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While most miRNA knockouts exhibit only subtle defects , a handful of miRNAs are profoundly required for development or physiology . A particularly compelling locus is Drosophila mir-279 , which was reported as essential to restrict the emergence of CO2-sensing neurons , to maintain circadian rhythm , and to regulate ovarian border cells . The mir-996 locus is located near mir-279 and bears a similar seed , but they otherwise have distinct , conserved , non-seed sequences , suggesting their evolutionary maintenance for separate functions . We generated single and double deletion mutants of the mir-279 and mir-996 hairpins , and cursory analysis suggested that miR-996 was dispensable . However , discrepancies in the strength of individual mir-279 deletion alleles led us to uncover that all extant mir-279 mutants are deficient for mature miR-996 , even though they retain its genomic locus . We therefore engineered a panel of genomic rescue transgenes into the double deletion background , allowing a pure assessment of miR-279 and miR-996 requirements . Surprisingly , detailed analyses of viability , olfactory neuron specification , and circadian rhythm indicate that miR-279 is completely dispensable . Instead , an endogenous supply of either mir-279 or mir-996 suffices for normal development and behavior . Sensor tests of nine key miR-279/996 targets showed their similar regulatory capacities , although transgenic gain-of-function experiments indicate partially distinct activities of these miRNAs that may underlie that co-maintenance in genomes . Altogether , we elucidate the unexpected genetics of this critical miRNA operon , and provide a foundation for their further study . More importantly , these studies demonstrate that multiple , vital , loss-of-function phenotypes can be rescued by endogenous expression of divergent seed family members , highlighting the importance of this miRNA region for in vivo function .
microRNAs ( miRNAs ) are ~22 nucleotide ( nt ) regulatory RNAs derived from hairpin precursors [1] , and there are 100s ~ 1000 miRNA loci in well-studied animal genomes [2] . As animal miRNAs regulate targets exhibiting as little as 7 nt of complementarity to their 5' regions ( principally nts 2–8 , known as the "seed region" ) , they coordinate large regulatory networks [3] . Collectively , the developmental and physiological impacts of miRNA-mediated regulation are extensive and substantial [4 , 5] . The first miRNAs discovered , nematode lin-4 and let-7 , exhibit strong developmental defects and have key individual targets that mediate substantial aspects of their phenotype [6–8] . As well , gain-of-function neural phenotypes associated with loss of 3' UTR elements from Notch target genes identified the functional logic of miRNA binding sites and highlighted additional key targets of miRNAs [9–11] . On the other hand , it is now well-appreciated that knockouts of individual miRNA genes frequently lack substantial phenotypes [12 , 13] , and that the typical range of miRNA-mediated repression is modest [14 , 15] . Such findings have been interpreted to reflect that miRNAs are usually for "fine-tuning" or "robustness" of gene expression [5 , 16] , but perhaps dispensable for major aspects of development , metabolism and behavior . Nevertheless , a handful of animal miRNA mutants exhibit dramatic phenotypes in one or more settings . A particularly compelling example is miR-279 [17 , 18] . This miRNA is one of just a few loci across all animals , including nematode lin-4 [19] , let-7 [8] , lsy-6 [20] , and mouse mir-96 [21 , 22] , to have emerged from forward loss-of-function genetics , attesting to the strength of its mutant phenotype . By comparison , virtually every other miRNA studied in intact animals originated from gain-of-function screening or a directed knockout ( although clearly in some cases these proved to have substantial effects ) . A mutant of mir-279 initially emerged from a genetic screen for altered patterning of olfactory neurons , yielding a line with ectopic CO2-sensing neurons in the maxillary palp [18] . This was associated with a transposon insertion near mir-279 , which was phenocopied by multiple mir-279 deletion alleles . In this setting , the transcription factors encoded by nerfin-1 and escargot are critical miR-279 targets [18 , 23] . Subsequent studies defined additional functions of miR-279 , including to mediate normal circadian activity [24] and for specification and migration of border cells [25] . Curiously , these other settings were associated with deregulation of JAK-STAT signaling , although via different mechanisms . miR-279 restricts the JAK-STAT ligand unpaired in circadian pacemaker cells [24] , whereas in ovarian border cells it represses the transcription factor STAT [25] . Altogether , these studies highlight diverse requirements for miR-279 in development and behavior . The mir-996 locus was later identified in the vicinity of mir-279 and shown to encode a similar seed , but they otherwise have distinct mature sequences and were originally suggested to derive from separate genes [26 , 27] . Notably , miR-996 has not been implicated in any biological processes , since available mir-279 deletion alleles do not affect the mir-996 locus , and mir-279 mutant phenotypes can be rescued by a genomic transgene that contains only mir-279 and lacks mir-996 sequence [18] . Indeed , the deep conservation of divergent non-seed regions of miR-279 and miR-996 , and the observation that mir-279 is ancestral and that mir-996 emerged more recently during arthropod evolution [28] , suggest that miR-996 may have neofunctionalized from miR-279 to acquire some distinct activity . In this study , we generated single and double mutants of mir-279 and mir-996 , and cursory examination suggested that miR-996 was dispensable for overt development and behavior , while miR-279 was essential . However , our studies unexpectedly reveal that all mir-279 single deletion mutants affect the expression of miR-996 . In essence , then , all studies of mir-279 mutants to date [18 , 23–25] have effectively been of double mutants . To remedy this , we engineered a defined set of backgrounds , using recombineered knockin/knockout transgenes introduced into the double deletion , to distinguish individual miRNA sequence requirements from dosage effects . We find that miR-996 contributes essential function in all known biological settings of miR-279 activity , such that a single genomic dose of either mir-996 or mir-279 provides nearly wildtype rescue to double deletion mutants in all characterized neural and non-neural settings . These data strongly support the notion that the seed region is the major determinant of in vivo miRNA function in animals .
The mir-996 hairpin is located ~1 . 5 kb downstream of the mir-279 hairpin ( Fig 1A ) . While they share seed regions , the rest of their mature regions are distinct and well-conserved ( Fig 1B ) . When we initially annotated mir-996 , its hairpin was embedded in the annotated gene CG31044 ( FlyBase Release 5 . 12 ) , which was predicted to encode a short , non-conserved , protein . At the time , we hypothesized that CG31044 might represent the primary transcript for mir-996 , distinct from mir-279 [26] . A similar inference of separate transcription units for these miRNAs was reported in a concurrent study [27] . Phylogenomic tracing indicates that mir-279 is ancestral and that mir-996 has adopted a derived sequence [28] . A third seed member , mir-286 , is genomically unlinked from the mir-279/996 cluster and moreover deployed in a spatially and temporally distinct manner , being essentially restricted to early embryogenesis [26 , 29 , 30] ( S1 Fig ) . Nevertheless , mature miR-279 is more similar to miR-286 than it is to miR-996 ( Fig 1A ) . This suggests that miR-279 and miR-996 are selected for distinct sequences , presumably related to some separable functions . Our previous efforts yielded two alleles in this region , [ex117] and [ex36] , that delete mir-279 but spare the mir-996 locus ( Fig 1A ) . As the phenotypes of these mutants were rescued by a ~3kb genomic transgene bearing only mir-279 , and lacked mir-996 sequence [18] , mir-279 appeared to be causal . Nevertheless , we were interested to assess whether miR-996 contributes to any biological settings known to depend on miR-279 . We screened excisions of a P element inserted downstream of mir-996 , and recovered two small deletions ( [ex187] and [ex310] ) that selectively remove this locus . We also recovered a longer deletion ( [ex15C] ) that removes both mir-279 and mir-996 loci , thus establishing an apparent allelic series of single and double mutants of these miRNAs ( Fig 1A ) . Both mir-996 single deletions were homozygous viable and lacked obvious morphological or behavioral defects . We measured the lifespan of mir-996[ex310] mutants and this was normal ( Fig 1C ) . We also analyzed the projections of GR21+ olfactory neurons . In wildtype , these CO2-sensing neurons are present only in the antenna and they project to ventral glomeruli . We visualized these in control ey-FLP; FRT82B MARCM clones generated in the GR21-Gal4 , UAS-synaptotagmin-GFP background , and stained for GFP-labeled projections to brains that were counterstained with nc82 ( Fig 1D ) . The GR21+ projections of mir-996 deletion MARCM clones were identical to wildtype ( Fig 1E ) . In contrast , mir-279 deletions induced ectopic medial projections ( Fig 1F and 1G ) , as described [18] , and reflected the generation of ectopic CO2-sensing neurons in the maxillary palp . Our newly generated mir-279/996 double deletion mutant [ex15C] exhibited similar gross phenotypes as mir-279[ex36] , with respect to lifespan ( Fig 1C ) and ectopic GR21+ projections ( Fig 1H ) . As well , the pharate lethality of homozygous [ex15C] mutants was well-rescued by the mir-279-only genomic transgene . Altogether , these findings were consistent with an interpretation that miR-279 is primarily responsible for essential genetic requirements of this two-miRNA locus . While the initial genetic data were reasonably explained by phenotypic dominance of miR-279 , certain other observations remained difficult to account for . Perhaps most germane was the fact that the mir-279[ex117] and mir-279[ex36] single deletions , both null for mir-279 , exhibited distinct viability . While mir-279[ex36] is lethal within a few days of eclosion , mir-279[ex117] adults can eclose and survive for weeks with optimal care , despite their locomotor difficulties ( Fig 1C ) . The discrepancy of these alleles was exploited in the circadian rhythm studies of Sehgal and colleagues; such behavioral studies require adult viability of at least one week [24] . Their analyses utilized a stock of mir-279[ex117] that had been outcrossed to remove potential second-site aberrations . A plausible model was that mir-279[ex36] bears an unlinked mutation responsible for its stronger defects . However , we were unable to recover homozygous mir-279[ex36] stocks that survived longer , even after extensive outcrossing of this mutant chromosome . In addition , flies carrying mir-279[ex36] in trans to a deficiency of the region showed the same gross phenotypes . Finally , we could rescue the viability and locomotor behavior of this mutant using the 3kb mir-279-only genomic transgene . Taken together , these findings suggested that phenotypic differences between mir-279[ex117] and mir-279[ex36] must reside extremely close to the mir-279 hairpin . For example , there might theoretically be another non-coding function of the primary mir-279 transcript , or even perhaps a peptide encoded by this region . However , another scenario we considered was that miR-996 might be affected by available miR-279 alleles . Northern analysis of small RNAs in different mir-279 and mir-996 alleles confirmed this hypothesis . The mir-996[ex310] homozygous mutant lacked mature miR-996 , validating its nature as a null allele and demonstrating specificity of the miR-996 probe; this mutant expressed miR-279 normally ( Fig 2A ) . Both mir-279 "single" alleles and the mir-279/996 double deletion failed to express mature miR-279 , as expected , but all of these mutants also proved deficient for miR-996 . mir-279[ex117] expressed <10% the normal level of miR-996 , and mir-279[ex36] did not detectably express miR-996 ( Fig 2B ) . We obtained similar results by examining different female tissues ( Fig 2 ) as well as male tissues ( S2 Fig ) . Therefore , we conclude that the available mir-279 "single" mutants are unexpectedly also strong or nearly null alleles of mir-996 . Consideration of current modENCODE transcriptomic data at the mir-279/996 region proved informative ( Fig 1 ) . Our small RNA analyses showed that miR-279 and miR-996 belong to the same expression cluster across diverse Drosophila tissue and cell line small RNA libraries [31] , indicating their coordinate deployment . Inspection of companion transcriptome data [32] revealed relatively continuous , although graded , levels of RNA-seq reads across the entire locus , consistent with the notion of a single primary mir-279/996 transcript . It is commonly observed in Drosophila that 3' fragments of Drosha-cleaved primary transcripts are less stable than 5' Drosha fragments [33 , 34] , and evidently at the mir-279/996 locus , the 3'-most fragment of its primary transcript is least stable of them all ( Fig 1A ) . On the basis of such transcriptome data , the provenance of CR31044 was expanded in the most recent FlyBase release ( 5 . 47 ) , such that it now includes both mir-279 and mir-996 ( Fig 1A ) . Analysis of capped analysis of gene expression ( CAGE ) data revealed a 5' transcription start site ~1kb upstream of the mir-279 hairpin . This lies <30nt downstream of a typical TATA box sequence ( GTATATAAA ) , suggesting that as the promoter for the mir-279/996 transcription unit . The deletion extents of the "mir-279" alleles , relative to the transcription start , were notable . mir-279[ex36] removes sequence upstream of the promoter , presumably explaining why this allele strongly compromises expression of the downstream miRNA . On the other hand , mir-279[ex117] deletes to within 14 nt of the transcription start site ( Fig 1A ) , which does not abolish , but apparently debilitates expression and/or processing of the intact mir-996 hairpin . Altogether , these molecular observations are consistent with our genetic inference that both miRNAs may contribute to mutant phenotypes uncovered by chromosomal aberrations of the region . Since the available allelic series did not permit assessment of phenotypes caused by specific loss of miR-279 , we sought an alternative strategy to analyze "clean" mir-279 and mir-996 mutant backgrounds . To do so , we recombineered a genomic transgene that includes the full 16 . 6kb intergenic region between the upstream ( CG14508 ) and downstream ( Ef1gamma ) protein-coding genes ( Fig 3A ) , and thus may be expected to confer full miRNA rescue . To permit direct comparison with variant forms , we used the phiC31 system to integrate transgenes into a common genomic site . The wildtype 16 . 6 kb transgene restored accumulation of mature miR-279 and miR-996 ( Fig 3B ) and fully rescued viability of mir-279/996[ex15C] double deletion homozygotes ( Fig 3C ) . Using this genomic fragment , we then generated a series of mutant transgenes in which we specifically deleted 100bp covering either the mir-279 or mir-996 hairpins ( 1x-mir-279 and 1x-mir-996 , which essentially serve as mir-996-KO and mir-279-KO transgenes , respectively ) or replaced either miRNA with the non-cognate hairpin ( 2x-mir-279 and 2x-mir-996 ) ( Fig 3A ) . We placed one copy of each transgene into mir-279/996[ex15C] homozygotes , and performed Northern blotting for the two miRNAs from adult females . These tests demonstrated specific expression of mature miR-279 and miR-996 in the designated genotypes ( Fig 1A ) . This confirmed their status as a bona fide panel of single mutants of the mir-279/996 locus , an allelic series that was not functionally fulfilled by corresponding single genomic deletions . For reasons that are not apparent , the amount of mature miR-996 from the transgenic copies , especially the reprogrammed allele , was not as robust as the endogenous chromosomal locus ( Fig 3B ) . Nevertheless , each of the four transgenes fully rescued adult viability of mir-279/996 double deletion backgrounds . This provided an initial view into the complicated genetics of this locus . Strikingly , endogenous expression of only a single copy of either 1x-mir-279 or 1x-mir-996 transgenes in the double mutant , thus recapitulating full knockout of either miRNA on top of heterozygosity for the other , was sufficient to rescue adult viability ( Fig 3C ) . This indicates that there is no essential requirement for the unique miR-279 sequence , and that one allele of either mir-279 or mir-996 supports normal viability of Drosophila . We proceeded to subject these engineered miRNA backgrounds to detailed phenotypic study , to ascertain the extent to which defects previously attributed to miR-279 might actually depend on the joint function of miR-279 and miR-996 . Under MARCM clonal conditions , mir-996 single hairpin deletions exhibit normal specification of CO2-sensing neurons within the antenna , and these project to ventral glomeruli in the central brain ( Fig 1D and 1E ) . In contrast , mir-279 single hairpin deletions , which we now recognize as deficient for mature miR-996 , exhibit ectopic CO2-sensing neurons in the palp , and these project to medial glomeruli . In particular , mir-279[ex117] ( which retains some expression of miR-996 ) exhibits a weaker GR21 phenotype than mir-279[ex36] or mir-279/996[ex15C] ( which are nearly null or definitively null for both miRNAs , respectively ) under clonal conditions ( Fig 1F–1H ) . These observations strongly hinted that endogenous miR-996 contributes to suppression of CO2-sensing neurons in the maxillary palp . We sought to test this under non-clonal conditions , which are expected to be more sensitive than clonal conditions , which may potentially be rendered less potent by perdurance . It is more difficult to obtain mir-279/996[ex15C] homozygotes compared to MARCM mutant adults , but these proved to exhibit strong and fully penetrant GR21 projection phenotypes ( Fig 4A ) . In fact , under these non-clonal conditions , this genuine double deletion mutant exhibited slightly stronger phenotypes than either of the shorter deletions that physically remove only mir-279 but compromise mir-996 expression ( Fig 4G ) . The mir-279/996[ex15C/ex15C] phenotype was fully rescued by a single insertion of the wildtype 16 . 6kb mir-279/996 transgene , validating its status as a fully functional genomic fragment ( Fig 4B ) . Moreover , single insertions of either "2x" transgene , in which mir-279 was substituted for mir-996 , and vice versa , also provided complete rescue of the ectopic CO2-sensing neurons ( Fig 4C and 4D ) . In more stringent assays , we then showed that single insertions of either "1x" transgenes , recapitulating mir-279-knockout and mir-996-knockout conditions , similarly provided essentially full suppression of the double deletion phenotype ( Fig 4E and 4F ) . We quantified these rescues , expressed as the relative amounts of ectopic GFP+ projections in the brain , in Fig 4G . The ectopic CO2 neuron phenotype was shown to be driven by derepression of specific miR-279 targets , namely the transcription factors encoded by nerfin-1 and escargot [18 , 23] . Although CO2-sensing neurons comprise only a small number of cells in the nervous system , we were able to detect massive derepression of nerfin-1 ( Fig 4H ) and substantial upregulation of escargot ( Fig 4I ) transcripts in whole mir-279/996[ex15C/ex15C] adult heads , relative to Canton S control heads . Based on the degree of misregulation , we infer that miR-279/996 must regulate nerfin-1 outside of the CO2-sensing apparatus . In mutant heads bearing either wildtype mir-279/996 genomic transgene , or mir-279-only or mir-996-only transgenes , we observed comparable restoration of nerfin-1 and escargot transcript levels by the various miRNAs ( Fig 4H and 4I ) . The rescued levels were consistently slightly greater than Canton S but were actually similar to mir-279/996[ex15C/+] heterozygotes in all cases . This might reflect marginally incomplete rescue by the transgenes , or alternatively , some genetic background variation between this control and the miRNA deletion genotypes . In either case , we conclude that this dramatic , fully-penetrant , neural cell specification phenotype requires the joint activity of the miR-279 and miR-996 miRNAs , and that either miRNA suffices to direct normal development of these neurons via joint repression of shared critical target genes . We next tested the potential involvement of miR-996 in maintenance of circadian behavior . We initially studied the hypomorphic mir-279[ex117] homozygous condition , and confirmed previous observations [24] that a majority of mir-279[ex117] mutant flies displayed arrhythmic locomotor activity in constant darkness ( Fig 5A and 5B ) . Unexpectedly , however , we further observed that 30% of mutant individuals were weakly rhythmic ( Fig 5C ) . The quantification of percentage of rhythmic animals , their circadian period , and their power of rhythmicity are shown in Table 1 . Restoration of either miR-279 or miR-996 on the [ex117] background , in either two doses or in a single dose , fully recovered behavioral rhythmicity ( Fig 5D–5F and Table 1 ) . The normalized activity profiles of all the different transgene combinations in the [ex117] background are provided in S3 Fig . We performed more stringent tests by asking if either miRNA could rescue the coordinate absence of both miR-279 and miR-996 . Circadian rhythm assays require flies to be mobile for at least a week . Since homozygous mir-279[ex36] and mir-279/996[ex15C] adult flies are poorly able to stand or walk , and die within a few days ( Fig 1C ) , these genotypes cannot be assayed for circadian behavior . Heterozygotes of the double deletion [ex15C] exhibit normal rhythmic behavior ( Fig 5G ) ; for comparison , the normalized activity pattern of surviving [ex117] homozygotes is shown in Fig 5H . When we assayed our panel of recombineered transgenes in mir-279/996[ex36/ex15C] transheterozygotes , which should be close to a null condition for the locus , we observed normal rhythmic behavior in all cases ( Table 1 ) . Finally , we performed the strictest test by assaying rescues of [ex15C] homozygotes . Strikingly , all transgene isoforms , including single mir-279 and mir-996 versions , fully restored the normal circadian clock in the mutant flies ( Fig 5I and 5J and Table 1 ) . Altogether , these data indicate that intact mir-996 expression fully complements loss of mir-279 in both nervous system development and adult neurophysiology . The evidence gathered indicates that miR-279 and miR-996 play surprisingly similar roles in diverse developmental and behavioral settings . Nevertheless , their distinct conserved 3' sequences ( Fig 1B ) suggests that these miRNAs have diversified in some way during evolution . As we showed in the head , endogenous miR-279 and miR-996 exhibit comparable activity to restrict the accumulation of nerfin-1 and escargot transcripts ( Fig 4H ) . To probe this further , we compared the gain-of-function activities of these miRNAs using luciferase sensor assays in S2 cells . We tested 3' UTR sensors for nine genes bearing conserved miR-279 family target sites , including all of those identified in previous in vivo studies . Transfection of ub-Gal4 and UAS-dsRed-miRNA expression constructs led to significant repression of all sensors ( Fig 6A ) . As a negative control , the Hairless 3' UTR does not contain any miR-279/996 binding site and the sensor was not repressed upon miRNA transfection ( Fig 6A ) . We verified comparable ectopic expression of both miR-279 and miR-996 in these transfection experiments ( Fig 6B ) . Therefore , the capacities of miR-279 and miR-996 in S2 cells are similar . To explore their ectopic activities in vivo , we overexpressed the miRNAs in the circadian neurons with tim-Gal4 . In accordance with previous studies [24] , overexpression of miR-279 in circadian tissues strongly disrupted the adult behavioral rhythm . In contrast to the unactivated transgene background , tim>mir-279 animals quickly became arrhythmic following their transfer to constant darkness ( Fig 6C ) . However , ectopic miR-996 only weakly affected circadian rhythm , with 13% arrhythmic flies observed with only one of the two insertions ( Fig 6D ) . Quantitative data for these genotypes is shown in Table 2 . This was not due to inability to produce miR-996 , since the degree of accumulation of ectopic miR-996 induced by tim-Gal4 was greater than for miR-279 ( Fig 6F ) . Such phenotypic differences suggested that miR-279 has stronger capacity to influence circadian cell activity , even though endogenous mir-996 is completely able to compensate for the absence of mir-279 . Nevertheless , given the substantial overlapping capacities of these miRNAs for target regulation ( Fig 6A ) , we asked whether increased levels of mir-996 could influence circadian rhythm . For these tests , we utilized tim-UAS-Gal4 , which auto-potentiates Gal4 expression in tim-expressing neurons ( Fig 6E ) . We used Northern blotting to verify that more mature miR-996 was generated in the latter condition ( Fig 6F ) . Interestingly , in this sensitized overexpression background , miR-996 induced substantial behavioral arrhythmia ( 81% and 44% of the independent UAS-mir-996 insertions exhibited arrhythmia , Fig 6D and Table 2 ) . In summary , these gain-of-function experiments reveal intrinsic differences between miR-279 and miR-996 , which otherwise exhibit surprising genetic redundancy under carefully controlled endogenous conditions . This is perhaps counter to normal expectation , in which overexpressed seed family miRNAs more typically exhibit similar properties even on the transcriptome level [35 , 36] , but may instead display functional distinctions under physiological settings .
Despite extensive experimental and computational evidence for the pervasive nature of animal miRNA target networks , genetic studies have not generally supported the notion that animals rely upon miRNAs to the same degree , as say , transcription factors and signaling pathways [37] . This was strikingly evident with the systematic knockout of C . elegans miRNAs , which revealed barely any developmental or behavioral phenotypes [12] . Similarly , a genomewide collection of D . melanogaster miRNA knockouts reveals a variety of phenotypes , but these are generally quantitative in nature and include few documented developmental defects [38] . While this might partly be due to functional overlap amongst members of similar miRNA families , compound knockouts of C . elegans miRNA families revealed overt consequences only for a minority of families [39 , 40] . Only upon further sensitization , by reducing the activity of other gene broad regulators , did additional miRNA knockouts exhibit phenotypes [41] . Together with studies of dozens of miRNAs that mostly exhibit phenotypes under sensitized conditions [42–44] , an emerging concept is that miRNAs mostly act as robustness factors [5 , 16] . Nevertheless , select miRNAs have proven to be essential for certain development or physiological processes . Our current studies affirm and extend the broad impact of the mir-279/mir-996 locus , which generates phenotypically critical miRNAs of profound impact . Together , these miRNAs are fully essential for organismal viability , for normal cell specification of olfactory neuron subtypes , and for rhythmic behavior via circadian pacemaker cells . An unexpected conclusion of this work was to uncover that these two miRNAs , previously inferred to derive from separate transcription units [26] , actually provide highly overlapping in vivo activities . Although the stringent evolutionary conservation of these miRNAs is de facto evidence that they are not truly "redundant" , we demonstrate using precise genetic engineering that single genomic copies of either mir-279 or mir-996 can fully compensate for the deletion of all four miRNA alleles in diverse developmental and physiological settings . A notable feature of the function of this miRNA family operon is that they mediate their effects through multiple key , setting-specific , targets . For example , the transcription factors encoded by nerfin-1 and escargot are the critical miR-279/996 targets whose de-repression induces ectopic CO2-sensing neurons , and whose heterozygosity confers substantial rescue of ectopic CO2-sensing neurons in flies that lack these miRNAs [18 , 23] . On the other hand , miR-279/996 have substantial effects on different aspects of the JAK-STAT signaling pathway in circadian pacemaker cells and ovarian border cells , by targeting the ligand unpaired [24] and the transcription factor STAT [25] , respectively . While manipulation of these various targets can substantially rescue setting-specific phenotypes caused by loss of miR-279 and miR-996 , none of them rescue the extremely abbreviated lifespan of the double mutant . It remains to be seen whether this marked phenotype is due to combined de-repression of multiple characterized targets , or to a different pathway or target network . Our newly characterized mir-279/996 alleles and rescue backgrounds comprise valuable reagents for future study of these miRNAs . Early genetic studies revealed the principle of miRNA seed targeting [10 , 11] , many years before the complementary miRNAs were identified [9 , 45] . Since then , a wealth of experimental studies have shown that ~7 nt seed matches are sufficient to confer substantial regulation by miRNAs , not only in culture cells [46] but also in the animal [47 , 48] . Genomewide studies show that seed-matching is the dominant mode of conserved target recognition [49–51] . In addition , overexpression tests clearly demonstrate that the dominant transcriptome signature induced by ectopic miRNAs is seed-based , and maintained even upon substitution of the remainder of the miRNA sequence [35 , 36] . Nevertheless , considerable debate continues about the contribution of "non-seed" target sites to miRNA networks . Notably , some of the earliest miRNA targets found , which definitively mediate regulatory interactions critical for development , lack continuous seed matching [7 , 8] . In the classic example of let-7:lin-41 pairing , the atypical architecture of a bulged seed supplemented by extended 3' pairing is proposed to permit specific recognition that cannot be achieved by other let-7 family members in C . elegans [40 , 52] . A number of subsequent directed studies define functional and/or conserved miRNA:target pairing configurations , characterized by 3' compensatory pairing , distributive complementarity , or centered pairing [47 , 53 , 54] . The existence of such sites has been attractive from the point of view that they might help explain strong evolutionary constraints on entire miRNA sequences , which are not satisfactorily explained by seed regions alone . However , it remains to be seen whether mutations of non-seed miRNA sequences are of consequence to in vivo miRNA-mediated phenotypes . On the other hand , available genetic studies suggest limits on the contribution of non-seed regions to in vivo miRNA phenotypes . For example , all known miRNA point mutant alleles , other than ones that broadly affect biogenesis , invariably prove to alter seed regions [6 , 8 , 21 , 22 , 55 , 56] . In addition , Horvitz and colleagues showed that embryo/larval lethality caused by compound mutations of the 8-member mir-35 family or the 6-member mir-51 family could both be rescued by individual family members [39] . Our studies of miR-279 and miR-996 provide a new testbed for this , since this locus is responsible for several of the most overt developmental and behavioral phenotypes ascribed to animal miRNAs . While we provide functional evidence that these miRNAs are not identical in regulatory capacity and/or processing , it is still striking from the biological viewpoint that multiple essential in vivo phenotypes can be fully satisfied by either of these non-seed divergent miRNAs . It should be informative to conduct similar studies for some of the other miRNAs that exhibit overt phenotypic requirements , potentially by systematic mutation of 3' regions , to assess their relative contribution to organismal phenotypes . With the advent of CRISPR/Cas9 genome engineering , it is now feasible to address these questions with respect to miRNA genes and miRNA sites in the genomes of intact organisms [57 , 58] .
The mir-996 single deletion and mir-279/996 double deletion alleles were generated by imprecise excision of P{EPgy2}CR31044[EY03350] , which is inserted 370bp downstream of the mir-996 hairpin . We crossed these to the TMS , Δ2-3/TM6B jumpstarter and induced transposition in P{EPgy2}CR31044[EY03350]/TMS , Δ2–3 animals . Following the segregation of TMS , Δ2–3 , we screened ~500 candidate excision chromosomes for deletions in the mir-279/mir-996 region using the the following PCR amplicons: mir279F excision CAAGAAACCACCCCGAGAAGAAGAAG mir279R excision AGCAGGTGTTACAGTTACACTCAAACG . The mir-996[ex310] deletion contains a 568 deletion with 9bp of P-element sequence left , while the mir-996[ex187] deletion contains a 584 bp deletion with 154bp of P-element sequence left . The mir-279/996[ex15C] allele bears a 2825bp deletion that removes both miRNA hairpins and retains 84bp of P-element sequence . The 3 . 0kb genomic sequence containing only mir-279 was cloned from the genome and inserted into the pBDP vector [59] . For the large rescue transgenes , we retrieved 16 . 6kb extending into both upstream and downstream protein-coding genes of the mir-279/996 locus from the BAC CH322-35G11 ( BACPAC Resources ) and cloned it into the attB-P[acman]-AmpR vector by recombineering as described [60] . The mir-279 or mir-996 hairpins were targeted with an rpsL-neo cassette ( Gene Bridges ) , which was flanked by the ~50bp left and right homology arms for the miRNA and carried two BbvCI restriction sites between the rpsL-neo cassette and the homology arms . We then deleted the rpsL-neo cassette from the targeted construct by BbvCI digestion and the remaining vector was re-ligated to generate the mir-279-1x or mir-996-1x construct . To generate the mir-279-2x construct , genomic fragments 13 . 5kb upstream and 3 . 0kb downstream of the mir-996 hairpin were retrieved from the CH322-35G11 BAC and cloned between the AscI and NotI sites of the attB-P[acman]-AmpR vector . The 5’ end of the upstream and 3' end of the downstream fragment were identical to the ends of the 16 . 6kb wild type genomic fragment . The mir-279 hairpin was PCR cloned and inserted to the 5' end of the mir-996 downstream fragment , then the resultant 3 . 1kb piece was digested out and ligated with the 13 . 5kb mir-996 upstream sequence to generate the mir-279-2x construct . Similar procedures were followed to generate the mir-996-2x construct . Such mir-279-2x and mir-996-2x constructs carried a NotI site at the 5' side and an AscI site at the 3' side of the ectopic hairpin . Sequences of the primers used are listed in S1 Table . Transgenes were generated using the phiC31 system ( BestGene Inc ) . The mir-279[ex117] ( also known as Δ1 . 2 ) and mir-279[ex36] ( also known as Δ1 . 9 ) alleles were generated in the lab previously [18] , but outcrossed stocks were obtained from Amita Sehgal [24] and used in this study . Other previously-described stocks utilized in this study include UAS-luc-mir-279 and UAS-DsRed-mir-996 [61] , tim-Gal4 and tim-UAS-Gal4 [62] , and the MARCM tester stock eyflp; Gr21-Gal4 , UAS-sytGFP; FRT82 , tubGal80 [18 , 63] . Total RNAs were extracted using Trizol LS ( Life Technologies ) following the manufacturer’s protocol . RNA samples were separated on 12% polyacrylamide denaturing gels ( National Diagnostics ) , transferred to the GeneScreen Plus ( Perkin Elmer ) membrane , crosslinked with UV light and hybridized with γ-32P-labeled LNA ( Exiqon ) antisense probes for miR-279 and miR-996 at 45°C overnight . Signals were exposed to an Imaging Plate ( Fujifilm ) for 2~3 days for appropriate signal intensity . 2S RNA was hybridized with DNA probe and exposed for 30 min as the loading control . Signal quantifications were performed in the Image Gauge software and levels of miR-279 and miR-996 in different genotypes were normalized to 2S rRNA . Late stage pupae of mir-279[ex117] , mir-279[ex36] and mir-279/996[ex15C] mutants were transferred from culture bottles to clean petri dishes humidified with wet Kimwipe papers . This was necessary because of the overall poor vigor of the mutant stocks . For flies carrying rescue transgenes , no extra care was needed as all wildtype and modified constructs fully restored normal robustness of the mutant backgrounds . Adult flies of all genotypes were collected within 24 hours of eclosion into normal food vials and maintained in room temperature ( 22°C ) in low density ( 10 flies per vial ) . Flies were transferred to fresh vials every day for mutants and every 3 to 5 days for rescued genotypes and scored for survivors across a time frame of 20 days . Again , the additional care was necessary to extend the lifespan of the mutants , which would normally succumb much more prematurely due to becoming trapped in the food . Adult heads were fixed on ice for 2~3 hours in the fixative solution containing 4% paraformaldehyde ( PFA ) and 0 . 2% Triton-X-100 in PBS ( 0 . 2% PBST ) . Heads were then rinsed with 0 . 2% PBST for 3 times and brains were dissected in the blocking solution ( 5% normal goat serum in 0 . 2% PBST ) . Both primary and secondary antibodies were incubated overnight at room temperature . Brains were washed 3 times with 0 . 2% PBST before and after the secondary antibody incubation and mounted in the Vectashield mounting medium with DAPI ( H-1200 , Vector Labs ) . Antibodies were used as follows: mouse-anti-nc82 ( 1:20 , Developmental Studies Hybridoma Bank ) , rabbit-anti-GFP ( 1:1000 , Invitrogen ) , and Alexa Fluor-488 , -568 secondary antibodies ( 1:500 , Molecular Probes ) . Flies were entrained in 12-hour light/12-hour dark ( LD ) cycles at 25°C for 5 days before transferred into constant darkness ( DD ) during the dark phase of the LD cycle . Locomotor activities of individual flies were recorded with the Drosophila Activity Monitor System ( TriKinetics ) every 1 min , data were then binned at 30 min with the DAMFileScan and the circadian period was calculated using ClockLab ( Actimetrics ) from data collected for 7 days in DD conditions . The power of rhythmicity was calculated as the chi-squared power above the significance line [64] . The 3' UTRs of Hairless and predicted miR-279 target genes were cloned between the XhoI and NotI sites of a modified psiCHECK-2 vector [65] . Sensor plasmid and Ub-Gal4 were cotransfected with UAS-dsRed-miRNA [61] or empty pUAST vector into S2-R+ cells with the Effectene ( Qiagene ) reagents . Luciferase levels were measured using the Dual-Glo Luciferase Assay System ( Promega ) . Primer sequences for 3' UTR cloning are listed in S1 Table . We prepared cDNA from Trizol-extracted RNA that was treated with DNase and reverse transcribed using QuantiTect Reverse Transcription Kit ( Qiagen ) . qPCR reactions were performed using SYBR select master mix ( Life Technologies ) . Data were normalized to Rpl32 amplification . Primer sequences for qPCR amplicons are listed in S1 Table .
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Amongst the small number of miRNA knockouts that exhibit substantially overt phenotypes , mutants of Drosophila mir-279 are notable . Previous studies have uncovered its essential requirements in a range of developmental and behavioral assays . Surprisingly , we find that the phenotypes attributed to mir-279 deletions depend on the unanticipated loss of expression of the downstream locus mir-996 , whose genomic locus is retained in extant mir-279 mutants . These miRNAs share their seed regions but are divergent elsewhere in the mature sequences . We use precise genetic engineering to show that a single endogenous copy of either mir-279 or mir-996 can fully rescue viability , olfactory neuron , and circadian rhythm defects of double deletion animals . These data and genetic reagents set a new foundation for developmental and behavioral studies of this critical miRNA locus . More generally , these data demonstrate that multiple loss-of-function phenotypes can be rescued by endogenous expression of divergent seed family members , highlighting the importance and potentially sufficiency of this region for in vivo function .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Multiple In Vivo Biological Processes Are Mediated by Functionally Redundant Activities of Drosophila mir-279 and mir-996
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Bacterial resistance to β-lactams may rely on acquired β-lactamases encoded by class 1 integron-borne genes . Rearrangement of integron cassette arrays is mediated by the integrase IntI1 . It has been previously established that integrase expression can be activated by the SOS response in vitro , leading to speculation that this is an important clinical mechanism of acquiring resistance . Here we report the first in vivo evidence of the impact of SOS response activated by the antibiotic treatment given to a patient and its output in terms of resistance development . We identified a new mechanism of modulation of antibiotic resistance in integrons , based on the insertion of a genetic element , the gcuF1 cassette , upstream of the integron-borne cassette blaOXA-28 encoding an extended spectrum β-lactamase . This insertion creates the fused protein GCUF1-OXA-28 and modulates the transcription , the translation , and the secretion of the β-lactamase in a Pseudomonas aeruginosa isolate ( S-Pae ) susceptible to the third generation cephalosporin ceftazidime . We found that the metronidazole , not an anti-pseudomonal antibiotic given to the first patient infected with S-Pae , triggered the SOS response that subsequently activated the integrase IntI1 expression . This resulted in the rearrangement of the integron gene cassette array , through excision of the gcuF1 cassette , and the full expression the β-lactamase in an isolate ( R-Pae ) highly resistant to ceftazidime , which further spread to other patients within our hospital . Our results demonstrate that in human hosts , the antibiotic-induced SOS response in pathogens could play a pivotal role in adaptation process of the bacteria .
Transferable genes encoding antibiotic resistance to major antibiotics ( e . g . β-lactams , aminoglycosides ) are often carried by class 1 integrons in Gram negative pathogens [1] . In these genetic elements the antibiotic resistance genes are carried inside mobile structures called gene cassettes , which generally correspond to a promoterless gene associated to a recombination site called attC , formerly called 59-be [2] , [3] . Gene cassette expression is driven by a promoter located in the integron platform upstream of the attI site , the primary site of cassette integration , and in the case of the class 1 integron , inside the intI1 gene which encodes the cassette recombinase [4] . This organization allows a positional regulation of the cassette's expression: the closer a gene cassette is located to attI , the higher is its expression [1] . In addition to this transcriptional attenuation along the cassette array , the decrease in expression can be due to problems of translational coupling [5] , [6] . Thus , gene expression in these elements can be modulated by the site-specific recombination events mediated by the integrase IntI1 [1] , [3] . The SOS response is a conserved regulatory network that is induced in response to DNA damage [7] . It also promotes integron rearrangements by controlling the expression of integrases with promoters that contain a LexA-binding motif [8] , [9] . During the SOS response , the RecA protein , bound to single stranded DNA , stimulates the cleavage of the repressor LexA , thus releasing the transcription of the LexA-controlled genes . The adaptations resulting from integron activity are thought to influence bacterial evolution , especially in Proteobacteria , where integrons are extremely common [3] . We have recently shown that common horizontal gene transfer processes , such as conjugation [10] , trigger the SOS response and ultimately the integron integrase expression . However , the most medically relevant SOS induction is certainly the one directed by antibiotic treatments . Hence , a number of antibiotics , including the β-lactams , aminoglycosides and fluoroquinolones , have been found to directly or indirectly provoke this stress response [11]–[13] . Despite this evidence from in vitro studies , the clinical significance of the SOS response on integron rearrangement and the dynamics of integron-based bacterial adaptation during human infections are unknown . So far , there are no examples of SOS-mediated antibiotic resistance occurring during therapeutic use of antibiotics , even though , as mentioned above , many of them stimulate the SOS response . Here , we witnessed the emergence in a hospitalized patient of an isolate of Pseudomonas aeruginosa highly resistant to the third generation cephalosporin ceftazidime , associated with the production of an extended-spectrum β-lactamase encoded by a class 1 integron-borne gene . This strain , highly resistant to ceftazidime , further became epidemic within the hospital . We discovered the mechanism , based on the excision of a gene cassette originally located upstream of the β-lactamase-encoding gene cassette , which modulated the expression of the transferable resistance gene . This patient had been previously treated with ceftazidime ( to treat the infection by P . aeruginosa ) and metronidazole ( to treat an infection by anaerobes ) . This led us to suspect the involvement of this treatment in the integron cassette array remodeling , through SOS response induction inside the patient . We demonstrated that the metronidazole , not an anti-pseudomonal antibiotic , is able to trigger the SOS response in P . aeruginosa , and subsequently activates the integrase IntI1 and cassette rearrangement . Deletion of the gcuF1 cassette and full expression the β-lactamase were obtained at high rates in vitro , supporting this scenario to explain the genesis , in the patient , of the R-Pae isolate from S-Pae after metronidazole treatment .
We detected the same clone of a multi-drug resistant clone of P . aeruginosa ( R-Pae ) in 13 adult patients by pulsed-field gel electrophoresis ( Figure S1 ) . R-Pae was resistant to potent anti-pseudomonal agents , including ceftazidime , cefepime , aztreonam , aminoglycosides , and fluoroquinolones ( Table S1 ) . A double-disk synergy test revealed a weak synergy between β-lactamase substrates ( ceftazidime or cefepime ) and β-lactamase inhibitors ( imipenem or clavulanate ) in these bacteria , suggesting production of an extended-spectrum β-lactamase ( oxacillinase ) of Ambler class D ( Figure S2 ) [14] . We searched for the most common genes encoding extended-spectrum oxacillinases in P . aeruginosa [15] by PCR for the blaOXA-10 , blaOXA-2 and blaOXA-1 groups [16] . Only the PCR specific to blaOXA-10 using primers OXA-10A and B ( see Table S2 ) was positive . As blaOXA-10 and variants are found as gene cassettes often borne by class 1 integrons [17] , we performed PCR experiments using primers specific to these integron platforms , directed to the conserved sequences flanking the variable cassette array , usually called 5′-CS and 3′-CS ( see Table S2 ) . A single 1673-bp amplicon was thus obtained , which was subsequently sequenced to reveal two resistance gene cassettes , namely aacA4 that determines a 6′-N-aminoglycoside acetyltransferase conferring high resistance to gentamicin and tobramycin [18] , and blaOXA-28 that encodes the extended-spectrum oxacillinase OXA-28 [19] ( Figure 1A ) . Additionally , quantification of the specific mRNA transcripts by RT-qPCR showed that the R-Pae1 isolate overexpressed the ampC gene encoding the intrinsic chromosomal cephalosporinase AmpC , when compared to the wild type reference strain of P . aeruginosa PAO1 ( Figure 2A ) . To determine whether bacterial resistance had emerged during the course of treatment , we retroactively analyzed the P . aeruginosa isolates archived from the first colonized patient ( patient 1 ) within 2 months before the isolation of R-Pae . The clonal strain S-Pae was isolated from the sputum of patient 1 , 28 days before the isolation of R-Pae in the patient's lung . R-Pae1 was isolated after a treatment with ceftazidime ( to treat the infection by P . aeruginosa ) and metronidazole ( to treat an infection by anaerobes ) . To our surprise , S-Pae was susceptible to ceftazidime ( MIC , 4 µg/ml ) despite the presence of an intact blaOXA-28 gene in its genome ( Figure 1B , C ) . S-Pae and R-Pae1 demonstrated an equivalent expression level of the cephalosporinase-encoding ampC ( Figure 2A ) . The other resistance mechanisms found in S-Pae and R-Pae1 ( efflux pump overproduction and porin loss ) do not alter the susceptibility to ceftazidime ( see Text S1 ) . Because of the lack of evidence for classical resistance mechanisms accounting for the difference in β-lactam susceptibilities in these isolates , we investigated the possible mechanism of resistance modulation . S-Pae differed from R-Pae1 by a 10-fold lower amount of blaOXA-28 transcripts ( Figure 2B ) and by the presence of a 319-bp cassette , gcuF1 , inserted immediately upstream of blaOXA-28 ( Figure 1B ) . Using nested-PCR , we demonstrated the presence of free circular cassettes of gcuF1 in S-Pae ( Figure 3A , B ) , demonstrating that the recombination between its own attC site and the aacA4 attC site was occurring , though at extremely low level [2] . Computational analysis of the nucleotide sequence of the S-Pae integron predicted the translation of a new ORF , a fused protein consisting of gcuF1 and blaOXA-28 ( GCUF1-OXA-28 ) . We were able to show that these two cassettes ( gcuF1 and blaOXA-28 ) could be transcribed in a single transcript . Hence , we could retrieve a specific amplicon after PCR amplification using cDNA prepared from S-Pae RNA as the matrix and with primers overlapping the junction between gcuF1 and blaOXA-28 ( Figure S3 ) . The GCUF1-OXA-28 peptide ( 368 residues ) was predicted to have a molecular weight of 40 . 1 kDa , compared with the 29 . 3-kDa native OXA-28 ( 266 residues ) . To confirm these data , both ORFs were expressed in Escherichia coli BL21 from plasmid pET-28a which adds an N-terminal polyHis tag . After purification , we found that their molecular weights estimated by SDS-PAGE were in full agreement with our predictions ( Figure 1D ) . This protein contained the original 19-residue long signal peptide now misplaced between the GCUF1 and the OXA-28 domains at position 103–121 of the GCUF1-OXA-28 protein ( Figure 1B and 4A ) . Since β-lactamases are periplasmic proteins and are produced as preproteins with an N-terminal peptide signal [20] , one would expect that the misplacement of the signal peptide in the GCUF1-OXA-28 protein will abolish the periplasmic process of the β-lactamase . However , cellular production of this altered protein conferred a residual resistance to ceftazidime ( MIC of ceftazidime , 8 µg/ml; gcuF1-blaOXA-28 in Figure 1C ) , suggesting the presence of an active and processed OXA-28 in the periplasm of the GCUF1-OXA28-producing isolate . To clarify this point , we cloned the blaOXA-28 and gcuF1-blaOXA-28 sequences into the broad host range vector pBTK27 to encode C-terminal His-tagged polypeptides that were expressed in the reference strain P . aeruginosa PA14ΔampC . Western-blot analysis of periplasmic extracts of GCUF1-OXA28-producing bacteria revealed the presence of a reduced amount of processed periplasmic OXA-28 ( Figure 1D ) , consistent with the lower resistance to ceftazidime when the gcuF1 cassette is inserted upstream of the blaOXA-28 ( Figure 1C ) . We used a directed mutagenesis approach to clarify the origin of periplasmic OXA-28 in S-Pae and determine whether it is due to the export processing of the fusion protein or to an internal translational initiation at the original OXA-28 start codon ( Figure 4A ) . In P . aeruginosa PA14ΔampC carrying a plasmid-borne gcuF1-blaOXA-28 , the in-frame insertion of a stop codon just upstream of blaOXA-28 reduced the resistance level to ceftazidime down to 2 µg/ml . We also tested the effect of the in frame deletion of the blaOXA-28 ribosome binding site ( GAAGGT ) , or its substitution by a sequence with no ribosome binding properties ( CTCTCT ) . Finally , we tested the substitution of the ATG start codon with either a GTC or a GTG valine codons , which have no or weak translation initiation power [21] . None of these mutations led to a change in resistance level ( Figure 1C ) . Hence , we confirmed that detected OXA-28 came entirely from the processing of the ORF2-OXA28 fusion protein , and that the inefficiency of its maturation was in part responsible for the low resistance level to ceftazidime . Additionally , the putative ribosome binding site of gcuF1 ( TTAGG ) is predicted to have a poor translation initiation efficiency [22] , [23] ( Figure 4A ) , likely leading to reduced translation of gcuF1-blaOXA-28 . In patient 1 , the transition from S-Pae to R-Pae1 was observed after treatment with two antibiotics , ceftazidime and metronidazole , both known to activate the bacterial SOS response [12] , [24] . Cassette expression in class 1 integrons can be controlled by two promoters , Pc and P2 , which can exist under various forms [4] . P2 is created by the insertion of three guanines between the potential −35 and −10 regions , but this insertion also disrupts the LexA binding box ( also called the SOS box ) of the intI1 promoter and abrogates the SOS control of intI1 expression [4] . Analysis of the S-Pae class 1 integron revealed a functional LexA-binding box overlapping the −10 box of the intI1 promoter [25] , thus the P2 promoter is absent and the cassettes' expression only relies on the strong Pc promoter variant PcS ( Figure 4B ) . It has been previously found that the encoded integrase IntI1 ( IntI1R32_N39 ) displayed the second highest excision activity of the four known existing variants [4] . In agreement with this , using nested-PCR , we confirmed the presence of free circular gcuF1 cassettes in S-Pae ( Figure 3A , B ) , occurring through recombination between its own attCgcuF1 site and the aacA4 attCaacA4 site , as observed previously for a few other cassettes [2] . LexA is the transcriptional repressor that binds the SOS box sequences to silence transcription . RecA , once activated by the presence of abnormal single strand DNA produced by a variety of stimuli that includes antibiotic exposure , induces the LexA autoproteolysis and releases the transcriptional silencing driven by LexA binding to SOS boxes . We hypothesized that the excision of the gcuF1 cassette by the integrase IntI1 and subsequent emergence of full resistance to ceftazidime in R-Pae1 was a result of the SOS response induced by antibiotic therapy in the patient . We quantified the expression of SOS pathway genes recA and lexA , as well as the integrase encoding gene intI1 by RT-qPCR after in vitro induction with metronidazole and ceftazidime . Our results indicated that in vitro exposure of S-PaeΔampC to the minimal inhibitory concentration of ceftazidime ( 2 µg/ml ) neither triggered the SOS response ( recA and lexA were not induced ) , nor enhanced the expression of the intI1 gene ( Figure 5A ) . We measured the frequency of ceftazidime-resistant mutant emergence by gcuF1 cassette excision in the same experimental conditions . Consistent with the integrase expression data , we found that the gcuF1 cassette excision frequency remained basal after exposure to ceftazidime ( Figure 5B ) . On the contrary , in vitro exposure of S-PaeΔampC to therapeutic concentrations of metronidazole ( 50 µg/ml for recA wild-type strain and 25 µg/ml for recA mutants ) triggered the SOS response , as indicated by the increased recA and lexA expression , and the increase in intI1 expression , gcuF1 cassette excision and a subsequent 34-fold enhancement of the frequency of emergence of ceftazidime-resistant mutants ( Figure 5A , B ) . As shown with the results obtained in recA-deleted and recA-complemented strains , the effect of metronidazole on the integron rearrangement fully depended on the presence of recA , confirming the role of the SOS induction for the cassette rearrangement ( Figure 5A , B ) . In mutants isolated both in patient 1 ( R-Pae1 ) and in vitro ( M-Pae ) , the gcuF1 cassette excision provoked full blaOXA-28 expression and a massive increase in resistance to ceftazidime ( Figure 1C ) .
In this study , we identified a new mechanism of modulation of antibiotic resistance in integrons . The positional regulation of gene cassette expression was already documented , but this was considered so far as only relying on the transcription attenuation process and on the lack of transcriptional coupling between genes carried in consecutive cassettes [6] . What we describe here is the presence of a genetic element , the gcuF1 cassette , upstream of the integron-borne β-lactamase cassette blaOXA-28 which modulates the transcription , translation , and secretion of this enzyme , all at once . The poor ribosome-binding site found upstream of gcuF1 ( Figure 4A ) [23] is likely responsible for the low production of the fusion protein detected , but is also likely responsible for the low level of blaOXA-28 mRNA . Indeed , it has been shown that a reduced ribosome binding to RBS can destabilize mRNA , which then becomes more vulnerable to endonucleolytic attack [26] . GcuF1 shares 78% identity with integron-borne orfD gene cassettes of unknown function that are frequently found in clinical strains of Pseudomonas sp . and Enterobacteriaceae [1] . The insertion of gcuF1 generates a fusion protein GCUF1-OXA-28 with a misplaced signal peptide between the GCUF1 and the OXA-28 domains . However , the GCUF1-OXA-28-producing bacteria still demonstrated residual resistance to ceftazidime , consistent with the presence of small amount of the processed OXA-28 in its periplasm . Using various mutants constructed in this aim , we established that the OXA-28 produced from gcuF1-blaOXA-28 was exclusively derived from cleavage at position 121 of the fusion protein GCUF1-OXA-28 ( Figure 4A ) . These data confirm that a protein with a misplaced cleavable leader sequence ( i . e . outside the N-terminus ) can be exported , although less efficiently , into the periplasm [27] . We showed that the gcuF1 cassette can be excised by the IntI1 integrase , leading to the production of a circularized cassette . The gcuF1 cassette carries an attC site with an unusual R″ box , with a T instead of a C in last position , as in the large majority of integron cassettes ( Figure 4A ) . The gcuF1 closest relative is a cassette found in the Acidovorax sp . JS42 genome ( GenBank accession number CP000539 ) , which is 87% identical over the whole cassette sequence , but shows a CC at this precise position . Thus the substitution of this dinucleotide by a single T explains why the spacer between the R″ and L″ boxes is reduced to 4 nucleotides , instead of the usual 5 nucleotides in gcuF1 ( Figure 4A ) . The last base of R″ is normally pairing with the first base of the R′ box in the single strand recombinogenic form of the attC site [28] . Frumerie and colleagues tested all possible base pairs ( C/G , A/T , G/C and T/A ) at this position in the predicted annealed R″/R′ , and found that all deeply decreased the recombination rate , by more than a hundred fold factor [29] . However the effect of single base substitutions at these positions is so far unknown , the observations made in our study suggest that substitution of the conserved C in R″ by a T does not abolish the attC recognition and recombination , but the effect of this mutation , as well as the one brought by the R″/L″ spacer reduction , on the rate of recombination needs to be established . We found that excision of the gcuF1 cassette from the original cassette array leads to increased resistance to ceftazidime ( Figure 1C ) . As the expression of IntI1 is controlled by the SOS response , we surmised the antibiotic treatment given in first instance to this patient ( ceftazidime and metronidazole ) to be responsible for the SOS induction episode that ultimately led to the IntI1-mediated gcuF1 deletion . We found that , in contrast to ceftazidime , in vitro exposure to therapeutic concentrations of metronidazole , an antimicrobial against which P . aeruginosa is naturally resistant , greatly enhanced the frequency of emergence of ceftazidime-resistant mutants . This phenotype is dependent on the excision of the gcuF1 cassette that is fully dependent on the SOS response , as attested by the lack of excision in a recA mutant ( Figure 5B ) . We speculate that in patient 1 , metronidazole likely promoted the SOS-dependent transition from S-Pae to R-Pae1 , which was further selected by ceftazidime therapy . Interestingly , Cipriano Souza et al . showed that previous consumption of metronidazole was an independent risk factor for acquisition of multi-drug resistant P . aeruginosa by hospitalized patients [30] . Metronidazole and related 5-nitroimidazoles are redox-active prodrugs . Metronidazole is widely used to treat anaerobic bacteria infections , ( e . g . Clostridium difficile ) , protozoa , and the microaerophilic Helicobacter pylori [31] . Bacterial nitroreductases , such as RdxA in H . pylori , catalyze the conversion of metronidazole to mutagenic products that directly interact with DNA bases [32] , [33] . This causes DNA helix destabilization and single- and double-strand DNA breakage [34] that activate the SOS response [7] , [24] . The effect of metronidazole in P . aeruginosa , in terms of DNA damage has still to be established , but one can speculate that the RdxA homolog in P . aeruginosa ( PA5190 in the PAO1 genome , http://www . pseudomonas . com ) could play a similar role in the metabolism of the metronidazole , and explain how this antibiotic triggers the SOS induction . Our data suggest that SOS induction by antibiotics can result in the development of integron-based resistance in vivo . SOS also enhances the rate of mutations [7] . This is of particular concern in P . aeruginosa in which multidrug resistance mainly arise from chromosomal mutations [35] . More generally , it may lead to undesired changes in the behavior of bacteria and their faster adaptation to hostile environments . This is alarming because apart from metronidazole , other major classes of antibiotics ( e . g . β-lactams , aminoglycosides , trimethoprim and fluoroquinolones ) can trigger the bacterial SOS response [11]–[13] . The expression of horizontally acquired antibiotic resistance mechanisms is tightly regulated; this may reduce the biological cost associated with resistance expression and account for the dissemination of susceptible strains carrying hidden resistance determinants [36] , [37] . Here , in S-Pae , expression of antibiotic resistance is silenced until antibiotic exposure triggers expression . This could represent an efficient evolutionary pathway for resistance determinants to be “switchable” and render bacteria fitness-neutral in the absence of antibiotic selection pressure [37] . Current policies for controlling the spread antibiotic resistance often rely on the detection of resistant bacteria , and on the assumption that resistance has a functional cost [38] . Future antibiotic restriction guidelines should consider the fact that resistance genes can spread latently in susceptible isolates with low biological cost . In summary , we describe a reversible mechanism modulating an acquired antibiotic resistance in bacteria . The metronidazole-induced SOS response favored the emergence in a patient of bacteria highly resistant to ceftazidime that could then spread to twelve other patients which were under antibiotic pressure . The suppression of the SOS response activation has been reported to enhance killing by antibiotics of E . coli and to increase survival of infected mice [39] , [40] . Efforts have been made to identify small molecules and short peptides that inhibit RecA activity , although the absence of potential adverse effects on Rad51 ( the human RecA homologue ) needs to be demonstrated [41]–[43] . Our results suggest an adaptive role for the antibiotic-induced SOS response in bacterial genome rearrangement in vivo within humans . Altogether , this supports the hypothesis that inhibition of RecA is a plausible therapeutic adjuvant in combined therapy to reduce the capacity to generate antibiotic-resistant mutants .
We identified a multidrug-resistant P . aeruginosa strain ( R-Pae ) in 13 patients hospitalized in the hematological ward of the University Hospital of Besançon ( France ) from March 2004 ( Patient 1 ) to December 2009 ( Patient 13 ) . The genetic similarity of P . aeruginosa clinical isolates was investigated by pulsed field gel electrophoresis ( PFGE; CHEF-DR III; Bio-Rad , Hercules , California ) with the use of DraI enzyme , as described elsewhere [44] . We retroactively analyzed the bacterial isolates of patient 1's early specimens . Twenty-eight days before pulmonary infection with R-Pae1 , this patient was colonized with S-Pae , a clonally-related isolate that was more susceptible to β-lactams than R-Pae . Early and late sputums only contented S-Pae and R-Pae , respectively . In review of the patient record , patient 1 was treated with ceftazidime ( 4 g/day for 8 days ) for P . aeruginosa and also with metronidazole ( 500 mg/day for 7 days ) for infections by anaerobes prior to the isolation of R-Pae1 . Oligonucleotides , bacterial strains and plasmids used for this study are detailed in the Tables S2 , S3 and S4 , respectively . The minimal inhibitory concentrations ( MICs ) of selected antibiotics were determined by the conventional Mueller-Hinton agar ( MHA ) dilution method , and interpreted according to CLSI ( Clinical and Laboratory Standards Institute ) guidelines [45] . The wild-type reference strain of P . aeruginosa PA14 was used as a control in susceptibility testing . MHA was supplemented with 1 mM of IPTG for strains carrying pBTK27 derivatives ( Table S4 ) . Total RNA was isolated from cultures at an absorbance at 600 nm of 1 . 0 ( or otherwise stated ) using the Qiagen RNeasy protocol ( Qiagen , Valencia , California ) . The RNA samples were treated with DNase ( Turbo DNAse; Ambion , Austin , Texas ) and further cleaned according to the manufacturer's protocol . Total RNA was quantified using the RiboGreen RNA Quantitation Kit ( Molecular Probes , Carlsbad , California ) . Total RNA was reverse transcribed with Superscript III reverse transcriptase ( Invitrogen , Carlsbad , California ) as specified by the supplier . Quantitative PCR was performed on an Mx4000 Multiplex QPCR System ( Stratagene , Santa Clara , California ) using samples in triplicate with 25 ng of total RNA in a 20 µl reaction using SYBR Green PCR Master Mix ( Applied Biosystems , Carlsbad , California ) and specific primers for housekeeping gene rpsL , blaOXA-28 , ampC , recA , lexA , and intI1 ( Table S2 ) . PCR cycling conditions consisted of 95°C for 10 min , and 40 cycles of 95°C for 15 s , 60°C for 1 min . After each assay , a dissociation curve was run to confirm specificity of all PCR amplicons . The mRNA levels of ampC and blaOXA-28 were normalized to that of reference gene rpsL [46] and expressed as a ratio to the levels in the isolate PA14 ( for ampC ) or R-Pae1 ( for blaOXA-28 ) in which the values were set at 1 . 00 . For recA , lexA , and intI1 genes , resulting Ct values were converted to nanograms , normalized to total RNA and expressed as the average of triplicate samples . We assessed the presence in S-Pae isolate of free circular forms of the gcuF1 cassette . Total DNA from isolates R-Pae1 ( without gcuF1 , taken as a control ) and S-Pae ( with gcuF1 ) were PCR amplified with primers circ1 and circ2 ( Figure 3A , Table S2 ) . The purified PCR products were used as templates for a second nested PCR with primers circ3 and circ4 . PCR products were visualized on an agarose gel . The PCR fragment obtained from S-Pae DNA was further sequenced to verify its specificity . To determine whether the DNA element gcuF1-blaOXA-28 could transcribe a functional transcript , we carried out RT-PCR reactions by using PCR primers overlapping the gcuF1-blaOXA-28 junction ( overlap1 and overlap2 , Table S1 ) , and cDNA prepared from S-Pae RNA as the matrix ( see above ) . The nucleotide sequence of the amplicon was determined to check for the specificity of the reaction . For the deletion of ampC from PA14 and S-Pae , approximately 1000-bp specific fragments upstream ( with primers AmpCdel1F/AmpCdel1R ) and downstream ( with primers AmpCdel2F/AmpCdel2R ) of ampC were PCR amplified from PA14 and S-Pae total DNAs , and used in an overlap extension reaction to create a single 2 , 000-bp product ( Table S1 ) . These products were cloned into Gateway-compatible gene replacement vector pEX18AmpGW [47] , yielding the plasmids pDelAmpC-PA14 and pDelAmpC-SPae ( Table S3 ) , which were then transformed into E . coli DH5α . For recA inactivation in S-PaeΔampC , 5′ ( ca . 450-bp ) and 3′ ( ca . 530-bp ) portions of recA were amplified separately with primer pairs RecAdel1F/RecAdel1R and RecAdel2F/RecAdel2R , respectively . The tetA gene was amplified from plasmid mini-CTX1 with primers TetF and TetR ( Table S1 ) . These three fragments were cloned simultaneously in a 4 way ligation in the EcoRI/HindIII sites of pEX18ap to yield plasmid pDelRecA ( Table S3 ) . The plasmids for ampC or recA deletion were transferred into the recipient strains ( PA14 or S-PaeΔampC ) by triparental mating that included the donor strain E . coli DH5α with strain E . coli HB101 ( containing helper plasmid pRK2013 ) , followed by selection with irgasan ( 25 µg/ml ) and carbenicillin ( 150 µg/ml for PA14 , 500 µg/ml for S-PaeΔampC ) and screening for P aeruginosa transconjugants with the deletion as previously described [48] . Deletion of the ampC and inactivation of recA were verified by PCR and sequencing . The resistance level to ceftazidime conferred by the production of OXA-28 , GCUF1-OXA-28 , and their derivatives was assessed by cloning gcuF1-blaOXA-28 ( PCRed from S-Pae with primers 1 and 3 ) and blaOXA-28 ( PCRed from R-Pae1 with primers 2 and 3 ) sequences into the broad host range vector pBTK27 . This yielded plasmids pBTK/gcuF1-oxa28 and pBTK/oxa28 , respectively , encoding C-terminal polypeptides that were expressed in the reference strain P . aeruginosa PA14ΔampC ( Table S3 ) . We used the plasmid pBTK/gcuF1-oxa28 as template for various mutageneses with a QuikChange kit ( Stratagene ) . We inserted a TGA stop codon downstream gcuF1 with mutagenic primers stop-F and stop-R , yielding plasmid pInsSTOP . We deleted in frame the GAAGGT sequence including the natural blaOXA-28 ribosome binding site ( GAAGG ) with mutagenic primers delRBS-F and delRBS-R , yielding plasmid pDelRBS , which encodes this GCUF1-OXA-28 variant missing amino acids E100 and G101 . We also substituted the sequence harboring the blaOXA-28 ribosome binding site with a sequence with no translation initiation power ( GAAGGT by CTCTCT ) using mutagenic primers replRBS-F and replRBS-R , yielding plasmid pReplRBS . The ATG start codon from blaOXA-28 was substituted by GTC or by GTG with mutagenic primers ( RepATG1-F/RepATG1-R and RepATG2-F/RepATG2-R , respectively ) yielding plasmid pReplATG1 and pReplATG2 , respectively , which encodes the M103V GCUF1-OXA-28 variant ( Tables S2 and S4 ) . All pBTK27-derivated plasmids were introduced into the reference strain P . aeruginosa PA14ΔampC by triparental mating ( see above ) to assess the resistance to ceftazidime . To determine the size of the encoded proteins , blaOXA-28 ( PCRed from R-Pae1 with primers 5 and 6 ) and gcuF1-blaOXA-28 sequences ( PCRed from S-Pae with primers 4 and 6 ) were cloned into the pET-28a vector ( Kmr; Novagen-Merck , Darmstadt , Germany ) at NheI/XhoI , yielding plasmids pET/oxa28 and pET/gcuF1-oxa28 , respectively , encoding N-terminal His-tagged polypeptides . The cloned gene products were expressed in E . coli BL21 ( DE3 ) by IPTG induction ( 0 . 2 mM ) to the exponentially growing cells ( A600 of 0 . 8 ) and left overnight at 20°C with shaking . Bacteria were harvested and lysed using standard protocols . The lysates were applied on a 5 ml Ni-NTA column ( Qiagen ) . His-tagged peptides were eluted with PBS supplemented with 250 mM imidazole . Eluted fractions were separated by 12% SDS-PAGE and transferred to nitrocellulose filters . Filters were hybridized with the His-detector Ni-HRP reagent ( KPL ) and the immune complexes were detected by the ECL-Plus chemiluminescent system ( GE Healthcare , Buckinghamshire , United Kingdom ) . To assess the presence of His-tagged OXA-28 or GCUF1-OXA-28 in the periplasm , the plasmids pBTK/oxa28 or pBTK/gcuF1-oxa28 in P . aeruginosa PA14ΔampC was induced by 1 mM IPTG for 4 h at 37°C . Periplasmic fractions were prepared by using Peripreps Periplasting kit ( Epicentre Biotechnologies , Madison , Wisconsin ) and analyzed by SDS-PAGE , transfer , and hybridization ( see below ) . The raw integrated density of the blots was assessed using the ImageJ 1 . 44p software ( National Institute of Health ) . We cloned recA ( PCRed from S-Pae with primers RecA1 and RecA2 ) sequence into the broad host range vector pBTK27 , yielding plasmid pBTK/recA ( Tables S2 and S4 ) . Plasmid pBTK/recA was introduced into the strain S-PaeΔampCΔrecA by triparental mating ( see above ) . S-PaeΔampC , S-PaeΔampCΔrecA , and S-PaeΔampCΔrecA carrying pBTK/recA or pBTK27 plasmids were used to determine the frequency of emergence of ceftazidime-resistant mutants by gcuF1 cassette excision . The gene ampC was deleted to avoid the emergence of resistant mutants overproducing this intrinsic β-lactamase . Bacteria were grown in LB broth ( Luria-Bertani ) overnight , then diluted 1∶250 and grown until OD600 = 0 . 3 . Half of the cultures were then exposed to antibiotics ( mitomycin C , 1 MIC for 1 . 5 h; ceftazidime , 1 MIC for 1 h; metronidazole , 1/40 MIC for 15 h ) . MICs of mitomycin C were 1 . 0/0 . 3 µg/ml , those of ceftazidime were 2/2 µg/ml and those of metronidazole were 2000/1000 µg/ml for S-Pae derivatives with or without the recA gene , respectively . Exposure to antibiotics in these conditions did not alter the growth rates of the bacteria . Metronidazole concentrations used for SOS response induction experiments ( 25 and 50 µg/ml ) were in the range of those found in the plasma of treated patients [49] . Cultures were collected by centrifugation and washed twice with 0 . 9% NaCl . Appropriate dilutions in 0 . 9% NaCl were plated on MH plates with or without ceftazidime at 50 µg/ml . We checked the gcuF1 excision by PCR in 133 clones growing on ceftazidime-containing media , obtained after exposure to mitomycin C , metronidazole and ceftazidime . Only one of them ( 0 . 8% ) still displayed the gcuF1 cassette . The gcuF1 excision rate under antibiotic stress was then estimated as the number of ceftazidime-resistant colonies divided by the number of plated cells . The result was expressed as the ratio of the excision rates with and without antibiotic ( for wild-type and ΔrecA strains ) and with or without recA ( for recA-complemented strain ) . All assays were independently performed at least 3 times . All the samples were subjected to microscopic observation to ascertain that no filamented cells were present . We confirmed that the isolate S-Pae was not a hypermutator ( see Text S1 ) . Approval and written informed consent from all subjects or their legally authorized representatives were obtained before study initiation . The study was approved by the ethical committee ‘Comité d'Etude Clinique’ of the Besançon hospital , Besançon , France . Student's t-tests were used to determine statistical significance for comparisons of gene expression ( Figure 5A ) and frequencies of emergence of ceftazidime-resistant mutants ( by gcuF1 excision; Figure 5B ) with and without antibiotic ( for wild-type and ΔrecA strains ) and with or without recA ( for recA-complemented strain ) . Data were log transformed and variance estimates were pooled over similar experiments , resulting in pooled estimates of standard error of 0 . 24 with 65 degrees of freedom for the t-tests of Figure 2A and of 0 . 54 with 27 degrees of freedom for the t-tests of Figure 5B . Graphical examination supports the assumption of normality and homogeneous variation across experiments for the gene expression data Figure 5A and frequency the emergence of ceftazidime-resistant mutant data Figure 5B expressed on a log scale . The chosen significance threshold was 0 . 05 for all tests .
|
The bacterial SOS response is a conserved regulatory network that is induced in response to DNA damage . Its activation in vitro leads to the emergence of resistance to antibiotics , leading to speculation that this is an important clinical mechanism of acquiring resistance . We found evidence here that antibiotic-induced SOS response plays a role in bacterial genome rearrangement in vivo within humans . The major classes of antibiotics can trigger the bacterial SOS response and our data raise questions about their wide use and their subsequent effect on the bacterial genetic adaptability . This suggests that emergence of antibiotic resistance during therapy could be reduced by inhibiting the bacterial SOS response . We showed that acquired resistance genes could spread latently in susceptible bacterial strains until needed . These findings could impact current policies for control of antibiotic resistance , which rely on the detection of resistant bacteria and on the assumption that resistance mechanisms have a functional cost to the bacteria . More generally , SOS response may spur changes in the behavior of bacteria and their faster adaptation to hostile environments , including humans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"bacteriology",
"medicine",
"bacterial",
"diseases",
"infectious",
"diseases",
"bacterial",
"evolution",
"biology",
"evolutionary",
"biology",
"genomic",
"evolution",
"microbiology",
"infectious",
"disease",
"control",
"bacterial",
"pathogens"
] |
2012
|
Evidence for Induction of Integron-Based Antibiotic Resistance by the SOS Response in a Clinical Setting
|
Bacterial pathogens and their toxins target host receptors , leading to aberrant behavior or host death by changing signaling events through subversion of host intracellular cAMP level . This is an efficient and widespread mechanism of microbial pathogenesis . Previous studies describe toxins that increase cAMP in host cells , resulting in death through G protein-coupled receptor ( GPCR ) signaling pathways by influencing adenylyl cyclase or G protein activity . G protein-coupled receptor kinase 2 ( GRK2 ) has a central role in regulation of GPCR desensitization . However , little information is available about the pathogenic mechanisms of toxins associated with GRK2 . Here , we reported a new bacterial toxin-Bacillus bombysepticus ( Bb ) α-toxin that was lethal to host . We showed that Bb α-toxin interacted with BmGRK2 . The data demonstrated that Bb α-toxin directly bound to BmGRK2 to promote death by affecting GPCR signaling pathways . This mechanism involved stimulation of Gαs , increase level of cAMP and activation of protein kinase A ( PKA ) . Activated cAMP/PKA signal transduction altered downstream effectors that affected homeostasis and fundamental biological processes , disturbing the structural and functional integrity of cells , resulting in death . Preventing cAMP/PKA signaling transduction by inhibitions ( NF449 or H-89 ) substantially reduced the pathogenicity of Bb α-toxin . The discovery of a toxin-induced host death specifically linked to GRK2 mediated signaling pathway suggested a new model for bacterial toxin action . Characterization of host genes whose expression and function are regulated by Bb α-toxin and GRK2 will offer a deeper understanding of the pathogenesis of infectious diseases caused by pathogens that elevate cAMP .
Infectious diseases caused by pathogens result in deaths . Mechanisms of how infection of hosts leads to death have been studied in detail for many pathogens that involve destruction of the cell membrane , inhibition of protein synthesis , or activation of second messenger pathways . However , many questions of bacterial pathogenesis are relatively unexplored , such as which pathogen proteins interact with the host and which infection mechanisms and pathways are commonly triggered by pathogens . These aspects of host-pathogen systems determine the fate of pathogen infections and disease outcomes . Interactions between host and pathogen in host-pathogen systems are vital for initiating infection . These interactions are associated with regulated pathways that govern a variety of cellular activities and bring about structural and functional disarray within cells that affect survival and fate of the host . An important aspect of host-pathogen systems is the mechanism by which toxins secreted by many pathogenic organisms alter signaling events through interaction with host cell receptors to elevate cellular cAMP concentrations , leading to aberrant activity or cell death . Toxins secreted by Bordetella pertussis enhance receptor-mediated GTP-induced activation of adenylate cyclase ( AC ) , resulting in increased cAMP [1] . Cholera toxin increases cAMP level through ADP ribosylation activity toward heterotrimeric G protein [2] . The acylpeptide of Bacillus subtilis shows the capacity of inhibitor for cAMP-degrading phosphodiesterases ( PDEs ) to increase the cAMP levels [3] . The binding of Cry1Ab toxin to Bt-R1 activates G protein to elevate cellular cAMP [4] . These toxins interact with different receptors , provoking cell death by altering signaling pathways to increase cAMP and influence downstream effectors . Therefore , subversion of host signal pathways to change cAMP levels via toxin interaction with receptors is an efficient and widespread mechanism of microbial pathogenesis . In the present study , we have identified and characterized a previously undescribed type of molecular mechanism by which bacterial pathogens increase host cAMP concentration . G protein-coupled receptor kinases ( GRK ) regulate G protein-coupled receptors ( GPCR ) that alter signal transducers with a direct or potential impact in cellular proliferation [5] . GRKs share a common structure comprising a well-conserved central catalytic domain and a C-terminal domain of variable length and structure [6] . Although GRKs show a different tissue expression profiles , subcellular localization , and action [7] , they mostly localize at the plasma membrane [8] . Among the family of GRKs , GRK2 have an essential physiological role in the control of growth and development by modulation of GPCRs [9] . Apart from the essential physiological function , changes in GRK2 abundance and activity are an important pathophysiological feature of diseases that have been identified in coronary artery disease [10] . Furthermore , GRK2 has been proposed as a multi-functional protein that interacts with a number of receptors , including EGFR [11] and insulin receptor [12] , which are involved in the regulation of several cellular functions controlling larval development . However , the potential involvement of GRK2 in infection of hosts by bacterial pathogens has not been addressed . In this study , we report on a molecular mechanism that used by the toxin of the bacterial pathogen Bacillus bombysepticus ( Bb ) . The toxin binds to GRK2 to promote larval death associated with interfering GPCR signaling pathway , which activates G protein , increases cAMP and stimulates protein kinase A ( PKA ) . Activation of the cAMP/PKA signaling initiates a series of events that affected homeostasis and fundamental biological processes such as the cytoskeleton or ion channels et al , disturbing the structural and functional integrity of cells , resulting in death . Characterization of host genes whose expression and function are regulated by Bb α-toxin and GRK2 will offer a deeper understanding of the pathogenesis of infectious diseases caused by pathogens that elevate cAMP .
To investigate the molecular mechanisms underlying the regulation of bacteria toxin production by Bb , we searched the genome sequence to identify Bb toxins responsible for Bb pathogenicity . The complete Bb sequence was obtained from high-throughput Solexa paired-end sequences [13] . We indentified a predicted extracellular protein that was named Bb α-toxin . The deduced amino acid sequence of Bb α-toxin displayed limited sequence similarity to a range of pore forming toxins as determined by a BlastP search . To investigate the expression pattern of Bb α-toxin during Bb pathogens infection silkworm , reverse transcription polymerase chain reaction ( RT-PCR ) was used . The result shows that Bb α-toxin was actually expressed by Bb during authentic infection of silkworm ( S1 Fig ) . In order to determine the role of Bb α-toxin in the pathogenesis of Bb , we generated a Bb α-toxin mutant strain ( ΔBb α-toxin ) by CRISPR/Cas 9 genome editing ( S2 Fig ) and tested its virulence in silkworm . Interestingly , the ΔBb α-toxin mutant strain demonstrated decreased pathogenicity . Survival analysis of 5th-instar silkworm larvae after infection with wild-type Bb or ΔBb α-toxin mutant strain showed that only 50% of the larvae infected with ΔBb α-toxin mutant died within 5 days , while 80% of larvae infected with wild-type Bb died by this point ( Fig 1A ) . The attenuated virulence of the ΔBb α-toxin mutant strain indicated that Bb α-toxin is involved in Bb pathogenicity . To further explore the relationship of biological function between toxic capacity of Bb α-toxin and Bb pathogenicity , a recombinant protein with an N-terminal His-tag was purified ( Fig 1B ) . In the present works , we examined the morphological changes associated with Bb α-toxin protein-treated BmE cells that are silkworm embryo derived cells . As seen in Fig 1C , time-lapse microscopy showed that BmE cells , upon toxin protein exposure , underwent dramatic cytological changes including altered size , shape , and lysis , as compared with untreated viable BmE cells . In order to better characterize the role of Bb α-toxin , we used trypan blue to assess cytotoxicity; cells treated with Bb α-toxin protein eventually died ( Fig 1D ) . Bioassays showed that larvae died after Bb α-toxin protein treatment ( S3A Fig ) , but untreated wild type silkworm ( WT ) survived ( S3B Fig ) . Another negative control of nontoxic CR12 protein was used to exclude the possibility of effects from protein purification ( S3C Fig ) . Survival rates of newly exuviated 5th-instar silkworm larvae were 54 . 4% for infection Bb α-toxin protein , and 100% for CR12 or controls ( Fig 1E ) , with an LD50 of 66 . 1 μg per larva at 5th-instar . The survival rate of 4th-instar larvae was 32 . 5% after Bb α-toxin infection ( Table 1 ) . The approximate LD50 value calculated for 4th-instar larvae was 34 . 5 μg per larva . The mortality statistics indicated that Bb α-toxin protein was cytotoxic and pathogenic for cells or silkworms . Bacterial toxins kill target cells through receptor-mediation and receptor-disruption of essential cytosolic function . In insects , midgut invasion by pathogens is studied because this is the first line of resistance and immune response . Midgut brush border membrane vesicles ( BBMVs ) have many receptors for bacterial toxins to participate in pathogenesis [14] . To determine whether or not Bb α-toxin protein toxicity involved the midgut , pull-down assays and far-western blots were performed . His-tag recombinant Bb α-toxin protein was incubated with total proteins isolated from midgut BBMVs and bound proteins were isolated by affinity chromatography . As shown in Fig 2A , a 70-kDa protein bound to Bb α-toxin was detected in pull-down assays . Far-western blots showed that the 70 kDa protein immunologically reacted with His-tag antibody ( Fig 2B ) . The band of 70 kDa was excised from gels for quantitative liquid chromatography-mass spectrometry ( qLC-MS ) analysis . The LC-MS analysis showed that one of the components in this band is GRK2 ( S1 Table ) , which was originally identified as the kinase that mediates GPCR desensitization and signal transduction [15] . BmGRK2 have transcription activity in BmE cells ( S4 Fig ) . To confirm the interaction of Bb α-toxin protein and BmGRK2 , the ORF of BmGRK2 was cloned by RT-PCR and recombinant BmGRK2-Flag was expressed and purified ( S5 Fig ) . BmGRK2-Flag protein was incubated with anti-Flag antibody bound to beads and crosslinked . After incubation with Bb α-toxin protein , BmGRK2-Flag-bound protein was purified by co-IP with anti-Flag antibody . Samples were separated by SDS-PAGE and probed with His-tag antibody ( Fig 2C ) . Anti-Flag antibody bound to beads incubated with Bb α-toxin protein was used as a negative control to exclude Bb α-toxin directly interacting with beads or anti-Flag antibody . Among the BmGRK2-Flag co-IP products , a product with the molecular mass of Bb α-toxin protein was detected by His-tag antibody ( Fig 2C ) . Far-western blots of Bb α-toxin and BmGRK2 proteins showed that BmGRK2 interacted with Bb α-toxin ( Fig 2D ) . Calculation of the apparent binding affinities obtained by the saturation ELISA binding assays ( Fig 2E ) revealed that Bb α-toxin protein bound BmGRK2 with high binding affinity ( Kd = 96 . 58 ) . The results of these assays confirmed that Bb α-toxin protein bound to BmGRK2 . In the model of GPCR signaling pathway , GRKs terminate GPCR signaling via desensitization of GPCRs by phosphorylation to prevent receptor and G protein association and degrade cAMP [16 , 17] . cAMP has been implicated in modulation of signaling related to cell death in bacteria to higher eukaryotes [18–20] . Based on Bb α-toxin protein binding to BmGRK2 , we hypothesized that a cAMP pathway was affected by Bb α-toxin protein effort the action of GRK2 , involving stimulation of G protein , cAMP and PKA . The production of cAMP is stimulated by Gα and subsequent cAMP binds to PKA to activate catalytic subunits of PKA that , in turn , phosphorylate downstream effector proteins [21 , 22] . To test our hypothesis , we analyzed the activity of signaling molecules after Bb α-toxin protein oral infection of silkworm larvae . Because elevation of intracellular cAMP levels is hallmark of GPCR pathway activation [23] , we measured intracellular cAMP of Bb α-toxin protein-treated silkworms and BmE cells at different times . In silkworm larvae continuously exposed to toxin , cAMP production was significantly and consistently increased in the midgut ( Fig 3A ) . In Bb α-toxin protein-exposed BmE cells , cAMP production increased in a time-dependent manner ( Fig 3B ) . To determine whether Bb α-toxin protein binding to BmGRK2 could exert an effect in GPCR signaling to alter the balance of intracellular cAMP levels , we examined cAMP accumulation in response to forkolin , a direct activator of membrane ACs that is known to promote cAMP production . The EC50 for forskolin-stimulated cAMP was significantly reduced in Bb α-toxin protein-treated BmE cells as compared with untreated-BmE cells ( Fig 3C ) , indicating that interaction with Bb α-toxin protein affected the function of GRK2 in a manner that appeared to lead to sensitization of GPCR receptor signaling . Thus , these results suggested that Bb α-toxin protein bound to BmGRK2 to alter the functional action of GRK2 in the GPCR pathway that is critical for stimulating cAMP production . The production of cAMP is controlled by activation of membrane-bound ACs , which is activated by Gαs [21 , 22] . To ascertain whether G protein activity was involved in the Bb α-toxin protein-induced pathway , we used two cell-permeable inhibitors , NF449 [24] , that selectively antagonizes Gαs , and NF023 [25] , a G protein antagonist that inhibits the G protein α-subunit Gαi . BmE cells preincubated ( 30 min ) with NF449 were less sensitive to Bb α-toxin protein than cells not treated with inhibitor ( Fig 4A ) . In fact , the concentration of cAMP was significantly reduced in NF449-treated cell when compared to NF449-untreated cells that exposed to Bb α-toxin protein ( Fig 4B ) . Moreover , cytotoxicity of Bb α-toxin protein decreased by 41 . 6% when BmE cells were incubated with NF449 ( Fig 4C ) . The Bb α-toxin protein-induced BmE cytotoxicity was not significantly inhibited by NF023 and caused cAMP levels to significantly increase as compared to the control ( Fig 4A–4C ) . As can be seen in vivo , silkworm larvae that were injected with NF449 and after 30 minutes , treated with Bb α-toxin protein had midgut cAMP levels that were less sensitive to Bb α-toxin than larvae not treated with NF449 inhibitor and had no effect by NF023 ( Fig 4D ) . To determine whether continuously high cAMP concentrations caused larval death , we also investigated toxicity of Bb α-toxin protein with injected inhibitors . The results showed that 62 . 5% of Bb α-toxin protein-treated larvae and 1 . 67% of controls died ( Fig 4E ) . The toxicity of Bb α-toxin protein decreased to 15 . 0% in larvae injected with NF449 ( Fig 4E ) . The 56 . 7% lethality for Bb α-toxin after injection with NF023 was not different from Bb α-toxin protein infection . We concluded from these results that stimulation of Gαs protein led to a continuous increase in production of cAMP and was involved directly in the toxicity of Bb α-toxin protein , causing death . Generally , PKA activity depends on cAMP concentration [26] . To determine whether the toxicity of Bb α-toxin protein was mediated by a cAMP/PKA signaling event , we tested the effects of PKA activity . A representative gel demonstrating the separation of phosphorylated and non-phosphorylated kemptide for PKA effective activity is shown . Qualitative assessment of PKA activity revealed that there were high levels in Bb α-toxin protein-treated BmE cells as compared to controls ( Fig 5A ) or silkworm larvae ( Fig 5B ) ; a rough estimate of PKA activity was calculated based on the phosphorylated kemptide . We found that activation of the PKA were terminated by NF449 inhibitor in vitro ( Fig 5C ) and in vivo ( Fig 5Da ) after Bb α-toxin protein treatment , whereas NF023 had no effect ( Fig 5C and 5Db ) . These results indicated that Bb α-toxin affected PKA activity . Next , we tested the effects of a PKA inhibitor , H-89; this competitive inhibitor interferes with the utilization of ATP by PKA . H-89 was introduced to BmE cells in a preincubation step followed by the addition of Bb α-toxin protein . The results showed that H-89 treatment of BmE cells reduced PKA activity when cells were exposed to Bb α-toxin protein ( Fig 5E ) . Moreover , the characteristic morphological changes were partially prevented and blocked cell death by PKA inhibitor ( Fig 5F ) . As can be seen in Fig 5G , the cytotoxicity of Bb α-toxin protein decreased by 38 . 23% when BmE cells were incubated with H-89 , indicating that the reduction or elimination of PKA activity abolishes the action of Bb α-toxin protein and prevents the death of cells . Indeed , inhibition of PKA activity by injection of H-89 resulted in a dosage-dependent decrease in the lethality of toxin-exposed larvae ( Fig 5H ) . These results demonstrated that inhibition of PKA abolished Bb α-toxin action and cAMP-dependent PKA was critical for Bb α-toxin action in mediating downstream death activity . Thus , the death of cell or larvae is stimulated by and requires PKA activity . Activated PKA phosphorylates substrates that control diverse cellular phenomena . Previous researchers have defined the cAMP-response element binding ( CREB ) proteins , an inducible transcription factor , as one of PKA substrates that mediate an increase in gene expression in response to cAMP/PKA signaling [27–29] . To evaluate the effect of PKA substrates on the PKA signaling pathway with toxicity of Bb α-toxin protein , western blotting analysis was performed to detect the expression of p-CREB , which is an indicator of PKA activity . Phosphorylation of CREB was significantly increased by the addition of Bb α-toxin protein in BmE cells ( Fig 6A ) and larvae ( Fig 6B ) . To examine if the requirement for Bb α-toxin protein in CREB-dependent transactivation was restricted to cAMP/PKA-mediated activation , we tested the effects of the inhibitors , NF449 , NF023 and H-89 . The cAMP level was significantly reduced by NF449 treatment as compared to cells that exposed to Bb α-toxin protein ( Fig 4B and 4D ) . NF449 treatment of BmE cells ( Fig 6C ) or silkworm larvae ( Fig 6D ) caused a significant decrease in the degree of phosphorylation of CREB , whereas this propensity was absent treated with NF023 ( Fig 6C and 6D ) . Furthermore , pretreatment of BmE cells with H-89 inhibitor significantly decreased the phosphorylation of CREB when cells were exposed to the Bb α-toxin protein ( Fig 6E ) . These results were consistent with the transcriptional level of CREB by qRT-PCR analysis ( S7 Fig ) . Therefore , these support the phosphorylation of CREB is occasioned by α-toxin .
In this article , we demonstrated that Bb α-toxin protein binding to BmGRK2 , a key GPCR regulatory kinase , led to continuous upregulation of its downstream effects , which include stimulation of Gαs , increase in cAMP and activation of PKA , to induce host death . Bb α-toxin protein exerted pathogenicity in toxin treatments of cells and larvae ( Fig 1C–1E and Table 1 ) . Overall , our data indicated that the effects of BmGRK2 binding with Bb α-toxin lead to a dysregulation in GPCR desensitization that influenced cAMP/PKA signaling , which was associated with toxicity ( Figs 2–6 ) . Increased intracellular cAMP levels is a hallmark of activation of cAMP-related signal transduction pathways that mediate cell death or growth [30 , 31] . Preventing cAMP production by inhibition of Gαs with NF449 substantially reduced the pathogenicity of Bb α-toxin protein , whereas inhibitor of Gαi ( NF023 ) had no effect on toxicity ( Fig 4 ) . The physiological effects of cAMP signaling are mediated via PKA effector molecules [32 , 33] . PKA activity assays showed that continuous , high-level activation of PKA in cell or larvae midguts after Bb α-toxin protein treatment was maintained following cAMP increase ( Fig 5A–5D ) . Pretreatment of larvae with H-89 , an inhibitor of PKA , protected host from the action of Bb α-toxin protein ( Fig 5F–5H ) . GRK2 regulates GPCR phosphorylation resulting in receptor desensitization that involved in modulation of most physiological processes [5 , 10] . GRK2 recruitment and activation affects the signals emanating from GPCRs to regulate the balance of intercellular cAMP levels and directly or potentially impacting cell cycle progression and proliferation [34] . Attenuation or depletion of Drosophila Gprk2 in embryos or adult flies induces dysfunction of muscles , loss of fibers , and flightless behavior [35] . In vertebrates , GRK2 hemizygous mice shows the importance of GRK2 in hypertension [36] . Furthermore , GRK2 ablation causes embryonic lethality , supporting that GRK2 has a central , general role in key cellular processes [37] . Similarly , alteration of GRK2 abundance and activity are associated with inflammation , cardiovascular disease , and tumors , suggesting that alterations contribute to initiation or development of pathologies [10 , 38 , 39] . We found that Bb α-toxin protein , which was lethal to host , interacted with BmGRK2 , indicating that continuous exposure to Bb α-toxin had prompted alterations GRK2 and coupled signaling components; in turn , this activated the cell death machinery . Bb α-toxin binding to BmGRK2 has an effect on action of GRK2 . This leads to untimely functional interactions of GRK2 with pathways that interrupt GPCR , resulting into impairing cell cycle progression associated with the progression of larvae death . This result reveals a previously unknown activity of GRK2 in silkworm development . Elevation of intracellular cAMP levels is hallmark of GPCR pathway activation [23] . GRK2 regulates the desensitization of GPCR that leads to rapid degradation of cAMP preventing downstream signal transduction [17 , 34] . cAMP is ubiquitous and crucial for pathogen-host interactions , which are implicated in modulation of signaling and promoting cell death in a variety of species [18–20] . Induction of cell death through elevation of host organism cAMP is an efficient and powerful evolutionary strategy by which pathogenic microbes overcome a host [1–4 , 40 , 41] . We demonstrate that cAMP concentrations consistently increased after Bb α-toxin protein treatment . Forskolin-stimulated cAMP is enhanced in Bb α-toxin protein-treated BmE cells as compared with untreated cells indicating that GPCR desensitization is reduced . This suggests that the binding of Bb α-toxin protein to BmGRK2 reduced the GRK2-mediated desensitization of GPCR signaling that enhanced signaling via cAMP . In mammals , many multipass plasma membrane-bound isoforms of cAMP-producing ACs have been identified [42 , 43] . Regulation of ACs is primarily by GPCR , which releases a GPCR-associated α-subunit of heterotrimeric G protein ( αβγ ) , that binds to Gα with ACs , leading to cAMP synthesis . Depending on the nature of the α-subunit , ACs either are inhibited ( αi ) or activated ( αs ) [43] . cAMP production and lethality were prevented by NF449 , an inhibitor of Gαs , after Bb α-toxin protein treatment demonstrating that regulators of the Gαs protein signaling were critical for cAMP synthesis involvement in the pathogenic toxicity of Bb α-toxin protein and increased cAMP levels drives death of host . Unfortunately , the results can not be used to determine how Bb α-toxin binding to GRK2 induces GPCR desensitization . However , these problems could be solved if we will identify the target of GRK2 from 90 putative GPCRs [44] and clarity the mechanism of GPCR desensitization in silkworm . Despite its preliminary character , this study can clearly indicate that Bb α-toxin protein led to death through Bb α-toxin binding to BmGRK2 and altering the balance of intracellular cAMP levels to promote host death . The cellular effects of cAMP are usually mediated by PKA [26] . Binding of cAMP to PKA activates the catalytic subunits of proteins that phosphorylate a set of target proteins to control diverse cellular phenomena [45] . Disruption of PKA activity results in the destabilization of cell processes [46] . Overexpression of the constitutively active PKA catalytic subunit led to dilated cardiomyopathy and cardiomyocyte hypertrophy [47] . Here , Bb α-toxin protein-induced cAMP production was able to trigger the activation of PKA and its downstream effectors , as evidenced by CREB transcription factor phosphorylation . The transcription factor CREB acts downstream of PKA signaling pathways . After phosphorylation on serine , CREB binds to the CREB binding protein ( CBP ) to regulate the transcription of various target genes involved in cell proliferation , differentiation and survival [48] . Abundant evidence suggest that phosphorylated CREB play a direct role in disease pathogenesis , including mediating the malignant behavior of tumor cells [49] or acute lymphoblastic leukemia [50] . However , it remains to be explored the mechanisms downstream of PKA/CREB signaling participates in the process of disease . Notwithstanding its limitation , this study does demonstrate the essential role of PKA activation in the Bb α-toxin protein pathogenic mechanism by experiments showing that mortality changes is impaired if PKA activity is inhibited by H-89 . Activated PKA alters downstream effectors to affect cell homeostasis , thereby regulating fundamental biological processes such as blood pressure or metabolism to dismantle cells . The cAMP/PKA signaling pathway is considered to be involved in metabolism , proliferation and development , and some researchers have further claimed that the stimulation of cAMP/PKA signal transduction pathway represents a novel mechanism for regulating cell death . These can be supported by the injection of cAMP , or ectopic expression of a constitutively activated form of Gα or PKA , induced cell death , implicating cAMP as the second messenger in the cell death pathway [51] . Studies with transgenic mouse models have revealed that deregulation of the cAMP/PKA pathway can cause apoptosis [52] . Specifically , as seen in cAMP/PKA pathway mediated cardiomyocyte apoptosis in Grave’s disease , which ca result in heart failure [53] . Deregulation of the cAMP/PKA pathway has been implicated in a range of human diseases [54] . In Drosophila , activation of the cAMP/PKA signaling pathway is required for cell death in wing epidermal cells [51] . Further , many pathogens are also known to interfere with host cell signaling to promote cell death via cAMP/PKA pathway [1–5] . The present work demonstrates that in cells and tissues , cAMP/PKA pathway has a major role in the regulation of cell death . Little is known about the downstream molecular mechanisms of cAMP/PKA-triggered cell death . Further analysis of the targets of cAMP/PKA will likely link this signaling pathway with the components that directly regulate cell death . In conclusion , our results demonstrated that host death occasioned by Bb α-toxin protein is a complex cellular response to pathology . This paper puts forward a previously undescribed model for bacterial toxin action ( Fig 7 ) . The model is a series of events that are confined to or associated with GRK2 action in the pathogenesis of Bb α-toxin protein and provide insights into molecular pathogenesis involving G protein activation , cAMP production , and PKA activation . In the model , Bb α-toxin binds specifically to BmGRK2 , affecting the signal transduction of the GPCR pathway . This action stimulates Gαs , resulting in accumulation of cAMP and activation PKA ( pathway 1 ) . cAMP/PKA signaling is required for cell death [51] . PKA is the key cell death component . Nevertheless , the underlying molecular mechanisms used by cAMP/PKA to control programmed cell death are complex and remain unclear . Determining how the molecular mechanisms of cAMP/PKA signaling pathway leads to death will be useful . Our model suggests a mechanism by which many bacterial toxins challenge hosts through toxin-receptor interaction , manipulating critical reactions associated with cellular responses . Nevertheless , the Bb α-toxin protein affects a signaling pathway involving GPCR through GRK2 . This mechanism does not resemble classical bacterial toxins that cause larval death through AC activity [1] , ADP ribosylation activity that modulates G protein activity [2 , 55] , inhibition of PDEs [3] , delivery of cAMP from its own cytosol into cytosol of macrophages [56] , or binding to Bt-R1[4] to elevate cellular cAMP concentrations . Likewise , rescue experiments using chemical inhibition show only partial restoration of phenotypes to WT levels . This may suggest additional pathways , or mechanisms , apart from cAMP/PKA that results in Bb a-toxin susceptibility . Alternatively , GRK2 is reported to both positively and negatively regulate signals downstream of receptor tyrosine kinases ( RTKs ) including IGF-1 , PDGF , EGF , insulin , and NGF receptors . These receptors are involved in cell cycle phases [57] , but whether these signaling pathways participate in the pathogenic mechanism of Bb α-toxin protein is unknown ( pathway 2 ) . It is possible that modulation of some other signaling pathway is not through pathway 1 or pathway 2 but through other unknown receptors ( pathway 3 ) . Therefore , further characterization of host genes whose expression and function are regulated by Bb α-toxin protein will offer a deeper understanding of the pathogenic mechanism of infectious diseases caused by pathogens .
The silkworm B . mori strain Dazao ( P50 ) was reared on fresh mulberry leaves at 25°C under a photoperiod of 12 h light and 12 h darkness . Under these conditions , the newly exuviated 4th-instar and 5th-instar larvae were used to further experiments . Bb , a bacterial pathogen of the silkworm , was kindly provided by Professor Yanwen Wang ( Silkworm Diseases Laboratory of Shandong Agriculture University , China ) . The BmE cells [58] were cultured in GRACE medium supplemented with 10% ( v/v ) fetal bovine serum ( FBS ) at 27°C . The procedure of Bb infection silkworm were according to Lin et al [59] . Total RNA was extracted using Bacterial RNA kit ( Omega ) following the manufacturer’s instructions . 2 ug of total RNA was then subjected to a Dnase treatment according to the manufacturer’s instructions ( Invitrogen ) . The RNA was reverse transcribed using M_MLV reverse transcriptase according to the manufacturer's instructions ( Promega , USA ) . The cDNA sample was used as the templates for RT-PCR . PCR was done with 27 cycles in a total reaction volume of 25 uL and products were analyzed by electrophoresis in 1% ( w/v ) agarose gels . To generate the pUC57-gRNA-Cas9 plasmid , we designed that promoter sequence according to Citorik et al . [60] and gRNA sequences were placed upstream and downstream of two BbsI enzyme sequences . Cas9 expression was driven by the T7 promoter . The whole sequence was synthesized and inserted into pUC5-T-simple plasmid using Genscript service , forming pUC57-gRNA-Cas9 . Guiding sequences of Bb α-toxin gRNAs were synthesized as two reverse complement oligos which were annealed and inserted into BbsI-treated pUC57-gRNA-Cas9 , forming pUC57-Bb α-toxin-gRNA-Cas9 . This plasmid was then used to generate the stain ΔBb α-toxin , which contained a deletion of the gene Bb α-toxin . Genomic DNA of Bb was extracted utilizing the E . Z . N . A . bacterial DNA kit ( Omega ) . The ORF of Bb α-toxin was cloned into the pET-28a expression vector using the BamHI and XhoI . Total RNA was extracted from the silkworm midgut tissues using TRIzol reagent ( Roche ) and reverse transcribed as described [59] . The ORF of BmGRK2 were cloned into the pET-28a expression with Flag tag on the C-terminal ends of the target sequences using XbaI and XhoI . The correct clones were transformed into Escherichia coli BL21 ( DE3 ) to express Bb α-toxin and BmGRK2 proteins . BmE cells were harvested and seeded in 96-well plates ( Costar ) at a 3×104 cell/well concentration and allowed to grow attached to the bottom surface of the plate . Growth medium was replaced with fresh medium containing Bb α-toxin at 50 μg/mL . After addition of Bb α-toxin , images of BmE cells were recorded at 0 , 60 , 180 , and 360 min with an Olympus TH4-200 microscope . BmE cells were preincubated for 30 min with NF449 ( 1μM ) , NF023 ( 1μM ) , or H-89 ( 20μM ) , respectively , before the addition of Bb α-toxin for cytotoxicity assays . Cell death was determined by typan blue exclusion . These experiments were performed according to the trypan blue staining kit protocol ( Beyotime ) . When observed with a microscope , the dead cells appeared blue and the live cells appeared colorless . The total number of cells per field was counted . Final values represent an average of 6 fields that were randomly selected for each treatment; three separate experiments were performed in triplicate . The BmE cells incubated with Bb α-toxin were treated with increasing concentrations of forskolin ( 0–100 μM ) for 60 min and lysed with cAMP Lysis Buffer . In addition , quantitative determination of cAMP , PKA activity , and phosphorylates of CREB with BmE cells were also analyzed after these treatment as below . Silkworm natural infections with Bb and ΔBb α-toxin strains were carried out as described previously by Huang et al . [61] and all infections were performed with bacterial preparations adjusted to an OD = 100 which correspond to 1 . 2E11 colony forming units per ml . Virulence assays were performed at least three times . The survival of silkworms after infection with Bb α-toxin was investigated . Newly exuviated 4th-instar and 5th-instar larvae were fed on fresh mulberry leaves coated with purified Bb α-toxin for a dose of 50 μg per larva . 120 larvae were tested and recorded for mortality within 5 days . Furthermore , the 5th-instar silkworm larvae were injected with NF023 ( 1 μM/mg ) , NF449 ( 1μM/mg ) , and H-89 ( 0–60 μM/g ) for 30 min , respectively , before Bb α-toxin treatment for bioassays . In addition , quantitative determination of cAMP , PKA activity , and phosphorylates of CREB were also analyzed after these treatment as below . Insect midguts were dissected from silkworm larvae and used to prepare the total proteins of midgut brush border membrane vesicles ( BBMVs ) by differential precipitation using MgCl2 [62] and stored at -80°C until use . For pull-down experiments , 200 ug total proteins were mixed with 100 ug recombinant Bb α-toxin with a His tag and incubated at 4°C with gentle agitation for 12 h . Ni-NTA resin ( 100 μL ) was added and incubated at 4°C for 30 min . After washing five times with PBS , protein was eluted from the resin with eluting buffer ( PBS with 500 mM imidazole ) . Eluted samples were separated on SDS-PAGE and analyzed by qLC-MS/MS which was done by Shanghai Applied Protein Technology Co . Ltd . For far-western blots , total proteins ( 50 μg ) were separated by 12% ( wt/vol ) SDS-PAGE and transferred to PVDF membranes . One membrane was used for direct immunoblotting using anti-His antibody ( 1:8 , 000 ) as a negative control . Another membrane was used for far-western blot analysis incubated with Bb α-toxin and probed with anti-His antibody ( 1:8 , 000 ) to detect protein interactions . All of the membranes were washed in TBST ( 20 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl , 0 . 05 Tween-20 ) five times , with each wash lasting 10 min , and the procedures as described for far-western blots was performed according to the method of Wu et al . [63] . To identify BmGRK2 protein interacted with Bb α-toxin , BmGRK2 with a Flag tag was used as bait in co-IP assays . BmGRK2-Flag protein ( 100 μg ) was incubated with anti-Flag antibody bound to Protein G magnetic beads ( Thermo Fisher Scientific ) and crosslinked using BS3 ( Thermo Fisher Scientific ) according to the manufacturer’s protocols . After washing the beads , Bb α-toxin ( 100 μg ) was incubated overnight at 4°C with gentle agitation . Complexes were eluted after washing in PBS five times . Absence of BmGRK2-Flag was the negative control . Eluted samples were separated using SDS-PAGE , followed by western blot analysis with anti-His antibody . Far-western blots were performed to identify interactions between BmGRK2 and Bb α-toxin as described above . A [add a space] summary of ELISA protocol was described by Lin [64] . After experimental treatments of BmE cells and silkworm larvae as above , samples were harvested and washed in buffered saline solution three times . All of the samples were dissected and sonicated in Cell lysis buffer 5 on ice , and freeze/thaw cycle twice before centrifugation at 600 g for 10 min . Supernatants were used for cAMP assays . cAMP levels were measured using cAMP Assay kits ( R&D Systems ) according to the manufacturer’s protocols . All samples and standards were assayed in duplicate , and the results were averaged . Concentration were determined by reference to a standard . Statistical analysis was analyzed with GraphPad using student t-tests . The BmE cells or midgut tissue samples after treatment were harvested , washed in PBS and resuspended in PKA extraction buffer ( 25 Mm Tris-HCl pH 7 . 4 , 0 . 5 mM EDTA , 0 . 5 mM EGTA , 10 Mm beta-mercaptoethanol , 1 μg/mL leupeptin , and 1μg/Ml ) that incubated on ice for homogenization . Samples were cleared by centrifugation and Supernatants were used for PKA assay . The qualitative activity measurements of PKA were used PepTag non-radioactive protein kinase assays ( Promega , WI , USA ) according to the manufacturer’s description . qRT-PCR and western blot was performed as described by Lin [64] . After experimental treatments of BmE cells and silkworm larvae as above , the total proteins were harvested and separated in 12% ( wt/vol ) SDS-PAGE , and proteins were transferred to PVDF membranes . The p-CREB antibody ( 1:1 , 000; Santa Cruz Biotechnology , Santa Cruz , CA ) and Histone H3 antibody ( 1:1000; Beyotime ) was used as the primary antibody to detect the phosphorylation of CREB . Statistical analysis was analyzed with GraphPad ( GraphPad Software , LaJolla , CA ) using student t-tests . No significant difference between the session is indicated; P>0 . 05 and statistically significant differences are indicated; * P<0 . 05 , **P<0 . 01 . The LC50 was calculated using GraphPad Prism software version 5 . 0 for Windows .
|
Interference with regulation of host signaling by pathogens can alter gene expression , leading to functional disarray in the host cells that causes abnormal division or death . Here , we propose a previously undescribed model for how bacterial toxins subvert host processes via interaction with GRK2 that influences cAMP/PKA signaling . Our findings provide new fundamental information about how bacterial pathogens regulate host signal transduction to cause death , which offers additional perspectives in host-pathogen systems . These findings will help to advance our understanding of bacteria pathogenic mechanism . Furthermore , these might extend to other microbial pathogenesis and assist in designing new or safer strategies against pathogens .
|
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2016
|
Bacillus bombysepticus α-Toxin Binding to G Protein-Coupled Receptor Kinase 2 Regulates cAMP/PKA Signaling Pathway to Induce Host Death
|
We assessed the role of myeloid dendritic cells ( mDCs ) in the outcome of SIV infection by comparing and contrasting their frequency , mobilization , phenotype , cytokine production and apoptosis in pathogenic ( pigtailed macaques , PTMs ) , nonpathogenic ( African green monkeys , AGMs ) and controlled ( rhesus macaques , RMs ) SIVagmSab infection . Through the identification of recently replicating cells , we demonstrated that mDC mobilization from the bone marrow occurred in all species postinfection , being most prominent in RMs . Circulating mDCs were depleted with disease progression in PTMs , recovered to baseline values after the viral peak in AGMs , and significantly increased at the time of virus control in RMs . Rapid disease progression in PTMs was associated with low baseline levels and incomplete recovery of circulating mDCs during chronic infection . mDC recruitment to the intestine occurred in all pathogenic scenarios , but loss of mucosal mDCs was associated only with progressive infection . Sustained mDC immune activation occurred throughout infection in PTMs and was associated with increased bystander apoptosis in blood and intestine . Conversely , mDC activation occurred only during acute infection in nonprogressive and controlled infections . Postinfection , circulating mDCs rapidly became unresponsive to TLR7/8 stimulation in all species . Yet , stimulation with LPS , a bacterial product translocated in circulation only in SIV-infected PTMs , induced mDC hyperactivation , apoptosis and excessive production of proinflammatory cytokines . After infection , spontaneous production of proinflammatory cytokines by mucosal mDCs increased only in progressor PTMs . We thus propose that mDCs promote tolerance to SIV in the biological systems that lack intestinal dysfunction . In progressive infections , mDC loss and excessive activation of residual mDCs by SIV and additional stimuli , such as translocated microbial products , enhance generalized immune activation and inflammation . Our results thus provide a mechanistic basis for the role of mDCs in the pathogenesis of AIDS and elucidate the causes of mDC loss during progressive HIV/SIV infections .
Myeloid dendritic cells ( mDCs ) are potent antigen-presenting cells which are responsible for initiating both innate and adaptive immune responses . mDCs stimulate NK , B and T cells [1] , [2] , but can also act as “watch-dogs” to sense and regulate aberrant immune activation , induce tolerance and thus prevent autoimmune diseases [2] . The ability of mDCs to act as immune sentinels is conferred by their capacity to migrate into lymphoid organs where they secrete cytokines and initiate immune responses [2] . Altogether these characteristics suggest that mDCs may have a critical role in the pathogenesis of human and simian immunodeficiency virus ( HIV/SIV ) infections , in which progression to AIDS is driven by excessive generalized immune activation and inflammation [3] . A considerable amount of correlative data reporting changes in mDC counts during pathogenic HIV/SIVmac infections has been published [4]–[14] , showing that mDC depletion from circulation occurs during acute HIV/SIV infection , at the time of peak viremia , and persists throughout the chronic infection and progression to AIDS [4] , [7] , [11] , [14] . The exact mechanism of mDC depletion is not yet understood . In SIV-infected nonhuman primates ( NHPs ) , mDC loss is inversely correlated with virus loads ( VLs ) [7] , the NHPs that rapidly progress to AIDS having high VLs and a profound loss of mDCs [3] . Conversely , SIVmac-infected rhesus macaques ( RMs ) with normal or delayed disease progression have lower VLs and increased numbers of circulating mDCs [7] . NHP studies corroborate reports in HIV-infected patients showing that antiretroviral therapy restores mDCs and suggesting that direct killing of mDCs by the virus is a potential mechanism . mDCs can indeed be infected by HIV-1 , the virus burden in mDCs isolated from chronically HIV-1-infected patients being considerable [15] . However , mDC depletion from circulation was also reported for HIV-2–infected patients and RMs infected with SIVmac ( which is closely related to HIV-2 ) , albeit mDCs appear to be less susceptible to HIV-2 infection [16] , suggesting that additional factors may be responsible for mDC depletion . Other studies have suggested that mDC loss from the circulation may occur through either increased apoptosis [17] or mDC recruitment to the lymph nodes ( LNs ) [7] , [18]–[20] . While mDCs are pivotal in shaping the mucosal microenvironment , no study to date has been carried out in either humans or animal models of HIV infection to explore whether or not the loss of circulating mDCs is due to recruitment to mucosal tissues such as the intestine , which is the main target of viral replication and where a high degree of inflammation occurs [21] . It was previously shown that a loss of the chemokine balance in the LN environment occurs in SIV-infected macaques and results in recruitment of mDCs to LNs [7] , . Similar changes may also occur in the mucosal tissues , leading to recruitment of immune cells , including activated mDCs , to these sites [24] . Thus , the exact role of mDCs in the pathogenesis of AIDS is unknown , yet may be essential for designing better vaccines and therapeutic interventions . Loss of mDCs during pathogenic HIV/SIV infections prevents a clear understanding of their precise role in HIV/SIV pathogenesis . One option towards realizing this goal may rely on comparing and contrasting the dynamics , trafficking and function of mDCs in lentiviral infections with variable pathogenic outcomes . There are three pathogenic outcomes of lentiviral infections: ( a ) The persistent progressive infection occurs in the majority of cases of HIV infection and SIVmac/smm infection of macaque species and is characterized by ( i ) massive , continuous viral replication [25]–[27] , with VL setpoint being predictive for the time of progression to AIDS [28]–[30]; ( ii ) continuous depletion of CD4+ T cells from the peripheral blood [31] , [32] that is more pronounced at mucosal sites [21] , [33]–[35]; and ( iii ) high levels of T cell immune activation [36] , [37] , the magnitude of which has been reported to be predictive of disease progression [36] , [37] . The interaction between these factors cripples the immune system and eventually results in severe immunodeficiency and death [31] , [32] , [38] . ( b ) The persistent nonprogressive infection is observed in African NHPs that are natural hosts of SIV , such as African green monkeys ( AGMs ) , sooty mangabeys ( SMs ) and mandrills ( reviewed in [39] ) , and is characterized by ( i ) active viral replication , with setpoint VLs similar to or even higher than those reported in pathogenic infections [40] , [41]; ( ii ) transient depletion of peripheral CD4+ T cells during acute infection that rebound to preinfection levels during chronic infection [39] , [40]; ( iii ) significant CD4+ T cell depletion in the intestine that can be partially restored during the chronic infection , regardless of significant viral replication [41]–[43]; ( iv ) low levels of CD4+ T cells expressing the CCR5 coreceptor in blood and tissues [44] , [45] , and ( v ) transient and moderate increases in immune activation and T cell proliferation during acute infection , which is resolved to near baseline levels with the transition to chronic infection [42] , [46] , [47] . Altogether , the action of these factors results in an active , persistent SIV infection , which only rarely progresses to AIDS in natural hosts [48] . ( c ) The controlled infection occurs in a minority ( 1–5% ) of HIV-infected individuals , which are defined as long-term nonprogressors ( LTNPs ) . LTNPs that have undetectable VLs for at least 1 year are referred to as elite controllers ( ECs ) ( reviewed in [49] ) . The main characteristics of LTNP infections are ( i ) infection for more than 7 years; ( ii ) stable CD4+ T cell counts greater than 600 cells/µl; ( iii ) low/undetectable levels of HIV in the peripheral blood; ( iv ) no symptoms of HIV-induced disease; and ( v ) presence of a vigorous immune response against HIV , with multifunctional , persistent CD4 and CD8 responses . ECs that control immune activation in addition to VLs are referred to as superelite controllers . Similarly , SIV infection in NHPs may result in a controlled infection: in a fraction of Indian RMs infected with SIVmac and carrying restrictive MHC genotypes ( MaMu A*01 , B*17 , B*08 ) , in Chinese macaques , or upon cross-species transmission of SIVs [50]–[55] . By exposing RMs to SIVagmSab , we have recently developed an animal model in which superelite control/functional cure of SIV infection occurs in 100% of cases [56] . To date , all information on mDCs in HIV/SIV infections is derived from studies performed on persistent progressive ( pathogenic ) infections ( reviewed in [15] ) . Very few of these studies focused on mDCs at mucosal sites [57]–[60] . Furthermore , to our knowledge , the role of mDCs in the pathogenesis of either persistent nonprogressive or controlled lentiviral infections has not yet been established . Hence , to test the hypothesis that distinct mDC profiles are associated with particular pathogenic outcomes , we dissected the kinetics of mDCs in blood , LNs and at mucosal sites in persistent progressive infections in PTMs , nonprogressive persistent infections in AGMs and controlled infections in RMs after inoculation with the same virus strain ( SIVagmSab ) . We report that the three pathological outcomes are associated with different dynamics of mDCs . To explain these outcomes , we examined the mobilization , migration , activation and apoptosis of mDCs in three animal models . We corroborated this descriptive data with mDC functional studies using cells isolated from blood and intestine from SIVagmSab-infected PTMs , AGMs and RMs . Altogether , our results indicate that mDCs play a significant role in driving the outcome of SIV infection .
Five PTMs , four AGMs , and five RMs were intravenously infected with the 300 TCID50 of SIVagmSab92018 [61] . All of the PTMs developed significant lymphadenopathy during the acute infection and exhibited signs of disease progression during the follow-up: two experienced weight loss during acute infection , followed by rapid progression to AIDS ( 78 and 104 days postinfection , dpi ) ; two additional PTMs progressed to AIDS within a year postinfection; the remaining PTM did not progress to AIDS during the follow-up , but showed profound CD4+ T cell depletion and persistently high VLs , which are indicative of progressive , pathogenic infection . The PTMs with AIDS presented with anorexia/cachexia , diarrhea , LN and spleen atrophy , neurological disease , thrombotic microangiopathy ( TMA ) , glomerulonephropathy and myocarditis . RMs had marked lymphadenopathy during acute infection . No clinical sign of infection was observed during acute infection in AGMs or during chronic infection in AGMs and RMs . Acute VLs peaked at day 8–14 dpi ( Figure 1a , b and c ) and were similarly high in all three species . However , there were slight species-specific variations , with PTMs having the highest VLs , followed by RMs and AGMs; the different peak levels correlated with different levels of CD4+ T cells expressing the CCR5 molecule ( the target cells of SIVagmSab ) , which are the highest in PTMs , intermediate in RMs and the lowest in AGMs [44] . In stark contrast to this uniform viral replication during the acute infection , chronic viral replication patterns were completely divergent between the three models ( Figure 1 ) : VLs remained high in PTMs and showed two patterns of chronic replication: noncontrolled in the two rapid progressors ( chronic VLs of 108 SIVagmSab RNA copies/ml , i . e . , a reduction of less than 1 . 5 logs from the peak VLs ) ; in the remaining three PTMs , a relative control was observed ( chronic VLs of 105–106 SIVagmSab RNA copies/ml , i . e . , a contraction of 3 logs from the peak VLs ) ( Figure 1a ) . Conversely , in AGMs VLs reached a setpoint level of 4×104 copies/ml by 42 dpi , which was maintained throughout the follow-up ( Figure 1b ) , in agreement with our previous studies showing that this setpoint can be maintained for decades in AGMs [62] . Finally , SIVagmSab-infected RMs gradually controlled VLs , which became undetectable from 60 dpi on and remained so throughout the follow-up ( Figure 1c ) , in agreement with our previous studies showing that viral control can extend beyond 5–6 years [56] . As a first step of dissecting the role of mDCs in driving different outcomes of SIV infection , we assessed their characteristics in blood , LN and intestine in the three species of NHPs prior to infection . We first characterized mDCs in PTMs , AGMs and RMs by applying the strategy previously reported to identify mDCs in RMs [6] , [7] and showed that selection based on Lineageneg , HLA-DR+ and CD11c+ identified mDCs with similar phenotypic characteristics in all three species in blood ( Figure 2a ) , LNs ( Figure 2b ) and intestine ( Figure 2c ) . Characterization of mDCs from blood , LNs and intestinal mucosa revealed , however , different levels of HLA-DR expression between the three sites ( Figure 2 ) : low in peripheral blood , high in the LN and intestine . This phenotypic variation is probably due to differences in mDC maturation stages: immature mDCs with low HLA-DR expression are present in circulation , while more mature mDCs expressing higher levels of HLA-DR are found in the LNs and mucosal tissues . Comparison of mDC baseline levels between PTMs showed that the two rapid progressor PTMs had lower peripheral baseline mDC counts compared to the normal progressors . As the number of rapid progressor PTMs included in the study group precluded statistical analysis of the data , we compared the baseline levels of mDCs between rapid and normal progressors in a larger cohort of PTMs from previous studies . This analysis confirmed an inverse correlation between the low numbers of circulating mDCs prior to infection and the rapid disease progression of SIVagmSab-infected PTMs ( Figure S1 ) , thus suggesting a protective role of mDCs during the course of HIV/SIV infection . Using a random effects model to analyze the dynamics of mDC during infection in all animals , we found that there were significant differences in the changes over time across the 3 infection models ( p = 0 . 0003 ) . We then analyzed in more detail the changes in circulating mDCs at key time points of SIVagm infection in the three species to determine whether or not differences in the dynamics of peripheral mDCs could explain the different pathogenic outcomes . Although dynamics of acute virus replication were similar in all three infections , the number of mDCs significantly dropped only in PTMs ( Figure 3a ) and AGMs ( Figure 3b ) , but not in RMs ( Figure 3c ) . Furthermore , the duration and timing of acute mDC depletion were different in PTMs and AGMs: rapid ( from 1 dpi ) and maintained throughout the acute infection in PTMs ( Figure 3a ) , while very transient , reaching significance only around the peak of viral replication in AGMs ( Figure 3b ) . mDC depletion from circulation prior to detectable viremia is not surprising , as in the mouse model the virus is already present in the LNs and that DCs are migrating to the LNs carrying the virus 30 minutes post intravenous HIV inoculation [63] . Major differences in the dynamics of mDCs became discernible between the three models during chronic SIV infection . In PTMs , progression to AIDS was characterized by a significant decline of mDCs ( Figures 3a ) . To confirm that mDC depletion is specifically associated with disease progression , we compared mDC counts prior to SIV infection and during chronic SIV infection in animals included in other studies and showed that mDCs are indeed lost with disease progression ( Figure S2 ) . Our results thus corroborate previous reports documenting loss of peripheral mDCs during pathogenic HIV/SIV infections [7] . In AGMs , mDC levels returned to near baseline levels with the transition to chronic infection , then were maintained throughout follow-up ( Figure 3b ) . Finally , in RMs , a 300% increase from the baseline mDC counts occurred during early chronic infection , coincident with the control of viral replication ( Figure 3c ) . Once the VLs were controlled , the circulating mDCs declined in RMs but remained significantly increased compared to baseline values long after the viremia was completely controlled ( Figure 3c ) . Our results show that the degree of mDC recovery during chronic infection rather than their acute depletion predicts disease progression in SIV-infected NHPs . These results corroborate previous studies showing that higher peripheral mDC counts are associated with slower disease progression in SIVmac-infected RMs [7] . We further investigated the mechanisms responsible for the distinct dynamics of peripheral mDCs observed in progressors vs nonprogressors vs controllers . To determine if mDC depletion is due to direct virus killing , we first correlated the absolute mDC counts with the VLs in each species between 8 and 120 dpi . The use of the same virus to infect these three distinct NHP species eliminates the variables related to the ability of different SIV/HIV to infect and thus directly kill mDCs . A strong negative correlation ( using mixed-effects models ) was found in AGMs between higher VLs and lower mDC counts ( p = 0 . 006 ) ( Figure 3e ) , yet we could not establish any significant association of the same parameters in PTMs ( p = 0 . 43 ) ( Figure 3d ) and RMs ( p = 0 . 15 ) ( Figure 3f ) infected with SIVagm . To assess the role of virus killing in inducing mDC loss , we further sorted mDCs from the LNs collected from all three species and showed that mDCs from PTMs , AGMs and RMs do not harbor detectable levels of SIVagmSab DNA ( data not shown ) . Conversely , a cellular fraction containing Lineage+ cells ( and representing a mixture of CD3+ T cells and B lymphocytes ) harbored high amounts of virus in all the three species included ( data not shown ) . This result is not surprising , as the recent description of SAMHD-1 restriction factor suggested that dendritic cells cannot be infected by primate lentiviruses that do not contain a vpx gene , as is the case of SIVagmSab92018 [64] . We next investigated if differences in mDC mobilization from the bone marrow in SIVagmSab-infected PTMs , AGMs and RMs can explain the variation in numbers of circulating mDCs in these three species . Since mDCs rarely undergo division in the periphery and tissues [2] and Ki-67 antigen is not expressed by cells in the resting phase ( G0 ) , Ki-67 expression by circulating mDCs is indicative of active or recent cell division , i . e . , production and mobilization from bone marrow , the primary site where mDCs undergo cell division and differentiation [65] . Overall , there was a significant difference in the dynamics of Ki67+ mDC in circulation among the three species ( p = 0 . 0054 ) . Ki-67 expression by peripheral mDCs increased in the PTMs throughout SIVagmSab infection ( Figure 4a ) , thus excluding the possibility that loss of mDC in this species is due to their lack of mobilization from the bone marrow . Even rapid progressor PTMs that had lower mDC counts at baseline showed a clear tendency to restore these cells in the periphery through increased mDC mobilization from bone marrow ( Figure S3 ) . This shows that the massive mDC loss is not due to a defect of bone marrow specific to the rapid progressors , but rather to other factors . Increased mDC mobilization from BM was also observed in AGMs ( Figure 4b ) and RMs ( Figure 4C ) in the early stages of SIVagm infection and was maintained throughout the follow-up . Interestingly , the highest mDC mobilization from bone marrow was observed in the RM controllers ( Figure 4C ) , which may be the mechanism behind the increased mDC counts observed in SIVagm-infected RMs . However , with the exception chronic RM infection , in which significant increases in Ki-67 expression by mDC are accompanied by significant increases in mDC counts ( Figure 3c ) , there were no other direct positive correlation between the degree of mDC mobilization from bone marrow and the peripheral mDC counts in the other species . Our results indicate that mDC mobilization from bone marrow occurs very early postinfection in all pathogenic scenarios and is maintained unaltered even during progression to AIDS . Yet , this increased mDC mobilization alone is not sufficient to preserve healthy counts of peripheral mDC in SIV-infected NHPs , and most likely other factors play a significant accessory role in the preservation or loss of these cells from the circulation . To determine whether or not mDC recruitment to LNs and intestine is responsible for their loss from circulation and to establish if this outcome drives disease progression , we first used flow cytometry to assess mDC dynamics in the LNs and intestine during acute and chronic SIV infection in the three types of SIV infection . We found differences in the changes in mDC frequency in LN over time across the species ( p = 0 . 0162 ) , as well as trends toward a difference in the intestine ( p = 0 . 0645 ) . Assessment of the mDC frequency in the LNs and intestine showed that they decreased during acute infection in PTMs in both compartments ( Figure 5a and 5d ) and were only partially restored during chronic infection . While mDC depletion in the intestine did not reach statistical significance during chronic infection of PTMs included in this study ( Figure 5d ) or in a larger cohort including PTMs from our previous studies ( Figure S4a ) , a subset of mDCs expressing CD103+ were preferentially lost at the mucosal sites in chronically-infected PTMs ( Figure S4b ) . This result corroborates data previously reported for other pathogenic SIV infections [60] . No significant changes in mDC counts occurred in the LNs in AGMs and RMs ( Figures 5b and c ) , while mDC percentages were significantly increased in the intestine in these two species at different time points of SIVagmSab infection ( Figures 5e and f ) . These immunophenotypic results were confirmed by immunohistochemistry ( IHC ) for CD11c ( Figures S5 , S6 , S7 and S8 ) . In general , there was a good match between IHC and flow cytometry data , indicating acute mDC depletion in the LN ( Figure S5 a–c ) and intestine ( Figure S6 ) in the progressive infection of the PTMs , increased during chronic infection in the gut of nonprogressor AGMs ( Figure S7 d–i ) and increased during acute infection in RM controllers ( Figure S8 d–i ) . IHC showed that , in the intestine , the majority of the CD11c+ cells were located in the Peyer's Patches ( Figure S7 d–f ) and in the lymphoid aggregates ( Figure S8 d–f ) , with only a small fraction of mDCs being present in the lamina propria ( Figures S6a–c , S7g–I and S8d–i ) . This points to potential differences in CD11c dynamics in different gut segments ( i . e . , inductive vs effector sites ) . Similarly , IHC results revealed differences in the dynamics of CD11c expression between superficial and mesenteric LNs in the pathogenic infection of PTMs ( Figure S5 ) . Our data showing increases in the mDC population in the intestinal compartment in persistent nonprogressive SIVagmSab infection in AGMs and in controlled SIVagmSab infection in RMs , suggest that mDC recruitment to these sites is not deleterious for SIV/HIV pathogenesis . In contrast , loss of mDCs from mucosal sites was correlated with disease progression in SIVagmSab-infected PTMs . The decreased numbers of mDCs in the LNs and intestine of PTMs may result from a lack of recruitment to these tissues . Yet , increased apoptosis of mDCs from circulation and LNs was previously reported in progressive HIV/SIVmac infections [7] . Consequently , we could not rule out mDC loss from lymphoid and mucosal tissues through a similar mechanism , and we assessed additional markers of mDC migration and recruitment to lymphoid sites . We measured Ki-67 expression on mDCs isolated from LN and intestine as a marker of their recent mobilization at these sites . We found significant differences over time across the three species , both in LN ( p = 0 . 0001 ) and in the intestine ( p = 0 . 0319 ) . Ki-67 expression rapidly increased on mDCs in the LNs and was maintained at high levels throughout infection in all three species , the highest increase being observed in AGMs ( Figures 6a , b , c ) . Slight , but consistent increases of Ki-67 expression by mucosal mDCs were observed in AGMs and RMs ( Figures 6e , f ) , especially during chronic SIV infection . Increased mDC mobilization to the intestine may explain the increased numbers of mDCs observed at this site in nonprogressive infections . Surprisingly , however , the most prominent increase in Ki-67 expression occurred in the intestine of SIVagmSab-infected PTMs , starting from the acute infection throughout the follow-up ( Figure 6d ) , suggesting that mDCs are also recruited to mucosal site in progressive infections . This observation is also supported by the observation of significant increases in α4β7 integrin expression on circulating mDCs isolated from both AGMs and PTMs ( Figure S9 ) , confirming their recruitment to the gut in the progressive infection [24] . Low levels of mDCs in the gut despite their documented massive recruitment indicate that additional factors are responsible for mDC depletion in the intestine of SIVagmSab-infected PTMs . To complete the characterization of mDC migration to either LNs or mucosal tissues , we examined the expression of chemokine receptors on circulating mDCs . We reasoned that , if mDC recruitment to LNs and tissues is driven by specific chemokine signaling , circulating mDCs that migrated to certain effector sites should express the necessary chemokine receptors to respond to chemokines from tissues or LNs . We thus measured the levels of CCR7 , a receptor for two chemokines ( CCL19 and CCL 21 ) usually expressed in the LNs , [66]–[68] and CCR5 , a receptor for chemokines ( CCL3 , CCL4 and CCL5 ) usually elevated in inflamed mucosal tissues [69] , to assess mDC migration to either LNs or mucosal tissues . In pathogenic SIV infection of PTMs , expression of both CCR5 and CCR7 chemokine receptors increased on peripheral mDCs , with a more prominent increase occurring for CCR5 ( Figures 6g and j ) . In the nonpathogenic SIV infection of AGMs , very high CCR7 expression , but lower CCR5 expression was observed in the peripheral mDCs ( Figures 6h and k ) . The decreased CCR5 expression on circulating mDCs from SIVagmSab-infected AGMs may suggest their lower recruitment to intestine in AGMs vs PTMs . However , the increases observed for all the other mobilization/recruitment markers and the observation that mDCs are increased in the intestine support recruitment of mDCs to the gut in AGMs . Finally , in the controlled SIVagmSab infection of RMs , circulating mDCs showed transient but significant increases of both CCR7 and CCR5 expression ( Figure 6i and l ) . Altogether , these results strongly support recruitment of mDCs to LNs and intestine in all the pathogenic scenarios . Two lines of evidence suggest that significant mDC apoptosis occurs in the pathogenic SIV infection of PTMs: ( a ) increased mDC mobilization from bone marrow ( increased Ki-67 ) , yet decreased levels of circulating mDCs; and ( b ) migration of activated mDCs to the intestine ( increased CCR5 and α4β7 expression on circulating mDCs and increased Ki-67 expression on intestinal mDCs ) , without consistent increase of mDCs in the intestine . As a result , we hypothesized that while mDCs are mobilized to mucosal tissues , once they arrive at this particular compartment they undergo apoptosis , which offsets their influx . To test this hypothesis , we examined the expression of activated Caspase-3 by mDCs from both intestine and peripheral blood . We gated on live mDCs that expressed activated Caspase-3 to specifically identify the cells that undergo apoptosis ( Figure 7a ) . Circulating mDCs from PTMs expressed increased levels of activated Caspase-3 during SIV infection ( Figure 7b ) . Conversely , activated Caspase-3 expression of mDCs did not significantly change at any time during SIV infection in AGMs or RMs ( Figure 7b ) . Similar kinetics of apoptotic mDCs were observed in the intestine , with activated Caspase-3 expression being increased during acute and chronic infection in PTMs , while remaining unchanged throughout infection in AGMs and RMs ( Figure 7c ) . In conclusion , our data clearly show that mDC loss in progressive infections results through bystander apoptosis , which is particularly severe at mucosal sites . Our further goal was to determine if mDC loss observed during progressive SIV infection in PTMs is due to immune activation-induced bystander apoptosis . We therefore assessed expression of CD80 and CD95 receptors on mDCs isolated from blood and tissues from PTMs , AGMs and RMs . Both CD80 and CD95 have been reported to increase during activation of mDCs [70] , [71] . Meanwhile , mDCs that express CD95 can potentially undergo apoptosis [72] . We found that immune activation of mDCs is global and sustained in PTMs , with both CD80 and CD95 being significantly increased throughout follow-up ( Figures 8a and b ) . mDC activation was less significant in AGMs , and limited to increases in CD80 expression during acute and , to a lesser extent , chronic infection , while increases in CD95 expression were not significant in this species ( Figures 8a and b ) . Finally , in SIVagmSab-infected RMs CD80 expression increased on circulating mDCs throughout acute infection and returned to baseline levels with the passage to chronic infection . Similarly , increased CD95 expression on circulating mDCs paralleled that of CD80 in RMs , being limited to the acute infection and returning to baseline during chronic infection ( Figure 8b ) . Interestingly , normalization of mDC activation occurred before complete control of SIVagmSab infection in RMs ( Figures 8a and b ) , in contrast to CD4+ T cell activation that normalizes long after the control of viral replication [56] . Collectively , our results identified distinct patterns of mDC immune activation in progressive , nonprogressive and controlled SIV infections that generally parallel the degree of mDC apoptosis . This point to a direct association between the degree of immune activation of innate effectors and their death . This mechanism has also been reported to account for at least a fraction of the CD4+ T cell loss during progression to AIDS in pathogenic HIV/SIV infection [37] . While massive mDC recruitment to intestinal sites occurred in all SIVagmSab disease models , apoptosis associated with increased immune activation of mDCs in both gut and periphery was detected only in the progressive model . We hypothesized that the mDC overstimulation and death unique to SIVagmSab-infected PTMs relies on the severe gut dysfunction , which is characteristic of this progressive infection model . It was reported that translocation of microbial products in the lamina propria and in the general circulation occurs in PTMs [73] , where they may enhance mDC stimulation in conjunction with the virus . In contrast , the integrity of the gut is maintained in nonprogressive and controlled infections [73] , [74] , thus exposing mDCs only to SIV stimulation . We addressed this hypothesis by comparing the ability of LPS ( as a surrogate for microbial products ) and R848 , a TLR7/8 agonist that stimulates mDCs through TLR8 , similar to SIV/HIV [75] , [76] ( as a surrogate for the virus ) , to induce immune activation and apoptosis in mDCs isolated from PTM blood prior to SIVagmSab infection . This experiment showed that increased immune activation ( documented as increased CD80 expression ) ( Figures 9a and c ) and apoptosis [measured as increased expression of Annexin V ( AnnV ) on the surface of live mDCs] ( Figures 9b and c ) occurred after stimulation with both LPS and R848 . LPS appeared to be a more potent inducer of mDC apoptosis , as shown by the higher levels of AnnV expression on mDCs stimulated with this TLR4 ligand ( Figure 9b and c ) . Altogether , our findings suggest that the combined action of SIV and translocated microbial products may result in excessive activation and apoptosis of circulating and mucosal mDCs in progressive SIV infections . To assess the impact of virus stimulation on mDC function , we compared the cytokine production of mDCs prior to and at critical time points during progressive , nonprogressive and controlled SIV infections . mDCs were isolated and stimulated with R848 [77] . This experiment showed that , within the same species , mDCs from acutely and chronically SIVagmSab-infected monkeys responded to TLR7/8 stimulation by decreasing IL-6 ( Figure 10a ) and TNF-α production ( Figure 10b ) . The decreased production of proinflammatory cytokines in response to viral stimulation occurred in all three NHP species , independent of the pathogenic outcome of SIVagm infection . Note however , that AGMs exhibited the lowest production of proinflammatory cytokines both before and after SIV infection ( Figure 10a and b ) . These results are in agreement with those previously reported in HIV infection [60] , [78] and suggest that SIV infection per se does not trigger proinflammatory cytokine production by mDCs . Since microbial translocation ( MT ) significantly increases only in pathogenic SIVagmSab infection of PTMs and not in nonprogressive SIVagmSab infection of AGMs and RMs [42] , [56] , we reasoned that mDCs might be stimulated to produce proinflammatory cytokines by the microbial products translocated from the gut during pathogenic SIV infections . We assessed TNF-α production in response to LPS stimulation of peripheral mDCs isolated from PTMs prior to SIV infection and during chronic infection . Our measurements revealed that TNF production is indeed increased in LPS-stimulated mDCs isolated from chronically SIV-infected PTMs ( Figure 11 ) , indicating that mDCs may contribute to the increased immune activation and inflammation observed during progressive SIV/HIV infection . To determine if mDCs contribute to the gut dysfunction described in progressive HIV/SIV infections , we compared the spontaneous production of proinflammatory cytokines ( TNF-α and IL-6 ) by unstimulated mDC isolated from the gut in normal vs . acutely and chronically SIVagmSab-infected PTMs . We also compared and contrasted these results with those obtained from assessment of the function of unstimulated intestinal mDCs isolated from normal , acutely and chronically SIVagmSab-infected AGMs and RMs which lack intestinal dysfunction [56] , [73] . No significant difference in cytokine production was observed in any of the three models between acutely infected and uninfected animals ( Figure 12 ) . TNF-α and IL-6 production by intestinal mDCs isolated from chronically SIVagmSab-infected PTMs was significantly greater than that of intestinal mDCs from uninfected PTMs ( Figure 12a and d ) . No modifications in mDC TNF-α and IL-6 production could be detected in chronically infected AGMs ( Figure 12b and e ) and RMs ( Figure 12c and f ) . Our results suggest that intestinal mDCs play a significant role in the etiology of the gut dysfunction characteristic of progressive infection . Furthermore , increased mDC production of TNF-α in the intestine is not beneficial and is associated with a poor clinical prognosis , in agreement with previous reports [79] . Finally , these data support our hypothesis that mDC overstimulation and increased proinflammatory cytokine production occurs only in the animal model in which microbial products are translocated into the lamina propria .
We report that , in agreement with previous reports [7] , a significant drop in peripheral mDC counts occurs during the pathogenic SIV infection of PTMs . However , our comparative analysis also identified a similar depletion of circulating mDCs in the nonpathogenic infection of AGMs . As such , our study suggests that the magnitude of the acute depletion of circulating mDCs is not predictive of SIV disease progression . Similarly , while it was previously reported that mDC counts at the setpoint are predictive of the outcome of infection [84] , our results could not establish such a correlation . In our study , mDC levels during chronic infection appear to be associated with the control of disease progression . Thus , in RMs , mDC levels increased over baseline levels coincident with the control of viral replication . Altogether , these features point to mDC restoration as one of the mechanisms through which control of SIV infection is achieved . Our results suggest that mDC depletion from circulation is not due to direct virus killing: ( i ) The three species exhibited similar high levels of viral replication during acute infection ( Figure 1 ) , yet mDCs were depleted only in PTMs and AGMs , but not in RMs . ( ii ) The kinetics of acute mDC depletion from circulation significantly diverged between PTMs and AGMs , in spite of similar dynamics of viral replication . ( iii ) mDC depletion occured prior to detectable viremia in PTMs . ( iv ) SIVagmSab does not have a vpx gene and therefore cannot infect dendritic cells because of the SAMHD1 restriction [64] , as documented in our study that failed to identify viral DNA in sorted mDCs from SIVagmSab-infected PTMs , AGMs and RMs . By documenting mDC depletion in a biological system that uses a virus that does not infect mDCs , our study demonstrates that factors other than direct virus killing are responsible for mDC depletion during SIV/HIV infection . We assessed mDC mobilization from the bone marrow by measuring Ki-67 expression . For the same purpose , other groups have used thymidine analogues such as Bromodeoxyuridine ( BrdU ) , which becomes incorporated into dividing mDCs . BrdU assesses mDC mobilization from the bone marrow and tracks the cells to tissues . However , BrdU induces mutations , limiting its use in long-term studies in large animal models [85] . Furthermore , studies reported that BrdU incorporation and Ki-67 expression in DCs have a similar value [86] . Therefore , we reasoned that measuring the expression of cell proliferation markers by mDCs is appropriate to assess trafficking in long-term studies and postulated that Ki-67 expression in tissue mDCs is due to rapid recruitment of cells that were recently mobilized into circulation from the bone marrow . We report that mDC mobilization from the bone marrow occurs in pathogenic , nonpathogenic and controlled SIV infections . Increased mobilization was observed throughout the follow-up and did not appear disrupted during the terminal stages of infection in the animals that progressed to AIDS . Furthermore , rapid progression to AIDS was not associated with a defect in mDC mobilization from the bone marrow . As such , our study failed to document any evidence that insufficient mobilization of mDCs from the bone marrow is a factor driving the drop of circulating mDCs . We took a series of steps to carefully assess mDC migration to the LNs and the gut . By immunophenotyping the mDCs from the LNs and intestine , we showed that increases of mDCs in the LNs and intestine occur in persistent nonprogressive SIVagm infection in AGMs and to a lesser extent in controlled SIVagm infection in RMs , suggesting mDC recruitment to these sites . Conversely , transient loss of mDCs from LNs and intestinal mucosal sites only occurred in SIVagm-infected PTMs . To explain this discrepancy between progressive and nonprogressive infections , we further assessed Ki-67 expression by mDCs at the LNs and mucosal sites , as a sign of their recent mobilization , and we showed that Ki-67 expression increased in all models throughout infection , suggesting massive mDC influx at these sites . Next , we assessed mDC surface expression of the chemokine receptors involved in cell mobilization to LNs [66]–[68] and mucosal [69] tissues to further document mDC mobilization to these sites in the three types of infections . Increased levels of CCR5 and CCR7 documented a massive influx of mDCs to both LNs and mucosal tissues in the pathogenic infection of PTMs . Conversely , in the nonpathogenic infection of AGMs , a low expression of CCR5 by the mDCs recapitulates previous reports in this species documenting a similar low CCR5 expression by CD4+ T cells [44] , [45] and suggests a reduced mDC migration to the intestine in this animal model . Meanwhile , transient increases of both CCR5 and CCR7 pointed to a brief mDC mobilization to both LNs and intestine in SIVagm-infected RMs and showed that mDC trafficking to these tissues returns to baseline when viral replication is controlled . We also assessed the expression of the intestine homing marker α4β7 integrin on mDCs and documented their migration to the gut in PTMs and AGMs . Finally , the flow cytometry results were confirmed by IHC . Altogether , our data demonstrate here for the first time that mDC mobilization to the LNs and mucosal tissues occurs in progressive , nonprogressive and controlled SIVagmSab infections and is a major factor responsible for their depletion from circulation . mDCs were either depleted or not significantly increased from the intestine during pathogenic infection , in spite of continuous recruitment to this site . As such , we assessed the expression of apoptosis markers by mDCs in progressive SIV infection of PTMs and demonstrated that mDC depletion is indeed due to increased programmed cell death . By comparing the results of in vitro stimulation of mDCs isolated from uninfected PTMs with R848 ( to mimic virus stimulation ) and LPS ( a surrogate for microbial products that are abundantly present in the lamina propria and general circulation in SIV-infected PTMs ) , we found that mDC hyperactivation and death is due to microbial products rather than the virus . These data corroborate our findings that a lower degree of mDC activation and no significant increase in apoptosis are associated with lack of MT , rather than low viremia , in the nonprogressive infections . While it is difficult to extrapolate in vivo the in vitro data and conclusively establish which is the major cause of mDC hyperactivation and death during SIV infection , our data supports a view in which a synergistic SIV and microbial mDC hyperstimulation are responsible for the depletion of this immune cell subset in pathogenic HIV/SIV infections . Conversely , in nonprogressive and controlled infections , mDC apoptosis may be kept at bay because , in these models , there is no MT associated with SIVagmSab infection and mDC stimulation occurs predominantly through direct virus action . This is a key question addressed by our study . Our complex approach allowed us to establish four lines of evidence in support of a positive role for mDCs in SIV infection: ( i ) We established a direct correlation between mDC counts and prognosis of SIV infection: animals with the lowest mDC levels prior to infection were rapid progressors; mDC depletion from blood and intestine was associated with disease progression; animals that completely recovered mDCs during chronic infection experienced delayed or no disease progression . ( ii ) While mDC mobilization from the bone marrow occurred in all the NHPs included in our study , increased mobilization was associated with a lack of disease progression during the follow up ( Figure 4 ) , suggesting possible mDC involvement in virus control . ( iii ) We documented mDC mobilization to LNs and intestine in all three species in response to SIV infection and showed that increased levels of mDCs in the intestine and LNs are associated with lack of disease progression/control of infection , while mDC loss at these sites is associated with progression to AIDS . ( iv ) The comparison between progressive , nonprogressive and controlled infections revealed that mDCs are not inducing immune activation and inflammation in the biological systems in which SIV replication is not associated with MT . Thus , mDC accumulation in the intestine in nonprogressive and controlled SIVagmSab infection of AGMs and RMs , respectively , is not associated with increases in immune activation and inflammation in these species . This is supported by our findings that peripheral mDC production of proinflammatory cytokines in response to TLR7/8 ligands after SIV infection is decreased in these species . Adequate preservation of mDCs may impact immune activation and inflammation in SIVagmSab-infected AGMs and RMs through additional pathways . It was recently reported that subsets of mDCs may play a significant role in inducing differentiation of Tregs , which may in turn control immune activation and promote tolerance to SIV infection [87] . Altogether , these features strongly support a role of mDCs in reducing the levels of immune activation and inflammation , maintaining gut integrity and possibly inducing tolerance to SIV in nonprogressive infections . Due to their depletion from both circulation and the intestine , it is virtually impossible to precisely define the role of mDCs in progressive infection . While our studies showed that mDCs isolated from PTMs show a similar hyporesponsiveness postinfection to TLR7/8 stimulation compared to nonprogressive infections , mDC activation was significantly higher in PTMs . Our results show that mDC hyperactivation and production of proinflammatory cytokines may be enhanced in progressive SIV infection by microbial products translocated to the circulation as a result of the severe SIV-associated gut dysfunction in these species . Finally , we documented increased TNF-α and IL-6 production by intestinal mDCs isolated from PTMs ( Figure 12 ) , suggesting an association between increased inflammatory cytokine production and poor clinical prognosis . Altogether , our results show that during progressive SIV/HIV infections , the beneficial role of mDCs may be obscured by either their excessive loss and/or by hyperfunction reflected in an overproduction of proinflammatory cytokines by residual mDCs in response to microbial products or opportunistic pathogens concurrent with HIV/SIV infection . In conclusion , we report that mDCs have different kinetics , immune activation/apoptotic patterns and functions in pathogenic , nonpathogenic and controlled SIV infection . Our results provide a mechanistic basis of the role of mDCs in the pathogenesis of AIDS . These results are informative for designing both therapeutic interventions and vaccinations aimed at controlling HIV infection .
All animals were housed and maintained at the University of Pittsburgh according to the standards of the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) , and experiments were approved by the University of Pittsburgh Institutional Animal Care and Use Committee ( IACUC ) . These studies were covered by two IACUC protocols: 0907039/12080831 Animal Model for SIV Infection Control ( Approved in 2009 and renewed in 2012 ) ; 0911844/12121250 Pathogenesis of SIV in African green monkeys ( Approved in 2009 and renewed in 2012 ) . The animals were fed and housed according to regulations set forth by the Guide for the Care and Use of Laboratory Animals and the Animal Welfare Act . Four AGMs , five RMs and five PTMs were included in the study . All animals were infected with plasma equivalent to 300 tissue culture infectious doses ( TCID50 ) of SIVagm . sab92018 [61] and followed for up to one year or until progression to AIDS . Animals were clinically monitored throughout the follow-up . Plasma VLs were quantified by real time-PCR as previously described [61] . Blood was collected from all animals at the following time points: prior to the infection ( 0 dpi ) , during acute SIVagm infection to closely monitor the innate responses ( 1 , 3 , 4 , 5 , 8 , 14 , 21 and 28 dpi ) , around the viral setpoint ( 35 and 42 dpi ) , and during chronic infection ( 60 , 72 , and 120 dpi ) . LN biopsies were performed prior to infection , at the peak of viral load ( during acute infection ) , and at necropsy ( during chronic infection ) . Intestinal resections ( 5–10 cm ) were surgically performed prior to infection , during acute infection and during chronic infection , as previously described [42] , [62] . Additional intestinal samples were collected at the necropsy . Within one hour after blood collection , plasma was harvested and peripheral blood mononuclear cells ( PBMCs ) were separated from the blood using Ficoll density gradient centrifugation . Lymphocytes from the intestine and LNs were isolated and stained for flow-cytometry , as previously described [42] , [61] , [62] . Lymphocytes were isolated from the axillary or inguinal LNs by gently mincing and pressing tissues through nylon mesh screens . Intestinal resections were processed as described previously to obtain an enriched mononuclear cell suspension [42] , [61] , [62] , [88] . Briefly , intestinal resections were minced mechanically , washed with EDTA and subjected to collagenase digestion followed by Percoll density gradient centrifugation . PBMCs , LNs and intestinal cells from PTMs , AGMs and RMs infected through the same route and with the same dose of SIVagm . sab as in previous protocols were also included to match infection time points when samples from the present study were not available , or to increase the number of animals and improve statistical significance . Whole peripheral blood , LN cell suspensions and intestinal mononuclear cell suspensions were stained with fluorescently-labeled antibodies to Lineage markers CD3 ( clone SP34-2; all antibodies from BD Bioscience , San Jose , CA , USA unless otherwise noted ) , CD14 ( M5E2 ) , CD163 ( GH1/6 , only in LN and intestine ) and CD20 ( 2H7 ) ]; HLA-DR ( G46-6 ) , CD11c ( S-HCL-3 ) , CD103 ( 2G5 ) CD45 ( D058-1283 ) , CD80 ( L307 . 4 ) , CD86 ( FUN-1 ) , CD95 ( DX2 ) , CCR5 ( 3A9 ) , CCR7 ( 3D12 , eBioscience , San Diego , CA , USA ) and α4β7 ( FIB504 ) . An amine-reactive fixable dead-cell dye ( Invitrogen , Grand Island , NY , USA ) was used to distinguish live from dead cells . For intracellular staining , cells were stained to identify live mDCs as described above , then fixed , permeabilized and stained for activated Caspase-3 ( Cas-3 , C92-605 ) and Ki-67 ( B56 ) . Flow cytometry acquisitions were performed on an LSR II flow cytometer and analyzed with FlowJo software ( Treestar , Ashland , OR , USA ) . mDCs were defined as CD45+ ( only in peripheral blood ) Lineage negative ( Lineageneg ) HLA-DR+ cells expressing CD11c ( Figure 2 ) . In the LNs , a broad Lineageneg HLA-DR+/++ gate was used to include all mDCs , as described previously ( Figure 2 ) . mDC populations positive for CD80 , CD86 , CD95 , CCR5 , CCR7 , α4β7 and Ki-67 were identified by staining with an isotype control antibody . Cas-3+ mDCs were identified as a distinctly separate population . A two-step TruCount technique was used to enumerate mDCs in blood , as previously reported [89] . The number of blood CD45+ cells was quantified using a precise volume of blood stained with antibodies in TruCount tubes ( BD Biosciences ) that contained a defined number of fluorescent beads to provide internal calibration . The number of blood mDCs was then calculated based on the ratio of mDCs to CD45 cells in whole blood at the same time point . Cryopreserved single-cell preparations of superficial lymph nodes collected at time of necropsy from various protocols were thawed and stained with CD3/C20-PE , CD14-FITC , HLA-DR-APC-Cy7 , CD11c-APC , CD123-PE-Cy7 , Live/Dead-Yellow Dye L-34959 from Molecular Probes ( Eugene , OR ) for 30 minutes at 4°C; then washed once with and resuspended in PBS supplemented with 0 . 5% BSA and 2 mM EDTA . mDCs ( Lineageneg CD14neg HLA-DR+ CD123neg CD11c+ ) along with Lineage+ cells , were sorted on a 3-laser FACSAria instrument using FACSDiva 6 software ( BD Biosciences ) and collected in polystyrene tubes containing RPMI+20% heat-inactivated FBS . The sorted cells were pelleted and frozen , and DNA was extracted with the DNeasy Blood and Tissue Kit ( Qiagen , Germantown , MD ) . Viral copy number was determined by qPCR of extracted DNA , as previously described [56] . IHC was performed on formalin-fixed , paraffin-embedded tissue samples . Four µm thick sections were deparaffinized , rehydrated , and rinsed . For antigen retrieval , the sections were microwaved in Vector Unmasking Solution and treated with 3% hydrogen peroxide . Sections were incubated with CD11c monoclonal primary antibody ( Novacastra , USA ) . Secondary antibodies were from Vector Vectastain ABC Elite Kit . For visualization , sections were treated with DAB ( Dako ) and counterstained with hematoxylin . mDC activation and apoptosis was measured after stimulation of total PBMCs for 24 hours with 10 µM of the TLR7/8 ligand R848 , ( Invivogen , San Diego , CA , USA ) or with 100 ng/ml Escherichia coli lipopolysaccharide ( LPS ) , ( Invivogen , San Diego , CA , USA ) . Cells were harvested and stained for dendritic cell markers , CD80 and AnnV . An amine-reactive fixable dead-cell dye ( Invitrogen , Grand Island , NY , USA ) was used to discriminate live from dead cells . Cells cultured in the absence of R848 and LPS were used as background control . Intracellular cytokine production by isolated mononuclear cells from blood and intestine was measured as described previously , with slight variation [7] . Briefly , cells were cultured for seven hours with 10 µM of the TLR7/8 ligand R848 ( Invivogen , San Diego , CA , USA ) or with Escherichia coli lipopolysaccharide ( LPS; 100 ng/ml; Invivogen , San Diego , CA , USA ) with and without the addition of 10 µg/mL brefeldin A ( Sigma , St . Louis , MO , USA ) after two hours . Cells were stained with surface-labeling antibodies as above then fixed and permeabilized prior to incubation with antibodies to TNF-α ( MAb11 ) , IL-6 ( MQ2-6A3 ) and IL-12 ( C8 . 6 , eBioscience ) and analyzed by flow cytometry . Cells ( mDCs ) cultured with R848 or LPS stimulation but without the addition of brefeldin A were used as background control . In each species , postinfection time point values for each parameter were compared with pre-infection values using the Mann-Whitney U test . GraphPad Prism 5 ( GraphPad Software ) was used for statistical analysis . Correlations were determined using the non-parametric Spearman rank test . Differences in temporal dynamics were analyzed using mixed-effects models , with macaque as the grouping factor to account for the repeated measurements made in each animal . For these analysis we used the nlme package [90] of R ( http://cran . r-project . org/ ) . All P<0 . 05 values were considered to be significant .
|
Myeloid dendritic cells ( mDCs ) are potent antigen-presenting cells that regulate both innate and adaptive immune responses and act as “watch-dogs” , sensing and controlling aberrant immune activation; as such , they may significantly impact the outcome of HIV/SIV infection . By comparing and contrasting the frequency , function , migration to tissues and levels of activation and apoptosis in progressive , nonprogressive and elite-controlled SIV infections , we investigated the mechanisms responsible for mDC loss in HIV/SIV infection and their role in driving progression to AIDS . We report that progression to AIDS is associated with low mDC preinfection levels and depletion throughout infection , due to massive migration of these cells to mucosal sites and excessive cell death by apoptosis . We also show that residual mDCs from blood and intestine have a high capacity to produce proinflammatory cytokines , thus contributing to the increased immune activation and inflammation characteristic of progressive infections .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2013
|
Kinetics of Myeloid Dendritic Cell Trafficking and Activation: Impact on Progressive, Nonprogressive and Controlled SIV Infections
|
Eumycetoma is a chronic granulomatous subcutaneous infectious disease , endemic in tropical and subtropical regions and most commonly caused by the fungus Madurella mycetomatis . Interestingly , although grain formation is key in mycetoma , its formation process and its susceptibility towards antifungal agents are not well understood . This is because grain formation cannot be induced in vitro; a mammalian host is necessary to induce its formation . Until now , invertebrate hosts were never used to study grain formation in M . mycetomatis . In this study we determined if larvae of the greater wax moth Galleria mellonella could be used to induce grain formation when infected with M . mycetomatis . Three different M . mycetomatis strains were selected and three different inocula for each strain were used to infect G . mellonella larvae , ranging from 0 . 04 mg/larvae to 4 mg/larvae . Larvae were monitored for 10 days . It appeared that most larvae survived the lowest inoculum , but at the highest inoculum all larvae died within the 10 day observation period . At all inocula tested , grains were formed within 4 hours after infection . The grains produced in the larvae resembled those formed in human and in mammalian hosts . In conclusion , the M . mycetomatis grain model in G . mellonella larvae described here could serve as a useful model to study the grain formation and therapeutic responses towards antifungal agents in the future .
Mycetoma is a chronic granulomatous subcutaneous infectious disease , characterized by massive deformities and disabilities . It is endemic in tropical and subtropical regions . It can be caused by 56 different micro-organisms , including both bacteria ( actinomyctoma ) and fungi ( eumycetoma ) [1] . The most common causative agent world-wide is the fungus Madurella mycetomatis [1] . A characteristic feature of mycetoma is the presence of grains inside the tissue . These grains are formed by the micro-organisms upon entering the human body , probably as a defense mechanism against the human immune system [2] . Since so many different micro-organisms are able to cause mycetoma , a large variety of grains can be formed . These grains can be of different color , size , and consistency , depending on the causative micro-organism [2] . The grains of the most common causative agent M . mycetomatis are black , firm , and brittle and are 0 . 5–1 mm in size [3] . They consist of densely packed fungal mycelia embedded in a hard and brown-black cement material . The chemical composition of these grains is not fully understood , but lipids , proteins , DHN-melanin , Cu , Zn and Ca are known to be present in the grain [4–6] . Surrounding the grain , an extensive granuloma formation is present , characterized by a large zone of neutrophils . Interestingly , although grain formation is key in mycetoma , its formation process is not well understood . This is because grain synthesis cannot be induced in vitro , a mammalian host is necessary [2 , 7 , 8] . In the past , mice and monkeys were used to induce grain formation and mimic M . mycetomatis mycetoma [7 , 9–12] , with various success rates . Histologically , the grains induced in mammal models indeed resemble the grains formed inside the human body , characterized by mycelia embedded in cement material and a neutrophil zone surrounding them . Since in both human patients and in infected animals neutrophils were found surrounding the mycetoma grain , these cell types might be important in the formation of the mycetoma grain . These cells , however , are probably not the sole factor involved , since exposure of M . mycetomatis towards neutrophils did not induce grain formation in vitro ( van de Sande , personal communication ) . Therefore a non-mammalian host in which neutrophil-like cells are present might be a suitable alternative to induce grain formation . One such host , is the larvae of the greater wax moth Galleria mellonella [13–19] . These larvae have an immune system with similarities to the mammal innate immune response [20] . The G . mellonella immune response consists of two tightly interconnected components: the cellular and the humoral responses [20] . The cellular response is mediated by hemocytes and involves responses such as phagocytosis [20] . The humoral defense is characterized by anti-microbial peptides , complement-like proteins such as peroxynectin , transferrin , lysozyme and defensin [20] . The process of phagocytosis , the production of reactive oxygen species and degranulation is similar between hemocytes and neutrophils [20] . Also receptors such as Toll like receptors and beta glucan receptors , and the formation of a neutrophil extracellular net are similar between hemocytes and neutrophils [20] . Until now , invertebrate hosts were never used to study the pathogenesis of mycetoma , but they might be appealing alternatives to the mammalian hosts since they are inexpensive to keep , easy to manipulate , and they can be kept at 37°C , which makes the comparison of pathogenic processes inside the human body possible [19–21] . Therefore , in the present study , we investigated if G . mellonella larvae can serve as an alternative model host to study the grain formation in M . mycetomatis . We evaluated the survival of larvae when infected with different M . mycetomatis isolates and we verified the presence of grains in the tissue by histopathology . Our results demonstrate that larvae of G . mellonella can be used as an alternative to mice to study grain formation in M . mycetomatis mycetoma .
Final sixth instar G . mellonella larvae were acquired from Vellinga Voedseldieren , Ridderkerk , The Netherlands and kept at room temperature on wood shavings in the dark until use . Larvae were used within 5 days of receipt . Larvae of approximately 300 to 500 mg showing no discoloration were selected for the experiments . M . mycetomatis strains mm55 , mm68 and cn796 were selected for this study based on their genetic differences ( different AFLP types ) and different morphology ( Fig 1 ) [22] . These isolates were isolated by direct culture of the black grains obtained by deep surgical biopsies from three different mycetoma patients seen in the Mycetoma Research Centre in Sudan . The strains were identified to the species level by morphology , polymerase chain reaction with M . mycetomatis specific primers and sequencing of the internal transcribed spacer [23] . The isolates were maintained in the laboratory on Sabouraud agar ( Difco laboratories , Becton and Dickinson , Sparks , USA ) . To prepare the inoculum for the G . mellonella larvae , M . mycetomatis mycelia were cut from agarplates and transferred to 200 ml colorless RPMI 1640 medium supplemented with L-glutamin ( 0 . 3 g/liter ) , 20 mM mopholinepropanesulfonic acid ( MOPS ) and chloramphenicol ( 100 mg/liter; Oxoid , Basingstroke , United Kingdom ) . The mycelia were disrupted by 20 s sonication at 28 micron ( Soniprep , Beun de Ronde , The Netherlands ) and incubated for 2 weeks at 37°C . To prepare the inocula , mycelia were separated and washed by vacuum filtration ( Nalgene , Abcoude , The Netherlands ) . Wet weights of the mycelia were determined and a suspension containing 100 mg wet weight per ml in phosphate-buffered saline ( PBS ) was sonicated for 2 min at 28 micron . This procedure does not kill the fungus , and results in a homogenous hyphal suspension [7] . The resulting homogenous suspension was washed once in PBS and further diluted to their final inoculum size . To confirm fungal viability and to exclude bacterial contamination , 10 μl of each suspension was cultured on blood agar and Sabouraud agar ( Difco Laboratories , Becton Dickinson ) for 2 weeks at 37°C . G . mellonella larvae were inoculated with various amounts of viable M . mycetomatis , ranging from 0 . 04 to 4 mg wet weight per larvae . Inoculation was performed by injecting 40 μl of the fungal suspension in the last left pro-leg with an insulin 29G U-100 needle ( BD diagnostics , Sparsk , USA ) . As controls , untouched larvae , larvae pricked with the needle and larvae injected with PBS were included . For the survival experiments , each group consisted of 15 larvae . To determine the burden of infection , each group consisted of 5 larvae . Larvae were kept at 37°C during the experiment . To monitor the course of infection , larvae were checked daily for survival for 10 days . If during these 10 days larvae formed pupa , these individuals were left out of the equation , since we could not ascertain that these individual larvae would have died during the infection or would have survived . At 4h , 24h , day 3 , day 7 and day 10 after inoculation larvae were dissected to determine if black grains were present by macroscopic observation and histology . If grains were present , culture on Sabouraud agar was performed to determine their viability . For histological observations , larvae were fixed in 10% buffered formalin . Since the larval exoskeleton is impenetrable to most fixative reagents , 100 μl of the 10% buffered formalin was injected into the larvae [24] . After 24 h fixation , whole larvae were dissected longitudinally into two halves with a scalpel and fixated in 10% formalin for at least another 48 h [24] . The two halves of larvae were routinely processed for histology . Sections were stained with hematoxylin and eosin ( HE ) , Grocott methanamine silver and Sirius red . To compare the grains formed in the Galleria mellonella larvae , histological slides were obtained from 3 human patients ( Mycetoma Research Centre ) and the mouse model developed by Ahmed et al [7] . One of the strains used to infect our G . mellonella larvae ( strain mm55 ) was previously used by Ahmed et al . for developing the mice model [7] . To determine the fungal burden 5 larvae from each group were sacrificed at 4h , 24h , 3 days , 7 days or 10 days post infection . To each larvae 10 chrome steal metal balls and 1 ml PBS were added . Larvae were homogenized in a Qiagen Tissue lyser for 5 min at 30 Hz . From each homogenized larvae 250 μl undiluted , 50 μl undiluted and 50 μl 1:10 diluted suspension was plated out on Sabouraud-gentamicin agar . Plates were incubated at 37°C for two weeks and the number of colony forming units per larvae ( CFUs ) was determined . To quantify the melanization , haemolymph of each larvae was collected 4h , 24h , 3 days , 7 days or 10 days post infection as described by others [25] . In short , the haemolymph was harvested by making a small incision below the last proleg of the larvae . The haemolymph was collected into clean 1 . 5 ml tubes and diluted 1:10 with IPS buffer ( Insect Physiological Saline: 150 mM sodium chloride , 5 mM potassium chloride , 10 mM Tris-HCl pH 6 . 9 , 10 mM EDTA and 30 mM sodium citrate ) immediately after collection . The optical density of the diluted heamolymph was measured at 405 nm to determine melanisation as described by others [24] . Each hemolymph was measured three times independently . To compare the survival curves the Log-rank test was performed . To compare differences in CFU count and melanization between the different groups , the Mann-Whitney test was performed . A p-value smaller than 0 . 05 was deemed significant . For the isolated strains and histological sections used from patients , written informed consent was obtained from all participants and ethical approval was obtained from Soba University Hospital Ethical Committee , Khartoum , Sudan . The histological sections used from mice were obtained from a previous study performed by Ahmed et al . [7] . For that study , approval was obtained from the Animal Care and Use Committee of the Erasmus Medical Center , Rotterdam , The Netherlands . The experimental protocols adhered to the rules specified in the Dutch Animal Experimentation Act of 1977 and the published Guidelines on the Protection of Experimental Animals by the Council of the European Community of 1986 .
To establish a grain model in G . mellonella larvae , three different M . mycetomatis isolates were selected . These isolates differed in morphology ( Fig 1 ) and genetic make-up [22] . As can be seen in Fig 2 , a concentration-dependent survival was noted when G . mellonella larvae were infected with M . mycetomatis strains mm55 , mm68 and cn796 . For mm55 and cn796 an inoculum of 4 mg wet weight per larvae resulted in death in all G . mellonella larvae within 7 and 8 days , respectively . This inoculum resulted in significant decreased survival compared to the PBS controls ( Log-rank , p<0 . 0001 for all strains ) , the 0 . 04 mg inoculum ( Log-rank , p<0 . 001 for strains mm55 and mm68 and p = 0 . 0009 for strain cn796 ) and the 0 . 4 mg inoculum ( Log-rank , p = 0 . 0003 for mm55 , p = 0 . 004 for mm68 and p = 0 . 003 for cn796 ) . An inoculum of 0 . 04 mg resulted in a 90% survival in the case of mm55 and only a 70% survival in the case of cn796 . The survival obtained when this inoculum was used , did not differ significantly from the survival obtained in the PBS injected controls ( Log-rank , p>0 . 05 for all strains ) . Since melanization is a key step in the antimicrobial response of G . mellonella upon infection , the pigmentation of the larvae after M . mycetomatis challenge was assessed both by observation and by measuring the melanization in the haemolymph ( Fig 3 ) . Larvae infected with 0 . 04 mg wet weight of mm55 , mm68 or cn796 showed no obvious melanization during the 10 days observation period . They looked similar to the non-infected larvae . When the haemolymph itself was assessed for melanization , a slight elevation was noted when compared to the uninfected controls , but this elevation was not significant ( Mann Whitney , p>0 . 05 ) ( Fig 3B and 3C and 3D ) . Larvae infected with 0 . 4 mg wet weight mm55 , mm68 and cn796 showed light melanization ( Fig 3A ) . Melanization occurred within 4 hours after inoculation of the fungi . As is seen in Fig 3B–3D , the haemolymph itself was significantly darker when compared to the uninfected controls ( Mann Whitney , p = 0 . 016 for mm55 and p = 0 . 008 for mm68 and cn796 ) . When larvae were infected with 4 mg wet weight of either one of the strains , strong melanization was noted within 4 hours after inoculation ( Fig 3A ) . The haemolymph of the surviving larvae was again significantly darker when compared to the uninfected controls ( Mann Whitney , p = 0 . 016 for mm55 and cn796 and p = 0 . 008 for mm68 ) . When melanization was present , this remained present until the end of the observation period ( day 10 ) . A key feature of mycetoma is the formation of grains . To observe if grains were formed during the infection , larvae were dissected 4h , 24h , 3 days , 7 days and 10 days after infection . As can be seen in Table 1 , even at the lowest inoculum , black spots were seen after 4h of infection . The lowest inoculum resulted in only a few black spots , while the highest inoculum resulted in numerous spots . To determine if the black spots indeed represented the characteristic mycetoma grains , histological slides were prepared and stained with HE , Grocott and Sirius red . These were compared to histological slides from patients and from mice ( Fig 4 ) . As is seen in Fig 5 , even after 4 hours grains were visible . At this time point grains were still forming , and although cement material was already present , some immune cells were seen within the grain . From day 3 onwards , these immune cells disappeared from within the grain , and the cement material was found throughout the grain . Characteristic of human and mouse grains is the presence of a collagen capsule surrounding the grain . No collagen capsule was present at 4h and 24h after inoculation . On day 3 , larvae infected with either mm55 or mm68 had formed collagen capsules ( Figs 4 and 5 ) . These capsules disappeared later in infection ( day 7 for mm55 and day 10 for mm68 ) . No capsule was found for Cn796 . Surprisingly , at the same time when the capsule disappeared , an increase in the presence of immune cells was noted . The fungal burden inside the larvae was assessed by culturing homogenate of sacrificed larvae . After one week of culturing on sabouraud agar , clear colonies were grown from grains originating from strains mm55 and cn796 , two weeks were necessary to discriminate the colonies of mm68 . Interestingly , the morphology of the colony originating from the grains was similar to the colony morphology of the isolate before passage through the larvae . As is seen in Fig 6 , the number of CFU was dependent on the starting inoculum . Significant higher CFUs were obtained for the 4 mg inoculum at time points 4h and 24h as for the 0 . 4 and 0 . 04 mg inoculum for all strains ( Mann-Whitney , p = 0 . 08 for all strains at 4h and p = 0 . 016 for mm55 , p = 0 . 05 for mm68 and p = 0 . 08 for cn796 at 24h ) . After 72h the number of CFUs dropped considerably , the initial differences in number of CFUs observed for the different inoculum sizes was no longer observed ( Mann-Whitney p>0 . 05 , for all strains at time points 72h , 168h and 700h ) . The number of CFU obtained from the homogenate of the sacrificed larvae was the highest at 4h and 24h after inoculation , the load lowered during the infection . The initial difference in CFU was comparable at 4h and 24h after infection for all strains ( Mann-Whitney , p>0 . 05 ) , but after 72h the number of CFU dropped considerably ( 4 mg inoculum , Mann-Whitney , p = 0 . 04 for strains mm55 and mm68 , and p = 0 . 01 for strain cn796 ) , Although at some time-points the load dropped below the detection limit of the method used , if individual grains were isolated and cultured from the infected larvae , M . mycetomatis could still be retrieved , even from larvae infected with the lowest inoculum . To determine if the three morphologically different M . mycetomatis isolates ( Fig 1 ) also differed in pathogenicity in G . mellonella larvae , we compared survival rates , melanisation of the infected larvae and the CFU obtained from infected larvae between the different isolates . For this we used a 4 mg inoculum for each M . mycetomatis isolate . In terms of survival , it appeared that the survival curves obtained for strain mm55 were comparable to those obtained for mm68 ( Log-rank , p = 0 . 24 ) and cn796 ( Log-rank , p = 0 . 13 ) . Only when strains mm68 and cn796 were compared a significant difference in survival was noted ( Log-rank , p = 0 . 027 ) , cn796 appeared to be more pathogenic to G . mellonella larvae than mm68 . In terms of melanisation , no significant difference in melanisation of the infected larvae was observed for each of the time points monitored ( Mann-Whitney , p>0 . 05 ) . In terms of CFU , initially significantly more CFUs were retrieved for strain mm55 when compared to mm68 ( t = 4h , Mann-Whitney , p = 0 . 008 ) but this difference was not observed at the later time points ( Mann-Whitney , p>0 . 05 ) . Also compared to cn796 , a higher CFU count was observed for mm55 . Significantly more CFUs were obtained for mm55 than for cn796 at 24h ( Mann-Whitney , p = 0 . 016 ) , at 72h ( Mann-Whitney , p = 0 . 034 ) and at 168h ( Mann-Whitney , p = 0 . 026 ) . No difference was observed when the number of CFU counts was compared between mm68 and cn796 ( Mann-Whitney , p>0 . 05 ) .
Mycetoma is a chronic granulomatous infection caused by either bacteria or fungi and characterized by the formation of grains [2] . Worldwide , the fungus Madurella mycetomatis is the most common causative agent of mycetoma [1] . Although the disease has been known for more than 200 years , still there are many factors of this disease which are not well understood , including the underlying host factors , the infection route , the process of grain formation and the therapeutic efficacy of the various drugs towards the causative agent [26] . Also simple diagnostic tools are currently lacking , and identification of the causative agent still relies on histology and culturing of the grain , which takes long and misidentifications are common [27] . In order to gain more insight in the pathogenesis of mycetoma and to develop rapid point of care diagnostic tools and effective therapies , a grain model is needed . Here we demonstrated that M . mycetomatis grains could be produced in the invertebrate host G . mellonella which resembled the grains formed in human patients and the mouse model developed by Ahmed et al . [7] . Histologically , the grains obtained in G . mellonella larvae resembled those obtained in mice and human , only the immune reaction surrounding the grain differed . In mice , a large neutrophil zone surrounding the grain was noted , even when the grain itself was encapsulated with collagen . In G . mellonella larvae , invasion of immune cells only started when the collagen capsule disappeared . The grain itself looked similar in all three hosts . Not surprisingly , the tissue reaction surrounding the grain was more similar between mice and human than between G . mellonella and human . Next to comparing the histology of the grains between the different hosts , our G . mellonella model can also be compared with the mouse model of Ahmed et al . in terms of pathogenicity of the fungus , the survival and the efficacy of grain formation . In terms of the pathogenicity of the fungus , in both models the M . mycetomatis inoculum was prepared in a similar manner by sonication . Furthermore , in both models M . mycetomatis strain mm55 was used . Ahmed et al , used inocula ranging from 0 . 8 mg to 120 mg per mouse , corresponding to approximately 0 . 04 mg to 8 mg M . mycetomatis per gram mouse [7] . We used a similar inoculum range in our G . mellonella model , namely 0 . 08 mg M . mycetomatis per gram larvae ( 0 . 04 mg/larvae ) to 8 mg/g ( 4 mg/larvae ) . In the mouse , complete survival was found at inocula up to 4 mg/g after 38 days , while only 37 , 5% of mice survived at an inoculum of 8 mg/g [7] . In terms of survival , the G . mellonella host appeared to be slightly more susceptible towards a M . mycetomatis infection , since even at concentrations as low as 0 . 08 mg/g , not all larvae survived . At an inoculum of 8 mg/g , no larval survival was noted . In terms of grain formation , the G . mellonella model appeared to be more efficient . In mice , an adjuvant was needed to induce grain formation , while in G . mellonella larvae no adjuvant was needed at all [7] . Furthermore , even at the lowest inoculum tested ( 0 . 04 mg/larvae ( 0 . 08 mg/g ) , grains were formed in all larvae . In mice , at such low concentrations no grains were obtained . Only at a concentration of 8 mg/g grains were formed in all inoculated mice [7] . Histologically , the M . mycetomatis grains formed in the G . mellonella larvae looked similar to those formed in mice or human , which could make this model a valuable model to further investigate the process of grain formation , to develop novel diagnostic tools or to determine the efficacy of antifungal agents against this protective structure . In order to investigate the process of grain formation , the grain model described here can be used to determine the pathogenicity of genetically modified M . mycetomatis isolates . Here we already showed that grains can be formed when different M . mycetomatis isolates are used and that small differences in terms of survival between these isolates were observed . By generating genetically modified M . mycetomatis isolates , and using them to infect G . mellonella larvae it can be determined which genes are essential in the grain formation process . For several other fungal infectious diseases the pathogenicity of genetic mutants have been studied in G . mellonella larvae [13 , 18] . Studying the pathogenicity of these genetic mutants in G . mellonella is only useful if they could predict that the results obtained are similar as in the patient . For other fungal infections it has been determined if various genetically modified isolates resulted in either enhanced or reduced pathogenicity in different hosts including insects and mammals . By studying the pathogenicity of Aspergillus fumigatus mutants in the siderophore and folate biosynthesis pathways in different hosts , it could be concluded that similar survival rates were obtained in G . mellonella larvae and mice [18] . In contrast , when the pathogenicity of these mutants were assessed in another invertebrate model , namely Drosophila melanogaster , no comparable results were obtained [18] , demonstrating that not all invertebrate hosts are able to mimic the outcomes of mammalian hosts . Also , 9 out of 10 different Candida albicans mutants which were tested for virulence in both G . mellonella larvae and mice , showed similar pathogenicity . Only the cph1efg1 double mutant of C . albicans differed , appeared to be virulent in the larvae and avirulent in mice [13] . Since for other fungal species similar results were obtained when the pathogenicity of genetically modified isolates were tested in G . mellonella larvae and in mice , it is possible that this approach can also be used in the future to determine which M . mycetomatis genes are essential for grain formation . Another way in which this G . mellonella grain model can be exploited in the future is to use it as source of fresh grains which can be used in the development of rapid point-of-care diagnostic tools . It will allow to develop antigen-based diagnostic tools and DNA-based diagnostic tools directly from the grain , which was not possible in the past . Grain specific antigens could be detected and novel methods to isolate DNA directly from patient material could be developed . This would make the culture step redundant for molecular diagnostic tools and would shorten the time to identification with more than 6 weeks . May be the most important use of this grain model in the near future is to study the effect of different antifungal agents against M . mycetomatis grains . Currently , only in vitro models have been used to determine the susceptibility of M . mycetomatis towards the different antifungal agents [28 , 29] . In these in vitro susceptibility assays , hyphal fragments were generated in RPMI medium and exposed to different concentrations of antifungal agents . It appeared that M . mycetomatis itself was highly susceptible towards azole antifungal agents , but it is difficult to translate these models directly to the patient situation since no grains are formed . The only study , which ever compared the susceptibility of in vitro grown M . mycetomatis isolates and grains already demonstrated that antifungal agents penetrate a hyphae better than a grain [30] . Furthermore , recently it was also demonstrated that although itraconazole has a better in vitro activity against M . mycetomatis than amphotericin B , it could not prevent grain formation in a mouse model , while amphotericin B could [31] . This indicates that there is indeed a difference between the activity of antifungal agents against M . mycetomatis hyphae in vitro and M . myctomatis grains in vivo . Therefore the infected G . mellonella larvae can be used to study the in vivo efficacy of the different antifungal agents against M . mycetomatis grains . In conclusion , in this study we show that the larvae of the greater wax moth G . mellonella can be used to induce grain formation in vivo and that the grains produced resemble the grains found in experimental mouse models and in human . The G . mellonella grain model could serve as a useful model to study the grain formation and therapeutic response in the future .
|
Mycetoma is a chronic subcutaneous infectious disease affecting different parts of the body but commonly seen in the foot . It can be caused by bacteria and fungi . Especially for fungi , the treatment options are meagre and therapeutic failures are common . In order to develop better therapeutic strategies for this disease , models are needed which mimic the state of the causative agent inside the patient . Unlike other fungal pathogens , mycetoma causative agents produce a protective structure surrounding the hyphae . The hyphae embedded in this protective material are called a grain . This grain cannot be produced in vitro . A mammalian host is needed . In search for alternatives for animal use , larvae of the greater wax moth are often used as model systems for various infectious diseases . We therefore determined if these larvae were able to produce mycetoma grains . In this paper we describe the development of a Madurella mycetomatis grain model in Galleria mellonella larvae . We compare the grains formed in the larvae with those obtained from human patients and a previously developed mouse model .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
A Madurella mycetomatis Grain Model in Galleria mellonella Larvae
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Cysticercosis ( CC ) is a tissue infection caused by the larval cysts of the pork tapeworm Taenia solium . It is usually acquired by eating contaminated food or drinking water . CC Cysts can develop in the muscles , the eyes , the brain , and/or the spinal cord . T . solium is found worldwide , but its prevalence has decreased in developed countries due to stricter meat inspection and better hygiene and sanitation . Nevertheless , CC is still a leading cause of seizures and epilepsy . In Spain , The disease is not nationally reportable and data on CC infected animals are also missing , despite the European Directive 2003/99/EC . We performed a retrospective descriptive study using the Spanish Hospitalization Minimum Data Set ( CMBD ) . Data with ICD-9 CM cysticercosis code ( “123 . 1” ) placed in first or second diagnostic position from 1997 to 2014 were analyzed . Hospitalization rates were calculated and clinical characteristics were described . Spatial distribution of cases and their temporal behavior were also assessed . A total of 1 , 912 hospital discharges with clinical cysticercosis were identified . From 1998 to 2008 , an increasing trend in the number of CC hospitalizations was observed , decreasing afterwards , in parallel with a decrease in the external migration rate . The Murcia region had the highest median hospitalization rate ( 13 . 37 hospitalizations/100 , 000 population ) , followed by Navarra and Madrid . The 16–44 age group was the most represented ( 63 . 6% ) . The three most frequent associated diagnoses were epilepsy and convulsions ( 49 . 5% ) , hydrocephalus ( 11 . 8% ) and encephalitis/myelitis/meningitis ( 11 . 6% ) . There is a need for a common strategy on data collection , monitoring and reporting , which would facilitate a more accurate picture on the CC epidemiological scenario . Even if most cases might be imported , improving the human and animal CC surveillance will result useful both in gaining extended disease knowledge and reducing morbidity and related-costs .
Cysticercosis ( CC ) is a parasitic tissue infection caused by larval cysts of the pork tapeworm Taenia solium [1] . Pigs , the CC natural intermediate hosts , become infected by eating tapeworm eggs in the feces of a human infected with a tapeworm . Human acquire CC through faecal-oral contamination with T . solium eggs from human tapeworm carriers [2] . Humans are the only definitive host for T . solium , while T . saginata CC is only a disease of cattle and has veterinary importance in beef and dairy production [1 , 3] . Among foods , uncooked vegetables are the major source [3] . Clinical syndromes related to CC are divided into neurocysticercosis and extraneural cysticercosis [2] [4] . Neurocysticercosis ( NCC ) is the greatest cause of acquired epilepsy worldwide , and is also increasingly seen in more developed countries because of immigration from endemic areas [2] . Other symptoms of CC include intracranial hypertension , hydrocephalus , meningoencephalitis , psychiatric disorder , stroke , and/or radiculopathy or myelopathy , if the spinal cord is involved [5] . The peak severity of CC has been estimated to occur 3–5 years after initial infection , but it can be delayed over 30 years [6] . Outside the central nervous system , cysticercosis causes no major symptoms besides the eye [2] . Postmortem studies in endemic areas suggest that 80% of NCC infections are asymptomatic . Consequently , many cases are never diagnosed or are found incidentally during imaging procedures [5] . Cysticercosis is a zoonosis of public health importance; with significant economic impacts on the health and meat sectors . The prevalence of CC in humans is highly variable within a country and between countries [1] . This variability is due to hygienic habits and socio-economic conditions , quality of meat inspection and culinary habits . CC is common throughout Latin America , most of Asia , sub-Saharan Africa , and parts of Oceania [7] . In Europe , human CC is not notifiable and therefore it is difficult to assess its epidemiology . Detection and reporting of animal CC cases is mainly based on official meat inspection [8] . CC was considered to be endemic in the past , however , some foci continue to be reported in Bulgaria , Latvia , Lithuania , Poland , Portugal , and Romania [9] . Sporadic cases of CC have also been documented in other EU countries , yet it is not known if transmission occurred recently or in the past . An effective surveillance system is lacking at European level , so the problem may be certainly underestimated [10] . Besides , the increasing number of migrants from endemic countries ( specially Latin America , Sub-Saharan Africa and Southeast Asia ) and international travelers has resulted in an increase of CC in Europe [11] . A particular epidemiological situation is present in Spain and Portugal , where a recent review carried out by Fabiani et al . showed a number of cases more than fivefold the total cases reported in the seventeen European countries analyzed [10] . Spain is the country reporting the highest number of imported cases of CC , probably because it hosts the largest number of migrants coming from Latin American in Europe [9] , where CC is highly prevalent [12] . Up to date , there is no surveillance system for CC disease implemented in Spain . However , hospitalized cases are recorded within the National Health System′s Hospital Discharge Records Database ( Conjunto Mínimo Básico de Datos or CMBD in Spanish ) belonging to the Spanish Ministry of Health . In this paper , we describe for the first time CC related hospitalizations in Spain between 1997 and 2014 , in terms of time , geographical distribution , and disease related individual characteristics .
We performed a retrospective descriptive study using the CMBD for the time period January 1st , 1997 to December 31st , 2014 . International Classification of Diseases , Ninth Revision , Clinical Modification ( ICD-9CM ) , the ICD version employed during the study period , was used for this purpose [13] . Registers with ICD-9 CM “other cestode infection” codes ( “123 . *” ) placed in first or second diagnostic position were analyzed . For further analysis , hospitalization discharges coded as cysticercosis ( ICD-9 CM code 123 . 1 ) were selected . The CMBD database receives notification from around 98% of the public hospitals in Spain [14] . The National Health System ( NHS ) provides free medical care to 99 . 5% of the Spanish population , although those persons not covered by the NHS can be attended at the public hospitals . Private hospitals represent only a small proportion of all hospital admissions . Since 2005 , CMBD also has a gradual coverage from private hospitals [15] . For each entry , we collected socio-demographic ( sex , age and autonomous community of residency ) and clinical data ( other diagnosis , type and department of admission , average length of hospitalization , non-invasive procedures and history of surgical intervention during the hospitalization , re-admission , outcome , hospitalization′s cost to the health care system , financing regime and diagnosis related group ( DRG ) ) . Age was categorized in four groups: 0–15 , 16–44 , 45–64 and ≥ 65 years old . These four age categories were selected to provide an overview of children , young adults , older adults and seniors . Other selected co-diagnoses were also explored . These diagnoses were assessed by searching all those ICD-9-CM codes possibly associated with CC in any diagnostic position , and included: diseases of the nervous system and sense organs , endocrine , metabolic and immunity disorders and HIV infection . Differences in proportions were assessed by the χ2 test and 95% confidence intervals ( 95% CI ) were calculated . ANOVA was used to compare differences in means . We used two-sided tests and p < 0 . 05 was considered significant . The average number of hospitalizations per year and autonomous community ( Comunidades Autónomas or CC . AA in Spanish ) were calculated in order to assess temporal and geographical patterns . Official population figures of the Spanish municipalities were used as population at risk for the study period 1998–2014 [16] . Data was missing for 1997 , thus the population data from the Intercensus Population Estimates were used for that year [17] . Migrations Statistics were also obtained from the Spanish National Statistical Institute . The external migration rates were computed by using residential variation statistic , only available for the period 2002–2014 [18] . Those countries considered endemic for CC ( Latin America , Southeast Asia and Sub-Saharan Africa ) were selected . External migration rates were computed for the whole country and separately for those CC . AA with the highest CC hospitalization rates . It was assumed that the age distribution of the population covered by these hospitals was similar to the general population . Results in terms of mean rates by CC . AA were plotted in maps for the whole study period using the Geographical Information System QGis free software version 2 . 18 . 13 . Data analysis was performed using STATA software version 12 . This study involves the use of patient medical data from The Spanish Centralized Hospital Discharge Database ( CMBD ) . These data are hosted by the Ministry of Health Social Services and Equality ( Ministerio de Sanidad , Servicios Sociales e Igualdad or MSSSI in Spanish ) . Researchers working in public and private institutions can request the databases by filling , signing and sending a questionnaire available at the MSSSI website . In this questionnaire a signed Confidentiality Commitment is required . All data are anonymized and de-identified by the MSSSI before it is provided to applicants . According to this Confidentiality Commitment signed with the MSSSI , researchers cannot provide the data to other researchers that must request the data directly to the MSSSI [14] .
A total of 2 , 067 hospital discharges with “other cestode infection” diagnosis placed as first or second diagnostic position were identified for the 18-year study period ( ICD-9-CM codes 123 . * ) . Out of them , 1 , 912 cases ( 92 , 5% ) were cysticercosis hospitalizations , while the remaining 155 hospitalizations were due to intestinal taeniasis ( Table 1 ) . The temporal distribution of hospitalizations related to clinical CC as first or second diagnosis during the 18-year study period is represented in Fig 1 . From 1998 to 2008 , an increasing trend in the number of CC hospitalizations was observed . Hospitalization rates peaked in 2008 and were afterwards followed by a steady decline , in parallel with a decrease in the external migration rate , which began to decline in 2006 . At national level , the median annual CC hospitalization rate was 4 . 22/100 , 000 population . Regarding the regional distribution , the Murcia region had the highest median CC hospitalization rate ( 13 . 37 hospitalizations/100 , 000 population ) , followed by Navarra ( 10 . 09/100 , 000 population ) , Madrid ( 9 . 32/100 , 000 population ) , Aragon ( 6 . 21/100 , 000 population ) and Rioja ( 6 . 04/100 , 000 population ) ( Fig 2 , S1 Table ) . Hospitalization trends from those regions with higher rates revealed different patterns . In Murcia , hospitalization rate reached high values in 2005 , 2008 and 2012 , with an overall decreasing trend . A similar trend was identified in Aragon , with peaks in 2004 and 2007 but a tendency to increase since 2011 . In Madrid and Navarra , the tendency in the number of hospitalization rates paralleled the external migration rate . In Rioja there was a peak between 2006 and 2008 ( Fig 3 ) . The mean age of the 1 , 912 CC hospitalized patients was 38 years ( range 0–97 ) , being the 16–44 age group the most represented . This age group also had the highest incidence rate ( 6 . 3/100 , 000 ) , followed by above 65 years old ( 3 . 4/100 , 000 ) , 45–64 years old ( 3 . 0/100 , 000 ) and under 15 ( 2 . 2/100 , 000 ) . The percentage of CC hospitalized men and women was similar . The majority of patients were discharged at home , decease occurring in 1 . 5% . The average length of stay for clinical CC hospitalizations was 13 days . We found a wide range for the hospitalization median cost with a median per patient of 29 , 327 . 4 euros . Other clinical characteristics are summarized in Table 2 . The most frequent diagnostics associated with clinical cysticercosis were diseases of the nervous system and sense organs , such as epilepsy and convulsions ( 49 . 5% ) , hydrocephalus ( 11 . 8% ) encephalitis/myelitis/meningitis ( 11 . 6% ) and occlusion of cerebral arteries/hemiplegia ( 4 . 2% ) , which could be related to subarachnoideal NCC . Of all hospitalizations with CC , surgical procedures in the central nervous system occurred in 520 hospitalizations ( 27 . 2% ) . The most frequent were: diagnostic procedures on spinal cord and spinal canal structures ( n = 367 ) , extracranial ventricular shunt and revision , removal , and irrigation of ventricular shunt ( n = 169 ) , procedures over the skull ( craniotomy ) , brain , and cerebral meninges ( n = 30 ) , and other neurosurgical procedures as ventriculostomy ( n = 52 ) . Regarding other non-neurological diagnoses , the most frequent were unspecified essential hypertension ( 9 . 1% ) , pure hypercholesterolemia ( 4 . 5% ) and unspecified hyperlipidemia ( 4 . 3% ) , diabetes mellitus ( 3 . 8% ) and some addictions such as tobacco use disorder ( 8 . 7% ) and alcohol dependence syndrome ( 2 . 3% ) ( S2 Table ) . Major diseases causing immunosuppression were checked in the database . Only 12 patients were seropositive for the HIV and 3 presented disorders of the immune system causing immunosuppression . Comparison of “immunosuppressed” and “immunocompetent” patients´ clinical characteristics was carried out . The exitus outcome was more frequent in the immunosuppressed group ( 6 . 7% vs 1 . 4% ) , although this difference was not significant ( p = 0 . 092 ) . The only significant difference was related to the hospitalizations time; immunosuppressed patients were hospitalized longer than immunocompetent patients ( p<0 . 001 ) .
Several considerations should be taken into account when interpreting the findings from this research . Our study included cases of CC requiring hospitalization ( clinical CC ) , which is not equivalent to the true CC incidence in the population . The CMBD provides information from a network of hospitals that covers more than 99% of the population living in Spain [14] , but it does not include cases managed in outpatient settings or asymptomatic cases , thus CMBD is still underestimating the real burden of CC in Spain . Other relevant limitations in the CMBD are: important individual data such as country of origin or possible related risk factors are missing; information about laboratory test results are not recorded; and the CMBD do not allow the follow-up of patients . Thus , further investigation is recommended . The use of hospital records data for epidemiological consideration may also be prone to imprecision due to the following reasons . The diagnosis of CC is complicated; imaging findings are rarely pathognomonic and immunodiagnostic tests vary in sensitivity and specificity [39] . Even if the diagnostic criteria have been agreed upon by international experts ‘consensus [40–42] , the standard diagnostic criteria in the daily clinical practice may differ . Finally , at national ( and also international ) level , data on the animal side of the CC epidemiological scenario are also missing . Despite the European Directive 2003/99/EC which recommends monitoring animal CC according to the epidemiological situation [43] , many countries do not report these cases , including Spain [44] . There is a need for a common strategy on data collection , monitoring and reporting , which would facilitate a more accurate picture on the CC epidemiological situation . Even if the majority of cases are imported , improving the human and animal CC surveillance will result useful both in gaining extended disease knowledge and reducing morbidity and related-costs .
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Cysticercosis ( CC ) , caused by the larval cysts of the pork tapeworm Taenia solium , is a neglected tropical disease ( NTD ) . It is acquired when worm eggs are ingested and the developing larvae migrate through the body and form cysts in tissues . Frequent in pigs , it can also affect humans , usually when they swallow T . solium egg-contaminated soil , water or food ( mainly vegetables ) or through self-infection or person to person transmission when hygiene practices are insufficient . CC is the most frequent preventable cause of epilepsy worldwide , and is estimated to cause 30% of all epilepsy cases in countries where the parasite is endemic . T . solium in humans has been emerging as a public health concern in Europe due to the increased number of diagnosed CC cases in recent decades . This situation has been linked to increased travels and migratory movements towards and from endemic countries . In Spain , there is no surveillance system in place . Thus , the CC disease burden remains unknown . This study provides an 18-year review of the epidemiological trends and patient characteristics in order to assess the impact of CC hospitalizations in Spain . In this way , we aimed to describe for the first time the national human CC scenario in Spain .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"hospitalizations",
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"european",
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"tropical",
"diseases",
"geographical",
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"europe"
] |
2018
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Clinical Cysticercosis epidemiology in Spain based on the hospital discharge database: What's new?
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Chronic immune activation ( IA ) is considered as the driving force of CD4+ T cell depletion and AIDS . Fundamental clues in the mechanisms that regulate IA could lie in natural hosts of SIV , such as African green monkeys ( AGMs ) . Here we investigated the role of innate immune cells and IFN-α in the control of IA in AGMs . AGMs displayed significant NK cell activation upon SIVagm infection , which was correlated with the levels of IFN-α . Moreover , we detected cytotoxic NK cells in lymph nodes during the early acute phase of SIVagm infection . Both plasmacytoid and myeloid dendritic cell ( pDC and mDC ) homing receptors were increased , but the maturation of mDCs , in particular of CD16+ mDCs , was more important than that of pDCs . Monitoring of 15 cytokines showed that those , which are known to be increased early in HIV-1/SIVmac pathogenic infections , such as IL-15 , IFN-α , MCP-1 and CXCL10/IP-10 , were significantly increased in AGMs as well . In contrast , cytokines generally induced in the later stage of acute pathogenic infection , such as IL-6 , IL-18 and TNF-α , were less or not increased , suggesting an early control of IA . We then treated AGMs daily with high doses of IFN-α from day 9 to 24 post-infection . No impact was observed on the activation or maturation profiles of mDCs , pDCs and NK cells . There was also no major difference in T cell activation or interferon-stimulated gene ( ISG ) expression profiles and no sign of disease progression . Thus , even after administration of high levels of IFN-α during acute infection , AGMs were still able to control IA , showing that IA control is independent of IFN-α levels . This suggests that the sustained ISG expression and IA in HIV/SIVmac infections involves non-IFN-α products .
Chronic immune activation during HIV infection is considered as the main driver of CD4+ T cell depletion and AIDS , and early T cell activation is a better predictor of the outcome of the infection than viral load [1] . Recent observations suggest that inflammation is even more important than T cell activation to predict disease progression and mortality [2] , [3] . Already in the acute primary phase of HIV-1 infection , the levels of soluble inflammatory mediators , such as IP-10 ( CXCL10 ) , were predictive of disease progression [4] , [5] . Type I IFN ( IFN-I ) , such as IFN-α , is an important component of innate immunity providing a first-line defense to viral infections , as well as bridging the innate and adaptive immune systems . This cytokine is mainly produced by plasmacytoid dendritic cells ( pDCs ) in viral infections . These cells interact with myeloid dendritic cells ( mDCs ) , NK cells , monocytes , T and B cells and contribute to the orchestration of the immune response . IFN-α production is critical for the activation of NK cells , enhancing IFN-γ secretion and their cytotoxicity . Reciprocally , NK cells can affect pDC maturation and function [6] . Thus , upon infection , a crosstalk is engaged between NK cells , pDCs and mDCs , an interplay that involves IFN-I activity coupled with the release of other soluble factors [7] . Upon recognizing HIV-1 , pDCs become activated , secreting high amounts of IFN-α and inflammatory cytokines , such as TNF-α [8] . This leads to bystander maturation of mDCs [9] . Both pDCs and mDCs are reduced in number and function in HIV-1 infected individuals in the circulation [10] . PDCs have been shown to migrate to lymph nodes ( LNs ) , gut and spleen and accumulate there [11]–[14] . As a matter of fact , the diminished responses seen in disease progressors might be explained by pDC exhaustion or trafficking to tissues [13] , [15] . Moreover , a defect in the pDC-NK cell cross-talk , due in large part to impaired NK cell responsiveness to IFN-α , has been described in HIV-1 infection [16] , [17] . Still the role of IFN-α in HIV infection is controversial . On the one hand , IFN-α may delay disease progression by inhibiting viral replication through the induction of cellular restriction factors and by stimulating various components of the immune response involved in the control of HIV [18] , [19] . A beneficial effect of IFN-α is also suggested by the observation of higher levels of pDCs and IFN-α production by TLR9-stimulated pDCs in HIV-infected long-term non-progressors [20] . On the other hand , IFN-α levels and type I interferon-stimulated gene ( ISG ) are markedly increased and sustained in progressors as compared to long-term non-progressors [21] , [22] . Indeed , in HIV-untreated patients , high levels of ISG , such as IP-10 , were associated with a more rapid CD4+ T cell depletion [4] , [23] . Thus , it has been suggested that IFN-α might exert deleterious effects through various mechanisms . It could fuel chronic immune activation by the induction of ISGs including chemokines able of attracting target cells to the site of viral replication [24] . It could also stimulate innate immune cells , such as NK cells , which will in turn produce cytokines ( IFN-γ , … ) and chemokines , and indirectly contribute to the activation of other cell types . Moreover , the up-regulation of the ISG TRAIL may induce apoptosis of uninfected CD4+ T lymphocytes [25] . Chronic high levels of IFN-α could also induce defects in the thymopoiesis and bias in T cell selection , thereby accelerating disease progression [26] . Fundamental clues regarding the role of inflammation in AIDS and the mechanisms that protect against it may lie in natural hosts of SIV , such as African green monkeys ( AGMs ) and sooty mangabeys ( SMs ) , which are asymptomatic carriers of SIV [27] , [28] . This protection against AIDS is seen despite virus replication levels in blood and gut similar to HIV-1 infected humans and SIVmac-infected macaques [29] . It is associated with an absence of chronic immune activation , lacking both chronic T cell activation and chronic inflammation [27] , [30]–[32] . This is not due to ignorance of the virus or to a functional defect of pDCs in sensing the virus [33]–[36] . Indeed , a vigorous innate immune response is triggered upon infection [34] , [36]–[40] . Thus , the acute phase of SIVagm infection is characterized by the recruitment of pDCs to LNs , IFN-α production , induction of ISG and corresponding protein ( ISP ) expression [34] , [36]–[40] . The levels of ISP strongly correlated with IFN-α levels during the acute phase of SIVagm infection [36] . However , there are major differences as compared to SIVmac infection: the levels of IFN-α produced in blood and LN were lower than those observed in SIVmac infection [35] , [36] , [38] . Moreover , in some reports , most cytokines were produced only to moderate levels in natural hosts and several pro-inflammatory cytokines were not induced at all , in contrast to the cytokine storm seen during pathogenic HIV-1/SIVmac infections [30] , [35] , [36] , [38] , [41]–[43] . Finally , ISGs , cytokines and T cell activation are down-regulated by the end of the acute phase in natural hosts and maintained as such . Thus , while in HIV/SIVmac pathogenic infections , immune activation persists , in natural hosts there are mechanisms that either prevent the onset of sustained inflammation or mechanisms that rapidly and efficiently turn them off . In this report , we investigated the effect of SIVagm infection in AGM on innate immune cell compartments , in particular pDCs , mDCs and NK cells , and tested whether exogenous administration of IFN-α would modify the development of antiviral responses , promote chronic inflammation and/or alter clinical parameters .
The innate immune responses were followed in six SIVagm . sab92018-infected AGMs between days 2 and 547 post-infection ( pi ) . We analyzed both blood and LNs . It is crucial indeed to study LNs because these are the sites where T and B cell responses are induced , shaped , and regulated and where correlates of protection were identified [44] , [45] . Consistent with previous reports , AGMs displayed high levels of SIV replication with a peak on day 9 pi coinciding with a transient decline in CD4+ T cell levels ( Figure 1A and B ) [30] , [36] , [46] . We monitored T cell proliferation and confirmed that the primary phase of SIVagm infection in AGMs is associated with a transient increase in the percentages of Ki-67+ T cells in blood and LNs ( Figure 1D and E ) [30] . The peak of Ki-67+ CD4+ T cells was observed between day 7 and 9 pi ( at day 9 , p = 0 . 031 ) , while the percentage of Ki-67+ CD8+ T cells reached a plateau on day 11 pi in blood ( p = 0 . 008 ) and a peak on day 25 pi in LN ( p = 0 . 016 ) . The Ki-67+ CD4+ and DN T cell frequencies subsequently decreased on day 11 and that of Ki-67+ CD8+ T cells after day 31 pi . To better understand the trafficking and function of mDCs , pDCs and NK cells in AGMs , we investigated the early changes in activation , maturation , function and homing markers of these cells in blood and LNs ( Figures 2 , 3 and 4 , respectively ) . The gating strategy used for flow cytometry analysis is depicted in Figure S1 . We first confirmed previous data on pDC and mDC dynamics during SIVagm infection ( data not shown ) [38] , [47] , [48] . We then studied two homing receptors for DCs: the homing inflammatory chemokine receptor CXCR3 , which is the receptor for CXCL9 , IP-10 and CXCL11 and CCR7 , which is a receptor for chemokines that are expressed constitutively in secondary lymphoid organs . In line with the increase of mDC frequency in LNs , expression of CXCR3 increased on these cells in blood and LNs during acute infection ( Figure 2A and B ) . Moreover , the percentage of CCR7+ mDCs was transiently increased in blood ( p = 0 . 039 at day 9 pi ) ( not shown ) while CCR7 levels on mDC surface did not increase ( Figure 2C and D ) . For pDCs , the expression levels of CCR7 were significantly increased up to day 14 pi , while the CXCR3 levels were not increased ( Figure 3A–D ) . Hence , CXCR3 and CCR7 showed opposite expression profiles on pDCs and mDCs . Still , both mDCs and pDCs showed an increase expression of one homing marker , concomitant with their increases in LNs [38] , [47] , [48] MDCs showed up-regulations of the maturation markers CD80 and CD86 in blood and LN at early time points of primary infection ( in blood: p = 0 . 008 at day 4 pi for CD86 and p = 0 . 008 at day 2 pi for CD80 , in LNs: p = 0 . 031 at day 9 for CD80 ) ( Figure 2F , G , I and J ) . In contrast , the expression of CD86 was not modulated on pDCs ( Figure 3E and F ) . It was surprising to see this discrepancy in maturation profiles between mDCs and pDCs . To confirm these findings , we analyzed the maturation profiles of mDCs and pDCs in the blood of another group of 8 AGMs infected with SIVagm . In this group , we further distinguished CD16+ mDCs ( inflammatory ) and CD16− mDCs ( Figure 2E , H and K ) . In addition to the maturation markers CD80 and CD86 , we also measured HLA-DR expression . The two CD16+ and CD16− mDC subsets were present in similar frequencies in blood ( not shown ) . Both mDC subsets displayed increases in the expression of CD80 , CD86 and HLA-DR . These maturation markers were more significantly increased on the CD16+ than on the CD16− subset ( Figure 2E , H and K ) . We confirmed the pDC phenotype in these 8 additional animals by staining for BDCA-2 ( Figure 3G–H ) . We choose to follow HLA-DR and CD40 as these markers are well known to be up-regulated when pDCs mature and CD40 expression is increased on pDCs in SIV/HIV pathogenic infection [49] , [50] . On AGM pDCs , the expression of HLA-DR was significantly down-regulated during the acute phase and the expression of the activation/maturation marker CD40 was not modulated ( Figure 3G and H ) . The expression of CCR7 was transiently increased ( p = 0 . 031 at day 4 pi ) in this group of AGM too ( not shown ) . These analyses thus confirm that mDCs show a more pronounced maturation profile than pDCs during SIVagm infection . The two main functional NK cell subsets ( cytolytic versus cytokine producers ) were analyzed . These two subsets were differentiated based on the expression of CD16 , the CD16+ subset being the predominant one in blood , as in humans and macaques ( Figure 4A ) . Similar to cynomolgus macaques , the CD56 marker cannot be used in AGM to differentiate the NK cell subsets [51] . Thus , NK cells were defined as CD3− CD20− HLA-DR− CD8α+ NKG2A+ CD16+/− ( Figure S1B and C ) , as in other studies on NK cells from macaques and SMs [39] , [52] . A significant transient decline of both subsets was observed in blood ( p = 0 . 031 at day 2 pi ) ( Figure 4A ) . NK cell numbers then progressively increased to reach 249% of the pre-infection levels at the end of primary infection ( day 25–31 pi ) for the major CD16+ subset , and 154% for the CD16− subset . They returned to baseline levels in the chronic phase ( not shown ) . In LNs , only few NK cells were detectable and most corresponded to the CD16− subset , similar to humans [53] . A significant decrease in the percentage of the CD16− subset in LNs was observed ( Figure 4B ) . The levels of CD16+ cells in LNs were too low to be followed . Thus , CD16+ NK cells in the blood exhibited maximal increase at the time of transition between acute and chronic phase , similar to what has been observed in SMs [39] . We monitored the activation profiles of CD16+ and CD16− NK cells in blood and of the CD16− NK cells in LNs . As shown in Figure 4C and E , the frequencies of Ki-67+ and CD69+ NK cells were markedly enhanced upon SIVagm infection in blood with a peak on day 11 pi . The activation profiles of CD16− NK cells in blood followed a similar kinetic than that of CD16+ NK cells ( not shown ) . The percentage of activated NK cells also highly increased in the LNs ( Figure 4D and F ) . To evaluate NK cell function , the surface expression of CD107a ( a surrogate marker for cytolytic function ) and intracellular expression of IFN-γ ( cytokine production ) were measured ( Figure 4G–J ) . The NK cell cytolytic activity was significantly increased only in LNs ( Figure 4G and H ) and no significant increase of IFN-γ production was observed in either blood or LNs ( Figure 4I and J ) . NK activation in blood and LNs was correlated with viral replication ( blood CD16+Ki-67%: Rs = 0 . 37 , p<0 . 001; LN CD16−Ki-67%: Rs = 0 . 54 , p = 0 . 002; blood CD16+CD69%: Rs = 0 . 4 , p<0 . 001; LN CD16−CD69%: Rs = 0 . 53 , p = 0 . 002 ) . The NK cell cytolytic activity in LNs was also correlated with viral load ( CD16−CD107a%: Rs = 0 . 54 , p = 0 . 003 ) . Thus , in SIVagm primary infection , NK cells were strongly activated and cytolytic NK cells increased in LNs . These increases were positively associated with viremia levels . Both NK cell activation and cytotoxic activity are stimulated by IFN-I , which is driven by virus . IL-15 plays a pivotal role in the development , survival and function of NK cells . We quantified IFN-I and IL-15 concentrations in blood and tissues . In line with previous reports for SIVagm infection [36] , [38] , [42] , the IFN-α levels in plasma were transiently increased during primary infection . In addition , we reveal an increase of IL-15 production . The animals displayed two peaks of IFN-α and IL-15 production , on days 2 and 9 pi , day 9 corresponding to the peak of plasma viremia ( Figure 5A and C ) . By day 11 pi , these levels were already decreased and below detection limit after day 14 pi for IFN-α . IFN-α and IL-15 were also measured in LNs ex vivo by collection of supernatants from the LN cell preparations ( Figure 5B and D ) . The IFN-α concentrations in these supernatants were increased between days 2 and 11 pi . The limited number of LNs that could be collected did not allow for the same close monitoring frequency as in blood and it is unclear whether two peaks of expression were present in the LN compartment as well . We found that NK cell activation in blood was correlated with the IFN-α levels ( CD69%: Rs = 0 . 39 , p<0 . 001; Ki-67%: Rs = 0 . 24 , p = 0 . 008 ) and the IL-15 levels in plasma ( CD69%: Rs = 0 . 25 , p = 0 . 009; Ki-67%: Rs = 0 . 28 , p = 0 . 003 ) . At the level of LNs , IFN-α was correlated with NK cell activation ( CD69%: Rs = 0 . 35 , p = 0 . 032; Ki-67%: Rs = 0 . 41 , p = 0 . 012 ) and cytotoxicity ( CD107a%: Rs = 0 . 5 , p = 0 . 002 ) , while IL-15 was only correlated with NK cell proliferation ( Ki-67%: Rs = 0 . 43 , p = 0 . 007 ) . The IFN-α levels also correlated with Ki-67+ CD4+ T cell levels ( Rs = 0 . 3 , p = 0 . 002 ) . Altogether , IFN-α and IL-15 correlated with NK activation and IFN-α with NK cytotoxic activity in LNs . We quantified thirteen additional cytokines in the plasma for the two AGM groups ( Figure 5 and S2 ) to determine the earliest kinetics of cytokines and search for differences with the cytokine storm reported in HIV-1 and SIVmac infections . Cytokines reported to be increased early during HIV-1 infection were selected , such as MCP-1 as well as “innate” cytokines , such as IL-12 . The early collection time points were chosen at very short intervals starting at 6 hours pi . Among the 15 cytokines studied in total , 8 were significantly up-regulated and 6 displayed a first peak on day 2 as well as a second increase on day 7 and/or 9 pi: IL-15 , IFN-α , IP-10 , MCP-1 , IFN-γ and IL-18 ( Figure 5 and S2 ) . IL-15 , IP-10 and MCP-1 are inducible by IFN . Their profiles strongly correlated with IFN-α levels ( IL-15: Rs = 0 . 53 , p<0 . 001; IP-10: Rs = 0 . 73 , p<0 . 001; MCP-1: Rs = 0 . 6 , p<0 . 001 ) ( Figure 5 ) . IL-8 was modestly increased at day 9 pi , while IL-12 was up-regulated only later on , on days 14 and 28 pi and even downregulated at time points just after the IFN-α peaks ( Figure S2 ) . This might be due to the fact that IL-12 is inhibited by IFN-α [54] . As a matter of fact , previous reports in SIVmac and HIV-1 infection showed that IL-12 levels increase late in primary infection , once the IFN-α levels decrease [43] , [55] . Strikingly , in SIV-infected AGMs , most of the other pro- and anti-inflammatory proteins and ISPs measured ( IL-6 , sTRAIL , TNF-α , IL-17 , TGF-β ) were not modulated ( Figure S2 ) . We wondered whether the first peak ( day 2 pi ) is specific for natural hosts . For those cytokines , which showed increases already on day 2 pi in AGMs , we also measured cytokines in two rhesus macaques infected with SIVmac251 ( Figure S3 ) . Also in these monkeys , a peak of cytokine production was observed on day 2 pi depending on the cytokine and the animal studied , suggesting that the early peak is not unique to AGMs . Most of studies conducted so far don't include such early time points . However , a similar early induction ( day 2–4 pi ) of IFN-α and some ISGs has been observed at mucosal sites of orally infected macaques [56] . We had already noticed such an early peak in previous studies [36] , [38] . The AGMs were infected here with a purified virus which therefore excludes that the early peak is due to contaminants in the inoculum . Finally , the data confirm previous reports showing that IFN-α levels are lower in acutely infected AGMs compared to macaques ( Figure S3B ) [35] , [36] , [38] . Altogether , the close monitoring of fifteen soluble factors showed that cytokines , which are known to be produced early during SIVmac infection in macaques or HIV-1 infection in humans , i . e . before , during or shortly after the viral peak , were all induced in SIVagm infection . In contrast , cytokines that are induced late during the acute phase of pathogenic infection were not or only moderately induced in AGMs . We tested whether the lower levels of IFN-α in SIVagm infection might dictate the outcome of infection , in particular with respect to the resolution of the inflammation . We therefore administered high doses of recombinant IFN-α ( r-mamu-IFN-α ) during the acute phase of SIVagm infection in an attempt to perturb the control of inflammation and abolish its resolution , which would be characterized by uncontrolled expression of ISGs and chronic immune activation . The r-mamu-IFN-α used for the in vivo treatment was first tested for its efficacy on AGM cells in vitro and in vivo ( Figure 6A–D and F ) . The same cytokine has been previously used in SMs without inducing any anti-IFN-α antibodies [57] . AGM PBMCs exposed to r-mamu-IFN-α in vitro up-regulated the expression of ISGs , such as Mx1 or IP-10 , to similar levels as in macaque PBMCs ( Figure 6A ) . Low doses of the r-mamu-IFN-α were already highly efficient for ISG induction , in line with previous data [36] . After a single in vivo injection of 5×105 IU of r-mamu-IFN-α , high levels of IFN-α were observed in plasma 1 hour post-treatment ( Figure 6B ) , leading to strong up-regulation of ISGs , such as IP-10 ( Figure 6C ) . Since the half-life of a similar human recombinant IFN-α has been estimated at 2–5 h in AGMs in vivo [58] , this could explain why the levels of IFN-α and IP-10 mRNA were already low at 24 h after administration despite the r-mamu-IFN-α being an IgG fusion protein [57] , [59] . In order to maintain robust levels of IFN-α and constantly high expression of ISGs , we injected r-mamu-IFN-α daily with a 10% increment every 2 days for 16 days . The safety and efficacy of such treatment were verified on a SIV-chronically-infected AGM . The latter displayed a 2 log10 decrease of the chronic viral load during the treatment ( Figure 6D ) , but no major difference in T cell activation ( Figure 6F ) , similar to data reported for chronically SIV-infected SMs [57] . Anti-IFN-α antibodies were not detected at any time point during or after treatment ( data not shown ) . Since r-mamu-IFN-α was efficient in cells from uninfected and chronically infected AGMs , we then tested whether such treatment would affect the resolution of immune activation during primary infection . The treatment was started on day 9 pi because after day 9 pi , endogenous IFN-α levels started to decrease , concomitantly with the diminution of other cytokines and ISPs such as IP-10 and MCP-1 and the decrease of activated NK cells and Ki-67+ CD4+ T cells . Also , based on data from the literature , we could not exclude that an initial inflammation during the first week of infection is necessary to establish infection . Finally , we did not want to interfere with the efficacy of the initial viral replication . Two AGMs were injected daily with r-mamu-IFN-α between day 9 and 24 pi . The virological and immunological profiles in the IFN-α-treated AGMs were compared to those of the 6 infected but untreated AGM ( Figures 6E , 6G , 6H , 7 , S3 and S4 ) . The administration of r-mamu-IFN-α during the acute phase of infection had no major effect on viral load ( Figure 6E ) , even if a slight but not significant decrease was observed as compared to untreated animals at the first time points after treatment . Body temperatures were elevated during the treatment period with IFN-α ( Figure S5 ) . The expression of the ISGs CXCL9 , IP-10 and CXCL11 were however comparable between treated and untreated AGMs in both PBMCs and LN cells ( Figure 7 ) . The treatment also did not result in a persistent T cell activation ( Figure 6G and H ) or CD4+ T cell loss over time ( data not shown ) . As IFN-α is known to exert direct and indirect effects on innate immune cells , we also investigated whether in the absence of a change in disease outcome , the IFN-α treatment would still have had an impact on NK cells , mDCs and pDCs ( Figure S4 ) . The administration of r-mamu-IFN-α in the context of the SIV primary infection did not affect their frequencies and did not induce an increase of maturation or activation of these innate immune cells . In summary , in spite of daily administration of high doses of IFN-α post peak of SIV replication , AGMs were still able to resolve inflammation and immune activation .
We aimed to study if the lower levels of IFN-α described during SIVagm infection as compared to SIVmac infections matter in the resolution of the inflammation in AGMs . The early host immune responses are an essential factor in determining the subsequent clinical course of disease . In mice , an early innate alteration significantly compromises the following immune responses and the host's ability to counteract the virus/parasite spread [60] , [61] . We tested here whether by artificially increasing IFN-α related inflammation during the acute phase of SIVagm infection , one can overcome the intrinsic control of immune activation in this natural host . In order to study the control of immune activation and not interfere with the establishment of viral infection , the treatment was administered between the plasma viral peak and the end of the acute phase of infection . Surprisingly , the treatment did not affect viral dynamics , control of inflammation or T cell activation . This is not due to a lack of sensitivity of AGM cells to the recombinant IFN-α used here . Indeed , the r-mamu-IFN-α molecule was functional in vitro and in vivo in healthy and chronically-infected AGMs . Moreover , when administered during the chronic phase of infection , our results paralleled those described in chronically-infected SMs treated with the same molecule , namely a reduction in viral load and increase in ISG expression in the absence of major increases of T cell activation [57] . Although the number of animals was low , the analyses show that r-mamu-IFN-α was fully active on AGM cells . It is possible that the lack of changes after IFN-α treatment during primary SIVagm infection was due to tolerance to the injected IFN-α . In SMs chronically infected with SIVsmm and treated with IFN-α , the effect of IFN-α was transient likely due to the induction of tolerance to such treatment as also reported in humans [62] , [63] . Here , the treatment was short , but refractoriness could have been induced by the previous response to endogenous high levels of IFN-α . Of note , we treated the animals starting from day 9 pi , corresponding to the peak of endogenous IFN-α production . Had we started the treatment on the day of infection , we cannot exclude that we might have seen an effect on ISGs or viral load . However , such protocol might have lowered the initial viral replication , which was in opposition with the aim of our study . Altogether , administration of IFN-α in the mid and late part of the acute phase did not change the outcome , suggesting that the resolution of inflammation in AGMs is not due to a difference in the levels of IFN-α production during primary infection . It has been suggested that the combination of antiretroviral therapy and interferon given during acute HIV infection may potentiate both innate and adaptive immune responses against HIV replication and/or reservoir levels [64] . Our study shows that in primary infection , IFN-α , when administered after the peak of viremia , does not affect viral replication or innate responses . It reduces viral load during chronic phase . Whether this is true for pathogenic infection , remains to be determined . However , the timing is very important and should be considered when such treatment is envisaged . We previously showed that during the acute phase of SIV infection the levels of ISGs , such as of IP-10 , strictly correlate with IFN-α levels [36] . Here , treatment with high doses of IFN-α did not lead to sustained ISG expression . This suggests that , at the transition of acute to chronic phase other factors than IFN-α predominantly drive ISG expressions in macaques and humans . Elevated expression levels of ISGs in chronic infection are associated with uncontrolled viremia and disease progression [23] , [65] . It could be that not the IFN-I production , but constant ISG expression are deleterious for the host . IP-10 has been reported to be an excellent marker of inflammation and disease progression [4] , [65]–[68] . IP-10 is inducible not only by IFN-I and IFN-γ , but also by other pro-inflammatory cytokines ( TNF-α , IL-1β , IL-18 ) [69]–[71] . Hence , it is possible that IFN-α alone is not sufficient , but that a combination with other factors , TNF-α for example , is also required to induce ISG expression . Moreover , even though ISGs are IFN-inducible , some were shown to be directly up-regulated through recognition of viral or bacterial products by pattern-recognition receptors in an IFN-independent manner [72]–[75] . Finally , an expansion of the enteric virome and microbial translocation are observed in chronic HIV-1 and SIVmac infections [76] , [77] . It could thus explain why macaques and humans maintain ISG expression but not AGMs who do not display microbial translocation or virome expansion . Other or additional factors might also play a role in the maintenance of ISG expressions during HIV-1/SIVmac infections . For instance , distinct IFN-α subtypes differently induce ISG expressions in vivo , while here , only IFN-α2 , which is considered the most abundant in viral infections , was used [69] , [78]–[80] . Altogether , our study indicates that the non-pathogenic outcome of SIVagm infection is not due to differences in IFN-α levels between AGM and macaques or humans . It does not exclude that the difference in outcome is related to different levels of ISG expression . It indicates however , that the mechanisms , which maintain high levels of ISG expression , are due to other or additional factors than IFN-α . Several studies have debated whether natural hosts display lower or similar immune activation levels during primary infection as compared to pathogenic infections . Some studies have reported weaker levels of T cell activation and cytokine concentrations during the acute phase of SIVagm or SIVsmm infection , whereas in other studies the levels were equivalent to those observed in SIVmac-infected macaques [30] , [36] , [41] , [81] . We performed a detailed follow-up of T cell activation and , to attempt to reconcile the discrepant cytokine profiles , we deciphered here the early production of cytokines in AGM . We included in the study the cytokines known to be induced very early during pathogenic infection , such as IL-15 [19] , [43] . Of note , the acute phase of SIVagm infection resulted in significant increases of early cytokines , including IL-15 , IP-10 and MCP-1 , similar to pathogenic infection . However , salient differences were observed for cytokines known to be produced in later stages of the acute phase of HIV-1/SIVmac infections . They were either not or only weakly induced in AGMs . In particular , IL-6 and TNF-α were not up-regulated ( Figure S2 ) . We hypothesize that the early cytokines , which are produced in AGMs during the first two weeks pi , confer a benefit to both the virus and the host . It would be beneficial to the virus as inflammation attracts target cells to the sites of infection . For the host , the induction of early innate responses ( restriction factors , NK cells , mDCs ) , would allow the development of antiviral innate and adaptive responses for partial control of viral replication . AGMs might have found a way to allow early inflammation resulting in productive infection while blocking the cytokine storm that takes place following the viral peak . This would avoid the sustained inflammatory environment . The dual pattern of cytokines that we observed might be explained by a differential susceptibility to activation by the innate cells . While pDCs show a normal sensing of SIVagm [36] , [37] , [40] leading to the production of IFN-α , other cells , for instance myeloid cells , such as mDC and macrophages , might not produce any cytokine . Indeed , a recent study reported that in contrast to SIVmac and HIV-1 infections , mDCs mature but do not show spontaneous production of pro-inflammatory cytokines such as TNF-α in primary SIVagm infection [48] . To understand what might be the key events of the innate response in natural hosts that allows them to maintain the inflammation under control , we investigated the effect of SIVagm infection in AGM on innate immune cell compartments , in particular pDCs , mDCs and NK cells . Little is known about those decisive early cellular responses in AGMs , in particular regarding pDC maturation and NK cell activation . In addition , for the first time the activation profiles of these three types of innate cells were analyzed concomitantly in the same animals . We analyzed the maturation and homing patterns of two sub-populations of mDCs , the CD16− and CD16+ subsets . These correspond to two major subsets of mDCs in humans [82] , [83] . The CD16+ mDCs displayed higher levels of activation and maturation than the CD16− subpopulation . Whether these cells play a distinct role in T cell activation or tolerance is unclear . PDCs displayed lower levels of maturation than mDCs . IFN-α production by pDCs is associated with their maturation stage . It has been shown that HIV skews pDCs toward a partially matured and persistently IFN-α-secreting phenotype which allows their survival [84] . Eventually , the partial maturation of pDCs in AGMs might be associated with their capacity of efficient IFN-α production during the acute phase of SIVagm infection . Egress of pDC precursors from bone marrow could then account for the return of IFN-α levels to baseline [85] . This is supported by the decrease of HLA-DR expression on the pDC's surface . On the contrary , a preferential maturation process at the expense of cytokine secretion might be occurring at the level of mDCs in AGMs , especially in presence of IFN-I , since IFN-I induces mDC maturation rather than cytokine secretion [83] . We also analyzed NK cells for the first time in the context of SIVagm infection . We observed a strong increase in proliferation and activation of NK cells during the acute phase of SIVagm infection . Our data support the observations reported in SMs on earlier and stronger NK cell responses than in SIVmac-infected macaques [39] . The rapid and strong increase in NK cell proliferation in AGMs might be a direct consequence of the early and robust production of IL-15 and IFN-α during primary SIVagm infection . No production of IFN-γ by NK cells was observed , while NK cell cytotoxicity was induced . It has been shown that IFN-α and IL-15 promote NK cell proliferation and survival , while IFN-α is able to increase NK cell cytotoxicity , and IL-12 to augment the secretion of IFN-γ [86] . This is in accordance with the fact that modest levels of IL-12 and high levels of IFN-α were detected , playing thus a putative role in establishing such protective NK cell responses . It was surprising to detect increases in NK cytotoxic function in LNs . One hallmark of SIV infection in natural hosts is the high viral load in blood and intestinal tissues , but low viral burden in LNs in the chronic phase of infection [29] , [87]–[90] . It has been suggested for SMs that the rapid and dramatic control of viral replication in LNs is associated with CD8+ T cell responses [89] . However , it is tempting to speculate that at least in the early stage of SIVagm infection in AGMs , NK cells could significantly contribute to the control of viral replication in LNs which in consequence could contribute to limit immune activation [91] . Altogether , our study provides evidence that the control of immune activation in SIVagm infection is not a consequence of lower levels of IFN-α production . We show that AGMs mount strong early innate immune responses as exemplified by the significant NK cell activation and production of early cytokines , such as IL-15 and MCP-1 . Our study indicates that the sustained ISG production in HIV/SIVmac infections is likely driven by additional or factors other than IFN-α , among which could be elevated pro-inflammatory cytokine levels , enteric virome expansion and microbial translocation . The data also suggest that mechanisms controlling inflammation are in place before the transition of the acute to the chronic phase , thus earlier than previously considered . Whether this is due to the establishment of inhibitory or tolerance mechanisms after the viral peak , or to a distinct susceptibility to infection or immune activation by specific immune cell subsets , needs to be further investigated .
Animals were housed in the facilities of the CEA ( “Commissariat à l'Energie Atomique” , Fontenay-aux-Roses , France ) and Institut Pasteur ( Paris , France ) ( CEA permit number: A 92-032-02 , Institut Pasteur permit number: A 78-100-3 ) . All experimental procedures were conducted in the CEA animal facility and in strict accordance with the international European guidelines 2010/63/UE about protection of animals used for experimentation and other scientific purposes ( French decree 2013-118 ) and with the recommendations of the Weatherall report . The monitoring of the animals was under the supervision of the veterinarians in charge of the animal facilities . All efforts were made to minimize suffering , including efforts to improve housing conditions and to provide enrichment opportunities ( e . g . , 12∶12 light dark schedule , provision of monkey biscuits supplemented with fresh fruit and constant water access , objects to manipulate , interaction with caregivers and research staff ) . All procedures were performed under anesthesia using 10 mg of ketamine per kg body weight . For deeper anesthesia required for lymph node removal a mixture of ketamine and xylazine was used . Paracetamol was given after the procedure . Euthanasia was performed prior to the development of any symptoms of disease ( e . g . , for macaques when the biological markers indicated progression towards disease , such as significant CD4+ T cell decline and increases of viremia ) . Euthanasia was done by IV injection of a lethal dose of pentobarbital . The CEA is in compliance with Standards for Human Care and Use of Laboratory of the Office for Laboratory Animal Welfare ( OLAW , USA ) under OLAW Assurance number #A5826-01 . Animal experimental protocols were approved by the Ethical Committee of Animal Experimentation ( CETEA-DSV , IDF , France ) ( Notification number: 10-051b ) . Eighteen Caribbean-origin African green monkeys ( Chlorocebus sabaeus ) and two Chinese rhesus macaques ( Macaca mulatta ) were used in the study . AGMs were infected by intravenous inoculation with 250 TCID50 of purified SIVagm . sab92018 , and macaques with 5000 AID50 of SIVmac251 , as previously described [90] . SIVagm . sab92018 has been purified by sucrose density gradient centrifugation and on Vivaspin 20 columns ( Vivaproducts ) . Neither IFN-α nor endotoxin ( LAL QCL-1000 Kit , Lonza ) were detected in the two viral stocks . Four AGMs were treated with the r-mamu IFN-α-IgFc by subcutaneous injection ( Resource for NHP Immune Reagents , Emory University , Atlanta , GA ) : 2 were used for the establishment of the treatment protocol and control of its efficiency in AGM , and 2 were treated during the acute phase of infection . When daily injections of 5×105 IU for over a period of 16 days were performed , the dose was increased by 10% every second day . Whole blood was collected from all AGMs . For the initial groups of monkeys , baseline blood collections were performed at 4 to 6 time points before infection ( days −30 , −28 , −23 , −21 , −19 and −16 ) to mimic the sampling of the acute phase and measure any difference linked to the sampling . No variation due to the sampling was observed . Blood was then collected during primary infection ( on days 2 , 4 , 7 , 9 , 11 , 14 and 25 ) and during the chronic phase ( days 31 , 59 , 85 , 122 , 183 , 241 , 354 , 456 , 547 or euthanasia ) . AGM peripheral LNs were obtained by excision before infection ( days −15 and/or −10 ) and after infection at the following days: 2 ( 3 AGMs ) , 7 ( 1 AGM ) , 9 ( 8 AGMs ) , 11 ( 7 AGMs ) , 14 ( 8 AGMs ) , 25 ( 7 AGMs ) and 547 pi or euthanasia ( 5 AGM ) . In a second group of 8 AGMs , blood was collected at 3 to 4 time points before infection ( days −40 , −30 , −20 , −10 ) , at very early time points during primary infection ( 6 hours post-infection and at days 1 , 2 , 4 , 7 , 9 , 11 , 14 , 28 pi ) and during the chronic phase ( days 42 and 63 pi ) . Panels of fluorochrome-labeled monoclonal antibodies ( mAbs ) that have been shown to be cross-reactive with AGMs , were used to label fresh whole blood and LN cells and were purchased from BD Biosciences unless otherwise stated: CD3 ( SP34-2 ) , CD4 ( L200 ) , CD8 ( Sk1 ) , CD20 ( 2H7 , ebioscience ) , HLA-DR ( L243 ) , CD16 ( 3G8 ) , NKG2A ( Z199 , Coulter ) , CD107a ( H4A3 ) , CXCR3 ( 1C6 ) , CD69 ( FN50 , ebioscience ) , CD123 ( 7G3 ) , BDCA-2 ( AC144 , Miltenyi ) , CD86 ( FUN-1 ) , CD40 ( HB14 , Caltag ) , CCR7 ( 3D12 , ebioscience ) , CD11c ( S-HCL-3 ) , CD80 ( L307 . 4 ) , IFN-γ ( 45-15 , Miltenyi ) , CD45 ( D058-1283 ) , Ki-67 ( MIB-1 , Dako ) as well as isotype controls . FcR Blocking Reagent ( Miltenyi ) was used to block unwanted binding of antibodies and increase the staining specificity of cell surface antigens . For detection of IFN-γ and CD107a , cells were pre-incubated for 4 hours with brefeldin A and monensin at 37°C prior to labeling with surface-binding antibodies and then fixed and permeabilized prior to incubation with IFN-γ antibody . Cells were run on a BD LSR-II flow cytometer system , collected with BD FACS Diva 6 . 0 software , and analyzed with FlowJo 8 . 8 . 7 ( TreeStar ) . Cytokines were measured in plasma and LN cell supernatants . LN cell supernatant consists of the medium in which the biopsy was collected and kept for 2–3 hours at 4°C . Cells were prepared by homogenization in the same medium and the supernatant was collected after centrifugation . Titers of bioactive IFN-α were determined as previously described [38] . The same test was used to search for plasma IFN-α antibodies that might have developed in response to the treatment . The other cytokines were quantified using the following ELISA kits: MONKEY IFN-gamma , IL-6 , IL-8 , IL-10 , IL-12/23p40 , TNF-alpha ( U-Cytech ) , Human IL-15 , CXCL10/IP-10 , CCL2/MCP-1 , TRAIL/TNFSF10 Quantikine Kits ( R&D ) , Human IL-17A Ready-SET-Go ( eBiosciences ) , Human IL-18 Kit ( MBL ) , Simian IFN-beta Kit ( USCN ) , TGF-β1 Multispecies Kit ( Invitrogen ) . To verify the cross-reactivity of the antibodies used in the ELISA kit for cytokines that have never been tested on AGM [30] , [36] , AGM PBMCs were stimulated in vitro and cytokines were measured in the supernatants ( data not shown ) . Plasma viral load was determined by real-time PCR [90] . Quantification of ISG transcripts was performed by real-time RT-PCR in triplicate using Taqman gene expression assays ( Life technologies ) . The expression of each gene was normalized against that of 18S rRNA [30] , [36] . To characterize each marker's progression ( figures 2–4 ) , a linear mixed effect model was used to account for multiple measurements within each AGM . Firstly , we graphically assessed that the marker's distribution was Gaussian; if not , a logarithmic transformation was used . Secondly , a LOWESS ( locally weighted scatterplot smoothing ) curve was used to assess whether the marker's trajectory looked linear or piecewise linear . Based on these trajectories , we introduced or not slopes . We indicated at which time-point the change of slope occurred . Finally , a mixed effect linear or piecewise-linear model was applied . When two slopes were introduced into the model , the difference between the two slopes was tested using Wald's test . The Wilcoxon matched-pairs signed rank test was used to evaluate whether there was a statistically significant difference in the level of one given marker at a given time point following inoculation when compared to the baseline medians ( day 0 ) , using Prism ( GraphPad ) . Baseline medians in blood consisted of 3 to 6 pre-infection values per animal for the flow cytometry and gene expression analysis , and 4 total pre-infection values per animal for the plasma cytokines study . In LNs , it consisted of 1 to 2 pre-infection values per animal for all the measurements . Finally , the Spearman rank test was used to assess the correlation between 2 continuous variables .
|
Chronic inflammation is considered as directly involved in AIDS pathogenesis . The role of IFN-α as a driving force of chronic inflammation is under debate . Natural hosts of SIV , such as African green monkeys ( AGMs ) , avoid chronic inflammation . We show for the first time that NK cells are strongly activated during acute SIVagm infection . This further demonstrates that AGMs mount a strong early innate immune response . Myeloid and plasmacytoid dendritic cells ( mDCs and pDCs ) homed to lymph nodes; however mDCs showed a stronger maturation profile than pDCs . Monitoring of cytokine profiles in plasma suggests that the control of inflammation in AGMs is starting earlier than previously considered , weeks before the end of the acute infection . We tested whether the capacity to control inflammation depends on the levels of IFN-α produced . When treated with high doses of IFN-α during acute SIVagm infection , AGMs did not show increase of immune activation or signs of disease progression . Our study provides evidence that the control of inflammation in SIVagm infection is not the consequence of weaker IFN-α levels . These data indicate that the sustained interferon-stimulated gene induction and chronic inflammation in HIV/SIVmac infections is driven by factors other than IFN-α .
|
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2014
|
Innate Immune Responses and Rapid Control of Inflammation in African Green Monkeys Treated or Not with Interferon-Alpha during Primary SIVagm Infection
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The emergence of Zika virus ( ZIKV ) in the New World has led to more than 200 , 000 human infections . Perinatal infection can cause severe neurological complications , including fetal and neonatal microcephaly , and in adults there is an association with Guillain-Barré syndrome ( GBS ) . ZIKV is transmitted to humans by Aedes sp . mosquitoes , yet little is known about its enzootic cycle in which transmission is thought to occur between arboreal Aedes sp . mosquitos and non-human primates . In the 1950s and ‘60s , several bat species were shown to be naturally and experimentally susceptible to ZIKV with acute viremia and seroconversion , and some developed neurological disease with viral antigen detected in the brain . Because of ZIKV emergence in the Americas , we sought to determine susceptibility of Jamaican fruit bats ( Artibeus jamaicensis ) , one of the most common bats in the New World . Bats were inoculated with ZIKV PRVABC59 but did not show signs of disease . Bats held to 28 days post-inoculation ( PI ) had detectable antibody by ELISA and viral RNA was detected by qRT-PCR in the brain , saliva and urine in some of the bats . Immunoreactivity using polyclonal anti-ZIKV antibody was detected in testes , brain , lung and salivary glands plus scrotal skin . Tropism for mononuclear cells , including macrophages/microglia and fibroblasts , was seen in the aforementioned organs in addition to testicular Leydig cells . The virus likely localized to the brain via infection of Iba1+ macrophage/microglial cells . Jamaican fruit bats , therefore , may be a useful animal model for the study of ZIKV infection . This work also raises the possibility that bats may have a role in Zika virus ecology in endemic regions , and that ZIKV may pose a wildlife disease threat to bat populations .
Zika virus ( ZIKV ) was first isolated from a sentinel rhesus macaque in Uganda in 1947 and subsequently from Aedes africanus mosquitoes in the same location [1] . The first human cases were identified in 1954 in Nigeria and serosurveys found evidence of a broad geographic distribution for ZIKV throughout Africa and Asia with sporadic cases in humans [2 , 3] . The first recognized ZIKV epidemic occurred in Yap State , Federated State of Micronesia in 2007 . An estimated 73% of residents were infected , and of those 18% presented with clinical disease [4] . In 2013 , a second epidemic occurred in French Polynesia with 28 , 000 cases reported . During the latter outbreak , the incidence rate of Guillain-Barré syndrome ( GBS ) increased 20-fold and first indication of a connection between ZIKV infection and GBS was established [5] . The virus spread to Brazil in 2015 [6 , 7] and has since disseminated throughout much of tropical South America , Central America , the Caribbean , and the southern United States , with more than 200 , 000 confirmed cases [8] . ZIKV can also cause congenital Zika syndrome ( CZS ) in naïve populations and is therefore a virus of high concern [3] . Zika virus is maintained in an urban cycle , transmitted between an Aedes mosquito vector and humans thereby maintaining endemicity [9] . It is generally accepted that the virus transmits between non-human primates and vectors in a sylvatic cycle; however , the sylvatic cycle has not been well characterized in the Old World and little is known about a New World sylvatic cycle [9 , 10] . Molecular analysis of ZIKV to better understand viral phylogenetics suggests that animal hosts affected viral evolution and therefore may play an important role in viral ecology [11] . In the 1950s and ‘60s , the susceptibility of bats to ZIKV was investigated . Shepherd and Williams [12] screened 172 wild bats from 12 different species in Uganda for antibodies against ZIKV and found 16/44 little free-tail bats ( Tadarida pumila ) and 26/36 Angolan free-tail bats ( T . condylura ) were seropositive by hemagglutination inhibition assay . Additionally , two Angolan free-tail bats were experimentally inoculated with ZIKV and serially bled to test for viremia . Both animals were viremic on days 2 , 4 and 6 as determined by paralysis in mice inoculated with the sera from those two bats [12] . Simpson and O’Sullivan [13] experimentally inoculated three straw-colored fruit bats ( Eidolon helvum ) , three Egyptian fruit bats ( Rousettus aegyptiacusi ) , and five Angolan free-tail bats . Two of the straw-colored fruit bats were viremic and had seroconverted . One of the Egyptian fruit bats was viremic and two had seroconverted . The Angolan free-tail bats were euthanized on days 1 , 3 , 5 , 7 and 10 days post inoculation and screened for viral tropism . At one day post infection , a kidney was trace positive [13] . Finally , Reagan et al . [14] inoculated 20 New World little brown bats ( Myotis lucifigus ) by 5 different routes: intracranial , intraperitoneal , intradermal , intrarectal and intranasal . Bats in all groups , with the exception of the intranasal group , developed fatal neurological disease 4–7 days post inoculation . Brain tissue was virus-positive in all animals with clinical disease , determined by inoculation of mice with brain homogenate suspension [14] . Considering the evidence that African bats are naturally susceptible to ZIKV and that little brown bats develop disease , the question emerged: could bats serve as a natural reservoir host for ZIKV in the New World ? To test this hypothesis , we inoculated Jamaican fruit bats ( Artibeus jamaicensis ) , among the most abundant bats in the Caribbean , Central America and Mexico , with ZIKV to examine virology , immunology and pathology of the infection . Although virus was detected in several organs , including the testes and brains , no overt clinical signs were detected , and substantial viremia or viruria was not evident . These results suggest that Jamaican fruit bats are unlikely to serve as amplification hosts but that ZIKV infection may constitute a wildlife disease threat to bats .
Bats for this project were obtained from the Colorado State University breeding colony approved by the Institutional Animal Care and Use Committee ( protocol 16-6512A ) . Two experimental infections were conducted; a pilot study and a time course study . In the pilot-study , three male bats ( AJ-z7 , AJ-z8 , AJ-z9 ) were intradermally inoculated with 7 . 5x105 plaque forming units ( pfu ) ZIKV , strain PRVABC59; a high dose to assess susceptibility . No signs of disease were apparent during this 28 day experiment; however , all three bats had antibody titers of 3200 on day 28 ( Table 1 ) . After demonstration of susceptibility in the pilot study , a time course study was conducted . Six male bats ( AJ-z1 through AJ-z6 ) were identically inoculated and two were euthanized at 2 , 5 and 10 days post inoculation ( dpi ) . No conspicuous signs of disease were observed in any of the inoculated bats . Necropsies immediately followed euthanasia and no significant gross pathology was evident . Quantitative probe-based reverse transcription PCR ( qRT-PCR ) was performed on serum-inoculated Vero cell supernatants , serum , brain , lung , liver , spleen , kidney , urinary bladder , prostate and testes from bats from both studies . In addition , urine collected during the time course study was similarly assayed . Urine from bats AJ-z6 at 3 dpi and AJ-z7 at 5 dpi had low levels of vRNA whereas bat AJ-z1 , euthanized at 2 dpi , had low levels of vRNA in its brain ( Fig 1 ) . All other samples were negative . Sera from AJ-z2 at 2 dpi , and AJ-z3 and AJ-z4 at 5 dpi were negative by ELISA . Sera were blind passaged on Vero E6 cells in an attempt to isolate ZIKV and all were negative for cpe and PCR .
Two bat infection experiments were conducted in this investigation; 1 ) a pilot study to determine susceptibility of Jamaican fruit bats to ZIKV infection , and 2 ) a time course study to better understand pathophysiology and chronology of events pertaining to the dynamics of viremia , viral tropism , replication and shedding of the virus in a New World bat species . The goal was to determine whether bats can be used as an animal model for ZIKV pathogenesis and to assess the possible role of bats in ZIKV ecology in the New World . In the pilot experiment , no signs of disease were apparent during the 28-day study . Sera collected at euthanasia indicated modest antibody titers of 3200 for each bat by ELISA ( Table 1 ) , whereas the human -convalescent control serum titer was ≥12 , 800 . Bats typically have low to modest antibody titers , perhaps due to limited somatic hypermutation and affinity maturation [15–22] . Concerning viremia , cell-serum supernatants , blind passage supernatants , and neat serum results were all negative . Although serum is routinely used for ZIKV diagnostics in humans , it may not be the most suitable sample [23–27] . In one investigation ZIKV patient had negative serum sample for the duration of the study , whereas whole blood yielded positive qRT-PCR results from days 9 to 101 [27] . One possible explanation for the phenomenon of negative serum in human patients is that the virus during acute infection disseminates via a cell-associated viremia or as novel findings suggest that the virus gets phagocytized in neutrophils and therefore whole blood is a more sensitive diagnostic sample than serum . Viruria is commonly detected in ZIKV-infected humans [26]; therefore , urine may be an equally important diagnostic sample with higher viral load in early infection when compared to blood in humans and other primates [23–26] . Although urine collection from bats was challenging , we collected urine from some of the inoculated bats in the time course study . AJ-z6 exhibited viruria only at 3 dpi , and AJ-z7 was equivocal only at 5 dpi , corroborating the findings in other mammals that urine may be a route of viral shedding early in infection . Urine from one human patient was positive from the first time point ( 6 dpi ) through 14 dpi and again on day 56 . Similarly , saliva from that same patient was positive from day nine through day 14 and again on day 49 [27] . Another investigation compared diagnostic samples of 80 infected patients and showed that urine was positive in 50 of them , whereas serum was only positive in 19 patients by qRT-PCR . The study concluded that viral loads in urine were ten-fold higher compared to serum and that uremia lasted longer [25] . These data corroborated the first study that identified ZIKV shed in urine in which there was a higher viral load in urine for longer duration compared to serum [26] . ZIKV RNA in plasma was detected in the bats by qRT-PCR between 2 and 6 dpi , but between 2 to 17 dpi in urine [26] . The lack of detectable viremia in the serum of bats is congruent with some of the human and NHP investigations in that viremia is low and short-lived . Detached renal pelvic urothelial cells and degenerate salivary gland ductular epithelium as seen in the current study will make urine and saliva equally important fluids to collect in order to maximize detection of ZIKV in the acute and established stages of infections . For this experiment , all male bats were used because female bats are prioritized for colony expansion . ZIKV exhibited tropism for the testes with strong immunoreactivity in reproductive organs ( Figs 4 & 7 ) . Histologically , minimal focal testicular degeneration in two bats ( Fig 2 ) suggests viral related pathology may be minimal . In humans it has yet to be completely elucidated what reproductive organs harbor ZIKV , it has been determined that semen contains ZIKV both in both vasectomized and unvasectomized men [27 , 28] . This suggests that ZIKV is sequestered in the testes and/or accessory sex glands . Mouse models have demonstrated ZIKV infection and associated pathology in the testes [29–31] of humanized BLT mouse model with infection primarily targeting macrophages and Leydig cells [32] . Limited investigation has been done relating to infection of accessory sex glands in mouse models , but one study that assessed the prostate found no virus , possibly due to differential expression of the receptor candidate in the testes but not in the prostate [29] . For this experiment the finding of viral antigen and viral RNA in the testes but not in the prostate is consistent with published animal models and may suggest the potential for bats to serve as another animal model . Three bats had histopathological alterations in the hippocampus at later time points and one bat had viral nucleic acid present in the brain as determined by qRT-PCR demonstrating tropism for the CNS , a tissue predilection also documented in humans and animal models . ZIKV has a predilection for nervous tissue in animal studies and disease manifestation in humans . As a neurological teratogen , ZIKV has been detected in the brain mononuclear cells in human newborns with fatal microcephaly and fetal miscarriages . Histological lesions are varied but may include parenchymal calcification , microglial nodules , gliosis , cell degeneration , mononuclear infiltration and necrosis [33–35] . In non-human animal models , evidence for viral tropism has been found in brain and/or peripheral nervous tissue [36–39] . In immunocompromised mouse models , the virus has a predilection for the brain but with the mice engineered for specific immune traits it is difficult to know to what extent this recapitulates natural ZIKV pathophysiology [40] . In the bats used in this experiment , evidence of ZIKV-induced pathology in the brain is consistent with what has been seen in human newborns and fetuses . The novel finding of co-localizing ZIKV antigen in bat Iba1+ microglial/macrophage cells lends support to the earlier evidence of microglial cell infection via Axl ligand bridging ZIKV particles to glial cells [41] . Iba1 ( aka , allograft inflammatory factor 1 , Aif1 ) is a microglia/macrophage-specific calcium-binding protein , which has actin-bundling activity that participates in membrane ruffling and phagocytic activity of activated microglia . Activated microglial cells appeared with increased ability of cell migration and phagocytosis , which is controlled by remodeling of membrane cytoskeleton [42] . The morphology of cells with co-localization in the brain of infected bats is consistent with activated microglia depicting prominent branched processes . Recent primate models in rhesus and cynomolgus macaques demonstrated similar viral distribution of ZIKV antigen to that in bats , described herein . High-level of ZIKV was evident in cerebellar neurons and the same studies documented involvement of Iba1 positive microglial cells in CNS infections . In primate models there is increasing evidence that ZIKV antigen was detected in individuals with the highest peak plasma viremia , which in part implies that ZIKV may initially seed the CNS by a passive spillover from circulating monocytes to resident microglial cells . This is further substantiated in all of human and animal studies , which did not show any evidence of disruption to BBB or viral distribution reminiscent of circumventricular distribution seen in alphavirus animal models [43] . In addition to brain and testes immunoreactivity , scrotal skin and mandibular salivary gland also harbored viral antigen . Distribution of viral antigen in bat tissues suggests that infection in this species recapitulates human infection , which is thought to start with infection of epidermal and dermal cells with subsequent dissemination to multiple organs including salivary glands as viral RNA can be detected in human saliva [44 , 45] . The histopathology for AJ-z5 , 5 dpi showed sialoadenitis and the presence ZIKV antigen by IHC ( Fig 3 ) . This suggests ZIKV may be shed in the saliva , although additional animal experiments need to be performed to confirm such a route of shedding . The results presented here suggest that Jamaican fruit bats may be a suitable animal model for examining ZIKV infection to elucidate its pathogenesis . Jamaican fruit bats may also serve as a model to ascertain sexual transmission , in utero transmission , teratogenesis and neurological pathophysiology . It may be that ZIKV is a wildlife disease threat for bats that could lead to infertility in some males , which could impact bat populations . ZIKV is thought to be maintained in two different distinct cycles: sylvatic—cycling between non-human primates ( NHP ) and mosquito species , and urban—cycling between humans and mosquito species [3] . While there are limited data on what mosquito species feed on Jamaican fruit bats , evidence for natural flavivirus infection has been identified in wild New World bats . Dengue virus ( DENV ) RNA and antibodies to DENV were detected in multiple species of bats , including Jamaican fruit bats , in Mexico [46] . Additionally , antibodies to DENV were detected in multiple bat species including those of the Artibeus genus in Costa Rica and Ecuador [47 , 48] . These data indirectly provide evidence for mosquito-bat interactions in the wild; either through consumption of bat-blood meals taken by mosquitoes or bat consumption of infected mosquitoes . As it pertains to a wildlife reservoir , wild NHPs have antibody to ZIKV including several monkey species trapped near Ziika Forest [10] , and wild and semi-captive orangutans in Borneo [49] . Not only have NHP been found to be seropositive , but also many other mammals , including rodents , horses , cows , and goats [50 , 51] . Furthermore , experimental inoculation of various North American species resulted in seroconversion ( cottontail rabbits , boar goats , pigs , and leopard frogs ) and demonstrated viremia ( nine-banded armadillo and leopard frogs ) [52] . Molecular epidemiology suggests animals play an important role in an enzootic cycle [11] . Much about the enzootic cycle of ZIKV has yet to be understood but it stands to reason that bats may be capable of maintaining the virus in nature . Jamaican fruit bats are found in northern South America , Central America , and the Caribbean—areas that now have ZIKV potentially exposing bat populations to the virus [8 , 53] . However , the data presented here suggest it is unlikely that Jamaican fruit bats can serve as amplification hosts of ZIKV , unless virus sequesters in some as-yet unidentified way that could lead to periodic shedding of virus . It may also be that some bats become persistently infected and can transmit sexually to maintain virus within populations of bats . Further experimental and field studies will be necessary to fully understand the ecological role of bats in ZIKV maintenance .
All animal procedures were approved by the Colorado State University ( CSU ) Institutional Animal Care and Use Committee ( protocol 16-6512A ) and were in compliance with U . S . Animal Welfare Act . CSU has a captive colony of Jamaican fruit bats ( Artibeus jamaicensis ) , a neotropical fruit bat indigenous to much of South America , Central America and the Caribbean [53] . Colony bats are kept in a free flight room measuring 19’w x 10’l x 9’h . Roosting baskets are hung from the ceiling throughout the room and drapes of different cloth material are positioned for hanging and roosting . Ambient temperature is maintained between 20°C and 25°C , with humidity between 50% and 70% , and a 12 hour light/12 hour dark light cycle via a computer-controlled system . Diets consist of a combination of fruits ( Shamrock Foods , Fort Collins , CO ) , Tekald primate diet ( Envigo , Huntington , UK ) , molasses , nonfat dry milk and cherry gelatin that are placed in multiple feeding trays around the room once a day . Fresh water is provided . In addition , fruit is hung around the room to stimulate foraging behavior and serve as enrichment . For infection experiments , bats were trapped using a butterfly net and placed in an 20”d x 12”w x 18”h cage for 24 hours prior to inoculations to allow for acclimation . Hanging clothes were provided for roosting and coverage . Food and water are placed in open trays in the bottom of the cage and changed daily . Tray liners were changed every two days , and cages and hanging clothes are changed every two weeks . Due to the social nature of these bats , minimums of two bats were kept in cages at all times to mitigate potential stress . Two sets of experiments were performed; a pilot study and a time course study . Zika virus strain PRVABC59 . PRVABC59 was isolated in 2015 by Centers for Disease Control and Prevention ( Fort Collins , CO ) from an infected individual who traveled to Puerto Rico ( GenBank accession no . HQ234499 ) . The virus stock titer is 3x107 plaque forming units ( pfu ) per ml of media , and the fourth passage was used for both studies . For the pilot study , three male bats were anesthetized with 1% to 3% isoflurane to effect with an oxygen flow rate of 1 . 5 L/min , administered with a gas mask . Animals were placed on a heating pad to maintain body temperature and respirations continuously monitored . The dorsum of each animal was disinfected with 70% ethanol and 25ul containing 7 . 5x105 p . f . u of virus was administered subcutaneously ( sc ) at the level of the scapula with a sterile hypodermic 25 gauge needle in a biosafety cabinet . When procedures were finished , bats were removed from isoflurane and placed back in the cage in ventral recumbency . Respirations were monitored until animal was fully awake and ambulated normally . Bats were identified as AJ-z7 , AJ-z8 and AJ-z9 . Animals were euthanized at 28 days post-inoculation ( dpi ) . For the time course study , six male bats were anesthetized under the same protocol as the pilot study . Animals were placed in ventral recumbency . After disinfecting the dorsum of each animal with 70% ethanol , 0 . 15mls of 1% lidocaine was administered sc at the level of the last rib with a 25 gauge sterile hypodermic needle as a local anesthetic . IPTT300 transponders ( BioMedic Data Systems , Inc . , Seaford , DE ) were inserted sc at the level of the caudal edge of the scapula . Twenty-five microliters containing 7 . 5x105 p . f . u of virus was administered sc at the level of the cranial edge of the scapula . Recovery followed the same protocol as for the pilot study bats . Animals were identified as AJ-z1 through AJ-z6 . AJ-z1 and AJ-z2 were euthanized at two dpi . AJ-z3 and AJ-z4 were euthanized at 5 dpi . AJ-z5 and AJ-z6 were euthanized at 10 dpi . Female bats were excluded from the study because they are prioritized for breeding to sustain and expand upon the colony . For the pilot study , bats were visually monitored twice daily for fourteen days , and then monitored once a day for an additional fourteen days . For the time course study , bats were monitored twice a day throughout the experiment . For both studies , energy levels , behavior , ability to ambulate , respirations , presence of oral or nasal discharge , and fecal consistency were all assessed . During the time course study urine was collected at 2 , 3 , 5 and 10 dpi from as many bats as possible . Urine was collected by allowing bats to grasp screen cloth with their feet and then the bat was placed in a clear solo cup ( Dart Container , Lake Forest , IL ) with the screen covering the top of the cup as a lid , and kept in place with a rubber band . This allowed the bats to hang in a clear container . Bats were monitored for 45 minutes . If they urinated , bats were removed from the collection contraption and placed back in the cage without disrupting the urine . Urine collection was attempted on all remaining bats at each time point , but not all bats would urinate at each collection attempt . Urine was successfully collected as follows: two dpi from AJ-z3 and AJ-z4; three dpi from AJ-z3 , AJ-z5 and AJ-z6; five dpi from AJ-z3 , AJ-z4 , AJ-z5 and AJ-z6; and ten dpi from AJ-z5 and AJ-z6 . Urine was pipetted off the surface of the cup with a sterile pipette tip and put in a 1 . 5 ml microcentrifuge tube and stored at -80°C for future use . Urine volume ranged between 5 ul and 15 ul . Bats were deeply anesthetized and maintained with 3% isoflurane and an oxygen flow rate of 1 . 5 L/min . Deep pain was assessed by firmly pinching skin and toes with forceps and assessed for any response . A thoracotomy was then performed with sterile standard scissors to puncture through the skin , muscle and diaphragm just caudal to the sternum and cut through the wall of the chest cavity caudally to cranially—removing and preventing negative pressure from building in the thorax . Cardiac blood was collected with a 21 gauge sterile needle inserted into the apex of the heart . A maximum blood volume of between 1 and 1 . 5mls is collected in a syringe and transferred to a red top tube ( RTT ) . RTTs sat at room temperature for one hour to allow a clot to form and then centrifuged at 1000 x g for 10 min at room temperature . Serum was removed from the clot , placed in a new microcentrifuge tube and stored at -20°C . Serum from bats at 2 and 5 dpi were used to assess for viremia . Serum from 10 dpi and the 28 dpi pilot study bats were used to determine antibody titers . Because blood draws yield a small volume of blood ( 50 μl whole blood for a non-terminal blood draw , 500 μl whole blood for terminal blood draw ) it was necessary to prioritize samples to optimize data retrieved . In order to assay the serum for viral RNA and perform serology , earlier time points were used to assess for viremia and later time points for seroconversion . Along with sample partitioning for data maximization , the small blood volume led to concerns that there would be an undetectably small viral load . To circumvent this issue , neat serum and 1:10 diluted serum were inoculated onto Vero cells to amplify any virus that may have been present at low levels . One blind passage on Vero cells was done and cell supernatants assayed by qRT-PCR . The remaining serum from three of the four bats was assayed directly for ZIKV RNA . Necropsies were performed immediately after euthanasia . Bats were assessed for gross pathology . The following tissues were collected for both experiments: heart , lung , liver , spleen , kidney , urinary bladder , prostate , testes , and brain . A portion of tissues were collected and kept at -80°C for RNA extraction , and a portion placed in 10% buffered formalin for histology at a 1:10 weight to volume ratio for histology . For a negative control animal a male bat was trapped from the colony and euthanized under the same protocol as the experimental infection bats . Vero E6 cells ( ATCC ) were propagated to 60% confluency in a 96-well tissue culture plate and infected with ZIKV strain PRVABC at an m . o . i . of 0 . 1 . After a one hour incubation period , unbound virus was removed and replaced with 2% FBS-DMEM and incubated for a maximum of three days . Media was then replaced with 85% acetone for 20 minutes at -20°C to fix virus-infected cells to plate and serve as an antigen for enzyme-linked-immunosorbent assay ( ELISA ) . Plates were stored at 4°C until use and used within two weeks . Plates were washed 5x with 0 . 05% Tween 20-PBS and blocked with SuperBlock T20 ( TBS ) Blocking Buffer ( Thermo Fisher Scientific , Waltham , MA ) for one hour at room temperature . Serum from an uninfected bat was used for a negative control . A convalescent human serum sample ( kindly provided by B . Foy , CSU ) was used as a positive control . A two-fold serial dilution was used starting at 1:100 to 1:12800 . Diluted serum was placed in wells and incubated for two hours at room temperature . Serum was removed and plates washed . HRP-conjugated protein A/G ( Thermo Fisher Scientific , Waltham , MA ) was added at a concentration of 2 μg/ml to each well , and incubated for 30 minutes at room temperature . HRP-conjugated protein A/G was used in place of a secondary antibody as it targets the Fc portion of an antibody , which is highly conserved and therefore can be used for multiple animal species [54] . Plates were washed and 150 μl of ABTS Peroxidase Substrate ( 2 component ) ( KPL , Gaithersburg , MD ) added according to manufacturers’ instructions , incubated at room temperature for 30 minutes , and then 150 μl of ABTS Peroxidase Stop solution ( KPL , Gaithersburg , MD ) added . Plates were read on an EMax Plus Microplate Reader ( Cambridge Scientific , Watertown , MA ) . Absorbance was measured at 405 nm and the limit of detectable response was set at three standard deviation values above mean negative control serum . TRIzol Reagent was used for RNA extraction from serum-cell supernatants , serum , urine and tissues according to Ambion , Life Technologies protocol . For tissues , approximately 50 mg of tissue was homogenized with one mL of TRIzol Reagent . A 5mm stainless steel bead ( Qiagen , Valencia , CA ) was used with a TissueLyser LT ( Qiagen , Valencia , CA ) at 50 Hz for 5 minutes . One ml of TRIzol was added to urine to 5 to 15 μl of urine . One ml of TRIzol was added to 160 μl of serum from AJ-z2 , AJ-z3 , and AJ-z4 . Two-hundred microliters of serum-cell supernatants were added to one ml of TRIzol . Samples were then incubated at room temperature for 5 minutes . Chloroform ( Thermo Fisher Scientific , Waltham , MA ) was added , samples were mixed , incubated for 3 minutes at room temperature and centrifuged at 12 , 000 x g for 15 minutes at 4°C . The aqueous phase was removed , 4 μg of glycogen ( Thermo Fisher Scientific , Waltham , MA ) and 100% molecular grade isopropanol added ( Thermo Fisher Scientific , Waltham , MA ) . Samples were incubated at room temperature for 10 minutes and then centrifuged at 12 , 000 x g for 10 minutes at 4°C . Supernatant was removed and 75% molecular grade ethanol ( Thermo Fisher Scientific , Waltham , MA ) was added to RNA pellet . Samples were vortexed and centrifuged at 7500 x g for 5 minutes at 4°C . Wash was removed and air-dried . RNA was resuspended in RNase-free water and stored at -80°C for future use . Vero cells were grown to 70 to 80% confluency in a 48-well tissue culture plate with 10% FBS-DMEM . Media was removed and 100 ul of bat serum from 2 dpi bats and 5 dpi bats was inoculated onto cells . Additionally , serum from each bat was diluted 10-fold in 2% FBS ( Millipore Sigma ) PBS supplemented with 1% calcium and magnesium , and inoculated onto cells . Samples were incubated for one hour at 37°C . Inoculum was removed and cells washed twice in sterile PBS . Two-percent FBS-DMEM was added to wells and plates were incubated at 37°C , 5% CO2 . Cells were assessed daily for cytopathology ( CPE ) through day 7 but none was observed . Two-hundred microliters of the supernatant was removed on day 7 and used for RNA extractions . An additional 100 μl of supernatant was blind passaged onto Vero cells at 70 to 80% confluency . Cells were incubated for one hour at 37°C , washed twice with sterile PBS and 2% FBS-DMEM added . On day seven , supernatant was removed and TRIzol extractions performed for RNA recovery . Serum was treated as such in an attempt to amplify viral load and increase assay sensitivity serum may not be the most sensitive diagnostic sample [23–26] . If any serum was remaining it was directly used for TRIzol RNA extractions . Serum samples remained from AJ-z2 at 2 dpi , and AJ-z3 and AJ-z4 at 5 dpi . No serum remained from AJ-z1 . Roche Real Time Ready RNA Virus Master Kit ( Roche , Indianapolis , IN ) was used on RNA extracted from serum-cell supernatants , serum , urine and tissue to assay for ZIKV RNA according to manufacturers’ instructions . Primers used were ZIKV 1086 ( CCGCTGCCCAACACAAG ) and ZIKV 1162c ( CCACTAACGTTCTTTTGCAGACAT ) . Probe was ZIKV 1107-FAM ( AGCCTACCTTGACAAGCAGTCAGACACTCAA ) [55] . Two-hundred nanograms of sample RNA was added to each reaction . Reactions were performed in duplicate . Standards were a non-infectious clone of full length ZIKV strain PRVABC59 by which concentration was determined through optical density . Molecular weight of the genome sequence was used to calculate copy number [56] . A log10 dilution series of the standard was made and linear regression used to determine copy number equivalents of positive samples . Amplification was performed according to manufacturers’ protocol for Roche Real Time Ready RNA Virus Master Kit ( Roche Diagnostics Corporation , Indianapolis , IN ) with PCR conditions as follows: 8 min at 50°C , 30 s at 95°C , and 45 cycles of 10 s at 95°C , 20 s at 60°C and 10 s at 72°C . Tissues fixed in 10%-buffered formalin were cut in and submitted to Colorado State University Veterinary Diagnostic Laboratory ( CSU VDL , Fort Collins , CO ) for paraffin embedding , sectioning and staining with hematoxylin and eosin , as well as immunohistochemistry ( IHC ) . Tissues cut in on bats to assess for histology included: heart , lung , liver , kidney , testes , prostate , urinary bladder and brain . Additionally , for AJ-z3 and AJ-z5 mandibular salivary gland was cut in . AJ-z4 had esophagus and lymphoid tissue that included palatine salivary gland cut in . Antibody for IHC was a polyclonal rabbit antibody that targets preM and E proteins of ZIKV and was provided by CSU VDL’s pathology department . The Bond-III automated instrument ( Leica Biosystems , Wetzlar , Germany ) was used for IHC staining . All slides were blindly read by a diplomat of the American College of Veterinary Pathologists . Brain tissues was prepared for immunohistochemical and immunofluorescence staining as previously reported [57] . Tissue was dehydrated by using a graded ethanol series of 70% ethanol for 2 h , 80% overnight , 90% for 2 h and 100% for 2 h . Brain tissues were then post-fixed in dimethylbenzene for 30 min and embedded in dimethylbenzene-paraffin at 60°C for 2 h , after which samples were embedded in a metal frame . Sagittal sections were collected at 5um thick . All dewaxing , antigen retrieval and immunofluorescence staining was automated using a Leica Bond RXM . In short , sections were dewaxed using ethanol and then boiled in antigen retrieval solution for 10 minutes . The cooled sections were incubated in 3% H2O2 for 15 min at room temperature and then blocked with 2% donkey and goat serum ( Millipore Sigma ) for 1 hour . Rabbit anti-Iba1 ( Wako Chemicals USA , Irvine , CA ) and 4G-2 Flavivirus E specific monoclonal antibodies ( CDC , Fort Collins ) were diluted in TBS to final concentrations of 1:250 and 1:50 , respectively . Sections were incubated in primary antibodies concurrently at room temperature for one hour . Following removal of unbound primary antibodies by washing , goat anti-rabbit secondary ( AlexaFluor-555 ) and donkey anti-mouse secondary ( AlexaFluor-647 ) was added and incubated for 1 hour at room temperature . Finally , DAPI counterstain ( Vector Laboratories , Burlingame , CA ) was applied and sections were washed with TBS prior to cover slipping for imaging . Stained sections were imaged on a Ziess LSM 800 with Airyscan laser-scanning confocal microscope ( Ziess , Oberkochen , Germany ) using a 63× oil immersion objective . Each field of view was imaged as a z-stack ( 8–10 planes , . 5-μm step size ) transformed into a single maximum projection image using the Ziess Zen ( blue ) imaging software .
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The rapid spread of Zika virus through a naïve population in the Americas resulted in novel and severe disease manifestations , including fetal and neonatal microcephaly , and GBS . These disease complications make understanding the pathology and ecology of ZIKV a priority . Captive Jamaican fruit bats were challenged with ZIKV to determine their susceptibility , to assess whether bats may play a role in virus ecology , and if they might serve as an animal model to better understand ZIKV pathophysiology . The bats became acutely infected and mounted an antibody response . Three terminally euthanized inoculated bats had antibody titers of 3200 , 28 days PI . Evidence of virus replication and associated pathologies were found in the brain , testes , lungs and salivary glands of some of the inoculated bats . The virus showed predilection for mononuclear cells , including resident Iba1+ macrophage/microglial cells , and Leydig cells . With no discernible disruption to the blood brain barrier nor distribution of viral antigen indicative of circumeventricular neuroinvasion , microglia cells may be a possible route of entry of ZIKV into brains of bats . Further investigations are needed to determine the mechanisms of neuroinvasion of ZIKV in bats , further determine feasibility of bats as an alternative animal-model for congenital Zika syndrome , and what role bats might play in ZIKV viral ecology .
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2019
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Experimental Zika virus infection of Jamaican fruit bats (Artibeus jamaicensis) and possible entry of virus into brain via activated microglial cells
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Horizontal transmission of cytomegaloviruses ( CMV ) occurs via prolonged excretion from mucosal surfaces . We used murine CMV ( MCMV ) infection to investigate the mechanisms of immune control in secretory organs . CD4 T cells were crucial to cease MCMV replication in the salivary gland ( SG ) via direct secretion of IFNγ that initiated antiviral signaling on non-hematopoietic cells . In contrast , CD4 T cell helper functions for CD8 T cells or B cells were dispensable . Despite SG-resident MCMV-specific CD8 T cells being able to produce IFNγ , the absence of MHC class I molecules on infected acinar glandular epithelial cells due to viral immune evasion , and the paucity of cross-presenting antigen presenting cells ( APCs ) prevented their local activation . Thus , local activation of MCMV-specific T cells is confined to the CD4 subset due to exclusive presentation of MCMV-derived antigens by MHC class II molecules on bystander APCs , resulting in IFNγ secretion interfering with viral replication in cells of non-hematopoietic origin .
Cytomegaloviruses ( CMVs ) , members of the β-herpesvirus family , establish a latent persistent infection . Although primary infection in immune-competent individuals is in general clinically silent , severe complications caused by reactivation or primary infection are frequent in immune-compromised patients such as transplant recipients or HIV patients . However , even in individuals with a competent immune system , CMV is detectable in mucosal secretions for a long period after primary encounter , representing the main source for both horizontal and vertical transmission [1] . As sustained replication and shedding of CMVs by the salivary gland ( SG ) into the saliva is one of the prime reasons for primary and secondary CMV infection ( reviewed in [2] , [3] ) , in addition to transmission via breast milk and genital secretions , it is of particular interest for the virus to evade its immune recognition in the SG . Prolonged shedding of CMV into the saliva is also observed in murine CMV infection ( MCMV ) , rendering it a valuable model to identify mechanisms of how CMVs are controlled in the SG [4] . On the host side , specific immune mechanisms are required to control viral replication in the SG: Depletion of CD4 T cells abolished viral control in the SG with sustained viral replication up to 10 weeks post infection [5] . Sustained MCMV replication is restricted in the SG to a particular cell subset , the acinar glandular epithelial cells ( AGECs ) . As systemic neutralization of IFNγ and TNFα abolished antiviral MCMV control in the SG , it was proposed , but never directly proven , that CD4 T cells control viral replication via secretion of IFNγ and TNFα [6] , [7] . MCMV-specific CD4 T cells were indeed found to produce both of these cytokines [8] , [9] , but it remains unclear whether CD4 T cells directly control MCMV replication via secretion of IFNγ and TNFα . Further , although never directly addressed experimentally , it was proposed that IFNγ secreted by virus-specific CD4 T cells may act on other immune cells such as NK cells to induce antiviral activities in these secondary effector cells and not directly on infected target cells [4] . In contrast to CD4 T cells , CD8 T cells and B cells seem to be dispensable for MCMV control in the SG [10] , [11] , [12] . Many open questions remain as to why CD4 T cells are so crucial to control MCMV in the SG and not - or to a lesser extent – in other tissues . Up to date it is unclear whether CD4 T cells are necessary to support the recruitment or function of other immune cell subsets in the SG during MCMV infection , as immune responses to MCMV have not been investigated comprehensively in this relevant tissue . To investigate in detail local immune control of MCMV replication in the SG , we identified the exact mechanisms of antiviral control exerted by CD4 T cells . We prove that production of IFNγ by CD4 T cells is essential to control MCMV replication and IFNγ needs to be sensed by radio-resistant cells and not by cells of the hematopoietic lineage . Despite being able to secrete IFNγ and outnumbering MCMV-specific CD4 T cells , MCMV-specific CD8 T cells are unable to control MCMV replication in the SG due to extensive virus-induced down-regulation of MHC molecules on infected AGECs . Hence , antigen recognition in the SG depends on local professional APCs having taken up and processed exogenous MCMV-derived antigens . Intriguingly , SG-resident APCs are unable to cross-present particulate antigens , resulting in severe paucity of MHC class I but largely intact MHC class II presentation of MCMV-derived epitopes on these APCs . Thereby MCMV efficiently evades its immune recognition and elimination by CD8 T cells , ensuring prolonged viral shedding into the saliva and promoting horizontal transmission .
CD4 T cells are crucial to control MCMV infection , especially in the SG where productive viral replication continues in the absence of these cells , at least for 10 weeks which was the longest observation period in previous studies [5] , [6] . We corroborated this finding and extended the observation period to more than one year , by comparing viral titers of two mouse strains that lack CD4 T cells , namely MHC class II knockout ( MHCII-/- ) and CD4 knockout mice ( CD4-/- ) , with wild type C57BL/6 ( B6 ) mice at different stages post MCMV-Δm157 ( herein after referred to MCMV ) infection . m157 is expressed on the surface of MCMV-infected host cells and interacts with the activating receptor Ly49H on NK cells of B6 mice , thereby leading to enhanced viral control . We deliberately used an m157 deletion mutant in our studies as most laboratory mouse strains as well as tested outbred mice lack expression of Ly49H [13] , [14] . Using plaque forming assays , presence of replicating MCMV was analyzed in several organs including the spleen , lung , liver and SG over more than one year . In the absence of CD4 T cells , increased viral titers and prolonged detection of replicating virus was observed for all organs examined . However , with the striking exception of the SG , infectious virus was eventually controlled in spleen , liver and lung ( Fig . S1 ) . In the SG infectious MCMV was still detectable in MHCII-/- mice , even more than one year post infection ( Fig . 1A ) . Surprisingly , in CD4-/- mice , MCMV replication in the SG was eventually controlled between 200 to 400 days post infection . CD4 T cells can control pathogens by several means , a prominent function being provision of help to either CD8 T cells or B cells which then control pathogen replication via cytotoxicity or production of antibodies . To test if CD4 T cells control MCMV by exerting helper mechanisms , we analyzed virus titers of MCMV infected CD8 T cell ( CD8-/- ) and B cell deficient mice ( JHT mice ) . SG virus titers in both of these mouse strains were comparable to wild type animals at four ( Fig . S2A ) and eight ( Fig . S2B ) weeks post infection; at both time points , however , viral titers were markedly increased in MHCII-/- mice . Our results are consistent with previous reports of efficient viral control in the absence of CD8 T cells and B cells [10] , [11] , [12] . It is possible , however , that MCMV-specific CD8 T cell responses were impaired in the SG of CD4 T deficient animals , leading to impaired viral control . This would be a plausible explanation for the requirement of CD4 T cells to control MCMV replication in the SG which has not been addressed so far . To exclude a role of T cell help for CD8 T cells in the SG , we analyzed CD8 T cell responses in CD4 T cell deficient and control B6 mice ( Fig . 1B-E ) . SG-resident lymphocytes were isolated and CD8 T cells specific for the epitopes M45 ( Fig . 1B , D ) and M38 ( Fig . 1C , E ) were quantified by tetramer staining . M45-specific CD8 T cell responses in mice lacking CD4 T cells were largely comparable to wild type mice and M38-specific CD8 T cell responses were generally increased in MHCII-/- and CD4-/- mice , indicating that MCMV-specific CD8 T cell responses in the SG were not impaired in absence of CD4 T cells up to 120 days post infection . Previous studies indicated that systemic administration of neutralizing antibodies specific for either IFNγ or TNFα could abolish CD4 T cell-mediated control of MCMV replication in adoptive transfer models [6] , [7] . Further , in CMV-seropositive humans , direct cytolytic activity exerted by virus-specific CD4 T cells was proposed [15] . Although IFNγ and TNFα are involved in the control mechanisms exerted by CD4 T cells , it remains unknown if CD4 T cells produce these cytokines themselves and which cell type these mediators act upon . To identify the effector molecules produced directly by CD4 T cells to control SG MCMV replication , we generated the following mixed bone marrow chimeras: CD4-/- recipients were γ-irradiated and reconstituted with 50% bone marrow of CD4-/- and 50% bone marrow of either IFNγ deficient ( IFNγ-/- ) , TNFα-deficient ( TNFα-/- ) , perforin-deficient ( PKOB ) , CD4-/- or B6 mice . By doing so , CD4 T cells present in the reconstituted animals were the only cell subset being entirely deficient for either IFNγ ( IFNγ-/-xCD4-/- ) , TNFα ( TNFα-/-xCD4-/- ) or perforin ( PKOBxCD4-/- ) . Control animals lacked CD4 T cells completely ( CD4-/-xCD4-/- ) or CD4 T cells were fully functional ( B6xCD4-/- ) . At the time point of infection , frequencies of CD4 T cells were comparable in all experimental groups harboring CD4 T cells ( data not shown ) . The chimeric mice were infected with a recombinant MCMV expressing the firefly luciferase under the control of the m157 promoter [16] . In these animals active viral replication is detectable by in vivo bioluminescence imaging after intraperitoneal administration of D-luciferin ( Fig . 2A ) . Furthermore , two to four months post infection MCMV titers were determined in the SG by plaque assay . Percentages of non-controllers ( viral titer above detection limit ) and the viral titers are shown in Fig . 2B and C . Viral control was impaired in ( CD4-/-xCD4-/- ) compared to ( B6xCD4-/- ) chimeras , corroborating our results in non-chimeric mice . While a comparable lack of MCMV control was observed in mice in which CD4 T cells were deficient for IFNγ production or in which CD4 T cells were completely absent , MCMV replication was similarly controlled in the SG of chimeric mice in which CD4 T cells lacked TNFα or perforin and in mice with fully functional CD4 T cells . To exclude impaired CD4 T cell priming in any of the experimental groups , frequencies of lung-derived MCMV-specific CD4 T cells secreting IFNγ or TNFα were analyzed and were comparable in all CD4-sufficient animals ( data not shown ) . These data strongly support the notion that CD4 T cells directly control MCMV replication in the SG by secretion of IFNγ . Next , we addressed the question whether IFNγ secreted by CD4 T cells inhibited viral replication in the SG by signaling via IFNγ receptors ( IFNγR ) expressed on non-hematopoietic cells ( including infected AGECs ) or by triggering IFNγR signaling on hematopoietic cells , thereby stimulating other immune cells such as NK cells to control viral replication . Using either IFNγR-/- or wild type mice as recipients or donors , bone marrow chimeras were generated that either lacked IFNγR completely ( IFNγR-/- → IFNγR-/- ) , that lacked IFNγR on radio-resistant cells ( B6 → IFNγR-/- ) , that lacked IFNγR on radio-sensitive cells ( IFNγR-/- → B6 ) or that expressed IFNγR on all cells ( B6 → B6 ) . 8 weeks post MCMV infection , viral titers increased in the SG when IFNγR was completely absent compared to wild type counterparts ( Fig . 2D ) . Importantly , increased lytic viral replication was only observed in mice which either completely lacked IFNγR expression or selectively on non-hematopoietic cells . In contrast , mice that lacked IFNγR only on hematopoietic cells controlled MCMV replication . 8 weeks post infection , MCMV-specific CD4 T cell responses were comparable between experimental groups ( data not shown ) . Hence , IFNγ secreted by CD4 T cells signaled on radio-resistant cells to suppress viral replication . CD8 T cells and NK cells from the spleen were shown to produce IFNγ upon MCMV infection [17] , [18] . Why is then exclusively IFNγ produced by CD4 T cells so crucial to control viral replication in the SG ? We first tested if IFNγ production by MCMV-specific CD8 T cells isolated from the SG was impaired . Numbers of CD8 T cells secreting IFNγ after ex vivo restimulation were much higher compared to MCMV-specific CD4 T cells at any time point examined ( Fig . 3A ) , indicating that MCMV-specific CD8 T cells per se were able to produce IFNγ . CD4 T cells were shown to be necessary for the development of fully functional CD8 T cells in a variety of infection models ( reviewed by [19] ) . Although the absence of CD4 T cells only slightly impacted the functionality of CD8 T cells isolated from the spleen , lung or liver [5] , [20] , [21] , [22] , the possibility remained that SG derived MCMV-specific CD8 T cell function was crucially dependent on CD4 T cells . However , in the SG , IFNγ-producing CD8 T cells specific for either the M45 or the M38 epitope were increased rather than decreased in the absence of CD4 T cells at various time points post MCMV infection ( Fig . 3B ) . Even IE3-specific CD8 T cell responses , which have previously been shown in the blood and spleen to largely depend on presence of CD4 T cells [22] , were detected to almost comparable levels in the SG in presence or absence of CD4 T cells . These data clearly argue against a role for CD4 T cells to support CD8 T cell function in the SG at the investigated time points . Although MCMV-specific CD8 T cells and NK cells were detectable in the SG tissue by flow cytometric analysis , their migration to close proximity of infected cells might be less efficient in comparison to MCMV-specific CD4 T cells . To determine the homing and tissue distribution of CD8 T cells and NK cells in relation to MCMV-infected cells , we performed confocal immunofluorescence analyses on tissue sections of the SG . B6 and MHCII-/- mice were infected with a GFP-expressing m157-deficient MCMV mutant . Three weeks later SG sections were stained for CD4 T cells , CD8 T cells , NK cells , B cells and CD11b as well as CD11c expressing cells ( Fig . 4 and Fig . S3 ) . As shown previously [5] , [23] , AGECs were the main cells of the SG that were infected with MCMV at this time point and displayed cell enlargement typical for MCMV infected cells ( Fig . 4 ) . Scattered throughout the SG tissue , dense foci of leukocyte infiltrates were detectable comprising not only CD4 and CD8 T cells but also CD11c and CD11b expressing as well as NK and B cells . However , these dense foci of leukocytes most often did not contain GFP+ cells ( Fig . 4 and Fig . S3; right columns ) . We propose that these infiltrates had previously surrounded a GFP-positive MCMV-infected cell that had been eliminated and is hence no longer detectable via GFP expression . GFP-expressing ( hence MCMV infected ) AGECs were either located distal , proximal or in very rare occasions within leukocyte infiltrates . Only few immune cells were in close proximity to infected cells when situated distal to infiltrates ( Figs . 4A , B and Fig . S3; left columns ) . Interestingly , in some infiltrates , cells displaying a lower GFP fluorescence intensity were detected which exhibited a much smaller cell size than GFP-expressing AGECs . Furthermore , their morphology was comparable to the surrounding cells of the infiltrate , suggesting that these cells were not directly infected cells but might rather be phagocytic cells which had taken up remnants of MCMV infected cells including GFP ( Fig . 4 , middle column ) . It was recently shown that IFNγ-secreting CD4 T cells are necessary for the migration of herpes simplex virus-specific CD8 T cells to the site of infection [24] . However , during MCMV infection , migration of CD8 T cells , B cells as well as NK cells , CD11c and CD11b expressing cells to infected cells of the SG was not impaired in the absence of CD4 T cells ( Fig . 4C and Figs . S3 and S4 ) . Distribution of these cells in the SG examined by immunohistological analysis were comparable between MCMV-infected MHCII-/- and B6 mice with the only exception that the former strain had increased numbers of infected foci ( Fig . 4C and Fig . S3 ) . We hypothesized that CD8 T cells , in contrast to CD4 T cells , are unable to control viral replication in the SG because they are not exposed to their cognate antigen in this particular organ . MCMV suppresses not only MHC class I but also MHC class II expression on infected cells [25] , [26] , [27] , [28] and inhibition of MHC class I expression might be more pronounced in AGECs than MHC class II expression . To test this hypothesis , sections of SG were stained for MHC class I and II expression three weeks post MCMV-GFP infection and analyzed by confocal microscopy . Neither MHC class I nor MHC class II molecules were detectable on directly infected GFP-positive cells when distal to cell infiltrates ( Fig . 5A and D ) . However , once proximate to leukocyte infiltrates , very few infected cells expressed very low levels of MHC class I and class II on their cell surface , but to a much lower extent than on the close-by leukocyte infiltrate ( Fig . 5B and E ) . Overall , 86% of all GFP-infected cells had no detectable surface expression of MHC class I and only 14% expressed MHC class I at very low levels . Comparably , only 14% of GFP-expressing cells in the SG were slightly positive for MHC class II on their cell surface . Low or absent MHC expression on directly infected cells stands in line with published data showing that MCMV very potently inhibits antigen presentation via down-regulation of MHC molecules in vitro [25] , [26] , [27] , [28] , [29] . MHC class I expression on uninfected AGECs was concentrated to the apical side of the cells and was overall low in comparison to leukocytes ( Fig . 5C ) . MHC class II expression was completely absent in uninfected AGECs ( data not shown ) . The comparably rare and low expression patterns of MHC class I and II by infected AGECs argues against the hypothesis that CD4 T cells would preferentially interact directly with MCMV-infected AGECs . Although directly infected cells of the SG lacked MHC class I and II expression , other cells present in close proximity expressed high levels of both these molecules ( Fig . 5 ) . It is likely that these non-infected bystander APCs are presenting MCMV-derived antigens to MCMV-specific T cells . As these bystander cells were clearly not directly infected since they lacked GFP expression , these cells would have to present MCMV antigens from an exogenous source . It is conceivable that MHC class II-restricted MCMV epitopes might be presented on these APCs much more efficiently than MHC class I-restricted epitopes . In the spleen , cross-presentation of exogenous antigens on MHC class I molecules is largely restricted to a distinct dendritic cell ( DC ) subset , identified by CD11c , MHCII , CD8α , DEC205 expression and concomitant absence of B220 and CD4 expression [30] . To test if SG-resident APCs are phenotypically related to cross-presenting splenic DCs , splenic DCs as well as SG-derived APCs were stained for surface expression of CD11c , MHC class II , B220 , DEC205 , CD4 , and CD8α at different time points post infection . CD11c+ , MHCII+ , CD8α+ , DEC205+ , B220− , CD4− cross-presenting DCs were detectable in the spleen of B6 mice at all the time points examined ( Fig . 6A and B ) . In contrast , this DC subset was almost completely absent in the SG isolated from naïve animals ( Fig . 6A ) . Even when the SG tissue was heavily infected by MCMV two and four weeks post infection , frequencies of CD11c+ , MHCII+ , CD8α+ , DEC205+ , B220− , CD4− cells among the overall SG-resident APC population remained very low ( <1 . 5% ) and total numbers remained far below their splenic counterparts . In peripheral organs such as the gastrointestinal tract , skin and the lung CD103+ DCs represent one of the major DC subset able to cross-present exogenous antigen to CD8 T cells [31] , [32] and on day 28 30% of the SG-resident CD11c+ I-Ab+ APCs expressed CD103 on their cell surface ( Fig . 6 A ) . To formally assess the capacity of SG-resident APCs to process and present exogenous antigen on MHC class I or class II molecules , we isolated SG-resident APCs and splenic DCs . APCs were left untreated or were pulsed with virus like particles ( VLPs ) linked either to the LCMV-derived MHC class I-restricted epitope gp33 or the MHC class II-restricted gp61 epitope . As controls , APCs were pulsed directly with the respective peptides . We used VLPs to assess cross-presentation capacities of splenic DCs and SG-derived APCs as they represent a well-characterized particulate antigen which has been previously shown to depend on cross-presentation for MHC class I antigen loading using splenic DCs [33] . Identical numbers of spleen- or SG-derived APCs were then added to naïve CFSE-labeled TCR transgenic CD8 T cells recognizing the gp33 epitope ( P14 cells ) or TCR transgenic CD4 T cells specific for the gp61 epitope ( Smarta cells ) . P14 cells recognized their antigen on splenic DCs either pulsed with VLP-gp33 or their cognate peptide ( Fig . 6C ) . However , salivary gland-derived APCs were not able to cross-present the cognate peptide to P14 T cells when pulsed with VLP-gp33 . SG-derived APCs were not per se unable to stimulate CD8 T cells as they were able to do so when pulsed with the gp33 peptide . In contrast , Smarta cells proliferated extensively when incubated with either SG-derived APCs or splenic DCs pulsed with VLP-gp61 . These results demonstrate , on a functional level , that SG-resident APCs are not able to cross-present exogenous antigens to CD8 T cells . As we could never detect GFP+ CD11c+ cells in any of the SG sections analyzed , we conclude that they are not directly infected by MCMV and hence do not present endogenous MCMV-derived antigens on MHC class I molecules . However , in line with the notion that SG-resident APCs are responsible for MCMV antigen presentation , we were able to detect in some rare cases CD11c+ MHC class I ( Fig . S5A ) or MHC class II ( Fig , S5B ) expressing cells with focal GFP inclusions which most likely represent APCs which had phagocytosed remnants of infected AGECs . Based on our functional in vitro data , we propose that these might be the very cells being responsible for local MCMV-derived antigen presentation to CD4 T cells . To directly assess whether the absent MHC class I expression on infected AGECs due to MCMV-encoded immune evasion genes was responsible for the exclusive CD4 T cell-mediated control of MCMV in the SG , we made use of a triple MCMV mutant lacking all three major MHC class I immune evasion genes m04 , m06 and m152 ( Δm04Δm06Δm152 ) [34] . B6 , CD4-/- and MHCII-/- mice were infected with Δm04Δm06Δm152 MCMV and its parental BAC-derived virus and viral titers were examined 28 days post infection in the SG . While MHCII-/- and CD4-/- mice were unable to control wild type ( WT ) MCMV replication , they were able to control Δm04Δm06Δm152 MCMV by day 28 post infection ( Fig . 7A ) , indicating that the strict requirement for CD4 T cells to control SG MCMV infection no longer holds in absence of MCMV-encoded MHC class I immune evasion genes . Consistent with the notion that CD8 T cells become major contributors for control of SG MCMV infection in absence of MHC class I immune evasion genes , CD8 T cell depletion resulted in significantly impaired control of Δm04Δm06Δm152 MCMV replication in the absence of CD4 T cells ( Fig . 7B ) .
In this study we unravel the underlying mechanisms of the unique requirement for CD4 T cells to control MCMV replication in the SG . We show that CD4 T cells control MCMV replication in the SG by exerting direct antiviral effector functions via IFNγ secretion and not by providing helper functions to CD8 T cells or B cells . While robust CD8 T cell responses were detected in presence or absence of CD4 T cells in the SG , they were unable to control MCMV replication due to the exquisite efficiency of MCMV to downregulate MHC class I expression on infected AGECs . In absence of MCMV-encoded MHC class I immune evasion genes , CD8 T cells gained ability to control SG MCMV infection also in absence of CD4 T cells . However , in the absence of antiviral active CD4 T cells , B cells and antibodies can contribute to control viral replication in the SG on the long term , as infectious virus was eventually cleared in CD4-/- mice in all organs examined as opposed to MHCII-/- mice . One major difference between MHCII-/- and CD4-/- mice is that the latter strain is able to mount isotype-switched antibody responses [35] , [36] . MCMV-binding IgG and neutralizing antibodies were indeed detectable in MCMV-infected CD4-/- mice , but were completely absent in MHCII-/- animals ( Fig . S6 and ) . As MCMV-specific antibodies were previously shown to inhibit viral dissemination during MCMV infection [12] , [37] , they are likely to be responsible for late differences in MCMV control observed between MHCII-/- and CD4-/- mice . In vitro , IFNγ and TNFα can exert strong and synergistic inhibition of MCMV replication in fibroblasts [38] . Systemic in vivo neutralization of IFNγ and TNFα in CD8 T cell depleted MCMV infected BALB/c mice or in MCMV-infected lethally irradiated mice transferred with MCMV-primed splenocytes resulted in decreased MCMV control [6] , [7] . Thus , CD4 T cells were suggested to act via secretion of IFNγ and TNFα to limit viral replication in the SG . However , in these studies , unlike ours , there was no formal proof that CD4 T cells themselves and no other cells secrete those cytokines . We were able to show for the first time that CD4 T cells exert direct antiviral mechanisms by producing IFNγ and to a much lower extent - if at all - via TNFα production or perforin-mediated effector mechanisms . At this point we cannot formally exclude that CD4 T cells might interact with other cells which then produce TNFα , thereby leading to control of viral replication in the SG . However , our observation that lytic MCMV replication is not controlled in chimeric mice which specifically lack IFNγR expression on non-hematopoietic cells would rather argue against a pivotal role of additional effectors including TNFα . It was proposed that NK-like cells might synergize with CD4 T cells to control viral replication in the SG [4] , due to the fact that mice depleted of NK cells by anti-NK1 . 1 or anti-asialoGM1 antibodies showed increased viral titers in the SG [11] , [39] , [40] , [41] . Our data does not exclude a synergism between CD4 T and NK cells , we can only conclude that a potential NK-CD4 T cell interaction is unlikely to be mediated via IFNγ . Further , the NK cell compartment in the SG was not affected by the absence of CD4 T cells: NK cells were present in the SG of CD4 T cell deficient animals , absolute numbers were comparable to wild type mice and we were unable to detect any differences in NK cell function between MHCII-/- and B6 mice ( Fig . S7 and Text S1 ) . Furthermore , SG-resident NK cells were recently shown to have functional deficits and peripheral NK cells appear to be poorly recruited to the SG during MCMV infection [42] . Finally , in contrast to many reports demonstrating a role for NK cells in limiting early MCMV replication [11] , [39] , [40] , [41] , [43] , a recent study showed a negative impact of NK cells on MCMV control due to curtailed MCMV-specific T cell responses [44] . Why then is IFNγ secreted by CD4 T cells so crucial to control MCMV in the SG whereas IFNγ production by other immune cells such as CD8 T cell or NK cells is insufficient ? Different reasons could account for this: a ) other immune cells might not produce IFNγ in the SG; b ) CD4 T cells but no other IFNγ-producing cells migrate into close proximity of infected cells; c ) CD4 T cells might enable other immune cells to migrate to infected cells; d ) CD4 T cells , in contrast to CD8 T cells , might recognize directly infected cells; e ) antigens might not be presented directly on infected cells but on bystander APCs that are unable to cross-present and hence to activate CD8 T cells . We show here evidence for the last scenario as both MCMV-specific CD8 as well as CD4 T cells from the SG were able to secrete IFNγ , total numbers of IFNγ+ CD8 T cells exceeded by far total numbers of IFNγ+ CD4 T cells , CD8 T cells were localized at the same sites as CD4 T cells in the SG , the cellular composition of the infiltrates were not changed in absence of CD4 T cells , most directly infected cells in the SG were MHC negative and SG-resident APCs were capable of processing and presenting particulate antigen on MHC class II but not MHC class I molecules . Importantly , we have no evidence for direct infection of SG APCs , at least at the time points analyzed ( Fig . 5 and data not shown ) . Furthermore , we found some MHC class I and II positive CD11c+ cells within densely populated leukocyte infiltrates in the SG which displayed discrete inclusion of cargo derived from MCMV-infected cells , suggestive of phagocytes which had taken up remnants of MCMV-infected AGECs . Our observations that we only rarely detected MHC class I and II molecules on directly infected AGECs stands in line with findings that MCMV down-regulates very efficiently MHC molecules , albeit this has mostly been shown in vitro for infected fibroblasts , macrophages and dendritic cells [25] , [26] , [27] , [28] , [29] . In fact our study demonstrates for the first time that MCMV can indeed suppress surface expression of MHC complexes on AGECs in vivo as well . MCMV expresses several gene products , so called immune-evasins , that impair antigen presentation on MHC class I molecules ( reviewed by [29] ) . Interestingly , genetic deletion of these immune-evasins resulted in increased MCMV immune control specifically in the SG in a CD8 T cell dependent manner in BALB/c mice [25] , suggesting that MCMV expressed immune-evasins are particularly potent in AGECs compared to cells of other tissues . We corroborated and extended this observation by showing that CD4 T cells were dispensable for MCMV control in the SG in the absence of the virus-encoded m04 , m06 and m152 immune-evasins . Up to date we were not able to study MHC class I expression on AGECs infected with Δm04Δm06Δm152 MCMV as the mutant strain currently available does not express GFP . In future experiments we will generate a GFP expressing Δm04Δm06Δm152 MCMV strain to address this open question . In summary , the results of our study strongly support a model in which virus-specific CD4 T cells but not CD8 T cells control viral replication in the SG , thereby eventually curtailing horizontal transmission ( Fig . 8 ) : In infected AGECs MCMV immune-evasins inhibit trafficking of MHC class I molecules to the cell surface and shut down the induction of MHC class II expression . MCMV antigens released by the infected cells or remnants of infected cells are taken up by local APCs which are unable to cross-present particulate antigens but able to process and present on MHC class II , thereby selectively inducing MCMV-specific CD4 T cells to secrete IFNγ . IFNγ binds to its receptors present on infected cells or / and on adjacent cells and exerts its antiviral function by interfering with viral replication or rendering cells resistant for subsequent MCMV infection .
This study was carried out in in strict accordance to the guidelines of the animal experimentation law ( SR 455 . 163; TVV ) of the Swiss Federal Government . The protocol was approved by Cantonal Veterinary Office of the canton of Zurich , Switzerland ( Permit number 145/2008 ) . All surgery was performed under isoflurane anesthesia and animals were treated pre- and post-surgically from day-1 to day+7 with the analgesic Buprenorphine . All efforts were made to minimize suffering . C57BL/6 , MHCII-/- [45] , CD4-/- [46] , CD8-/- , B6 . 129P-Igh-Jtm1Cgn ( JHT ) [47] , IFNγ-/- ( Charles River Laboratories ) , TNFα-/- ( B6 . 129-TNFtm1Ljo , [48] ) , PKOB [49] and IFNγR-deficient mice were kept under specific pathogen free ( SPF ) conditions and were infected intravenously with 106 plaque forming units ( PFU ) of MCMV-Δm157 , MCMV wt , MCMV-Δ04Δ06Δ152 , MCMV-Δm157-luciferase , or 5×105 PFU MCMV-Δm157-GFP between 6 and 12 weeks of age . For CD8 T cell depletion , mice were injected i . p . with 0 . 2 mg of purified anti-mouse CD8 monoclonal antibody ( YTS 169 . 4 , [50] ) 3 and 1 days before infection and then weekly . The MCMV-derived M45aa985-993 and M38aa316-323 as well as the LCMV-derived gp33 and gp61 peptides were purchased from NeoMPS ( Strasbourg , France ) . Production of a crude lysate of MCMV infected cells was previously described [8] . Production and purification of VLP-gp33 ( HBcAg-p33 ) and VLP-gp61 ( HBcAg-p33 ) was previously described [51] . CD4-/- mice were irradiated ( 950 rad γ ) and reconstituted with a 1∶1 mixture of bone marrow from CD4-/- and CD4-/- or IFNγ-/- or TNFα-/- or PKOB or C57BL/6 mice . In other experiments , irradiated IFNγR-/- or C57BL/6 recipients were reconstituted with bone marrow of IFNγR-/- or C57BL/6 mice . Generation of the recombinant MCMV-Δm157 ( lacking the m157 gene of MCMV ) was previously described [8] . The m157-deficient MCMV mutant expressing the luciferase gene was a kind gift of Dr . M . Mach ( Erlangen , Germany ) [16] . The MCMV mutant expressing green fluorescent protein ( GFP ) under the m157 promoter was described before [52] as well as MCMV-Δm04Δm06Δm152 [34] . MCMV was propagated on mouse embryonic fibroblasts ( MEFs ) and viral titers were determined using plaque forming assays as described in [53] . Shaved mice were injected intravenously with 0 . 5 mg D-luciferin in 200 µL PBS and immediately anaesthetized using isoflurane . Ten minutes after luciferin injection , bioluminescence was recorded over a 300-second integration period by a cooled CCD camera system ( IVIS Imaging System 200 Series , Caliper Life Sciences AG , Oftringen , Switzerland ) . Anesthesia was maintained during imaging by nose cone delivery of the anesthetic . Relative intensities of transmitted light from the in vivo bioluminescence were represented as pseudocolor imaging . PE-conjugated peptide-MHC class I tetrameric complexes were generated as previously described [54] . The following monoclonal antibodies were either purchased from BD Pharmingen ( Allschwil , Switzerland ) or from BioLegend ( Lucerna Chem AG , Luzern , Switzerland ) and used for stainings: anti-CD8 ( FITC , PerCP , APC , PacificBlue , APC-Cy7 ) , anti-CD4 ( PE , PerCP , PacificBlue ) , anti-IFNγ ( APC ) , anti-B220 ( PE-Cy7 ) , anti-Ly6C ( FITC ) , CD11b ( PerCP ) , anti-CD11c ( APC ) , anti-I-Ab ( PE ) . Lymphocytes were isolated from spleen , lung , liver and SG as previously described [55] . Cells were surface stained with directly labeled Abs or peptide-MHC class I tetramer complexes followed by erythrocyte lysis . For intracellular cytokine stainings , CD8 T cells were stimulated with 1 µg/ml peptide and CD4 T cells with 10 µg/ml cell lysate in the presence of 10 µg/ml brefeldin A ( Sigma-Aldrich ) at 37°C for 6 h . Cell surface staining was done as described above , followed by fixation and permeabilization using 500 µl of FIX/Perm solution ( FACSLyse diluted to 2x concentration and 0 . 05% Tween 20 ) for 10 min at room temperature . Cells were washed and stained with directly labeled Abs against IFNγ and TNFα . Multiparameter flow cytometric analysis was performed using a FACS LSRII flow cytometer ( BD , Allschwil , Switzerland ) with FACS DIVA software ( BD , Allschwil , Switzerland ) . List mode data were analyzed using FlowJo software ( Treestar , San Carlos , CA ) . SG were isolated from infected animals , fixed for 1 h in PBS containing 4% PFA at 4°C and incubated overnight in PBS containing 20% sucrose , followed by tissue embedding in O . C . T . compound ( Sakura , Torrance , CA ) , snap-freezing in liquid N2 and storing at −80°C . Cryosections of 16 µm thickness were prepared , completely air-dried for 2 to 6 hours before recovery by a brief rinse in PBS . Sections were blocked ( PBS with 10% FCS ) for one hour . Primary antibody ( CD4 ( clone YTS191 . 1 ) , CD8 ( clone YTS156 ) , CD11c ( BD Pharmingen ) , I-Ab ( BioLegend ) , MHC class I ( Santa Cruz ) ) was added in staining buffer ( 1% mouse serum in PBS ) for one hour before extensive washing with PBS . Incubation with secondary antibodies ( anti-hamster IgG ( Cy3/Cy5 ) , Phalloidin ( 647-labeled Fluoprobes ) , anti-rat IgG ( Cy3/ Cy5 ) , all purchased from Sigma-Aldrich ) containing DAPI ( Sigma-Aldrich ) diluted in staining buffer lasted one hour , followed by extensive washing and fixation with 1% PFA and mounting with VectaShield ( Vector Laboratories , Burlingame , CA ) . Samples were viewed and analyzed with an inverted confocal microscope ( Axiovert 200 , Carl Zeiss , Inc . ) , equipped with an oil-phase contrast objective ( Plan Neofluar , Carl Zeiss , Inc . ) , an ultraview confocal head ( PerkinElmer , location ) and a krypton argon laser ( 643-RYB-A01 , Melles Griot ) . Data analysis was done with Velocity ( Improvision ) . One month post MCMV infection four to five spleens and SG were digested using Liberase TL Research Grade ( Roche , Rotkreuz , Switzerland ) and DNaseI according to manufacturer's instruction , except that SG tissue was dissociated previous to digestion and in between two incubation periods of 20 minutes . DCs from the spleen and APCs from the SG were purified with CD11c MACS beads according to manufacturer's instruction , pulsed with either 1 µg/ ml VLPs or 10−8 M peptide for 1 hour at 37°C . After intensive washing , APCs were counted and purity was determined by FACS analysis . Equal numbers of splenic DCs and SG APCs ( varying inter experimentally between 0 . 5 to 1×105 cells ) were added to 105 TCR transgenic CD8 or CD4 T cells previously labeled with CFSE ( Invitrogen , Basel , Switzerland; end concentration 2 mM ) and incubated for 5 days . CD8 and CD4 T cells were MACS-purified from spleens of Ly5 . 1+ P14 or Ly5 . 1+ Smarta transgenic mice , respectively . 5 days later cells were labeled with antibodies against CD4 , CD8 and Ly5 . 1 and analyzed by FACS .
|
Cytomegaloviruses ( CMVs ) infect 50 to 90 % of the world's population and cause severe clinical complication in immunosuppressed individuals . An important tissue for horizontal transmission is the salivary gland ( SG ) . CD4 T cells are crucial for viral control in this organ . However , how CD4 T cells control MCMV and why CD8 T cells , important effector cells in other organs , are inefficient in the SG , remains unclear . Here we show that CD4 T cells exert direct antiviral effector rather than helper functions by secretion of IFNγ acting on non-hematopoietic cells . Although SG-resident CD8 T cells were able to produce IFNγ and outnumbered CD4 T cells , absence of MHC class I expression on infected cells due to CMV-encoded immune evasion genes and concomitant absence of cross-presenting antigen presenting cells prohibited antigen recognition by CD8 T cells . Deletion of CMV-encoded immune evasion genes enabled CD8 T cells to control MCMV replication in the SG in absence of CD4 T cells . Hence , CMV control depends on direct antiviral functions of CD4 T cells because of exclusive MHC class II-restricted CMV antigen presentation by bystander APCs in the SG , exemplifying a strategy of effective immune evasion by which CMVs to promote their own transmission .
|
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2011
|
Absence of Cross-Presenting Cells in the Salivary Gland and Viral Immune Evasion Confine Cytomegalovirus Immune Control to Effector CD4 T Cells
|
A robust ( long and thick ) root system is characteristic of upland japonica rice adapted to drought conditions . Using deep sequencing and large scale phenotyping data of 795 rice accessions and an integrated strategy combining results from high resolution mapping by GWAS and linkage mapping , comprehensive analyses of genomic , transcriptomic and haplotype data , we identified large numbers of QTLs affecting rice root length and thickness ( RL and RT ) and shortlisted relatively few candidate genes for many of the identified small-effect QTLs . Forty four and 97 QTL candidate genes for RL and RT were identified , and five of the RL QTL candidates were validated by T-DNA insertional mutation; all have diverse functions and are involved in root development . This work demonstrated a powerful strategy for highly efficient cloning of moderate- and small-effect QTLs that is difficult using the classical map-based cloning approach . Population analyses of the 795 accessions , 202 additional upland landraces , and 446 wild rice accessions based on random SNPs and SNPs within robust loci suggested that there could be much less diversity in robust-root candidate genes among upland japonica accessions than in other ecotypes . Further analysis of nucleotide diversity and allele frequency in the robust loci among different ecotypes and wild rice accessions showed that almost all alleles could be detected in wild rice , and pyramiding of robust-root alleles could be an important genetic characteristic of upland japonica . Given that geographical distribution of upland landraces , we suggest that during domestication of upland japonica , the strongest pyramiding of robust-root alleles makes it a unique ecotype adapted to aerobic conditions .
Asian cultivated rice ( Oryza sativa L . , O . sativa ) is a staple food for half the world’s population . Grown in diverse environments worldwide , O . sativa is also well-known for its rich-within-species diversity with two major subspecies , indica ( also referred to as Xian ) and japonica ( also referred to as Geng ) and subpopulation differentiation [1–6] . Upland rice , particularly upland japonica rice , represents a predominant ecotype grown under aerobic and rain-fed conditions in mountainous areas of Southwest China , South and Southeast Asia , Africa and Latin America [7–11] . Most upland rice accessions have a robust root ( long and thick ) system [10 , 12 , 13] that confers the ability of upland to adapt to aerobic and drought-prone conditions of rain-fed , hilly environments by absorbing more water and nutrients from deeper soil zones [14–18] , implying that robust roots could be a key physiological feature selected during adaptation to aerobic conditions of upland rice . Thus , exploration of loci and natural alleles underlying robust roots and adaptation analysis of upland rice using these loci are conducive to developing new rice varieties with improved drought resistance and water use efficiency , when plant breeders are facing an increasing challenge of water shortage caused by global climate change . Breeding rice lines with robust roots based on phenotypic selection has been challenging because conventional breeding approaches based on phenotypic selection are ineffective for improving root traits [19–21] . Thus , tremendous efforts have been made in identifying QTLs/genes contributing to robust roots to facilitate marker- or genome-assisted breeding of drought resistance rice varieties by improving root traits . To date , many QTLs for root length ( or depth ) and thickness in rice have been identified in bi-parental crosses , such as DRO1 , DRO2 , DRO3 , SOR1 , qSOR1 , qRL6 . 1 , qRL7 and qRT9 [22–30] . OsbHLH120 that controls root thickness was cloned by map-based cloning using introgression lines [23 , 31] . Additionally , DRO1 affects deep rooting by controlling root angle and the mutation of SOR1 leads to observed deficiency in root gravitropic response and soil-surface rooting [30 , 32] . Traditional methods using linkage mapping and map-based cloning for QTL identification and cloning are laborious and inefficient . Thus , many questions remain to be answered in understanding allelic diversity at QTL controlling robust roots in the primary gene pool of rice and their roles in the adaptation to aerobic conditions of upland rice . Genome-wide association and transcriptome analyses have become feasible with recent advances in high-throughput sequencing technologies , which enable capture of genomic variation affecting complex traits . These include genome-wide association study ( GWAS ) [33–36] , genome-wide screening of elite single-nucleotide polymorphism ( SNP ) alleles [12] , determination of genome-wide expression profiles under different drought stress conditions [37–39] , MutMap based on whole-genome sequencing of bulked DNA of F2 segregants [40] , Ho-LAMap that joins GWAS and multiple bi-parental linkage analysis [41] . Using these methods , wide natural variation was explored at loci associated with root architecture , such as Nal1 , OsJAZ1 and Nced [12 , 36] . Although each of these approaches has advantages and disadvantages in revealing specific aspects of the genetic and/or molecular mechanisms underlying complex traits , when taken together , they provide a powerful strategy for genetic and molecular dissection of complex traits . Based on hydrological conditions ranging from fully aerobic , temporarily and fully anaerobic , four major rice ecosystems were categorized by International Rice Research Institute ( IRRI ) , including upland , rainfed lowland , irrigated ( also called lowland or paddy ) and flood-prone ( also called deepwater ) [7 , 33 , 42 , 43] . From the 1970s many morphologic and genetic studies distinguished upland and lowland rice genotypes , and investigated the domestication of upland rice [4 , 13 , 18 , 44] . Among morphological traits for drought resistance , such as root characteristics [45] , leaf rolling [46] and ratio of shoot and root [47] , root length and root thickness have been positively related to field drought resistance [48 , 49] , implying that robust roots can be used to distinguish upland and lowland rice . Further genetic differentiation between upland and lowland rice was explored by simple sequence repeats ( SSRs ) located in expressed sequence tags ( ESTs ) isolated from rice in response to drought treatment [44 , 50] . These previous studies suggested that there was considerable morphologic and genetic differentiation between upland and lowland rice . Recently , a phylogenetic analysis of 3 , 029 , 822 SNPs detected from 166 rice accessions ( 82 lowland accessions , 84 upland accessions and 25 wild rice accessions ) showed that all upland japonica accessions clustered together , suggesting that upland japonica has a single origin [18] . Despite these advances , deep sequencing of a wider collection of O . sativa was needed to investigate the adaptation to aerobic conditions of upland rice . Moreover , given differences in phylogenetic trees using whole-genome sequencing data and domestication loci [2] , genetic analysis using loci associated with robust roots may be a better way to gain insights into the adaptive domestication process of upland rice . In this study , we demonstrate an integrated strategy that combined high resolution mapping by GWAS and linkage mapping , comprehensive bioinformatics analyses of genomic , transcriptomic and haplotype data from deep genomic and transcriptomic sequencing , and large scale phenotyping of two large sets of O . sativa accessions for genetic dissection and high efficiency cloning of small-effect QTLs underlying robust roots in rice . Our results revealed insights into the adaptive domestication history of upland rice as a unique O . sativa ecotype that is adapted to the aerobic conditions of highlands in tropical and subtropical environments .
The 795 O . sativa accessions used were from 41 countries worldwide , including a 189 accession mini-core collection retaining 70 . 65% of the genotypic variation and 76 . 97% of the phenotypic variation of 4 , 310 Chinese O . sativa accessions [51] , and 525 lines in the International Rice Molecular Breeding Network [52] , including 40 upland accessions ( S1 Table ) . By comparison with the RGAP 7 Nipponbare reference genome , we obtained 15 , 133 , 187 SNPs from the 795 genomes with an average sequencing depth of 15× , including 12 , 648 , 777 SNPs with missing rates ≤ 50% ( S1A Fig ) . After removing SNPs with missing rates > 50% and minor allele frequencies < 5% , we identified 3 , 291 , 151 ( 3 . 3 million ) SNPs representing high density . Using 154 , 516 SNPs with missing rates ≤ 50% , minor allele frequencies ≥ 5% and r2 of linkage disequilibrium ( LD ) ≤ 0 . 3 , we calculated varying levels of K means , the Indica and Japonica varietal groups appeared clearly at K = 2 ( S1B and S1C Fig ) , which was supported by principal component ( PC ) plotting , and a kinship matrix constructed using 3 . 3 million SNPs and neighbor-joining tree constructed using 54 , 853 evenly distributed SNPs ( Fig 1A and 1B , S1D Fig ) . Referring to the recent research results of 3 , 010 rice accessions [1] , we classified the 795 O . sativa accessions into two major subspecies , indica ( 506 accessions ) and japonica ( 289 accessions ) for further phenotypic analysis and GWAS in two subpopulations ( Fig 1A and 1B , S1 Table ) . The 795 O . sativa accessions showed significant amount of variations for the measured root traits , ranging from 8 . 1 to 19 . 4 cm for root length ( RL ) and from 0 . 46 to 0 . 95 mm for root thickness ( RT ) ( S2 Fig , S2 Table ) . Upland japonica accessions had mean RL and RT of 14 . 5±1 . 6 cm and 0 . 79±0 . 07 mm , significantly higher than those of the other three ecotypes ( upland indica , lowland indica and lowland japonica ) ( Fig 1C and 1D ) . No obvious differences in mean root weight were detected between the upland japonica and other ecotypes , although there was considerable variation in root weight among different accessions within each subpopulation ( S3 Fig , S2 Table ) . Clearly , upland japonica accessions as a group showed extreme phenotypes for robust root systems , and our population showed sufficient variation in RL and RT to uncover the genetic architecture of robust roots . Using the root trait data and 3 . 3 million SNPs , we performed GWAS to identify important QTLs for RL and RT . To minimize false positives due to population structure [35 , 53 , 54] , we compared the general linear model ( GLM ) ( S4 and S5 Figs ) and compressed mixed linear model ( CMLM ) ( Fig 2 ) and determined that the CMLM model effectively minimized false positive rates in detecting RL and RT QTLs in our populations . A threshold of -log ( P ) = 4 was determined using a conditional permutation test ( S6 Fig ) . We defined a QTL as including ≥ 3 clustered significant SNPs within distances ≤ 170 kb between adjacent ones , given LD decay values of ~123 kb and ~167 kb in indica and japonica populations , respectively [53] . With this criterion we identified 21 , 14 and 4 RL QTLs in the whole , indica and japonica populations , respectively ( Fig 2 ) . Detailed comparisons indicated that 1 of 4 QTLs in japonica and 7 of 14 QTLs in indica mapped to identical positions as RL QTLs identified in the whole population , while most other QTLs detected in both subpopulations were near those identified in the whole population , indicating that the slight differences in position of detected RL QTLs resulted from LD and population structure ( Fig 2 , S3 Table ) . We also identified 22 , 28 and 46 RT QTLs in the whole , indica and japonica populations ( Fig 2 , S4 Table ) . Given that most of RL QTLs in both subpopulations could be identified in the whole population , the 21 RL QTLs in the whole population and 96 RT QTLs in all three populations were considered target QTLs for further exploration of functional genes . We took a four-step integrated strategy to determine the high-confidence candidate genes for the detected RL and RT QTLs ( see Materials and Methods for detail ) . First , we constructed two varietal pools for RL and RT from each of the japonica and indica subpopulations with each pool containing 20 accessions with extreme phenotypes , i . e . a long-root pool and a short-root pool for RL , or a thick-root pool and a thin-root pool for RT ( S1 Table ) . We then performed pooled-χ2-tests to compare the allele frequencies of all significant SNPs at the target QTLs between the two pools for each root trait because varieties with extreme phenotypes were expected to possess more QTL alleles for increased trait value , whereas those of extremely low phenotypic values would have fewer . Within the 21 RL QTLs , there were 254 annotated genes containing 857 significant SNPs ( S3 Table ) . Among them , there were 174 genes with 282 large-effect SNPs in the open reading frames ( ORFs ) and 141 genes with 271 significant SNPs in their 2000 bp upstream promoter regions . The pooled-χ2-tests based on allelic frequency differences for SNPs around each detected QTL between the long-root and short-root pools allowed us to reduce the number of significant SNPs ( candidate genes ) from 857 to 230 ( from 254 to 85 ) for 18 RL QTLs with QTL alleles for increased root length were much more abundant in the long-root varieties than in the short-root varieties at all 230 SNPs ( Fig 3A , S5 and S6 Tables ) . We then compared the QTLs identified by GWAS with QTLs detected previously by linkage mapping in two bi-parental crosses and found 10 [55–60] of the 21 RL QTLs and 23 [22 , 23 , 25 , 59 , 61] of the 96 RT QTLs were common ( S7 Table ) . We screened all 380 SNPs significantly associated with RL in 10 common QTL regions containing 102 candidate genes and found that 119 of these SNPs located in 35 genes in 7 QTLs showed differences between the parents of the bi-parental populations ( S8 Table ) . Using the results of pooled-χ2-tests , we shortlisted the candidate genes from 254 to 81 within 17 RL QTLs ( Fig 3B and 3C , S7 Fig ) . There were 146 significant SNPs in the 81 genes , including 82 SNPs in ORFs of 42 candidate genes , while 77 SNPs in the promoters of 46 candidate genes ( S9 Table ) . These candidate genes included OsGH3 . 1 , WOX11 and OsBOR1 with known functions in root development . Similar analyses were performed on the 96 RT QTLs and resulted in 466 genes as the most likely candidates for the 29 RT QTLs ( S10 Table ) . In steps 3 and 4 , to further reduce the number of candidate genes for the identified root trait QTLs , we performed transcriptomic analyses on 6 robust-root japonica upland varieties and 6 non-robust-root lowland japonica lines . We expected that most real QTL genes in these two pools of varieties might indicate differences in three patterns: root-specific expression , differential expression between two pools , and no differential expression but having significant phenotypic differences between alleles at non-synonymous SNP . Based on thresholds of ( RPKM , Reads Per Kilobase per Million mapped reads ) RPKMRobust-roots/RPKMControls > 1 . 3 or < 0 . 77 between the robust-root varieties and non-robust controls in our transcriptomic data , we identified 44 candidate genes for 16 RL QTLs , including 23 genes showing root-specific differential expression between the long-root and short-root pools , 37 genes containing many large-effect non-synonymous SNPs or InDels between the long-root and short-root groups , and 5 specifically expressed root genes ( Fig 4A , S11–S13 Tables ) . Applying the same strategy , 97 candidate genes were shortlisted for 20 of the RT QTLs ( S14 and S15 Tables ) . Of all these candidate genes , six ( OsGH3 . 1 for qRL1-3 , WOX11 for qRL7-2 , OsBOR1 for qRL12-2 , OsPTR6 for qRT4-4 , OsBRXL3 for qRT4-4 , and RSS3 for qRT11-4 ) appeared to be the most likely QTL candidate genes based on the combined genome mapping and transcriptomic evidence ( Figs 2 and 4B ) . OsGH3 . 1 ( LOC_Os01g57610 ) is an indole-3-acetic acid ( IAA ) amido synthetase , a member of the GH3 family in which several genes are known to control root architecture and drought resistance [62–64] . WOX11 ( LOC_Os07g48560 ) was reported to be a key regulator specifically expressed in rice crown roots [65 , 66] . OsBOR1 ( LOC_Os12g37840 ) was reported to regulate root development in rice grown under limited boron availability [67] . Of the three genes for RT , over-expression of OsPTR6 ( LOC_Os04g50950 ) increases root growth in rice [68] , whereas OsBRXL3 ( LOC_Os04g51172 ) is a member of the BREVIS RADIX family , which is known to influence cell proliferation and root elongation in Arabidopsis and rice [69] . RSS3 ( LOC_Os11g25920 ) regulates root cell elongation under salinity stress [70] . Fig 5 , S8 and S9 Figs show results from haplotype analyses using all non-synonymous SNPs and InDels within the ORFs and promoters of all candidate genes for the identified RL and RT QTLs . Significant phenotypic differences were detected between or among different haplotypes at 9 loci for 7 RL QTLs and 14 candidate genes for 7 RT QTLs with 2–5 major haplotypes at each of them . Gene ontology analysis indicated that 16 of the 23 genes have diverse biological functions and the remaining 7 genes have no known functions ( Table 1 ) . For RL candidate genes , we detected 3 haplotypes at LOC_Os03g50980 ( OsSIZ2 ) with Hap1 present only in some indica accessions . Hap2 was the predominant one and was associated with longer RL in both indica and japonica populations , while Hap3 had a low frequency and was associated with shorter roots in both populations ( Fig 5D ) . Similarly , three major haplotypes were detected at LOC_Os11g43320 ( OsRL11 . 1 ) with Hap1 associated with longer roots . Its frequency was high in indica and low in japonica ( Fig 5E ) . The remaining 7 RL loci showed greater indica-japonica differentiation , with 1–3 of the haplotypes absent in either indica or japonica accessions ( Fig 5A–5C , S8 Fig ) . There were japonica-specific allele variations for RT on LOC_Os01g57610 ( OsGH3 . 1 ) and LOC_Os08g43040 ( OsRL8 . 2 ) since their haplotype differences were present only in japonica accessions , whereas LOC_Os03g50470 ( OsRL3 . 2 ) showed indica-specific allele variations for RT . We detected 2 to 5 haplotypes and observed a similar indica-japonica differentiation at the 14 candidate loci for RT QTLs ( S9 Fig ) . Of these , LOC_Os02g13400 ( OsRT2 . 1 ) , LOC_Os04g02780 ( OsRT4 . 1 ) , LOC_Os04g31000 ( OsRT4 . 4 ) , and LOC_Os06g37840 ( OsRT6 . 2 ) showed japonica-specific allele variations for RT since significant phenotypic differences were present only between or among japonica haplotypes . Similarly , LOC_Os04g30920 ( OsRT4 . 3 ) , LOC_Os04g31010 ( OsRT4 . 5 ) , LOC_Os04g31020 ( OsRT4 . 6 ) , LOC_Os04g31120 ( OsRT4 . 7 ) , LOC_Os04g51060 ( OsRT4 . 8 ) , LOC_Os04g51080 ( OsRT4 . 9 ) , and LOC_Os08g15050 ( OsRT8 . 2 ) possessed indica-specific allele variation . Only three loci , LOC_Os04g30490 ( OsRT4 . 2 ) , LOC_Os06g36810 ( OsRT6 . 2 ) , LOC_Os08g14660 ( OsRT8 . 1 ) showed significant phenotypic differences between haplotypes in both indica and japonica populations . To validate the candidate genes related to robust roots , five mutant lines in Dongjin or Hwayoung genetic backgrounds ( Ti-OsRL3 . 3 , Ti-OsSIZ2 , Ti-OsRL7 . 1 , Ti-OsRL8 . 2 and Ti-OsRL11 . 1 ) each containing a T-DNA insertion in one of the five RL candidate genes ( LOC_Os03g50350 , LOC_Os03g50980 , LOC_Os07g03160 , LOC_Os08g43040 and LOC_Os11g43320 ) ( S10 Fig , S16 Table ) for the 4 RL QTLs were evaluated on MS medium for RL , root number and shoot length , together with the Dongjin or Hwayoung wild types and controls ( mutant lines in HsfA4a , LOC_Os01g54550 ) . The homozygous and heterozygous mutant plants at all loci , except for Ti-OsSIZ2 showed highly significant reductions in root and shoot growth compared with the wild types and controls ( Fig 6A and 6D ) . Ti-OsRL3 . 3 mutant ( LOC_Os03g50350 ) plants in particular had no primary root development 5 days after germination , even though the crown roots initiated in the same way as the wild type ( Fig 6B and 6C , S11 Fig ) . Curiously , mutant Ti-OsSIZ2 ( LOC_Os03g50980 ) showed a slight but non-significant reduction in RL , but a highly significant shoot length reduction when compared with the wild type ( Fig 6D ) . There was a highly significant difference between Ti-OsRL3 . 3 and the wild type in root number ( Fig 6D ) . OsSIZ2 was previously shown to complement a dwarf phenotype in Arabidopsis [71] , and its homolog ( OsSIZ1 ) affected primary root length , crown root number and plant height by regulating auxin accumulation [72 , 73] . Thus , the detected RL QTL , qRL3-3 , could be due to either of the two non-synonymous SNPs in LOC_Os03g50350 , whereas LOC_Os03g50980 was located in a different LD block and showed no interaction with LOC_Os03g50350 ( S12 Fig ) . Initially , we constructed a phylogenetic tree of 997 O . sativa accessions ( with 242 upland rice landraces from 21 countries ) and 446 wild rice ( O . rufipogon ) accessions [2] based on the evenly distributed 90 , 838 SNPs ( S13 Fig ) . The 242 upland rice landrace accessions were divided into 90 tropical japonicas , 41 temperate japonicas , 3 japonicas ( japonica but not clearly into tropical or temperate ecotypes ) , 73 indicas and 35 intermediate-types ( cultivated rice but not clearly into japonica or indica ) . We then constructed a phylogenetic tree of 997 O . sativa and 446 wild rice accessions ( Fig 7A , S14 Fig ) using 5 , 779 SNPs in the robust-root candidate genes . The upland rice landraces in japonica and indica were clearly separated at two nodes after differentiation of japonica and indica . By further comparing their phenotypic and genotypic differences , we found most upland landraces with robust roots and very high frequencies of long-root and thick-root alleles were clustered in two sub-branches within subspecies japonica and indica , which were designated L-T-root ecotypes ( Fig 7A and 7B ) . Japonica L-T-root ecotypes included 141 upland accessions ( 89 tropical japonica , 36 temperate japonica , 1 japonica and 15 intermediate ) , whereas the indica L-T-root ecotypes included 59 upland accessions ( 51 indica and 8 intermediate ) ( S14 Fig , S1 Table ) . Close examination of the geographical distribution of the upland landraces indicated that the majority of japonica L-T-root accessions were from hilly areas with rain-fed farming practices [7 , 74–78] in South China , Southeast Asia and Africa , as were most indica L-T-root landraces from the rain-fed areas of South China , Southeast Asia and India ( S1 Table ) . Clearly , those upland landraces became adapted to the rain-fed upland aerobic conditions of the respective hilly areas . These results suggested that the lower diversity in robust-root candidate genes was a distinctive characteristic of upland rice in adapting to aerobic conditions ( Fig 7A and 7B ) . We then examined the ancestral states of 59 and 136 non-synonymous SNPs associated with RL and RT; 53 of 59 RL alleles and 108 of 136 RT alleles were detected in the 446 wild accessions . In particular , the frequencies of the long-root and thick-root alleles were lowest in the three wild rice populations , Or-I ( 49% and 43% , respectively ) , Or-Ⅱ ( 53% and 40% ) and Or-Ⅲ ( 55% and 38% ) [2] , followed by indica ( 57% and 54% ) , lowland temperate japonica ( 79% and 45% ) , lowland tropical japonica ( 77% and 58% ) , and upland temperate japonica ( 85% and 57% ) , whereas upland tropical japonica had the highest long-root and thick-root allele frequencies at 86% and 64% ( Fig 7C ) . The results suggested that almost all alleles were present in wild rice , and there was obvious pyramiding of robust-root alleles in cultivated rice , especially in upland japonica . Given that a reduction in nucleotide diversity was an important selective signature [2] , we computed the ratios of genetic diversity between different branches ( πnon-L-T-root/πL-T-root ) in japonica and indica . Twenty-six of the 44 RL candidates and 56 of the 97 RT candidates were under selection with greatly reduced diversity in japonica L-T-root accessions ( πnon-L-T-root/πL-T-root > 4 ) , whereas only 3 of the 44 RL candidates and 17 of the 97 RT candidates were under selection in indica L-T-root accessions ( Fig 7D , S17 Table ) . When comparing the genetic diversity in the wild rice accessions with the japonica and indica subspecies , ( πw/πJ and πw/πI ) , we found 16 of the 44 RL and 9 of 97 RT candidate genes showing strong selective signals in subspecies japonica , in contrast to only 1 of 97 RT candidates in indica ( Fig 7D , S17 Table ) . The mean value of πnon-L-T-root/πL-T-root in japonica for all candidate genes was 14 . 6 , which is much higher than those of πnon-L-T-root/πL-T-root in indica ( 2 . 1 ) , πw/πJ ( 3 . 7 ) and πw/πI ( 1 . 2 ) ( S17 Table ) . Eleven RL candidate genes and 16 RT candidate genes showed very strong selective signals with much higher πnon-L-T-root/πL-T-root value in japonica ( > 20 ) , and should be key genes for aerobic adaptation of upland japonica ( S17 Table ) . Among them , genes OsRL3 . 3 and OsSIZ2 with πnon-L-T-root/πL-T-root values in japonica of 102 . 1 and 29 . 6 were shown to be associated with RL by T-DNA insertion mutant lines ( Table 1 ) . Further Tajima’s D Test was performed for each of the robust-root candidate genes in wild rice , and the four ecotypes of cultivated rice . Tajima’s D values of 5 , 3 , 48 , 56 and 92 genes were negative in wild rice , non-L-T-root in indica , L-T-root in indica , non-L-T-root in japonica and L-T-root in japonica , respectively . Only one , 0 , 7 , 4 and 66 gene showed strong selection with Tajima’s D < -1 in above separated groups ( S17 Table ) . These observations indicated that significantly more robust-root candidate genes had undergone natural selection during adaptation to aerobic conditions of the upland japonica accessions than in other ecotypes . Therefore , we concluded that almost all robust-root alleles were inherited from wild rice , and they gradually accumulated in 4 japonica ecotypes during and after domestication of tropical and temperate japonica subpopulations , and more robust-root alleles were accumulated by natural and artificial selection in the upland japonica ecotype than in other ecotypes in adapting to aerobic conditions ( Fig 7E ) .
Considerable effort has been made to understand the genes underlying variation of complex traits by cloning QTLs . However , QTL cloning by using classical map-based approach is time-consuming and effective only for large-effect QTLs . Cloning QTLs of moderate to small effect is highly challenging owing to the difficulty of phenotyping large populations , particularly for plant root traits such as RL and RT that are difficult to measure under field conditions . In this study we adopted a modified hydroponics system ( see Materials and Methods ) to measure root traits in rice . This allowed genetic potential to be assessed in the absence of stress [33 , 79] . Genetically , it remains an unanswered question as to whether large-effect QTLs are primary sources of genetic variation for complex quantitative traits , even though most cloned QTLs in rice and other plants are regulatory genes with large and pleiotropic effects on multiple traits [80 , 81] . Thus , identification of large numbers of RL and RT QTLs and cloning five of the RL QTLs of small effect in this study should be considered highly successful . This improvement was attributed to our integrated strategy that combined results from high resolution mapping by GWAS and linkage mapping , and comprehensive bioinformatic analyses of genomic , transcriptomic and haplotype data to shortlist QTL candidate genes plus validation of key candidate genes by insertional mutants . With the increasing availability of various kinds of -omics data and genetic stocks such as genome-wide insertional mutants and introgression lines [82–85] , it is expected that the integrated strategy demonstrated here will be widely applied in large scale molecular dissection of genes underlying complex traits by efficient cloning and characterization of small- and moderate-effect QTLs . In this respect , our results revealed several unique properties of genes underlying variation of complex traits and shed light on how natural QTL alleles contribute to robust-root systems and adaptation of rice to the aerobic conditions . Our results indicated that major haplotypes , consisting of non-synonymous SNPs in the coding sequence ( CDS ) and/or promoter regions within single loci , represent the most important allelic diversity of moderate- and small-effect QTLs underlying robust-root variation in the rice populations . This conclusion was supported by at least five pieces of evidence . First , when examining large numbers of candidate genes in >100 RL and RT QTL regions , we detected 2 to 5 predominant haplotypes ( alleles ) associated with significant differences in RL or RT , each comprising 2 to 23 non-synonymous SNPs in the CDS or promoter region of each RL and RT QTL candidate locus , including the 5 validated RL QTL genes ( Fig 6 , S12 Fig ) . Second , compared with random SNPs , specific haplotypes at single loci showed much greater differentiation in gene frequency among populations , indicating they were important targets of selection during adaptation to aerobic conditions . Third , phenotypic differences between different haplotypes at each of the cloned RL gene loci , though statistically significant , were quite small and did not appear to have pleiotropic effects when compared with lines having insertional mutations in the same genes . Fourth , phenotypic differences between haplotypes at many of these QTL gene loci could be , at least partially , due to differences at the transcriptomic level . This suggests that fine tuning of complex traits in crop improvement can be achieved by careful manipulation of cloned QTL genes at the transcriptional level using various molecular technologies . Fifth , QTL genes affecting the same RL or RT phenotype appeared to have diverse molecular functions , and few of them were regulatory genes ( Table 1 ) . These properties distinguish the QTL genes identified in this study from most cloned large-effect QTL genes reported previously . Nevertheless , it remains a challenge to understand how different alleles at moderate- to small-effect QTL loci control complex traits at the molecular level and how they interact with the environment . O . sativa is well known for its rich diversity and wide subspecies and population differentiation [2 , 3] . The domestication analysis using 446 O . rufipogon and 1 , 083 O . sativa accessions indicated that japonica was first domesticated from a specific population of O . rufipogon in the Pearl River Valley in southern China [2] . However , adaptation process to aerobic conditions of upland japonica rice remains unclear . Our previous survey of 50 , 265 rice landraces ( 33 , 665 indica and 16 , 784 japonica ) indicated that upland japonica landraces were distributed primarily in hilly areas of four southern and southeastern China provinces ( 41% in Yunnan , 10% in Guangxi , 16% in Guizhou and 21% in Hainan , respectively ) [11] ( S15 Fig ) . The genetic structure of O . sativa indicated that japonica was clearly differentiated between soil water regime ecotypes ( uplands and lowlands ) in Yunnan [9] , Guizhou [86] and many parts of China [4–6] , caused by different environments and cropping systems . Based on previous studies of geographical distribution and population structure , we have reasons to believe that upland japonica is a unique ecotype with specific morphologies and genotypes associated with drought resistance . Consistent with previous research , our study verified that robust roots are characteristics of upland rice [10 , 12 , 13] . Therefore , we analyzed adaptation process to aerobic conditions of upland japonica rice based on 5 , 779 SNPs in the robust-root candidate genes . The phylogenetic tree showed that there was lower diversity and higher population structure consistency in robust-root candidate genes in the L-T root ecotype in japonica ( 89 tropical japonica , 36 temperate japonica , 1 japonica and 15 intermediate ) , suggesting that the genotype of robust roots in upland rice was different from that in other ecotypes . By further analysis of nucleotide diversity and allele frequency at robust loci , our study provided new insights into adaptive domestication of the upland rice ecotype . In summary , upland rice accumulated more robust-root alleles in adapting to aerobic conditions during domestication than present in other ecotypes . Additionally , we suggest that the upland japonica ecotype is a typical upland rice ecotype with the most robust roots , highest number of robust-root alleles , and strongest selective signals among all tested ecotypes .
A total of 795 O . sativa accessions from the 3000 Rice Genome Project ( 3KRGP ) [83 , 84] were used for identification of RL and RT QTLs , including mini-core collections constructed by Zichao Li in China Agricultural University and 525 lines in the International Rice Molecular Breeding Network constructed by Zhikang Li in Chinese Academy of Agricultural Science [51 , 52] . All information of cultural type for these rice accessions were from The Catalog of Rice Germplasm Resources in China and IRRI , respectively ( S1 Table ) [11] . For an adaptive domestication analysis , we added 202 additional upland landrace accessions from 19 countries from 3KRGP and 446 O . rufipogon accessions from Asia and Oceania [2] . The background information of cultural type for the additional 202 upland landrace accessions was from IRRI . All 795 O . sativa accessions were used for phenotyping RL and RT . Two robust-root varieties , HGL and IRAT109 , and two lowland temperate japonica varieties , Nipponbare and Yuefu , were used as L-T-root and non-L-T-root checks . Four additional long-root varieties ( CH1052 , CH1086 , CH1198 and CH1183 ) and four non-long-root varieties ( CH1179 , CH1008 , CH1283 and CH1122 ) were also used as checks in transcriptomic analysis ( S1 Table ) . The hydroponic culture experiment was conducted at China Agricultural University in 2014 . Seed of each accession were washed with distilled-water and germinated at 32°C for 64 hours . Five uniformly germinated seeds of each variety were placed into five wells on a plastic foam frame with gauze bottom ( 30 cm × 42 cm ) containing 130 ( 10 wells × 13 rows ) wells , with 110 plants of 22 varieties and 20 plants of two bordering rows ( S16 Fig ) . Two frames were floated in a plastic box ( 60 cm × 42 cm × 18 cm ) containing Yoshida nutrient solution [87] . The pH ( 5 . 5 ) and concentration of the nutrient solution were adjusted twice daily with NaOH and distilled water , and the solution was replaced weekly . Plants were grown under natural conditions but protected with tarpaulins on rainy days . The experiment was conducted twice as two replications from May 24 to June 15 and from June 23 to July 15 in 2014 , during which the average daily temperatures fluctuated from 19 . 9°C to 31 . 7°C and from 23 . 9°C to 32 . 9°C , respectively . Seedlings were allowed to grow for 23 days and then five plants per variety were sampled and measured for RL , RT and root weight using a method described previously [23] . The mean values of all five plants of each accession were used as the input data in the data analyses . Sequencing data of the 795 and additional upland landraces were obtained from the 3KRGP , and had an average sequencing depth of 15× and generated > 15 million SNPs and > 2 million small InDels when compared with the Nipponbare reference genome [84] . The sequencing data of 446 O . rufipogon accessions were downloaded from the public database [2] ( S1 File ) . Six robust-root cultivars and 6 non-robust-root controls were used for transcriptomic analyses . The 12 varieties were planted in pot and field experiments . At the four-leaf stage , roots and shoots of each variety were sampled for extraction of total RNA using TRIzol reagent . High-quality RNA was used for construction of RNA-seq libraries with three biological replicates for each sampled variety . Upon completion of sequencing libraries using the TruSeq RNA sample preparation kit ( Illumina ) , sequencing was performed on an Illumina HiSeq 2500 . More than 12 . 8 million 101-bp simple-ends were generated in each sample . Mapping of RNAseq reads and transcript abundance RPKM was performed by the Cufflinks package version 2 . 0 , based on the rice reference genome and gene model annotation file ( GFF transformed GTF file ) from the Ensembl database ( http://plants . ensembl . org/index . html ) . The basic information of the transcriptomic data is provided in S18 Table . Based on 3 . 3 million un-imputed SNPs with missing rates ≤ 50% and minor allele frequencies ≥ 5% , we further extracted 154 , 516 SNPs with missing rates ≤ 50% , minor allele frequencies ≥ 5% and r2 of LD ≤ 0 . 3 using PLINK [88] . The software ADMIXTURE 1 . 3 was used to calculate varying levels of K ( K = 1–10 ) , and K = 8 was a suggesting modeling choice ( S1B and S1C Fig ) [89] . Based on 3 . 3 million un-imputed SNPs with missing rates ≤ 50% and minor allele frequencies ≥ 5% , principal component ( PC ) and kinship analyses were computed to verify population structure and relationship among the 795 cultivated rice accessions in software GAPIT . The first three PCs were used to construct the PC matrix . We performed GWAS with a Compressed Mixed Linear Model ( CMLM ) with PC and kinship and GLM with PC using default settings of software GAPIT . A conditional permutation was used to determine the significance threshold . We divided the phenotype ( Y ) of each accession into the original genotypic effect ( G ) and the fixed effect of population structure ( P ) . P was estimated by the average effect of each PC in the PC matrix on each individual through regression analysis of each PC on Y; the remainder was G after excluding P from Y . G was randomly reshuffled as Gr , and the new phenotype of each accession was reconstructed as P + Gr . The conditional permutation test was executed using CMLM with the same parameters and PC matrix . A total of 1 , 000 sets of P + Gr were performed for root length and root thickness . SNPs with -log ( P ) ≥ 2 in the GWAS using original phenotypes were extracted to improve the computational efficiency . Finally a threshold of -log ( P ) = 4 was determined , which was higher than the 95th percentile of 1 , 000 conditional permutation tests . LD heatmaps of two candidate genes ( OsRL3 . 3 and OsSIZ2 ) were generated using the R package “LD heatmaps” . For screening SNPs associated with target traits , we constructed two pools for RL and RT by selecting accessions with extreme phenotypes from typical indica and japonica populations , then performed a chi-square test on the allele frequency of each SNP executed by in-house Perl scripts . To reduce genetic differences in unrelated target trait between polar pools , we took no account of varieties at PC1 values ranging from -400 to 300 . Varieties with PC1 < -400 were defined as typical japonica and varieties with PC1 > 300 were considered to be typical indica ( Fig 1A ) . Each pool included 20 varieties with extreme phenotypes in typical indica and japonica , respectively ( S1 Table ) . After haplotype analysis of candidate genes for robust roots , we obtained T-DNA insertion mutants for 9 RL candidate genes . Six mutant plants containing T-DNA insertions ( Ti-OsRL3 . 3 , Ti-OsSIZ2 , Ti-OsRL7 . 1 , Ti-Os8 . 2 , Ti-Os11 . 1 and HsfA4a ) in ‘Dongjin’ or ‘Hwayoung’ background were from the POSTECH Biotech Center , Republic of Korea . Seeds of the six mutant lines were surface-sterilized with 75% ethanol for 2 min and in 20% NaClO for 30 min , and thoroughly washed with sterile water . Medium cultivation experiments were conducted in a glasshouse with a 14h light/10h darkness and temperature 28°C using MS Medium , pH 5 . 8 ( S17 Fig ) . After 12 days , RL , RT and root weight were measured with a ruler and an electronic scales . DNA was extracted from leaves and was used for identification of homozygous and heterozygous mutants by PCR using gene-specific primers ( LP and RP ) coupled with a T-DNA-specific primer ( RB or LB ) . PCR was conducted with an initial step of incubation at 95°C for 5 min , followed by a second step of 35 cycles of 95°C for 40s , 58°C ( 55°C ) for 40s , and 72°C for 1 min 20s . The PCR products were genotyped by a sequencing company . We downloaded the SNP set ( 7 , 970 , 357 ) of 1 , 529 rice lines from a recent study and selected SNP data for the 446 wild rice accessions that were simply classified into three types Or-I , Or-II and Or-III in this study [2] . Since this dataset used IRGSP4 Nipponbare genome as reference , we extracted the 200 bp flanking regions from each side of the target SNP ( 200+1+200 = 401 bp ) from the IRGSP4 reference and mapped 401 bp sequences to the RGAP 7 reference by BWA software [90] . We converted the SNP coordinate of IRGSP4 to RGAP 7 based on the mapping result and then integrated the genetic information for 997 cultivated rice accessions and 446 wild rice accessions following extraction of 5 , 779 SNPs within robust-root candidate genes ( 44 genes for RL and 97 genes for RT ) from 1 , 443 accessions ( S1 File ) . A neighbor-joining tree based on the polymorphisms was generated using Tassel 5 and Mega 6 , and the nucleotide diversity ( π ) [91] was calculated using an in-house Perl script ( S1 File ) . We also extracted 90 , 838 SNPs evenly distributed throughout the genomes of 1 , 443 accessions , and a constructed neighbor-joining tree as control .
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Asian cultivated rice is well-known for its rich-within-species diversity with two major subspecies , indica and japonica and subpopulation differentiation . A robust ( long and thick ) root system that is characteristic of upland japonica rice represents a predominant ecotype grown under aerobic and rain-fed conditions . In this study , we identified candidate genes for root length and root thickness , and validated five root length candidates by T-DNA insertional mutations . Further analyses of an Asian cultivated and wild rice population were performed based on random SNPs and SNPs within robust loci . The findings hold promise for application in improving drought resistance and also reveal the adaptive domestication history of upland rice as a unique Asian cultivated rice ecotype .
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2018
|
Loci and natural alleles underlying robust roots and adaptive domestication of upland ecotype rice in aerobic conditions
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It is presently unclear how much individual community members contribute to the overall metabolic output of a gut microbiota . To address this question , we used the honey bee , which harbors a relatively simple and remarkably conserved gut microbiota with striking parallels to the mammalian system and importance for bee health . Using untargeted metabolomics , we profiled metabolic changes in gnotobiotic bees that were colonized with the complete microbiota reconstituted from cultured strains . We then determined the contribution of individual community members in mono-colonized bees and recapitulated our findings using in vitro cultures . Our results show that the honey bee gut microbiota utilizes a wide range of pollen-derived substrates , including flavonoids and outer pollen wall components , suggesting a key role for degradation of recalcitrant secondary plant metabolites and pollen digestion . In turn , multiple species were responsible for the accumulation of organic acids and aromatic compound degradation intermediates . Moreover , a specific gut symbiont , Bifidobacterium asteroides , stimulated the production of host hormones known to impact bee development . While we found evidence for cross-feeding interactions , approximately 80% of the identified metabolic changes were also observed in mono-colonized bees , with Lactobacilli being responsible for the largest share of the metabolic output . These results show that , despite prolonged evolutionary associations , honey bee gut bacteria can independently establish and metabolize a wide range of compounds in the gut . Our study reveals diverse bacterial functions that are likely to contribute to bee health and provide fundamental insights into how metabolic activities are partitioned within gut communities .
Metabolic activities of the microbiota are key for symbiotic interactions in the gut and impact health and disease of the host in manifold ways . Gut bacteria facilitate the breakdown of refractory or toxic dietary compounds [1–3] , produce metabolites that promote host growth and physiology [4–7] , and modulate immune functions in the gut [8] and other tissues [9 , 10] . Moreover , metabolic activity is the basis for energy and biomass production , resulting in bacterial growth and the occupation of ecological niches conferring colonization resistance against pathogenic microbes [11] . Substrates of gut bacteria predominantly originate from the diet of the host [2 , 12] , making diet the major modulator of the composition and metabolic activity of the gut microbiota [13 , 14] . The substantial metabolic potential of the animal gut microbiota has been profiled by the direct sequencing of functional gene content ( i . e . , shotgun metagenomics ) [15–18] . However , it is challenging to predict functional metabolic output from such sequencing data . With recent advances in the coverage and throughput of untargeted screening metabolomics [19–21] , it has become feasible to quantify metabolic changes in microbiota or host tissues at large coverage and throughput . Besides identifying metabolites connected to human health and disease [22–30] , untargeted screening metabolomics holds considerable promise to unravel metabolic functions of individual microbiota members in animals with divergent dietary preferences . However , such mono-colonization studies are complicated by the highly variable and species-rich composition of most animal microbiota . Thus , gut communities of reduced complexity are valuable models to disentangle metabolic functions of the constituent species . Like mammals , honey bees harbor a highly specialized gut microbiota . However , in contrast to mammals , the honey bee gut microbiota is surprisingly simple and consistent , with seven species ( categorized by clustering at 97% sequence identity of the 16S rRNA ) accounting on average for >90% of the entire gut community in bees sampled across continents [31] . This microbiota is composed of four Proteobacteria ( Gilliamella apicola , Snodgrassella alvi , Frischella perrara , and Bartonella apis ) , which mostly reside in the ileum , and two Firmicutes ( Lactobacillus spp . Firm-4 and Firm-5 ) and one Actinobacterium ( B . asteroides ) , which are predominantly found in the rectum . These specific locations suggest that bacteria occupy different metabolic niches in the bee gut and potentially engage in syntrophic interactions [32 , 33] . The honey bee gut microbiota has marked effects on the host . It promotes host weight gain and hormone signaling under laboratory settings [34] and stimulates the immune system of the host [35 , 36] . In addition , honey bees are ecologically and economically essential pollinators that have experienced increased mortality in recent years [37 , 38] , which could in part be due to disturbances of their microbiota composition [39–42] . Genomic analyses and in vitro experiments have shown that fermentation of sugars and complex carbohydrates ( e . g . , pectin ) into organic acids [15 , 32 , 43 , 44] is a prominent metabolic activity of the gut microbiota [34] . Lacking , however , is a detailed understanding of the consumption of diet-derived substrates and how individual community members contribute to the metabolic activities in vivo . For instance , it is elusive whether analogously to mammals , recalcitrant dietary compounds ( especially from pollen ) are broken down by the microbiota in the hindgut ( i . e . , large intestine composed of ileum and rectum ) , while more accessible compounds are reportedly absorbed by the host in the midgut ( i . e . , small intestine ) [45–47] . To profile the metabolic output of the honey bee gut microbiota and its individual members , we employed gnotobiotic bee colonizations and in vitro experiments in conjunction with untargeted metabolomics ( Fig 1 ) . We first characterized robust metabolic differences between microbiota-depleted bees and bees colonized with a reconstituted community composed of the seven major bacterial species of the gut microbiota . Subsequently , we analyzed bees colonized with each community member separately to assay their potential contribution to the overall metabolic output of the gut microbiota . Finally , we recapitulated our results in vitro using pollen-conditioned medium . Our systematic approach provides unprecedented insights into the metabolic activities of the honey bee gut microbiota and demonstrates the possibility to use metabolomics in combination with gnotobiotic animal models to disentangle functions of individual gut microbiota members .
To characterize the metabolic output of the honey bee gut microbiota , we colonized newly emerged bees with selected bacterial strains previously isolated from the bee gut . The reconstituted bacterial community consisted of 11 strains ( S1 Table ) covering the seven predominant species of the bee gut microbiota described above . We used two strains for G . apicola and four strains for Firm-5 in order to cover the extensive genetic diversity within these species [44 , 48] . Exposure of newly emerged adult bees to this community resulted in the successful establishment of all seven species , with a total of approximately 109 bacterial cells per gut after 10 d of colonization; hereafter , these are referred to as CL bees ( Fig 2A ) . In contrast , non-colonized bees had total bacterial loads of <106 cells per gut , an observation consistent with previous studies [32 , 49] . In the following , we will refer to these bees as microbiota-depleted ( MD ) because they were not colonized with detectable levels of typical honey bee gut bacteria as determined with species-specific qPCR primers ( <105 bacterial cells , except for one bee that was slightly above this cut-off for Firm-5 ) ( Fig 2B ) . However , these bees may have harbored low levels of environmental microbes , as they were not kept under sterile conditions , especially in cases in which the bacterial loads were slightly above our detection limit of 105 bacterial cells ( Fig 2A ) . It is also important to point out that newly emerged bees can occasionally be contaminated with specific bee gut bacteria , resulting in “MD” bees that in fact are colonized . Therefore , to be able to exclude such bees from further analysis , it is essential to determine the microbiota status of gnotobiotic bees using the qPCR assays presented in this study or an equivalent method . Compared to hive bees of the same age , bacterial abundances of most species were slightly elevated in CL bees . However , in both groups the Firm-5 species was consistently the most abundant community member , while B . apis colonized at relatively low levels . This is in line with recent 16S rRNA gene-based community analyses [31 , 50 , 51] , and we thus conclude that the selected strains assembled into a structured community resembling the native honey bee gut microbiota . Overall , this analysis validates our gnotobiotic bee system as a tool for microbiota reconstitution experiments and enables the study of microbiota functions under controlled laboratory conditions . To reveal microbiota-induced metabolome changes in the gut , we dissected the combined mid- and hindgut of MD , CL , and hive bees and analyzed water-extracted homogenates of these gut samples by untargeted metabolomics [21] . In total , we detected 24 , 899 mass-to-charge features ( ions ) , 1 , 079 of which could be annotated by matching their accurate mass-to-sum formulas of compounds in the full Kyoto Encyclopedia of Genes and Genomes ( KEGG ) database ( S2A Data ) . These 1 , 079 ions putatively correspond to 3 , 270 metabolites ( S2B Data ) , since this method cannot separate isobaric compounds . For statistical analysis , we continued with the annotated ions , and for ion changes with multiple annotations , we provided the most likely annotation based on information from literature and genomic data . Principal component analysis on the ion intensities revealed that CL and MD bees separate into two distinct clusters , which suggests colonization-specific metabolic profiles ( S1 Fig ) . In two independent experiments , a total of 372 ions exhibited significant changes between CL and MD bees ( Welch’s t test , Benjamini and Hochberg adjusted [BH adj . ] P ≤ 0 . 01 , S2C Data ) . A subset of 240 ions ( 65% ) were more abundant in MD bees , suggesting that the cognate metabolites are utilized by the gut microbiota . These ions are hereafter referred to as bacterial substrates . Conversely , 132 ions were more abundant in CL bees and are hereafter referred to as bacterial products , indicating that they are produced either by the microbiota or by the host in response to the microbiota . To facilitate the biological interpretation of these multitude metabolic changes , we carried out two analyses . First , we looked at whether certain compound classes were overrepresented among the subsets of bacterial substrates and products ( S3A Data ) . Second , we sorted ion changes based on their ability to explain the difference between the CL and MD metabolome profiles in an Orthogonal Projection of Least Squares-Differentiation Analysis ( OPLS-DA ) ( Fig 3 ) [52] . We first focused on the 240 substrate ions that were more abundant in MD versus CL bees and potentially correspond to metabolites utilized by the microbiota ( S2C Data ) . We found 3 compound classes to be strongly enriched: “flavonoids” ( 20 of 36 annotated ions , one-sided Fisher’s exact test , P < 0 . 001 ) and both “purine nucleosides and analogues” and “pyrimidine nucleosides and analogues” ( in total eight of nine annotated ions , both P < 0 . 01 ) . Seven flavonoids , three nucleosides , and a nucleoside precursor ( orotate , m/z 155 . 009 ) were also among the 28 substrate ions with the most discriminatory power for distinguishing CL versus MD bees as based on OPLS-DA ( Fig 3 ) . Other ions among these most discriminatory substrates included two ω-hydroxy acids ( m/z 315 . 254 and m/z 331 . 248 ) and three phenolamides ( m/z 582 . 260 , m/z 630 . 245 , and m/z 233 . 129 ) from the outer pollen wall , as well as quinate ( m/z 191 . 056 ) and citrate ( m/z 191 . 019 ) , both of which had previously been predicted to be utilized by certain community members of the honey bee gut microbiota [53 , 54] . Because they are the most remarkable groups among the identified substrates , nucleosides , flavonoids , and pollen wall-specific compounds will be discussed in more detail . We next looked into the 132 ions that were more abundant in CL versus MD bees and thus represent possible metabolites produced by the microbiota ( S2C Data ) . Again , we used enrichment analyses and OPLS-DA ( Fig 3 ) to prioritize the most important product ions . Three compound classes were to some extent enriched among the bacterial products ( S3A Data ) : “carboxylic acids and derivatives” ( seven of 26 annotated ions , one-sided Fisher’s exact test , P < 0 . 03 ) , “fatty acids and conjugates” ( seven of 29 annotated ions P < 0 . 05 ) , and “eicosanoids” ( five of eight annotated ions , P < 0 . 01 ) . To assess how much of the total metabolic output can be identified in hive bees under natural conditions , we analyzed the gut metabolome of 10-d-old hive bees that were exposed to social interactions and natural dietary resources and were colonized by the native gut microbiota . Principal component analysis revealed that hive bees clustered separately from CL bees ( S1 Fig ) . This may be explained in part by the different diet of hive bees , the presence of multiple strains in a bee colony , and the impact of the environment on the gut metabolism . However , we found that 27 of the 28 most discriminatory substrate ions and 15 of the 22 most discriminatory product ions showed qualitatively the same changes in hive bees as in CL bees ( S2D Data , Welch’s t test , BH adj . P ≤ 0 . 01 ) . On the substrate side , this included most flavonoid ions , all nucleosides , quinate , and citrate , as well as the ions annotated as ω-hydroxy acids and phenolamides from the outer pollen wall . On the product side , we found four of the five prostaglandins and one of the juvenile hormone derivatives to be significantly increased in hive bees relative to MD bees , suggesting that these host-derived metabolites are also induced under natural conditions . Moreover , ions corresponding to fermentation products were either significantly increased ( sebacic acid and valerate ) or showed a trend towards increased levels ( succinate and pimelate ) in hive bees . The same was the case for the four ions corresponding to possible degradation products of flavonoids ( hydroxy- and dihydroxyphenylpropionate , maleylacetate , and hydroxy-3-oxoadipate; S2A Data ) . We conclude that the remarkable overlap of metabolic changes between hive bees and CL bees highlights the relevance of our findings . We thus far presented evidence for substrates and products of the complete microbiota in the honey bee gut . To elucidate which community members might be responsible for these transformations , we conducted mono-colonizations of MD bees with all seven bacterial species ( again using a mix of four and two strains together for Firm-5 and G . apicola , respectively ) . All species successfully established in the gut of MD bees , with other community members being generally below the limit of detection ( <105 bacterial cells ) ( S5 Fig ) . We again extracted metabolites from the mid- and hindgut of individual bees to address how many of the 372 robust ion changes can be explained by one or multiple mono-colonizations ( S2A Data ) , i . e . , show qualitatively the same change as in CL bees ( analysis of variance [ANOVA] followed by Tukey honest significant difference [HSD] post hoc test at 99% confidence , P ≤ 0 . 05 ) ( S7 Data and S8 Data ) . Extended results of this analysis can be found in S2A Data . Remarkably , using these significance cutoffs , 299 of the 372 ( 80% ) robust changes between MD and CL bees could be explained by one or multiple mono-colonizations . This included 201 ( 84% ) substrate and 98 ( 74% ) product ions . The two Lactobacilli species ( Firm-5 and Firm-4 ) explained most changes , followed by B . asteroides and the two Gammaproteobacteria ( Fig 4A ) . Interestingly , the relative contribution to substrate conversion and product accumulation varied between mono-colonizations . For example , B . asteroides contributed relatively little to the conversion of substrates but seemed to be responsible for the production of a relatively large fraction of bacterial products . The Firm-4 species showed the opposite pattern , explaining relatively many bacterial substrates but a small fraction of bacterial products . Ion changes identified in CL bees but not in any of the mono-colonizations ( in total 20% ) may be due to our strict significance cutoffs , additive metabolic activities , or concerted functions of the community members , such as cross-feeding or interspecies metabolic feedback . Our in vivo results strongly suggest that specific gut bacteria utilize distinct substrates from the pollen diet of bees . This prompted us to test ( 1 ) whether the bacterial species could grow in vitro on a pollen-based culture medium and ( 2 ) whether this would result in the metabolic conversions of the same compounds as was observed in vivo . To this end , we water-extracted metabolites from the same pollen batch that was used for feeding the bees and analyzed the metabolic composition of this extract using untargeted and targeted metabolomics . Detailed results are presented in S4 Text and show that pollen extract contains physiologically meaningful levels of nutrients and is expectedly enriched in “amino acids and derivatives , ” “flavonoids , ” “monosaccharides , ” and “carboxylic acids and derivatives” ( S6 Fig ) . Strikingly , all community members , except for S . alvi , showed substantial growth in the presence of this pollen extract compared to the nutrient-limited base media in which little or no growth was observed after 16 h of incubation ( Fig 5A ) . We then profiled the metabolome of growth media before and after bacterial incubation in a separate metabolomics experiment . We annotated a total of 1 , 031 ions ( S9 Data ) , of which 427 ( 41% ) were also present among the 1 , 079 ions from the in vivo dataset . In line with their growth profiles , the largest number of depleted metabolites ( log2 ( FC ) ≥ |1| and Welch’s t test BH adj . P ≤ 0 . 01 ) was found for the growth cultures of Firm-5 , followed by G . apicola , Firm-4 , F . perrara , B . asteroides , B . apis , and S . alvi ( Fig 5A ) . Using strict criteria , we identified 17 ions ( 13 pollen-derived substrates and four bacterial products ) , which were explained in vivo and in vitro by the same species ( S7 Fig and S3 Table ) . Seven of these 13 substrates belonged to the most discriminatory substrate ions for CL versus MD bees ( Fig 3 ) : three flavonoids ( quercitrin , afzelin , and rutin ) , one nucleoside ( inosine ) , and ions annotated as quinate , citrate , and 2-fuorate . The fact that different community members were responsible for the conversion of some of these substrates ( B . asteroides , Firm-4 , Firm-5 , F . perrara , B . apis , and G . apicola ) demonstrates that our in vitro cultures allowed us to recapitulate metabolic activities covering the entire community . We found remarkably overlapping substrate specificity for four flavonoids in vitro and in vivo , with the Firm-5 species being the only member capable of converting rutin and scolymoside , while quercitrin and afzelin were also utilized by Firm-4 , and quercitrin was additionally used by B . asteroides and B . apis ( Fig 5A ) . Among the four in vitro recapitulated products were three of the four ions corresponding to putative breakdown products of flavonoids ( S7 Fig and S3 Table ) . These ions accumulated in vivo and in vitro in the presence of Firm-4 and/or Firm-5 , providing further evidence for breakdown of the polyphenolic ring structure of flavonoids . However , we also found that deglycosylated flavonoids ( i . e . , aglycones ) significantly accumulated in cultures of Firm-5 and showed a trend towards accumulation for Firm-4 and B . asteroides ( Fig 5A ) . Based on these results , we propose that flavonoid degradation involves two steps ( Fig 5B ) : ( 1 ) deglycosylation of sugar residues and their subsequent fermentation and ( 2 ) the breakdown of the polyphenol backbone . The second step could be relatively slow , explaining why aglycones accumulated in vitro ( 16 h ) , but not in vivo ( 10 d ) . Alternatively , the accumulation of some of these aromatic compound degradation intermediates could also result from the metabolism of other substrates such as aromatic amino acids . An obvious difference in our in vitro experiments compared to the in vivo situation is the absence of the host , which may predigest pollen grains before gut bacteria utilize pollen-derived metabolites . For example , certain sugars and amino acids are expected to be present in lower amounts in vivo because of host absorption . Conversely , the host may also provide physicochemical conditions that support the growth of some community members . This could explain the poor growth of S . alvi in vitro , especially as in vivo S . alvi is tightly associated with the gut epithelium and other gut bacteria such as G . apicola [77] . Microbial species in gut communities can organize into food chains , where one species provides metabolites that can be utilized by others . Such metabolites may be released from insoluble dietary particles via bacterial degradation or can be generated as waste products of metabolism [2] . To identify possible metabolic interactions between community members of the bee gut microbiota , we focused on ions that in vivo significantly accumulated in some mono-colonizations and were depleted in others ( S2A Data ) . A total of 27 ions showed such opposing changes between two or several mono-colonizations ( S4 Table ) . An example of a potential metabolic interaction identified in our dataset is the liberation and consumption of one of the major bacterial substrates in CL bees , 9-10-18-trihydroxystearate ( m/z 331 . 248 ) , originating from the outer pollen wall . In our mono-colonization experiments , the corresponding ion was depleted in Firm-4 and B . asteroides but accumulated in the case of Firm-5 and G . apicola ( Fig 4C ) . This suggests that the latter two species facilitate the release of this ω-hydroxy acid from the outer pollen wall , possibly rendering it more accessible for further degradation by Firm-4 and B . asteroides . A second example is pyruvate ( m/z 87 . 008 ) , which substantially accumulated in the gut of bees mono-colonized with G . apicola but was utilized as a substrate by other bacteria such as S . alvi and Firm-5 ( Fig 6A ) . A syntrophic interaction between G . apicola and S . alvi had previously been suggested , because these bacteria are colocalized on the epithelial surface of the ileum [77] and harbor complementary metabolic capacities [32] . To test for potential cross-feeding of pyruvate from G . apicola to S . alvi , we supplemented the pollen-conditioned medium of S . alvi with culture supernatant of G . apicola cultures . The growth of S . alvi was slightly but significantly improved in the conditioned medium compared to the control medium ( Fig 6B ) . Metabolome analysis ( S10 Data ) revealed six ions that accumulated during the growth of G . apicola and were utilized from the conditioned medium by S . alvi ( Fig 6C ) . Besides pyruvate , these were ions corresponding to three putative fermentation products , a nucleoside derivative , and hydroxyphenylpropionate . We determined the concentration of pyruvate biochemically and showed that G . apicola indeed produces high levels of pyruvate ( approximately 4 mM ) and that this is subsequently utilized by S . alvi ( S8 Fig ) . These results confirm our predictions from the in vivo dataset and show that bee gut bacteria engage in cross-feeding interactions . While not essential for gut colonization in itself as based on our mono-colonization experiments , such interactions may be important for community assembly and resilience and reflect the longstanding coexistence among these gut bacteria . The simple composition and experimental amenability of the honey bee gut microbiota facilitated our systems-level approach . We reconstituted the honey bee gut microbiota from cultured strains , characterized the metabolic output of the complete microbiota , identified the contributions of individual community members in vivo , and recapitulated their activities in vitro . Our results provide unprecedented insights into the metabolic functions of bee gut bacteria . As in the mammalian and termite gut ecosystem [1 , 68] , we conclude that most substrates utilized by the bee gut microbiota are indigestible compounds that originate from the diet of the host and accumulate in the hindgut where bacterial density is the highest ( Fig 7 ) . Such compounds include plant metabolites from the outer pollen wall , such as ω-hydroxy acids , phenolamides , and flavonoid glycosides . While one of the bee gut bacteria had previously been identified as utilizing a major pollen polysaccharide ( pectin ) [5] , our data provides , to our knowledge , the first evidence for a role of the gut microbiota in breaking down outer pollen wall components . Bacterial fermentation of these pollen-derived compounds resulted in the accumulation of organic acids ( e . g . , succinate ) and putative polyphenol degradation products , which are likely to impact the physicochemical conditions in the colonized gut . In addition , we found that host-derived signaling molecules are induced by B . asteroides , suggesting a specific interaction of this gut symbiont with the host . Based on the mono-colonization experiments , we conclude that most metabolic output of the bee gut microbiota can be explained by the metabolic activities of individual community members . This suggests different metabolic niches in the gut , which could be in part explained by the distinct distribution of bacteria along the gut [77] . While we have found evidence for cross-feeding ( e . g . , between G . apicola and S . alvi ) , the metabolic exchange between bacteria seems not to be essential for gut colonization , as each community member was able to colonize on its own . This may also be the case for gut bacteria of other animals , as they cannot rely on the presence of specific interaction partners in the highly dynamic gut environment but rather adapt to diet-derived nutrients . However , the bee gut microbiota is relatively simple , and interspecies metabolic exchanges may be essential to establish in more complex communities such as those of the termite or mammalian gut . The metabolic activities identified in this study are likely underlying the symbiotic functions of the bee gut microbiota and thus may be directly linked to the microbiota’s impact on bee health and physiology [34 , 41] . The large metabolic overlap between colonized and hive bees demonstrates the relevance of our findings and validates our gnotobiotic bee model . Moreover , our study highlights the versatility of high-throughput untargeted metabolomics to disentangle metabolic functions in microbial ecosystems . We believe that this systematic approach can be extended to other gnotobiotic animals to enable a better understanding of the diversity of metabolic activities and functions that are present in microbial communities .
Newly emerged bees were obtained from a healthy-looking colony of Apis mellifera carnica located at the University of Lausanne . In short , dark-eyed pupae were carefully removed from capped brood cells with sterile tweezers and transferred to sterilized plastic boxes as described previously [36] . Boxes with pupae were kept with a source of sterile sugar water ( 50% sucrose solution , w/v ) at 35°C with 80%–90% humidity for 2 d until the bees emerged , followed by a reduction in temperature to 32°C . For each box , one or two newly emerged bees were dissected , and their homogenized hindguts ( in 1 ml 1x PBS ) cultured on growth media as described below . To minimize the chance of including contaminated bees in colonization experiments , we excluded cages for which bacterial growth was observed for the tested bees . For the colonization of newly emerged bees , bacterial strains were inoculated from glycerol stocks and restreaked twice . Details on bacterial strains and culture conditions can be found in S1 Table . Bacterial cells were harvested and resuspended in 1x PBS/sugar water ( 1:1 , v/v ) at an OD600 of 1 . For colonization , bacterial suspensions were added to a source of sterilized pollen and provided to the newly emerged bees ( for details , see S5 Text ) . MD bees were kept under the same conditions , with the same food sources , but without being exposed to bacteria . The mid- and hindgut ( S9 Fig ) of gnotobiotic bees were dissected at day 10 post colonization and stored at −80°C until further use . To obtain age-controlled hive bees , several brood frames without adult bees were transferred from the hive to a ventilated Styrofoam box that was kept in an incubator at 32–34°C overnight . The next morning , the newly emerged bees were collected , marked on the thorax with a pen , and reintroduced into the hive . These bees were recollected 10 d later , and their mid- and hindguts were dissected and stored at −80°C until further use . The colonization experiment was repeated at two different time points of the year ( spring and fall , referred to as experiment 1 and experiment 2 in this study ) . Whenever possible , we included bees from both experiments in our analysis , such as for CL and MD bees . However , this was not possible for all mono-colonizations because of bacterial contaminations ( as detected by qPCR ) or in a few cases because of the presence of above-threshold viral loads . The precise numbers of bees included per condition are listed in S5 Table . Bacterial loads were determined by qPCR using universal bacterial and species-specific 16S rRNA primers on DNA samples obtained from the gut tissues used for metabolomics analysis . Details on DNA/RNA extraction methods are given in S5 Text . Each DNA sample was screened with 11 different sets of primers targeting the actin gene of A . mellifera , the universal 16S rRNA region , and the species-specific 16S rRNA region of nine bacterial species , including the seven species used in this study and two non-core species frequently found in the gut of A . mellifera: Alpha-2 . 1 and Lactobacillus kunkeei . Primers used for this qPCR analysis are listed in S2 Table . We also screened all gut samples for the presence of viruses . Samples that were contaminated with other bacteria than the desired ones ( i . e . , >105 bacteria cells detected by qPCR ) or that had high virus titers were excluded from the analysis where possible ( S5 Table , S5 Fig and S5 Text ) . The minimum information for publication of qPCR experiments ( MIQE ) guidelines were followed throughout the data analysis of the qPCR experiments [78] . Details on the qPCR analysis can be found in S5 Text . Bacteria were precultured on solid media from −80°C glycerol stocks before liquid cultures were inoculated for in vitro growth experiments . For G . apicola ELS0169 , S . alvi wkB2 , F . perrara PEB0191 , and B . apis PEB0149 , we used a modified M9 minimal medium supplemented with casamino acids and vitamins ( http://dx . doi . org/10 . 17504/protocols . io . kdqcs5w ) . For B . asteroides ESL0170 , the Firm-5 strains , and Firm-4 Hon2N , we used carbohydrate-free MRS ( cfMRS ) medium [79] . Bacteria were harvested from plates or spun down from overnight liquid cultures ( the latter only for Lactobacilli and B . asteroides ) and resuspended in the corresponding minimal medium . Freshly prepared liquid cultures were supplemented with either 10% ( v/v ) ddH2O or pollen extract and inoculated at a final OD600 of 0 . 05 ( see S5 Text for details on pollen extract preparation ) . Half of the culture was immediately processed to determine colony-forming units ( CFUs ) and to harvest supernatants for metabolomics at time point 0 h , i . e . , before growth . The other half of the culture was incubated for 16 h according to the conditions listed in S1 Table and then processed in the same way as the sample taken at time point 0 h . For CFU counting , serial dilutions were plated on solid media and incubated under the species-specific culturing conditions . For metabolomics analysis , the remaining bacterial culture was spun down at 20 , 000x g at room temperature for 10 min , and 300 μl of the culture supernatant was transferred to a fresh tube stored at −80°C until further processing . Five replicates were included for each species and treatment group . For the cross-feeding experiment , G . apicola strain ESL0169 was grown for 8 h in pollen-supplemented M9 medium as described above to an OD600 of 0 . 11–0 . 15 . Cultures were subsequently sterile filtered and mixed with fresh pollen-supplemented M9 medium 1:1 ( v/v ) in a total volume of 3 ml in a 12-well plate . For the control condition , non-inoculated pollen-supplemented M9 medium was incubated for 8 h , sterile filtered , and mixed with fresh pollen-supplemented M9 medium 1:1 ( v/v ) . Then , S . alvi wkB2 was added to each well at a final OD600 of 0 . 05 . The growth of S . alvi was assessed by OD600 measurements of a 100-μl aliquot in a 96-well plate with FLUOstar Omega microplate reader ( Huberlab , Switzerland ) . As S . alvi tends to form aggregates , each culture was thoroughly mixed by pipetting up and down before transferring the aliquot and recording the OD600 . For metabolomics analysis , supernatants were sampled at time points 0 h and 8 h for the G . apicola cultures and at time points 0 , 16 , 36 , and 72 h for the S . alvi cultures . For biochemical quantification of pyruvate , we used a Pyruvate Assay Procedure kit ( K-PYRUV , Megazyme , United States ) according to the manufacturer’s microplate assay instructions . Samples of 8 , 6 , 4 , 2 , 1 , 0 . 5 , 0 . 25 , and 0 . 125 mM pyruvate were used to generate the standard curve ( slope = 0 . 062 , intercept = 0 . 023 , R2 = 0 . 987 ) . Standards and samples were measured in triplicate at 340 nm with an Infinite M200PRO microplate reader ( Tecan , Switzerland ) . Metabolites from gut and pollen samples were water-extracted after mechanical disruption , and supernatants from the in vitro experiments were harvested by centrifugation . Gut samples were preselected based on their wet-weight ( arithmetic mean 55 . 1 mg , standard deviation 9 . 9 ) . Ten times more water than the gut wet weight ( v/w ) was added , and the samples were homogenized with 0 . 1 mm zirconia beads ( 0 . 1 mm dia . Zirconia/Silica beads; Carl Roth ) in a Fast-Prep24 5G homogenizer ( MP Biomedicals ) at 6 m/s for 45 s . While most of the homogenate was snap-frozen in liquid nitrogen for subsequent DNA/RNA extraction , aliquots of 100 μl were diluted 1:1 with water for metabolite extractions . To do so , the diluted aliquots were incubated in a preheated thermomixer at 80°C and 1 , 400 rpm for 3 min . After each minute , the samples were vortexed for 10 s . Subsequently , the samples were centrifuged at 20 , 000x g and 4°C for 5 min , and 150 μl of the resulting supernatant was transferred to a new tube and centrifuged again at 20 , 000x g for 30 min . Samples for untargeted metabolomics analysis were further diluted 10x in water . All samples were stored at −80°C before metabolomics analysis . For untargeted analysis , samples were injected into an Agilent 6550 time-of-flight mass spectrometer ( ESI-iFunnel Q-TOF , Agilent Technologies ) operated in negative mode , at 4 Ghz , high resolution , and with a mass / charge ( m/z ) range of 50−1 , 000 [21] . The mobile phase was 60:40 isopropanol:water ( v/v ) and 1 mM NH4F at pH 9 . 0 supplemented with hexakis ( 1H , 1H , 3H- tetrafluoropropoxy ) phosphazine and 3-amino-1-propanesulfonic acid for online mass correction . After processing of raw data as described in [21] , m/z features ( ions ) were annotated by matching their accurate mass-to-sum formulas of compounds in the KEGG database with 0 . 001 Da mass accuracy and accounting for deprotonation [M-H+]- . The complete KEGG database was used because it has broad coverage of plant , bacterial , and insect metabolic pathways . Notably , this metabolomics method cannot distinguish between isobaric compounds , e . g . , metabolites having identical m/z values , and in-source fragmentation cannot be accounted for . The raw data of samples from the three sets of experiments ( bee gut samples , in vitro supernatants , and cross-feeding supernatants ) were processed and annotated separately to accommodate their different sample matrices or times of measurement . These data can be explored in S2 Data , S9 Data and S10 Data . Raw data processing and annotation took place in MATLAB ( MATLAB 2015b , The Mathworks , Natick ) as described previously [21] , and downstream processing and statistical tests were performed in R ( version 3 . 3 . 2 , R Foundation for Statistical Computing , Vienna , Austria ) . Selected metabolite samples were measured in targeted fashion using ultra-high-pressure chromatography-coupled tandem mass spectrometry as described before [72] . Metabolite quantifications were performed by interpolating observed intensities to a standard curve of the metabolite using a linear model ( R2 ≥ 0 . 95 ) . Metabolites with standard curves of R2 ≤ 0 . 95 or in which intensities had to be extrapolated can be interpreted as relative changes only and were labeled in grey in all plots ( S6B Fig ) . We used the weights of the extracted material to express the concentrations in millimole per gram of gut or gram of pollen . The dataset can be found in S6 Data . Flavonoid ions were targeted for MS/MS fragmentation as [M-H+]- electrospray derivatives with a window size of ± 4 m/z in Q1 . Fragmentation of the precursor ion was performed by collision-induced dissociation at 0 , 10 , 20 , and 40 eV collision energy , and fragment-ion spectra were recorded in scanning mode by high-resolution time-of-flight MS . Spectra were interpreted using MetFrag [80] , and spectral cosine similarity scores were calculated between reference spectra that were obtained in-house or library spectra from MassBank of North America ( MoNA , http://mona . fiehnlab . ucdavis . edu/ ) . For further details , see S5 Text . All steps of the downstream data analysis were performed in R ( R Foundation for Statistical Computing , Vienna , Austria ) . Samples from double injections ( technical replicates ) were confirmed to be highly similar and averaged . Subsequent analyses were performed on these averaged ion intensities , which are available in S2D Data , S9 Data and S10 Data . Principal component analysis ( pca function in R ) on ion intensities was used for the multivariate inspection of co-clustering of samples from different groups . For reasons of transparency , we carried out four different PCAs on all annotated ions or the subset of ions with robust changes between CL and MD bees and on log2-normalized or Z-score normalized ion intensities . Z-score transformation was used to remove the domination of high-intensity ions . Ions that were deemed robustly different between the CL and MD bees were those that were significantly different ( Welch t test with Benjamini and Hochberg correction ≤ 0 . 01 , t . test and p . adjust ( x , method =“BH” ) in R ) between CL and MD in both independent experiments . Differences between MD and CL samples were expressed as log2 ( fold change ) values for both experiments separately and for pooled data of both experiments ( see S2A Data ) . Fold changes were based on the arithmetic mean of the CL samples divided by the arithmetic mean of the MD samples . The standard error of the log2 ( fold change ) was computed as the square root of the sum of the squared standard errors of the log2-transformed intensities of both CL and MD . Enrichment analyses were computed on compound class categories from KEGG ( in-house database ) , which are added in the column “compound class” in S2A Data . Some ions with ambiguous annotations had a compound class associated with multiple of these annotations . However , supported by the observation that compound classes between alternative annotations were often the same ( or highly related ) , only the compound class of the first annotation was used as input for one-sided Fischer’s exact tests ( fisher . test ( x , alternative =“greater” ) in R ) on a 2 x 2 contingency table for every compound class . Compound classes associated with a single ion were removed from the results because they were deemed not biologically meaningful . Ions were sorted based on to what extent they are responsible for explaining the separation between the CL and MD groups from experiment 2 . To do this , these datasets were selected as the input for an OPLS-DA ( opls from the ropls R package ) . The correlation and covariance between the log10-transformed ion intensities of included samples and the opls “scoreMN” output were computed with the cor and cov functions in R , respectively . The resulting scores were plotted in a so-called S-plot ( Fig 3 ) . The substrate and product ions most responsible for the separation were selected based on an absolute correlation ≥0 . 8 and an absolute covariance of ≥5 . Because such analyses can be prone to overfitting , we tested the sensitivity of the most discriminatory ion selection by implementing a “leave one out strategy” and concluded that the selected ions are robust ( >80% present in all 1 , 000 permutations ) . One-way ANOVA ( aov adjusted with TukeyHSD ( x , conf . level = 0 . 99 ) in R ) was performed between all bee gut samples after selecting the relevant samples from the data matrix and normalizing the intensities to ion standard ( Z- ) scores ( i . e . , by row ) by subtracting for every ion its arithmetic mean intensity and dividing the resulting values by the standard deviation of its respective ion intensity . The results of the full ANOVA analysis can be explored in S7 Data . For this study , the focus was on differences between any group and MD bees , which were considered significant when having a Tukey HSD post hoc adjusted P value ≤ 0 . 05 . When for a specific mono-colonization group this significance cut-off was met and the direction of the change was the same as that for CL versus MD , the ion was considered to be “explained” by this group . In order to enrich for pollen ions , we only considered ions with an arithmetic mean intensity of ≥10 , 000 in the pollen samples , in addition to being highly significantly different from water-matrix control samples ( Welch t test with BH correction ≤ 0 . 001 , t . test and p . adjust ( x , method = “BH” ) in R combined with log2 ( fold change ) difference of ≥ 2 ) . For the in vitro data ( S9 Data ) , the goal was to identify pollen substrates and bacterial products for which changes in levels were observed in vivo and in vitro . Pollen ions were mapped by matching the top annotation formula of both datasets . For all media-strain combinations , we performed a statistical comparison ( Welch t test with BH correction , t . test and p . adjust ( x , method =“BH” ) in R ) between the time points 16 h and 0 h and considered only those ions with a log2 ( fold change ) of ≥|1| and BH adj . P value of ≤0 . 01 as significant in vitro products or substrates . In order to be certain that only pollen-derived substrates were included , for every strain only ions that displayed a significant negative log2 ( fold change ) exclusively in the base medium supplemented with pollen extract were considered as in vitro pollen substrates . To identify ions that might be cross-fed between G . apicola and S . alvi , ions were selected that increased during the growth of G . apicola and were depleted when S . alvi was grown in this conditioned medium mixed 1:1 with fresh base medium . To do this , all ion intensities for both strains ( S10 Data ) were split and transformed to log2 ( fold change ) with respect to the first time point of sampling . Ions that had a log2 ( fold change ) of ≥1 during G . apicola growth and a log2 ( fold change ) of ≤−1 during S . alvi growth were selected . The raw data and R code for recapitulating the metabolomics data analysis can be found in S11 Data .
|
Honey bees are important pollinators that harbor a relatively simple gut microbiota with striking parallels to the mammalian system . This makes them relevant models to study gut microbiota functions and their impact on host health . We applied untargeted metabolomics to characterize metabolic changes induced by the gut microbiota and to characterize the contributions of the major community members . We find that the gut microbiota digests recalcitrant substrates derived from the pollen-based diet of bees . Most metabolic changes could be explained by the activity of individual community members , suggesting substrate specificity and independent metabolic functions . We did identify some cross-feeding interactions between species , including for pyruvate . Our study provides novel insights into the functional understanding of the bee gut microbiota and provides a framework for applying untargeted metabolomics to disentangle metabolic functions of gut bacteria .
|
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"Abstract",
"Introduction",
"Results",
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"discussion",
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"and",
"methods"
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"invertebrates",
"plant",
"anatomy",
"gut",
"bacteria",
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"microbiology",
"animals",
"pollen",
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2017
|
Disentangling metabolic functions of bacteria in the honey bee gut
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Aedes aegypti , is the major dengue vector and a worldwide public health threat combated basically by chemical insecticides . In this study , the vectorial competence of Ae . aegypti co-infected with a mildly virulent Metarhizium anisopliae and fed with blood infected with the DENV-2 virus , was examined . The study encompassed three bioassays ( B ) . In B1 the median lethal time ( LT50 ) of Ae . aegypti exposed to M . anisopliae was determined in four treatments: co-infected ( CI ) , single-fungus infection ( SF ) , single-virus infection ( SV ) and control ( C ) . In B2 , the mortality and viral infection rate in midgut and in head were registered in fifty females of CI and in SV . In B3 , the same treatments as in B1 but with females separated individually were tested to evaluate the effect on fecundity and gonotrophic cycle length . Survival in CI and SF females was 70% shorter than the one of those in SV and control . Overall viral infection rate in CI and SV were 76 and 84% but the mortality at day six post-infection was 78% ( 54% infected ) and 6% respectively . Survivors with virus in head at day seven post-infection were 12 and 64% in both CI and SV mosquitoes . Fecundity and gonotrophic cycle length were reduced in 52 and 40% in CI compared to the ones in control . Fungus-induced mortality for the CI group was 78% . Of the survivors , 12% ( 6/50 ) could potentially transmit DENV-2 , as opposed to 64% ( 32/50 ) of the SV group , meaning a 5-fold reduction in the number of infective mosquitoes . This is the first report on a fungus that reduces the vectorial capacity of Ae . aegypti infected with the DENV-2 virus .
The susceptibility of Aedes aegypti adults to infection with Beauveria bassiana was first reported in the late 1960s [1] . However the potential of entomopathogenic Ascomycetes ( Hypocreales ) as adulticides of vector mosquitoes was largely overlooked until Metarhizium anisopliae was demonstrated to induce mortality of Culex quinquefasciatus and Anopheles gambiae [2]; and sequentially both M . anisopliae and B . bassiana have been tested against Ae . aegypti and Aedes albopictus [3] . The successful infection of adult female mosquitoes has been made via direct contact [4] , [5] and also via auto-dissemination from males to females when mating [6] , [7] . The increasing interest in exploring these fungi as biocontrol agents of dengue vectors stems from the fact that they are ubiquitously available , relatively cheap to mass-produce , and kill mosquitoes effectively [8] . In addition to the infection studies , attention has also been focused on other topics such as determining their safety to public health [9] , and the effect of different surfaces on the infectivity of conidia to resting mosquitoes [10] . Likewise , some devices with inoculum baited with lures have also been tested for attracting and infecting adults to avoiding domiciliary sprayings [11] . Metarhizium anisopliae pathogenesis to insects has been widely documented [12] . The fungus is hemibiotrophic [13] . Conidia germination and cuticle perforation last around 24 hours [14] . After penetration , the pathogen produces hyphal bodies or blastospores invading the whole host's hemocele , depleting nutrients and killing the insect by starvation , dehydration , and toxemia [15] . It is therefore proposed that the rapid fungal invasion could affect the survival of the DENV virus if both are present in the same female of Ae . aegypti , weakening its vectorial competence . Here , we fed Ae . aegypti females with DENV-2-infected human blood , and/or exposed them to M . anisopliae conidia to produce single-fungus ( SF ) , single-virus ( SV ) and co-infected ( CI ) mosquitoes . The parameters evaluated included mosquito survival , fecundity and first gonotrophic cycle ( GC ) length , plus the viral infection rate in the midgut and head .
The DENV-2 Yuc 18500 strain was isolated from blood of a sick person at Merida city in 2008; it is deposited at the “Collection of Arboviruses isolated at the Yucatán Peninsula” of the Regional Research Center “Dr Hideyo Noguchi” , University of Yucatan ( UADY ) , Mexico; its use in this study was approved by written consent given by Dr . Fernando Andrade-Narvaez , Chair , Bioethics Committee of the Regional Research Center “Dr Hideyo Noguchi” , University of Yucatan ( UADY ) , Merida , Yucatan , Mexico . In addition , all members of the Bioethics Committee provided informed consent . This strain was used to infect C6/36 Aedes albopictus cells at a starting viral titer of 1 . 5 particles/cell . C6/36 cells were grown in Leibovitz's medium but the infected ones were held in medium containing 2% fetal bovine serum . The Ma-CBG-1 strain of M . anisopliae was isolated from soil collected at rural habitats around the city of Saltillo; cultured on potato-dextrose-agar; and passaged three times through living hosts ( Ae . aegypti females ) before the study . At 12 days post-infection ( PI ) the virus was harvested . Mosquitoes used in the study were derived from a colony of Ae . aegypti that was established in 2008 with larvae collected in Monterrey , México . Four to seven day-old female Ae . aegypti mosquitoes were exposed to single infections of either Ma-CBG-1 M . anisopliae strain at 1 . 6×108 conidia mL−1 ( SF ) or DENV-2 ( SV ) ; and both fungus and virus ( CI ) . Fungal infections were done as described previously [7] . For the virus infections , females were confined in 1-liter glass flasks , and were fed on 2 , 320 µL defibrinated human blood and 680 µL of virus suspension containing a titer of 1×107 plaque-forming units ( PFU ) mL−1 , for 1 hour via a water-jacketed membrane feeding apparatus [16] . Control mosquitoes ( C ) and those in the SF group were fed with non-virus infected blood in the same manner . After blood feeding , mosquitoes were anaesthetized by exposing the flask to 4°C for 25 minutes , and then only blood-fed females were transferred to the holding containers for each bioassay . Three bioassays ( B ) were conducted . In B1 , the survival of Ae . aegypti females was compared between each treatment: CI , SF , SV , and control . Fifty mosquitoes per treatments were used , encompassing two replicates of 25 each in a 1-liter plastic flask . The 25 females per replicate were randomly selected from , those emerged from larvae ( 200 larvae/liter ) of the same plastic tray and all replicates were conformed by adults emerged from larvae of different plastic trays , origin ( eggs ) and handling . Dead insects in treatments were recorded and removed daily . The cadavers were submerged twice in 1% chlorine solution , washed in distilled H2O , and placed in humid chambers for conidiation . This bioassay was run until the last insect died . In B2 the viral infection rate in the midgut and head of mosquitoes was examined . Treatments were CI and SV , with also fifty insects per treatment and two replicates as in B1 . Dead females were registered daily until six days PI without registering sporulation . At day 7 PI all surviving mosquitoes were cold-killed . Day 7 was the cut-off point because this is the average extrinsic incubation period ( EIP ) for the DENV-2 in Ae . aegypti [17] , [18] and these studies were conducted at 30 ( ±2 ) °C . Mosquito midguts and heads were dissected on a glass slide containing 10 µL of phosphate-buffered saline; then were fixed and placed into 0 . 2 mL Gold-PCR tubes containing 150 µL of 4% paraformaldehyde and kept at 4°C until further analysis . In B3 , sixty females were used per treatment to assess the impact of fungal infection on fecundity and length of the first GC . The same treatments as in B1 were set up but with three replicates of 20 females each . However here , the mosquitoes of each replicate were individually separated into 40-ml capped-vials , containing a small amount of water and cardboard to record daily oviposition for each individual female . In B2 , the viral infection rate in the females sacrificed at 7 days PI , as well as in cadavers collected at 1–6 days , was determined using PCR . Details of protocols for viral RNA purification , cDNA synthesis , primer sequences , PCR conditions , and detection of PCR products by agarose gel electrophoresis , were reported earlier [16] . The preparation of midguts and heads , the immunofluorescent assay , and stain of female's tissues have also been published elsewhere [18] . Daily mortality rate was used to compute the median lethal time ( LT50 ) per treatment with the Kaplan-Meier model; the model was stratified by replicate number to account for dependencies for mosquitoes which were held within each replicate . In B2 , two 2×3 cross tabulation analyses by χ2 using Fisher's exact test were applied to the percentages of females with viral infection in the midgut , then with disseminated infection in head and non-DENV-2-infected , across the CI and SV treatments; the first one was an overall analysis while the second was only for the three groups of surviving females across both treatments on 7 days PI . The Fisher's exact test was used because the sample sizes were small . In B3 , a 2×2 cross tabulation analysis by χ2 was applied to the percentages of ovipositing females in both CI and SV treatments; moreover , a one-way analysis of variance was applied for fecundity and GC; means were contrasted with a Ryan test . All analyses were performed with SAS [19] .
In B1 , the overall survival varied among the four groups ( χ2 = 237 . 25 , df = 3 , p<0 . 0001 ) ; further analysis indicated that there was no statistical difference in the survival of DENV-2 infected ( CI ) and non-DENV-2-infected Ae . aegypti females ( SF ) ( χ2 = 2 . 87 , df = 1 , p>0 . 05 ) . The LT50 of fungal infected females was 6 . 93 ( range , 6 . 59–7 . 27 days , 50 samples ) for CI and 7 . 23 ( range , 6 . 80–7 . 66 days , 50 samples ) for SF . The same occurred for the survival of females infected only with the virus ( SV ) which was similar to the uninfected controls ( χ2 = 0 . 21 , df = 1 , p>0 . 05 ) . The LT50 for SV and control mosquitoes was 24 . 00 ( range , 23 . 08–24 . 98 days , 50 samples ) and 24 . 83 ( range , 24 . 06–25 . 60 days , 50 samples ) days respectively . Overall , M . anisopliae reduced the survival ( as indicated by the LT50 values ) of both CI and SF mosquitoes by ≈70% ( Figure 1 ) . The sporulation rates of cadavers collected from the CI and SV treatments was 85% on average for both experimental factors . Therefore , regardless of the virus , the fungus killed 85% of mosquitoes in both treatments . In B2 at 7 days PI , the mortality rate of SV females was only 6% ( 3/50; Figure 2a ) , while in CI mosquitoes there was 78% ( 39/50; Figure 2b ) mortality . For the SV treatment after 7 days , the percentage of surviving mosquitoes with virus in the midgut , head or non-DENV-2-infected was 15% ( 7 ) , 64% ( 32 ) and 17% ( 8 ) respectively . For the CI treatment after 7 days , the percentage of surviving mosquitoes with virus in the midgut , head and non-DENV-2-infected was 10% ( 5 ) , 12% ( 6 ) and 0% ( 0 ) respectively . As such , for mosquitoes surviving to 7 days PI , there was a reduced proportion which were able to develop a head infection between the CI and SV groups ( χ2 = 6 . 14 , df = 2 , p<0 . 05 ) . However , an assessment of vectorial capacity should also account for mortality . When mortality is considered , 64% ( 32/50 ) of the mosquitoes in the SV treatment were alive and potentially able to transmit at 7 days PI; compared with only 12% ( 6/50 ) in the CI group ( χ2 = 17 . 99 , df = 1 , p<0 . 0001 ) . Therefore , there was a 5-fold reduction in the number of potential infective females due to the high mortality of females before they were able to complete the EIP . In B3 , the percentage of fungus-infected individuals which oviposited within 7 days was only 56% ( 23/41 ) for CI and 42% ( 18/42 ) for SF . This was ≈50% less females than observed for the non-fungus treatments where 100% of females oviposited ( SV = 59/59 and Control = 59/59 ( χ2 = 294 . 00 , df = 3 , p<0 . 0001 ) . Of the females which laid at least one egg , fungus-infected females generally laid less eggs . The mean number of eggs per female for fungal-infected treatments were 21 . 75 ( range , 19 . 10–24 . 40 eggs , 20 samples ) in CI and 21 . 65 ( range , 18 . 19–25 . 11 eggs , 20 samples ) in SF , contrasting with the non-fungus treatments where the mean number of eggs per female was 45 . 76 ( range , 40 . 45–51 . 07 eggs , 20 samples ) in SV and 46 . 58 ( range , 41 . 23–51 . 93 eggs , 20 samples ) in CI . Therefore , the fecundity of Ae . aegypti was reduced by 52% ( Figure 3 ) ( F = 22 . 95 , df = 3 , p<0 . 001 ) . The infection of females with M . anisopliae was also observed to accelerate the oogenesis . The GC of fungus-infected females was 2 . 65 ( range , 2 . 48–2 . 82 days , 20 samples ) in CI and 3 . 46 ( range , 3 . 25–3 . 67 days , 20 samples ) in SF and both of these were shorter than the GC of SV and uninfected mosquitoes which were 5 . 31 ( range , 4 . 96–5 . 66 days , 20 samples ) and 5 . 35 ( range , 4 . 91–5 . 79 days , 20 samples ) days respectively ( F = 14 . 15 , df = 3 , p<0 . 001 ) . Thus , the fungal co-infection diminished the length of the GC by 40% ( from 5 to 3 days ) .
In Ae . aegypti co-infected with DENV-2 and with a Mexican strain of M . anisopliae there was a 5-fold reduction in the number of mosquitoes which survived the EIP and with potential to transmit dengue . The most sensitive component of vectorial capacity ( C ) as defined by the Ross-MacDonald model is daily survival [20] , [21] . M . anisopliae infection killed the majority of females ( 78–88% ) before surviving the 7-day EIP . Furthermore , of the fungus-infected mosquitoes which did survive the EIP , there was a reduced chance of them becoming infectious after a feeding on DENV-2 infected blood . To express more clearly the impact of the fungus on C of the dengue vector , we computed this index taken our own data and others from literature: Daily survival probability ( p ) for SV and CI females was computed by regressing the number of survivors [ln ( X+1 ) ] on days PI up to day 6 , and were 0 . 98 and 0 . 75 with determination coefficients of R2 = 0 . 62 and 0 . 98 , respectively ( Figure 4 ) . These different rates mean a reduction in p of 0 . 23 by the fungal effect . The C model ma3bpn/-ln ( p ) and their components are defined as follows: m = “daily biting rate” , a3 = “the human biting rate” powered to the number of blood meals per GC , which is at least three for Ae . aegypti [22]; a = 1/GC , b = proportion of infectious females at the EIP which were 0 . 64 and 0 . 12 for SV and CI groups , respectively , and pn/-ln ( p ) = the expected infective life in days after the EIP . Now in Monterrey , MX , the Ae . aegypti annual population is bimodal with the highest peak in October , where a time series of 19 consecutive days of human-landing captures allowed to estimate a m = 37 bites/human/day [23]; then keeping constant m = 37 , a = 1/GC days ( 5 days for SV and 3 for CI ) and n = EIP = 7 days , the calculation results in a C = 8 . 14 and 0 . 07 for SV and CI , respectively; this means that the fungus reduced the C by 116 times in CI compared to the one of SV-infected females . This would be a drastic impact of M . anisopliae on the vectorial capacity of Ae . aegypti in field . Comparatively , the 64% of females that fed on DENV-2 infected blood and became infectious within 7 days , is two-fold superior than 27% and 30% recorded in Ae . aegypti from Texas , USA , and Chiapas , Mexico infected with the DENV-2 Southeast Asia strain , respectively [24] , but is similar to the competence of Ae . aegypti from Australia [25] . The fungus was able to quickly invade the tissues and cells causing the early death of CI mosquitoes before many of them were able to survive the entire EIP . This observation is supported by previous work which detected M . anisopliae hyphal bodies circulating in the hemolymph of Locusta migratoria manilensis at day 2 PI [26] . Generally cuticle perforation and hemocele invasion last around 20 hours [14] . Once the host is invaded , the fungus starts to consume nutrients and propagate . By day 2–3 PI the fungus releases detectable levels of toxins and enzymes [27] , [28] , compromising the immune system of the mosquitoes triggering mortality , as was observed in CI and SF females by 3 days PI . Similarly , this competition for nutrients between the host and fungus , could also explain the 52% reduction in fecundity and a shortening of the GC from 5 to 3 days in B3 . In a previous study , we conducted with this fungus but with a highly virulent strain ( Ma-CBG-2 ) found that in mycosed females the fecundity was reduced to almost zero [7] . In addition , the Anopheles gambiae female mosquitoes tend to take smaller blood meals after becoming fungus-infected [29] , [30] and this also plausibly explains the reduced number of females which actually laid eggs . In the current experiments , we controlled for any possible biases of dengue infection on fecundity by using the four experimental factors ( CI , SV , SF and control ) . It is a general knowledge that entomopathogens usually induce changes in vector behavior and physiology [31] , [32] . The shorten of GC observed in B3 could be part of an adaptive strategy by the female mosquitoes in achieving reproduction before the pathogens drastically deplete the nutritive resources required for the egg development . It could also be an adaptive behavior by the mosquitoes which are unlikely to survive the virulence of the pathogens as earlier observed in crickets infected with bacteria and parasites [33] , [34] , while the oogenesis of Schistocerca gregaria and Ae . aegypti was speeded up by M . anisopliae and B . bassiana [35] , [36] . However , instead of curtailment of reproductive potential of the insects , some pathogens have been known to enhance the reproductive success of their host through higher production of offspring early in life; a phenomenon often referred as “fecundity compensation” [37] . These reactions may partly be mediated by the immune system of the insects in response to the infection as earlier posited by researchers [33] , [37] . Concerning the impact of the virus , no effect of dengue infection on fecundity was noted , contrary to previous research . There is some evidence that the dengue virus exerts low mortality rates on Ae . aegypti adults , as well on its fecundity , as was recently reported for Brazil , where 4–5 day-old Ae . aegypti females were fed with rabbit blood mixed with 3 . 6×105 PFU mL−1 of the DENV-2 ( strain 16681 ) [38] . The authors found that the longevity of infected mosquitoes averaged 26 days , which is similar to the 24 days observed here in SV females . They reported a viral infection of 66% which is also similar to the 68% ( 6 females with virus in head and 5 with virus in midgut ) obtained in SV females at 7 days PI that we found; the viral effect on fecundity was examined by a logistic regression; however the authors only reported that the mean number of eggs per GC through five GCs tended to be lower in infected mosquitoes , without mentioning the specific means . The reduced fecundity may be beneficial for mosquito control because the population size of consecutive generations will be proportionally smaller; however this reduction occurred concomitantly with a shorter GC , which not necessarily implies only a faster reproduction . A shorter GC may also alter the natural mortality of populations by increased exposure to predation and other adverse environmental factors when search more often for blood-meals . It is possible that the direct mortality observed in our laboratory study may be compounded such indirect increases of mortality under field conditions . In conclusion , M . anisopliae has the potential to drastically affecting the vectorial capacity of Ae . aegypti in the field and could be accomplish without necessarily using a highly virulent strain ( LT50 = 3–4 days ) of the fungus . Whether these conditions could be fulfilled in field is the aim for an ongoing investigation by our team .
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Dengue is a worldwide public health problem . There is not an effective vaccine yet; the chemical struggle against its transmitter , the mosquito Aedes aegypti , is onerous and erratic , and the community participation to eliminate vector breeding sites is unconfident . Here , we examined mosquitoes fed on human blood mixed with the Dengue virus , by exposure to the fungus Metarhizium anisopliae , to test whether the fungus halts the viral dissemination from midgut to head in co-infected ( CI ) insects . We found an overall viral infection rate in CI mosquitoes of 76% but infected or not , most ( 78% ) died before or at day six post-infection; only six ( 12% ) out of 50 , survivors had virus in head and were potentially infectious at day seven post-infection . A higher infection ( 84% ) was observed in single-virus infected mosquitoes , but they suffered only 6% mortality after 6 days and 32 ( 64% ) survivors tested positive for virus in head after 7 days . Survival , fecundity and ovaric cycle of CI mosquitoes were reduced in 70 , 52 and 40% in comparison to the ones of control . Therefore , if the fungus caused a 5-fold reduction in the number of infectious mosquitoes , it has potential to be evaluated against the Dengue transmitter in field .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"biology"
] |
2013
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Vectorial Capacity of Aedes aegypti for Dengue Virus Type 2 Is Reduced with Co-infection of Metarhizium anisopliae
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Crossing-over is a central feature of meiosis . Meiotic crossover ( CO ) sites are spatially patterned along chromosomes . CO-designation at one position disfavors subsequent CO-designation ( s ) nearby , as described by the classical phenomenon of CO interference . If multiple designations occur , COs tend to be evenly spaced . We have previously proposed a mechanical model by which CO patterning could occur . The central feature of a mechanical mechanism is that communication along the chromosomes , as required for CO interference , can occur by redistribution of mechanical stress . Here we further explore the nature of the beam-film model , its ability to quantitatively explain CO patterns in detail in several organisms , and its implications for three important patterning-related phenomena: CO homeostasis , the fact that the level of zero-CO bivalents can be low ( the “obligatory CO” ) , and the occurrence of non-interfering COs . Relationships to other models are discussed .
Crossover ( CO ) recombination interactions occur stochastically at different positions in different meiotic nuclei . Nonetheless , along a given chromosome , COs tend to be evenly spaced . This interesting phenomenon implies the existence of communication along chromosomes , the nature of which is not understood . CO patterning , commonly known as “CO interference” , was originally detected from genetic studies in Drosophila [1] , [2] . It was found that the frequency of meiotic gametes exhibiting two crossovers close together along the same chromosome ( “double COs” ) was lower than that expected for their independent occurrence . The implication was that occurrence of one CO ( or more correctly one CO-designation ) “interferes” with the occurrence of another CO ( CO-designation ) nearby . We previously proposed a model for CO patterning in which macroscopic mechanical properties of chromosomes play governing roles via accumulation , relief and redistribution of stress ( Figure 1A ) [3] , [4] . In that model , a chromosome with an array of precursor interactions comes under mechanical stress along its length . Eventually , a first interaction “goes critical” , undergoing a stress-promoted molecular change which designates it to eventually mature as a CO . By its intrinsic nature , this change results in local relief of stress . That local relaxation then redistributes outward in the immediate vicinity of its nucleation point , in both directions , dissipating with distance . A new stress distribution is thereby produced , with the stress level reduced in the vicinity of the CO-designation site , to a decreasing extent with increasing distance from its nucleation point . This effect disfavors occurrence of additional ( stress-promoted ) CO designations in the affected region . The spreading inhibitory signal comprises “CO interference” . More such CO-designations may then occur , sequentially , each accompanied by spreading interference . Each subsequent event will tend to occur in a region where the stress level remains higher , which will necessarily tend to be regions far away from prior CO-designated sites . Thus , as more and more designation events occur , they tend to fill in the holes between prior events , ultimately producing an evenly-spaced array . The most attractive feature of this proposed mechanism is the fact that redistribution of stress is an intrinsic feature of any mechanical system , thus comprising a built-in communication network as required for spreading CO interference . CO-designated interactions then undergo multiple additional biochemical steps to finally become mature CO products [5] . Precursors that do not undergo CO-designation mature to other fates , predominantly inter-homolog non-crossovers ( NCOs ) . CO patterning by the above stress-and-stress relief mechanism can be modeled quantitatively by analogy with a known physical system that exhibits analogous behavior , giving the beam-film ( BF ) model [3] . We note that BF model simulations can be applied to any mechanism whose effects are described by the same mathematical expressions as the beam-film case . In such a more general formulation ( Figure 1B ) , there is again an array of precursor interactions . That array would be acted upon by a “Designation Driving Force” ( DDF ) . Event-designations would occur sequentially ( or nearly so ) . Each designation would set up a spreading inhibitory effect that spreads outward in both directions , decreasing in strength with increasing distance , thereby decreasing the ability of the affected precursors to respond to the DDF . When multiple designation/interference events occur , they would produce an evenly-spaced array . Maturation of CO-designated and not-CO-designated interactions ensues . The present study adds several new features to the BF simulation program and explores in further detail the predictions and implications of the BF model ( whether mechanical or general ) . We evaluate the ability of the model to quantitatively explain experimental CO pattern data sets in budding yeast , tomato , grasshopper and Drosophila . Our results show that the logic and mathematics of the BF model are remarkably robust in explaining experimental data . New information of biological interest also emerges . We then present detailed considerations of three phenomena of interest , the so-called “obligatory CO” and “CO homeostasis” , and the nature of “non-interfering COs” . We discuss how these phenomena are explained by the BF model and show that BF predictions can very accurately explain experimental data pertaining to these effects . Overall , the presented results show that BF simulation analysis is a useful approach for exploring experimental CO patterns . Other applications of this analysis are presented elsewhere . The current study has also provided new criteria for characterization of CO patterns using Coefficient of Coincidence analysis and illustrates both short-comings and useful applications of gamma distribution analysis . Relationships of the BF model to other models are discussed .
CO data sets , whether experimental or from BF simulations , comprise descriptions of the positions of individual COs along the lengths of each of a large number of different chromosomes ( “bivalents” ) . Each bivalent represents the outcome of CO-designation in a single meiotic nucleus; the entire data set comprises the outcomes of CO patterning for a particular chromosome in many nuclei . BF simulations require specification of three types of parameters ( Table 1 ) . One set describes the nature of the precursor array upon which CO-designation acts; a second set describes features of the patterning process per se; and a third precursor specifies the efficiency with which a designated event matures into a detectable CO or CO-correlated signal . Application of the BF model to an experimental data set permits the identification of a set of parameter values for which simulated CO patterns most closely match those observed experimentally ( general strategy described and illustrated in Figure S4 ) . Best-fit simulation analysis for data sets from yeast , Drosophila , tomato and grasshopper demonstrates that the logic and mathematics of the BF model can describe experimental CO patterns with a high degree of quantitative accuracy . This conclusion is evident in descriptions of CoC and ED patterns as described in this section ( III ) . Additional evidence is provided by applications and extensions of BF simulation analysis to CO homeostasis , the obligatory CO and non-interfering COs as described in sections IV–VI . Inspection of experimental CoC relationships has also provided new information regarding the metric of CO interference in tomato and the fact that interference spreads across centromere regions ( in grasshopper , as previously described , and also in tomato and yeast ) . Experimental evidence has revealed that variations in the level of recombination-initiating double-strand breaks ( DSBs ) are not accompanied by corresponding variations in the number of COs . When DSB levels are either reduced or increased , CO levels are not reduced or increased commensurately [15] , [34] , [43]–[46] . This phenomenon is referred to as CO homeostasis [43] . According to the BF model , CO homeostasis is dependent upon , and in fact is a direct consequence of , CO interference ( Figure 11A ) , as proposed [43] , [46] . In the absence of interference , the probability that a precursor will give rise to a CO is a function only of its own intrinsic properties , independent of the presence/absence of other precursors nearby . Thus , as the number of precursors decreases , the number of COs will decrease proportionately . In contrast , if interference is present , each individual precursor is subject to interference that emanates across its position from CO-designation events at neighboring positions . The lower the number of precursors , the less this effect will be . Thus , assuming a fixed level of CO interference , the frequency of COs per precursor will increase as the number of precursors decrease . Put another way: as the density of precursors decreases , the ratio of COs to precursors increases , even though there is no change in CO interference . Importantly , since CO homeostasis requires CO interference , its magnitude will also depend on the strength of CO interference as discussed below . Regular segregation of homologs to opposite poles at the first meiotic division requires that they be physically connected . During meiosis in all organisms , in at least one sex and usually both , the requisite physical connection is provided by the combined effects of a crossover between non-sister chromatids of homologs and connections between sister chromatids along the chromosome arms . Correspondingly , in such organisms , in wild-type meiosis , every bivalent almost always acquires at least one CO [47] . This first CO that is essential for homolog segregation is often referred to as the “obligatory CO” . In fact , the obligatory CO is simply a biological imperative: the level of zero-CO chromosomes should be low . The CO patterning process , by whatever mechanism , must somehow explain this feature . In most situations , the frequency of zero-CO bivalents is extremely low ( <10−3 ) , but higher frequencies also occur in certain wild-type situations as well as in certain mutants ( below ) . In some models for CO patterning , the obligatory CO is ensured by a specific “added” feature of the patterning process ( e . g . the King and Mortimer model; Discussion ) . In contrast , in the beam-film model , the requirement for one CO per bivalent is satisfied as an intrinsic consequence of the basic functioning of the process , as follows . In some organisms , a significant fraction of COs arises outside of the patterning process . The existence of these “non-interfering” COs is most rigorously documented for budding yeast , where the number of “non-interfering” COs is ∼30% among total COs ( by compassion the number of patterned COs defined by analysis of CO-correlated Zip2/Zip3 foci with the number of total COs from genetic and microarray analyses ) ( e . g . [22] , [31] , [52] , [53]; below ) . The origin of non-interfering COs is unknown . One possibility is that they arise from the majority subset of interactions that do not undergo CO-designation [5] . By this model ( “Scenario 1”; Figure 14A left ) , not-CO-designated interactions would mostly mature to NCOs but sometimes would mature to COs , analogously to the situation in mitotic DSB-initiated recombinational repair [54] . Alternatively , such COs might arise from some other set of DSBs that arise outside of the normal process , e . g . because they occur later in prophase after CO-designation is completed or earlier in prophase before patterning conditions are established ( “Scenario 2”; Figure 14A right ) . Both scenarios can be examined using the BF simulation program . To simulate the outcome of Scenario 1 , where non-patterned COs arise from non-designated interactions left over after patterning , a standard CO-designation BF simulation is performed to define the interfering COs; the precursors that have not undergone CO-designation are then used as the starting array of precursors for a second round of CO-designation . In this second round , COs are randomly selected from among the precursors remaining after the first round of designation . The COs resulting from the two simulations are then combined and the total pattern is analyzed . To model Scenario 2 , in which non-patterned COs arise from an unrelated set of precursors , a standard CO-designation BF simulation is performed to define interfering COs . Then a second , independent simulation is performed using a specified number of precursors that are unrelated to the first set and random selection of COs from among that precursor set . COs generated by the two types of simulations are then again combined and analyzed . CoC relationships for total COs ( interfering plus non-interfering ) will depend significantly on whether the precursors that give rise to the “non-interfering” COs are evenly or randomly spaced along the chromosomes . CoC curves for total COs reflect the combined inputs of CoC relationships for interfering COs and non-interfering COs . CoC curves for interfering COs are affected only modestly by even-versus-random spacing due to the overriding effects of CO interference ( above; e . g . Figure 14B left ) . However , non-interfering CO relationships are a direct reflection of precursor relationships , which differ dramatically in the two cases . For precursors , CoC = 1 for random spacing and significant “interference” for even spacing; Figure 14B second from left ) . CoC relationships for non-interfering COs alone exhibit the same features ( Figure 14B , rightmost two panels ) . These differences are directly visible in CoC curves for total COs , with greater or lesser prominence according to the relative abundance of non-interfering COs versus interfering COs ( Figure 14C ) . Notably , CoC relationships for Scenario 1 , where precursors exhibit the even spacing defined by BF best-fit simulations ( E = 0 . 6 ) , show a qualitatively different shape than CoC relationships under Scenario 2 . Given this framework , we defined CoC curves for total COs along yeast chromosomes IV and XV as defined by microarray analysis ( Figure 14D left panel ) . The general shapes of these experimental curves correspond qualitatively to those predicted for emergence of non-interfering COs from an evenly-spaced precursor array , with a closer correspondence to those predicted for Scenario 1 than to those predicted for Scenario 2 ( compare Figure 14D left panel with Figure 14C ) . This impression is further supported by BF simulations . To model Scenario 1 , we began with the set of best-fit parameters defined for interfering COs ( Zip3 foci ) above ( Figure 6I ) and generated predicted total CoC curves , assuming that non-interfering COs comprise 30% of the total ( above ) , for each of the three possible case of non-interfering COs: Scenario 1 ( where precursors are assumed to be evenly spaced as for interfering COs ) ; and Scenario 2 with precursors assumed to be either evenly or randomly spaced ( Figure 14D , second panel from left ) . The CoC curve for the first of these three cases has the same shape as the experimental CoC curves for total COs ( compare Figure 14D left and second from left panels ) and direct comparison shows that it gives a quite good quantitative match with the experimental curves ( Figure 14D third panel from left ) . Scenario 2 with evenly-spaced precursors is a less good match ( Figure 14D , right panel ) . Scenario 2 with randomly-spaced precursors ( Figure 14D , second panel from left , red ) is a quite poor match ( not shown ) . These analyses suggest that , in yeast , non-interfering COs arise from the not-CO-designated precursors as a minority outcome of the “NCO” default pathway ( Figure 14A , Scenario 1 ) . Many studies of CO interference characterize CO patterns by defining a gamma distribution that best describes an experimentally observed distribution of the distances between adjacent COs , often with the assumption ( implicit or explicit ) that a higher value of the gamma shape parameter ( ν ) corresponds to “stronger” CO interference ( e . g . [11] ) . We have examined the way in which ( ν ) varies as a function of changes in the values of several BF parameters . Variations in L or Smax increase or decrease the value of ( ν ) in correlation with increased or decreased LCOC and in opposition to the average number of COs per bivalent ( Figure 15AB , compare green line and blue/pink distributions with red and black lines ) . This is the pattern expected for a change in the “strength of interference” . In contrast , the value of ( ν ) is also altered by variations in M or N , which have little or no effect on LCOC; moreover , the change in ( ν ) co-varies with the change in the average number of COs per bivalent ( Figure 15CD , compare green line and blue/pink distributions with red and black lines ) . The BF model thus implies that a change in the value of ( ν ) , e . g . in a mutant as compared to wild type , may or may not imply a change in the patterning process per se . However , comparison of the variation in ( ν ) with the variation in average COs per bivalent can distinguish between the two possibilities , with opposing variation implying a patterning difference and co-variation implying a difference in some other feature .
This is true not only with respect to CoC and ED relationships but with respect to more detailed effects such as CO homeostasis and the obligatory CO . These matches , and the information that emerges there-from , support the notion that the basic logic of the BF model provides a robust and useful way of thinking about CO patterning . These matches are also specifically supportive of the proposed mechanical stress-and-stress relief mechanism . In budding yeast: ( i ) CO patterning has the same basic features for shorter and longer chromosomes; ( ii ) Mlh1 is required specifically for CO maturation not for CO patterning; and ( iii ) Precursors are evenly spaced , as shown by both CoC analysis and analysis of total ( interfering-plus-non-interfering ) COs . In tomato ( and , to be described elsewhere , in budding yeast ) , the metric of CO interference is physical chromosome length ( µm ) not genomic length ( Mb ) . In the case of tomato , differences in CoC relationships expressed in the two different metrics is attributable to differential packaging of heterochromatin versus euchromatin along the chromosome plus differential proportions of heterochromatic versus euchromatic regions among different chromosomes . In tomato and yeast , as previously described for grasshopper , human and several other organisms , crossover interference spreads across centromeres with the same metric as along chromosome arms . In budding yeast , non-interfering COs arise from evenly-spaced precursors , most probably by occasional resolution of NCO-fated precursors to the CO fate . With respect to CO homeostasis , the importance of CO interference as a determinant in the strength of homeostasis is emphasized and BF simulations are shown to permit accurate quantitative descriptions of homeostasis . Also , the strength of homeostasis can be seen to reflect the ratio of interference distance ( LCoC ) to the distance between adjacent precursors . With respect to the obligatory CO , the general logic of the BF model ( Figure 1 ) suggests that occurrence of a low level of zero-CO chromosomes is independent of CO interference ( and precursor spacing ) and is achieved by an appropriate evolved constellation of all other parameters . Explanations can also be provided for several known cases where the level of zero-CO chromosomes is unusually high , but interference is robust , and potential explanations for other mutant phenotypes are suggested . Importantly , the logic of the beam-film model predicts the existence of mutants that lack interference but still exhibit the obligatory CO , evidence for which will be presented elsewhere . The central issue for CO patterning is how information is communicated along the chromosomes . Three general types of mechanisms have been envisioned . ( 1 ) A molecular signal spreads along the chromosomes , e . g . as in the polymerization model of King and Mortimer [55] or the “counting model” of Stahl and colleagues [20] , [35] . ( 2 ) A biochemical reaction/diffusion process surfs along the chromosomes [56] , as recently described in detail for bacterial systems [57] , [58] . ( 3 ) Communication occurs via redistribution of mechanical stress , as in the beam-film model [3] , [4] or via other mechanical mechanisms ( e . g . [59] ) . The counting model can provide good explanations of experimental data; however , the underlying mechanism is contradicted by experimental findings ( [43]; but see [60] ) . No specific reaction/diffusion mechanism has been suggested thus far for CO interference . The King and Mortimer model and the beam-film model are significantly different , in three respects . First , in the King and Mortimer model , the final array of COs reflects the relative rates of CO-designation and polymerization . Thus it is the kinetics of the system that governs its outcome . In the beam-film model , where interference arises immediately after each CO-designation , kinetics does not play a role . Second , in the King and Mortimer model , the interference signal continues to spread until it runs into another signal approaching from the opposite direction . In the beam-film model , the interference signal is nucleated and spreads for an intrinsically limited distance , with an intrinsic tendency to dissipate with distance from its nucleation site . Third , the King and Mortimer model envisioned that precursors were Poisson distributed among chromosomes . As a result , significant numbers of chromosomes would initially acquire no precursors if the average number of precursors is low and thus would never give a CO , thereby giving an unacceptably high level of zero-CO chromosomes . To compensate for this effect , the model proposed that the effect of interference was to release encountered precursors , which then rebound in regions that were not yet affected by interference ( and thus on chromosomes with no precursors ) . This precursor turnover would ensure that all chromosomes achieved a precursor that could ultimately give a CO . Because of this feature , the King and Mortimer model envisions that interference is required to ensure a low level of zero-CO chromosomes ( i . e . to ensure the “obligatory CO” ) . By the beam-film model , instead , precursors do not turn over and interference is not required to ensure a low level of zero-CO chromosomes , which results instead from an appropriate constellation of other features , as described above . The beam-film model predicts the existence of mutants that are defective in interference but do not exhibit an increase in the frequency of zero-CO chromosomes .
Yeasts SK1 strains ( Figure 6 and S7 ) are described in Table S1 . In all strains , ZIP3 carries a MYC epitope tag; a construct expressing LacI-GFP and is integrated at either LEU2 or URA3 , and a lacO array [61] is inserted at HMR ( chromosome III ) , Scp1 ( Chromosome XV ) or Chromosome IV telomere ( SGD1522198 ) to specifically label each chromosomes by binding of LacI-GFP . Pachytene chromosomes exhibit ∼65 foci of Zip2 , Zip3 and Msh4 , with strong colocalization of Zip3 and Msh4 foci ( [31] , [62]; this work ) . Zip2 foci [63] exhibit interference as defined by CoC relationships for random adjacent pairs of intervals [31] . We further show here that Zip2 and Zip3 foci exhibit interference as defined by full CoC relationships along specific individual chromosomes ( Figure 6 and Figure S5 ) . Zip2 and Zip3 foci also both occur specifically on association sites of zip1Δ chromosomes [31] , [64] . The total number of COs per yeast nucleus as defined by microarray and genetic analysis is ∼90 [22] , [52] , [65] implying that Zip2/Zip3/Msh4 foci represent 65/90 = 70% of the total . Correspondingly , mutant analysis suggests that “non-interfering” COs comprise ∼30% of total COs ( e . g . [50] ) . Additionally , BF analysis accurately explains CoC relationships for total COs on the assumption of 70% patterned COs and 30% “non-interfering” COs ( Figure 14 , Results ) . Synchronous meiotic cultures ( SPS sporulation procedure from [66] ) were prepared and harvested at a time when pachytene nuclei are most abundant ( ∼4–5 hours ) . Cells were spheroplasted and chromosomes spread on glass slides according to Loidl et al . and Kim et al . [67] , [68] . Primary antibodies were mouse monoclonal anti-myc , goat polyclonal anti-Zip1 ( Santa Cruz ) and rabbit polyclonal anti-GFP ( Molecular Probes ) . Each was diluted appropriately in the above BSA/TBS blocking buffer . Secondary antibodies were donkey anti-mouse , donkey anti-goat , and donkey anti-rabbit IgG labeled with Alexa488 , Alexa645 or 594 and Alexa555 ( Molecular Probes ) , respectively . Stained slides were mounted in Slow Fade Light or Prolong Gold Antifade ( Molecular Probes ) . Spread chromosomes were visualized on an Axioplan IEmot microscope ( Zeiss ) with appropriate filters . Images were collected using Metamorph ( Molecular Devices ) image acquisition and analysis software . Acquired images were then analyzed with Image J software ( NIH ) , with total SC length and positions of Zip3 foci for the specifically labeled bivalent were measured from the lacO/LacIGFP-labeled end to the other end ( Figure 6B bottom ) . For each type of chromosome analyzed ( III , IV and XV ) in each experiment , measurements were made for >300 bivalents , one from each of a corresponding number of spread nuclei . Resulting data were transferred into an EXCEL worksheet for further analysis . Coefficient of coincidence ( CoC ) curves were generated from SC length and Zip3 focus positions determined as described above . Each analyzed bivalent was divided into a series of intervals of 0 . 1 µm in length ( corresponding to the resolution with which adjacent Zip3 foci can be resolved ) . Chromosome III , IV and XV were thus usually divided into 9 , 42 and 30 intervals with equal size , respectively . Each chromosome length was normalized to 100% and each Zip3 focus position was also normalized correspondingly . Each Zip3 focus was then assigned to a specific interval according to its coordinate . The total frequency of bivalents having a Zip3 focus in each interval was calculated . For each pair of intervals , the frequency of bivalents having a Zip3 focus in both intervals was determined to give the “observed” frequency of double COs . For each pair of intervals , the total CO frequencies for the two intervals were multiplied to give the frequency of double COs “expected” on the hypothesis of independent occurrence . The ratio of these two values is the CoC . Thus in each pair of intervals , CoC = ( Obs DCO ) / ( Pred DCO ) . CoC values for all pairs of intervals can be plotted as a function of the distance between the midpoints of the two involved intervals ( “inter-interval distance” ) . However , for all of the data shown here , the CoC values from all pairs of intervals having same inter-interval distance were averaged and this average CoC was plotted as a function of inter-interval distance ( e . g . Figure 6C and others ) . The previous Beam Film program [3] was rewritten in MATLAB ( R2010a ) for easy use and modified to include more features as described in the text . Extensive details regarding program structure and application are provided in the Protocol S1 section . However , briefly , there are three options in the software that serve three different purposes: The Chorthippus L3 chiasmata data were generously provided by Gareth Jones ( University of Birmingham , UK ) . The Drosophila X-chromosome crossover data are from [33] . The tomato ( S . lycopersicum ) Mlh1 foci date are from [38] ( generously provided by F . Lhuissier ) . Zip2 data in the S . cerevisiae BR background are from [31] ( generously provided by J . Fung ) .
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Spatial patterning is a common feature of biological systems at all length scales , from molecular to multi-organismic . Meiosis is the specialized cellular program in which a diploid cell gives rise to haploid gametes for sexual reproduction . Crossing-over between homologous maternal and paternal chromosomes ( homologs ) is a central feature of this program , playing a role not only for increasing genetic diversity but also for ensuring regular segregation of homologs at the first meiotic division . The distribution of crossovers ( COs ) along meiotic chromosomes is a paradigmatic example of spatial patterning . Crossovers occur at different positions in different meiotic nuclei but , nonetheless , tend to be evenly spaced along the chromosomes . We previously-described a mechanical “stress and stress relief” model for CO patterning with an accompanying mathematical description ( the “beam-film model” ) . In this paper we explore the roles of mathematical parameters in this model; show that it can very accurately describe experimental data sets from several organisms , in considerably quantitative depth; and discuss implications of the model for several phenomena that are directly related to crossover patterning , including the features which can ensure that every chromosome always acquires at least one crossover .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology"
] |
2014
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Crossover Patterning by the Beam-Film Model: Analysis and Implications
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Histidine kinases ( HKs ) are dimeric receptors that participate in most adaptive responses to environmental changes in prokaryotes . Although it is well established that stimulus perception triggers autophosphorylation in many HKs , little is known on how the input signal propagates through the HAMP domain to control the transient interaction between the histidine-containing and ATP-binding domains during the catalytic reaction . Here we report crystal structures of the full cytoplasmic region of CpxA , a prototypical HK involved in Escherichia coli response to envelope stress . The structural ensemble , which includes the Michaelis complex , unveils HK activation as a highly dynamic process , in which HAMP modulates the segmental mobility of the central HK α-helices to promote a strong conformational and dynamical asymmetry that characterizes the kinase-active state . A mechanical model based on our structural and biochemical data provides insights into HAMP-mediated signal transduction , the autophosphorylation reaction mechanism , and the symmetry-dependent control of HK kinase/phosphatase functional states .
Bacteria commonly use two-component signal transduction pathways to couple environmental stimuli to adaptive responses [1] . Typically composed of a sensor histidine kinase ( HK ) and a response regulator ( RR ) , two-component systems ( TCSs ) are abundant in bacterial genomes , reflecting the diversity of stimuli that bacteria are capable of perceiving and responding to [2]–[4] . Independently of the nature of input signals , TCSs share a common signal transduction mechanism [3] . Stimulus perception triggers a two-step phosphorylation cascade consisting in the autophosphorylation of the HK at a conserved histidine residue followed by the transference of the phosphoryl group to an aspartate in the cognate RR , usually a transcription factor that initiates the bacterial response by modifying target gene ( s ) expression . HKs are homodimeric receptors with a modular architecture , which in its simplest form consists of a sensor domain and a cytoplasmic transmitter core . Whereas the signal-dependent sensor domains are highly variable , the transmitter core is more conserved . It contains a dimerization and histidine phosphotransfer ( DHp ) domain coupled to a catalytic ATP-binding ( CA ) domain . Mainly depending on the cellular location of the sensor domain ( either extracytoplasmic , membrane-anchored , or cytosolic ) , a wide variety of HK architectures exists [4] , [5] . However , the prevalent form of HKs is a transmembrane receptor with an extracytoplasmic sensor domain coupled to the cytoplasmic catalytic core through an adaptor HAMP domain [6] , a small protein module that is commonly found in Histidine kinases , Adenyl cyclases , Methyl-accepting chemotaxis proteins and Phosphatases . During the last few years , a number of structural studies have shed light into various aspects of the TCS mechanism of signal transduction . Crystal structures of isolated HK sensor domains have revealed the conformational consequences of stimulus perception [7]–[9] , whereas those of HK transmitter cores illustrated the conversion between different functional states through DHp interhelical rearrangements [10] , [11] , and two structures of cytoplasmic HK–RR complexes provided the structural basis underlying the RR dephosphosphorylation reaction [12] , [13] . Nevertheless , some important questions on TCS signal transduction and autophosphorylation mechanisms remain largely unanswered . For instance , it is poorly understood how a stimuli-induced symmetry/asymmetry switch regulates the HK catalytic activities . Moreover , a full structural characterization of the autophosphorylation reaction remains elusive , possibly hampered by the intrinsic flexibility and highly dynamic nature of the catalytic core [10] , as well as by the transient nature of the reaction intermediate in HKs [14] . There is also a lack of structural data on prototypic ( HAMP-containing ) HKs . So far , none of the HKs for which the transmitter core has been crystallized [10]–[13] , [15] belong to the predominant HK subfamily containing HAMP domains directly linked to the catalytic core [4] . To gain insight into the mode of action of prototypical HKs , we chose to focus on Escherichia coli CpxA , the HK component of the Cpx signaling system that regulates an envelope stress response in E . coli [16] , [17] . The sensor kinase CpxA senses a variety of envelope stresses , including misfolded proteins , and transduces this signal to the RR CpxR through conserved phosphotransfer reactions . Phosphorylated CpxR then activates transcription of genes whose products are involved in protein physiology in the envelope [18] . Cpx regulon includes a large number of members [19] , mainly periplasmic chaperones and proteases [20]–[23] . In addition to CpxA and CpxR , the Cpx system includes the auxiliary periplamic protein CpxP , a dimeric α-helical protein [24] , [25] that could interact with the CpxA periplasmic sensor domain and was shown to tightly regulate autokinase activity in vivo [26] and in vitro [27] . Here we report the 3D structures of the full cytoplasmic region of CpxA in several different crystal forms . These structures show CpxA adopting different asymmetric conformations , including a state that corresponds to the autophosphorylating Michaelis complex and provides insights into the reaction mechanism . The ensemble of structures illustrates the highly dynamic and strongly asymmetric nature of the HK kinase-active state . Based on our mutagenesis , structural , and biochemical data , we propose a mechanical model in which propagation of the conformational signal through the HAMP domain can regulate autokinase activity by inducing segmental movements and pronounced bending of the central HK α-helices , which in turn control the mobility of the ATP-binding catalytic domains .
The full cytoplasmic region of E . coli CpxA ( CpxAHDC ) , encompassing the HAMP signaling domain ( residues 188–237 ) and the DHp and CA domains of the catalytic core ( residues 238–457 ) , has been produced as recombinant protein for structural studies ( Figure 1A ) . The 3D structures of wild-type CpxAHDC ( or the point mutant CpxAHDC_M228V ) in complex with nucleotides have been determined in five distinct crystalline environments ( Table 1 ) using either single-wavelength anomalous diffraction ( SAD ) or molecular replacement methods ( see Materials and Methods ) . The CpxAHDC structure consists of a homodimer , in which the HAMP and DHp domains from both subunits assemble into an elongated central coiled-coil region flanked on either side by the two CA domains ( Figure 1B ) . The polypeptide chains of the HAMP domain were fully traced into SAD-phased experimental electron density maps of the CpxAHDC orthorhombic and trigonal crystal forms ( Figure S1 ) . The domains are formed by two parallel helices ( α1 and N-terminal part of α2 ) linked by a long connector ( Figure 1B ) , which despite a low sequence identity adopt a dimeric four-helix bundle similar to the Af1503 and VicK HAMP structures ( Figure S2 ) [15] , [28]–[30] . In some crystal forms , however , only the C-terminal α2 helix from both monomers could be traced in the electron density maps , because CpxAHDC has a tendency to establish interactions via its central-core helices , leading to the formation of dimers-of-dimers that sterically interfere with the positioning of helix α1 ( Figure S3A ) . Indeed , previous biophysical studies of full-length CpxA reconstituted in nanodiscs had indicated that the protein could exist as a mixture of dimers and tetramers ( Subrini , PhD thesis , 2011 ) . We carried out SAXS studies of CpxAHDC at different protein concentrations , which also demonstrate that CpxAHDC is prone to associate into dimers-of-dimers , as scattering data suggest a concentration-dependent equilibrium between dimers and tetramers in solution ( Figure S3 ) . Guinier analysis shows no significant nonspecific protein aggregation , albeit the Rg dependence with protein concentration points to interparticle interactions that would likely favor the tetrameric state at protein concentrations similar to those used for the crystallization trials ( >10 mg/ml ) . However , extrapolation of these data to very low concentration suggests that CpxA homodimers are the functionally relevant form of the protein , given the low protein abundance at the E . coli inner membrane . The DHp antiparallel two-helix hairpins ( helices α2 and α3 ) from both protomers also assemble into a four-helix bundle ( Figure 1B ) as observed in other HKs structures [10] , [11] , [31] . Both the HAMP and DHp coiled coils display the characteristic heptad repeats periodicity , in which the inner heptad positions a and d are occupied by hydrophobic residues . Besides the presence of a heptad repeat stutter at Leu232 [32] , HAMP and DHp domains merge into a long continuous helix ( α2 ) ( Figure 1B ) , in a very similar way as previously reported for the chimeric fusion between AF1053 HAMP and DHp EnvZ [28] . At the C-terminus of each monomer , DHp helix α3 connects through a short flexible linker ( residues 300–304 ) to the CA domain ( Figure 1B ) . These catalytic domains adopt the ATP-binding Bergerat fold [33] , a two-layer 3α/5β sandwich fold typical of the GHKL ( bacterial gyrase , HSP90 , histidine kinase , MutL ) superfamily . In HKs , this topology is characterized by four conserved sequence motifs termed G1 , G2 , F , and N boxes ( Figure S4 ) that include residues engaged in direct contacts with the bound ATP or confer flexibility to a flexible lid loop that surround the nucleotide binding cleft . The 3D structure of CpxAHDC is highly asymmetric , not only by a different orientation of the CA domains , but also as a consequence of two potential bending points in the central helix α2 ( Figure 1B ) . The first kink at Ser238 is located at the boundary between the HAMP and DHp domains , where there is a discontinuity between the parallel ( HAMP ) and the antiparallel ( DHp ) four-helix bundles . Helical bending at this position is probably required to avoid steric clashes at a layer of polar residues ( Gln239–Gln240 ) . The second kink is caused by the presence of a highly conserved proline residue ( Pro253 ) in the proximity of the phosphorylatable His248 , and has been also observed in other HK structures , such as HK853 [11] , KinB [34] , and VicK [15] . As a consequence of different bending angles in the two central α3 helices , the overall structure of the homodimer displays a curved asymmetric conformation ( Figure S5 ) , similar to that reported for the wild-type Af1503 HAMP–EnvZ DHp chimera [28] . It is interesting to note that the presence of two-helix–disrupting centers within α2 implies segmental helical movements in response to any torsional or orientational stress signal transmitted through the HAMP domain , which could easily result in asymmetric helical bending of the DHp domain . When considering together all CpxAHDC crystal structures , it transpires that the DHp helical bending promotes a strikingly different dynamic behavior of the two CA domains in the homodimer , primarily due to distinct interactions with the central four-helix bundle . Thus , whereas one CA domain ( from subunit A ) is retained in a fixed—inactive—conformation close to the DHp helices ( α2 from subunit B and α3 from subunit A ) through a relatively large contact interface ( ∼1 , 200 Å2 ) , the other CA domain with apparent unrestrained mobility adopts disparate orientations ( Figure 2A ) and displays significantly smaller CA–DHp surface contact areas , the largest of which ( ∼800 Å2 ) corresponds to the autophosphorylating Michaelis complex ( see below ) . This strongly suggests a highly dynamic autophosphorylation process , leading primarily to a single phosphorylation event per homodimer , a feature already described for various HKs [10] , [35] , [36] . In fact , in vitro autokinase activity assays show that CpxAHDC phosphorylation is never complete ( Figure 2B ) , with only ∼70% of the protein phosphorylated at steady state ( Figure 2C ) . These results suggest that CpxA could be mainly hemiphosphorylated in vivo under stress conditions , as the higher in vitro autophosphorylation efficiency might be attributed to subunit exchange during the assay , as observed for EnvZ [37] . In all homodimers , the fixed CA domain is sequestered by the DHp domain in an inactive kinase conformation , with the ATP-γP positioned far away from the phosphorylatable His248 . Instead , the active site formed by the second , mobile CA domain is poised for catalysis in three independent structures crystallized in two distinct crystal forms ( trigonal and hexagonal space groups , Table 1 ) , with the ATP γ-P and the acceptor His248 Nε atom well positioned for the phosphotransfer reaction to proceed ( Figure 3A ) . The catalytic cores ( DHp + CA domains ) are very similar to each other in the three independent crystal structures ( Figure S6 ) ; they can be superimposed with r . m . s . d . of 0 . 9–1 . 4 Å and display an identical network of hydrogen bonding interactions involving key functional residues for ATP binding and catalysis . This highly conserved active site geometry in distinct crystalline environments emphasizes the stability of the CA–DHp interface in the Michaelis complex and lends further support to its functional relevance . Most residues interacting with the nucleotide belong to the CA domain ( Figure S7 ) , except for the phosphoacceptor His248 and the conserved Arg251 , both of which make strong hydrogen bonding interactions with oxygen atoms from the ATP γ-P . The ATP phosphate groups are further stabilized in the binding pocket at the correct position for catalysis through additional contacts made by their oxygen atoms with the backbone amide nitrogen atoms of Leu419 , Gly420 , and Leu421 , the hydroxyl groups of Thr417 and Tyr364 , the guanidinium group of Arg363 , and the carboxamide group of Asn360 ( Figure 3A and Figure S7 ) . Most of these residues belong to conserved sequence motifs in the HK family [38]; in particular , Asn360 is a strictly conserved residue from the N-box in the CA domain that is essential for the autophosphorylation reaction [39] , [40] . The geometry of the active site points to Glu249 , the amino acid adjacent to the phosphoacceptor His248 in the DHp domain , as a key functional residue . Its carboxylic side chain makes hydrogen-bonding interactions with both the Nδ atom of His248 and the carboxamide group of Asn356 from the CA domain ( Figure 3A ) . These interactions favor the deprotonation of the His248 Nε atom , which performs the nucleophilic attack on the ATP γ-P ( Figure 3B ) . Consistent with such a role , mutations of this glutamate were found to abolish kinase activity in other HKs such as NtrB [41] and CdrS [42] , and an acidic residue at this position ( immediately following the phosphoacceptor histidine ) is highly conserved in major HK subfamilies HisKA ( Pfam00512 ) and HisKA_3 ( Pfam07730 ) , which represents about 90% of all known HK sequences . Further evidence supporting a key functional role of this residue is provided by the structural comparison of the CpxA active site with that of the chemotaxis HK CheA [43] . Although the two proteins have a different topology , a CheA glutamic acid ( Glu67 ) is spatially equivalent to CpxA Glu249 and interacts in an analogous way with the phosphoacceptor His45 ( Figure S8 ) , strongly suggesting a similar functional role for the acidic residue in the autophosphorylation mechanism . Also important is the interaction between Glu249 and Asn356 across the DHp–CA interface ( Figure 3A ) , which could help to achieve the correct disposition and polarization of Glu249 for catalysis . In agreement with this hypothesis , we found that the substitution Asn356–Tyr presented a strong kinase-deficient phenotype ( Table 2 ) in a genetic screen designed to search for mutations that affect kinase activity in vivo ( see below ) . It is interesting to note that a conserved glutamate at the equivalent position of CpxA Asn356 acts as a general base in the catalytic reaction of ATPases from the GHKL superfamily , which share a similar ATP-binding domain with HKs [44] , [45] . In the Michaelis complex , the CA carrying the ATP molecule and the phosphorylatable His belong to different subunits of the homodimer , which structurally confirms trans-autophosphorylation for CpxA ( Figure 3C ) . However , it is worth noting that trans-phosphorylation does not seem to be a generic feature of HKs; recent work by Ashenberg and coworkers [46] has shown that the cis/trans character of the reaction depends not on the local structure of the active site , but on the handedness of the DHp domain , which is determined by the loop at the base of the DHp four-helix bundle ( i . e . , between helices α2 and α3 in CpxA; Figure 1 ) . This model is now further confirmed by the structural comparison of CpxA ( trans-autotophosphorylation ) and VicK ( cis-autophosphorylation , [15] ) . Although both proteins have opposite handedness of the central four-helix bundle , their structures revealed a very similar active CA–DHp interaction ( Figure S9 ) . In addition to the polar interactions described above , two hydrophobic residues from the CA domain ( Phe403 in the F box and Leu419 in the G2 box ) play a key dual role in stabilizing the DHp–CA interface for both the active and inactive CA conformations ( Figure 3D ) . In the active conformation , the side chain of Leu419 is in contact with the phosphorylatable His248 , while Phe403 interacts with Leu292 and the aliphatic moiety of Arg296 in DHp helix α3 from the opposite monomer . In the second , inactive , subunit the same two residues participate in anchoring the CA domain to a hydrophobic binding pocket mainly defined by residues Met287 , Leu291 , and Met 294 from helix α3 ( in the same monomer ) and residues Leu242 , Ile246 , and Leu250 from helix α2 ( in the opposite monomer ) , accounting for a larger DHp–CA domain interface . Interestingly , some of these residues correspond to the a and d core positions of the DHp coiled-coil , which become partially accessible in one of the monomers due to the asymmetric conformation ( see below ) . These differences in the hydrophobic anchoring of Phe403 and Leu419 to the DHp domain ( Figure 3D ) might also contribute to modulate ATP binding affinity during the catalytic cycle . We have observed that the CpxAHDC homodimer exhibited half-occupation of the active site in some crystal forms , and in these cases , only the less mobile CA domains ( i . e . , those sequestered by stronger hydrophobic interactions with the DHp domain ) consistently displayed a bound nucleotide covered by the lid loop ( Figure S10 ) , whereas the CA domains with higher mobility have an empty nucleotide-binding cleft . These observations suggest that proper positioning of the lid loop through specific interactions with the DHp domain might trigger ATP binding . This hypothesis is consistent with recent observations from Mycobacterium tuberculosis DosS [47] , which is unable to bind ATP until the CA domain is properly positioned for catalysis , and is reminiscent of previous findings on CheA dimers [48] , where the two sites have largely different affinities for ATP . In our case , a lower nucleotide substrate affinity for the floppy CA domains might facilitate nucleotide turnover during the HK activation cycle . Despite the overall asymmetry of the CpxA homodimer , the HAMP four-helix bundle is perfectly symmetric as was also observed in the Af1503 , Aer2 , and VicK structures [15] , [28]–[30] , [49] , [50] . A strict 2-fold axis passing along the length of the coiled-coil relates the two HAMP subunits ( Figure 4A ) , which adopts a packing mode closer to a canonical knobs-into-holes model [51] than the unusual complementary x-da observed in the wild-type Af1503 structures [28]–[30] . However , a negative slope in the Crick angle deviation plot [29] , [52] suggests a departure from an ideal knobs-into-holes packing mode due to a weak heptad repeat periodicity at α1 ( Figure 4B ) . Overall , the observed CpxA–HAMP conformation resembles that observed in several Af1503 HAMP mutants associated with a kinase-active state [28]–[30] . The similarities in helical packing modes and overall helical bending between CpxA and the Af1503 HAMP–EnvZ DHp fusion chimera ( Figure S5 ) prompted us to investigate whether CpxA follows an analogous two-state mechanism of signal transduction . Based on studies of Af1503 , Hulko et al . [30] proposed a model for HAMP-mediated signal transduction , in which the HAMP coiled-coil can adopt two distinct conformations depending on the input signal . In particular , they found that the single point mutation ( Ala291–Val ) in Af1503 displaced the equilibrium between the two forms [30] and partially reversed the helical bending asymmetry of the wild-type Af1503–EnvZ crystal structure [28] . We therefore decided to introduce the equivalent mutation in CpxA ( Ala197–Val; see Figure S2 ) . However , the responses to extracytoplasmic stress due to the overproduction of a folding-defective mutant of MalE in the periplasm ( MalE31 , [53] ) , or to the presence of phenethyl alcohol in the inner membrane [54] , were similar in bacteria expressing either CpxAA197V or wild-type CpxA ( Figure 4C ) . To gain further insight into the signaling state ( s ) of the HAMP domain , we set up a genetic screening to search for mutations that affect the CpxA kinase activity in vivo . For this , a plasmid encoding a CpxA variant , which lacks the periplasmic sensor domain ( CpxAΔP ) and therefore displays constitutive kinase activity , was randomly mutagenized and transformed into a cpxA-null strain background . In these cells , the high β-galactosidase activity of the transcriptional fusion ( correlated to the kinase activity ) provided a means to isolate intragenic suppressors that confer a kinase-deficient/phosphatase-dominant ( K−/P+; i . e . , Lac− ) phenotype , easily monitored on X-gal containing plates . From 48 Lac− colonies examined , the identified mutations were reconstructed into the wild-type cpxA gene and their in vivo activity was evaluated ( Table 2 ) . The strongest phenotype corresponded to the point mutation Met228–Val in the HAMP , which completely abolished the Cpx-system response ( Figure 4C ) . In vitro studies revealed that the point mutant CpxAHDC_M228V is less efficient than wild-type CpxA in phosphorylating CpxR ( Figure 4D ) , at least in part as a consequence of a reduced autophosphorylation rate ( Figure 4E ) . Because Met228 occupies a heptad-repeat inner position in the four-helix bundle , its substitution by a smaller valine side-chain would promote a rearrangement of the interhelical packing to avoid the creation of a cavity within the hydrophobic core ( Figure 4A ) . Consistent with a more stable four-helix bundle packing , our SAXS data suggest that the Met228–Val substitution decreases the tendency of CpxAHDC to assemble into tetramers ( dimers of dimers ) , which partially disrupts the overall HAMP structure . Thus , in contrast to wild-type CpxAHDC , CpxAHDC_M228V is mainly dimeric in solution , with a relatively constant Rg value at different protein concentrations ( Table S1 and Figure S3D ) . As described above , our biochemical and structural data indicate that HAMP functioning is consistent with a two-state model for signal transduction [49] , [55] , in which different types of stress ( torsional , rotational , or translational ) coming from the membrane would break the conformational symmetry of the homodimer by inducing pronounced helical bending of the full CpxA transmitter core and a strong dynamical asymmetry in CA mobility ( Figure 5 ) . An important issue here is that , due to the presence of the helix-disrupting points at Ser238 and Pro253 in the long α2 helix , the overall bending movement would arise from segmental helical mobility . Thus , the movements of the central part of helix α2 ( between Ser238 and Pro253 , roughly corresponding to the H-box ) in each monomer differ from those of the HAMP domain ( i . e . , α1 and N-terminus of α2 in both monomers ) and the membrane-distal part of the DHp domain ( i . e . , C-terminus of α2 and α3 in both monomers ) , both of which rotate as separate rigid bodies . As a consequence , in the asymmetric ( kinase-active ) state , the C-terminus of α3 in one monomer moves away from the central part of α2 in the other monomer , thus partially exposing hydrophobic core residues ( Figure 5 , insert ) . This exposure creates a binding interface that retains one CA domain in a fixed inactive conformation ( Figure 3D ) . Instead , on the opposite face of the DHp domain , the same bending movements release the second CA domain , which becomes available to form a transient active site configuration poised for catalysis ( Figure 3A ) . This mechanical model highlights the importance of sequestering the CA domain ( through interactions with the DHp domain ) as a major mechanism to switch off kinase activity [28] , and possibly to bring the catalytic core into phosphatase-active ( symmetric ) or phosphotransferase-active ( asymmetric ) states . Taken together , our results point to the importance of conformational and dynamical asymmetry in modulating the autophosphorylation reaction in HKs , in much the same way as ligand-induced asymmetry of homodimeric HK sensor domains was found to play a major role in two-component signal transduction [8] , [9] .
CpxAHDC construct was cloned into the expression vector pET28a+ ( Novagen ) and expressed in E . coli BLI5 cells . His-tagged CpxAHDC was purified by immobilized metal affinity chromatography followed by gel filtration using standard procedures . For the production of seleno-methionyl ( SeMet ) CpxAHDC , the same plasmid was transformed into the autotrophic E . coli B834 ( DE3 ) strain . Differing only in the growth media , SelenoMet Medium ( MDL ) , and L-selenomethionine ( 50 mg/L ) instead of LB , SetMet proteins were purified exactly as the unlabeled ones . Mass spectrometry was used to confirm SeMet incorporation . Full-length CpxA was expressed , solubilized , and purified as described previously [56] . Purified proteins were concentrated using ultrafiltration VivaSpin devices ( 30 kDa MWCO , Millipore ) to ∼15 mg/ml prior crystallization trials . Crystals were obtained by vapor diffusion at 18°C . All crystal forms grew under similar crystallization conditions [100 mM Tris-HCl ( pH 8 . 5 ) , 1 . 75 M Ammonium sulphate , and 25% ( v/v ) glycerol] , with the exception of the two monoclinic forms , which were obtained using a solution containing 100 mM Tris-HCl ( pH 8 . 5 ) , 25% ( w/v ) PEG3350 , and 200 mM lithium sulphate as precipitant . In most of the cases , crystals appeared in a few days and reached their maximum size within 2 wk . Data were collected at ESRF and Soleil synchrotron beamlines ( Table 1 ) . Data were indexed and integrated with program XDS [57] , followed by space group determination with Pointless , and scaling with Aimless from the CCP4 software package [58] . Diffraction data from Se-labeled protein crystals were used to solve the structures of the trigonal ( P3121 ) and hexagonal ( P6122 ) crystal forms . Se atom substructure and phases were estimated using the AutoSol wizard of PHENIX [59] . After density modification , the resulting electron density maps were readily interpretable for most of the protein chain . Initial models were manually built with Coot [60] by fitting into the experimental map template helices and a homology model of the CA domain . The orthorhombic crystal form ( C2221 ) was solved by SAD from a Pt derivative dataset at 4 . 1 Å resolution . Heavy atom positions and phases were estimated using truncated data at 6 . 5 Å resolution ( the anomalous measurability limit ) with the AutoSol wizard . Despite the poor quality of the experimental map , it was possible to locate the CA domains and build a main-chain trace for the rest of the protein . Given the nonisomorphism between the Pt-derivative and the native dataset at 3 . 65 Å resolution , an extensive rigid body refinement was necessary to reorient the three dimers present in the native ASU prior to further refinement . Monoclinic crystal forms were determined by molecular replacement with Phaser [61] using the previously solved structures as search models . All models were refined with Buster [62] including as restrains NCS , TLS , a reference model , and experimental phases when available . Final models were validated with MolProbity [63] . Data collection and refinement statistics are shown in Table 1 . Figures were generated and rendered with PyMOL [64] . SAXS data were collected at 20°C at beamline BM29 at the ESRF . Data reduction and analysis following the standard procedures were performed using the ATSAS program package [65] . All samples were in 25 mM Tris-HCl buffer , pH 8 . 5 , containing 300 mM NaCl , 5 mM MgCl2 , 5% ( v/v ) glycerol , and 0 . 5 mM TECP . Plasmid pLCBA was constructed by subcloning cpxA from pIV3cpxA [49] into the low-copy pLCB vector [66] . This plasmid expressed cpxA about 1 . 5-fold in strains grown in LB at 37°C in the absence of arabinose ( Betton , unpublished results ) . The deletion of the periplasmic sensor domain of CpxA was made by the one-step site-directed deletion method [67] using pLCBA as DNA template , and the primers 5′-ATCTAGATTCACCGCTATTACTGCTGATTGTCACCATG-3′ and 5′-TAGATCTAACCCATTGTAGTTTTGGTTGTAGTTGTG-3′ . The PCR fragment was first treated with DpnI , and then with XbaI before ligation and transformation . The resulting plasmid , pLCBAΔP , encoded a CpxA variant in which the residues 29–163 are substituted by NLDS . Error-prone PCR was performed using the GeneMorph II Random Mutagenesis kit ( Agilent Technologies ) according to the manufacturer's instructions . Amplification using about 325 ng of cpxAΔP gene as DNA template and 20 thermal cycles was performed to aim for 1∼3 mutations/kb . The purified PCR fragment was digested , ligated into pLCB , and transformed into NS54 E . coli strain . Approximately 20 , 000 transformants were platted on LB-agar containing X-gal and 48 white colonies were picked for confirming the linkage of the Lac− phenotype to the plasmid . After DNA sequencing , single suppressor mutations were moved by site-directed mutagenesis on the wild-type cpxA gene cloned into pLCBA . To validate the genetic analysis , β-galactosidase activities conferred by the single mutant cpxA alleles were assayed in vivo in NS54 strain . NS54 cells harboring the various pLCB derivatives were grown overnight in LB medium containing chloramphenicol at 37°C , subcultured ( 1∶100 ) into 5 ml of the same medium , and then grown to midlog phase at 37°C . The β-galactosidase activity of permeabilized cells was assayed as described previously [53] . A minimum of four independent determinations was averaged to obtain the indicated values . Autokinase activity on soluble CpxAHDC and phosphotransfer from full-length CpxA to CpxRN ( receiver domain ) were assayed by Phos-tag acrylamide gel electrophoresis . Autokinase activity assays on full-length CpxA ( as Brij35 complexes ) were performed using radioactive ATP as described [56] . For phosphotransfer assays , full-length CpxA ( 10 µM ) in buffer H ( 25 mM HEPES , pH 7 . 8 , containing 100 mM NaCl , 50 mM KCl , 0 . 05% Brij35 , and 5 mM MgCl2 ) was phosphorylated with 100 µM ATP at 25°C for 20 min . Then , purified CpxRN was added to the reaction to a final concentration of 20 µM . Samples were withdrawn at various time intervals , mixed with SDS sample buffer , and kept on ice until all reactions were completed . Phospho-proteins were separated by Phos-tag acrylamide gel electrophoresis . Polyacrylamide gels were polymerized with 25 µM Phos-tag acrylamide and 50 µM MnCl2 . Following electrophoresis , gels were stained with Coomassie blue and analyzed by densitometry using the software Quantity One ( Biorad ) . Data were fitted using a nonlinear regression with program Kaleidagraph ( Synergy Software ) . The crystal structures of CpxA-nucleotide complexes described in this article have been deposited in the Protein Data Bank under accession numbers 4BIU ( CpxAHDC–ADP , orthorhombic C2221 form ) , 4BIV ( CpxAHDC–ATP , trigonal P3121 form ) , 4BIW ( CpxAHDC_M228V–AMPPNP , hexagonal P6122 form ) , 4BC0 ( CpxAHDC–ATP , hexagonal P6122 form ) , 4BIX ( CpxAHDC_M228V–ADP , monoclinic C2 form ) , and 4BIY ( CpxAHDC_M228V–ADP , monoclinic I2 form ) .
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Bacteria use two-component signal transduction systems ( TCSs ) , composed of a histidine kinase ( HK ) and a downstream response regulator ( RR ) , to perceive a broad range of external stimuli and trigger an appropriate response . Prototypical HKs are transmembrane homodimeric receptors with an external sensor region coupled to a cytoplasmic catalytic core through an adaptor HAMP domain . By a mechanism that is still poorly understood , the external input signal propagates through the HAMP domain to control the activity of the catalytic domain , which adds a phosphate tag , first to the HK itself ( “autophosphorylation” ) , ready for subsequent transfer to the RR . In this study , we report the first detailed structures of the full cytoplasmic domain of a prototypical HK , CpxA , which is involved in the detection and response to damage of the cell envelope of Escherichia coli . The structures provide snapshots of different kinase states along the autophosphorylation reaction . Our combined genetic , biochemical , and structural data point to a mechanical model for HK activation in which the input signal induces coordinated helical motions that regulate catalysis by asymmetrically repositioning the catalytic domains .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"proteins",
"protein",
"structure",
"biology",
"microbiology"
] |
2014
|
Segmental Helical Motions and Dynamical Asymmetry Modulate Histidine Kinase Autophosphorylation
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The normalization model of attention proposes that attention can affect performance by response- or contrast-gain changes , depending on the size of the stimulus and attention field . Here , we manipulated the attention field by emotional valence , negative faces versus positive faces , while holding stimulus size constant in a spatial cueing task . We observed changes in the cueing effect consonant with changes in response gain for negative faces and contrast gain for positive faces . Neuroimaging experiments confirmed that subjects’ attention fields were narrowed for negative faces and broadened for positive faces . Importantly , across subjects , the self-reported emotional strength of negative faces and positive faces correlated , respectively , both with response- and contrast-gain changes and with primary visual cortex ( V1 ) narrowed and broadened attention fields . Effective connectivity analysis showed that the emotional valence-dependent attention field was closely associated with feedback from the dorsolateral prefrontal cortex ( DLPFC ) to V1 . These findings indicate a crucial involvement of DLPFC in the normalization processes of emotional attention .
Attentional selection is the mechanism by which the subset of incoming information is preferentially processed at the expense of distractors . Numerous studies have suggested that attentional selection modulates both visual performance and neuronal activity in striate and extrastriate visual cortices [1] . However , studies have found disparate attentional selection effects on stimulus-evoked neural responses , such as the contrast-response function ( CRF ) [2 , 3] . Some have reported that attentional selection primarily enhances neural responses to high-contrast stimuli ( response gain ) [4–9] , whereas others have reported that attentional selection primarily enhances neural responses to medium-contrast stimuli ( contrast gain ) [2 , 3 , 10 , 11] . Still others have reported that attentional selection either enhances the entire contrast range or produces a combination of both response-gain and contrast-gain changes [12–16] . The normalization model of attention suggests that these seemingly conflicting modulatory effects of attention on sensory responses in the visual cortex may depend on two factors: the stimulus size and the attention field size [6 , 17–19] . Changes in the relative size of these two factors can tip the balance between neuronal excitatory and inhibitory processes , thereby resulting in response-gain changes , contrast-gain changes , or various combinations of the two [19] . Specifically , this model predicts that attention increases response gain when the stimulus is large and the attention field is small and increases contrast gain when the stimulus is small and the attention field is large . Previous psychophysical [17] and electroencephalography [20] studies have reported that the pattern of both behavioral performance and steady-state visual evoked potentials is consistent with the normalization model of attention . However , little is known about whether emotional attention also shapes perception by means of the normalization framework . Emotional stimuli , both negative and positive emotion , tend to attract attention in humans as well as other primates [21–26] . However , there is a critical distinction between the perceptual correlates of negative and positive emotions , with negative emotion narrowing and positive emotion broadening the scope of attention or perception [27–30] . For example , negative emotion shows lower and positive emotion shows higher sensory responses to unattended extrafoveal stimuli than neutral emotion [31] . The narrowing of the attention field by negative emotion is sometimes referred to as “weapon focus , ” in which peripheral details of stimuli are more poorly encoded , as measured in later memory [32] and repeated adaptation [31] . Similarly , negative emotion is associated with a greater tendency to perceive local components of visuospatial stimuli [33] , whereas positive emotion is associated with a greater tendency to perceive their global components [34] . Therefore , emotional stimuli , negative versus positive , offer a unique opportunity to change the size of the attention field relative to the stimulus , differentially modulating the gain of attentional selection . Here , the size of the attention field was manipulated by emotional valence—negative faces versus positive faces—while the stimulus size was held constant , and the stimulus contrast was varied in a spatial cueing task [19 , 35] . We measured the gain pattern of CRFs on the spatial cueing effect derived by the emotional faces and empirically revealed an interaction between emotion and attention: gain modulation depended on emotional valence , with a change in the spatial cueing effect consonant with a change in response gain for negative faces and a change in contrast gain for positive faces . A functional magnetic resonance imaging ( fMRI ) experiment confirmed that subjects’ attention fields were narrowed and broadened by negative faces and positive faces , respectively , as indexed by the decreased and increased primary visual cortex ( V1 ) responses to flanking gratings . Furthermore , the self-reported emotional strength of the emotional faces significantly correlated with the psychophysical gain modulations , and with the V1 blood oxygenation-level-dependent ( BOLD ) signal changes , across individual subjects . Finally , effective connectivity analysis showed that emotional valence controlled the attention field through the modulation of feedback from the dorsolateral prefrontal cortex ( DLPFC ) to V1 . These findings indicate that emotional attention interacts with the normalization processes depending on emotional valence , which is best explained by feedback modulation to the visual cortex from DLPFC .
In the psychophysical experiment , subjects performed an orientation discrimination task on one of two target grating patches; each was presented at five different contrasts ( the contrasts of both gratings were identical on any given trial and covaried across trials in random order ) . Covert attention ( without eye movements , S1 Fig ) was captured by the emotional face ( negative or positive ) , which also modulated the attention field: negative faces narrowed and positive faces broadened the attention field ( Fig 1B and 1C ) . A response cue at the stimulus offset indicated the target location , yielding congruent cue ( the emotional face matched the response cue ) and incongruent cue ( mismatched ) conditions ( Fig 1A ) . Comparing performance accuracy ( d′ ) for congruent and incongruent trials revealed the spatial cueing effect for each target contrast . The mean d′ plotted as psychometric functions of stimulus contrast and emotional valence are shown in Fig 2A: the negative emotion yielded a pattern that qualitatively resembled response gain ( left ) , and the positive emotion yielded a pattern that qualitatively resembled contrast gain ( right ) . The measured psychometric function for each emotional valence ( negative and positive ) and each trial condition ( congruent and incongruent ) was fit with the standard Naka–Rushton equation [37] . The two parameters d' max ( asymptotic performance at high-contrast levels ) and c50 ( the contrast yielding half-maximum performance ) determined response gain and contrast gain , respectively . The exponent n ( slope ) was fixed at 2 in the current analysis [17 , 38] . The d' max for emotional valence ( negative and positive ) and trial conditions ( congruent and incongruent ) are shown in Fig 2B and were submitted to a repeated-measures ANOVA with emotional valence and trial condition as within-subjects factors . The main effect of emotional valence ( F1 , 22 = 1 . 734 , p = 0 . 201 ) was not significant , but the main effect of the trial condition ( F1 , 22 = 34 . 971 , p < 0 . 001 ) and the interaction between these two factors ( F1 , 22 = 13 . 742 , p = 0 . 001 ) were both significant . Further t tests showed that the d' max of congruent trials was higher than that of incongruent trials ( t22 = 14 . 422 , p < 0 . 001 ) for negative emotion , but not for positive emotion ( t22 = 0 . 789 , p = 0 . 438 ) ; the d' max for negative emotion was higher than that for positive emotion in the congruent trials ( t22 = 2 . 181 , p = 0 . 040 ) , but not in the incongruent trials ( t22 = 0 . 083 , p = 0 . 934 ) . Similarly , for the c50 ( Fig 2C ) , the main effect of emotional valence was not significant ( F1 , 22 = 1 . 072 , p = 0 . 312 ) , but the main effect of the trial condition ( F1 , 22 = 40 . 884 , p < 0 . 001 ) and the interaction between these two factors ( F1 , 22 = 30 . 950 , p < 0 . 001 ) were both significant . Further t tests showed that the c50 of congruent trials was lower than that of incongruent trials for positive emotion ( t22 = −7 . 676 , p < 0 . 001 ) , but not for negative emotion ( t22 = −1 . 377 , p = 0 . 182 ) ; the c50 for negative emotion was lower than that for positive emotion in the incongruent trials ( t22 = −2 . 172 , p = 0 . 041 ) , but not in the congruent trials ( t22 = 0 . 464 , p = 0 . 647 ) . These results thus suggest that gain modulation of attentional selection depends on emotional valence . To evaluate further the role of emotional valence in the gain modulation of attention , we calculated the correlation coefficients between the self-reported emotional strength of the faces and psychophysical measures ( d' max and c50 ) across individual subjects . The self-reported emotional strength of negative faces significantly correlated with the d' max difference between congruent and incongruent trials ( r = 0 . 536 , p = 0 . 008 , Fig 2D , left ) , but not with the c50 difference between congruent and incongruent trials ( r = 0 . 014 , p = 0 . 948 , Fig 2E , left ) . Conversely , the self-reported emotional strength of positive faces significantly correlated with the c50 difference between congruent and incongruent trials ( r = −0 . 536 , p = 0 . 008 , Fig 2E , right ) , but not with the d' max difference between congruent and incongruent trials ( r = 0 . 205 , p = 0 . 348 , Fig 2D , right ) . These results thus demonstrate a close relationship between emotional valence and gain modulation of attentional selection ( response-gain and contrast-gain changes in psychophysical performance ) . Furthermore , given that subjects performed the negative and positive sessions on two different days ( the order of the two sessions was counterbalanced across subjects ) , we performed an additional analysis to confirm that the order of these two sessions did not influence our psychophysical results ( S2 Fig ) . To directly investigate whether negative emotion narrowed and positive emotion broadened subjects’ attention fields , a block-design fMRI experiment was designed to measure the V1 responses to task-irrelevant gratings ( Fig 3A ) . Each run consisted of 12 stimulus blocks of 16 s , interleaved with 12 blank intervals of 16 s . There were 6 kinds of stimulus blocks: 2 ( visual field: left/right ) × 3 ( emotional valence: negative/neutral/positive ) , and each stimulus block was randomly repeated two times in each run . For each type of emotional valence , data from the left and right visual fields were pooled together for analysis . Each stimulus block consisted of 8 trials; on each trial , a target face was centered at 4 . 65° eccentricity in the left or right hemifield and flanked by four gratings . The center-to-center distance between the target face and nearby gratings and between the target face and far gratings was 2 . 54° and 4 . 52° , respectively ( Fig 3A and 3B ) . The target face and flanking gratings were presented for 0 . 3 s , followed by a 1 . 7-s fixation interval , and subjects were asked to discriminate the gender of the target face ( male or female ) while maintaining central fixation throughout the trial ( Fig 3C ) . The accuracy rates ( mean percent correct ± standard error of the mean [SEM] ) were 91 . 35% ± 1 . 09% , 91 . 95% ± 0 . 86% , and 92 . 44% ± 1 . 15% , while the reaction times ( mean reaction time ± SEM ) were 813 . 06 ± 19 . 95 ms , 821 . 96 ± 19 . 09 ms , and 827 . 94 ± 20 . 16 ms for negative , neutral , and positive conditions , respectively . For these measurements , there was no significant difference ( all p > 0 . 05 ) in subject performance among the three types of emotional valence of the target faces . Regions of interest ( ROIs ) in V1 were defined as the cortical regions responding significantly to the target face , nearby gratings , and far gratings ( Fig 3B ) . We focused our analysis on V1 because activated areas in extrastriate cortex that corresponded to these three different stimuli showed a great deal of overlap . BOLD signals were extracted from these ROIs and then averaged according to emotional valence . For each stimulus block , the 2 s preceding the block served as a baseline , and the mean BOLD signal from 5 s to 16 s after stimulus onset was used as a measure of the response amplitude . The BOLD amplitudes in V1 evoked by the target face and flanking gratings ( nearby + far ) are shown in Fig 4B and 4C , respectively , and were submitted to a repeated-measures ANOVA with emotional valence as a within-subjects factor . For the target face , the main effect of emotional valence was not significant ( F2 , 28 = 2 . 416 , p = 0 . 112 ) . For the flanking gratings , however , the main effect of emotional valence was significant ( F2 , 28 = 16 . 582 , p = 0 . 001 ) ; post hoc paired t tests revealed that V1 response during the neutral condition was significantly lower than that during the positive condition ( t14 = −4 . 165 , p = 0 . 003 ) but significantly higher than that during the negative condition ( t14 = 3 . 806 , p = 0 . 006 ) . We further evaluated the role of emotional valence in the modulation of V1 responses to flanking gratings and calculated the correlation coefficients between the self-reported emotional strength of the faces and fMRI measures across individual subjects . Compared to the neutral condition , the decreased BOLD signal in the negative condition and the increased BOLD signal in the positive condition correlated significantly with the self-reported emotional strength of negative faces ( r = −0 . 746 , p = 0 . 001 , Fig 4D , left ) and positive faces ( r = 0 . 633 , p = 0 . 011 , Fig 4D , right ) , respectively . Moreover , these decreased and increased BOLD signals also correlated significantly with the response-gain ( Fig 4F , left ) and contrast-gain ( Fig 4G , right ) changes , respectively , in the psychophysical experiment . Our results thus indicated that negative emotion decreased and positive emotion increased the encoding of flanking gratings , as indexed by the BOLD signal changes in V1 evoked by four gratings ( nearby + far ) . However , at least three potential mechanisms could explain the same result: ( 1 ) emotional valence modulates the scope of perceptual encoding , with negative emotion narrowing and positive emotion broadening the attention field ( S4A Fig ) ; ( 2 ) emotional valence modulates the brain state ( e . g . , arousal ) , with negative emotion decreasing and positive emotion increasing the V1 signal ( S4B Fig ) ; or ( 3 ) a combination of hypotheses 1 and 2 , with negative emotion narrowing the attention field and decreasing the V1 signal and positive emotion broadening the attention field and increasing the V1 signal ( S4C Fig ) . Accordingly , for each emotional condition , we analyzed the BOLD amplitudes in V1 evoked by nearby gratings and far gratings separately . We hypothesized that these different mechanisms would show different patterns in V1 responses to nearby gratings and far gratings ( S4 Fig ) . The BOLD amplitudes in V1 evoked by nearby gratings and far gratings are shown in Fig 4E ( left and right , respectively ) and were submitted to a repeated-measures ANOVA with emotional valence ( negative , neutral , and positive ) and grating distance ( nearby and far ) as within-subjects factors . The main effect of emotional valence ( F2 , 28 = 17 . 227 , p = 0 . 001 ) , the main effect of the grating distance ( F1 , 14 = 8 . 140 , p = 0 . 013 ) , and the interaction between these two factors ( F2 , 28 = 8 . 887 , p = 0 . 003 ) were all significant . Thus , these data were submitted to a further simple effect analysis . For the nearby gratings , the main effect of emotional valence was significant ( F2 , 28 = 13 . 487 , p = 0 . 002 ) ; post hoc paired t tests revealed that there was no significant difference between neutral and positive conditions ( t14 = −1 . 866 , p = 0 . 250 ) , and both were significantly higher than the negative condition ( neutral versus negative: t14 = 5 . 211 , p < 0 . 001; positive versus negative: t14 = 3 . 672 , p = 0 . 008 ) . For the far gratings , the main effect of emotional valence was also significant ( F2 , 28 = 18 . 989 , p < 0 . 001 ) ; post hoc paired t tests revealed that there was no significant difference between negative and neutral conditions ( t14 = −1 . 900 , p = 0 . 235 ) , and both were significantly lower than the positive condition ( negative versus positive: t14 = −4 . 322 , p = 0 . 002; neutral versus positive: t14 = −6 . 426 , p < 0 . 001 ) . For both the negative and neutral conditions , the nearby gratings were significantly higher than the far gratings ( negative: t14 = 2 . 849 , p = 0 . 013; neutral: t14 = 3 . 366 , p = 0 . 005 ) , but significant for the positive condition ( t14 = 2 . 160 , p = 0 . 049 ) . These findings are consistent with the first hypothesis that emotional valence modulates the scope of perceptual encoding in V1 by narrowing and broadening the attention field . To examine potential cortical or subcortical area ( s ) that showed a consistent pattern of activation with that in V1 , where negative and positive emotions modulated its responses to flanking gratings in opposite ways ( Fig 4C ) , we performed a group analysis and did a whole-brain search for cortical and subcortical area ( s ) that showed opposite modulations of flanking gratings for negative and positive emotions , relative to the neutral condition . The results showed that only early visual cortical areas , the pulvinar thalamic nucleus , and DLPFC demonstrated this effect . The BOLD amplitudes in the pulvinar and DLPFC for the three types of emotional valence are shown in Fig 4H and 4J , respectively , and were submitted to a repeated-measures ANOVA with emotional valence as a within-subjects factor . The main effect in both the pulvinar ( F2 , 28 = 9 . 092 , p = 0 . 001 ) and DLPFC ( F2 , 28 = 23 . 081 , p < 0 . 001 ) was significant; post hoc paired t tests revealed that , for the pulvinar , the negative condition was significantly higher than that during the positive condition ( t14 = 3 . 801 , p = 0 . 006 ) , but no significant difference was found between the neutral and negative conditions or between the neutral and positive conditions ( all p > 0 . 05 ) . For the DLPFC , however , the neutral condition was significantly lower than that during the negative condition ( t14 = −5 . 336 , p < 0 . 001 ) but significantly higher than that during the positive condition ( t14 = 3 . 779 , p = 0 . 006 ) . Furthermore , we found that V1 responses to flanking gratings were significantly correlated with DLPFC responses ( Fig 4K ) , but not with pulvinar responses ( Fig 4I ) . Compared to the neutral condition , V1’s decreased BOLD signal in the negative condition and increased BOLD signal in the positive condition correlated significantly with DLPFC`s increased BOLD signal in the negative condition ( r = −0 . 631 , p = 0 . 012 ) and decreased BOLD signal in the positive condition ( r = −0 . 704 , p = 0 . 003 ) , respectively . Taken together , these findings suggest that the modulation of the attention field size in V1 by emotional valence may be derived by feedback from DLPFC . Additionally , to further exclude the possibility that emotional valence modulation of the attention field size in V1 could be derived from feedback from other attention-specific ( i . e . , the frontal eye field [FEF] and the posterior parietal cortex [PPC] ) or emotion-specific ( i . e . , the amygdala and medial orbitofrontal cortex [mOFC] ) cortical areas , we performed a supplemental analysis and found that the BOLD responses in both the amygdala and mOFC , but not in either FEF or PPC , were significantly modulated by emotional valence . For the amygdala , as well as mOFC , both the negative and positive conditions were significantly higher than the neutral condition; however , no significant difference was found between these two conditions ( S5 Fig ) , showing an inconsistent pattern of activation with that in V1 , where the negative condition was significantly lower than the positive condition . To directly confirm whether emotional valence modulated the attention field size in V1 through the modulation of feedback from DLPFC , we used dynamic causal modeling ( DCM ) to examine functional changes in directional connectivity among the amygdala , DLPFC , the pulvinar , and V1 related to negative and positive emotions . The pulvinar was selected in the models since it showed a consistent pattern of activation with DLPFC ( Fig 4H ) , while the amygdala was selected in the models since it is well known as a critical brain area for emotion processing [24] , showing significantly greater responses to emotional faces than neutral faces ( S5 Fig ) . Given the extrinsic visual input into V1 , we defined seven different models with modulatory inputs ( either the negative emotion or positive emotion , Fig 5A ) . The modulatory inputs could modulate the feedback from the amygdala ( Model 1 ) , from the pulvinar ( Model 2 ) , from both the amygdala and pulvinar ( Model 3 ) , from DLPFC ( Model 4 ) , from both the amygdala and DLPFC ( Model 5 ) , from both DLPFC and the pulvinar ( Model 6 ) , and from all three areas ( Model 7 ) to V1 . We examined these seven models for modeling the modulatory effect by negative and positive emotions and fit each of these seven models for each subject . For negative emotion , we computed the exceedance probability of each model [39] . The result showed that Models 1 through 7 had exceedance probabilities of 2 . 55% , 5 . 14% , 2 . 88% , 30 . 45% , 14 . 35% , 23 . 57% , and 21 . 05% , respectively , suggesting that Model 4 was the best one to explain the modulatory effect by negative emotion ( Fig 5B , up ) . The negative emotion significantly increased the feedback connectivity from the amygdala to both DLPFC ( t14 = 2 . 906 , p = 0 . 011 ) and the pulvinar ( t14 = 2 . 213 , p = 0 . 044 ) but decreased the feedback connectivity from DLPFC to V1 ( t14 = −3 . 792 , p = 0 . 002 ) ( Fig 5C , up ) . For positive emotion , the exceedance probabilities of Model 1 to Model 7 were 2 . 89% , 3 . 98% , 5 . 37% , 32 . 24% , 15 . 60% , 21 . 02% , and 18 . 91% , respectively , suggesting that the modulatory effect by positive emotion was also best explained by Model 4 ( Fig 5B , down ) . However , the positive emotion significantly decreased the feedback connectivity from the amygdala to both DLPFC ( t14 = −2 . 743 , p = 0 . 016 ) and the pulvinar ( t14 = −2 . 573 , p = 0 . 022 ) but increased the feedback connectivity from DLPFC to V1 ( t14 = 3 . 923 , p = 0 . 002 ) ( Fig 5C , down ) . Furthermore , we calculated the correlation coefficients between V1 responses and the effective connection strengths ( the sum of the intrinsic and modulatory connectivities ) from DLPFC to V1 across individual subjects . Compared to the neutral condition , the decreased V1 BOLD signal in the negative condition ( r = 0 . 620 , p = 0 . 014 ) and the increased V1 BOLD signal in the positive condition ( r = 0 . 587 , p = 0 . 021 ) correlated significantly with feedback connectivity from DLPFC to V1 ( Fig 5D ) . Additionally , a supplemental DCM analysis with mOFC , instead of the amygdala , showed significantly greater responses to emotional faces than neutral faces , confirming these results ( S6 Fig ) . Together , these results further support the idea that emotional valence-dependent modulations of the attention field size in V1 may be derived by feedback from DLPFC .
This study examined whether emotional attention shapes perception via a normalization framework . The normalization model of attention proposes that attention can affect performance by response- or contrast-gain changes , depending on the stimulus size and the attention field size [19] . Previous studies have suggested that negative emotion could narrow and positive emotion could broaden the scope of perceptual encoding [29 , 31 , 32] , which offers a unique opportunity to change the size of the attention field relative to the stimulus size . Here , we measured the gain pattern of CRFs on the spatial cueing effect derived from negative and positive faces . We found a change in the spatial cueing effect consistent with a change in response gain for negative faces and in contrast gain for positive faces . The fMRI experiment confirmed that emotional valence modulated the attention field in V1; negative faces decreased and positive faces increased V1 responses to flanking gratings . Importantly , across subjects , the self-reported emotional strength of negative and positive faces correlated , respectively , both with response- and contrast-gain changes and with V1 decreased and increased responses to flanking gratings . Furthermore , effective connectivity analysis showed that the V1 attention field size controlled by emotional valence was best explained by increased and decreased feedback from DLPFC to V1 . Our data provide , to our knowledge , the first neural evidence that emotional attention interacts with normalization processes depending on emotional valence . Our behavioral data can be interpreted by a hypothesis that behavioral performance is limited by the neuronal activity with an additive , independent , and identically distributed noise , and the decision-making process with a maximum-likelihood decision rule [40] . Performance accuracy d' , used in both Herrmann et al . [17] and our studies , is proportional to the signal-to-noise ratio of the underlying neuronal responses . Thus , it can reflect in parallel any change in neuronal CRFs in our study . Indeed , we found that a change in the cueing effect ( Fig 2A ) was consonant with a change in response gain of CRF ( Fig 1B ) for negative faces and a change in contrast gain of CRF ( Fig 1C ) for positive faces . These emotional valence-dependent gain modulations of attentional selection not only are consistent with existing psychophysical [30 , 32 , 41] and brain imaging [31 , 42] studies suggesting that negative emotion narrows and positive emotion broadens the scope of perceptual encoding , but also support and extend the normalization model of attention [19] . This model proposes that , in the absence of attention ( e . g . , in the incongruent cue condition ) , two factors determine the firing rate of a visually responsive neuron . One is the stimulus drive ( excitatory component ) determined by the contrast of the stimulus placed in the receptive field of a neuron . The other is the suppressive drive ( inhibitory component ) determined by the summed activity of other neighboring neurons , which serves to normalize the overall spike rate of the given neuron via mutual inhibition [43 , 44] . Attention ( e . g . , in the congruent cue condition ) modulates the pattern of neural activity by altering the balance between these excitatory and inhibitory components , depending on the relative sizes of the attention field to the stimulus size , and thereby exhibiting response-gain changes , contrast-gain changes , and various combinations of the two . In our study , given the fixed size of the target stimuli in the spatial cueing task , the narrowed attention field by negative emotion led to response-gain changes because attentional gain enhanced the entire stimulus drive but enhanced only the center of the suppressive drive . Conversely , the broadened attention field by positive emotion led to contrast-gain changes because attentional gain was applied equally to the stimulus and suppressive drives . The fMRI data confirmed these emotional valence-dependent changes of the attention field; negative emotion narrowed and positive emotion broadened the attention field in V1 . Importantly , this result cannot be explained by brain state changes or by the combination of brain state and attention field changes ( S4 Fig ) . Moreover , the result cannot be explained by a number of other factors , such as low-level features , task difficulty , target face processing , or eye movement . First , the size and contrast of the flanking gratings were identical on any given trial , and the phase and orientation were random across trials , suggesting no physical difference of the gratings among the different emotional conditions . Second , during scanning , the flanking gratings were never task relevant for the subjects , who performed a gender discrimination task on the faces . There was no significant difference in subject performance among the three types of emotional valence of the faces , suggesting no difference in task difficulty . Third , the finding of no significant activation difference in V1 for the emotional faces excluded the possibility of a trade-off between attention to the target face and flanking gratings ( Fig 4B ) . Finally , the eye movement data showed that the subjects’ eye movements were small and their eye position distributions were statistically indistinguishable for the three types of emotional valence ( S3 Fig ) . Although our eye movement data were recorded in a psychophysics lab ( outside the scanner ) , it should be noted that the recordings were made when subjects performed the same task as the one in the fMRI experiments . Differences in eye movements for the three types of emotional valence may be a potential confound , but it is highly unlikely since our recordings outside the scanner did not detect any such differences . One should note that emotional valence-dependent modulations of attention fields in our study were indexed by the decreased and increased V1 responses to flanking gratings , which were irrelevant and presumably ignored while subjects attended to the target face . Thus , how does emotional valence differentially modulate V1 responses to these distractors ? Previous neurophysiological and brain neuroimaging studies have implicated prefrontal areas in the filtering of distractors [45–50] , and our findings are consistent with such an influence . Our findings suggest that distractor suppression by emotional valence in V1 could be associated with feedback from DLPFC . First , DLPFC responses were significantly modulated by emotional valence and showed a pattern of activation consistent with that in V1 , where negative and positive emotions modulated its responses to task-irrelevant distractors in opposite ways . This consistent pattern of activation between V1 and DLPFC was also confirmed by a group analysis and a whole-brain search for cortical and subcortical area ( s ) that showed opposite responses for negative and positive emotions ( Fig 4J ) . Second , V1 responses to flanking distractors were significantly predicted by DLPFC responses ( Fig 4K ) . Finally , the DCM analysis indicated that negative emotion increased and positive emotion decreased suppression from DLPFC to V1 , and these suppression effects significantly predicted the V1 responses to flanking distractors ( Fig 5C and 5D ) . Our study succeeded in linking emotional valence-dependent feedback from the DLPFC to V1 directly with distractor suppression . Based on our fMRI findings , in conjunction with existing neurophysiological [51] , behavioral [30 , 32] , and neuroimaging [42 , 52] data , we speculate that emotional valence-dependent distractor suppression is derived from DLPFC influences on the scope of inhibitory control . Inhibitory control is thought to limit the amount of information entering the focus of attention [53] , which , in turn , affects the scope of attentional selection , and DLPFC is thought to play a very important role in this function [48] . We speculate that , in our study , when the emotional faces were negative , both feedback from the amygdala to DLPFC and the BOLD signal in DLPFC increased and thus decreased effective connectivity ( i . e . , increased suppression ) from DLPFC to V1 , as revealed by the DCM analysis , which then would increase the inhibitory control , in other words , increase the inhibition of ignored distractors , resulting in a narrowed focus of attention and reduced processing of the flanking gratings . Conversely , when the emotional faces were positive , both feedback from the amygdala to DLPFC and the BOLD signal in DLPFC decreased and thus increased effective connectivity ( i . e . , decreased suppression ) from DLPFC to V1 , which would decrease the inhibition of ignored distractors , resulting in a broadened scope of attention and flanking gratings that were more fully processed . It should be noted that our speculation only provides a possible mechanism for emotional valence-dependent attention field in V1 , which should be tested with neurophysiological techniques in the future . Our results also indicate that the pulvinar may be involved in emotional valence-dependent modulations of distractor suppression in V1 , consistent with previous lesion [54] , neurophysiological [55] , and brain neuroimaging [56] studies , implicating the pulvinar’s involvement in the filtering of unwanted information . However , it is important to note that the ROIs in the pulvinar defined in our study were across dorsal and ventral parts . The dorsal pulvinar predominantly projects to areas within the frontoparietal network and superior anterior temporal cortex [57]; the ventral pulvinar , conversely , exhibits reciprocal connections with successive occipitotemporal cortical areas along the ventral processing stream [58 , 59] . Thus , further work is needed to use high-spatial resolution fMRI or neurophysiological techniques to parse the relative contributions of the dorsal and ventral pulvinar to emotional valence-dependent modulations of distractor suppression . One should note that our results cannot be explained by a number of other factors , including poststimulus modulation by the response cue , greater attention directed to negative faces , or an effect of emotional faces on decisional rather than perceptual processing of the target . First , although previous studies have suggested that the poststimulus cue ( for example , the response cue in our study ) can influence not only subjects’ nonperceptual decision [60] but also the perception of stimuli presented before it [15 , 61 , 62] , the response cue in our study was totally randomized and uninformative about the target; we thus believe that our psychophysical results cannot be explained by the response cue . Second , although previous studies have found that negative faces tend to attract more attention and show a greater response than positive faces [25 , 26] , this effect was not obtained in our study; no significant difference in response to negative and positive faces was found in V1 ( Fig 4B ) , the amygdala , or mOFC ( S5 Fig ) , thus eliminating the specific impact of negative faces as a factor affecting our fMRI results . Finally , if the emotional faces ( negative versus positive ) affected subjects’ decisional rather than their perceptual processing of the target stimuli , then these two conditions should have produced different responses in the orbitofrontal cortex ( OFC ) , an area critically involved in decision making [63 , 64]; however , no significant difference between these two conditions was found in OFC ( S5 Fig ) , indicating that the observed difference between the negative and positive conditions was most likely caused by perceptual rather than decision-making processes . In addition , our study used the normalization model to predict and explain psychophysical performance only . Our fMRI experiment did not measure the BOLD response for different contrast levels but instead examined whether negative emotion narrowed and positive emotion broadened subjects’ attention fields . The design of our fMRI study took into account the results of several papers reporting that the attentional effect on the BOLD response is constant across different contrast levels , showing a baseline increase/additive shift rather than either a response gain or contrast gain [12 , 14 , 15] . In those studies , the attention field was manipulated by focused ( narrowed attention field ) and distributed ( broadened attention field ) cues . These two cues , however , either enhanced the entire contrast range or produced a combination of both response-gain and contrast-gain changes , indicating inconsistent predictions of the normalization model [19] . Previous studies have suggested that their results may be because BOLD signals integrate the activity across neurons showing different attention modulatory effects , which would result in various combinations of both response and contrast gains [15 , 16] . Moreover , attention-triggered BOLD signals can be driven by both bottom-up stimuli and top-down goals [14 , 65]; hence , the increased BOLD signals to the attended low and mid-contrast stimuli may be mainly driven by the top-down modulation rather than the bottom-up stimuli . In sum , our study provides strong evidence that gain modulation of emotional attention depends on emotional valence . Negative emotion and positive emotion modulate the attention field in V1 in opposite ways , maybe depending on the increased or decreased feedback from DLPFC , thereby changing the suppression of distractors [53] . The prominent role of the prefrontal cortex in distractor suppression evident here is consistent with recent neurophysiological findings that have begun to address how prefrontal areas directly influence sensory representations to filter out distractors [49 , 51 , 66] . Identifying DLPFC as a potential neural substrate of emotional valence-dependent normalization processing of attention gives insight into how the interaction between emotion and attention shapes our experience of the world .
A total of 23 human subjects ( 8 males , 21–41 y old ) participated in the study . All 23 participated in the psychophysical experiment , and 15 ( 8 males , 21–41 y old ) of them participated in the fMRI experiment . All subjects were naїve to the purpose of the study . They reported normal or corrected-to-normal vision and had no known neurological , psychiatric , or visual disorders . They gave written informed consent in accordance with protocols approved by the National Institute of Mental Health ( NIMH ) Institutional Review Board ( 93-M-0170 ) . Forty angry , forty happy , and forty neutral faces were chosen from the NimStim Set of Facial Expressions ( http://www . macbrain . org/resources . htm ) [36] . All faces were masked to exclude ears , neck , hair , and hairline and were scaled to the same size ( diameter: 2 . 2° ) ( Fig 1A ) . In the psychophysical experiment , a pair of faces were centered in the left and right hemifields at 4 . 65° eccentricity , one of which was an emotional face . Target gratings ( spatial frequency: 4 . 0 cycles/°; diameter: 2 . 2°; phase: random ) were presented at five possible contrasts: 0 . 03 , 0 . 08 , 0 . 20 , 0 . 45 , and 0 . 75 . In the fMRI experiment , a single face ( diameter: 2 . 2° ) was centered in either the left or right hemifield at 4 . 65° eccentricity and was flanked by four gratings ( diameter: 1 . 4°; spatial frequency: 4 . 0 cycles/°; contrast: 0 . 20; phase: random; orientation: randomly chosen from 0° to 180° ) . The center-to-center distance between the face and nearby gratings and between the face and far gratings was 2 . 54° and 4 . 52° , respectively ( Fig 3A and 3B ) . Visual stimuli were displayed on a BENQ LCD monitor ( model: XL2420Z; refresh rate: 60 Hz; resolution: 1 , 920 × 1 , 080; size: 24 in ) at a viewing distance of 57 cm . The subjects’ head position was stabilized using a chin rest . A white fixation ( diameter: 0 . 1° ) cross was always present at the center of the monitor . Each trial began with central fixation . A pair of faces ( one emotional ) were presented in the left and right hemifields for 150 ms , followed by a 50 ms fixation interval . The emotional face served as a cue to attract covert spatial attention . Then , a pair of gratings ( with identical contrasts ) were presented for 33 ms in the left and right hemifields at 4 . 65° eccentricity , one of which was the target . Subjects were asked to press one of two buttons to indicate the orientation of the grating ( leftward or rightward tilted ) and received auditory feedback if their response was incorrect . Target location was indicated by a peripheral 100 ms response cue ( 0 . 5° white line ) above one of the grating locations , but not at the grating location to avoid masking . A congruent cue was defined as a match between the emotional face location and response cue location ( half the trials ) ; an incongruent cue was defined as a mismatch ( half the trials ) . Participants were explicitly told that the emotional faces were randomized and uninformative about the target location ( Fig 1A ) . The experiment consisted of two sessions ( negative emotion and positive emotion of the faces ) , with the two sessions occurring on different days; the order of the two sessions was counterbalanced across subjects . Each session consisted of 30 blocks; each block had 80 trials , from randomly interleaving 16 trials from each of the five contrasts . Contrast varied from trial to trial in randomly shuffled order , and stimuli were presented briefly ( i . e . , 33 ms ) to avoid any possible dependence of attentional state on stimulus contrast . The attentional effect for each grating contrast was quantified as the difference between the performance accuracy ( d' ) in the congruent and incongruent cue conditions . After each session , subjects were asked to rate ( on a seven-point Likert scale ) their self-perception of the emotional strength of each emotional face . For each subject , the self-reported emotional strength of positive and negative emotion was the mean rating for 40 positive and 40 negative faces , respectively . To quantitatively examine the pattern of gain ( either response or contrast gain ) separately for positive emotion and negative emotion , for each subject , performance—i . e . , d' = z ( hit rate ) –z ( false alarm rate ) —was assessed across experimental blocks for each contrast and each trial condition ( congruent and incongruent ) . A rightward response to a rightward stimulus tilt was ( arbitrarily ) considered to be a hit , and a rightward response to a leftward stimulus was considered to be a false alarm . For each subject , the mean d' contrast response functions ( CRFs ) obtained for congruent and incongruent trials were fit with the standard Naka–Rushton equation [37]: d′ ( c ) =d′max ( cn/[cn+c50n] ) , where d' is performance as a function of contrast ( c ) , d' max determines the asymptotic performance at high contrasts , c50 is the contrast corresponding to half the asymptotic performance , and n is an exponent that determines the slope of the CRFs . In this analysis , n was fixed at 2 [17 , 38] . Using a block design , the experiment consisted of six functional runs . Each run consisted of 12 stimulus blocks of 16 s , interleaved with 12 blank intervals of 16 s . There were 6 different stimulus blocks: 2 ( visual field: left/right ) × 3 ( emotional valence: negative/neutral/positive ) . Each stimulus block was randomly repeated two times in each run , and consisted of 8 trials; on each trial , a face flanked by four gratings was presented for 0 . 3 s , followed by a 1 . 7-s fixation interval , and subjects were asked to discriminate the gender of the face ( male or female ) while maintaining central fixation throughout the trial ( Fig 3C ) . The V1 boundary was defined by a standard phase-encoded method developed by Sereno et al . [67] and Engel et al . [68] , in which subjects viewed rotating wedge and expanding ring stimuli that created traveling waves of neural activity in visual cortex . A block-design scan was used to localize the ROIs in V1 corresponding to the target face , nearby gratings , and far gratings ( Fig 3B ) . The localizer scan consisted of 12 stimulus blocks of 12 s , interleaved with 12 blank intervals of 12 s . In a stimulus block , subjects passively viewed 8-Hz flickering patches . Each block type was repeated four times in the run , which lasted 288 s . MRI data were collected using a 3T Siemens Trio scanner with a 32-channel phase-array coil . In the scanner , the stimuli were back-projected via a video projector ( refresh rate: 60 Hz; spatial resolution: 1 , 280 × 800 ) onto a translucent screen placed inside the scanner bore . Subjects viewed the stimuli through a mirror located above their eyes . The viewing distance was 115 cm . BOLD signals were measured with an echo-planar imaging sequence ( TR: 2 , 000 ms; TE: 30 ms; FOV: 192 × 192 mm2; matrix: 64 × 64; flip angle: 90°; slice thickness: 3 mm; gap: 0 mm; number of slices: 34; slice orientation: axial ) . The bottom slice was positioned at the bottom of the temporal lobes . A 3D MPRAGE structural dataset ( resolution: 1 ×1 × 1 mm3; TR: 2 , 600 ms; TE: 30 ms; FOV: 256 × 224 mm2; flip angle: 8°; number of slices: 176; slice orientation: sagittal ) was collected in the same session before the functional scans . Subjects underwent two sessions , one for retinotopic mapping and the other for the main experiment . The anatomical volume for each subject in the retinotopic mapping session was transformed into a brain space that was common for all subjects [69] and then inflated using BrainVoyager QX . Functional volumes in both sessions for each subject were preprocessed , including 3-D motion correction , linear trend removal , and high-pass ( 0 . 015 Hz ) filtering using BrainVoyager QX [70] . Head motion within any fMRI session was <2 mm for all subjects . The images were then aligned to the anatomical volume from the retinotopic mapping session and transformed into Talairach space [69] . The first 8 s of BOLD signals were discarded to minimize transient magnetic saturation effects . A general linear model ( GLM ) procedure was used to determine the V1’s boundary and ROI analysis . V1 boundaries were delineated by a standard retinotopic mapping method [67 , 68] . The ROIs within V1 were defined as regions that responded more strongly to the flickering patches than to the blank screen ( p < 10−3 , uncorrected ) . To directly confirm whether emotional valence modulated the attention field size in V1 through the modulation of feedback from DLPFC , we applied DCM analysis in SPM10 to our fMRI data [39] . For each subject and each hemisphere , using BrainVoyager QX , the amygdala and V1 ( including dorsal and ventral parts ) voxels were identified as those activated by the emotional block and the flanking gratings at a significance level of p < 0 . 005 , respectively; both the pulvinar and DLPFC voxels were identified as those activated by the stimulus block at a significance level of p < 0 . 005 . The mean Talairach coordinates of these voxels and the standard errors across subjects in the amygdala , dorsal V1 , ventral V1 , the pulvinar , and DLPFC were [−22 ± 1 . 4 , −7 ± 1 . 0 , −13 ± 1 . 1] , [−7 ± 0 . 8 , −93 ± 1 . 0 , −12 ± 1 . 3] , [−3 ± 1 . 1 , −84 ± 1 . 1 , −16 ± 1 . 2] , [−18 ± 1 . 9 , −27 ± 1 . 2 , 7 ± 0 . 9] , and [−44 ± 1 . 6 , 25 ± 1 . 7 , 29 ± 2 . 7] for the left hemisphere and [25 ± 1 . 6 , −9 ± 1 . 0 , −15 ± 1 . 0] , [7 ± 1 . 1 , −94 ± 0 . 6 , −8 ± 2 . 2] , [3 ± 0 . 8 , −83 ± 1 . 1 , −14 ± 1 . 7] , [17 ± 1 . 7 , −29 ± 1 . 0 , 7 ± 0 . 9] , and [45 ± 1 . 7 , 21 ± 3 . 1 , 31 ± 2 . 3] for the right hemisphere , respectively . For each subject and each hemisphere , these Talairach coordinates were converted to Montreal Neurological Institute ( MNI ) coordinates using the tal2mni conversion utility ( http://imaging . mrc-cbu . cam . ac . uk/downloads/MNI2tal/tal2mni . m ) . In SPM , for each of these areas , we extracted voxels within a 4-mm sphere centered on the most significant voxel and used their time series for the DCM analysis . The estimated DCM parameters were later averaged across dorsal and ventral V1 and the two hemispheres using the Bayesian model averaging method [39] . DCMs have three sets of parameters: ( 1 ) extrinsic input into one or more regions; ( 2 ) intrinsic connectivities among the modeled regions; and ( 3 ) bilinear parameters encoding the modulations of the specified intrinsic connections by experimental manipulations [39 , 71 , 72] . The third set of parameters is used to quantify modulatory effects , which reflect increases or decreases in connectivity between two regions given some experimental manipulation , compared with the intrinsic connections between the same regions that capture connectivity in the absence of experimental manipulation . FMRI data were modeled using GLM , with regressors for negative , neutral , and positive emotions , as well as a fourth condition comprising all visual input . The fourth condition was added specifically for the DCM analysis to be used as a direct visual input . Given the extrinsic visual input into V1 , we defined seven different models with modulatory inputs ( either the negative emotion or positive emotion , Fig 5A ) . The modulatory inputs could modulate feedback from the amygdala ( Model 1 ) , from the pulvinar ( Model 2 ) , from both the amygdala and pulvinar ( Model 3 ) , from DLPFC ( Model 4 ) , from both the amygdala and DLPFC ( Model 5 ) , from both DLPFC and the pulvinar ( Model 6 ) , and from all three areas ( Model 7 ) to V1 . We examined these seven models for modeling the modulatory effect by negative and positive emotions . We fit each of these seven models for each subject . Using a hierarchical Bayesian approach [73] , we compared the seven models by computing the exceedance probability of each model , i . e . , the probability to which a given model is more likely than any other included model to have generated data from a randomly selected subject [39 , 71 , 72] . In the best model ( Model 4 ) , we examined the modulatory effects by negative and positive emotions . Eye movements were recorded with an ASL EyeTrac 6000 ( Applied Science Laboratories , Bedford , Massachusetts ) in a psychophysics lab ( outside the scanner ) . Its temporal resolution was 60 Hz , and its spatial resolution was 0 . 25° . Recording was performed when subjects performed the same task as the psychophysical and fMRI experiments . S1 Fig and S3 Fig show that subjects’ eye movements were small and statistically indistinguishable across all conditions .
|
Attentional selection is the mechanism by which the subset of incoming information is preferentially processed at the expense of distractors . The normalization model of attention suggests that attention-triggered modulatory effects on sensory responses in the visual cortex depend on two factors: the stimulus size and the attention field size . However , little is known regarding whether emotional attention shapes perception by means of the normalization framework . To test this hypothesis , we manipulated the attention field by emotional valence—negative faces versus positive faces—while holding the stimulus size constant in a spatial cueing task . We observed that attention increased response gain for negative faces , with the largest cueing effects occurring at high contrasts and little to no effect at low and mid-contrasts; however , attention increased contrast gain for positive faces , with the largest cueing effects occurring at mid-contrasts and little to no effect at low and high contrasts . A complementary neuroimaging experiment confirmed that subjects' attention fields were narrowed for negative faces and broadened for positive faces . Across subjects , the self-reported emotional strength of negative faces and positive faces correlated , respectively , both with response-gain and contrast-gain changes and with narrowed and broadened attention fields in the primary visual cortex . Mechanistically , we found that the emotional valence-dependent attention field was closely associated with feedback from the dorsolateral prefrontal cortex to the primary visual cortex . Our findings provide evidence for a normalization framework for emotional attention and for the critical role of feedback from the prefrontal cortex to the early visual cortex in this normalization .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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2016
|
A Normalization Framework for Emotional Attention
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Trichomonas vaginalis is a common sexually transmitted parasite that colonizes the human urogential tract where it remains extracellular and adheres to epithelial cells . Infections range from asymptomatic to highly inflammatory , depending on the host and the parasite strain . Here , we use a combination of methodologies including cell fractionation , immunofluorescence and electron microscopy , RNA , proteomic and cytokine analyses and cell adherence assays to examine pathogenic properties of T . vaginalis . We have found that T . vaginalis produces and secretes microvesicles with physical and biochemical properties similar to mammalian exosomes . The parasite-derived exosomes are characterized by the presence of RNA and core , conserved exosomal proteins as well as parasite-specific proteins . We demonstrate that T . vaginalis exosomes fuse with and deliver their contents to host cells and modulate host cell immune responses . Moreover , exosomes from highly adherent parasite strains increase the adherence of poorly adherent parasites to vaginal and prostate epithelial cells . In contrast , exosomes from poorly adherent strains had no measurable effect on parasite adherence . Exosomes from parasite strains that preferentially bind prostate cells increased binding of parasites to these cells relative to vaginal cells . In addition to establishing that parasite exosomes act to modulate host∶parasite interactions , these studies are the first to reveal a potential role for exosomes in promoting parasite∶parasite communication and host cell colonization .
The parasite Trichomonas vaginalis causes the most common non-viral sexually transmitted infection , with an estimated 275 million people infected each year worldwide [1] . Disease manifestations may include vaginitis , cervicitis , urethritis , pelvic inflammatory disease , and adverse birth outcomes [2] . T . vaginalis infection may also increase risk of HIV transmission and the incidence and severity of cervical and prostate cancer [3]–[5] . Despite the need to define key pathogenic properties of the parasite in order to prevent and control the infection , little is known about parasite or host factors involved in pathogenesis [6] , [7] As an extracellular parasite residing in the urogenital tract , T . vaginalis must adhere to epithelial cells as an initial step towards colonizing the host and establishing infection . Although several families of membrane proteins and secreted proteases have been proposed to play roles in host cell attachment [7]–[10] only three T . vaginalis surface molecules have been shown to be involved in attachment of the parasite to host epithelial cells . The best studied is an abundant lipoglycan ( TvLG ) [11]–[13] that binds to galectin-1 , the only host cell receptor described for T . vaginalis [14] . Two related surface proteins of unknown function are known to increase the attachment of T . vaginalis to host cells when expressed in the parasite [15] . A recent analysis of the surface membrane proteome of T . vaginalis revealed that at least three tetraspanin ( Tsp ) proteins of the nine found in the genome are present on the parasite's surface [15] , [16] . Tsps are involved in a wide variety of activities in mammalian cells including attachment , fusion , motility , migration , and proliferation [17] . Of the 33 human tetraspanins , a small subset including CD63 , CD9 , CD81 , CD82 are constitutive components of exosomes and various other tetraspanins may be present depending on cell of origin and cellular environment [18] . Tsps are also present in all examined mammalian exosomes and , as such , are routinely used as markers for these small secreted extracellular vesicles [19] , [20] . Exosomes are 30–100 nm membrane-bound vesicles derived from endocytic compartments that are secreted into the extracellular milieu . Studies of mammalian cells have established that exosomes package specific cargo used for intercellular communication [21] , immune modulation and surveillance and the metastasis of diverse tumor cells [18] , [22]–[24] . Roles in antigen presentation , the delivery of surface receptors and the transfer of RNA to recipient cells have also been described for exosomes [25]–[27] . Exosomes have recently been shown to be released by pathogens or mammalian cells infected with pathogens [22] , [28] . However , to date only a handful of non-mammalian cell types: the fungi Histoplasma , Cryptococcus , Paracoccidiodes , the nematode C . elegans and the parasite Leishmania [29]–[31] have been shown to produce exosomes . Here we report that T . vaginalis secretes exosomes with physical characteristics and protein components similar to mammalian exosomes . Our analyses demonstrate that parasite exosomes mediate both host∶parasite and parasite∶parasite interactions and play a role in the attachment of the parasite to host epithelial cells . T . vaginalis exosomes are also shown to fuse with and deliver their contents to host cells thereby modulating host cell immune response . These studies are the first to indicate a role for exosomes in promoting host cell colonization and parasite∶parasite communication .
As previously published in the surface proteome , three tetraspanin ( Tsp ) membrane proteins were identified [15] , [16] . Examination of exogenously expressed , hemagglutinin ( HA ) tagged Tvag_019180 ( Tsp1 ) revealed it is mainly on the plasma membrane ( Fig . 1A ) [15] . However , when parasites expressing Tsp1-HA are exposed for an hour or longer to ectocervical cells ( Ects ) the protein accumulates in large vesicular bodies within the parasite ( Fig . 1B ) . These structures are reminiscent of mammalian multivesicular bodies that give rise to secreted exosomes . As mammalian Tsps are enriched in exosomes [19] , [26] , these data raised the possibility that T . vaginalis secretes Tsp-containing vesicles . As shown in Fig . 1C , this membrane protein is secreted , consistent with the secretion of exosomes by T . vaginalis . To test whether T . vaginalis produces exosomes , vesicles were isolated from parasite growth media through a series of ultracentrifugation steps , similar to that described for isolating mammalian exosomes [32] . Examination of the preparation by electron microscopy ( EM ) revealed cup-shaped vesicles of ∼50–100 nm ( Fig . 2A ) , similar in size and shape to mammalian exosomes [33] . To determine if the vesicles had the density reported for mammalian exosomes , vesicles containing a hemagglutinin ( HA ) tagged Tsp1 ( Tsp1-HA ) for tracking purposes were floated on a linear sucrose density gradient . The Tsp1-HA-tagged vesicles were found to have densities of 1 . 03–1 . 25 g/cm3 ( Fig . 2B ) , similar to that reported for mammalian exosomes ( ∼1 . 1–1 . 2 g/cm3 , [34] ) . The higher MW band in Fig . 2B is most likely a homodimer of Tsp1 as tetraspanin proteins are known to form dimers [35] . To determine vesicle size variation , we used nanoparticle tracking analysis ( Nanosight , Costa Mesa , CA ) to directly examine millions of vesicles . This analysis showed that the size of vesicles peak with a mean diameter of 95 nm and 83 . 3% are between 50–150 nm in size ( Fig . 2C and 2D ) . We then determined whether these vesicles contain RNA using an Agilent 2000 Bioanalyzer , as mammalian exosomes have been reported to deliver miRNAs and mRNAs to recipient cells [26] . A heterogenous population of small RNAs ranging in size from between 25 and 200 nt were found ( Fig . 3A and3B ) . Taken together , the size , morphology , density , and presence of Tsp1 and RNA indicates that T . vaginalis produces and excretes exosomes . SDS-PAGE followed by silver staining of proteins of exosomes and whole cell lysates normalized by protein concentration indicates an enrichment of specific proteins in exosomes ( Fig . 4A ) . To define the proteins packaged in adherent T . vaginalis B7RC2 strain exosomes and compare their contents with exosomes from other eukaryotes , we determined their protein composition using MudPIT-based proteomic mass spectrometry . Proteins with two or more identified peptides that were found in at least three of seven MudPIT analyses were included in the exosome proteome and revealed a total of 215 proteins ( Table S1 ) . These inclusion parameters are conservative compared with other exosomal proteome analyses wherein proteins identified in two of ten experiments or proteins with one peptide in a single experiment were included [36] , [37] . When compared with the compiled list of common mammalian exosome proteins in ExoCarta [38] , we found that T . vaginalis exosomes contained orthologs of approximately 73% of mammalian exosome proteomes and 39 . 5% are orthologous to Leishmania exosomal proteins [39] . The extensive overlap with mammalian exosomal proteins was surprising as roughly 2/3 of T . vaginalis genes have no mammalian orthologs [40] . Shared proteins represent 60 core conserved exosome protein/protein families such as tetraspanins , Alix , Rabs , Hsp70 , subunits of heterotrimeric G proteins and TcTP [25] as well as hypothetical proteins identified by BLAST analyses as similar to mammalian exosomal proteins . Identified proteins were sorted into functional groups by BLAST analyses and genome annotation and assigned a predicted function ( Fig . 4B ) . Fourteen percent are signaling proteins , 14% are metabolic enzymes , 13% are cytoskeletal proteins , 8% are involved in transport and 6% are vacuolar proteins . The remaining proteins include 32 hypothetical proteins ( 15% of total ) of unknown function as well as proteins involved in protein folding and other cellular activities ( Fig . 4B and Table S1 ) . It is notable that one protein in the exosome proteome Tvag_452120 ( TvG402 ) was previously found to localize to in large vesicular structures within T . vaginalis [41] . Additionally , 24 . 6% were previously found in the T . vaginalis surface proteome [15] ) . Nine percent have predicted transmembrane domains and 3 . 7% have predicted signal peptides as annotated in the TrichDB database ( Figure 4C ) . It should be noted , however , that T . vaginalis membrane proteins often appear to lack conventional , identifiable N-terminal signal peptides or transmembrane domains [15] , making it difficult to predict whether many exosomal proteins are membrane associated or soluble . Nevertheless , the high degree of conservation between mammalian and T . vaginalis exosome proteomes firmly establishes that T . vaginalis secretes exosomes . Novel proteins that may have a role in T . vaginalis pathogenesis are also present in the exosome proteome . Interestingly we identified surface proteins ( Tvag_240680 , BspA family ) and proteases ( Tvag_224980 , metallopeptidase ) thought to be involved in pathogenesis [6] , [40] , [42] , [43] . For example , Tvag_340570 is related to two surface proteins involved in parasite attachment which are significantly more abundant in highly adherent versus poorly adherent parasites [15] . These adhesion related surface proteins are part of a ∼150 member family , 30% of which have EST evidence for expression ( www . trichdb . org ) and 18% of which were found in the surface proteome [15] . Orthologs of virulence proteins characterized in other parasites ( Tvag_371800 , GP63-like ) and those potentially involved in host immune regulation ( Tvag_137880 , peptidyl proyl isomerase A also known as cyclophilin A ) are also notable . Many of the identified exosomal proteins are members of very large gene families [40]; however , only 1 or 2 members are packaged in the exosome , indicating a specificity in expression and/or packaging of specific proteins . For example , there are 911 putative BspA family proteins , ∼30% of which have EST , RT-PCR , or microarray data to support expression [43] and while 11 were found in the surface proteome only 1 was identified in the exosome proteome . Additionally there are ∼700 proteases encoded in the genome , of which ∼120 are annotated as metallopeptidases with 60% having EST evidence for expression and only 3 are found in the exosome proteome . Similarly , ∼90 GP63-like proteases are annotated in the genome , 33% have been shown to be expressed , 16 were found in the surface proteome [15] , while only 1 was identified in the exosome proteome . Future studies on the differential expression of proteins packaged into exosomes by T . vaginalis strains with varying virulent phenotypes should help identify exosomal proteins involved in pathogenesis . . Increasing evidence indicates exosomes are capable of mediating cell∶cell communication , leading to intercellular transfer of molecules [44] , [45] . Having established that T . vaginalis exosomes contain several proteins potentially involved in pathogenesis , we hypothesized that these exosomes could be used by the parasite during infection . The host cells first encountered by T . vaginalis during infection that are the primary site of replication and survival are ectocervical cells ( Ects ) [46] . We examined whether T . vaginalis exosomes associates with Ects by labeling exosomal membranes with BODIPY-PC [47] , followed by incubation with Ects . After extensive washing to remove free exosomes , Ects were examined by microscopy and the fluorescent BODIPY-PC was found to label the Ects ( Fig . 5A ) . This is in contrast with that observed using BODIPY-PC labeled hydrogenosomes [48] ( Fig . 5A ) which are not capable of transferring BODIPY-PC to Ects . These data indicate that T . vaginalis exosomes deliver their contents to host cells . To directly examine whether soluble protein in the parasite exosomes are delivered to host cells we utilized a split-GFP system [49] . Ects were transiently transfected with the large S1-10 fragment of GFP . Exosomes were purified from T . vaginalis expressing a soluble exosome protein ( Tvag_180840 , TcTP or Tvag_137880 , peptidyl proyl isomerase A ) tagged with the small S11 fragment of GFP . These exosomes were then incubated with GFPS1-10 transfected Ects . GFP fluorescence will only be observed if the S11 fragment fused in frame with an exosomal protein is delivered to the cytoplasm of an Ect containing the S1-10 fragment . As shown in Fig . 5B , fluorescence was observed specifically in exosome-treated and not in vehicle treated Ects . After normalization by transfection efficiency which was ∼30% , quantification of the data estimates that exosomes fused with ∼50% of the Ects ( Fig . 5C ) . These results provide strong evidence that T . vaginalis exosomes fuse with and deliver their contents to host cells . We hypothesized that T . vaginalis exosomes may modulate the Ect immune response , as these cells are involved in host innate immunity [12] , [50]–[53] . Specifically , we examined the proinflammatory cytokines IL6 and IL8 secreted by Ects in response to both exosomes and parasites . To allow comparison , we used the concentration of exosomes predicted to be produced by the number of parasites used in the same experiment as calculated from exosome isolation yields . Quantification of IL6 secretion by Ects demonstrated that T . vaginalis exosomes induce this cytokine to approximately the same extent as parasites ( Fig . 6A ) . Exosomes were also shown to elicit IL8 secretion; however , the response is only ∼50% of that observed when Ects are exposed to parasites ( Fig . 6B ) . It is notable that using equivalent amount ( 9 ug ) of either T . vaginalis hydrogenosomes , cytosol or exosomal supernatant did not induce a cytokine response ( Fig . 6E ) . Because IL6 is an acute inflammation protein and IL8 is involved in a long-term inflammatory process [54] we further hypothesized that the exosomes secreted by T . vaginalis could potentially prime host cells for parasite infection . We found pretreating Ects with exosomes did not affect their subsequent IL6 cytokine production ( Fig . 6C ) . However , as shown in Fig . 6D , preincubation of Ects with exosomes prior to the addition of T . vaginalis parasites led to a significant inhibition of IL8 secretion by the Ects . These results indicate that T . vaginalis exosomes specifically modulate IL8 , but not IL6 production by Ects . Secreted exosomes can potentially interact with other parasites in the population such that exosomes from one parasite might influence another . As attachment to host cells is critical for establishing infection [6] , [7] , we examined whether exosomes purified from B7RC2 , a strain 20-fold more adherent than the lab strain G3 [15] , affects the attachment of the less adherent strain G3 to Ects . The ability of exosomes to mediate both parasite∶parasite and parasite∶host cell adherence was assessed . The following were preincubated with B7RC2 exosomes for 1 hour prior to performing attachment assays: 1 ) G3 parasites only 2 ) Ects only or 3 ) both Ects and G3 parasites . Unincorporated exosomes were washed away before performing the attachment assay and as a negative control BSA was used instead of exosomes . Based on average yields of exosomes per parasite number , the amount of exosomes oredicted to be secreted by the quantity of parasites used in our assays was used for all preincubation treatments . Preincubation of B7RC2 exosomes with G3 parasites resulted in a 2-fold increase in G3 attachment to Ects showing that exosomes from one parasite can affect attachment of another parasite ( Fig . 7A ) . Preincubation of B7RC2 exosomes with Ects resulted in a 3-fold increase in G3 attachment to Ects showing that exosomes alter the host cell and result in increased parasite attachment . Interestingly , this effect is additive as a fivefold increase in G3 attachment was observed when both the G3 parasites and Ects are preincubated with B7RC2 exosomes ( Fig . 7A ) . As losses result during isolation of exosomes , we believe the amount of exosomes utilized in our experiments is conservative , however a dose curve ( Fig . S2 ) shows that increasing amounts of B7RC2 exosomes does increase G3 adherence . Contrary to that observed using B7RC2 exosomes , preincubation of parasites , Ects , or both , with exosomes from the poorly-adherent G3 strain did not increase G3 attachment to Ects ( Fig . 7B ) . Furthermore preincubation of B7RC2 exosomes on B7RC2 parasites only had a slight effect on attachment to Ects ( Fig . 7C ) . This is likely due to a saturation effect as the B7RC2 parasites produce and secrete their own exosomes . Doing the identical experiment except replacing exosomes with equivalent amounts of T . vaginalis hydrogenosomes or cytosol does not result in increased parasite attachment ( Fig . S1A and Fig . S1B , respectively ) . These data indicate that exosomes from a highly adherent strain can increase parasite attachment of a less adherent strain to host cells . Moreover , they indicate that exosomes can mediate both intraspecies and interspecies interactions . To test whether the parasite∶parasite and parasite∶host interactions observed using the B7RC2 strain is generally observed with other parasite strains , we expanded our G3 attachment assay to include exosomes purified from several strains . We found that exosomes from poorly adherent strains like T1 or RU384 [55] do not significantly increase adherence of G3 parasites while those from more highly adherent strains like MSA1103 or LSU160 [55] do increase attachment of G3 to Ects ( Fig . 8 ) . As MSA1103 and LSU160 strains are more adherent ( 3 and 2 fold respectively ) to the benign prostate epithelium cell line ( BPH1 ) than the female Ect host cells [55] , we tested whether exosomes from these two strains result in a greater increase in G3 parasite adherence to BPH1 cells as compared to Ects . We found that while exosomes from LSU160 and MSA1103 increased attachment to Ects ∼2 fold , they increased attachment to BPH1 cells by ∼6 and ∼4 fold respectively ( Fig . 9 ) . In contrast a difference was not observed when preincubating with B7RC2 exosomes consistent with B7BC2 parasites displaying no preference for attachment to Ects versus BPH1 [55] . Together , these results indicate that exosomes package pathogenic factors specific to the strain that produces them and that exosomes from highly adherent strains may contribute to the parasites' ability to colonize specific niches of the male and female urogenital tract .
This study , the first to identify and characterize exosomes from the parasite T . vaginalis , reveals a role for these secreted vesicles in host∶parasite interactions . We have isolated T . vaginalis exosomes from the adherent B7RC2 strain and shown that they are remarkably similar to mammalian exosomes in size , structure and core protein components [22] , [25] , [56] . The extensive ( 73% ) overlap between the T . vaginalis and mammalian exosomal proteomes demonstrate the specific packaging of proteins within exosomes as the majority of T . vaginalis proteins have no orthologs in humans [40] . Minimal overlap between the T . vaginalis exosomal proteome and other reported T . vaginalis proteomes [7] , [15] , [57] and the presence of only 1–2 proteins from large , expressed protein families [6] , [40] likewise argues for specificity in exosomal protein content . Exosome biogenesis and the selective packaging of exosomal proteins is poorly understood [20] , [58] , [59]; however , the overlap of the exosome proteomes of this highly divergent parasite and that of humans indicates the conservation of underlying mechanisms . Thus the ease of culturing T . vaginalis and its high exosomes yield makes this parasite a good model for studying the basic biological properties of exosomes . In addition to the core proteins conserved between T . vaginalis and mammalian exosomes , many proteins unique to T . vaginalis exosomes were also identified . Thirty-two are conserved hypothetical proteins of unknown function . Others , including several surface proteins and proteases , have been implicated in pathogenesis . Future experiments characterizing the exosomal contents from strains of parasites of varying virulence will assist in identifying and characterizing specific exosomeal proteins that affect pathogenesis . Unique T . vaginalis exosomal proteins may be critical for mediating host∶parasite interactions In this regard , another parallel can be drawn between T . vaginalis and mammalian exosomes as the latter are involved in interactions that lead to pathologies such as cancer proliferation and HIV transmission between different cell types [28] , [60]–[63] . Using a split-GFP assay , parasite exosomes were shown to fuse with and deliver their contents to host cells . T . vaginalis exosomes were also shown to modulate host cell cytokine production . T . vaginalis may use exosomes to manipulate host defense responses similar to the secretion of virulence factors and vesicles by bacteria [22] , [64] , [65] . Exosomes were found to induce an IL6 response in Ects and to down regulate the IL8 response to parasites . IL8 is involved in the recruitment of neutrophils to the site of infection and persists in its active form within the immediate environment for longer than other chemoattractants [12] , [66] . Thus by dampening the IL8 response of Ects to parasites , T . vaginalis exosomes may play a critical role in establishing a successful chronic infection . IL6 is a chief stimulator of proteins in acute inflammation and suppresses the level of other proinflammatory cytokines in an acute response [67] . IL6 can also stimulate IL-1 receptor antagonist , an anti-inflammatory mediator , to control tissue inflammatory responses [68] . Thus T . vaginalis exosomes may lead to the regulation of IL6 and IL8 secretion , thus priming the urogenital tract for parasite colonization . Both proinflammatory and immunosuppressive responses to T . vaginalis infection or its double-stranded RNA virus have been described in various in vitro and mouse studies [69] , [70] . Future studies aimed at investigating the effects of exosomes on various host effector cells recruited during infection will be necessary to understand the molecular mechanisms by which exosomes modulate host immune responses . T . vaginalis exosomes were found to substantially increase the adherence of this sexually-transmitted parasite to female ( Ect ) and male ( BPH1 ) epithelial cells via an effect on both parasites and host cells . As an extracellular parasite , attachment of T . vaginalis to host cells is vital for survival and pathogenesis . Similar to that described for exosomes produced by different mammalian tissues and cells [23] , [56] , [71] , T . vaginalis exosomes have strain-specific characteristics . Exosomes from highly adherent parasites are capable of increasing the adherence of poorly adhering parasite to host cells , whereas those from poorly adhering parasites are not . Furthermore , exosomes from parasite strains that preferentially bind BPH1 cells are more effective in increasing adherence to BPH1 versus Ects . Taken together , our data indicate T . vaginalis exosomes may package strain specific , perhaps even host cell specific , virulence factors . Future studies on differential packaging of factors in exosomes between strains may lend insight into differences attributed to virulent and less virulent phenotypes . T . vaginalis exosomes could potentially be found in vaginal secretions or urine of infected individuals and thus serve as biomarkers for infection . Testing for infection in females currently requires gynecology visits [2] , [72] and there is a lack of a non-invasive , quick method for diagnosing T . vaginalis infection in men [73] . Exosomes have been successfully isolated from a variety of easily obtained bodily fluids including blood , amniotic fluid , saliva , and urine [74] . It has been shown that urine contains abundant amounts of exosomes from prostatic secretions [75] and hence could contain exosomes from T . vaginalis in infected patients . Thus the isolation of T . vaginalis exosomes or detection of exosomal contents may provide a non-invasive means to diagnose infection . Furthermore , the ability of T . vaginalis exosomes to deliver soluble substances to host cells provides the potential for parasite exosomes to be used therapeutically . Mammalian exosomes and their RNA and protein contents have been shown to regulate a variety of cellular pathways by modulating gene expression in recipient cells [76] , [77] . The characterization of T . vaginalis exosomes and their protein cargo sets the stage for determining specific parasite factors likely to modulate host cell response and affect infection outcomes . The small RNAs packaged inside T . vaginalis exosomes may also modulate parasite∶parasite or parasite∶host interactions . The possibility that these small RNAs are novel parasite miRNAs that are delivered to the host cell to modulate gene activity is an appealing idea . The use of exosomes by a strictly extracellular parasite represents a novel method by which the parasite may deliver proteins and/or RNA to the host to manipulate host cell responses while remaining extracellular . The delivery of strain specific exosomal contents can impact both host immune response as well as parasite attachment to host cells ( Fig . 10 ) . A better understanding of how exosomes increase parasite adherence to host cells and modulate host cell responses will provide insights into pathogenesis and possibly new avenues for diagnosis and therapy .
T . vaginalis strains ( B7RC2 , G3 , T1 , RU384 , MSA1103 , LSU160 ) were cultured in TYM medium supplemented with 10% horse serum , 10 U/ml penicillin/10 ug/ml streptomycin ( Invitrogen ) , 180 uM ferrous ammonium sulphate and 28 uM sulfosalicylic acid [78] . 100 ug/mL G418 ( Invitrogen ) was added to culture of the Tsp1-HA ( Tvag_019180 ) , Tvag_180840 , and Tvag_137880 transfectants . Parasites were grown at 37°C and passaged daily for ≤2 weeks . The human cervical epithelial cell line Ect1 E6/E7 ( ATCC CRL-2614 ) was grown as described [46] except without additional CaCl2 . The human benign prostate epithelial line BPH1 [79] was grown as described [80] . T . vaginalis at a density of ∼1 . 0×106 parasites/ml , were washed 3X and resuspended in TYM media without serum for 4 hrs after which parasites were removed by centrifugation at 500×g . The cell-free media was filtered through 0 . 22 um filter and concentrated using a Vivaflow 200 100 , 000 MWCO PES ( Sartorious Stedium ) . Exosomes were pelleted by ultracentrifugation at 100 , 000×g for 75 min using a TLA100 rotor followed by resuspension in 2 mL cold PBS+1X HALT protease inhibitors ( Thermo Scientific ) . Exosomes were concentrated further by ultracentrifugation at 100 , 000×g for 70 min and resuspended in 100–300 uL PBS to be purified by floatation on a linear sucrose gradient as described ( Raposo , 1996 ) . Pellets were resuspended by boiling for 5 min in Laemmli sample buffer and stored at −20°C in PBS or immediately resolved by SDS-PAGE followed by western blot analysis or silver staining using standard procedures . Fractions transferred to PVDF membranes were blocked with 5% TBST-milk and probed with an anti-HA antibody ( 1∶5000 , Covance ) followed by reaction with anti-mouse-HRP ( Jackson Labs ) . Exosome protein concentration was assayed using the Pierce BCA Kit ( Thermo Scientific ) . Freshly isolated exosomes were directly adsorbed onto charged carbon-coated grids , contrasted with 1% uranyl acetate , and examined using a transmission electron microscope . Total RNA was isolated from freshly isolated exosomes or whole parasites using the MirVana Paris kit ( Invitrogen ) according to manufacturer's protocol . The eukaryote total RNA Nano assay ( RNA 6000 Nano kit , Agilent ) was used to analyze the RNA on an Agilent 2100 Bioanalyzer at the UCLA Clinical Microarray Core . Parasites were incubated with Ects for 15 or 60 min . Cells were fixed in 4% formaldehyde for 20 min , permeabilized with 0 . 2% Triton X-100 in PBS , blocked with 3% BSA in PBS ( PBS-BSA ) , incubated with a 1∶1 , 000 dilution of anti-HA primary antibody ( Covance ) , washed , and then incubated with a 1∶5000 dilution of Alexa Fluor-conjugated secondary antibody ( Molecular Probes ) . The coverslips were mounted using ProLong Gold Antifade reagent with DAPI ( Invitrogen ) . Stained parasites were examined using an Axioskop 2 epifluorescence microscope ( Zeiss ) , and images were recorded with an AxioCam camera and processed with the AxioVision 3 . 2 program ( Zeiss ) . Parasites at log growth were resuspended in 5% PBS-sucrose at a density of 1×106 parasites/mL at 16°C or 37°C for 1 hour . Parasites were pelleted by centrifugation and the supernatant was filtered through 0 . 22 um filter to remove cell debris and concentrated using an Amicon filter . The pellet was resuspened to the same volume as the filtered supernatant ( 700 uL ) , and 15 uL of each was subjected to SDS-PAGE . Western blot analysis was performed using anti-HA ( 1∶5000; Covance ) to detect Tsp1 , anti-β-tubulin ( 1∶1000; Sigma ) and anti-neomycin phosphotransferase II ( 1∶2500; Jackson Labs ) . Secondary antibodies were anti-mouse-HRP ( 1∶25000 ) and anti-rabbit-HRP ( 1∶25000 ) . Exosomes were fractionated on 12% Tris-glycine gels ( Invitrogen ) followed by fixation with 10% methanol and 7% acetic acid . Gel slices were excised and treated with 100 mM ammonium bicarbonate ( Fisher ) and 50% acetonitrile ( ACN ) . Disulphide bonds were reduced with 10 mM DTT and SH groups alkylated with 50 mM iodoacetamide ( Sigma ) . After washing , gel pieces were dehydrated with ACN then rehydrated on ice with 20 ng/uL trypsin , 40 mM ammonium bicarbonate , 9% ACN , and incubated overnight at 37°C . Acidic peptides were extracted by the addition of 100 mM ammonium bicarbonate and extraction of basic peptides was performed with 2 . 5% trifluoroacetic acid ( TFA ) . The supernatants were combined , dried in a speed-vac . The dried samples were resuspended in digestion buffer ( 100 mM Tris-HCl , pH 8 , 8M urea ) , proteolytically digested by the sequential addition of Lys-C and trypsin proteases and subjected to MudPIT analyses as described [15] . 10 uM BODIPY-PC ( 2-decanoyl-1- ( O- ( 11- ( 4 , 4-difluoro-5 , 7-dimethyl-4-bora-3a , 4a-diaza-s-indacene-3-propionyl ) amino ) undecyl ) -sn-glycero-3-phosphocholine; Invitrogen ) was used to label exosomal or hydrogenosomal membranes for 30 min at 4°C in the dark . Excess lipids were removed by two 500X volume washes with PBS and ultracentrifugation at 50 , 000 rpm for 1 hr . BODIPY-PC labeled exosomes or hydrogenosomes were added to Ects for 24 hr after which cells were washed with warm media 3X followed by fixation with 4% formaldehyde and mounting with ProLong Antifade reagent with DAPI ( Invitrogen ) . Cells were visualized as described for immunolocalization . The pCMV-mGFP-S1-10 mammalian optimized plasmid ( Theranostech , Inc ) was transiently transfected into Ect cells using Fugene HD ( Promega ) according to manufacturer's protocol . Tvag_137880 and Tvag_180840 were cloned into Master-Neo plasmid with S11 fused in frame using PCR and parasites were transfected and cultured as described [81] . Exosomes containing S11-tagged proteins were added 48 hr post transfection , allowed to interact for 4 hr and cultures were then washed 3X to remove unfused exosomes . Cells were fixed with 4% formaldehyde and mounted with ProLong Gold antifade reagent with DAPI ( Invitrogen ) and viewed as described for immunolocalization . Ects were plated in 48-well plates and grown to confluence . 5×105 parasites or 9 ug exosomes were added for 6 hr and the supernatant was then removed . Cells/debris were removed by spinning at 5000 rpm for 10 min . Supernatants were stored at −20°C . IL6 and IL8 was quantified using IL6 or IL8 ELISA kits from AssayPro following manufacturer's directions . For priming experiments , exosomes , BSA , or PBS was added to host cells for 12 hr after which host cells were rinsed 3X with prewarmed KSFM ( Invitrogen ) and 5×105 parasites were added for 6 hr . Data were normalized as percent of cytokine secretion using 9 ug BSA without the addition of parasites . Attachment of parasites to Ect or BPH1 cells was performed as described [15] . Briefly , CellTracker Blue CMAC ( Invitrogen ) labeled parasites were added to confluent monolayer of host cells ( 1∶3 parasite∶host cell ratio ) for 30 min . Coverslips were subsequently rinsed in PBS to remove unattached parasites , fixed with 4% formaldehyde ( Polysciences , Inc ) , and mounted on slides with Mowiol ( Calbiochem ) . Fifteen 10X magnification fields were analyzed per coverslip with three coverslips per treatment per experiment . Fluorescent parasites adhered to host cells were quantified using ImageJ . . When examining the role of exosomes in attachment , exosomes were preincubated with Ects , parasites , or both for 1 hr prior to attachment followed by washing with warm media to remove remaining exosomes . All experiments were performed 3–5 times with 3 coverslips per treatment per experiment . Fold change in parasite number was calculated by totaling the number of parasites for 15 images/coverslip , and averaging all coverslips per treatment condition and dividing by the same number derived using negative control BSA samples . Graphs were made and statistical analyses performed using Microsoft Excel 2010 . Independent experiments were performed a minimum of 3 times with at least three technical replicates per experiment . Two sample t-tests were used to determine significance . Data are expressed as standard error of the mean ( ± SEM ) .
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Trichomoniasis , the most common non-viral sexually transmitted disease worldwide , infects over 275 million people annually . Infection results from the colonization of the human urogenital tract by the parasite Trichomonas vaginalis . To establish and maintain infection the parasite adheres to host cells , a process that is poorly understood . Here , we show that T . vaginalis secretes small vesicles called exosomes that are capable of fusing with and delivering their contents to host cells . Parasite exosomes were found to induce changes in the host cell and to mediate the interaction of T . vaginalis with host by increasing the adherence of the parasite to host cells . Exosomes have been primarily studied in mammalian cells where they have been shown to mediate intercellular communication and have been implicated in processes including development , antigen presentation and cancer metastasis . Our data extend the function of exosomes to mediating host∶parasite interactions , cellular communication between two species and promoting colonization of an extracellular parasite . Research on T . vaginalis exosomes holds the potential for developing applications that would allow exosomes to be used in detecting and diagnosing trichomoniasis and for targeting drugs to the site of infection .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"cell",
"biology",
"microbial",
"pathogens",
"host-pathogen",
"interaction",
"biology",
"microbiology",
"pathogenesis",
"parasitology"
] |
2013
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Trichomonas vaginalis Exosomes Deliver Cargo to Host Cells and Mediate Host∶Parasite Interactions
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Many cellular responses to surrounding cues require temporally concerted transcriptional regulation of multiple genes . In prokaryotic cells , a single-input-module motif with one transcription factor regulating multiple target genes can generate coordinated gene expression . In eukaryotic cells , transcriptional activity of a gene is affected by not only transcription factors but also the epigenetic modifications and three-dimensional chromosome structure of the gene . To examine how local gene environment and transcription factor regulation are coupled , we performed a combined analysis of time-course RNA-seq data of TGF-β treated MCF10A cells and related epigenomic and Hi-C data . Using Dynamic Regulatory Events Miner ( DREM ) , we clustered differentially expressed genes based on gene expression profiles and associated transcription factors . Genes in each class have similar temporal gene expression patterns and share common transcription factors . Next , we defined a set of linear and radial distribution functions , as used in statistical physics , to measure the distributions of genes within a class both spatially and linearly along the genomic sequence . Remarkably , genes within the same class despite sometimes being separated by tens of million bases ( Mb ) along genomic sequence show a significantly higher tendency to be spatially close despite sometimes being separated by tens of Mb along the genomic sequence than those belonging to different classes do . Analyses extended to the process of mouse nervous system development arrived at similar conclusions . Future studies will be able to test whether this spatial organization of chromosomes contributes to concerted gene expression .
A cell continuously receives signals from its local environment and accordingly adjusts cellular programs , such as cell proliferation , motility and metabolism [1] . Typically , regulation of a cellular process requires changes in the expression of a group of genes in a temporally coordinated manner [2] . How such coordinated regulation is achieved is a central question that remains poorly addressed . A mechanism of such regulation is through specific interaction network structures of transcription factors ( TFs ) . TFs bind to certain DNA sites and regulate transcriptional activities of their targeted genes . A TF can regulate multiple target genes to form a so-called single-input-module ( SIM , or fan-out ) [3] . This SIM network motif appears in a high frequency to coordinate the expression of genes with related functions such as those in bacterial metabolic pathways [4] . Gene regulation in eukaryotic cells is more complex since the three-dimensional structure of DNA has a more profound impact on gene transcription than that in prokaryotic cells . For instance , a nucleosome structure with a high packing level limits gene accessibility [5] . Furthermore , epigenetic modifications can strongly influence gene transcription [6] . It is not fully understood how these different regulation mechanisms collectively control the expression of a group of genes . To examine how multiple levels of regulation lead to concerted expression of gene groups , we analyzed the temporal gene expression profiles of TGF-β treated human mammary epithelial MCF10A cells in the context of histone modification patterns and chromosome structures derived from Hi-C data . The TGF-β family is crucial for regulating a complex signal transduction network in embryonic and fetal development , and is also involved in multiple physiological and pathological processes such as wound healing and cancer progression [7] . Its signaling event starts from membrane embedded TGF-β receptors , which bind active TGF-β molecules from the extracellular environment [8] . The TGF-β signal is then transmitted into the cell through a signal transduction network and triggers a cascade of cellular responses . The latter is achieved through temporally coordinated expression changes of groups of genes with related functions such as cell proliferation , metabolism , and motility [9] . TGF-β also induces a global reprogramming of cell epigenome [10] , which reinforces cellular responses for committed cell phenotype transition . We also analyzed temporal gene expression together with histone modifications and chromosome structures during mouse neural differentiation , another well-defined model for studying cell phenotype transition [11 , 12] . Specifically , we analyzed a recently published dataset that combined Hi-C , RNA-seq , and ChIP-seq studies on the differentiation process from mouse embryonic stem cells ( ESCs ) to neural progenitor cells ( NPCs ) then to cortical neurons ( CNs ) [13] . In both the TGF-β response and neural differentiation systems , our analyses reveal that genes co-regulated by a common TF ( s ) have the tendency to be spatially close , even if they are distant along the linear genome sequence .
MCF10A cells were purchased from the American Type Culture Collection ( ATCC ) and were cultured in the DMEM/F12 ( 1:1 ) medium ( Gibco ) with 5% horse serum ( Gibco ) , 100 μg/ml of human epidermal growth factor ( PeproTech ) , 10 mg/ml of insulin ( Sigma ) , 10 mg/ml of hydrocortisone ( Sigma ) , 0 . 5 mg/ml of cholera toxin ( Sigma ) , and 1x penicillin-streptomycin ( Gibco ) . Cells were cultured at 37°C with 5% CO2 with a medium change every the other day . We induced the cells with 4 ng/ml human recombinant TGF-β1 ( Cell signaling ) . Total RNA was isolated from the cell pellets with an RNA extraction kit ( Qiagen , Cat No . 74104 ) . All RNA extracts were confirmed with high quality ( RQN score = 10 . 0 ) using the Fragment AnalyzerTM platform ( Advanced Analytical Technologies , Inc ) . Libraries were prepared using the NEBNext Ultra RNA Library Prep Kit for Illumina ( NEB , Cat No . E7530L ) according to the manufacturer's instructions . Briefly , mRNA was first isolated from total RNA with oligo d ( T ) 25 beads ( all volumes were halved except for washing steps , NEB , Cat No . E7490S ) . Next , purified mRNA was denatured and melted into small fragments , and subjected to random priming and extension for reverse transcription . After that , double-stranded cDNA was end-repaired , dA-tailed , adaptor ligated , and amplified with 12 PCR cycles . Constructed libraries were subjected to purification and quality control; the final quality-ensured libraries were pooled and sequenced on an Illumina HiSeq 4000 instrument for 150 bp paired-end sequencing . Paired-end cleaned reads were aligned to the human reference genome hg19 ( UCSC ) using TopHat ( v 2 . 1 . 1 ) with default parameters . The BAM files of mapped reads were used to annotate transcripts and calculate the FPKM values using the Cufflinks , Cuffquant , Cuffnorm suite [14] . Differentially expressed ( DE ) genes were identified between any two time points with the criteria: fold change >2 or < 0 . 5 and FDR < 0 . 05 . The FPKM values of genes from the RNA-seq dataset were further cleaned up using custom R scripts . Hierarchical clustering of genes was performed using an R package ( pheatmap ) . Gene expression and TF regulation based Hidden Markov Model ( HMM ) clustering was performed with the DREM2 software [15] . RNA-seq results of ESC , NPC and neuron cells were downloaded from the GEO database under the accession number GSE96107 . Hi-C data were downloaded from the GEO database ( MCF10A , GEO:GSE66733; mouse nervous system GEO:GSE96107 ) . Chromosome structures were constructed using an R package ( igraph ) . Clustering of bins was obtained with the fast-greedy algorithm [16] . Physical distances between bins were estimated with a Matlab code provided by Lesne et al . [17] . This code uses a Shrec3D algorithm , which first relates the Hi-C contact frequency between every two genomic sites with a spatial distance , then approximates the actual distance between the two sites by their shortest-path distance on a contact graph . This algorithm alleviates uncertainty of reconstructing the spatial distance between two distal sites only by their own contact frequency .
We used MCF10A cells , a non-tumorigenic human mammary epithelial cell line , as a major in vitro model to study in this work . This cell line has been widely used to study the TGF-β induced epithelial-to-mesenchymal transition ( EMT ) [1 , 19] ( Fig 1A ) . Cells were treated with 4 ng/ml TGF-β for 12 hours , 2 , 3 , 5 , 8 , 12 , and 21 days ( Fig 1B ) . Untreated MCF10A cells showed typical epithelial morphology with tight cell-to-cell adherence . With TGF-β treatment , we observed progressive morphological changes indicating the transformation from epithelial to mesenchymal phenotype . From day 2 to day 5 , cells started to show loosened intercellular adherence . After day 5 , some cells appeared with expanded cell size and extended long cell axis . With further TGF-β treatment , more cells acquired a spindle-like shape . On day 21 , only a small fraction of cells still maintained epithelial morphology and most cells had undergone EMT . Next , we performed RNA-seq studies to uncover changes of gene expression accompanying EMT . At each time point , we harvested cell samples and extracted RNA . The RNA-seq results revealed that about 33% of human genes were differentially expressed upon TGF-β treatment . Principal component analysis ( PCA ) over these ~ 7000 DE genes showed an expected larger separation between gene expression profiles of samples from different time points than those of replicate samples from the same time point ( Fig 1C ) . The global transcriptome change over time reflected in the PCA space was consistent with the gradual morphological change of cells over time and the previous report that TGF-β-induced EMT proceeded through intermediate states [19] . To further examine the temporal patterns and functions of the DE genes , we performed hierarchical clustering ( HC ) analysis . The analysis divided the DE genes into seven HC classes based on similar expression patterns in each ( Fig 2A ) [20] . Among the seven HC classes , class I with ~1 , 700 genes exhibit a monotonically decreasing pattern , and class II of ~2 , 000 genes exhibit a monotonically increasing pattern . Another two classes III and IV show transient up and transient down dynamics , respectively . The remaining three classes V-VII display wavy dynamic patterns to varying degrees . Gene ontological ( GO ) analysis ( S2 Fig ) revealed that genes in each class are typically involved in multiple cellular processes . For example , genes in the decreasing class ( class I ) are related to RNA polymerase I activity and snoRNA binding . These two functions are related to the RNA metabolic process , including ribosomal RNA production , modification , and binding to regulatory factors . The observation that these genes are down-regulated is consistent with previous reports that under TGF-β treatment cells are under growth arrest until they finish EMT [21] . Histone modifications can also affect gene expression [22] . To investigate the relationship between histone modification and gene expression , we integrated genome-wide H3K4me3 and H3K4ac profiles obtained by Messier et al . [23] with our RNA-seq data . Both H3K4me3 and H3K4ac are histone modification marks that are associated with active or poised genes [24] . We used H3K4me3 and H3K4ac profiles of all human genes as a control , and examined the marks in each HC class . The results in Fig 2B show that all HC classes have elevated H3K4me3 and H3K4ac compared to the control , and there is no apparent difference between different classes . Each HC class also has a broad bimodal distribution . That is , genes within an HC class do not share common histone modification patterns . Given that histone modification patterns correlate with local chromosome structures [25] , these results suggest that genes from the same HC class have heterogeneous local chromosome environments . Next , we adopted a different clustering scheme , the Dynamic Regulatory Events Miner ( DREM ) , which clusters genes by combining gene expression time series with additional pre-established transcriptional networks [26] . Fig 3A shows the clustering results analyzed with DREM2 based on a Hidden Markov Model ( HMM ) [15] . At each conjunction node , genes are assigned to different branches based on their expression trend and upstream regulators ( transcription factors on this node ) . Genes from an upstream branch can become key regulators at subsequent nodes [15 , 26] . It reveals a hierarchy of gene regulation during the process of TGF-β-induced phenotype change . With DREM2 the DE genes were clustered prominently into 46 branches with 19 nodes at the conjunction sites and 25 end classes . For clarity , we call the latter HMM classes to distinguish from the HC classes that are based on expression only . Compared to the HC classes , HMM classes showed finer dynamic patterns and GO enrichment information ( S1 Table ) . For example , genes in the first seven HMM classes all had increased expression , but differed in their detailed temporal profiles . Genes in class C1 increased their expression to high levels already on day 2 . Genes related to metalloendopeptidase activity were enriched in this class by over 17 fold with respect to the reference genes . Four of the matrix metalloproteinases ( mmps ) , mmp2/7/11/13 , are also in this class . These four MMPs are known to degrade components of extracellular matrix proteins such as gelatin , fibronectin , and laminin , and mediate biological activities including migration , mammary epithelial cell apoptosis , and EMT [27] . Heparin binding genes were another type of highly enriched genes . These genes , such as periostin ( postn ) , fibronectin ( fn1 ) , are also known to be related to matrix or cell membrane formation and thus affect cell migration and adhesion [28] . Another class of early activation genes , class C2 , was also enriched with genes related to cell matrix and membrane structure . Among them five of the pcdh family members , including pcdh7/a4/b9/b10/b13 , are integral membrane proteins that are involved in cell-cell recognition and adhesion [29] . In general , genes within each HMM class had narrower distributions and thus higher similarity of histone modification patterns ( Fig 3B ) than those of the HC classes do ( Fig 2B ) . Therefore , genes clustered through the DREM2 analysis based on common TFs and similar dynamic profiles tend to have closely related functions . As mentioned above , local chromosomal DNA environment affects gene transcriptional activity . We wondered whether genes sharing similar expression patterns and common regulatory factors , as in an HMM cluster identified by the DREM2 analysis , are also spatially close and share similar local DNA environment . To test this hypothesis , we first examined gene arrangement along the linear genome sequences . We divided the whole human genome into bins with a resolution of 1 Mb , a typical size of a topologically associated domain ( TAD ) . Then we matched all genes to the relevant bins based on their genomic positions . Statistical analysis of all the genes spreading along the chromosomes showed that genes are not evenly distributed along the DNA sequences ( Fig 4A ) . Most bins have less than ten genes , and globally one third of the bins are gene-free . By contrast , ~3% of the bins ( a total of less than 100 bins ) contained 17% of the overall human genes . This uneven distribution was slightly more profound for the DE genes under TGF-β treatment: DE genes resided in less than half of the bins and 17% of DE genes were enriched in only 2 . 5% of the bins . To further examine the gene distribution along chromosomes , we defined an averaged linear distribution function σL ( see Materials and Methods for details ) . It measured how the chromosomal density of a group of genes of interest changes with respect to the transcription starting site ( TSS ) of a given gene . For a given gene x belonging to an HMM class α as a tagged gene , we divided the DNA sequences along both sides flanking the TSS of x into bins with a size of 125 kb ( S1A Fig , r = 125 kb ) , and counted the fraction of HMM class α genes in each bin . We then repeated this process by choosing every gene in the HMM class α as the tagged gene , and calculated the average density of HMM class α genes ( σαL ( i ) ) in the i-th bin with respect to the tagged gene . For statistical comparison , we also calculated a similar σαLA ( i ) for all human genes and σαLD ( i ) for all DE genes with respect to the tagged HMM class α genes as controls . If there were no class-specific gene clustering along the genomic sequence , one would expect that σαL ( i ) =〈niα+n−iα〉αNα−1=〈ni+n−i〉αNα−1Nα−1N=σαLA ( i ) =σαLD ( i ) within statistical errors ( see Materials and Methods for explanation of terms ) . Instead , the σL values of more than half of HMM classes were not significantly higher than those of DE gene and all human gene controls . The upper left panel of Fig 4B shows HMM class C23 as an example . Only five HMM classes showed statistically significant increases of σL values over controls ( although the increases are small ) within the first pair of bins ( ≤ 125 kb ) , indicating relative accumulations of genes from the same HMM class; one of them ( HMM class C24 ) is shown in Fig 4B upper right panel . Next , we investigated the spatial arrangement of the DE genes using a set of available Hi-C data from MCF10A cells [30] . Following an approach used in statistical mechanics [18] , we defined a set of radial distribution functions ( σαβR ( i ) ) that measured the average radial density ( rd ) of HMM class β genes and residing inside the i-th evenly divided spherically shell relative to a tagged class α gene , and averaged the rd values over all class α genes ( S1B Fig ) . For comparison we also defined two controls σαAR ( i ) ) and σαDR ( i ) ) , where the class β genes were replaced by all human genes and all DE genes , respectively . If there were no HMM class-specific gene spatial clustering , one would expect that within statistical errors , σααR ( i ) =σαβR ( i ) =σαAR ( i ) ) =σαDR ( i ) . According to this metric , however , genes in the classes C23 and C24 , as discussed above , exhibited substantial spatial clustering . Genes in class C24 tended to be spatially close ( Fig 4B bottom right ) , likely due to their arrangement in a linear sequence . Notably , genes in class C23 also showed significantly enhanced spatial co-localization . With respect to a tagged C23 gene , the rd values of C23 genes within the first shell was more than doubled than that of all genes , which means that even some C23 genes that are not close along the linear sequence come close spatially . To visualize such spatial clustering of genes from an HMM class , we generated a two-dimensional plot of 1-Mb bins on chromosome 1 based on bin-bin contact frequencies obtained from the Hi-C data ( Fig 4C ) . The red boxes show spatial aggregation of genes on chromosome 1 that belong to the two classes , respectively . Further analysis revealed significant gene spatial clustering in the first shell for all HMM classes compared to that of the controls ( Fig 4D ) , and showed that spatial clustering mainly takes place within each HMM class ( S3A Fig ) . That is , genes sharing a common upstream regulator have an enhanced tendency to be spatially close . We also examined how the genes within the first shell of a tagged gene are distributed along the chromosome sequence ( S3B Fig ) . While a large contribution to the average radial gene density ( σααR ( 0 ) ) came from genes that were already close along the chromosome sequence , some gene elements as far as ~ 50 Mb apart resided spatially close . Next , we asked whether the observed spatial clustering of genes with related function is beyond the TGF-β induction of MCF10A cells . To this end , we performed similar DREM2 and linear/spatial gene density analyses on the differentiation of mouse ESCs into NPCs then CNs ( Fig 5A ) , for which both RNA-seq and Hi-C data for the three developmental stages were reported by Bonev et al . [13] . A DREM2/HMM analysis clustered ~ 20 , 000 mouse genes into seven classes based on both their expression patterns during neuron cell differentiation and TF regulation ( Fig 5B ) . For both ESC and CN cells , radial distributions ( Fig 5C and 5D and S4A Fig ) show that genes within the same HMM class have a slightly enhanced tendency to cluster spatially in the first shell compared to the control groups . We observed a similar tendency for NPC cells but to a less extent . Compared to the MCF10A cell data , the ESC-CN system showed less enhanced spatial clustering within individual HMM classes relative to that of the control . We reasoned that DREM2 clustering was more coarse-grained in the ESC-CN system due to the limited number of time points in the available RNA-seq datasets . Each HMM class is thus likely composed of multiple sub-classes regulated by different TFs . The expected effect of spatial clustering within each sub-class ( σμμR ( 0 ) /σμAR ( 0 ) >1 ) is then reduced by their spatial relation to other sub-classes ( σμνR ( 0 ) /σμAR ( 0 ) ≈1 ) , where μ and ν refer to two sub-classes within one HMM class . Apparently , the ratio reaches an asymptotic value of one if there is only one HMM class . This reduction due to unresolved class mixtures was less severe for MCF10A cells , for which the DREM2 clustering was finer . To support this hypothesis , we reanalyzed the MCF10A RNA-seq data assuming that one can only identify nine HMM classes branched on day 2 ( S4B Fig ) , and eight of them are mixtures of the finer classes obtained from analyzing RNA-seq data at all time points ( as shown in Fig 3A ) . As expected , S4C Fig shows that the extent of spatial clustering of genes within each class is reduced as compared to those shown in Fig 4D .
Recent studies on chromosome conformations have revealed the existence of structural units such as promoter-enhancer hubs , topologically associated domains ( TADs ) , and meta-TADs and demonstrated that these structural units play important roles in gene regulation [31–34] . Several studies focusing on specific genomic regions have shown correlation between gene expression and local chromosome structures [35 , 36] . In this work , we provide a genome-wide perspective on the relationship between chromosome structure and gene regulation by integrating the RNA-seq and Hi-C data . We first only used the expression data and grouped genes that share similar temporal expression patterns and are co-regulated by common TFs together . We found that genes within each group display a significantly enhanced tendency to be clustered spatially in the three-dimensional chromosome structure , regardless whether these genes are close ( < 1 Mb ) along the genome sequence or separated by as far as tens of Mb . This observation further suggests that the three-dimensional chromosome structure is part of a multi-layer gene regulation program . Our analysis reveals two related mechanisms that achieve spatial clustering of genes subject to common regulators . Some genes are located close in chromosome sequence and consequently spatially close . By contrast , some genes that are far apart along chromosome sequence can also become adjacent spatially by forming three-dimensional structures . TFs may actively orchestrate such chromosome structure organization [37 , 38] . Alternatively , other DNA binding factors such as long non-coding RNAs and transcription initiation complexes can drag associated chromosomal regions together to form enhancer-promoter hub structures . These hub structures may facilitate TF binding and related cooperative regulation such as phase separated molecular assemblies [39] . Functionally , spatial co-localization may contribute to temporally coordinated regulation of a group of genes in eukaryotic cells . This co-localization can be viewed as a further refinement of the SIM network motif first noticed in prokaryotic cells . Spatial co-localization may facilitate simultaneous regulation of local chromosomal environment of these genes , such as DNA methylation and histone modification , and chromosome compaction , all of which affect gene expression activities . Indeed , a recent study on Drosophila embryos shows that a group of genes separated by genomic distance but pulled together by an enhancer element exhibit similar expression fluctuation patterns [40] . Our analysis of the MCF10A data , however , has a number of limitations . While we performed RNA-seq analysis of MCF10A cells at a number of time points during TGF-β treatment , the lack of simultaneous time-course Hi-C and epigenomic data prevented us from analyzing how spatial clustering may change dynamically upon the change of gene expression status . In addition , having the RNA-seq , Hi-C and epigenomic datasets obtained from different labs also raises a concern of potential cell line drifting during culture . It is desirable to have an integrated set of parallel RNA-seq , epigenomic and Hi-C measurements from the same batch of cells , similar to how the ESC differentiation was studied by Bonev et al . [13] but at more time points . Together with the gene regulatory network analysis , such datasets would permit finer clustering and identifying gene groups that each contains multiple spatially clustered , co-regulated and functionally related genes , and examining to what extent these units are either cell type specific or conserved among different cell types . In summary , based on an integrated analysis of transcriptome , epigenome , and chromosome 3D structural information we propose a mechanism for concerted regulation of gene groups that can be further evaluated with more systematically measured datasets . That is , concerted gene regulation can be achieved through a common trans regulator ( s ) and the spatial co-localization of target genes . This observation further suggests that genes may be spatially organized into functional units , consistent with the hierarchical patterns and long-range interactions revealed by chromosome structure studies [36 , 41] . The relationship between gene expression and chromosome structure can be better understood by grouping genes into finer HMM classes based on their expression patterns and regulatory elements .
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Cellular responses to environmental stimulation are often accompanied by changes in gene expression patterns . Genes are linearly arranged along chromosomal DNA , which folds into a three-dimensional structure . The chromosome structure affects gene expression activities and is regulated by multiple events such as histone modifications and DNA binding of transcription factors . A basic question is how these mechanisms work together to regulate gene expression . In this study , we analyzed temporal gene expression patterns in the context of chromosome structure both in a human cell line under TGF-β treatment and during mouse nervous system development . In both cases , we observed that genes regulated by common transcription factors have an enhanced tendency to be spatially close . Our analysis suggests that spatial co-localization of genes may facilitate the concerted gene expression .
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2019
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Spatial clustering and common regulatory elements correlate with coordinated gene expression
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Scientific and technological advances that enable the tuning of integrated regulatory components to match network and system requirements are critical to reliably control the function of biological systems . RNA provides a promising building block for the construction of tunable regulatory components based on its rich regulatory capacity and our current understanding of the sequence–function relationship . One prominent example of RNA-based regulatory components is riboswitches , genetic elements that mediate ligand control of gene expression through diverse regulatory mechanisms . While characterization of natural and synthetic riboswitches has revealed that riboswitch function can be modulated through sequence alteration , no quantitative frameworks exist to investigate or guide riboswitch tuning . Here , we combined mathematical modeling and experimental approaches to investigate the relationship between riboswitch function and performance . Model results demonstrated that the competition between reversible and irreversible rate constants dictates performance for different regulatory mechanisms . We also found that practical system restrictions , such as an upper limit on ligand concentration , can significantly alter the requirements for riboswitch performance , necessitating alternative tuning strategies . Previous experimental data for natural and synthetic riboswitches as well as experiments conducted in this work support model predictions . From our results , we developed a set of general design principles for synthetic riboswitches . Our results also provide a foundation from which to investigate how natural riboswitches are tuned to meet systems-level regulatory demands .
The breadth of function exhibited by biological systems provides a foundation from which to develop solutions to global challenges including sustainability , renewable energy production , material synthesis , and medical advancement . Underlying these systems-level functions are regulatory components that evaluate molecular information in the extracellular and intracellular environments and ultimately translate that information into phenotypic responses over varying time scales . The properties of individual regulatory components and genetic networks composed of these components are tuned to control critical functions , including survival in fluctuating environments [1] , [2] , minimization of energy expenditure in metabolism [3] , [4] , developmental fate assignment [5] , and proper information transmission through regulatory cascades [6]–[8] . To more effectively approach the reliable construction of synthetic biological systems , it is critical to advance our understanding of the degree to which individual component properties are tuned in natural systems , the underlying mechanisms that support tuning of biological components , and the effect of tuned components on resulting systems-level functions . Riboswitches are RNA-based regulatory components that mediate ligand control of gene expression . Natural riboswitches have been identified in all three kingdoms of life [9] and primarily function by sensing a variety of essential cofactors , amino acids , and nucleotides and regulating the expression levels of proteins in associated metabolic pathways [10] . Riboswitches typically exploit three properties of RNA to translate changes in ligand concentration to changes in the expression of a target protein: specific and high affinity ligand binding by aptamer sequences , formation of distinct functional conformations primarily dictated by base-pairing interactions , and diverse gene expression regulatory mechanisms based on the central location of mRNA in the process of gene expression . With the exception of the glmS ribozyme [11] , [12] , natural riboswitches function through a general mechanism in which the RNA molecule can primarily adopt two conformations and ligand binding to the formed aptamer in one conformation biases partitioning toward the ligand-bound conformation . Each conformation is associated with differential regulatory activities such that increasing ligand concentrations either increase ( ON behavior ) or decrease ( OFF behavior ) gene expression depending on which conformation contains the formed aptamer . Synthetic riboswitches have been constructed based on this functional mechanism to expand on the regulatory potential exhibited by natural riboswitches [13] , [14] . There has been significant interest in engineering riboswitches as tailored ligand-responsive genetic control elements by integrating aptamers selected in vitro [15] against diverse molecular ligands appropriate for different applications . Natural and synthetic riboswitches have been demonstrated to be highly tunable regulatory components . Targeted nucleotide changes in synthetic riboswitches can shift the response curve [16]–[20] . Studies of natural riboswitches functioning through transcriptional termination found that the time lag between transcription of the aptamer and the terminator stem can tune the effective ligand concentration at which a half-maximal response is achieved ( EC50 ) [21] , [22] . These previous studies explored tuning in limited contexts by focusing on one aspect of riboswitch function ( EC50 ) for one type of regulatory mechanism . However , advancing the characterization or design of new riboswitches requires a general quantitative framework that applies broadly to different regulatory mechanisms . Due to the link between RNA secondary structure and function and the relative ease with which RNA molecules can be modeled , riboswitches present an interesting class of regulatory components through which researchers can examine links between physical composition , tuned component response properties , and resulting systems-level behavior . In this study , we employed mathematical modeling to explore how the dynamics of riboswitch function dictate its performance , where performance is evaluated based on the response curve quantitatively linking ligand concentration and protein levels . To draw general conclusions regarding riboswitch performance , we considered three representative regulatory mechanisms: transcriptional termination , translational repression , and mRNA destabilization . Parameter space for all three mechanisms was surveyed in order to understand the relationship between model parameters and riboswitch performance . Our results show that the competition between reversible and mechanism-specific irreversible rate constants primarily dictates riboswitch performance and response curve tuning properties . Complete dominance of irreversible rate constants renders a riboswitch non-functional , although ligand binding during transcription can rescue riboswitch performance . Our results also demonstrate that placing an upper limit on the ligand concentration alters the observed tuning properties such that a maximum dynamic range exists for intermediate conformational partitioning . Model predictions are supported by published experimental data and new data obtained through the modification of a synthetic riboswitch . We provide a set of design principles for the construction of synthetic riboswitches based on our modeling results . In addition , our results lend insights into the inherent flexibility and potential biological relevance of tuning of natural riboswitches .
We started with a detailed molecular description of riboswitch function ( Figure S1 ) that accounts for folding and ligand binding during discrete steps of transcription . Three regulatory mechanisms were considered: translational repression , transcriptional termination , and mRNA destabilization . Translational repression occurs through ribosome binding site ( RBS ) sequestration in a double-stranded secondary structure that prevents ribosome recruitment . Transcriptional termination occurs through a rho-independent mechanism such that hairpin formation directly upstream of a polyuridine stretch induces dissociation of the transcript from the template and the polymerase . We also considered the regulatory mechanism of a recently-described synthetic riboswitch that undergoes ribozyme self-cleavage [19] , thereby initiating mRNA destabilization [23] . In these examples , two inter-converting conformations ( A/B ) are associated with differential protein levels subject to the specified regulatory mechanism . Ligand binding to the formed aptamer harbored in B promotes conformational stabilization , thereby increasing the prevalence of B . We assigned a rate constant to each mechanistic step in the models to yield a detailed relationship between ligand concentration ( L ) and protein levels ( P ) . In all models , transcriptional initiation produces a partial-length riboswitch in either conformation A ( kfA ) or B ( kfB ) to reflect transcriptional folding . Transcription is broken into discrete steps that represent different sequence lengths . Each step determines the extent of conformational switching ( k1 , k1′ ) , the ability to bind and release ligand ( k2 , k2′ ) , and the rate of progression to the next step ( kE ) . For transcriptional termination , riboswitches effectively choose between termination ( kTA , kTB ) and extension ( kMA , kMB ) after transcription of the terminator stem . To ensure that both conformations make the decision with the same frequency , we set the sum of termination and extension rate constants for each conformation equal to a single parameter kM: ( 1 ) Following transcription of the full-length riboswitch for translational repression and mRNA destabilization or extension through the terminator stem for transcriptional termination , the transcript can be translated into protein ( kPA , kPB ) or undergo degradation ( kdMA , kdMB ) . A single constant is assigned when the rate constants are equal between conformations ( kP , kdM ) . Values for the rate constants can vary widely depending on the organism and regulatory mechanism ( Table 1 ) . Therefore , we explored how each rate constant contributes to riboswitch performance . Riboswitch performance was evaluated under steady-state conditions for both ON and OFF behaviors by calculating a collection of performance descriptors that define the response curve ( Figure 1A ) : EC50 , dynamic range ( η ) defined as the difference between high and low protein levels , basal protein levels ( P ( L = 0 ) ) , and ligand-saturating protein levels ( P ( L→∞ ) ) . While the dynamic range can be alternatively defined as the ratio of high and low protein levels , we selected the difference definition based on the mathematical symmetry between the equations representing ON and OFF behaviors ( Text S1 ) . Transcription can be considered as a discrete multistep process ( Figure S1 ) . The conformations that can form at each step depend on the ordering of elements along the riboswitch sequence , such as the relative location of the aptamer or gene regulatory elements . Matching the number of steps and parameter values to particular sequence configurations becomes burdensome and restricts the elucidation of general principles . Therefore , we simplified the transcription process by assuming that synthesized transcripts appear in either conformation A or B and are immediately subject to conformational partitioning , ligand binding , and the regulatory mechanism ( Figure 1B–D ) . As a result , the outcome of the transcription process is reflected by biased folding into either conformation A ( kfA ) or conformation B ( kfB ) . This simplification excludes ligand binding during transcription , which has been demonstrated for natural riboswitches functioning through transcriptional termination [21] , [22] . Therefore , we separately accounted for ligand binding during transcription in our analyses . We first derived expressions for the performance descriptors—dynamic range , EC50 , and basal and ligand-saturating levels—for riboswitches functioning through each regulatory mechanism ( Text S2 ) . From these derivations two common parameters emerged: the partitioning constant ( K1 = k1′/k1 ) and the ligand association constant ( K2 = k2/k2′ ) . K1 reflects the relative stability of conformation A and is present in all performance descriptor expressions . K2 reflects the affinity between the aptamer and its cognate ligand and is only present in the expression for EC50 . For all regulatory mechanisms , K1 and K2 reflect reversible conformational switching and ligand association , the core processes of riboswitch function . These processes are opposed by irreversible events that deplete the abundance of both conformations: mRNA degradation for translational repression and mRNA destabilization , and the riboswitch's decision to terminate or extend for transcriptional termination . The ratio between the rate constants for irreversible and reversible events is prevalent in all expressions for the performance descriptors ( Text S2 ) . This ratio is encapsulated in two reduced parameters γ1 and γ2: ( 2 ) ( 3 ) Here , kiA and kiB represent the irreversible rate constants for conformation A or B , respectively , for translational repression ( kdM ) , transcriptional termination ( kM ) , and mRNA degradation ( kdMA and kdMB ) . Three operating regimes can be defined based on the ratio of reversible and irreversible rate constants within γ1 and γ2 . The first regime occurs when both reversible rate constants dominate ( γ1 , γ2 converge to one ) , the second begins when either of the reversible rate constants is balanced with the associated irreversible rate constant ( either γ1 or γ2 is less than one ) , and the third begins when the irreversible rate constant kiA dominates over k1 ( γ1 converge to zero ) . Each regime is generally determined by the competition between reversible and irreversible rate constants . We next evaluated the tuning properties of each regime for all regulatory mechanisms . For dominating reversible rate constants ( γ1 = γ2 = 1 ) , a riboswitch molecule can sample both conformations and bind and unbind ligand many times before the irreversible event occurs . We define this regime as ‘thermodynamically-driven’ in accord with previous uses of this term in the study of natural riboswitches [21] , [24] , [25] , since energetics dictate the prevalence of each conformation . In the thermodynamically-driven regime , riboswitch function is captured for the three regulatory mechanisms by a general molecular description ( Figure 2A ) . The associated response curve is captured by a single equation that includes the partitioning constant ( K1 ) , the aptamer association constant ( K2 ) , mRNA degradation rate constants ( kdMA , kdMB ) , and representative regulatory activities of conformations A ( KA ) and B ( KB ) : ( 4 ) The values of KA and KB depend on the selected regulatory mechanism and are provided in Text S2 . Parameter variation has a unique effect on the response curve for both ON ( Figure 2B and 2C ) and OFF behaviors ( Figure S2 ) . Increasing K1 stabilizes conformation A , resulting in more riboswitch molecules adopting this conformation . Since conformation A has lower regulatory activity for ON behavior ( KA<KB ) , basal levels decrease . Concomitantly , EC50 increases as higher ligand concentrations are required to offset the decreased abundance of conformation B . Increasing K2 reduces EC50 as expected when aptamer affinity is modulated . However , K2 has no effect on dynamic range and ligand-saturating levels since we assumed sufficient ligand can be added to saturate the response curve . Previous mutational studies of two synthetic riboswitches [16] , [17] support these model predictions . However , these studies examined trans-acting mechanisms , calling into question whether model insights apply to cis-acting mechanisms . Finally , rate constants distinct from the core processes of riboswitch function such as transcription initiation ( kf ) and protein decay ( kdP ) affect both basal levels and dynamic range by modulating the steady-state mRNA and protein levels . Stabilizing conformation A ( increasing K1 ) improves the dynamic range to an upper limit set by the regulatory activities ( KA , KB ) and mRNA degradation rate constants ( kdMA , kdMB ) associated with each conformation ( Figure 2D ) . While all four parameters affect the dynamic range , kdMA and kdMB also impact the dependence of the dynamic range on conformational partitioning ( Figure S3 ) . This latter effect results from the dominant influence of the larger mRNA degradation rate on steady-state mRNA levels , which can be countered by biasing partitioning toward the more stable conformation . Therefore , when conformation A degrades faster ( higher kdMA , ON behavior ) , less partitioning toward conformation A ( lower K1 ) is required to separate basal and ligand-saturating levels , whereas more partitioning toward conformation A ( higher K1 ) is required when conformation B degrades faster ( higher kdMB , OFF behavior ) . As a result , thermodynamically-driven riboswitches functioning through mRNA destabilization require more ( OFF behavior ) or less ( ON behavior ) partitioning toward conformation A to achieve a larger dynamic range . In contrast , riboswitches functioning through translational repression and transcriptional termination display similar trends in dynamic range as a function of conformational partitioning for ON and OFF behaviors as the degradation rate constants are the same for each conformation . EC50 is also dependent on the value of K1 according to the following relationship: ( 5 ) EC50 approaches the lower limit set by the aptamer dissociation constant when riboswitches principally adopt conformation B ( low K1 ) . Therefore , although stabilizing conformation A ( increasing K1 ) can improve the dynamic range , excessive stabilization can be detrimental due to the increase in EC50 . As a result , tuning strategies based on increasing K1 require higher ligand concentrations to access the improved dynamic range . The ratio of the mRNA degradation rate constants in the expression for EC50 offsets the modified dependence of the dynamic range on K1 for riboswitches functioning through mRNA destabilization . Therefore , riboswitches functioning through any of the regulatory mechanisms exhibit the same trade-off between EC50 and dynamic range . The second regime begins when either of the irreversible rate constants balances the associated reversible rate constant ( either γ1 or γ2 is between zero and one ) . We call this regime the ‘kinetically-driven’ regime in accord with uses of this term in the study of natural riboswitches [21] , [22] , where performance is driven by kinetics over energetics . In this regime , riboswitch molecules have fewer opportunities to sample both conformations and bind and release ligand before the irreversible event occurs , where the number of opportunities is governed by the competition between reversible and irreversible rate constants . Since γ1 is coupled to K1 and kfA while γ2 is coupled to K2 , both γ1 and γ2 are anticipated to have a significant impact on the response curve and impart several tuning properties distinct to this regime . We initially use riboswitches functioning through transcriptional termination to highlight two of these tuning properties ( Figure 3A–C ) . First , irreversible rate constants modulate all performance descriptors , often at a cost to riboswitch performance . As the rate constant for terminator stem formation ( kM ) increases , riboswitch molecules become trapped in a given conformation after transcriptional folding or conformational switching as reflected in γ1 . This effect reduces the dynamic range ( Figure 3A ) and shifts basal and ligand-saturating levels according to the extent of transcriptional folding ( Figure 3B ) . The reduction in dynamic range can be offset by increasing the overall mRNA and protein abundance through modulation of the rates of transcription ( kf ) , translation ( kP ) , and mRNA ( kdM ) and protein ( kdP ) degradation . However , such changes also increase basal levels . γ1 and γ2 both impact EC50 according to the following relationship: ( 6 ) Since γ1 and γ2 reflect the ratios of kM/k1 and kM/k2′ , respectively , the relationship between EC50 and kM depends on both conformational switching ( k1 ) and ligand release ( k2′ ) ( Figure 3C ) . Increasing kM , which reduces both γ1 and γ2 , can increase or decrease EC50 based on the opposing contributions of γ1 and γ2 . Reducing γ1 decreases EC50 by restricting the time available to switch between conformations , while reducing γ2 increases EC50 by decreasing the half-life of the ligand-aptamer complex ( BL ) . Therefore , the relative values of k1 and k2′ must be known to predict the effect of modulating the irreversible rate constant on EC50 . Second , biased transcriptional folding can modulate the relationship between irreversible rate constants and the dynamic range ( Figure 3A ) . When transcriptional folding is biased toward conformation A ( kfB/kf = 0 ) , riboswitch molecules must have sufficient time to switch between conformations to maintain activity . Therefore , the dynamic range declines as kM approaches and surpasses k1 . In contrast , when transcriptional folding is biased toward conformation B ( kfB/kf = 1 ) , riboswitch molecules must switch to conformation A before the irreversible event occurs . In this case , kM must exceed the sum 2k1+k1′ to reduce the dynamic range . As a result , biasing transcriptional folding toward conformation B in the kinetically-driven regime increases the dynamic range . A third tuning property is associated with riboswitches functioning through mRNA degradation . The rate constants for mRNA degradation ( kdMA , kdMB ) are responsible for both the irreversible event and the steady-state basal and ligand-saturating levels , resulting in complex tuning properties ( Figure S4 ) . Increasing either kdMA ( ON behavior ) or kdMB ( OFF behavior ) initially improves the dynamic range by separating the steady-state basal and ligand-saturating levels . However , the impact of larger values of kdMA and kdMB depends on riboswitch behavior . For ON behavior , if a riboswitch predominantly folds into conformation A during transcription ( kfB/kf = 0 ) , then values of kdMA in excess of k1 diminish the dynamic range as conformation A is degraded before it can switch conformations . However , if a riboswitch predominantly folds into conformation B during transcription ( kfB/kf = 1 ) , then the dynamic range plateaus as each molecule either binds ligand or irreversibly switches to conformation A . In contrast , transcriptional folding has a negligible impact on the relationship between the dynamic range and kdMB for OFF behavior , since molecules that adopt conformation A will switch to conformation B before undergoing degradation . Furthermore , as observed for the thermodynamically-driven regime , more partitioning toward conformation A ( higher K1 ) is required to counteract the influence of kdMB on mRNA steady-state levels . Increasing kdMB eventually dominates basal levels when partitioning is maintained , leading to a loss in the dynamic range ( Figure S3 ) . As such , a tailored design approach is required to account for the difference between ON and OFF behavior for kinetically-driven riboswitches functioning through mRNA destabilization . Transcriptional folding is a key tuning parameter for ON behavior and should be the predominant focus before tuning the degradation rate of conformation A . In contrast , transcriptional folding can be largely ignored for OFF behavior , and the degradation rate of conformation B must be properly tuned to optimize the dynamic range . Higher irreversible rate constants require increased ligand concentrations to achieve a diminishing change in protein levels . We define the ‘non-functional’ regime as one in which riboswitches are effectively trapped in the conformation formed during transcriptional folding ( γ1 = 0 ) . In this regime , ligand has a negligible effect on performance . The fast time scales of terminator stem formation and mRNA cleavage may drive riboswitches functioning through these regulatory mechanisms into this regime . Our analysis of the kinetically-driven regime revealed that performance can be preserved by biasing transcriptional folding toward conformation B and ensuring that k1′ exceeds the irreversible rate constant kiA . However , these approaches do not alleviate the increased EC50 caused by the reduced half-life of the ligand-aptamer complex ( BL ) when γ2 approaches 0 . As a potential solution , studies of natural riboswitches have suggested that ligand binding during transcription can preserve EC50 [21] , [22] . Therefore , we examined the effect of ligand binding during transcription under the assumption that conformation B is solely available ( kfB/kf = 1 ) prior to polymerase extension ( kE ) through the gene regulatory element responsible for the irreversible event . We examined ligand binding during transcription for riboswitches functioning through transcriptional termination ( Figure 4A ) . We assumed that terminator stem formation ( kM ) occurs much faster than ligand release ( k2′ ) and the progression from conformation A to B ( k1 ) to limit consideration to non-functional riboswitches . Under these assumptions , the dynamic range is dependent on the ratio of read-through efficiencies for conformations A ( kMA/kM ) and B ( kMB/kM ) , the progression from conformation B to A ( k1′ ) , and the rate of terminator stem formation ( kM ) . The dynamic range is maximized when conformational progression occurs much faster than terminator stem formation ( Figure 4B ) as predicted from our analysis of the kinetically-driven regime ( Figure 3A ) . An in vitro study of the ribD FMN riboswitch operating through transcriptional termination yielded a reduced dynamic range when removing the polymerase pause site in the terminator sequence , increasing the nucleotide concentration , and withholding the NusA protein responsible for increasing polymerase residence time at pause sites [22] . These manipulations are expected to reflect an increase in kM and thus support our model predictions . If increasing k1′ above kM maximizes the dynamic range , riboswitches operating in this regime are expected to display strong stabilization of conformation A reflecting rapid progression from conformation B . In support of this claim , full-length riboswitches operating under transcriptional termination strongly prefer the aptamer-disrupted conformation and exhibit negligible ligand binding affinity [22] , [25] , [26] . EC50 tuning properties are strikingly different for riboswitches in which ligand binding during transcription allows for improved performance than those for thermodynamically-driven riboswitches . EC50 depends on model parameters in Figure 4A according to the following relationship: ( 6 ) Both ligand release ( k2′ ) and the time necessary to transcribe the sequences required for the formation of conformation A ( kE ) have a significant impact on the value of EC50 ( Figure 4C ) . Interestingly , tuning of kE decouples EC50 and basal levels such that EC50 can equal the aptamer dissociation constant ( k2/k2′ ) without impacting the dynamic range . In contrast , the EC50 of a thermodynamically-driven riboswitch approaches the aptamer dissociation constant as conformation B is stabilized , resulting in a concomitant decrease in the dynamic range ( Figure 2D ) . A previous theoretical study of the pbuE adenine riboswitch using experimentally measured kinetic rates also concluded that modulating polymerase extension time can tune EC50 when the extension time is not significantly slower than ligand release [21] . In our analyses thus far , we assumed that the maximum ligand concentration always saturates the response curve . However , studies of synthetic riboswitches have demonstrated that the response curve may not be saturated by the accessible upper limit in ligand concentration ( Figure 5A ) due to various system properties including aptamer affinity , ligand solubility , permeability of the ligand across the cell membrane , and cytotoxicity of the ligand [16] , [17] , [19] , [27]–[29] . Furthermore , natural riboswitches may regularly function in response to physiologically-relevant changes in metabolite concentrations that are much smaller than the ∼1000-fold range necessary to access the full riboswitch response curve . To assess the effect of establishing an upper limit to the ligand concentration , we evaluated the response curve descriptors for a maximum ligand concentration of L' . An apparent EC50 ( EC50APP ) was calculated according to protein levels at L = 0 and L' . Restricting L' alters the dependence of the dynamic range ( Figure 5B ) and the apparent EC50 ( Figure 5C ) on model parameters as illustrated for riboswitches operating in the thermodynamically-driven regime . L' acts as a system restriction that prevents access to the full response curve such that increasing K1 shifts the actual EC50 beyond L' , thereby reducing the maximum dynamic range that can be achieved . This behavior was recently observed for a trans-acting synthetic riboswitch operating under a limited ligand concentration range [17] , supporting model predictions . Reflecting this behavior , the apparent EC50 has the following dependence: ( 7 ) where the apparent EC50 converges to L'/2 as expected for a linear response when L' is below the EC50 for an unbounded ligand concentration range ( Figure 5D ) . Our modeling results demonstrate that restricting the ligand concentration upper limit reduces riboswitch performance and establishes a unique relationship between dynamic range and conformational partitioning . In addition to serving as a design constraint for synthetic riboswitches , natural riboswitches may inherently operate under defined limits in ligand concentration . Future experiments may focus on measuring the physiologically-relevant metabolite concentration range experienced by natural riboswitches to examine what section of the response curve is utilized . To begin evaluating how the predicted tuning trends apply to both natural and synthetic riboswitches , we physically manipulated a recently-described synthetic riboswitch functioning through translational repression that up-regulates gene expression ( ON behavior ) in the presence of theophylline [18] ( Figure 6A ) . Under the naming convention from Figure 1B , conformation A comprises a base-paired structure between the aptamer and RBS , while conformation B includes a formed aptamer and a single-stranded RBS . This riboswitch was selected because it closely resembles natural riboswitches functioning through translational repression , experimental data suggest that this riboswitch operates in the thermodynamically-driven regime [18] , the ligand concentration upper limit does not saturate the response curve [28] , and the demonstration that different sequences yield different response curves suggests riboswitch tuning [18] . A theophylline concentration of 1 mM was used as an upper limit , as exceeding this concentration inhibited cell growth . In studies performed by Lynch and coworkers , sequences associated with desirable response curves were identified by randomization of the RBS and screening for variants with low basal activity and a large activity increase in the presence of theophylline . Since the randomized sequence was located in a region responsible for conformational partitioning and translation , mutations most likely reflect simultaneous modulation of KA , KB , and K1 . We therefore sought to introduce directed mutations to solely modulate individual model parameters and test model predictions for a thermodynamically-driven riboswitch with a ligand concentration upper limit that prevents response curve saturation . We examined two model predictions that could not be supported with available experimental data for cis-acting riboswitches: ( 1 ) solely modulating conformational partitioning ( K1 ) affects both EC50 and basal levels ( Figure 2B ) , and ( 2 ) the dynamic range can be optimized by modulating K1 when the ligand concentration upper limit does not saturate the response curve ( Figure 5B ) . We modulated K1 by introducing systematic mutations into the aptamer stem while preserving the RBS sequence ( m1–4; Figure 6A ) . Mutant sequences were ordered with increasing K1 based on the energetic difference between conformations predicted by the RNA folding algorithm Mfold [17] . The mutations were not anticipated to significantly affect aptamer affinity ( K2 ) [30] , [31] or translational efficiency for either conformation ( KA , KB ) . Additional mutants were examined that are predicted to entirely favor either conformation A ( mA ) or conformation B ( mB ) to establish the regulatory activity of either conformation . Riboswitch performance was evaluated by measuring β-Galactosidase levels over a range of theophylline concentrations . The introduced mutations altered the response curve in agreement with model predictions ( Figure 6B–D ) . Protein levels in the presence and absence of theophylline correlated with the relative stability of conformation A . Furthermore , complete stabilization of conformation A ( mA ) and conformation B ( mB ) established respective lower and upper limits for the observed expression levels . As predicted for a non-saturating value of L' , an intermediate conformational partitioning value optimized the dynamic range to a value that was below the maximum dynamic range ( ηmax = 15 , 600 MU ) ( Figure 6B ) , and EC50 approached 0 . 5 mM ( L'/2 ) for increased stabilization of conformation A ( Figure 6C and 6D ) . Dynamic range optimization is clearly observed when evaluating the ratio of high and low protein levels , which is predicted to display the same qualitative tuning behavior ( Figure S5 ) . The data agree with our model predictions for K1 modulation in the thermodynamically-driven regime under conditions where the ligand concentration upper limit does not saturate the response curve , although we cannot rule out the possibility that stabilization of conformation A inadvertently drove the riboswitch into the kinetically-driven regime . The introduction of the aptamer sequence to the regulatory element decreased the regulatory activity of conformation B as observed when comparing protein levels for mB and a construct harboring only the RBS and aptamer basal stem ( empty; Figure 6B ) . Our previous construction and characterization of a trans-acting synthetic riboswitch functioning through RNA interference [17] also showed sub-maximum dynamic range optimization when the ligand concentration was limiting and compromised activity of the regulatory element due to introduction of the aptamer element of the riboswitch . Thus , the results support the extension of our model predictions to synthetic riboswitches . In addition , our modeling results may have direct implications for the performance and tuning of natural riboswitches based on the similarity between the synthetic riboswitch examined here and natural riboswitches operating under translational repression .
Synthetic riboswitches can be divided into two categories based on the intended application: inducible regulators and autonomous regulators . The applicable category depends on the identity and source of the detected ligand and requires distinct approaches to riboswitch design . We provide the following design principles assembled from our modeling results to guide the design of synthetic riboswitches as inducible or autonomous regulatory systems . The desired properties of inducible regulatory systems include large dynamic ranges , low basal expression levels , and titratable control over expression levels . Selecting an effective regulatory mechanism is critical since numerous factors reduce the dynamic range , such as conformational partitioning , dominating irreversible rates , upper limits to ligand concentration , and reduced gene regulatory efficiencies from the incorporation of other riboswitch elements [17] , [19] . A design that is biased toward forming the disrupted-aptamer conformation ( high K1 ) will generally increase the dynamic range , although such strategies require higher ligand concentrations to modulate protein levels . The rates of events separate from core riboswitch processes , such as transcription , translation , and protein decay , can be modulated to increase the dynamic range difference at the expense of increased basal levels . The selected regulatory mechanism will likely dictate the values of the irreversible rate constants and thus the operating regime . In support of this , studies on natural riboswitches have suggested a consistent pairing between translational repression and the thermodynamically-driven regime [25] and transcriptional termination and the non-functional regime with ligand binding during transcription [21] , [22] , [25] , [26] , [33] . Therefore , the design of inducible regulatory systems may rely on the tuning properties associated with each regime . While thermodynamically-driven riboswitches generally provide for the largest dynamic range , kinetically-driven and non-functional riboswitches can be designed to perform similarly using insights from our modeling efforts . In general , placing the aptamer toward the 5′ end of the riboswitch sequence will preserve the dynamic range by biasing transcriptional folding toward conformation B . The exception is OFF-behaving riboswitches acting through mRNA destabilization , which are insensitive to biased transcriptional folding ( Figure S4 ) . In addition , introducing pause sites and ensuring rapid conformational switching from the aptamer-formed conformation ( k1′ ) will allow kinetically-driven and non-functional riboswitches to exploit ligand binding during transcription , thereby decreasing the amount of ligand required to induce gene expression . In many practical applications , system restrictions will limit the accessible range of the response curve ( Figure 7A–C ) . Such limitations need to be addressed through parameter tuning in order to access the appropriate section of the response curve . For most biological systems , a predominant restriction is a limit to the maximum ligand concentration . In situations where the maximum ligand concentration does not saturate the response curve , designs for thermodynamically-driven riboswitches should be based on intermediate conformational partitioning values ( K1 ) that achieve a suboptimal maximum dynamic range . An alternative strategy is the design of non-functional riboswitches that bind ligand during transcription , which can respond to ligand at lower concentrations without sacrificing the dynamic range . Genes often exist in regulatory networks that dictate cellular phenotype such that complex relationships exist between the expression levels of individual genes and systems-level functions . To effectively regulate these genes with synthetic riboswitches , a variety of tuning strategies must be employed to tune the response curve to operate within system restrictions . The properties of the regulated gene , its integration into a biological network , and the ultimate systems-level functions must be considered . One can envision an application-specific regulatory niche that defines the acceptable ranges of basal and maximum-ligand expression levels for proper system performance ( Figure 7B ) . For example , the properties of a given system may require that the riboswitch be tuned to minimize basal expression over maximizing dynamic range , such as when the regulated enzyme exhibits high activity or cytotoxicity . The engineering of synthetic riboswitches that act as autonomous regulatory systems presents an even greater design challenge . Here , the upper and lower ligand concentrations that the system fluctuates between establish the accessible section of the response curve such that the regulatory niche is further restricted ( Figure 7C ) . For example , riboswitches responsive to an endogenous central metabolite will likely be operating under a defined concentration range characteristic of the organism and the environment . In this case , the response curve must be tuned to place the desired expression levels at the limits of this defined concentration range by modulating the appropriate performance descriptors . Depending on system restrictions , proper tuning of riboswitches acting as autonomous control systems may require minimization of basal levels , operation across higher expression levels , or maximization of the change in expression levels . Many parameters can potentially be modulated to tune the response curve . However , current practical considerations favor the modulation of a subset of these parameters in the laboratory . As one example , a given riboswitch may require a higher EC50 value to meet the performance requirements . Aptamer affinity ( K2 ) , conformational partitioning ( K1 ) , and the irreversible rates associated with the gene regulatory mechanism can be modulated to increase EC50 . However , rational modulation of aptamer affinity is restrictive since most mutations effectively abolish ligand binding , while the method and ease of modulating irreversible rates depend on the regulatory mechanism . Modulating conformational partitioning is an attractive approach since simple base-pairing interactions principally establish each conformation . However , conformational partitioning also impacts basal levels and the dynamic range , such that other parameters may need to be modulated to compensate for any undesired changes . Thus , the effective design of synthetic riboswitches requires knowledge of the relationship between riboswitch sequence and model parameters and may require the coordinated modulation of multiple parameters to meet application-specific performance requirements . The relationship between riboswitch sequence and model parameters depends in part on the composition framework used in the riboswitch design . A synthetic riboswitch can be designed such that parameters map to individual domains [16] , [17] , [19] or multiple domains [18] , [42] , [43] . Each design strategy offers distinct advantages depending on whether rational design or random screening is used to select riboswitch sequences . Individual domain mapping strategies allow for insulated control over each parameter and domain swapping without requiring redesign of the riboswitch , thereby presenting significant advantages for rational design approaches . Multiple domain mapping strategies may be more desirable for random screening approaches , where assigning multiple parameters to a single sequence domain can reduce the number of randomized nucleotides required to sufficiently sample parameter space . Natural riboswitches primarily serve as key autonomous regulators of diverse metabolic processes [10] . Recent characterization of eleven known S-adenosylmethionine riboswitches in Bacillus subtilis demonstrated that these riboswitches exhibit a diverse range of values for basal expression levels , EC50 , and dynamic range [44] , suggesting that natural riboswitches are finely tuned to match their occupied regulatory niche . However , this study is the only one to date to characterize the response curves of multiple natural riboswitches responsive to the same ligand . Two questions emerge from these observations and our modeling results that underlie the biological utilization of natural riboswitches as dynamic regulators of metabolism: ( 1 ) how finely tuned are natural riboswitches to their regulatory niche , and ( 2 ) what sequence modifications are associated with response curve tuning ? Understanding the extent to which natural riboswitches are tuned to their regulatory niches will provide insights into riboswitch utilization and the underlying principles of genetic regulatory control . Similar to the tuning of synthetic riboswitches to match their intended regulatory niche , investigating the extent and biological relevance of natural riboswitch tuning requires knowledge of the functional properties of the regulated genes and their contribution to cellular fitness . Furthermore , the typical ligand concentration range encountered in the intracellular environment designates the operational section of the response curve , such that determining this range is critical to advancing our understanding of natural riboswitch tuning within regulatory niches . The composition of a natural riboswitch dictates the relationship between its sequence and model parameters . One way to gain insights into this relationship is investigating sequence deviations between natural riboswitches in the same organism or different organisms that recognize the same ligand and employ the same regulatory mechanism . Using the response curve as a basis of comparison , these mutations may be neutral or shift the response curve in line with modulation of single or multiple parameters . Identifying which parameters are modulated will provide insights into how accessible each parameter is to random point mutations and how evolution effectively tunes the response curve through parameter modulation . Advances in our understanding of the biological utilization of natural riboswitches will enable researchers to better define regulatory niches in a biological system and more effectively design synthetic riboswitches to match these niches . Beyond riboswitch design and implementation , insights into the fine-tuning of natural regulatory components and networks will enable the construction of biological networks that reliably control systems-level functions .
All modeling assumptions and methods are fully described in Text S2 . Briefly , time-dependent differential equations were generated using mass action kinetics to describe each mechanistic step in the simplified molecular descriptions of riboswitch function for translational repression , transcriptional termination , and mRNA degradation . The resulting equations were simplified by assuming steady-state conditions . Relevant tuning properties were identified based on the impact of model parameters on the response curve descriptors , including dynamic range ( η ) defined as the difference between high and low protein levels , ligand concentration to induce a half-maximal response ( EC50 ) , basal protein levels ( P ( L = 0 ) ) , and maximum-ligand protein levels ( P ( L→L' or ∞ ) ) . pSAL8 . 3 served as the base plasmid for all experimental studies [18] . A theophylline-dependent synthetic riboswitch functioning through translational repression resides between the upstream Ptac1 promoter and the downstream Tn10-β-Galactosidase fusion gene . Mutant sequences were cloned into the unique KpnI and HindIII restriction sites located directly upstream of the riboswitch and approximately 200 nucleotides into the fusion gene coding region . Primers harboring mutant sequences ( Table S1 ) and a 5′ KpnI site were used to PCR amplify the 5′ untranslated region extending through the HindIII restriction site . The resulting PCR product was digested with KpnI/HindIII , ligated into pSAL8 . 3 digested with the same restriction enzymes , and transformed into Escherichia coli strain DH10B . Assembled plasmid constructs were verified by sequencing ( Laragen ) . All molecular biology reagents and enzymes were obtained from New England Biolabs . β-Galactosidase assays were conducted using E . coli DH10B cells harboring the pSAL8 . 3 plasmid mutants based on modifications to previously described protocols [18] , [45] . Cells harboring each construct were grown overnight in Luria-Bertani ( LB ) broth supplemented with 50 µg/ml ampicillin . Overnight cultures were back-diluted into three separate wells containing 500 µl LB broth with 50 µg/ml ampicillin and the appropriate concentration of theophylline and grown at 37°C for 3 hrs with shaking at 210 RPM . Approximately 3 µl of the overnight culture was added to each well . Following the 3-hr incubation with shaking , optical density was recorded by transferring 175 µl into a 96-well microplate with a µClear bottom ( Greiner ) and measuring on a Safire fluorescence plate reader ( Tecan ) . Cells were lysed by mixing 20 µl of culture with 80 µl permeabilization solution ( 100 mM Na2HPO4 , 20 mM KCl , 2 mM MgSO4 , 0 . 6 mg/ml CTAB , 0 . 4 mg/ml sodium deoxycholate , and 5 . 4 µl/ml β-mercaptoethanol ) and mixed at room temperature for approximately 10 min . In a fresh 96-well microplate , 25 µl of the lysed culture was mixed with 150 µl substrate solution ( 60 mM Na2HPO4 , 40 mM NaH2PO4 , 1 mg/ml ONPG , and 5 . 4 µl/ml β-mercaptoethanol ) . ONPG hydrolysis was stopped with the addition of 75 µl 1 M Na2CO3 when a faint yellow color was observed . Absorbance at 420 nm was then measured on the fluorescence plate reader and protein levels were calculated in Miller Units ( MU ) : ( 8 ) where t is in minutes and absorbance values reflect the difference between each sample and blank media . The MU value of cells carrying a blank plasmid was also subtracted from each sample measurement .
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Riboswitches are RNA-based components that integrate ligand binding and gene regulation to dynamically respond to molecular signals within cells . Natural riboswitches are employed to regulate metabolism and other cellular processes , while synthetic riboswitches have been constructed to expand the sensory and regulatory capabilities exhibited in nature . Characterization studies have revealed that sequence modifications can tune properties of the riboswitch response curve , which links ligand concentration to expression levels . Tunability is critical when matching component properties to the regulatory demands of biological systems; however , the characterization of riboswitch tuning strategies is complicated by the integration of numerous regulatory mechanisms and various processes , such as RNA folding and turnover , that impact riboswitch performance . To develop a generalized framework for examining quantitative aspects of riboswitch tuning , we modeled the kinetics of riboswitch function operating under common regulatory mechanisms . Our results reveal that riboswitch performance is primarily dictated by the competition between reversible and mechanism-specific irreversible rate constants . We demonstrate that practical system restrictions can significantly alter the requirements for riboswitch performance , necessitating a variety of tuning strategies . We developed design principles to guide the construction of synthetic riboswitches and a quantitative framework from which to investigate how natural riboswitches are tuned to meet systems-level regulatory demands .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"computational",
"biology/transcriptional",
"regulation",
"molecular",
"biology/mrna",
"stability",
"molecular",
"biology/transcription",
"elongation",
"biotechnology/bioengineering",
"molecular",
"biology/translational",
"regulation"
] |
2009
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Design Principles for Riboswitch Function
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The phytohormone abscisic acid ( ABA ) regulates plant growth , development and responses to biotic and abiotic stresses . The core ABA signaling pathway consists of three major components: ABA receptor ( PYR1/PYLs ) , type 2C Protein Phosphatase ( PP2C ) and SNF1-related protein kinase 2 ( SnRK2 ) . Nevertheless , the complexity of ABA signaling remains to be explored . To uncover new components of ABA signal transduction pathways , we performed a yeast two-hybrid screen for SnRK2-interacting proteins . We found that Type One Protein Phosphatase 1 ( TOPP1 ) and its regulatory protein , At Inhibitor-2 ( AtI-2 ) , physically interact with SnRK2s and also with PYLs . TOPP1 inhibited the kinase activity of SnRK2 . 6 , and this inhibition could be enhanced by AtI-2 . Transactivation assays showed that TOPP1 and AtI-2 negatively regulated the SnRK2 . 2/3/6-mediated activation of the ABA responsive reporter gene RD29B , supporting a negative role of TOPP1 and AtI-2 in ABA signaling . Consistent with these findings , topp1 and ati-2 mutant plants displayed hypersensitivities to ABA and salt treatments , and transcriptome analysis of TOPP1 and AtI-2 knockout plants revealed an increased expression of multiple ABA-responsive genes in the mutants . Taken together , our results uncover TOPP1 and AtI-2 as negative regulators of ABA signaling .
The phytohormone abscisic acid ( ABA ) in plants controls a variety of developmental processes such as seed dormancy , germination , root/shoot growth , flowering and senescence [1–3] . When plants encounter stressful conditions , ABA can rapidly induce the reprogramming of gene expression and trigger multiple physiological responses such as stomatal closure to reduce water loss in plants [4–6] . Given the importance of ABA in regulating various aspects of plant growth and stress responses , it is critical to understand the molecular mechanisms of ABA action in response to adverse environmental conditions . Arabidopsis ABA insensitive 1 ( abi1-1 ) and abi2-1 mutants were isolated in genetic screens for ABA insensitivity phenotypes [7] . Both mutants showed dominant-negative effects during seed germination , seedling growth and stomatal closure . The discovery that ABI1 and ABI2 encode homologous type 2C Serine/Threonine ( Ser/Thr ) phosphatases ( PP2Cs ) revealed critical roles for phosphatase-mediated dephosphorylation in regulating ABA signaling ( [8] [9] [10] ) . It has been shown that these clade A PP2Cs negatively regulate the ABA signal transduction pathway [11] . The SNF1-related protein kinases ( SnRK2s ) also regulate ABA signaling . The Arabidopsis mutant ost1-1/snrk2 . 6 ( Open Stomata 1 , also known as SnRK2 . 6 ) displays impaired ABA-induced stomatal closure and defective light-induced stomatal opening [12] . Ten SnRK2 members ( SnRK2 . 1–10 ) have been identified in Arabidopsis [13]; however only four ( SnRK2 . 2/2 . 3/2 . 6/2 . 8 ) can be activated by ABA in a protoplast transient expression assay , implying that SnRK2 members may function in both ABA-dependent and independent signaling pathways [13 , 14] . ABA-induced gene expression and other ABA responses were almost completely blocked in snrk2 . 2/3/6 triple mutant plants [15–17] , demonstrating that these three SnRK2s are critical positive regulators of ABA signaling . It was shown that the clade A PP2Cs bind to the SnRK2s and dephosphorylate key Ser/Thr residues in the activation loop of SnRK2s [18 , 19] . In recent years , ABA receptors were identified as the PYRABACTIN RESISTANCE 1 ( PYR1 ) /PYR1-Like ( PYL ) /REGULATORY COMPONENT OF ABA RECEPTOR ( RCAR ) family of proteins [20 , 21] . ABA binding to the receptors triggers a conformational change in the receptors , allowing them to associate with PP2Cs and thus disrupting the PP2C-SnRK2 interactions and releasing the SnRK2s from PP2Cs-mediated inhibition [20–26] . Activated SnRK2s subsequently phosphorylate downstream target proteins such as ion channels in the plasma membrane and basic-domain leucine zipper ( bZIP ) transcription factors in the nucleus [4–6 , 27] . To identify potential new components of the ABA signaling pathway , we performed a yeast two-hybrid screen employing SnRK2 . 6 as bait . We discovered that Type One Phosphatase 1 ( TOPP1 ) and its regulatory protein , Arabidopsis Inhibitor-2 ( AtI-2 ) , interacted with several SnRK2s and PYLs . Biochemical and physiological evidence indicates that TOPP1 and AtI-2 coordinately inactivate SnRK2s and , in turn , negatively regulate the ABA signal transduction pathway . topp1 and ati-2 mutant plants are hypersensitive to ABA and salt , consistent with their function in ABA signaling . Furthermore , transcriptome analysis revealed that TOPP1 and AtI-2 co-regulate groups of overlapping genes in response to ABA . Taken together , our work identifies new components of the ABA signaling pathway .
To identify potential new components in early ABA signaling , we used SnRK2 . 6 as bait to find interacting proteins in a yeast two-hybrid ( Y2H ) screen . Full-length SnRK2 . 6 cDNA was fused to the yeast GAL4 DNA Binding Domain ( BD ) . A yeast strain carrying the bait construct was transformed with a cDNA plasmid library in which the cDNAs were fused to the GAL4 DNA Activation Domain ( AD ) . One of the potential interactions identified was between TYPE ONE PROTEIN PHOSPHATASE 1 ( TOPP1 ) and SnRK2 . 6 . We cloned the full-length coding sequence of TOPP1 to validate and extend the potential interaction between TOPP1 and ten members of the SnRK2 family . Yeast co-expressing AD-TOPP1 and several members of SnRK2 family ( SnRK2 . 2/3/4/6/8 ) fused to the BD could grow on the selection medium as well as the positive control that co-expressed AD-ABI1 and BD-SnRK2 . 6 ( Fig 1A ) , indicating that TOPP1 interacts with the SnRK2s . Similarly , Y2H assays suggested that TOPP2 also interacts with SnRK2 . 2 and SnRK2 . 6 , out of several SnRK2s tested ( S1 Fig ) . From data mining of SnRK2-interacting proteins within the Arabidopsis thaliana Protein Interaction Network [28] , we noticed that ARABIDOPSIS Protein Phosphatase Inhibitor-2 ( AtI-2 ) was predicted to interact with SnRK2 . 6 . To evaluate potential interactions between AtI-2 and SnRK2s , we cloned the full-length coding sequence of AtI-2 and fused it to the AD vector . Consistent with previous report ( Templeton et al , 2011 ) , we found that AtI-2 interacted with TOPP1 in the Y2H assay . In addition , yeast co-transformed with AD-AtI-2 and several BD-SnRK2s ( SnRK2 . 2/3/6/8 ) also grew on the selection medium ( Fig 1B ) . Taken together , our Y2H results indicated that both TOPP1 and its regulatory protein AtI-2 physically interact with some SnRK2 family members . To validate the interaction results obtained from the Y2H assays , we performed a split-luciferase complementation ( Split-LUC ) assay in tobacco leaves . As illustrated in Fig 1C , the co-expression of AtI-2-nLUC with cLUC-TOPP1 reconstituted luciferase activity , and served as our positive control . In contrast , co-expressing nLUC/cLUC , nLUC/cLUC-TOPP1 , or AtI-2-nLUC/cLUC resulted in only background luciferase signals ( Fig 1C ) . The co-expression of nLUC-tagged SnRK2 . 2 , SnRK2 . 3 or SnRK2 . 6 with cLUC-TOPP1 produced detectable luciferase activity , confirming the results from the Y2H assay . Similarly , the co-expression of SnRK2 . 2- , SnRK2 . 3- , or SnRK2 . 6-nLUC with cLUC-AtI-2 led to luciferase activity that was comparable to another positive control , which co-expressed SnRK2 . 6-nLUC and ABI1-cLUC ( Fig 1D ) . In addition , we performed in vitro pull down ( S2 Fig ) and co-immunoprecipitation assays in protoplasts , and the results support the interactions between SnRK2 . 6 and TOPP1 ( Fig 1E ) and between AtI-2 and SnRK2 . 6 ( Fig 1F ) . In mammals , INHIBITOR-2 ( I-2 ) interacts with Protein Phosphatase 1 ( PP1 ) . I-2 promotes substrate recognition by PP1 and the formation of a trimeric I-2/PP1/substrate protein complex [29 , 30] . To investigate whether a similar trimeric Ati-2/TOPP1/SnRK2 complex might be possible , we co-expressed FLAG-tagged AtI-2 with cLUC-TOPP1 and SnRK2 . 6-nLUC in leaves . As shown in Fig 1G , the co-expression of SnRK2 . 6-nLUC with cLUC-TOPP1 generated a detectable but relatively weak luciferase signal . In contrast , the co-expression of FLAG-AtI-2 , SnRK2 . 6-nLUC and cLUC-TOPP1 produced a much stronger luciferase signal . Western blot analysis confirmed that cLUC-TOPP1 and SnRK2 . 6-nLUC were expressed at comparable levels in the two samples ( Fig 1G ) . These data suggest the possibility that AtI-2 may facilitate the association between TOPP1 and SnRK2s , and that the proteins might form a trimeric complex . In the presence of ABA , the ABA receptors PYR1/PYLs associate with and inactivate the PP2Cs [23 , 31] . Since TOPP1 encodes a Ser-Thr phosphatase , we considered the possibility that TOPP1 might also interact with the ABA receptors . We performed Y2H assays to evaluate potential interactions between TOPP1 and PYLs . As shown in Fig 2A , yeast co-expressing AD-TOPP1 and BD-PYL4 , -PYL9 or -PYL11 grew in the selection media in the absence of ABA . The exogenous application of ABA enhanced the interactions between TOPP1 and these PYLs ( PYL4/9/11 ) ( Fig 2A ) . Since we found that both TOPP1 and AtI-2 interact with SnRK2s , we subsequently investigated whether , like TOPP1 , AtI-2 may also interact with PYLs . Yeast co-transformed with AD-AtI-2 and BD-PYLs in yeast showed detectable growth on the SD-Leu/Trp/His medium , although the growth was less than yeast co-transformed with TOPP1 and PYLs , suggesting a weaker interaction ( Fig 2B ) . Among fourteen PYLs , PYL11 showed the strongest interaction with AtI-2 , and this interaction was largely dependent on ABA . We validated some of these protein interactions with the Split-LUC assays . We tested PYL11 since it displayed stronger interactions with TOPP1 and AtI-2 in the Y2H assays . The nLUC and cLUC empty constructs expressed together or with individual fusion constructs resulted in only background luciferase activity . However , we found that co-expression of cLUC-TOPP1 with PYL11-nLUC produced a strong luciferase signal , comparable to the positive control of AtI-2-nLUC with cLUC-TOPP1 ( Fig 2C ) , confirming that TOPP1 interacts with PYL11 . Similarly , the co-expression of PYL11-nLUC with AtI-2-cLUC or the positive control ABI1-cLUC also exhibited strong luciferase activity ( Fig 2D ) , verifying the interaction between AtI-2 and PYL11 . To test whether AtI-2 may enhance the interaction between TOPP1 and PYL11 , cLUC-TOPP1 and PYL11-nLUC were co-expressed with or without FLAG-AtI-2 . As illustrated in Fig 2E , the luciferase signal generated by co-expression of cLUC-TOPP1 and PYL11-nLUC was notably increased by FLAG-AtI-2 . Further western blot results showed equivalent protein levels of cLUC-TOPP1 and PYL11-nLUC . The data indicated that AtI-2 may also promote the interaction between TOPP1 and PYLs ( Fig 2E ) . TOPP1 encodes a Ser-Thr protein phosphatase in plants [32] . We purified GST-tagged TOPP1 ( GST-TOPP1 ) and incubated it with the general substrate p-Nitrophenyl Phosphate ( pNPP ) . As demonstrated in S3A Fig , TOPP1 displayed a phosphatase activity , which was reduced by the addition of AtI-2 . Next , we tested whether TOPP1 affects SnRK2 . 6 activity . When TOPP1 , SnRK2 . 6 and the SnRK2 substrate GST-ABF2 ( Gly73 to Gln119 ) were incubated together , the SnRK2 . 6-mediated phosphorylation of ABF2 was reduced by TOPP1 in a dose-dependent manner ( Fig 3A ) . Incubation of SnRK2 . 6 together with TOPP1 led to an inhibition of SnRK2 . 6 autophosphorylation ( S3B Fig ) . Together , these data suggest that TOPP1 could dephosphorylate and inhibit the kinase activity of SnRK2 . 6 in vitro . Since AtI-2 appeared to enhance the association of TOPP1 with SnRK2 . 6 in our Y2H and Split-LUC experiments , we sought to determine if AtI-2 also affects SnRK2 . 6 activity . We incubated recombinant SnRK2 . 6 with its substrate ABF2 and increasing amounts of GST-AtI-2 . SnRK2 . 6-mediated ABF2 phosphorylation remained unaltered ( Fig 3B ) , suggesting that AtI-2 does not affect SnRK2 . 6 activity by itself in vitro . However , AtI-2 enhanced the TOPP1-mediated reduction in ABF2 phosphorylation by SnRK2 . 6 ( Fig 3C ) . The result also shows that SnRK2 . 6 does not phosphorylate AtI-2 in vitro ( Fig 3C ) . It has been known that PYLs bind to PP2Cs to inhibit their phosphatase activities . Thus , we wanted to test whether PYLs could also inhibit the phosphatase activity of TOPP1 . We selected PYL11 as it showed the strongest interaction with TOPP1 and AtI-2 in our Y2H assay ( Fig 2 ) . As shown in S3C Fig , His-tagged PYL11 suppressed the phosphatase activity of TOPP1 in a dose-dependent manner in vitro . We found that TOPP1 reduced SnRK2 . 6-mediated phosphorylation of ABF2 , and that this reduction was enhanced by AtI-2 ( Fig 3A and 3C ) . As PYL11 was shown to inhibit the phosphatase activity of TOPP1 in vitro , we further introduced PYL11 to examine the potential effect of PYL11 on the inhibitory activity of TOPP1-AtI-2 complex on SnRK2 . 6 . The addition of PYL11 did not result in a substantial increase in SnRK2 . 6 phosphorylation of ABF2 , although the phosphorylation appeared slightly enhanced in the presence of ABA ( Fig 3D ) . The results from the in vitro phosphorylation assays above suggest that TOPP1 and AtI2 may form a complex to suppress the kinase activity of SnRK2 . 6 . To determine whether a TOPP1-AtI-2 complex functions as a negative regulator in ABA signal transduction , we performed transient expression assays in Arabidopsis mesophyll protoplasts . We employed a protoplast system in which ABA induces SnRK2 . 2/3/6-dependent ABF2 phosphorylation and , in turn , the expression of an ABA-responsive LUC reporter gene driven by the RD29B promoter ( RD29B-LUC ) [24] . We co-expressed TOPP1 and/or AtI-2 with SnRK2 . 2/3/6 , ABF2 and RD29B-LUC in protoplasts with or without 5 μM ABA . GUS was also co-expressed to determine the transfection efficiency . As expected , we found that ABA induced RD29B-LUC expression in protoplasts co-expressing SnRK2 . 6 and ABF2 , and the induction was suppressed by co-expressing ABI1 ( Fig 4A ) . The additional co-expression of TOPP1 or AtI-2 with SnRK2 . 6 and ABF2 also suppressed ABA-induction of RD29B-LUC , indicating a negative role for TOPP1 and AtI-2 in ABA signaling ( Fig 4A ) . Moreover , co-expression of both TOPP1 and AtI-2 with SnRK2 . 6 and ABF2 further suppressed RD29B-LUC induction by ABA . Together , these data suggest that TOPP1 and AtI-2 may function in a complex to suppress ABA-induced SnRK2 . 6 kinase activity ( Fig 4A ) . Similar results were obtained for SnRK2 . 2 and SnRK2 . 3 , indicating that SnRK2 . 2/2 . 3-dependent ABF2 phosphorylation and activation of RD29B-LUC was also inhibited by a TOPP1/AtI-2 complex ( S4A and S4B Fig ) . As reported previously [33] , co-expressing PYL11 in the protoplasts released the suppression of RD29B-LUC expression by ABI1 ( Fig 4A ) . Similarly , we found that PYL11 could release the inhibition of RD29B-LUC by TOPP1 , AtI-2 , and by the TOPP1-AtI-2 complex ( Fig 4A ) , although it is difficult to tell whether the effect of PYL11 was due to inhibition of the co-transfected TOPP1 or due to inhibition of endogenous clade A PP2Cs . These protoplast transient expression results suggest that TOPP1 and AtI-2 collaborate to negatively regulate ABA signaling in a manner similar to the PP2Cs . To acquire additional evidence that TOPP1 and AtI-2 negatively regulate SnRK2s in vivo , we performed an in-gel kinase assay to examine endogenous SnRK2 activity in WT , and T-DNA insertion mutants of TOPP1 ( topp1 ) and AtI-2 ( ati-2 ) . We isolated homozygous topp1 and ati-2 mutant plants and genomic DNA PCR confirmed that topp1 and ati-2 lines were homozygous as shown in S5A and S5B Fig . Previous studies have shown that the ABA-induced endogenous kinase activities of SnRK2s can be detected by in-gel kinase assays using histones as a substrate [13 , 34] . Our in-gel kinase assays revealed no detectable difference in the endogenous activities of SnRK2 . 2/3/6 in WT and mutant plants in the absence of ABA . However , upon ABA treatment for one hour , the phosphorylation signals corresponding to the endogenous kinase activities of SnRK2 . 2/3/6 were higher in topp1 and ati-2 mutant plants than those in the WT . As a control , no SnRK2 . 2/3/6 activities were detected in the snrk2 . 2/2 . 3/2 . 6 triple mutant ( Fig 4B ) . These data suggest that the TOPP1 and AtI-2 mutations resulted in increased kinase activities of SnRK2 . 2/3/6 in plants , further supporting their negative role in regulating ABA-induction of SnRK2 . 2/3/6 activities . We generated transgenic lines with the promoters of TOPP1 or AtI-2 driving a β-glucuronidase ( GUS ) reporter to help determine the expression patterns of TOPP1 and AtI-2 in plants . Seeds of transgenic plants expressing ProTOPP1:: GUS and ProAtI-2:: GUS were first germinated on MS plates and then transferred to soil to examine GUS expression at different developmental stages . GUS staining of germinating seeds and 4-day-old seedlings showed that ProTOPP1:: GUS is expressed mainly in the cotyledon , while ProAtI-2: GUS is expressed throughout the whole seedling , with some enrichment in the roots ( Fig 5A and 5B ) . In two-week-old seedlings , GUS activity was detected in all true leaves and roots but not in the cotyledons in ProTOPP1:: GUS transgenic lines , whereas ProAtI-2: GUS was strongly expressed in all tissues ( Fig 5C and 5D ) . In adult plants , GUS activity was detected in rosette and cauline leaves but not in the stems ( Fig 5E and 5F ) . ProAtI-2::GUS transgenic plants displayed higher GUS activity in the flower and silique than ProTOPP1:GUS transgenic plants ( Fig 5G and 5H ) . ProTOPP1:: GUS expression was much weaker overall compared to that of ProAtI-2: GUS , possibly because that some regulatory sequences may be missing from the TOPP1 promoter in the construct ( Fig 5 ) , which makes it difficult to conclude whether TOPP1 is not expressed in certain tissues . To help understand the potential function of TOPP1 and AtI-2 in regulating ABA and stress responses in plants , we generated TOPP1 and AtI-2 transgenic lines by transforming topp1 or ati-2 mutant plants ( S5A and S5B Fig ) with 35Spro::TOPP1-HA and 35Spro::AtI-2-FLAG , respectively . qRT-PCR assays showed that TOPP1 and AtI-2 are not expressed in their respective mutants but are highly expressed in the transgenic lines ( S5C Fig ) . When germinated on MS plates with 0 . 5 μM ABA , the topp1 and ati-2 mutations did not affect seed germination , but the topp1 mutant plants showed a slightly reduced rate of green cotyledon expansion compared with wild type plants while ati-2 displayed a significantly lower rate of green cotyledon expansion ( Fig 6A and 6B ) . The expression of 35Spro::TOPP1-HA and 35Spro::AtI-2-FLAG rescued the post-germination hypersensitivity phenotypes of the loss-of-function mutants; the green cotyledon expansion rates in the transgenic plants were slightly higher than that in the WT ( Fig 6A and 6B ) . We also examined post-germination root growth . Although the root length of topp1 or ati-2 mutants was indistinguishable from WT ( S6 Fig ) , we found that the roots of the transgenic lines expressing 35Spro::TOPP1-HA and 35Spro::AtI-2-FLAG were significantly longer than WT roots after growing on ABA-containing MS plates , thus revealing an increased tolerance to exogenous ABA ( Fig 6C and 6D ) . We also tested whether topp1 and ati-2 mutant plants may have altered sensitivity to salt stress . Similar to their ABA-hypersensitive seed germination phenotype , both topp1 and ati-2 exhibited increased sensitivities to salt stress , with severely retarded germination and barely any green cotyledon expansion in the presence of 100 mM NaCl . Consistently , the expression of 35Spro::TOPP1-HA and 35Spro::AtI-2-FLAG suppressed the hypersensitivity of the loss-of-function mutants ( Figs 6B and S7 ) . Moreover , water loss rate from detached leaves was reduced in ati-2 compared with WT plants while topp1 showed an overall rate of water loss that was similar to the WT although the rate appeared to be unsteady ( S8A Fig ) . In contrast , the 35Spro::TOPP1-HA expression lines showed an increased water loss compared to WT whereas the 35Spro::AtI-2-FLAG expression lines were indistinguishable from WT ( S8B Fig ) . Together , the genetic evidence suggests that TOPP1 and AtI-2 negatively regulate ABA sensitivity during seed germination and early seedling growth . To investigate the genome-wide effect of TOPP1 and AtI-2 on ABA-induced gene expression changes , we performed RNA-sequencing experiments . In total , 76 and 89 genes displayed at least 1 . 2-fold changes in expression level in topp1 and ati-2 mutant plants relative to WT plants upon ABA treatment ( Fig 7A ) . Of these , 66 genes were common in both mutant plants , indicating that they were co-regulated by TOPP1 and AtI-2 . Interestingly , several ABA-responsive genes such as RESPONSIVE TO DESSICATION 29A ( RD29A ) , COLD-REGULATED 15A ( COR15A ) and COR47 were in the list of differentially expressed ( DE ) genes co-regulated by TOPP1 and AtI-2 ( S1 Dataset ) . The heat map generated with the DE genes revealed a highly similar pattern between topp1 and ati-2 ( Fig 7B ) . Gene ontology analysis indicated that the DE genes co-regulated by TOPP1 and AtI-2 were enriched in stress-related biological processes ( Fig 7C ) , consistent with TOPP1 and AtI-2 functioning in ABA and salt stress responses . To validate the results from RNA-seq , we examined the expression of several ABA-responsive genes including RD29A , RD29B , RESPONSIVE TO ABA 18 ( RAB18 ) , COR15A and COR47 in the presence or absence of 50 μM ABA for 3 hours by quantitative Real-Time PCR ( RT-qPCR ) . In the presence of ABA , the expression of these genes was substantially higher in both ati-2 and topp1 mutant plants compared to WT plants ( Fig 8 ) . In contrast , the expression of NINE-CIS-EPOXYCAROTENOID DIOXYGENASE 3 ( NCED3 ) , which encodes a key enzyme in ABA biosynthesis , was induced less by ABA in topp1 mutant plants relative to WT ( Fig 8 ) . These results further support the conclusion that AtI-2 and TOPP1 negatively regulate ABA signaling .
Although substantial progress has been made in our understanding of ABA perception and signal transduction , the complexity of the signaling pathway remains to be fully explored . In the present study , we have provided new insights into ABA signaling by identifying two new components , TOPP1 and its regulatory protein AtI-2 . Our results suggest that TOPP1 and AtI-2 form a complex and negatively regulate ABA signaling through a physical interaction with PYLs and SnRK2s in plants . TOPP1 and AtI-2 function together to inhibit SnRK2s , but the inhibition appears to be partially released by PYL11 . Therefore , we propose that TOPP1 and possibly TOPP1 paralogs function together with AtI-2 and act analogously to the PP2Cs ( Fig 9 ) , but due to their different biochemical and expression features from the PP2Cs , they may confer additional specificity and flexibility to ABA signaling . TOPP1 belongs to the serine/threonine phosphatase family ( PSPs ) , which is composed of three major groups , including phosphoprotein phosphatases ( PPPs ) , metal-dependent protein phosphatases ( represented as PP2Cs ) , and the aspartate-based phosphatases [35] . Representative subgroups of the PPP family can be further categorized as PP1 , PP2A , PP2B , PP4 , PP5 , PP6 , and PP7 [35] . PP1 is a widely expressed catalytic subunit across all eukaryotes , with a Mg2+/ Mn2+-dependent activity in vitro [36] . Unlike PP2Cs , which contain both catalytic and regulatory domains within the same polypeptide chain , PP1 is tightly controlled by its regulatory proteins that affect its substrate specificity and catalytic activity [36] . In mammals , PP1 is known to be involved in controlling various cellular processes such as glycogen metabolism , cell division and RNA splicing [37 , 38] . In Arabidopsis , PP1 is annotated as TOPPs , with nine members ubiquitously expressed in most tissues [36 , 39] . Previous studies have shown that ABA-invoked PYLs bind and inhibit PP2Cs [40] . We found that TOPP1 interacts with some PYLs . The TOPP1-PYL interactions were ABA-independent , but could be enhanced by ABA . In comparison with the PP2Cs , TOPP1 appeared to display different patterns of pairwise interaction with PYLs . Since some PYLs have been reported to function preferentially under certain abiotic stress conditions [41 , 42] , it is possible that TOPP1 functions with certain PYLs to transduce a tissue- or stress-specific signal . Although numerous kinases in plants are well studied , only a few phosphatases have been characterized thus far . There are more than 1050 kinases reported in the Arabidopsis genome , whereas less than 150 annotated catalytic subunits of phosphatases have been reported [43] . Therefore , PP1 may mediate multiple cellular responses by targeting different kinases . Here , we provided biochemical evidence for TOPP1-mediated inhibition of SnRK2s in vitro and in vivo . Consistently , the endogenous SnRK2 . 2/2 . 3/2 . 6 activities in topp1 mutant were higher than those in WT plants after ABA treatment , further supporting the negative roles of TOPP1and AtI-2 in the ABA signaling pathway . To date , Arabidopsis Inhibitor-2 ( AtI-2 ) , Inhibitor-3 ( I-3 ) and PP1 regulatory subunit 2-like protein 1 ( PRSL1 ) have been characterized as regulatory proteins of TOPPs in Arabidopsis [44 , 45] . AtI-2 is known to interact with TOPPs through three conserved motifs identified by bioinformatics analysis . Biochemical studies of AtI-2 revealed its role in inhibiting the phosphatase activities of TOPPs1-9 in vitro [46] . It has been shown that TOPPs co-localize with AtI-2 in both the cytosol and nucleus , with an enriched signal in the nucleus [46 , 47] . Recently , it was reported that TOPP1 along with AtI-2 participated in stomatal opening downstream of the blue-light sensing kinase phototropin and upstream of the H+-ATPase [47 , 48] . Our results suggest that AtI-2 could function beyond inhibition of TOPPs in plants . Our spilt-LUC assays indicated that an enhancement by AtI-2 on the interactions between SnRK2 . 6 and TOPP1 or PYL11 . These results indicate the possibility that AtI-2 may stabilize the interaction of TOPP1 with its targets . Although AtI-2 did not suppress SnRK2 . 6 activity alone in vitro , it was able to enhance the inactivation of SnRK2 . 6 by TOPP1 , which was probably achieved by facilitating the TOPP1-SnRK2 . 6 interaction . On the contrary , the transactivation data in protoplasts revealed that AtI-2 alone could suppress the SnRK2 . 2/3/6-ABF2 mediated RD29B-LUC expressions , which is not fully consistent with the results obtained from in vitro kinase assay . One explanation for this discrepancy would be that AtI-2 could promote the activities of endogenous TOPPs in the protoplasts . Alternatively , AtI-2 could also suppress SnRK2s activities by modulating other phosphatases or undefined factors . In addition , gene expression analyses showed an overlapping function of TOPP1 and AtI-2 , as they co-regulated various groups of genes in response to ABA . Together , the in vitro and in vivo data provide strong evidence for the coordinated inhibition of ABA signaling by AtI-2-TOPP1 . In summary , we have provided new insights into ABA signaling by characterizing the roles of two new components , TOPP1 and AtI-2 , in the pathway . AtI-2 promotes TOPP1 suppression of SnRK2s , thus negatively regulating ABA signaling . Our results suggest that the TOPP1-AtI-2 complex helps to fine-tune the core signaling pathway mediated by PP2Cs .
Arabidopsis thaliana Columbia-0 ecotype ( Col-0 ) was used in this study . T-DNA insertion mutants , topp1 ( SALK_057537 ) , and ati-2 ( SALK_110571C ) were obtained from the Arabidopsis Biological Resource Center ( ABRC , Columbus , OH ) . Homozygous mutants were isolated by genomic PCR . ( Primer sequences are listed in S2 Dataset ) . Double mutant snrk2 . 2/2 . 3 was obtained as described in Fujii et al , 2007 [15] . Arabidopsis seedlings were grown on horizontal Murashige and Skoog ( MS ) medium containing full MS salts , 3% ( w/v ) sucrose , and 0 . 6% ( w/v ) agar , pH 5 . 7 in growth chamber at 23°C under long day photoperiod condition ( 16 h light/8 h dark ) . Root growth inhibition assays and ABA- or salt-mediated seed germination and were performed as described previously ( Fujii et al , 2009 ) . The seeds were harvested at the same time and were used for the germination , cotyledon green expansion and post-germination root growth assays . For post-germination root growth assays , 3-days-old seedlings were first germinated on vertical MS medium and then were transferred to ABA-supplemented medium ( 10 μM ) and the primary root growth was measured at 7 days after transfer . Nicotiana benthamiana was grown in growth room under 16 h light/8 h dark . One-month-old tobacco plants were used for transient expression assays . The full-length coding sequences of SnRK2s and PYLs were PCR amplified using Phusion high fidelity Taq polymerase ( New England Biolabs ) , cloned into the pGBKT7 vector ( Gal4 DNA binding domain; Clontech ) , and used to screen a cDNA library for interacting proteins . An Arabidopsis cDNA library was prepared by Clontech in the pGADT7-RecAB vector ( Gal4 activation domain; Clontech ) . Primers used for the Y2H assay were listed in S2 Dataset . To confirm the protein interactions , pGADT7 plasmids containing TOPP1 or AtI-2 were co-transformed with members of pGBKT7-SnRK2s or pBDGal4-PYLs into Saccharomyces cerevisiae AH109 cells according to the standard yeast PEG transformation method [49] . Successfully transformed colonies were identified on yeast SD medium lacking Leu and Trp . To verify the protein interactions , colonies were transferred to selective SD medium lacking Leu , Trp , His in the absence or presence of 10 μM ABA . 1 mM 3-amino-1 , 2 , 4-triazole ( 3-AT ) was added to reduce the self-activation effect of BD-SnRK2s . To determine the intensity of protein interaction , saturated yeast cultures were diluted to 10−1 , 10−2 and 10−3 and spotted onto selection medium . Photographs were taken after 4 days incubation at 30°C . To make proTOPP1:GUS and proAtI-2:GUS constructs , DNA fragments covering roughly 2 kb upstream to the translational initiation start codon sites were amplified by PCR from Col-0 genomic DNA as templates , the PCR products were then inserted into pENTR/D-TOPO vectors according to the manual ( Invitrogen ) . Those promoter fragments were next transferred to destination vector pMDC162 by LR reaction with Gateway LR Clonase II enzyme mix ( Invitrogen ) . To generate overexpression lines , the coding sequences of TOPP1 and AtI-2 were amplified from Col-0 cDNA and then cloned into pENTR/D-TOPO vectors . The coding sequences were subsequently transferred to destination vectors to generate CaMV 35S promoter driven TOPP1 ( pEarley201-TOPP1 ) and AtI-2 ( pEarley202-AtI-2 ) . After sequencing , those destination constructs were transformed into Agrobacterium tumefaciens GV3101 and were transformed into their mutants by the floral dipping method [50] . Transformed T0 seeds were harvested and were selected by Hygromycin B or Basta . Resistant T1 seedlings were grown in soil to obtain T2 generation . Homozygous T3 plants were used for phenotyping or GUS staining . The coding sequences of proteins described in our work were amplified by PCR using primers listed in Supplement data set 2 . The PCR products were first cloned into pENTER vector ( Invitrogen ) and then transferred to nLUC/cLUC vectors via LR reactions . Split-LUC complementation assay was performed by transient expression in leaves of N . benthamiana by agrobacterium-mediated infiltration . Briefly , Agrobacterium GV3101 strains carrying cLUC and nLUC constructs were cultured at 28°C overnight and then were re-suspended in the injection buffer ( 10mM MgCl2 , 10mM MES and 100 μM Acetosyringone , pH 5 . 6 ) and incubated at room temperature for at least 3 h . Corresponding cLUC and nLUC constructs were equally mixed ( final concentration OD600 = 0 . 5/each ) and infiltrated into leaves of N . benthamiana . The infiltrated leaves were covered with plastic lid to maintain high humidity . After 2 days infiltration , leaves co-expressing different constructs were then examined for LUC activity by applying the LUC substrate D-luciferin ( Promega ) with CCD camera equipped with Winview software ( Princeton instruments ) . The expressions of n/cLUC fusion proteins were determined by western blot with poly anti-Luciferase antibody ( Sigma ) . The coding sequences of TOPP1 and AtI-2 were amplified by PCR and were cloned into pGEX4T1 vector ( Amersham ) with EcoRI/XhoI ( TOPP1 ) and BamHI/XhoI ( AtI-2 ) , respectively . Recombinant proteins were purified using glutathione–agarose beads ( GST ) ( Sigma-Aldrich ) . PYL11 was cloned into pET28a vector ( Novagen ) , and MBP-SnRK2 . 6 was prepared as reported in Fujii . et al 2009 . Escherichia coli ( E . coli ) BL21 ( DE3 ) strains carrying pET28a-PYL11 and pMal-c2X-SnRK2 . 6 constructs were purified by Ni-NTA agarose ( QIAGEN ) and amylose resin ( NEB ) , respectively as described by manufacturer’s protocol . pGEX4T1-ABF2 ( Gly-73 to Gln-119 ) was prepared as described in Fujii . et al 2007 . Primers used in this study are listed in S2 Dataset . Recombinant proteins were expressed and purified from E . coli according to the manufacturer’s protocol . Prey proteins of interest were incubated with the immobilized bait protein for 4 hours at 4°C . After binding in vitro , unbound prey proteins were washed with 1XPBS buffer for 4 times and the eluted proteins were boiled in 2XSDS loading dye . The protein samples were then separated by SDS-PAGE . The co-IP assays were performed in Arabidopsis protoplasts as described [51] . Full length of SnRK2 . 6 was amplified and cloned into pHBT95 with MYC tag through transfer PCR . The indicated plasmids were isolated using Maxi Prep kits ( Qiagen ) . Briefly , After co-transformation , protoplasts were collected and suspended in 2 mL lysis buffer ( 50 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl , 1 mM EDTA , 1 mM DDT , 0 . 1% ( v/v ) Triton X-100 , and 1× protease inhibitor cocktail from Sigma plus 1 mM PMSF ) in ice for 15 min and then centrifuged at 12000 rpm for 10 min at 4°C . The supernatant was incubated with pre-balanced 20 μL monoclonal anti-HA-agarose antibody ( Sigma ) or anti-MYC-agarose ( Abcam ) at 4°C for at least 4 h with gentle rotation . The beads were washed at least four times with lysis buffer and boiled in 50 μL of 1×SDS loading buffer for 5 min . Samples were subjected to Western blots and detected with indicated antibodies . General substrate p-nitrophenyl phosphate ( pNPP , Sigma ) was used to measure the phosphatase activity of TOPP1 . Reactions were performed in assay buffer containing 50 mM Tris-HCl , pH 7 . 5 , 2 mM MnCl2 , 1 mM EDTA , 0 . 5% β-mercaptoethanol , 2 mg/ml BSA and 50 mM pNPP . An increasing gradient of recombinant protein GST-AtI-2 or His-PYL11 was incubated with GST-TOPP1 at 37°C for 1 h . After incubation , the reactions were quenched with 5 volumes of 0 . 5 M EDTA . The hydrolysis of pNPP was measured by following the absorbance at 405 nM ( A405 ) . The purified recombinant proteins GST-TOPP1 , GST-AtI-2 and MBP-SnRK2 . 6 were incubated at room temperature for 20 min in kinase reaction buffer ( 25 mM Tris-HCl pH 7 . 4 , 12 mM MnCl2 , 1 mM DTT ) . After pre-incubation , GST-ABF2 and 1 μCi [ϒ-32P] ATP were added and incubated at 30°C for another 15min . After the reaction , 4X SDS-PAGE sample buffer was added to the reaction mixture and boiled for 5 min , the samples were separated by SDS-PAGE . Radioactivities of MBP-SnRK2 . 6 and GST-ABF2 were detected by the phosphoimager ( BIO-RAD ) . This assay was performed as described in Wang et al 2010 . Briefly , seeds were germinated in liquid MS medium with 1 . 5% sucrose for 4 days and then were transferred to control MS medium or MS medium containing 50 μM ABA for 1 h . Total protein was extracted in lysis buffer containing 5 mM EDTA , 5 mM EGTA , 2 mM DTT , 10 mM NaF , 10 mM Na3VO4 , 50 mM β-glycerophosphate , 5% glycerol , 1 mM phenylmethylsulfonyl fluoride ( PMSF ) , 5 μg/mL leupeptin , 5 μg/mL aprotinin , and 100 mM HEPES-KOH , pH 7 . 5 . After centrifugation at 14 , 000 rpm for 20 min , protein concentrations of supernatants were determined using Bradford reagent ( BIO-RAD ) with BSA as standard [52] . Total proteins ( 15 μg/lane ) were separated in a 10% SDS-PAGE gel containing 2 mg/mL histone as a substrate . The gel was washed for 30 min three times with washing buffer ( 0 . 5 mM DTT , 5 mM NaF , 0 . 1 mM Na3VO4 , 0 . 5 mg/mL BSA , 0 . 1% Triton X-100 , and 25 mM Tris-HCl , pH 7 . 5 ) , and then incubated for 1 h at room temperature with renatured buffer ( 1 mM DTT , 5 mM NaF , 0 . 1 mM Na3VO4 , and 25 mM Tris-HCl , pH 7 . 5 ) , and at 4°C overnight . After 30 min of incubation in reaction solution ( 2 mM EGTA , 12 mM MgCl2 , 1 mM DTT , 0 . 1 mM Na3VO4 , and 25 mM Tris-HCl , pH 7 . 5 ) at room temperature , the gel was incubated in 30 mL of reaction solution supplemented with 50 μCi of [ϒ-32P]ATP and 200 nM cold ATP for 60 min at room temperature . The gel was washed with 5% TCA and 1% sodium pyrophosphate for total 6 h with five time changes of buffer . The gel was dried and exposed to phosphoscreen overnight . Protoplast transient assays were performed as described ( Fujii et al 2009 ) . All the plasmids used in this assay were purified using QIAGEN Plasmid Maxi or Midi Kit . Briefly , plants were grown under 8 h light/16 h dark photoperiod condition . Leaf strips were incubated in enzyme solution ( 20 mM MES , pH 5 . 7 , 1 . 5% ( w/v ) cellulase R10 , 0 . 4% ( w/v ) macerozyme R10 ( Yakult Pharmaceutical Industry ) , 0 . 4 M mannitol , 20 mM KCl , 10 mM CaCl2 , 1 mM 2-mercaptoethanol and 0 . 1% BSA ) for vacuum infiltration for 30 min and then were incubated for another 3 h at room temperature under the dark condition . Protoplasts was next filtered with a 75 μm nylon mesh and centrifuged at 100 g for 2 min in a 30 mL round bottomed tube , pellets were resuspended in W5 solution gently and precipitated at room temperature for 30 min . Before transformation , protoplasts were replaced with MMg solution ( 4 mM MES , pH 5 . 7 , 0 . 4 M mannitol and 15 mM MgCl2 ) to a final concentration of 2×105 cell/ml . Protoplasts ( 200 μL ) were mixed with indicated plasmids and 220 μL PEG solution ( 40% w/v PEG-4000 , 0 . 2 M mannitol , and 100 mM CaCl2 ) and then mixed thoroughly . Protoplast and plasmids were incubated for 5 min , and washed with 440 μl of W5 solution; later pellets were resuspended in 50 μL of WI solution ( 4 mM MES , pH 5 . 7 , 0 . 5 M mannitol and 20 mM KCl ) . After transfection , protoplasts were incubated in WI solution without or with 5 μM ABA under light , and protoplasts were harvested after 4 h incubation , then frozen in liquid N2 and stored at -80°C . The frozen protoplasts were resuspended in 50 μL lysis buffer ( 2 . 5 mM Tris-phosphate , pH 7 . 8 , 1 mM DTT , 2 mM DACTAA , 10% ( v/v ) glycerol and 1% ( v/v ) Triton X-100 ) , and 20 μL of protoplast lysates were mixed with 100 μL of D-luciferin mix ( Promega ) for the measurement of LUC luminescence intensity with Wallac VICTOR2 plate reader ( Perkin Elmer ) . Another 2 μL protoplast lysates were mixed with 10 μL of 4-methylumbelliferyl β-D-glucuronide ( MUG ) substrate ( 10 mM Tris-HCl , pH 8 , 1 mM MUG ( Gold Bio Tech ) and 2 mM MgCl2 ) , incubated for 30 min at 37°C , then added 100 μL of 0 . 2 M Na2CO3 . The GUS activity was detected using the plate reader with the excitation filter at 355 nm and the emission filter at 460 nm . For quantitative real-time PCR , 1 μg of total RNA extracted with Trizol reagent ( Invitrogen ) was used for the first-strand cDNA synthesis by qScript Flex cDNA Synthesis kit ( Quanta ) as instructed by the manufacturer . The cDNA reaction mixture was diluted three times , and 2 μL was used as a template in a 15 μL qRT-PCR reaction ( Quanta ) . PCR was performed after a preincubation at 95°C for 3 min followed by 40 cycles of denaturation at 95°C for 15 sec , annealing at 55°C for 15 sec , and extension at 72°C for 10 sec . All the reactions were performed in the BIO-RAD real-time PCR detection system . Each experiment was replicated three times . The primers used in RT-qPCR are listed in S2 Dataset . Three biological replicates of WT , ati-2 , topp1 mutant seeds were germinated in ½ MS liquid medium and grown up to 2-week-old with continuous shaking at 25°C . The seedlings were then treated with either mock or 3 hr of 50 μM ABA at room temperature . The total RNA was then isolated with Trizol reagent ( Invitrogen ) according to the manufacturer’s instruction and sequencing were carried out by Shanghai stress center . Reads of RNA-seq were mapped to Arabidopsis reference genome ( TAIR10 ) using TopHat using default parameters . Read count for each gene were obtained using feature Counts in subread . Feature differentially expressed ( DE ) genes were identified using DESeq ( adjusted p-value < 0 . 05 and at least 4-fold change with ABA treatment ) . TOPP1 or AtI-2-dependent genes were defined under the criteria: 1 ) genes were differentially expressed after ABA treatment in both WT and topp1 ( or ati-2 ) mutant; 2 ) gene expression level in topp1 ( or ati-2 ) mutant were at least 1 . 2 fold higher or lower than that in WT after ABA treatment . GO enrichment analysis were performed using WEGO [53] . Germinating seeds or 2 or 3-week-old seedlings were incubated in GUS staining buffer ( 1 mM K3Fe ( CN ) 6 , 0 . 1% Triton X-100 , 10 mM EDTA , 100 mM phosphate buffer ( NaPO4 , pH 7 . 0 , and 2 mM X-Gluc ) at 37°C for 16 h and the non-specific staining were removed with 50% ethanol . Tissue specific gene expression were observed by Leica EZ4 imaging system . Fully expanded adult leaves were excised from each genotype and left at room temperature , the fresh weight was recorded at the indicated time point . Water loss rate was expressed as the percentage of initial fresh weight . The experiment was repeated at least three times .
|
The phytohormone abscisic acid ( ABA ) regulates multiple developmental processes such as seed dormancy , germination , root/shoot growth , flowering and senescence in plants . Although the core ABA perception and signaling pathway has been elucidated , the complexity of the pathway remains to be exploited . In the present work , we uncovered two new proteins , TOPP1 and its regulatory protein AtI-2 , interact with both ABA receptor PYLs and their downstream positive regulator SnRK2s . In addition to their physical interaction , TOPP1 could inhibit the kinase activity of SnRK2s and this inhibition could be further enhanced by AtI-2 , which is likely due to a promotion of the interaction between TOPP1 and SnRK2s by AtI-2 . topp1 and ati-2 mutants exhibited hypersensitivity to ABA and salt treatments; and transcriptome studies revealed multiple ABA-responsive genes were up-regulated in the mutants . In summary , our work identified two new components , TOPP1 and AtI-2 , and characterized their negative roles in ABA signaling .
|
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"Methods"
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2016
|
Type One Protein Phosphatase 1 and Its Regulatory Protein Inhibitor 2 Negatively Regulate ABA Signaling
|
MicroRNAs ( miRNAs ) play key roles in the initiation and progression of various cancers by regulating genes . Regulatory interactions between genes and miRNAs are complex , as multiple miRNAs can regulate multiple genes . In addtion , these interactions vary from patient to patient and even among patients with the same cancer type , as cancer development is a heterogeneous process . These relationships are more complicated because transcription factors and other regulatory molecules can also regulate miRNAs and genes . Hence , it is important to identify the complex relationships between genes and miRNAs in cancer . In this study , we propose a computational approach to constructing modules that represent these relationships by integrating the expression data of genes and miRNAs with gene-gene interaction data . First , we used a biclustering algorithm to construct modules consisting of a subset of genes and a subset of samples to incorporate the heterogeneity of cancer cells . Second , we combined gene-gene interactions to include genes that play important roles in cancer-related pathways . Then , we selected miRNAs that are closely associated with genes in the modules based on a Gaussian Bayesian network and Bayesian Information Criteria . When we applied our approach to ovarian cancer and glioblastoma ( GBM ) data sets , 33 and 54 modules were constructed , respectively . In these modules , 91% and 94% of ovarian cancer and GBM modules , respectively , were explained either by direct regulation between genes and miRNAs or by indirect relationships via transcription factors . In addition , 48 . 4% and 74 . 0% of modules from ovarian cancer and GBM , respectively , were enriched with cancer-related pathways , and 51 . 7% and 71 . 7% of miRNAs in modules were ovarian cancer-related miRNAs and GBM-related miRNAs , respectively . Finally , we extensively analyzed significant modules and showed that most genes in these modules were related to ovarian cancer and GBM .
Cancer is one of the leading causes of death worldwide . Although remarkable progress has been achieved in cancer therapies , the molecular mechanisms of cancer have not yet been fully identified . Among various regulations of cancer-related genes and pathways in several stages , the regulation of genes by microRNAs ( miRNAs ) in cancer cells has drawn particular attention , because many miRNAs are located in chromosomal regions that are frequently altered in cancer [1] . MiRNAs are small RNAs , known as important regulators of genes through binding to 3’ UTR regions of target genes [2] . In many cancer types , miRNAs have been studied as important biomarkers for diagnosis and prognosis of cancer , as many miRNAs function as oncogenes or tumor suppressors by regulating other oncogenes or tumor suppressor genes [1 , 3] . Because miRNAs regulate genes by binding to the 3’ UTR regions of genes , many methods were developed to identify conserved sequence regions between miRNAs and mRNAs [4] . However , sequence-based approaches generate many false positive bindings sites and cannot identify functional changes of genes . Hence , the expressions of genes and miRNAs were also integrated to address possible negative correlations between the two sets of expression data [5 , 6] . With the advances in high throughput technologies , large-scale mRNA expression and miRNA expression data sets from the same tumor samples have become available , due to collaborative efforts such as The Cancer Genome Atlas ( TCGA ) project . [7 , 8] . These data sets enable researchers to apply computational approaches to identify relationships between mRNAs and miRNAs and help understand their effects in cancer . Another important approach to understanding relationships between mRNAs and miRNAs is to analyze multiple genes and miRNAs simultaneously by constructing modules of them rather than analyzing each gene-miRNA pair separately [5 , 9 , 10] . It is widely known that a miRNA can regulate multiple genes [11] , and a gene can be targeted by multiple miRNAs [12] . Changes in these numerous relationships can significantly alter the biological functions or signaling pathways associated with a specific cancer [13] . Although it is known that several pathways , such as the p53 and TGF-beta signaling pathways , are related to ovarian cancer [14 , 15] , the functions of miRNAs in these pathways have not yet been fully explained . Although a few algorithms for finding gene-miRNA modules have been proposed , improvements are still needed . Peng et al . [5] proposed a bi-clique approach based on a gene-miRNA correlation matrix; however , most of the modules contained only one miRNA , and a few modules contained at most three miRNAs . Hence , it may be difficult to address multiple relationships between genes and miRNAs . Zhang et al . [6] integrated miRNAs , gene expression and gene-gene interactions based on a non-negative matrix factorization ( NMF ) framework [16] . The decomposed matrix components were considered as gene-miRNA regulatory modules . Although many modules were enriched with known pathways , the relationships between genes and miRNAs were not explained . Relationships between genes and miRNAs become even more complicated because molecules such as transcription factors or signal transducers regulate genes and miRNAs . For example , p53 , the most frequently mutated gene in cancer , regulates hundreds of genes and a set of miRNAs , including miR-24 family , miR-145 , miR-107 , and miR-192 [17 , 18] . In [19] , the authors constructed modules that contain highly correlated genes and miRNAs in their expression levels and found that miR-200a regulates the transcription factor ZEB1 , which regulates genes contained in the same module as miR-200a . To enhance the understanding of relationships between genes and miRNAs , we propose a framework that combines a biclustering approach and a Gaussian Bayesian network . Using the biclustering approach , gene-sample modules are first constructed based on gene expression and gene-gene interaction data sets . Here , a subset of genes that are correlated with each other in a subset of samples is clustered , because gene aberrations are different among patients , even if cancer occurs in the same organ or tissue type [20] . Next , using a Gaussian Bayesian network , gene-miRNA modules are constructed to identify miRNAs that regulate genes in gene-sample modules . Here , we use the expression data on genes and miRNA . When we applied our approach to ovarian cancer data sets and glioblastoma ( GBM ) data sets from TCGA , we identified several modules consisting of genes and miRNAs related to ovarian cancer and GBM . In many modules , relationships between genes and miRNAs were explained either by direct regulations of genes by miRNAs or by indirect relationships via transcription factors . In addition , functional pathway enrichment tests using several biological and signaling pathways demonstrated that these modules were biologically coherent . Based on ratios of cancer-related genes and cancer-related miRNAs , we extensively analyzed several significant modules and performed network analyses of these modules to demonstrate the regulation of genes by miRNAs .
Ovarian cancer . We collected mRNA expression and miRNA expression data sets for 587 tumor samples and 8 unmatched normal samples for ovarian cancer from TCGA [8]; mRNA and miRNA expression data were generated using an Affymetrix HG-U133A microarray and an Agilent H-miRNA_8X15K microarray , respectively . We normalized the expression levels of 12 , 042 genes using log2 ratios between tumor samples and the average of normal samples for each gene , and then selected 2 , 933 differentially expressed genes using a t-test ( p-value < 0 . 001 ) . Similarly , we normalized the expression levels of 479 miRNAs using the log2 ratios between tumor samples and the average of normal samples for each miRNA ( Fig . 1 ( A ) ) . Glioblastoma . We collected mRNA expression and miRNA expression data sets for 482 tumor samples and 10 unmatched normal samples for GBM [7] . These data sets were generated using the same microarray platforms used in the ovarian cancer study . After normalization , we selected 4 , 059 differentially expressed genes using a t-test ( Bonferroni corrected p-value < 0 . 05 ) . We used the expression levels of 423 miRNAs normalized using normal samples . Selecting a p-value threshold for a t-test . The degree of expression changes depending on the cancer type . In this study , the number of differentially expressed genes was small in ovarian cancer compared to GBM . Hence , we used a less strict threshold for ovarian cancer . Gene-gene interactions . We collected gene-gene interaction data from the HPRD database [21] . In this study , we first hypothesized that if a group of genes has similar expression tendencies in a subset of samples , and they are differentially expressed in these samples , then these genes might be related to similar functions or pathways in the development of cancer . We also hypothesized that a gene might have multiple functions and could function in several pathways . To incorporate these hypotheses , we use a biclustering algorithm to allow the duplication of genes and samples in multiple clusters . First , we construct a matrix of differentially expressed genes and samples , and then we normalize the expression values for each gene using a z-score to determine the tendency toward changes of gene expression in the samples . Next , we apply a SAMBA biclustering algorithm [22] to the normalized matrix to construct modules in which genes and samples are highly correlated ( Fig . 1 ( B ) ) . The SAMBA biclustering algorithm models gene expression data in a bipartite graph G = ( U , V , E ) , where genes in V are represented as nodes on one side and samples in U on the other side . There is an edge in E between a gene v in V and a sample u in U if the expression value of gene v changes significantly in sample u , having high absolute expression values . The biclustering algorithm generates subgraphs from the bipartite graph , in which most of the genes are connected to most of the samples as edges . These subgraphs represent highly correlated gene-sample clusters , where the tendency toward gene expression changes is similar for a subset of samples . Additional details are provided in Fig . S1 . We calculate the statistical significance of each module based on a null hypothesis that the expression level of a gene is independent of the expression level of other genes for samples in a module , assessing that the average Pearson correlation coefficients ( PCCs ) of gene expression levels for genes in the module are higher than the ones from random modules for selected samples . For each module , we conduct the following test . ( Step 1 ) Construct a random module by randomly selecting the same numbers of genes and samples from the normalized matrix . ( Step 2 ) Calculate the PCC matrix of expression level values of genes in the module across a subset of samples . Then , calculate the average value of the PCC matrix , excluding diagonal elements . ( Step 3 ) Repeat Steps 1 and 2 N times , letting the average value from the i-th permutation serve as the randomavg ( i ) . ( Step 4 ) Let the average PCC value of genes in the observed module be the moduleavg . ( Step 5 ) Calculate the p-value of the observed module using the following equation , where I is an indicator function . p − v a l u e = ∑ i = 1 N I ( m o d u l e a v g < r a n d o m a v g ( i ) ) N When we calculate the p-value , we try to take into account that observed modules are not independent of each other as genes overlap among modules . Hence , we construct random modules where genes in the modules share the same overlap ratio as the observed modules . Recent research has shown that not all of the genes in cancer-related pathways undergo expression or genomic changes [23] . Consequently , certain genes that play important roles in cancer-related pathways might not be differentially expressed . To include functionally related genes in the gene-sample modules , we expand the gene-sample modules using a gene-gene interaction network . If a gene interacts directly with at least one gene in a module , then this gene can be regarded as a candidate gene for the module . For each module , we collect candidate genes and calculate the average PCC values of expressions between a candidate gene and the genes in the module . We add candidate genes to the module in descending order from the gene having the highest PCC value until the average PCC values of the expressions of genes in the module do not increase . Because a set of genes with similar expression changes might be regulated by common miRNAs , we construct gene-miRNA modules by including regulating miRNAs in the gene-sample modules . For this task , we employ a Bayesian network model . Bayesian networks have been extensively used for analyzing gene expression patterns [24] . They are useful in modeling local dependencies and causal influences among variables . Hence , we estimate dependencies between expression values of genes and expression values of miRNAs based on a Bayesian network model . A joint distribution of genes X = {X1 , X2 , … , Xn} and miRNAs Y = {Y1 , Y2 , … Ym} is represented by a Gaussian Bayesian network . If Xi is normally distributed around a mean that linearly depends on its parents , then the conditional probability of Xi given its parents PaG ( Xi ) = {Yj , … Yk} can be represented by P ( X i | P a G ( X i ) ) = P ( X i | Y j , … , Y k ) ∼ N ( a 0 + ∑ j ' a j ' · Y j ' , σ 2 ) ( 1 ) Then , the likelihood of X and Y can be represented by L ( X , Y ) = P ( X 1 , X 2 , … , X n , Y 1 , Y 2 , … Y m ) = ∏ i = 1 n P ( X i | P a G ( X i ) ) ( 2 ) To determine which sets of miRNAs explain the expression levels of genes in gene-sample modules , we use a Bayesian information criterion ( BIC ) as a measure for determining a Bayesian network structure between genes and miRNAs , which can be represented by B I C = l o g ( L ) - l o g M 2 + O ( 1 ) , ( 3 ) where M is the sum of the number of genes and miRNAs . To determine the parents PaG ( Xi ) of a gene Xi yielding the optimal BIC score , we should consider all combinations of miRNAs; however , this approach is highly time-consuming . To reduce the search space , we select candidate miRNAs whose average of absolute Spearman’s rank correlation coefficient ( SCC ) values for genes in a given module are within the top T% among all miRNAs . Note that we use SCC values for selecting candidate miRNAs to reduce the effects of possible outliers in the PCC . From candidate miRNAs , we first add a miRNA with the highest SCC value as a regulator and calculate the BIC score . Then , we add miRNAs with the next highest SCC values , until adding more miRNAs no longer improves the BIC score . After adding miRNAs to gene-sample modules using the above approach , modules with fewer than two miRNAs are filtered out because these modules cannot represent the combinatorial effects of genes and miRNAs . Finally , gene-miRNA modules are obtained . To validate the relationships between genes and miRNAs in the modules , we consider four cases of gene regulations . In the first case , genes are directly bound and regulated by miRNAs . To validate this case , we select gene-miRNA pairs from miRTarbase [25] and MicroCosm ( http://www . ebi . ac . uk/enright-srv/microcosm/htdocs/targets/v5/ ) . Interacting pairs in miRTarbase are validated by various molecular experiments . Among them , reporter assays and western blot analysis confirm direct interactions . We compare the gene-miRNA pairs in our modules with these direct interactions in miRTarbase . MicroCosm provides computationally predicted binding sites for miRNAs in genomic sequences . Among these pairs , we select only gene-miRNA pairs with a negative correlation in expression values . From this process , we collect target genes for each miRNA , which we use for validation . Then , we perform a hypergeometric test for each miRNA in the modules to check for enrichment of genes in a module against the target genes of a miRNA . However , certain genes in the modules are not directly regulated by miRNAs , even though the expressions of the genes and the miRNAs are highly correlated . To investigate this indirect relationship , we introduce transcription factors ( TFs ) . We confirm relationships between miRNAs and TFs by manually searching the literature for evidence of cases where miRNAs are regulated by TFs or TFs are regulated by miRNAs . In the second case , we consider a relationship in which the miRNAs in a module regulate TFs , and these TFs regulate genes in the module . Here , it is not necessary that TFs be members of the module . We identify relationships between TFs and genes using the ChIP-X database [26] . For each TF in the database , we perform a hypergeometric test to determine if there is enrichment of genes in a module against the target genes of the TF . Here , the correlation of expression values between the miRNA and the TF must be negative , and the correlation values between the TF and the mRNA can be either positive or negative . In the third case , genes and miRNAs are regulated by a common TF . In this case , correlations of expression values between gene-TF and miRNA-TF should be both positive or both negative . In the fourth case , interacting pairs in miRTarbase [25] , experimentally validated by the coexpression of miRNA and mRNA , are used to validate gene-miRNA pairs in our module . Molecular experiments for this case include quantitative real-time PCR ( qPCR ) , microarrays , stable isotope labeling with amino acids in culture ( SILAC ) and pulsed SILAC . To determine the functional relevance of the modules , we test whether the genes from the modules are enriched for specific biological functions or signaling pathways . We perform a pathway enrichment test using gene ontology ( GO ) biological process terms [27] , KEGG pathways [28] , and BioCarta pathways ( http://www . biocarta . com ) . First , we download these pathways from GSEA ( http://www . broadinstitute . org/gsea ) and apply a hypergeometric test to each module , obtaining the p-values . We exclude biological functions or signaling pathways containing more than 300 genes , as such functions are too general . Supplementary Fig . S2 shows the distribution of GO biological functions as well as KEGG and BioCarta pathways . It can be seen that 51 of 825 GO terms contain more than 300 genes . To address any issues with multiple comparisons , we compute the q-values from the p-values based on a Benjamini & Hochberg correction . Then , we use a q-value < 0 . 05 for the enrichment threshold . To validate that modules are related to the specific cancer , we first examine whether enriched pathways are related to the cancer being evaluated . For this task , we collect 2 , 032 cancer genes from the allOnco database ( http://www . bushmanlab . org/links/genelists ) , which is a collection of list of cancer genes from several databases [29–32] , 379 ovarian cancer genes from the Dragon Database for Exploration of Ovarian Cancer Genes ( DDOC [33] ) , and 98 GBM genes from the literature ( [34 , 35] ) . Then , we calculate the ratios of these cancer genes in the modules . We also collect 100 ovarian cancer miRNAs and 92 GBM miRNAs from the Human miRNA & Disease Database ( HMDD [36] ) . Then , we calculate the ratios of ovarian cancer-related miRNAs in the modules . Genes involved in the development of cancer vary depending on cancer subtypes . In several papers [8 , 37–39] , the expression levels of marker genes are used to determine the subtype . For example , GBM samples were classified as a proneural subtype if marker genes DLL3 , NKX2–2 , SOX2 , ERBB3 , and OLIG2 were overexpressed [8] . Similarly , we check whether modules identified by our approach are related to a specific subtype of cancers using marker genes . For this task , we perform the following two steps . In the first step , we cluster all samples into subtypes using hierarchical clustering with a dynamic tree cut [40] . For clustering , we use genes with high variability across the samples . Then , we assign each cluster to a subtype of cancers if known marker genes of cancer subtypes are overexpressed or underexpressed . If a cluster is not related to any subtype or is related to more than one subtype , that cluster is not assigned to any subtype . In the second step , for each module , we use marker genes of the subtype to compare the expression levels of the marker genes of samples in a module to the expression levels of samples in the other subtype clusters using the t-test . If the p-values of markers genes of the subtype are significant , we consider the module to be related to the given subtype .
To construct gene-sample modules , we applied the SAMBA biclustering algorithm to the gene expression matrix , allowing duplication of genes and samples in modules using an overlap factor of 0 . 5 in [0 , 1] , where 1 represents non-overlap . For ovarian cancer and GBM , we identified 90 and 135 modules , respectively , that represent similar tendencies of gene expression changes for a subset of samples . After performing 1000 permutation tests , we selected 58 and 88 modules with a q-value < 0 . 05 for ovarian cancer and GBM , respectively . Then , we enlarged these modules by adding genes using gene-gene interactions . On average , we added 15 and 33 genes to each module for ovarian cancer and GBM , respectively . We constructed gene-miRNA modules from gene-sample modules by including miRNAs . As described in the Methods section , we pre-selected the candidate miRNAs based on the SCC values between the genes and the miRNAs and then added miRNAs to the module , which increased the BIC score . As shown in Fig . 2 , we applied 20 different SCC thresholds ( T% in [1% , 20%] of candidate miRNAs among all miRNAs ) to reduce the search space . In Fig . 2 ( A ) , the number of modules for ovarian cancer decreased as the thresholds decreased . We observed similar trends when the PCC was used instead of the SCC or when we did not integrate the gene-gene interaction data . Fig . 2 also shows that the ratios of cancer genes , ovarian cancer genes , and ovarian cancer miRNAs were similar for various SCC thresholds > 5% , and that these ratios increased when SCC thresholds decreased . Fig . S3 shows similar results for GBM . Note that we filtered out modules with fewer than two miRNAs , as such modules cannot represent the combinatorial effects of genes and miRNAs . Among the various thresholds for candidate miRNAs , we selected a value of 3% ( SCC value = 0 . 157 for ovarian cancer and 0 . 194 for GBM ) for further analysis and constructed 33 and 54 modules for ovarian cancer and GBM , respectively . Tables S1 , S2 , S3 , and S4 present lists of genes and miRNAs for the modules . For ovarian cancer , the average size of the modules was 34 genes and 10 miRNAs . On average , 19 . 1% of genes were cancer genes , 5 . 7% were ovarian cancer genes , and 51 . 7% of miRNAs were ovarian cancer-related miRNAs in the ovarian cancer modules . When combining genes and miRNAs from all modules , 18 . 6% ( 145 out of 777 ) of genes were cancer genes , 6 . 0% ( 47 out of 777 genes ) were ovarian cancer genes , and 43 . 5% ( 47 out of 108 ) of miRNAs were ovarian cancer-related miRNAs . Based on the pathway enrichment test , 48 . 4% of the modules were enriched with biological functions or signaling pathways , and most of the modules contained at least one ovarian cancer gene . Table 1 shows ovarian cancer genes and miRNAs for the selected modules . Table S5 presents lists of cancer genes , ovarian cancer genes , and ovarian cancer miRNAs for all of the ovarian cancer modules . For GBM , the average numbers of genes and miRNAs for each module were 66 genes and 14 miRNAs . In the GBM modules , on average , 23 . 2% of the genes were cancer genes , 1 . 2% were GBM-related genes , and 71 . 7% of the miRNAs were GBM-related miRNAs . For all genes and miRNAs in the GBM modules , 20 . 6% ( 386 out of 1867 ) of the genes were cancer genes , 1 . 7% ( 32 out of 1867 genes ) were GBM-related genes , and 48 . 4% ( 46 out of 95 ) of the miRNAs were GBM-related miRNAs . Table S6 presents lists of cancer genes , GBM genes , and GBM miRNAs for all of the GBM modules . Based on the pathway enrichment test , 74 . 0% of the modules were enriched in biological functions or signaling pathways . Because our approach includes genes belonging to multiple modules , we calculated the overlap ratios of genes and miRNAs among the modules . The overlap ratio is defined as ∣m1 ∩ m2∣/∣m1 ∪ m2∣ , where m1 and m2 are the number of genes or miRNAs in module 1 and module 2 , respectively . Figs . S4 and S5 show the overlap ratios among the modules . The average overlap ratios of genes were 1 . 6% and 2 . 0% for ovarian cancer and GBM , respectively , and the average overlap ratios of miRNAs were 7 . 3% and 14 . 2% for ovarian cancer and GBM , respectively . The overlap ratios of miRNAs are higher than the overlap ratio of genes , indicating that a miRNA regulates many genes involved in several pathways . As described in the Methods section , we examined the direct relationships between genes and miRNAs and their indirect relationships through TFs in the identified modules , as well as experimentally validated interactions between genes and miRNAs . For the ovarian cancer modules , we tested the direct relationship based on whether potential targets of a miRNA in the module were enriched for the genes in the same module using MicroCosm . Table 2 shows 8 miRNAs and their target genes in 12 ovarian cancer modules . For example , in Table 2 , let-7b may directly regulate several genes ( ESPL1 , DEPDC1 , BUB1B , AURKB and UBE2C ) in module 33 . Additionally , 19 gene-miRNA direct interaction pairs that were experimentally confirmed in miRTarbase are shown in Table 3 . Previously , it was confirmed using a luciferase reporter assay and the western blot method that miR-93 targets E2F1 . Also , it was confirmed using a luciferase reporter assay that miR-125b targets BCL3 in ovarian cancer cell [41] . All 156 gene-miRNA interaction pairs experimentally validated in miRTarbase are shown in Table S7 , which includes both direct and coexpression based interactions . Table S8 shows the indirect relationships in 19 ovarian cancer modules , where genes and miRNAs are co-regulated by the same TF . Note that some TFs are not members of the modules . Regulation of miRNAs by TFs is validated by literature evidence ( PubMed IDs are shown in the table ) , and the significance of the regulations of the genes in the modules by TFs was demonstrated using p-values that were obtained based on the ChIP-X database [26] . In many modules , one TF regulates multiple miRNAs and multiple genes . For example , Fig . 3 ( A ) shows ovarian cancer module 22 , in which the TF EGR1 positively regulates several genes ( AEBP1 , COL1A1 , COL5A1 , COL5A3 , COL6A1 , ITGA5 , LOXL2 , MMP11 , MMP2 and THBS2 ) and miRNAs ( miR-214 and miR-152 ) . Fig . 3 ( B ) shows ovarian cancer module 8 , in which EGR1 positively regulates several genes ( AQP1 , BGN , CALB2 , CEND1 , COL1A1 , COMP , HNT , IRX5 , ITGA5 and ITGB1 ) and miRNAs ( miR-214 , miR-152 , miR-199a and miR-199b ) in the module at the same time . In both cases , we can infer that the genes and miRNAs are indirectly related via EGR1 . Table S9 shows another type of indirect relationship in ovarian cancer , where miRNAs regulate TFs , and the TFs regulate genes in 14 ovarian cancer modules . Regulation of TFs by miRNAs was found in the literature , and is shown in the third column of the table . One example of this relationship is shown in Fig . 3 ( C ) : let-7b directly regulates the TF BACH1 , and BACH1 regulates several genes ( BUB1 , CCNA2 , CENPF , MCM10 , BIRC5 , TK1 , OIP5 , KIF11 , RRM2 and CENPA ) ; miR-156b and let-7b regulate the TF E2F1 , which regulates several genes and other miRNAs in the modules; and miR-101 , miR-29a , miR-29b and miR-29c regulate the TF MYCN , which regulates genes in the module . This module is related to ovarian cancer-related pathways such as those involved in mitosis and the cell cycle . Similarly , relationships among genes , miRNAs , and TFs in GBM modules are shown in Fig . 4 and in Tables S10 , S11 , and S12 . Table S10 shows 8 miRNAs and their target genes in 12 GBM modules . Genes targeted by miRNAs were highly enriched in these modules . In addition , Tables S11 and S12 show indirect relationships between genes and miRNAs through TFs . Fig . 4 ( A ) shows one example of an indirect relationship in GBM module 11 , where even though genes might not be directly regulated by miRNAs , they are indirectly related via two TFs: RUNX1 and TCF4 . For ease of reference , the genes in module 11 were divided into three groups ( GA , GB and GC ) : the TF RUNX1 positively regulates miR-221 , miR-222 , and genes in GA and GB; miR-155 negatively regulates the TF TCF4; and TCF4 positively regulates genes in GB and GC . Similarly , Fig . 4 ( B ) shows that miR-29a regulates the TF MYCN , which regulates several genes and miR-93 in GBM module 5 . Experimentally validated 438 gene-miRNA interactions from the miRTarbase are shown in Table S13 , including 112 direct interactions . In addition , we verified in the literature that miR-21 interacts with BMPR2 and miR-222 interacts with ICAM1 in GBM cell [42] . Fig . S6 summarizes these direct and indirect relationships in the ovarian cancer and GBM modules . These analyses show that , in total , 91% ( 30 out of 33 ) of ovarian cancer modules and 94% ( 51 out of 54 ) of GBM modules can be explained by direct regulations or indirect relationships , which allows us to understand how genes are regulated in modules . To determine the functional relevance of modules identified in ovarian cancer , we performed pathway enrichment tests for GO biological processes , KEGG pathways , and BioCarta pathways . We found that 16 out of 33 modules ( 48 . 4% ) were enriched in at least one function . Table 4 presents enriched functions or signaling pathways for selected modules . Several modules have many enriched functions or pathways related to ovarian cancer , such as the p53 signaling pathway [43] , ECM receptor interactions [44] , and cell cycles [45] . Tables S14 , S15 , and S16 present lists of all enriched pathways . As mentioned previously , on average , 19 . 1% of genes in our modules were cancer genes and 5 . 7% were ovarian cancer genes . Our further manual literature search revealed that most of the cancer genes in several modules are also ovarian cancer-related genes , suggesting that cancer genes in the modules have a high potential to be ovarian cancer-related genes . In addition , most of the enriched modules had at least one ovarian cancer gene , supporting the idea that all enriched modules might be related to ovarian cancer . Therefore , we extensively analyzed modules 22 and 8 because module 22 has a relatively high fraction of ovarian cancer genes ( 12 . 8% ) and cancer genes ( 28 . 2% ) and is enriched for important pathways in ovarian cancer , and module 8 also contains a high fraction of ovarian cancer genes ( 18 . 5% ) , cancer genes ( 33 . 3% ) , and three enriched pathways related to ovarian cancer . Fig . 5 shows a network representation of module 22 , where 25 genes ( 2 genes are not shown ) and 6 miRNAs are presented as nodes . In this module , 5 genes ( FN1 , MMP2 , MMP1 , PLAU , and SPARC ) , colored in green , were identified as ovarian cancer-related genes in the DDOC database . Moreover , the literature showed that 14 genes ( ITGA5 , COL6A1 , THBS2 , COL1A1 , MMP19 , MMP11 , CTSK , ECM1 , GREM1 , VCAN , LOXL2 , ADAM12 , FAP , and INHBA ) , colored in pink , are ovarian cancer genes ( shown in Table S17 ) and that these genes have high-average SCC values with at least one miRNA colored in sky blue . Most of the genes enriched in ECM receptor interaction , focal adhesion and proteolysis pathways are green or pink nodes , suggesting that these pathways are closely related to ovarian cancer . The literature confirms that these pathways are related to ovarian cancer [44 , 46 , 47] . In this module , COL3A1 might be related to ovarian cancer , as it is a known cancer gene targeted by all ovarian cancer miRNAs and belongs to ECM receptor and focal adhesion pathways . COL5A1 and COL5A3 are also likely to be ovarian cancer genes: they are targeted by ovarian cancer miRNAs and enriched in the above pathways , although they are not known cancer genes . Similarly , DPT also might be an ovarian cancer gene , as it is a cancer gene and is targeted by all ovarian cancer miRNAs . Evidence in the literature shows that the previously known ovarian cancer-related miRNAs miR-152 , miR-22 , and miR-214 are also related to enriched pathways in this module: miR-152 is involved in ECM-receptor-interaction [48 , 49] , and miR-22 and miR-214 regulate the AKT/PTEN pathway and the p53 signaling pathway [50 , 51] , which are highly related to the ECM-receptor , focal adhesion and proteolysis pathways [52–55] . These observations support the idea that genes and miRNAs interact with each other and play critical roles at the pathway level . Fig . 6 illustrates module 8 , which contains 34 genes and 8 miRNAs ( 5 genes are not shown ) . Because several genes and miRNAs are duplicated in module 22 , the same pathways ( ECM receptor and focal adhesion ) are enriched . However , other important pathways in ovarian cancer , such as the TGF-beta signaling pathway and the complement and coagulation cascades pathway , are also enriched [56] . From this module , COL16A1 , COL3A1 , and COL1A2 are likely to be ovarian cancer genes , as they are cancer genes and are enriched with at least one pathway containing ovarian cancer genes . For miRNAs , several articles support that miR-199a , miR-199b , miR-214 , and miR-382 are involved in the TGF-beta signaling pathway [57–60] , and that miR-22 regulates the AKT/PTEN pathway [50 , 51] , which is closely related to the TGF-beta signaling pathway in several cancers [50 , 61] . We performed pathway enrichment tests for modules identified from the GBM data set . Of 54 modules tested , 40 ( 74% ) were enriched with at least one function . Several modules had many enriched functions or pathways related to GBM , such as the p53 signaling pathway [62] , the ERBB signaling pathway [63] , and the MAPK signaling pathway [64] . Tables S18 , S19 , and S20 present lists of enriched pathways . As mentioned above , on average , 23 . 2% of genes in the modules were cancer genes , and 1 . 2% were GBM genes . A list of GBM genes was extracted from two articles [34 , 35] . Similarly to ovarian cancer , the literature results demonstrated that most of the cancer genes in our modules were also GBM-related genes , suggesting that cancer genes in the modules are likely to be related to GBM . We extensively analyzed module 11 because this module contained many GBM-related genes and pathways . Fig . 7 illustrates a network presentation of module 11 , where 74 genes ( 15 genes are not shown ) and 7 miRNAs are presented as nodes . In this module , 4 genes ( MAPK1 , CDKN1A , SHC1 , and ERBB2 ) , colored in green , are GBM genes that were validated by the literature . Most of the genes on the left side of Fig . 7 are cancer genes and are enriched with at least one pathway , including the p53 , ERBB , and GRNH signaling pathways . CBLC might be involved in the development of GBM because it is a cancer gene and is contained in the ERBB signaling pathway , an important GBM-related pathway that includes four GBM genes in this module . Additionally , the literature shows that miRNAs in this module function together in the enriched pathways: miR-34a , miR-135 , miR-21 , mi-222 , miR-221 , miR-27a , and miR-34b are involved in the p53 signaling pathway [65–71] and the MAPK signaling pathway [71–75] , and miR-34a , miR-135 , miR-21 , miR-222 , and miR-221 are involved in the ERBB signaling pathway [76–79] . In Bell et al . [8] , ovarian cancer was classified into four ovarian cancer subtypes depending on the expression levels of marker genes: “immunoreactive , ” “proliferative , ” “differentiated , ” and “mesenchymal . ” The immunoreactive subtype was identified by the chemokine receptor CXCR3 and its ligands CXCL11 and CXCL10 , indicating that considerable expression changes of these genes are important markers for identifying the subtype . The proliferative subtype was identified by the overexpression of transcription factors HMGA2 and SOX11 , proliferation marker genes such as MCM2 and PCNA , and underexpression of MUC1 and MUC16 , which are known ovarian tumor marker genes . The differentiated subtype was identified by overexpression of MUC16 , MUC1 and SLPI . Finally , the mesenchymal subtype was identified by overexpression of FAP and ANGPTL2 . In this study , we used the marker genes described above to determine which subtype was related to the majority of samples in the modules . First , we calculated the average expression level of the marker gene in the samples belonging to the module . Fig . 8 ( A ) represents the average expression levels of the 12 subtype marker genes across 33 ovarian cancer modules , showing that the expression levels of marker genes vary depending on the modules . As explained in the Methods section , we identified the cancer subtypes of samples by performing a hierarchical clustering with a dynamic tree cut ( minModuleSize = 30 ) using gene expression data , and then we calculated the p-values of marker genes for the identified modules . As shown in Fig . 8 ( B ) , among marker genes in the immunoreactive subtype , CXCL10 is underexpressed in module 5 ( p-value: 0 . 08 ) , and all of the marker genes ( CXCL10 , CXCL11 and CXCR3 ) are overexpressed in module 18 ( p-values: 0 . 04 , 0 . 02 and 0 . 67 ) . Marker genes of the mesenchymal subtype are overexpressed in module 10 ( p-values: 0 . 0003 and 0 . 0002 ) , module 23 ( p-values: 0 . 03 and 0 . 66 ) , and module 32 ( p-values: 0 . 02 and 0 . 09 ) . In Verhaak et al . [37] , GBM was classified into four subtypes depending on the marker genes: “proneural , ” “neural , ” “classical , ” and “mesenchymal . ” It was observed that marker genes DLL3 , NKX2–2 , SOX2 , ERBB3 , and OLIG2 were overexpressed in the proneural subtype; marker genes FBXO3 , GABRB2 , SNCG and MBP were overexpressed in the neural subtype; FGFR3 , PDGFA , EGFR , AKT2 , and NES were overexpressed in the classical subtype; and CASP1 , CASP4 , CASP5 , CASP8 , ILR4 , CHI3L1 , TRADD , TLR2 , TLR4 , and RELB were overexpressed in the mesenchymal subtype . Note that marker genes of the GBM subtype were overexpressed in samples belonging to that subtype , while marker genes of other GBM subtypes were underexpressed in those samples . For GBM , we first calculated the average expression levels of marker genes . Fig . 9 ( A ) presents the average expression levels of the 23 subtype marker genes across 54 GBM modules , and shows the distinct expression levels of marker genes depending on the modules . Fig . 9 ( B ) shows 6 modules related to GBM marker genes . Marker genes in the proneural subtype ( DLL3 , NKX2–2 , SOX2 , ERBB3 and OLIG2 ) are overexpressed in module 7 ( p-values: 0 . 01 , 0 . 001 , 0 . 0002 , 0 . 07 and 0 . 004 ) and module 15 ( p-values: 0 . 001 , 0 . 00003 , 0 . 002 , 0 . 017 and 0 . 007 ) . All of the marker genes in the mesenchymal subtype ( CASP1 , CASP4 , CASP5 , CASP8 , ILR4 , CHI3L1 , TRADD , TLR2 and RELB ) , except TLR4 , are overexpressed in module 22 ( p-values: 0 . 001 , 0 . 001 , 0 . 003 , 0 . 022 , 0 . 048 , 0 . 001 , 0 . 036 and 0 . 0004 ) . Two marker genes ( SNCG and MBP ) in the neural subtype are overexpressed in module 32 ( p-values: 0 . 07 and 0 . 0001 ) , all of the marker genes in the neural subtype ( FBXO3 , GABRB2 , SNCG and MBP ) are overexpressed in module 45 ( p-values: 0 . 02 , 0 . 02 , 0 . 11 and 0 . 02 ) , and two marker genes in the neural subtype ( FBXO and MBP ) are overexpressed in module 51 ( p-values: 0 . 05 and 0 . 03 ) . In addition , we obtained the subtype classification of GBM samples from Carro et al . [80] , which shares 162 samples in common with our study ( proneural: 62 , neural: 22 , classical: 35 and mesenchymal: 53 ) . When we used these subtypes of samples for the enrichment of a particular subtype in our modules through a hypergeometric test , we confirmed that modules 32 and 45 are closely related to the neural subtype ( p-values: 0 . 053 and 0 . 018 ) . Zhang et al . [6] previously showed that their NMF approach outperformed the bi-clique algorithm proposed by Peng et al . [5] . Hence , we assessed the performance of our approach by comparing it with the NMF approach using TCGA ovarian cancer data . By applying our criteria to the modules generated from their approach , we selected modules having at least one gene and two human miRNAs . As a result , we removed 7 out of 50 modules . Fig . 10 shows that the ratio of modules containing enriched pathways in the NMF approach was slightly higher than the ratios of our modules . However , the average number of enriched pathways in our modules was larger than that in the NMF approach . When we compared enriched pathways , two approaches had 43 common pathways , including ovarian cancer-related pathways such as the immune response , ECM-receptor , and TGF-Beta signaling pathways . In addition , 71 pathways were enriched only in our modules and 67 pathways only in the NMF modules , indicating that the two approaches most likely complement each other and capture different pathways related to ovarian cancer . Table S21 lists the common pathways and pathways enriched in each approach . Additionally , modules identified by our approach contain more differentially expressed genes and cancer-related genes , because we primarily used differentially expressed genes , which provide more chances to incorporate cancer type-specific genes . In Zhang et al . [6] , the modules contain a small fraction of differentially expressed genes and cancer-related genes , because 12 , 456 genes were used after filtering out genes with small absolute values and little variation . When we computed the overlap ratios of differentially expressed gene , most genes in our modules ( 79 . 4% , 617 out of 777 genes ) were differentially expressed . However , modules generated by Zhang et al . [6] contained 28 . 3% ( 462 out of 1630 genes ) differentially expressed genes on average . When we compared ratios of cancer genes , ovarian cancer genes , and ovarian cancer miRNAs in modules , our approach outperformed the NMF approach , as shown in Fig . 10 . The difference between the NMF approach and ours from a methodological viewpoint is that our approach can be more flexibly generalized to incorporate other regulatory components . In our approach , gene-sample modules are first constructed , and then miRNAs regulating genes are added to the modules ( generating gene-miRNA modules ) . To demonstrate the range of our approach , we incorporated DNA copy number aberrations ( CNAs ) as another type of regulators in gene-sample modules . As a result , 23 out of 58 ovarian cancer gene-sample modules were explained by the regulation of CNAs , and 15 ovarian cancer gene-sample modules were explained by both miRNAs and CNAs . A detailed analysis regarding regulations by CNAs is provided in the Discussion section . By contrast , the NMF approach simultaneously incorporates gene-expression , miRNA expression , gene-gene interaction , and gene-miRNA sequence prediction information . Hence , when other regulators are included , they generate modules , where correlations between genes and regulators are simultaneously high . Indeed , in another paper from the same authors [81] , they extended their NMF model to incorporate miRNAs , genes , and methylation of genes . In the generated modules , correlations of the expression levels of these three data sets were coordinately high due to a common basis matrix . Although it is a good approach , it omits modules representing the regulation of genes by a single type of regulators when incorporating multiple regulators . Additionally , we compared our approach with the Context-Specific MicroRNA analysis ( COSMIC ) algorithm [82] using TCGA ovarian cancer data . COSMIC combines gene-miRNA target prediction information , mRNA expression , and miRNA expression data . The modules constructed by the COSMIC algorithm consisted of a single miRNA and genes , which indicated that several genes are regulated by the miRNA . When we applied a q-value threshold of < 0 . 05 to 479 identified modules , 102 modules were obtained . Since COSMIC generates modules consisting of a single miRNA , it is difficult to directly compare COSMIC with our approach . Hence , we applied pathway enrichment tests using GO biological processes and BioCarta and KEGG pathways with a q-value threshold of < 0 . 05 to these 102 modules , and observed that 25 . 5% ( 27 out of 102 ) of the modules were significantly enriched . This enrichment ratio is lower than the value obtained using our approach ( 48 . 4% ) . However , we need to consider that the higher enrichment ratio in our approach is partially because two studies developed algorithms using different data sets and different assumptions . We incorporated gene-gene interactions and indirect interactions among genes and miRNAs based on mRNA expressions and miRNA expressions , while COSMIC incorporated direct interactions using sequence information of genes and miRNAs , which might reduce false positive interactions . In spite of the differences , the two approaches had 26 common pathways , including ovarian cancer-related pathways such as the ECM-receptor , DNA replication , and the G2 pathway . In addition , 88 and 38 pathways were enriched only in our modules and only in the COSMIC algorithm , respectively . Table S22 lists the common pathways as well as the pathways enriched in each approach .
In this study , we developed an approach to constructing gene-miRNA modules by integrating genes and miRNAs . We applied our approach to ovarian cancer and GBM data sets from the TCGA project . Finally , we constructed 33 modules for ovarian cancer and 54 modules for GBM . We employed gene-gene interactions to include genes with high absolute correlations with genes in the modules , because some important cancer-related genes might not be clustered together by the biclustering algorithm or might not be differentially expressed . Fig . 2 shows that incorporating gene-gene interactions increased the performance in terms of the average number of enriched terms , the number of modules with at least one enriched pathway , and the ratios of cancer-related genes and cancer-related miRNAs . Although we used gene-gene interactions to add biologically relevant genes to modules in the proposed approach , gene-gene interactions can be used to filter out biologically irrelevant genes from modules to reduce false positives . However , because the currently available human gene-gene interactions are not complete , closely related but unidentified genes might also be filtered out . It is an important challenge to incorporate gene-gene interactions to reduce false positive genes in modules , while true relevant genes still remain . We will address this issue in our future work . Because the identified modules might miss relevant interactions , we measured a potential false negative rate using miRTarbase . Let NG be the number of common genes in the modules and miRTarbase , and let NG_interaction be the number of common genes that interact with the same miRNAs in the modules and miRTarbase . Then , 1 - NG_interaction / NG might be a potential false negative rate . As a result , the rates of false negative were 0 . 789 ( 1–118/559 ) for ovarian cancer and 0 . 775 ( 1–316/1405 ) for GBM , respectively . However , the false negative rate should be adjusted when more accurate miRNA-gene interaction data become available , as this ratio is estimated based on all gene-miRNA interactions from miRTarbase and is not based on the specific cancer type and miRTarbase , which itself contains only a fraction of the gene-miRNA interactions . In the Results section , we described a functional enrichment test of genes in modules using GO terms , KEGG , and BioCarta pathways . Although we employed a widely used approach in the enrichment test , a hypergeometric test followed by a Benjamini & Hochberg method for multiple comparison correction , several issues that require further improvement still remain . For the first issue , the Benjamini & Hochberg method hypothesizes independence of the terms , while the biological processes in various ontologies represent a hierarchical structure and inter-correlation . Thus , we performed an additional enrichment test for ovarian cancer and GBM modules using TANGO [83] , which considers dependencies among biological pathways . It corrects p-values by computing the distribution of enrichment p-values in a large number of randomly generated gene sets of the same size . For ovarian cancer , 16 of 33 modules ( 48% ) were enriched with at least one GO biological process term . For GBM , 28 out of 54 modules ( 48% ) were enriched with at least one term . Tables S23 and S24 list all pathways enriched in each cancer . Further , Fig . S7 shows a comparison of the two approaches ( a Benjamini & Hochberg method and TANGO ) in terms of the ratio of enriched modules and the number of enriched terms . Although there are small differences in the two approaches , both approaches confirm that a large fraction of our identified modules were enriched with biologically relevant terms . For the second issue , because annotated pathways in GO terms , KEGG , and BioCarta pathways are still incomplete , validations on these pathways might miss biologically related sets of genes . An approach to reveal the pathways unannotated in GO , KEGG and BioCarta is to search for evidence about gene functions in the literature , and then to analyze them collectively . As part of such efforts , we manually searched scientific articles on ovarian cancer-related genes and GBM-related genes ( Table S17 ) , and relationships among genes , microRNAs , and TFs ( Tables S8 , S9 , S11 , and S12 ) . However , this approach only solves the above problem partially so a more systematic approach is called for . Very few efforts , including LitVan ( http://www . c2b2 . columbia . edu/danapeerlab/html/software . html ) , have been developed to carry out an automatic literature search to connect genes with over-represented biological terms in millions of scientific articles . Although we attempted to analyze our modules using such tools , either there are no currently available tools or websites are not connected . Hence , we will further analyze modules for functional enrichments in the future . Certain oncogenes and tumor-suppressor genes such as P53 and PTEN may play important roles in many cancer types rather than only in specific cancer type . Hence , we examined how many genes in the identified modules were specific to ovarian cancer or GBM . We collected 1393 genes from five cancer type specific databases: the DDOC [33] , GBM genes from the literature [34 , 35] , the Cervical Cancer gene Database ( CCDB ) [84] , the Dragon Database of Genes associated with Prostate Cancer ( DDPC ) [85] , and Lung Cancer Gene Database ( LUGEND ) . We refer to genes contained only in the DDOC as potentially ovarian cancer specific genes . Although these genes are not compared with genes from all types of cancers , it might helpful to remove common cancer genes . Among the 47 DDOC genes included in our ovarian cancer modules , 18 genes were potentially ovarian cancer specific genes . Similarly , among the 32 GBM genes included in our GBM modules , 7 genes were potentially GBM cancer specific genes . Lists of these cancer type specific genes are shown in Table S25 . The accuracy of the identified modules might be largely dependent on the quality of the data sets . In this study , we used TCGA microarray data sets , as in many previous reports they have been used to identify core genes and pathways significantly related to ovarian cancer and GBM . Additionally , when TCGA microarray data sets were compared to RNA-Seq data from the same samples , their expression values were highly correlated in most cases [86] confirming that these data sets are less dependent on a particular platform . The proposed approach can be generalized to incorporate other regulatory components . To demonstrate the range of applicability of our approach and to provide additional support of biological relevance to the modules , we incorporated somatic DNA copy numbers from the paired patients of gene expression data . For this task , we downloaded TCGA level 3 data sets that provide segmented copy number ratio data compared to normal samples . We first recalculated the copy number aberration ratios for every 1 MB region and filtered out regions whose absolute copy number ratio values are less than 0 . 2 , corresponding to 99 . 9% among all ratio values . Then , CNA regions were incorporated into gene-sample modules based on correlations between genes in modules and CNA regions . As a result , for the ovarian cancer modules , 23 out of 58 gene-sample modules were explained by the regulation of CNAs , and genes in 15 out of 33 gene-miRNA modules ( 45% ) were also regulated by CNAs , as shown in Table S26 . In particular , genes in several modules were located in the regulating CNA regions , indicating that the expression of genes in the modules might be directly affected by CNAs . DNA copy numbers in the chr 1: 32 . 1 MB - 53 . 4 MB region were highly correlated with genes in ovarian cancer module 9 with a PCC value of 0 . 301 , and 13 out of 18 genes in the module ( CDCA8 , C1orf109 , AK2 , SNIP1 , GNL2 , RLF , TRIT1 , YRDC , RRAGC , PPIE , PSMB2 , MED8 and COL9A2 ) were located in this CNA region . Similarly , the DNA copy numbers in the chr 1: 180 . 6 MB - 247 . 9 MB region were highly correlated with genes in ovarian cancer module 23 with a PCC value of 0 . 319 , and most of genes ( 14 out of 19 genes ) in this module were located in this region . Additionally , for ovarian cancer module 29 , DNA copy numbers in chr 1: 31 . 9 MB - 59 . 1 MB regions have a high correlation value ( 0 . 345 ) with gene in the module , and 78 . 3% of the genes are located in this region . For GBM , 26 out of 88 gene-sample modules were explained by regulation of the DNA copy numbers shown in Table S27 , and 19 out of 54 gene-miRNA modules ( 35% ) were commonly regulated by CNAs and miRNAs .
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A microRNA ( miRNA ) is a small RNA molecule that regulates the expression of mRNA genes . A miRNA can regulate multiple genes , and a gene can be regulated by multiple miRNAs . The regulation of genes by miRNAs may vary from patient to patient , even if they suffer from the same type of cancer . In this study , we identify the relationships between genes and miRNAs in cancer patients using expression data . Because these relationships are complicated by the involvement of transcription factors , which are among the most influential regulators of genes , we also attempt to explain the triple relationship among genes , miRNAs , and transcription factors . We constructed modules consisting of a set of genes and miRNAs , in which the expression levels are highly correlated . In most of these modules , genes and miRNAs are related to specific cancer types; their relationships are explained both by direct regulation of genes by miRNAs and by indirect relationships via transcription factors .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
A Computational Approach to Identifying Gene-microRNA Modules in Cancer
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A major challenge in ecology is forecasting the effects of species' extinctions , a pressing problem given current human impacts on the planet . Consequences of species losses such as secondary extinctions are difficult to forecast because species are not isolated , but interact instead in a complex network of ecological relationships . Because of their mutual dependence , the loss of a single species can cascade in multiple coextinctions . Here we show that an algorithm adapted from the one Google uses to rank web-pages can order species according to their importance for coextinctions , providing the sequence of losses that results in the fastest collapse of the network . Moreover , we use the algorithm to bridge the gap between qualitative ( who eats whom ) and quantitative ( at what rate ) descriptions of food webs . We show that our simple algorithm finds the best possible solution for the problem of assigning importance from the perspective of secondary extinctions in all analyzed networks . Our approach relies on network structure , but applies regardless of the specific dynamical model of species' interactions , because it identifies the subset of coextinctions common to all possible models , those that will happen with certainty given the complete loss of prey of a given predator . Results show that previous measures of importance based on the concept of “hubs” or number of connections , as well as centrality measures , do not identify the most effective extinction sequence . The proposed algorithm provides a basis for further developments in the analysis of extinction risk in ecosystems .
The robustness of ecosystems to species losses is a central question in ecology given the current pace of extinctions and the many species threatened by human impacts [1]–[3] . The loss of species in complex ecological networks can cascade into further extinctions because of the mutual dependence of species . Of all the possible causes leading to these “cascading” extinctions , the simplest case to analyze is that of species left with no exploitable resources [4]–[8] . These extinctions due to lack of nutrient flows represent the most predictable subset of secondary losses and also the best case scenario , since the addition of other effects [9] , [10] , related to the loss of dynamical regulation , will result in additional losses . The former scenario is the simplest to analyze because the extinction of consumers that are left with no resources will happen with certainty , unless the consumers can switch to a different set of resources . Because modern data sets are obtained by sampling extensively a system over time , it is unlikely that potential resources resulting from switching prey go unregistered . If these potential interactions have been included in the prey of a given predator , then the dynamics of extinction for this flow-based case are completely described by the network structure . This simple analysis also represents the best case scenario , since other causes of extinction such as low population abundance can increase the loss of species in response to the original disturbance , but cannot prevent flow-based extinctions from happening . From the flow-based perspective , the effects of a single species loss can be easily analyzed [7] , but those of multiple losses and sequences of extinctions rapidly become an intractable problem . Species' importance in this context has been traditionally measured using local network properties , such as the number of species' connections [4] , [5] . In particular , species with a large number of links are considered keystones ( or hubs [11] ) for the robustness of ecological networks [5] , [6] , [8] , [12] . A different take on species' importance in networks makes use of centrality measures: species that are central mediate the interaction among those that are more peripheral and therefore should be considered the most important species [13]–[15] . Here we propose a new algorithm for assessing the importance of species for food web robustness that takes into account the full topology of the network . When species importance from the perspective of robustness is correctly measured , the ordered removal of species according to this ranking should lead to the fastest collapse of the network . Our approach inspired by PageRankTM , the algorithm at the heart of GoogleTM [16] , uses a recursive definition: a species is important if important species rely on it for their survival . Results show that the algorithm outperforms all other measures of species importance from the perspective of fastest route to collapse . Moreover , it performs as well as a genetic algorithm [17] , [18] , an evolutionary intensive search that can evaluate millions of solutions , even if the eigenvector implementation is much simpler and faster . A biological interpretation of species importance follows naturally as the amount of matter flowing through a given species , for both qualitative networks constructed from the presence and absence of links , and quantitative networks for which interaction strengths are explicitly specified [19]–[21] . The proposed approach provides the basis for a more comprehensive treatment of extinction risk in food webs .
The World Wide Web is a directed network in which web pages ( nodes ) are connected with each other by hyper-links . We can write a matrix in which the presence and absence of a link from the row-page to the column-page are represented as entries and , respectively . PageRankTM rates pages as important if they receive links from pages that are in turn also rated as important . The PageRankTM algorithm solves this recursive definition using a clever application of linear algebra [16] . Each page is assigned an importance , and each link ( exiting page to enter page ) carries an equal fraction of the importance value . The importance of a page is the sum of the importance assigned to the incoming connections . The recursive problem can be solved by building a matrix in which each element represents the fraction of importance assigned to a linkand given by . When matrix satisfies two conditions ( it is both irreducible and primitive [16] ) , then the problem of assigning importance is solved by computing a fundamental and well-known quantity in linear algebra , the eigenvector associated with the dominant eigenvalue of . If the two conditions are met , the Perron-Frobenius Theorem guarantees the existence of this dominant eigenvector ( Text S1 ) . One main problem , besides the numerical challenge of computing the eigenvectors of a matrix with several billions rows and columns , is that the World Wide Web is not irreducible [16] . For irreducible matrices , the associated network must be strongly connected , with any two nodes connected by a directed pathway . Because he WWW clearly does not meet this condition , the matrix is modified by applying a “damping factor” , . A new matrix is constructed with entries , where is the number of nodes in the network . The damping factor effectively mimics the probability that a user browsing the web can decide to move directly to another ( random ) page [16] . The eigenvector is then computed for . Here we propose an algorithm to rank the importance of species for food web robustness that uses a similar principle . Nutrients move from one species to another in a food web through feeding links . For their survival , species must be able to receive energy and matter from primary producers through some pathway in the network [7] , [22] . Thus , we define a species as important if it supports ( directly or indirectly ) other species that are in turn important . The problem is similar to that of ranking web pages , with the difference that now importance moves in the opposite direction than that of the links ( i . e . a web page is important if important pages point to it; species are important if they point to important species ) . Also food webs are neither irreducible nor primitive , but we can find a biologically sound solution to this problem . A damping factor would be completely unrealistic since nutrients cannot randomly “jump” among links in the food web . We make instead two observations: first , all matter in the food web must originate from primary producers who receive it from the external environment and channel it through the food web to all other species through feeding pathways [21] , [23] . We therefore attach to the network a special node ( a “root” ) that points to all the primary producers [7] , [22] . Second , every species has an intrinsic loss of matter which can be represented by adding a link from every node to the root . This process represents the buildup of detritus that in turn is partly recycled into the food web [21] , [23] . With these two modifications any food web becomes irreducible and primitive ( Fig . 1 , Text S1 ) and we can now solve the problem of assigning importance by computing the eigenvector associated with the dominant eigenvalue . For simplicity , we consider the normalized eigenvector so that . Recent research on food web robustness has emphasized the role of connectivity: species with a high number of connections are likely to be essential for the survival of other species [4]–[8] . In-silico extinction experiments also showed that random removal sequences rarely cascade in the secondary loss of species , whereas the removal of highly connected species is likely to generate many secondary extinctions . Another line of research borrowed measures of centrality from sociology . Central species mediate the spread of disturbances through the network . In this sense , species with high centrality would be considered “keystone” to the maintenance of connectivity in networks [13]–[15] . To test our algorithm , we performed in-silico extinction experiments in which a single species is removed at each step and the number of secondary extinctions is recorded . We compared several simple algorithms: a ) the removal of the most connected species at each step ( , where we measured the number of connections coming out of each node ) ; b ) the removal of species according to closeness centrality ( ) : nodes are highly central from this point of view if they have short distance to many nodes in the network; c ) the removal according to betweeness centrality ( ) : a node has high betweeness if it lies on the shortest path between many couples of nodes; d ) removal according to dominators ( ) : a node dominates another if all the paths from “root” to contain - the removal of will therefore drive extinct [7]; finally , e ) we removed according to the eigenvector-based algorithm outlined above ( ) . All the algorithms are “greedy”: at each step , we compute the “importance” of each node according to a particular algorithm , and we remove the one with the highest importance . The procedure is repeated until all the species have gone extinct or have been removed . The algorithms are explained in detail in the Text S1 . For each extinction sequence , we measured the “extinction area” , a quantity that equals 1 when all species go extinct after the first removal and tends to 1/2 when no secondary extinction is observed ( Fig . 2 ) . In this way , we can assess the performance of each algorithm with a single number . If important species are removed early on , then the area will be larger . The algorithms could yield ties - nodes with the same importance . Whenever we encountered ties , we considered all the possible sequences of extintions that may result exploring all the ties . Therefore , algorithms with low ranking power ( i . e . yielding many ties ) could produce very many extinction sequences . We followed all extinction sequences generated by ties whenever they were less than half a million . If there were more possible solutions , we analyzed the first half million . We applied all the algorithms to 12 published food webs ( Table 1 ) . For each algorithm and network , we tracked the total number of solutions produced by the algorithm , the minimum , maximum and mean “extinction area” and the number of solutions yielding the maximum area ( Text S1 ) . We then evaluated the value of the maximal extinction area . Because the number of possible removal sequences is where is the number of species in the network , the enumeration of all possible cases is clearly unfeasible . We therefore programmed a Genetic Algorithm [17] ( ) that seeks to find the best possible sequence using an evolutionary search . This type of algorithm has been shown to be effective for similar problems in food web theory [18] , even when computationally expensive and when its performance declines with food web size . Here , the search performs at least as well as the best among the other algorithms , as expected for an effective search ( Fig . 3 ) .
In all cases , the best solution for the degree-based algorithm ( ) and the closeness centrality ( ) did not match the genetic algorithm ( ) : these measures do not correctly identify the fastest route to collapse ( Fig . 3 ) . Betweeness centrality yields an area as large as that of the in only 1 case ( benguela ) . The dominators-based procedure finds the best solution in 2/3 of the cases . The eigenvector-based algorithm finds the best solution in 11 cases out of 12 . To improve the algorithm , we build upon a previous approach of ours [22] , based on the observation that not all the links in a food web contribute to robustness . The idea that more complex networks would contain a multiplicity of pathways that would in turn render the networks more robust was put forward by MacArthur more than fifty years ago [24] . We recently showed that , while this is generally true , some links do not contribute to robustness , while others dampen the effects of species removal and increase robustness ( Fig . 1 ) [22] . Thus links can be classified as “redundant” or “functional” from the perspective of their effects on secondary extinctions . From this classification , one can obtain a simplified food web by removing all redundant connections , that has exactly the same robustness properties than the original network in terms of the secondary extinctions . For the algorithm , then , we repeated the removal sequence experiment but we computed for the simplified food web obtained by first removing redundant connections . The results indicate that the algorithm is capable of finding the best solution provided by the in all cases ( Fig . 3 , Text S1 ) .
We have developed two algorithms to rank species in food webs according to their role in extinction cascades . We considered a flow-based perspective in which species go extinct if they lack a connection through some pathways to primary producers . Although it is evident that many other types of extinctions can increase total species loss , the subset considered here provides a baseline and corresponds to the best case scenario in which the minimum impact to the network is taken into account . Species left with no resources will go extinct , unless they can switch their choice of prey sufficiently fast . It is known that species can exhibit this type of adaptive behavior in response to the relative abundance of prey , with consequences for the stability of predator-prey systems [25] . Because the food webs we have analyzed are sampled in the field over time and space , it is most likely that the links included in the networks already reflect prey switching . An important source of additional secondary extinctions will be related to the population dynamics of species . The complete consideration of dynamics with a system of nonlinear differential equations that simulates the outcome of species losses , will only increase the number of species predicted to go extinct by the simplest scenario . The analysis of removal effects remains very challenging if not prohibitive for large ecological networks ( but see [9] , [10] , [26] ) , requiring information most often unavailable on the functional form of a large number of interactions and their associated parameters , the exploration of different assumptions and a huge parameter space . The simple and elegant solution for the flow-based case provides a baseline from which additional impacts can be considered . The results obtained here with a simple algorithm emphasize that the position of a species in the food web , rather than its sheer number of connections , is the main determinant of its impact on extinction cascades . This contrasts with the emphasis given so far to the number of connections and to the concept of “hubs” in networks . We have shown that the performance of the algorithm , which considers only the neighbors of a given species , is considerably worse than that of the eigenvector based algorithms at finding the fastest route to collapse . The latter algorithms solve the problem of importance by considering the full topology of the network and the particular position that each species occupies . We further showed that an algorithm that first removes “redundant” connections provides a valuable improvement , because it relies on the functional role of connections in maintaining the flow of nutrients through the food web . Interestingly , a parallel problem has been analyzed in computer science ( Text S1 ) . Srinivasan et al . [27] have shown that many realistic removal sequences are not likely to cascade in massive species' losses , with the loss of threatened species not necessarily resulting in further extinctions . It is therefore difficult to discriminate importance among species whose removal has little direct effect on network structure . The eigenvector approach provides a simple and effective way of comparing species importance even when their removal does not result in extinction cascades . This should help assessing the relative importance of threatened species for network robustness and from the perspective of network structure . Coll et al . analyzed the effect of actual human-induced extinction in the Mediterranean sea and found that removing commercially valuable species had typically a higher impact than random removals , but lower than maximum degree driven removals [28] . The dominant eigenvector has also a simple biological interpretation . To show this , we assume for the moment that we can fully describe the interacting community by means of differential equations representing the dynamics of species' abundance , . We further consider that the system is at a feasible equilibrium point ( for all species , ) . For this case , we can measure the flow of biomass entering and exiting each species ( for example , in kilograms of biomass per year per hectare ) and the amount entering and exiting each node must be equal given the equilibrium condition [19]–[21] . These quantities are proportional to the eigenvector used here: specifically , provides an estimate of the flow through each species ( Text S1 , Fig . S1 ) . In the absence of available information on diet preferences , measures the flow that each species would receive if each of its prey provided equal amounts of nutrients . When quantitative information on these inputs is available , and the flow-based description become exactly equivalent ( Text S1 , Fig . S2 ) . The proposed algorithm further provides a measure of eigenvector centrality in directed , rooted networks . Other centrality measures have been proposed to evaluate species importance [13]–[15] , but they typically consider undirected networks and have not been adapted to food webs . This is reflected in the poor performance achieved by the centrality algorithms . Here we have shown that consideration of ecological knowledge on food web processes can improve algorithms that have been developed in other branches of science . It should be possible to adapt the methods presented here to other types of biological networks , especially metabolic ones . For food webs , the next challenge is to add other dynamical effects to this framework , to obtain a more complete description of extinction risk in complex ecological networks .
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Predicting the consequences of species' extinction is a crucial problem in ecology . Species are not isolated , but connected to each others in tangled networks of relationships known as food webs . In this work we want to determine which species are critical as they support many other species . The fact that species are not independent , however , makes the problem difficult to solve . Moreover , the number of possible “importance'” rankings for species is too high to allow a solution by enumeration . Here we take a “reverse engineering” approach: we study how we can make biodiversity collapse in the most efficient way in order to investigate which species cause the most damage if removed . We show that adapting the algorithm Google uses for ranking web pages always solves this seemingly intractable problem , finding the most efficient route to collapse . The algorithm works in this sense better than all the others previously proposed and lays the foundation for a complete analysis of extinction risk in ecosystems .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"ecology/theoretical",
"ecology",
"ecology/community",
"ecology",
"and",
"biodiversity",
"ecology",
"computational",
"biology"
] |
2009
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Googling Food Webs: Can an Eigenvector Measure Species' Importance for Coextinctions?
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Haploid high quality reference genomes are an important resource in genomic research projects . A consequence is that DNA fragments carrying the reference allele will be more likely to map successfully , or receive higher quality scores . This reference bias can have effects on downstream population genomic analysis when heterozygous sites are falsely considered homozygous for the reference allele . In palaeogenomic studies of human populations , mapping against the human reference genome is used to identify endogenous human sequences . Ancient DNA studies usually operate with low sequencing coverages and fragmentation of DNA molecules causes a large proportion of the sequenced fragments to be shorter than 50 bp—reducing the amount of accepted mismatches , and increasing the probability of multiple matching sites in the genome . These ancient DNA specific properties are potentially exacerbating the impact of reference bias on downstream analyses , especially since most studies of ancient human populations use pseudo-haploid data , i . e . they randomly sample only one sequencing read per site . We show that reference bias is pervasive in published ancient DNA sequence data of prehistoric humans with some differences between individual genomic regions . We illustrate that the strength of reference bias is negatively correlated with fragment length . Most genomic regions we investigated show little to no mapping bias but even a small proportion of sites with bias can impact analyses of those particular loci or slightly skew genome-wide estimates . Therefore , reference bias has the potential to cause minor but significant differences in the results of downstream analyses such as population allele sharing , heterozygosity estimates and estimates of archaic ancestry . These spurious results highlight how important it is to be aware of these technical artifacts and that we need strategies to mitigate the effect . Therefore , we suggest some post-mapping filtering strategies to resolve reference bias which help to reduce its impact substantially .
The possibility to sequence whole genomes in a cost-efficient way has revolutionized the way how we do genetic and population genetic research . Annotated , high-quality reference genomes are a cornerstone for resequencing surveys which aim to study the genetic variation and demographic history of an entire species . Resequencing studies usually align the sequences of all studied individuals to a linear haploid reference sequence originating from a single individual or a mosaic of several individuals . In each site , this haploid sequence will only represent a single allele out of the entire genetic variation of the species . An inherent consequence is some degree of bias towards the alleles present in that reference sequence ( “reference bias” ) . Sequencing reads carrying an alternative allele will naturally have mismatches in the alignment to the reference genome and consequently have lower mapping scores than reads carrying the same allele as the reference . This effect increases with genetic distance from the reference genome , which is of particular interest when using a reference genome from a related species for mapping [1–3] . Generally , reference bias can influence variant calling by missing alternative alleles or by wrongly calling heterozygous sites as homozygous for the reference reference allele [4 , 5] which is known to influence estimates of heterozygosity and allele frequencies [6–8] . The field of palaeogenomics and the population genomic analysis of DNA obtained from hominin remains has led to a number of important insights and groundbreaking results in recent years , including admixture between different hominin groups , migrations of prehistoric humans and the evolution of different phenotypes [9–14] . DNA preservation poses a major challenge for these studies , as fragmentation causes most authentic sequences to be shorter than 100 bp , and deamination damage increases the number of mismatches and can even mimic genetic variation at transition sites [15–17] . In addition to fragmentation and other post-mortem damages , low coverage data is a major limiting factor for ancient DNA studies . These low coverages do not permit calling diploid genotypes so a very common approach is to use “pseudo-haploid” data: at each known single nucleotide polymorphisms ( SNP ) site one sequencing read is picked in order to represent a haploid genotype of that individual . The single read is either chosen at random or to represent the most common allele among all reads mapping to the site . This approach would not introduce bias if the reads were a random representation of the chromosomes carried by the individual . Reference bias , however , would introduce some skew towards the reference allele at heterozygous sites . These characteristics of ancient DNA and practices used in palaeogenomic studies make them particularly vulnerable to reference bias [18–20] . It has been shown that pseudo-haploid data can be more biased than imputed genotypes [21] , and that reference bias and fragment length artifacts can interfere with phylogenetic classifications [3] . Reference bias can influence downstream analyses if these are based on estimating allele frequencies in a population , or studying pairwise allele sharing between individuals and groups . This study investigates the presence and impact of reference bias in studies of prehistoric human populations using genomic ancient DNA . We first illustrate its abundance in published data from ancient human and archaic hominins , and illustrate how it is influenced by standard data processing . We then show how reference bias can influence some basic population genetic analyses such as population affinities and heterozygosity . Finally , we discuss some possible data filtering strategies in order to mitigate reference bias in ancient DNA studies .
We first investigate whether reference bias is present in published ancient DNA data . We restrict our analysis to known biallelic SNPs , as most population genomic analyses are using SNPs and the allele frequencies at those positions . In particular , we are only using transversion polymorphisms ( to avoid the effect of post-mortem deaminations ) and sites identified to be polymorphic in a world-wide set of modern human populations [22] . We investigate supposedly heterozygous sites ( defined as sites covered by at least 10 reads with at least 25% representing the minor allele ) in a set of published medium to high coverage human and hominin genomes ( Table 1 ) . We note that our approach does not include any rescaling of base qualities , as such approaches usually take the reference allele into account which may amplify reference bias . At a heterozygous site , a DNA extract of an individual should contain the same number of reference and alternative fragments . We observe that after mapping to the human reference genome the average proportion of alternative alleles is lower than the expected 50 percent for most of the individuals investigated ( Fig 1 ) , regardless of whether they represent libraries with enzymatically repaired post-mortem deamination damage or not ( Table 1 ) . Samples for which we used SNP capture data ( Table 1 ) [31 , 32] show slightly stronger reference bias than shotgun sequenced samples but they are also characterized by shorter fragments which can influence the strength of reference bias ( see below ) . For comparison , we added six high-coverage modern individuals from diverse continental origin [35] which also show proportions below 50 percent but higher than most ancient individuals highlighting that some degree of mapping bias could be present in NGS data of modern populations as well . As sequence fragments carrying the alternative allele will show an elevated number of mismatches to the reference genome , mapping quality seems a natural filter to avoid reference bias . Consistent with this expectation , we see a slightly stronger reference bias for stricter mapping quality filters . Lowering the mapping quality cutoff can have other detrimental effects , however , for example an enrichment of microbial contamination [36] or sequences not uniquely mapping to a particular region of the genome . As the qualities of the base calls have not been rescaled after mapping to the reference genome , we do not see an effect of different minimum base quality thresholds on reference bias ( S1 Fig ) . Post-mortem deamination is a major issue in the analysis of ancient DNA data creating additional mismatches between the sequence reads and the reference genome . We were surprised to see individuals with enzymatic repair of these damages did not systematically perform better in Fig 1 as the expected number of mismatches would be lower for those . To follow up on this , we investigated individual libraries ( both damage-repaired and non damage-repaired ) of the Vindija Neandertal [34] separately ( S2 Fig ) . All non-damage repaired libraries together show a stronger reference bias than the single damage-repaired library but the latter is also characterized by longer fragment length ( see observations below ) . Furthermore , single non-damage repaired libraries show both higher and lower reference bias without a clear trend which suggests that the influence of post-mortem damage on reference bias is not major . Investigating pairwise correlations between the proportion of alternative alleles at sites considered heterozygous in both individuals shows significantly positive correlations in most cases ( S1 Table ) . This indicates that the strength of reference ( and alternative ) bias may differ regionally across the genome , so there could be an effect of sequence context and uniqueness of the specific sequences across the genome . The highest correlations are observed between samples from the same study or produced by the same institute suggesting that similar wet lab techniques also influence this effect . To investigate the distribution of reference bias instead of just averages as above , we modified original reads to carry opposite alleles at each SNP site and remapped them . We created such a virtual read set for the Scandinavian Mesolithic hunter-gatherer sf12 and the Siberian Ust’-Ishim individual . In total , 1 , 022 , 747 SNPs were identified for sf12 , and 1 , 022 , 605 for Ust’-Ishim . Out of these , 63 . 04% and 87 . 90% , respectively , showed the perfect allelic balance of 0 . 5 as expected by design from the dataset . The smaller number of balanced SNPs for sf12 is mainly due to increased resolution of twice the number of sequencing reads as a single non-matching read would cause deviations from the perfect 50/50 ratio in this analysis . We only considered reads that map back to their original location from the first mapping round . A very limited number of SNPs were also affected by reads that mapped back with sufficient quality , but to a different genomic location . The proportions of alternative alleles are summarized in Table 2 . Notably , there is a subset of SNPs showing alternative as opposed to reference bias . There is also a subset of SNPs where the bias is total , i . e . only one of the two alleles is ever mapped back successfully within this dataset . The distribution across the genome of sites deviating from the balanced case is similar to the overall density of the SNPs used—all chromosomes and chromosomal regions are affected . We also checked the overlap between the two individuals . 1 , 022 , 343 SNPs fulfilled the uniqueness filtering conditions and were successfully identified in at least one read in sf12 as well as Ust’-Ishim . Out of these , 584 , 434 ( 57 . 07% ) showed perfect allelic balance in both individuals . To investigate further , we also tried 1 , 693 , 337 SGDP transversion SNPs without applying the mappability filter . This naturally increased the number of identified SNPs , but at the cost of an even lower proportion of SNPs in perfect allelic balance , and markedly fatter tails in the distribution ( 0 . 97% with an allele fraction below 0 . 4 for sf12 , vs . 0 . 09% with the filtering in place ) . Most mapping strategies set the number of allowed mismatches relative to the length of the sequenced fragment . Therefore , shorter fragments might show a stronger reference bias than long fragments . To investigate this , we used the 57x genome generated for the Scandinavian Mesolithic hunter-gatherer sf12 [25] and partitioned the data into fragment length bins . The large amount of data allows us to still have a sufficient number of SNPs covered at 10x or more for each of the length bins . Somewhat expectedly , shorter fragments display a stronger reference bias than longer sequences ( Fig 2A ) . Generally , fragment length might be a main driver of reference bias across all samples as the mode of each individual’s fragment size distribution is highly correlated with the average proportion of alternative alleles at heterozygous sites ( Pearson’s r = 0 . 496 , p = 0 . 0118; Fig 2B ) . This also has an effect on the proportion of sites considered heterozygous among all sites analyzed which can be seen as a relative measure for the individual’s heterozygosity ( Fig 2C ) . In fact , different fragment length bins of the same individual produce heterozygosity estimates that do not overlap in their 95% confidence interval ( Fig 2C ) . This represents a general limitation for estimating heterozygosity from ancient DNA data which may to some degree explain the generally low diversity estimates for many prehistoric groups [37–39] . The potential of obtaining significantly different estimates for the same population genetic statistic may also have enormous effects on other downstream analyses such as allele sharing and population structure . In order to investigate the influence of reference bias on measures of allele sharing , we calculated different combinations of D statistics of the form D ( Chimp , X; Y , Z ) , where X is a modern human population , and Y and Z are two different treatments of the same individual sf12 . Therefore , the expectation for D is 0 , but differences in reference bias between Y and Z could lead to spurious allele sharing between population X and a deviation from 0 . Negative values of D indicate more allele sharing of X with Y while positive values indicate an excess of shared alleles between X and Z . The populations X were grouped by continental origin and we calculated the statistics separately for whole genome shotgun data ( SGDP ) [22] and populations genotyped using a SNP array ( HO ) [23] . We use four different versions of genotypes for sf12 . First , we compare pseudo-haploid calls ( random allele per site with minimum mapping and base quality of 30 ) to diploid genotype calls ( Fig 3A and 3C ) . This comparison assumes that the diploid calls are less affected by reference bias as slight deviations from a 50/50-ratio at heterozygous sites should be tolerated by a diploid genotype caller but random sampling would be biased towards the reference allele . This is supported by the D statistic D ( chimp , reference_genome; sf12_hapl , sf12_dipl ) < 0 ( Z = −7 . 2 ) , indicating more allele sharing between the reference and the pseudo-haploid calls . For this illustration , we are using diploid genotype calls from GATK as we are only looking at the variation at known SNP sites . We note that different calling methods might also introduce other types of technical artefacts and that genotype callers specifically developed for ancient DNA [40–42] are preferable when calling novel variants from ancient DNA data as they incorporate post-mortem damage and other ancient DNA specific properties . Second , we compare randomly sampled reads of different fragment length categories ( Fig 3B and 3D ) as longer ( 75-80 bp ) fragments should exhibit less reference bias than short ( 35-40 bp ) fragments ( see above ) , which is supported by the D statistic D ( chimp , reference_genome; sf12_short , sf12_long ) < 0 ( Z = −5 . 8 ) , indicating more allele sharing between the reference and pseudo-haploid calls from short fragments . In general , we observe a deviation from zero in most cases highlighting the effect of reference bias on these statistics ( Fig 3 ) . Surprisingly , the directions of this bias differ between the HO data ( SNP genotyping array ) and the SGDP data ( whole genome sequencing ) , which suggests that different reference data sets are also affected by reference bias at different degrees . Even when investigating the modern populations at only sites that were covered in both data sets , we see differences in the relative heterozygosity for the same individual between the data sets ( S3 Fig ) . The SNP array data ( HO ) consistently shows lower heterozygosities and a higher count of reference alleles for all individuals which might be a consequence of the different calling algorithms employed for these fundamentally different data types . This represents a potential batch effect which also needs to be considered when merging different reference data sets . Affinities to populations of different geographic origin vary in their sensitivity to reference bias but little general trends are observable . Western Eurasian populations show a strong deviation from 0 in all tests . Some of these individual tests would have achieved nominal significance ( assuming a significance threshold of |Z| > 2 and no correction for multiple testing ) . Notably , African populations show the strongest deviation in the short versus long comparison in the SGDP data set while they exhibit almost no bias in the same comparison using the HO data . As the biases do not seem to show a consistent tendency , we cannot directly conclude that recent ancient DNA papers have been systematically biased in some direction . The shifts appear to be dataset and test specific so some results could still be driven by spurious affinities due to reference bias . The human reference genome sequence is a mosaic of the genomes of different individuals , and population specific segments might not be well represented in the reference assembly [43] . The geographic origin of the specific segments should have an impact on the population genetic affinities as the reference allele will more likely be found in specific geographic regions . We obtained information on the local ancestry of the human reference genome from [44] . According to this estimate 15 . 6% of the reference genome can be assigned to African , 5 . 0% to East Asian and 30 . 0% to European origin while the origin for 49 . 4% is uncertain . We re-calculate D statistics for the different parts of the genome separately , restricting the analysis to the SGDP data . The impact of reference bias differs between the different ancestries ( Fig 4 ) . Generally , reference bias is weakest for reference segments of African origin . Notably , African populations show the strongest deviations from 0 in this case . Sequences mapping to the European segments of the reference show a strong reference bias with slight differences between continental populations . Several tests show nominal significance ( |Z| > 2 ) for higher allele sharing of the modern group with the more biased version of sf12 . Reference bias at the East Asian segments of the reference genome seems intermediate but the D statistics also show large variation and noise which may be due to the only small proportion of the reference genome that could confidently be assigned to an East Asian origin [44] . Finally , we explore whether reference bias can affect estimates of archaic ancestry . We estimate the Neandertal ancestry proportion in sf12 as done by [34]: α = f 4 ( s f 12 , M b u t i ; A l t a i N e a , C h i m p ) f 4 ( V i n d i j a N e a , M b u t i ; A l t a i N e a , C h i m p ) We use eight different combinations of diploid and pseudo-haploid calls for sf12 as well as the two Neandertals in this statistic ( Table 3 ) . The 95% confidence intervals of all estimates overlap but point estimates differ by up to 2 . 85% when using all pseudo-haploid versus all diploid calls . The African segments of the reference genome yield the lowest point estimates ( as low as 0 . 72% ) —none of these estimates are significantly different from 0 . These numbers alone would not allow to show the presence of archaic admixture in non-African populations—a pattern that has been confirmed using a range of methods other than f statistics during the last decade [12] . These different estimates highlight some of the sensitivities of f4-ratios not just to the choice of reference populations [45] but also to technical artifacts . After establishing the abundance and potential effect of reference bias , we investigated two simple post-mapping filtering approaches to mitigate reference bias . The two agents involved in the process are the reference genome and the sequence fragments or reads . First , we modified reads that successfully mapped to a SNP site with a match of the reference allele to carry the alternative allele . These modified reads were re-mapped to the reference genome and they passed the filtering if they still mapped to the same position of the genome with no indels . Second , we prepared a modified version of the reference genome which carried a randomly chosen third base ( neither the reference base nor the known alternative allele ) at all 1 , 022 , 984 sites . A similar approach has been used to study ultra-short fragments in sequence data from archaic hominins [46] . All reads originally mapping to the SNP sites were re-mapped to this modified reference genome , and again only reads that mapped to the same location and without indels passed the filtering . Finally , we used both filters on the same BAM file . All scripts used for filtering can be found at https://bitbucket . org/tguenther/refbias/ . The filtering approaches increase the average proportion of the alternative allele at heterozygous sites ( Fig 5A ) . Mapping to a modified reference genome shows a slightly better improvement than using modified reads , while combining both filters yields the best results in most cases . A small number of samples shows a 50/50-ratio after filtering but most are still significantly below that ratio while three samples even show a slight alternative bias after mapping to the modified reference genome . The limited success of filtering is not surprising as the filtering is only applied to reads that have previously mapped to a single reference genome so the data before filtering does not represent a 50/50-ratio , and removing some reference allele reads cannot completely account for the non-reference reads lost earlier . This is most evident in the samples for which data was not available as raw data including unmapped reads ( Table 1 ) illustrating the importance of sharing all data . Some of these data sets only included mapped reads after running bwa [47] with lower maximum edit distance parameters ( -n 0 . 04 ) than our pipeline which does not leave much room for improvement after filtering . Another possible reason for deviation from a 50/50-ratio at heterozygous sites could be low levels of modern contamination which may lead to a slight over-representation of the reference allele before mapping [33 , 42 , 48] . Comparing the outcome of the filters to different fragment length categories shows a similar pattern: the bias is decreased but some length categories still display differences in their relative heterozygosity ( Fig 5B ) . We also checked the effect of the filtering on allele sharing with different continental groups by calculating D ( chimp , X; sf12_short , sf12_dualfilter ) which compares the short fragments of sf12 ( i . e . high reference bias ) with the version after applying both filters ( S4 Fig ) . This is an extreme example to illustrate the effect . The stronger reference bias of the short fragments and the improvement through filtering is indicated by D ( chimp , reference_genome; sf12_short , sf12_dualfilter ) < 0 ( Z = −4 . 3 ) . In this particular case , D statistics tend to be shifted towards the short fragments of sf12 for Americans , Central and East Asians , and Oceanian populations while the tests of Western Eurasian and South Asian populations tend more towards the filtered version of sf12 . For the filtered version of sf12 , a subsequent analysis of continental ancestry proportions ( e . g . using clustering methods [49] or methods based on f statistics [50 , 51] ) could have estimated lower proportions of American , Central and East Asian ancestry , and higher proportions of Western Eurasian and South Asian ancestry . We also compared the filtered version of sf12 to the two treatments with less reference bias , pseudo-haploid calls from long fragments ( S5 Fig ) and diploid genotype calls ( S6 Fig ) . Consistent with the results shown in Fig 5 , there is still some residual reference bias in the filtered data for both comparisons ( D ( chimp , reference_genome; sf12_long , sf12_dualfilter ) > 0 , Z = 1 . 9; D ( chimp , reference_genome; sf12_diploid , sf12_dualfilter ) > 0 , Z = 3 . 6 ) but the effect is weaker than in the comparison above ( S4 Fig ) . Furthermore , while the D statistics still show skewed results ( S5 and S6 Figs ) , the trends are similar for all continental groups suggesting a reduced impact on downstream analyses .
Our analysis highlights that reference bias is pervasive in ancient DNA data used to study prehistoric populations . While the strength of the effect differs between applications and data set , it is clear that reference bias has the potential to create spurious results in population genomic analyses . Furthermore , even when the overall presence of bias is limited , it is important to assess whether subsets of variants are prone to strong systematic bias , including the possible presence of alternative bias . We are entering a time where sample sizes in ancient DNA studies reach one hundred and beyond , while the questions focus on more and more detailed patterns and subtle differences . At the same time , sampling starts to involve older remains and remains from more challenging environments—both of which are usually associated with poor preservation and shorter fragments . Therefore it seems crucial to avoid reference bias or other biases such as batch effects or ascertainment biases as much as possible , and to develop and apply computational strategies to mitigate the impact of these issues .
We selected medium to high coverage data from 22 different individuals representing data generated by different research groups with different wet lab strategies , covering different geographic regions and time periods ( Table 1 ) . For anatomically modern human samples , we tried to use data as raw as possible but some publications only provided the data after mapping and filtering . The general pipeline for these samples was identical to previous studies [25 , 72] . Reads were mapped to the 1000 genomes version of the human reference genome hg19 using bwa [47] with non-default parameters -l 16500 -n 0 . 01 -o 2 . Subsequently , PCR duplicates and fragments shorter than 35 bp were filtered [73] . We restricted our analysis to a set of known biallelic transversion variants to avoid an effect of post-mortem damage . We selected 107 , 404 transversions from the Human Origins genotyping array [23 , 50] as well as 1 , 022 , 984 transversions which were at at least 5% allele frequency in the public data of the Simons Genome Diversity Project ( SGDP ) [22] and were located in parts of the genome which are uniquely mappable with 35bp reads [33 , 35] . To detect reference bias , we are looking at supposedly heterozygous sites where one would expect reads to map in a 50/50-ratio on average if no bias existed . We define a heterozygous site as a SNP for which we observe at least ten reads with between 25 to 75% of those representing the alternative allele . These reads are assessed using samtools mpileup version 1 . 5 [74] employing the -B option to turn off base quality rescaling . For the high coverage genome of sf12 [25] as well as the high coverage archaic genomes [33–35] we also generated diploid genotype calls similar to the pipeline described in [25] . Briefly , base qualities of the first five base pairs of each read as well as the last five base pairs were set to 2 to avoid residual deamination . Picard version 1 . 118 [75] was used to add read groups to the files followed by indel realignment with GATK 3 . 5 . 0 [76] based on reference indels identified in phase 1 of the 1000 genomes project [77] . Finally , diploid genotypes were called with GATK’s UnifiedGenotyper employing the parameters -stand_call_conf 50 . 0 , -stand_emit_conf 50 . 0 , -mbq 30 , -contamination 0 . 02 and –output_mode EMIT_ALL_SITES using dbSNP version 142 as known SNPs . Genotype calls not flagged as low quality calls at investigated SNP sites were extracted from the VCF files using vcftools [78] . When creating the virtual read sets with known perfect heterozygosity in all SNPs , we started out from all reads mapping to SNPs in our marker set , where the read had an original mapping quality of 30 , and a base quality of 30 at the SNP base pair . No filter was placed on coverage as this process was fully executed per-read . This joint read set of original and modified reads thus had perfectly balanced allele ratios for all SNPs . The full set was remapped with our pipeline , and SNPs were grouped based on the observed alternative allele fraction among all reads that again mapped to their respective SNPs with mapping quality of at least 30 . In order to investigate the population genetic effect of reference bias , we calculated D and f statistics [50] . These statistics are based on pairwise allele sharing , so they should be sensitive to spurious allele sharing due to reference bias . D statistics were calculated with popstats [79] , f4 ratios were calculated ADMIXTOOLS [50] , and standard errors were calculated employing a weighted block jackknife with a block size of 5 Mbp . We used the chimpanzee reference genome as an outgroup .
|
Mapping next-generation sequencing reads to a single linear reference genomes comes with the inherent problem that alleles not found in the reference sequence will achieve lower mapping scores . This reference bias can cause heterozygous sites to be falsely called as homozygous which will have an effect on downstream analysis of the data . We investigate this issue in published ancient DNA data from human populations and find that reference bias is a pervasive phenomenon across data sets . The effect is exacerbated in paleogenomic data due to the short fragments of authentic ancient DNA and the common practice of using pseudo-haploid data . We show that—depending on the circumstances—reference bias can lead to slightly skewed results in population genetic analyses such as estimates of heterozygosity , studies of population affinities or estimates of archaic ancestry . Finally , we propose filtering strategies to alleviate the impact of reference bias . We make the scripts used for filtering publicly available .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"heterozygosity",
"population",
"genetics",
"ancient",
"dna",
"alleles",
"genome",
"analysis",
"paleontology",
"dna",
"paleogenetics",
"molecular",
"genetics",
"population",
"biology",
"genomic",
"libraries",
"molecular",
"biology",
"genetic",
"loci",
"biochemistry",
"nucleic",
"acids",
"heredity",
"earth",
"sciences",
"genetics",
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"genomics",
"evolutionary",
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"genomics",
"statistics",
"computational",
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] |
2019
|
The presence and impact of reference bias on population genomic studies of prehistoric human populations
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Virus infections induce CD8+ T cell responses comprised of a large population of terminal effector cells and a smaller subset of long-lived memory cells . The transcription factors regulating the relative expansion versus the long-term survival potential of anti-viral CD8+ T cells are not completely understood . We identified ZBTB32 as a transcription factor that is transiently expressed in effector CD8+ T cells . After acute virus infection , CD8+ T cells deficient in ZBTB32 showed enhanced virus-specific CD8+ T cell responses , and generated increased numbers of virus-specific memory cells; in contrast , persistent expression of ZBTB32 suppressed memory cell formation . The dysregulation of CD8+ T cell responses in the absence of ZBTB32 was catastrophic , as Zbtb32-/- mice succumbed to a systemic viral infection and showed evidence of severe lung pathology . We found that ZBTB32 and Blimp-1 were co-expressed following CD8+ T cell activation , bound to each other , and cooperatively regulated Blimp-1 target genes Eomes and Cd27 . These findings demonstrate that ZBTB32 is a key transcription factor in CD8+ effector T cells that is required for the balanced regulation of effector versus memory responses to infection .
The anti-viral CD8+ T cell response has been the topic of intense investigation over recent years , beginning with early ground-breaking studies demonstrating that , at early times post-infection , effector cells destined to die could be distinguished from those destined to populate the long-lived memory pool [1] . Molecular analysis of these subsets has revealed complex networks of transcription factors regulating the numbers , the phenotypes , and the survival potential of antiviral CD8+ T cells in models of both acute and chronic infections [2–4] . Contributing to this complexity , lineage-tracing experiments showed that the clonal responses of individual CD8+ T cells activated in vivo exhibited dramatic heterogeneity , and further , that this heterogeneity was already apparent at early times post-infection [5 , 6] . These studies also showed an inverse correlation between T cell family size at the peak of the response and the expression of memory T cell markers . Furthermore , mathematical modeling of these data indicated a linear pattern of differentiation with memory precursor cells arising first , undergoing limited proliferation , followed by a small number of these cells undergoing massive expansion to comprise the majority of the terminal effector population . Single-cell RNA-seq data have elaborated on these findings , identifying subpopulations of activated CD8+ T cells that show effector-like and memory-like gene expression profiles that can be seen as early as the first cell division [7] . While the source of the variability in clonal T cell responses is not currently known , one likely possibility is a variation in local concentrations of antigen and inflammatory cytokines , as these signals have been shown to regulate the magnitude of antiviral CD8+ T cell responses and the generation of memory cells [8–12] . Thus , transcription factors that are upregulated by a combination of TCR and inflammatory cytokine signals would be likely candidates to contribute to the regulation of clonal T cell responses . One such transcription factor is Blimp-1 ( encoded by Prdm1 ) , which has a critical role in promoting the terminal differentiation of CD8+ effector T cells [13 , 14] . We have recently shown that Blimp-1 acts as an epigenetic regulator and enhances the numbers of short-lived effector cells , while suppressing the development of memory-precursor CD8+ T cells [15] . Here we identify a second transcription factor , ZBTB32/ROG , as being rapidly upregulated in anti-viral CD8+ T cells in a TCR- and inflammatory cytokine-dependent manner . ZBTB32 belongs to the POK ( Poxviruses and Zinc-finger ( POZ ) and Krüppel ) family of proteins , most of which are transcriptional repressors , such as PLZF ( Promyelocytic leukemia Zn finger protein ) and BCL6 ( B cell lymphoma-6 ) [16] . ZBTB32 is expressed in T and B cells upon activation [17–22] , and has been shown to inhibit IL-4 gene activation by recruiting histone deacetylase ( HDAC ) 1 and 2 [19] . Zbtb32-deficient CD4+ T cells showed enhanced proliferation and cytokine production following in vitro stimulation [18 , 20 , 21] . Consistent with this , overexpression of ZBTB32 in BDC2 . 5 CD4+ T cells suppressed T cell proliferation and cytokine production [23] . Zbtb32-deficient CD8+ T cells were found to have enhanced responses to MCMV infection , whereas the opposite effect was observed in NK cells responding to the infection [24] . In addition , studies of plasma cell differentiation suggested that ZBTB32 and Blimp-1 form a complex to regulate the Ciita and H2 genes during this process [22] . Recently , ZBTB32 was shown to be a negative regulator of memory B cell recall responses [25] . Nonetheless , the function of ZBTB32 in regulating anti-viral CD8+ T cell responses in vivo is currently not known . Here we addressed the function of ZBTB32 in CD8+ T cell responses to both acute and chronic virus infections . We found that mice deficient in Zbtb32 generated an enhanced anti-viral CD8+ T cell response during acute virus infection and had increased memory CD8+ T cell populations; conversely the sustained expression of Zbtb32 in virus-specific CD8+ T cells dampened the anti-viral T cell response . Molecular analysis demonstrated that Zbtb32 induction following TCR plus cytokine stimulation resulted from STAT1 , STAT4 or STAT5 binding to the regulatory region of the Zbtb32 locus , and that later in the response , Zbtb32 was repressed by Blimp-1 . Finally , we showed that ZBTB32 and Blimp-1 acted cooperatively to mediate repressive chromatin modifications at key target genes during the peak of the anti-viral CD8+ T cell response , thereby dictating the magnitude of the response and the numbers of memory T cells generated .
In CD8+ T cells , ZBTB32 was up-regulated upon stimulation with α-CD3/CD28 ( Fig 1A ) . We then examined the cytokines involved in the induction of Zbtb32 mRNA . Primary CD8+ T cells were pre-activated with α-CD3/CD28 , and then cultured in a panel of cytokines ( Fig 1B ) . Zbtb32 mRNA was up-regulated in response to IL-2 , IFNβ and IL-12 ( Fig 1B ) . Moreover , Chromatin immunoprecipitation ( ChIP ) assays at the Zbtb32 locus revealed that IL-2 , IFNβ and IL-12 could induce STAT5 , STAT1 and STAT4 binding , respectively , to the Zbtb32 proximal promoter ( AmpA ) and the 5’ UTR region ( AmpB ) , but not to a non-specific region of the locus ( AmpC ) ( Fig 1C and 1D and S1A Fig ) . Genome-wide STAT5 ChIP-seq analysis [26] showed that both dimeric and tetrameric forms of STAT5A and STAT5B bound to the regulatory region of Zbtb32 upon IL-2 stimulation and was associated with increased H3-Ac modification ( Fig 1D ) . Moreover , STAT5 binding was associated with active gene transcription and histone modifications , based on increased RNA polymerase II ( Pol II ) binding and high amounts of permissive H3-Ac , H3K4me3 , but low amounts of repressive H3K27me3 modifications , compared to a non-specific region of the locus ( AmpC ) ( Fig 1E and S1B Fig ) . STAT1 and 4 binding were also associated with active gene transcription and histone modifications at STAT binding regions ( Fig 1E and S1B Fig ) . These findings demonstrated that IL-2 , IFNβ and IL-12 signaling could each initiate STAT binding , leading to the establishment of an active chromatin state at the Zbtb32 locus in CD8+ T cells . To determine the function of ZBTB32 in CD8+ T cells , we first analyzed the kinetics of Zbtb32 mRNA expression in CD8+ T cells responding to acute LCMV-Armstrong infection , and found a sharp peak of maximal expression at day 6 post-infection ( Fig 2A ) . Next , we compared the CD8+ T cell responses of Zbtb32-/- [21] versus wild type ( WT ) mice following LCMV-Armstrong infection . Uninfected Zbtb32-/- mice showed normal development , with no apparent defects in the maturation or the proportions of lymphocytes in the thymus , spleen or lymph nodes ( S2A and S2B Fig ) , as previously reported [20 , 21] . After infection , we found that Zbtb32-/- mice had higher proportions and absolute numbers of LCMV-specific CD8+ T cells at days 8 and 45 post-infection ( Fig 2B ) , a result confirmed by ex vivo IFNγ production ( Fig 2C ) . Further , Zbtb32-/- mice generated increased proportions of cells capable of producing IFNγ , TNFα and IL-2 simultaneously ( Fig 2D ) , an indication of enhanced memory cell formation [27] . Viral titers in the spleens of infected Zbtb32-/- mice were similar to WT controls , indicating that the increased magnitude of the CD8+ T cell response was not due to impaired viral clearance ( S3A Fig ) . Additionally , we observed no differences in granzyme B expression between the two groups of mice ( S3B Fig ) . To test whether these findings were generalizable to other infection models , we examined T cell responses to Vaccinia virus ( VACV ) infection . As shown , splenocytes from Zbtb32-/- VACV-infected mice had a higher proportion and absolute number of IFNγ+ virus-specific CD8+ T cells as compared to WT controls ( Fig 2E and S3C Fig ) . Overall , these results indicate that ZBTB32 is induced early and functions to limit effector T cell responses during acute virus infections . Previously-reported data examined the responses of Zbtb32-/- versus WT CD8+ T cells in mixed bone marrow chimeras infected with MCMV . In this study , MCMV-specific Zbtb32-/- CD8+ T cells showed enhanced expansion compared to controls at D7 post-infection , indicating a CD8+ T cell-intrinsic role for ZBTB32 [24] . To address this issue more directly , Zbtb32-/- and WT P14 splenocytes were transferred into WT recipients , followed by infection with LCMV-Armstrong . At days 9 and 15 , we observed a higher proportion and absolute number of Zbtb32-/- compared to WT P14 cells ( Fig 3A ) , as well as a greater proportion of Zbtb32-/- P14 cells expressing the memory markers CD27 and CXCR3 [28 , 29] at day 9 post-infection ( Fig 3B , top ) . By day 15 post-infection , Zbtb32-/- P14 cells were enriched in memory precursor effector cells ( MPEC; IL-7Rhi KLRG-1lo ) and a greater proportion expressed CD27 , CXCR3 and CD62L compared to WT controls ( Fig 3B , bottom ) . Zbtb32-/- P14 cells were also enriched for triple IFNγ , TNFα , and IL-2 cytokine-producing populations ( Fig 3C ) , and had increased expression of the transcription factor EOMES that promotes persistence of memory CD8+ T cells [30] ( Fig 3D ) . These data indicated that ZBTB32 expression in CD8+ T cells limited effector T cell expansion and the generation of memory precursor cells . Overall these findings suggested that persistent ZBTB32 expression would suppress T cell responses and memory generation . To test this , activated P14 cells transduced with retroviruses ( RV ) expressing ZBTB32 or a mock RV control were mixed 1:1 , and co-transferred into recipient mice that were then infected with LCMV-Armstrong ( Fig 3E ) . Expression of GFP after in vitro culture indicated that there were similar transduction efficiencies for each RV ( Fig 3E ) . At days 14 and 45 post-infection , P14 cells transduced with the ZBTB32-RV were reduced in proportion compared to those transduced with the mock-RV control ( Fig 3F ) . Furthermore , ZBTB32-RV transduced GFP+ cells at days 14 and 45 had a reduced proportion and MFI ( mean fluorescent intensity ) of IL-7R expression compared to control cells ( Fig 3G ) . These gain-of-function studies confirmed that ZBTB32 normally functions to limit T cell responses and the generation of memory CD8+ T cells . To address whether differences between WT and Zbtb32-/- T cells were maintained into the memory phase , transferred Zbtb32-/- and WT P14 cells were analyzed at day 30 post-LCMV-Armstrong infection . We found approximately three-fold more Zbtb32-/- P14 than WT cells ( Fig 4A ) . Furthermore , Zbtb32-/- P14 cells were enriched in MPEC and had greater proportions of cells expressing CD27 , CXCR3 and CD62L ( Fig 4B ) , correlating with enhanced cytokine production ( Fig 4C and 4D ) . To confirm that memory Zbtb32-/- P14 cells were in fact bona fide memory T cell populations , we tested their recall response to a secondary LCMV challenge . Zbtb32-/- or WT P14 cells were sorted at day 30 post-primary LCMV-Armstrong infection , and equal numbers were adoptively transferred into naïve hosts , which were then challenged with LCMV-Armstrong ( Fig 4E ) . At day 5 post-challenge , a higher proportion and absolute number of Zbtb32-/- P14 cells were found compared to WT controls ( Fig 4F ) , indicating that , on a per-cell basis , Zbtb32-/- CD8+ memory T cells were able to expand more robustly to secondary challenge compared to controls . Since Zbtb32-/- mice generated an enhanced CD8+ T cell response to acute LCMV-Armstrong infection , we addressed whether ZBTB32 plays a role in regulating T cell exhaustion during chronic infection with LCMV-Clone 13 ( clone 13 ) [31 , 32] . When Zbtb32-/- mice were infected with high-dose clone 13 ( 2x106 pfu/mouse ) , approximately 70% of the mice succumbed by two weeks post-infection ( Fig 5A , top ) , and showed more severe weight loss than controls ( Fig 5A , bottom ) . A previous study showed that mortality during high dose LCMV-clone 13 infection is often associated with increased lung immunopathology [32] . Lung sections from naïve and day 10 clone 13-infected Zbtb32-/- and WT mice were examined and scored on an arbitrary scale from 1–5 ( 1 = healthy , 5 = severe disease ) . Lungs of infected Zbtb32-/- mice at day 10 had increased pathology as compared to controls ( Fig 5B ) , along with lung histology scores indicative of enhanced disease ( Fig 5C ) . Both spleens and lungs of clone 13-infected Zbtb32-/- mice had approximately two-fold more virus-specific CD8+ T cells at day 10 post-infection compared to controls ( Fig 5D ) . In addition , viral clearance in the spleens of Zbtb32-/- mice was enhanced compared to WT controls , although no significant differences were observed in the liver at this time point ( Fig 5E ) . CD4+ T cell-depletion of Zbtb32-/- mice and WT controls prior to infection with clone 13 did not alter the survival of Zbtb32-/- mice , although it did lead to a less severe loss in body weight of both WT and Zbtb32-/- clone 13-infected mice ( S4A and S4B Fig ) . To address whether ZBTB32 had a CD8+ T cell-intrinsic role in regulating exhaustion along with limiting T cell expansion , we performed an adoptive transfer experiment . WT and Zbtb32-/- P14 cells were mixed 1:1 , transferred into recipients , and examined at day 14 post-infection with clone 13 . Not only did Zbtb32-/- P14 cells dominate the response relative to WT P14 cells ( Fig 5F ) , but the Zbtb32-/- P14 cells had reduced expression of characteristic exhaustion markers , PD-1 , CD160 , LAG-3 and 2B4 ( Fig 5G ) . Thus , these results demonstrated that the suppression of antiviral CD8+ T cell responses mediated by ZBTB32 in WT cells is critical in controlling excessive effector T cell responses and promoting T cell exhaustion to prevent immunopathology . To understand the mechanism by which ZBTB32 regulates immune response during LCMV-Armstrong infection , we chose to assess candidates genes previously shown to regulate effector-memory CD8+ T cell differentiation and survival [2–4 , 33] , and compared mRNA levels in WT and Zbtb32-/- P14 cells at several timepoints post-infection ( Fig 6A and S5A Fig ) . Among these genes , mRNA and protein expression of Eomes and CD27 , two genes known to promote the persistence and survival of memory CD8+ T cells [15 , 28–30] , were significantly enhanced in the absence of ZBTB32 ( Figs 6A , 3B and 3D ) . Therefore , we focused on understanding the mechanism by which ZBTB32 regulated expression of these two genes . ChIP assays verified the direct regulation of Eomes and CD27 by ZBTB32 ( Amp1 ) ( Fig 6B and 6C ) . As expected , there was no binding of ZBTB32 to any of these sites in Zbtb32-/- CD8+ T cells , nor was ZBTB32 binding detected at non-specific regions ( Amp2 ) of each gene ( Fig 6C ) . Since ZBTB32 is known to recruit histone modifying enzymes to the Il4 gene [19] , we determined whether HDAC1 and HDAC2 were present at ZBTB32-binding regions of Eomes and Cd27 in WT CD8+ T cells , and found both factors present; furthermore , this binding was greatly reduced in Zbtb32-/- CD8+ T cells ( Fig 6D ) . The ZBTB32-dependent binding of HDAC1 and HDAC2 was not detected at control loci ( Amp2 ) of either gene ( S6A Fig ) . We next determined whether ZBTB32 regulated histone modifications and transcription of these genes . Along with high amounts of Pol II and p300 binding at Amp1 of Eomes and Cd27 , CD8+ T cells isolated from Zbtb32-/- mice at day 6 post-LCMV-Armstrong infection had increased amounts of H3Ac , H3K4me3 and H3K36me3 , modifications that correlate with a permissive chromatin state [34] , and reduced amounts of repressive H3K9me2 , H3K9me3 and H3K27me3 modifications [34] , compared to CD8+ T cells from WT mice ( Fig 6E ) . In contrast , no ZBTB32-dependent differences in factor binding or repressive histone modifications were observed at non-specific regions ( Amp2 ) of Eomes and Cd27 , nor at the transcription start site ( Amp ) of Cd8a as a control ( S6B–S6D Fig ) . Together , these data demonstrated that ZBTB32 induced a repressive chromatin state at regulatory regions of target genes in CD8+ T cells upon LCMV infection , thereby suppressing the transcription of genes important for the formation of long-lived memory T cells . ZBTB32 has been shown to bind to Blimp-1 in Raji B cell lines [22] . To address whether ZBTB32 interacted with Blimp-1 in activated CD8+ T cells , a single cell-based proximity ligation assay was performed ( Fig 7A ) . Following TCR plus cytokine stimulation leading to high-level Blimp-1 [15] and ZBTB32 expression ( Fig 1A ) , ZBTB32 and Blimp-1 were found to interact in CD8+ T cells ( Fig 7A ) . As a positive control , we verified the interaction of Blimp-1 and HDAC2 , as described previously ( Fig 7A ) [15] . The interaction of ZBTB32 and Blimp-1 was also confirmed by co-immunoprecipitation from activated primary T cells ( Fig 7B ) . Furthermore , ZBTB32 and Blimp-1 bound together on target genes Eomes and Cd27 , as revealed by sequential ChIP experiments ( ChIP-reChIP ) performed on chromatin from virus-specific T cells ( Fig 7C ) . These data indicated that ZBTB32 is in close proximity to Blimp-1 on both the Eomes and Cd27 genes , providing evidence that ZBTB32 and Blimp-1 may co-operatively regulate target genes in primary T cells activated in vivo . Cd27 [13 , 15] and Eomes [15] are Blimp-1 targets genes in CD8+ T cells . To determine if Blimp-1 and ZBTB32 bind cooperatively to shared targets , we examined the binding of each factor in virus-specific CD8+ T cells in the presence versus the absence of the other factor . The absence of Prdm1 attenuated ZBTB32 binding to the regulatory regions of Eomes and Cd27 at day 7 post-LCMV-Armstrong infection , and in reciprocal fashion , the Zbtb32-deficiency attenuated Blimp-1 binding ( Amp1 in Eomes or Cd27 ) ( Fig 7D ) . These data directly demonstrated that ZBTB32 and Blimp-1 co-operate in their binding to the regulatory regions of these two genes . We have previously reported that Blimp-1 regulates the Il2ra gene by direct binding on the regulatory region of its gene locus [15] , as shown ( Fig 7E ) ; however , unlike Eomes and Cd27 , Blimp-1 binding to this target gene was independent of ZBTB32 . We conclude that a subset of Blimp-1-regulated genes requires the cooperative activity of ZBTB32 , whereas other Blimp-1 target genes are ZBTB32-independent . Blimp-1 is a transcriptional repressor known to regulate the differentiation of effector T cells [13–15 , 35 , 36] . Unlike the transient expression of ZBTB32 in CD8+ T cells responding to acute virus infection , Blimp-1 expression is maintained in virus-specific cells well into the memory time points [13 , 37] . Yet by day 14 post-LCMV infection , when Blimp-1 levels were still readily detectable ( Fig 8A and 8B ) , CD8+ T cells have begun to express surface receptors characteristic of memory cells . This prompted us to investigate whether Blimp-1 regulated ZBTB32 expression , or vice-versa . We found that the transcript levels for Zbtb32 were substantially elevated in Prdm1-/- CD44hi CD8+ T cells isolated at days 8 and 14 post-LCMV-Armstrong infection compared to WT controls ( Fig 8C ) . In contrast , Prdm1 transcript levels were slightly decreased at day 6 in the absence of ZBTB32 , whereas no differences were observed at days 8 and 10 post-infection in the presence versus the absence ZBTB32 ( S5A Fig ) . Consistent with the marked increase in Zbtb32 mRNA levels in the absence of Blimp-1 , ChIP experiments revealed that Blimp-1 bound to the regulatory region of the Zbtb32 gene ( Fig 8D and S5B Fig ) , indicating that Blimp-1 represses Zbtb32 expression during the late stage of the anti-viral immune response . The striking similarity between our data on anti-viral CD8+ T cell responses in Zbtb32-/- mice compared to that seen following infection of Prdm1-/- mice [13–15] prompted us to examine if the double-deficiency of ZBTB32 and Blimp-1 enhances the generation of virus-specific CD8+ T cells , compared to a single-deficiency of each factor . While each single knockout line had increased numbers of LCMV-specific CD8+ T cells , the double-deficient mice had a further increase , consistent with elevated numbers of virus-specific cells expressing the memory markers CD27 and CXCR3 ( Fig 8E–8H ) . Interestingly , in spite of the aberrantly high expression of ZBTB32 in Prdm1-/- T cells , ZBTB32 in the absence of Blimp-1 does not prevent the overexpansion of CD8+ effector and memory T cells . This may be due to the requirement for both factors to repress Eomes and Cd27 expression . Overall , these results indicate that ZBTB32 functions together with Blimp-1 to limit both effector T cell responses and memory development during acute virus infections .
Our data show that ZBTB32 plays a unique non-redundant role in limiting T cell responses and memory generation during acute virus infection , and that this regulation is essential to prevent lethality in a model of persistent virus infection . ZBTB32 is induced by TCR plus inflammatory cytokine signaling . Further , in the absence of ZBTB32 , Blimp-1 fails to bind to the proximal regulatory regions of Eomes and Cd27 , and that these genes fail to undergo repressive chromatin modifications , leading to premature upregulation of memory cell genes that promote long-term cell survival ( see S7 Fig ) . These findings highlight the importance of preventing the accumulation of excessive numbers of effector T cells at early timepoints post-infection , as a means of preventing T cell-mediated immunopathology . While Blimp-1 is well known to regulate the terminal differentiation of several cell types , ZBTB32 has been less well-studied in immune cells . Our data indicate that Blimp-1 and ZBTB32 acting together function in CD8 regulatory networks recently described [2–4] . In the absence of Blimp-1 , virus-specific CD8+ T cells are increased in number , but have reduced expression of cytolytic molecules , such as perforin and granzyme B [13 , 14 , 36] . In contrast , Zbtb32-deficient T cells have no defect in cytolytic molecules , but like the Blimp-1-deficient CD8+ T cells , have increased potential to generate long-lived memory cells . These data indicate that Blimp-1 regulates a distinct set of genes , in addition to the target genes shared with ZBTB32 . Our previous studies identified Il2ra , Cd27 and Eomes as representative genes regulated by Blimp-1 [15] . We now show that Cd27 and Eomes are co-regulated by ZBTB32 , whereas Il2ra is a target of Blimp-1 alone . The findings from recent studies help provide insight into the functions of ZBTB32 and Blimp-1 [5–7 , 38 , 39] . Of particular interest , the studies of Lin et al indicate a sudden change in CD8+ T cell lineage determination after the 3rd-4th cell division post-stimulation , a timepoint that coincides with ~day 3 post-infection [38] . At this time , activated CD8+ T cells that initially resemble memory precursor cells ( TCF1hi ) begin to generate daughter cells with divergent lineage potentials , seen as TCF1hi versus TCF1lo . The findings indicate that TCF1hi cells continue to divide and generate both subsets; in contrast , TCF1lo cells , which acquire features of terminal effector cells , can only generate TCF1lo daughter cells that are unable to contribute to the long-lived memory pool . Tying these data to our studies of Blimp-1 and ZBTB32 , we have found that Blimp-1 is a direct transcriptional repressor of Tcf7 ( the gene encoding TCF1 ) in CD4+ T cells , a mechanism that contributes to the divergent differentiation of activated CD4+ T cells into TH1 versus TFH lineages [40] . We also show here that Tcf7 is a Blimp-1 target in CD8+ T cells isolated from LCMV-Armstrong-infected mice ( S8A and S8B Fig ) [15] . Together , these data suggest that Blimp-1 and ZBTB32 function to establish the balance of these two groups of CD8+ effector T cells . Consistent with this model , Blimp-1 and ZBTB32 expression peak in parallel with the robust proliferative expansion of the short-lived effector response . Given the strong influence of inflammatory cytokines on the up-regulation of both Prdm1 and Zbtb32 mRNA levels , together with the known role of these cytokines in promoting a more robust effector response [8–12] , it is likely that the most robustly-proliferating cells are expressing high levels of Blimp-1 and ZBTB32 . This scenario is consistent with our findings that Zbtb32-/- anti-viral CD8+ T cells are both increased in numbers and exhibit a pronounced increase in memory cell markers at the peak of the response . Also consistent are previous studies showing increased proliferation of Zbtb32-/- T cells following stimulation in vitro [18 , 20] , a feature likely attributed to enhanced cell survival . This model proposes that the cells with the highest Blimp-1 and ZBTB32 expression would likely die , due to the repression of genes required for long-term survival , and the remaining less differentiated effector cells would further down-regulate ZBTB32 expression due to Blimp-1-mediated repression . This final phase would allow the remaining cells to re-express the memory cell markers needed for long-term survival , thereby generating the normal small proportion of memory cells . Our findings of increased numbers of long-lived memory cells in Zbtb32-/- mice indicate the obvious dysregulation of this response in the absence of ZBTB32-mediated repression . One surprising finding in our study was that Zbtb32-/- mice show a high mortality rate following infection with high-dose LCMV-clone 13 . In WT mice , fatality from clone 13 infection is avoided due to a process of clonal exhaustion , in which the majority of virus-specific effector CD8+ T cells undergo cell death , and many of the remaining cells become functionally nonresponsive [31] . In Zbtb32-/- mice , virus-specific CD8+ T cells are more numerous and less exhausted as indicated with reduced expression of exhaustion markers , correlating with a reduction in viral titers in the spleen at day 10 post-high-dose LCMV-clone 13 infection . Our current data do not indicate whether the unrestrained expansion of Zbtb32-/- CD8+ T cells results from enhanced responses to persistent antigen or to inflammatory cytokines . While adoptive transfer experiments , as well as data from mixed bone marrow chimeras [24] , confirm that the altered CD8+ T cell response is intrinsic to the loss of ZBTB32 in CD8+ T cells , other transcription factors may function in a regulatory network together with ZBTB32 to control exhaustion during chronic infection [41–46] . Thus , de-repression of ZBTB32-regulated genes might account for the failure of Zbtb32-/- T cells to undergo exhaustion , leading to the fatal immunopathology observed in response to LCMV-clone 13 . These findings , that Zbtb32-/- CD8+ T cells are refractory to clonal exhaustion in the presence of persistent antigen stimulation , raises the interesting possibility that manipulation of ZBTB32 activity may be useful in the context of cancer immunotherapy . Our data identify ZBTB32 as a necessary co-factor for Blimp-1-mediated regulation of key memory cell genes , Eomes and Cd27 in CD8+ T cells . Furthermore , our LCMV infection studies suggest that this cooperation is required to program anti-viral CD8+ T cells for the terminal effector fate , leading ultimately to cell death , a program that appears essential for efficient clonal exhaustion . Unlike Blimp-1 , ZBTB32 is only transiently expressed over the course of the anti-viral response . These findings leave open the possibility that Blimp-1 in the absence of ZBTB32 may function together with other factors , such as HOBIT protein ( homolog of Blimp-1 in T cells ) expressed in tissue-resident lymphocytes [47–49] . In future , continued biochemical , genetic , and molecular analysis of anti-viral CD8+ T cells at each phase of the response will be invaluable in resolving the multiple cell subsets contributing to the overall population of protective anti-viral T cells .
C57BL/6J male mice were purchased from the Jackson Laboratory ( Bar Harbor , ME ) . P14 transgenic mice were bred onto CD90 . 1 and CD45 . 1 C57BL/6 backgrounds to distinguish the transgenic cells from wild-type ( WT ) cells after adoptive transfer into C57BL/6 ( CD90 . 2+ CD45 . 2+ ) mice . Zbtb32-/- mice and Prdm1-/- mice were kindly provided by I-Cheng Ho ( Harvard Medical School , Brigham and Women's Hospital ) and Alexander Tarakhovsky ( Rockefeller University ) , respectively [15 , 21 , 50] and Zbtb32-/- mice were crossed to P14 TCR transgenic CD90 . 1 or CD45 . 1 mice for adoptive transfer studies . This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the U . S . National Institutes of Health . All animal experiments were approved by the Institutional Animal Care and Use Committee ( IACUC ) of the University of Massachusetts Medical School ( UMMS ) ( Animal Welfare Assurance #A-1068-15 ) . Mice were bred and housed in specific pathogen free conditions at the UMMS in accordance with the guidelines of the IACUC of UMMS and all efforts were made to minimize suffering of mice . LCMV , strain Armstrong , was propagated in baby hamster kidney ( BHK ) -21 cells obtained from the American Tissue Culture Collection ( ATCC ) as previously described [51] . Mice were inoculated intraperitoneally ( i . p . ) with 0 . 1 ml containing 5 x 104 PFU ( plaque forming units ) of LCMV in PBS . The Clone 13 variant of LCMV was propagated in BHK-21 cells [52] and titrated by plaque assay on african green monkey kidney ( Vero ) cells ( ATCC ) . Mice were infected intravenously ( i . v . ) with 2 × 106 ( high dose ) PFU of LCMV , strain Clone 13 . In some experiments , mice were inoculated i . p . with 1 × 106 PFU of Vaccinia virus ( VACV ) , strain Western Reserve . Anti-mouse CD8α ( 53–6 . 7 ) , CD4 ( RM4-5 ) , CD45 . 2 ( 104 ) , CD27 ( LG . 310 ) , CD122 ( TM . BETA-1 ) , CD25 ( PC61 ) , CD62L ( MEL-14 ) , TCRβ ( H57-597 ) and NK1 . 1 ( PK136 ) antibodies were purchased from BD Pharmingen . Anti-mouse CD44 ( IM7 ) , CD45 . 1 ( A20 ) , CD90 . 1 ( HIS51 ) , CD90 . 2 ( 53–2 . 1 ) , CXCR3 ( CXCR3-173 ) , KLRG-1 ( 2F1 ) , CD127 ( A7R34 ) , PD-1 ( J43 ) , CD160 ( ebioCNX46-3 ) , LAG-3 ( ebioC9B7W ) , 2B4 ( ebio244F4 ) , EOMES ( Dan11mag ) , Granzyme B ( NGZB ) and TCRδ ( ebioGL3 ) antibodies were purchased from eBiosciences . To stain samples for intracellular antigens , FOXP3/Transcription factor staining buffer kit ( eBiosciences ) was used . For intracellular cytokine assays , samples were incubated for 5 hours ex-vivo with 1μM LCMV-specific NP396-404 or GP33-41 peptide or 1μM VACV-specific B8R or K3L peptide in the presence of Golgi Plug ( BD Biosciences ) . Cells were then permeabilized using the cytofix/cytoperm kit ( BD Biosciences ) followed by intracellular staining for IFNγ ( XMG1 . 2; eBiosciences ) , IL-2 ( JES65H4; BD Pharmigen ) and TNF ( MP6-XT22; Biolegend ) . Samples were analyzed on an LSRII flow cytometer ( BD Biosciences ) , and data were further analyzed using FlowJo ( Tree Star ) . For identification of virus-specific CD8+ T cells , cells from infected mice were incubated with PE- or APC-conjugated H-2Db-NP396–404 or -GP33-41 tetramers for 1 hour at 4°C followed by staining for surface antigens . Unless otherwise noted virus-specific CD8+ T cell responses were tracked by transferring 2x105 ( CD90 . 1+ or CD45 . 1+ ) splenocytes into congenic C57BL/6 hosts ( CD90 . 2+ CD45 . 2+ ) . Recall responses of Zbtb32-/- or WT P14 CD8+ T cells at day 30 post-LCMV infection were compared by transferring sorted 1x105 P14 CD8+ T cells ( WT or Zbtb32-/- ) into naïve hosts , which were subsequently infected with LCMV-Armstrong . Wherever noted , total CD8+ T cells were isolated with the CD8+ T Cell Isolation Kit II ( Miltenyi Biotec ) , and LCMV-specific P14+ CD8+ T cells were further sorted on a FACS Aria cell sorter ( BD Biosciences ) . WT and Zbtb32-/- mice were depleted of CD4+ T cells by i . p . injection of anti-CD4 ( clone GK1 . 5 ) or control IgG2b ( Iso ) at a dose of 1mg/mouse at day -1 and day 3 of LCMV-clone 13 infection . Spleens or livers from LCMV-infected WT , Prdm1-/- or Zbtb32-/- mice were homogenized and virus was tittered by plaque assay on Vero cells , as previously described [51] . Lung tissue sections from high dose LCMV-Clone 13-infected mice were fixed in 10% paraformaldehyde and stained with hematoxylin and eosin ( H&E ) . Total RNA was isolated , converted to cDNA , and analyzed by real-time quantitative PCR amplification on a Bio-Rad iCycler ( Bio-Rad ) , using the iQ SYBR Green Supermix ( Bio-Rad ) as previously described [15] . The primers used for RT-PCR were described in S9 Fig . The primers for Cd27 and Actb were purchased from Real Time Primers , LLC . CD8+ T cells were cyto-spun onto positively-charged microscope slides ( Fisher , 12-550-20 ) and washed with cold PBS twice , followed by fixation with 4% paraformaldehyde at 25°C for 10 min . Fixed cells were washed with PBS twice and permeabilized in 0 . 5% Triton X-100/PBS at 4°C for 6 min , followed by washing with 70% EtOH . After blocking samples in the Duolink II blocking solution for 30 min at 37°C , samples were incubated at 4°C overnight with αBlimp-1 and αHDAC2 , αBlimp-1 and αZBTB32 , or mouse IgG and rabbit IgG as a control , followed by the Duolink II proximity ligation assay according to the manufacturer’s instructions . Samples were counter-stained for nuclei ( blue; DAPI ) . The signals ( Red ) from each pair of PLA probes were detected using laser-scanning confocal microscopy ( Leica TCS SP5 II ) with a 63x phase contrast oil immersion objective ( numerical aperture = 1 . 3 ) . The nuclei images were captured using the UV laser . Duolink II Detection kit ( DUO92008 ) , Duolink II PLA probe Mouse Plus ( DUO92001 ) and Duolink II PLA probe Rabbit Minus ( DUO92005 ) were purchased from Olink Bioscience . For production of retroviruses , human embryonic kidney 293T cells obtained from the American Tissue Culture Collection ( ATCC ) were transfected with 2 μg of retroviral DNA ( Zbtb32 in pMigR1-GFP or pMigR1-GFP empty vector ) and 1μg of pCL-Eco packaging DNA ( Addgene ) , and retroviral supernatants were collected after two days . The Zbtb32 cDNA was kindly provided by I-Cheng Ho ( Harvard Medical School , Brigham and Women's Hospital ) and subcloned into pMigR1-GFP vector , and then verified by DNA sequencing . Congenically-marked WT P14 CD8+ T cells were stimulated in vitro with αCD3 and αCD28 ( eBiosciences ) for 24 hours , and then transduced by spin-infection ( 2000 rpm , 25°C , 1 hour ) with ZBTB32-expressing retrovirus ( Zbtb32 RV ) or mock retrovirus ( mock RV ) . The two populations were mixed 1:1 , and then 1x106 cells were transferred into recipients , which were infected with LCMV-Armstrong . A subset of transduced P14 cells was cultured in vitro for an additional 2 days , and the transduction efficiency assessed by GFP fluorescence . At days 14 and 45 post-transfer and -LCMV infection , P14 cells were analyzed for their frequencies and for IL-7R expression on each population . CD8+ T cells were fixed for 10 min at 25°C with 1% formaldehyde , and then quenched for 5 min at 25°C with 125mM Glycine ( Sigma-Aldrich ) . The cells were washed twice in ice-cold 0 . 5% BSA-PBS . ChIP analysis was performed on 2x105 cells using the ChIP Assay Kit ( Millipore ) following the manufacturer’s instructions . The antibodies used were αSTAT1 ( Santa Cruz , sc-346 ) , αSTAT4 ( Santa Cruz , sc-486 ) , αSTAT5 ( R&D systems , PA-ST5A ) , αSTAT6 ( Santa Cruz , sc-374021 ) , αBlimp-1 ( Santa Cruz , sc-66015 ) , αHDAC1 ( Abcam , ab7028-50 ) , αHDAC2 ( Invitrogen , 51–5100 ) , αPol II ( Santa Cruz , sc-9001 ) , αp300 ( Santa Cruz , sc-584 ) , αH3Ac ( Millipore , 06–599 ) , αH3K4me3 ( Millipore , 17–614 ) , αH3K36me3 ( Abcam , ab9050 ) , αH3K9me2 ( Abcam , ab-1220 ) , αH3K9me3 ( Millipore , 17–625 ) , or αH3K27me3 ( Abcam , ab-6002 ) . The αZBTB32 antibody was generated by NeoBiolab , by immunizing rabbits with the chemically synthesized peptide: cys-GLGSPGEKQKPEKDFRSN ( amino acids 141–160 ) as previously described [19] . ZBTB32-specific antibodies were affinity purified by binding to beads conjugated with a GST-fusion protein containing the N-terminal 1–165 acids of ZBTB32 and then purified antibodies were verified using ChIP assay ( S10 Fig ) . To identify potential ZBTB32 binding sites in the Eomes , Cd27 and Il2ra gene loci , we used the reported ZBTB32 binding motif [19 , 53 , 54] and performed a motif search using the Motif Alignment and Search Tool in the MEME Suite ( v4 . 12 . 0 . ) . Immunoprecipitated DNA ( 2 μl from a total of 50 μl ) was quantified by real-time quantitative PCR amplification on a Bio-Rad iCycler , using the iQ SYBR Green Supermix ( Bio-Rad ) . As a control , input DNA purified from chromatin before immunoprecipitation was used . For the second round of ChIP ( ChIP-reChIP ) , eluates from αBlimp-1 or mouse IgG immunoprecipitation were taken prior to reverse-crosslinking , and were diluted 10-fold in ChIP dilution buffer , and diluted eluates were incubated at 4°C on rotator overnight with αZBTB32 or rabbit IgG coupled with Dynabeads Sheep-αRabbit I magnetic beads ( Invitrogen , Dynabeads M-280 Sheep αRabbit IgG ) . Immune complexes with magnetic beads were collected on the magnet , washed 5 times with LiCl wash buffer ( Millipore ) and then washed two times with TE buffer . Immune complexes were eluted in 500μl elution buffer ( 1%SDS , 0 . 1M NaHCO3 ) , followed by reverse-crosslinking in a 65°C water bath overnight . DNA fragments were recovered by a Qiagen PCR Cleanup kit . Real-time quantitative PCR amplification was performed with 5 μl from a total of 50 μl of the immunoprecipitated DNA . PCR primers for RT-PCR and for ChIP Q-PCR are described in S9 Fig . Statistical differences between samples were analyzed with an unpaired Students t test ( *p≤0 . 05 , **p≤0 . 01 , ***p≤0 . 001 , ****p≤0 . 0001 ) . All error bars in the manuscript represent the Standard Error of the Mean ( SEM ) .
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CD8+ T lymphocytes are essential for immune protection against viruses . In response to an infection , these cells are activated , proliferate , and generate antiviral effector cells that eradicate the infection . Following this , the majority of these effector cells die , leaving a small subset of long-lived virus-specific memory T cells . Our study identifies a transcription factor , ZBTB32 , that is required for the regulation of CD8+ T cell responses . In its absence , antiviral CD8+ T cell numbers increase to abnormally high levels , and generate an overabundance of memory T cells . When this dysregulated response occurs following infection with a virus that cannot be rapidly eliminated by the immune system , the infected animals die from immune-mediated tissue damage , indicating the importance of this pathway .
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2017
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Transient expression of ZBTB32 in anti-viral CD8+ T cells limits the magnitude of the effector response and the generation of memory
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The genetic diversity of pathogens , and interactions between genotypes , can strongly influence pathogen phenotypes such as transmissibility and virulence . For vector-borne pathogens , both mammalian hosts and arthropod vectors may limit pathogen genotypic diversity ( number of unique genotypes circulating in an area ) by preventing infection or transmission of particular genotypes . Mammalian hosts often act as “ecological filters” for pathogen diversity , where novel variants are frequently eliminated because of stochastic events or fitness costs . However , whether vectors can serve a similar role in limiting pathogen diversity is less clear . Here we show using Francisella novicida and a natural tick vector of Francisella spp . ( Dermacentor andersoni ) , that the tick vector acted as a stronger ecological filter for pathogen diversity compared to the mammalian host . When both mice and ticks were exposed to mixtures of F . novicida genotypes , significantly fewer genotypes co-colonized ticks compared to mice . In both ticks and mice , increased genotypic diversity negatively affected the recovery of available genotypes . Competition among genotypes contributed to the reduction of diversity during infection of the tick midgut , as genotypes not recovered from tick midguts during mixed genotype infections were recovered from tick midguts during individual genotype infection . Mediated by stochastic and selective forces , pathogen genotype diversity was markedly reduced in the tick . We incorporated our experimental results into a model to demonstrate how vector population dynamics , especially vector-to-host ratio , strongly affected pathogen genotypic diversity in a population over time . Understanding pathogen genotypic population dynamics will aid in identification of the variables that most strongly affect pathogen transmission and disease ecology .
Genetic diversity within a single microbial species can lead to infection of hosts with mixtures of pathogen genotypes . Remarkably , studies across numerous systems have demonstrated that mixed-genotype infections are more common than infections with a single clonal variant [1]–[5] . The degree of genotypic diversity , defined here as the number of unique genotypes within a population , has been associated with pathogen transmission rates and virulence [6]–[9] . For example , greater numbers of circulating Plasmodium faliciparum genotypes were positively correlated with increased virulence or a greater probability of transmission [6] , [8] . Competition experiments among Dengue virus serotypes resulted in the more virulent serotype being selected at the expense of less virulent serotypes during both human and mosquito infection [7] . Additionally , during the early years of West Nile virus circulation in New York , transmission intensity was associated with increases in viral genetic diversity [9] . The capacity of hosts to sustain multiple pathogen genotypes , and the within-host interactions among co-infecting genotypes , can impact pathogen transmission , virulence , and immune evasion . However , for pathogens that cycle among multiple host species , especially vector-borne pathogens that cycle between disparate species ( mammals and arthropods ) , the impact of genotypic diversity and genotypic interactions on individual genotype transmission and infection success is largely unknown . Vector-borne pathogens , which cause diseases of importance for human and animal health , therefore provide a platform to study how genotypic diversity and interactions among genotypes affect colonization of the vector and resulting pathogen transmission . Genetic diversity is a hallmark of vector-borne pathogens . Numerous studies have described the circulation and infection of individual hosts or vectors with multiple genotypes of bacterial ( e . g . , Anaplasma sp . , Borrelia sp . ) , viral ( e . g . , West Nile virus , Dengue virus ) or protozoal ( e . g . , Trypanosoma sp . , Plasmodium sp . ) vector-borne pathogens [2] , [5] , [10]–[17] . Competition among vector-borne pathogen genotypes within the mammalian host is common , with competitive success frequently achieved by the more virulent genotype [1] , [4] , [18]–[22] . For example , in experiments with P . falciparum and B . burgdorferi , the more virulent genotype replicated to greater levels compared to the competitor , resulting in numerical dominance and preferential transmission . Whether similar genotypic diversity-limiting competition occurs within the arthropod vector is unknown . Further , most studies examine the interactions of only two genotypes at a time; therefore , whether the degree of pathogen genotypic diversity influences the number of genotypes able to infect individual hosts and particularly individual vectors is similarly unknown . Similar to other tick-borne bacterial pathogens , natural genetic variation within Francisella tularensis , including subspecies , is well described [23]–[28] . For example , using multiple loci variable-number tandem repeat analysis on only two loci , 10 unique F . tularensis genotypes were recovered from ticks; with the most genotypic diversity found in areas with the greatest prevalence of F . tularensis in ticks [23] . The large degree of circulating genotypic diversity observed in that study was indicative of long-standing enzootic transmission of multiple genotypes [23] . Additionally , unlike the majority of tick-borne bacterial pathogens which are refractory to genetic manipulation , F . tularensis subsp . novicida ( herein referred to as F . novicida ) can be genetically manipulated with relative ease , and thus can serve as a powerful model to address broader questions concerning tick-borne bacterial pathogens . Here , we used a set of differentiable Francisella novicida transposon mutants and Dermacentor andersoni ticks , which are a natural vector of Francisella sp . [29] , to investigate how genotypic diversity affects the success of individual genotypes in colonizing the tick vector as compared to the mammalian host . Specifically , we determined ( i ) if similar numbers of genotypes were able to co-infect mice and ticks , ( ii ) whether exposure of hosts and vectors to differing numbers of genotypes affected the proportion of genotypes able to be recovered from the host or vector , and ( iii ) if competition limits the ability of certain genotypes to colonize the vector . To address these questions , pools of F . novicida genotypes of varying diversity were inoculated into mice . The genotypes able to infect mice , be acquired by feeding D . andersoni nymphs , and persist in the tick midgut through the molt to the adult stage at population and individual host and vector levels were identified . As the tick midgut is the primary site of colonization for most tick-borne pathogens , it serves as a relevant location to examine the effects that varying genotypic diversity has on individual genotype transmission success between host and vector [30] , [31] . Finally , we designed a population model to demonstrate how variations in pathogen genotypic diversity , vector and host abundance , and vector-to-host ratios could influence the retention of genotypic diversity in a pathogen population over time .
We first determined whether the breadth of pathogen genotypic diversity is similarly sustained among mice and ticks at a population level . In all experiments ‘genotypic diversity’ refers to the number of different genotypes , the ‘vector’ refers to the tick and the ‘host’ refers to the mouse . The genotypes ‘available’ to colonize mice and ticks will refer to those genotypes that were inoculated into mice and those genotypes that were detected in terminal mouse blood during peak bacteremia , respectively . Our experiments were initiated by infection of mice , instead of ticks , because of the difficulty and more importantly the variability of artificially infecting ticks . To simulate diverse genotype populations we used differentiable F . novicida transposon-containing genotypes in two large pools ( Pool A = 93 genotypes , Pool B = 94 genotypes ) each comprised of a different set of F . novicida transposon-containing genotypes ( Table S1 ) . Genotypes were identified in mouse blood at peak bacteremia ( concurrent with completion of nymph feeding ) and in adult tick midguts . Ticks fed as nymphs on infected mice over the entire duration of mouse bacteremia and genotypes were identified from the midgut of ticks after the infected nymphs molted to adults . This time point was specifically chosen to avoid detection of genotypes present in the undigested blood meal and confirm that any detected genotype ( s ) were able to infect and be transstadially maintained in the tick midgut . One limitation of this approach is that we were unable to determine if genotypic diversity was lost prior to or during early infection of the midgut or during transstadial transmission . Our readout of genotype success is colonization of the adult tick midgut , a time point which reflects the cumulative loss of genotypic diversity at any prior point during tick infection . Of the genotypes present in the large-pools , 84% and 81% of Pool A and Pool B genotypes were recovered from their respective mouse cohorts ( Table 1 ) . As these large pools encompassed genotypes with variable fitness , it was expected that some genotypes would not be recovered . Of the genotypes that successfully colonized mice , 76% and 54% of genotypes from Pool A and Pool B , respectively , were also acquired by the feeding nymph cohort and transstadially maintained in tick midguts ( Table 1 ) . The percentage of genotypes recovered from large-pools was significantly lower for ticks compared to mice ( χ2 = 13 . 5 , P = 0 . 0002 ) . These results demonstrate that at a population level , despite simultaneous exposure to a large number of genotypes , not all available genotypes colonize mice and ticks . The inability of some in vitro generated genotypes to colonize mice was expected given the presence of the introduced transposon; however , the results also suggested additional loss of genotype diversity upon infection of the tick cohort . To determine whether reducing genotypic diversity affected the recovery of genotypes from ticks during mixed-genotype infections , genotypes from pools A and B that had successfully infected mice but were not recovered from ticks were divided into three smaller pools ( Pool C = 16 genotypes , Pool D = 17 genotypes , Pool E = 16 genotypes ) and the experiment was repeated ( Table S1 ) . As expected , all of the genotypes in the small pools ( Pools C–E ) were recovered from their respective mouse cohorts ( Table 1 ) . Interestingly , 81 , 88 , and 94% of genotypes the from small-genotype pools C , D , and E , respectively , were recovered from their respective tick cohorts despite not being recovered from ticks during the large-genotype pool experiments ( Table 1 ) . Similar to the large-pools , the percentage of genotypes recovered from ticks was significantly lower compared to mice ( χ2 = 6 . 39 , P = 0 . 012 ) . In summary , at a population level , a smaller proportion of available genotypes were recovered from ticks as compared to the mammalian host irrespective of the size of the genotype pool . Further , a greater proportion of available genotypes were recovered from ticks when genotypic diversity was reduced ( χ2 = 9 . 30 , P = 0 . 0023 ) . These results support that at a population level , F . novicida genotype diversity is not equally sustained by mammalian hosts and tick vectors , and suggests that the latter serve as greater ecological filters for F . novicida diversity . To determine if the observation that the greater reduction in genotypic diversity in the vector population compared to the mammalian host population was also reflected at the level of an individual , we identified the F . novicida genotype ( s ) that colonized individual mice and ticks . For example , if 59 genotypes were recovered from the population of ticks that fed upon mice inoculated with 93 genotypes in Pool A , we determined whether an individual tick was colonized by all or subsets of those 59 genotypes . In the large-genotype pool experiments , individual mice were colonized by a significantly greater percentage of the available genotypes ( 78 and 53% of the available genotypes in pools A and B , respectively , colonized individual mice ) compared with individual ticks ( 12 and 10% of the available genotypes in pools A and B , respectively , colonized individual ticks ) ( χ2 = 707 . 4 , P<0 . 001 ) ( Figure 1 ) . With regard to ticks in the large-genotype pool experiments , ticks were exposed to a mean of 62 genotypes while feeding on infected mice , and individual ticks were colonized with a mean of 8 . 5 genotypes ( range = 1 to 25 , median = 6 . 5 ) ( Figure S1 ) . These results indicate that the observed genotype diversity sustained by ticks at a population level was the cumulative product of individual ticks infected with subsets of the available genotypes . To determine if reducing genotypic diversity affected the overall number or proportion of genotypes recovered we identified the genotypes that colonized individual mice and ticks from the small-genotype pool experiments . Similar to the large-genotype pool experiments , a significantly smaller proportion of the available genotypes colonized individual ticks ( 23 , 29 , and 21% from Pools C–E , respectively ) compared to individual mice ( 100 , 82 , and 100% from Pools C–E , respectively ) ( χ2 = 227 . 5 , P<0 . 0001 ) in the small-genotype pool experiments ( Figure 1 ) . In the small-genotype pool experiments overall , ticks were exposed to a mean of 14 . 3 genotypes and individual ticks were colonized by a mean of 4 genotypes ( range = 1 to 11 , median = 3 . 5 ) ( Figure S1 ) . Examining genotype recovery from individual mice and ticks supported the population level genotype recovery results , and demonstrate that genotype diversity is most severely constrained in the tick . Further , the degree of genotypic diversity influenced both the mean number and proportion of genotypes that colonized ticks . Ticks exposed to more diverse F . novicida populations were colonized by a greater total number of genotypes ( Z = 2 . 14 , P = 0 . 033 ) , but a smaller proportion of the available genotypes ( χ2 = 44 . 8 , P<0 . 0001 ) as compared to ticks exposed to less diverse genotype populations ( Figure 1 , S1 ) . To determine if the low number of genotypes colonizing ticks compared to mice was the result of a few dominating genotypes , the number of times each genotype was recovered from each tick and mouse was quantified . In general , individual genotypes were recovered from a greater proportion of mice than ticks ( Figure S2 , S3 ) . On average an individual genotype was recovered from significantly fewer ticks in large pools ( 11% ) compared to small pools ( 24% ) ( χ2 = 871 . 1 , P<0 . 001 ) . Thus the reduction in genotype diversity during tick infection was not the result of a small subset of genotypes infecting ticks at a greater frequency . Importantly , identification of different genotype combinations from individual ticks that fed upon similarly infected mice indicated that ticks were exposed to a wider array of genotypes then those that were recovered from an individual tick . Further , since ticks fed on mice during their entire duration of bacteremia ( approximately 3 days ) , ticks were likely exposed to all or most or the genotypes identified in the terminal mouse blood . Therefore , the decreased genotype diversity observed in ticks is unlikely to be due to limited sampling opportunities or exposure to a limited number of genotypes . The reduction in F . novicida genotypic diversity upon infection of ticks at both the population and individual level may reflect competition among genotypes . Alternatively , this reduction in diversity may be due to the inability of specific genotypes to infect the tick . To test these hypotheses , the only six genotypes ( Genotype 1–6 , Table S4 ) that were consistently recovered from mice but absent from ticks in pooled genotype experiments were further explored . First , we determined if each of these six genotypes , when inoculated individually into mice , were able to colonize feeding ticks . All six genotypes colonized both mice and ticks at infection levels ( CFU/ml mouse blood or tick midgut ) similar to wild-type with the exception of Genotype 3 that failed to colonize infect ticks ( Figure 2A , B ) ( F5 , 40 = 0 . 88 , P = 0 . 50 ) . Moreover , with the exception of Genotypes 3 , the other genotypes were recovered from a similar proportion of ticks as wild-type ( P>0 . 30 for all comparisons ) ( Figure 2C ) . As all of these genotypes , except Genotype 3 , were competent to infect ticks , each was examined in 1∶1 competition experiments with wild-type to determine if a single additional genotype [wild-type] produced sufficient competition to result in competitive exclusion or suppression of the genotype of interest . In addition to wild-type , the competing genotype in all competition experiments was recovered from the terminal mouse blood ( 1 . 1×106 , 2 . 1×107 , 2 . 3×103 , 1 . 9×105 , 4 . 0×106 , and 1 . 6×105 CFU/ml blood for Genotypes 1–6 , respectively ) , thus confirming that ticks were exposed to the genotype of interest during feeding . The mean wild-type bacterial level recovered from terminal blood during competition with individual genotypes was 1 . 6×107 cfu/ml blood . During competition with wild-type , Genotypes 3 , 4 and 6 , which had the lowest bacteremia in mice , failed to colonize ticks ( Figure 3A ) . The absence of Genotype 3 in ticks during competition was expected as , when alone , it resulted in a low bacteremia in mice and was not recovered from ticks ( Figure 2 ) . The absence of Genotypes 4 and 6 during competition with wild-type is indicative of competitive exclusion as these genotypes , when alone , had similar infection levels in mice and ticks compared to wild-type . When examined individually , both Genotype 4 and 6 were similar to wild-type in terms of both percent infected ticks ( χ2 = 1 . 05 , P = 0 . 30 for both comparisons ) and midgut infection level ( F2 , 26 = 0 . 18 , P = 0 . 83 ) ( Figure 2B , 2C ) . Genotypes 1 , 2 , and 5 were able to colonize ticks during competition with wild-type ( Figure 3 ) ; however , a smaller percentage of ticks were colonized by these genotypes compared with wild-type ( χ2 = 3 . 60 , P = 0 . 058 , χ2 = 3 . 81 , P = 0 . 051 , χ2 = 3 . 53 , P = 0 . 060 for genotypes 1 , 2 , and 5 , respectively ) . In ticks colonized by Genotypes 1 , 2 , or 5 , colonization by wild-type was also observed . Although wild-type could exclude Genotypes 1 , 2 , or 5 in individual ticks , none of these three genotypes excluded wild-type . Moreover , Genotypes 1 and 2 established significantly lower infection levels in the tick midgut compared to wild-type indicating that these two genotypes were competitively suppressed by wild-type ( Figure 3B ) for Genotype 1 and wild-type , t13 = 2 . 59 , P = 0 . 023; for Genotypes 2 and wild-type , ( t12 = 3 . 87 , P = 0 . 0022 ) . Interestingly and despite a lower colonization prevalence compared to wild-type , Genotype 5 achieved infection levels in the tick midgut similar to wild-type ( Figure 3 ) ( t15 = 0 . 42 , P = 0 . 68 ) . As a control to demonstrate that wild-type specifically out-competed Genotypes 1–6 , we performed a 1∶1 competition assay with wild-type and Genotype 7 , which has a transposon in a non-coding region ( isftu-2 ) , and behaves similarly to wild-type in both mice and ticks [29] . In the terminal mouse blood the bacterial levels for wild-type and Genotype 7 were 9 . 3×106 and 7 . 0×106 CFU/ml blood , respectively , confirming ticks were exposed to both genotypes . Equal proportions of ticks were colonized by Genotype 7 and wild-type together , Genotype 7 alone , and wild-type alone . In ticks that were co-infected , both Genotype 7 and wild-type achieved similar infection levels in the tick midgut ( Figure 3 ) ( t6 = 0 . 25 , P = 0 . 81 ) . The equal success of Genotype 7 and wild-type in colonizing ticks during competition with one another demonstrated that Genotypes 1–6 were diminished or excluded due to competition rather than random effects . In summary , these results indicate that Genotypes 1–6 have a fitness disadvantage in the vector as compared to wild-type as co-infection of any of these genotypes with wild-type results in their competitive exclusion ( e . g . , Genotype 3 , 4 , and 6 ) or competitive suppression ( e . g . , Genotypes 1 , 2 , and 5 ) . This demonstrates that co-infection with a single , more fit genotype is sufficient to alter the success of the competing genotype even if the less fit competitor is competent upon single-infection . Further , both competitive suppression and competitive exclusion offer explanations for the loss of genotypic diversity observed during pathogen infection of ticks . Our experiments suggest that pathogen genotypic diversity is restricted within the tick vector at both population and individual levels . This restriction in diversity is most pronounced within individual ticks , suggesting that the abundance of ticks will strongly affect pathogen genotypic diversity within an environment . To further explore how variations in vector and host populations influence pathogen genotypic diversity , we developed a simple population model that incorporated data from our experiments . The model contained separate functions for vectors ( ticks ) , hosts ( mice ) , and pathogens ( Francisella genotypes ) ( Figure S4 ) . We used this model to investigate how vector-to-host ratios , vector and host abundance , and the initial number of pathogen genotypes within a population influenced the overall maintenance of genotype diversity in the population . With all model conditions , individual mice harbored greater pathogen genotypic diversity than ticks ( Figure 4 , S5 , S6 ) . Thus , rare pathogen genotypes were more likely to be lost from the vector population than from the mammalian host population . At the population level , vector-to-host ratios strongly influenced the retention of pathogen genotypic diversity ( Figure 4 ) . When vector densities declined and vector-to-host ratios approached 1 , pathogen genotypic diversity rapidly declined as individual genotypes were lost from the system . In contrast , high vector-to-host ratios increased the retention of genotypic diversity because the filtering effects of individual ticks were reduced due to large population sizes ( Figure 4 ) . Variation in vector or host abundance did not influence pathogen genotypic diversity as strongly as vector-to-host ratios; however , in general , larger vector and host populations led to greater maintenance of pathogen genotypic diversity ( Figure S5 ) . Initial pathogen genotypic diversity also influenced the number of pathogen genotypes maintained in the vector and host populations ( Figure S6 ) . Not surprisingly , both vectors and hosts individually harbored more pathogen genotypes when the number of initial genotypes was greater . However , the proportion of genotypes in the population infecting individual vectors and hosts declined with greater initial pathogen genotypic diversity ( Figure S6 ) as observed in our experiments with large- and small-genotype pools ( Figure 2 ) . Thus , the model showed a trade-off between the raw number of pathogen genotypes that infected individual vectors and hosts and the proportion of the pathogen genotype population they represented .
Our results demonstrate that pathogen genotypic diversity is restricted to a greater degree in the tick vector as compared to the mammalian host . Moreover , the extent to which hosts and vectors contribute to the maintenance of pathogen genotypic diversity is influenced by the initial degree of genotypic diversity in the pathogen population and competition among genotypes during infection of the vector . Within a host or vector , competitive interactions among genotypes can result in the reduction or elimination of one or more genotypes . Studies on co-infecting Plasmodium genotypes illustrate how a more virulent genotype can competitively suppress or prevent a less virulent genotype from being transmitted between the mammalian host and mosquito vector [32]–[35] . Additionally , studies on arboviruses such as West Nile virus and Dengue virus have demonstrated that genotypic diversity can be correlated with transmissibility or virulence [7] , [9] . Similar to our results , intrahost examination of West Nile virus revealed that viral genetic diversity was restricted in mosquito midguts compared to the input pool [36] , [37] . Interestingly , however , despite a reduction of viral diversity in the mosquito midgut , corresponding salivary samples were similar in diversity to the input pool , perhaps contributed to accumulation of mutations as a result of relaxed purifying selection during infection of the mosquito [36] . In this study , F . novicida genotype diversity was not equally sustained by mice and ticks , and the greatest restriction in genotypic diversity occurred in individual ticks . This reduction in diversity was mediated by a combination of both stochastic and selective forces , and was unlikely to be an artifact of tick feeding . Based on our results , despite exposure to a large array of mutants , individual ticks were not able to support the same number of mutants as mice . One possible reason for genotypic restriction is that resources for bacterial colonization , such as nutrient availability or receptors for cell entry , are more limited in ticks than in mice , which could lead to competition among genotypes for limited resources . Several lines of evidence suggest that strong competition among genotypes occurred in ticks . First , individual ticks that fed upon the same mouse infected with up to 94 genotypes were colonized by different combinations of genotypes . Second , five genotypes not recovered from ticks during pooled-genotype experiments were competent to colonize ticks , in most cases to wild-type levels , in the absence of a second genotype . Third , in competition assays with wild-type and a wild-type-like genotype ( Genotype 7 ) , both were equally able to compete and colonize ticks , which further indicated that the absence of Genotypes 1–6 from the pooled-genotype experiments was not random . Our experimental design allowed us to examine the genotypic diversity that was sustained by ticks from the genotypes present during the nymphal blood meals to recovery of genotypes from adult tick midguts . This period of time encompassed several points where genotypic diversity could have been lost in the tick midgut including during initial entry into the nymph midgut , early replication and colonization events in the nymph midgut , transstadial transmission from nymph to adult , or continued colonization in the adult midgut . Although our results clearly demonstrate that competition is occurring among F . novicida genotypes during infection of the tick vector , it is interesting to note that a previous study speculated that facilitative interactions among genotypes in mixed B . burgdorferi genotype infections conferred an advantage for the bacteria to establish and maintain infection in ticks [15] , [38] . It is possible that such interactions may occur in this system . Additional variables that could further influence competition among genotypes and contribute to the observed reduction in genotype diversity in ticks include the infection level for an individual genotype , transmission priority ( the order in which genotypes are transmitted ) , and genotype fitness . With regard to the latter two variables , our results indicated that reduction in fitness can in some instances overwhelm the stochastic forces that dictate tick infection by pathogen genotypes [29] , [39] , [40] . Overall , F . novicida bacterial levels did not vary based on genotype diversity and were similar to previously reported single-genotype infection levels [29] . This suggests that ticks have an infection threshold limit for F . novicida , such that as the number of genotypes a tick is exposed to increases , the maximum infection level of any individual genotype is proportionately reduced [29] . Therefore , genotypes that are able to replicate first will have a greater opportunity to colonize the tick while reducing the amount of available resources for incoming genotypes ( founder effect ) [12] . Additionally , greater numerical success in one host or vector will confer a greater probability of subsequent transmission . The transmission priority of genotypes between mice and ticks was stochastic , such that ticks had an opportunity to acquire the genotypes that colonized mice relative to the genotype-specific infection level in mice . Transmission priority is potentially important if resources are more limited within the tick and monopolized by genotypes on a “first come , first serve” basis . In most pooled-genotype experiments , genotype recovery was random and ticks were colonized by small subsets of the available genotypes in different combinations . Although we strived to initiate our pooled-genotype experiments with equal ratios of genotypes , four genotypes in Pool B were recovered from a greater percentage of mice and ticks implying that they had a numerical advantage in the initial inoculum , maintained that advantage while colonizing mice and were available at a greater frequency for feeding ticks to acquire ( Figure S2 ) . These four genotypes , which were recovered from a greater percentage of ticks than the other genotypes comprising Pool B , provide evidence for genotypes with an initial advantage having greater transmission and colonization success . Importantly , although these four genotypes were identified in a greater percentage of ticks , they were not the sole genotypes observed and were commonly identified in individual ticks with less frequently occurring genotypes . These results are similar to those of a Trypanosoma brucei study and recently a B . burgdorferi study where vector acquisition of genotypes from mice , infected with multiple , similarly fit pathogen genotypes , was noted as random and the first genotype able to infect an individual vector had an advantage during dissemination to other tissues and in subsequent transmission [12] , [41] . Stochastic forces also play a prominent role in shaping arboviral transmission , and has been demonstrated for West Nile Virus and Venezuelan equine encephalitis virus [42] , [43] . Genotype fitness can influence competitive ability as well as virulence as demonstrated by co-infection studies using genotypes with known fitness differences [33]–[35] . A range of fitness among the F . novicida genotypes examined was expected , depending on the location of the transposon . The overall genetic similarly of genotype populations suggests that the majority likely shared similar abilities to infect mice and ticks ( Table 1 ) . We surmised that the six genotypes absent from ticks in the pooled-genotype experiments were out-competed . This postulation was supported by the results of the 1∶1 competition assays between wild-type and Genotypes 1–6 , where wild-type succeeded disproportionately in terms of infection prevalence and infection load compared to the competing genotype . The competition assay between wild-type and Genotype 7 confirmed that if Genotypes 1–6 had been similarly fit as wild-type , they would have succeeded to a similar extent as Genotype 7 did during competition with wild-type . The finding that Genotypes 1–6 were able to colonize ticks during single-genotype experiments but not in during competition with more fit genotypes supported the notion that the location of the transposon in these genotypes exacts some fitness cost , although the exact mechanism by which this is occurring remains unknown . In the field , genotypic diversity is likely to be dynamic and heavily influenced by environmental variables . Genotypic diversity , when measured , generally occurs as insertions , deletions , and polymorphisms in individual and small numbers of nucleotides [44]–[46] . Additionally , gene duplications and deletions do occur [47] . While insertions are over-represented in our population , the use of naturally occurring genotypes is not possible , as a collection of greater than 150 different genotypes that can easily be distinguished one from another do not exist for any tick borne bacterial pathogen . Importantly , the alterations in phenotype in our population are likely highly variable and represent a broad spectrum , from complete knock-out of gene function to no alteration in gene function . Thus , while the type of genetic mutation represented in our population is limited as compared to a natural population , a broad spectrum of alterations in phenotype is likely to be represented . Further , in our experiments more mutational robustness was observed in the vertebrate , however , within a host infected with naturally occurring genotypes those genotypes could possess very different fitness abilities , thus altering the outcome of within-host interactions and ongoing transmission . To extrapolate our results to a broader range of field scenarios we created a model to explore how variations in vector-to-host ratio , vector and host abundance , and initial pathogen genotypic diversity affected the retention of pathogen genotypic diversity in a population over time . These variables were selected because our experimental data indicated that the greatest restriction in F . novicida genotype diversity occurred during colonization of ticks compared to mice . We assumed that there was no mortality of vectors and hosts , and thus the model likely over-estimated the conservation of diversity ( as pathogen genotypes might be lost from dying vectors and hosts ) . Our modeling results suggested that local extinction of pathogen genotypes , and genotypic diversity overall , is more likely to be affected during pathogen infection of ticks . Vector-to-host ratio was the most important variable in the maintenance of pathogen genotypic diversity over time in a population; however , abundance of vectors and hosts , and initial pathogen genotypic diversity also contributed . Finally , our model was conservative in design in that it assumed equal fitness among genotypes , that all ticks fed , and does not incorporate the addition of new genotypes beyond those initially present . If additional values are known , derivations of this model could be used to examine these variables which could result in accelerated specific genotype extinction or retention . Although our model featured a tick-borne pathogen , our experimental results and model predictions are in line with epidemiological data of other vector-borne pathogens , including Plasmodium spp . , where areas of high transmission are associated with abundant vector populations that collectively support a great diversity of pathogen genotypes [6] . Most vector-borne pathogen studies examining genotype co-infection to date either survey the circulating pathogen genotypes in an area or conduct competition assays among pairs of genotypes , frequently differing drastically in fitness ( e . g . , attenuated versus virulent , transmissible versus not transmissible ) [2] , [5] , [11] , [15] , [19] , [48]–[50] . Knowledge gaps exist regarding the role of the vector in supporting or restricting pathogen genotype diversity in a population . In this study , both the experimental data and population modeling data revealed that the tick vector acted as a greater ecological filter for pathogen genotypic diversity compared to the mammalian host . This restriction of F . novicida genotypic diversity in ticks was further affected by the initial amount of genotypic diversity and competition among genotypes . Extrapolation of our results in a model revealed variables , including vector-to-host ratio , which over many generations played important roles in the maintenance of pathogen genotypic diversity . The marked reduction in genotypic diversity within the tick indicates that intervention strategies targeting the pathogen within the tick , such as introduction of highly competitive genotypes , are likely to be effective in disrupting disease transmission . Further , understanding how pathogen genotypic diversity and genotype interactions within the host and vector affect colonization success is essential to understanding pathogen transmission , selection and disease ecology .
This study was carried out in accordance with the following: Animal Welfare Act ( 9 CFR Ch . 1 Subpart C 2 . 31 ( c ) ( 1–8 ) ) , Guide for the care and use of Agricultural Animals in Agricultural Research and Training ( Chap . 1 ) , and the Public Health Service Policy on Humane Care and Use of Laboratory Animals ( Section IV . B . ( 1–8 ) ) . All protocols involving the use of animals were approved by the Washington State University Institutional Animal Care and Use Committee ( IACUC ) ( ASAF Number: 3686 and 4430 ) . Dermacentor andersoni ( Reynold's Creek ) nymphs were obtained from a colony maintained by USDA-ARS-ADRU ( Pullman , WA ) . All nymphs were fed on C57BL/6 mice [29] . After inoculation with F . novicida , mice were monitored twice daily for signs of illness . At the onset of severe illness ( ruffled fur , hunched posture , ocular or nasal discharge , ataxia , etc ) , mice were euthanized and blood was cultured to determine bacteremia as described in the following section . If mice did not develop disease , they were euthanized upon completion of nymph feeding and bacteremia similarly determined via culture . In some experiments , adult D . andersoni were fed to repletion on male New Zealand white rabbits ( Western Oregon Rabbit Company , Philomath , OR ) [29] . Rabbits remained asymptomatic and were culture negative at the end of tick feeding . Wild-type F . novicida ( U112 ) or transposon mutants containing a kanamycin resistance cassette [51] ( Table S1 ) were used in all experiments . All F . novicida mutant genotypes were cultured in tryptic soy broth ( TSB ) or on tryptic soy agar ( TSA ) containing 0 . 1% L-cysteine and kanamycin ( 15 µg/ml ) ( kanamycin was omitted when culturing wild-type F . novicida ) [29] . Briefly , F . novicida broth cultures were incubated at 37°C and 225 rpm either overnight or for 3 hr , depending on the experiment . F . novicida agar cultures were incubated at 37°C for 48 hrs and the resulting colony forming units ( CFU ) enumerated . To recover F . novicida from blood , whole blood was plated from individual mice . To recover F . novicida from ticks , tick midguts were individually dissected and homogenized in Lysing Matrix H tubes ( MP Biomedical , Solon , OH ) containing 500 µl of 1× PBS for 13 seconds at 3 M/s and plated . For all samples , genomic DNA ( gDNA ) was isolated from lawns ( >10 , 000 CFU ) of F . novicida culture using a DNeasy kit ( Qiagen , Valencia , CA ) . Individual F . novicida genotypes were identified by PCR amplification of a 350–700 bp fragment using a universal primer located within the kanamycin cassette and a genotype-specific primer in the adjacent sequence ( Table S2 ) . Individual reactions included 2× GoTaq Mastermix ( Promega , Madison , WI ) , 2 . 5-µM of each primer , and 50-ng of gDNA template . Thermocycler conditions were as follows: Step 1 ( ×1 ) , 94°C for 2 min; Step 2 ( ×35 ) , 94°C for 45 sec , 54°C for 45 sec , 72°C for 45 sec; Step 3 ( ×1 ) , 72°C for 5 min . Following electrophoresis , PCR products were visualized on a 2% agarose gel containing SYBR Safe ( Invitrogen , Carlsbad , CA ) . To simulate diverse genotype infections , clones from two randomly chosen plates ( NR-8058 and NR-8065 , BEI Resources , Manassas , VA ) from a F . novicida transposon mutant library were used to assemble the large pools ( Pool A , n = 93; B , n = 94 ) for infection assays ( Table S1 ) . The populations of genotypes that comprised the small pools ( Pool C , n = 16; D , n = 17; E , n = 16 ) ( Table S1 ) , were those that were recovered from the mouse blood but not the tick midgut in the large pool infection assays . To generate diverse inocula , glycerol stocks of individual F . novicida genotypes were each inoculated into a single well in a 96-well plate containing 1 ml of TSB and grown overnight . Overnight cultures were sub-inoculated into fresh TSB for a starting concentration of 1∶1500 ( 1 µl overnight culture into 1 . 5 ml of TSB ) . Cultures were incubated for 3 hr and 50 µl of individual genotype cultures were combined to generate the mixed genotype inocula . An OD600 measurement was obtained for the combined culture and the appropriate dilutions were made in 1× PBS for a final concentration of 4000 CFU ( ∼40 CFU/genotype ) or 1000 CFU ( ∼60 CFU/genotype ) in 100 µl for the large- and small-pool infection assays , respectively . Mice infested with D . andersoni nymphs were intraperitoneally inoculated ( 6 mice/pool and 3 mice/pool for the large and small pool infection assays , respectively ) . To verify that all genotypes were present in the inoculum , an aliquot was plated , allowed to grow to a lawn ( >10 , 000 CFU ) and re-suspended in 5 ml 1× PBS , from which 100 µl was used for gDNA extraction , and examined by genotype-specific PCR . Terminal mouse blood was plated and the resulting bacterial cultures examined to determine the bacterial load and identify the genotypes that successfully infected the mice and thus were available for the feeding nymphs to acquire . After feeding , nymphs were incubated at 25°C and allowed to molt to adults . For the large pool infection assays , the infected adult ticks were fed on a naïve rabbit to expand the F . novicida infection load in the tick midgut; however , we later determined this extra feeding was not necessary to detect the population of genotypes in the tick midgut and was omitted in subsequent infection assays . Once molting to adults was complete , midguts were dissected from individual ticks , homogenized and plated , and the resulting bacterial lawns were examined to determine the bacterial level and identify the genotypes that had colonized the tick midgut and had been transstadially maintained . Bacterial lawns derived from blood ( n = 3 ) or midgut cultures ( n = 10 to 12 ) were processed as described above from individual mice or ticks and the F . novicida genotype population determined from individual or pooled ( combine aliquots of re-suspended culture from like inoculated/exposed mice or ticks ) samples . In the infection assays using multiple genotypes , described above , six transposon-containing genotypes were consistently recovered from the mouse blood but not the tick midgut . These genotypes were then tested in individual infection assays and competition assays with wild-type . As a control for the competition assays , a transposon-containing genotype that has a phenotype similar to that of wild-type [29] was used . For individual genotype infection assays , the inoculum was prepared as previously described with each mouse receiving 1000 CFU of a single genotype . Detection of the individual genotype in the inoculum , mouse blood , and tick midgut was accomplished by culture and the identity of the genotype was verified by PCR . For competition assays , a 1∶1 ratio ( 500∶500 CFU ) of two different genotypes were injected into mice in the same manner as described above . To enumerate each genotype within blood or midgut , CFUs were calculated by dual plating samples on antibiotic-free and kanamycin-containing TSA plates . This allowed enumeration of the transposon-containing genotype ( CFU on kanamycin-containing TSA plates ) and wild-type ( CFU on antibiotic-free plates minus CFU enumerated on the reciprocal kanamycin-containing TSA plates ) . The ratio of wild-type to transposon-containing genotype was determined for each competition assay in the inoculum , mouse blood , and adult tick midgut . Six to twelve ticks were assessed for each competition pairing . We developed a population model that incorporated data from the experiments to explore how variation in vector and host populations would influence pathogen genotypic diversity over a range of vector and host conditions . The model contained functions for vectors ( ticks ) , hosts ( mice ) , and pathogens ( Francisella genotypes ) ( Figure S5 ) . The model was initiated by allocating pathogen genotypes to a population of mice , with each mouse receiving all pathogen genotypes . These pathogen genotypes were then tracked over time in both tick and mice populations . The model had a generational time step , and at each time step uninfected ticks attached to mice , fed , and acquired pathogens . Infected ticks then molted and fed on uninfected mice ( i . e . , the next generation ) , transmitting pathogens in the process ( Figure S5 ) . The model was individual-based , such that each tick only acquired pathogens from the mouse it fed on; similarly , mice only acquired pathogens from ticks that fed upon them . The probability that an uninfected tick acquired pathogen genotype g from mouse m was: ( 1 ) where is the number of pathogen genotypes harbored by mouse m . Thus , the maximum probability of a tick acquiring any genotype was 28% . The model was stochastic , and a random number was drawn from a uniform distribution between 0–1 and compared with to determine whether ticks acquired each pathogen genotype . In turn , the probability that an uninfected mouse acquired a pathogen genotype g from ticks was as follows : ( 2 ) where is the number of pathogen genotypes harbored by tick t . The summation adds up the total number of genotypes for all ticks that fed on each particular mouse ( total = n ) . Thus , the maximum probability of a mouse acquiring any genotype was 100% . Like ticks , mice were modeled individually and the pathogen genotypes they acquired were stochastic . Equations for Pt and Pm were generated by fitting linear model to data from the experiments . One limitation of the model is that using linear functions sets a maximum acquisition value that may be lower than the probability for “fit” genotypes . However , such functions were used to approximate the average genotype . This was done because it is difficult to assume the proportion of genotypes that would be “fit” ( i . e . , have a higher acquisition probability ) and “unfit” ( i . e . , have a lower acquisition probability ) in natural populations; therefore , we only modeled the “average genotype” . We did explore alternative forms of the acquisition function with a greater maximum acquisition value . However , with any form of the model our qualitative results on the role of vector-to-host density , initial vector and host abundance , and initial pathogen diversity did not change . Thus , we only present results of this simple model that did not distinguish between genotypes in terms of fitness . While simple , results with this model were used to demonstrate how diversity might be maintained in natural population with varying conditions . In the baseline set of simulations , there were 100 mice , 1 , 000 ticks and 100 pathogen genotypes . However , these values were varied in sensitivity analyses to investigate the effects of different vector-to-host ratios , differences in vector and host abundance , and different initial pathogen genotypic diversity on the maintenance of pathogen genotypic diversity over time ( Table S3 ) . For each set of initial conditions , the model was run for 100 generations to examine the maintenance of pathogen genotypic diversity over time in ticks and mice . For each set of initial model conditions ( Table S3 ) , we ran the model 1 , 000 times to account for the stochastic nature of the model . Results presented represent the average values from these 1 , 000 simulations . All statistical analyses were conducted using JMP Statistical Discovery Software Version 11 ( Cary , NC ) . We used logistic regression to explore effects of genotype diversity ( i . e . , pool size ) , genotype group nested within pool size , and host ( mouse vs . tick ) , and all two-way interactions on the recovery of genotypes from hosts and vectors . Genotype group was not significant in these analyses , ( χ2 = 3 . 36 , P = 0 . 34 ) , and so final analyses were only run with the factors genotype diversity , host , and their interaction . In these analyses , the number of genotypes recovered or not recovered from hosts and vectors were binomial count data . To look at the number of genotypes recovered from individual ticks in differing pool sizes ( large vs small ) we used non-parametric Wilcoxon tests , as data on the number of genotypes recovered were non-normal . For single wild-type or genotype infection assays , we used an ANOVA to compare F . novicida genotype bacterial levels to wild-type bacterial levels during single-genotype infection experiments . For 1∶1 competition experiments between wild-type and a select genotype , we used two-sample t-tests to compare wild-type and genotype bacterial levels in tick midguts . Moreover , we used Fisher's exact tests to determine the proportion of ticks that were infected with each genotype compared to wild-type in these 1∶1 competition assays . For all analyzes , an α value of 0 . 05 was used to determine statistical significance .
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Co-infection , the presence of multiple genotypes of the same pathogen species within an infected individual , is common . Genotype diversity , defined as the number of unique genotypes , and the interaction between genotypes , can strongly influence virulence and pathogen transmission . Understanding how genotypic diversity affects transmission of pathogens that naturally cycle among disparate hosts , such as vector-borne pathogens , is especially important as the capacity of the host and vector to sustain genotypic diversity may differ . To address this , we exposed Dermacentor andersoni ticks , via infected mice , to variably diverse populations of Francisella novicida genotypes . Interestingly , we found that ticks served as greater ecological filters for genotypic diversity compared to mice . This loss in genotypic diversity was due to both stochastic and selective forces . Based on these data and a model , we determined that high numbers of ticks in an environment support high genotypic diversity , while genotypic diversity will be lost rapidly in environments with low tick numbers . Together , these results provide evidence that vector population dynamics , vector-to-host ratios , and competition among pathogen genotypes play critical roles in the maintenance of pathogen genotypic diversity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"vector-borne",
"diseases",
"microbiology",
"bacterial",
"diseases",
"population",
"modeling",
"bacterial",
"pathogens",
"veterinary",
"science",
"animal",
"models",
"of",
"infection",
"infectious",
"diseases",
"veterinary",
"diseases",
"zoonoses",
"veterinary",
"microbiology",
"medical",
"microbiology",
"microbial",
"pathogens",
"infectious",
"disease",
"modeling",
"francisella",
"tularensis",
"rabbit",
"fever",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"tularemia"
] |
2014
|
Restriction of Francisella novicida Genetic Diversity during Infection of the Vector Midgut
|
Coxiella burnetii is an intracellular pathogen that causes human Q fever , a disease that normally presents as a severe flu-like illness . Due to high infectivity and disease severity , the pathogen is considered a risk group 3 organism . Full-length lipopolysaccharide ( LPS ) is required for full virulence and disease by C . burnetii and is the only virulence factor currently defined by infection of an immunocompetent animal . Transition of virulent phase I bacteria with smooth LPS , to avirulent phase II bacteria with rough LPS , occurs during in vitro passage . Semi-rough intermediate forms are also observed . Here , the genetic basis of LPS phase conversion was investigated to obtain a more complete understanding of C . burnetii pathogenesis . Whole genome sequencing of strains producing intermediate and/or phase II LPS identified several common mutations in predicted LPS biosynthesis genes . After passage in broth culture for 30 weeks , phase I strains from different genomic groups exhibited similar phase transition kinetics and elevation of mutations in LPS biosynthesis genes . Targeted mutagenesis and genetic complementation using a new C . burnetii nutritional selection system based on lysine auxotrophy confirmed that six of the mutated genes were necessary for production of phase I LPS . Disruption of two of these genes in a C . burnetii phase I strain resulted in production of phase II LPS , suggesting inhibition of the encoded enzymes could represent a new therapeutic strategy for treatment of Q fever . Additionally , targeted mutagenesis of genes encoding LPS biosynthesis enzymes can now be used to construct new phase II strains from different genomic groups for use in pathogen-host studies at a risk group 2 level .
Coxiella burnetii is a gram-negative intracellular bacterial pathogen and the etiological agent of the zoonosis Q fever . Sheep , goats , and dairy cattle are considered relevant animal reservoirs for human infection . Infection generally results from inhalation of aerosols containing the highly infectious and stable bacterium that arise from contaminated animal products [1] . Q fever typically presents as an acute flu-like illness that often goes untreated . In rare occasions , Q fever manifests as a more serious persistent focalized infection ( formerly termed chronic Q fever ) that can result in endocarditis or vascular disease [2] . These infections require long-term treatment with doxycycline and hydroxychloroquine . While treatment significantly reduces mortality , it is not always effective , resulting in disease relapse [3–5] . Recent Q fever outbreaks in The Netherlands [6 , 7] and U . S . [8] highlight Q fever as a public health threat . C . burnetii undergoes a virulent-to-avirulent transition involving lipopolysaccharide ( LPS ) truncation known as phase variation [9] . The seminal work of Fiset and Stoker [10 , 11] provided the conceptual framework for C . burnetii phase variation , a term originally coined to describe the peculiar serological behavior of C . burnetii strains . They demonstrated that organisms in phase II react with early ( < 20 days ) and late ( >20 days ) sera derived from infected guinea pigs , while organisms in phase I react only with late sera . The phenomenon was associated with culture history , i . e . , natural isolates with phase I reactivity convert to phase II reactivity after approximately 10 passages in embryonated hen’s eggs . Fiset et al . [10] suggested that , although low passage C . burnetii produce both phase I and phase II antigens , phase II antigens are “masked” by phase I antigens and unavailable for antibody interactions in vitro . He further speculated that phase I and phase II antigens are biochemically different , with phase I and phase II antigens being “poor” and “good” antigens , respectively . These insightful propositions made over 60 years ago were validated upon showing that phase I antigen is LPS O-antigen [12–14] which sterically inhibits binding of antibodies directed against phase II antigens ( surface proteins ) , deposition of complement , and recognition of toll-like receptor ligands , such as lipoproteins , from innate immune recognition by dendritic cells [12–17] . Accordingly , high passage and clonal phase II variants lack O-antigen altogether and are avirulent in a guinea pig infection model [12–14 , 18 , 19] . Biochemical analysis and genomic sequencing of the isogenic virulent Nine Mile phase I ( NMI ) ( RSA493 ) and avirulent Nine Mile phase II ( NMII ) ( RSA439 ) strains indicate LPS is the sole factor responsible for the disparate virulence of the two strains , and that O-antigen is the primary surface antigen recognized by phase I antiserum [14 , 20 , 21] . Indeed , fixed , whole-cell phase II bacteria generated by 20 egg passages of phase I bacteria are 100 to 300 times less effective as vaccines [22] . Thus , the only available vaccine for protection against Q fever ( Q-Vax ) , is a formalin-inactivated preparation of whole cells of the virulent Henzerling phase I strain that is licensed for use in Australia [23] . Distinct genomic groups of C . burnetii produce structurally and antigenically different phase I LPS molecules [24]; however , cross protection is observed when vaccinated animals are challenged with heterologous phase I strains , indicating the presence of common epitopes [18 , 22 , 25] . The critical importance of full-length LPS in protective immunity induced by C . burnetii phase I vaccines suggests subunit vaccines based solely on protein antigens will be ineffective . C . burnetii LPS phase variation is analogous to the smooth-to-rough LPS transition seen in the Enterobacteriaceae . C . burnetii phase variation results from gene mutation and is not to be confused with phase variation of other gram-negative bacteria , which is generally a reversible on-off regulatory process associated with production of several virulence factors , such as fimbriae , capsule , and LPS antigenicity [26] . C . burnetii LPS is the only virulence factor defined by infection of an immunocompetent animal [18] . Virulent organisms , producing full-length phase I ( smooth ) LPS and isolated from infections and natural sources , convert to avirulent organisms producing truncated phase II ( rough ) LPS upon serial passage in embryonated hen’s eggs , cell culture , or synthetic medium [11 , 27–29] . The selective pressure promoting LPS transition is thought to involve energy conservation [30 , 31] . The sugars comprising the inner/outer core regions and the repeating O-antigen subunits of LPS from the NMI strain have been identified [32–34] . Two unusual O-antigen sugars unique to C . burnetii LPS are virenose ( 6-deoxy-3-C-methylgulose ) and dihydrohydroxystreptose ( 3-C- ( hydroxymethyl ) -L-lyxose ) [32] . Unlike other gram-negative bacteria , where O-antigen generally has a defined repeat size [35] , C . burnetii O-antigen has populations that differ in size and composition [24 , 36]; consequently , its carbohydrate structure remains unresolved . A strain isolated from placental tissue of a guinea pig persistently infected with NMI produces an intermediate length LPS [37 , 38] . This strain , termed Nine Mile Crazy ( NMC ) ( RSA514 ) , produces an LPS that weakly reacts with polyclonal anti-phase I LPS antiserum , a result that correlates with the absence of repeating O-antigen subunits and virenose [37 , 39] . The LPS structure of NMII contains tetra-acylated lipid A linked to an inner core consisting of three 3-deoxy-D-manno-2-octulosonic acid ( KDO ) molecules , two terminal D-mannose molecules , and 2- and 3 , 4-linked D-glycero-D-manno-heptose molecules [33 , 40–42] . The lipid A of NMI and NMII is identical and fails to signal through toll-like receptor ( TLR ) 2 or TLR4 [43] . Phase II LPS is missing both the outer core and repeating O-antigen sugars , including dihydrohydroxystreptose and virenose [44] . The genetic lesion ( s ) resulting in C . burnetii phase I to phase II transition are undefined . NMC and NMII contain large overlapping chromosomal deletions that eliminate cbu0676 to cbu0700 ( 31 , 570 bp ) and cbu0678 to cbu698 ( 25 , 997 bp ) , respectively [45 , 46] . This region contains genes implicated in virenose synthesis [47 , 48] . The lack of virenose in the LPS of NMC and NMII agrees with this hypothesis [33 , 39] . The large deletion of NMC accounts for an intermediate length LPS . However , the smaller deletion of NMII does not explain missing outer core and O-antigen sugars [49] . The large chromosomal deletion of NMII , avirulence in a guinea pig model of infection , and lack of reversion to phase I LPS are the basis for classifying NMII as a risk group 2 organism . Remaining C . burnetii strains are considered risk group 3 bacteria [18 , 50 , 51] . The precise genetic lesion ( s ) accounting for the rough LPS of NMII is unknown . Indeed , additional phase II strains lacking a large deletion have been characterized [52 , 53] . LPS gene expression profiling did not identify mutations responsible for transition to phase II [52 , 53] . In this study , we show that phase variation occurs in a similar fashion between C . burnetii of different genomic groups , resulting in common intermediate and phase II LPS forms . Site-directed mutagenesis of virulent C . burnetii , using a new method of genetic selection based on C . burnetii lysine auxotrophy , identified several mutations responsible for LPS phase transition . In particular , mutation of cbu0533 , encoding a undecaprenyl-phosphate alpha-N-acetylglucosamine phosphotransferase , is the genetic lesion responsible for the phase II LPS of NMII . Collectively , our results reveal the genetic complexity of LPS modifications by C . burnetii that are directly related to virulence potential .
A distinguishing property of phase I and phase II C . burnetii is surface charge [18 , 54] . The absence of O-antigen sugars in phase II strains is associated with hydrophobicity and spontaneous agglutination [18 , 54] . These properties allow distinction from hydrophilic phase I bacteria during axenic growth . Phase II bacteria clump in liquid media and form opaque colonies on agarose plates with a defined border . Conversely , phase I bacteria disperse in media and form translucent colonies with an undefined border ( S1 Fig ) . These growth phenotypes are useful scores for LPS phase transition in C . burnetii . NMI , NMC , and NMII are isogenic strains producing prototypical phase I , intermediate , and phase II LPS forms , respectively ( Fig 1A ) [52] . The specificity of monoclonal antibodies against LPS of NMI ( anti-phase I LPS ) , NMC ( anti-intermediate LPS ) and NMII ( anti-phase II LPS ) were examined by immunoblot ( Fig 1B ) . Anti-phase I LPS antibody reacted with O-antigen of full-length LPS as indicated by a laddering profile above 15 kDa not obvious by silver stain ( Fig 1A ) . The anti-intermediate LPS antibody reacted with an intermediate size LPS of ~11 kDa in both NMI and NMC . The anti-phase II LPS antibody reacted with an ~3 kDa LPS specific to NMII [14] . Representative LPS structures are depicted in Fig 1C [31 , 36 , 39 , 42 , 44 , 55] . These antibodies were used in remaining experiments to characterize LPS produced by wild type and mutant strains of C . burnetii . C . burnetii phase variation is described as a non-reversible shift from full-length LPS of virulent phase I bacteria to truncated LPS of avirulent phase II bacteria [11 , 56] . Ftacek et al . previously demonstrated that serial passage of the C . burnetii Priscilla phase I strain in embryonated hen’s eggs results in sequential transition from high-to-intermediate-to-low molecular weight LPS molecules [27] . To further examine LPS phase variation in C . burnetii , Nine Mile ( RSA363 ) , S ( Q217 ) , G ( Q212 ) , and Dugway ( 7E65-68 ) phase I strains were serially passaged weekly for 30 weeks in the synthetic medium acidified citrate cysteine medium-2 ( ACCM-2 ) , and LPS isolated at passage 2 , 10 , 20 and 30 . These strains are isolated from disparate sources and fall within different genomic groups ( S1 Table ) . Also included was NMC . LPS profiles following passage were examined by silver stain and immunoblot ( Fig 2 and S2 Fig ) . At passage 2 , the phase I LPS profile of each phase I strain was unique , indicating different forms of LPS exist within the Coxiella genus , as previously reported [14 , 24 , 33] . During subsequent passage , LPS profiles changed in a similar fashion with the appearance of intermediate ( ~11 kDa ) and phase II ( ~3 kDa ) LPS forms . The rate of change differed slightly among phase I strains . At passage 30 , S ( Q217 ) , G ( Q212 ) , and Dugway ( 7E65-68 ) had similar intermediate and phase II LPS profiles . However , phase II LPS of S ( Q217 ) appeared to decrease as passage number increased , suggesting reversion back to an intermediate LPS form . Also , intermediate LPS of Nine Mile ( RSA363 ) decreased with a coincident increase in an upper phase II LPS form ( ~6 kDa ) . This same form was also faintly observed in Dugway ( 7E65-68 ) LPS starting at passage 10 ( Fig 2 ) . A previous study also showed an upper phase II LPS form following chick embryo passage of the Priscilla strain [27] . Passage of NMC resulted in an overall decrease in intermediate LPS and a subsequent increase in phase II LPS ( S2A Fig ) . LPS of passaged strains was further examined by immunoblot ( S2B Fig ) . Consistent with silver staining , phase I LPS ( >15 kDa ) decreased during passage with a coincident appearance of intermediate and phase II LPS . The ~6 kDa phase II LPS form was not recognized by anti-phase II antibody . Bacterial LPS transition from smooth-to-rough is usually associated with mutation of genes involved in LPS biosynthesis [30 , 57] . To discover mutations associated with phase transition in C . burnetii , we characterized the LPS profiles and genomes of five strains previously serotyped as phase II [58] ( Fig 3 ) . The LPS profile of Australia ( RSA297 ) and Australia ( RSA425 ) were identical by silver stain , with each showing intermediate and phase II LPS ( Fig 3A ) . Accordingly , immunoblot showed reactivity to anti-intermediate LPS antibody , but not to anti-phase I LPS antibody ( Fig 3B ) . Interestingly , LPS of Australia ( RSA297 ) did not react with anti-phase II LPS antibody , suggesting the Australia strains have different phase II LPS structures . The LPS of California ( RSA350 ) mainly consisted of phase II LPS ( ~3 kDa ) with a small amount of phase I LPS detectable only by immunoblot ( Fig 3B ) . A clone of this strain derived by micromanipulation [59] , California ( RSA350 ) C2 , contained only phase II LPS ( Fig 3B ) . Similarly , M44 ( RSA461 ) C1 , a clonal strain obtained by plaque formation [60] , displayed only phase II LPS ( Fig 3A and 3B ) . To define the genetic mutations associated with production of intermediate and phase II LPS , we conducted whole genome sequencing of the five phase II strains , in addition to NMC and NMII ( Fig 3 ) . The identified mutations are listed in S2 Table and include mutations in four predicted LPS biosynthesis genes: cbu0678 , cbu0533 , cbu0845 , and cbu1657 ( Fig 3C ) . Mutation of cbu0678 was common to all strains , including a complete deletion in NMC and a partial deletion in NMII . The cbu0678 gene is located in a chromosomal region implicated in virenose biosynthesis [46–49] . A single mutation in cbu0533 was specific to NMII . CBU0533 has homology to E . coli WecA ( Rfe ) , which is a undecaprenyl-phosphate alpha-N-acetylglucosamine phosphotransferase that initiates O-antigen synthesis [61] . Two different disruptive mutations in cbu0845 were present in California ( RSA350 ) and M44 ( RSA461 ) C1 strains . CBU0845 has homology to the GDP-mannose dehydrogenase family of LPS enzymes , which in Pseudomonas aeruginosa , is involved in O-antigen synthesis [62] . Mutated cbu1657 was present in Australia ( RSA297 ) and Australia ( RSA425 ) , although only ~28% of sequence reads in Australia RSA425 had this mutation . CBU1657 has homology to an alpha-L-glycero-D-manno-heptose beta-1 , 4-glucosyltransferase , which in Klebsiella pneumoniae , transfers glucose to heptose I in the inner core [63] . The mutation of cbu0678 in all sequenced strains suggested the gene is required for transition of intermediate to phase I LPS . CBU0678 has homology to CBU1655 , which is annotated as a bifunctional sugar kinase/adenylyltransferase containing two domains ( Fig 4A ) . Domain I of CBU1655 is a D-glycero-D-manno-heptose-7-phosphate 1-kinase while domain II is a D-glycero-D-manno-heptose-1-phosphate adenylyltransferase . Based on homology to E . coli HldE , CBU1655 is predicted to synthesize ADP-D-glycero-β-D-manno-heptose [64] , a sugar found in the C . burnetii inner core [42] ( Fig 4B ) . CBU0678 has an unconventional , reversed domain arrangement ( Fig 4A ) . All cbu0678 mutations result in a truncated C-terminus that eliminates the predicted active site of domain I [64] ( Fig 4A ) . To define the roles of CBU0678 and CBU1655 in LPS biosynthesis , the encoding genes were mutated in NMI and NMII , respectively . The cbu0678 mutant ( cbu0678tr ) was engineered to express a truncated version of the protein containing amino acids ( AA ) 1 to 437 . This generates the mutation observed in NMII [46] ( Fig 4A ) . NMI cbu0678tr contained intermediate and phase II LPS , but not phase I LPS ( Fig 5A and 5B ) . Expression of a wild type copy of cbu0678 in NMI cbu0678tr rescued production of phase I LPS ( Fig 5A and 5B ) . These data indicate that cbu0678 is essential for production of phase I LPS and that disruption of the enzyme results in an intermediate length LPS . The small amount of observed phase II LPS is a consequence of passage in synthetic medium , which is required to create and clone the mutant . The predicted function of CBU1655 is synthesis of the first heptose of the inner core of C . burnetii LPS . To test this idea , cbu1655 was deleted in NMII . Isolation of LPS from NMII Δcbu1655 using a traditional hot phenol method was unsuccessful , a result possibly explained by a less water-soluble form of LPS lacking heptose and mannose [14] . Therefore , a modified LPS extraction method was used [65] . NMII Δcbu1655 produced a deep rough phase II LPS that was smaller than NMII ( Fig 5C ) that did not react with anti-phase II LPS antibody ( Fig 5D ) . These data are consistent with CBU1655-directed synthesis of the first heptose in C . burnetii LPS . Expression of cbu1655 in the mutant rescued production of NMII phase II LPS , confirming cbu1655 involvement in inner core biosynthesis . The predicted LPS structures of cbu0678tr and Δcbu1655 are depicted in Fig 5E . CBU0533 is a predicted undecaprenyl-phosphate alpha-N-acetylglucosamine phosphotransferase . In E . coli , the enzyme is responsible for initiation of O-antigen elongation [66] . NMII cbu0533 contains a 3 bp in-frame deletion that results in elimination of the first leucine ( AA 168 ) in a string of five leucine residues ( S3 Fig ) . This leucine motif is predicted to comprise a membrane-spanning region of the protein . To examine the role of cbu0533 in LPS biosynthesis , cbu0533 was deleted in NMI using a newly developed nutritional selection system based on lysine auxotrophy of C . burnetii [67] . Rescue of C . burnetii growth in ACCM-D media lacking lysine is accomplished by expression of lysCA ( lpp1774 ) from Legionella pneumophila ( S4 Fig ) . The LPS profile of NMI Δcbu0533 was identical to that of NMII ( Fig 6A and 6B ) . Complementation here and elsewhere employed gDNA from wild-type NMI ( comp-I ) or NMII ( comp-II ) . Expression of a wild-type copy of cbu0533 ( comp-I ) in NMI Δcbu0533 rescued production of phase I LPS . However , expression of cbu0533 , containing the 3 bp in-frame deletion ( comp-II ) , did not rescue ( Fig 6A and 6B ) , suggesting the missing leucine residue of NMII CBU0533 is essential for function . To further examine the importance of leucine 168 , we expressed NMI cbu0533 in NMII . Expression resulted in the production of an intermediate LPS ( Fig 6C and 6D ) . No phase I LPS was seen . This result was expected as full complementation of NMII with one gene to produce phase I LPS is not possible due to the large chromosomal deletion ( cbu0678 to cbu0698 ) of the strain [45 , 46] . Indeed , as demonstrated in Fig 5 , disruption of cbu0678 alone results in production of an intermediate LPS . The leucine residue deleted in NMII CBU0533 is directly downstream of the predicted active site of WecA ( S3 Fig ) [68] , suggesting the change may influence enzyme function . The active site of E . coli WecA has two aspartate residues , D156 and D159 , ( S3 Fig ) that are conserved in C . burnetii [61] . To examine if D156 is critical for CBU0533 function , we expressed a mutant of cbu0533 in NMI Δcbu0533 where D156 is replaced with a cysteine residue ( cbu0533-D156C ) . The cbu0533-D156C construct did not complement NMI Δcbu0533 ( Fig 6C and 6D ) , confirming D156 is necessary for function . The predicted structures of NMI Δcbu0533 , NMI Δcbu0533comp-I , and NMII cbu0533comp-I are depicted in Fig 6E . Together , these results indicate that disruption of CBU0533 results in the severely truncated phase II LPS of NMII . Domain analysis of CBU0845 suggested the enzyme is a GDP-mannose dehydrogenase . Phase II LPS profiles of California ( RSA350 ) , California ( RSA350 ) C2 , and M44 ( RSA461 ) C1 correlated with mutation of cbu0845 , but not cbu0533 ( Fig 3 ) . Thus , CBU0845 likely directs synthesis of mannose in the C . burnetii outer core [41] , the absence of which results in phase II LPS . Two mutations in cbu0845 were identified by whole genome sequencing ( S2 Table ) . Ninety-three percent of cbu0845 sequence reads from California ( RSA350 ) contained a single base pair deletion that causes a frameshift at amino acid 257 ( S5 Fig ) . M44 ( RSA461 ) C1 is clonal for a 24 base pair deletion that eliminates AA 280 to 287 ( S5 Fig ) . Both strains also have mutations in cbu0678 , necessary for intermediate-to-phase I transition , that cause a frameshift . However , in the case of California ( RSA350 ) , the mutation was found in only 19% of the sequence reads . Consistent with the presence of ~7% wild type cbu0845 in California ( RSA350 ) , a small amount of phase I LPS was detected by immunoblot that was undetectable by silver stain ( Fig 7A , 7B and 7C ) . Expression of wild-type cbu0845 restored production of phase I LPS . Expression of wild type cbu0845 in California ( RSA350 ) C2 , which is clonal for mutations in cbu0845 and cbu0678 ( Fig 7C ) , resulted in production of intermediate LPS , but not phase I LPS , consistent with the cbu0678 mutation preventing production of phase I LPS . Expression of wild type cbu0845 in M44 ( RSA461 ) C1 , also clonal for mutations in cbu0845 and cbu0678 ( Fig 7C ) , resulted in production of an intermediate LPS . A model for the predicted LPS structures of these strains is depicted in Fig 7D . These results confirm that mutation of cbu0845 results in generation of phase II LPS . Moreover , they show a potential two-step transition to phase II LPS . As shown in Fig 3 , LPS from the Australia ( RSA297 ) strain produces a phase II LPS that does not react with antibody generated against NMII LPS . Sequencing of Australia ( RSA425 ) and Australia ( RSA297 ) identified a single base pair deletion in cbu1657 that results in a frameshift ( S2 Table ) . CBU1657 has homology to alpha-L-glycero-D-manno-heptose beta-1 , 4-glucosyltransferase . However , C . burnetii LPS contains D-glycero-D-manno-heptose , and not L-glycero-D-manno-heptose [41] . Based on the known structure of NMII phase II LPS [41 , 42] , we predicted that CBU1657 adds mannose to the first heptose of phase II LPS . This hypothesis is based on mannose having a 1 , 4 linkage to heptose I in phase II LPS and that cbu1657 is predicted to encode a beta-1 , 4-glucosyltransferase [42 , 59] . NMII Δcbu1657 produced an LPS slightly smaller than NMII that was not recognized by anti-phase II LPS antibody ( Fig 8A and 8B ) . Reactivity was restored by expression of wild type cbu1657 . Expression of cbu1657 in Australia ( RSA297 ) restored reactivity to anti-phase II LPS antibody ( Fig 8C and 8D ) . These results show that mutation of cbu1657 results in an antigenically-distinct phase II LPS that is smaller than NMII LPS , and that the 1 , 4-linked mannose is a necessary component of the epitope recognized by anti-phase II LPS antibody . Models of the LPS structures produced by the strains examined above are depicted in Fig 8E . Experiments thus far utilized strains serologically in phase II to find mutations related to LPS transition . Ftacek et al , ( 2000 ) showed that serial passage of the Priscilla strain of C . burnetii in embryonated hen’s eggs results in loss of phase I LPS and accumulation of intermediate and phase II LPS forms [27] . Consistent with this finding , we demonstrated 30 passes of five C . burnetii strains in axenic media results in similar LPS changes ( Fig 2 and S2 Fig ) . To examine global mutational changes associated with LPS transition , DNA was isolated from NMI ( RSA363 ) , S ( Q217 ) , G ( Q212 ) , Dugway ( 7E65-68 ) , and NMC after 2 , 10 , 20 and 30 passes and sequenced . Fourteen mutations in 11 predicted LPS biosynthesis genes were identified ( Table 1 ) . In general , the frequency of mutation correlated with passage number . For example , NMI ( RSA363 ) contained a 4 bp insertion in cbu0839 resulting in a frameshift . This mutation increased in frequency to 30 . 4% of sequence reads at passage 10 and corresponded with the appearance of phase II LPS forms of ~3 and 6 kDa ( Fig 2 ) . Mutations in non-LPS biosynthetic genes were also identified , but their frequency did not increase with passage . To confirm the cbu0839 mutation was responsible for phase II LPS , the gene was deleted in NMI ( RSA363 ) . The LPS profile of NMI Δcbu0839 exhibited the ~3 and 6 kDa phase II LPS forms of passage 30 NMI RSA363 , with no intermediate or phase I LPS forms ( Fig 9A and 9B , S2B Fig ) . Expression of wild type cbu0839 in NMI Δcbu0839 restored production of phase I LPS ( Fig 9A and 9B ) . A model of the predicted LPS structure of NMI Δcbu0839 is depicted in Fig 9C . This result indicated that mutations that accumulate in LPS-related genes after in vitro passage are associated with a changing LPS profile . Consistent with this hypothesis , passage of NMC resulted in the accumulation of the same 3 bp deletion in cbu0533 responsible for phase II LPS of NMII ( Table 1 , S2A Fig ) . Interestingly , accumulation of this mutation was also identified in phase II Turkey ( RSA315 ) and Ohio ( RSA338 ) , which is a phase II variant of phase I Ohio ( RSA270 ) that lacks this mutation [69 , 70] , suggesting cbu0533 is a frequent mutational target in phase variation . Insertion and deletion mutations generally disrupt protein function whereas point mutations can have disparate effects . Sequencing of passaged strains identified point mutations in homologs of cbu0533 and cbu0678 in S ( Q217 ) and G ( Q212 ) , respectively . The cbu0533 homolog of S ( Q217 ) accumulated a point mutation conferring a T138M change . The mutational frequency was highest at passage 10 ( 44 . 0% ) , then decreased in passage 20 ( 16 . 9% ) and 30 ( 0 . 6% ) , which corresponded to the level of phase II LPS ( Fig 2 and S2B Fig ) . To determine the effect of this mutation , cbu0533-T138M was expressed in NMI Δcbu0533 . Expression of cbu0533-T138M did not result in the same phase I LPS profile as expression of wild type cbu0533 ( Fig 10A and 10B ) . Specifically , LPS bands between 10 and 20 kDa were missing . These data suggested that CBU0533-T138M may retain partial function . The appearance of mutations conferring P74A and G369R changes in domain II and domain I , respectively , of the cbu0678 homolog of G ( Q212 ) , coincided with a phase I to intermediate LPS change ( Fig 4A ) . In E . coli , these domains function independently of each other , suggesting either mutation could affect enzyme activity [64] . Expression of cbu0678-P74A in NMI cbu0678tr resulted in LPS lacking most of its O-antigen , with only a single band detected by anti-phase I LPS antibody ( Fig 10C and 10D ) . Expression of cbu0678-G369R or the double mutant cbu0678-P74A/G369R in NMI cbu0678tr failed to restore phase I LPS production ( Fig 10C and 10D ) . Thus , both point mutations affect function of CBU0678 . A model of the predicted LPS structures resulting from these mutations is depicted in Fig 10E . Collectively , these results demonstrate that serial passage of C . burnetii in axenic media causes accumulation of mutations that disrupt LPS biosynthesis genes .
As a critical virulence factor , understanding synthesis and modification of C . burnetii LPS is essential for expanding our knowledge of Q fever pathogenesis and protective immunity . Here , we identified natural mutational pathways that generate three distinct phase II LPS molecules , two of which are antigenically unique based on antibody reactivity . A fourth deep-rough phase II was generated by gene knockout . Phase II strains grow in eukaryotic host cells , indicating extended LPS structure is unnecessary for lysosomal resistance ( S1 Table ) [58] . A gene necessary for virenose synthesis and O-antigen addition to an intermediate LPS form was also identified . Organisms synthesizing this form can be derived from a chronically-infected animal ( i . e . , NMC ) , as well as during in vitro passage [38] . It is interesting to speculate on whether organisms producing intermediate LPS are found in persistent focalized infections of humans , potentially as a result of defective immune clearance . This study provides important insight into C . burnetii phase transition in addition to identifying several steps in LPS biosynthesis . Disparate mutations in cbu0678 of five C . burnetii phase II strains indicate this gene is a common target during phase variation . Disruption of cbu0678 causes full-length LPS of phase I bacteria to convert to an intermediate length LPS similar to that of NMC . The function of CBU0678 is unknown . The enzyme is annotated as a bifunctional sugar kinase/adenylyltransferase with homology to CBU1655 . The domain structure of CBU1655 is reversed from CBU0678 , and we show it is involved in synthesis of the inner core . Mutation of cbu0533 or cbu0845 causes phase I to phase II LPS transition . Indeed , a 3 base pair deletion in cbu0533 is solely responsible for the truncated phase II LPS of NMII . This result solves the puzzle as to why NMC , with a 31 . 5 kb chromosomal deletion of LPS-encoding genes that overlaps the deletion of NMII , produces a larger molecular weight intermediate LPS [46 , 49 , 52] . The function of CBU0533 is unknown . CBU0533 has homology to WecA , which in E . coli encodes a undecaprenyl-phosphate alpha-N-acetylglucosamine phosphotransferase that catalyzes the first step in O-antigen synthesis [61] . However , cbu0533 does not complement an E . coli ΔwecA mutant [31] , suggesting it has a different function in C . burnetii . Consistent with this hypothesis , deletion of cbu0533 in C . burnetii results in loss of LPS outer core and O-antigen , suggesting CBU0533 catalyzes the first step in outer core biosynthesis . High frequencies of the same 3 bp deletion were also found in cbu0533 homologues of phase II Turkey ( RSA315 ) and Ohio ( RSA338 ) [69 , 70] . Thus , cbu0533 is a common mutational target during transition to phase II . Two separate mutations in cbu0845 were found in two strains of C . burnetii: California ( RSA350 ) and M44 ( RSA461 ) C1 . CBU0845 encodes a protein annotated as a GDP-mannose dehydrogenase . The presence of mannose in the outer core of C . burnetii LPS [41] , and the LPS profile produced by natural cbu0845 mutants , suggests that CBU0845 is required for mannose production . Collectively , cbu0533 and cbu0845 are mutational hotspots for generating phase II LPS . Insight into additional genetic lesions underlying C . burnetii phase variation was achieved by weekly serial passage of five strains in axenic media for 30 weeks . LPS profiles show that phase variation in C . burnetii strains from distinct genomic groups occurs in a similar fashion . Phase II LPS was detected in less than 10 passages in axenic medium , indicating a rapid transition from phase I to phase II . This is consistent with previous work showing phase II LPS after 10 passages of phase I C . burnetii in embryonated chicken eggs and tissue culture [27 , 29] . In the later report , loss of phase I LPS may have been accelerated due to enhanced infectivity of phase II bacteria for cultured host cells , a behavior attributed to the anionic surface charge of phase II bacteria due to the loss of carbohydrate O-antigen [18 , 54] . Sequencing of passage variants identified 14 mutations in 11 predicted LPS-associated genes that accumulate in frequency during passage . Novel mutations in cbu0678 and cbu0533 were identified in G ( Q212 ) and S ( Q217 ) , respectively . Subsequent functional analysis of the gene products indicate that these mutations affect protein function . In many gram-negative bacteria , genes required for core and O-antigen synthesis are linked [71] . C . burnetii has three chromosomal regions enriched in genes required for O-antigen synthesis . The genes cbu0825 to cbu0856 , cbu0664 to cbu0704 , and cbu1831 to cbu1838 are implicated in synthesis of dihydrohydroxystreptose , virenose , and sugar components of O-antigen , respectively [72] . Consistent with this organization , additional high frequency mutations were found in cbu0676 and cbu0677 that are in an operon with cbu0678 [73] . Based on co-expression with cbu0678 , we predict mutation of cbu0676 and cbu0677 will also result in an intermediate length LPS due to the lack of virenose synthesis . Interestingly , accumulation of mutations in genes not involved in LPS synthesis was less evident , which again suggests a selective advantage during axenic growth for disruption of LPS biosynthesis . With the mutational data identified here , we developed a model for genetic lesions underlying C . burnetii phase variation ( Fig 11 ) . Full-length phase I LPS is required for growth in mammals and potentially arthropods , but not in immunoincompetent host cells or axenic media [18 , 30 , 74] . Thus , serial passage in axenic media results in the loss of repeating O-antigen . This can occur via accumulation of mutations in genes that result in a semi-rough , intermediate LPS ( e . g . cbu0678 ) , or alternatively , mutations that directly result in a rough , phase II LPS ( e . g . cbu0839 , cbu0533 , or cbu0845 ) . Transition from a semi-rough intermediate length LPS ( e . g . NMC ) to a phase II LPS occurs via mutation of cbu0533 or cbu0845 . Finally , additional mutations in cbu1657 and/or cbu1655 can occur that result in further modification or shortening of phase II LPS structure . Antibiotic treatment of persistent focal infections can be problematic , and doxycycline-resistant strains have been described [3 , 4 , 75] . Thus , there is need for new Q fever therapeutics . Increasing antibiotic resistance has resulted in development of small molecule inhibitors that target bacterial LPS and cell wall synthesis [76–80] . The most common LPS targets are LpxA , LpxD , and LpxC , which catalyze the first three steps in lipid A biosynthesis [81] . Additional targets are GmhB and WecA , that are involved in inner core and O-antigen biosynthesis , respectively [82–84] . In Mycobacterium tuberculosis , inhibition of WecA by the caprazamycin derivative CPZEN-45 inhibits cell wall biosynthesis [78] . The two C . burnetii LPS enzymes , CBU0533 and CBU0845 , identified in this study as necessary for elongatation of phase II LPS , are potential targets for inhibition . The encoding genes are conserved in all C . burnetii strains and inhibition of these enzymes in vivo would likely convert phase I to phase II bacteria , which are effectively cleared by the host immune system [18] . Such a treatment might resolve persistent focalized infection with or without antibiotic therapy . In summary , this study utilized a newly described axenic medium ( ACCM-D ) [67] and a nutritional selection system to generate directed mutations in C . burnetii LPS genes . The process for creating gene deletions in virulent C . burnetii is like that of the avirulent risk group 2 strain NMII [85–87] . A caveat is that the passaging required to expand and clone transformants of phase I C . burnetii results in a mixed population of organisms producing intermediate or phase II LPS . This problem can be subverted at the cloning stage by picking colonies distinctive of phase I C . burnetii . Multiple mutational pathways directing phase variation by the organism were revealed . Identification of genes involved in phase II transition allows generation of phase II variants beyond the NMII reference strain . Regulatory approval of these strains for risk group 2 containment would enable host-pathogen studies of C . burnetii within different genetic groups .
The bacterial strains used in this study are listed in S1 Table . Wild type C . burnetii and genetic transformants were grown microaerobically in ACCM-2 as previously described [88] . For nutritional selection of transformants , strains were grown in ACCM-D minus lysine [67] . E . coli Stellar ( BD Clontech ) and PIR1 ( ThermoFisher Scientific ) cells were used for recombinant DNA procedures and cultivated in Luria broth . E . coli transformants were selected on LB agar plates containing 50 μg of kanamycin/ml or 10 μg of chloramphenicol/ml . Genes conferring resistance to chloramphenicol , kanamycin , or ampicillin are approved for C . burnetii genetic transformation studies by the Rocky Mountain Laboratories Institutional Biosafety Committee and the Centers for Disease Control and Prevention , Division of Select Agents and Toxins Program . Australia ( RSA425 ) , Australia ( RSA297 ) , California 16 ( RSA350 ) , California 16 ( RSA350 ) C2 , and M44 ( RSA461 ) C1 were grown in ACCM-2 for 7 days . Nine Mile ( RSA363 ) , G ( Q212 ) , S ( Q217 ) , Dugway ( 7E65-68 ) , and Nine Mile Crazy ( RSA514 ) were grown in ACCM-2 for 7 days and serially passaged each week for 30 weeks . Genomic DNA was isolated from C . burnetii strains using the PowerMicrobial Maxi DNA isolation kit ( Mo Bio ) with an additional step of boiling for 30 minutes prior to physical disruption of the cells . DNA was sequenced using an Illumina MiSeq instrument to generate read pairs as previously described [20 , 70] . Raw FASTQ reads for each sample were quality trimmed using trimmomatic tool , version 0 . 3 [89] with the following settings: PE ILLUMINCLIP:Truseq3-PE . fa:2:30:10 CROP:225 LEADING:10 TRAILING:10 SLIDINGWINDOW:4:15 MINLEN:36 . Quality trimmed reads were then assembled into contiguous sequences ( contigs ) using SPAdes Genome Assembler , version 3 . 9 . 1 , and the -careful flag and kmer lengths of 21 , 33 , 55 , 77 , 99 , 127 . Contigs with coverage less than 2 and shorter than 200 base pairs were discarded . The draft genomes were submitted to Genbank for annotation using the NCBI Prokaryotic Genome Annotation pipeline ( PGAP ) . Annotation stats and accession numbers for each genome are given in S3 Table . Raw sequenced reads for all genomes were submitted to the NCBI sequence read archive ( https://www . ncbi . nlm . nih . gov/sra/ ) with SRA accession numbers for each sample given in S4 Table . Following trimming , sequence reads were aligned to the Nine Mile RSA493 chromosome ( NC_002971 . 4 ) and plasmid ( NC_004704 . 2 ) sequences using Bowtie2 [90] . The SAMtools package was then used to create sorted BAM ( Binary Alignment/Map ) files [91] . Small nucleotide polymorphisms ( SNPs ) present in NMI ( RSA363 ) , NMII ( RSA439 ) , NMC ( RSA514 ) , Australia ( RSA297 ) , Australia ( RSA425 ) , California ( RSA350 ) , California ( RSA350 ) C2 , and M44 ( RSA461 ) C1 were identified by importing BAM files into Geneious version 10 . 2 . 2 ( Biomatters ) . Mutation frequency was determined using the Find Variations/SNPs function with a minimum coverage of 10 and minimum variant frequency of 0 . 85 . BAM files from passaged strains were uploaded into Geneious and analyzed for mutation frequency using the Find Variations/SNPs function with a minimum coverage of 10 and minimum variant frequency of 0 . 1 . C . burnetii LPS was extracted using a modified hot phenol method as described [52] . LPS from “deep rough” C . burnetii mutants was isolated using a modified LPS microextraction protocol [65] . Briefly , bacterial cells harvested from a 30 ml ACCM-2 culture were suspended in 100 μL water and boiled for 10 minutes . Samples were cooled , 1 mg of DNaseI ( Sigma ) added , then incubated for 2 hours at 37°C . One-hundred microliters of lysis buffer [4% SDS , 4% β-mercaptoethanol , 0 . 1% bromophenol blue , 10% glycerol , 1 M Tris-HCl ( pH 6 . 8 ) ] was added and samples heated at 100°C for 10 minutes . Five microliters proteinase K ( 20 mg/mL ) was added to the cooled samples , which were incubated in a 55°C water bath overnight . Samples were boiled for 5 minutes to inactivate proteinase K . Samples for silver or glycoprotein staining were electrophoresed on 16% tricine-SDS-PAGE gels [92] . Samples for immunoblotting were electrophoresed on 12% or 16% glycine-SDS-PAGE gels . LPS bands were sized using the Precision Plus Dual color ( Bio-Rad ) or SeeBlue Plus2 prestained protein ladders ( ThermoFisher Scientific ) . In gel LPS was stained using SilverQuest or the GelCode Glycoprotein staining kit following the manufacturer’s instructions ( ThermoFisher Scientific ) . For immunoblotting , LPS samples were transferred to PVDF membrane . Mouse monoclonal antibodies AAB-COX-MAB ( Bei Resources ) , 1E4 ( a generous gift of Guoguan . Zhang , University of Missouri-Columbia ) [93] , and A6 [59] were used to label phase I , intermediate , and phase II LPS molecules , respectively . Antibodies were used at dilutions of 1:10000 ( AAB-COX-MAB ) , 1:10000 ( 1E4 ) , and 1:50 ( A6 ) . Reacting LPS was detected using IgG secondary antibody conjugated to horseradish peroxidase and chemiluminescence using Supersignal West Pico chemiluminescent substrate ( ThermoFisher Scientific ) . Plasmids and oligonucleotide primers used in this study are listed in S1 and S5 Tables , respectively . Restriction endonucleases were obtained from New England BioLabs . PCR was conducted using Accuprime Pfx or Taq polymerase ( ThermoFisher Scientific ) . Oligonucleotide primers were purchased from Integrated DNA technologies . All cloning reactions were performed using the In-Fusion HD PCR cloning kit ( BD Clontech ) , and the resulting reactions transformed into either E . coli PIR1 or Stellar competent cells . Plasmid construction is described in S6 Table . All genetic manipulations of NMII predicted to lengthen LPS and potentially increase virulence were approved by the Rocky Mountain Laboratories Institutional Biosafety Committee and were conducted under risk group 3 conditions . Nine Mile phase I bacteria were electroporated with 20 μg of suicide plasmid DNA ( pJC-Kan::cbu0678tr-5'3'-CAT , pJC-CAT::cbu0533-5'3'-lysCA or pJC-CAT::cbu0839-5'3'-lysCA ) as previously described [88] . Co-integrants were selected by culture of bacteria in ACCM-2 plus 1% FBS containing chloramphenicol ( final concentration of 3 μg/ml ) and kanamycin ( final concentration of 400 μg/ml ) , or in ACCM-D lacking lysine ( ACCM-D-lys ) plus 1% FBS . Resolution of pJC-Kan::cbu0678tr-5'3'-CAT co-integrants was accomplished by culture for 7 days in ACCM-2 supplemented with chloramphenicol prior to subculture for 4 days in ACCM-2 containing 1% sucrose and chloramphenicol . Resolution of pJC-CAT::cbu0533-5'3'-lysCA and pJC-CAT::cbu0839-5'3'-lysCA co-integrants was accomplished by culture in ACCM-D-lys containing 1% sucrose for 4 days . Surviving transformants were expanded by culture in ACCM-2 containing chloramphenicol ( NMI cbu0678tr ) or ACCM-D-lys ( NMI Δcbu0533 and NMI Δcbu0839 ) until growth was visible . The NMI cbu0678tr mutant was cloned by picking colonies on ACCM-2 agarose as previously described [88] . The NMI Δcbu0533 and NMI Δcbu0839 mutants were cloned by top spreading 100 μl of diluted 7 day culture on 0 . 25% ACCM-D-lys agarose followed by a 7 day incubation . Picked bacterial colonies were expanded in their respective media . Verification of NMI cbu0678tr , NMI Δcbu0533 , and NMI Δcbu0839 mutants was conducted by PCR using CBU0678tr-KO-F/CBU0678tr-KO-R , CBU0533-KO-F/CBU0533-KO-R , and CBU0839KO-F/CBU0839KO-R primer pairs , respectively , and gDNA from wild-type or mutant bacteria . Complementation of C . burnetii NMI cbu0678tr , NMI Δcbu0533 , and NMI Δcbu0839 was achieved by transformation of mutant strains with pJB-Kan::cbu0678comp-I , pMiniTn7T-CAT::cbu0533comp-I or pMiniTn7T-CAT::cbu0839comp-I , respectively [86 , 88] . To assess the role of mutations found in NMII cbu0533 , the mutant S ( Q217 ) homolog of cbu0533 ( T138M ) , or the active site mutant of NMI cbu0533 ( D156C ) , NMI Δcbu0533 was transformed with pMiniTn7T-CAT::cbu0533comp-II , pMiniTn7T-CAT::cbu0533comp-T138M , or pMiniTn7T-CAT::cbu0533comp-D156C , respectively . Mutations responsible for P74A and G369R changes in the G ( Q212 ) homologue of CBU0678 were examined by transformation of NMI cbu0678tr with pJB-Kan::cbu0678comp-P74A , pJB-Kan::cbu0678comp-G369R or pJB-Kan::cbu0678comp-P74A/G369R . Generation of C . burnetii Δcbu1655 and Δcbu1657 in NMII was achieved using the suicide plasmids pJC-Kan::cbu1655-5'3'-CAT and pJC-CAT::cbu1657-5'3'-lysCA and methods described above for NMI cbu0678tr and NMI Δcbu0533 , respectively . Verification of NMII Δcbu1655 and NMII Δcbu1657 was confirmed by PCR of gDNA from wild-type or mutant bacteria using CBU1655-KO-F/CBU1655-KO-R and CBU1657-KO-F/CBU1657-KO-R primer pairs , respectively . C . burnetii NMII Δcbu1655 and NMII Δcbu1657 mutants were complemented using pMiniTn7T-Kan::cbu1655comp-II and pMiniTn7T-CAT::cbu1657comp-II , respectively [86] . M44 ( RSA461 ) C1 was cloned from M44 ( RSA459 ) by plaque assay [60] . California 16 ( RSA350 ) C2 was cloned by micromanipulation [59] . M44 ( RSA461 ) C1 , California ( RSA350 ) , and California ( RSA350 ) C2 strains were complemented with pMiniTn7T-CAT::cbu0845comp-I [86] . Australia ( RSA297 ) was complemented with pJB-CAT::cbu1657comp-II [88] . The complete genome sequence of C . burnetii NMC ( RSA514 ) , Australia ( RSA297 ) , Australia ( RSA425 ) , M44 ( RSA461 ) C1 , California 16 ( RSA350 ) C2 have been deposited in GenBank under the accession numbers listed in S3 Table . Sequence read archive files of Australia ( RSA297 ) , Australia ( RSA425 ) , M44 ( RSA461 ) C1 , NMI ( RSA363 ) , NMC ( RSA514 ) , California 16 ( RSA350 ) , California 16 ( RSA350 ) C2 , Dugway ( 7E65-68 ) , and passage variants ( P2 , P10 , P20 and P30 ) of Nine Mile ( RSA363 ) , Nine Mile Crazy ( RSA514 ) , G ( Q212 ) , S ( Q217 ) , and Dugway ( 7E65-68 ) have been deposited in Genbank under the SRA accession numbers listed in S4 Table .
|
Coxiella burnetii is the causative agent of Q fever , an acute febrile illness that can develop into a persistent focalized infection , such as endocarditis or vascular disease . Currently , the only licensed vaccine against Q fever is Q-Vax , a formalin-inactivated whole-cell preparation of the virulent C . burnetii Henzerling strain that is only available in Australia . Full-length ( smooth ) LPS is required for full virulence and efficacious Q fever vaccines . Indeed , various immune assays show LPS as an immunodominant antigen . Upon serial passage of C . burnetii in embryonated hen’s eggs , tissue culture , or synthetic medium , a smooth-to-rough ( truncated ) LPS transition occurs that results in avirulence . Using laboratory strains in various stages of phase variation , we defined several genetic pathways associated with LPS phase transition . In addition to defining genes responsible for production of a critical virulence factor , this study reveals LPS enzymes early in the biosynthetic pathway that can be subjected to small molecule screens to identify compounds that inhibit production of phase I LPS . The resulting loss of C . burnetii pathogenicity may aid immune clearance of the organism to alleviate human disease .
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2018
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Genetic mechanisms of Coxiella burnetii lipopolysaccharide phase variation
|
Imatinib mesylate ( Gleevec ) inhibits Abl1 , c-Kit , and related protein tyrosine kinases ( PTKs ) and serves as a therapeutic for chronic myelogenous leukemia and gastrointestinal stromal tumors . Imatinib also has efficacy against various pathogens , including pathogenic mycobacteria , where it decreases bacterial load in mice , albeit at doses below those used for treating cancer . We report that imatinib at such low doses unexpectedly induces differentiation of hematopoietic stem cells and progenitors in the bone marrow , augments myelopoiesis but not lymphopoiesis , and increases numbers of myeloid cells in blood and spleen . Whereas progenitor differentiation relies on partial inhibition of c-Kit by imatinib , lineage commitment depends upon inhibition of other PTKs . Thus , imatinib mimics “emergency hematopoiesis , ” a physiological innate immune response to infection . Increasing neutrophil numbers by adoptive transfer sufficed to reduce mycobacterial load , and imatinib reduced bacterial load of Franciscella spp . , which do not utilize imatinib-sensitive PTKs for pathogenesis . Thus , potentiation of the immune response by imatinib at low doses may facilitate clearance of diverse microbial pathogens .
Signaling by protein tyrosine kinases ( PTKs ) mediates a variety of cellular processes including migration , morphogenesis , stress responses , and cytoskeletal reorganization [1 , 2] . Dysregulation of PTK activity causes a variety of diseases , including cancer . One such cancer , chronic myelogenous leukemia ( CML ) , is associated with a characteristic translocation between chromosomes 9 and 22 , called the “Philadelphia chromosome ( Ph ) , ” which encodes a fusion protein composed of breakpoint cluster region ( BCR ) protein and the PTK Abl1 , called BCR-ABL[3] . Expression of BCR-ABL in hematopoietic stem cells ( HSCs ) results in aberrant proliferation of Ph+ stem cells and the accumulation of myeloid cells in the bone marrow and blood . Over the last decade , development of small molecule inhibitors of Abl1 and BCR-ABL , such as imatinib mesylate ( imatinib , Gleevec ) , have dramatically reduced mortality rates in patients with CML and related cancers [4–8] . Imatinib selectively kills Ph+ myeloid lineage cells in the bone marrow and periphery , whose survival depends on expression of BCR-ABL . However , the drug does not appear to affect survival of Ph+ hematopoietic stem cells ( HSCs ) , nor of Ph- cells [9] . Imatinib inhibits several other structurally related PTKs in a dose-dependent manner [10–13] . Nanomolar concentrations of imatinib inhibit c-Abl1 and c-Abl2 , platelet-derived growth factor receptor alpha ( PDGFRα ) and beta ( PDGFRβ ) , and the stem cell receptor ( c-Kit ) , whereas micromolar concentrations inhibit macrophage colony-stimulating factor receptor ( m-CSFR or c-fms ) [13] . Accordingly , imatinib also has efficacy against gastrointestinal stromal tumors ( GISTs ) , which are caused by dysregulated c-Kit or PDGFRα [14] . Various pathogens utilize activation of Abl1 and related PTKs to facilitate intracellular survival , intracellular trafficking , and spread from cell to cell [15] . These include diarrheagenic Escherichia coli , Pseudomonas , Salmonella , Shigella , Helicobacter , Anaplasma , Chlamydia , and pathogenic mycobacteria amongst bacteria , and filoviruses , HIV , Coxsackie virus , Kaposi sarcoma virus , Polyomaviruses , and orthopoxviruses amongst viruses , as well as the human parasite Leishmania [16–33] . For pathogenic mycobacteria including Mycobacterium tuberculosis ( Mtb ) and Mycobacterium marinum ( Mm ) , imatinib enhances trafficking of the bacteria into acidified vesicles [30 , 33] , whereas for orthopoxviruses , the drug prevents Abl-dependent dissemination of the virus [31 , 32] . In contrast to the wealth of information on the role of PTKs in cancer and microbial pathogenesis , information on how PTK inhibitors function in vivo remains more limited . Historically , the therapeutic effects of imatinib have been attributed to its cell autonomous effects on tumor cells expressing oncogenic kinases , or to its inhibition of cellular kinases and pathogenesis in infected cells . However , recent evidence suggests that imatinib also regulates the immune response . Imatinib inhibits T cell signaling in vitro , and reportedly causes immunosuppression and even neutropenia in some patients , especially at high doses [34] . By contrast , other data suggests that the therapeutic effect of imatinib may actually require the immune system . Thus , imatinib remains effective in vivo even against engrafted GIST cells that are unresponsive to the drug in vitro , an effect attributed to stimulation of cross talk between dendritic cells and natural killer cells , which have attendant anti-tumor activity [35] . Moreover , recent reports indicate that imatinib relieves c-Kit-dependent immunosuppression by regulatory T cells , which in turn potentiate anti-tumor CD8+ T cell responses [36] . Such immunostimulatory effects may be important for infectious diseases as well . We measured the effects of imatinib on innate immune responses , particularly the increased numbers of myeloid cells typically seen following bacterial infection . We demonstrate that low doses of imatinib activate myelopoiesis in the bone marrow and increase the number of myeloid cells in the bone marrow , blood , and spleen , which enhances a physiological antimicrobial response to infection .
Previous studies have shown that imatinib maximally reduces mycobacterial load in mice when administered at 66mg/kg/d , whereas higher doses ( e . g . 200mg/kg/d ) proved much less effective [30] . Doses of 66mg/kg/d resulted in steady state serum levels of 57+/-21ng/ml ( ~100nM ) in mice . Such serum levels would be considered sub-therapeutic in humans , where doses of 400mg QD result in serum levels of ~1500 to ~3000 ng/ml ( 2–5 μM ) [37 , 38] . To characterize the effect of imatinib at a dose of 66mg/kg/d on the composition of immune cells , the drug was delivered to uninfected mice or those infected with Mm beginning one day prior to infection and continuing for the duration of the experiment . Blood and spleens were harvested seven days after infection and lymphoid , myeloid and dendritic cell ( DC ) populations enumerated by flow cytometry ( Figs . 1A , B; S1A-S1C ) . Imatinib significantly increased the number of myeloid-derived cells in the blood and spleen in both infected and uninfected animals . No difference in cell numbers was evident in animals left untreated or treated with the carrier ( water ) . The number of neutrophils , defined as CD11b+ Ly6Cint Gr-1hi SSCint , increased on average by 14-fold in blood , and 22-fold in spleen in imatinib-treated mice compared to uninfected untreated controls ( Fig . 1A; representative plots in S1B Fig . ) . Infection with Mm alone increased neutrophil numbers by ~3 and 6-fold in the blood and spleen , respectively . Neutrophil numbers in animals treated with imatinib and infected increased by 15- and 25-fold in blood and spleen , respectively , compared to numbers observed in uninfected controls ( Figs . 1A; S1B ) . Monocytes , eosinophils , natural killer ( NK ) cells , and CD8− DCs likewise increased in number following imatinib treatment with or without infection , though to a lesser extent than neutrophils ( Figs . 1A , B; S1C ) . Imatinib also increased numbers of CD8+DCs in the spleen compared to uninfected controls ( S1C Fig ) . By contrast , imatinib produced no change in the numbers of T cells or B cells ( Fig . 1B ) . Thus , imatinib caused a large-scale increase in cells of the myeloid lineage , as well as changes in CD8+ DCs and NK cells . To validate the expansion of the myeloid compartment , histological sections of spleens were analyzed seven days after imatinib treatment and/or infection with Mm . As shown in Fig . 1C , the splenic architecture was largely maintained with drug treatment or infection , with CD169+ marginal zone macrophages ( green ) clearly demarcating lymphocyte areas from F4/80+ CD11b+ red pulp ( blue , Fig . 1C ) . However , the expansion of myeloid cells following imatinib treatment was clearly evident , with accumulation of CD11b+ cells evident ( red , Fig . 1C ) , particularly in the areas of the red pulp adjacent to the marginal zones . To determine whether the number of myeloid cells with imatinib transiently increased , uninfected mice were treated with drug for up to 27 days , the longest time tested , and the numbers of immune cells in the blood determined at weekly intervals . Increases in numbers of neutrophils and monocytes persisted for as long as the drug was administered , whereas the numbers of eosinophils , DCs , B cells and T cells did not significantly increase over this time period ( e . g . Fig . 1D ) . We next determined whether the increased number of myeloid cells in blood and spleen seen at 66mg/kg/d resulted from increased production in the bone marrow . Giemsa staining of femurs from imatinib-treated mice revealed a pronounced increase in marrow cellularity ( Fig . 2A ) . Moreover , centrifuged pellets of bone marrow from mice treated with imatinib appeared white , a hallmark of neutrophilia , compared to marrow from untreated mice , which was pink . The numbers of neutrophils and monocytes in bone marrow from imatinib-treated mice increased by 4- and 3-fold , respectively ( Fig . 2B; gating schema in S2A Fig ) . The numbers of mature B cells , T cells , DCs , eosinophils , and NK cells in the bone marrow did not change significantly upon treatment with imatinib ( Figs . 2B; S2B ) . Thus , accumulation of mature myeloid cells in the marrow was positively correlated with increases in mature cells in the blood and spleen at a dose of 66mg/kg/d . By contrast , with infection the number of mature myeloid cells in the bone marrow did not significantly increase with or without imatinib . Because increases in numbers of mature cells were evident in blood and peripheral tissues ( Fig . 1 ) , we surmise that infection augmented migration of mature cells from the bone marrow . The bone marrow is the primary site of post-natal hematopoiesis , where dormant hematopoietic stem cells ( HSC ) become activated [39 , 40] and sequentially differentiate into four identifiable multipotent progenitor cell types ( MPP1-MPP4 ) [41] , myeloid/lymphoid or erythroid/myeloid progenitors [42 , 43] , and finally , into a variety of both immature cells and mature cells , which migrate out of the bone marrow . HSCs are phenotypically distinguished from more mature cells primarily based on their capacity for efficient immune reconstitution upon transplant into lethally irradiated animals [39 , 40] . We next assessed the effects of imatinib on numbers of neutrophil precursors , HSCs , and multipotent progenitors in bone marrow by flow cytometry . Despite increases in the numbers of mature neutrophils in the bone marrow , no effects of imatinib were evident on the number of neutrophil precursors , including promyelocytes , myelocytes , and metamyelocytes ( S3A and S3B Fig ) . By contrast , the fraction of LineagenegSca1+c-Kit+ ( LSK ) cells [44] , which include MPPs and HSCs , increased by 2-fold on average with imatinib treatment at 66 mg/kg/day , and to an even greater extent with infection ( Figs . 3A; S4A and S4B ) . Notably accumulation of LSK cells evident with infection markedly decreased with imatinib treatment ( Fig . 3A ) . Using markers defined by Wilson et al . [41] ( S4A Fig ) , we next determined whether the observed increase in numbers of LSK cells was due to an effect on HSCs , MPP1 , MPP2 , MPP3 , or MPP4 cells . As shown in Fig . 3B , the number of HSCs remained unchanged with imatinib alone at 66 mg/kg/day , but increased by up to 5-fold with Mm infection . Notably , reduced accumulation of HSCs in infected animals was evident upon treatment with imatinib . Numbers of MPP2 , MPP3 and MPP4 cells increased with imatinib at 66 mg/kg/day , with the greatest accumulation compared to control animals evident with MPP3 and MPP4 cells ( Fig . 3C ) . Infection alone produced an accumulation of MPP1 , MPP2 , MPP3 , and MPP4 cells , and , as with HSCs , MPP1 and MPP2 cells accumulated to a lesser extent with infection plus imatinib compared to infection alone ( Fig . 3C ) . Together , these data indicate that ( i ) imatinib at 66 mg/kg/day does not induce accumulation of HSCs , whereas infection does; ( ii ) imatinib nevertheless reduces accumulation of HSCs upon infection , suggesting that the drug may increase flux of HSCs to progenitors; and ( iii ) imatinib regulates accumulation of subsets of MPPs , an effect also evident with infection . To determine whether imatinib caused an expansion of transplantable HSCs , we assessed the engraftment capacity of these cells in a competitive repopulation assay [45] . Marrow derived from control CD45 . 2 C57Bl/6 mice or CD45 . 2 C57Bl/6 mice treated with imatinib for seven days was mixed with marrow derived from untreated CD45 . 2 GFP+C57Bl/6 mice . Irradiated congenic CD45 . 1+ C57Bl/6 mice were then injected with either the control/GFP mixture or the imatinib/GFP mixture . At 4 and 12 months post-transplant , mice were bled and the relative numbers of blood cells derived from GFP- , naïve- or imatinib-treated donor mice , or recipient mice determined by flow cytometry . At all time points , the proportion of blood cells derived from naïve or imatinib-treated animals were similar , indicating a lack of competitive advantage of bone marrow from imatinib-treated mice ( Fig . 3D ) , in accordance with the absence of a statistically significant accumulation of HSCs ( Fig . 3B ) . Because HSCs represent less than ~0 . 003% of the total marrow [41] , it remained possible that the competition assay with whole marrow might not resolve small differences in the HSC numbers between imatinib-treated or naive mice . To assess this possibility , competitive transplant experiments using just the LSK fraction from naïve or imatinib-treated animals were performed . As shown in Fig . 3E , at four months post transplant , proportions of mature cells derived from imatinib-treated mice were unchanged compared to their naïve counterparts . Together , these data suggest that imatinib does not cause an increase in the number of transplantable HSCs , in accordance with observations showing that HSCs do not accumulate upon treatment with the drug ( Fig . 3B ) . However , these data do not rule out the possibility that imatinib increases the capacity of a fraction of activated HSCs to asymmetrically divide and rapidly differentiate into MPPs , a result suggested by data with infection plus imatinib ( Fig . 3A-C ) . To further characterize the effects of imatinib on the expansion and differentiation of myeloid progenitors , bone marrow from imatinib-treated mice was plated in semi-solid media and analyzed by Colony Forming Cell ( CFC ) assay to detect and quantify colonies of granulocyte-macrophage hematopoietic progenitors ( CFU-GM ) . As shown in Fig . 4A , bone marrow derived from mice treated with imatinib at 66mg/kg/d or 200 mg/kg/d for seven days yielded ~34% more CFU-GM colonies compared to marrow from naïve mice . These data suggest that treatment with imatinib induces an irreversible commitment of HSCs into progenitors that can differentiate ex vivo into myeloid cells . To determine whether cells from naïve animals could likewise be induced to differentiate into myeloid-type colonies when treated with imatinib in culture , CFC assays were performed on naïve bone marrow cultured with various concentrations of drug . As shown in Fig . 4B , addition of imatinib at 50 nM caused a 33% increase in CFU-GM , whereas concentrations exceeding 500 nM , were without effect . We also assessed the effects of PTK inhibitors in CFC assays using marrow derived from human donors . Addition of low concentrations of imatinib ( 50 nM ) to the media maximally increased the number of CFU-GM by 42% compared to untreated marrow ( Fig . 4C ) . By contrast , and in accordance with previous reports [46] concentrations at or exceeding 500nM were without effect . Together , these data suggest that imatinib induced an irreversible differentiation of HSCs or progenitors into myeloid cells in a dose-dependent fashion , and that imatinib effects on myelopoiesis in vivo are recapitulated in cultures of murine and human cells in vitro . Partial inhibition of c-Kit affects myeloid and lymphoid cells in the blood . Previous studies suggested that administration of high concentrations ( 1mg ) of the c-Kit neutralizing antibody ACK2 to C57Bl/6 mice ablated hematopoietic progenitors [47] . However , because the effects of imatinib appeared strongly dose-dependent , we reasoned that partial inhibition of c-Kit might recapitulate some or all effects of the drug . To test this hypothesis , we assessed the effects of ACK2 at lower concentrations ( 0 . 3ng , 3ng , 3μg and 30μg; Fig . 4D ) . Increased overall numbers of myeloid cells , including neutrophils , eosinophils and monocytes , were evident in the blood of mice treated with 3 μg of ACK2 ( ~2 fold on average ) , relative to an isotype control ( Fig . 4D ) , though the effect was not to the same extent as that seen with imatinib . However , unlike imatinib , ACK2 also increased the numbers of lymphoid lineage cells by ~1 . 5 fold ( Fig . 4D ) . We were unable to assess effects of ACK2 on numbers of MPPs directly because the antibody interfered with bone marrow staining panels . Nevertheless , low doses of ACK2 , which may partially inhibit c-Kit kinase activity or reduce levels of c-Kit protein , appeared sufficient to induce expansion of leukocytes in the blood . Such an effect is consistent with an increase in myeloid and lymphoid progenitors . The observation that ACK2 affects both myeloid and lymphoid cells whereas imatinib predominantly affects myeloid cells suggests that inhibition of other kinases governs myeloid lineage commitment . We also tested the effect of the ACK2 antibody in the context of infection . Although treatment with the antibody resulted in a ~2-fold decrease in CFU on average ( S4C Fig ) . Although these data did not reach the 0 . 05 level of statistical significance , they “trended” in the right direction . We surmise that the limited efficacy is due to the fact that the antibody is not as efficient as the drug in inducing myelopoiesis . We next assessed numbers of myeloid cells in the blood and bone marrow of uninfected mice treated with imatinib at 200mg/kg/d , a dose previously shown to have no anti-mycobacterial effects . As shown in Fig . 4E , whereas imatinib at 66mg/kg/d increased the percentage of neutrophils in the blood compared to untreated controls from 18% to 60% ( see also Fig . 1 ) , with 200mg/kg/d no such increase was evident; similar dosage effects were apparent with monocytes in the blood . Interestingly , in the bone marrow imatinib at 66 or 200mg/kg/d induced comparable increases in numbers of mature neutrophils and monocytes ( Fig . 4F ) . Accordingly , bone marrow derived from mice treated with imatinib 200 mg/kg/d for seven days yielded ~40% more CFU-GM colonies compared to marrow from naïve mice , an increase comparable to that observed with marrow from animals treated with 66mg/kg/d ( Fig . 4A ) . Likewise , comparable increases in numbers of LSKs , HSCs , and MPP2 , MPP3 and MPP4 cells were evident with imatinib at doses of 66 and 200mg/kg/d ( Fig . 3A-C ) . Thus , accumulation of mature myeloid cells in the marrow was positively correlated with increases in mature cells in the blood and spleen at 66mg/kg/d , but no such correlation was evident at doses of 200mg/kg/d or higher . Notably , infection plus 200mg/kg/d imatinib did not induce substantial increases in the numbers of myeloid cells in the blood and spleen beyond that seen with infection alone . Together , these data suggest that imatinib facilitates exodus of myeloid cells from the bone marrow only at lower doses , but inhibits this process at higher doses . Because doses of 66mg/kg/d have proven most effective in bacterial infection studies [30] , our subsequent analysis focused on effects of the drug at this dose and not at higher doses . Retention of neutrophils within the bone marrow is correlated with expression of CXCR4 on the cell surface [48] , whereas migration of neutrophils into the blood and to peripheral sites is associated with surface expression of CXCR2 [48–50] . Treatment with imatinib at 66mg/kg/d increased the percentage of CXCR2hiCXCR4low neutrophils by 17% in uninfected mice and by 9% in infected mice , compared to control animals ( Fig . 5A ) . Accordingly , imatinib increased the median fluorescence intensity ( MFI ) of CXCR2 on neutrophils by 1 . 8-fold and 1 . 5-fold in uninfected and infected mice , respectively ( Fig . 5B ) . Thus , imatinib at 66mg/kg/d increased the surface expression of CXCR2 and the proportion of CXCR2hiCXCR4low neutrophils in the bone marrow . These data are consistent with an increased capacity of neutrophils to migrate from the bone marrow to the blood at 66mg/kg/d , and with the observed increase of neutrophils in the blood at this dose . Proinflammatory stimuli cause neutrophils to become activated and mobilize secondary and primary granules , which fuse with the plasma membrane and release antimicrobial compounds [51] . Neutrophil degranulation can be quantified by the surface expression of CD66b , a marker for secondary granules , or CD63 , a marker for primary granules ( Fig . 5C ) . In naïve mice , imatinib at 66 mg/kg/d did not alter the surface expression of CD66b or CD63 on neutrophils in the bone marrow , blood or spleen , indicating that , although the neutrophil numbers increased , the neutrophils were not activated ( Figs . 5C and S5A ) . However , in the context of a Mm infection , neutrophils from control animals or animals treated with imatinib displayed increased surface expression of CD66b and CD63 relative to uninfected controls ( Fig . 5C ) . This effect was most pronounced in the spleen , the site of the greatest concentration of bacteria [30] . Together , these data suggest that neutrophils are not activated by imatinib at low doses , but retain the capacity to become so upon infection . Following activation or phagocytosis , neutrophils undergo apoptosis [52] , which is characterized by cleavage of pro-caspase-3 or -7 ( caspase-3/7 ) into enzymatically active forms [53] . Neutrophils from mice treated with imatinib at 66 mg/kg/d did not display significant differences in levels of active caspase-3/7 compared to untreated mice ( Figs . 5C; S5A ) . However , neutrophils in the spleens of Mm infected mice showed significantly elevated levels of caspase-3 and 7 whether treated with imatinib or not ( Figs . 5C; S5A ) , in accordance with reports that neutrophils become activated in response to mycobacterial infection [52] . These data indicate that imatinib does not alter apoptosis in activated neutrophils . To determine whether an increase in neutrophil numbers alone was sufficient to reduce bacterial load following infection with Mm , adoptive transfer experiments were performed . Neutrophils were purified from either control or imatinib-treated animals . Recipient mice were then injected with 4x106 neutrophils derived from either control or imatinb-treated animals , and then infected with Mm . This number of transferred neutrophils represented less than half that found in the spleens of animals treated with imatinib; however , levels equivalent to that with drug could not be achieved because purifying larger numbers proved unfeasible . Bacterial load in infected organs was assessed 48h after adoptive transfer and infection . As shown in Fig . 5D , increasing the overall number of neutrophils , whether derived from PBS- or imatinib-treated mice , reduced CFUs in the spleen by ~2-fold , and no significant difference was evident between neutrophils derived from control or imatinib-treated animals . Thus , increasing neutrophil numbers alone is sufficient to reduce mycobacterial load , and imatinib does not augment the specific killing capacity of neutrophils . Notably , other myeloid cell types that increase in number with imatinib may also contribute to the observed reduction in CFU . Moreover , despite administration of sufficient anti-Ly6G antibody ( 1A8; [54] ) to saturate binding sites on neutrophils in imatinib-treated mice , only 30–40% of the neutrophils could be depleted ( S5B and S5C Figs ) . These data suggest that administration of high concentrations of 1A8 under neutrophilic conditions saturates cellular removal mechanisms . Because such mechanisms may also contribute to removal of infected cells , and because only a fraction of the cells can be depleted , using such methods to evaluate the role of neutrophils in imatinib-mediated reduction of CFUs has proven untenable . Myeloid cells , and particularly neutrophils , are required to contain infections caused by a variety of pathogenic bacteria [55–57] . The observation that imatinib dramatically increased myeloid cell numbers led us to ask whether the drug might be effective against other bacterial infections , which , unlike mycobacteria [30 , 33] , do not utilize Abl or other imatinib-sensitive kinases for pathogenesis . Growth and intracellular survival of the Francisella species F . novicida ( Fn ) and F . holarctica ( LVS , the live vaccine strain ) , in either broth or in macrophages remained insensitive to imatinib ( S6A-S6D Fig ) . Because these bacterial strains are lethal in mice within a few days of infection , imatinib was provided at 66 mg/kg/d for one week prior to infection with Fn or LVS , and throughout the course of infection ( 48hrs for Fn and 5 days for LVS ) . Imatinib reduced Fn and LVS CFU in the spleen and skin of infected animals by up to 10-fold compared to untreated animals ( Fig . 6A , B ) . In addition , pathology at the site of infection with LVS was assessed . Lesions in mice treated with imatinib were either reduced in size or absent compared to controls ( Fig . 6C , D ) . Imatinib was likewise effective against F . tularensis ( Ft ) , reducing CFUs in blood and spleen by on average 8-fold and 15-fold , respectively ( Fig . 6E ) . By contrast , imatinib at a dose of 200mg/kg/d was without effect on CFU ( S6E Fig ) . Unlike Mm , Franciscella infection did not activate a strong emergency response , and appeared to suppress immune cell numbers . Thus , with LVS infection , numbers of neutrophils remained constant , but numbers of monocytes , B , T , and NK cells decreased ( Figs . 6F and S6F ) , perhaps reflecting a partial suppression of immune function or killing of infected cells by the bacteria . With infection plus imatinib ( 66mg/kg/d ) , numbers of neutrophils and monocytes increased , although only the neutrophil increase reached statistical significance ( p<0 . 05 ) ; imatinib was without effect on T , B , or NK cells ( S6F Fig ) . Thus , imatinib may counter myelosuppressive effects of Franciscella infection by increasing myelopoiesis , or decreasing bacterial CFU , or both . Moreover , these data suggest that imatinib may provide a protective effect against a broad range of pathogens , including those whose intracellular survival does not depend on the activity of Abl1 and other imatinib-sensitive kinases .
Several lines of evidence suggest that imatinib , at the low doses used in this study , activates dormant HSCs , which rapidly differentiate into MPPs and mature myeloid cells . Low levels of imatinib did not cause accumulation of HSCs nor augment their transplantability . Nevertheless , the drug reduced accumulation of HSCs and MPP1 and MPP2 in infected mice ( Fig . 3B , C ) , suggesting that the drug facilitates flux of early stem cells and progenitors into more differentiated cell types . Data presented here suggest that partial inhibition of c-Kit may result in expansion of HSCs and/or MPPs but not their differentiation . Thus , low doses of anti-c-Kit mAb ACK2 increased numbers of cells of both myeloid and lymphoid origin ( Fig . 4D ) , whereas imatinib appeared to both expand HSCs and MPPs , and direct MPPs predominantly towards myeloid lineages ( e . g . Figs . 1–3 ) . HSCs express c-Kit , and c-Kit ligand both promotes self-renewal of HSCs and maintains quiescence [58–61] . In accordance with our observations , G-CSF , which mobilizes HSCs and augments granulopoiesis [41] , also induces the production of proteases that cleave c-Kit and its ligand , thereby reducing c-Kit activity [62] . Finally , mice with mutations in kit regulators ( C57BL/6J-KitW-sh ) display both increased numbers of myeloid cells in the bone marrow and peripheral neutrophilia [63] . However , because these mice contain more than thirty other mutations , it has not been possible to ascribe these effects to c-Kit directly . Recent transplantation experiments have highlighted the importance of c-Kit surface expression and signaling levels in regulating self-renewal of HSCs ( c-Kitlo ) versus their differentiation ( c-Kithi ) , with the c-Kitlo cells giving rise to c-Kithi cells , but not vice versa [61] . c-Kithi HSCs in turn support long-term lympho-myeloid grafts , although they exhibit a bias towards the megakaryocytic lineage [61] . Our data with the low doses of ACK2 antibody suggest that partial inhibition of c-Kit may govern transition of activated HSCs into MPPs and expansion of MPPs . However , the observation that low doses of the ACK2 antibody induce increases in both myeloid and lymphoid cells suggests that lineage determination by imatinib is regulated by kinases other than c-Kit . In this regard , inhibition of PDGFR , also an imatinib target , has been associated with differentiation of megakaryocytes [64] . Imatinib may cause differentiation of myeloid lineage cells by either inhibiting lymphoid differentiation , or alternatively , augmenting myeloid differentiation , perhaps via effects on lymphoid-myeloid progenitors distal to MPPs [42 , 43] . Finally , although accumulation of MPPs and mature myeloid cells in the bone marrow was evident at doses of 66 and 200mg/kg/d , accumulation of myeloid cells in the blood occurred only at the lower dose ( Figs . 3 and 4 ) . Surface expression of CXCR2 increased on mature neutrophils in the marrow in animals treated with 66mg/kg/d , consistent with an increased capacity to migrate out of the marrow . Notably , even with infection , fewer myeloid cells were evident in the periphery at high doses compared to low doses . Thus , whereas low doses of imatinib facilitate exodus of myeloid cells out of the bone marrow , higher doses appear to inhibit this process . As summarized in Fig . 6G , imatinib doses of 66mg/kg/d appear to regulate hematopoiesis at three distinct steps: ( i ) flux of HSCs and MPPs , ( ii ) maturation of myeloid but not lymphoid progenitors and ( iii ) migration of mature myeloid cells to the blood and peripheral organs . At higher doses ( 200mg/kg/d ) migration appears inhibited . In all these ways , imatinib at doses of 66mg/kg/d mimics “emergency hematopoiesis” [65] , a natural response to infection that results in increased numbers of circulating myeloid cells , particularly monocytes and neutrophils . Notably , at optimal doses , the effect of imatinib on myelopoiesis appears more pronounced than that seen with infection ( Figs . 1A , 3 , 4 ) . Differences in half-life of imatinib ( ~1 . 5 hrs in mice versus ~15 hours in humans ) preclude direct comparisons of applied doses between the two species , However , comparisons can be made by considering steady state levels of the drug . In mice , imatinib at doses of 66mg/kg/d yields steady state levels in the serum of ~57ng/ml ( ~100nM ) , which corresponds to less than 5% of that observed in CML patients treated with the minimal clinical dose of imatinib at 400mg QD ( ~1500ng/ml trough to 3000 peak , or 2 . 5–5μM ) [37 , 38] . Thus , increased myelopoiesis seen in vivo in mice and in vitro using cultured murine and human bone marrow cells ( Figs . 1 , 2 , and 4 ) , would likely not be evident at doses currently prescribed for CML patients . Indeed , myelo-suppression has been most commonly associated with doses higher than 400mg QD or with long-term administration of the drug in people [66] . However , it is noteworthy that in rare instances , GIST patients exhibit a dermal rash called Sweet’s syndrome [67] , which is characterized by a localized neutrophilia . The rash abates when the drug is discontinued . It remains possible that individuals who contract Sweet’s syndrome are either non-compliant with the treatment regimen , or rapidly metabolize imatinib , either of which could result in lower levels of drug in the blood . In addition , Druker and colleagues noted in their initial clinical study with imatinib that half of those assigned to receive 25 , 50 , or 85 mg imatinib QD were removed from the study within two months because of elevated white-cell or platelet counts , which required therapy prohibited by the protocol [68] . Although not definitive because of the underlying leukemia , these doses are at the upper range we would expect might cause induction of myelopoiesis , and taken together with our data showing increases in CFC numbers with human marrow at concentrations of 5–50 nM ( Fig . 4 ) , suggest that this effect will be evident in humans , a prospect we are currently testing . Our results raise the possibility that imatinib may be useful in treating several conditions associated with dysregulation of neutrophil homeostasis , which result in increased risk of severe infection . These include hereditary disorders such as cyclical neutropenia , cancer , autoimmune diseases , microbial infections and myelosuppressive chemotherapeutics . Administration of G-CSF mitigates neutropenia in a variety of conditions [69–71] by stimulating the production of neutrophils in the marrow and their migration into the blood [72] . However , G-CSF has been associated with deleterious outcomes against infections . For example , G-CSF blunts helper T cell responses and increases regulatory T cell responses and recapitulates a “super-shedder” phenotype characterized by excretion of high levels of Salmonella and hyperinflammation [73] . Imatinib , by contrast , has the opposite effect , causing reductions in bacterial load [30] , decreased regulatory T cell responses , and augmented antigen presentation [36 , 74 , 75] . The difference may be that imatinib induces myelopoiesis and an overall increase in all myeloid cells , whereas G-CSF only induces granulopoiesis and neutrophilia , or , alternatively , that G-CSF has additional effects on other cell types [71] . Thus , imatinib may be useful for patients suffering neutropenia and have fewer deleterious side effects than G-CSF , a prospect we are currently testing . We have proposed that imatinib may be useful in treating a broad range of infections caused by bacterial and viral pathogens that use Abl1 , Abl2 or other imatinib-sensitive PTKs for pathogenesis [76] . These include , for example , poxviruses , filoviruses ( Ebola ) , and Mtb [23 , 28 , 30–32 , 76 , 77] . Different dosing strategies may apply to different pathogens depending on the mechanism of action . Thus , for poxviruses and filoviruses , imatinib effects likely depend on inhibition of viral dissemination , which requires Abl-family kinases [23 , 31 , 32] . Notably , the optimal dose for inhibiting Abl-family kinases and treating poxvirus infections with imatinib is 200mg/kg/d , a dose that stimulates myelopoiesis but without attendant increases in myeloid cell numbers in blood and spleen . By contrast , other pathogens , particularly those that trigger antimicrobial responses mediated by neutrophils and macrophages , may be more susceptible at lower doses of imatinib . Mycobacteria infections are optimally responsive to imatinib at 66mg/kg/d [30] , a dose that triggers both myelopoiesis in the marrow , and increases in myeloid cells in blood and spleen . In acute Mtb infections , macrophages , followed by neutrophils , transiently increase in numbers in the lungs , reaching the highest levels just prior to arrival of DCs [54] . Both neutrophils and macrophages become infected , and depleting neutrophils increases the frequency of Mtb-infected DCs in the lungs , but decreases trafficking of DCs to the mesenteric lymph nodes , which precludes DC-initiated adaptive responses [54] . Thus , neutrophils may promote adaptive responses to Mtb by delivering bacterial antigens to DCs in a way that enables DC migration , and allows more effective antigen presentation and activation of naive CD4 T cells . Other evidence suggests that the anti-infective state induced by imatinib in the host and mediated by myeloid cells , resembles that seen in human patients who are protected from TB . First , studies of initially IGRA-negative TB household contacts indicate that low baseline neutrophil count is a predictor of subsequent IGRA conversion [78] . Thus , protected individuals may have , by virtue of continuous or repeated exposure , a heightened basal myeloid response that provides protection , and which resembles that induced by imatinib . Moreover , Kaushal and colleagues have shown in primates that mutants of Mtb , which are cleared by the immune response , induce a strong hematopoietic response , whereas Mtb does not [79] . Thus , Mtb may suppress the emergency response , which may be overcome by imatinib . Finally , Fletcher and colleagues have shown that BCG vaccinees who remain unprotected from TB have transcriptional signatures that may be indicative of either low myeloid responses or hyperactive ones , whereas protected individuals have an intermediate response ( H . Fletcher , personal communication ) . Current efforts are aimed at determining whether these protective responses resemble those seen with imatinib . Like Mtb , resolution of Franciscella infections depends on neutrophils [57] , and Franciscella appears to suppress both the emergency response ( Fig . 6F ) , and adaptive responses ( S6F Fig ) . Our observations suggest that Francisella spp . do not require imatinib-sensitive kinases for pathogenesis in vitro , yet are still susceptible to imatinib in vivo ( Figs . 6 and S6 ) . Together , our data raise the possibility that imatinib may have utility against a wide range of pathogens that do not necessarily utilize Abl-family kinases for pathogenesis , by overcoming pathogen strategies to limit or subvert the emergency response . Moreover , agents such as imatinib may even be efficacious against strains resistant to conventional antibiotics , and may even act synergistically with co-administered antibiotics , a result suggested by our previous studies [30] . To realize the potential of imatinib as an immunomodulatory therapeutic for Mtb infections will require a balanced inflammatory response , without favoring hyper- or hypo-inflammation , which have been shown to be deleterious ( e . g . [80–83] ) . Notably , the hematopoietic response generated by imatinib is titratable with dose . This response comprises an increase in numbers of all myeloid cells , thereby providing a limit on inflammation [84] , rather than an increase in a single cell type , such as neutrophils , which can by themselves induce significant damage [85] . Nevertheless , heterogeneity in immune response ( e . g . [82] ) or disease stage could affect how an individual responds to up-regulation of the emergency response . Thus , careful dosing regimens and treatment at appropriate disease stages , in conjunction with assessments of diagnostic biomarkers and clinical signs will be required to ensure optimal activity of the drugs with minimal toxicity . A more complete discussion of the promise and caveats associated with host directed therapeutics for TB , including imatinib , as well as trial design and measures or efficacy , is reviewed elsewhere by one of us ( D . K . ;[77] ) . In summary , we demonstrate a surprising immune-stimulatory effect of imatinib on myelopoiesis , which depends in part on c-Kit and occurs at subclinical doses . These observations have important implications for the use of imatinib as an immunostimulatory therapeutic against neutropenia and against infectious pathogens , including those that do not utilize host imatinib-sensitive kinases for pathogenesis .
For analysis of immune cells in whole blood , bone marrow and collagenase-digested splenocytes [86] were incubated with blocking mAb 2 . 4G2 anti-FcγRIII/I and live/dead probe ( Alexa Fluor 430; Invitrogen , Grand Island , NY ) . Cells were labeled with CD11b ( M1/70 ) , B220 ( RA3-6B2 ) , and Ly6C ( AL-21 ) antibodies from BD Biosciences ( San Jose CA ) , CD19 ( MB19-1 ) , Thy1 . 2 ( 53–2 . 1 ) , F4/80 ( BM8 ) , CD11c ( N418 ) , CD8α ( 53–6 . 7 ) , Gr-1 ( RB6-8C5 ) from eBioscience ( San Diego , CA ) and NK1 . 1 ( PK136 ) from BioLegend . Cells were then stained with Streptavidin ( QDot655; Invitrogen ) before fixation . To isolate neutrophils for activation and degranulation assays , blood , bone marrow and spleen cell samples were processed on ice and in PBS-EDTA buffer to prevent activation of the cells . Bone marrow and spleen cells were homogenized and then filtered . Then , cells were incubated with blocking mAb 2 . 4G2 anti-FcγRIII/I ( BD Biosciences ) and live/dead probe ( Yellow; Invitrogen ) along with labeled CD11b ( M1/70 ) , B220 ( RA3-6B2 ) , Ly6C ( AL-21 ) , and CD66b ( G10F5 ) antibodies from BD Biosciences , Ly6G ( 1A8 ) , CXCR2 ( TG11 ) , CD63 ( MEM-259 ) from BioLegend ( San Diego , CA ) , CXCR4 ( TG12 ) from eBiosciences and FLICA probe for caspases 3/7 from Novus Biologicals ( Littleton , CO ) . Notably , there is good cross-species reactivity with mouse neutrophils with the anti-human CD66b antibody ( clone G10F5; [87] ) . All samples were acquired on a BD Biosciences LSR II ( BD Biosciences and analyzed using FlowJo ( TreeStar , Inc; Ashland , OR ) . To deplete neutrophils in naïve or imatinib treated mice , 300ug of anti-Ly6G antibody clone 1A8 or 2A3 isotype control were administered one day prior to imatinib treatment and 2 days post treatment as described previously [54] . Blood was collected on the third day after imatinib treatment and neutrophil numbers were determined by flow cytometry . Both fluorescently labeled anti-Ly6G ( clone 1A8 ) and anti-Gr-1 ( specific for Ly6G and Ly6C ) antibodies were unable to bind some neutrophils from 1A8-treated naïve or imatinib-treated mice owing to continued presence of 1A8 depleting antibody occluding the epitope shared by both antibodies . Thus , full enumeration of blood neutrophils was achieved with an anti-DEC-205 fluorescently labeled antibody in conjunction with an anti-Ly6C antibody . Neutrophil depletion of mice with 1A8 reduced median numbers of blood neutrophils to ~6% of the number seen in naïve animals , in line with previous reports [54] . However , depletion of imatinib-treated mice reduced numbers to only 62% of that seen in naïve animals or to 20% of that seen in imatinib-treated animals . Thus , our data suggest that in the presence of imatinib , the maximum number of neutrophils possible were depleted with 1A8 , and increasing the concentration of 1A8 would not deplete more , and therefore that the mechanism by which neutrophils were removed from circulation appeared to be saturated in the presence of imatinib plus 1A8 . Together these data suggest that we could not efficiently deplete neutrophils under these conditions . Moreover , because of this inefficiency and because cellular depletion mechanisms likewise are required to remove infected cells , we could not evaluate whether neutrophils were necessary for imatinib-mediated reduction of CFU . To identify LSK , HSC , and multipotent progenitor populations ( MPP1-4 ) , bone marrow was flushed from femurs with DMEM plus 10% fetal bovine serum and labeled with the following mAbs: CD34 ( HM34 ) and erythroid cells ( TER-119 ) from Biolegend , CD135 ( A2F10 ) from eBiosciences , sca-1 ( D7 ) , c-Kit/CD117 ( 2B8 ) and the following biotinylated mAbs ( CD19 lineage ( ID3 ) , NK1 . 1 ( PK136 ) , B220 ( RA3-6B2 ) , GR1 ( RB6-8C5 ) , CD11b ( M1/70 ) , CD19 ( 1D3 ) , CD4 ( GK1 . 5 ) , CD8 ( 53–6 . 7 ) CD150 ( Q38-480 ) CD48 ( HM48-1 ) , followed by a secondary stain with streptavidin , from BD Biosciences . Cell numbers are expressed as per two femurs based on cell counts or , alternatively fluorescent beads to measure the concentration of cells during flow cytometry . Both methodologies yielded similar numbers of cell subsets . Bone marrow was flushed from donor femurs with sterile PBS containing 1% heat-inactivated fetal calf serum ( PBS/FCS ) . Bone marrow cells were stained with a cocktail of antibodies from BD Biosciences: biotinylated lineage 1 antibodies ( CD3 , CD11b , CD19 , CD49b , IgM , and Ter119 ) , PE-conjugated lineage 2 antibodies ( CD4 , CD8 , GR-1 , and I-Ab ) , as well as , B220 PE-Cy5 , c-kit APC , and Sca-1 PE-Cy7 , followed by streptavidin APC-Cy7 . For some experiments , cells were sorted using a FACS-Aria cell sorter and data analyzed using Diva Version 5 . 1 software ( both from BD Biosciences ) . After initial scatter-based gating to exclude doublets , the B220− population was further gated to identify and sort the lineage- Sca-1+ c-Kit+ ( LSK ) cell population that contains hematopoietic stem cells [44] . Murine bone marrow ( BM ) cells were flushed from femurs and tibias using Iscove’s MDM ( Stem Cell Technologies; Vancouver , BC , Canada ) containing 2%FBS . Cells were triturated and filtered through a nylon screen to obtain a single-cell suspension . Human BM was obtained by aspiration from the posterior iliac crest in an IRB-approved protocol that enrolled normal volunteers ( see below ) and was depleted of RBCs by treatment with 0 . 8% Ammonium Chloride Solution ( Stem Cell Technologies ) according to manufacturer’s instructions . BM was plated in duplicate ( 2 × 104 nucleated cells/35 mm dish for murine BM , 5 x 104 nucleated cells/35 mm dish for human BM ) in semisolid methylcellulose medium containing stem cell factor ( SCF ) , interleukin-3 ( IL-3 ) , erythropoietin ( Epo ) , and either interleukin-6 ( IL-6 ) for murine BM ( MethoCult M3434 , Stem Cell Technologies ) , or granulocyte/macrophage colony stimulating factor ( GM-CSF ) for human BM ( MethoCult H4434 , Stem Cell Technologies ) , plus or minus the indicated concentrations of imatinib . Plates were incubated at 37°C , 5% CO2 and >95% humidity and Granulocyte-Macrophage colonies ( CFU-GM ) were identified by morphology and counted after 10–12 ( murine ) or 14–16 ( human ) days of incubation . In the first experiment , 2x106 bone marrow cells from naïve CD45 . 2 C57Bl/6 mice or CD45 . 2 C57Bl/6 mice treated with imatinib for 7 days were mixed with 2x106 bone marrow cells from untreated CD45 . 2 GFP-C57Bl/6 mice , and either the naïve-GFP BM mixture or the imatinib-GFP BM mixture injected I . V . into the tail vein of a congenic CD45 . 1+ C57Bl/6 recipient mice . At four and twelve months post-transplantation , mice were bled and the relative numbers of WBC derived from GFP animals , or from either naïve- or imatinib-treated donors , or recipient mice were analyzed by flow cytometry , taking advantage of the GFP label and CD45 . 1 congenic marker . In the second experiment , 5000 FACS-sorted LSK cells from naïve- or imatinib-treated mice were co-transplanted with 300 , 000 untreated BM cells from GFP+ mice . The relative numbers of WBC derived from GFP , or either naïve- or imatinib-treated donors , or recipient mice were determined 4 to 12 months post-transplantation . For experiments with imatinib , the mesylate salt was dissolved in water and loaded into Alzet pumps ( Braintree Scientific , 1007D or 2002; Cupertino , CA ) capable of dispensing a continuous flow of drug at doses ranging from 1 , to 300mg/kg/day . Pumps were inserted subcutaneously into anesthetized 6-week old male C57Bl/6 mice ( Jackson Laboratories; Bar Harbor , ME ) . At all doses tested , no weight loss or other adverse events were evident in uninfected animals . Alzet pumps were inserted 24 h to 7 days prior to manipulation or infection , and drug delivery was maintained for the duration of the experiment ( 7 to 28 days ) . Some variation in CFU in Mm infection , or with plaque forming units ( PFU ) with vaccinia virus was evident when using different sources or lots of drug . The drug lot used in this study achieved a steady state serum concentration in the blood of 57+/-21ng/ml at 66mg/kg/d , and 165ng/ml +/- 66ng/ml at 200mg/kg/d . Discrepancies between lots or sources were accounted for by the fact that different lots of drug applied at the same dose sometimes yielded different steady state concentrations of drug in the blood; however , no phenotypic differences were evident when animals were dosed such that serum levels were equivalent . All the data shown in this paper used a single lot of drug , but the phenotypes have been reproduced with different lots from different manufacturers . Together , these data highlight the utility of normalizing phenotypes to the steady state concentration of drug in the blood rather than to the administered dose . Mice were injected intravenously with 3μg of c-Kit neutralizing antibody ACK2 ( eBiosciences ) , or 3μg of IgG2b isotype control ( eBiosciences ) , every 48h for seven days at the indicated concentrations . Blood was collected from mice at day seven and the number of myeloid cells and lymphocytes was determined by flow cytometry . M . marinum ( Mm ) strain 1218R ( ATCC 927 ) , a fish outbreak isolate , was grown in Middlebrook 7H9 broth ( 7H9 ) ( BBL Microbiology Systems , Cockeysville , MD ) supplemented with ADC ( Difco Laboratories , Detroit , MI , ) and 0 . 05% Tween 80 ( Mtb ) ( Sigma-Aldrich , St . Louis , MO ) or 0 . 025% Tween 80 ( Mm ) . For CFU assays 7H10 agar supplemented with 10% oleic acid-albumin-dextrose-catalase ( OADC ) was used ( Difco Laboratories , Sparks , MD ) . For in vivo Mm infections , bacterial stocks were grown at 30°C for 2 days to an OD600 of 0 . 4 ( Eppendorf , BioPhotometer; Hamburg , Germany ) , the cells were diluted with PBS to 105 CFU/100ul . F . novicida ( Fn ) strain U112 overnight cultures were grown at 37°C with aeration in tryptic soy broth ( TSB; Difco ) supplemented with 0 . 02% L-cysteine ( Sigma-Aldrich ) while LVS cultures were grown in modified Mueller-Hinton broth ( mMHB ) supplemented with 1 mM CaCl2 , 1 mM MgCl2 , 0 . 1% glucose ( Sigma-Aldrich ) , 2% Isovitalex ( Difco ) , and 0 . 025% ferric pyrophosphate . For macrophage CFU assays , Fn was plated for enumeration on tryptic soy agar ( TSA; Difco ) and supplemented with 0 . 01% L-cysteine . Mouse macrophage cell line J774A . 1 ( ATCC TIB-67 ) was maintained in Dulbecco’s modified Eagle Medium ( DMEM ) . For in vivo CFU assays , Fn experiments were plated on modified Mueller Hinton ( mMH ) ( Difco/BD ) plates supplemented with 0 . 025% ferric pyrophosphate ( Sigma-Aldrich ) , 0 . 1% glucose , and 0 . 01% L-cysteine . For both macrophage and in vivo assays , LVS was plated on mMH agar supplemented with 2% Isovitalex . For M . marinum infections , six-week old male C57Bl/6 mice were injected in the tail vein with active growing cultures at ~105 CFU/mouse . The number of bacteria injected for each experiment was determined by retrospective plating and was ~2 . 5x105 CFU/mouse . Seven days after infection , blood , spleen and bone marrow were harvested . For CFU , spleens were weighed and homogenized in a Tissuemise ( Fisher Scientific , Hampton , NH ) in 1 ml PBS . Each homogenate was diluted and spread on 7H10 agar . Colonies were scored after seven days of incubation at 30°C . Total weight of the organ and colonies per ml of the homogenized organ were used to determine CFU/gram . For Francisella infections , C57Bl/6 mice were infected with ~6x106 F . novicida ) or ~2 x 105 F . holartica ( LVS strain ) , or F . tularensis ( Ft; strain Schu S4 ) . After 48 hours with Fn , or 5 days with LVS , of 4 days with Ft mice from both types of infections were sacrificed and the spleen , liver , and skin at the site of infection were harvested , homogenized , plated for CFU on MH plates , and incubated overnight at 37°C . Neutrophils were purified from the collagenase-digested spleens derived from control or imatinib-treated mice . Splenocytes were first depleted of B cells with anti-CD19 coated microbeads ( Miltenyi; San Diego , CA ) then neutrophils were positively selected by anti-Ly6G+ microbeads . Purity was assessed on the Ly6G+ enriched fraction using the parameters listed above . Neutrophils , defined as CD11b+Gr-1hiLy6Clow . Ly6G+ fractions were 100% pure from imatinib-treated mice and 80% pure from naïve mice . We routinely purified ~1x106 neutrophils from the spleen of a naïve mouse and ~8x106 from the spleen of an imatinib treated mouse . For adoptive transfers , 4x106 neutrophils were injected into the left lateral tail vein of naïve recipients . Immediately following the adoptive transfer , 105 Mm were injected into the right lateral tail vein of the mouse . Spleens were harvested 48 hours after the neutrophil transfer and infection , and bacterial CFU determined as described above . Imatinib-treated ( 66 . 7mg/kg/day ) or water control pumps were implanted one day prior to infection . Day 7 post-treatment , spleens were harvested , frozen in optimum cutting temperature ( OCT ) compound , cut into 6μm sections , mounted on slides , and then fixed with %100 acetone for 10 min at −20°C . Following rehydration in PBS , slides were permeabilized with PBS +0 . 5% Triton X-100 +1% BSA , blocked with normal rat serum and anti-FcRIII/I antibodies and then incubated with anti-CD169-FITC ( Serotec; Oxford , UK ) , CD11b-PE ( eBioscience ) , and F4/80-biotin ( eBioscience ) . Tissues were incubated with Streptavidin-APC and mounted with Prolong-GOLD ( with DAPI ) ( Invitrogen ) . Images were captured using the x10 objective on a Zeiss Axioscope ( Carl Zeiss , Germany ) and analyzed using ImageJ ( National Institute of Mental Health ) and DoubleTake ( Echo One , Denmark ) software . Bone slices from naïve mice or mice treated with imatinib at 66 mg/kg/d and stained with Giemsa and imaged on a Zeiss 200M microscope at x100 or x400 magnification . Statistical analysis was done using either of two non-parametric tests including the Mann-Whitney test to compare two samples , or the Kruskal Wallis test to compare multiple subsets within a group ( e . g . with or without infection and with or without drug ) . In both tests , the data were pooled and the values ranked . The statistic calculates the probability that the observed ranks of a subset of observations represent a random sampling from the population as a whole , or a significantly different population compared to the group as a whole . Values less than or equal to 0 . 05 were considered statistically significant . For comparing amongst averaged data from several experiments , a t-test was used to determine significance . For some experiments , bone marrow was obtained from normal human volunteers enrolled in an institutional observational study conducted at the Emoryt transplant center to evaluate the central immune response in healthy volunteers . Samples used in this paper were from normal bone marrow donors who did not have signs of disease including malignancy nor gastrointestinal infection ( viral , bacterial , fungal , protozoal ) within two weeks of the day of collection . Samples used in this paper were obtained from bone marrow unused for other aspects of the study . The protocol was reviewed by the Emory University Institutional Review Board ( IRB-00060350 ) . Since samples for this study involved the use of samples obtained as part of an ongoing study where written informed consent was obtained for storage and use of samples in other studies , it qualifies for a waiver of informed consent for this study . Samples from 5 patients were used in this study . All mouse studies were reviewed and approved by the Emory Institutional Animal Care and Use Committee ( DAR-2001392-020615BN ) , which reviews the animal care and use projects . The committee adheres to specific national and international regulations regarding the ethical treatment of animals as specified by the National Institutes of Health .
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Host-directed therapeutics ( HDTs ) for infectious diseases target cellular mechanisms used by pathogens to move into , through , or out of cells . The Abl tyrosine kinase ( TK ) inhibitor and cancer therapeutic imatinib mesylate ( Gleevec ) , for example , has activity against bacterial and viral pathogens via effects on pathogen entry ( polyomaviruses ) , intracellular transit ( Mycobacteria ) and exit ( poxviruses and filoviruses ) . Other HDTs target the host immune system by suppressing or activating circulating innate and adaptive cells . Here we report that imatinib at doses that are effective in clearing Mycobacterial infections but which are 10-fold lower than those used for cancer , mimics a physiological innate response to infection in the bone marrow , called the “emergency response , ” in which hematopoietic stem cells and multipotent progenitors expand and differentiate into mature myeloid cells that migrate to peripheral sites . Imatinib effects occur in part via partial inhibition of c-Kit , suggesting a mechanism by which c-Kit controls the earliest stages of hematopoiesis . Mimicking a physiological antimicrobial response may make imatinib broadly useful . Accordingly , imatinib also has efficacy against infections caused by Franciscella spp . , which do not use imatinib-sensitive TKs for pathogenesis . These observations identify myelopoiesis as an important target for HDTs , and provide information on how to dose imatinib for clinical use .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Low Doses of Imatinib Induce Myelopoiesis and Enhance Host Anti-microbial Immunity
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Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes , offering insights into tumour etiology , features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically . We present a novel machine learning formalism for improved signature inference , based on multi-modal correlated topic models ( MMCTM ) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data . We exemplify the utility of our approach on two hormone driven , DNA repair deficient cancers: breast and ovary ( n = 755 samples total ) . We show how introducing correlated structure both within and between modes of mutation can increase accuracy of signature discovery , particularly in the context of sparse data . Our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification , and provides a statistical modeling framework to incorporate additional features of interest for future studies .
Patterns of mutation in cancer genomes reflect both endogenous and exogenous mutagenic processes [1] , allowing inference of causative mechanisms , prognostic associations [2] , and clinically actionable [3–6] vulnerabilities in tumors . Many mutational processes leave distinct genomic “footprints” , measurable via nucleotide substitution patterns [1] , localised mutation densities , and patterns of structural variation ( SV ) . As such , each mutagenic source ( whether exogenous or endogenous ) changes DNA in a characteristic manner , at genomic locations with preferred chemical and structural characteristics . Exogenous insults such as ultra-violet radiation and tobacco smoke-associated mutagens ( e . g . benzo[a]pyrene ) induce single nucleotide variants ( SNVs ) with characteristic C→T ( at CC or TC dinucleotides ) [7] and C→A mutation patterns [8] , respectively; endogenous APOBEC activity mediates enzymatic 5-methylcytosine deamination , resulting in C→T substitution patterns at TC dinucleotides [7] . Cancer cells can also acquire endogenous mutator phenotypes , accumulating mutations [7] due to DNA repair deficiencies . Defective DNA repair processes induce both point mutations and structural variations [9] , and include several mechanistic classes such as mismatch repair deficiency ( MMRD ) , homologous recombination deficiency ( HRD ) , microhomology mediated end-joining , and breakage fusion bridge processes . Defective DNA repair has been exploited in therapeutic regimes , including immune checkpoint blockade for mismatch repair deficiency [6] , and synthetic lethal approaches for HRD [4 , 5] , underscoring their clinical importance . Both point mutation signatures [10] and structural variation signatures [11] have been studied extensively as independent features of cancer genomes , mostly through non-negative matrix factorization ( NMF ) approaches [1 , 3 , 12–15] . As increasing numbers of whole genomes are generated from tumors in international consortia and focused investigator research , the need for robust signature inference methods is acute . Additional computational methods have been proposed [16–19] , however no approaches jointly infer signatures from both point mutation and structural variations . We contend that systematic , integrative analysis of point mutation and structural variation processes enhances ability to exploit signatures for subgroup discovery , prognostic and therapeutic stratification , clinical prediction , and driver gene association . Latent Dirichlet allocation ( LDA ) [20] , a popular and effective approach for natural language document analysis , is well suited to the task of mutation signature inference . Although LDA was designed to extract topics from documents , these concepts can be mapped to mutation signatures and somatic mutation catalogues derived from tissue samples , respectively . In this paper we introduce the correlated topic model ( CTM ) [21] , an extension of LDA which incorporates signature correlation , and a multi-modal correlated topic model ( mf-CTM . dt in Salomatin et al . [22] , hereafter referred to as MMCTM ) . A modality is a particular kind of data , and in this report SNV and SV counts are two distinct modalities . The MMCTM thereby jointly infers signatures from multiple mutation types , such as SNVs and SVs . Signature correlations can arise through a mutational process generating multiple signatures , as with the HRD-associated SNV and SV signatures . C→T substitutions caused by APOBEC cytidine deaminases have also been shown to cluster around SV breakpoints [12] . Correlations between mechanistically independent signatures can also occur; for example , COSMIC SNV signatures 1 and 5 are both correlated with age of diagnosis in some cancer types [23] . We set out to investigate whether statistical modeling that could encode correlations between signatures could enhance accuracy in signature analysis . We show how integrating SNV and SV signature probability correlation improves mutation signature inference relative to NMF and standard topic modeling methods . By incorporating statistical correlation and multiple modalities , more information is provided to the model , improving inference further , while still maintaining distinct signatures for each modality . Motivated by the need to better understand mutation signatures in the context of DNA repair deficiency , we analysed breast and ovarian tumour genomes . We applied the MMCTM to SNV and SV somatic mutations derived from whole genomes ( breast [13] and ovarian [2]; 755 samples total ) , performing joint statistical inference of signatures . Our results reveal correlated topic models as an important analytic advance over standard approaches . Rigorous benchmarking over mutation signatures inferred from previously published mutation corpora was used to establish metrics for comparison . We show systematically how correlation integration improves inference , especially in the context of sparse mutation counts , and where SNVs and SVs are considered jointly . In addition , we report novel strata using MMCTM-derived signatures , including patient groups exhibiting combined whole genome SNV and SV signature profiles from breast and ovary cancers . We automatically recovered BRCA1-like and BRCA2-like homologous recombination repair deficient breast and ovarian cancers , where the tumors bearing the well known SNV HRD signature were reproducibly split on the basis of SVs . In aggregate , our study reveals the importance of simultaneously considering multiple classes of genomic disruption as a route to expanding mutation signature discovery , and their downstream impact on novel stratification across human cancers .
We developed a suite of probabilistic correlated topic models ( Fig 1 ) to evaluate their utility in signature discovery . We describe the models here briefly and refer to S1 Text for more detailed descriptions . Topic models represent mutation signatures as discrete distributions over unique mutation categories ( e . g . C→T substitutions at TCT trinucleotides ) . Each sample is then represented as a discrete distribution over signatures . How the sample-signature distributions are generated differ between LDA ( Fig 1a and 1d ) and the correlated topic models . In LDA , this variable is drawn from a Dirichlet distribution [20] . With the correlated topic models , however , it comes from the transformation of a variable that is distributed according to a multivariate Gaussian distribution [21] ( Fig 1b , 1c and 1d ) . By using the multivariate Gaussian , the covariance of signature probabilities across samples can be captured . The multi-modal extension of the CTM ( i . e MMCTM ) encodes mutation counts and signatures for different modalities ( e . g . SNVs and SVs ) independently , except for the sample-signature probabilities which are all modeled using the same Gaussian distribution , allowing for cross-modality correlations . We also developed a set of “independent” feature models based on the method introduced by Shiraishi et al . [16]—independent-feature LDA , CTM , and MMCTM ( ILDA , ICTM , IMMCTM , S1 Fig , S1 Table , S1 Text ) . These models can treat each mutation feature ( e . g . substitution type , flanking nucleotide ) independently . That is , one feature for the mutation itself ( say , C→T ) , and features for each piece of contextual information ( e . g . 5′ A and 3′ G ) . Using this scheme , we drastically reduced the number of feature values: assuming 6 SNV types , and 2 flanking nucleotides the number of feature values is reduced from 6 * 4 * 4 = 96 to 6 + 4 + 4 = 14 [16] . We studied mutation signatures in 560 breast [13] and 195 ovarian [2 , 24] cancer genomes ( S2 Table ) . Each dataset was analyzed separately to avoid biases from differences in sample sequencing , data-processing or annotation . We constructed SNV features using the 6 types of pyrimidine-centric substitutions ( C→A , C→G , C→T , T→A , T→C , T→G ) , and their flanking nucleotides . For example , a C→T substitution with an upstream A and downstream G is represented as the item “A[C→T]G” . We defined SV features by rearrangement type ( deletion , tandem duplication , inversion , foldback inversion ( FBI ) , translocation ) , number of homologous nucleotides around the breakpoints ( 0–1 , 2–5 , >5 ) , and breakpoint distance ( <10kbp , 10–100kbp , 100kbp–1Mbp , 1–10Mbp , >10Mbp , except for translocations ) . Foldback inversions are inverted duplications caused by breakage-fusion-bridge cycles . We then computed counts of mutations , categorized as described above . The resulting count matrices were provided as input to LDA , CTM , MMCTM , and NMF ( S1 Table ) . We compared NMF to the LDA , CTM , and MMCTM topic models . As NMF is commonly applied to normalized mutation counts , we also compared output from this alternative NMF procedure ( NMF-NORM ) . Each method was run on input mutation counts constructed in an identical manner ( e . g . for SNVS , 96 counts for each sample ) , and methods were compared using three different benchmarks: i ) average per-mutation predictive log-likelihood; ii ) logistic regression prediction accuracy of HRD labels; and iii ) the mean absolute error of inferred solutions compared to a synthetic reference dataset . For log-likelihood comparisons , we performed 5-fold cross validation , repeated 10 times , on the 560 breast cancer dataset . In each comparison , we fit SNV and SV signatures to four folds , leaving out a test fold ( 112 samples ) . We split mutation counts from each test fold sample into two parts , inferred sample-signature probabilities with one portion , and computed average per-mutation predictive log-likelihood values with the other portion . By evaluating each method on data different than those used for parameter estimation , we alleviated the risk of over-fitting parameters . This evaluation procedure only required estimated mutation signatures and sample-signature probabilities from each method , and did not depend on other model details , e . g . signature correlation structure . The average per-mutation predictive log-likelihood is an established comparison metric used in the topic modeling literature [25–27] , and is also not directly optimized by any method here ( unlike e . g . reconstruction error which is directly minimized by NMF ) . Although a likelihood-based metric may seem more applicable to the probabilistic models , NMF can be interpreted as maximum likelihood estimation of the “signature” and “activity” matrices under certain conditions ( e . g . using Euclidean distance for the cost function maps NMF to a Gaussian emission model ) [17 , 28] . We first compared performance as a function of the number of signatures , fitting models over a range of 2–12 SNV and SV signatures ( Fig 2a , S1 Dataset ) . For SNV signatures , LDA , CTM , and MMCTM performed similarly , and were consistently higher than the NMF methods across the full range of signature numbers . For SV signatures , the probabilistic topic models’ performance was consistently higher than the NMF models , and improved until a plateau was reached with an inflection point at 5 . Within the topic models , the CTM and MMCTM showed better performance than LDA . NMF-NORM performance degraded with >5 signatures , and NMF performance degraded with >6 signatures . Correlated topic models performed better than their non-correlated analogues at inferring SV signatures , possibly due to relatively low input counts for SV features . To explore this further , we compared performance over a range of mutation count fractions ( Fig 2b , S2 Dataset ) . When subsetting SNV counts , LDA , CTM , and MMCTM performed roughly equally until only 1% of mutation counts were retained , at which point LDA performance became worse than the CTM and MMCTM . With fewer SV counts , the MMCTM performed better than the CTM , and both outperformed LDA . Importantly , correlated topic models were the least affected by reducing mutation counts , whereas NMF-NORM exhibited the worst performance decline , indicating that correlated models were in general more robust to data sparsity . Further , fixing the MMCTM covariance matrix during inference reduced it’s performance with fewer counts ( Fig 2c , S2 Dataset ) , underlining the benefit of modeling signature correlations . We next compared the ability of these methods to provide informative , low-dimensional representations of samples , using signatures to stratify patients ( Fig 3 , S3 Dataset ) . We trained each method 10 times with random initializations on the full breast cancer dataset . We then trained a logistic regression classifier with the per-sample signature probabilities from each run as input features , and published labels from HRDetect [3] . HRD prediction accuracy scores were computed using 5-fold cross-validation . When the classifier was trained on only SNV signature probabilities , LDA , CTM , and MMCTM performed equally well . NMF and NMF-NORM generally performed worse . With SVs , the MMCTM signature probabilities provided the best accuracy , followed by the CTM and LDA . When the classifier was trained on both SNV and SV signature probabilities , the CTM and MMCTM performed better than other methods , further supporting the advantage of correlated models . We then tested each method on a simulated dataset based on SNV and SV counts from 560 breast cancers [13] ( Fig 4 , S2 Fig , S4 Dataset ) . Briefly , we used NMF to fit signature probabilities to a set of distinct SNV and SV signatures previously identified in this dataset ( COSMIC 1 , 2 , 3 , 13; RS 1 , 2 , 3 , 5; see Methods ) [13] . We note that using NMF-based signatures and estimated signature probabilities likely biased results in favour of NMF . Using the signatures , estimated signature probabilities , and mutation counts per sample , we generated 20 new sets of counts ( 560 synthetic tumour samples each ) by sampling from a Poisson distribution . We then repeated the experiment by generating synthetic datasets with only 1% and 10% of the original SNVs and SVs . Signatures and signature probabilities were estimated using each method , selecting the best solution from 500 restarts , and the mean absolute error ( MAE ) was calculated between estimated and reference values . While all methods generally performed well at recovering SNV signatures ( all median MAE <0 . 01 , except for LDA in COSMIC 2 with 1% counts ) , NMF-NORM performed worst at inferring SV signatures ( adjusted t-test p-values <0 . 05 , S2 Fig ) . The relatively low MAE even with reduced mutation counts also indicated that these methods are able to detect similar signatures as with a full set of mutations . Considering signature probabilities with full counts ( Fig 4 ) , NMF performed best for COSMIC 1 ( except v . s . NMF-NORM ) , COSMIC 3 , COSMIC 13 , and the SV signatures , except v . s . CTM in RS 3 ( adjusted t-test p-values <0 . 05 ) . NMF-NORM was worst for COSMIC 2 , 3 , and 13 ( adjusted t-test p-values <0 . 05 ) . However , with 1% of the original SNV counts , the MMCTM did better than other methods for COSMIC 1 , 3 , & 13 , and both the MMCTM and CTM did best for COSMIC 2 ( adjusted t-test p-values <0 . 05 ) . With 10% SV counts , the MMCTM did best for RS 2 and 5 . The CTM and MMCTM both did better than other methods for RS 1 and 3 ( adjusted t-test p-values <0 . 05 ) . The performances of the independent-feature models ( ILDA , ICTM , IMMCTM ) were also robust to low mutation counts , as previously described [16] , and they typically worked well for SV signature estimation . However , they are generally worse than the MMCTM at inferring SNV signatures , and were not considered for subsequent analysis ( S3 and S4 Figs ) . Overall , correlated topic models produced superior predictive mutation signature distributions and low-dimensional representations of samples . This was especially true when each sample had few mutations , as for SVs . We also found similar patterns in log-likelihood comparisons using the smaller ovarian cancer dataset ( S4 Fig ) , except we detected no major differences between the CTM and MMCTM . Performance of probabilistic topic models was stable across a range of topic hyperparameter values ( S3d Fig ) , and across random restarts compared to NMF ( S5 Fig ) , although randomization schemes differ across these two classes of methods . We next analysed mutations from the 560 breast cancer genomes [13] with the MMCTM for stratification analysis ( S6a Fig ) . We simultaneously fit 6 SNV and 7 SV signatures to counts of SNVs and SVs ( Fig 5a and 5b , S7 Fig , S5 Dataset , see Methods for signature count selection ) . We found SNV signatures similar to those previously identified with proposed etiologies ( S8 Fig ) , including the age-related ( Age , COSMIC 1 ) , APOBEC ( APOBEC-1 & APOBEC-2 , COSMIC 2 & 13 ) , MMRD ( COSMIC 20 ) , and HRD ( COSMIC 3 ) signatures . Additionally we found an SNV signature of unknown etiology , UNK ( COSMIC 17 ) . We identified SV signatures including small , medium , and large tandem duplications ( S-Dup , M-Dup , L-Dup ) , deletions ( Del ) , intrachromosomal SVs ( Intra-Chr & L-Intra-Chr ) , and translocations ( Tr ) . Some signatures were more likely to co-occur in the same tumour , possibly reflecting common etiology . According to the MMCTM model , the two APOBEC signatures were positively correlated ( Pearson’s r = 0 . 34 ) ( Fig 5d , S6 Dataset ) , and the HRD SNV signature was positively correlated with the S-Dup signature ( r = 0 . 3 ) , as expected . The Age signature was positively correlated with Intra-Chr ( r = 0 . 66 ) , L-Intra-Chr ( r = 0 . 53 ) , and Tr ( r = 0 . 38 ) SV signatures . We next performed unsupervised clustering over tumours on joint per-tumour SNV and SV signature probabilities ( Fig 5c , S6b and S9 Figs , S7 and S8 Dataset , see Methods ) . The resulting 7 groups included two ( clusters 1 & 2 , n = 164 & 147 ) enriched for the Age signature ( see S10a Fig , S9 Dataset for significant cluster-signature associations ) . Cluster 1 was enriched for the Tr signature , and both clusters 1 & 2 were enriched for Intra-Chr and L-Intra-Chr . While the Age signature was most correlated with patient age at diagnosis ( r = 0 . 23 , adjusted p-value << 0 . 0001 ) , Intra-Chr was second most correlated ( r = 0 . 20 , adjusted p-value << 0 . 0001 ) . Cluster 1 was associated with Luminal A cancers with relatively fewer SNVs , and contained tumours from generally older patients ( see Fig 5e , S10 Dataset for significant cluster-annotation associations ) . This implies that older patients may be more likely to have accumulated SVs in their cancers’ etiology as function of background rates , indicating a putative SV-related age signature for breast cancer . We also observed clusters with BRCA1/BRCA2 mutations and methylation ( clusters 3 & 4 , n = 79 & 71 ) , as previously described [13] . These tumours typically exhibited an HRD phenotype , and had elevated probability of the HRD SNV signature . Cluster 3 was associated with the S-Dup & M-Dup SV signatures , and more BRCA1 , RB1 , and PTEN driver mutations than expected by chance . As expected , cluster 3 patients were predominantly from the Basal PAM50 class . Cluster 4 was associated with the Del signature , and BRCA2 mutation . In contrast to cluster 1 , patients in cluster 3 also tended to be younger than patients in other clusters . The majority ( 87% ) of BRCA1/2 samples fell into clusters 3 & 4 , although BRCA1/2 mutant tumours that fell outside these clusters often had evidence of HRD , albeit with increased probability of unrelated signatures ( e . g . L-Dup in cluster 6 ) . Of patients predicted by HRDetect [3] to harbour HRD , 97% fell within the BRCA1/2 ( clusters 3 & 4 ) groups , demonstrating that the MMCTM output provides a substrate upon which known biological clusters are recovered , with further stratification as a result of SNV and SV integration . Cluster 5 ( n = 62 ) was enriched for the APOBEC-1 , APOBEC-2 , Intra-Chr , and L-Intra-Chr signatures , and was also enriched for HER2-positive tumours , relating Her2-amplification and APOBEC deamination processes for approximately 11% of breast cancers , as previously reported [29] . Cluster 6 ( n = 29 ) was the only group enriched for L-Dup , and also contained older patients than expected by chance . Cluster 7 ( n = 8 ) was associated with defective DNA mismatch repair ( MMRD ) , and the MMRD SNV signature , consistent with previous reports [30] . A recent analysis of ovarian tumours revealed a novel high-grade serous ovarian carcinoma ( HGSC ) sub-group with relatively worse prognosis , characterized by increased frequency of foldback inversions ( FBI ) [2] . Their analysis combined NMF-based SNV signature analysis with ad-hoc SV and copy number variant ( CNV ) features . Here we expanded on some of their findings using the MMCTM on a merged data set consisting of 133 samples from Wang et al . [2] and 62 samples from the International Cancer Genome Consortium ( ICGC ) ovarian cancer whole genome dataset [31] . We fit 6 SNV and 7 SV signatures to mutation counts from the 195 ovarian cancer genomes ( Fig 6a and 6b , S11 Fig , S5 Dataset , see Methods for signature count selection ) , including endometrioid carcinomas ( ENOC ) , clear cell carcinomas ( CCOC ) , granulosa cell tumours ( GCT ) , and HGSC ( S2 Table ) . Amongst the resultant SNV signatures were the previously described Age ( COSMIC 1 ) , APOBEC ( COSMIC 13 ) , HRD ( COSMIC 3 ) , MMRD-1 ( COSMIC 20 ) , MMRD-2 ( COSMIC 26 ) , and POLE ( COSMIC 10 ) signatures ( S8 Fig , see also for a comparison to the breast SNV signatures ) . The SVs included signatures for small , medium , and large tandem duplications ( S-Dup , M-Dup , L-Dup ) ; deletions ( Del ) ; FBI , inversions , and deletions ( FBI/Inv/Del ) ; intrachromosomal SVs ( Intra-Chr ) ; and translocations ( Tr ) . The association of deletions with FBI can be understood in terms of the underlying cause of FBI: breakage-fusion-bridge cycles . After the loss of a telomere , sister chromatids fuse and are then pulled apart during mitosis , producing one chromosome with a foldback inversion and another with a terminal deletion . We clustered the tumours according to their joint standardized SNV and SV signature probabilities , which resulted in 11 groups ( Fig 6c , S12 Fig , S7 and S8 Dataset ) . While the original study identified one HRD signature group [2] , our analysis here produced two major HRD clusters ( 1 & 4 , n = 34 & 23 ) , roughly defined by tumours with S-Dup and M-Dup ( see S10 Fig , S9 Dataset for cluster-signature associations ) coupled with loss of BRCA1 ( see Fig 6d , S10 Dataset for cluster-annotation associations ) , and small deletions ( Del ) coupled with loss of BRCA2 , respectively . The association of BRCA1/2 status with tandem duplication and deletion SV signatures has been reported in breast cancer tumours [13] , and was reflected in our analysis of the 560 breast cancer dataset ( Fig 5 , described above ) , providing strong evidence for BRCA1-like and BRCA2-like HRD sub-strata crossing tumour types . Cluster 2 ( n = 32 ) , 5 ( n = 20 ) , 7 ( n = 14 ) , and 9 ( n = 8 ) were all enriched for the FBI/Inv/Del signature . Cluster 9 also included all microsatellite instable ( MSI ) ENOC tumours , and was also associated with MMRD-1 , Age , and Del signatures , along with higher numbers of SNVs , and KMT2B and RPL22 mutations . Cluster 3 ( n = 25 ) contained mainly CCOC and ENOC tumours enriched for the Age , L-Dup , and Tr signatures . Cluster 6 ( n = 16 ) included tumours highly enriched for APOBEC signature probability . Cluster 7 ( n = 14 ) was associated with the HRD SNV signature as well as Del and FBI/Inv/Del . Cluster 8 ( n = 11 ) was only enriched for the Intra-Chr signature . Cluster 10 ( n = 6 ) was similar to the BRCA1 cluster ( 1 ) , but was more strongly associated with the M-Dup signature . Another small cluster of mainly HGSC tumours ( 11 , n = 6 ) , was associated with higher probability of the L-Dup signature , and CDK12 mutations , an association supported by a previous study [32] . By inspecting the signature correlations output by the MMCTM model ( Fig 6g , S6 Dataset ) we saw that the HRD SNV signature was positively correlated with the S-Dup ( r = 0 . 12 ) signature , as may be expected from the underlying biology of these signatures . The Age signature is positively correlated with the L-Dup ( r = 0 . 45 ) and FBI/Inv/Del ( r = 0 . 37 ) signatures . MMRD-1 is positively correlated with the S-Dup ( r = 0 . 26 ) , and Del ( r = 0 . 53 ) SV signatures . HGSC patient groups , defined by their standardized mutation signature probabilities , differed in survival rates . We defined 5 HGSC groups ( see Methods ) , representing BRCA1-mutant ( clusters 1 , 10; n = 36 ) , BRCA2-mutant ( cluster 4 , n = 19 ) , FBI ( clusters 2 , 5 , 7; n = 50 ) , Intra-Chr ( cluster 8 , n = 8 ) , and CDK12-like tandem duplicator tumours ( cluster 11 , n = 5 ) . We compared overall-survival amongst the HGSC super-clusters using the Kaplan-Meier method ( Fig 6e and 6f ) . The BRCA2/deletion cluster had the highest survival rate , while the CDK12/tandem duplicator group had the worst . Comparing the HGSC clusters in a pairwise fashion , the CDK12 group had worse survival than the BRCA1 and BRCA2 groups ( adjusted log-rank p-value < 0 . 05 ) . The FBI group had worse survival than the BRCA2 group ( adjusted log-rank p-value < 0 . 05 ) . The BRCA1/tandem-duplication group had an intermediate survival rate , but the survival curve was not significantly different than those of the FBI or BRCA2 groups ( adjusted log-rank p > 0 . 05 ) . While FBI was previously identified as a marker for poor prognosis [2] , activity of a mutational process linked with loss of CDK12 and producing 100kbp–1Mbp tandem duplications could indicate even worse outcomes . Overall , the MMCTM analysis represented a refinement of signature-based prognostic stratification in HGSC indicating BRCA2-like HRD as the best performing group of patients , followed by BRCA1-like HRD , Intra-Chr , FBI , and CDK12-like tandem duplicators . To evaluate the reproducibility of signatures inferred using the MMCTM , we applied the method to the two independent HGSC datasets included in our ovarian cancer analysis above . Specifically , 59 samples previously published by our group , and 62 samples from ICGC . Each HGSC group contained signatures that were similar between both groups , including HRD associated SNV and SV signatures . Both groups also showed a segregation of BRCA1- and BRCA2-like cases based on per-sample signature probabilities ( S13 and S14 Figs ) . We also compared SNV signatures inferred in the ovarian and breast datasets to each other and to the COSMIC signatures ( S8 Fig ) . In both datasets we found signatures similar to those previously reported to occur in ovary and breast cancers [2 , 10 , 13] , including the APOBEC; HRD; and age-associated signatures , demonstrating the ability of the MMCTM to capture established signatures .
Through integrated statistical inference and analysis of SNV and SV mutation signatures , our results reveal at once correlated signatures and patient stratification within DNA repair deficient tumours . Our findings have several implications for the field . The use of structural variations in signature analysis is less common than for point mutations , in part due to the relative paucity of whole-genome sequencing datasets . Here , we show the significant new value from their joint interpretation , and set the framework for their simultaneous consideration across a broad range of tumour types . Moreover , our results demonstrate that correlated statistical modeling improves signature inference in the context of sparse mutation counts . The HRD point mutational signature is well described , but automated association of tandem duplications within BRCA1-like and interstitial deletions within BRCA2-like cancers represents an important refinement , reproduced here in two independent cancer types , with data from two independent studies . Furthermore , we show in the ovarian cancer cohort how this has prognostic implication , superseding what could be derived from gene-based biomarkers ( i . e . if only BRCA1 and BRCA2 mutation status were considered ) . We have introduced a new formalism for mutation signature analysis in cancer genomes . Our approach models the correlation between signatures , which provides their performance increase . However , when no correlations exist between signature probabilities , this method will likely not provide much benefit . In these situations , a researcher may opt to use an alternative , such as NMF or LDA . Nevertheless , signature correlations exist in at least breast and ovarian cancer , as shown in this report , and we believe analysis of other cancer types will benefit from our approach . The topic models discussed in this manuscript produce signature probabilities , as opposed to activity estimates , which requires a subtle difference in interpretation . Signature probabilities are related to activities , but they indicate the probability of signatures generating a mutation , rather than the proportion of mutations generated by a signature . The topic models discussed output non-zero signature probabilities for each sample , due to their Bayesian formulation . Since every sample is unlikely to have experienced activity from every detected signature , one may wish to set a probability threshold to determine active signatures for downstream analysis . However , the optimal choice of probability threshold is a matter for future investigation . Correlated topic models are significantly more robust to reduced mutation burden , which can occur in a number of scenarios . We have already described that signature extraction from SVs , at the level detected in the breast and ovarian datasets analysed here , benefits from correlated signature modeling . Analysis of other low-count mutation types may also benefit , for example mutations called from exome or single-cell sequencing experiments . Importantly , the statistical framework of the MMCTM is flexible and extensible . While here we show the advantage of integrated SNV and SV analysis , the MMCTM can seamlessly integrate other count-based features such as copy number events , double strand breaks , and telomeric insertions . As the field develops , we suggest a robust and extensible framework will be required to encode and integrate multiple feature types of the genome as they relate to mutational processes . The advantage of our relatively simple SNV and SV integration is evident and motivates further advances through multi-modal statistical modelling leading to richer biological interpretations of endogenous and potentially exogenous processes . In conclusion , our findings reinforce the importance of an integrated , holistic view of multiple classes of genomic scarring to drive discovery and characterization of mutation processes across human cancers .
Nucleotides flanking SNVs were extracted from human reference GRCh37 . The number of each type of SNV ( e . g . C→T ) with a particular flanking sequence was counted . SV calls were split according to type ( deletion , tandem duplication , inversion , foldback-inversion , translocation ) , the level of homology ( 0–1 , 2–5 , >5 bp ) , and breakpoint distance ( <10kbp , 10–100kbp , 100 kbp–1Mbp , 1–10Mbp , >10Mbp ) , then counted . Foldback inversion calls were not included in the breast cancer dataset . Breakpoint distance bins are those used in a previous study on SV signatures [13] . Breakpoint distance was not calculated for translocations , as the concept is not applicable for this class of SVs . SNV and SV counts per sample were computed from the mutations used for signature analysis . Additional ovary sample gene mutation annotations were computed from SNV and indel calls according to the original paper . For LDA and ILDA , parameters were inferred using mean-field Variational Bayes . For CTM , MMCTM , ICTM and IMMCTM , parameter inference was performed using mean-field variational EM . The MMCTM updates and derivations can be found in Salomatin et al . [22] . See S1 Text for detailed descriptions of the topic models . When using only a single mutation type , the MMCTM reduces to the CTM described by Blei and Lafferty [21] ( similarly for the IMMCTM and ICTM ) . Therefore , the CTM and ICTM parameters were inferred using the MMCTM and IMMCTM implementations , but with counts from a single mutation type . The CTM , ICTM , LDA , ILDA , and NMF methods were used to compute SNV or SV signatures separately . The probabilistic topic models were implemented similarly using the Julia language v0 . 6 . 3 [33] . NMF models were fit using the coordinate descent solver implementation in the Scikit-learn library [34] v0 . 19 . 1 . NMF was run on both raw and normalized mutation counts . Normalization was performed by dividing mutation counts by sample totals , for each mutation type . For log-likelihood-based comparisons , mutation counts were split according to a stratified 10 × 5 cross validation scheme; For each histotype , samples were split into 5 training and test sets . The splitting procedure was performed 10 times , resulting in 50 training and test sets . Each method was run on each training set and evaluated on each corresponding test set , using random initialization . Random initialization for the topic models involved generating random positive integer values for the variational signature-mutation dirichlet parameters . Evaluation was performed by randomly splitting the mutations in each test sample into observed and hidden sets . Signature probabilities for each test sample were estimated using the observed test mutation counts , then the per-mutation predictive log likelihood was computed using the hidden test mutation counts . Methods were tested over a range of 2–12 signatures , as well as over a range of count subsets . Multi-modal topic models were given the same number of signatures for SNVs and SVs . An additional , similar comparison was performed by fitting the MMCTM to this data with covariance fixed to the identity matrix . Count subset comparisons were performed by removing mutations from each genome , retaining only a given fraction . Mutations were randomly selected according to their type ( e . g . C ( C→T ) T ) and relative type proportions . These mutations were removed and the genome mutation counts updated . The updated mutation counts were then input to the compared methods . SNVs were subset to 1 , 5 , 10% , while keeping SVs at 100% . SVs were subset to 10 , 15 , 20% , while keeping SNVs at 100% . For the breast cancer dataset , the number of SNV and SV signatures was fixed at 5 , selected by observing the log-likelihood curves in the above benchmarking experiment ( S3 Fig ) with the objective of choosing a “fair” value . For the ovarian cancer dataset , the number of SNV and SV signatures was fixed to 6 and 5 , respectively . The stability of method solutions were also compared over 100 random restarts on 4/5 of the breast cancer dataset . Solutions were evaluated on the remaining 1/5 of the samples in the manner described earlier . Predictive log likelihoods were computed on test sets with signatures for SNVs and SVs separately . The likelihood computation involves the signatures fit with the training data , sample-signature probabilities estimated using the observed test counts , and the hidden test counts . The average per-mutation predictive log likelihood for a particular mutation type is given in Eq 1 . l = ∑ d D ∑ n N d log ( ∑ k K p ( X n d ∣ ϕ k ) p ( Z n d = k ∣ θ d ) ) ∑ d D N d ( 1 ) where D is the number of samples , Nd is the number of mutations in sample d , K is the number of signatures , X is the mutations in sample d , Z is the mutation-signature indicators , ϕk is the signature-mutation distribution , and θd is the sample-signature distribution . For comparisons involving the breast cancer dataset , foldback inversion counts were not provided to NMF as these SV types were not included in this dataset . When evaluating the NMF solutions , the outputs are normalized to produce valid probability distributions that can be used for the log-likelihood calculations . Since NMF does not take into account uncertainty during estimation , the sum of probabilities calculation above can occasionally produce zeros . To avoid taking log ( 0 ) , we add 10−16 to the sum of probabilities for NMF . Topic model signature-mutation and sample-signature distribution point-estimates were obtained by taking the mean of their variational posterior distributions . For the logistic regression classifier-based comparisons , each signature detection method was trained 10 times with 2–10 signatures , using the full 560 breast cancer dataset . For multi-modal methods , the same number of SNV and SV signatures was given . The sample-signature distributions were used as training data for the classifier along with previously published HRDetect-derived labels . HRDetect negative cases were subsampled for each method run to produce balanced datasets for training and evaluation , with 124 positive and negative labels each . Three types of tests were performed: using only SNV , only SV , or both SNV and SV sample-signature distributions . Stratified 5-fold cross-validation was performed for each test , resulting in 5 × 10 = 50 scores for each method , training data type , and setting of the number of signatures . The output score of cross validation is the mean accuracy of the logistic regression classifier . Parameter inference was performed using the Scikit-learn [34] v0 . 19 . 1 implementation with the liblinear solver and maximum 10 , 000 iterations . Simulated datasets were generated by first selecting COSMIC SNV signatures 1 , 2 , 3 , 13 , and breast cancer SV signatures [13] RS 1 , 2 , 3 , and 5 . These SNV signatures were reported as present in the breast cancer dataset [13] , and they are qualitatively distinct from each other . SV signatures largely defined by clustered breakpoints were excluded as that feature was not included in this analysis . Reference signature probabilities were estimated using NMF , the given signatures , and counts for the 560 breast cancer dataset . 10 synthetic datasets were generated , where for each mutation type in each sample , counts were generated by drawing from a Poisson distribution with rate equal to the number of mutations in the sample multiplied by the reference signature matrix and the sample’s signature probability vector . This approach is similar to that used in a previous study [18] . This procedure was repeated using the reference signatures , signature probabilities , and mutation counts subsetted to 1% SNVs and 10% SVs . Signatures and signature probabilities per dataset were then estimated by running each method 500 times with random restarts and choosing the best solution per method based on predictive log-likelihood . Topic model signature hyperparameters were set to 1 . 0 . Estimated signatures were then matched to the reference signatures , and the mean absolute differences between the reference and estimated values were computed . Signature matching was performed by finding the pairwise combination of estimated and reference signatures that gave the lowest mean absolute error . Then the matching procedure was repeated for the rest of the signatures , while ignoring previously assigned reference signatures . The number of signatures to estimate in the breast and ovarian datasets was selected by inspecting the log-likelihood curves from the benchmarking experiment , using the elbow curve method ( S15 Fig ) . The number of signatures to estimate in the two HGSC datasets was selected by fitting the MMCTM to approximately half the mutations in each sample , and computing the average per-mutation log-likelihood on the other half of the mutations . This differs from the benchmarking cross-validation scheme in that it takes in account all samples in the dataset . The model was initially fit to each dataset 1000 times for a limited number of iterations . α hyper-parameters were set to 0 . 1 . Each restart is run until the relative difference in predictive log likelihood on the training data was < 10−4 between iterations . The restart with the best mean rank of the SNV and SV predictive log likelihoods was selected for fitting to convergence with a tolerance of 10−5 . Samples were clustered using sample-signature probabilities for SNV and SV signatures together . Signature probabilities were converted to Z-scores for each signature across samples . By standardizing the probabilities , the inter-sample differences of low-prevalence signatures are given increased emphasis relative to higher-prevalence signatures . Hierarchical agglomerative clustering was performed using the Euclidean metric , and Ward linkage . Discrete clusters were formed using the R dynamicTreeCut package [35] v1 . 63 with method = “hybrid” , deepSplit = FALSE , and minClusterSize = 3 . Enrichment of a sample cluster’s signature probability was tested using an unequal variance one-sided t-test against the signature probabilities of other clusters . For the breast cancer dataset , cluster associations with ER , PR , HER2 , MMRD , and PAM50 status were performed with a two-tailed Fisher’s exact test . Differences in Age or the number of SNVs and SVs were tested with two-tailed unequal variance t-tests . Driver gene mutation and HRDetect prediction associations were computed using a blocked permutation test . The permutation tests were performed as follows: For each cluster , “new” clusters were generated by sampling tumour samples without replacement from the full dataset . New clusters maintained the same ER , PR , and HER2 status composition as the original cluster . The difference in proportions of samples with the annotation of interest between the new cluster and all other samples was computed . Two-tailed p-values were calculated using Eq 2: p = 1 + ∑ n N I ( a b s ( s ′ ) ≥ a b s ( s ) ) 1 + N ( 2 ) where N is the number of permutations ( generated clusters , here 10 , 000 ) , and s is the statistic of interest for the original cluster ( e . g . difference in proportions of samples with loss of TP53 ) , and s′ is the same statistic for a generated cluster . This procedure attempts to correct for correlations between the tested annotations and ER , PR , and HER2 status . Gene mutation status and MSI cluster associations in ovarian cancer were tested with the blocked permutation test described above , accounting for histotype rather than ER , PR , and HER2 status . Differences in SNV and SV counts were performed with two-tailed unequal variance t-tests . Due to the presence of a POLE mutant sample with a very high number of SNVs , t-tests for this statistic were performed on count ranks . The unequal variance t-test on ranked data is a robust alternative to Student’s t-test and the Mann-Whitney U test when assumptions are violated [36] . Cluster-signature and cluster-annotation p-values within each dataset were corrected using the Benjamini & Hochberg method [37] . HGSC samples grouped according to the hierarchical clustering were compared by estimating overall-survival Kaplan-Meier curves for each cluster , using the R survival package . Clusters 2 , 5 , and 7 were grouped as they were all enriched for the FBI/Inv/Del signature , and had no significant difference in survival outcome . We call this the “FBI” group . Similarly , cluster 10 was grouped with cluster 1 as it contained BRCA1 mutant patients with similar signature profiles . P-values were calculated using the log-rank test . Pairwise survival curve comparison p-values were adjusted using the Benjamini & Hochberg method [37] implemented in the R p . adjust function . Topic model code is available in a GitHub repository: https://github . com/shahcompbio/MultiModalMuSig . jl .
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Over time DNA accumulates mutations from a variety of sources . Some mutations result from external mutagens , such as UV radiation , while others result from processes occurring within the cell itself . Each of these sources can impart characteristic patterns of mutations on the genome , known as mutation signatures , which can be detected using computational techniques . Loss of DNA repair mechanisms can leave specific mutation signatures in the genomes of cancer cells . To identify cancers with broken DNA-repair processes , accurate methods are needed for detecting mutation signatures and , in particular , their activities or probabilities within individual cancers . In this paper , we introduce a class of statistical modeling methods used for natural language processing , known as “topic models” , that outperform standard methods for signature analysis . We show that topic models that incorporate signature probability correlations across cancers perform best , while jointly analyzing multiple mutation types improves robustness to low mutation counts .
|
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"Abstract",
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"Results",
"Discussion",
"Materials",
"and",
"methods"
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2019
|
Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models
|
Dengue is a major mosquito-borne viral disease and an important public health problem . Identifying which factors are important determinants in the risk of dengue infection is critical in supporting and guiding preventive measures . In South-East Asia , half of all reported fatal infections are recorded in Indonesia , yet little is known about the epidemiology of dengue in this country . Hospital-reported dengue cases in Banyumas regency , Central Java were examined to build Bayesian spatial and spatio-temporal models assessing the influence of climatic , demographic and socio-economic factors on the risk of dengue infection . A socio-economic factor linking employment type and economic status was the most influential on the risk of dengue infection in the Regency . Other factors such as access to healthcare facilities and night-time temperature were also found to be associated with higher risk of reported dengue infection but had limited explanatory power . Our data suggest that dengue infections are triggered by indoor transmission events linked to socio-economic factors ( employment type , economic status ) . Preventive measures in this area should therefore target also specific environments such as schools and work areas to attempt and reduce dengue burden in this community . Although our analysis did not account for factors such as variations in immunity which need further investigation , this study can advise preventive measures in areas with similar patterns of reported dengue cases and environment .
Dengue virus ( DENV ) is a positive-strand RNA virus ( Flaviviridae ) that is spread by Aedes aegypti and also Ae . albopictus mosquitoes [1] . DENV infection rates worldwide may have been underestimated , with a recent study suggesting 390 million infections per year [2] . Dengue disease has been recognised as a public health problem in Indonesia since it was first detected in 1968 in the cities of Jakarta and Surabaya [3] . Since then , Indonesia has experienced periodic outbreaks of dengue with increasing numbers of infections and severity [3 , 4] , recording nearly a third of the DENV cases and half of all fatality cases reported in South-East Asia between 2001 and 2010 [5] . Nonetheless , dengue is very poorly researched in Indonesia [6] . Multiple factors , including environmental , biological and demographic factors are believed to be important in DENV transmission [2 , 7] . Climate is considered a driving force behind DENV epidemics and transmission; behaviour , ecological , demographical and socio-economical changes/conditions however , are key determinants in local dengue risk ( see for example [8–11] ) . To clarify which factors principally influence DENV epidemiology , long-term data on climate and other socio-ecological changes should be evaluated against each other in spatio-temporal models [12–17] . The fundamental challenge for determining risk factors of DENV infection is how to best develop epidemiological models at a regional and/or local level with complete data [18] . Several statistical methods have been used to determine the relationship between dengue and putative explanatory variables [12] . Both the multiplicity of approaches to designing risk maps and the large number of predictors found associated with the risk of dengue render it necessary to evaluate risk factors for each geographical situation [13] . A recent comprehensive review of studies using modelling approaches to assess dengue risk highlighted the variety of modelling approaches , and suggested that high resolution risk maps factors such as human movement and housing might be more useful for assessing dengue occurrence than climatic data [19 , 20] . This suggests that dengue risk assessments need to take into account a variety of variables that reflect local conditions . In this study we carried out a large scale analysis of demographic , socio-economic and environmental variables to determine the most influential factors related to local DENV infections in Java , Indonesia . In particular , we built Bayesian spatial and spatio-temporal models to evaluate the influence of determinants that may vary locally while accounting for spatio-temporal variations in climate . This conceptual shift from universal to multivariate local analysis will allow the development of better strategies for dengue prevention control in the study area , optimising control activities to its unique characteristics as has been done for other vector-borne disease such as Malaria [21] . This is also a clear improvement from previous risk models as spatio-temporal variations in climate have often been omitted when analysing dengue surveillance data [18] .
Studies conducted here were carried out with ethical approval from the University of Glasgow ( Project Number: 2012082 ) and the Ministry of National Education , Faculty of Medicine Gadjah Mada University , Medical and Health Research Ethics Committee ( KE/FK/323/EC ) . Data were analyzed anonymously . All hospital-reported dengue cases ( confirmed by ELISA or based on clinical symptoms; leading to hospitalization/hospital stays ) in Banyumas Regency , Central Java , from January 1st 2000 to December 31st 2013 were extracted from central databases at the Banyumas Regency Health office and aggregated at village-level ( Fig 1 ) . Cases were allocated to a village from the address stated in the hospital report form . Population data were gathered for all 329 villages in Banyumas Regency from the Indonesian 2010 census . Between 2001 and 2010 , Central Java experienced limited population growth of <4% [22] . For the purpose of analysis , we therefore assumed that the underlying at-risk population remained stable throughout the study period ( Fig 2A ) . However , to account for potential local biases due to temporal changes in population , land cover data for the year 2000 and 2010 were extracted from maps of insular Southeast Asia , freely available at the Centre for Remote Imaging , Sensing and Processing ( CRISP ) of the National University of Singapore [23] . For each village , the proportions of cells classified as urban cover ( Fig 2B ) and the proportions of cells classified as plantation cover were calculated for both the year 2000 and 2010 . Differences between yearly proportions of coverage were computed as a proxy to the amount of changes that occurred in the population distribution ( Fig 2C ) . In total , 53 socio-economic descriptors were recorded in the census data , providing information on the age structure , working status and education level for each village . Given the strong correlation between these variables , we constructed proxies of socio-economic status for each village using principal component analysis ( PCA ) ( S1 Appendix ) . Two significant axes ( p<0 . 001 ) explained a total of 60 . 2% of the total variation in the data , with the first and second explaining 42 . 2% and 18 . 2% respectively . For the purpose of analysis , these measures were standardised to range between 0 and 100 . The first axis ( PCA1 , Fig 2D ) provides information regarding employment type and education levels in each village , where a higher number represents an increase in the number of people that are better educated , and are employed as civil servants or business and services industry . The second axis ( PCA2 , Fig 2E ) provides information on the age structure in each village , with 0 indicating villages with a high proportion of retired people , whereas 100 indicates those with a high proportion of working families . Three variables were created to adjust for possible confounding . Firstly , a normalized population density per village was calculated by computing the log10-transformed number of inhabitants recorded in 2010 per squared kilometres . This variable was created as it was assumed that mosquito biting rate may vary as a function of the availability of the host population by creating more cases in highly populated areas of the Regency . The second variable is the shortest Euclidean distance between the centroid of each village to the nearest hospital ( Fig 2F ) . This variable was computed as a proxy for access to health care facilities and , thereby , misreporting . Finally , the presence or absence of a hospital in the boundaries of village was recorded as a binary predictor variable . Environmental data from various sources and with various spatial and temporal resolutions were extracted ( S1 Appendix ) . Digital elevation data was downloaded from the Consultative Group on International Agricultural Research-Consortium for Spatial Information [24] . Data on the proxies of precipitation were obtained from the WorldClim—Global Climate Data [25] . All data on the enhanced vegetation index ( EVI ) , day-time ( LST ) and night-time land surface temperatures ( nLST ) that were available over the study period were sourced from the Terra satellite product version-5 from the Moderate Resolution Imaging Spectroradiometer [26] . For all environmental measures ( EVI , LST , nLST , precipitations ) , summary statistics were computed for each individual cell over all selected layers . Summary statistics considered in these analyses were: the overall mean , standard deviation , minimum and maximum , as well as similar estimates for the dry and rainy seasons . The dry season was considered to be occurring between May and September . Village-level data was collated by computing the mean estimates of all digital cells ( either representing the global or yearly measures ) which were overlaid by the boundaries of each village . Fig 2G–2I illustrate the spatial variability of a few of the considered environmental measures . A Bayesian Poisson spatial analysis was carried out to quantify the effect of factors influencing the number of people reported to have a dengue infection in the Banyumas regency during the period 2000 to 2013 . For this analysis the outcome was the number of reported dengue cases in each village , y = {y1 , ⋯ , yn} , during the period of interest ( see above ) . The number of cases was adjusted for the frequency-dependence of the transmission by including an offset term corresponding to the expected number of dengue cases for each village . The expected number of cases was considered as emerging from a homogenous process and proportional to the population of each village . We used integrated nested Laplace approximations ( INLA ) [27] to do fast approximate Bayesian inference . Analyses were done in R ( version 3 . 1 . 1 ) and the R-INLA package [28] . For all analyses , two model structures were considered ( see S1 Appendix for details ) . Model 1 was a spatial-only Poisson regression model , developed upon the cumulative number of cases recorded during the study period in each village , and with variance components being only related to villages . Model 2 was a spatial-temporal Poisson regression model , developed upon the number of cases recorded per year in each village , and in which spatial , temporal and spatio-temporal variance components are considered . While Model 1 quantifies the overall strength of the association between the number of dengue cases in each village and spatially-structured epidemiological factors hypothesized to influence disease transmission and reporting during the study period , Model 2 tests for the resilience of these associations to both temporal and spatio-temporal structures in the data . It is worth noting that Model 1 considered all potential explanatory , village-level environmental and climatic variables as fixed for the whole 14-year study period , whereas Model 2 considered these variables to change for each year of the study period . Details for implementing spatial and spatio-temporal statistical models with INLA are provided elsewhere [29 , 30] . Because spatial and spatio-temporal variance components can be modelled in different ways [29] , the most parsimonious model structure for both models was selected prior to analyses according to model’s Deviance Information Criteria ( DIC ) [31] and mean logarithmic scores [32] ( see S1 Appendix for further details ) . The structure of the spatial ( Model 1 ) and spatio-temporal ( Model 2 ) models which best represent the data were formulated such as: ηi=log ( ρi ) =α+∑k∞βkxki+σi Model 1 ηit=log ( ρit ) =α+∑k∞βkxkit+νi+σi+ϕt+γt+δit Model 2 where the ratio ρi and ρit are the standard morbidity ratio ( SMR , i . e . observed-to-expected dengue cases ) for each village i during the entire study period and in each year t , respectively; α the intercept , quantifying the average dengue rate in the regency; xki and xkit the value of the kth potential risk factor ( or fixed effects ) for each village i and for each village i in each year t , respectively . The components σi and νi are the structured and unstructured random effects specific to each village i; whereas γt and ϕt represent the temporally structured and unstructured random effect . Finally , the component δit represents the interaction between space and time . Here , δit is assumed being the results of interaction between the two unstructured effects νi and ϕt . Consequently , we assume no spatial and/or temporal structure on the interaction . Therefore , δit , similarly to the other random effects , is following a normal distribution such as δit∼N ( 0 , τ ) . We used uninformative priors for all variables tested individually in order to estimate the parameters that fitted best the data , as no previous data were available for Indonesia . Briefly , the priors assigned to the model coefficients were normally distributed , as were the coefficients of the posterior distributions . In both models , the priors assigned to the fixed parameters were uninformative with distribution ∼N ( 0 , 0 ) for the intercept and ∼N ( 0 , 0 . 001 ) for other fixed effects ( i . e . with a normal distribution of mean 0 and with precision of 0 or 0 . 001 for intercept or other fixed effect , respectively ) , as was the prior distribution assigned to the various random effects . For all random effects , the precision τ was defined as θ = log ( τ ) , where the initial values of the hyperparameter θ followed a log-gamma distribution ( parameter values: scale = 1 , shape = 5e−04 ) . For each model structure ( i . e . Model 1 and Model 2 ) , each variable was examined individually in a univariate analysis . Variables with an 80% credible interval ( Cr . I . ) of their posterior distribution which does not overlap 0 and that are not correlated to other variables ( Pearson’s correlation coefficient , r , smaller than 0 . 7 ) were then taken forward to the multivariate statistical analysis . A stepwise elimination process was applied to retain associated variables , along with biologically plausible two-way interactions . The DIC was used to compare model performance . Variables were considered significantly associated with the number of reported cases in a village if their 95% credible interval of their posterior distribution does not overlap 0 . The association of all significant climatic variables with the risk of dengue infection was further investigated by evaluating their shape and strength over various biologically-relevant ranges . In addition , the stability of all associations was checked by systematic removal of variables . Quality of model inferences were evaluated by comparing model-based predictions with observed village-level values of SMR , first through visual comparison , and then by using Pearson’s product moment correlation coefficient ( r ) . The uncertainty associated with the posterior means can also be mapped and provide useful information [29 , 33] . In particular , we were interested to identify which villages show excess dengue risk through the whole study period and in each individual year . To do so , we plotted the spatial distribution of the posterior probabilities for the spatial random effect p ( exp ( σi ) > 1|y ) resulting from both Model 1 and Model 2 . Similarly , we plotted the spatial distribution of the posterior probabilities for the spatio-temporal effect p ( exp ( δi ) > 1|y ) resulting from Model 2 . As defined in [29] , an increased risk with a small level of associated uncertainty is indicated in villages with a spatial ( or spatio-temporal ) relative risk above 1 and an associated posterior probabilities above 0 . 8 .
For the period 2000 to 2013 , 3810 DENV cases were recorded by Banyumas Regency Health office which corresponds to a mean incidence of 17 . 5 reported cases per 100 000 inhabitants . The temporal trends follow closely with national figures , showing a sharp increase from the year 2000 and peaks of incidence in 2008 , 2010 and 2013 ( Fig 1B ) . The village-level risk of dengue cases in the Regency varies between 0 and 6 . 2 more cases than what was expected ( Fig 1A ) , with the largest standardized morbidity ratio values centred on the main urban area of Purwokerto , the capital of the Banyumas Regency . Of the 76 putative explanatory variables , 56 were individually associated with the village-level risk of dengue cases ( S1 Fig ) . Population density and the first socio-economic axis were highly correlated ( Pearson’s r>0 . 7 ) and only the first socio-economic axis has been considered further since it explained a greater amount of variability . The socio-economic variable , proxy for the level of education and employment structure in each village , was the most important risk factor in the final spatial model ( Table 1 ) and showed a positive association with the village-level risk during the study period . Villages with large numbers of individuals that are better educated , and people that are employed as civil servants or in business and services were significantly more at-risk of reporting dengue infection during the study period . Distance to the nearest hospital was also negatively associated with the risk of dengue cases , suggesting a significant risk of underreporting for every kilometre of distance further away from the closest hospital or health centre . The average minimum night-time temperature was the only significant environmental risk factor , revealing an increasing risk of dengue infection with a decreasing minimum night-time temperature . The risk of dengue infection was increased by 28% ( incidence risk ratio ( IRR ) = 1 . 28 , 95% Cr . I . 1 . 04 to 1 . 58 ) in areas with night time temperatures between 10°C and 15°C , and by 64% ( IRR = 1 . 64 , 95% Cr . I . 1 . 20 to 2 . 27 ) in areas with night time temperatures below 10°C , compared to areas with a temperature of 15°C and greater . We assessed the explanatory performance of the spatial model by examining how the model predictions agree with the observations ( Fig 3A ) and calculating the Pearson’s correlation coefficient between observations and predictions . A value of one indicates perfect correspondence between the model inferences and the observations . The Pearson’s correlation coefficient was 0 . 984 ( 95% CI 0 . 98 to 0 . 99 , p<0 . 0001 ) , indicating a high concordance of the model inferences with observations . However , such a high concordance may be explained by the spatial effect alone . Looking at the spatial random process ( Fig 4A ) , it is clear that the spatial structure relating to the risk of dengue infection observed in the Regency ( Fig 1A ) has been explained by the three significant explanatory variables . Additionally , the residual spatial process revealed areas where the risk of dengue infection is significantly high ( Fig 4B ) but is explained neither by environmental nor socio-economic variables . The area around the capital city of Purwokerto still remains at higher risk , indicating that the risk of dengue infection was not entirely accounted for by the model’s predictors in this location . In addition , villages in the South-West of the Regency may undergo different processes leading to disease than other areas . We further examined how resilient our results were to spatio-temporal changes observed in the Regency during the study period . S2 Fig provides the outcome of the univariate analysis whereas Table 2 provides the posterior estimates of the best explanatory multivariate model for the annual incidence of dengue in the regency . Taking the spatio-temporal effect into account for changes in the disease process due to periods of particularly high ( or low ) incidence in particular villages did not change our overall conclusions . Both influential explanatory variables ( Table 2 ) and the structure of the residual spatial process ( Fig 4C ) remained unchanged . Comparing mean inferences from the spatio-temporal model with mean observed annual SMR for each village ( Fig 3B ) shows good concordance of the model inferences with observations , with a Pearson’s correlation coefficient of 0 . 944 ( 95% CI 0 . 93 to 0 . 95 , p<0 . 0001 ) . The location of villages showing significant higher residual annual incidence , as indicated by village-specific posterior probabilities p ( exp ( δit ) > 1|y ) > 0 . 8 , is shown in S3 Fig , revealing that villages with hotspots of infection may change over time . The explanations for these spatio-temporal variations that affect villages may include movement of people between villages , change of habits and behaviour , virus strain replacement and host responses evolving ( e . g . herd and individual immunity ) . It would require in depth analysis of such villages to determine the exact reasons for this observation . This however , does not affect the overall conclusions of the study .
In this study , we assessed the influence of various risk factors for dengue infection in the Banyumas Regency , Central Java , Indonesia . This study uses a wealth of data from 2000–2013 to precisely evaluate the influence of putative village-level risk factors . Over a large target population of almost two million people at risk and spread out over 329 villages , we developed spatial and spatio-temporal dengue risk models [18] that have previously been lacking for this important South-East Asian country . We found that increasing village-level socio-economic profile was the most significant contributor to risk of DENV infection . This finding is consistent with the general phenomenon of wealthier populations ( with high levels of education and employment ) being more likely to seek healthcare when infected with dengue than poor populations [34] , and thereby being more likely to be reported as infected . However , as most hospitals in the Regency are located in villages with larger proportion of people of high socio-economics status , including hospital proximity in our analysis should have accounted for any over-reporting biases due to high socio-economics . Although we cannot categorically rule out that some small bias remains , most of the observed influence of socio-economics should be attributable to the risk of dengue infection rather than the probability of reporting . As such , our results suggest that urbanized areas , in which people have better jobs and socio-economic conditions , have a greater risk of dengue infections . Village-level risk of DENV infection can be expected to result from an increased risk of exposure to vector mosquitoes ( mostly Ae . aegypti ) . In this situation , two alternative source of exposure can be advanced to explain such pattern . Firstly , urbanized areas may contain more artificial breeding sites ( such as buckets , water-storage containers , aquariums , traditional bath tubes ) than in rural areas , increasing the risk of dengue infection . However , recent entomological survey in four villages of the Regency [35] showed artificial breeding sites were found equally important regardless of villages’ dengue status ( i . e . endemic , sporadic or dengue-free ) . Secondly , urbanized areas may promote exposure to adult vectors , particularly to Ae . aegypti . While this is consistent with the capture rate of this species in urban areas in the Regency [35] , reasons for the presence of high adult vector population in urban areas are not known and at least one rural area in this study showed comparable numbers of adult Ae . aegypti in the rainy season . However , the clear absence of influence of most village-level environmental variables on the risk of dengue infection suggests a more complex vector ecology , most likely related to the greater presence of suitable micro-environments in urban areas than elsewhere in the Regency . Nevertheless , given that Ae . aegypti prefers to feed indoor , exposure may either occur at home or inside work spaces , or in their close vicinity . Of particular interest , presence of adult Ae . aegypti inside work spaces could potentially result in increased exposure , depending for example on the nature of the business or work ( for example those with important human movement or transit ) and subsequently allow dengue to spread more efficiently between people . Past studies have suggested the importance of human movement in dengue transmission even at local level , and our data may be a further reflection of such findings [9 , 10 , 17 , 36] . If such a hypothesis is correct , it would suggest that control measures to eliminate vector mosquitoes inside work spaces or homes should be a primary aim to reduce dengue cases in the Regency . Instead , current programmes rely largely on large scale efforts to control mosquito populations , spraying insecticide and larvicides inside villages . Such control programmes may not be fully effective to control domestic mosquitoes such as Ae . aegypti within indoor areas ( where control is most required ) but may lead to the development of resistance in vector populations [37 , 38] . The role of socio-economic factors is increasingly recognised as an important factor in the local risk of DENV infection , with worsening socio-economic conditions and lack of knowledge enhancing the risk of exposure [39 , 40] . However , such data are difficult to compare , limiting our capacity to draw a clear and consistent picture on the impact of socio-economics on the overall risk of DENV infection . Our results highlight the importance of local risk assessments , not only providing critical information to improve targeted control strategies but also identifying current weaknesses in dengue surveillance . Indeed , there was an increasing risk of underreporting DENV cases with increasing distance to health centres/hospitals in the Regency . Such an approach also identified areas ( Fig 4 ) where the risk of infection was not totally explained by our model . Whether it is due to other factors that were not accounted for by the various variables tested in this study or the circulation of other dengue-like diseases is unknown . Furthermore , our data capture patients with severe symptoms and who are hospitalized , and this may not always accurately reflect virus circulation . It may , for example , indicate introduction and circulation of new serotypes or dengue-like pathogens and subsequently more severe clinical outcome . Alternatively , this may reflect unwillingness or inability to obtain medical support with distance to medical support which may be reflected in the observation outlined above ( increased risk of underreporting with distance to health centres/hospitals ) . These possibilities require further investigation . Moreover patients who may seek treatment in neighbouring regencies are not captured; but for example in the West of the Regency ( Fig 4B and 4C ) such an effect would result in an artificial decrease in dengue cases yet there is excess of risk of dengue cases that is not explained by our model . Nevertheless , our results highlight the difference in the epidemiology of dengue between urban and rural areas and thereby raise the importance of modulating surveillance systems accordingly . Recent studies have also increasingly analysed the role of temperature variation on dengue transmission , which is important in natural settings and impacts vector capacity and competence [41 , 42] . In this study we noticed an increased risk ( see Tables 1 and 2 ) for dengue infection at low night time temperatures , which may seem counterintuitive . Interestingly one study described that large temperature variations at a low mean temperature resulted in shorter extrinsic incubation periods , and thus possibly higher transmission potential [42] . Although such a reduction in extrinsic incubation period cannot be rejected to explain our finding , it is unlikely to be the main reason since extreme low temperatures are not suitable for mosquito activities . Instead , we believe that low night time temperatures may influence human behaviour , for example by staying indoors at nights when cold , and correlate to increased risk associated with indoor exposure . While this hypothesis may be plausible , further studies are required to untangle the cause ( s ) behind our findings . This extensive study provides , to our knowledge , the first in-depth assessment of factors influencing the risk of DENV infection in Indonesia . It can form the base for tracking risk factors in Indonesia and elsewhere in South-East Asia . This study did not take into account factors such as variations in immunity at individual/local level which may influence disease dynamics and these are deficiencies that need to be address in further studies . We suggest nonetheless that we have identified features that may help improving control and surveillance strategies in Java and these findings need implemented and follow-up studies carried out to verify that they can lead to a reduction in dengue cases in this area .
|
Dengue has been detected in Indonesia and transmission is progressively rising to account for half of all fatal dengue cases recorded in South-East Asia . However , little is known about the epidemiology of the virus , which may hamper the effectiveness of preventive strategies carried out in the country . Identifying local risk factors is critical in assuring preventive measures are efficiently targeted . In this study we carried out a spatial , long-term analysis of all hospital-reported dengue cases in the 329 villages of the Banyumas Regency of Central Java , Indonesia between 2000 and 2013 . We determined which variables , including environmental factors , geography , socio-economic status , and access to health services , significantly impacted the village-level risk of dengue infection between 2000 and 2013 . Our data give a large scale overview of dengue risk factors in this Regency and to our knowledge is the first study of such scale in Indonesia . Our findings have the potential to guide targeted preventive planning locally as we identify intervention points in the socio-economic make up of this Regency . The outcomes of this study need to be confirmed in other affected areas of Indonesia .
|
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2016
|
The Importance of Socio-Economic Versus Environmental Risk Factors for Reported Dengue Cases in Java, Indonesia
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The appropriate development of conidia and appressoria is critical in the disease cycle of many fungal pathogens , including Magnaporthe oryzae . A total of eight genes ( MoHOX1 to MoHOX8 ) encoding putative homeobox transcription factors ( TFs ) were identified from the M . oryzae genome . Knockout mutants for each MoHOX gene were obtained via homology-dependent gene replacement . Two mutants , ΔMohox3 and ΔMohox5 , exhibited no difference to wild-type in growth , conidiation , conidium size , conidial germination , appressorium formation , and pathogenicity . However , the ΔMohox1 showed a dramatic reduction in hyphal growth and increase in melanin pigmentation , compared to those in wild-type . ΔMohox4 and ΔMohox6 showed significantly reduced conidium size and hyphal growth , respectively . ΔMohox8 formed normal appressoria , but failed in pathogenicity , probably due to defects in the development of penetration peg and invasive growth . It is most notable that asexual reproduction was completely abolished in ΔMohox2 , in which no conidia formed . ΔMohox2 was still pathogenic through hypha-driven appressoria in a manner similar to that of the wild-type . However , ΔMohox7 was unable to form appressoria either on conidial germ tubes , or at hyphal tips , being non-pathogenic . These factors indicate that M . oryzae is able to cause foliar disease via hyphal appressorium-mediated penetration , and MoHOX7 is mutually required to drive appressorium formation from hyphae and germ tubes . Transcriptional analyses suggest that the functioning of M . oryzae homeobox TFs is mediated through the regulation of gene expression and is affected by cAMP and Ca2+ signaling and/or MAPK pathways . The divergent roles of this gene set may help reveal how the genome and regulatory pathways evolved within the rice blast pathogen and close relatives .
Magnaporthe oryzae is an ascomycete fungus and the causal agent of rice blast , the most destructive disease of rice worldwide . The annual yield loss from rice blast is equivalent to rice that could feed about 60 million people [1] . Rice blast has served as an important model for studying molecular plant-pathogen interactions because of its economic significance and genetic tractability of the host and pathogen . More recently , the availability of the genome sequences of both rice and the fungal pathogen has provided a new platform to understand molecular pathogenesis at the genome level [2]–[4] . Like most fungal pathogens , conidia ( asexual spores ) of M . oryzae play a central role in the disease cycle . The conidia attach to the surface of host plants upon hydration and produce germ tubes . This fungus develops a specialized infection structure , an appressorium , at the tip of the germ tube . The appressorium generates enormous turgor pressure ( >8 MPa ) by accumulating osmolytes including glycerol for penetration through the mechanical rupture of host cell barriers [5] . After penetration , the fungus develops invasive hyphae , colonizes host cells , and produces massive conidia via conidiogenesis , serving as secondary inocula for new infections . This infection cycle may occur many times during the growing season , resulting in explosive disease development . Therefore , understanding the molecular mechanisms involved in conidiation and appressorium development is a prerequisite to provide novel strategies for disease management . During the last couple of decades , considerable progress has been made in understanding signaling pathways that regulate the infection-related development of this fungus . These include the mitogen-activated protein kinase ( MAPK ) signaling cascade [6] , [7] , signaling pathways dependent on secondary messengers such as Ca2+ and cAMP [8]–[13] , and G-protein-mediated signaling pathways [14] , [15] . For example , deletion of genes involved in cAMP and Ca2+ signaling pathways has revealed that both are required not only for infection-related fungal development but also for pathogenicity [16] , [17] . Disruption of Gα subunits and the MAP kinase gene also indicated the involvement of G-protein and MAP kinase signaling in vegetative growth , sexual reproduction , and pathogenicity in M . oryzae [15] . Most studies have focused on well-known upstream signaling pathways , but relatively little information is available on the downstream regulators of appressorium development . Conidiogenesis is a complex process that involves a cascade of morphological events . M . oryzae produces three-celled conidia on a conidiophore , a specialized structure elongated through apical extension of an aerial hypha . Unlike vegetative hyphae , conidiophores rarely branch and their growth is modestly determinate . Several conidia , mostly three to five are arrayed at the tip of a conidiophore in a sympodial pattern after the occurrence of rounds of mitosis . The molecular biology of conidiation has been characterized in detail for Aspergillus nidulans and Neurospora crassa [18] , [19] . The transcription factor ( TF ) genes brlA , abaA , and wetA are key regulators in the central regulatory pathway of A . nidulans conidiation , which coordinately regulates the order of gene activation during conidiophore development and spore maturation . Several other genes , FlbB , FlbC , FlbD , and FlbE , act as early regulators that activate the central regulatory pathway in response to the product of FluG activity [19] . In N . crassa , conidium-specific con genes have been described [20] . The fl gene , which encodes a TF , and genes ( frq , wc-1 , and wc-2 ) that act in the Neurospora circadian clock regulate the morphological transition from filamentous growth to conidiation [18] , [21] . Relatively little information exists on conidiation in M . oryzae despite its central role in the disease cycle . Deciphering the conidiation pathways may reveal key determinants of initiation and the progress of conidiation , which may provide potential targets for disease control . A few genes are involved in conidial morphology in M . oryzae . Mutations at the SMO locus cause abnormally shaped conidia [22] . A mutation of the Acropetal gene causes head-to-tail arrays of conidia [23] . Deletion of the CON7 gene encoding a zinc finger TF also causes abnormal conidia with less septa and/or protuberances at the basal scar [24]–[26] . In addition to genes involved in conidial ontology , a recent study showed that a zinc finger TF-coding gene , named COS1 for conidiophore stalk-less1 , is indispensible for an early stage of conidiophore development [27] . Transcriptional regulation is a major mechanism by which alterations in the expression of specific subsets of genes determine development and differentiation in cells . DNA-binding TFs play a pivotal role in the transcriptional regulation of specific target genes necessary for such processes in response to physiological or environmental stimuli . A comparative genome-wide analysis revealed that a variety of TFs are abundantly present in metazoans , including fungi [28] . M . oryzae appears to possess over 400 TF genes , but only a few have been characterized [10] , [24] , [27] , [29] , [30] . Homeobox TF genes contain highly conserved sequences coding for the DNA-binding motif called the homeodomain . This group of homeobox TFs was first discovered in Drosophila melanogaster in which they specify the body plan along the antero-posterior axis [31] , [32] . Numerous genes for homeobox TFs have since been identified in eukaryotes , including fungi . Several studies have established the involvement of homeodomain proteins in mating and sexual differentiation in fungi [33]–[37] . A gene encoding a homeodomain protein also controls hyphal morphology and conidiogenesis in Podospora anserina [38] . In Ustilago maydis , homeobox TFs regulate the hyphal growth , pathogenicity and sexual cycle [39] . It is therefore evident that homeobox TFs play important regulatory roles in morphogenesis and pathogenesis in plant-pathogenic fungi . As a first step in deciphering the biological functions of TF genes in M . oryzae , we comprehensively searched currently available sequences at the genome-wide level for the existence of homeobox TFs in the fungal kingdom . This analysis revealed that a total of eight genes encoding putative homeobox TFs exist in M . oryzae , which were here named MoHOX1 to MoHOX8 . Similarly , other fungal species appear to possess multiple copies of these genes . To further characterize the roles of MoHOXs in M . oryzae biology , deletion mutants of each MoHOX gene were generated through a homology-dependant gene replacement strategy . Analyses of the various transformants including ΔMohox mutants demonstrated that homeobox TFs function as stage-specific regulators in fungal development and pathogenicity in M . oryzae . In particular , MoHOX2 and MoHOX7 are indispensable for conidiation and appressorium development , respectively . Our findings would provide new insight into the transcriptional regulation of infection-related morphogenesis at the genome level .
Members of the homeobox TF family possess a conserved DNA-binding motif called the homeodomain [40] . Using InterPro terms ( IPR001356 and IPR003120 ) for homeodomains , 216 homeobox TFs were retrieved from 22 eukaryotic microbe genomes , including eight ( MoHOX1 to MoHOX8 ) in M . oryzae ( Table S1 ) . The MoHOX genes separated into eight distinct clades in the phylogenetic tree ( Figure 1 ) . This suggests that expansion of the DNA-binding TF family may be linked to functional divergence , as hypothesized previously [41] . The MoHOX-clades , except the MoHOX8-clade , which contains a divergent form of the homeodomain , embrace homeobox TFs belonging only to the subphylum Pezizomycotina , but not to the subphylum Saccharomycotina or phylum Basidiomycota , suggesting that homeobox TFs have evolved in a lineage-specific manner [41] , [42] . In order to characterize the roles of homeobox genes in M . oryzae development and pathogenicity , constructs for the targeted disruption of the MoHOX genes were generated using a split-marker deletion method or double-joint PCR method ( Figure S1 ) . Eight deletion mutants were generated in which all of the MoHOX genes were replaced with a hygromycin resistance cassette , as confirmed by DNA blot and RT-PCR analyses using a gene-specific probe and sets of PCR primers ( Figure S1 , Table S2 ) . The strains of the wild-type and transformants generated in this study are presented in Table 1 . The effects of the deletion of MoHOX genes on M . oryzae development and pathogenicity are summarized in Table 2 . In brief , deletion mutants of each MoHOX gene exhibited unique phenotypes in fungal development and pathogenicity , such as mycelial growth , conidial morphology , conidiation , and appressorium formation . The ΔMohox1 mutant showed a significant reduction in vegetative growth , but increase in melanin pigmentation ( Figure S2 ) . The ΔMohox6 mutant also exhibited a significant reduction in vegetative growth , whereas other phenotypes in the ΔMohox6 mutant were indistinguishable from those of the wild-type . Conidiation ( asexual reproduction ) was completely abrogated in the ΔMohox2 mutant , but this defect was recovered when the wild-type copy of the MoHOX2 gene was transformed into the mutant . The ΔMohox4 mutant produced shorter and smaller conidia in both length and width compared to those of the wild-type ( Figure S3 ) . The ΔMohox7 mutant was unable to form appressoria on hydrophobic surfaces , while its other phenotypes were similar to those of the wild-type . However , the ΔMohox8 mutant formed appressoria , but was nonpathogenic due to a defect in penetration . The mst12 mutant , carrying a partial deletion in the MST12 gene , shows the same phenotypes as the ΔMohox8 mutant , confirming that they are the same gene [30] . No distinguishable phenotypes were observed in ΔMohox3 and ΔMohox5 , as compared to the wild-type . Based on our phenotypic observations of MoHOX deletion mutants , the mutants ΔMohox2 and ΔMohox7 were chosen for detailed studies as their phenotypes are associated with important developmental stages in the M . oryzae disease cycle . Quantitative measurement of conidia reconfirmed that conidial production was completely abolished in the ΔMohox2 mutant on V8 juice or oatmeal agar media . However , this defect in conidiation was fully recovered in the complemented transformant Mohox2c , to a similar extent as in the wild-type ( Table 2 ) . The other phenotypes in the ΔMohox2 mutant were quite similar to those in the wild-type and complemented transformant Mohox2c ( Table 2 , Figure S4 ) , indicating that MoHOX2 is specifically involved in conidiation . Microscopic observation was performed to carefully define the effect of the MoHOX2 deletion on conidial formation ( Figure 2 ) . The wild-type and Mohox2c developed pear-shaped conidia on a conidiophore in a sympodial pattern 18 h after incubation ( Figure 2A and 2C ) . However , no conidia formed in ΔMohox2 after prolonged incubation under conidial induction conditions , even though conidiophore development appeared to be normal in the mutant . In order to determine if MoHOX2 is also involved in conidiophore development , we stained patches of aerial mycelia with lactophenol aniline blue to distinguish conidiophores from other aerial hyphae [27] . Microscopic examination revealed that conidiophores developed in the ΔMohox2 mutant , as in the wild-type and Mohox2c ( Figure 2B ) . These results indicate that MoHOX2 is a specific regulator that is essential for conidial development . To evaluate the role of MoHOX2 during M . oryzae disease development , a pathogenicity assay was performed by inoculating susceptible rice leaves with mycelial agar plugs , rather than a conidial suspension , because ΔMohox2 is unable to produce conidia . The inoculation with ΔMohox2 mycelial blocks caused blast lesions similar to the wild-type ( Figure 3A ) . Given that successful lesion development requires the development of appressoria on germ tubes of conidia for penetration into plant cells , this result led us to examine diseased tissue using microscopy . As expected , many appressorium-like structures were observed on the surfaces of rice leaves inoculated with either ΔMohox2 or wild-type mycelial agar plugs ( data not shown ) . To investigate whether these appressorium-like structures contribute to disease development , we carried out a penetration assay , in which a mycelial agar plug ( 6 mm in diameter ) was placed on onion epidermal cells . Both the wild-type and ΔMohox2 developed appressoria specifically at tips of hyphae on onion cells and invasive hyphal growth was subsequently observed underneath the appressorium inside the cell ( Figure 3B ) . Appressoria that formed on hyphal tips of the wild-type and ΔMohox2 were indistinguishable in shape , size , and melanization . In order to evaluate whether a hydrophobic surface is conductive to appressorial formation by hyphae , an agar plug containing actively growing hyphae was placed on either the hydrophobic or hydrophilic surface of Gelbond film . Unexpectedly , both surfaces induced appressorial formation at hyphal tips of the wild-type and ΔMohox2 ( Figure 3C ) . As seen in pathogenicity assays with ΔMohox2 mycelia , conidia from Mohox2c caused typical necrotic lesions on foliar parts of the host plant , similar to those caused by the wild-type ( Figure 3A ) . During appressorium-mediated penetration , lipid droplets abundant in conidium move into the incipient appressorium and degrade at the onset of turgor generation [43] . To understand the functional role of hypha-driven appressoria , we examined the temporal and spatial occurrence of lipid droplets using Nile red staining and epifluorescence microscopy . Unlike in conidia , lipid droplets were not initially detected in hyphae of the wild-type or ΔMohox2 on appressorium-inductive surfaces until 24 h ( Figure 3D ) . However , lipid droplets became abundant in hyphae , and translocated into nascent appressoria 48 h after inoculation . The process was completed by 72 h . Considering that translocation of lipid droplets into nascent appressoria on conidial germ tubes occurs within 4 h [43] , such a delayed process seems to be associated with the de novo synthesis of lipid droplets and hypha-driven appressorium development . Taken together , these results indicate that MoHOX2 is essential for conidiogenesis , but dispensable for appressorium formation and pathogenicity . Also , M . oryzae can form hypha-driven appressoria that can cause foliar disease . Quantitative real-time RT-PCR ( qRT-PCR ) was used to examine the expression pattern of MoHOX2 under various conditions . The MoHOX2 gene was found to be constitutively expressed during development ( Figure 4A ) . MoHOX2 transcript levels were dramatically higher during conidiation but lower during invasive growth , as compared to other stages . This pattern of MoHOX2 expression appears to be correlated with a functional role for MoHOX2 in conidiation . To evaluate the impact of upstream signaling pathways on MoHOX2 expression , we measured the expression levels of the MoHOX2 gene in mutant backgrounds related to signal transduction ( Figure 4B , Table 1 ) . Significantly reduced MoHOX2 expression ( greater than two-fold ) was found in the adenylate cyclase mutant Δmac1 and the phospholipase C mutant ΔMoplc1 . The expression of the MoHOX2 gene changed slightly , but not significantly , less than two-fold in other mutants , including the mutants Δmocrz1 for a calcineurin-responsive TF , ΔcpkA for a cAMP-dependent protein kinase catalytic subunit , Δmck1 for a MAPKKK , and Δpmk1 for a MAPK . These results suggest that the expression of MoHOX2 is co-regulated by cAMP and Ca2+-dependent pathways . Since MoHOX2 is a putative homeobox TF , specifically required for an earlier stage of conidiation , it is reasonable to speculate that MoHOX2 acts as a TF that modulates the expression of other conidiation-related genes . To determine the impact of MoHOX2 deletion on the expression of conidiation-related M . oryzae genes and other M . oryzae genes that are orthologs to conidiation-related genes in other fungi ( Table 3 ) , their expression levels were measured in the ΔMohox2 mutant background . The expression of the genes MoAPS1 , COS1 , and Con7 was significantly upregulated ( Figure 4C ) , as were the M . oryzae genes MoCon6 and MoCon8 , orthologs to N . crassa Con-6 and Con-8 ( Figure 4D ) . These results indicate that genes tested are not direct targets of MoHOX2 . However , the altered levels of gene expression in ΔMohox2 suggest that MoHOX2 functions as a key TF of downstream gene expression leading to conidiogenesis . The deletion of MoHOX7 completely abolished the ability to form appressoria while other phenotypes were not affected ( Table 2 ) . Microscopic examination revealed that wild-type conidia formed appressoria on the tip ends of germ tubes 6 h after incubation on a hydrophobic surface ( Figure 5A ) . In contrast , the ΔMohox7 mutant failed to develop appressoria; instead , the germ tubes in ΔMohox7 elongated abnormally and appeared to have several rounds of swellings and hooking until 12 h . During prolonged incubation ( 24 h ) of the ΔMohox7 mutant , its germ tubes grew like vegetative hyphae , with branches rather than repeating recognition steps ( Figure 5A ) . Nile red staining of germ tubes of the ΔMohox7 mutant revealed a large accumulation of lipid droplets until swelling and hooking occurred , before switching to vegetative hyphal growth ( Figure 5A ) . These defects in the ΔMohox7 mutant were repaired in the complemented transformant Mohox7c . This suggests that MoHOX7 plays a critical role in appressorium development rather than the recognition of cues that induce appressorium formation . Next , we tested whether MoHOX7 is also required to form appressoria at hyphal tips . Not surprisingly , the ΔMohox7 mutant did not form an appressorium on hydrophobic and hydrophilic surfaces , although non-melanized swellings and hooking were found on tips of hyphae ( Figure 5B ) , as observed with conidial germ tubes in Figure 5A . In contrast , the wild-type , Mohox7c , and MoHOX7e formed appressoria at the tips of mycelia ( Figure 5A ) . This supports the idea that MoHOX7 is essential for the development of appressoria , both from hyphae and germ tubes . Infection assays on rice were performed to test whether the ΔMohox7 mutant can cause disease on host tissues . Conidial suspensions were sprayed onto 3-week-old rice seedlings . The wild-type caused blast lesions on the plant , but the ΔMohox7 mutant produced no lesions in plant cells ( Figure 6A ) . These defects in the ΔMohox7 mutant were fully restored to the wild-type levels in Mohox7c . To test the role of MoHOX7 in penetration , onion epidermis and rice leaf sheath cells were inoculated with a conidial suspension . The wild-type penetrated into epidermal cells and grew invasively ( Figure 6B ) . In contrast , the ΔMohox7 mutant was unable to penetrate into plant surfaces . Germ tubes appeared to have rounds of swellings and hooking before vegetative growth began on plant surfaces ( Figure 6B ) . To determine whether the ΔMohox7 mutant can perform invasive growth in plant cells , a conidial suspension was infiltrated into rice leaves by injection using a syringe . Both the wild-type and ΔMohox7 developed blast lesions , indicating that the mutant still had the ability to grow inside plant cells ( Figure 6C ) . These results suggest that the functioning of MoHOX7 is not required for further growth inside the host , and is strictly limited to the stage of appressorial development . qRT-PCR was performed to determine the transcription level of MoHOX7 during developmental stages in M . oryzae . The transcription of MoHOX7 was dramatically upregulated during appressorium formation ( >29-fold ) , conidiation ( >12-fold ) , and in invasive growth in planta ( >four-fold ) at 78 h after inoculation , compared to its expressions during mycelial growth ( Figure 7A ) . The highest expression of MoHOX7 during appressorium development was consistent with its role in appressorium-mediated disease development , as shown in Figure 5 . To investigate whether signaling pathways associated with appressorium-mediated penetration affect the expression of the MoHOX7 gene , the levels of MoHOX7 transcripts were measured in several mutants related to signal transduction ( Table 1 , Figure 7B ) . Much lower levels of MoHOX7 transcripts were observed in ΔMoplc1 and Δmac1 mutants , whereas the expression of the MoHOX7 gene was not significantly affected in the mutants Δcpka , Δmck1 , or Δpmk1 . This suggests that the expression of MoHOX7 is regulated by Ca2+ and cAMP-dependent signaling pathways ( Figure 7B ) . To test if such signaling molecules can restore appressorium formation in the ΔMohox7 mutant , a conidial suspension of ΔMohox7 on hydrophilic and hydrophobic surfaces was treated with the chemicals 1 , 16-hexadecanediol ( HDD ) , cAMP , CaCl2 , and 1 , 2-dioctanoyl-sn-glycerol ( DOG ) , a diacylglycerol analogue . None of these molecules complemented the defects in appressorium formation in the ΔMohox7 mutant on both the hydrophilic and hydrophobic surfaces , while these molecules increased appressorium formation in the wild-type on the hydrophilic surface ( Table S3 ) . This result suggests that MoHOX7 is a key downstream regulator of appressorium development .
Conidiogenesis and appressorium development are key steps in the colonization of host plants by many fungal pathogens . These processes are controlled by a precise developmental program in response to stimuli from the host and environment . Organisms have evolved regulatory networks to ensure the correct timing and spatial pattern of the developmental events . Transcription factors ( TFs ) play important roles in fungal development and pathogenicity as regulators in biological networks . The functional analysis of TFs provides new insights into a controlling network that governs fungal development and pathogenicity . In an effort to understand developmental biology in M . oryzae , we identified eight homeobox TFs ( MoHOX1 to MoHOX8 ) as candidates for development-controlling genes since they are well-known regulators of development and differentiation in other organisms [29] , [35] , [36] . MoHOX2 proved essential for conidiogenesis . The disruption of MoHOX2 completely abolished the ability of M . oryzae to produce conidia , even though conidiophore development was normal . The deletion of this gene did not affect any other developmental stages , such as hyphal growth , appressorium formation , and penetration , except for a subtle difference . Our study suggests that MoHOX2 is a stage-specific regulator of an earlier step of conidiogenesis . This phenotypic feature in the mutant is interesting and unique because the deletion of any of the other genes related to conidiogenesis caused pleiotropic defects in M . oryzae . The Δcon7 mutant , affected in a zinc finger TF , develops morphologically abnormal conidia and never forms an appressorium [24] . A very recent study reported the interesting finding that COS1 , which encodes another zinc finger TF , is a determinant of conidiophore formation and melanin pigmentation [27] . The authors also observed that the Δcos1 mutant , unlike the wild-type , developed appressorium-like structures on the host surface and disease symptoms in a mycelial inoculation test , and speculated that COS1 may have a role in an unknown mechanism involved in mycelia-mediated infection . However , we believe that the wild-type can form appressoria at hyphal tips as a strategy for host infection , although there may be strain-dependent differences . Such hypha-driven appressoria in the ΔMohox2 and wild-type mediated penetration into host cells caused typical symptoms of rice blast , indicating a functional similarity to conidial germ tube-driven appressoria in M . oryzae disease development . Hyphal appressorium-mediated penetration by M . oryzae may not predominantly occur in nature due to limitations in the spatial distribution of hyphae and on hyphal longevity . However , members of non-sporulating fungi develop sclerotia as survival and inoculum structures , in which hyphae become interwoven , aggregated , melanized , and dehydrated [44] , [45] . These fungal pathogens penetrate host epidermal cells by means of infection cushions , aggregated forms of branched hyphae , after their perception of host factors [46] . The ΔMohox7 mutant , defective in appressorium formation on conidial germ tubes , was also unable to form appressoria at hyphal tips , and the defect was not recovered with the addition of exogenous cAMP . This indicates that MoHOX7 regulates appressorium development in both hyphae and conidial germ tubes . The role of MoHOX7 in appressorium development has been predicted by the study of a large deletion mutant , Δpth12 ( NCBI accession number DQ060925 ) , which was generated by restriction enzyme-mediated integration ( REMI ) , but no detailed characterization of Δpth12 has been performed . Although entry into the host is possible through natural surface openings such as stomata , this is not believed to be the predominant mode for spread of infection; the ΔMohox7 mutant consistently failed to colonize unwounded host leaves . Independent of appressorium-mediated infection , M . oryzae has evolved other strategies to cause disease on hosts . During root infection , hyphae swellings resembling the hyphopodia of root-infecting fungi are associated with root invasion , leading to systemic disease development similar to typical foliar disease [47] . This evidence supports the idea that M . oryzae has evolved hypha-mediated infection structures to gain entry into their hosts . A variety of signals induce appressorium formation , including surface hardness , hydrophobicity , adhesion quality , and host molecules [48]–[51] . The hyphae of the ΔMohox2 mutant and wild-type developed appressoria upon sensing hydrophobic surfaces , consistent with a previous observation [52] . In addition , the hyphae were also able to form appressoria on hydrophilic surfaces , unlike conidial germ tubes . This suggests that there is an unknown pathway that regulates the development of the hypha-driven appressoria after sensing environmental cues such as the surface hardness . The strong adhesion of conidial germ tubes to substrates is needed for appressorial development , and we also observed that tight adhesion of hyphae is a prerequisite to formation of appressoria at the tips . Adhesion , therefore , appears to be a critical step prior to the initiation of infection , not only for the prevention of dislodgement , but also for subsequent correct development . After perceiving inductive signals , germ tubes of conidia exhibit hooking and swelling related to appressorium formation . Thus , the continued hooking and swelling in the ΔMohox7 mutant indicates that MoHOX7 is not involved in signal recognition . In contrast , the deletion of MagB , which encodes a Gα subunit involved in sensing a surface cue , causes the formation of long and straight germ tubes [15] . The biological processes that mediate a functional appressorium from a conidial germ tube are very complicated , based on molecular and cytological evidence such as autophagy [53] , changes in metabolism [43] , cell cycle control [53] , and the generation of turgor pressure [5] . A body of evidence has demonstrated that conserved signaling pathways are associated with the coordination of biological changes related to appressorial development and maturation . However , the downstream molecular pathways that are activated in developing appressoria remain mostly uncharacterized . Filling this gap would require a specific aim at characterizing tissue-specific regulators . Signaling pathways often converge on transcriptional regulation during cell development . Interestingly , the MoHOX2 and MoHOX7 genes were significantly downregulated in the two signaling-defective mutants Δmac1 and ΔMoplc1 [12] , [17] . Given that both adenylate cyclase and phospholipase C are associated with the generation of signaling molecules , such as cAMP and Ca2+ , it is possible that cAMP and/or Ca2+-dependent signaling pathways are involved in modulating the functions these two MoHOX genes . As most homeobox TFs in other species are constitutively expressed [54] , it is not surprising that MoHOX2 and MoHOX7 were constitutively expressed during development , but most highly expressed during conidiation and appressorium formation , respectively . Many TFs are also post-translationally regulated , especially by ( de ) phosphorylation [55] , [56] . This event typically occurs in response to external stimuli , which lead to the modulation of the DNA-binding activity of homeobox TFs . This suggests that the two MoHOX proteins are phosphorylated or dephosphorylated by a kinase or phosphatase , respectively . The identification of such regulators responsible for ( de ) phosphorylating MoHOX proteins is in progress using a yeast two-hybrid system . Previously , a yeast two-hybrid study showed that PMK1 interacts with MST12 ( MoHOX8 ) , suggesting that PMK1 regulates MST12 to control invasive growth [30] , [57] . PMK1 is a well-known MAP kinase essential for appressorium formation and invasive growth in M . oryzae [6] . Since MST12 is not involved in appressorium formation , there may be other TFs regulated by PMK1 that are involved in appressorium formation . Considering that MoHOX7 is another homeobox TF that is crucial for appressorium formation , PMK1 might interact with MoHOX7 for the regulation of appressorium development . In summary , we have demonstrated that members of the homeobox TF family function as stage-specific regulators during M . oryzae development and pathogenicity . MoHOX1 , -2 , -4 , -6 , -7 , and -8 are specifically associated with hyphal growth and pigmentation , asexual reproduction , conidial morphology , mycelial growth , appressorium development , and invasive growth , respectively . Detailed molecular and cytological analyses revealed that deletion of the MoHOX2 gene entirely abolished asexual reproduction , while other stages , including conidiophore development appeared normal . Also , the data showed that MoHOX7 is a key regulator , essential for appressorium development on both hyphal tips and conidial germ tubes . These results provide evidence that M . oryzae is able to cause foliar disease via hypha-driven appressoria , after sensing environmental cues . Our studies will help to unveil the regulatory mechanisms involved in conidiation and appressorium formation and contribute to development of novel strategies for rice blast control .
Magnaporthe oryzae strain KJ201 was obtained from the Center for Fungal Genetic Resources ( CFGR ) and was used as the wild-type stain in this study . The strain and its transformants were routinely grown at 25°C under continuous fluorescent light on oatmeal ( 50 g oatmeal per liter ) agar medium or V8 ( 4% V8 juice ) agar medium . DNA and RNA were isolated from mycelia , which were grown in liquid complete medium ( 0 . 6% yeast extract , 0 . 6% tryptone , 1% sucrose ) for 4 days . Conidia were obtained from 10-day cultures on oatmeal agar media by rubbing the mycelia with water followed by filtration through Miracloth ( Calbiochem , San Diego , USA ) . Germinated conidia were obtained by placing drops of conidial suspension on hydrophobic surfaces for 3 h . Appressoria were obtained by holding germinated conidia for an additional 3 h . For the phenotype assay , complete medium was used to measure the vegetative growth and colony characteristics [58] . Oatmeal agar medium and V8 juice agar medium were used to measure conidiation and conidial morphology . Genomic DNA was isolated using two different methods , depending on the experimental purpose . Genomic DNA for general experiments was isolated according to a standard method [59] . Genomic DNA for PCR screening of transformants was prepared using the quick and safe method [60] . Restriction enzyme digestion , agarose gel separation , and DNA gel blotting were performed following standard procedures [59] . DNA hybridization probes were labeled with 32P using the Rediprime II Random Prime Labeling System kit ( Amersham Pharmacia Biotech , Piscataway , NJ , USA ) according to the manufacturer's instructions . The hybridization membrane was exposed to a Phosphorimager ( BAS-2040 , Fuji Photo Film , Tokyo , Japan ) and visualized with the Phosphorimager software . Total RNA was isolated from frozen fungal mycelia using the Easy-Spin total RNA extraction kit ( Intron Biotechnology , Seongnam , Korea ) following the manufacturer's instructions . To measure the relative abundance of MoHOX2 and MoHOX7 transcripts in mutant backgrounds listed in Table 1 , RNAs of the mutants were extracted from mycelia grown in CM liquid medium for 4 days at 25°C in a 120-rpm orbital shaker . The primer sets used to detect transcripts of conidiogenesis-related genes from M . oryzae , A . nidulans , and N . crassa , sets of primers are listed in Table 3 and Table S2 . For RT-PCR and quantitative real time RT-PCR ( qRT-PCR ) , 5 µg of total RNA were reverse transcribed into first-strand cDNA using the oligo ( dT ) primer with the ImProm-II Reverse Transcription System kit ( Promega , Madison , WI , USA ) according to manufacturer's instructions . For detecting transcripts of the two complements , RT-PCR was conducted with primer pairs MoHOX2_ORF_F/MoHOX2_ORF_R and MoHOX7_ORF_F2/MoHOX2_ORF_R ( Table S2 ) . RT-PCR was performed in 20-µl reaction mixtures containing 100 ng cDNA , 2 . 5mM of dNTP mix , 2 µl 10×PCR buffer , 1 µl ( 10 pmol ) of each primer , and 1 unit of Taq polymerase . In all , 30 cycles of RT-PCR were run on a Perkin-Elmer 9720 DNA thermal cycler . The β-tubulin gene was included as a control . Real-time quantitative reverse transcription PCR ( qRT-PCR ) reactions were performed following previously established procedures [61] . The AB7500 Real-Time PCR system ( Applied Biosystems , Foster city , CA , USA ) was used for PCRs that consisted of 3 min at 95°C ( 1 cycle ) followed by 15 s at 95°C , 30 s at 60°C , and 30 s at 72°C ( 40 cycles ) . Each qRT-PCR mixture ( final volume 10 µl ) contained 5 µl of Power SYBR® Green PCR Master Mix ( Applied Biosystems ) , 3 µl of forward and reverse primers ( 100 nM concentrations for each ) and 2 µl of cDNA template ( 12 . 5 ng/µl ) . The oligonucleotide sequences used for each gene are listed in Table S2 . To compare the relative abundance of target gene transcripts , the average threshold cycle ( Ct ) was normalized to that of β-tubulin ( MGG00604 ) for each of the treated samples as 2−ΔCt , where −ΔCt = ( Ct , target gene−Ct , β-tubulin ) . Fold changes during fungal development and infectious growth in liquid CM were calculated as 2−ΔΔCt , where −ΔΔCt = ( Ct , target gene−Ct , β-tubulin ) test condition− ( Ct , WT−Ct , β-tubulin ) CM [10] . qRT-PCR was performed with three independent pools of tissues in two sets of experimental replicates . Vegetative growth was measured on complete agar medium on 10 days after inoculation , with three replicates . The ability to produce conidia was measured by counting the number of conidia from 6-day-old V8 juice agar plates as described previously [7] . Conidia were collected by flooding the plate with 5 ml of sterilized distilled water . The number of conidia was counted using a hemacytometer under a microscope . Conidiophore development was monitored as previously described [23] . Conidial size was measured as width by length under a microscope . Conidial germination and appressorium formation were measured on a hydrophobic coverslip . Conidia were harvested from 10-day-old oatmeal agar culture plates using sterilized distilled water . A conidial suspension of 40 µl was dropped onto a coverslip following adjustment of its concentration to approximately 5×104 spores/ml . Drops were placed in a moistened box and incubated at 25°C . After 9 h of incubation , the percentage of conidia germinating and germinated conidia-forming appressoria was determined by microscopic examination of at least 100 conidia per replicate in at least three independent experiments , with three replicates per experiment . Plant penetration assays were performed using onion epidermis and rice sheaths , as previously described [62] . These experiments were replicated three times . For the pathogenicity assay , conidia were harvested from 8 to 10-day-old cultures on oatmeal agar plates and 10 ml of conidial suspension ( 105 conidia/ml ) containing 250 ppm Tween 20 were sprayed onto susceptible rice seedlings ( Oryza sativa cv . Nakdongbyeo ) at the three- to four-leaf stage . The inoculated plants were kept in a dew chamber at 25°C for 24 h in darkness and moved to a growth chamber with a photoperiod of 16 h with fluorescent lights . Disease severity was measured at 7 days after inoculation , as previously described [63] . For the infiltration infection assay , 100 µl of conidial suspension were injected into three points per leaf of 4-week-old rice plants . These experiments were replicated three times . Homeobox TFs were identified using InterPro terms ( IPR001356 and IPR003120 ) for homeodomains via the pipeline of Fungal Transcription Factor Database ( http://ftfd . snu . ac . kr/ ) [64] . Amino acid sequences of homeobox TFs were aligned using CLUSTAL W 1 . 83 [65] . Phylogenetic trees were constructed using the neighbor-joining method [66] with the aid of FTFD [64] . All sequence alignments were tested with a bootstrap method using 10 , 000 repetitions . The domain architecture of homeobox TFs was determined by InterProScan [67] and presented using the CFGP ( http://cfgp . snu . ac . kr/ ) [68] .
|
Pathogens have evolved diverse strategies to cause disease . Magnaporthe oryzae is the fungal phytopathogen that causes rice blast and is considered an important model for understanding mechanisms in fungal development and pathogenicity . Asexual reproduction and infection-related development play key roles in M . oryzae disease development . The conidium of M . oryzae differentiates a specialized structure , an appressorium . The appressorium generates turgor pressure that allows penetration through the mechanical rupture of host cuticle layers . After colonizing host cells , the fungus produces massive conidia via conidiogenesis , serving as secondary propagules for the polycyclic disease . To elucidate molecular mechanisms in asexual reproduction and appressorium-mediated disease development , we identified eight homeobox transcription factors through a genome-wide in silico analysis . Characterization using deletion mutants revealed that each homeobox TF functions as a stage-specific regulator for conidial shape , hyphal growth , conidiation , appressorium development , and invasive growth during M . oryzae development . Notably , conidiation and appressorium development were entirely abolished in ΔMohox2 and ΔMohox7 , respectively . This study also provides evidence that M . oryzae is able to cause rice blast by means of hypha-driven appressoria upon responses to host signaling factors . This study will aid in the understanding of regulatory networks associated with fungal development and pathogenicity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology/morphogenesis",
"and",
"cell",
"biology",
"infectious",
"diseases/fungal",
"infections",
"developmental",
"biology/cell",
"differentiation",
"genetics",
"and",
"genomics/gene",
"function",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis"
] |
2009
|
Homeobox Transcription Factors Are Required for Conidiation and Appressorium Development in the Rice Blast Fungus Magnaporthe oryzae
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Systems-level consolidation refers to the time-dependent reorganisation of memory traces in the neocortex , a process in which the ventromedial prefrontal cortex ( vmPFC ) has been implicated . Capturing the precise temporal evolution of this crucial process in humans has long proved elusive . Here , we used multivariate methods and a longitudinal functional magnetic resonance imaging ( fMRI ) design to detect , with high granularity , the extent to which autobiographical memories of different ages were represented in vmPFC and how this changed over time . We observed an unexpected time course of vmPFC recruitment during retrieval , rising and falling around an initial peak of 8–12 months , before reengaging for older 2- and 5-year-old memories . This pattern was replicated in 2 independent sets of memories . Moreover , it was further replicated in a follow-up study 8 months later with the same participants and memories , for which the individual memory representations had undergone their hypothesised strengthening or weakening over time . We conclude that the temporal engagement of vmPFC in memory retrieval seems to be nonmonotonic , revealing a complex relationship between systems-level consolidation and prefrontal cortex recruitment that is unaccounted for by current theories .
We possess a remarkable ability to retrieve , with ease , one single experience from a lifetime of memories . How these individual autobiographical memories are represented in the brain over time is a central question of memory neuroscience that remains unanswered . Consolidation takes place on two levels , which differ on both a spatial and temporal scale . On a cellular level , the stabilisation of new memory traces through modification of synaptic connectivity takes only a few hours [1] and is heavily dependent upon the hippocampus [2–5] . On a much longer timescale , the neocortex integrates new memories , a form of consolidation termed ‘systems-level’ [6] . The precise timeframe of this process is unknown . A related long-standing debate that has contributed to this uncertainty is whether or not the hippocampus ever relinquishes its role in autobiographical memory retrieval . One theory asserts that the hippocampus is not involved in the retrieval of memories after they have become fully consolidated to the neocortex [7] . Alternate views maintain that vivid , detailed autobiographical memories retain a permanent reliance on the hippocampus for their expression [8–12] . An undisputed feature of systems-level consolidation , however , is the strengthening of neural representations in the neocortex over time . Clarity on the time course of systems-level consolidation is therefore more likely to be achieved through scrutiny of its neocortical targets . While theoretical accounts often fail to specify these cortical locations , animal experiments have consistently implicated the medial prefrontal cortex . While this region has been associated with the formation [13 , 14] and recall of recently acquired memories [15–17] , in rodents , it appears to be disproportionately involved in the retrieval of memories learned weeks previously [18–26] . The dependency on this region , which emerges over time , is facilitated by postlearning activation [27] and structural changes [28–30] . The evolutionary expansion of prefrontal cortex in humans makes it challenging to make direct anatomical comparisons with rodents , but the ventromedial prefrontal cortex ( vmPFC ) has been proposed as a homologous site of long-term memory consolidation [31] . It may appear surprising that an association between impaired autobiographical memory retrieval and vmPFC lesions has only recently started to be more precisely characterised [32] . However , there are a number of confounding factors in this field [33]—nonselectivity of vmPFC lesions , methodological differences in memory elicitation , and the tendency of patients with vmPFC damage to recollect events that have never occurred , a phenomenon known as confabulation [34] . Numerous functional magnetic resonance imaging ( fMRI ) studies of vmPFC activity during autobiographical memory recall have been conducted but with inconclusive results . Delay-dependent increases in retrieval-related activity have been observed in some studies [35 , 36] but not others [37–39] . Autobiographical memory , in particular , induces robust vmPFC engagement [40] , but it is unclear whether this activity increases [41] , decreases [42] , or remains constant in accordance with memory remoteness [43–52] . A powerful method of fMRI analysis that can help to bridge the empirical gap between the human and animal literatures is multivoxel pattern analysis ( MVPA ) , because of its increased sensitivity to specific neural representations [53] . Using this approach , Bonnici and colleagues [54] demonstrated that remote 10-year-old autobiographical memories were more detectable in the vmPFC than recent 2-week-old autobiographical memories , consistent with its proposed role as a long-term consolidation site . This difference was not apparent in other cortical areas , nor did it emerge from a standard univariate analysis . A follow-up study 2 years later with the same participants and memories demonstrated that the original 2-week-old memories were now as detectable in the vmPFC as the remote memories [55] . This suggested the recent memories had been fully consolidated in the vmPFC after just 2 years and perhaps even sooner . The identification of this 2-year time window represented an opportunity to resolve the time course of systems-level consolidation with high precision . To do so , we sampled memories from 4-month intervals spanning a 2-year period and compared their neural representations using fMRI . As opposed to the pattern-classification approach employed by Bonnici and colleagues [54] to decode the neural signatures of individual memories , we used representational similarity analysis ( RSA ) [56] . This method compares the consistency of neural patterns across repetitions of a single memory against all other unrelated memories to detect its unique informational content in a region of interest ( ROI ) . Differences in the strength of memory representations across time periods were interpreted as delay-dependent engagement of the vmPFC . To verify observed time-sensitive differences , we followed the neural evolution of individual memories in a follow-up study with the same participants and memories 8 months later . The selection of numerous time points characterised the consolidation process with unprecedented temporal resolution , while the longitudinal design was an opportunity not only to replicate these findings but to observe systems-level consolidation in action . Systems-level consolidation is generally assumed to be an incremental process; therefore , we considered a gradual linear trajectory of vmPFC recruitment as the most likely outcome . The alternative hypothesis was a rapid strengthening of vmPFC neural representations in the first few months after an event . The results conformed to neither scenario and revealed an unexpected temporal relationship—a transient recruitment of the vmPFC beginning in the months following the initial experience , followed by an enduring signature of more remote memories . The second , longitudinal experiment confirmed this finding . This is the first demonstration , to our knowledge , of such a temporal dissociation in vmPFC-mediated memory retrieval .
One week prior to the fMRI scan , with the assistance of personal photographs , participants ( n = 30 ) verbally recalled and rated the characteristics of autobiographical memories from 8 time periods: memories that were 0 . 5 months old ( 0 . 5 M , i . e . , 2-week-old memories ) , 4 M , 8 M , 12 M , 16 M , 20 M , 24 M , and also 60 M old—these latter memories serving as a definitive benchmark for remote ( 5-year-old ) memories ( see Materials and methods , Fig 1A ) . Two memories from each time period that were sufficiently vivid , detailed , specific , and unique in time and place were chosen for subsequent recall in the scanner . This meant that there were 2 full sets of memories . Participants created a short phrase pertaining to each autobiographical memory , which was paired with the photograph to facilitate recall during the subsequent fMRI scan . The nonmonotonic pattern we observed in the fMRI data did not manifest itself in the subjective or objective behavioural data . In fact , the only difference in those data was higher ratings for the most recent 0 . 5 M old memories . However , these were paradoxically the most weakly represented memories in the vmPFC , meaning the neural patterns were not driven by memory quality . The objective scoring of the memories confirmed comparable levels of detail provided for all memories , without any significant drop in episodic detail or increase in the amount of semantic information provided as a function of time . Therefore , the amount or nature of the memory details were not contributing factors . Nevertheless , to verify that the results genuinely represented the neural correlates of memory purely as a function of age , one would need to study the effects of the passage of time on the individual neural representations . Therefore , we invited the participants to revisit 8 months later to recall the same memories again both overtly and during scanning; 16 of the participants agreed to return . In order to generate specific predictions for the neural representations during Experiment 2 , we took the actual data for the 16 subjects from Experiment 1 who returned 8 months later ( Fig 5 green line , in which the nonmonotonic pattern is still clearly evident ) and shifted them forwards by 2 time points to simulate the expected pattern 8 months later ( Fig 5 pink dotted line ) . Note that for the 28 M and 32 M time periods in Experiment 2 , we assumed they would have the same level of detectability as 24 M old memories , given the absence of data relating to these time periods from Experiment 1 . We further assumed the neural representations between 60 M and 68 M would be unchanged . A comparison of the original and simulated neural representation scores yielded a number of clear hypotheses about how memory representations would change over time in the vmPFC . Two-week-old memories should become detectable 8 months later , while the original 4 M and 8 M old memories should not differ in their representational strength . Twelve-month-old memories from Experiment 1 should be significantly less detectable , whereas 16 M old memories should remain unchanged . The original 20 M old memories should be better represented at 28 M , whereas the 24-and 60-month old-memories from Experiment 1 were not predicted to change over time . One week prior to the fMRI scan , with the assistance of the personal photographs and previously chosen phrases that were used as cues in Experiment 1 , the participants verbally recalled and rated the characteristics of their autobiographical memories just as they had done 8 months previously ( see Materials and methods and Fig 6A ) .
Over the course of consolidation in this study , the vmPFC twice alternated between disengagement and engagement , indicative of 4 separate stages . Below we consider , based on the latest theoretical developments and empirical research on systems-level consolidation and vmPFC functioning , the time-dependent processes which could underlie such a nonmonotonic pattern . The current findings have potential implications for the two dominant theoretical perspectives on systems-level consolidation . Standard consolidation theory [7] predicts that the passage of time promotes the strengthening of neural representations in the neocortex , but the duration of this process in humans is poorly specified . The current results suggest this process is accomplished over a relatively fast timescale on the order of months . The alternative perspective on consolidation , multiple trace theory and transformation hypothesis [10] , posits that over time , consolidation promotes the emergence of schematic , gist-like representations in the neocortex , which complement the original detailed memory . The reengagement of the vmPFC at 2 years in this study may reflect the emergence of these generalised representations to facilitate specific recall at more remote time points . Therefore , the consolidation of new memories in the neocortex may be reasonably rapid , whereas the transformation of these engrams may take place over a much longer timescale . Using an autobiographical memory paradigm to study consolidation is preferable to laboratory-based episodic memory tests by virtue of its ecological validity , availability of temporally distant stimuli , clinical significance , and context-dependent equivalence to animal tasks . However , studying autobiographical memory carries with it potential confounds that can affect interpretation of results . In the sections that follow , we consider why these factors cannot account for our observations . Older memories may yield a higher RSA score if they are more consistently recalled . Here , however , participants actually rated 0 . 5 M memories as more consistently recalled than 60-month-old memories . Older memories were not impoverished in detail when compared to the detail available for recent memories . Moreover , an inspection of interview transcripts across experiments revealed participants rarely offered new details for previous memories when retested , countering the suggestion that increased detectability of old memories may arise from the insertion of new episodic or semantic details [77] . The consistency in recalled detail across experiments could be attributable to participants recalling in Experiment 2 what they had said during Experiment 1 . However , whether or not participants remembered by proxy is irrelevant , as they still recalled the specific details of the original event , removing forgetting as a potential explanation of changes in neural patterns over time . Retrieving a memory initiates reconsolidation , a transient state in which memories are vulnerable to interference [78 , 79] . Therefore , repeated retrieval may cause this process to have an influence on neural representations . However , all memories were recalled 1 week before the fMRI scan , so if such an effect was present , it would be matched across time points . Retrieval at this stage may also accelerate consolidation [80] , yet if this were a major influence , we would likely have found 0 . 5 M memories to be more detectable than they were . Further repetition of memories within the scanner in Experiment 1 took place over a timescale that could not affect consolidation processes or interpretation of the initial neural data . Nevertheless , this could arguably affect vmPFC engagement over a longer period of time [81] and thus perturb the natural course of consolidation , influencing the results of Experiment 2 . However , given that 7 out of the 8 specifically hypothesised temporally sensitive changes in neural representations were supported , an altered or accelerated consolidation time course appears highly unlikely . Again , recall recency was matched in Experiment 2 by the memory interview , and recall frequency between experiments was low . Taking a more general and parsimonious perspective , the ratings demonstrate that , naturally , all memories are recalled on an occasional basis ( Table A in S1 Table ) ; therefore , it seems highly unlikely that a mere six repetitions within a scanning session would significantly alter the time course of systems-level consolidation . It should also be noted that successful detection of neural patterns relied on the specific content of each memory rather than being due to generic time-related retrieval processes ( S4 Fig ) . One alternative to the current two-experiment longitudinal design to limit repetition across experiments would be to have a different group of participants with different memories for the second experiment . However , the strength of the current approach was the ability to track the transformation in neural patterns of the same memories over time . An alternative interpretation of the time-sensitive vmPFC engagement is a systematic bias in the content of selected memories—for example , annual events coinciding across all participants , such as a seasonal holiday . However , recruitment took place over a period of 5 months in an evenly spaced manner , ensuring that such events did not fall into the same temporal windows across participants . The occurrence of personal events such as birthdays was also random across participants . The use of personal photographs as memory cues also limited the reliance on time of year as a method for strategically retrieving memories . Furthermore , the nature of memory sampling was that unique , rather than generic , events were eligible , reducing the likelihood of events that were repeated annually being included . Memory detectability was high at 12-month intervals such as 1 , 2 , and 5 years in this study , suggesting perhaps it is easier to recall events that have taken place at a similar time of year to the present . However , this should have been reflected in behavioural ratings and equivalently strong neural representations for recent memories , but neither was observed . Most importantly , if content rather than time-related consolidation was the main influence on memory detectability , then we would not have observed any change in neural representation scores from Experiment 1 to Experiment 2 , rather than the hypothesised shifts that emerged . A related concern is that memories across time differ in nature because they differ in availability . Successful memory search is biased towards recency , meaning there are more events to choose from in the last few weeks than in remote time periods . Here , this confound is circumvented by design , given that search was equivalently constrained and facilitated at each time point by the frequency at which participants took photographs , which was not assumed to change in a major way over time . These enduring ‘snap-shots’ of memory , located within tight temporal windows ( see Materials and methods ) , meant that memory selection was not confounded by retrieval difficulty or availability . It could also be argued that selection of time points for this study should have been biased towards recency , given that most forgetting occurs in the weeks and months after learning . However , it is important to dissociate systems-level consolidation from forgetting , as they are separate processes that are assumed to follow different time courses . Memory forgetting follows an exponential decay [82] , whereas systems-level consolidation has generally been assumed , until now , to be gradual and linear [83] . Our study was concerned only with vivid , unique memories that were likely to persist through the systems-level consolidation process . A further potential concern regarding memory selection is that recent and remote memories that are comprised of equivalent levels of detail must be qualitatively different in some way . For example , selected remote memories must have been highly salient at the time of encoding to retain such high levels of detail . However , the underlying assumption that individual memories invariably become detail impoverished over time does not necessarily hold . While the volume of memories one can recall decreases over time [84] , the amount of details one can recall from individual consolidated memories can actually increase over a 1-year delay [85] . While generalised representations are thought to emerge over the course of consolidation , they do not necessarily replace the original detailed memories [10] , and the equivalent level of detail provided by participants across the two experiments here would suggest that memory specificity can be preserved over time . Furthermore , the possibility that remote memory selection may still be biased towards more salient memories is rendered unlikely by the method of memory sampling employed here . Because memories were chosen only from available photographic cues , the salience of recent and remote events was determined at the time of taking the photograph and not during experimentation . These photographs served as potent triggers of remote memories that were not necessarily more salient than recent memories and that may not have otherwise come to mind using a free-recall paradigm . In addition , one would expect more salient remote memories to score higher than recent memories on subjective ratings of vividness , personal significance , or valence , but this was not the case . Therefore , stronger neural representations at more remote time points were likely due to consolidation-related processes rather than qualitative difference between recent and remote experiences at the time of encoding . Given that the medial prefrontal cortex is often associated with value and emotional processing [86] , could these factors have influenced the current findings ? Humans display a bias towards consolidating positive memories [87] , and remembered information is more likely to be valued than that which is forgotten [88] . Activity in vmPFC during autobiographical memory recall has been found to be modulated by both the personal significance and emotional content of memories [89] . However , in the current two experiments , memories were matched across time periods on these variables , and the selection of memories through photographs taken on a day-to-day basis also mitigated against this effect . In the 8 months between experiments , memories either remained unchanged or decreased slightly in their subjective ratings of significance and positivity , suggesting that these factors are an unlikely driving force behind the observed remote memory representations in vmPFC . For example , if recent memories in Experiment 1 were not well represented in vmPFC because they were relatively insignificant , there is no reason to expect them to be more so 8 months later , yet their neural representation strengthened over time nonetheless . A methodological discrepancy between this experiment and that conducted by Bonnici and colleagues [54] is the additional use of a photograph to assist in cueing memories . One possible interpretation of the neural representation scores is they represent a role for the vmPFC in the maintenance of visual working memory following cue offset . However , the prefrontal cortex is unlikely to contribute to maintenance of visual information [90] . Furthermore , if this was the driving effect behind neural representations here , the effect would be equivalent across time periods , yet it was not . There is , however , an obvious inconsistency between the findings of the current study and that of Bonnici and colleagues [54] . Unlike that study , we did not detect representations of 0 . 5 M old memories in vmPFC . It could be that the support vector machine classification–based MVPA used by Bonnici and colleagues [54] is more sensitive to detection of memory representations than RSA; however , the current study was not optimised for such an analysis , because it necessitated an increased ratio of conditions to trials . Nonetheless , the increase in memory representation scores from recent to remote memories was replicated and additionally refined in the current study with superior temporal precision . One observation that was consistent with the Bonnici findings was the detection of remote memories in the hippocampus , which also supports theories positing a perpetual role for this region in the vivid retrieval of autobiographical memories [10 , 12] . However , the weak detectability observed at more recent time points may reflect a limitation of the RSA approach employed here to detect sparsely encoded hippocampal patterns , which may be overcome by a more targeted subfield analysis [91] . There are , however , distinct advantages to the use of RSA over pattern-classification MVPA . RSA is optimal for a condition-rich design , as it allows for the relationships between many conditions to be observed . For example , in the current experiment , a visual inspection of the group RSA matrix ( S1 Fig ) does not reveal an obvious clustering of recent or remote memories that would indicate content-independent neural patterns related to general retrieval processes . The approach employed by Bonnici and colleagues [54] assessed the distinctiveness of memories within each time point from each other in order to detect memory representations . Should the neural patterns of a single memory become more consistent over time , yet also more similar to memories of the same age because of generic time-dependent mechanisms of retrieval , pattern classification would fail to detect a representation when one is present . In the current study , however , the two can be assessed separately , revealing memories at each time point become distinct from both memories of all other ages ( Fig 4A ) and identically aged memories ( Fig 4C ) . The machine-learning approach employed by Bonnici and colleagues [54] to decode memory representations also requires the division of data into ‘training’ and ‘testing’ sets to classify unseen neural patterns [53] . This reduces the number of trials available for analysis , which would have been suboptimal for the current design because it would have necessitated an increased number of conditions and fewer trials per memory , whereas this restriction is not a necessity for RSA . Finally , because the pattern classification approach used by Bonnici and colleagues [54] compared memories from each time point directly to each other , they could not be analysed independently . In the current RSA design , the two sets of memories could be analysed separately from each other to ascertain if the temporal patterns could be replicated in an independent set of data . As is evident in Fig 4B , the nonmonotonic pattern of vmPFC recruitment was present in both sets of memories . The suitability of each MVPA method , therefore , depends on the study design and the research questions being posed . In the light of our hypotheses , Experiment 2 generated one anomalous finding . Twenty-four-month-old memories from Experiment 1 were no longer well represented 8 months later . Why memories around 32 M of age are not as reliant on vmPFC is unclear , but unlike other time periods , we cannot verify this finding in the current experiment , as we did not sample 32 M memories during Experiment 1 . The current results revealed that the recruitment of the vmPFC during the expression of autobiographical memories depends on the exact stage of systems-level consolidation and that retrieval involves multiple sequential time-sensitive processes . These temporal patterns were remarkably preserved across completely different sets of memories in one experiment and closely replicated in a subsequent longitudinal experiment with the same participants and memories . These findings support the notion that the vmPFC becomes increasingly important over time for the retrieval of remote memories . Two particularly novel findings emerged . First , this process occurs relatively quickly , by 4 months following an experience . Second , vmPFC involvement after this time fluctuates in a highly consistent manner , depending on the precise age of the memory in question . Further work is clearly needed to explore the implications of these novel results . Overall , we conclude that our vmPFC findings may be explained by a dynamic interaction between the changing strength of a memory trace , the availability of temporally adjacent memories , and the concomitant differential strategies and schemas that are deployed to support the successful recollection of past experiences .
This study was approved by the local research ethics committee ( University College London Research Ethics Committee , approval reference 6743/002 ) . All investigations were conducted according to the principles expressed in the Declaration of Helsinki . Written informed consent was obtained for each participant .
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Our past experiences are captured in autobiographical memories that allow us to recollect events from our lives long after they originally occurred . A part of the brain’s frontal lobe , called the ventromedial prefrontal cortex ( vmPFC ) , is known to be important for supporting autobiographical memories , especially as memories become more remote . The precise temporal profile of the vmPFC’s involvement is unclear , yet this information is vital if we are to understand how memories change over time and the mechanisms involved . In this study , we sought to establish the time course of vmPFC engagement in the recollection of autobiographical memories while participants recalled memories of different ages during functional magnetic resonance imaging ( fMRI ) . Using a method that detects the brain activity patterns associated with individual memories , we found that memory-specific neural patterns in vmPFC became more distinct over the first few months after a memory was formed , but then this initial involvement of vmPFC subsided after 1 year . However , more remote memories ( 2 years and older ) appeared to reengage vmPFC once again . This temporal profile is difficult to accommodate within any single existing theory . Consequently , our results provoke a rethink about how memories evolve over time and the role played by the vmPFC .
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2018
|
Nonmonotonic recruitment of ventromedial prefrontal cortex during remote memory recall
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Molecular circadian clocks are interconnected via neural networks . In Drosophila , PIGMENT-DISPERSING FACTOR ( PDF ) acts as a master network regulator with dual functions in synchronizing molecular oscillations between disparate PDF ( + ) and PDF ( − ) circadian pacemaker neurons and controlling pacemaker neuron output . Yet the mechanisms by which PDF functions are not clear . We demonstrate that genetic inhibition of protein kinase A ( PKA ) in PDF ( − ) clock neurons can phenocopy PDF mutants while activated PKA can partially rescue PDF receptor mutants . PKA subunit transcripts are also under clock control in non-PDF DN1p neurons . To address the core clock target of PDF , we rescued per in PDF neurons of arrhythmic per01 mutants . PDF neuron rescue induced high amplitude rhythms in the clock component TIMELESS ( TIM ) in per-less DN1p neurons . Complete loss of PDF or PKA inhibition also results in reduced TIM levels in non-PDF neurons of per01 flies . To address how PDF impacts pacemaker neuron output , we focally applied PDF to DN1p neurons and found that it acutely depolarizes and increases firing rates of DN1p neurons . Surprisingly , these effects are reduced in the presence of an adenylate cyclase inhibitor , yet persist in the presence of PKA inhibition . We have provided evidence for a signaling mechanism ( PKA ) and a molecular target ( TIM ) by which PDF resets and synchronizes clocks and demonstrates an acute direct excitatory effect of PDF on target neurons to control neuronal output . The identification of TIM as a target of PDF signaling suggests it is a multimodal integrator of cell autonomous clock , environmental light , and neural network signaling . Moreover , these data reveal a bifurcation of PKA-dependent clock effects and PKA-independent output effects . Taken together , our results provide a molecular and cellular basis for the dual functions of PDF in clock resetting and pacemaker output .
Circadian clocks endow organisms with the ability to predict and respond adaptively to daily changes in the environment . In many taxa , these clocks consist of cell-autonomous molecular feedback loops , producing ∼24-hour oscillations at the mRNA and protein levels . In insects and mammals these clocks are also connected in neural networks that stabilize and synchronize these molecular feedback loops and communicate timing information to regulate daily behavior . How network and cell-autonomous mechanisms collaborate to produce robust circadian rhythms remains a major question . In Drosophila , the molecular circadian clock consists of a set of interlocked transcriptional feedback loops in which the basic helix-loop-helix per-arnt-sim ( bHLH-PAS ) domain transcription factor CLOCK ( CLK ) forms a heterodimer with CYCLE ( CYC ) and binds E-boxes in the promoter regions of period ( per ) , timeless ( tim ) , vrille ( vri ) , Par-domain protein 1ε ( Pdp1ε ) and clockwork orange ( cwo ) , promoting their transcription ( reviewed in [1] ) . PDP1ε and VRI feed back to regulate the Clk and cryptochrome ( cry ) promoters [2] , [3] , while CWO feeds back to regulate CLK/CYC activation at E-boxes [4]–[7] . PER and TIM proteins dimerize in the cytosol and are each required for their subsequent localization to the nucleus where PER inhibits CLK/CYC–mediated activation [8]–[14] . The CRY photoreceptor mediates light resetting via TIM degradation [15]–[18] . Clock function is evident as 24-h oscillations in the mRNA and protein levels of most of these clock components . The activity , stability , and subcellular localization of these proteins are largely controlled post-translationally by daily phosphorylation rhythms and subsequently by ubiquitin/proteasome dependent degradation [17] , [19]–[25] . In contrast to transcriptional regulators , significant oscillations have not been described for these post-translational regulators with the exception of the PP2A subunits tws and wdb [26] . In insects and mammals , intercellular signaling among pacemaker neurons in neural networks has been found to be critical for synchronizing molecular clocks . The Drosophila pacemaker network is comprised of ∼150 neurons of which specific subgroups regulate discrete aspects of behavior in light-dark ( LD ) and constant darkness conditions ( DD ) [27] . Two of these groups—all but one sLNv and all lLNvs ( small and large ventral-lateral neurons ) express the neuropeptide PIGMENT DISPERSING FACTOR ( PDF ) . The s-LNvs rhythmically express PDF in the dorsally projecting terminals that terminate near the DN1 [28] . Loss of function of either pdf or its receptor pdfr results in strongly reduced morning anticipation , an evening activity peak that is phase-advanced by 1 h relative to wild-type , and strongly reduced DD rhythmicity [29]–[41] . Ablation of PDF neurons results in similar phenotypes suggesting that PDF is the major transmitter of these neurons [37] . Transgenic rescue of pdfr mutants showed morning anticipation could be attributed to function in the DN1p neurons , while evening anticipation phenotypes mapped to non-PDF neurons , including the PDF ( − ) sLNv , the CRY+ subset of the LNd , and the DN1 [35] , [41] . PDF coordinates molecular oscillations between disparate circadian pacemaker neurons and mediates pacemaker neuron output downstream of the clock . tim and cry mRNA oscillations in pacemaker neurons are damped in pdf01 mutants [42] . The timing of nuclear entry of PER protein in sLNv becomes phase-dispersed in DD in pdf01 flies [43] . PER expression in the LNd and DN1 cells is phase advanced on the first day of DD and subsequently damps [34] , [43] . Analysis of TIM protein levels and a PER-luciferase fusion reporter suggested that the clocks in the different cell groups in the network can both advance or delay in response to PDF signaling [44] . Interestingly , while pdfr mutants exhibit notably reduced morning anticipation , PER oscillations in sLNvs and DN1s are comparable to wild-type flies under LD conditions [35] , suggesting that PDF also mediates pacemaker neuron output in clock target neurons . Acute activation and silencing of neuronal activity observed by PDF injection in cockroaches are consistent with this latter mechanism [45] . However , the underlying signaling pathways mediating these dual PDF functions within non-PDF clock neurons , i . e . , clock resetting and neural output , have not been identified .
PDFR is expressed in most of the pacemaker neurons and most of these neurons respond to PDF application in ex vivo preparations with increases in cAMP levels [46] , [47] . We tested the function of the cAMP-dependent protein kinase A ( PKA ) in clock neuron-driven behavior . cAMP is the canonical activator of PKA activity . cAMP binds the PKA regulatory ( R ) subunit releasing the catalytic ( C ) subunit to phosphorylate substrates ( reviewed in [48] ) . While PKA signaling has been implicated in circadian clock function [49] , [50] , its precise role in mediating PDFR signaling has not been defined . Using the Gal4-UAS system , we expressed a type I regulatory subunit of PKA that is defective for cAMP binding ( U-PKA-R1dn ) , thereby rendering endogenous catalytic subunits insensitive to cAMP activation [51] , [52] . Initially we drove expression using circadian drivers , such as cwo-G4 ( also known as c632a; [5] ) and examined behavior under standard 12:12LD conditions . cwo-G4 is a GAL4 enhancer trap insertion just upstream of the transcription start site of core clock gene cwo and drives expression in clock neurons [5] . In addition to expression in the PDF clock neurons [5] , we examined cwo-G4 driven nuclear green fluorescent protein ( GFP ) expression in pacemaker neurons using PER co-labeling . We observed GFP expression mainly in the circadian pacemaker neurons , with limited expression in other brain regions ( Figure S1; unpublished data ) . Among pacemaker neurons , we observed GFP in non-PDF clock neurons , including all of the LNds and the PDF ( − ) s-LNv , as well as a number of DN1s and DN3s ( Figure S1 ) . Broad expression of PKA-R1dn with cwo-G4 ( Figure 1C ) was able to phenocopy many features seen in pdfr mutants ( Figure 1D ) . These flies exhibited reduced morning anticipation and phase advanced evening activity under LD cycles , and were nearly arrhythmic in constant darkness , thus mimicking the three canonical behavioral phenotypes of pdf and pdfr mutants ( Figure 1A–1E; Tables 1 and 2 ) . Given that the majority of PDFR functions mapped to non-PDF clock neurons , we then asked whether these PKA-R1dn effects were due to its expression in non-PDF neurons by blocking PDF neuron expression with pdf-G80 . Importantly , we did not detect cwo-G4 driven GFP expression in the PDF ( + ) neurons , verifying effective inhibition of cwo-G4 activity in those neurons by pdf-G80 ( Figure S1 ) . We observed once again that these flies behaved similarly to pdf and pdfr mutants , exhibiting the three hallmark phenotypes of reduced morning anticipation , phase-advanced evening activity , and reduced rhythmicity in constant darkness ( Figure 1F–1J; Table 1 ) , providing strong evidence for PKA function in non-PDF neurons in mediating PDF effects in circadian neurons . The addition of pdf-G80 did improve the rhythmicity ( power-significance [P-S] ) in constant darkness , suggesting that PKA activity in the PDF neurons contributes to a portion of this phenotype ( Table 2 , p<0 . 005 ) . Nonetheless , the morning and evening phenotypes continue to map to non-PDF neurons . Given that cwo-G4 also drives limited expression in some non-circadian areas we decided to examine PKA function using an independent circadian driver . Previous work from our laboratory demonstrated that morning and evening anticipation phenotypes of pdfr mutant behavior can be rescued by expressing U-pdfr in non-PDF neurons using cry13-G4 and pdf-G80 [35] . Here we expressed U-PKA-R1dn in PDF ( − ) circadian cells using this Gal4/Gal80 combination and found that inhibition of PKA activity also results in morning and evening anticipation similar to pdfr- ( Figure 2A , 2B , 2D , 2E; Table 1 ) . If PDFR and PKA are operating in the same pathway , we would expect that U-PKA-R1dn expression in a pdfrhan5304 ( pdfr- ) mutant background would exhibit phenotypes comparable to either PKA-R1dn or pdfr mutants alone . Indeed , we found no differences in morning or evening behaviors among pdfr- , PKA-R1dn expression , or PKA-R1dn expression in the pdfr- background using cry13-G4/pdf-G80 ( Figure 2B , 2C; Table 1 ) , suggesting that PDFR and PKA operate in a common pathway within these cells . Rescue or suppression of mutant receptor function by expression of activated downstream signaling components is a powerful in vivo method to elucidate signaling pathways [53] . We also attempted to suppress pdfr mutant phenotypes by expressing a PKA catalytic subunit ( U-PKA-mC* ) , which is defective for regulatory subunit binding and is therefore constitutively active [52] . We expressed PKA-mC* in PDF ( − ) circadian neurons in pdfr mutants and observed that it rescued the reduced morning anticipation and phase-advanced evening activity onset characteristic of pdfr mutants ( Figure 2; Table 1 ) . There were no notable effects of PKA-mC* expression in a wild-type background in LD ( Figure 2F–2J; Table 1 ) . While PKA-mC* rescued LD phenotypes , we did not observe rescue of DD rhythms , suggesting that PKA-mC* is not expressed at the appropriate levels , cAMP inducibility may be required , and/or that non-PKA signaling pathways may contribute to PDFR function in DD ( Table 2 ) . We obtained similar results in the absence of pdf-G80 , thus allowing PKA-mC* expression in the PDF neurons , suggesting that lack of PKA activity in PDF neurons is not responsible for the lack of DD rhythm rescue by PKA-mC* ( Table 2 ) . Taken together , our genetic evidence , especially our ability to bypass PDFR function with an activated form of PKA , indicate that PKA is a major mediator of PDFR signaling in non-PDF clock neurons . PKA activity is both necessary and sufficient for the execution of most PDFR-mediated behaviors . We have previously shown that we can rescue pdfr mutant morning anticipation and DD rhythmicity , but not evening anticipation , by expressing wild-type pdfr only in DN1p circadian neurons using the Clk4 . 1-G4 driver [41] . To test whether PKA also functions in the DN1p we expressed PKA-R1dn using Clk4 . 1-G4 and found modestly reduced morning anticipation and DD rhythmicity , but no change in evening anticipation , complementing our observations for pdfr rescue ( Figure 3A–3D; Tables 1 and 2 ) [35] , [41] . Given that cwo-G4/pdf-G80 driven PKA-RIdn exhibited more robust morning and DD phenotypes ( Tables 1 and 2 ) , we hypothesize that non-PDF , non-DN1p cells may also contribute to these phenotypes and/or this driver combination more strongly inhibits PKA within the DN1p than Clk4 . 1-G4 . To address the neural substrates of PKA function in evening anticipation , we used the mai179-G4 driver in combination with pdf-G80 , which drives expression in the single PDF ( − ) sLNv , and the CRY ( + ) subset of LNd with variable expression in a 1–2 DN1s [30] , [54] , [55] . Here we found that PKA inhibition phase advances evening anticipation similarly to pdfr mutants ( Figure S2 ) . In keeping with a model in which the CRY+ LNd and fifth PDF ( − ) sLNv selectively regulate evening anticipation , PKA-R1dn driven by mai179-G4/pdf-G80 had no effect on morning anticipation ( Figure S2; Table 1 ) or DD rhythms ( Table 2 ) . Rhythms in PDF levels are apparent in the terminals of LNv neurons and rhythmic PDF release is thought to contribute to the temporal encoding of the PDF signal; however , rhythmic PDF may not be necessary for rhythmic behavior or clock function [28] , [40] , [56] . We hypothesized that rhythmic control of signal processing within cells receiving the PDF signal may contribute to the robustness of this pathway . To determine whether PKA subunit transcripts are under circadian clock control in DN1p clock neurons , we expressed UAS-membrane GFP ( U-mGFP ) using Clk4 . 1-G4 and isolated these neurons by fluorescence-activated cell sorting ( FACS ) . After RNA isolation and linear amplification ( see Materials and Methods ) , we examined the transcript levels of the three catalytic PKA subunits ( C1 , C2 , and C3 ) and two regulatory subunits ( R1 and R2 ) by quantitative real-time ( RT ) -PCR . Transcript levels of three PKA subunits ( PKA-C1 , PKA-R1 , and PKA-R2 ) oscillate in phase , with coincident peaks in the mid-day ( ZT4 ) ( Figure 3E–3G ) . Peak transcript levels were reduced and rhythms were not detected in the per01 mutants consistent with circadian clock control . The two other PKA transcripts ( C2 , C3 ) were near the limits of quantitative detection . Thus , not only is PKA activity likely controlled via rhythmic inputs of PDF-driven cAMP production but PKA is also rhythmically controlled at the level of gene expression . We hypothesize that these dual mechanisms collaborate to provide a robust time-of-day PKA signal to synchronize non-PDF to PDF oscillators . The ability of PDF neurons to reset molecular clocks in non-PDF neurons has been powerfully demonstrated by selective manipulation of circadian period in PDF neurons and examination of molecular oscillations in non-clock cells [39] , [41] , [55] . To determine the direct molecular targets of PDF neuron signaling , we rescued clock function selectively in PDF neurons of arrhythmic per01 mutants and assayed molecular oscillations in per-less non-PDF neurons . By examining molecular changes in per01 non-PDF neurons , we removed the possibility that identified changes would be indirect through a functioning circadian clock and/or per . In addition , we examined molecular changes on the first day of constant darkness , removing the possibility that light signaling via CRY is responsible for any changes . We rescued per in PDF cells ( per01;pdf-G4/+;U-per16/+; “pdfPER” flies ) and examined non-PDF-expressing circadian cells ( LNd and DN1 ) in brains stained for the clock components TIM ( Figure 4 ) and PDP1ε ( Figure 5 ) . First we demonstrated that transgenically supplied PER cycles in the sLNvs and rescues oscillations in TIM levels and nuclear localization in those cells ( CT24 ) , indicating at least a partially rescued sLNv clock consistent with prior studies ( Figure 4A ) [55] . We then examined the consequences of the rescued sLNv clock on non-PDF per01 DN1 and LNd neurons . Consistent with the fact that these cells lack per or a fully functioning circadian clock , TIM is predominantly expressed in the cytoplasm at all times of day ( Figure 4 ) [13] . However we found stark changes in the levels and phase of TIM oscillation in DN1 neurons despite the lack of PER . In per01 controls , we observed a low amplitude TIM oscillation with an inappropriate day-time peak ( Figure 4 ) . In pdfPER rescue flies , TIM cycling amplitude increases with elevated peak levels and its oscillation phase is synchonized with that of TIM in the PDF neurons ( Figure 4 ) . TIM levels and phase in LNds are also modified by the PDF-neuron clock , but these effects are much smaller than those in the DN1s ( Figure 4 ) , consistent with our prior finding that the DN1 are more strongly reset by PDF neurons than the LNd [41] . To determine if these effects on TIM are specific , we also assayed a second clock component PDP1ε , a core circadian transcription factor directly activated by CLK/CYC [3] . We found that PDP1ε exhibits comparable levels between per01 and pdfPER rescue flies with only a modest change at ZT18 in the LNd ( Figure 5 ) . The absence of strong effects on PDP1ε suggests that PDF-mediated inputs to the molecular clock are unique to TIM . This result also argues strongly against large PDF effects on CLK/CYC driven transcription , a major determinant of PDP1ε levels [3] . The strength of the effects on TIM evident in the absence of a functioning clock , per and light , suggest that TIM is a direct target of PDF signaling . Our results suggest PKA mediates PDF effects and that PDF targets TIM . To test whether PKA can influence TIM levels , we expressed PKA-R1dn in non-PDF circadian cells using cwo-G4 in combination with pdf-G80 in a per01 mutant background and examined TIM levels ( Figure 6 ) . Flies were entrained and dissected at four time points across the LD cycle , and stained for TIM . We observe reduced peak levels of TIM in both the LNd and DN1 at ZT24 with a non-significant trend developing by ZT12 during the light period . In these per01 flies , TIM remains in the cytoplasm throughout the 24-h cycle . These results indicate that PKA activity positively regulates TIM accumulation in the LNds and DN1s in the absence of a functioning clock suggesting a direct effect on TIM . We have shown that the PDF-cell clock specifically regulates TIM in non-PDF circadian cells ( Figures 4 and 5 ) and that reducing PKA activity in non-PDF cells leads to reduced TIM levels ( Figure 6 ) . Our behavior data show that PKA acts in the PDF signaling pathway downstream of PDFR ( Figures 1 and 2 ) . We therefore determined whether loss of PDF would mimic reduced PKA activity and result in reduced TIM . Here we examined loss of PDF ( pdf01 ) in an arrhythmic per01 mutant background and examined the effects of the PDF peptide on TIM at the end of DD1 ( CT24 ) . We compared per01 and per01;;pdf01 flies and observed that TIM is strongly reduced in the absence of PDF in both LNd and DN1 neurons ( Figure 7 ) . We observe a nearly 50% reduction in TIM staining intensity in the absence of PDF , which is comparable to or even larger than the effect than we observed with PKA inhibition . One possibility is that expression of PKA-R1dn may not completely interrupt the signaling cascade , whereas pdf01 is a confirmed null mutation [37] . These results provide further independent support for the hypothesis that the PDF>PDFR>PKA signaling pathway influences the core molecular clock by promoting the accumulation or stability of TIM . Reduced morning anticipation in pdfr mutants coupled to an absence of significant core clock effects in LD suggested that PDF morning function is via effects on neuronal output [35] . We hypothesized that these effects may be mediated by direct effects on neuronal activity . Our previous work had identified the DN1p as functional targets of PDF on morning anticipation using rescue of pdfr mutants [41] . To determine if this reflects a direct interaction , we selectively labeled the DN1p using U-GFP in combination with Clk4 . 1-G4 and the LNv using anti-PDF and found that the arbors of each extensively co-mingle ( Figure 8A ) . To test if DN1p neurons are direct targets of PDF neurons , we employed GFP reconstitution across synaptic partners ( GRASP ) [57] . Here we expressed one fragment of GFP ( GFP11 ) on the extracellular surface of the LNv neurons using pdf-LexA and its complementary fragment ( GFP1-10 ) on the extracellular surface of the DN1p neurons using Clk4 . 1-G4 . We observe robust fluorescence in the dorsal terminals consistent with extensive physical contacts ( Figures 8B , 8C , and S4 ) . Co-labeling of sLNv-DN1p GRASP with PDF finds that PDF signal is in close proximity to GRASP signals suggesting that sites of physical contact are potential release sites for PDF ( Figure 8B and 8C ) . To examine PDF signaling mechanisms that control neuronal output , we first performed live imaging on the DN1p neurons on explanted brains . Using the Clk4 . 1-G4 driver in combination with the FRET sensor U-Epac1 ( 50A ) [58] , we measured the variation of [cAMP] following focal PDF application specifically in the DN1p neurons . Prior studies had used bath application of PDF to examine changes in cAMP and thus , the observed effects could be due to indirect activation [47] . Following focal PDF application to the DN1p neurons ( Figure S3A ) , we observed a decrease in the ratio YFP/CFP indicating an increase of [cAMP] ( Figure S3B–S3D ) . Thus , as suggested by prior studies [47] , we confirm that PDF increases cAMP levels in the DN1p . To resolve whether PDF acutely controls DN1p neuronal activity , we developed patch clamp electrophysiology of the DN1p subset of pacemaker neurons and assayed the response of these cells to focal PDF application . We performed cell-attached recordings in combination with live calcium imaging on the DN1ps by simultaneously recording firing frequency and [Ca2+]i using Clk4 . 1-G4 driving expression of the GCaMP6f calcium indicator [59] . Focal PDF application acutely stimulates the DN1ps by increasing the instant firing frequency of the neurons ( Figure 9A and B ) . This increase in neuronal activity is directly correlated with an increase in [Ca2+]i as measured by the GCaMP6f calcium indicator ( Figure 9C ) . Using whole-cell current clamp recordings , we found that PDF both acutely depolarizes and increases action potential firing rates ( Figure 10A ) . This excitatory effect of PDF is dependent on its receptor as we could not detect any effect of PDF on the membrane potential or firing frequency in cells lacking PDFR ( Figure 10A ) . The PDF-evoked depolarization was present after blocking action potential firing , and thus most synaptic transmission , with the voltage gated sodium channel blocker TTX indicating that PDF acts on the DN1ps directly ( Figure 10B ) . Surprisingly the PKA inhibitor H89 did not block these effects indicating that PDF activates a PKA-independent pathway to acutely activate neurons ( Figure 10C ) . To independently confirm the dispensability of PKA signaling we recorded from DN1p neurons expressing the dominant-negative PKA-R1 ( Figure 10D and 10E ) . PDF application still depolarizes and increases in firing frequency further supporting the hypothesis that PDF acts independently of PKA activation to regulate membrane excitability . We next examined the potential role of cAMP as the intracellular component mediating the acute PDF effect on membrane activity . First , we demonstrated that the adenylate cyclase inhibitor MANT-GTPγS blocks the PDF induced excitation ( Figure 11A–11C ) . Conversely , forskolin ( an adenylate cyclase activator ) and direct dialysis of cAMP into the cell induces a depolarization similar to the PDF evoked response ( Figure 11D and 11E ) . Finally , the cAMP induced depolarization and activation was present in the neurons expressing PKA-R1dn ( Figure 11E ) . Taken together these data indicate that cAMP , rather than other upstream signaling components is responsible for the PDF effects on excitability . In voltage clamp mode , PDF application acutely induces an inward current at negative potentials and a positive shift in the reversal potential ( Figure 12A–12C ) . This inward current is TTX-insensitive ( Figure 12C ) and is attenuated after reduction of extracellular sodium ( Figure 12F ) . Furthermore , focal application of the adenylate cyclase activator forskolin or direct intracellular dialysis of cAMP induce an inward current like PDF ( Figure 12D and 12E ) . The properties of the observed current—neuropeptide induction , TTX resistance as well as PKA independence—are consistent with a cyclic-nucleotide-gated channel ( CNG ) [60] . Thus , using this novel patch clamp analysis and focal application of PDF , we demonstrate that PDF acts as an excitatory neurotransmitter that acutely increases firing rate and calcium , likely in a PKA-independent manner . The rapidity of PDF effects on excitablility argues strongly that they are direct and not via clock resetting .
Intercellular communication has emerged as a critical element in circadian pacemaker function in multicellular animals . PDF acts as a master neural network regulator coordinating molecular oscillations between disparate circadian pacemaker neurons in Drosophila . Yet how PDF signaling resets circadian clocks as well as acutely regulates neural activity has not been clearly defined . Here we provide evidence that the PDF signaling pathway works through two mechanisms to regulate circadian behavior; a clock resetting pathway that targets the core clock protein TIMELESS ( TIM ) via PKA to maintain synchronous molecular oscillations throughout the pacemaker network , and a neural activity pathway that acutely increases the firing rate of pacemaker neurons independent of PKA ( Figure 13 ) . Here we provide in vivo genetic evidence for a role for PKA in mediating PDF neuropeptide effects on behavior , including demonstration of clock control of PKA subunits in PDF target neurons . While prior work had demonstrated a role for PKA in circadian behavior , these studies observed effects under conditions of PKA overexpression in mutant conditions [61] , failed to link PKA to PDF receptor signaling [49] , [61]–[64] , or impaired cAMP or PKA throughout the fly [49] , [63] or the circadian network [62] , [65] . We show that inhibition of PKA in a subset of non-PDF neurons can mimic the advanced circadian activity in the evening observed in PDF or PDF receptor mutants . Moreover , expression of an activated form of PKA can rescue most pdfr mutant phenotypes , providing powerful genetic evidence that PKA is mediating PDFR signaling in non-PDF circadian pacemaker neurons ( Figure 13 ) . Using refined cell-specific manipulations , we dissected the functional neuroanatomy of PKA function . These studies demonstrated DN1p PKA contributes to morning anticipation and DD rhythms and PDF ( − ) sLNv and CRY+ LNd functions in evening anticipation , similar to the division of labor we previously observed for pdfr rescue [35] , [41] . Nonetheless , the finding of modest effects of DN1p PKA on morning anticipation in the face of a prominent role for non-PDF cells ( Table 1 ) suggest that other non-PDF cells make a contribution and/or that the DN1p function is via PDF effects on neuronal excitability upstream of PKA . While a previous study linked a PDF-coupled adenylate cyclase ( AC3 ) function in the PDF neurons to morning anticipation in sLNv , it is not known if this adenylate cyclase may also couple to other receptors that may mediate these effects , for example , by regulating PDF release rather than PDF receptor signaling [31] . We did observe that PDF neurons also contribute to DD rhythmicity effects of PKA ( Table 2 ) , consistent with the more distributed function of PDFR in DD rhythmicity . Taken together , these data provide a circuit map for PKA function in mediating PDF effects in the central pacemaker network . A central feature of core circadian clock components is their time-of-day dependent expression , providing the mechanistic basis of biological timekeeping . To address whether the clock actively controls PKA expression or activity , we assessed transcript levels using FACS isolation of the DN1p . Here we show that both regulatory subunits ( R1 and R2 ) and one of the three catalytic subunits ( C1 ) of PKA show robust rhythms with a peak during the mid-day . Coordinate oscillations of both regulatory and catalytic subunits of PKA should result in daily increases in the sensitivity to PDFR activation . Rhythmic PKA expression also provides a mechanistic basis for rhythmic behavior under conditions when PDF oscillations are not apparent [29] , [56] . PKA rhythms are abolished in mutants of the classical core clock component per01 , indicating these oscillations are clock controlled . Given that these PKA transcripts peak in the mid day ( ZT4; Figure 3E–3G ) at a time when CLK activity is low [66] and that peak PKA transcript levels are reduced in the per01 mutant suggest that it is not directly CLK-activated . Interestingly , expression of a bacterial sodium channel in the larval sLNv can induce PKA-C1 transcript expression [62] , suggesting that clock-driven changes in neuronal activity may mediate PKA transcript rhythms . While the peak of PKA subunit transcription in the DN1p in the mid day is not coincident with the requirement of PDFR signaling for morning anticipatory behavior , the rate of accumulation and half-life of PKA protein in these cells is not known . Regardless , these results indicate that pacemaker neurons rhythmically control their sensitivity to PDF inputs , suggesting that rhythmic PDF-driven cAMP production and rhythmic PKA transcription collaborate to provide a robust time-of-day specific signal to synchronize non-PDF to PDF oscillators ( Figure 13 ) . In addition to demonstrating a key role for PKA in PDFR signaling , we also reveal important in vivo evidence that PDF neuronal signaling selectively targets the circadian clock component TIM in non-PDF neurons , providing a molecular basis for network influence on core molecular clocks ( Figure 13 ) . To address the core clock target of PDF signaling , we set up a complex genetic scenario in which we used the arrhythmic per01 mutant as a background and rescued per ( and thus , clock function ) only in PDF neurons . We then asked how rescued clock function in PDF neurons impacts molecular clock components in per01 non-PDF clock neurons . We found that these rhythmic PDF neurons are able to drive high amplitude and appropriately phased DN1 molecular oscillations in the core clock component TIM but not in another clock component PDP1ε , suggesting that TIM is the specific target of PDF signaling in the molecular clockwork . We observe reduced effects on TIM in the LNd , perhaps due to the fact that the rescue of per01 in PDF neurons is not complete ( Figure 4 ) [55] . Moreover , it is unlikely that PDF is inducing an intact clock in per01 non-PDF neurons . In addition to the weak or absent PDP1ε oscillations , clock-driven oscillations in TIM nuclear localization are not observed with TIM remaining cytoplasmic , consistent with studies indicating that PER is required for TIM nuclear localization [10] , [13] . Loss of PDF in a per01 background results in reduced TIM levels in both the LNd and DN1 ( Figure 7 ) . These robust TIM effects in the LNd may reflect a more extreme perturbation of PDF signaling in the null mutant and/or the potential of non-PDF transmitters to influence LNd TIM levels in the PDF cell rescue . Nonetheless , our data support the view that PDF signaling is specifically regulating TIM rather than reconstituting a clock in per01 target neurons . Our work suggests that PKA is an important intermediary between PDF and TIM ( Figure 13 ) . Inhibition of PKA in non-PDF neurons in per01 mutants reduces TIM levels in LNds and DN1s ( Figure 6 ) , consistent with a role for PKA in promoting TIM accumulation or stability . The more strongly evident effects of PKA on TIM than in the PDF cell rescue context may reflect the incomplete PDF cell rescue , more robust PKA manipulation with dominant negative expression , and/or PDF or PKA-independent effects of PDF cells on LNd TIM levels . PKA effects on TIM in the absence of per or a fully functioning clock suggest that these effects are direct . PKA has also been implicated in activating CLK driven transcription . However , these effects are modest and observed under conditions of PKA overexpression in cultured S2 cells [67] . Moreover , PKA does not phosphorylate CLK in vitro [67] . Our finding that PDP1ε in non-PDF cells is not strongly affected by rescue of the molecular clock in PDF neurons further supports the hypothesis that CLK activity is not an in vivo target of PDF/PKA . TIM contains numerous consensus PKA phosphorylation sites and is robustly phosphorylated by PKA in vitro [68] . Our findings that reduction of PKA function reduces TIM levels suggests a positive role for PKA in TIM accumulation or stability . Our finding that TIM levels in LNd and DN1 neurons are reduced in the absence of PDF peptide provides strong independent verification for the PDF>PDFR>PKA pathway in targeting TIM to influence the core molecular clock . Notably , we did not observe any effects on TIM in DD as assayed by Western blot after PKA inhibition in the eye ( unpublished data ) , suggesting PKA pathway function in the core clock may be restricted to the pacemaker neurons . Comparable changes in TIM due to light pulses are associated with significant phase shifts [69] that are comparable to , or even exceed , those evening phase effects observed in pdf mutants or with PKA-R1dn expression , suggesting that these TIM effects are biologically meaningful . The finding that TIM responds to PDF and PKA could explain observed interactions between PDF and CRY signaling . Altering the pace of PDF-cell clocks can reset non-PDF clocks . However under standard LD conditions , PDF-cell clocks are only able to reset evening phase after mutation of the CRY photoreceptor , indicating that CRY antagonizes PDFR signaling [70] . Our identification of TIM as a common target of CRY and PDFR signaling provides a plausible mechanism for these phenotypes: CRY-mediated degradation of TIM may render pacemaker neurons insensitive to PDF receptor inputs thus explaining the CRY-dependence of PDF effects on evening phase . Thus , TIM is a multimodal integrator of core clock , environmental , and network pathways of entraining and maintaining clocks in the pacemaker network: ( 1 ) tim is transcribed by the CLK/CYC heterodimer and is thus regulated directly by the core feedback loop; ( 2 ) TIM protein levels are controlled by environmental light via CRY-mediated degradation; and ( 3 ) we demonstrate that TIM responds to network signals via PDF signaling , likely directly mediated post-transcriptionally by PKA . In addition to elucidating signaling mechanisms that link PDF to core clocks , we also defined mechanisms by which PDF acutely regulates neuronal activity . While our work suggests that PDF acts via changes in protein abundance to reset clocks , our previous work suggested that PDF also has effects on pacemaker neuron output , specifically morning behavior , that are independent of resetting clocks [35] . In fact , PDF injection in cockroaches acutely regulates neuronal activity [45] . However , the precise nature and mechanism by which PDF achieves these effects are not clear . Here we have developed patch clamp electrophysiology of the DN1p subset of neurons and assayed the response of these cells to focal PDF application . We focally applied PDF to these neurons and found that PDF both acutely depolarizes and increases action potential firing rates in a PDFR dependent manner , indicating that PDF is acutely excitatory and providing a mechanistic basis for effects on pacemaker neuron output ( Figure 9A ) . Consistent with our data in the DN1p , membrane-tethered PDF peptide expressed in the PDF+ LNv depolarizes the sLNv [29] . Surprisingly PKA inhibiton ( by H89 or the expression of a dominant negative PKA ) did not block these effects , while adenylate cyclase inhibition did block them , indicating that PDF activates a cAMP-dependent , PKA-independent pathway to acutely activate neurons ( Figures 10B–10E and 11A–11E ) . We note that genetic inhibition of PKA in the DN1ps only modestly reduces morning anticipation , suggesting a potential role for this PKA-independent pathway in morning behavior ( Figure 3B and 3C ) . Given the properties of the PDF-induced current we hypothesize that PDF-driven cAMP activates a cyclic nucleotide gated channel to acutely depolarize and activate target neurons ( Figure 13 ) . Our model is consistent with the role of G-alpha-s and cAMP in mediating PDF effects in the sLNv on morning and evening activity allocation [29] . However , the role of PKA was not examined this study . While we cannot exclude the possibility of direct or indirect cross talk between pathways , these data reveal a bifurcation of the PDF receptor signaling pathway: a PKA-dependent fork contributes to synchronization of the molecular clocks via regulation of TIM and a PKA-independent fork acutely induces neuronal activity ( Figure 13 ) , thus providing mechanistic bases for the dual functions of PDF in the Drosophila circadian pacemaker network .
Fly lines carrying U-PKA-mC* and U-PKA-R1dn ( also known as BDK33 ) were a generous gift from Daniel Kalderon [52] . UAS-CD4::spGFP1-10 and LexAop-CD4:spGFP11 flies were the gift of Kristen Scott [71] . The latter transgene was recombined with pdf-LexA ( a gift of Michael Rosbash [72] ) . Lines carrying combinations of these and other transgenes or mutants were constructed using standard genetic crosses . Tim was genotyped for the s/ls alternative start site polymorphism using previously described primers ( Peschel 2004 ) . TIM staining in the per01;pdf-G4;U-per16 rescue context was completed three times: in two trials the tim genotypes were s/ls , and in one trial the control was ls/ls , while the pdfPER flies were s/ls . The staining results were comparable between these conditions and were combined . Strains for TIM staining with PKA-R1dn expression ( Figure 6 ) were s/ls . Fly behavior was recorded using the Drosophila Activity Monitoring system ( Trikinetics ) and analyzed using ClockLab and the Counting Macro as described [73] . Briefly , male flies were fed on 5% sucrose-agar medium in 5LD7DD conditions at 25C . LD eductions were obtained using averaged data in 30-minute bins across days 2–5 of the behavior run . DD period and rhythmicity data were calculated in ClockLab with period measurements taken only from flies in which the Power-Significance ( P-S ) ≥10 . Morning anticipation was calculated using a variant of the method described in [32] . Activity from each of four days of LD behavior recorded for each individual fly were analyzed such that the morning index ( MI ) = ( ( total activity 3 h prior to lights-on ) / ( total activity 6 h prior to lights-on ) ) − ( 0 . 5 ) . 0 . 5 was subtracted so flat activity over the six hours analyzed is equal to 0 . In cases where no activity counts occurred in the 6 hours before lights-on , resulting in an undefined 0/0 , the ratio was set to 0 . 5 , indicating no change in activity over that time period . The timing of evening activity onset was calculated as previously described [35] with the onset time defined as the first time point in the four 30-min bin sliding window with the largest increase in activity prior to lights-off . Genotypes were compared by Student's two-tailed t-test . Flies to be stained were entrained for five to seven 12-h light , 12-h dark ( LD ) cycles at 25°C and either dissected and fixed at the indicated timepoint for LD staining ( Figure 6 ) or transferred to constant darkness and dissected and fixed for DD1 staining ( Figures 4 and 5 ) . Brains were dissected in PBS ( pH 7 . 5 ) and fixed in 3 . 7% formaldehyde in PBS for 1 h shaking at room temperature . Brains were then washed 3× in PBS and primary antibody solution was added . Guinea pig anti-TIM ( 1∶2 , 000 ) , rabbit anti-PER ( 1∶16 , 000 ) , and mouse anti-PDF ( 1∶500 ) ( Developmental Studies Hybridoma Bank ) were incubated overnight shaking at 4°C in a solution of PBS , 10% goat normal serum ( GNS ) , and 0 . 3% Triton X-100 . For stains involving rabbit anti-PDP ( 1∶200 ) brains were dissected and fixed as above , except after fixation and 3× washes with PBS , brains were subject to a 1-h permeablization in PBS +1% Triton X-100 and primary antibody solution was incubated for 3 days in PBS with 0 . 3% Triton X-100 and 10% GNS . After the primary incubation , for all stains , brains were washed 3× in PBS +0 . 3% Triton X-100 and secondary antibodies ( for PDF , PER , TIM staining: anti-mouse Alexa647 , anti-guinea-pig Alexa 488 , anti-rabbit Alexa 594; for PDF , PDP1ε staining: anti-mouse Alexa 594 , anti-rabbit Alexa 488 ) ( all dyes from Molecular Probes - Invitrogen ) were each added at 1∶500 . Brain images were taken on a Nikon E800 laser-scanning confocal microscope using a 60× A 1 . 40 N . A . objective with laser , filter , and gain settings remaining constant within each experiment . Channels were scanned sequentially . Confocal Z-stacks were analyzed in NIH ImageJ software . Intensity measurements were taken from single confocal sections at approximately the middle of each cell . Nearby areas of similar area to the cells being measured were selected for each cell group in each hemisphere as a measurement of background staining . The background measurement for each cell group in each hemisphere was subtracted from the intensity measurement for each cell in that group . Background-subtracted values were then averaged across all brains in that experiment . Image measurements were normalized prior to combining data from independent experiments . For Figures 4 and 5 , each experiment was individually normalized such that pdfPER Rescue at CT6 = 1 . For Figure 6 , data were normalized to Control at ZT6 = 1 . For Figure 7 , data were normalized to per01 at CT24 = 1 . Data from independent experiments were combined post-normalization to obtain the final graphs . Images from Figures 4 , 5 , and 6 are displayed using the inverted 5 Ramps lookup table within ImageJ for ease of viewing images with low signal . Staining data were statistically analyzed by one-way ANOVA and Tukey's pairwise comparisons . GRASP signal and mouse anti-PDF stained brains were fixed , mounted , and imaged as above , except using anti-mouse Alexa 594 to label PDF . Both 40× and 60× objective images were collected in 1 micron steps through the region containing staining , or through the entire dorsal brain for non-labeled parental control lines . Cells were processed as described previously [74] . Before FACS cell sorting cells were filtered using 100 micron filter . Propidium iodide ( Sigma , 130 ng/ul ) was added to distinguish between dead and alive cells . Cells were sorted on Aria II FACS Cell Sorter ( BD Biosciences ) into an extraction buffer from the PicoPure RNA extraction kit ( Arcturus ) . Transcripts were obtained from 40 to 45 brains ( yielding 300–500 DN1p neurons ) per time-point . Subsequently , the cells were lysed and stored at −80°C until RNA extraction as described previously [74] . Cells were processed as described previously [75] . cDNA from two independent replicates per genotype were analyzed per time-point on a BioRad CFX384 real-time PCR system . mRNA was quantified as described previously [74] . One-way ANOVA was used to determine statistically significant differences between time-points within each genotype ( p<0 . 05 ) . The following primers were used to examine pka expression: pka-R1 , F primer , 5′-ACTTTGGCGAGATTGCTCTG-3′; R primer , 5′-CGGACAACGATACGAAACTG-3′; pka-R2 , F primer , 5′-CTACGAACGCATGAATCTGG-3′; R primer , 5′-GCCGAAGTACTGTCCCTTGC-3′; pka-C1 , F primer , 5′-ATCGCTGGCATCGTAGTCG-3′; R primer , 5′-AAGGCGCTTGGTTAAGACG-3′ . Brains from male adults Drosophila ( 7–14 days old ) were removed from their heads in ice-cold recording solution . After removing the connective tissue , air sacs , and trachea with fine forceps , the brains were transferred to a recording chamber and were held ventral side down by a harp slice grid ( ALA scientific ) . No enzymatic treatment was used to avoid altering ion channels function on the cell surface . Brains were allowed to rest in continuously flowing oxygenated saline ( 95% oxygen and 5% carbon dioxide ) for at least 10 min and no more than 2 h before recording . Perfusion with oxygenated saline was continued throughout the recording period . Whole brain electrophysiology and imaging experiments were performed on an Ultima two-photon laser scanning microscope ( Prairie Technologies ) equipped with galvanometers driving a Coherent Chameleon laser . Fluorescence was detected with photomultiplier tube . Images were acquired with an upright Zeiss Axiovert microscope with a 40×0 . 9 numerical aperture water immersion objective at 512×512 pixel resolution and 1-µm steps . Current-clamp recordings were performed with pipettes ( 10–14 MΩ ) filled with internal solution . To visualize the recorded cell , Alexa Fluor 594 biocytin ( 10 µM ) was added into the intracellular solution . Recordings were made using Axopatch 200B patch-clamp amplifier , Digidata 1320 A , and pCLAMP software ( Axon Instruments ) . The extracellular recording solution contains in mM: 101 NaCl , 1 CaCl2 , 4 MgCl2 , 3 KCl , 5 glucose , 1 . 25 NaH2PO4 , and 20 . 7 NaHCO3 ( pH 7 . 2 , 250 mOsm ) . The internal solution contains in mM: 102 K-gluconate , 0 . 085 CaCl2 1 . 7 , MgCl2 , 17 NaCl , 0 . 94 EGTA , 8 . 5 HEPES , 4 Mg-ATP , 0 . 3 Tris-GTP , and 14 phosphocreatine ( di-tris salt ) ( pH 7 . 2 , 235 Osm ) . For simultaneous cell attached and live calcium-imaging recordings , the Drosophila DN1ps neurons were visualized with GCaMP6f indicator [59] . The x-y images of GCaMP6f fluorescence were acquired at 10–20 Hz . GCaMP6f fluorescence was excited at 840 nm and was captured at wavelengths between 490 and 540 nM using a bandpass filter . Changes in intracellular cAMP concentration were imaged using the Epac1-cAMPs indicator . Whole brain imaging experiments were performed using hemolymph-like HL3 saline [76] ( in mM: NaCl 70 , KCl 5 , CaCl2 1 . 5 , MgCl2 20 , NaHCO3 10 , D-trehalose dihydrate 5 , sucrose 115 , Hepes 5 , pH adjusted at 7 . 1 with NaOH 1 M ) . After dissection , whole brains were placed with HL3 solution in the experimental chamber ( POC-R perfusion chamber , Zeiss ) and placed on the stage of an Axiovert 200 M inverted microscope attached to a Zeiss 510 Meta/ConfoCor3 Laser Scanning unit ( Zeiss ) available through the Northwestern University Biological Imaging Facility . The x-y confocal images of Epac1-camps fluorescence were acquired at 2–4 Hz using a Zeiss planApochromat 20×0 . 8 N . A . objective . Epac1-camps fluorescence were excited at 454 nm by a 200 mW argon ion laser and were captured at wavelengths between 470 and 500 nM for CFP and between 510 and 550 nM for YFP using a bandpass filter . The pinhole was set to provide a confocal optical slice of 10 µm . Epac1-camps fluorescence intensity was normalized to the average fluorescence intensity in the images captured before neurotransmitter application and the ratio YFP/CFP was calculated . PDF ( 50 µM , dissolved in recording solution , GenScript ) or forskolin ( Sigma ) was applied focally for 10 s to the recorded cells via pressure ejection ( 0 . 5–1 psi ) from a glass pipette ( 5–10 µM ) placed in the vicinity of the cell . TTX ( Tocris ) and/or H89 ( Sigma ) were bath applied by exchanging the recording solution . cAMP ( 10 µM , Sigma ) and MANT-GTPγS ( 500 nM , Sigma ) were diluted into the intracellular solution .
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Circadian clocks provide a mechanism for predicting and adapting behavioral and physiological processes to 24-hour rhythms in the environment . In animal nervous systems , cell-autonomous molecular oscillators are coupled via neural networks that control daily patterns of activity . A major neuropeptide synchronizing neural oscillators in the Drosophila clock network is PIGMENT DISPERSING FACTOR ( PDF ) . Here we identify a fork in the processing of the PDF signal in circadian neurons to independently reset the molecular clock and regulate neuronal activity . We show that the cAMP-activated protein kinase A ( PKA ) in circadian neurons is necessary and sufficient for many PDF-dependent behaviors . In addition , we find that a PDF>PDF receptor>PKA pathway targets the clock component TIMELESS to control molecular oscillators , and that this process may be influenced by rhythmic expression of PKA . We show that this pathway splits at the level of cAMP generation , with PDF and cAMP acutely increasing the activity of clock neurons in a PKA-independent manner . Thus , PDF operates via dual signaling pathways: one via PKA to reset clocks and the other via cAMP to acutely control activity . These results have broad implications given the conserved involvement of neuropeptide signaling in synchronizing clocks in circadian neural networks .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"invertebrates",
"molecular",
"neuroscience",
"neurochemistry",
"neural",
"networks",
"neuroscience",
"animals",
"gene",
"function",
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] |
2014
|
Dual PDF Signaling Pathways Reset Clocks Via TIMELESS and Acutely Excite Target Neurons to Control Circadian Behavior
|
Lung disease caused by nontuberculous mycobacteria ( NTM ) is an emerging infectious disease of global significance . Epidemiologic studies have shown the Hawaiian Islands have the highest prevalence of NTM lung infections in the United States . However , potential environmental reservoirs and species diversity have not been characterized . In this cross-sectional study , we describe molecular and phylogenetic comparisons of NTM isolated from 172 household plumbing biofilms and soil samples from 62 non-patient households and 15 respiratory specimens . Although non-uniform geographic sampling and availability of patient information were limitations , Mycobacterium chimaera was found to be the dominant species in both environmental and respiratory specimens . In contrast to previous studies from the continental U . S . , no Mycobacterium avium was identified . Mycobacterium intracellulare was found only in respiratory specimens and a soil sample . We conclude that Hawai’i’s household water sources contain a unique composition of Mycobacterium avium complex ( MAC ) , increasing our appreciation of NTM organisms of pulmonary importance in tropical environments .
Nontuberculous mycobacteria ( NTM ) are ubiquitous inhabitants of natural and human-engineered environments . To date , there are over 175 species of NTM with standing in nomenclature [1] . They range in virulence from benign environmental microorganisms to difficult-to-treat human pathogens [2] . Potentially pathogenic NTM have been documented in households , institutions ( i . e . , hospital premise plumbing ) , and soil [3] . In the continental United States ( U . S . ) , household plumbing and environmental aerosols are thought to be important point sources of infection [4–8] . The most common NTM species to cause lung disease in the continental U . S . are those of the Mycobacterium avium complex ( MAC ) –slowly growing mycobacteria ( SGM ) that include Mycobacterium avium subsp . “hominissuis” and Mycobacterium intracellulare [9] . Clinically relevant environmental rapidly growing mycobacteria ( RGM ) include Mycobacterium abscessus subsp . abscessus , massiliense , and bolletii as well as the closely related species , Mycobacterium chelonae [10] . The current hypothesis is that NTM lung infections follow exposure to NTM from the home or other environmental source . [6] . Of interest , the predominant NTM species responsible for lung disease varies by geographic region , suggesting that environmental conditions ( e . g . , pH , oxygen , organic matter , and salinity ) and the presence of other microorganisms influence NTM species numbers and diversity [11] . Despite the almost universal exposure to environmental NTM , pulmonary infections are relatively rare in otherwise healthy , non-bronchiectatic individuals and more common in individuals with abnormal lung architecture such as bronchiectasis and emphysema [12] . Nevertheless , it is important to identify the environmental niches that harbor potentially pathogenic NTM in geographical areas with a high prevalence of disease . In the U . S . , the Hawaiian Islands were found to have the highest period prevalence of NTM lung disease ( 396 cases/100 , 000 persons for a total ten year time period ) in a sampling of 2 . 3 million Medicare Part B beneficiaries enrolled from 1997 to 2007 [13] . In a follow-up study , spatial modeling revealed high-prevalence locations for NTM lung disease in this state [14] . The Hawaiian Islands also showed the highest age-adjusted mortality rates from NTM lung disease in the U . S . , particularly in women over 55 years of age [15] . The high prevalence of NTM lung disease in the Hawaiian Islands provided the impetus to explore potential sources of infection and to determine the predominating NTM species in both environmental and clinical specimens . These islands are recognized for their unique island geology , flora , and fauna which are largely impacted by the tropical climate and isolation of the archipelago in the Pacific Ocean . Unlike most areas in the continental U . S . for which surface water serves as the primary public water source , underground aquifers provide water there . The Hawaiian Islands are also home to the highest number of elderly Asian-Pacific Islanders in the U . S . —a population previously recognized to be more susceptible to NTM infection [14] . To better understand NTM lung disease as a neglected tropical disease of emerging importance in this geographic area , the objective of the current work was to employ state-of-the-art molecular techniques to describe the indigenous NTM species composition in indoor and outdoor environments . A secondary objective was to analyze the genetic relatedness between the Hawaiian Island environmental NTM specimens ( including 15 patient respiratory specimens ) and continental U . S . NTM isolates .
In this cross-sectional study , we use the term “Hawaiian Islands” to designate the eight islands of the State of Hawai’i; the term “Hawai’i” refers to the youngest and largest island among the eight islands . Sample collection was conducted between December 2012 and January 2013 . Samples were collected from 62 non-patient households located on the islands of Oahu , Molokai , Kauai , and Hawai’i . Detailed written instructions for collecting household water biofilms and soil samples were provided to local residents who volunteered to collect samples from their home as part of this study . As NTM are most commonly found in premise plumbing biofilms , samples were obtained by swabbing with sterile cotton-tipped applicators the inner surface of showerheads , kitchen and bath faucets , kitchen sink sprayers , refrigerator water dispensers , laundry room sinks , and shower drains [5 , 6] . Samples from random sites in outdoor gardens or yards were also collected by clearing away surface leaves and other detritus and then scooping soil from the top five centimeters of ground into sterile 50 ml conical screw cap tubes as described [16] . Respiratory isolates of slowly-growing NTM recovered from 15 de-identified Oahu patients suspected of mycobacterial lung disease whose sputum had been submitted for mycobacterial culture were randomly selected from saved isolates at Diagnostic Laboratory Services , Inc . ( Aiea , HI ) . Mycobacterium tuberculosis was not recovered in any of these sputum samples where NTM were isolated . As these were de-identified patient residual isolates , where only age and gender were noted from routinely ordered laboratory testing , Institutional Review Board ( IRB ) consent was waived . However , it was impossible to determine whether these patients met current American Thoracic Society/Infectious Disease Society of America ( ATS/IDSA ) diagnostic criteria for NTM pulmonary disease as private health information were delinked [9] . Genome identification of environmental and patient NTM isolates was conducted through the amplification and sequencing of a 723 bp segment of the RNA polymerase beta subunit ( rpoB ) gene , also known as region 5 [17] . Sequences were trimmed for quality and compared against rpoB type strain sequences deposited in the National Center for Biotechnology Information ( NCBI ) GenBank using the BLAST algorithm . Definitions of species by single genes or spacer region were those of the Clinical Laboratory Standards Institute ( CLSI ) [18] . A sequence similarity cutoff of ≥ 98 . 3% was used to determine the species identification according to previously described cutoffs validated by studies of rapidly-growing mycobacteria [17] . The sequencing of NTM strains derived from patients was approved by the National Jewish Health Human Subject IRB . To determine whether NTM isolates from the Hawaiian Islands have shared sequence similarity with isolates obtained elsewhere , NTM type strains were included in genetic analyses . Type strains are denoted by superscript “T” and include M . porcinum CIP 105392 T , M . abscessus subsp . abscessus ATCC 19977T , M . abscessus subsp . bolletii CIP 108541T , M . chelonae ATCC 35752T , and M . chimaera CIP 107892 T . Additionally , 33 clinical respiratory isolates of M . chimaera ( one per patient ) from seven other states–Maryland , Texas , Louisiana , North Carolina , Oregon , Mississippi , and Arkansas–submitted for molecular identification to the Nocardia/Mycobacteria Research Laboratory , University of Texas Health Science Center , Tyler , Texas were included . Those isolates were identified to species by partial 16S rRNA and region 5 rpoB gene sequencing . This work was approved by the Human Subjects Committee of the University of Texas Health Science Center , Tyler , Texas . Partial rpoB gene sequences from 166 Hawaiian Island NTM isolates and 33 M . chimaera isolates from the continental U . S . were deposited in the GenBank nucleotide database . The GenBank accession numbers for type strain and representative isolate rpoB gene sequences of M . porcinum , M . abscessus , M . chelonae , and M . chimaera from NCBI are also listed in S1 Table Partial rpoB sequences of respiratory and environmental NTM isolates ( n = 166 ) were aligned using MUSCLE [19] and sequence alignments were trimmed to remove missing data from the ends of the final alignment . Phylogenetic trees were generated using the neighbor-joining method based on the number of nucleotide differences and uniform rates among sites while omitting any sites in the alignment with gaps or missing data in MEGA version 6 [20] . For rpoB sequence variant analyses , only sequences greater than 600bp and with no ambiguous base calls were included . Sequences were grouped by species and compared to selected type and non-type strain sequences from NCBI . The PopART population genetics software was used to examine intraspecies sequence variation , generate species-specific rpoB sequence variant networks , and label isolates by isolation source: i . e . , kitchen , bathroom , soil , patient [21] . For the M . porcinum , M . abscessus , and M . chelonae analyses , the environmental Hawaiian Island isolates and both type and non-type strains were included . For the M . chimaera analysis , environmental and clinical Hawaiian Island isolates , type , and non-type strains , as well as clinical isolates from seven states across the continental U . S . were included . Statistical analyses were performed using R version 2 . 13 . 2 [22] . Fisher’s Exact Tests were used to evaluate differences in proportions of NTM species or species groups between household areas ( i . e . , bathroom , kitchen , and soil ) or sample type ( biofilm and soil ) .
From a total of 62 households across four islands ( Fig 1A ) , a total of 172 biofilm and soil samples were collected . The majority of the samples ( n = 134 , 78% ) were collected from Oahu and included 35 showerheads ( 26% ) , 41 kitchen faucets ( 31% ) , 6 bathroom sink faucets ( 4% ) , 2 refrigerator water taps ( 1% ) , 3 other biofilm samples from laundry room faucets ( 2% ) , and 47 soil samples ( 35% ) . The remaining 38 samples ( 22% ) were collected from 13 households on the neighbor islands . Among all 172 biofilm and soil samples collected from the 62 households , NTM were isolated from 44% of samples ( 75/172 ) ( Table 1 ) . NTM were identified in nearly half of the samples on Oahu ( 65/134 , 49% ) and in approximately a quarter of samples from the neighbor islands ( 10/38 , 26% ) . Overall , the NTM culture positivity rate for biofilms was 59% ( 67/113 ) , which was significantly greater than for soil ( 14% , 8/59; p = 6 . 0x10-9 ) . The majority of the environmental samples collected were from 49 households in seven different towns on Oahu , the most populated island ( Fig 1B ) . NTM were recovered by culture from 82% of the Oahu households ( Fig 1A ) . For the neighboring islands , NTM were also recovered in households on Kauai , Molokai , and Hawai’i ( Fig 1A ) . Among the 62 collective households sampled in this study , only 14 had no NTM isolated ( 23% ) . However , the number of households with one , two , and three different NTM species isolated were 26/62 ( 42% ) , 18/62 ( 29% ) , and 4/62 ( 6% ) , respectively ( Fig 1C ) . Overall , the majority of households ( 43/62 , 69% ) had at least one clinically relevant species of MAC , M . abscessus subsp . , or M . chelonae— ( Table 2 ) . To determine the diversity of NTM in non-household sites , 13 environmental samples ( n = 7 biofilm and n = 6 soil ) were collected from eight public areas on Oahu and Kauai ( Table 3 ) . On Oahu , a total of six biofilms from public sites were collected including gymnasium showerheads and water fountain taps . Four soil samples were also collected from public sites on Oahu . Two water biofilm and two soil samples were collected from public sites on Kauai . One Oahu public site soil sample contained M . chimaera ( 1/6 = 17% ) and one biofilm sample contained M . chelonae ( 1/7 = 14% ) , but the majority ( 5/13 = 38% ) yielded other RGM species ( i . e . , M . barrassiae , M . alvei , and M . septicum ) . RpoB sequences from four distinct isolates did not have NCBI database matches above 95% sequence identity , suggesting they represent novel species . Among the 75 environmental samples from the households that were NTM culture-positive , 20 different NTM species were identified ( Fig 2A ) and 17% ( 13/75 ) grew out multiple NTM species . The most common species recovered from households were MAC organisms with M . chimaera being the predominant species ( 42/75 , 56% ) ( Fig 2B , left ) . The next most frequently isolated species were M . chelonae ( 12/75 , 12% ) and M . porcinum ( 11/75 , 11% ) . All isolates of M . abscessus were confirmed as M . abscessus subsp . abscessus ( 10/75 , 10% ) [23 , 24] . Less frequently isolated NTM species ( <10% ) included M . phocaicum , M . gadium , M . alvei , M . gordonae , M . paraffinicum , M . marseillense , and M . colombiense . No isolates of M . avium or M . intracellulare were recovered from household biofilm samples , though M . intracellulare was isolated from a single soil sample . While M . chimaera and M . chelonae were identified in non-household samples , the majority classified as other NTM included potentially novel species ( Fig 2B , right ) . To determine whether NTM were present in particular household locations , the frequencies of NTM recovery between bathroom biofilms , kitchen biofilms , and soil were compared ( Fig 3 ) . M . chimaera was frequently identified from both bathroom ( 22/34 , 65% ) and kitchen ( 15/30 , 50% ) biofilms and was also identified in soil ( 2/7 , 29% ) . M . porcinum was overrepresented in bathroom ( 8/34 , 24% ) compared to kitchen biofilms ( 2/30 , 7%; p = 0 . 09 ) , while M . chelonae was significantly more common in kitchen ( 9/30 , 35% ) compared to bathroom biofilms ( 3/34 , 9%; *p = 0 . 05 ) . M . abscessus was observed in similar proportions between bathroom ( 5/34 , 15% ) and kitchen ( 4/30 , 13% ) biofilms . M . porcinum , M . chelonae , and M . abscessus were not recovered from soil . M . marseillense was recovered only from soil and not identified in any of the household biofilm samples . NTM species that showed low prevalence in our study ( i . e . , one isolate per species identified in the entire sample set and labeled “other RGM” and “other SGM” ) were primarily isolated from soil samples . To examine population diversity among RGM isolates from individual households , rpoB sequences of M . porcinum , M . abscessus , and M . chelonae were analyzed ( Fig 4 ) . Type and non-type strain rpoB sequences were included for comparison . In the M . porcinum dataset ( n = 25 sequences ) , a total of seven sequence variants were identified ( Fig 4A ) . All isolates from the bathroom , kitchen , and outside faucets were in the same sequence variant group as the M . porcinum type strain , CIP 105392T , except for one kitchen isolate that contained a single SNP difference . Among all M . abscessus sequences ( Hawaiian Island and type/reference strains; n = 38 ) , six sequence variants of subsp . abscessus , four variants of subsp . massiliense , and one of subsp . bolletii ( Fig 4B ) were identified . Environmental M . abscessus isolates grouped with other M . abscessus subsp . abscessus and the majority of M . abscessus isolates ( 13/16 = 81% ) shared an identical rpoB sequence with the type strain , ATCC 19977T . Three additional isolates differed by one SNP each from the ATCC 19977T type strain . Finally , M . chelonae isolates ( Fig 4C ) showed the greatest rpoB sequence variation with a total of 14 rpoB sequence variants . Hawaiian Island M . chelonae isolates fell into seven rpoB sequence variant groups , but the majority ( 15/20 = 80% ) fell into two main subgroups: one group ( 6/15 and 40% ) sharing the M . chelonae ATCC 19237 rpoB variant and a second group ( 5/15 and 33% ) related to the M . chelonae ATCC 35752T rpoB variant . As the majority of the Hawaiian Island environmental NTM isolates from this study were M . chimaera , 15 random respiratory SGM isolates from Oahu patients presenting to a pulmonary clinic with suspected mycobacterial lung disease were used as pilot samples to evaluate for the presence of M . chimaera in clinical specimens . As a group , the median age of the 15 patients was 75 years ( 95% CI , 68; 81 years ) and 67% ( 10/15 ) were female ( Table 4 ) . Ten isolates were identified as M . chimaera ( 10/15 , 67% ) four as M . intracellulare ( 4/15 , 27% ) , and one as M . marseillense ( 1/15 , 6% ) . Of the ten patients with M . chimaera , 60% ( 6/10 ) were female . All four patients with M . intracellulare were female ( 100%; 5/5 ) and the patient with M . marseillense was male ( Table 4 ) . M . avium was not identified from any of the Oahu clinical isolates . To measure the genetic similarity among a diverse collection of environmental and clinical M . chimaera , we analyzed rpoB sequence variation between the 57 Hawaiian Island environmental M . chimaera isolates and the 10 Oahu respiratory M . chimaera isolates . However , the rpoB sequence of one clinical M . chimaera isolate was excluded from these analyses due to the presence of ambiguous bases . Also included were NCBI non-type strains ( n = 2 ) , type strains ( n = 2 ) , and other M . chimaera respiratory isolates ( n = 33 ) from seven states in the continental U . S . In total , 103 M . chimaera sequences were analyzed and only two rpoB sequence variants were observed ( Fig 5 ) . The larger variant subgroup comprised over 90% of the isolates including all of the Oahu respiratory and biofilm M . chimaera isolates . This group also contained the majority of continental U . S . clinical isolates and the CIP107892T type strain . The smaller variant subgroup contained continental U . S . clinical isolates , non-type strains from NCBI , and Hawaiian Island soil isolates .
To our knowledge , this is the first assessment of environmental NTM prevalence and species composition in the Hawaiian Islands . This archipelago is approximately halfway between the continental U . S . and Asia; thus , one might speculate that the spectrum of NTM observed mirrors the results from other environmental studies from the continental U . S . or Asia . Due to the prevalence of M . avium subsp . “hominissuis” reported in studies from the continental U . S . and Japan [25–27] , we suspected this species would be prevalent in Hawaiian Island household biofilms and patient samples; however , it was seemingly absent , at least in the samples examined in this study . In general , NTM are rare in groundwater [29] whereas M . avium subsp . “hominissuis” has been isolated from surface water sources [28] . Aquifers provide most of the drinking water in the Hawaiian Islands [30] which may be one reason for the lack of M . avium detection in our samples . However , given the widespread prevalence of M . chimaera and the RGM in Hawaiian Island household biofilms , local aquifers may be a potential reservoir for M . chimaera and other NTM . Future studies are needed to examine this hypothesis . To date , species diversity assessments of environmental NTM in other tropical Pacific Islands remains scant . A recent study described the identification of the M . fortuitum complex in Polynesian residents with suspected tuberculosis [31] and other reports from the area highlight NTM-associated skin disease [32 , 33] . On Australia , M . intracellulare was reported as the species responsible for most lung disease cases and yet only M . avium subsp . “hominissuis” , M . kansasii , and M . abscessus isolates had a species that match between patients and their household water system [34 , 35] . An unexpected finding of this study was the frequent identification of M . chimaera from both the environmental samples collected from bathroom , kitchen , and soil samples ( Fig 3 ) and patient isolates with suspected mycobacterial lung disease . Although the number of patient isolates was small and their disease status were not known , the correspondence between the high proportion of both environmental and clinical M . chimaera isolates is intriguing and offers direction for future investigations . M . chimaera was first described in 2004 [36] and was recently reported to cause health-care associated infections after open-heart surgery with the use of heater-cooler units [37 , 38] . As this is a relatively newly described species , there are no simple methods to differentiate M . chimaera from M . intracellulare . Furthermore , low frequency of presence in lung samples of patients from Germany , Italy , Zambia , and China [39–41] is most likely due to its misidentification as M . intracellulare . A greater adoption of more refined molecular methods to distinguish M . chimaera from M . intracellulare has facilitated the more precise speciation of M . chimaera ( 33 ) . In a previous U . S . study , water biofilm isolates originally reported as M . intracellulare , proved to be M . chimaera or other MAC-X [4] . Provisionally , it appears that the main environmental source of M . chimaera in the Hawaiian Islands are water biofilms and less from the soil ( Fig 3 ) , whereas M . intracellulare was absent in water biofilms and only recovered from soil , consistent with the finding of others 4 ( Fig 3 , other SGM ) . Soil should also be regarded as a potential reservoir for M . marseillense . Among our environmental samples , M . porcinum , M . chelonae , and M . abscessus were the most frequently identified RGM species . The M . fortuitum complex including M . porcinum were found to comprise the majority of clinical isolates examined in French Polynesia ( 42/87 , 48% ) using partial rpoB gene sequencing [31] . Of these , M . porcinum was identified in three patients who fulfilled ATS criteria for NTM lung disease . To our knowledge , M . porcinum infections have not yet been reported in the Hawaiian Islands , but the organism has been isolated from water supplies in other U . S . areas ( e . g . , Texas ) [42 , 43] . M . abscessus was recently associated with an outbreak in cystic fibrosis patients at a hospital in Hawai’i [44] . M . chelonae infection was reported in a case study of an individual from Hawai’i after laser in situ keratomileusis ( LASIK ) surgery [45] . It is important to mention that among the environmental samples in this study , these particular RGM were more commonly identified in bathroom and kitchen biofilm samples and absent from soil ( Fig 3 ) , suggesting a preferential environmental niche for these particular RGM species . Phylogenetic analyses were performed to evaluate whether the genetic diversity among environmental NTM species identified from the Hawaiian Island samples differed from those collected from the continental U . S . A relatively high genetic diversity among M . chelonae was observed with four major rpoB subgroups present , while most isolates of M . porcinum and M . abscessus belonged to one major genetic group per species ( Fig 4 ) . The presence of only two genetic subtypes of M . chimaera among a geographically diverse population of environmental and suspect respiratory Oahu specimens , as well as clinical isolates from seven other states in the continental U . S . suggests a low level of genetic divergence occurring in this species ( Fig 5 ) . Whole genome sequence comparisons will be necessary to improve our understanding of the genetic relationships between environmental and respiratory populations of M . chimaera . This study has some limitations including the following in methodology: ( i ) we were unable to consistently collect a large number of samples from the same indoor sites for each participating household , ( ii ) a sampling bias exists as the majority of samples were collected from Oahu ( home to the majority of the state’s population ) with only a few household samples collected from the less populated Molokai , Kauai , and Hawai’i and none from Kaho’olawe , Maui , Lanai , or Ni’ihau , and ( iii ) instead of a single person conducting all environmental sampling , household areas were sampled by local citizens , which added a layer of non-equivalency to the process of sample collection . To reduce non-uniformity in the collection process , we applied a well-accepted citizen science approach to minimize variability introduced by handling of samples by different people [46] . Although we cannot be certain our findings represent the true geographic diversity of NTM in the Hawaiian Islands , this work describes the largest study of environmental NTM in this geographic area with a documented high NTM disease burden . We would advocate for a larger , randomized systematic study of the distribution of environmental NTM in future work . To the best of our knowledge , all environmental samples were from households whose occupants are not known to have NTM lung disease; thus , it will be imperative to sample NTM patient households in a larger future study especially as a more thorough comparison of prevalence and numbers of NTM species in patients and their local environment can be assessed . We were also unable to confirm that the clinical isolates used in this study were etiological agents of respiratory disease or due to benign colonization from environmental exposures . Additionally , this pilot clinical isolate panel did not contain any RGM . Nevertheless , the observation that M . chimaera was the most common species in both environmental and clinical isolates examined suggests the possibility of environmental exposures and clinical NTM lung disease . To determine whether NTM in the household environment contributes to clinical disease , we hope to initiate a large-scale genomic study of matched household and clinical NTM isolates from NTM-infected Hawai’i patients who fulfill ATS/IDSA criteria for lung disease . Undoubtedly , the data collectively presented in this study will be valuable in guiding the design of a more comprehensive study . In summary , this study describes environmental sampling , microbiological selection , and molecular identification to determine the NTM species diversity in the Hawaiian Island environment . The observation that M . chimaera was the most common NTM species identified in both our Hawai’i environmental samples as well as in a small sampling of respiratory specimens from patients with suspected mycobacterial lung disease suggests that M . chimaera may be an important environmentally acquired respiratory pathogen . Furthermore , M . chimaera may be unique in prevalence in tropical climates such as Hawai’i . Additional studies with systematic collection of matched environmental and respiratory specimens , high-resolution genotyping methods , and correlation with demographic and epidemiological data ( i . e . age , gender together with ethnicity and host risk and genetic factors ) will be necessary to further characterize this observation and the important clinical implications .
|
In the U . S . , the Hawaiian Islands have the highest number of nontuberculous mycobacterial ( NTM ) lung disease cases per capita . The tropical climate , geographical isolation of the islands , and aquifer water sources may have influence such prevalence . Previous studies suggest that NTM thrive in water biofilms and soil . To broaden our understanding of potential environmental reservoirs and species composition of NTM in the Hawaiian Islands , we sampled environmental sites and examined patient isolates . Our recovery and identification of Mycobacterium chimaera and several other clinically relevant NTM species and the absence of Mycobacterium avium in both the indigenous environment and clinical specimens underscore the need for further studies to define the environmental factors that drive NTM lung disease and species composition in high prevalence locations such as the Hawaiian Islands .
|
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2016
|
Environmental Nontuberculous Mycobacteria in the Hawaiian Islands
|
Since platelet intracellular calcium mobilization [Ca ( t ) ]i controls granule release , cyclooxygenase-1 and integrin activation , and phosphatidylserine exposure , blood clotting simulations require prediction of platelet [Ca ( t ) ]i in response to combinatorial agonists . Pairwise Agonist Scanning ( PAS ) deployed all single and pairwise combinations of six agonists ( ADP , convulxin , thrombin , U46619 , iloprost and GSNO used at 0 . 1 , 1 , and 10xEC50; 154 conditions including a null condition ) to stimulate platelet P2Y1/P2Y12 GPVI , PAR1/PAR4 , TP , IP receptors , and guanylate cyclase , respectively , in Factor Xa-inhibited ( 250 nM apixaban ) , diluted platelet rich plasma that had been loaded with the calcium dye Fluo-4 NW . PAS of 10 healthy donors provided [Ca ( t ) ]i data for training 10 neural networks ( NN , 2-layer/12-nodes ) per donor . Trinary stimulations were then conducted at all 0 . 1x and 1xEC50 doses ( 160 conditions ) as was a sampling of 45 higher ordered combinations ( four to six agonists ) . The NN-ensemble average was a calcium calculator that accurately predicted [Ca ( t ) ]i beyond the single and binary training set for trinary stimulations ( R = 0 . 924 ) . The 160 trinary synergy scores , a normalized metric of signaling crosstalk , were also well predicted ( R = 0 . 850 ) as were the calcium dynamics ( R = 0 . 871 ) and high-dimensional synergy scores ( R = 0 . 695 ) for the 45 higher ordered conditions . The calculator even predicted sequential addition experiments ( n = 54 conditions , R = 0 . 921 ) . NN-ensemble is a fast calcium calculator , ideal for multiscale clotting simulations that include spatiotemporal concentrations of ADP , collagen , thrombin , thromboxane , prostacyclin , and nitric oxide .
Platelet activation during heart attack and stroke occurs through combined signaling pathways involving various receptors responding to collagen , thrombin , ADP , and thromboxane . Endothelial production of prostacyclin is highly protective against thrombotic platelet activation as revealed by the known cardiovascular risks of COX-2 inhibitors . Similarly , endothelial production of NO has many cardiovascular effects via vasodilation and platelet inhibition . The clinical importance of these pathways is seen in the number of drugs in clinical trials or approved that target GPVI signaling , thromboxane , ADP , or thrombin . More than 50 million U . S . adults take aspirin to inhibit platelet COX-1 production of thromboxane in order to reduce long-term risk of cardiovascular disease [1] . Clopidogrel antagonizes ADP activation of platelet P2Y12 receptors and is widely prescribed . Numerous anticoagulants are approved to target the generation or activity of thrombin . Platelet activation occurs through multiple signaling pathways in which agonists bind specific receptors on the platelet to trigger signaling in a dose-dependent manner . During a clotting episode , platelets respond to exposed surface collagen , released ADP , synthesized thromboxane , and the serine protease thrombin , all while being simultaneously modulated by endothelial derived nitric oxide and prostacyclin . These receptor-mediated signaling pathways are not independent and significant crosstalk can occur ( Fig . 1A ) . The Pairwise Agonist Scanning ( PAS ) method was first developed by Chatterjee et al . ( 2010 ) using EDTA-treated platelet rich plasma ( PRP ) to quantify and predict the interactions between multiple pathways ( S1 Fig . ) [2] . The PAS method measures platelet calcium responses to all individual and pairwise combinations of agonists at low , medium and high concentrations ( 154 conditions total for six agonists at 0 . 1 , 1 , and 10xEC50 , including a null condition ) . Because EDTA chelates extracellular calcium and prevents store operated calcium entry ( SOCE ) , the measured calcium data obtained using EDTA-treated PRP is enriched in the regulatory events surrounding IP3-mediated calcium release from the dense tubular system ( DTS ) . With PAS data , Chatterjee et al . were able to train an artificial neural network ( NN ) to predict platelet calcium response to combinations of agonists beyond the training data , such as trinary combinations , sequential additions of agonists , and combinatorial responses of four to six agonists [2] . The NN model builds an estimate of higher order interactions ( response to >2 agonists ) by combination of the measured binary interactions . A metric called the pairwise synergy score was defined to quantify the extent of cross-talk between pairs of agonists ( Fig . 1B ) [2] . The pairwise synergy score ( Sij ) was defined to be the difference between the integrated area ( area under the curve ) for the combined response to agonists i and j relative to the integrated area for both the individual agonist responses added together , normalized to the maximum absolute Sij [2] . In other words , the synergy score is a measure of deviation of the platelet response from the simple additive response of each agonist . A positive Sij value indicates synergistic behavior between agonists i and j whereas a negative Sij value indicates antagonistic or saturating behavior and Sij = 0 represents a purely additive response . Since many platelet pathways are triggered distally of IP3-released calcium and SOCE , the intracellular calcium concentrations can be used as a global metric of platelet activation . Calcium is the central “node” in platelet signaling , in that elevated calcium levels are central to downstream clotting events such as integrin activation , granule release , shape change , and phosphatidylserine exposure by platelets [3–5] . The ability to predict dynamic calcium traces for combinations of agonists enables the targeting of specific platelet pathways to increase or decrease platelet activity so as to achieve desired clinical outcome . NN trained on PAS data provides accurate calcium responses to different dose combinations of important agonists and is essential for simulating platelet function under flow . As an in silico predictor of calcium regulation , the NNs trained by PAS can be embedded in multi-scale simulations of platelet deposition under flow conditions . In the work of Flamm et al . ( 2011 ) [6] , NN were trained via PAS using calcium-containing PRP and then used to predict platelet deposition rates on collagen in the absence of thrombin by accounting for platelet signaling in response to laboratory analogs of collagen , ADP , thromboxane , and prostacyclin . A universal platelet calcium calculator provides a reference for platelet function of a healthy human . Platelet gain of function or loss of function in patients can therefore be measured in a high dimensional approach using the PAS method . Furthermore , since the specific pathways in the platelet that contribute to platelet gain or loss of function can be identified by PAS as well , PAS can be used to predict the sensitivity and resistance of drugs that target those specific agonist pathways , even loss of function mutations have been discovered with PAS [6] . Additionally , the calcium calculator can be embedded into a multiscale simulation of clotting under defined hemodynamic conditions . In the current study , the PAS method was expanded for the use of exogenously added thrombin in the presence of normal calcium and included the potent platelet inhibitors iloprost and nitric oxide ( NO ) . Thrombin is an extremely potent platelet activator via cleavage of platelet PAR1 and PAR4 receptors . Additionally , to estimate average healthy human platelet responses , calcium data was obtained from 10 healthy donors as an ensemble-averaged predictor of platelet calcium . Distinct from the prior PAS in Chatterjee et . al ( 2010 ) where PAR1 and PAR4 agonist peptides were used with platelet rich plasma ( PRP ) treated with EDTA , the current study required a means to study exogeneously added thrombin without endogenous production of thrombin in PRP with normal calcium . In the current experimental design , blood was drawn into 250 nM apixaban ( Ki = 0 . 08 nM ) [7] , a direct Factor Xa inhibitor , which does not alter extracellular calcium levels but prevents endogenous thrombin generation . This assay therefore allows the contribution of SOCE and includes the signaling distal of thrombin proteolytic activity on PAR1 and PAR4 . Furthermore , the NN-ensemble method was employed to increase accuracy and robustness in NN predictions .
Whole blood was drawn by venipuncture from healthy donors according to the University of Pennsylvania Institutional Review Board guidelines ( protocol number: 805305 ) , into a syringe containing apixaban ( SelleckChem ) with a final concentration of 250 nM . Donors self-reported to be free of any medications or alcohol use for three days prior to the blood draw . Female donors self-reported to not using oral contraceptives . Platelet rich plasma ( PRP ) was then obtained by subjecting the whole blood sample to centrifugation at 120g for 12 minutes . Then , 2 ml of PRP was incubated with a vial ( single microplate size ) of Fluo-4 NW dye mixture ( Invitrogen ) reconstituted with 7 . 8 ml of HEPES buffer and 200 μL of 77 mg/ml reconstituted probenecid ( Invitrogen ) for 30 minutes [2] . All single and pairwise combinations of six agonists ( ADP , convulxin , thrombin , U46619 , iloprost and GSNO ) at low , medium and high concentrations ( 0 . 1 , 1 , and 10X EC50 ) , as well as a buffer condition ( 154 conditions total x 2 replicates ) were dispensed into a 384-well plate ( called the ‘agonist plate’ ) using a high throughput liquid handler ( PerkinElmer Janus ) . The PAS agonists were: ADP ( P2Y1/P2Y12 activator , EC50 = 1 μM ) , convulxin ( GPVI activator , EC50 = 2 nM ) , thrombin ( PAR1/PAR4 activator , EC50 = 20 nM ) , U46619 ( TP activator , EC50 = 1 μM ) , iloprost ( IP activator , EC50 = 0 . 5 μM ) and GSNO ( NO donor , EC50 = 7 μM ) ( S2 Fig . ) . ADP and GSNO were obtained from Sigma-Aldrich , convulxin from Pentapharm , thrombin from Haematologic Technologies Inc . , U46619 and iloprost from Tocris Bioscience . After incubation with dye , the PRP was dispensed into a 384-well plate ( called the ‘read plate’ ) . Both the agonist and read plate were loaded into a Molecular Devices FlexStation 3 , a fluorescence reader with auto-pipetting capabilities . Agonists were dispensed to a column of wells containing the PRP , where well fluorescence F ( t ) was read and normalized by the pre-dispense baseline . Specifically , 20 μL of agonist was added to 30 μL of PRP in each well , giving a final volume of 50 μL . In each well , the final concentration of PRP after agonist addition was 12% PRP by vol . , and the volume of calcium dye was 15 μL ( 30% dye by vol . ) . Readings were taken in intervals of 2 . 5 seconds . The fluorescence was read for 20 seconds before dispense , and readings were taken for 210 seconds after each dispense ( EX/EM , 485 nm/525 nm ) . The entire plate was read , column-wise , in under 90 minutes . PAS was conducted on PRP from ten donors ( 50% male ) , each in replicate on two different days ( 20 PAS experiments total ) . In separate tests using indomethacin ( Sigma-Aldrich ) to block COX1 and apyrase ( Sigma-Aldrich ) to degrade released ADP , there was no evidence for autocrine signaling in the dilute PRP conditions of the experiment ( S3 Fig . ) , as previously found with EDTA-treated PRP [2] . In experiments with trinary mixtures of agonists , all single and trinary combinations of the same six agonists at two different concentrations ( 0 . 1x and 1x EC50 ) , as well as a null buffer condition ( 173 conditions total x 2 replicates ) were prepared in the agonist plate . This experiment was done once for each of the 10 PAS donors . There are 3 , 402 possible conditions involving four , five , or six agonists ( higher order combinations of agonists ) at low , medium and high concentrations . The higher order combination space was sampled in equal proportions ( approximately 1 . 3% each of 4 to 6 agonist conditions ) . Thus , a total of n = 45 higher order combinations were sampled ( 16 four-agonist , 19 five-agonist , and 10 six-agonist conditions ) . These experiments were done seven times spanning five donors ( two repeat experiments for two of the donors ) , neither of which were present in the PAS training set . In the sequential addition experiments , all conditions involving sequential addition of three agonists ( ADP , convulxin and U46619 ) at three different concentrations and a null buffer condition ( 55 conditions total x 2 replicates ) were prepared in the agonist plate . The sequential addition experiment was done once on a single donor . The pairwise synergy score ( Sij ) was defined to be the difference between the integrated calcium for the combined response to ij-agonists and the sum of the integrated calcium for both the individual agonist responses used independently , normalized by scaling to the maximum absolute synergy score observed in the experiment ( Fig . 1B ) [2] . Synergy scores range from -1 to 1 ( positive , synergistic; 0 , additive; negative , antagonistic ) . Trinary synergy scores ( Sijk ) were also similarly calculated as the difference of the combined response to ijk-agonists from the response for all three individual agonist responses . In general , synergy scores ( Sn ) are defined by Eq . 1 , where the variable Ai represents the integrated calcium for the response to agonist i used independently , and A1…n is the area under the curve for the response to agonists 1 through n used simultaneously ( n = 6 maximum for the six agonists deployed ) . The replicate wells in each PAS experiment duplicate were averaged before training . A 2-layer , 12-node dynamic neural network ( NN ) , as employed in Chatterjee et al . ( 2010 ) , was trained on each averaged PAS experiment 10 times ( n = 100 ) , each time with a different set of initial weights and randomized division of 154 single and pairwise time course data into training and validation sets ( 90%/10% ) ( Fig . 1B ) . All neural network model construction and training were done using the MATLAB Neural Network Toolbox ( MathWorks ) . Training on a NN was done for a maximum of 1000 epochs , where each epoch was one pass through the training set followed by testing of the validation set . The training set vectors were used to optimize the NN weights and the validation set was used to test the weights during training so as to ensure the NN did not over fit to the training set . Early stopping was also employed , in which training would stop if the validation set error did not improve after five epochs . At the end of the training of each NN , the optimized NN weights would typically match the predicted time series to the experimental time series with a mean squared error anywhere on the order of 10–4 to 10–2 . Each of the 100 trained NNs was then given the trinary experiment agonist concentration inputs and the resulting calcium time trace predictions were averaged to give an overall prediction for the average trinary experiment platelet response . The resulting synergy scores were also calculated and compared to the actual synergy scores for the trinary experiments . Similarly , each of the 100 trained NNs were given the higher order combination and sequential addition experimental concentration inputs , and the resulting calcium time trace predictions were compared to the measured values . A summary of the experimental and computational workflow is shown in Fig . 2 . In the testing of NN predictive abilities , output from all 100 NNs were averaged to give a final prediction . This approach is an ensemble method [8] . Ensemble methods are commonly used to overcome the inherent instability problem with NNs [8] . NNs ( along with decision trees , multivariate adaptive regression splines etc . ) are inherently unstable in that small differences in training data or conditions ( e . g . initial weights ) may cause variations in final predictions . Generating an ensemble of NNs and combining their outputs to produce a single prediction has been proven by Cunningham et al ( 2000 ) to be a robust solution to this problem [8] . In the testing of the NN on higher order combination experiments , two additional donors ( one male , one female ) were used in the training set ( 12 donors for a total of 120 NNs ) . None of the 12 donors used in the training set were present in the testing donor set ( five donors for a total of seven experiments ) . The success of the higher order combination predictions suggests that the ensemble NN was sufficiently robust to predict outcomes of donor aggregates without their donor-specific PAS data during training , and that the NN ensemble has learned calcium mobilization patterns of an average healthy human . Furthermore , the sequential addition experiment predictions points to the ability of the ensemble NN to predict the outcome of an individual donor , not just aggregate outcomes of donors , and without having PAS data of that specific testing donor during training .
The 10x10 NN-ensemble was trained on the pairwise agonist scanning ( PAS ) experiments of 10 donors and predicted the measured average pairwise calcium traces of those donors ( Fig . 3A-C ) with a correlation coefficient of R = 0 . 975 ( Fig . 3C ) . Fig . 3A indicates all 154 single and pairwise conditions used in the PAS with the corresponding calcium time traces ( Fig . 3B ) and NN-predicted calcium time traces ( Fig . 3C ) . From Fig . 3A-C , the calcium response to convulxin rose slowly , but became highly elevated and was sustained . In contrast , calcium responses to ADP or U46619 displayed rapid onset but were weaker and more transient than calcium responses to convulxin and thrombin . Interestingly , the thrombin response displayed rapid calcium mobilization , prominent elevation , and was more sustained than calcium responses observed in earlier studies with PAR1 and PAR4 agonist peptides [2] . A total of 135 binary synergy scores ( all pairs of six agonists at three concentrations ) for the PAS experiment and the NN-prediction are shown in vector form ( Fig . 3D ) , representing the average human platelet phenotype . Both experimental and NN-predicted synergy values were plotted in heat map form in Fig . 3D . Similar to the time series prediction , the NN ensemble was able to predict the measured average pairwise synergy scores of those ten donors ( R = 0 . 937 ) ( Fig . 3E ) . Many synergy values clustered around the center of the range ( Sij ∼ 0 , additive ) with slightly more negative values extending to full antagonism ( Sij ∼ -1 ) . The maximum synergy score did not exceed 0 . 5 . The most negative synergies were found with pairwise mixtures that included iloprost . The experimental and NN-predicted synergy scores were also arranged by dose and agonist pairs ( Fig . 3F ) . From Fig . 3F , iloprost was inhibiting for all agonists used in combination with it . GSNO was also inhibiting for most conditions , however , low dose GSNO slightly potentiated thrombin-induced calcium response . The combination of medium or high dose thrombin with medium dose convulxin was particularly synergistic , consistent with several findings [9 , 10] . Also , thrombin signaling was synergistic with thromboxane signaling which is a novel observation since both agonists signal through Gq . To test the predictive capability of the NN-ensemble beyond the training set , the NN-ensemble was used to predict the calcium output of trinary agonist stimulations . To avoid saturation effects of agonist induced signaling , the trinary combination experiments comprised all trinary combinations of six agonists at only the low and medium doses ( Fig . 4A ) . The NN ensemble trained only on the PAS experiments of ten donors in duplicate was able to predict the measured average trinary calcium traces of those donors with high accuracy ( R = 0 . 924 ) ( Fig . 4B-C ) . This demonstrated the de novo predictive capability of the neural network model . There were 160 trinary synergy scores for the trinary experiment consisting of all trinary combinations of six agonists at two different concentrations ( 0 . 1 and 1x EC50 ) . The NN trained only on the PAS experiments of ten donors in duplicate was able to predict the measured average trinary synergy scores of those ten donors with a correlation coefficient of R = 0 . 850 . ( Fig . 4D ) . The synergy scores plotted for the trinary experiments in Fig . 4D , though also clustered around zero , extended more toward 1 ( synergistic ) compared to the binary synergy scores ( Fig . 3E ) , which extended toward -1 ( antagonistic ) . This is expected , in part , because the trinary experiments were sampled across only the low and medium dose ranges , thus , there were fewer instances of saturation due to platelet activation by high doses of multiple agonists . Furthermore , only low and medium doses of the inhibitors iloprost and GSNO were used , so their strongest inhibitory/antagonistic effects at 10x EC50 were not present in the trinary data ( but were present in the binary experiments ) . To illustrate the predictive power of the NN-ensemble , the full time series plots of a random sampling of the 160 trinary conditions are shown in Fig . 5 . Full time series plots of all 160 trinary conditions rescaled to 0 . 5 are also shown in S6 Fig . . For the trinary stimulations , the predicted calcium time traces fit the experimental data over the full time domain with remarkable accuracy . The trinary agonist experiments embed information about platelet signaling during in vivo hemostasis , thrombosis , or bleeding . For example , during the early stages of vessel wall injury , platelets are activated by collagen of the damaged vessel wall [11] which can also generate thrombin via the extrinsic pathway ( distal of tissue factor ) . Concomitantly , endothelium-derived nitric oxide and prostacyclin modulate platelet functions . Therefore at this early stage the platelet is mainly exposed to these three agonists: exposed collagen , prevailing nitric oxide and prostacyclin , while thrombin is dynamically generated . Soluble agonists such as ADP and thromboxane become critically important during platelet mass build-up ( sometimes called secondary aggregation ) when activated platelets release ADP from dense granules and generate thromboxane via COX-1 . Recent in vivo and in vitro studies reveal that the platelets in the “core” are exposed to high levels of thrombin , while the outer shell of platelets see little thrombin but are especially sensitive to the presence of thromboxane [12–16] . The higher order test of the NN-ensemble comprised a 45-condition sampling of the full experimental space in equal proportions ( n = 45 total combinations: 16 four-agonist , 19 five-agonist , and 10 six-agonist conditions ) ( Fig . 6A ) . In this subsequent higher order experiment , the donor pool for the generation of the PAS training dataset was expanded to 12 individuals , 10 of which were also previously used in the prediction of trinary combination outcomes . The higher order experiments were an aggregate of seven experiments spanning five donors , none of whom were utilized in the PAS training dataset . The NN-ensemble trained only on the PAS experiments of 12 donors was able to predict the calcium traces of the higher order combination experiments with sufficiently high accuracy ( R = 0 . 871 ) ( Fig . 6A-C ) . This higher order experiment represented a most challenging test of the de novo predictive capability of the neural network model , more so than the trinary combination experiments of Fig . 4 . With up to six stimuli present , this experiment triggers an extraordinary range of signaling complexity in the platelet . The NN-ensemble trained only on the PAS experiments was able to predict the measured synergy scores with a correlation coefficient of R = 0 . 6953 ( Fig . 6D ) . Compared to the synergy scores of the binary ( Fig . 3E ) and trinary experiments ( Fig . 4D ) , the synergy scores of the higher order combination experiments tended to be more antagonistic due to saturation effects . Many of the measured and predicted higher ordered synergy scores were additive ( S∼0 ) with none being highly synergistic ( all S < 0 . 25 ) which occurs for saturated signaling by only a few of the agonists in the mixture . During saturation , a maximal amount of calcium is released by IP3 or conveyed by SOCE . Therefore , the actual calcium mobilization caused by high doses of ≥4 activating agonists was not expected to exceed the sum of calcium release due the individual agonists . The time series plots of all 45 conditions in the higher order combination experiment ( Fig . 6C and Fig . 7 ) indicated that the NN-predicted time calcium time traces tended to consistently under predict calcium traces involving high dose convulxin . This may be because the NN had not been trained on any data that involves calcium levels as high as that triggered by combinations of 4–6 agonists including a high dose of the potent activator convulxin . Another theory is that the NN may have over predicted the saturation effects in calcium responses that may result from combinations of multiple agonists in addition to high dose convulxin . Furthermore , the NN predictions underestimated the effect of iloprost and GSNO , in that combinations that involved those agonists tended to have calcium level predictions that were higher than the experimental values . However , the overall shape of almost all the predictions fits the experimental time traces rather well , indicating that the NN ensemble was able to capture the kinetics of these higher order combination experiments . There was no apparent trend in calcium trace prediction accuracy with increasing number of agonist ( four-agonist conditions had R = 0 . 82332 , five-agonist conditions had R = 0 . 90699 , six-agonist conditions had R = 0 . 79398 ) . However , this sampling of 45 conditions was only 1 . 3% of the complete experimental space of 3 , 402 possible conditions . A sequential addition experiment was done with all permutations for a two-dispense experiment of three agonists ( ADP , convulxin and U46619 ) in the full dose range , i . e . 54 conditions total ( Fig . 8A ) . Calcium time traces were plotted in heat map form in Fig . 8B; the arrows indicate the time at which the corresponding agonists in Fig . 8A were dispensed . The NN trained on the PAS experiments , in which agonist pairs were added simultaneously , was also able to predict the calcium output of the sequential addition experiment with a correlation coefficient of R = 0 . 921 ( Fig . 8C ) . The plots of both the experimental and predicted time series are shown in Fig . 9 . Due to limitations of working with isolated platelets in vitro ( requiring use within 3 hr from venipuncture ) , the PAS calcium readings for sequential addition tests required a 260 sec window per column for the 24 columns of the 384-well plate . The shorter 260 sec training interval still provided accurate predictions for the whole 780 sec window because many of the PAS calcium traces following agonist-mediated increases had decayed to resting levels within the 260 sec training interval . This was true of all agonist combinations except for those involving convulxin , which generated sustained calcium levels in the 260 sec window . Within this training interval , the PAS had captured the full kinetic effects of all the agonists except for convulxin . Extending the training window to 300 sec , where the second dispense in the sequential addition experiment had happened , would not confer additional information to the NN because convulxin responses would not have started decaying at the end of 300 seconds . Therefore , the NN trained on the PAS equipped with this kinetic information , in addition to crosstalk information between these six agonists from the pairwise conditions , was able to predict calcium responses to two agonists added sequentially with sufficiently high accuracy ( R = 0 . 921 ) , with a tendency for mild over prediction of calcium compared to the experimental time series . Nonetheless , the shape of the NN and measured calcium traces were quite similar ( Fig . 9 ) . As expected , in conditions where convulxin was added first in the sequential addition experiments , the NN predicted a sustained calcium level instead of a slight decay in calcium response toward the end of the 780 sec window , since this decay had not been captured in the 260 sec window of the PAS experiment used in NN training . The synergy scores ( Sij ) reduced more than 25 , 000 calcium time points from a PAS experiment to a 135-parameter vector . Each synergy score is the most succinct first order measure of crosstalk between specific pairs of agonists at specific doses . The discovery of new synergistic and antagonistic effects between specific combinations of agonists via synergy scores can motivate efforts to study the underlying biochemical mechanisms ( e . g . the thrombin-thromboxane positive synergy is unexpected since both signal through Gq ) . Furthermore , the synergy score may underlie a drug risk ( as seen with COX-2 inhibition therapy [2] ) , a patient-specific drug sensitivity or resistance . Future bottom-up models that predict the 135-parameter synergy vector may require interactions and pathways not explicitly represented in the currently prevailing platelet signaling model of Fig . 1A . Synergy scores of the 10-donor , 20-experiment averaged PAS experiments were further analyzed to gain insight into platelet signaling . The same was done for the averaged trinary combination experiments and the higher order combination experiments . The mean of the standard deviation between donors for a given synergy score in the experiment is 0 . 0932 for binary dataset , 0 . 1722 for the trinary dataset , and 0 . 2029 for the higher order dataset . This reflects the variation in a given synergy score between donors . As the number of agonists involved increased , signaling complexity increased , resulting in larger donor variations in a given synergy score . The mean of the pairwise Sij was very close to zero ( Sij = - 0 . 0626 ) , and the maximum synergy score was 0 . 3461 . The mean of the trinary synergy scores was slightly less negative ( Sijk = -0 . 0401 ) , and the maximum synergy score was 1 , meaning that the maximum absolute synergy score is a synergistic one . Because trinary conditions were only sampled at the low and medium dose , calcium saturation from multiple high doses of agonists was avoided , therefore the synergy scores tended to be more synergistic . The mean of the higher order combination synergy scores was quite antagonistic ( S = -0 . 3484 ) , and the maximum synergy score was also very small ( S = 0 . 1085 ) . As more agonists are involved , platelet signaling tends to reach saturation , which shifts the mean synergy score towards antagonism , and lowers the maximum synergy score . For the PAS and higher order combination experiments , the synergy score with the maximum magnitude was antagonistic ( i . e . minimum synergy score is -1 for binary and higher order experiment averages ) because iloprost and GSNO are both strong inhibitors in this assay . For the average PAS experiment , the most antagonistic synergy score involved a high dose of iloprost; for the higher order experiment , it involved low dose iloprost and high dose GSNO . The most antagonistic synergy score for the trinary experiment is -0 . 5395 and it involves medium dose iloprost . Interestingly , the synergy metric indicates that IP receptor activation by iloprost , a prostacyclin mimetic , was a more potent inhibitor of calcium mobilization compared to that observed with the activation of guanylate cyclase via GSNO release of NO ( Fig . 3F ) . This was confirmed by the calculation of GSNO and iloprost percent inhibitions of various agonists used in this assay ( S1 Table ) . It is also interesting to note that low and medium dose of GSNO slightly potentiated the medium dose thrombin-induced calcium release ( 15 . 73% and 13 . 32% increase respectively ) , consistent with previous findings that low levels of the NO donor sodium nitroprusside slightly potentiated thrombin-induced calcium release via store-operated calcium entry ( SOCE ) , whereas higher levels inhibited thrombin-induced increases in calcium [17] . Similarly , in our assay , high dose GSNO inhibited the calcium release of medium dose thrombin ( 39 . 11% inhibition ) . The maximum synergy score for the PAS experiment involved a high dose of convulxin and medium dose of thrombin ( Sij = 0 . 3461 ) ; the maximum of the trinary experiment involved medium dose convulxin , thrombin and GSNO ( Sijk = 1 ) ; the maximum of the higher order combination experiment involved high dose ADP , medium dose thrombin , high dose U46619 and low dose GSNO ( S = 0 . 1085 ) . In fact , four of the five strongest positive pairwise synergies involved convulxin and thrombin ( the fifth was medium dose thrombin and U46619 ) . Of the five strongest positive synergies in the trinary experiments , medium dose convulxin and thrombin were involved in all of them , low and medium dose GSNO was involved in two of them . Of the five strongest positive synergies in the higher order experiments , thrombin and U46619 were present in four of them; low and medium dose GSNO is involved in four of them , convulxin and thrombin were present in two of them . It is apparent that convulxin and thrombin used together gave the strongest synergistic effects , thrombin and U46619 used together also accounted for some of the strongest synergistic effects . For the trinary and higher order experiments , the presence of low and medium dose GSNO was also implicated in the highest synergy scores , and only when thrombin was present , which supports the previous finding that low levels of NO potentiates thrombin-induced calcium release via SOCE [17] . The synergistic effects between convulxin and thrombin were similarly found by Keuren et al . [9] and it was thought that thrombin-mediated influx of platelet extracellular calcium ( through PAR1 but not PAR4 ) enhances the collagen induced procoagulant response . This may explain why the synergistic effects between convulxin and the PAR1 agonist were not as prominent in previous PAS work that was done in the absence of extracellular calcium [1] . Another study that found similar synergistic effects between sub threshold concentrations of thrombin and GPVI showed that the synergism was independent of Src kinases and Syk [10] . As previously observed with EDTA-treated PRP activated by PAR1 agonist peptide and U46619 [2] , thrombin activation of PAR1/PAR4 and the thromboxane mimetic U46619 were synergistic ( Sij = 0 . 1127 at medium dose of thrombin and low dose U46619 ) , and especially at medium doses of thrombin and U46619 ( Sij = 0 . 1766 ) . Iloprost and GSNO when used alone or together with each other had no effect on platelet calcium ( top 9 binary conditions of Fig . 3A-C ) , as expected . However , these compounds elevate cAMP and cGMP to attenuate calcium mobilization [18–20] and this inhibition was clearly seen in the calcium traces ( Figs . 5 , 7 ) . Iloprost was a potent , rapid , and sustained inhibitor of convulxin activity ( 99 . 6–99 . 7% inhibition overall ) ( S4B , S4H Fig . ) , indicating that IP signaling was more rapid than GPVI signaling . Furthermore , iloprost was more potent than GSNO in inhibiting activity of all agonists this assay , for example , medium dose GSNO caused only 18 . 6% inhibition of convulxin activity ( S1 Table ) . Convulxin caused slow platelet activation since it must multimerize GPVI to induce signaling [21] . During thrombin activation of PAR1/4 , the inhibition by iloprost was also rapid ( as seen by the offset in peak calcium levels ) , but was incomplete initially and became more pronounced after approximately 25 seconds post-stimulation ( S4D , S4J Fig . ) . Low and medium levels of iloprost ( 0 . 1 and 1 x EC50 ) resulted in similar inhibition of thrombin calcium release ( ∼76 . 3 to 78 . 9% inhibition ( S4D , S4J Fig . ) . In the experiments with thrombin , iloprost may have a diffusive and kinetic advantage over thrombin which must cleave PAR1/4 whereas iloprost simply must the bind IP receptor . A similar pattern was observed for ADP ( S4M , S4N Fig . ) . However , there was no offset in peak calcium levels , potentially due to similar diffusive and binding kinetics of these two small molecules for their respective receptors . The inhibition by iloprost of ADP signaling only began after ADP-induced calcium level peaked around 20 seconds post-stimulation ( 41 . 5% to 71 . 7% inhibition at 0 . 1 and 1 x EC50 , respectively ) . During U46619 stimulation of TP receptor , the inhibition by iloprost was apparent immediately after dispense and increased after calcium levels peaked ( ∼ 20 sec post-stimulation ) . Iloprost was slightly more potent against U46619 compared to ADP , causing 87 . 4% to 91 . 6% inhibition at 0 . 1 and 1 x EC50 , respectively ( S4F , S4L Fig . ) . Overall , iloprost was more fast-acting and potent against the slower signaling agonists such as convulxin ( 99 . 6–99 . 7% inhibition ) or thrombin ( ∼76–79% inhibition ) that required receptor multimerization or enzymatic cleavage , compared to small molecules that rapidly equilibrated with their receptors such as U46619 ( 87–92% inhibition ) or ADP ( 41–72% inhibition ) . Iloprost may be less active against ADP compared to U46619 since ADP also binds the P2Y12 receptor which antagonizes cAMP pathways ( Fig . 1A ) . When ADP and convulxin were used simultaneously , ADP signaling dominated early calcium mobilization while convulxin signaling maintained sustained calcium levels . ( S4A , S4G Fig . ) With combined ADP/convulxin stimulation , medium dose iloprost resulted in only 71 . 1% inhibition ( S4G Fig . ) since it was not a complete blocker of the early signaling ( at t < 30 sec ) induced by ADP . A similar trend occurred with thrombin/convulxin co-stimulation ( S4C , S4I Fig . ) , however , iloprost was more effective in this case ( 83 . 5% inhibition with medium dose iloprost , S4I Fig . ) since thrombin/convulxin co-stimulation elevated calcium relatively slowly . When the weaker agonist U46619 ( compared to ADP ) was used with convulxin , iloprost remained a very potent inhibitor ( 98 . 9% inhibition with medium dose iloprost , S4E , S4K Fig . ) . The range ( i . e . intradonor variation ) of individual NN predictions over 10 to 12 donors for single and pairwise agonist conditions ( S5 Fig . ) was comparable to the experimental observations , as expected for NNs trained exactly on those conditions ( S5A , S5B Fig . ) . Similarly , the range of NN predictions for trinary agonist conditions matched the range for the experimental values as well ( S5C Fig . ) . The range of the higher order NN predictions ( ≥ 4 agonists ) was somewhat larger than the range observed in the corresponding individual experiments ( S5D–S5F Fig . ) . Clearly for ≥ 4 agonists , the signaling pathways span a very complex platelet biology beyond the dimensionality of the pairwise training data . Nonetheless , the range of the NN predictions reflected to a large degree the range of the experiment itself . For example , the range of the NN-predictions in S5E Fig . was smaller than the range in S5D Fig . ; the same trend was reflected in the range of the actual experiments . Furthermore , for ≥ 4 agonists , the experimental data comprises seven experiments spanning five donors , whereas the NN used in training spanned 12 donors , which in part explains why simulation ranges were larger than ranges of experimental observations . The NN range over all 120 NNs in S5D Fig . was larger than the experimental ranges , potentially reflecting donor variation but more importantly reflecting the difficulty of predicting higher dimensional responses . Despite the substantial range of individual NN predictions for the four-agonist condition depicted in S5D Fig . , the mean of the NN predictions predicted the mean response of the actual experiments , a benefit of the NN-ensemble approach for predicting a pooled population dynamic .
While the full complexity of receptor mediated signaling in platelets extends well beyond the known pathways indicated in Fig . 1A , a top-down approach using pairwise agonist scanning ( PAS ) provides an efficient data-driven method to predict platelet function . By using data obtained from multiple donors and training multiple NNs for each donor , a NN-ensemble ( Fig . 2 ) allowed accurate prediction of 135 binary stimulations and 135 synergy parameters ( Fig . 3 ) . The synergy vector composed of the 135 synergy parameters is an experimentally measured human platelet phenotype ( healthy adult male and female ) that is fully predicted by the NN-ensemble . Furthermore , the NN-ensemble provided suitable prediction beyond the binary training set to predict trinary responses ( Figs . 4–5 ) , higher ordered responses ( Figs . 6–7 ) , and response to sequential stimuli ( Figs . 8–9 ) . The major components of hemostasis and thrombosis that regulate platelet activation state are now quantitatively captured in the NN-ensemble . For large scale simulation of blood function , a user may specify or calculate any combination of the six agonists at different concentrations to produce a dynamic platelet calcium response representative of a healthy human donor . The NN is able to do this mainly because individual and pairwise interactions dominate platelet calcium signaling crosstalk in this assay , and because all single agonists are sampled across each of their full dose ranges [2] . An ordinary differential equation ( ODE ) model describing the calcium mobilization mediated by all six PAS agonists would likely require an estimated >500 kinetic parameters , many of which are unavailable [2] . From the perspective of a data-driven and top-down approach , NNs have proven quite robust and well matched to PAS data sets . Also NNs are ideal for multiscale simulations that involve crosstalk between receptors . However , NNs are not mechanistic models for identification or quantification of basic biochemical mechanisms . To facilitate future mechanistic model building , the full calcium data set is provided ( S1 Dataset ) . For example , future mechanistic models of receptor signaling and crosstalk should be testable against the 135-parameter synergy map we measured . Such mechanistic models should account for RGS proteins , PKC , cAMP/PKA , cGMP/PKG , and phosphodiesterase pathways , as well as receptor desensitization pathways including ITIM/Shp2 phosphatase , receptor internalization , and receptor shedding pathways , along with regulation of store operated calcium entry . Improved predictive capability is not necessarily an outcome of constraining of NN nodes and linkages to a preconceived reaction topology ( Fig . 1 ) . The calcium experiments that the NN was trained on included the contributions of SOCE because Apixaban was used in place of a calcium chelator as an anticoagulant . The effect of autocrinic effects by ADP and thromboxane secretion , however , were not significant in the PAS assay due to the dilute conditions of the assay ( S3 Fig . ) . Such autocrinic effects however are naturally captured in multiscale simulations that include convection-diffusion of soluble agonists [6] . Other important inside-out signaling downstream of calcium mobilization , such as integrin engagement , granule release , shape change , and phosphatidylserine exposure can be simulated by incorporating the calcium model into a larger fine or coarse-grain model . For example , in Flamm et al . ( 2011 ) , NN were trained via PAS using calcium-containing PRP and then used to predict platelet deposition rates on collagen in the absence of thrombin by accounting for platelet signaling in response to laboratory analogs of collagen , ADP , thromboxane , and prostacyclin [6] . Alternatively , the PAS methodology has also been adapted by Jaeger et al . [22] for flow cytometry instead of calcium measurements , so as to quantify inside-out signaling events such as: integrin α ( IIb ) β ( 3 ) activation , P-selectin exposure , and PS exposure using PAC-1 , anti-P-selectin antibody , and annexin V , respectively . The NN ensemble trained only on pairwise data was able to predict the calcium output of higher order agonist combination experiments with reasonably high accuracy . This was potentially due to a sparsity-of-effects principle: a system is largely dictated by main effects and lower order interaction [23] . Adding trinary conditions to the training data might theoretically improve prediction accuracy when it comes to higher order combinations . The improvement in accuracy should be a measure of the information content of trinary data . In separate studies , we tested the utility of adding trinary stimulation data to the PAS training set in order to enhance the predictive capability of the NN-ensemble . Trinary data were incorporated into the NN ensemble in 3 different ways , each time controlling for the number of NNs in the ensemble . The first set of trinary data collected comprised 160 trinary combinations of all six agonists at only low and medium concentrations , repeated on 10 donors . Adding this trinary data reduced the higher-order combination prediction accuracy from R = 0 . 824 to R = 0 . 784 , and synergy score accuracy decreased from 0 . 66936 to 0 . 66145 . The second set of trinary data collected comprised 27 combinations of only three agonists ( ADP , convulxin , U46619 ) in the full dose range , repeated on eight donors . Adding this trinary data did not substantially affect the prediction accuracy ( calcium trace accuracy from R = 0 . 810 to R = 0 . 806 , and synergy correlation from R = 0 . 66367 to R = 0 . 68387 ) . The third set of trinary data collected was an unbiased , random sampling ( n = 54 ) of the complete trinary space done on a single donor , who was not part of the original PAS dataset . Adding this trinary data increased prediction accuracy of the time courses ( from R = 0 . 871 to R = 0 . 906 ) , but reduced the accuracy of synergy scores prediction from R = 0 . 6953 to R = 0 . 53925 . We conclude that incorporating a randomly sampled trinary dataset can moderately increase prediction accuracy , whereas adding a biased sample of the trinary space does not increase accuracy . In fact , adding a sample of the trinary space that does not span the full dose range may reduce accuracy . However , even in the best of the three scenarios tested , adding trinary data to the PAS training dataset did not substantially improve accuracy . The 120-NN ensemble ( R = 0 . 87134 , mean-squared error MSE = 0 . 0129 ) was more accurate than the average individual NN within the ensemble ( R average = 0 . 6566 , MSE average = 0 . 0501 ) in predicting the calcium traces of higher order combination experiments . Even though the most accurate individual NN in the 120-NN ensemble ( R = 0 . 91391 , MSE = 0 . 0092 ) was more accurate than the ensemble itself , its prediction output was significantly noisier , and its synergy score prediction accuracy was much lower ( R = 0 . 6953 to R = 0 . 4609 ) . The ensemble approach reduced variances in prediction output , increased accuracy above the average NN in the 120-NN ensemble , consistent with previous findings [24] , and is generally thought to be more robust [25–28] . Ensemble pruning is often used to eliminate individual models from the ensemble based on certain criteria so as to improve the new ensembles predictive ability [28] . Using the interquartile range ( IQR ) outlier detection method , eight high outliers spanning seven different donors were identified in the mean-squared error ( MSE ) measurements of each of the 120 individual NNs . Removing the eight most inaccurate individual NNs from the ensemble did not improve the ensemble accuracy ( R-value improved slightly from 0 . 87134 to 0 . 87768 , but MSE increased slightly from 0 . 0129 to 0 . 0148 , synergy score R decreased slightly from 0 . 6953 to 0 . 68632 , and noise in ensemble predictions increased ) . Diversity of the models ( in this case originating from the different random initial weights generated at the beginning of each NN training ) comprising an ensemble is important for accuracy and robustness [29–31] . To increase diversity , heterogeneous ensembles may be used instead; for example , changing the training parameters of select individual NNs or incorporating into the NN-ensemble regression models other than NNs [28] . We found that the NN-ensemble was able to account for the dynamics and magnitude of one pathway relative to that of another . Iloprost was a very potent antagonist in this assay . In vivo , prostacyclin activates IP to increase cAMP and cGMP-dependent protein kinases pKA and pKG . Both pKA and pKG phosphorylate RGS18 ( a G-protein regulator ) , which eventually turns off Gq-signaling , the main activation pathway for U46619 and thrombin ( via PAR-1 ) [32] . ADP signaling through P2Y1 also goes through Gq-signaling . However , ADP also signals through the P2Y12 receptor , which involves the Gi protein ( Fig . 1A ) . Thrombin signaling through PAR4 can go through either Gq or Gi . The existence of alternate signaling routes ( through Gi ) explain why iloprost inhibition is markedly less potent for ADP , and slightly less effective for thrombin as well . Furthermore , signaling through the Gi protein inhibits the rise in adenylate cyclase ( precursor of cAMP which decreases intracellular calcium levels ) . As expected from studies of “coated” platelets [33] and studies with similar findings [9 , 10] , convulxin and thrombin were quite synergistic in the PAS assay . Additionally , thrombin and TP receptor signaling were somewhat synergistic , consistent with previous findings [2 , 6] . In future work , the NN-ensemble can be incorporated in multi-scale and hierarchical simulations of bleeding or clotting with linkages to vascular pathophysiology . While exogenously added thrombin was used in PAS , not all of this thrombin may reach the platelet due to antithrombin . This may right shift the thrombin potency [34] . Iloprost and GSNO were also included in this assay to recapitulate endothelial-derived prostacyclin and nitric oxide effects on platelet function . The development of a healthy human platelet calcium calculator can enable various applications such as predicting thrombosis or hemostasis under flow condition or extracting information from in vitro diagnostics , potentially using platelets from patients with cardiovascular disease risks .
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Platelets regulate clotting during injury to prevent blood loss . Hyperactive platelets may increase risk of thrombosis , whereas hypoactive platelets may increase risk of bleeding . Platelets are activated during a clotting event by agonists , through different signaling pathways , all of which converge on intracellular calcium mobilization . Calcium mobilization is a global metric of platelet activation . Predicting platelet response to different combinations of agonists is essential to scoring bleeding or clotting risks or drug response . We collected pairwise agonist scanning data , in which platelets are activated by all single and pairwise combinations of six important agonists at low , medium and high doses , from 10 donors and subsequently trained artificial neural networks . The combined trained model was able to predict the dynamic calcium time traces of combinations of three , four , five and six agonists at various dose ranges , as well as conditions where agonists were added sequentially . The data-driven neural network model is computationally fast and is able to capture a significant level of signaling complexity within the human platelet .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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A Human Platelet Calcium Calculator Trained by Pairwise Agonist Scanning
|
Wing patterning in Heliconius butterflies is a longstanding example of both Müllerian mimicry and phenotypic radiation under strong natural selection . The loci controlling such patterns are “hotspots” for adaptive evolution with great allelic diversity across different species in the genus . We characterise nucleotide variation , genotype-by-phenotype associations , linkage disequilibrium , and candidate gene expression at two loci and across multiple hybrid zones in Heliconius melpomene and relatives . Alleles at HmB control the presence or absence of the red forewing band , while alleles at HmYb control the yellow hindwing bar . Across HmYb two regions , separated by ∼100 kb , show significant genotype-by-phenotype associations that are replicated across independent hybrid zones . In contrast , at HmB a single peak of association indicates the likely position of functional sites at three genes , encoding a kinesin , a G-protein coupled receptor , and an mRNA splicing factor . At both HmYb and HmB there is evidence for enhanced linkage disequilibrium ( LD ) between associated sites separated by up to 14 kb , suggesting that multiple sites are under selection . However , there was no evidence for reduced variation or deviations from neutrality that might indicate a recent selective sweep , consistent with these alleles being relatively old . Of the three genes showing an association with the HmB locus , the kinesin shows differences in wing disc expression between races that are replicated in the co-mimic , Heliconius erato , providing striking evidence for parallel changes in gene expression between Müllerian co-mimics . Wing patterning loci in Heliconius melpomene therefore show a haplotype structure maintained by selection , but no evidence for a recent selective sweep . The complex genetic pattern contrasts with the simple genetic basis of many adaptive traits studied previously , but may provide a better model for most adaptation in natural populations that has arisen over millions rather than tens of years .
One of the central outstanding questions in evolutionary biology concerns the predictability of evolution . Specifically , can we predict the number and effect size of genes involved in evolution , and the types of genetic changes that underlie particular kinds of evolution ? Striking empirical examples of repeated use of the same genes in similar evolutionary changes suggest that common patterns may emerge even across distantly related taxa [1] , [2] . A few such systems are well understood , for example the genetic changes involved in melanic pigmentation in vertebrates such as rock pocket and beach mice [3] , [4] , or the adaptation of stickleback fish to freshwater habitats involving loss of spines and lateral plates [5] , [6] . Some general patterns are beginning to emerge from such studies . First , a few loci of major phenotypic effect are commonly involved in adaptation , and second , the same genes can be involved repeatedly across divergent lineages [2] . In addition , both cis-regulatory , structural and null mutations can play a role in adaptation , although intriguingly there is a suggestion that cis-regulatory change may be more important in inter-specific versus intra-specific adaptation [7] . To date , however , these patterns are inferred from just a handful of well-studied examples . Heliconius butterflies offer an excellent model system in which to address these questions as their wing patterns are under simple Mendelian genetic control and subject to strong natural selection [8] . The bright colour patterns function to advertise the toxicity of the butterflies to predators leading to stabilising selection and Müllerian mimicry , whereby sympatric species evolve a common colour pattern in order to share the cost of predator learning [9] . Strikingly , wing patterns are controlled by a small number of genomic ‘hotspots’ with a disproportionate influence on both divergent and convergent wing patterns [8] , [10] . Classic crossing experiments have established that tightly linked loci on two linkage groups control most of the variation both within Heliconius melpomene and between closely related species [8] . More recently , the development of molecular markers has facilitated comparative mapping between more distantly related species . This has shown homology in genomic location of patterning loci between both the co-mimics H . melpomene and Heliconius erato , and a third species , Heliconius numata , with entirely different wing patterns [11] . Thus , much as the MC1R locus is repeatedly involved in adaptive melanic pigmentation in vertebrates [2] , repeated involvement of the same genomic regions also underlies the complex wing patterns of Heliconius butterflies . However , the specific genetic changes underlying phenotypic diversity in Heliconius remain unknown . Here and in a companion paper [12] we take advantage of Heliconius mimicry as a system to study the population genetic patterns around loci involved in parallel phenotypic changes . Patterns of genetic variation in natural populations are commonly used to identify genes under selection , either through association studies that correlate genetic and phenotypic variation [13] , or through genome-wide scans for reduced variation or other signals of recent selection [14]–[16] . Such studies rely on the fact that selection on a locus also influences patterns of genetic variation at surrounding loci , through genetic ‘hitchhiking’ [17] . For example , after the recent and rapid rise of advantageous alleles , such during the evolution of insecticide resistance , there is often very strong linkage disequilibrium and dramatic reductions in nucleotide variability [18] , [19] . In contrast , morphological adaptation in the wild is likely to be more ancient , with selection sustained over longer time periods , such that the influence on surrounding genetic variation may be more difficult to detect . Thus , between morphologically divergent forms the classic signature of a selective sweep may have decayed , due to the combined effects of recombination and accumulation of derived mutations . In Heliconius , however , we can take advantage of natural hybrid zones , where admixture occurs between divergent geographic races , to carry out analysis of genotype-by-phenotype association . In particular , studies of a parallel hybrid zone in Peru between races of the co-mimics H . erato and H . melpomene have estimated that selection is extremely strong , on the order of a 20–50% reduced survival of foreign colour pattern morphs [20] . Crossing experiments have shown that Heliconius races typically differ at just two or three loci with major phenotypic effect on wing pattern [21] . Theory predicts that in such zones where relatively few genes are under strong selection , the barrier to gene flow at the rest of the genome will be weak [22] . This contrasts with many hybrid zones where many genes across the genome are under selection , leading to strong linkage disequilibrium and a genome-wide barrier to gene flow [23]–[25] . Thus , the situation in Heliconius offers an unusual opportunity to study populations that are strongly morphologically differentiated but nonetheless free to exchange genes across most of the genome . Previous demographic studies of Heliconius have demonstrated genetic differentiation on a regional scale , for example between the Guiana Shield , Amazon basin and Pacific slopes , but extensive gene flow between local populations ( [26]–[29]; but see [30] ) . Thus , when comparing populations fixed for alternate wing patterns on either side of a narrow hybrid zone , we would predict little or no genetic differentiation across most of the genome . In contrast , around functional sites controlling wing patterning we expect fixed differences between pattern races , offering a powerful system for detecting sites associated with adaptation even in the absence of the signal of a classic ‘selective sweep’ . Specifically , we here take advantage of our recent positional cloning of two such loci located on different chromosomes , the H . melpomene HmYb and HmB loci . Alleles at these loci are responsible for controlling presence/absence of the hindwing yellow bar and red forewing band respectively . In this study , we fully sequence and annotate the HmB genomic region [31] , and study patterns of genetic variation at both loci between species and races in the H . melpomene clade . Our work in H . melpomene has led to cloning of the corresponding region in H . erato , allowing for comparative analysis of both regions across species [12] . At both loci there is greater genetic divergence at markers linked to wing pattern as compared to unlinked regions , with significant peaks of genotype-by-phenotype association . Nonetheless , there is no strong signature of a recent selective sweep and sites associated with phenotype are interspersed with sites showing no such association , implying that wing patterning alleles are relatively ancient . However , a strong signature of divergence , and evidence for long-range haplotype structure associated with wing pattern divergence demonstrates a clear influence of selection on population variation . One of the three genes implicated at the HmB locus , a kinesin , shows striking differences in wing pattern expression between races and throughout wing development , making it a strong candidate for the HmB gene .
We characterised the HmB locus by sequencing of 980 kb of H . melpomene genomic sequence from seven BAC clones , representing a 721 kb tile path , based on previous fine-mapping of this region [31] . All seven BAC clones were annotated by comparison with transcriptome sequence and protein databases , using a combination of manual BLAST comparison , the KAIKOGAAS lepidopteran genome annotation tool and the MAKER annotation pipeline [32] . Twenty-four genes with homology to known or predicted genes ( BLAST cut off expect value 4 . 0e-08 ) were identified across the region , including several wing patterning candidates involved in development . Putative biological functions based on gene homology or predicted functional domains include cellular differentiation ( Mad ) , protein binding ( similar to CG7872 , GPCR , INCS7 ) , splicing ( slu7 ) , protein-protein interactions ( leucine rich repeats or LRR ) , transport ( Monocarboxylate transporter 14 , kinesin ) , basic cell function ( RpS13 , NADH , TPP5 ) and a transcription factor ( six/sine ) ( Figure 1 and Table S1 ) . In addition , seven predicted transcription units , without introns or homology to known genes , were also identified . This complements our recent description of the HmYb genomic region , which has been narrowed down to a 323 kb genomic region containing 20 genes ( Figure 1; [33] ) . Overall gene content and order is largely conserved across both regions between the co-mimic species , H . erato and H . melpomene ( Figure S1 ) . A direct comparison of gene order was not possible , as several H . erato BAC clones are not yet fully assembled , however a single copy of all 24 predicted H . melpomene HmB genes was identified in H . erato ( data not shown ) , and a similar pattern is seen at HmYb where sequence is available for H . erato . We estimated population structure at HmYb markers for three pairwise population comparisons , two involving races of H . melpomene ( amaryllis vs aglaope and melpomene vs rosina ) and one pair of hybridizing species ( H . pachinus vs . H . cydno ) ( Figure 2 , Table S2 , and Table S3 ) . In all cases the former taxon shows the presence of the yellow hindwing bar , which is absent in the latter of each pair . For the melpomene vs rosina comparison , 21 coding and non-coding regions were sampled , representing a total of 11 , 578 bp of sequence , for amaryllis vs aglaope eight regions representing 4085 bp and for H . pachinus vs . H . cydno 12 regions representing 5596 bp . Between both races and species there was greater genomic divergence across the HmYb region as compared to unlinked genes ( Table 1 ) . In total 806 variable sites were tested for HmYb , generating two genomic regions with significant genotype-by-phenotype association . First , both pairs of H . melpomene races showed significant associations around the HM00007 , HM00008 and HM00010 genes ( Figure 3 , Table S4 ) . Second , the strongest peaks of association between the species H . cydno and H . pachinus and between H . m . amaryllis and H . m . aglaope were around the HM00023 and HM00024 genes ( Figure 3 ) . This was most marked in the comparison between H . cydno and H . pachinus , which showed 14 significantly associated SNPs in this region ( in HM00024 , the region adjacent to it and a non-coding region near HM00025 ) . Such a pattern was not seen at any of 16 unlinked loci studied previously for this species pair [34] . In all cases however , there was a pattern of sites associated with phenotype being interspersed with others showing no or little association . The two races of H . melpomene , that differ in presence of the HmB red forewing band , were also studied for HmB linked markers [20] . H . melpomene amaryllis has a red forewing band , controlled by the dominant HmB allele , which is absent in H . melpomene aglaope ( Figure 2 ) . H . m . aglaope and H . m . amaryllis were screened for a total of seven genes across the HmB region plus the closely linked gene MRSP , covering a total of 5391 bp ( Table S3 ) . Again , linked HmB markers showed a higher overall level of genetic differentiation between races as compared to unlinked genes ( Table 1 ) . In total 40 variable SNPs were tested for genotype-by-phenotype association , of which 16 showed a significant association ( Figure 3 , Table S5 ) . The peak of association was located around the slu7 , kinesin and GPCR genes ( Figure 3 ) . Several SNPs in these three genes were fixed in H . m . amaryllis , with the alternate variant at high frequency ( >90% ) in H . m . aglaope . The lack of completely fixed differences between any of the comparisons suggests that we have not yet sampled causative sites , or that there is epistasis between the variable sites with phenotype determined by allelic combinations . Selective sweeps act to reduce genetic variability and alter the frequency spectrum of haplotypes . However , interestingly , in contrast to more recently evolved phenotypes such as pesticide resistance , there was no clear signal of recent selection in our data . Levels of nucleotide diversity varied between genes , but with no clear pattern of reduced variation in any population , or around the genomic regions showing genotype-by-phenotype associations ( Table S6 ) . There were three adjacent genes in the H . m . aglaope population that showed a significantly negative Tajima's D ( Table S6 ) , although this region was not in the peak of genetic association with phenotype . Otherwise , there was no consistent pattern across either region , with a few individual genes showing significant deviations from neutrality for particular populations , among markers linked and unlinked to colour pattern loci ( Table S6 ) . Overall levels of nucleotide diversity were lower than in the corresponding populations of H . erato , consistent with the observed smaller population sizes of H . melpomene in the field [27] . There was , however , good evidence for strong linkage disequilibrium across colour pattern regions , most notably around the three associated genes in the HmB region ( Figure 4 ) . LD analysis was restricted to the H . m . aglaope and H . m . amaryllis population samples , which had larger sample sizes ( n>20 per population ) . When LD was plotted against genetic distance , there was strong LD ( 0 . 5>r2>0 . 9 ) between sites separated by a distance of up to 14 kb ( Figure S2A ) . This long distance LD was primarily due to sites associated with phenotype at both HmB and HmYb – when such sites were removed from the analysis long distance LD virtually disappears ( Figure S2B ) . The only exceptions were a few pairwise comparisons between sites in the Slu7 and GPCR genes with high LD . These are not between sites associated with phenotype , but could presumably have resulted from a history of selection across this region ( Figure S2B ) . The analysis excluding sites associated with phenotype indicates that r2 values rapidly decay to a background level below about 0 . 3 at a distance of around 500 bp . This is further indicated in analysis of LD within genes , where strong linkage disequilibrium was seen up to a distance of around 500 bp , which similarly decayed rapidly ( Figure S3 ) . Thus , the background pattern of rapid decay in LD would appear to be similar in H . melpomene and H . erato , but in H . melpomene this is overlaid by long-range haplotype structure that is associated with wing pattern divergence [12] . For the HmB locus , where our population genetic analysis indicates a clear candidate region , we followed up with analysis of gene expression in developing wing discs . Four genes were chosen for quantitative expression analysis in developing wings , either because they were located in the peak of genetic differentiation ( Slu7 , Kinesin and GPCR ) , or as being a nearby candidate locus for pattern specification ( Mad ) . The same populations studied above were not available as live material , so we instead used two forms available from commercial suppliers , H . melpomene malleti and H . melpomene cythera , which have yellow and red forewing bands respectively , providing a similar comparison as that between H . m . aglaope and H . m . amaryllis . First , forewings were removed from three pupal developmental stages of H . melpomene cythera , and dissected into three wing segments , to examine spatial gene expression between proximal , medial ( HmB ) and distal regions ( expression analysis “between wing segments” ) . Second , whole forewings and hindwings were separately dissected from 5th instar larvae and early pupae of H . melpomene malleti and H . melpomene cythera for expression analysis ‘between races’ . The most striking result was a strong contribution to the prediction of gene expression by race at the kinesin gene , with H . m . malleti having an overall higher expression than H . m . cythera ( Figure 5 ) . Analysis using Bayesian Model Averaging gave Pr ( β≠0 ) = 66 . 5 , indicating a probability of 66 . 5% that the coefficient of variation for race in this experiment was greater than zero . The fold-change in expression across all samples represented 2 . 6 times greater expression in H . m . malleti as compared to H . m . cythera . In the ‘between wing segments’ experiment , the kinesin gene showed higher expression in the early pupal ( EP ) stage but no differences between different segments of the wing , consequently developmental stage made a strong contribution to gene expression prediction ( Pr ( β≠0 ) = 89 . 6; Figure 5 ) . In the ‘between wing segments’ experiment , Slu 7 showed higher expression in later developmental stages ( highest in the ommochrome only stage [OO] ) , but no differences between segments of the wing ( developmental stage Pr ( β≠0 ) = 100 ) . Race made a contribution to the prediction of gene expression in the ‘between races’ experiment ( Pr ( β≠0 ) = 86 . 5 ) , with H . m . cythera having a 1 . 52-fold overall higher expression than H . m . malleti ( Figure 5 ) . No main variables made a contribution to GPCR gene expression in either ‘between wing segments’ or ‘between races’ experiments . The Mad gene showed an increase in expression levels during pupal development , with highest expression at the latest stage surveyed ( developmental stage Pr ( β≠0 ) = 94 ) , but no differences between races or wing segments ( Figure 5 ) .
Hybrid zones in H . melpomene separate forms that are only differentiated at a few major colour pattern loci . Such zones provide a powerful system for identifying adaptive genetic changes . Genes under selection are strongly differentiated , but there is only a weak barrier to gene flow at the rest of the genome [22] . This is confirmed by the lack of genetic differentiation at loci unlinked to colour pattern seen in our data . In contrast , there was increased genetic differentiation across the candidate regions in all cases ( Table 1 ) , implying genetic hitch-hiking around sites under colour pattern selection . At the HmYb locus , levels of differentiation are greater between reproductively isolated species , as compared to races , consistent with higher overall levels of reproductive isolation and ‘divergence hitch-hiking’ between species [36] , [37] . Across both genomic regions , there were clear peaks of genotype-by-phenotype association . Our sampling of multiple phenotypic comparisons at the HmYb locus offers the first comparative analysis of parallel phenotypic evolution in Heliconius , and indicates that at a fine scale the genes responsible for parallel changes in phenotype are shared , but not identical across different hybrid zones . Most strikingly , the region around HM00024 ( LRR-3 ) shows a very marked peak in both the H . pachinus vs . H . cydno and the H . m . amaryllis vs H . m . aglaope hybrid zones , and is also highlighted in H . erato [12] . The genes in this region are therefore the most strongly implicated in regulation of wing pattern for HmYb . In addition however , another peak around HM00008 and HM00010 is shared between the two H . melpomene inter-racial comparisons , but not with H . erato . The repeatability of these patterns across independent hybrid zones suggests that multiple associations are not merely the stochastic results of selection at a single causative site , but rather imply a role for multiple functional sites underlying wing pattern differences . This is supported by the observation of increased linkage disequilibrium between associated sites at the HmYb locus as compared to background levels , suggesting that selection is maintaining particular allelic combinations across this region ( Figure S1 ) . The comparison across the HmB region also gave a clear peak of genotype-by-phenotype association near three genes , kinesin , GPCR and Slu7 . In this case the association was stronger than between races at the HmYb locus , with several SNPs across these three genes being completely fixed in H . m . amaryllis and with a frequency difference of >95% between races . This is also a much stronger signal of differentiation than seen in comparisons between races of H . erato at this locus [12] . Furthermore , unlike the pattern in H . erato , there was evidence for extensive LD between associated sites up to 14 kb apart . As at HmYb , against a background of estimated r2 values below 0 . 3 , the long-range LD at HmB similarly suggests a haplotype structure maintained by selection . However , in neither genomic region studied here was there consistent evidence for a recent selective sweep in any of the populations . This is perhaps to be expected given the age of the H . melpomene radiation ( ≈250 , 000–500 , 000 years [38] ) , and contrasts with the pattern seen in very recently evolved traits such as pesticide resistance . Similar to H . erato , levels of genetic variation showed no consistent pattern between linked and unlinked loci . Although three adjacent genes did show a significantly negative Tajima's D at the HmB locus , these did not correspond to the region of strong population differentiation , suggesting no strong deviations from neutrality due to wing pattern selection . Furthermore , both LD and genotype-by-phenotype analyses indicated that sites associated with phenotype were interspersed with variable sites showing no such association . The data are therefore consistent with the wing patterning alleles of both H . melpomene and H . erato being relatively ancient , such that considerable genetic variation has arisen subsequent to any initial selective sweep . An alternative is that the wing pattern alleles spread in a ‘soft’ sweep , with an initial period in which the novel allele was found at low frequency for an extended period , giving ample opportunity for recombination around selected sites [39] . Thus , the peaks of genetic differentiation seen between races are consistent with the strong selection known to act on wing pattern [29] , but have presumably been associated with a long history of hybridization and recombination between the races such that any signal of a recent selective sweep has been erased . The results therefore appear to support the ‘shifting balance’ model for the evolution of Heliconius colour pattern races [40] , whereby novel wing patterns arise and spread through otherwise continuous populations behind moving hybrid zones [41] . The ‘Pleistocene refuge’ model seems less likely , as recent contact after extended periods of geographic isolation would presumably have left a stronger signal of genetic differentiation between divergent races , perhaps across the genome but especially more strongly in regions linked to patterning loci [42] . Overall , the stronger signal of both haplotype structure and genotype-by-phenotype association in H . melpomene , as compared to H . erato , is also consistent with what is known of their biology . First , H . erato tends to be more widespread and have larger population sizes [27] , leading to higher levels of recombination and a more rapid breakdown of linkage disequilibrium generated by selection . Second , H . melpomene is thought to be the mimic of H . erato , such that the pattern alleles may have arisen more recently in the former . Nonetheless , in both species the genetic divergence is highly localised when compared to other recent examples , such as ecological races of the pea aphid Acyrthosiphon pisum , where islands of divergence extend several cM around QTL under divergent selection [36] , or freshwater races of sticklebacks where linkage disequilibrium is extensive around the locus controlling dermal plate morphology [5] . The data are perhaps more similar to that seen in two ecomorphs of the inter-tidal snail , the Rough periwinkle ( Littorina saxatilis ) , where regions of differentiation are limited to a few hundred base pairs [37] . High recombination and localised islands of divergence mean that in the future , population genetic analysis is likely to be a powerful tool for isolating functional sites in Heliconius . The development of scale-covered wings , pigmentation and an elaborate patterning system are evolutionary innovations of the Lepidoptera and have been cited as an example of the redeployment of conserved gene networks [43] . Notably , both the hedgehog and wingless signalling pathways are involved in wing eyespot formation [44] , [45] . Similarly , studies of Drosophila wing pigmentation have implicated cis-regulatory regions of shared pigment biosynthesis genes in regulating inter-specific differences [46] , [47] . However , no obvious members of either canonical signalling or pigment biosynthesis pathways are present in the HmB region , similar to the pattern already found in HmYb [33] . However , in contrast to a candidate gene approach , the linkage mapping method we have taken makes no a priori assumptions regarding the identity of the wing patterning genes . Instead we have cloned the genetic region controlling natural variation in a striking phenotype , and in doing so ruled out a role for any of the wing pattern ‘toolkit’ genes described previously . Of course , these two approaches may be complementary , and the locus that is controlling the wing phenotype may be an upstream regulator of classic ‘toolkit’ genes . Indeed , the intriguing observation that the ‘Bigeye’ pattern mutant of Bicyclus anynana maps to the homologous chromosome of HmYb might indicate that the gene networks uncovered in Heliconius may have a general role in butterfly wing patterning [48] . At the HmYb locus , two regions are implicated , first around the genes HM00007 , HM00008 and HM00010 . These genes have putative orthologues in Drosophila ( CG14870/B9 protein , CG5098 and CG3184 ) about which little is known , except that HM00010 contains WD40 repeats that are likely to be involved in protein-protein interaction . More striking is the association found around HM00024 ( LRR-3 ) , which was replicated in two of the comparisons studied here and in H . erato . This gene shows similarity to Sur-8 in Drosophila , consistent with a possible role in signal transduction , and is also adjacent to HM00023 , a probable non-coding transcript with complex patterns of alternative splicing that may have a regulatory function [33] . Overall , therefore , the coding regions implicated do not show any homology to well characterized genes in other species . The only candidate gene for HmYb suggested by sequence similarity was the transcription factor Unkempt which , unlike in H . erato , showed no significant association with wing pattern in H . melpomene . Against a background of multiple associated sites , and extensive LD across three genes , there is one strong candidate for the HmB gene . In both H . melpomene and H . erato when yellow and red forewing band forms are compared , the former showed much higher expression levels of the putative kinesin , HM01018 , in late larval and early pupal stages , suggesting a role for this locus in wing pattern specification . Members of the kinesin superfamily play diverse roles in cellular organization , using a catalytic motor to move along microtubules and transport organelles and molecules [49] . Homology predicts the H . melpomene kinesin contains an N-terminal motor domain , yet has no sequence similarity to other eukaryotes in the C-terminal cargo domain , making it difficult to speculate regarding the function of this gene . In Drosophila , kinesin proteins can function to determine cell polarity and RNA localisation during embryogenesis , and are therefore known to play a role in pattern specification at a cellular level [50] . Furthermore , movement of melanocytes in vertebrate pigmentation involves kinesin molecules [51] , and there has been speculation regarding a possible role in invertebrates [52] . Localisation of pigments , or pigment precursors in cellular vesicles might similarly be involved in butterfly wing pigmentation . It therefore seems plausible that increased levels of the kinesin expression across the whole wing regulate either scale cell fate or pigment localization , and thus ultimately specify pattern formation . Our data shows that adaptive changes in Heliconius are clustered in the genome across multiple independent hybrid zones . This adds to what is already known from crossing experiments , showing that two patterning loci , HmB and HmD , controlling the red forewing band and orange hindwing rays respectively , are tightly linked , as are HmYb and HmSb that similarly control the yellow hindwing bar and white margin [31] . There is also evidence for genetic associations between these colour pattern loci and other adaptive traits , including mate and host plant preference [53][R . Merrill and C . Jiggins , Unpub . ] . Thus , these regions are functional ‘hotspots’ in the Heliconius genome that play a disproportionately large role in pattern specification and indeed , in speciation . The population data described here offer novel insights into the genetic architecture of these regions . First , there is significant association of both genetic variation and long distance haplotype structure with wing pattern at both loci , indicating the influence of strong mimicry selection on the genome . Nonetheless , consistent with previous estimates of the age of H . melpomene ( ≈250 , 000–500 , 000 years [38] ) , there is no evidence for a recent selective sweep involving reduced variation or significant deviations from neutrality . This might be an indication that these alleles are indeed part of an ancestral ‘toolkit’ of wing patterning variants that are shared between taxa through hybridization [54] , or simply that there has been sufficient time since novel variants arose for the signal of a selective sweep to have been erased . This pattern may be more representative of most natural adaptation , which has arisen over millions of years , as compared to recently derived traits under strong man-made selection , such as pesticide resistance or domestication . In summary , our analysis of the population genetics of these regions offers novel insights into the evolutionary history of a spectacular parallel adaptive radiation .
A BAC tile path containing seven clones was constructed across the HmB locus [31] and sequenced by the Wellcome Trust Sanger Institute to HTG phase 3 quality; AEHM-22C5 ( 160673 bp ) CU462842 , AEHM-7G5 ( 179057 bp ) CU462858 , AEHM-28L23 ( 125911 bp ) CU467808 , AEHM-27I5 ( 126643 bp ) CU467807 , AEHM-19L14 ( 136956 bp ) CU672261 , AEHM-21P16 ( 129716 bp ) CU681835 and AEHM-28F19 ( 122304 bp ) CU672275 . Linkage mapping has shown that the HmD locus overlaps the HmB locus , and extends beyond clone AEHM-22C5 . A gap in the BAC library prevented this region being fully sequenced so we cannot be sure that HmD is among the loci studied here . For sequencing and annotation of the HmYb locus , see Ferguson et al . [33] . BAC clones in the HmB tile path were individually annotated with 454-EST contigs and a repeat database , using the annotation pipeline MAKER [32] , which identifies repeats , aligns ESTs and proteins to a DNA sequence , produces ab initio gene predictions , and automatically synthesizes the data into gene annotations having evidence-based quality indices . The 454-EST contigs were generated from normalised cDNA from wing disc mRNA of two H . melpomene races and sequenced using 454 FLX ( 48Mb for H . m . malleti and 103Mb for H . m . cythera [33] ) . Genome sequence repeat motifs were characterized by screening 32 , 528 H . melpomene BAC end sequences ( generated by Sanger di-deoxy sequencing ) with the ReRep pipeline [55] , which is specifically optimised for identifying repetitive structures in a genome survey sequence ( GSS ) dataset . BLAST searches were performed against the non-redundant UniRef100 database , and gene predictions were generated using SNAP ( Semi-HMM-based Nucleic Acid Parser ) [56] . The resulting gff files from the MAKER pipeline were analyzed and viewed with the Apollo Genome Annotation Curation Tool [57] version 1 . 9 . 6 . ( Table S2 ) . Adult butterflies were collected , wings removed and bodies preserved in 20% DMSO , 0 . 2 M EDTA salt saturated solution . Genomic DNA was extracted using DNeasy blood and tissue kit ( Qiagen ) . H . cydno galanthus ( n = 33 ) and H . pachinus ( n = 22 ) were collected from Costa Rica; H . melpoomene melpomene ( n = 16 ) and H . melpomene rosina ( n = 19 ) were collected from Panama and Venezuela; H . melpomene aglaope ( n = 30 ) and H . melpomene amaryllis ( n = 30 ) were collected from Peru ( Figure 1 ) . Two admixed individuals were included in the sample for H . melpomene amaryllis ( see Figure S4 ) . The Peruvian race H . m . aglaope is phenotypically almost identical to the Ecuadorean form H . m . malleti used in transcriptome sequencing . PCRs contained 10–50 ng of genomic DNA , 1× reaction buffer , 2 . 0 mM MgCl2 , 0 . 1 mM dNTP , 50 pmol of each primer , 0 . 25 units of Taq polymerase ( Bio-Line ) . Thermal cycling conditions were 94°C 1 min , 35 cycles of 94°C 15 sec , annealing 30 sec , 72°C 60 sec , and a final extension of 72°C . Prior to sequencing , products were incubated with 2 units of Exonuclease 1 ( NEB ) and 1 unit of Shrimp Alkaline Phosphatase ( Fermentas ) for 40 minutes at 37°C then 80°C for 20 minutes . Sequencing was performed in 10 µl reactions using 1–3 µl of template , 1× reaction buffer , 1µl BigDye terminator v3 . 1 ( Applied Biosystems ) and 0 . 32 pmol primer and run in a thermal cycler for 25 cycles of 95°C 30 seconds , 50°C 20 seconds , 60°C 4 minutes . Products were then sequenced using an ABI3730 capillary sequencer , analyzed using CodonCode Aligner software and single nucleotide polymorphisms identified manually . Genes contained insertion/deletion ( IN/DEL ) variation were trimmed to exclude such regions from analysis , or cloned using Promega pGEM-T Easy kit before sequencing ( Table S1 ) . Fragments of seven genes across the HmB region , plus the linked gene MRSP ( approx . 1 cM from the HmB locus ) , were sequenced , along with three control genes unlinked to wing colour patterning loci ( Table S1 ) . In total , 19 genomic fragments across the HmYb candidate region and three fragments linked to HmYb , but outside the candidate region , were amplified from at least 5 individuals from one or more of the pairs of hybridising subspecies/species . In addition 3–5 unlinked genes were sequenced for each pair of hybridizing H . melpomene subspecies . Haplotypes were inferred using Phase/Unphase implemented in DnaSP [58] . Further population genetic analysis was carried out in DnaSP including calculation of nucleotide diversity ( π ) [59] , Tajima's D for each population using all segregating sites [60] , and FST between colour pattern races at each locus [61] . The statistical significance of Tajima's D estimates was estimated using the two-tailed test assuming D follows a beta-distribution as proposed by Tajima and implemented in DnaSP [60] . Samples of the race H . m . melpomene collected from distant localities in Panama and Venezuela were analyzed separately and compared independently with H . m . rosina . Comparisons between FST values for genetic markers within the colour pattern regions ( as defined by the linkage study ) and unlinked markers were carried out for each pair of populations using a Mann-Whitney U test implemented in Minitab v15 . 1 ( Minitab Inc . ) . For the H . cydno/H . pachinus comparison background levels of differentiation were obtained from 16 previously published genes [34] . We determined if any SNPs were associated with a colour pattern phenotype using a chi-squared linear trend test [62] , [63] . This test assumes a linear relationship between the phenotype and genotype and applies a chi-square goodness-of-fit test to determine if the genotype at a SNP is significantly associated with a particular wing colour pattern . In total 679 variable sites were tested for HmYb in H . melpomene , 127 in H . cydno/pachinus , 40 variable sites for HmB between H . m . aglaope and H . m . amaryllis and 20 sites at unlinked loci . Colour pattern genotypes at the HmYb and HmB loci were scored as 0 . 0 or 1 . 0 representing alternative homozygotes , and 0 . 5 for heterozygotes . Although HmB is dominant , two hybrid individuals with the dominant red forewing HmB allele and orange rayed HmD alleles were scored as heterozygotes , as recombinants between HmB and HmD are rare . A stringent significance cut-off was calculated using a Bonferroni correction applied to all 866 sites tested ( −Log10 ( 0 . 05/866 ) = 4 . 24 ) . Linkage disequilibrium across HmYb and HmB was calculated for all population samples with a sample size ≥20 . This restricted the analysis to the H . m . aglaope and H . m . amaryllis populations , and gave a total of 40 sites for HmB , 24 for HmYb and 20 for unlinked loci with a rare allele frequency greater than 0 . 05 that were considered informative for LD analysis . Multi-allelic sites that had a minor allele with a frequency less than 0 . 05 were condensed to bi-allelic SNPs by merging the minor allele genotypes . LD was estimated using the commonly used composite estimate of LD method described by Weir [62] , which does not assume HWE or that haplotypes are known ( see also [12] ) . The R package LDHeatmap was used to visualize LD across both regions [64] . LD estimates are presented for combined H . m . aglaope and H . m . amaryllis populations . Quantitative real time PCR ( qPCR ) was used to determine the relative transcript levels of Mad , slu7 , kinesin and GPCR . RNA was extracted from forewings of H . m . cythera pupae at three developmental stages; EP ( Early Pupae ∼48 hours after pupation , poorly developed wings , no veins ) , PO ( Pre-Ommochrome – well developed wings with veins but no pigmentation ) and OO ( Only-Ommochrome – red band visible but no melanin present ) [65] . Each of these forewings was dissected into three sections to compare gene expression between wing regions ( P = proximal , B = region that develops the red HmB band and D = distal ) totalling 27 samples ( 3 individuals per stage ×3 developmental stages ×3 wing segments ) . To compare pattern races we used H . m . cythera and H . m . malleti individuals . The former has a red forewing band , which is absent in the latter . RNA was collected from whole forewings and hindwings of three individuals from two developmental stages ( late 5th instar larvae and EP ) , totalling 24 samples ( 3 individuals ×2 wings ×2 developmental stages ×2 races ) . Total RNA ( 500 ng ) was reverse transcribed with random hexamers and 1 µl of the resulting cDNA template ( 25ng ) was combined with 200 nM of each primer in 25 µl of total reaction volume containing 1× SYBR Green master mix ( SensiMix , Quantace ) . The reactions were subjected to 40 cycles of amplification in an Opticon 2 DNA engine ( MJ Research ) under the following conditions: 95°C for 15 min and then 40 cycles of 94°C for 15 sec , 55 . 4°–60°C for 30 sec , 72°C for 30 sec followed by a final incubation at 72°C for 10 min . Melting curves were generated between 55° and 90° with readings taken every 0 . 2° for each of the products to check that only a single product was generated . We sequenced at least one product from each set of primers to confirm identity and size . For most loci , the amplified fragments spanned at least one intron , ensuring that genomic DNA contamination could be identified . We used two housekeeping control genes for normalization in the ‘between wing segments’ experiment; EF1-α and RpS3A . However , as results of these two genes were consistent , only EF1-α was used in the ‘between race’ experiment . qPCRs on target and control loci were always assayed using product from the same cDNA synthesis reaction . Furthermore all samples were assayed 3 times but , because differences in gene expression between replicates were always <0 . 05× ( see below ) , we report only the average value for each sample . Statistical methods for qPCR experiments are given in Text S1 .
|
The diversity of wing patterns in Heliconius butterflies is a longstanding example of both Müllerian mimicry and adaptive radiation . The genetic regions controlling such patterns are “hotspots” for adaptive evolution , with small regions of the genome controlling major changes in wing pattern . Across multiple hybrid zones in Heliconius melpomene and related species , we no find no strong population signal of recent selection . Nonetheless , we find significant associations between genetic variation and wing pattern at multiple sites . This suggests patterning alleles are relatively old , and might be a better model for most natural adaptation , in contrast to the simple genetic basis of recent human-induced selection such as pesticide resistance . Strikingly , across the region controlling the red forewing band , a very strong association with phenotype implicates three genes as potentially being involved in control of wing pattern . One of these , a kinesin gene , shows parallel differences in expression levels between divergent forms in the two mimetic species , making it a strong candidate for control of wing pattern . These results show that mimicry involves parallel changes in gene expression and strongly suggest a role for this gene in control of wing pattern .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"evolutionary",
"biology/animal",
"genetics",
"evolutionary",
"biology/evolutionary",
"and",
"comparative",
"genetics",
"evolutionary",
"biology/genomics",
"evolutionary",
"biology/bioinformatics",
"evolutionary",
"biology/pattern",
"formation",
"evolutionary",
"biology",
"evolutionary",
"biology/developmental",
"evolution"
] |
2010
|
Genomic Hotspots for Adaptation: The Population Genetics of Müllerian Mimicry in the Heliconius melpomene Clade
|
Protein knots , mostly regarded as intriguing oddities , are gradually being recognized as significant structural motifs . Seven distinctly knotted folds have already been identified . It is by and large unclear how these exceptional structures actually fold , and only recently , experiments and simulations have begun to shed some light on this issue . In checking the new protein structures submitted to the Protein Data Bank , we encountered the most complex and the smallest knots to date: A recently uncovered α-haloacid dehalogenase structure contains a knot with six crossings , a so-called Stevedore knot , in a projection onto a plane . The smallest protein knot is present in an as yet unclassified protein fragment that consists of only 92 amino acids . The topological complexity of the Stevedore knot presents a puzzle as to how it could possibly fold . To unravel this enigma , we performed folding simulations with a structure-based coarse-grained model and uncovered a possible mechanism by which the knot forms in a single loop flip .
In the last decade , our knowledge about structure and characteristics of proteins has considerably expanded . The ability of proteins of small and medium size to fold into native structures is attributed to a minimally frustrated free energy landscape , which allows for fast and robust folding [1] , [2] . In recent years , however , a new class of proteins with knotted topologies emerged [3] , [4] , [5] , [6] , [7] that broadened the scope of possible folding landscapes . Not withstanding our daily experiences with shoelaces and cables , knots are mathematically only properly defined in closed loops , and not on open strings . In proteins , however , this issue can be resolved by connecting the termini ( which are usually located on the surface ) by an external loop [3] , [4] , [7] . This approach actually corresponds to a more practical definition of knottedness in which we demand that a knot remains on a string and tightens when we pull on both ends . After such closure , mathematical algorithms like the Alexander polynomial [8] can be employed to determine the type of knot ( a topological invariant ) . Knots are usually classified according to the minimum number of crossings in a projection onto a plane . Most knotted proteins discovered to date are quite simple . Out of the seven distinctly knotted folds discovered to date ( see Table 1 ) , four are simple trefoil knots ( 31 ) with 3 crossings , two are figure-eight knots ( 41 ) with 4 crossings , and only one fold is made up of five crossings ( 52 ) . Most of the knots in protein structures , however , were initially undetected from their structures since finding them by visual inspection is fairly hard , requiring a computational approach . Even though some pioneering experiments [9] , [10] , [11] , [12] , [13] have began to shed some light on how these peculiar structures fold and unfold , still little is known about the exact mechanisms involved . Recently , this subject was addressed with simulations of structure-based coarse-grained models [14] , [15] that suggested for the first time potential folding mechanisms and unfolding pathways [14] , [15] , [16] , [17] , [18] , [19] for knotted proteins . It has been suggested that folding of knotted proteins may proceed through an unfolded but knotted intermediate by simulations which include non-native contacts [14] , or by formation of slipknot conformations [15] ( segments containing a knot which disappears when protein as a whole is considered ) in conjunction with partial folding and refolding ( backtracking ) events [20] . The slipknot conformations allow the protein to overcome topological barriers in the free energy landscape which might otherwise lead to kinetic traps [21] , [22] , [23] . In a more general context , it is also intriguing to ask if the folding of complex knots can be reconciled with the folding funnel hypothesis [1] , [2] or nucleation mechanisms [24] . In this paper we present the most complex and also the smallest , knotted proteins known to date . To shed some light on potential folding routes of the former , we undertook molecular dynamics simulations with a coarse-grained model which only includes native contacts . Even though it is intrinsically difficult to fold such a large protein with a simple structure-based model , a small fraction of our trajectories ( 6 out 1000 ) folded into the knotted native state . Based on these simulations we propose a new mechanism by which this complex protein knot may fold in a single flipping movement . The proposed mechanism differs from mechanisms suggested before as it involves the flipping of a large loop over a mostly folded structure rather than folding via mostly unstructured knotted intermediates [14] .
It is difficult to imagine how proteins can actually fold into topologically elaborate structures like the 61 knot displayed in fig . 1a . Complex knots , however , are not necessarily difficult to tie . There are actually quite a few rather complicated knots , including the Stevedore knot in DehI , which can be transformed into unknots by removing a single crossing . Likewise , these knots can typically be formed in a single movement which simplifies the folding of these peculiar structures considerably . Recently , Taylor [31] predicted that complex protein knots discovered in the future will most likely belong to this class which is corroborated by the discovery of the Stevedore knot in DehI . As indicated in [31] , knots of arbitrary complexity can be obtained by twisting a loop in a string over and over again before threading one end through the loop . Even though this way of creating knots may appear as an attractive protein folding scenario due to its simplicity , our results suggest a somewhat different potential mechanism , which is able to reduce topological constraints and fold DehI in a single movement . Two loops are crucial for the formation of the 61 knot in DehI: a smaller loop which we call S-loop containing amino acids 64 to 135 and a slightly bigger loop termed B-loop ranging from amino acid 135 to 234 . Note that the latter includes the proline rich unstructured segment mentioned earlier . The analysis of the crystallographic B-factor ( see fig . S1 ) reveals that the center of the S-loop , the beginning and the end of the B-loop , as well as the unstructured proline rich segment , are particularly mobile . In addition , a very mobile unstructured segment around amino acid 240 provides additional flexibility to the C-terminus . Note that if the B-loop is flipped over to the other side of the protein , the Stevedore knot disentangles in a single step . In an attempt to elucidate the folding route of DehI , we undertook molecular dynamics simulations with a coarse-grained structure based Go-model [1] , [32] , [33] of DehI which does not include non-native interactions . With this model we were able to fold six trajectories ( out of 1000 ) into the 61 knot ( with more than 90% of native contacts ) . We emphasize that this number should not be associated with experimental folding rates . Folding large knotted proteins with a generic structure-based model without non-native interactions is extremely difficult as the protein has to undergo a series of twists and threading movements in correct order while collapsing . As demonstrated in Ref . [14] the addition of non-native interactions will increase the folding rate substantially , however , at the cost of introducing a bias . There is also a strong dependence of successful folding events on protein size . For example , in Ref . [15] a rather simple and short trefoil knot in an RNA methyltransferase , folded successfully in only 2% of all cases with the same underlying model . On the other hand we succeeded in folding 2efv with 100% success rate [34] . For comparison the number of amino acids in 2efv is roughly two times smaller than the number of amino acids in the methyltransferase , which again is roughly two times smaller than the number of amino acids in the dehalogenase . While acknowledging such limitations of coarse-grained models , we are still confident in deducing a potential folding pathway from the analysis of the successful trajectories , in particular because all six trajectories are very similar . Fig . 2 shows an actual folding trajectory . The S-loop is colored red , the B-loop green and the C-terminus blue . Two very similar potential folding routes were observed in our simulations . In both routes , the two loops form in the beginning by twists ( fig . 2a ) of the partially unfolded protein such that B- and S-loop are aligned ( fig . 2b ) . In the first route , the C-terminus is threaded through the S-loop ( which needs to twist once again – fig . 2c ) before the B-loop flips over the S-loop . In the second route the steps are interchanged: the B-loop flips over the S-loop and the C-terminus ( shaded in light blue in fig . 2c ) . A figure-eight ( 41 ) knot forms as a result before the C-terminus manages to thread through the S-loop to reach the native state . In both cases , the C-terminus moves through the S-loop via a slipknot conformation ( fig . 2c ) . Note that loop flipping and threading are typically accomplished with backtracking events [20] for topologically frustrated proteins [21] . Similar conformational changes during folding mechanisms have been observed in other topologically non-trivial structures . The rotation of a proline rich loop was also observed in a big slipknotted protein , Thymidine Kinase [15] . Slipknot intermediates appear in the folding mechanism for the trefoil knot in Methyltransferase [15] as well . Unfortunately , the size and complexity of the protein does not allow us to study the full thermodynamic process and reconstruct the free energy profile along a reaction coordinate . However , kinetic data allow us to distinguish some characteristic times from which we can deduce a likely folding mechanism . In fig . 3 we investigate the rate-limiting step in the folding of the Stevedore knot . On the left panel , we plot the time it takes to thread the C-terminus through the S-loop ( tc ) against the time it takes to flip the B-loop over the S-loop . Solid symbols are trajectories associated with route I ( 0→61 ) , and open symbols are trajectories associated with route II ( 0→41→61 ) . In the first pathway , the flipping of the B-loop takes longer than the threading of the C-terminus in two out of three cases . In the second pathway ( and the third trajectory associated with route I ) , the threading of the C-terminus through the S-loop occurs shortly after the flipping of the B-loop . In both scenarios , the flipping of the B-loop over the S-loop is the rate-limiting step . Once this is achieved , the protein is essentially folded ( fig . 3b ) . The flipping of the B-loop can therefore be associated with an entropic barrier in the folding free energy . From an analysis of the order at which contacts occur ( fig . S2 ) it is possible to deduce the occurrence of a first small barrier , which is associated with the formation and twisting of B- and S-loop before the B-loop flips . Hence , we believe a three-state folding scenario is more likely than a two-state scenario . In order to study the unfolding pathway , we raised the temperature above the folding temperature . Even though some native contacts are lost at higher temperatures , the global mechanism is by and large reversed as compared to the folding routes ( see fig . S3 ) . To check how topological complexity restricts the free energy landscape the protein topology was changed from 61 to 41 ( by eliminating a crossing , as previously performed with a different protein in Ref . [35] ) . This slight modification increases the folding ability of DehI substantially to 11% , suggesting that complexity of the knot is an important parameter in determining the foldability of a protein .
Our analysis of the Protein Data Bank revealed the most complex protein knot in α-haloacid Dehalogenase DehI and the shortest ( so far unclassified ) knotted protein known to date . This discovery underscores that knots in the backbone of proteins are significant structural motifs that appear at different levels of protein complexity and might offer new insight in the understanding of protein folding mechanisms . The finding of the smallest knotted protein ( which is almost half the size of all previously known protein knots ) may eventually enable us to study the folding of knotted proteins with more sophisticated all-atom simulations . We investigated the folding route of the most topologically complex protein knot with molecular dynamics simulations of a structure-based model . The analysis of successful folding trajectories suggests that the Stevedore ( 61 ) knot in DehI folds via a simple mechanism: a large twisted loop in the protein flips over another previously twisted loop , thus essentially creating the six-fold knot in a single movement . Thus , the topological complexity of the Stevedore knot in DehI can be overcome and explained in the context of classical theories of protein folding [1] , [2] , [36] . The flipping of a loop over a mostly folded structure constitutes a new scenario in the folding of knotted proteins which differs , e . g . , from the folding of knots via partially unstructured knotted intermediates [14] . Our mechanism also includes previously observed elements like the threading of slipknot conformations through loops [15] . These mechanisms can be essential for folding into topologically challenging structures and provide a general framework for the understanding of knotted proteins .
The programs used to detect knots are identical to those used in our previous work [7] . To determine whether or not a structure is knotted , we reduce the protein to its backbone , and draw two lines outward starting at the termini in the direction of the connection line between the center of mass of the backbone and the respective ends . The knot type is determined by computing the Alexander polynomial , which is also implemented on our protein knot detection server ( http://knots . mit . edu . ) [37] . For a detailed discussion of our methods , the reader is referred to Ref . [7] . Note that this class of structure based models was not created with protein knots in mind and is very prone to fold into topologically frustrated states . Even though Go-models can be adapted to enhance the formation of knots [14] we refrained from this approach because we did not want to impose any bias . We applied a structure based coarse-grained model with only native contacts [32] , [33] . In total we folded 1000 trajectories of DehI at temperature T = 0 . 48 out of which 6 folded into a 61 knot . Furthermore , we observed 737 unknotted conformations , 85 trefoil ( 31 ) , 167 figure-eight ( 41 ) and five 52 knots . Higher and lower temperatures resulted in a lower rate of 61 formation . After the structure was simplified to a figure-eight knot , 11% of all configurations folded into the native state ( with more than 95% native contacts . )
|
Knots are ubiquitous in many aspects of our life , but remain elusive in proteins . The multitude of protein structures archived in the Protein Data Bank can be grouped into several hundred patterns , but only a handful are folded into knots . Combing through the recently added structures we found several novel knotted proteins . A microbial enzyme that catalyzes the breakdown of pollutants is the most complex protein knot encountered so far ( similar to a knot used by stevedores for lifting cargo ) . The smallest knotted protein on the other hand consists of only 92 amino acids . The existence of these complex motifs demonstrates that the ability of self assembly goes far beyond normal expectations . Aided by computer simulations we present evidence which suggests that the Stevedore protein knot , despite its topological complexity , may actually form in a single flipping movement .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"computational",
"biology/molecular",
"dynamics",
"computational",
"biology/macromolecular",
"structure",
"analysis"
] |
2010
|
A Stevedore's Protein Knot
|
Although fitness landscapes are central to evolutionary theory , so far no biologically realistic examples for large-scale fitness landscapes have been described . Most currently available biological examples are restricted to very few loci or alleles and therefore do not capture the high dimensionality characteristic of real fitness landscapes . Here we analyze large-scale fitness landscapes that are based on predictive models for in vitro replicative fitness of HIV-1 . We find that these landscapes are characterized by large correlation lengths , considerable neutrality , and high ruggedness and that these properties depend only weakly on whether fitness is measured in the absence or presence of different antiretrovirals . Accordingly , adaptive processes on these landscapes depend sensitively on the initial conditions . While the relative extent to which mutations affect fitness on their own ( main effects ) or in combination with other mutations ( epistasis ) is a strong determinant of these properties , the fitness landscape of HIV-1 is considerably less rugged , less neutral , and more correlated than expected from the distribution of main effects and epistatic interactions alone . Overall this study confirms theoretical conjectures about the complexity of biological fitness landscapes and the importance of the high dimensionality of the genetic space in which adaptation takes place .
The fitness landscape is one of the central concepts in evolutionary biology . Ever since Sewall Wright [1] , it has been used to study and conceptualize the process of long-term evolutionary adaptation . Fundamentally , knowledge of fitness landscapes is required to translate microevolutionary adaptation ( i . e . changes in gene frequencies ) into macroevolutionary change ( i . e . speciation events and large-scale phenotypic modifications ) . One of the major limitations of the concept of fitness landscapes , however , is the near complete lack of knowledge of any large-scale and biologically realistic fitness landscapes . Most of the landscapes currently available are restricted to very few loci or alleles [2] , [3] , [4] , [5] , [6] , [7] , [8] . Due to their limitation in size , these landscapes do not allow the study of properties that might arise from the high dimensionality that is characteristic for real fitness landscapes . Current examples for large-scale landscapes are based on RNA secondary structure [9] or enzymatic activity of RNA [10] . However , the relation of RNA structure to fitness is unclear and the relation between enzymatic activity and fitness is often highly non-linear [11] . The centrality of the concept of fitness landscapes for evolutionary biology , combined with the absence of good biological examples has necessitated the study of theoretically conceived and idealized fitness landscapes , often tailored to the particular question studied . The so-called NK landscapes are an example for a broad class of theoretical fitness landscapes [12] , which have tunable ruggedness ranging from smooth , single-peaked Mount-Fujiyama-like landscapes to maximally rugged uncorrelated landscapes , in which the fitness of each sequence is independent of the fitness of its neighbors . These NK landscapes have been used , among other things , to study properties of landscapes arising from high dimensionality [13] . Landscapes based on neutral networks [14] , [15] reconcile Kimura's neutral theory [16] with natural selection , and have been used to explain phenomena such as punctuated equilibria observed for example in the evolution of the antigenic profile of influenza [17] . The related holey landscapes , which consist of a network of high-fitness genotypes with embedded fitness-holes , have been very influential as models of speciation [18] . All these examples have been tremendously valuable in studying processes of evolutionary adaptation , but are purely conceptual and it is unclear to what extent they reflect properties of real fitness landscapes . Recent progress in high throughput data generation now allows measuring both fitness and genotype for a large number of mutants [19] , [20] , [21] . Combining such data sets with appropriate computational methods enables for the first time the reconstruction of large-scale and biologically realistic fitness landscapes . Here we analyze fitness landscapes that are based on predictive models for fitness of HIV in an in vitro replication assay [21] . These models predict fitness based on estimated effects of individual mutations ( main effects ) and of pair-wise combinations of mutations ( epistasis ) and can thus be considered as a quadratic approximation to the real HIV fitness landscape .
The fitness landscapes analyzed here are based on statistical models that are based on extensive measurements of in vitro replicative fitness . These models allow to predict the fitness of HIV from amino acid sequences ( see Materials and Methods and ref . [21] ) . The entire landscape consists of approximately 21800≅10600 fitness values . Clearly , it is impossible to generate all these values despite the fact that the predictive model would allow in principle to compute the fitness for any sequence . Therefore , we describe the properties of the fitness landscapes by using summary statistics based on different types of random or directed walks on these landscapes . Specifically we use such walks to compute three measures that characterize different properties of the landscapes: ruggedness , correlation length and neutrality ( see Materials and Methods ) . Ruggedness refers to the number of local fitness optima; i . e . genotypes whose fitness exceeds that of every one of its neighbors . We determine ruggedness as the number of different local optima reached by adaptive walks that climb the fitness landscape by means of steepest ascent from random positions on the landscape . These adaptive walks always move to that neighboring sequence , which has the highest fitness of all the neighboring sequences . Local optima act as attractors for such steepest-ascent walks: if a walk is started within the “attraction domain” of the optimum , the walk will converge to this optimum . Depending on the structure of the landscape , such walks need not end up in the same optima , even if they are started from similar initial conditions . Conversely , walks that end up in the same optima need not originate from similar areas of the fitness landscape . We use such simple hill-climbing walks here as tools to analyze structural properties of the underlying fitness landscape such as ruggedness or the attraction domain of the local optima . To characterize the process of adaptation of populations evolving on these fitness landscapes such hill-climbing walks have limited validity and may overly simplify more complex aspects of evolution . The correlation length quantifies to what extent proximity in sequence space translates into similarity in fitness . To measure correlation length , we perform random walks , which start at a random genotype in the landscape and then randomly move in each step to neighboring genotypes . Recording the fitness values along such a random walk we then determine correlation length as the characteristic distance over which the autocorrelation of fitness decays . Neutrality measures to what extent populations can move on the landscape without changing their fitness . To measure neutrality , we perform quasi-neutral walks , where random steps to neighboring genotypes are only accepted if they do not change fitness by more than a defined small threshold value . We determine neutrality as the maximal distance from the starting genotype that is attained by such a neutral walk . We first explore these measures for a reference landscape ( RL ) , which is based on the model that best predicts replicative capacity in the drug-free environment ( see Materials and Methods and ref . [21] ) . We then examine different variations of the RL ( see Materials and Methods ) in order to explore how these measures depend on the features of the underlying landscape . These variations include landscapes based on fitness measured in the presence of different antiretroviral drugs; landscapes , in which the strength of epistasis is reduced; and landscapes in which the coefficients determining main effects and pair-wise epistasis in the RL are randomized ( see below ) . The RL is characterized by a large number of optima , a large correlation length and considerable neutrality ( Figure 1 ) . For an increasing number of starting points we find an increasing number of optima , with only a weak saturation of the increase up to 105 different starting points . For the 105 starting points tested in Figure 1 , on average every fourth one leads to a different optimum . By contrast , in a completely smooth landscape such walks would always converge to the same optimum . The large number of optima indicate ( Figure 1A ) a high degree of ruggedness and hence a multitude of basins of attraction ( i . e . the set of starting points from which adaptive walks end up in a given optimum ) . Moreover , the starting genotype of an adaptive walk has a strong influence on the long-term evolutionary trajectory and neighboring starting points can lead to completely different trajectories ( Figure 1B ) . The basins of attraction of the different optima differ greatly in structure . Some optima have an attraction domain that is confined to a small region of sequence space and is sparsely distributed within this region ( type α in Figure 1B ) . Other optima have an attraction domain , which also covers a small region of sequence space , but is densely distributed in that region ( type β ) . Finally , optima of the last type have an attraction domain that spans a large part of sequence space but is only sparsely distributed ( type γ ) . The long correlation length of random walks on the RL ( Figure 1C ) indicate that almost all loci characterizing a genotype ( here , genotypes consist of 404 loci , see Materials and Methods ) have to be mutated until the memory of the initial fitness value is lost . In the landscapes considered here , mutations may have extremely small effects , but they are never completely neutral . To define a sensible concept of neutrality we therefore need to define a threshold for the maximal fitness effect that a mutation is allowed to have to be considered neutral . The exploration range of the resulting quasi-neutral walks strongly depends on the magnitude of this threshold ( see Figure 1D ) . If this threshold is 10−4 or lower , the exploration range is very small with a maximal distance of 5–10 mutations . For thresholds of 10−3 or higher , on the other hand , neutral walks can reach considerable distances of 100 mutations or more . Thus , although there are no fully neutral mutations in the RL , the landscape is characterized by large networks over which fitness changes only minimally . Comparing the RL to the corresponding best-fit landscapes for 15 different environments each characterized by the presence of a different antiretroviral drug ( see Materials and Methods ) , we find that ruggedness , correlation length , and neutrality are of similar magnitude in all these environments ( Figure 2 ) . However , the no-drug environment exhibits less neutrality and longer correlation length than all environments with antiretrovirals present . Interestingly , there are also consistent differences between drug-classes . Figure 2 shows results for the 3 main classes of antiretroviral drugs: protease inhibitors ( green ) , nucleoside reverse transcriptase inhibitors ( blue ) , and non-nucleoside reverse transcriptase inhibitors ( cyan ) . For instance , the landscape is particularly rugged in the presence of protease inhibitors . Interestingly , protease inhibitors are also known to have the most complex resistance profiles [22] , [23] , [24] , in the sense that resistance against them is mediated by a large number of interacting mutations . To assess the impact of the strength of epitasis relative to that of main effects , we consider alternative landscapes in which fitness interactions between mutations are weaker . We chose the RL as a reference because it has the highest predictive power ( see [21] and Figure S1 ) . However , this landscape might overestimate the role of epistasis for statistical reasons ( see Materials and Methods ) . A landscape based on a more conservative estimate of the role of epistasis can be obtained by fitting a hierarchical model that first estimates the effects of individual mutations ( “main effects” ) and then uses pair-wise interactions between mutations ( “epistasis” ) to explain the remaining variance in the biological data ( see Materials and Methods ) . This hierarchical landscape ( HL ) has a predictive power almost equal to that of the RL ( see Materials and Methods ) . As both the RL and the HL represent equally valid approximations of the true biological fitness landscape , the true magnitude of epistasis will presumably lie between these two extremes . To further explore the role of epistasis we can reduce its magnitude beyond the realistic range by reducing all epistatic interactions in the HL by a factor ε ( 0≤ε≤1 ) . The predictive power of the corresponding landscapes ( HLε ) decreases for small ε , but even for ε = 0 the predictive power is only reduced by 13% ( see Materials and Methods ) . This continuum of the HLε landscapes allows us to study the effect of the relative strength of epistasis on ruggedness , correlation length and neutrality . We find that ruggedness and neutrality consistently increase with the magnitude of epistatic effects ( Figure 3 ) . With increasing epistasis , the HLε gradually shift from a single-peaked smooth landscape without neutral networks ( ε = 0 ) to a very rugged landscape with large quasi-neutral networks ( ε = 1 ) . In contrast to the other two measures , correlation length in the HLε depends only weakly on epistasis . All three measures continue to exhibit the same type of dependence on epistasis when switching from the HL to the more epistatic RL ( Figure 3 ) . Taken together , these results indicate that the relative strength of pair-wise epistasis is a major determinant of the structure of fitness landscapes . An intuition for the impact of the strength of epistasis on ruggedness can be obtained as follows: If main effects dominate , a given mutation is always either beneficial or deleterious , independent of its background . However , if epistatic interactions dominate , a change in the genetic background can turn a beneficial mutation into deleterious one and vice versa . Thus the landscape only has one peak if main effects dominate , but may have multiple peaks if epistatic effects dominate . Note that epistasis need not necessarily increase ruggedness . For example , this would not be the case if most epistatic interactions were of the same sign ( as has often been assumed [25] , [26] ) . Thus the increase in ruggedness with epistasis is a particular feature of the landscapes studied here . The fact that neutrality increases with epistasis might seem contradictory at first , given that epistasis contributes to the selective effects of mutations . One should note , however , that non-trivial neutrality ( i . e . a mutation being neutral in some genetic backgrounds but not in others ) requires epistasis by definition . This type of non-trivial neutrality is responsible for the observed increase in neutrality with increasing epistasis . In contrast to the trivial type of neutrality that would be due to synonymous mutations , the neutrality observed here is exclusively due to the cancelling out of selective effects . Finally , the correlation length of a random walk decreases with the strength of epistatic effects for the following reason: In the case of independent loci , correlation is lost if on average every locus has been mutated . If loci interact epistatically , then a mutation at one locus affects the fitness-contribution of the mutations at other loci as well and hence the number of changes required to loose correlation decreases . Given these interpretations , our results ( that ruggedness and correlation length are high ) suggest that epistasis is strong enough to increase ruggedness , but too weak to strongly affect correlation length . Interestingly Fontana et al . [9] , [27] found that landscapes in which fitness is predicted by RNA secondary structure combine neutrality and ruggedness similarly to the landscapes described here . However , these RNA-derived landscapes exhibit short correlation lengths , in contrast to our HIV landscapes . As correlation length decreases with the strength of epistatic interaction ( see Figure 3 ) , one reason for this difference might be that the epistatic or pair-wise interactions are much stronger in RNA landscapes than in the HIV landscapes analyzed here . The strong impact of the relative strength of main effects and epistasis raises the question whether the properties of fitness landscapes also depend on the detailed correlation structure between different epistatic effects and main effects or whether they are only determined by the distributions of these effects . In order to address this question , we use three different schemes to randomize the main and epistatic effects underlying the RL ( Figure 4 ) and then measure ruggedness , neutrality and correlation length for the different types of randomized landscapes . We find that , despite its large number of peaks , the RL is still considerably smoother ( smaller number of peaks ) than the randomized landscapes ( see Figure 4A ) . Furthermore , the RL is also less neutral and more correlated ( see Figure 4C and 4B ) . This implies that the fitness landscape of HIV is considerably less rugged , less neutral , and more correlated than expected from the distribution of main effects and epistatic interactions alone . Moreover , it suggests that , although the overall strength of epistasis is an important factor , knowledge of the distributions of main effects and epistatic interactions does not fully characterize fitness landscapes in general , because the correlational structure between epistatic effects plays an essential role in determining the properties of the landscapes . It should be noted that the structure of the fitness landscapes discussed here might be affected by selection biases in the data used for the development the fitness-prediction model . The viral isolates have been obtained from HIV-infected individuals and therefore the mutations found in these isolates do not represent a random sample of all possible mutations . On the one hand , because all isolates harbour replication competent viruses , the sample is biased against lethal or highly deleterious mutations . On the other hand , most viral isolates carry drug resistance mutations . These resistance mutations are beneficial in the presence , but typically detrimental in absence of drugs . Hence , in the drug free environment ( or in an environment containing drugs to which a give mutation does not confer resistance ) the isolates may be enriched in deleterious mutations . In any event , the mutations found in the isolates represent the standing variation of mutations that are present on the level of the host population . Clearly , however , it is likely that the complete fitness landscape of HIV does contain much more fitness-holes/troughs than the landscapes described here , because of the observation bias against lethal mutants . Comparing the fitness landscape of HIV with various theoretical landscapes that have been used to study evolutionary processes [12] , [14] , [15] , [18] shows that these classical landscapes can capture certain aspects of a real landscape while failing to describe others . For example , the fitness landscape of HIV resembles uncorrelated landscapes with regard to the high ruggedness . However , unlike uncorrelated landscapes , it is characterized by considerable neutrality and large correlation length . In these respects , the HIV fitness landscape is closer to neutral landscapes ( such as holey landscapes ) or single-peaked Mount-Fujiyama-like landscapes . Finally , the structure of the attraction domains—in particular the existence of attraction domains which are at the same time very sparsely and widely distributed—strongly contrasts the situation in low dimensional spaces . Overall , these results highlight the complexity and the high dimensionality that need to be taken into account to describe adaptive processes in real biological systems .
The fitness-landscapes analyzed here are based on models that predict the fitness of HIV from amino acid sequences . Fitness is measured as the reproductive capacity ( RC ) of HIV-derived amplicons ( representing all of Protease ( PR ) and most of Reverse Transcriptase ( RT ) ) inserted into a constant backbone of a resistance test vector . The models are then trained to predict this fitness from the amino-acid sequence of the amplicons . Although the fitness , which is predicted by these models , is an in-vitro RC , we could show in [28] that this predicted RC is significantly correlated to HIV virus load in vivo . Details on the experimental measurement of the RC values and on inferring the predictor have been published in [29] and [21] . Here , we briefly reiterate the principles of the models fitted to the data . In essence , the predictor is based on fitting the data consisting of amino acid sequences ( s ) , coded here as a binary string , and the corresponding RC values ( w ) with the following model ( M1 ) For the purpose of this paper , sij denotes the presence ( sij = 1 ) or absence ( sij = 0 ) of allele j at position i . ( Although the present work is restricted to this simple binary case , a more general definition is used in the data fitting procedure [21]: If an ambiguity in the population sequencing is consistent with several amino acids at a given position , then sij denotes the probability of allele j at position i . ) Thus s is a valid sequence only if for all positions i , . In total , there are 1859 alleles at 404 positions . The vast majority ( 1848/1859 ) of these alleles are amino acids ( thus not all possible amino acids at the 404 positions are allowed ) ; only 11 alleles correspond to either insertions or deletions . The model parameters I , sij and εij;kl can be interpreted as intercept , main effects ( effects of individual alleles on their own ) , and epistatic effects ( effects of an allele at one locus in combination with an allele at another locus ) . As the number of parameters exceeds the number of data-points , the model M1 has been fitted to the data on the basis of a machine learning approach ( generalized kernel ridge regression ) . With this approach over-fitting is no concern because the sub-dataset on which the predictor is evaluated is independent from the sub-dataset from which the predictor is inferred ( see [21] ) . Note that equation ( M1 ) can also be written as a second order cluster expansion [30] of the log-fitness ( M2 ) where Sj denotes the allele at position j of the amino-acid sequence , the impact on log-fitness of allele Si at position i , and denotes the combined impact of allele Si at position i and Sj at position j . The first-order effects in equation ( M2 ) correspond to the main-effects in equation ( M1 ) and the second order effects to the epistatic effects in equation ( M1 ) . For instance if i is a bi-allelic locus with alleles Si/Si' and k/k' denote the position corresponding to those alleles in the binary representation used above , then = mk and = mk' . The different landscapes are all based on model M1 , but differ with respect to the relative weight that is given to epistasis and main effects: Figure S1 shows the predictive power of the different models . If not stated otherwise , the RC values underlying the fitness-landscapes RL , HL and HLε are measured in the absence of drugs . In addition we consider 15 alternative versions of the RL based on RC values measured in the presence of 15 different single drugs . The drugs used here are the protease inhibitors amprenavir ( AMP ) , indinavir ( IDV ) , lopinavir ( LPV ) , nelfinavir ( NFV ) , ritonavir ( RTV ) , and saquinavir ( SQV ) , the 6 nucleoside reverse transcriptase inhibitors abacavir ( ABC ) , didanosine ( ddI ) , lamivudine ( 3TC ) , stavudine ( d4T ) , zidovudine ( ZDV ) , and tenofovir ( TFV ) and the non-nucleoside reverse transcriptase inhibitors delavirdine ( DLV ) , efavirenz ( EFV ) , and nevirapine ( NVP ) . For each drug , the replicative capacity of a virus on drugs was given by the interpolated value measured at the drug concentration at which the NL4-3 based control virus has 10% of its replicative capacity in the absence of drug ( i . e . the IC90 for NL4-3 is used as the reference drug concentration for every subsequent measurement ) [21] . The landscapes are characterized by adaptive , neutral and random walks . Each walk consists of a series/succession of genotypes s0→s1→s2→s3… . . The different types of walks differ with respect to the updating rule ( i . e . on how genotype sk+1 is determined from sk ) . Unless stated otherwise the start genotype of each walk is chosen randomly , i . e . at each position one of the possible alleles is chosen randomly and independently from alleles at the other positions . The ruggedness of the fitness landscapes is measured as the number of different end-points reached from a pre-specified number of steepest-ascent hill climbing walks ( SAHCW ) starting from different , random start genotypes . In each step sk→sk+1 of a SAHCW , the fitness of all single mutants of sk is determined . If the single mutant with the maximal fitness ( smax ) is less fit than sk , the walk is terminated , as sk represents a local maximum . Otherwise , the fittest single mutant smax is chosen as the next genotype in the walk ( sk+1 = smax ) . The neutrality of fitness landscapes is measured as the range explored by quasi-neutral walks ( QNW ) of a pre-specified length L ( typically 1000 steps ) . In each step sk→sk+1 of a QNW , a single random amino-acid substitution is performed on the genotype sk , yielding the genotype sk' . If the log-fitness of sk' differs by less than ε from the log-fitness of sk , then sk' is chosen as the next step in the QNW ( sk+1 = sk' ) . If the difference is larger than or equal to ε , the step is rejected and an alternative single mutant of sk is probed in the same way for its neutrality etc . If after 104 trials , no quasi-neutral mutation has been found , the QNW stays at sk ( sk+1 = sk ) . The range of such a neutral walk is determined as the maximal Hamming distance of one the genotypes s1… . sL from the start-genotype s0 . The correlation length of a fitness landscape is measured as the inverse decay rate of the autocorrelation of the log-fitness along random walks ( RW ) . Specifically , a pre-specified number ( typically 105 ) of random walks are initiated each from a different random start genotype . In each step of a given RW a single randomly chose amino acid substitution is performed . The autocorrelation after k steps is then determined aswhere the brackets refer to averaging over all random walks performed . An exponential decay is then fitted to these autocorrelation coefficients by performing a linear least-square fit according toThe correlation length is then given by 1/β .
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Evolutionary adaptation can be understood as populations moving uphill on landscapes , in which height corresponds to evolutionary fitness . Although such fitness landscapes are central to evolutionary theory , there is currently a lack of biologically realistic examples . Here we analyze large-scale fitness landscapes derived from in vitro fitness measurements of HIV-1 . We find that these landscapes are very rugged and that , accordingly , adaptive processes on these landscapes depend sensitively on the initial conditions . Moreover , the landscapes contain large networks along which fitness changes only minimally . While the relative extent to which mutations affect fitness on their own or in combination with other mutations is a strong determinant of these properties , the fitness landscape of HIV-1 is considerably less rugged than expected from the individual and pair-wise effects of mutations . Overall this study confirms theoretical conjectures about the complexity of biological fitness landscapes and the importance of the high dimensionality of the genetic space in which adaptation takes place .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"population",
"biology",
"evolutionary",
"biology"
] |
2012
|
Exploring the Complexity of the HIV-1 Fitness Landscape
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Epstein-Barr virus ( EBV ) is etiologically linked to infectious mononucleosis and several human cancers . EBV encodes a conserved protein kinase BGLF4 that plays a key role in the viral life cycle . To provide new insight into the host proteins regulated by BGLF4 , we utilized stable isotope labeling by amino acids in cell culture ( SILAC ) -based quantitative proteomics to compare site-specific phosphorylation in BGLF4-expressing Akata B cells . Our analysis revealed BGLF4-mediated hyperphosphorylation of 3 , 046 unique sites corresponding to 1 , 328 proteins . Frequency analysis of these phosphosites revealed a proline-rich motif signature downstream of BGLF4 , indicating a broader substrate recognition for BGLF4 than its cellular ortholog cyclin-dependent kinase 1 ( CDK1 ) . Further , motif analysis of the hyperphosphorylated sites revealed enrichment in ATM , ATR and Aurora kinase substrates while functional analyses revealed significant enrichment of pathways related to the DNA damage response ( DDR ) , mitosis and cell cycle . Phosphorylation of proteins associated with the mitotic spindle assembly checkpoint ( SAC ) indicated checkpoint activation , an event that inactivates the anaphase promoting complex/cyclosome , APC/C . Furthermore , we demonstrated that BGLF4 binds to and directly phosphorylates the key cellular proteins PP1 , MPS1 and CDC20 that lie upstream of SAC activation and APC/C inhibition . Consistent with APC/C inactivation , we found that BGLF4 stabilizes the expression of many known APC/C substrates . We also noted hyperphosphorylation of 22 proteins associated the nuclear pore complex , which may contribute to nuclear pore disassembly and SAC activation . A drug that inhibits mitotic checkpoint activation also suppressed the accumulation of extracellular EBV virus . Taken together , our data reveal that , in addition to the DDR , manipulation of mitotic kinase signaling and SAC activation are mechanisms associated with lytic EBV replication . All MS data have been deposited in the ProteomeXchange with identifier PXD002411 ( http://proteomecentral . proteomexchange . org/dataset/PXD002411 ) .
Infection with Epstein-Barr virus ( EBV ) , a ubiquitous herpesvirus , is associated with malignant disease , including Burkitt lymphoma , nasopharyngeal carcinoma , gastric carcinoma , and post-transplant lymphoproliferative disease [1 , 2] . While EBV latency proteins drive proliferation , lytic EBV gene products have also been implicated in tumorigenesis [3 , 4] . The EBV protein kinase BGLF4 , an early lytic gene product , is conserved across the order herpesviridae [5 , 6] . Due to its unique nature and key role in infectious virus production [7 , 8] , BGLF4 and its downstream effectors are potentially druggable targets [6 , 9–11] . BGLF4 phosphorylates both viral and cellular proteins [6 , 12] to generate an environment suitable for efficient viral replication . BGLF4 phosphorylates EBV latency and lytic proteins to regulate their transactivation activity [13–15] and expression [16–18] . It also phosphorylates EBV encoded replication proteins to facilitate lytic DNA replication [19–21] . In addition , BGLF4 interacts with and phosphorylates host cellular proteins involved in DNA replication to block cellular chromosomal DNA replication [22] , create a pseudo-S phase environment [23 , 24] and initiates a DNA damage response ( DDR ) beneficial for viral replication [6] . BGLF4 also phosphorylates host cellular proteins to alter microtubule dynamics [25] , disrupt the nuclear lamina [24 , 26] and affect nuclear pore permeability [27] . Protein SUMOylation is modified by BGLF4 in a kinase activity and SUMO-binding dependent manner [28 , 29] and BGLF4 phosphorylates IRF3 and UXT to suppress host immune responses and NF-κB signaling , respectively [30–32] . Taken together , these studies indicate that BGLF4 affects many aspects of the cellular environment . However , a global overview of BGLF4 regulated signaling events in cells is still lacking . Recent advances in proteomics have enabled the discovery of potential kinase substrates in vitro and in vivo [6 , 33–35] . In particular , quantitative phosphoproteomics based on stable isotope labeling by amino acids in cell culture ( SILAC ) has greatly facilitated the elucidation of global phosphorylation regulation in signaling pathways mediated by TSLP [36] , IL-33 [37] , oncogenic PI3KCA mutations [34] , DDR and mitosis pathways [38–40] . In this study , we employed a SILAC-based quantitative phosphoproteomic approach to dissect BGLF4-regulated downstream signaling events . We quantified 7 , 568 unique phosphorylation sites including 3 , 046 hyperphosphorylated sites in 1 , 328 proteins . Bioinformatics and network analyses revealed that BGLF4 regulates many proteins involved in the modulation of the DDR , mitosis and nuclear transport , in part through regulation of cellular kinase and phosphatase activity . Integration of protein phosphorylation events predicted activation of the spindle assembly checkpoint ( SAC ) by BGLF4 and hence the inhibition of the anaphase promoting complex/cyclosome ( APC/C ) E3 ubiquitin ligase activity . Parallel proteomic analysis of the nuclear proteome revealed increased protein levels for multiple known APC/C substrates and this was confirmed by western blot analysis . This study significantly expands the information on host protein phosphorylation regulated by the EBV conserved protein kinase and provides a foundation for the development of new anti-herpesvirus strategies .
Akata ( EBV+ ) B cell lines modified to express doxycycline inducible BGLF4 or vector control [21] were used to examine the changes in protein expression and phosphorylation levels that occurred subsequent to doxycycline induction of BGLF4 expression . Akata ( EBV+ ) -BGLF4 cells were grown in light medium while vector control Akata ( EBV+ ) cells were grown in medium supplemented with heavy isotopes of lysine and arginine ( 13C6 , 15N2-lysine and 13C6 , 15N4-arginine ) . Doxycycline was added for 48 hours prior to harvesting . Western blotting with antibody that recognizes phospho-serine in the context of a CDK substrate motif ( [K/R]-S-P-X-[K/R] ) confirmed that BGLF4 induced changes in protein phosphorylation under these conditions ( S1 Fig , upper panel ) . An increase in γ-H2AX protein , a known downstream result of BGLF4 expression , was also documented at 48 hr along with induction of BGLF4 itself ( S1 Fig , lower panel ) . In contrast , there was no obvious change in the overall pattern of phosphorylation of protein kinase A ( PKA ) substrates as detected by western blotting with an antibody that detects phospho-serine or -threonine in the context of RRxS/T ( S1 Fig , middle panel ) . For MS analysis , nitrogen cavitation was carried out [41] and the nuclear material was lysed and digested with trypsin . The tryptic peptides were further fractionated using high pH reversed-phase liquid chromatography ( bRPLC ) and 12 fractions were collected . Phosphopeptides were enriched and analyzed on an LTQ-Orbitrap Elite mass spectrometer . For cell nuclear proteome analysis , 12 fractions were analyzed on an LTQ-Orbitrap Velos mass spectrometer . Protein and phosphorylation site identification and quantitation were carried out using Thermo Proteome Discoverer ( PD 2 . 0 ) software suite ( Fig 1A ) . Statistical analysis of quantified phosphopeptides showed a good correlation between two biological replicates ( S2 Fig ) . A total of 7 , 568 unique phosphosites from 2 , 525 proteins were quantified in the MS analyses ( S1 Table ) . For the nuclear proteome , a total of 2 , 565 proteins were quantified ( S2 Table ) . Phosphopeptides that showed a 2-fold or greater change in BGLF4 expressing Akata ( EBV+ ) cells versus vector control Akata ( EBV+ ) cells were considered to be hyper or hypophosphorylated , while those that changed less than 1 . 2-fold were considered as unregulated . Comparison of the light: heavy ratios of the detected phosphopeptides revealed that 3 , 046 unique phosphosites ( 40% ) were hyperphosphorylated in BGLF4-expressing cells ( Fig 1B , 1D and S1 Table ) . In contrast , only 115 out of 2 , 565 quantified proteins ( 4% ) were overexpressed by 2-fold or more in the same condition ( Fig 1C , 1D and S2 Table ) . The hyperphosphorylated phosphopeptides correlate to 1 , 328 unique proteins . For 688 of the 1 , 328 proteins , information was also obtained on protein levels . Of these 688 , 93% showed less than 2-fold increase in total protein level ( Fig 1E ) . Thus increased phosphorylation , in general , represented an increase in post-translational modification in the absence of a change in protein level . The activities of cell kinases and their counterbalancing phosphatases are regulated by post-translational modifications . BGLF4 therefore has the potential to indirectly manipulate the cellular environment by activation or repression of specific members of these protein families . A known example is BGLF4-induced phosphorylation and activation of the acetyl transferase TIP60 that then acetylates and activates the ATM kinase [6] . In the current MS analysis , increased phosphorylation at ATM S2996 was detected ( Figs 2A and S3 ) . This is a site of ATM auto-phosphorylation and hence a mark of ATM activity [42] . Phosphorylation of T180 , detected in our screen ( Fig 2A ) , is one of two activating phosphorylation sites on MAPK14/p38α that is introduced by the upstream MEK kinase following various cellular stresses including DNA damage and viral protein expression [43–45] . The activation of CHK2 and p38α can lead to the phosphorylation of phosphatase CDC25C at S216 [45 , 46] , which was also detected in our current analysis ( Fig 2A ) . The function of the other key DNA damage signaling regulator , ATR , is also modified during herpesvirus infections . ATR down-stream signaling is blocked in latently EBV infected cells by STAT3 [47] and in herpes simplex -1 infected cells undergoing lytic replication , ATR signaling is inhibited by the viral helicase-primase complex although ATR pathway proteins are involved in the replication process itself [48 , 49] . We detected increased phosphorylation of ATR ( Figs 2A and S3 ) , consistent with activated phosphorylation [50] . Y15 and T14 are inhibitory phosphorylation sites of CDK1 while singly phosphorylated T161 is an active form of CDK1 [51] . The most abundant form of CDK1 detected at S/G2 and G2/M is triple phosphorylated and is inactive . The phosphorylation status of CDK1 in cells is balanced by kinases ( WEE1 and MYT1 ) and phosphatase CDC25C . The phosphorylation and inhibition of CDC25C might contribute to the increased phosphorylation of CDK1 . Consistent with CDC25 inhibition , increased phosphorylation of CDK1 was detected at Y15 , T14 and T161 after BGLF4 expression ( Figs 2A and S3 ) . This suggests that the majority of the CDK1 in BGLF4 expressing cells is in an inactive form . In addition to CDC25C , we also detected increased phosphorylation of protein phosphatase 1 ( PP1 ) ( Fig 2A ) . PP1 is normally inactivated by CDK1 mediated phosphorylation [52 , 53] . The inactivation site in PP1β is T316 , in PP1γ is T311 and in PP1α is T320 . Inactivation of PP1 would strengthen kinase signaling by removing a component of negative feedback control . Interestingly , in one of the PP1 targeting partners , CDCA2/RepoMan , we observed hyperphosphorylation at two sites ( T423 and S429 ) that are in or adjacent to the RVxF PP1 binding site on CDCA2 , locations that are known to inhibit PP1 binding [54] and would thus also perturb PP1 substrate targeting . Western blot analyses were performed to confirm the presence of inactivating phosphorylation sites in CDK1 and PP1 . In the case of PP1 , PP1α was used to illustrate that all PP1 isoforms were subject to regulation by BGLF4 . Akata ( EBV+ ) -BGLF4 and Akata ( EBV+ ) -vector cells were treated with doxycycline for 48 hrs , harvested and the lysates subjected to western blotting with phospho-specific antibodies . Increased phosphorylation of CDK1 at Y15 ( Fig 2B ) and PP1α at T320 ( Fig 2C ) was observed in the presence of wild-type BGLF4 expression . We had previously shown that BGLF4 phosphorylation of TIP60 was a key upstream event in the BGLF4 induced activation of DNA damage signaling [6] . PP1 regulates a wide range of cellular functions including several aspects of the DDR [55] . We investigated whether there might be a linkage between PP1α and TIP60 . An immunoprecipitation assay performed using V5-PP1α and Flag-TIP60 transfected cells revealed co-precipitation of PP1α with Flag–TIP60 ( Fig 2D , lane 2 ) . No PP1α was seen in the Flag immunoprecipitate from the vector control co-transfected cells ( Fig 2D , lane 3 ) . A co-precipitation assay was also performed on Flag-BGLF4 and V5-PP1α transfected cells . Co-precipitation of PP1α with BGLF4 was detected ( Fig 2D , lane 1 ) . However , when the relative amounts of Flag-BGLF4 and Flag-TIP60 present in the direct precipitates is taken into account , the association of BGLF4 with PP1α appears relatively weak compared to the PP1 and TIP60 interaction . The interaction of PP1α and TIP60 suggests that TIP60 phosphorylation may be normally regulated by PP1α . On the other hand , BGLF4 induced PP1 inhibition might also lead to p38α activation [56] ( Fig 2E ) . Activated p38α could trigger TIP60 activity by phosphorylation of T139 [57 , 58] , which was detected with an approximately 10-fold increase in our screen ( S1 Table ) . Therefore , BGLF4 appears to regulate multiple kinases and phosphatases to modulate the final signaling readout ( Fig 2E ) . The preference for amino acids surrounding the phosphorylation site is one of the major mechanisms that contributes to kinase specificity [59] . Induction of BGLF4 would be expected to result in the phosphopeptides that were direct BGLF4 substrates being in the majority . PhosphoSitePlus ( PSP ) Logo Generator was used to analyze the unique phosphopeptides that showed a 2-fold or more increase in phosphorylation after BGLF4 induction . The dominant signature detected was an *S/TP motif where the *S/T is the phosphorylated serine/threonine . There were 2 , 927 *S/T phosphopeptides that were enriched in the -4 to -7 and +1 to +7 positions with prolines ( Fig 3A ) . Further analysis of phospho-peptides derived from the shared targets of our MS analysis and the in vitro protein array screening [6] reveals a similar motif signature for BGLF4 ( Fig 3C ) . Analysis of the 1 , 793 phosphopeptides that showed no change in phosphorylation after BGLF4 induction revealed that the *S/TP motif in these phosphopeptides was enriched in the +2 to +6 positions with aspartate and glutamate residues and in the -3 , -5 and -7 positions with arginine residues ( Fig 3B ) . Taken together , the phosphorylation signatures observed suggest that having proline residues surrounding the *S/TP favors BGLF4 phosphorylation while the presence of positively and negatively charged amino acids surrounding the *S/TP is unfavorable for BGLF4 phosphorylation . Motif-X was used to further extract the individual phospho-serine and phospho-threonine motifs that were statistically enriched in the phosphopeptides that showed 2-fold or more up-regulation . The *SP motif ( 1 , 276 phosphopeptides ) detected in this analysis showed either enrichment of lysine in the +7 position or no preferred amino acids in the 13 amino acids surrounding the *SP ( Fig 3D and 3E ) . There were 453 *TP motif related sequences that showed either enrichment of proline/aspartic acid in the +2 position ( Fig 3I–3J ) or no preferred amino acids in the 13 amino acids surrounding the *TP ( Fig 3K ) . These sequences may also represent BGLF4 phosphorylation sites . The data also reinforce the concept that BGLF4 has a substrate range that is expanded beyond CDK1 substrates as was previously observed in an in vitro analysis of EBV protein substrates . In that screen ~50% of BGLF4 substrates were not CDK1 substrates [21] . As illustrated in Fig 2A , BGLF4 expression affects the activity of cellular kinases AURKA/Aurora A , AURKB/Aurora B , ATM and ATR . The Motif-X program identified an RRx*S motif ( 55 phosphopeptides ) ( Fig 3E ) and two *SQ motifs ( 138 phosphopeptides ) ( Fig 3F and 3G ) . RRx*S is a signature of Aurora kinases , serine/threonine kinases that regulate mitosis while *SQ is the core motif for the ATM and ATR serine/threonine DNA damage associated kinases . These results suggest that BGLF4 might simultaneously regulate the DDR and mitotic signaling through phosphorylation . In our previous study using human protein microarrays , we identified genes involved in the DNA damage pathway as being statistically over-represented in BGLF4 substrates [6] . In that study , 20 DNA damage pathway proteins were found to be phosphorylated in vitro by BGLF4 . The current analysis examined the effects of BGLF4 expression in cultured cells and so detected not only direct BGLF4 phosphorylation events but also phosphorylation events mediated through the subsequent activation of downstream kinases . To obtain a more complete picture of the impact of BGLF4 on the DNA damage pathway , a list of proteins was generated that showed a 2-fold or more increase in phosphorylation after BGLF4 induction and were DNA damage-related as assessed by the David Gene Functional Classification tool ( https://david . ncifcrf . gov/ ) plus literature curation ( S3 Table ) . Key proteins identified are represented in the DNA damage pathway in Fig 4 . The data reveal extensive phosphorylation events downstream of the ATM kinase in proteins that participate in DNA repair with a significant enrichment of proteins in the MRN ( MRE11-RAD50-NBS1/NBN ) complex which functions in homologous recombination repair . A more limited number of phosphorylation events were detected on proteins downstream of ATR . These results reinforce the important role of the DDR in viral replication . We detected the phosphorylation mediated activation of several key kinases involved in the regulation of mitosis , including AURKA/Aurora A , AURKB/Aurora B , PLK1 and ATM ( Fig 2A ) . The motif analysis also revealed a signature for the activity of Aurora kinases ( Fig 3F ) , kinases that regulate aspects of mitosis including mitotic entry , sister chromatid cohesion and the SAC [60] . Gene Ontology ( GO ) analysis of the proteins whose phosphorylation was increased by 2-fold or more following BGLF4 induction yielded “Cell Cycle” and “M Phase” as two of the highest scoring groupings in the category of biological process ( S4 Fig ) . In light of this information , we assembled a list of proteins that showed a 2-fold or more increase in phosphorylation after BGLF4 induction and were mitosis-related as assessed by the David Gene Functional Classification tool plus literature curation ( S4 Table ) . Key proteins identified are colored in the mitosis network shown in Fig 5 . CDK1/Cyclin B is a key regulator of the G2/M transition and M-phase phosphorylation . By manually examining the hyperphosphorylated sites , we extracted 233 phosphopeptides with a canonical CDK1 motif signature ( S5 Table ) . However , in BGLF4 expressing cells , the phosphorylation activity of CDK1 appears to be suppressed and we anticipate that CDK1 kinase activity would be replaced by BGLF4 . The data depicted in Fig 5 show widespread phosphorylation of substrates that lie downstream of the mitotic kinases CDK1 , AURKA/Aurora A , AURKB/Aurora B and PLK1 . This signaling impacts on multiple mitotic processes including spindle assembly , chromosome condensation , sister-chromatid cohesion , chromosome separation and cytokinesis [60–63] . In addition to the mitotic kinase signaling described above , BGLF4 impacts other aspects of the mitotic environment . Proteins showing increased phosphorylation after BGLF4 induction include components of the SAC or mitotic checkpoint complex ( MCC ) and the APC/C ( Fig 5 ) . APC/C has two co-activators , CDC20 and CDH1 , which associate with APC/C at different stages of the cell cycle [64] . APC/CCDC20 is critical for orderly progression through mitosis . The mitotic checkpoint proteins , BUBR1 , BUB3 , and MAD2 , bind to CDC20 to form the SAC which sequesters CDC20 and directly inhibits activation of APC/CCDC20 . Inhibitory phosphorylation of CDC20 by CDK1 [65 , 66] and BUB1 [67] also contributes to CDC20 inhibition . Examination of the proteins showing differential phosphorylation after BGLF4 induction revealed multiple phosphorylation events that could impact on APC/C CDC20 activity ( Fig 6A ) . MAD2L1BP ( p31Comet ) binds to Mad2 , preventing Mad2 activation and silencing the SAC [68] . Phosphorylation of p31Comet at S102 reduces the affinity of p31Comet for Mad2 and promotes SAC activity [69] . In our analysis we observed an increase in the phosphorylation of p31Comet at S102 ( S134 in the isoform in our analysis ) ( S1 Table ) . We also detected a 5-fold increase in the phosphorylation of CDC20 at T70 . Phosphorylation at this conserved site of Xenopus CDC20 correlates with a lack of association of CDC20 with APC/C [66] . Phosphorylation of MPS1/TTK at S436 has been linked to its activation [70] . Activated MPS1 can trigger the phosphorylation of kinetochore protein KNL1 to initiate SAC activation [71] . We detected an approximately 3-fold increase of MPS1 phosphorylation at this same kinase activation site ( Fig 2A ) . Further , we detected a dramatic increase in BUB1 phosphorylation at S314 ( Figs 2A and S3 ) , a site that is phosphorylated by ATM and is essential for BUB1 kinase activity and for spindle checkpoint activation [72] . Active BUB1 also phosphorylates CDC20 and inhibits the ubiquitin ligase activity of APC/CCDC20 catalytically [67] . To test whether BGLF4 interacts with key mitotic proteins in cells , we performed a co-immunoprecipitation assay of transfected BGLF4 with MPS1 , BUB1 , CDC20 or p31Comet . Interestingly , we found that BGLF4 interacts with MPS1 , CDC20 and p31Comet but not BUB1 ( Fig 6B ) , suggesting that BGLF4 may phosphorylate MPS1 , CDC20 and p31Comet directly and regulate BUB1 phosphorylation through ATM activation . To test whether BGLF4 could directly phosphorylate the proteins related to SAC activation . We performed an in vitro kinase assay using purified BGLF4 and GST-tagged MPS1 , PP1α and CDC20 . Because MPS1 itself is a protein kinase , we used only a segment of MPS1 ( aa 410–517 ) that contains a known phosphosite ( S436 ) in the kinase assay . As shown in Fig 6C , WT BGLF4 but not KD mutant BGLF4 , phosphorylated the positive control TIP60 ( a . a . 1–290 ) as well as MPS1 ( a . a . 410–517 ) , PP1α and CDC20 . In the case of CDC20 , we detected three phosphorylation bands corresponding to bands for GST tagged CDC20 and two N-terminal fragments detected by immunoblot using an anti-N-terminal CDC20 antibody ( S5 Fig ) . These results are consistent with the hyperphosphophorylation of the N-terminal CDC20 ( S41 and T70 ) seen in our MS analysis ( S1 Table ) . Taken together , our results demonstrate that BGLF4 binds to and directly phosphorylates key cellular proteins to activate the SAC and block APC/C activity ( Fig 6A ) . We tested whether BGLF4 induced phosphorylation was also seen during the course of EBV lytic reactivation using phospho-specific antibodies . As shown in Fig 7A , the phosphorylation of PP1α on T320 was dramatically increased in the EBV+ cells but not the EBV- cells . In addition to PP1α , we also detected the hyperphosphorylation of a mitosis-related protein in both EBV replicating cells and BGLF4-expressing cells ( Fig 7B ) . The phosphorylation signals detected correlate well with BGLF4 expression ( Fig 7B , lanes 2–4 and 13–14 ) . APC/C is a multi-protein complex that has ubiquitin ligase activity . Substrates contain recognition elements such as the D-box , KEN-box and A-box [73] and multiple substrates and potential substrates have been identified [40 , 74–76] . Among the proteins detected in our MS analysis were 21 known APC/C substrates whose protein levels were either unchanged or increased ( 0 . 8- to 7 . 1-fold ) following BGLF4 expression ( Fig 8A ) . In contrast , only 3 known APC/C substrates had decreased protein levels after BGLF4 expression ( 0 . 5 to 0 . 7-fold change ) . Consistent with our MS data , increased protein levels of Aurora A , Aurora B , NUSAP1 , Cyclin B1 , and TOP2A were confirmed by western blot analysis upon wild-type BGLF4 induction ( Fig 8B ) . In contrast , there was no protein level increase with the induction of SUMO-binding deficient or kinase dead BGLF4 mutants ( Fig 8B ) , indicating that both SUMO binding and kinase activity are required for the BGLF4 induced increase in these APC/C substrates . In addition , we also confirmed the accumulation of Aurora B and TOP2A in lytically induced EBV+ cells ( Fig 8C ) . These observations suggest that , by activating the SAC and blocking APC/C activity , BGLF4 may regulate the stability of proteins critical for viral replication . To evaluate the contribution of SAC activation to EBV replication , we tested the effects of the inhibitor reversine which has been shown to inhibit the SAC [77] . Interestingly , we found that the amount of EBV virus released to the medium was dramatically reduced by treatment with reversine at doses of minimal toxicity . In contrast , the intracellular viral DNA level was less affected by reversine , suggesting that SAC activation has a role in the viral life cycle subsequent to the DNA replication step ( Fig 8D and 8E ) . Nuclear envelope breakdown takes place at the transition into prometaphase and is a critical requirement for mitotic entry . Hyperphosphorylation of nuclear pore proteins takes place during mitosis and we noted increased phosphorylation of multiple nuclear pore proteins after BGLF4 expression ( Fig 9 and S6 Table ) . Phosphorylation of the nucleoporin NUP98 has been identified as being particularly important for nuclear pore disassembly [78] . NUP98 is phosphorylated by the kinases Nek6 and CDK1 . We detected 11 hyperphosphorylation events on NUP98 after BGLF4 expression , including increased phosphorylation of one of two Nek6 sites ( S608 ) and three of five CDK1 sites ( T546 , S612 and S623 ) ( S6 Table ) . BGLF4 has previously been shown to interact with the nuclear pore proteins NUP62 and NUP153 and to increase the permeability of the nuclear pore for large proteins [27] . Here we detected increased phosphorylation on 6 sites of NUP153 that may be potentially phosphorylated by BGLF4 . The increased phosphorylation of importins ( KPNA2/3/4/5 ) and RAGAP1-SUMO1 also suggests that BGLF4 might regulate the relocalization of importins and RAGAP1-SUMO1 through phosphorylation and therefore enhance nuclear transport of viral proteins [27] . This is supported by the observation that BGLF4 increased the expression of KPNA2 and KPNA4 at the protein level in the nuclear fraction by 2- to 3-fold in our proteomic analysis ( S2 Table ) .
We utilized SILAC-based quantitative proteomics approaches to globally identify the downstream phosphorylation events regulated by the EBV protein kinase BGLF4 . Our study revealed that BGLF4 initiates widespread changes in protein phosphorylation to impact multiple cellular processes , in particular , the DDR , mitosis , cell cycle and nuclear transport . This study provides the most comprehensive survey of quantified phosphorylation events triggered by a conserved herpesvirus protein kinase . We provide insight into the substrate motifs phosphorylated after expression of the EBV conserved herpesvirus protein kinase ( Fig 3 ) . The phosphorylation triggered by BGLF4 was partially mediated by BGLF4 modulation of cellular kinase and phosphatase activity . We detected activation phosphorylation events on ATM , ATR , Aurora A , BUB1 , PLK1 and p38α and inhibitory phosphorylation events on phosphatases CDC25C and PP1 ( Fig 2A ) . We also observed increased protein levels of Aurora kinases A and B upon BGLF4 induction ( Fig 8A and 8B ) . Consistent with these observations , motifs resembling those of ATM/ATR and Aurora A/B were extracted from the BGLF4 up-regulated phosphoproteins ( Fig 3F–3H ) . Although BGLF4 partially mimics cellular CDK activity [24] , the large number of proline-directed phosphopeptides without CDK1 motif features reinforces the previous observation [21] that BGLF4 has a broader substrate specificity than CDK1 ( Fig 3 ) . A recent study on phosphorylation induced by murine γ-herpesvirus 68 ( MHV68 ) infection also found a CDK motif signature . In that case the signature was generated predominantly by the D-type cyclin ortholog ORF72 rather than the BGLF4 ortholog ORF36 [79] . D-type cyclins are encoded by gamma 2 herpesviruses ( rhadinoviruses ) but not the gamma 1 herpesviruses ( lymphocryptoviruses ) of which EBV is a member . These observations suggest that EBV BGLF4 and MHV68 ORF36 protein kinases may have evolved differing substrate specificities and emphasize the potential for selective activation of CDK1-like substrates during herpesvirus replication . We provide the largest dataset on the DDR pathway regulated by the conserved herpesvirus protein kinases . Previously , we found that BGLF4 regulates the host DDR through the TIP60/ATM pathway [6] . The ATM dependent DDR plays a critical role in EBV lytic reactivation in both B cells and epithelial cells [6 , 80] . The current study significantly expands the list of DDR proteins regulated by BGLF4 ( Fig 4 and S3 Table ) . In addition to manipulation of ATM signaling , BGLF4 also positively regulated ATR . Not all the hyperphosphorylated sites in the DDR pathway contain an ATM/ATR motif ( S3 Table ) , suggesting that BGLF4 may directly phosphorylate certain DDR proteins or modulate other cellular kinase or phosphatase activity to impact the final signaling readout . Our study suggests an extensive participation of DDR proteins in viral life cycle . The importance of our finding is further supported by the fact that DDR signaling also plays a key role in the replication of a variety of viruses [81–84] and that viral latency proteins tend to block the DDR to facilitate latency establishment [6 , 85–87] . We now show that BGLF4 also increases the phosphorylation of a large number of proteins involved in the mitotic phase of the cell cycle ( Fig 5 and S4 Table ) . EBV genome amplification arrests the cell in early S phase to create a pseudo-S phase environment [88] and BGLF4 is known to contribute to the pseudo-S phase environment through Rb and p27 phosphorylation [23 , 24] . Virus-induced pseudo-mitosis is also observed in cells replicating human cytomegalovirus ( HCMV ) [89] . The widespread regulation of mitotic signaling by BGLF4 seen in our study suggests that the mitosis-like environment is intimately involved in EBV replication and maturation . A new insight into BGLF4 regulated mitotic signaling comes from the evidence of SAC activation through protein phosphorylation ( Figs 6 and 7 ) . The key regulators of the SAC , CDC20 , BUB1 and MPS1 , are phosphorylated either directly by BGLF4 or via BGLF4 mediated activation of ATM . SAC activation plays a critical role in inhibiting APC/C ubiquitin ligase activity . The central role of the APC/C complex in regulating the cell cycle has led to the APC/C being targeted by viruses to benefit their life cycles [90 , 91] . We found that the protein level of multiple known APC/C substrates is increased upon BGLF4 induction , indicating inhibition of APC/C activity by BGLF4 ( Fig 8A and 8B ) . Furthermore , we also demonstrated that the BGLF4 induced accumulation of TOP2A and Aurora B can be observed in lytically induced Akata ( EBV+ ) cells ( Fig 8C ) . The APC/C target TOP2A has previously been shown to be important for both EBV and KSHV replication [92–95] . During HCMV infection , the viral protein kinase UL97 , together with UL21a , down-regulates APC/C activity by targeting its subunits [96] . We anticipate that , during EBV lytic replication , other viral proteins may also co-ordinate with BGLF4 to selectively regulate the SAC readout and the ultimate stability of individual APC/C components . Interestingly , our study indicates that SUMO binding and kinase activity are critical for BGLF4 induced APC/C inhibition ( Fig 8B ) as well as the phosphorylation of PP1 ( Fig 2C ) , TIP60 , ATM , KAP1 and H2AX [29] . BGLF4 induction of the DDR is driven by BGLF4/TIP60/ATM signaling [6] . There is cross-talk between the DDR and the SAC that is mediated by ATM [97–99] . The effectiveness of ATM inhibitors in blocking EBV replication [6] may therefore be partially explained by the participation of ATM in two checkpoints whose modulation is critical for EBV replication . Importantly , we have demonstrated that the amount of extracellular EBV virus released is significantly suppressed by a small molecule inhibitor reversine ( Fig 8D and 8E ) . Reversine is reported to block SAC activation by inhibiting the mitotic kinase activity of MPS1/TTK and Aurora A and B . Recently reversine was also shown to reverse the aberrant prolonged mitosis duration triggered by human papillomavirus ( HPV ) E6/E7 [77 , 100 , 101] . Taken together , our observations suggest that the mitotic signaling triggered by BGLF4 plays an important role in the production of mature extracellular virus . Lytic viral replication is also associated with inhibition of APC/C activity in HCMV and high risk HPV infections [90 , 102–104] although in these cases the inhibition occurred through sequestration or degradation of APC/C subunits . More frequently described are examples of viral latency proteins that promote APC/C activity . HPV16 E6 and E7 up-regulate CDC20 and Ubch10 and promote APC/C activity [105] while EBV EBNA2 induces MAD2 degradation and possibly activation of APC/C [106] and human T cell lymphotropic virus type 1 ( HTLV-1 ) tax promotes APC/C activity through binding to CDC20 and APC3 [107] . Hepatitis B virus ( HBV ) X protein activates APC/C through inhibiting the association between BubR1 and CDC20 [108] and simian virus 40 ( SV40 ) large T antigen disrupts the MCC/SAC complex and promotes APC/C activity [109] . Thus the latency and lytic phases of the virus life cycle are diametrically opposed in their interactions with the SAC . We also provide evidence that the nuclear pore complex is extensively regulated by BGLF4 phosphorylation . BGLF4 has been shown to target the nuclear pore complex through interaction with nucleoporins and BGLF4 mediated phosphorylation of NUP62 and NUP153 has been implicated in the nuclear transport of viral proteins [27 , 110] . We noted 6 phosphorylation sites on NUP153 after BGLF4 induction ( S6 Table ) . In addition , our current study identified phosphorylation sites on other proteins involved in the nuclear pore complex including NUP35 , NUP50 , NUP98 , NUP107 , NUP133 , NUP188 , NUP210 , NUP214 , ELYS , NDC1 , POM121 , XPO1 , CSNE1L and TPR ( Fig 9 and S6 Table ) . Phosphorylation of these proteins may play a role in BGLF4 induced nuclear pore disassembly [78] . Interestingly , a recent study also detected the phosphorylation of NUP98 and NUP153 by the HCMV protein kinase UL97 [111] , indicating the conserved role of these viral kinases in targeting the nuclear pore complex during viral replication . The nuclear pore complex has also been implicated in regulation of the SAC [112] . The Mad1-Mad2 complex is a key mediator of the SAC . In interphase , Mad1-Mad2 is docked at the nucleoplasmic side of the nuclear pore complex by interactions with the nuclear basket protein TPR [113 , 114] . Upon disassembly of the nuclear pore , the Mad1-Mad2 complex is recruited to BUB1 to allow SAC formation [115] . BGLF4 may therefore also contribute to SAC activity via nuclear pore disassembly . In summary , our study suggests that the EBV protein kinase BGLF4 integrates multiple signaling events , including the DDR , mitotic signaling and phosphorylation of the nuclear pore complex , to induce SAC activation and APC/C inhibition . The information obtained gives valuable insight into viral manipulation of host signaling pathways that facilitate lytic replication and thus provides a foundation for the design of therapeutic strategies to limit EBV-associated disease .
The Akata ( EBV+ ) -tet-Vector , Akata ( EBV+ ) -tet-BGLF4 , Akata ( EBV+ ) -tet-BGLF4 ( mSIM-N ) , Akata ( EBV+ ) -tet-BGLF4 ( KD ) , Akata-BX1 ( EBV+ ) , Akata ( EBV+ ) and Akata-4E3 ( EBV- ) cells were grown in RPMI 1640 media supplemented with 10% FBS ( Cat# 26140079 , Thermo Fisher Scientific ) in 5% CO2 at 37°C [6 , 29] . 293T cells were grown in DMEM media supplemented with10% FBS ( Cat# 26140079 , Thermo Fisher Scientific ) in 5% CO2 at 37°C . The construction of Akata ( EBV+ ) -tet-Vector , Akata ( EBV+ ) -tet-BGLF4 , Akata ( EBV+ ) -tet-BGLF4 ( mSIM-N ) , and Akata ( EBV+ ) -tet-BGLF4 ( KD ) cell lines was described previously [21] . In order to label cells with stable isotopic amino acids , Akata ( EBV+ ) -tet-BGLF4 and Akata ( EBV+ ) -tet-Vector cells were propagated in RPMI 1640 SILAC media deficient in both L-lysine and L-arginine ( Cat# 88421 , Thermo Fisher Scientific ) and supplemented with light lysine ( 12C6 14N2-K ) and arginine ( 12C6 14N4-R ) for light state ( Cat#s L-9037 and A-8094 , Sigma ) , and 13C6 15N2-K and 13C6 15N4-R for heavy state labeling ( Cat#s CNLM-291-H-1 and CNLM-539-H-1 , Cambridge Isotope Laboratories ) . Cells were cultured for at least 6 doubling times for complete incorporation . V5-PP1α plasmid was kindly provided by Christine Neuveut [116] . Flag-TIP60 and Flag-BGLF4 were described previously [6 , 29] . MPS1 and BUB1 were obtained from William Hahn and David Root ( Addgene plasmid #s 23857 and 23612 ) [117] and were recloned as Flag-MPS1 and Flag-BUB1 in an SG5 expression vector . HA-cdc20 was a gift from Marc Kirschner ( Addgene plasmid # 11594 ) [64] . pcDNA5 FRT TO myc p31Comet was a gift from Stephen Taylor ( Addgene plasmid # 59833 ) [68] . TIP60 ( a . a . 1–290 ) , MPS1 ( a . a . 410–517 ) , PP1α and CDC20 were cloned into a modified GEX-2T vector ( GH413 ) . Halo-BGLF4 ( WT ) and Halo-BGLF4 ( KD ) were constructed by cloning WT and KD BGLF4 into pHTN HaloTag CMV-neo Vector ( Cat# G7721 , Promega ) . Cells were harvested and lysed in 2x SDS-PAGE sample buffer and boiled for 5 minutes . The samples were separated on 4–20% TGX gels ( Biorad ) , transferred to PVDF membranes , and probed with primary and horseradish peroxidase-conjugated secondary antibodies . Primary antibodies purchased from Cell Signaling Technology were: anti-phospho-CDC2/CDK1-Y15 ( Cat# 9111 ) , anti-CDC2/CDK1 ( Cat# 9112 ) , anti-Phospho-PP1α-T320 ( Cat# 2581 ) , anti-PP1α ( Cat# 2582 ) , anti-γH2AX ( Cat# 2595 ) , anti-Cyclin B1 ( Cat# 12231 ) , anti-TOP2A ( Cat# 12286 ) , anti-CDC20 ( Cat# 4823 ) , Aurora kinase sampler kit ( Cat# 3875 ) and phosphor-Ser/Thr kinase substrate antibody sampler kit ( Cat# 9920 ) . Rabbit anti-β-actin polyclonal antibody ( Cat# A5441 ) , anti-Flag ( Cat# F7425 ) , anti-NUSAP1 ( Cat# SAB4502109 ) and mouse anti-Myc antibodies ( Cat# M4439 ) were obtained from Sigma Aldrich . Rat anti-hemagglutinin ( HA ) high-affinity antibody ( Cat# 11-867-431-001 ) was obtained from Roche . Mouse anti-V5 and anti-V5-HRP antibodies ( Cat# R960-25 and R961-25 ) were obtained from Invitrogen . Anti-phospho-Ser/Thr-Pro MPM2 antibody ( Cat# 05–368 ) was purchased from Millipore . Normal rat IgG ( Cat# sc-2026 ) and mouse IgG ( Cat# sc-2025 ) were obtained from Santa Cruz . Mouse anti-BGLF4 antibody has been described previously [118] . Cells were the treated with doxycycline for 48 hrs and then counted twice using a hemocytometer and confirmed to be greater than 95% viable by Trypan blue exclusion . Equal numbers of cells ( 1x109 ) from light and heavy conditions were mixed and cells were centrifuged at 400 x g for 5 min . Pellets were washed twice by re-suspending in 250 ml of pre-chilled Dulbecco’s PBS ( Cat# 10010–049 , Thermo Fisher Scientific ) . Nuclear fractionation was performed using nitrogen cavitation as previously described [41] with some modifications . Briefly , the combined cells were lysed in Hypotonic Lysis Buffer ( 20 mM HEPES pH 7 . 4 , 10 mM KCl , 2 mM MgCl2 and 2mM CaCl2 ) plus protease ( protease inhibitor tablet , Roche Cat# 05892791001 ) and phosphatase inhibitors [ ( 2 . 5 mM sodium pyrophosphate ( Na4P2O7 ) , 1 mM sodium orthovanadate ( Na3VO4 ) , 1 mM β-glycerophosphate , 10 mM sodium fluoride ( NaF ) ] for subcellular fractionation by nitrogen cavitation at 200 psi for 10 min on ice ( 4639 Cell Disruption Vessel , Parr Instrument Company ) . Cell disruption with intact nuclei was confirmed by trypan blue staining . The lysate was centrifuged at 1000 x g for 10 min at 4°C to collect the crude nuclei and then resuspended in 10 ml Nuclear Wash Solution ( Hypotonic Lysis Buffer plus 0 . 25 M sucrose and protease/phosphatase inhibitors ) . The nuclei were harvested by centrifugation at 1000 x g for 10 min at 4°C and then were re-suspended in 10 ml freshly prepared Urea Lysis Buffer ( 20 mM HEPES pH 8 . 0 , 9 M urea and protease/phosphatase inhibitors ) for sonication . The nuclear lysate was centrifuged at 15 , 000 x g for 10 min at 18°C . The supernatant was stored in -80°C for proteomics analysis . Protein concentration ( ~4 mg/ml ) was measured by BCA assay ( Pierce , Cat# 23227 ) . Peptides were prepared by an in-solution tryptic digestion protocol with modifications [119] . Briefly , nuclear lysate ( in Urea Lysis Buffer ) was reduced with 4 . 5 mM dithiothreitol and alkylated with 10 mM iodoacetamide . For tryptic digestion , protein extracts were diluted in 20 mM HEPES pH 8 . 0 to a final concentration of 2 M urea and incubated with TPCK-treated trypsin ( Cat# LS003744 , Worthington Biochemical ) at 25°C overnight . Protein digests were acidified by 1% v/v trifluoroacetic acid ( TFA ) and subjected to centrifugation at 2 , 000 × g at 25°C for 5 min . The supernatant of the protein digests was loaded onto a Sep-Pak C18 column ( Cat# WAT051910 , Waters , Columbia , MD ) equilibrated with 0 . 1% v/v TFA . Columns were washed with 6 ml of 0 . 1% v/v TFA twice and peptides were eluted in 2 ml of 40% v/v acetonitrile ( ACN ) with 0 . 1% v/v TFA three times . Eluted peptides were lyophilized and subjected to high pH reversed-phase liquid chromatography ( bRPLC ) fractionation . Peptides were fractionated by bRPLC as described earlier [120 , 121] . Briefly , 12 mg lyophilized peptide mixture was re-suspended in buffer A [10 mM triethylammonium bicarbonate ( TEABC ) ] and fractionated by bRPLC chromatography on an Agilent 1100 LC system using a linear gradient of 8 to 60% buffer B ( 10 mM TEABC in 90% ACN ) for 60 min at a flow rate of 1 ml per min . A total of 96 fractions were collected , concatenated to 12 fractions , and vacuum dried . For the nuclear proteomic analysis , 10% of the peptides from each fraction were used . For the phosphoproteomic analysis , the remaining 90% of the peptides were subjected to TiO2-based phosphopeptide enrichment using 5 um titansphere beads ( Cat# 5020–75000 , GL Sciences , Japan ) . Peptides were mixed with beads in a 1:1 ratio and incubated at RT for 30 minutes . Peptides were then washed with 80% ACN in 3% TFA , eluted using a 4% ammonia solution and immediately neutralized with 4% TFA . Eluted peptides were vacuum dried , re-suspended in 30 μL 0 . 1% TFA , and desalted using C18 StageTips . The eluted peptides were subjected to LC-MS/MS analysis . The total nuclear peptides were analyzed on an LTQ-Orbitrap Velos mass spectrometer interfaced with Easy-nLC II nanoflow LC system ( Thermo Scientific ) . The mass spectrometer was operated in the data dependent mode , precursor and product ions were selected and measured using Orbitrap mass analyzer . The peptides were loaded onto a pre-column ( 75 μm x 2 cm , Magic C18 AQ 5 μm , 120 Å ) and resolved on an analytical column ( 75 μm x 20 cm , Magic C18 AQ 3 μm , 120 Å ) in 0 . 1% v/v formic acid and eluted using an ACN gradient ( 3–35% v/v ) containing 0 . 1% v/v formic acid for 95 minutes and a total run time of 120 minutes . The settings were: a ) Precursor scans acquired ( FTMS ) from 350–1 , 800 m/z at 60 , 000 resolution; and b ) MS2 scans acquired ( FTMS ) - fragmented using higher energy collisional dissociation ( HCD ) fragmentation of the 10 most intense ions ( isolation width: 1 . 90 m/z; normalized collision energy: 35%; activation time = 0 . 1 ms ) at 30 , 000 resolution . The enriched phosphopeptides were analyzed on an LTQ-Orbitrap Elite mass spectrometer interfaced with Easy-nLC II nanoflow LC system ( Thermo Scientific ) . The peptide digests were reconstituted in 0 . 1% formic acid and loaded onto a trap column ( 75 μm x 2 cm ) packed in-house with Magic C18 AQ ( Michrom Bioresources , Inc . , Auburn , CA , USA ) . Peptides were resolved on an analytical column ( 75 μm x 50 cm ) at a flow rate of 300 nL/min using a linear gradient of 10–35% solvent B ( 0 . 1% formic acid in 95% ACN ) over 85 min . The total run time including sample loading and column reconditioning was 120 min . Data dependent acquisition with full scans in 350–1700 m/z range was carried out using an Orbitrap mass analyzer at a mass resolution of 120 , 000 at 400 m/z . The fifteen most intense precursor ions from a survey scan were selected for MS/MS fragmentation using higher energy collisional associated dissociation ( HCD ) fragmentation with 32% normalized collision energy and detected at a mass resolution of 30 , 000 at 400 m/z . Automatic gain control ( AGC ) target was set to 1x106 for MS and 5x104 ions for MS/MS with a maximum accumulation time of 200 ms . Dynamic exclusion was set for 30 seconds with a 10 ppm mass window . Internal calibration was carried out using lock mass option ( m/z 445 . 1200025 ) using ambient air . The MS derived data were screened using MASCOT ( Version 2 . 2 . 0 ) and SEQUEST search algorithms against a human RefSeq database ( version 70 ) plus EBV database from the Akata strain [122] using Proteome Discoverer 2 . 0 ( Thermo Scientific ) . The search parameters for both algorithms included: carbamidomethylation of cysteine residues as a fixed modification and protein N-terminal acetylation , oxidation at methionine , phosphorylation at serine , threonine and tyrosine and SILAC labeling 13C6 15N2-K and 13C6 15N4-R as variable modifications . MS/MS spectra were searched with a precursor mass tolerance of 20 ppm and fragment mass tolerance of 0 . 1 Da . Trypsin was specified as the protease and a maximum of two missed cleavages were allowed . The data were screened against a target decoy database and the false discovery rate was set to 1% at the peptide level . The protein and peptide data were extracted using top one peptide rank and high peptide confidence filters . The false discovery rate ( FDR ) was calculated by enabling the peptide sequence analysis using a decoy database and a cut-off of 1% peptide and protein level FDR used for identifications . The SILAC ratio for each phosphopeptide-spectrum match ( phosphoPSM ) was calculated by the quantitation node and the probability of phosphorylation for each Ser/Thr/Tyr site on each peptide was calculated by the ptmRS node in the Proteome Discoverer and phosphosite probabilities greater that 75% were extracted for further analyses . Peptides with ratios greater than 2 . 0-fold were considered as regulated and used for further analysis . The MS proteomics data have been deposited to the ProteomeXchange Consortium ( http://proteomecentral . proteomexchange . org ) via the PRIDE partner repository with the dataset identifier PXD002411 . The surrounding sequence ( 7 amino acid residues on either side ) for each identified phosphorylation site was extracted from the RefSeq database ( version 70 ) . For phosphorylation sites that were located at the N-or C-termini , the surrounding sequence could not be extended in this fashion and they were excluded from further motif analysis . The Motif-X algorithm ( http://motif-x . med . harvard . edu/ ) was used to extract motifs . The significance threshold was set to p<1 x 10−10 . The minimum occurrence of the motif was set to 30 for pSer/pThr peptides against an IPI Human proteome background . Motif logos were generated by WebLogo ( http://weblogo . berkeley . edu/ ) . PhosphoSitePlus ( PSP ) Logo Generator was used to plot the frequency of amino acid residues on each position ( http://www . phosphosite . org/sequenceLogoAction . do ) . Residues above the midline were over-represented and those below were under-represented . The occurrence of each Gene Ontology ( GO ) was obtained from the DAVID Bioinformatics Resource ( https://david . ncifcrf . gov/ ) . The DNA damage response pathway and mitosis maps were constructed by manual curation of the literature combined with the information from the DAVID Bioinformatics Resource . GST-tagged proteins were expressed and purified as described previously [29 , 123] . Briefly , Escherichia coli BL21 cells were transformed with expression vectors [TIP60 ( a . a . 1–290 ) , MPS1 ( a . a . 410–517 ) , PP1α and CDC20] and then cultured in LB medium at 37°C until the A600 reached 0 . 6 . The bacteria were induced by adding 0 . 1 mM IPTG ( isopropyl-β-D-thiogalactopyranoside ) at 16°C for 12 to 16 hrs . Bacteria were harvested and then lysed by sonication . GST fusion proteins were purified by affinity chromatography using Glutathione-Sepharose 4B ( Cat# 17-0756-01 , GE Healthcare ) according to the manufacturer’s instructions . Wild-type ( WT ) and kinase-dead ( KD ) BGLF4 were purified from 239T cells using the Halotag Mammalian Protein Purification System ( Cat# G6790 , Promega ) according the manufacturer’s instructions . Briefly , Halo-tagged WT and KD BGLF4 were transfected into Hela cells . Two T175 flasks of transfected cells were harvested 48 hrs post-transfection at 100% confluence and lysed with 25 ml HaloTag Protein Purification Buffer ( 50 mM HEPES pH7 . 5 , 150 mM NaCl , 1mM DTT , 1mM EDTA and 0 . 05% IGEPAL CA-630 ) with Protease Inhibitor Cocktail . WT and KD Halo-BGLF4 were enriched using the Halo-tag resin and BGLF4 was eluted from the resin by washing 3 times with 0 . 5 ml HaloTag Protein Purification Buffer containing 20 ul Halo-TEV protease . Purified GST-Tip60 ( a . a . 1–290 ) , GST-PP1α , GST-MPS1 ( a . a . 410–517 ) and GST-CDC20 on beads were washed twice with Kinase Buffer ( 20 mM MOPS pH 7 . 2 , 25 mM β-glycerophosphate , 5 mM EGTA , 1 mM Na3VO4 and 1 mM dithiothreitol ) . Each sample was incubated in 40 ul Kinase Buffer containing 0 . 1 ( v/v ) magnesium-ATP cocktail buffer ( Cat# 20–113; Upstate ) , 0 . 2 μCi of [γ-32P]ATP ( Cat# Blue5027 PerkinElmer ) and 6 μl of WT or KD BGLF4 for 30 min at 30°C with vortexing every 2 to 3 minutes . Finally , reaction mixtures were washed twice with ice-cold Kinase Buffer and separated by gel electrophoresis . Radiolabeled proteins were detected by autoradiography . Akata ( EBV+ ) cells were untreated or treated with IgG ( 1:200 , Cat# 55087 , MP Biomedicals ) for 3 hrs and then reversine ( Cat# R3904 , Sigma ) was added for 5 days . To measure EBV virus release , virion-associated DNA in the culture supernatant was determined by PCR analysis using BALF5 primers as previously described [29] . The supernatant ( 180 μl ) was treated with 4 μl RQ1 DNase ( Cat# M6101 , Promega ) for 1 h at 37°C , and reactions were stopped by adding 20 μl of Stop Buffer and incubation at 65°C for 10 min . Then 12 . 5 μl Proteinase K ( 20 mg/ml; Cat# 4333793 , Invitrogen ) and 25 μl 10% ( w/v ) SDS were added into the reaction mixtures which were incubated for 1 h at 65°C . DNA was purified by twice phenol-chloroform extraction followed by isopropanol/sodium acetate precipitation . To measure cell associated viral DNA , total genomic DNA was extracted using the Genomic DNA Purification Kit ( Cat# A1120 , Promega ) and relative viral DNA content was determined by PCR analysis using BALF5 primers .
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Epstein-Barr virus ( EBV ) is a herpesvirus that is associated with B cell and epithelial human cancers . Herpesviruses encode a protein kinase which is an important regulator of lytic virus replication and is consequently a target for anti-viral drug development . The EBV genome encodes for a serine/threonine protein kinase called BGLF4 . Previous work on BGLF4 has largely focused on its cyclin-dependent kinase 1 ( CDK1 ) -like activity . The range of BGLF4 cellular substrates and the full impact of BGLF4 on the intracellular microenvironment still remain to be elucidated . Here , we utilized unbiased quantitative phosphoproteomic approach to dissect the changes in the cellular phosphoproteome that are mediated by BGLF4 . Our MS analyses revealed extensive hyperphosphorylation of substrates that are normally targeted by CDK1 , Ataxia telangiectasia mutated ( ATM ) , Ataxia telangiectasia and Rad3-related ( ATR ) proteins and Aurora kinases . The up-regulated phosphoproteins were functionally linked to the DNA damage response , mitosis and cell cycle pathways . Our data demonstrate widespread changes in the cellular phosphoproteome that occur upon BGLF4 expression and suggest that manipulation of the DNA damage and mitotic kinase signaling pathways are central to efficient EBV lytic replication .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Phosphoproteomic Profiling Reveals Epstein-Barr Virus Protein Kinase Integration of DNA Damage Response and Mitotic Signaling
|
Syphilis is a Sexually Transmitted Infection ( IST ) with significant importance to public health , due to its impact during pregnancy ( Gestational Syphilis—GS ) ; especially because syphilis can affect fetus and neonates’ development ( mother-to-child transmission—MTCT of syphilis ) , by increasing susceptibility to abortion , premature birth , skeletal malformations , meningitis and pneumonia . Measures to control and eliminate MTCT of syphilis have failed on the last few years in Brazil and this research aimed to identify the seasonality of notified cases of syphilis in a region of São Paulo state . The studied region , Pontal do Paranapanema , comprises 32 cities located in the West of São Paulo state , in Brazil . Data collected from the National System of Aggravations and Notification ( SINAN ) website was used to calculate the incidence rate of GS and MTCT . The incidence rate of GS was acquired dividing number of cases by number of women in each municipality and MTCT using number of live births in each year ( from 2007 to 2013 ) in each municipality . This result was then , standardized multiplying incidence rate by 10 , 000 and expressed as incidence/10 , 000 women or live births , for GS and MTCT , respectively . To identify possible endemic/epidemic periods , a control diagram was performed using the standard deviation ( SD ) of incidence rate . Thematic maps representing the spatial distribution of incidence rates were constructed using a Geographic Information System software ( GIS , based on cartographic vector available on the Brazilian Institute of Geography and Statistics ( IBGE ) website . Eighty cases of GS and 61 cases of MTCT were notified in the studied region . An increase of GS notification was detected in the Pontal do Paranapanema in 2011 followed by an increase in number of MTCT cases in the subsequent year , suggesting inefficacy in the treatment during gestational period . Most of those cases were reported on February and November which suggested seasonality for this IST in the region . The control diagram , based on the inputs collected from SINAN , showed no endemic period; however , the most susceptible month to happen an endemic event of GS and MTCT was February . Our study provided a new methodology to understand the syphilis dynamics as a potential tool to improve the success of future measures to control and possibly eliminate MTCT of syphilis .
Treponema pallidum is a spirochete bacterium that causes syphilis , a sexually transmitted infection ( STI ) transmitted due to contact with infected lesions . However , the transmission is not limited to this route since pregnant women infected with the microorganism can develop gestational syphilis ( GS ) what in its turn may lead to hematogenic transmission of T . pallidum to fetus , causing mother-to-child transmission ( MTCT ) of syphilis [1 , 2] . In Brazil 37 , 436 pregnant women and 20 , 474 children were notified with syphilis in 2016 [3] . These numbers represent an increase of 10% in comparison to 2015 , and 40% in comparison to 2010 [3] . In despite of implementation of health care programs for pregnant women aimed to control and eliminate GS and MTCT in the country , syphilis incidence has increased significantly and lack of diagnosis may have led to underreported GS and MTCT cases [4 , 5] . Aiming to overcome those health systems failures , innovative approaches to study GS and MTCT are necessary . Identification of seasonality and incidence as well as specific factors related to pregnant women and child infection , may help control and prevent GS and MTCT outbreaks . According to Bertolozzi and colleagues ( 2009 ) , the vulnerability of a region to a particular disease is related to socioeconomic and environmental factors that involve individuals [6] . Pontal do Paranapanema is one of the most vulnerable regions of the state of São Paulo , with low family income , high school dropout rates and inadequate sanitation and health conditions [7 , 8] , thus characterizing a region of vulnerability to infectious diseases . Epidemic and seasonal patterns of infectious diseases are widely used by public health systems to monitor communicable diseases , especially vector-borne infections [9 , 10] . Although the study of seasonal dynamics of STIs is an approach extensively used to characterize other diseases [11] , it is not frequently used to understand GS and MTCT . Aiming to overcome the need for epidemiological studies to understand GS and MTCT in the Pontal do Paranapanema , in this study we described a complex analysis of this region regarding to gestational and MTCT of syphilis . We included spatial distribution of GS and MTCT seasonal and epidemic patterns , as well as characteristics of pregnant and child diagnosed and notified with this infection from 2007 to 2013 , living in the Pontal do Paranapanema . Our data describes the behavior of the disease transmission and suggest improvement in prevention and screening campaigns to effectively prevent syphilis in Brazil .
Pontal do Paranapanema region is located in the extreme west of the state of São Paulo , southeastern Brazil , with a total area of 18 , 844 km2 and population of 583 , 703 people mostly concentrated in urban areas ( 89 . 74% ) ( Fig 1 ) [12] . Cases of GS and MTCT were collected from National System of Aggravations and Notification ( SINAN ) , using the DATASUS database [13] . We included in this study 32 cities located in São Paulo state ( Pontal do Paranapanema region ) , that reported or not cases of GS and MTCT of syphilis from 2007 to 2013 . The incidence of gestational cases of syphilis ( IGS ) was calculated dividing number of cases ( CN ) notified each year by number of women of each city , while the incidence of MTCT ( IMTCT ) was calculated dividing number of cases reported each year by number of live births ( LB ) born each year in each municipality . Both incidences were standardized to 10 , 000 ( standardized incidence rate ) . Data of number of women and live births were collected from Brazilian Institute of Geography and Statistics ( IBGE ) [12] . Incidence rate of gestational syphilis ( IGS ) IGS=CNwomenx10 , 000 Incidence rate of mother-to-child transmission ( IMTCT ) of syphilis IMTCT=CNLBx10 , 000 The state incidence rate was calculated by adding the average incidence of each city in the state of São Paulo , excluding 32 located in Pontal do Paranapanema and , dividing by the number of cities ( 614 cities ) included in the calculation ) . This result was used to compare rates of GS and MTCT in the cities of the Pontal do Paranapanema region and the remaining regions in the state of São Paulo . Data related to social characteristics of each city were collected from IBGE [12] . Among the variables were territorial extension , number of inhabitants , presence of fluvial areas , poverty index and the nature of health service offered to the population ( public or private ) . In addition , numbers of settlements and lots in each city were obtained from the website of the Institute of Lands of São Paulo state ( ITESP ) [14] . To identify male prisons units in the Pontal do Paranapanema , the Department of Penitentiary Administration ( SAP ) database was consulted [15] . Characteristics about pregnant women and newborns diagnosed with syphilis were collected from DATASUS [13] . For pregnant women diagnosed with gestational syphilis variables about ethnicity ( white or black ) , clinical classification of disease ( latent , primary , secondary or tertiary ) , tests ( treponemal or non-treponemal ) , educational level and residence location ( rural or urban area ) were collected . Regarding to newborns diagnosed with MTCT we identified variable about mother’s prenatal tests ( if performed or not ) , pregnancy period of diagnosis ( during prenatal , labor or post childbirth ) , treatment of partner ( if performed or not ) and residence location ( rural or urban area ) . In order to verify whether the incidence rate of MTCT presented a correlation with GS rate , in the period from 2007 to 2013 , we used Pearson Correlation coefficients , tested for the hypothesis , statistically different from zero . All analyzes were performed using R [16] software and adopting a 5% level of significance . Control charts were developed for identification of seasonal trends [17] , constructed in steps , as previously described [18] . First , arithmetic mean of incidence of each month individually in each year ( 2008 to 2013 for GS and 2007 to 2013 for MTCT was calculated ( Seasonal Index–SI ) . Calculus of SI was made by adding Monthly Average Incidence ( MAI ) of cases divided by years in which there were monthly notifications published in SINAN , according to the following equations . Gestational Syphilis: SI=MAIjan2008+MAIjan2009…MAIjan20136years MTCT of Syphilis: SI=MAIjan2007+MAIjan2008…MAIjan20137years The year of 2007 was not included for calculate seasonal index of GS and , consequently , construction of control chart , due to the absence of monthly information about this year on the database . The next step to construct the control chart was to identify alert and control limits of reported cases . Firstly , the annually standard deviation ( σ ) of SI was calculated . Secondly , monthly Upper Alert Limit ( UAL ) , Lower Alert Limit ( LAL ) , Upper Control Limit ( UCL ) and Lower Control Limit ( LCL ) , were calculated using the following equations: UpperAlertLimit:UAL=SI+ ( 2*σ ) LowerAlertLimit:LAL=SI− ( 2*σ ) . UpperControlLimit:UCL=SI+ ( 3*σ ) LowerControlLimit:LCL=SI− ( 3*σ ) When results for LAL and LCL presented negative values , those were considered equals to zero [18] . Thematic maps representing the spatial distribution of incidence rates were designed according to previous reports [19] , using a Geographic Information System software ( GIS ) , based on cartographic vector available by IBGE . The bases used consisted on 1 ) political-administrative limits of the cities belonging to the Pontal do Paranapanema; 2 ) cartography based on hydrography in the region , both in shapefile format .
According to DATASUS , a total of 14 , 849 cases of GS and 8 , 365 cases of MTCT were notified in São Paulo state . Eighty cases of GS were notified in 14 out of 32 cities located on the Pontal do Paranapanema region and 61 cases of MTCT of syphilis were notified in 15 cities . Statically , incidence rate of GS was lower in Pontal do Paranapanema than the other regions in São Paulo state in 2007 , 2008 , 2012 and 2013 ( p<0 . 050 ) and equal in 2010 and 2011 . MTCT incidence rate in Pontal was higher in 2009 but similar to the other regions in the other years investigated . Incidence rate of GS increased exponentially after 2009 until 2012 in Pontal do Paranapanema , with the highest rate determined in 2011 . The incidence rate of MTCT of syphilis was relatively higher when compared with GS during the period studied , reaching the peak in 2012 . For MTCT of syphilis this value ranged from 3 . 67 to 23 . 29 while GS from 0 . 12 to 0 . 81 . A tendential increase of rates as of 2009 was presented in both cases , with subsequent peak in 2011 to GS and 2012 to MTCT ( Fig 2 ) . Correlation between GS and MTCT incidence rates in the region was significative among the months analyzed ( p = 0 . 002 ) ( S1 Fig , S2 Table ) . Seasonal pattern showed higher number of cases reported during late Spring and Summer ( between November and February ) . Lower incidence of cases was noticed during the Autumn and Winter ( between March and October ) ( Fig 3 ) . Our data do not contain epidemic episodes in this population , since no Upper Alert Limit ( UAL ) and UCL ( Upper Control Limit ) overlap was observed . February month presented higher amplitude indicating that during this month higher number of cases of GS and MTCT are expected to be notified . Similar pattern was observed in November with less intensity for both cases . Smaller amplitude was seen during September and October for GS whilst May and September presented smaller amplitude for MTCT ( Figs 4 and 5 ) . Aiming to analyze possible endemic outbreaks , we found that higher concentration of reported cases of GS and MTCT was observed in cities in the border between Pontal do Paranapanema and Mato Grosso do Sul state , as well as in the Pontal and Parana state . Incoherence of notification was noticed in Narandiba , Taciba , Santo Anastácio , Santo Expedito and Álvares Machado cities , since there was notification of MTCT in the absence of notification of cases of GS . The cities of Euclides da Cunha Paulista , Rosana and Ribeirão dos Índios presented absence of MTCT cases even in the presence of cases of OS , suggesting a better control of transmission to the fetus/neonate . Distribution of reported cases of GS and MTCT , as well as incidence rate of cases , are shown in Figs 6 and 7 , respectively . Variables of interest including infrastructural socioeconomic and health characteristics of the city were analyzed to better characterize incidence of GS and MTCT ( Table 1 ) . Primarily , our hypothesis was that women residing in rural settlements had more chance to be part of the underreported cases , since those places are located generally in the peripheral area of city with no health services nearby . However , we have noticed that cities with rural settlements reported cases of GS and no cases of MTCT , suggesting better approach to prevent MTCT of syphilis . In addition , there were no correlations between the poverty index , nature of the health service and incidence of reported case . Although there are not female prison units in the Pontal do Paranapanema , we decided to investigate whether high population rate in male prison units would be related to GS or MTCT cases in the cities investigated , since transmission of syphilis to women can occur in those institutions during intimate visits . Our results showed that the highest rate of prisoners per habitants were in Marabá Paulista e Caiuá cities ( >20% ) ( Table 2 ) . Both cities did not reported GS or MTCT cases in the period investigated . Presidente Venceslau , Martinópolis and Presidente Prudente presented cases of GS and MTCT with similar demography of prisoners per habitants , 8 . 2 and 9 . 4% respectively . Thus , we concluded that male overpopulation in prison units is not related to transmission of gestational or MTCT of syphilis in Pontal do Paranapanema region . Important variables were identified during analysis of children diagnosed with MTCT syphilis . Surprisingly , 95% of child diagnosed with MTCT of syphilis were born from mothers enrolled in pre-natal care that included screening for syphilis . Our data also has shown that 54 . 10% of mothers received the diagnose of GS during pre-natal care while 45 . 90% were diagnosed in intrapartum and postpartum periods . A total of 93% of mothers declared to live in urban areas and 65% of partners having sexual relationship with pregnant women has not conducted the correct treatment indicate by physician after diagnosis of syphilis ( Table 3 ) . Other variable found in MTCT of syphilis diagnosis , related to gender , mother’s pre-natal care , diagnosis of gestational syphilis and prognostic are shown on Table 3 . We analyzed data from pregnant women diagnosed with syphilis who gave birth to a child with syphilis as well as the type of diagnosis test performed . From a total of 80 cases in the region , the most common syphilis clinical stages were secondary , primary and latent syphilis , in order of appearance . Non-treponemal test ( screening test with higher sensibility ) was positive in 96% of the cases while treponemal test ( confirmatory test with higher specificity ) was positivein 66% of them . Eighteen percent of the mothers were not submitted to a confirmatory treponemal test . Most mothers diagnosed with GS ( 86 . 25% ) presented low educational level and had attended school for less than 10 years ( Table 4 ) .
In this study , we identified the epidemiological profile of syphilis in both maternal and newborn population in the Pontal of Paranapanema region . Despite accessible diagnosis and treatment , cases of syphilis are still increasing in Brazil , suggesting a failure to control and eliminate this STI . Recent data , made available by Epidemiological Surveillance agencies , indicates a worrying increase in the number of cases of GS and MTCT throughout the country [5 , 20 , 21] . Therefore , innovative approaches are needed to understand the dynamics of this disease in the population . Study of temporal variations on infectious diseases allows the understanding of the transmission dynamics in a certain population [22] . For this evaluation , we used the seasonal index , which consists of graphic sampling from the behavior of the disease over a period of time , and the control diagram that allows the identification of variations and mathematical limits for the prediction of endemic episodes [23] . Brazil's epidemiological surveillance uses a large scale control chart to understand the dynamics of infectious diseases [17 , 24] , mainly to comprehend dynamics of vector diseases that undergoes direct influence from climate change and then are classified as seasonal diseases [25] . Regarding gestational and mother-to-child transmission of syphilis , no studies were found in the literature using this tool to understand the disease dynamics , thus leading to the innovative approach in our study . To identify epidemic episodes of GS and MTCT , we analyzed cases reported monthly from 2007 to 2013 in cities located in the Pontal do Paranapanema . Analysis of control chart allows the identification of endemic peaks when the index is greater than the Upper Control Limit . The alert thresholds may indicate a biased increase to a period of epidemic , leading to the health surveillance system to take precautions in order to avoid an epidemy [17 , 26] . In our study , we did not observe outbreaks since neither GS nor MTCT showed overlap of limits . Araujo and colleagues , based on observational and variational temporal analysis of cases reported in Brazil , found a MTCT rate ranging from 1 . 7 to 2 . 10 per 1 , 000 live births in the years 2003 to 2008 [27] , very close to the values identified in our study , from 0 . 37 to 2 . 30 ( 2007 to 2013 ) . They also reported a GS detection coefficient of 2 . 5 for every 1 , 000 inhabitants during 2003 to 2008 , higher than our result from 0 . 12 to 0 . 81 for 10 , 000 inhabitants . The significant increase in cases of gestational syphilis observed from 2011 may be related to the implementation of the national program "Rede Cegonha" and a consequent increase in cases detected . This program established periodic tests for syphilis detection during pre-natal , intrapartum and postpartum . In the latter , the newborn is assisted until two years old . In addition , a strategic agenda of the Epidemiological Surveillance was elaborated for the years 2011 to 2015 , with the objective of reaching 100% of the pregnant women undergoing prenatal care undergoing the syphilis test [28 , 29] . We suggest a failure in the GS cases notification system , since cities that reported MTCT cases did not necessarily report GS cases . This bias can explain the low rates of GS cases and the overall difficult to effectively screen the disease in the country . This observation has been previously reported by Komka e Lago ( 2007 ) , which identified 64% of notification rate for MTCT of syphilis in a reference hospital in the state of Tocantins , Brazil [30] . Failure to correctly diagnose syphilis during pregnancy culminates in non-treatment or inadequate adherence to treatment [31] . The inadequate treatment of GS cases reported in 2011 may have influenced the significant increase in SC cases in the year 2012 , which corroborates the data reported by Muricy e Pinto ( 2015 ) . According to them , 87 . 2% of the pregnant women living in the Federal District receive adequate prenatal care and 52 . 6% were diagnosed with syphilis . Only 22% of infected pregnant women were adequately treated and 24 . 8% of the partners , which makes reinfection possible even after treatment , increasing the chances of transmission of syphilis to the fetus [32] . Using analysis of alert range and case control limits , we determined that the month with the highest number of cases was February . Previously in Brazil , Passos and collegues ( 2010 ) investigated whether Carnival could influence the transmission and the number of cases of STIs . According to the authors , there was no correlation between the transmission and number of cases of STIs during the carnival period[33] . February is also summer time in the country and infectious diseases rates increase in warm and rainy periods in Brazil as the vector multiply better in this period . Then , more people seek for medical assistance , increasing diagnosis of infectious diseases [34] . Another important factor is the increase of travel during summer vacation , when people could potentially increase transmission of T . pallidum by sexual intercourse [35] . Finally , public campaigns against syphilis in the country are held on November and February . Those campaigns promote strategies of diagnosis and detections , then it also may explain the higher rate of detection in February . However , we have not observed any correlations between incidence of syphilis and the entire summer in the period analyzed , even considering that T . pallidum develop symptoms , at least , 30 days after infection [36] . There were discrepancies between reported cases of GS and MTCT , suggesting incorrect treatment or misdiagnosis of syphilis during gestational period . In the cities of Narandiba ( 2011 e 2012 ) , Álvares Machado ( 2010 ) , Santo Anastácio ( 2012 ) , Santo Expedito ( 2013 ) and Taciba ( 2010 ) cases of MTCT were reported , but no cases of GS were registered . Between 2010 and 2013 underreported cases of GS in Pontal do Paranapanema reached 13 . 74% . Underreporting of gestational syphilis cases in Brazil is a common fact already reported by other authors [37 , 38] . Komka and Lago ( 2007 ) , emphasizes that MTCT of syphilis is also affected by underreporting because of late diagnosis of GS leads to high mortality rates for MTCT [30] . Our data suggest a failure in the diagnosis and/or treatment of GS in the population from 2007 to 2013 . In more than 90% of MTCT cases the mothers had access to prenatal care . Only half of the mothers were diagnosed with GS during the prenatal care period ( 54 . 10% ) . Another sizable portion was diagnosed later , during delivery or curettage ( 42 . 62% ) . Prenatal care coverage is an important factor in the diagnosis and consequently the late search for prenatal care during pregnancy may lead to an increased risk of syphilis transmission to the fetus [39 , 40] . Therefore , although almost 100% of the pregnant women who had children diagnosed with MTCT of syphilis in the Pontal do Paranapanema have been attended by prenatal care , not all of them were diagnosed early . Hence , the difficulty of treatment and infection of the fetus is not limited to screening errors , but also limitation on search for health care service by pregnant women . Among the children diagnosed with MTCT , more than 65% were not submitted to any treatment . This important finding agrees with previous studies highlighting the importance of treating the partner to avoid reinfection of the mother and consequently to the fetus [40 , 41] . In a study performed in 2014 in Porto Alegre city , located in South of Brazil , it was found that less than 50% of the partners are treated for syphilis and there were limitations on the notification of treatment [42] . As a consequence of this behavior , reinfection of mothers can occur at any time during pregnancy , being most worrying during last weeks of pregnancy , when there is no more time for treatment before delivery , despite the stage maternal disease [43] . Most of the mothers diagnosed with GS in the present study are white . This finding reinforces the need for studies focused on the distribution and analysis of population considering that different regions may present singular profiles and that previous reports showed high prevalence of gestational syphilis among non-white women [44] . In addition , is important to consider that more than 60% of the Brazilian population is comprised by mixed/brown and black people . Subsequently , analysis of these profiles may assist in better tracking and planning effective approaches to control and elimination of syphilis . Reported cases of gestational and MTCT of syphilis identified in this study express a fragility in the notification system , since cities notifying MTCT do not necessarily report syphilis in pregnant women . Therefore , it is necessary to improve the reporting system so the data can provide better conditions to monitor and control transmission of syphilis . The use of the tools presented here can contribute to improve health strategies aimed to prevent and control syphilis , as well as other sexually transmitted infections in pregnant women in Brazil .
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Syphilis is a sexually transmitted infection that can be transmitted to child during pregnancy , or postpartum , by an infected mother . Syphilis in children can interfere in the development and , in some cases , may lead to death . We analyzed cases of syphilis during pregnancy and mother-to-child transmission of syphilis from 2007 to 2013 occurred in a Brazilian region . We used a public online website provided by the Brazilian government to obtain information about cases , generate map locations and design a transmission pattern in the region . During the period investigated , we identified 80 cases of syphilis in pregnant woman and 61 related to mother-to-child transmission , both notified mainly during Summer ( from November to February ) . Children infection might be related to incorrect treatment of mother and the partner . Not only the mother must be treated but the partner as well , otherwise women may be infected during pregnancy , transmitting syphilis to fetus . This study may help in the establishment of measures to control and eliminate mother-to-child transmission of syphilis .
|
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2019
|
Mother-to-child transmission and gestational syphilis: Spatial-temporal epidemiology and demographics in a Brazilian region
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Although the importance of humoral immunity to malaria has been established , factors that control antibody production are poorly understood . Follicular helper T cells ( Tfh cells ) are pivotal for generating high-affinity , long-lived antibody responses . While it has been proposed that expansion of antigen-specific Tfh cells , interleukin ( IL ) 21 production and robust germinal center formation are associated with protection against malaria in mice , whether Tfh cells are found during Plasmodium vivax ( P . vivax ) infection and if they play a role during disease remains unknown . Our goal was to define the role of Tfh cells during P . vivax malaria . We demonstrate that P . vivax infection triggers IL-21 production and an increase in Tfh cells ( PD-1+ICOS+CXCR5+CD45RO+CD4+CD3+ ) . As expected , FACS-sorted Tfh cells , the primary source of IL-21 , induced immunoglobulin production by purified naïve B cells . Furthermore , we found that P . vivax infection alters the B cell compartment and these alterations were dependent on the number of previous infections . First exposure leads to increased proportions of activated and atypical memory B cells and decreased frequencies of classical memory B cells , whereas patients that experienced multiple episodes displayed lower proportions of atypical B cells and higher frequencies of classical memory B cells . Despite the limited sample size , but consistent with the latter finding , the data suggest that patients who had more than five infections harbored more Tfh cells and produce more specific antibodies . P . vivax infection triggers IL-21 production by Tfh that impact B cell responses in humans .
Malaria , caused by the protozoan parasite Plasmodium , remains one of the most widespread and mortality-causing infectious diseases worldwide . Plasmodium vivax is the most frequent cause of recurring malaria and infects 130–390 million people each year , representing approximately 50% of all malaria cases [1] . Through constant reinfection , adult individuals acquire clinical immunity against severe disease by controlling infection , regardless of the parasite species . These individuals can become asymptomatic parasite carriers of both asexual blood-stage and infective sexual gametocyte stages [2] . Clinical immunity depends on antibodies [3] , however it is assumed that protective humoral responses to malaria are short-lived , slowly develop after multiple exposures to parasites and can be lost in the absence of regular exposure [4] . In addition to the clinical amelioration , resolution of malaria depends on generation of pathogen-specific antibodies . T follicular helper cells ( Tfh cells ) are key orchestrators of the germinal center ( GC ) reactions that drive the generation of plasma cells that secrete high-affinity antibodies to resolve primary infection and long-lived memory B cells that maintain protection against re-infection [5] . Tfh cells can be distinguished from other Th populations based on anatomical localization , effector functions , development requirements and homing properties [6] . Tfh cells priming is driven by cognate interaction between naive CD4+ T cells and conventional dendritic cells producing IL-6 and IL-21 that induce B-cell lymphoma 6 protein ( Bcl-6 ) , a transcriptional repressor promoting expression of C-X-C chemokine receptor type 5 ( CXCR5 ) . CXCR5 endows lymphocytes with the capacity to migrate to B cell follicles rich in C-X-C motif chemokine ligand 13 ( CXCL13 ) [7 , 8] . Tfh cells motility is also regulated by ICOS-ICOS-L ( Inducible T-cell Costimulator ) interactions between Tfh cells and non-cognate B cells at the T-B border , which potentiates Tfh cells migration into the follicle [9] . Once in the follicle , Tfh cells activity depends on cognate interactions with B cells , which further reinforces Tfh cells differentiation and function [9 , 10] . Therefore , their unique phenotype is critical for their development and function [7 , 9 , 11 , 12 , 13] . Cytokine production triggered by microbes at the onset of infection can also influence Tfh cell development [14] . Indeed , the absence of IL-21 results in reduced antibody production and in decreased GC B cell numbers that correlate with to a profound defect in GC formation [15] . In human blood , CXCR5+CD4+ T cells display Tfh cell functional properties , including being able to efficiently induce naïve B cells to produce immunoglobulin via IL-21 secretion , and are believed to represent the circulating memory counterpart of the Tfh cells from lymphoid tissues [11] . Protection in an experimental malaria vaccination protocol was associated with enhanced expansion of antigen-specific Tfh cells and robust GC formation [16] . Moreover , the absence of IL-21 , produced by T cells , abrogates P . chabaudi-specific immunoglobulin secretion and memory B cell responses [17] . In addition , in mice , severe malaria induces impaired Tfh cells differentiation and defective germinal centers . In this case , despite IL-21 production , Tfh cells expressed low levels of programmed cell death protein 1 ( PD-1 ) and CXCR5 and co-expressed Th1-associated molecules [18] . Currently , there is only one published study assessing Tfh cells in humans infected with P . falciparum , which showed that while malaria drives Th1 cytokine responses and Th1-like Tfh cells , their activation status did not correlate with antibody production [19] . Given the importance of T cell-dependent antibody responses in malaria , we attempted to assess circulating Tfh cells and define their role during P . vivax infection . To address this issue , we phenotypically and functionally characterized T and B cell subsets in the peripheral blood from patients experiencing acute malaria episodes . We demonstrate that P . vivax infection triggers an increase in circulating Tfh cells during acute infection and that Tfh cells are the primary sources of IL-21 and induce immunoglobulin production by naïve B cells . Moreover , P . vivax malaria alters the B cell compartments and these alterations are dependent on the number of malaria re-infections . Taken together , our findings indicate that circulating Tfh cells may be a marker of humoral responses against Plasmodium infection in humans .
The malaria group consisted of three females ( 12 . 5% ) and 21 males ( 87 . 5% ) with an age range from 18 to 56 years ( median 33 . 61 ± SD 8 . 88 years old ) . In Brazil , malaria is an occupational disease and therefore affects mostly males . Thirty three percent asserted primary malaria infection , 38% reported 1–5 previous malaria episodes and 29% reported more than five previous malaria episodes . Numbers of malaria episodes were confirmed by electronic records obtained from the Ministry of Health ( Sivep ) . All patients presented clinical symptoms of malaria and P . vivax parasitaemia ranged from 2 . 24 to 12 , 641 . 45 ( median 18 . 39 ± SD 3 . 281 ) parasites/μL ( S1 Table ) . Peripheral blood mononuclear cells ( PBMC ) viability after thawing , analyzed by flow cytometry using Acqua or Violet Live/Dead ( Invitrogen ) , was similar between malaria patients ( median 82 . 21 ± SD 13 . 26 ) and healthy individuals ( median 86 . 02 ± SD 5 . 094 ) . Healthy donors included in the studies were from the same endemic area and had not had any malaria episode by the date the blood samples were collected . They did not present with any other symptoms and were not on medication for any chronic disease . Studies on healthy adults have shown that blood CXCR5+CD4+ T cells are the circulating counterparts of GC Tfh cells in secondary lymphoid tissue [11] . We investigated the expression of molecules expressed by activated and memory cells , and those that define Tfh cells , in PBMC from P . vivax-infected patients before treatment ( BT ) and after treatment with chloroquine and primaquine ( AT ) . As an additional control , we analyzed PBMC from healthy donors ( HD ) . No alterations were found in the frequencies of memory ( CD45RO+ ) , activated ( CXCR5+ ) and activated memory ( CXCR5+CD45RO+ ) CD4+ T cells during acute malaria when comparing the same patients BT and AT ( Fig 1A ) . However , we found that the frequency of ICOS expressing cells was significantly increased in the memory , activated memory and total CD4+ T lymphocyte compartments in malaria patients BT ( Fig 1B ) . However , AT ICOS expression returned to the levels observed on CD4+ T cells from healthy donors ( HD ) ( Fig 1B , S3 Table ) . Furthermore , we observed increased frequencies of CD40L+ cells among CD45RO+ , CXCR5+CD45RO+ and total CD4+ T cells during acute malaria ( Fig 1C ) . Moreover , acute P . vivax infection also triggered the expression of PD-1 , another member of the B7-CD28 family [20] , on CD4+ , CD45RO+CD4+ and CXCR5+CD45RO+CD4+ T cells ( Fig 1D ) . Taken together , our data demonstrate that ICOS , CD40L and PD-1 are upregulated by CD4+ T cells during P . vivax infection . To characterize Tfh cells , we examined the simultaneous expression of CD3 , CD4 , CD45RO , CXCR5 , ICOS and PD-1 by CD4+ T cells among total PMBC ( PD-1+ICOS+CXCR5+CD45RO+CD4+CD3+ cells ) , and , consistent with the data described above , we observed a significant higher frequency of circulating Tfh cells during acute malaria compared to the same patients AT ( Fig 2A ) and to HD ( S3 Table ) . Moreover , significantly higher levels of IL-21 were observed in plasma from P . vivax-infected patients BT compared to AT . Higher levels of IL-6 , IL-10 and interferon-gamma ( IFN-γ ) were also found during acute malaria ( Fig 2B ) . These observations suggest that P . vivax infection triggers production of IL-21 and promotes Tfh cells expansion . A hallmark of Tfh cells is production of IL-21 , which drives the growth and differentiation of B cells and isotype switching [21] . To evaluate whether Tfh cells from P . vivax-infected patients secreted IL-21 and contributed to B cells differentiation , distinct CD4+ T cell subsets were FACS-sorted and cultured with different B cell subsets ( S1A Fig ) . We observed that Tfh cells from P . vivax-infected patients produced significantly higher levels of IL-21 than memory or naïve CD4+ T cells when co-cultured with different B cell subsets in the presence or absence of staphylococcal endotoxin B ( SEB ) ( S1B Fig ) . In addition , in the presence of P . vivax-infected reticulocytes or SEB , we observed increased IgG production by naïve B cells co-cultured with Tfh cells compared to naïve B cells co-cultured with memory CD4+ T cells ( S1C Fig ) . Moreover , higher frequency of IL-21 producing Tfh cells was observed among acutely malaria patients when compared to HD ( S5 Fig ) . Taken together , these findings suggest that IL-21 producing Tfh cells play an important role in the activation of B cells and antibody production during malaria . Characteristics of Tfh cells may partially overlap with Th1 , Th2 , and Th17 cells , contributing to the plasticity of the Tfh cells lineage [11] . To determine whether such flexibility was altered during malaria we evaluated the expression of CXCR3 and CC chemokine receptor 6 ( CCR6 ) by CD4+ T cell populations in P . vivax-infected patients BT and AT . We found that P . vivax infection did not alter the proportions of CXCR3+CCR6- ( Th1 ) , CXCR3-CCR6- ( Th2 ) and CXCR3-CCR6+ ( Th17 ) cells within the memory ( CD45RO+ ) and activated memory ( CXCR5+ CD45RO+ ) CD4+ T cell subsets ( Fig 3 , middle and lower panels ) . However , the frequencies of CXCR3+CCR6- and CXCR3-CCR6+ Tfh cells were significantly higher during acute malaria when compared to the same patients AT ( Fig 3 , upper panels ) . In addition , a significantly lower frequency of CXCR3-CCR6- Tfh cells was found BT ( Fig 3 , upper panels ) . These data suggest that P . vivax infection selectively influences the plasticity of Tfh cells . We next assessed circulating levels of total or 19-kDa Merozoite Surface Protein-1 ( PvMSP-119 ) -specific IgM and IgG in P . vivax-infected patients , BT and AT . As expected , total IgM and IgG levels were not altered upon infection ( Fig 4A , left panels ) . Furthermore , although acutely infected patients displayed similar levels of MSP-119-specific IgM , they had higher levels of MSP-119-specific IgG when compared to the same patients AT ( Fig 4A , right panels ) . It has been described that IL-21 is a switch factor for the production of IgG1 and IgG3 by human B cells [22] . Indeed , we observed that the reactivity indices of anti-PvMSP1-19 IgG1 , IgG3 and IgG4 antibodies were significantly higher during malaria when compared to the same patients AT ( Fig 4B ) . Antibody levels against AMA-1 were assessed in the same patients ( S6 Fig ) . Distinct from MSP-119-specific immunoglobulin levels , acutely infected patients had higher levels of AMA-1-specific IgM but similar levels of IgG when compared to the same patients AT ( S6A Fig ) . Moreover , different from MSP-119-specific antibodies , the levels of circulating IgG2 and IgG4 were higher after treatment than before treatment ( S6B Fig ) . These results indicate that different P . vivax antigens may trigger distinct malaria specific IgM and IgG responses and that MSP-119-specific antibodies are induce during acute infection and their levels quickly decrease after treatment . Since IL-21 levels and IgG responses were elevated during malaria , we sought to investigate whether P . vivax infection altered the proportions of circulating B cell subsets . We found that P . vivax infection did not change the frequency of either total B cells or immature B cells ( Fig 5A ) . However , when B cell subsets were assessed , we observed the proportions of activated memory ( CD27+CD21- ) and atypical memory ( CD27-CD21- ) B cells were significantly higher in P . vivax infected patients than in the same individuals AT . Conversely , treatment was associated with significantly higher frequencies of classical memory ( CD27+CD21+ ) and naïve ( CD27-CD21+ ) B cells ( Fig 5B ) . Since immunity to malaria requires repeated exposure to parasites and can be lost in absence of infection , we investigated the proportion of plasma cells during infection and AT ( Fig 5C ) . Plasma cells have been defined as B cells that express CD38 but not CD20 [23] , or as B cells that do not express both CD20 and CD21 [24 , 25] . Since most CD20-CD21- B cells are also CD38+ , the majority of cells identified using these strategies are coincident . We found that P . vivax infection triggered a significant increase in the frequency of plasma cells ( CD21-CD20- within CD19+ B cells ) , which decreased AT to levels similar to those found in HD ( Fig 5C , S3 Table ) . However , there were no significant changes in the percentage of IgG-expressing plasma cells in the circulation , either BT or AT ( Fig 5D ) . Nevertheless , P . vivax infection induced increased frequencies of PD-1+ , CD38+ and Ki67+ plasma cells , as well as subsets co-expressing IgG/PD-1 and Ki67/CD38 , indicating activation ( Fig 5D ) . Although the proportions of CD38+ , Ki67+ and CD38+Ki67+ plasma cells were diminished AT , they remained above the levels observed in HD ( Fig 5D , S3 Table ) . Together , these observations indicate that treatment triggered a contraction in plasma cells but was not sufficient to decrease their activation status to physiological levels . When patients were stratified based on the number of malaria infections , higher levels of IgG antibodies specific for Apical Membrane Antigen 1 ( PvAMA-1 ) and PvMSP-119 were observed in individuals who were repeatedly infected with the parasite ( Fig 6A ) . Moreover , this stratification revealed that multiple infections modulated the proportions of B cell subsets ( Fig 6B–6D , S4 Table ) . While patients with acute malaria displayed an increase in the proportion of activated memory and atypical memory B cells compared to HD or patients AT ( Fig 5B , S3 Table ) , this was not the case in patients who had previously been infected by P . vivax since they had lower frequencies of these two subsets ( Fig 6B ) . In addition , patients undergoing primary infection displayed significantly higher frequencies of total plasma cells and of Ki67+ and Ki67+CD38+ expressing plasma cells compared to those who had experienced prior infection ( Fig 6C ) . Conversely , significantly higher proportions of classical memory B cells were found in patients who had been infected on multiple occasions ( Fig 6B ) . Moreover , patients who experienced more than five malaria episodes had increased proportions of circulating Tfh cells , compared to patients undergoing primary infection ( Fig 6D , left graph ) . A significant positive correlation analysis was observed when numbers of malaria episodes were plotted against proportions of Tfh cells ( Fig 6D , right graph ) . When the same analyses were performed after treatment , the differences between patients experiencing their first infection versus those who had more than one malaria episode were lost , suggesting that the presence of the parasite boosts the B cell response and determines its pattern ( S2 Fig ) . We then performed a detailed correlation analysis between the various cell types analyzed and the soluble parameters assessed . The analysis revealed that before treatment , malaria patients , regardless of the number of infections , presented three clusters of nodes with large number of neighborhood connections: ( i ) B-cell subsets , ( ii ) plasma cell subsets and ( iii ) anti-MSP1/anti-AMA antibodies ( S3 Fig ) . When patients were categorized according to the number of malaria episodes , these clusters were segregated into distinct subgroups of patients showing relevant connections with Tfh cells . Thus , patients undergoing primary infection presented a cluster of nodes with connections preferentially composed by B-cell subsets , particularly classical and atypical B-cells . Patients who experienced 2–5 malaria episodes exhibited most connections among antibodies , while patients who had more than 5 malaria episodes displayed a less intricate network , but strong correlations involved the plasma cell cluster , antibodies and Tfh cells . Moreover , we plotted in radar graphs all the parameters evaluated considering the number of malaria episodes . Tfh cells , B cells , plasma cells and their subsets , cytokines and antibodies are represented clockwise ( Fig 7 ) . The inner circle represents the 50th percentile , which was taken as threshold to segregate higher and lower expression/production . The immunological parameters evaluated in patients undergoing the first malaria fill a small area of the radar graph , especially the area representing the antibodies ( Fig 7 , upper panel ) . Some of the cell subsets , such as immature , activated , atypical B cells and plasma cells are expanded in this group of patients . There is a clear expansion of the area composed by the antibodies subclasses as the patients are repeated exposed by P . vivax ( Fig 7 , lower panels ) . The main alterations observed in the B cell compartment are the increase of the area occupied by classical memory and decrease of the area occupied by atypical memory B cells in patients who had from 2 to 5 malaria episodes ( Fig 7 , lower , left panel ) . As the number of malaria episodes increases , the areas representing antibody levels and proportions of Tfh cells increase ( Fig 7 , lower , right panel ) . These data indicate that re-infection by Plasmodium vivax triggers circulating Tfh cells , promotes differentiation of B cells into classical memory B cells and boosts antibody levels that together provide an efficient humoral immune response .
While the role of Tfh cells in the GC reaction has been reported in several studies , the presence and function of their circulating counterpart has only been recently accepted . Our findings clearly demonstrate that P . vivax infection triggers an increase in the proportion of circulating Tfh cells that are the main source of IL-21 and are able to induce immunoglobulin production by naïve B cells . Our observations are consistent with reports demonstrating that CXCR5+CD4+ T cells represent memory Tfh cells that regulate B cell responses [11] . Memory cells are long-lived , and their longevity is dependent on their ability to undergo homeostatic proliferation in the absence of antigen [26] . P . vivax infection does not alter the frequencies of memory and CXCR5 expressing CD4+ T cell compartments . However , the infection induced expression of activation and co-stimulatory molecules , ICOS and CD40L , on CD4+ T cells , known to be crucial for T-B interactions providing help for B cell activation , maturation and antibody production [19 , 27 , 28 , 29] . Among the T lymphocytes , Tfh cells are the specialized subset in helping B cell responses [30] . A rigorous characterization of GC Tfh cells in both mice and humans takes into account the expression of CD3 , CD4 , CD45RO , CXCR5 , ICOS , PD-1 and the transcription factor Bcl-6 [10 , 31 , 32] . It has been reported in mice that , after providing help to B cells , Tfh cells may exit the GC , downregulate Bcl-6 , and circulate in the blood [33] . Based on this phenotype , a significantly higher frequency of circulating Tfh cells is found during malaria infection in humans . It is well accepted that immunoglobulin production is essential to an effective immune response against Plasmodium and this production is T-dependent [34] . However , in human , there is only one published report in malaria caused by P . falciparum , demonstrating that circulating PD-1+CXCR5+CD4+ T cells from patients have characteristics of GC Tfh cells , and our study represents the first report of an increase in Tfh cells following P . vivax infection [19 , 35] . Consistent with the expansion of Tfh cells during P . vivax infection , we found increased levels of IL-21 , a key cytokine produced by Tfh cells that , along with IL-4 and IL-10 , promotes growth , differentiation and class switching of B cells [36] . A better understanding of the ability of the circulating Tfh cells to promote B cell differentiation into plasma cells is still lacking . By purifying T and B cell subsets we demonstrated that circulating Tfh cells from malaria infected patients are the main source of IL-21 and trigger antibody production by naïve B cells . Higher levels of IL-10 , IL-6 and IFN-γ were also observed in the circulation of P . vivax-infected patients . Cytokines play essential roles in all phases of Tfh cells differentiation and several studies show that infections regulate the development and activity of this cell subset through modulation of cytokine production . lL-6 participates in the induction of early differentiation of Tfh cells , mainly acting through either Signal Transducer and Activator of Transcription 1 ( STAT1 ) or STAT3 to trans-activate Bcl-6 [37] . Large amounts of IL-10 are also detected in cultures of CXCR5+CD4+ T and naive B cells , and blocking IL-10 results in a partial inhibition of immunoglobulin production [11] . On the other hand , excessive IFN-γ and TNF may limit Tfh cells function and GC B cell responses during the blood stage of experimental Plasmodium infection [38 , 39] . Another very recent study showed that experimental Plasmodium infection-induced type I IFN limit Tfh cells accumulation and humoral immunity though secretion of IL-10 and IFN-γ by T regulatory cells [40] . Moreover , it was described that IFNAR1-signalling is associated with impaired GC B cell formation , antibody production and Tfh cell differentiation [41] . Studies suggest that the Tfh cells compartment is heterogeneous and that some Tfh cells are able to secrete cytokines characteristic of other T helper cell subsets [34 , 42] . Depending upon stimulus and microenvironment , Tfh cells can express T-box transcription factor ( Tbet ) , transcription factor GATA-binding protein 3 ( Gata3 ) , or retinoic acid-related orphan receptor-gamma T ( RORγt ) , which results in a diversity of Tfh cell subsets producing low levels of other Th-like cytokines with different abilities to regulate B cell responses [34 , 43] . Tfh cell subsets can also be distinguished based on the expression of the chemokine receptors CXCR3 and CCR6: CXCR3+CCR6− cells expressed T-bet , CXCR3−CCR6− cells expressed GATA3 and CXCR3−CCR6+ cells expressed RORγT [11 , 44] . P . vivax infection induced an increase in both CXCR3+CCR6- and CXCR3-CCR6+ Tfh cell subsets and a lower frequency of CXCR3-CCR6- expressing cells . These alterations were specific to Tfh cells , since no differences were observed in memory and activated memory CD4+ T cells . According to Obeng-Adjei and colleagues ( 2015 ) , during malaria caused by P . falciparum , the CXCR3- Tfh cell subset is better than the Th1-like CXCR3+ subset in helping B cells , but no correlation was found between Tfh cells and B lymphocytes or immunoglobulin production [19] . Antibodies are crucial to naturally acquired protective immunity against blood stage malaria , with functions that include inhibition of merozoite invasion , blocking cytoadherence , and improving phagocytic activity of monocytes and macrophages [45] . In this work , infected patients displayed higher levels of MSP-119-specific IgG than the same patients AT . The same was not observed for AMA-1-specific total IgG response . Moreover , a more integrated analysis of the proportion of the patients considered to represent the high producers of antibodies show that the more malaria episodes the patient had , the more antigen-specific antibodies they produce . Indeed , a meta-analysis study discuss that despite a great heterogeneity of humoral response observed where P . vivax infection is endemic , IgG response to a number of antigens is associated with increase antibody levels [46] . The same is observed when the dynamics of the antibody response is assessed during P . falciparum infection . Despite the antibody response seems to be short-lived , data revealed a modest increase in antibody reactivity with age [47] . The same group showed that the expansion of memory B cells and antibody compartments depends on parasite exposure rather than age [48] . Increased reactivity indices of IgG1 and IgG3 anti- PvMSP-119 were observed during acute malaria [49] . PvMSP-119-reactive-IgG1 antibodies predominate in individuals living in Brazilian endemic areas with different levels of exposure . The association between isotypes and protection to P . falciparum infection is not clear but it has been accepted that IgG1 and IgG3 are considered cytophilic and protective , whereas IgG2 and IgG4 may even block protective mechanisms [50] . Higher levels of IgG1 and IgG3 are also observed in subjects with long-term exposure in P . vivax infection [51] . Previous data reported a decrease in total B cells following acute P . falciparum and P . vivax infections [52] . Moreover , another study show that some exposure to P . falciparum does not result in stable populations of antigen-specific memory B cells [53] . More recently , the decrease in B lymphocytes was associated with an expansion of transitional ( immature ) B cells in children following P . falciparum infection [54] . Our study shows that P . vivax infection does not alter proportions of total and immature B cells , but striking changes are seen in memory and naïve compartments . It is believed that recurrent infections are necessary to maintain acquired immunity to malaria and avoid short-lived antibody responses due to defective or suboptimal responses of memory B cells [5] . Indeed , individuals living in endemic areas develop high levels of Plasmodium-specific antibodies and exhibit resistance to malaria infections , or at least to clinical symptoms [55] . However , it has been described that repeated malaria infections in areas of high endemic exposure can lead to B cell anergy or exhaustion and expansion of atypical memory B cells [25] . The role of atypical memory B cells in the context of malaria remains unclear . Atypical memory B cells represent up to 40% of all circulating B lymphocytes [25] , but they are uncommon in healthy individuals living in malaria-free regions . It has been postulated that they contribute to the production of short-lived antibodies due to the generation of short-lived plasma cells [56] . However , others have raised the possibility that their expansion during P . falciparum infection is beneficial and may promote protection from clinical disease by modulating the immune response [25] . Our results show that P . vivax infection triggers higher proportions of atypical memory B cells and lower frequencies of classical memory B cells . Previous study showed that higher proportions of IgD- atypical memory B cells are observed in P . vivax exposed pregnant and non-pregnant women compared to non-exposed individuals [57] . An controlled P . vivax human infection study showed an enhanced ability of atypical memory B cells to proliferate just after treatment initiation , which was lost 35 days later [58] . Atypical memory B cells were increased in patients living in low transmission P . falciparum area , and further increase in high transmission region [24] . Interestingly , even in our study area where malaria is not highly endemic , single- and multiple-infected individuals displayed distinct proportions of atypical and classical memory B cells . Although atypical memory B cells were increased during acute malaria , first time infected patients had higher frequencies compared to individuals who had more than one episode of malaria . The contrary was observed for classical memory B cells; individuals who had more than one malaria episode had higher proportions of this cell subpopulation . The alterations observed in atypical and classical cell subsets , promoting the decrease of the latter and the increase of the former is accompanied by an important increase in the antigen-specific antibody levels . The changes observed in the balance between atypical and classical memory B cells in patients with multiple infections suggests that an efficient memory response following repetitive malaria exposure is maintained by classical memory B cells . P . vivax infection also induces expansion of plasma cells and their activation as assessed by IgG , PD-1 , CD38 and Ki67 expression . However , when patients were segregated by malaria episodes , higher proportions of plasma cells and Ki67+ and Ki67+CD38+ plasma cells were found in first-time infected individuals . Despite this observation , it is important to mention that even with lower proportions of plasma cells , multiply-infected patients produced higher levels of P . vivax-specific IgG . A possibility is that plasma cells from re-exposed patients migrate from the circulation to other lymphoid tissues , such as bone marrow , or even that they respond more efficiently than their counterparts from individuals undergoing primary infection . Indeed , in a highly malaria transmission endemic area , very short-lived antibody responses to malaria were associated with younger individuals who had the fewest number of malaria infections [56] , suggesting that long-lived humoral responses develop after repeated infections . In summary , Plasmodium vivax infection triggers an increase in the proportion of circulating Tfh cells . Purification of Tfh cells from malaria patients confirmed that these were the main source of IL-21 and therefore likely to play an important role in the induction of protective humoral immunity . P . vivax infection also induces changes in B cell compartments , with multiple infections driving an increase in classical memory B cells that was accompanied with high levels of specific antibodies . The identification of bona fide circulating Tfh cells during P . vivax infection suggests that novel vaccination strategies should aim to prime strong Tfh cells responses in order to generate effective and long-lasting humoral immunity .
P . vivax-infected patients ( n = 24 , 87 . 5% male and 12 . 5% female , 18–56 years old ) with uncomplicated malaria were included in this study and received medical care at Centro de Pesquisa de Medicina Tropical de Rondônia in Porto Velho , Rondônia , a malaria endemic area in Brazil . Peripheral blood was collected from adults , 18 years or older individuals , BT and after being diagnosed with Plasmodium vivax infection by thick blood smear film . Blood samples were collected again 30–45 days AT . Infection by a single Plasmodium species was confirmed by polymerase chain reaction ( PCR ) . Patients were treated according to the Brazilian Ministry of Health guidelines . Clinical characteristics and laboratory data are shown in S1 Table . Blood samples were also obtained from healthy donors ( HD ) , who never had malaria , from Porto Velho ( 2 female and 4 male , 18–50 years old ) . This study was performed under protocols reviewed and approved by the Ethical Committees on Human Experimentation from Centro de Pesquisas René Rachou , Fiocruz ( CEP-CPqRR 665281 , CAAE: 30492014 . 9 . 0000 . 5091 ) . All patients were adults and were enrolled in the study after providing written informed consent . Total levels of IgM and IgG were measured in culture supernatants using Human IgG and IgM total enzyme-linked immunosorbent assay ( ELISA ) Ready-Set-Go ! ( eBioscience ) according to the manufacturer’s instructions . IgM , IgG and IgG’s subclasses anti-AMA-1 and anti-MSP-119 were measured in plasma and supernatants of cell cultures by ELISA . ELISA plates were coated with the recombinant proteins PvAMA-1 and PvMSP-119 ( 1μg/well ) produced as previously described [59 , 60] . Plasma samples were added to each well at a final dilution of 1:100 . The presence of bound IgA , IgM , IgG and subclasses of IgG was detected using tetramethylbenzidine ( Sigma ) at 10mg/mL diluted in phosphate-citrate buffer ( pH 5 . 0 ) containing hydrogen peroxide ( 0 . 03% [vol/vol] ) . The final optical density ( OD ) at 450nm was obtained by using a VERSAmax microplate reader . The results were expressed as reactivity index ( RI = the ratio between the OD 450nm values obtained from the sample and the values of the cut-off ) . Cut-off points were set at three standard deviations above the mean OD 450nm of plasma from 24 individuals who had never been exposed to malaria . Values of RI > 1 . 0 were considered positive [27] . PBMC from heparinized peripheral blood samples were prepared by centrifugation with Ficoll-Hypaque gradient ( GE Healthcare Life Sciences ) and then frozen in fetal bovine serum ( FBS ) ( GIBCO , Life Technologies ) with 20% dimethyl sulphoxide ( SIGMA ) . PBMC were thawed in RPMI 1640 ( Sigma-Aldrich ) 10% FBS and 20U/mL benzonase nuclease ( Novagen ) . Cells were washed in phosphate-buffered saline ( PBS ) , and stained with Violet or Acqua Live/Dead ( Invitrogen ) and with monoclonal antibodies . PBMC were washed , fixed and permeabilized ( FoxP3 staining buffer Set , eBioscience ) according to the manufacturer’s instructions . Cells were incubated with antibodies against intracellular proteins , washed , fixed and acquired on an LSR-FORTESSA . The antibodies used to define Tfh cells and B cell subpopulations are described in S2 Table . Doublets were removed using a forward scatter area ( FSC-A ) versus height ( FSC-H ) gate . Events were gated in function of Time versus size scatter area ( SSC-A ) and several combinations of fluorochromes to exclude debris flux interruptions . Dead cells were excluded using a Live/Dead gate versus CD3 or CD19 to phenotype T and B cells respectively ( Fig 1 ) . The following combinations of molecules were used to define specific cell subsets: T cells: CD4+CD45RO- naïve cells; CD4+CD45RO+CXCR5- memory cells; CD4+CD45RO+CXCR5+ activated memory cells; CCD4+CD45RO+CXCR5+ICOS+PD-1+ Tfh cells . Within each subpopulation the expression of CD154 indicated antigen-specific cell activation , CCR6-CXCR3- expressing cells were defined as Th2 or Th2-like cells; CCR6+CXCR3- as Th17 or Th17-like cells and CCR6-CXCR3+ as Th1 or Th1-like cells . CD3-CD14-CD19+ cells were selected for the analysis of B cell subsets: CD19+CD10+ immature cells; CD27+CD21+ classical memory cells; CD27-CD21+ naïve cells; CD27+CD21- activated memory B cells; CD27-CD21- atypical memory B cells; CD20-CD21- plasma cells; CD20+CD21+CD38+ activated plasma cells , as previously described [24 , 25] . FlowJo X and GraphPad PrismV5 . 0 ( GraphPad-Software ) were used for data analysis and graphic presentation . IL-21 levels were assessed in plasma and supernatants of cell cultures by Human IL-21 ELISA Reading-Set-Go ! kit ( 2nd Generation ) ( eBioscience ) according to the manufacturer’s instructions and analyzed with the software Softmax . IFN-γ , IL-6 , IL-10 , IL-17 levels were assessed in the same samples using the BD Cytometric Bead Array Human Th1/Th2/Th17 and Human Inflammatory Kits according to the manufacture’s instructions . Samples were acquired using BD FACSVerse system with the BD FACSuite software , analyzed by FCAP Array software and GraphPad Prism . After PBMC preparation CD14+ monocytes , CD66b+ neutrophils and lymphocytes were FACS-sorted using a FACSAria II ( BD Biosciences ) . A second round of sorting was performed to further purify lymphocytes into CD19+CD21+CD27- B naïve cells , CD19+CD27+ memory B cells and CD4+ T cells . The latter subpopulation was further sorted into CD4+CD45RO- naïve T cells , CD4+CD45RO+CXCR5- memory T cells and CD4+CD45RO+CXCR5+ Tfh cells . The frequency of PD1 and ICOS expressing Tfh cells was assessed in Tfh cells . Anti-CD66b was used to exclude contamination by neutrophils . Purity of sorted cells was >95% . Antibodies used to sort T , B cell subpopulations , monocytes are described in the item Immunophenotyping and intracellular cytokine assessment . The red blood cell pellet from the Ficoll-Hypaque density gradient centrifugation was harvested and washed three times and then resuspended in RPMI to a final hematocrit of 10% . Five milliliters of this suspension was overlaid on 5mL of a 45% Percoll ( Sigma Aldrich ) solution in a 15mL tube . After centrifugation , floating mature Pv-reticulocytes ( Pv-Ret ) were collected , washed three times and then resuspended in 1mL RPMI [27] . Purified subpopulations were cultivated for 5 and 9 days for assessment of cytokine and immunoglobulin levels . The cultures were performed in RPMI supplemented with penicillin ( 50U/mL ) , streptomycin ( 50ug/mL ) and 10% FBS , with stimulation using SEB ( 1 μg/mL ) or Pv-Ret ( ratio of 1:1 , Pv-Ret:monocytes ) . 2x104 of naïve T cells or memory T cells or Tfh cells were added to each well alongside the same number of naïve B cells or memory B cells ( ratio of 1:1 ) in the presence of autologous sera ( 5% ) and CD14+ cells were added as antigen presenting cells . ( 20% of total of cells ) . Identification of the Plasmodium species ( P . vivax , P . falciparum and P . ovale ) was done by nested PCR using 0 . 2mL of peripheral blood that targets variant sequences in the small subunit rRNA gene . The real-time PCR mix was prepared with Syber green PCR master mix ( Life Technologies ) , P . vivax species-specific primers ( 75nM ) and DNA from blood samples . The real-time PCR was performed in an ABI Prism system 7500 ( Applied Biosystems , Foster City , CA ) as follows: 95°C for 10 min , 40 cycles of 95°C for 15s , 60°C for 1min . Statistical analysis was performed using GraphPad Prism V5 . 0 . Differences were considered statistically significant when p≤0 . 05 . Because of the complexity of the experiments performed with purified T and B cell subsets that were present at very low frequencies in the circulation , a p<0 . 10 is also reported for appreciation . Results were analyzed using a two-tailed paired t-test . Wilcoxon’s test was used when paired samples data did not fit a Gaussian distribution . Radar graphs were used to analyze the overall immune response taking to account the number of malaria episodes of each patient . The percentage of high producers was calculated for each parameter to create an overall signature . The inner circle represents the 50th percentile , which was taken as threshold to segregate higher and lower expression/production based on the median . Microsoft Excel Software was used for creating radar graphs . Cytoscape V3 . 2 . 0 , an open access software , was used for integrating the multiple parameters assessed in the study . Networks were built for each group of patients segregated based on the number of malaria episodes . Correlation analysis was performed using Spearman’s ( GraphPad PrismV5 . 0 ) . Lines were drawn to connect and show associations between attributes , classified as positive ( solid line ) or negative ( dashed line ) . Lines are displayed with distinct thickness , representing the correlation scores , categorized as strong positive ( r ≥ 0 . 68; thick black line ) , moderate positive ( 0 . 36 ≤ r < 0 . 68; thin black line ) , strong negative ( r ≤ -0 . 68; thick gray dashed line ) , moderate negative ( -0 . 68 < r ≤-0 . 36; thin gray equal dashed line ) .
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Plasmodium vivax is the most widely spread malaria parasite species and represents a significant impediment to social and economic development in endemic countries . Our goal was to assess the importance of T follicular helper cells in the development of the immune response during malaria . We found that P . vivax infection promotes expansion of circulating Tfh cells that secrete IL-21 to boost immunoglobulin production by B-cells . Accordingly , malaria infection led to marked changes in B cell subpopulations , including expansion of plasma cells and increased production of antigen-specific IgG1 and IgG3 . Re-exposure to P . vivax led to amplified Tfh cells cell responses that were concomitantly associated with increased frequencies of classical memory B cells . Thus , Tfh cells that are induced during P . vivax infection could impact the efficiency of humoral immune responses that underlie protective immunity .
|
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2017
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T follicular helper cells regulate the activation of B lymphocytes and antibody production during Plasmodium vivax infection
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The replication of human polyomavirus JCV , which causes Progressive Multifocal Leukoencephalopathy , is initiated by the virally encoded T-antigen ( T-ag ) . The structure of the JC virus T-ag origin-binding domain ( OBD ) was recently solved by X-ray crystallography . This structure revealed that the OBD contains a C-terminal pocket , and that residues from the multifunctional A1 and B2 motifs situated on a neighboring OBD molecule dock into the pocket . Related studies established that a mutation in a pocket residue ( F258L ) rendered JCV T-ag unable to support JCV DNA replication . To establish why this mutation inactivated JCV T-ag , we have solved the structure of the F258L JCV T-ag OBD mutant . Based on this structure , it is concluded that the structural consequences of the F258L mutation are limited to the pocket region . Further analyses , utilizing the available polyomavirus OBD structures , indicate that the F258 region is highly dynamic and that the relative positions of F258 are governed by DNA binding . The possible functional consequences of the DNA dependent rearrangements , including promotion of OBD cycling at the replication fork , are discussed .
There are presently fourteen known human polyomavirus family members [1 , 2] . Reasons for interest in these viruses include the diseases they are associated with , particularly in immunocompromised individuals [3–5] . As examples , JC virus ( JCV ) causes the often fatal demyelinating disease Progressive Multifocal Leukoencephalopathy ( PML ) ( [6]; reviewed in [7] ) ; Merkel cell polyomavirus causes Merkel cell carcinoma , a rare but highly aggressive skin cancer [8 , 9] and BK polyomavirus causes BK nephropathy [10 , 11] . Further interest in polyomaviruses stems from the profound insights they have provided into basic cellular process , such as DNA replication ( e . g . , [12–17] ) and the mechanisms that underlie cellular transformation ( e . g . , [3 , 18–20] ) . Polyomaviruses have small double stranded DNA genomes [14] that contain a regulatory region that is termed the non-coding control region ( NCCR ) . The NCCR contains the origin of replication as well as the promoter and enhancer elements ( reviewed in [21 , 22] ) . An additional feature of polyomavirus genomes is the "early region" that encodes several proteins , including the large T-antigen ( T-ag; reviewed in [15 , 23 , 24] ) . T-ag is the only virally encoded protein needed for replication; therefore , it has been the target of numerous studies designed to understand its multiple roles during the duplication of the viral genome ( reviewed in [12 , 15 , 25] ) . For example , polyomavirus T-ag's have been the focus of a number of recent structural studies ( reviewed in [25 , 26] ) . These structural studies have provided critical insights into the initiation process , such as establishing how the GAGGC pentanucleotides in the polyomavirus replication origins are recognized by the origin binding domains ( OBD ) within T-ag [27–32] . Related studies , conducted with the SV40 T-ag OBD , suggest that following site-specific DNA binding , the OBDs undergo rearrangements , including "spiral formation" [32–35] . Additional experiments have revealed the multiple roles played by the T-ag helicase domains during origin melting [36–38] , oligomerization [39 , 40] and helicase activities ( [39 , 40] ) . Based on these studies , models depicting T-ag's multiple roles during the initiation of SV40 DNA replication have been proposed ( e . g . , [25 , 26 , 41] ) . To further our understanding of the initiation of polyomavirus DNA replication , we recently initiated structural studies of JCV T-ag . In particular , we solved the structure of the JCV T-ag OBD [42] . One of the interesting findings of the JCV T-ag OBD structure was the presence of a C-terminal "pocket" . This structure also revealed that the pocket serves as the binding site for T-ag residues from the A1/B2 loops situated on a neighboring OBD subunit [42] . The binding of the A1/B2 loops to the pocket was of interest given that the initial function of the A1/B2 loops is to site-specifically bind the pentanucleotides in the core origin ( [43 , 44]; reviewed in [45] ) . These observations provided further evidence that the A1/B2 loops are multifunctional ( reviewed in [26] ) and that the interaction of the A1 & B2 loops with the pocket is a critical step that takes place at later stages of the initiation process ( e . g . , during the oligomerization of T-ag on the core origin ( [42]; reviewed in [26] ) . Additional evidence for the hypothesis that the pocket in the OBD plays a critical role during JCV replication was derived from studies of the JCV T-ag F258L mutant [42] . The F258L "pocket mutation" had no effect on levels of T-ag expression , but for unknown reasons it inactivated T-ag dependent JCV replication [42 , 46] . Therefore , given our interest in the JCV T-ag OBD pocket , and its role ( s ) during JCV DNA replication , we elected to examine the structural basis for the inactivation of JCV replication by the F258L T-ag mutation . The results of these experiments , presented herein , prompted us to examine the dynamic properties of the C-terminal regions of polyomavirus T-ag OBDs . These structure-based analyses indicate that the C-terminal region of the SV40 T-ag OBDs move as a function of DNA binding . The possible biological consequences of these DNA dependent movements are discussed .
An expression vector encoding the JCV T-ag OBD ( residues 132–261 ) , that was termed pGEX1λT JC-OBD , was previously described [42] . Using the Quikchange Kit ( Agilent Technologies ) , JCV-OBD residue Phe 258 was mutated to Leu . The oligonucleotides used for the mutagenesis were 5’-GGCCTTAAGGAGCATGACCTTAACCCAGAATAATCG-3’ ( the mutated base is underlined ) and its complement . The resulting plasmid was termed pGEX1λT JCV-OBD-F258L . The same mutation had been previously introduced into full-length JCV T-ag [42] , using the Quikchange Kit , plasmid pCMVneo JCVT-ag and the oligonucleotides listed above . The resulting plasmid was termed pCMV-JCVT-ag F258L . DNA Sequencing at the Tufts University Core Facility ( TUCF ) confirmed the sequence of the F258L mutants in plasmids pGEX1λT JCV-OBD-F258L and pCMV-JCVT-ag F258L . Sequence alignments were performed with the program Clustal Omega at the EMBL-EBI website [47] . The aligned sequences were displayed using the program JalView [48] . The wild type JCV T-ag OBD was purified using a previously described protocol [42] . The JCV T-ag OBD F258L mutant protein was purified from BL21 cells using the identical procedures used to purify the wt JCV T-ag OBD [42] . Once purified , the JCV wt and F258L OBD proteins were stored at -80°C in storage buffer ( 20 mM Tris pH 8 . 0 , 50 mM NaCl , 10% glycerol , 1 mM EDTA , 0 . 1 mM PMSF and 5 mM DTT ) . The subcellular localization of the F258L JCV T-ag mutant within C33A cells was determined by immunofluorescence ( [49] and references therein . A detailed protocol describing the steps needed to detect JCV T-ag within C33A cells was previously published [46] ) . T-ag was detected using the Pab 416 monoclonal Ab ( Santa Cruz Biotechnology ) and a secondary goat anti-mouse antibody , conjugated with Alexa 488 ( Life technologies ) . The cells were visualized using a Zeiss Axiovert 200M microscope and the data were analyzed using the OpenLab software package from Perkin Elmer . The ITC studies were conducted with a VP-ITC calorimeter ( MicroCal , Northampton , MA ) . Prior to conducting the ITC studies , the dsDNA oligonucleotide and the JCV T-ag OBD proteins ( both wt and the F258L mutant ) were buffer-exchanged , using PD-10 columns ( GE Healthcare ) , into 10 mM Sodium Phosphate , pH 7 . 0 , 50 mM NaCl and 5 mM DTT . Protein and DNA concentrations were determined spectrophotometrically , using extinction coefficients calculated with the ProtParam web server and the Integrated DNA Technologies ( IDT ) website , respectively . The data were analyzed using the Origin software provided by the manufacturer . Binding isotherms and KD measurements were performed essentially as described [61 , 62] . The reactions were performed in 96-well plates ( OptiPlate-96 F HB black microplate , Perkin Elmer ) , in a final volume of 150 μl containing 15 nM of fluorescein-labeled probe and the indicated concentrations of T-ag OBD in buffer containing 20 mM Hepes pH 7 . 4 , 50 mM NaCl , 0 . 01% NP40 and 0 . 1 mM DTT . Fluorescence readings were taken on a Victor3V 1420 Multilabel HTS Counter ( Perkin Elmer ) using the 485 nm/535 nm filter sets . Background fluorescence from buffer was subtracted and polarization ( P ) and anisotropy ( A ) values were defined as P = ( III−I⊥ ) / ( III + I⊥ ) and A = ( III−I⊥ ) / ( III + 2I⊥ ) , where III and I⊥ are the fluorescence intensities recorded in the parallel and perpendicular orientations respective to the orientation of the excitation polarizer . Fluorescein-labeled oligonucleotides were purchased with the fluorophore attached at the 5`end by a six-carbon linker ( IDT ) . Duplex DNA probes were prepared by annealing each fluorescein-labeled oligonucleotide to a complementary oligonucleotide . Apparent KD values were obtained from direct binding isotherms by nonlinear least-squares regression of the data as previously described [62] .
We previously characterized , via immunofluorescence , the sub-cellular localization of JCV T-ag in C33A cells; a cell line that supports robust levels of JCV DNA replication [46] . It was concluded that JCV T-ag is predominantly in the nuclei of these cells where it is distributed in a punctate manner . Given that T-ag localization to the nucleus is essential for the replication of polyomaviruses ( e . g . , [63 , 64] ) , we elected to determine if T-ag containing the F258L mutation is also preferentially localized to the nuclei in C33A cells . Inspection of Fig 3 establishes that as with wt T-ag , full-length JCV T-ag containing the F258L mutation is largely in the nucleus ( 83% vs ~88%; respectively ) . Similar to wt T-ag , a much lower percentage of cells contain the mutant in both the nuclei and the cytoplasm ( ~16 . 2 vs 12%; respectively ) or exclusively in the cytoplasm ( ~ 0 . 833 vs 0 . 3%; respectively ) . Thus , the failure of the JCV T-ag F258L mutant to support viral replication is not due to defects in its subcellular localization . The next step taken to establish the defect caused by the F258L mutation was the determination of the structure of the JCV T-ag OBD F258L mutant . The F258L mutant was purified using previously described methods ( materials and methods section ) . As shown in Table 1 , the F258L JCV T-ag OBD crystallized in the I41 space group , diffracted to 2 . 7 Å and contained one molecule in the asymmetric unit cell . The structure of the JCV T-ag F258L OBD mutant is presented in Fig 4A , only residues 133–259 are visible in the crystal structure . As with the wt JCV OBD [42] , the topology of the OBD F258L mutant is a five-stranded antiparallel B-sheet sandwiched , on either side , by two helices . The leucine at position 258 is in orange and shown in a ball and stick representation . The multifunctional A1 and B2 loops are shown in red and blue , respectively . The A1 loop is the "DNA free" or "apo-conformation" ( [42]; reviewed in [26] ) . Inspection of Fig 4B establishes that the previously described C-terminal pocket [42] is also a feature of the JCV T-ag OBD F258L mutant . A superposition of the F258L JCV T-ag OBD mutant with the wt JCV OBD ( [42]; 4LIF ) is shown in Fig 4C; it is apparent that these two structures are nearly identical ( RMSD of 0 . 43 Å over 127 C-alphas ) . The spatial overlap between residues F258 with L258 is indicated ( Fig 4C; ( green and orange residues , respectively ) ) . Finally , the structure also revealed that , owing to the relatively small size of leucine , the F258L substitution created a small cavity that was filled by Leu 258 moving closer to the core of the OBD ( Fig 4C insert ) . The initial function of the A1 and B2 loops is site-specific DNA binding to the viral origin [44 , 65 , 66] . Spatially , the A1 and B2 loops are relatively close ( ~ 15 Å ) to residue 258 ( Fig 4A ) . Thus , while the two structures are nearly identical ( Fig 4C ) , subtle but significant structural differences may alter the DNA binding specificity of the F258L mutant and thus account for the replication defect associated with this JCV T-antigen mutant . Therefore , we elected to determine if the F258L T-ag OBD is altered in terms of its ability to bind DNA in a site-specific manner . The DNA binding studies support the conclusion that the F258L mutation does not significantly alter the structure of the DNA binding A1 and B2 loops . Therefore , we next focused on another prominent feature of the JCV T-ag OBD , the subunit-subunit interface it forms . In the previously reported wt JCV T-ag OBD crystal structures [42] , we observed a relatively small interface ( ~550 Å2 ) between neighboring OBD molecules . The interface was formed by the A1 & B2 regions of one OBD interacting with the C-terminal pocket of a neighboring OBD; thus , the OBDs were arranged in a head-to-tail manner [42] . Furthermore , the wt JCV T-ag OBD interface was analogous to the OBD:OBD interface observed in previous SV40 crystal structures wherein the OBDs formed a crystallographic spiral having 6 molecules/turn [33 , 34] . In light of the SV40 studies , we hypothesized that the interface observed in the structures of the wt JCV T-ag OBDs is similar to that formed during hexamerization of full-length T-antigen [42] . The JCV T-ag OBD containing the F258L mutation crystallized in the same space group as one of the wild type JCV OBDs , suggesting a very similar , but not identical interface as the wild type . Therefore , to refine our understanding of the defect in the F258L mutant , the differences between the interfaces formed by the wt and F258L T-ag OBDs were analyzed in detail . The studies presented in Figs 7 and 8 provide additional evidence that in the apo form of the JCV T-ag OBDs , the residue 258 containing C-terminal region plays a critical role in forming the interface with the A1/B2 loops . The A1/B2 regions are , however , initially involved in site-specific binding to DNA in the viral origin [43 , 44 , 65] . Moreover , DNA is known to alter the conformation of the A1 loop in SV40 T-ag ( e . g . , [27 , 67] ) . These observations raised the question of whether DNA binding is also altering the conformation of the C-terminal region of polyomavirus T-ag OBDs . A superposition of residue 258 in the apo forms of the JCV OBDs solved to date , including residue 258L ( in orange ) , is presented in Fig 9A ( left ) . Extending these analyses , a superposition of both JCV residue 258 , and SV40 residue 257 ( equivalent to JCV OBD residue F258 ) in all of the apo forms of the JCV and SV40 OBDs is presented in Fig 9A ( right ) . It is apparent that in the absence of DNA , the phenylalanines , and the single leucine present in the 258L mutant , adopt a highly conserved conformation . Regarding the question of whether these structures change upon DNA binding , co-structures of the JCV T-ag OBD bound to DNA have yet to be determined . Therefore , to ascertain whether structural changes occur within the C-terminal region of the OBDs upon DNA binding , the analyses were conducted with the previously determined co-structures of the SV40 T-ag OBDs . A superposition of the co-structures of the SV40 T-ag OBDs bound to DNA ( [27 , 28 , 35] ) is presented in Fig 9B ( left ) . Of interest , in several of the DNA bound co-structures , the position of residue F257 is altered . Moreover , the co-structure of a larger fragment of T-ag ( i . e . , the OBD & helicase containing T-ag131-627 fragment ) bound to DNA has also been determined [31] . Fig 9B ( right ) presents just the F257 containing regions in this larger co-structure . It is apparent that in this DNA bound co-structure , residue F257 is also in a "non-apo" position . To more clearly illustrate the DNA dependent shifts in the C-terminus of the OBDs , all of the structures from JCV and SV40 T-ags were superimposed ( Fig 9C; same coloring schemes as in Fig 9A and 9B ) . It is apparent from this figure that DNA binding causes the region around SV40 F257/ JCV F258 to become highly dynamic . Indeed , the position of F257 varies by as much as 13 Å ( see insert ) ; nearly the same distance moved by the beta-hairpin in the helicase domain as a function of ATP hydrolysis [40] . Therefore , it is concluded that DNA binding to the A1/B2 motifs alters the conformation of the C-terminal region of the SV40 T-ag OBD . Finally , an interesting feature of the SV40 T-ag OBD co-structures solved to date is that the F257 residues in the distal orientations are often positioned opposite aromatic residues; an indication that stacking interactions may help to stabilize the individual conformations ( Fig 10 ) . Given that it lacks an aromatic ring , the F258L mutant would fail to make these stacking interactions . Therefore , in addition to perturbing the wt interface ( Fig 8 ) , the failure of the F258L mutant to form the observed stacking interactions could be an additional reason why it is defective for viral replication .
There are several possible functional consequences of the DNA dependent conformational changes in the OBD C-termini; one being that they play a role during T-ag assembly on the origin . Support for this hypothesis includes the finding that SV40 T-ag residue F183 is needed for oligomerization ( [62 , 65]; in JCV T-ag , the analogous residue ( i . e . , F184 ) sits at the base of the C-terminal OBD pocket ) . DNA dependent conformational changes in the OBD might also play a role in the poorly understood melting of the central or “site II” region of the core origin [68] . In addition , DNA dependent conformational changes in the OBD C-termini may promote DNA replication at later stages , such as when T-ag serves as the helicase at replication forks . We previously proposed that when T-ag is functioning at replication forks , that the SV40 T-ag OBDs are proximal to the ds/ss fork ( Fig 11A; reviewed in [26] ) . Studies of the closely related papillomavirus E1 hexameric helicase also place the N-terminal DNA binding domain ( DBD ) at the replication fork [69–71] . Among the advantages of having the T-ag OBDs arranged at the replication fork is that as with the nonplanar DnaB spiral assembly [72] , the OBD subunits could engage the ds/ss fork via a "hand-over-hand" mechanism; a process that could promote DNA unwinding . Consistent with this proposal , we previously reported that the SV40 T-ag OBDs may assemble into nonplanar hexameric spirals ( [33–35]; reviewed in [26] ) . An additional advantage of hexameric spirals is that in one terminal monomer of the spiral the A1/B2 region is free and thus available for interactions with DNA . In contrast , were the OBDs to adopt a planar flat ring assembly , all of the A1/B2 regions would be involved in interface formation and thus unavailable to bind to DNA . When the studies summarized above are considered in terms of our current findings they suggest a mechanism for promoting cycling of the JCV T-ag OBD monomers at the replication fork . According to this model , when the A1/ B2 regions in OBD subunit A , the terminal monomer within the spiral that is colored purple , interact with DNA at the fork ( Fig 11B; left side ) , the DNA dependent structural changes in the C-terminus of the OBD will be induced ( Fig 9C ) . This in turn promotes the disruption of the interface formed by the proximal pair of monomers within the spiral ( subunits A & B ) . Consistent with this proposal , when bound to DNA the SV40 T-ag OBDs do not form "apo" like interfaces ( reviewed in [26] ) . Once the interface is disrupted and subunit A is released from the spiral , the A1/B2 region is exposed on what had been the penultimate monomer in the spiral ( subunit B ( colored in blue ) ) . Following engagement of the ds/ss forked DNA by the newly exposed A1/B2 regions on subunit B ( Fig 11B; right side ) , the DNA dependent cycle of interface disruption will be repeated . Thus , according to the DNA dependent "interface disruption" model , the splitting apart of the OBD/OBD interface is an active process that does not depend upon the relatively inefficient thermal breakage of the interface . Validation of the DNA dependent "interface disruption" model will require additional structural studies . For example , co-structures of full-length JCV T-ag or T-ag domains bound to DNA are needed to confirm the predicted DNA dependent movements within the C-termini of the JCV T-ag OBDs . Related studies are required to determine if DNA dependent movements occur in other polyomavirus OBDs . Also warranted are structural studies of the OBD:OBD interface formed in the context of T-ag hexamers . It is noted , however , that analogous DNA dependent changes in the DBD of papillomavirus E1 have not been reported [73 , 74] . Why these DNA dependent changes are detected in the SV40 T-ag OBD , but not in the papillomavirus E1 DBD , remains to be determined . Nevertheless , recent experiments with E1 have established that the fork proximal origin-recognition domains play critical roles in regulating helicase activity [69] and that strand separation takes place inside E1 in a chamber N-terminal to the helicase domain [71] . In addition , the "interface disruption" model makes certain predictions that need to be tested . For example , the model suggests that upon encountering a gap in duplex DNA , the DNA dependent conformational changes in the OBD will not be induced . This would promote maintenance of the interface and possibly pausing of the T-ag helicase at the gap . Termination at gaps and nicks has been previously reported for prokaryotic ( e . g . , [75 , 76] ) and eukaryotic ( e . g . , [77 , 78] ) hexameric helicases . However , it is not yet known if gaps and other forms of DNA damage cause the polyomavirus or papillomavirus hexameric helicases to pause . When completed , these experiments will address the generality of the DNA dependent structural changes within the C-termini of the polyomavirus OBDs and establish the functional consequences of these movements . Finally , MCM complexes are also known to form left-handed lock washer structures [79] . Thus , it will be of interest to determine if fork DNA promotes the cycling of MCM subunits .
|
A conserved feature of Polyomavirus T-antigens is a phenylalanine situated at the C-termini of their origin-binding domains ( OBDs ) . Using the T-antigen encoded by JC virus , we have investigated why this residue is critical for viral DNA replication . The studies presented herein establish that the consequences of this mutation are limited to the interface formed by the docking of the phenylalanine containing C-terminal pocket region of the OBD with the multifunctional A1/B2 region . Related studies indicate that the conformation of the C-terminal region of the OBD is altered by DNA binding . These observations suggest a model whereby cycling of the OBDs within the hexameric spiral structure at the replication fork is promoted by DNA binding .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2016
|
Structural Based Analyses of the JC Virus T-Antigen F258L Mutant Provides Evidence for DNA Dependent Conformational Changes in the C-Termini of Polyomavirus Origin Binding Domains
|
Mass drug administration ( MDA ) with ivermectin for onchocerciasis was provided in Guatemala’s Central Endemic Zone ( CEZ ) over a 24 year period ( 1988–2011 ) . Elimination of Onchocerca volvulus transmission was declared in 2015 after a three year post MDA surveillance period ( 2012–2014 ) showed no evidence of recrudescence . The purpose of the present study was to evaluate the knowledge , attitudes and practices ( KAP ) towards onchocerciasis and ivermectin among residents in the post endemic CEZ . A major interest in this study was to determine what community residents thought about the end of the ivermectin MDA program . A total of 148 interviews were conducted in November 2014 in four formerly hyperendemic communities using a standard questionnaire on smart phones . The majority ( 69% ) of respondents knew that the MDA program had ended because the disease was no longer present in their communities , but a slight majority ( 53% ) was personally unsure that onchocerciasis had really been eliminated . Sixty-three percent wanted to continue to receive ivermectin because of this uncertainty , or because ivermectin is effective against intestinal worms . Eighty-nine percent of respondents said that they would seek medical attention immediately if a family member had symptoms of onchocerciasis ( especially the presence of a nodule ) , which is a finding very important for ongoing surveillance . Many respondents wanted to continue receive ivermectin and more than half did not believe onchocerciasis had been eliminated . The ministry of health outreach services should be prepared to address ongoing concerns about onchocerciasis in the post endemic CEZ .
Human onchocerciasis ( River Blindness ) is caused by the filarial nematode Onchocerca volvulus that is transmitted by the bite of infected Simulium black flies that breed in rapid flowing rivers and streams . The adult worms of O . volvulus live mainly in subcutaneous nodules , which are often palpable , where the fertilized female worms during their 9–14 years of reproductive life release embryos called microfilariae . The microfilariae are predominantly found in the dermis where they can be ingested by female black fly during a blood meal . Microfilariae so ingested can develop into infective third stage larvae , which may then be transmitted to another person . No animal reservoirs exist . The great majority of microfilariae eventually die in the human tissues , causing complex immunological mediated manifestations leading to skin disease , lymphatic complications and ocular lesions . [1 , 2] Onchocerciasis occurs primarily in Africa , but Yemen and six countries in the Americas are also affected [3 , 4] although the transmission of the parasite has been eliminated in Colombia [5] , Ecuador [6] , Mexico [7] , and ( it is believed ) Guatemala [8] . In Guatemala , onchocerciasis transmission occurred in four geographically distinct transmission zones ( ‘foci’ ) and about half of the 220 , 000 persons at risk in the country resided in the focus known as the Central Endemic Zone ( CEZ ) [8 , 9] . The Ministry of Public Health and Social Assistance ( MSPAS ) , began onchocerciasis control efforts in 1935 with the surgical removal of nodules by uniformed health workers organized into brigades [9 , 10] . The decision by Merck & Company in 1987 to donate ivermectin ( Mectizan ) resulted in a shift to a strategy from nodulectomy to mass drug administration ( MDA ) that became operational in all Guatemalan foci by the year 2000 [8] . There were important challenges to establishing an ivermectin based MDA strategy . First , a single dose of ivermectin does not kill the adult parasites , only the microfilariae . A single dose of ivermectin prevents repopulation of the skin with microfilaria for about six months [1] . To block transmission of the parasite by the vectors , ivermectin needs to be given twice per year with high treatment coverage ( >85% of the MDA eligible population , which excludes children under five years of age and pregnant or mothers breastfeeding infants younger than one month old [1] . Second , high treatment coverage needs to be maintained for many years until the adult worm population in the transmission zone becomes incapable of sustaining itself , even if MDA is stopped [11] . Third , initial ivermectin treatment is often associated with localized or systemic allergic reactions , and fever , resulting from the destruction of microfilaria . While these reactions diminish with repeated treatments , fear of getting sick from the medicine was an important cause of low MDA uptake ( coverage ) early in the Guatemalan MDA program [12 , 13] . Fourth ( and what can be seen as an advantage perhaps rather than a challenge ) ivermectin is also effective against most ectoparasites ( lice , scabies ) and many intestinal helminthes ( especially the human roundworm , Ascaris lumbricoides ) [14 , 15] . The visible and valued experience of passing these large worms commonly led populations under MDA to value the deworming effect associated with the treatment above that for which the health education messages ( onchocerciasis ) have been designed [13 , 16] . In order to understand community values and concerns related to onchocerciasis and ivermectin MDA , two knowledge attitude and practices ( KAP ) surveys were conducted in the early years of the onchocerciasis program by our group . The results from the surveys were to be used to design health education messages and communication strategies to improve levels of participation in the MDA program . The first survey was conducted in communities that had not yet experienced ivermectin . It indicated that people used the term ‘filaria’ to describe onchocerciasis , which was defined as a nodule under the skin that could somehow affect the vision; surgical removal of the nodule ( nodulectomy ) by MSPAS brigades was the only recognized treatment . Transmission of filaria by a biting insect was largely understood , but the term microfilaria ( the target of the ivermectin ) was not recognized . In a free listing of diseases afflicting the communities , followed by their ranking based on paired comparisons , we learned that respondents ranked filaria 13th in seriousness compared to other commonly recognized conditions afflicting their communities [16] . Our second KAP survey was prompted by the observation that residents in some communities refused to take ivermectin when offered [13] . This survey was focused on exploring community concerns and the reasons for low participation rates . Fear of adverse reactions following treatment was the principal reason for rejecting treatment . Health education messages to address these concerns were designed to inform the community more about the complex lifecycle of the parasite , and explain that as the numbers of microfilaria diminished in the body with frequent treatment , the severity of adverse reactions would diminish . A cumulative total of 2 . 9 million ivermectin treatments were delivered in the 321 communities targeted by the CEZ MDA program between 1988 and 2011 . The biannual ( every six months ) treatments were delivered as directly observed treatments at the community level , and after the year 2000 , treatment coverage exceeded 85% of the eligible population . Based on the epidemiological assessments that showed no infection in residents and vectors sampled in sentinel areas , and very low ( <0 . 1% ) O . volvulus antibody in young children in a population-based survey throughout the CEZ , MDA was halted in 2012 . A three-year post treatment surveillance period ( PTS ) was launched during which health workers continued to visit the communities to explain the reason for discontinuation of MDA and to promote the need for continued vigilance to ensure disease elimination and monitoring for a possible recrudescence of the disease . PTS ended in 2014 with the successful completion of an entomological survey in sentinel and non-sentinel areas that showed continued interruption of transmission [8] . The KAP study reported herein was conducted in November 2014 , thirty four months after MDA had ended . It explored community resident’s understanding of the reason for ending the MDA program and the depth of the belief of its success . It also included a reexamination of their knowledge of onchocerciasis , and its seriousness compared to other diseases .
The study was conducted in 2014 in three privately-owned coffee plantations ( “fincas” ) in the Department of Suchitepéquez [Santa Isabel ( 14°32'44" , -91°9'14" ) , Los Tarrales ( 14°31'18" , -91°8'14" ) , and Los Andes ( 14°31'37" , -91°11'25" ) ] ( Fig 1 ) previously surveyed in the second ( 1992 ) KAP survey [13] . A fourth 2014 study site was an independent community [La Estrellita ( 14°28'51" , -91°2'52" ) ] in the Department of Chimaltenango that had not been previously surveyed in KAP studies . All four communities were within a radius of 15 km of survey sites in the first KAP study . In addition , all four communities were sentinel villages for the onchocerciasis program and as such had more frequent interactions with the Ministry of Health teams through the years [8] . Prior to the MDA intervention , prevalence of microfilaria in skin biopsies in the surveyed villages were: Los Andes 74% ( based on a survey in 1988 ) , Santa Isabel 90% ( 1981 ) , Los Tarrales 65% ( 1981 ) and La Estrellita 56% ( 1994 ) . Surveys in these villages conducted in 2007–2010 showed no microfilaria infection ( prevalence 0% ) [8 , 17] . A stratified random sampling by location was originally devised to estimate proportions of variables with binomial distribution [18] , applying the finite population correction , considering an α = 0 . 05 , a ratio of 0 . 5 , a 90% of response rate and a design effect of 1 . 5 to account for the decrease of precision due to clustering . Due to the low number of households in each community , and considering the sample size increase after adjusting for response rate and the design effect , it was decided to include all occupied households ( census ) in the survey to ensure the highest possible precision in the estimates for each community . Male and female heads of household with a minimum of six years of community residence were interviewed . When the head-of-household or an adult family member was not present , a return visit was scheduled for the next day , and if still absent , a third visit was scheduled . If , after three visits to the same household , the head–of-household or a responsible adult resident of the household was not present , the household was not included in the survey . The questionnaires were designed to take approximately 30 minutes and included open and closed questions organized into five sections: ( 1 ) All illnesses affecting community residents , ( 2 ) onchocerciasis ( filaria ) symptoms and transmission , ( 3 ) the treatment of onchocerciasis , ( 4 ) the reasons for halting MDA , and 5 ) the post-treatment period . Surveys were conducted by field technicians from the Universidad Del Valle de Guatemala and responses of participants were recorded on smartphones . The questionnaires and smartphone programs were pre-tested in the CEZ communities Nueva Providencia and Monte Llano located in the Departments of Sololá and Chimaltenango , respectively ( Fig 1 ) . To explore the perceived seriousness of onchocerciasis a list of Illnesses was created by the respondents by using the same methodology applied in the original KAP survey , asking the questions “What illnesses affect this community ? ” “What else ? ” “What else ? ” The 15 most frequently reported illnesses were selected and paired with every other illness to generate 105 pairs . Subsequently , using a paired comparison format , the 105 illness pairs were randomized and presented orally to a subsample of heads of households in the four communities . Each person was asked to indicate the illness in each pair that they considered to be the most serious ( grave ) . This resulted in a list of the 15 most common illnesses ranked in order of perceived seriousness . The ranking of perceived seriousness of common illnesses generated in the 2014 survey was compared with the ranking generated by respondents in our 1991 survey . The unit of analysis for the survey was the individual respondent . The answers to open-ended questions were coded and organized in major categories for subsequent analysis . Data were managed and analyzed using the R statistical software version 3 . 2 . 1 [19] , and the Chi-square test for count data was used to assess independence in the contingency tables . The Bonferroni [20] correction was applied to decrease the probability of Type I error in multiple comparisons . The protocol for this study deemed non-research ( evaluation of a public health program ) by the Institutional Review Boards of Emory University and the Centers for Disease Control and Prevention , the Ethics Committee of the Center for Health Studies ( CES-UVG ) and by the Vector Borne Diseases Program of the MSPAS . Written informed consent was obtained from all survey participants .
Residents of 148 ( 88 . 1% ) of the 168 total households in the four communities were interviewed . Among the 20 households not interviewed , 12 ( 7 . 1% ) were excluded due to absence of a qualified adult respondent , 6 ( 3 . 5% ) due to unwillingness to participate , and 2 ( 1 . 2% ) due to less than 6 years in residence . There were no significant differences in household participation rates among the four villages . The 148 interviews conducted in the four communities were as follows: 36 ( 24% ) in Los Andes , 27 ( 18% ) in Santa Isabel , 37 ( 25% ) in Los Tarrales and 48 ( 33% ) in La Estrellita . The self-reported age of participants ranged from 18 to 110 , with a median age of 42 , and 60% of respondents were female . There were no significant differences between communities in length of residence , which ranged from 6 to 110 years with a median of 36 years . The paired comparison format was presented orally to a subsample ( 108 or 73% ) of interviewed households . Rank order based on pairs analysis that reflected perceived seriousness of common illnesses , compared with results from the 1991 survey [12] , are shown in Table 1 . The 2014 post-elimination survey revealed that diabetes , blindness ( ceguera ) , hepatitis , vomiting ( vomitos ) and diarrhea ( diarrea ) were the five illnesses considered most serious . This 2014 list contained only two ( vomiting and dysentery ) of the 5 priorities listed in the 1991 pre-MDA survey [intestinal worms ( lombrices ) , measles ( sarampion ) , vomiting , dysentery ( disenteria ) , and pertussis ( tos ferina ) ] . Intestinal worms dropped from first place in 1991 to sixth in 2014 . Onchocerciasis ( filaria ) was ranked higher in the post elimination survey ( eighth place ) compared to the pre-MDA survey ( 13th place ) . Diabetes , blindness and hepatitis , the illnesses considered most serious in the 2014 survey , did not appear among the top 15 conditions listed in 1991 . Ninety-four percent of respondents recognized the term filaria in the 2014 survey ( compared to 100% in 1991 ) . Most respondents ( 84% ) were able to correctly describe the symptoms of onchocerciasis and almost all of these ( 97% ) reported that the symptoms of filaria included the presence of nodules , cutaneous manifestations and eye disease . There was evidence that knowledge of onchocerciasis transmission and etiology had improved compared to 1991 . Seventy percent knew that the disease was caused by the bite of an insect ( compared to 50% in 1991 ) and 47% correctly defined the condition as being worm ( compared to 39% in 1991 ) . However , of the107 respondents in this survey who provided either a correct response for the definition ( worm ) or for the cause ( insect bite ) of the disease , only 44% knew both ( Fig 2 ) , which was not significantly different from the 35% who knew both in the 1991 survey ( p = 0 . 19 ) . Ninety-three percent of all respondents reported knowing someone in the past in their community who had contracted the disease , and 73% reported having had onchocerciasis . Knowledge of having been personally infected was strongly related to age with older people more likely to have had the illness ( p < . 001 , X2 = 37 . 67 , df = 3 ) . Of those previously infected , 63% recognized their own infection by the presence of subcutaneous nodules or visual impairment , while 37% reported that they had been diagnosed by uniformed brigade health workers of the MSPAS . Of the 34% who indicated that they had not had the disease , 89% of these had undergone an examination by palpation for nodules or a skin biopsy by brigade health workers . Ninety-nine percent of respondents confirmed that health workers had come to their communities to deliver pills ( las pastillas ) to treat onchocerciasis and of these , 99% reported having taken the medication . When asked if they knew the name of the tablets , 64% of those interviewed indicated they did and 96% of these correctly identified it as Mectizan or ivermectin . When asked how the tablet worked against filaria , 50% of the responses indicated the pill killed microfilaria , 36% said it killed filaria and 13% indicated that it prevented the disease ( i . e . , it was understood to be prophylactic ) . Seventy-six percent of the respondents knew that ivermectin could cause side effects ( molestias ) , but no one could recall having seeing such adverse events after treatment for many years . When asked “Does the pill for filaria help to cure other illnesses ? ” 67 ( 45% ) respondents indicated “yes” , of these 47 ( 70% ) indicated that the pill cured intestinal worms , 15 ( 22% ) said the pill cured ectoparasites such as lice ( piojos ) and scabies ( granos ) and 5 ( 8% ) responded the pill cured both these conditions . The majority ( 69% ) of respondents said that the MDA program had ended because the disease was no longer present in their communities , but almost a third ( 31% ) did not know why MDA was halted . However , when all respondents were pressed by asking personal opinion , ( “Do you believe that onchocerciasis has been eliminated in your community” ? ) , over half ( 53% ) of all respondents gave responses showing skepticism . Nineteen percent said they were unsure that elimination had taken place , while 34% were certain it had not ( by responding ‘no’ to the question ) . When asked why they responded in this way , the ‘doubters’ said “because there are still people sick with la filaria” ( 72% ) or that “there are still flies that transmit the disease” ( 28% ) . Several respondents also expressed concern about the children who had been too young to take the treatment , assuming that these untreated children were at greater risk than those who had benefited from the MDA program . There was a significant association between those who indicated that they missed receiving ivermectin treatment and those who expressed the opinion that that onchocerciasis may not have , or definitely had not , been eliminated in their community . ( p < . 05 , X2 = 11 . 3 , df = 1 ) . Ninety-three respondents ( 63% ) said they missed receiving their twice per year ivermectin treatments; there were no age or sex differences associated with the desire to continue receiving the pill . When those who missed taking the pill were asked why , 87% answered that because the medicine was needed to prevent onchocerciasis , with the remaining 13% wanting to take treatment to treat their intestinal worms ( lombrices ) and/or ectoparasites ( piojos , granos ) . Educational and promotional activities during the 3-year PTS period ( 2012–2014 ) took three forms . The first was the MSPAS house to house ‘briefings’ at the individual community level that involved the same uniformed brigades that had provided MDA and conducted the monitoring and evaluation activities associated with the program . The second form of educational activity involved musical community celebrations , crafting activities , and parades ( ‘victory marches’ ) through the streets of larger communities and towns in the CEZ . These celebratory events involved a non-profit artistic troupe called “Caja Lúdica” ( http://www . cajaludica . org/ ) . The artists and the MSPAS health workers dressed in colorful costumes as the black flies , worms ( filaria ) , and Mectizan bottles; the artists often performed acrobatics while on stilts . Residents of the communities were encouraged to participate in the parades and associated activities . The third approach was to provide information through short radio ‘jingles’ on onchocerciasis . A critical element in all forms of community health education messaging was to ask the population to keep alert to new cases of onchocerciasis ( especially the appearance of nodules ) . Forty three percent interviewees reported that they had been exposed to one or both of these PTS educational and promotional activities . Most of these ( 54% ) had attended at least one community briefing with a health worker , with 33% having attended a large Caja Lúdica celebration . The remainder ( 13% ) reported having heard about the MSPAS visits or large celebrations from a neighbor . Only 6% of respondents reported that they had attended a briefing , or participated in a celebratory parade , in a community other than their own . Among persons interviewed in La Estrellita , where Caja Lúdica celebration held a parade , the overwhelming majority ( 99% ) preferred the large celebrations to MSPAS briefings , saying that the former were more entertaining and informative . Only one person reported hearing a radio jingle related to onchocerciasis elimination . When asked “If someone in your family presented symptoms of filaria what would you do ? ” , 89% responded that they would go to the health clinic . However , when asked “How quickly would you go to the health clinic ? ” , only 56% replied that they would do so immediately . With the cessation of the six monthly MDA program , Ministry of Health community outreach activities were perceived to have decreased . Although 117 ( 79% ) of the respondents indicated that the community had received visits from health workers , only 39 ( 28% ) identified these visits as related to onchocerciasis . The most frequent health brigade outreach activities , identified by 72% of respondents , were related to vector control for malaria and dengue , or childhood vaccination campaigns .
The purpose of the present study was to evaluate KAP towards onchocerciasis and ivermectin among residents in the post endemic CEZ . A major interest in this study was to determine what community residents thought about the end of the ivermectin MDA program . We found that three years after cessation of a two decade long ivermectin MDA program for onchocerciasis elimination in Guatemala , many residents wanted the ivermectin MDA program reinstated because of their uncertainty about onchocerciasis elimination , and their desire to have treatment for intestinal worms and ectoparasites . The vast majority of those questioned said that they would seek medical attention immediately if a family member presented symptoms of onchocerciasis ( especially the finding of a nodule ) , which is a practice very important for ongoing post endemic surveillance . The ministry of health outreach services , which has been reduced since the halting of the ivermectin MDA program , should be properly resourced to be prepare to address such reports , and seek to understand ongoing concerns of the populations residing in onchocerciasis post MDA Guatemala .
|
Human onchocerciasis ( also known as ‘River Blindness’ ) is a parasitic disease of the skin and eyes whose etiological agent ( Onchocerca volvulus ) is transmitted by black flies . Guatemala was the country in the Americas most afflicted by onchocerciasis , and the largest focus of onchocerciasis in Guatemala was the Central Endemic Zone ( CEZ ) . In the late 1980’s the Guatemalan ministry of health launched an onchocerciasis transmission elimination program based on mass drug administration ( MDA ) with the medicine ivermectin ( Mectizan ) in affected communities . Health education messages based on community knowledge , attitudes and practices ( KAP ) surveys were designed to enhance understanding and sustain participation in the MDA program . The first KAP survey ( published 1991 ) recommended the elements of the health education program . The second KAP survey ( published 1995 ) identified barriers preventing the achievement of adequate treatment coverage , and recommended adjustments to program activities to improve participation . This third KAP survey was conducted in 2014 , almost 3 years after cessation of the MDA program . Results indicated that many respondents wanted ivermectin MDA to continue in their community because of uncertainty about onchocerciasis elimination , or in order to treat intestinal worms . The ministry of health outreach services should be prepared to address ongoing concerns of the populations residing in post endemic , post MDA onchocerciasis Guatemala .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
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] |
2016
|
A Knowledge, Attitudes and Practices Survey Conducted Three Years after Halting Ivermectin Mass Treatment for Onchocerciasis in Guatemala
|
Morphotype switch is a cellular response to external and internal cues . The Cryptococcus neoformans species complex can undergo morphological transitions between the yeast and the hypha form , and such morphological changes profoundly affect cryptococcal interaction with various hosts . Filamentation in Cryptococcus was historically considered a mating response towards pheromone . Recent studies indicate the existence of pheromone-independent signaling pathways but their identity or the effectors remain unknown . Here , we demonstrated that glucosamine stimulated the C . neoformans species complex to undergo self-filamentation . Glucosamine-stimulated filamentation was independent of the key components of the pheromone pathway , which is distinct from pheromone-elicited filamentation . Glucosamine stimulated self-filamentation in H99 , a highly virulent serotype A clinical isolate and a widely used reference strain . Through a genetic screen of the deletion sets made in the H99 background , we found that Crz1 , a transcription factor downstream of calcineurin , was essential for glucosamine-stimulated filamentation despite its dispensability for pheromone-mediated filamentation . Glucosamine promoted Crz1 translocation from the cytoplasm to the nucleus . Interestingly , multiple components of the high osmolality glycerol response ( HOG ) pathway , consisting of the phosphorelay system and some of the Hog1 MAPK module , acted as repressors of glucosamine-elicited filamentation through their calcineurin-opposing effect on Crz1’s nuclear translocation . Surprisingly , glucosamine-stimulated filamentation did not require Hog1 itself and was distinct from the conventional general stress response . The results demonstrate that Cryptococcus can resort to multiple genetic pathways for morphological transition in response to different stimuli . Given that the filamentous form attenuates cryptococcal virulence and is immune-stimulatory in mammalian models , the findings suggest that morphogenesis is a fertile ground for future investigation into novel means to compromise cryptococcal pathogenesis .
The opportunistic environmental fungal pathogen , Cryptococcus neoformans , is a leading killer of HIV-infected patients worldwide [1 , 2] . It causes 1 million infections and more than half a million deaths each year worldwide [1] . C . neoformans is a species complex containing several serotypes ( A , D , and AD hybrids ) [3] . Among these serotypes , serotype A ( C . neoformans var . grubii ) causes approximately 95% of all cryptococcosis cases [4 , 5] , whereas serotype D ( C . neoformans var . neoformans ) is responsible for about 5% of the cases . The current anti-cryptococcal treatments rely primarily on azole antifungals with or without the induction therapy with amphotericin B [6] . The mortality rates of cryptococcosis are unacceptably high ( ~10–75% ) [1 , 7–9] . To further compound the problem , the emergence of resistance to azole drugs has been observed in multiple regions around the world [10–14] and relapse frequently occurs following treatment largely due to failure to clear the original infection [12 , 15] . Thus , it is of great value to identify cryptococcal specific programs that can be used for new antifungal or vaccine development . Morphotype switch between yeast and hypha is a cellular adaptation tightly linked to the virulence of dimorphic fungal pathogens [16–18] . In Candida albicans , proteins associated with hyphal growth are shown to impact its pathogenicity and some of them provide the bases for vaccine development [19–23] . Our previous studies in C . neoformans demonstrated that morphotypes ( yeast or filament ) are tightly linked to pathogenicity of this fungus as well [24–26] . Znf2 , the master regulator of filamentation , is a potent anti-virulent factor . Deletion of this zinc finger transcription factor locks cells in yeast form and enhances fungal virulence in a murine model of cryptococcosis . Conversely , overexpression of ZNF2 drives cells to the hyphal form and attenuates/abolishes the ability of C . neoformans to cause fatal infections [24–26] . Cryptococcus cells with ZNF2 overexpression stimulate protective immune responses in the host and provide protection to the animal against a subsequent challenge by the highly virulent serotype A clinical isolate H99 [26] . These findings indicate that activation of the filamentation program could drastically compromise cryptococcal virulence . Thus , the yeast-to-hypha morphological transition provides an important avenue to explore alternative measures for the prevention and/or treatment of cryptococcal infections . C . neoformans species is not considered a conventional dimorphic fungus due to the historical association of the yeast-to-hypha transition with mating . The mating response is controlled by the pheromone pathway composed of the pheromone , the pheromone receptor , the Cpk1 mitogen-activated protein kinase ( MAPK ) module , and the ultimate HMG domain transcription factor Mat2 [25 , 27–32] . The pheromone pathway promotes self-filamentation during unisexual development or dikaryotic filamentation during a-α bisexual development . As expected , the pheromone pathway is activated under mating-inducing conditions ( e . g . dehydration , nutrition limitation , V8 juice , and darkness ) . However , the host environment is not favorable for mating and the pheromone pathway exerts no or minimal impact on virulence [25 , 29 , 33 , 34] . Recent studies with C . neoformans serotype D isolates indicate that the pheromone pathway is essential for filamentation during a-α bisexual mating [24 , 25 , 35–37] , but it is not necessary for self-filamentation in a unisexual population under certain conditions [38–41] . Given the largely unisexual population of C . neoformans ( α >99% , a <1% ) , it is important to identify pheromone-independent pathways that can control self-filamentation . Although all serotypes of the C . neoformans species complex are expected to possess the ability to undergo self-filamentation , self-filamentation is often observed in serotype D isolates and rarely in serotype A isolates [42–45] , including the highly virulent clinical isolate and the most widely used serotype A reference strain H99 [46–48] . This hinders the investigation of morphological transition in C . neoformans as many resources are generated for the H99 background , including a congenic pair , gene deletion sets , a well-annotated genome , and vast literatures about cryptococcal biology and pathology [49–52] . The rarity of self-filamentation in serotype A isolates challenge the possibility of mitigating the diseases caused by the C . neoformans species complex through activating the filamentation program . Here , we found that glucosamine stimulated self-filamentation in both serotype D and serotype A strains , including H99 . Although we found that both N-acetyl-glucosamine ( GlcNAc ) and glucosamine could stimulate filamentation in another fungal pathogen C . albicans , GlcNAc showed no effect on filamentation in C . neoformans . We demonstrated that filamentation in C . neoformans evoked by glucosamine was independent of the pheromone pathway . By genetic screens , we discovered that Crz1 , a transcription factor downstream of calcineurin , was required for this process . The requirement of Crz1 for filamentation is specific to the response elicited by glucosamine , as Crz1 is not critical for filamentation elicited by pheromone [38] . We demonstrated that glucosamine promoted the translocation of Crz1 from the cytoplasm to the nucleus where it could exert its function as a transcription factor . Not surprisingly , we found that the catalytic and regulatory subunits of the phosphatase calcineurin , Cna1 and Cnb1 , were essential for the nuclear translocation of Crz1 and for filamentation . Interestingly , multiple components in the HOG pathway , except Hog1 itself , acted as repressors of glucosamine-elicited filamentation through their calcineurin-opposing effect on Crz1’s nuclear translocation . Deletion of these kinases increased the basal level of nucleus-localized Crz1 . These findings indicate that C . neoformans can resort to different genetic pathways for morphological transition in response to different stimuli , paving the way for future investigation to identify signals and targets that can be used to manipulate morphogenesis of this fungal pathogen in vivo .
Wild-type H99 does not undergo self-filamentation under all mating-inducing conditions . Here , we decided to test the effect of different carbon sources based on previous studies in other dimorphic fungal pathogens such as Candida albicans [53–56] , Histoplasma capsulatum , and Blastomyces dermatitidis [57] where N-acetyl-glucosamine ( GlcNAc ) activates hyphal growth . Here , we used YP medium ( 1% yeast extract and 2% peptone ) as the base medium and supplemented it with different carbon sources at the final concentrations of 2% . We included 6-carbon sugars ( glucose , galactose , and inositol ) , amino sugars ( N-methyl-glucosamine , N-acetyl glucosamine , and glucosamine ) , and other carbon sources ( glycerol , ethanol , and sodium acetate ) . None of the carbon sources tested stimulated filamentation in H99 , with the exception of glucosamine ( Fig 1A ) . Filamentation induced by glucosamine was unlikely to be an effect of carbon repression , as the non-metabolizable glucose analog 2-deoxyl glucose did not trigger filamentation in H99 ( Fig 1A ) . The filamentation stimulated by glucosamine was also unlikely to be a general effect due to the activation of the hexamine metabolism pathway , as N-methyl-glucosamine and N-acetyl glucosamine both failed to stimulate filamentation in H99 ( Fig 1A ) . The effect of glucosamine on filamentation was dose-dependent , as glucosamine at lower concentrations ( <0 . 5% ) did not evoke obvious hyphal growth in H99 ( Fig 1B ) . Robust filamentation was observed when glucosamine was present at concentrations higher than 1% ( Fig 1B ) . The addition of other carbon sources ( e . g . glucose , galactose , or GlcNAc ) inhibited filamentation in H99 ( Fig 1C ) . This suggests potential competitive inhibition of glucosamine by other carbon sources . Although not all strains could produce hyphae on the glucosamine medium , glucosamine-stimulated filamentation was not limited to H99 . Some other isolates of either serotype A or serotype D ( e . g . XL280 ) self-filamented on glucosamine medium ( Fig 1D ) . Interestingly , glucosamine-stimulated filamentation not only in C . neoformans , but also in some C . albicans strains ( S1 Fig ) . This suggests that glucosamine could be a general signal for fungal morphogenesis . C . neoformans typically undergoes yeast-to-hypha transition during a-α bisexual mating or during unisexual development . Two transcription factors , Mat2 and Znf2 , were demonstrated to be critical for hyphal growth during sexual development [24 , 25 , 37] . Mat2 controls the pheromone pathway and plays a central role in cell fusion [25] . Under mating-inducing conditions ( e . g . on V8 medium ) , Mat2 activates Znf2 , the master regulator of filamentation [24 , 25] . However , under mating-suppressing conditions ( e . g . on YPD medium ) , overexpression of MAT2 fails to activate Znf2 despite high levels of pheromone and C . neoformans remains in the yeast form [24] . We first examined the effect of glucosamine on the activity of Mat2 and Znf2 in wild-type H99 . We measured the transcript levels of their target genes , which reflected the activities of these transcription factors . The pheromone gene MFα is the most upregulated gene controlled by Mat2 during both bisexual and unisexual development [25 , 37] . The filamentation marker gene CFL1 is one of the highly expressed genes upregulated by Znf2 [24 , 58 , 59] . We found that the transcript levels of MFα and CFL1 increased more than 100 and 300 folds respectively when H99 was cultured on glucosamine medium compared to that of the base medium at 96 hours ( Fig 2A ) , indicating the activation of both Mat2 and Znf2 . To examine if self-filamentation in H99 evoked by glucosamine relies on Mat2 or/and Znf2 , we tested the znf2Δ mutant and the mat2Δ mutant on glucosamine medium . As expected , the MFα transcript level was no longer induced by glucosamine in the mat2Δ mutant ( Fig 2A ) . By contrast , a strong induction of the filamentation marker CFL1 at a level comparable to that in wild-type H99 was observed in the mat2Δ mutant ( Fig 2A ) . Consistent with the expression of the filamentation marker CFL1 , the mat2Δ mutant self-filamented on glucosamine medium ( Fig 2B ) . A strain overexpressing MAT2 ( MAT2oe ) also self-filamented on glucosamine medium . The result indicates that Mat2 is not essential for glucosamine-stimulated filamentation . In contrast to the mat2Δ mutant , there was no increase but rather a modest reduction in the CFL1 transcript level in the znf2Δ mutant on glucosamine medium compared to that of the base medium ( Fig 2A ) . The MFα transcript level increased slightly ( ~4 fold ) in this mutant ( Fig 2A ) . The low level of CFL1 in the znf2Δ mutant was consistent with its non-filamentous phenotype on glucosamine medium ( Fig 2B ) . Collectively , these observations indicate that Znf2 , but not Mat2 , is required for glucosamine-stimulated filamentation in H99 . The self-filamentation observed in H99 and the mat2Δ mutant was a response to glucosamine . The wild-type H99 , the mat2Δ mutant , or the znf2Δ mutant is incapable of self-filamentation on V8 medium ( S2 Fig ) . To determine if the dispensability of Mat2 and the essentiality of Znf2 in glucosamine-stimulated filamentation are conserved in C . neoformans , we further tested the mat2Δ mutant and the znf2Δ mutant made in the serotype D XL280 background . No hyphal growth was observed in the znf2Δ mutant while the mat2Δ mutant filamented similarly as the wild-type control on glucosamine medium ( S2 Fig ) . This is again different from filamentation observed on mating-inducing V8 medium where Mat2 is required ( S2 Fig ) [25 , 38] . This result corroborates the conclusion that filamentation elicited by glucosamine requires the morphogenesis regulator Znf2 , but not the pheromone pathway regulator Mat2 . To further verify that the pheromone pathway is not critical for glucosamine-stimulated filamentation , we tested additional mutants in the H99 background with disruption in the following key components of this pathway ( Fig 2C ) , namely the pheromone receptor Ste3 [60 , 61] , the pheromone receptor like protein Cpr2 [62] , Gβ subunit Gpb1 [64] , a PAK kinase Ste20α [65] , the MAPK kinase kinase Ste11α [33] , the MAPK kinase Ste7 [32] , and the MAPK Cpk1 [32] . We also included Gα subunit Gpa1 [63] that regulates mating through the cAMP/PKA pathway in our test . Except for the ste3αΔ and the ste7Δ mutants , all other gene deletion mutants tested filamented on glucosamine medium ( Fig 2C ) . The ste3αΔ could eventually produce some filaments after prolonged incubation . To verify that the blocked filamentation observed in the ste7Δ mutant is not an artifact , we tested multiple ste7Δ isolates generated in both mating type a and α backgrounds . All the ste7Δ mutants tested showed only yeast growth on glucosamine medium , indicating the unique role of Ste7 in filamentation compared to other components of the pheromone pathway . Collectively , the results indicate that the pheromone pathway overall is dispensable for filamentation induced by glucosamine . To identify genes that are involved in filamentation triggered by glucosamine , we screened approximately 2500 gene deletion mutants made in the H99 background for altered filamentation on glucosamine medium . The strains screened included the partial genome deletion set generated by Dr . Hiten Madhani’s group in 2015 , and the transcription factor and the kinase deletion sets generated by Dr . Yong-Sun Bahn’s group [66 , 67] . Among the deletion mutants tested , two genes encoding the glucosamine-6-phosphate deaminase Gnd1 ( gene locus # CNAG_06098 ) and the glucosamine 6-phosphate N-acetyltransferase Gnat1 ( gene locus # CNAG_05695 ) are involved in the hexamine metabolism pathway ( S3A Fig ) . The gnd1Δ mutant was unable to grow in the presence of glucosamine ( even at concentrations lower than 0 . 1% ) ( S3B Fig ) , suggesting that the Gnd1 is essential for the growth of C . neoformans under such conditions . GNAT1 was not essential for growth on glucosamine medium . However , the gnat1Δ mutant filamented as well as , if not better than , the wild-type H99 on glucosamine medium ( S3C Fig ) . The result suggests that the hexamine metabolism is unlikely to be responsible for the filamentous growth elicited by glucosamine . We classified the mutants screened with altered filamentation into four groups: non-filamentous group , decreased filamentation , increased filamentation , and hyper-filamentation ( S4 Fig and S1 Table ) . The fact that mutants with reduced/abolished filamentation and mutants with enhanced filamentation were recovered from the screen indicates that there are both repressors and activators of filamentation in response to glucosamine . Among mutants with enhanced filamentation , several were in the HOG pathway [68 , 69] ( Fig 3A ) , including the tco1Δ , ssk1Δ , ssk2Δ , and pbs2Δ mutants ( Fig 3B ) . However , disruption of Hog1 itself , the downstream MAPK of this pathway , did not impact filamentation ( Fig 3B ) . This observation suggests that glucosamine may not trigger the same response as osmotic stress . Consistent with this idea , the crz1Δ mutant is as resistant to osmotic stress caused by NaCl as the wild type ( more details later . See S8 Fig ) . Among the gene deletion mutants that showed blocked filamentation on glucosamine medium were the calcineurin mutants . These include the mutants with disruption in genes encoding the calcineurin catalytic subunit Cna1 [70 , 71] , the calcineurin regulatory subunit Cnb1 [70 , 72] , and the calcineurin downstream zinc finger transcription factor Crz1 ( aka Sp1 ) [73–75] ( Fig 4A and 4B , S1 Table ) . The mutant defective in the calcineurin binding protein Cbp1 [76 , 77] showed reduced filamentation ( Fig 4B ) . Treatment with the calcineurin inhibitor FK506 blocked the wild-type H99 from undergoing filamentation on glucosamine medium ( Fig 4C ) , a phenotype similar to the cna1Δ , cnb1Δ , and crz11Δ mutants ( Fig 4B ) . Thus , the two pathways appear to exert opposing effects on glucosamine-stimulated self-filamentation in H99: the phosphorelay system and Ssk2-Pbs2 upstream of the Hog1 MAPK pathway suppress filamentation while the calcineurin pathway is required for filamentation . Calcineurin transduces signals ( e . g . elevated level of calcium ) by dephosphorylating the downstream targets ( Fig 4A ) . The transcription factor Crz1 is one of the targets of calcineurin , and not all responses controlled by calcineurin depend on Crz1 [78 , 79] . For instance , the cna1Δ and cnb1Δ mutants displayed severe growth defect at 37°C and these mutants were hyper-sensitive to cell wall stress induced by Calcofluor White or Congo red [73–75 , 78] ( S5 Fig ) . By contrast , the crz1Δ mutant showed only slightly increased sensitivity to cell wall stress and heat stress ( S5 Fig ) . Nonetheless , the crz1Δ mutant , like the calcineurin mutants ( cna1Δ and cnb1Δ ) , was abolished in filamentation induced by glucosamine ( Fig 4B ) . This suggests that Crz1 is a major effector of the calcineurin pathway in regulating filamentation in response to glucosamine . We then examined if overexpression of CRZ1 could promote filamentation on glucosamine medium . To this end , we placed the CRZ1 gene under the control of the constitutively active GPD1 promoter [24 , 80] . We introduced these constructs into the wild-type H99 or the crz1Δ mutant . We found that overexpression of CRZ1 enhanced filamentation ( Fig 4D and Fig 5B ) . The enhancement in filamentation by CRZ1 overexpression was specific to the induction by glucosamine , as CRZ1 overexpression did not confer self-filamentation to either wild-type H99 or the corresponding crz1Δ mutant when cells were cultured alone on V8 medium ( S6A Fig ) . Furthermore , the deletion of CRZ1 or the overexpression of CRZ1 did not affect the ability of the strain to cross with a wild-type partner of the opposite mating type based on the observation that there was no notable difference between the crosses crz1Δ α x a , CRZ1oe α x a , and α x a ( S6B Fig ) . These findings suggest that alteration of the expression level of CRZ1 does not impact mating efficiency controlled by the pheromone pathway , consistent with the recent finding in a serotype D strain [38] . To examine the genetic relationship between Crz1 and Znf2 in the regulation of filamentation in response to glucosamine , we first measured the transcript levels of ZNF2 and CRZ1 in the znf2Δ mutant and the crz1Δ mutant . The CRZ1 transcript level on glucosamine medium was comparable to that of the base medium in wild type and its transcript level was also comparable between the wild type and the znf2Δ mutant ( Fig 5A ) . This result indicates that neither Znf2 nor glucosamine has much impact on CRZ1 at the transcript level . On the other hand , the ZNF2 transcript level in wild-type H99 increased more than 6 fold on glucosamine medium and the degree of induction was much reduced in the crz1Δ mutant ( 2–3 fold ) ( Fig 5A ) . This suggests that deletion of CRZ1 attenuated the induction of ZNF2 elicited by glucosamine . Furthermore , overexpression of CRZ1 failed to confer filamentation to the znf2Δ mutant while overexpression of ZNF2 restored filamentation in the crz1Δ mutant on glucosamine medium ( Fig 5B ) . Collectively , these epistatic results indicate that Crz1 functions upstream of Znf2 in response to glucosamine . We then examined if disruption of Crz1 affects the subcellular localization of Znf2 after the Znf2 protein is made . For this purpose , we introduced the PCTR4-mCherry-ZNF2 construct into the crz1Δ mutant and the wild-type H99 background . The mCherry-Znf2 signal was localized to the nucleus in both the crz1Δ mutant background and the wild-type background ( Fig 5C ) . Collectively , the results suggest that Crz1 regulates ZNF2 at the transcript level and it functions upstream of Znf2 , and Crz1 does not affect the subcellular localization of the Znf2 protein . Calcineurin is known to dephosphorylate Crz1 in response to certain stimuli like calcium or heat shock . Dephosphorylation causes the translocation of Crz1 from the cytosol to the nucleus for it to function as a transcription factor in C . neoformans [73–75] ( Fig 4A ) . To examine if glucosamine affects the subcellular translocation of Crz1 , we placed mCherry tagged Crz1 under the control of the constitutively active GPD1 promoter . The exposure to either calcium or high temperature , two known stimuli of calcineurin , indeed stimulated mCherry-Crz1 in this overexpression strain to relocate from the cytosol into the nucleus ( Fig 6A ) . As reported previously [75] , NaCl induced granular localization of Crz1 in the cytosol ( Fig 6A ) , indicating that the nuclear translocation of Crz1 is stimulus-specific . We then tested the effect of glucosamine on the subcellular localization of Crz1 . Remarkably , greater than 90% of the cryptococcal population showed nuclear localization of Crz1 in the presence of glucosamine ( Fig 6A and 6B ) . This indicates that glucosamine , like calcium , greatly increases the translocation of Crz1 from the cytosol into the nucleus . The translocation of Crz1 to the nucleus in response to calcium and glucosamine was not affected by Znf2 ( Fig 6C and 6D ) , consistent with Crz1 functioning upstream of Znf2 . To verify that the nuclear translocation effect of glucosamine was not an artifact due to CRZ1 overexpression , we tested Crz1-mCherry placed under the control of its native promoter that was used in a recent study [79] . Again , glucosamine greatly increased the population of cryptococcal cells with nucleus-localized Crz1 , as did calcium and the exposure to high temperature ( S7 Fig ) . This finding indicates that glucosamine enhances nuclear translocation of the Crz1 protein regardless of its gene expression level . If glucosamine activates filamentation through its effect on the translocation of Crz1 , we hypothesized that overexpression of CRZ1 would be futile in the absence of a functional calcineurin . Indeed , no filamentation was observed when CRZ1 was overexpressed in the cna1Δ mutant or the cnb1Δ mutant ( Fig 7A ) . Similarly , overexpression of CRZ1 did not restore the temperature sensitivity of the cna1Δ mutant ( S8 Fig ) . Consistent with our hypothesis , Crz1 showed only cytoplasmic localization in the calcineurin cna1Δ and cnb1Δ mutants , regardless whether the cells were cultured in YPD medium or in glucosamine medium ( Fig 7B and 7C ) . The cbp1Δ mutant showed reduced filamentation and overexpression of CRZ1 in cbp1Δ restored filamentation ( Fig 7A ) . Consistently , Crz1 was more concentrated in the nucleus in this mutant background ( Fig 7B and 7C ) . The results demonstrate the essential role of calcineurin in controlling the nuclear translocation of Crz1 , which correlates with robustness in filamentation . Multiple components of the HOG pathway , namely Tco1 ( hybrid histidine kinase ) , Ssk1 ( response regulator ) , Ssk2 ( MAPKKK ) , and Pbs2 ( MAPKK ) , suppress glucosamine-stimulated filamentation given that disruption of these components enhanced filamentation ( Fig 3 ) . We decided to examine if the HOG pathway components suppress filamentation through Crz1 . For this purpose , we made double gene deletion mutants ssk2Δ crz1Δ and pbs2Δ crz1Δ and examined their phenotypes on glucosamine medium . The ssk2Δ and pbs2Δ single mutants showed enhanced filamentation on glucosamine medium ( Fig 3 , Fig 8A and 8B ) . The ssk2Δ crz1Δ and pbs2Δ crz1Δ double mutants were non-filamentous on glucosamine medium ( Fig 8A ) , similar to the crz1Δ single mutant . This result suggests that Crz1 is essential in the regulation of filamentation in response to glucosamine and it functions downstream of Ssk2 and Pbs2 . We postulate that the HOG pathway components may oppose the effect of calcineurin and suppress the nuclear translocation of Crz1 . If this hypothesis is valid , then disruption of the HOG pathway components would increase the level of nucleus-localized Crz1 . To test this hypothesis , we constructed mCherry labeled Crz1 in the ssk1Δ mutant , the ssk2Δ mutant , and the pbs2Δ mutant by crossing these strains to XW252 ( PCRZ1-Crz1-mCherry , GFP-Nop1 ) [79] . We then examined the subcellular localization of Crz1-mCherry in the absence of these HOG pathway components . We found that most cells showed nuclear localized Crz1-mCherry next to the nucleolus marker GFP-Nop1 in the absence of Ssk1 , Ssk2 , or Pbs2 even when these cells were cultured in YPD medium at 22°C without any stimulus ( Fig 8C ) . Upon induction with glucosamine , almost all cells showed nuclear localized Crz1 , regardless whether the SSK1 , SSK2 , or PBS2 gene was intact or not ( Fig 8C ) . Thus , the absence of the HOG pathway upstream components increased the basal level of nuclear localized Crz1 , which may have enhanced the initiation of filamentation in the ssk1Δ , ssk2Δ , or pbs2Δ mutant on glucosamine medium . In contrast to the deletion of SSK1 , SSK2 , or PBS2 , the deletion of HOG1 gene did not significantly enhance the basal level of nuclear-translocation in the absence of glucosamine compared to the wild type ( generated by crossing PGPD1-mCherry-CRZ1 to hog1Δ ) ( left panel in Fig 8D ) . Nonetheless , the treatment of glucosamine stimulated the translocation of cytosolic Crz1 into the nucleus in the hog1Δ strain , just like the wild type ( right panel in Fig 8D ) . Thus , Hog1 , the downstream MAPK of the HOG pathway , appears to be dispensable for glucosamine-stimulated filamentation . In the wild-type H99 strain , Hog1 is known to be highly phosphorylated under normal growth conditions and it undergoes dephosphorylation in response to osmotic shock [81] . Indeed , we observed reduced level of phosphorylation of Hog1 in response to osmotic stress caused by NaCl ( Fig 8E ) . However , no apparent change in Hog1 phosphorylation was observed in response to glucosamine ( Fig 8E ) . This result indicates that Hog1 phosphorylation is not affected by glucosamine , which corroborates the dispensability of Hog1 in glucosamine-stimulated filamentation .
C . neoformans could undergo yeast-to-hypha transition and this morphotype switch is linked to its virulence potential . Yeast is the virulent form , whereas the filamentous form is attenuated in virulence in mammalian models of cryptococcosis ( [82] and references therein ) . Our previous studies demonstrated that upregulation of ZNF2 is sufficient to drive C . neoformans to undergo filamentation and to abolish/attenuate virulence [24–26] . Thus , activation of filamentation could potentially be used to mitigate cryptococcosis if suitable effectors that can trigger cryptococcal filamentation program in vivo can be identified . In addition , the filamentous form of Cryptococcus elicits protective immune-responses in a mammalian host [26] , providing a platform for future vaccine development . Because the pheromone pathway has no or minimal impact on virulence and C . neoformans infections are largely caused by serotype A α isolates ( α >99% among serotype A isolates ) , it is of great value to identify conserved signals and pathways that control self-filamentation independent of the pheromone pathway . Self-filamentation in C . neoformans is mostly observed in serotype D isolates and rarely in serotype A isolates . The widely used and highly virulent serotype A reference strain H99 , for instance , has not been observed to undergo self-filamentation under laboratory conditions despite numerous attempts . Here we found that H99 can undergo self-filamentation in response to glucosamine and this morphological transition is independent of the pheromone pathway . Why glucosamine , but not any other carbon-source tested , triggers self-filamentation in H99 remains mysterious . Glucosamine is the subunit of chitosan from Cryptococcus cell wall . Chitosan is the deacetylated form of chitin , and chitin is a common cell wall component in fungi and in the exoskeletons of arthropods , such as the shells of crustaceans and the outer coverings of insects . It is possible that the presence of glucosamine , rather than N-acetyl glucosamine , the subunit of chitin , serves as a unique danger signal to Cryptococcus . Alternatively , unknown secondary signals triggered by glucosamine are the real signals stimuli of filamentation . Regardless of the true biological meaning of glucosamine , the identification of pathways that control self-filamentation in natural serotype A strains like H99 represents an important advance in the endeavors to understand the regulation of cryptococcal dimorphism , which was primarily considered a response to pheromone . By screening approximately 2500 gene deletion mutants for altered filamentation on glucosamine medium , we found that the transcription factor Crz1 was critical for glucosamine-induced filamentation: deletion of CRZ1 abolished filamentation and overexpression of CRZ1 enhanced filamentation on glucosamine medium . Crz1 appears to regulate filamentation specifically in response to glucosamine , as neither deletion nor overexpression of CRZ1 showed any effect on cryptococcal yeast-to-hypha transition during mating on V8 medium ( S6 Fig ) [38] . We found that the pheromone pathway responding to mating cues was overall dispensable for filamentation in response to glucosamine ( Fig 2 ) . Glucosamine strongly induced Crz1 to translocate from the cytosol to the nucleus , where it can exert its function as a transcription factor . Two pathways converged on Crz1 and play important but opposing roles . One pathway is the expected calcineurin pathway known to dephosphorylate Crz1 , which required for its nuclear translocation [75 , 79] . Indeed , Crz1 was retained in the cytoplasm in the absence of calcineurin catalytic subunit Cna1 or the regulatory subunit Cnb1 ( Fig 7B and 7C ) . Interestingly , the absence of Cbp1 didn’t affect Crz1’s translocation into the nucleus ( Fig 7C ) . This offers a plausible explanation for the lack of dramatic phenotype of the cbp1Δ mutant , in contrast to the non-filamentous phenotype of the cna1Δ and the cnb1Δ mutant on glucosamine medium ( Fig 4B ) . The other pathway is the HOG components upstream of the Hog1 MAPK , which is known for their regulation of a variety of environmental stress responses . We found that the HOG components inhibited filamentation on glucosamine medium and suppressed the nuclear translocation of Crz1 ( Fig 8C ) , likely through their direct or indirect effect on Crz1 phosphorylation that counter-balances the phosphatase activity of calcineurin ( Fig 9 ) . In another fungal pathogen Aspergillus fumigatus , CrzA ( Crz1 homolog ) translocates to the nucleus upon osmotic stress caused by NaCl or sorbitol [83] . CrzA also directly upregulates the expression of the histidine kinase PhkB and the MAPKKK SskB of the osmotic sensing pathway by binding to their promoters [83] . Thus in A . fumigatus , CrzA plays a role in osmotic stress response [83] , and there appears to be a positive feedback regulation between the osmotic sensing pathway and CrzA in A . fumigatus . Unlike CrzA in A . fumigatus , Crz1 in Cryptococcus translocates to granule-like structures in the cytoplasm after osmotic stress [46] ( Fig 6 ) . Consistent with its cytoplasmic localization in response to osmotic stress , the crz1Δ mutant was as resistant to the osmotic stress caused by NaCl as the wild type ( S8 Fig ) . Overexpression of CRZ1 in the pbs2Δ mutant also failed to restore pbs2Δ’s sensitivity to osmotic stress ( S8 Fig ) . These findings are consistent with the idea that Crz1 is not critical for the osmotic stress response in C . neoformans ( S8 Fig ) . The calcineurin pathway is known to control growth , stress responses , morphogenesis and pathogenicity in various fungal species [70–72 , 78 , 84–90] . However , Crz1 , the established downstream target of calcineurin , appears to be more specific in promoting hyphal growth than the adaptation to the general stresses based on previous studies in A . fumigatus [91 , 92] and Candida species [85 , 93] . Interestingly , the HOG pathway plays a more suppressive role in hyphal growth as demonstrated in Candida species [94–96] ) and in Cryptococcus neoformans during bisexual mating [69 , 81] . Thus the opposing effect between the calcineurin pathway and the HOG pathway on hyphal growth might be conserved in multiple fungal species . Whether Crz1 is the conserved conjunction of these two pathways in regulating filamentation in these fungal species is yet to be determined . We believe that the upstream components of the HOG pathway normally suppress the translocation of Crz1 to the nucleus based on the elevated basal level of nuclear Crz1 in the corresponding deletion mutants in the absence of any stimuli ( Fig 8C ) . This suggests that the upstream components of the HOG pathway inactivate Crz1 , possibly by enabling the phosphorylation of Crz1 either directly or indirectly , and consequently opposing the activity of calcineurin . It is important to note that nuclear localization of Crz1 is necessary , but not sufficient to drive filamentation in the absence of glucosamine . This is evident given that some cells showed nuclear localized Crz1 even in YPD medium , but all cells grew in the yeast form under that condition . This is also consistent with the observation that heat-shock and calcium , although both stimulate nuclear translocation of Crz1 , were unable to elicit filamentation in H99 in the absence of glucosamine . Thus , a yet unknown factor affected by glucosamine , in addition to the requirement of Crz1 nuclear translocation , has to be involved to enable filamentation . One interesting observation is that not all components in a well-established pathway behave in the same fashion . For example , most key components in the pheromone pathway , including the transcription factor Mat2 , are dispensable for self-filamentation induced by glucosamine . However , ste7Δ is non-filamentous on glucosamine medium . This finding is surprising given that ste7Δ and mat2Δ are both non-filamentous with identical transcriptomes under mating-inducing conditions during both bisexual and unisexual development [25 , 32 , 97] . Thus , the distinct phenotype of ste7Δ on glucosamine medium suggests that Ste7 might have additional functions besides its established role in pheromone sensing and response . Another example is Hog1 in the HOG pathway . Most upstream components of the HOG pathway suppress filamentation on glucosamine medium , but the MAPK Hog1 itself shows no or minimal involvement in this process . We postulate that there is a divergence in the downstream effectors of this phosphorelay system in response to osmotic stress or glucosamine . Hog1 is activated in response to osmotic stress when Crz1 is being concentrated in granules in the cytoplasm in Cryptococcus [75] ( Fig 6A ) . In contrast , Crz1 is localized to the nucleus in response to glucosamine . How different effectors are activated by the same phosphorelay system , what controls the multiple distinct subcellular localizations of Crz1 , and what prevents cross-activation of the downstream effectors remain to be investigated .
Strains were stored as glycerol stocks in -80°C . Freshly streaked cells were used for experiments . The three deletion sets made in the H99 background were obtained from the Fungal Genetics Stock Center ( FGSC ) and the information about these strains can be obtained from the FGSC website ( http://www . fgsc . net/crypto/crypto . htm ) . Other strains were listed in S2 Table . Cryptococcal cells were maintained on YPD medium ( 20 peptone , 10 yeast extract , 20 glucose , 20 agar , gram/liter ) unless stated otherwise . For the filamentation assay , the YP medium ( 20 peptone , 10 yeast extract , 20 agar , gram/liter ) was used as the base medium . All the different carbon sources tested were made to the final concentration of 2% . When testing filamentation on YPGlcN medium ( 20 peptone , 10 yeast extract , 20 glucosamine , 20 agar , gram/liter ) , 3 μl of cells ( optical density OD600 = 1 ) of the tested strains were dropped onto the agar medium . Cells were cultured at 30°C for two days before being transferred to 22°C for additional incubation of 4 to 7 days in the dark . To test the effect of the calcineurin inhibitor FK506 on filamentation , FK506 was added to the YPGlcN medium at the final concentration of 1 μg/ml . To test the dose-dependent effects of glucosamine on filamentation , glucosamine were added to the YP base medium to the final concentration of 0 , 0 . 2% , 0 . 5% , 1% , and 2% . To test the effect of the addition of another carbon source to the YPGlcN medium on filamentation , 2% galactose , glycerol , or xylose was added to the YPGlcN medium ( 2% glucosamine ) . To test thermo-tolerance , cells of the tested strains with 5x serial dilutions ( OD600 = 10 , 2 , 0 . 4 , 0 . 08 , 0 . 016 , and 0 . 0032 ) were dropped onto YPD medium and incubated at 30°C or 37°C for 2 days . To test the susceptibility to cell wall stress , cells of the indicated strains were serial diluted and spotted onto YPD medium , YPD with 0 . 2% Congo Red , or YPD medium with 10 μg/ml of Calcofluor white . Cells were then incubated at 30°C for 2 days . RNA extraction and qPCR were performed as we described previously [24] . For the transcript measurements used in Fig 2 , strains H99 , mat2Δ , and znf2Δ were cultured on YPD medium or glucosamine medium at 30°C for 2 days , and then were transferred to 22°C for additional 2 days before cells were harvested . For the transcript measurements used in Fig 5 , Strains H99 , crz1Δ , and znf2Δ were cultured on the YP-glucosamine or YP base medium at 30°C for 2 days , and then incubated at 22°C for additional incubation . Cells were harvested at the time points ( 0 , 2 days , 4 days , and 6 days ) as indicated in the figures . Harvested cells were washed with cold water , frozen in liquid nitrogen , and then lyophilized . Lyophilized cells were broken into fine powder with glass beads and total RNA was extracted with the PureLink RNA Mini Kit ( life technology ) according to the manufacture’s instruction . First strand cDNA was synthesized with Superscript III cDNA synthesis kit ( Invitrogen ) according to the manufacture’s instruction . The house-keeping gene TEF1 was used as the endogenous control . The relative transcript levels were determined using the comparative ΔΔCt method as described previously [24] . Three biological replicates were performed for each sample and their values were used to calculate the mean and the standard error . Primers used for realtime PCR were listed in S3 Table . All the gene deletion mutants in the serotype A background used in this study generated by the Lin’s group or Bahn’s group were made in the same H99 background ( see S2 Table for strains used in this study ) . The 2015 gene deletion set deposited by Dr . Hiten Madhani’s lab and the transcription factor and kinase gene deletion sets deposited by Dr . Bahn’s lab are available from the Fungal Genetics Stock Center . http://www . fgsc . net/crypto/crypto . htm ) . These mutants were also generated in the same H99 background . The mutants were screened on the YP-glucosamine ( 2% ) medium after replicating from 96 well plates as described earlier for the filamentation assays . Strains with altered filamentation were selected based on comparison with other strains on the same plate during the initial screen . These mutant phenotypes were further confirmed in the secondary screen with the wild type H99 control . For the genes and pathways that were further characterized in this study , including the pheromone pathway , the calcineurin pathway , and the Hog1 pathway , separate mutants were obtained from the original sources where the mutations were verified in the previously published work . These strains and their sources/references were listed in S2 Table . To generate the knockout construct , 1 kb of the 5’ and 3’ flanking sequences bordering the open reading frame of the gene of interest were amplified using the genomic DNA of the wild-type strain as the template . They were then fused with the NEO or NAT dominant drug marker amplified from the plasmid pAI1 or pJAF1 by overlap PCR as we described previously [98] . The knockout constructs were introduced into appropriate recipient strains by biolistic transformation as described previously [99] . The transformants grown on selective medium ( YPD+NAT or YPD+G418/NEO ) were then screened for gene replacement via homologous recombination events by diagnostic PCR as described previously [98] . To generate CRZ1 or ZNF2 overexpression strains , the open reading frame of the CRZ1 or the ZNF2 gene were first amplified by PCR with specifically designed primers with FseI/PacI cut sites at the ends . After digestion , the digested products were ligated into the PGPD1 vector or the PCTR4 vector where the ORF was placed downstream of the GPD1 or the CTR4 promoter , as we described previously [58 , 59] . The resulting plasmids were then linearized and introduced into the recipient strains as indicated in the text by biolistic transformation . All the primers used for constructing or confirming gene deletion or gene overexpression were listed in S3 Table . Yeast cells of α and a mating partners were mixed together on V8 juice agar medium ( 5% V8 juice , 0 . 5 g/L KH2PO4 , 4% agar , pH adjusted to 5 ) . The mixed culture was then incubated for 2 weeks at 22°C in the dark until spores were produced following filamentation . Cells from V8 medium were transferred to fresh YPD agar medium and spores were micro-manipulated with a dissecting microscope . The mating type of the germinated spores was determined by successful mating of their derived colonies with either JEC20a or JEC21α . Genetic linkage between the presence of the drug marker and the observed mutant phenotype was established by analyzing the dissected spores as we described previously [42] . To characterize the subcellular localization of Znf2 and Crz1 , mCherry was fused to the N-terminus of Znf2 or Crz1 in frame . The ORF of CRZ1 or ZNF2 with PacI recognition site at 3’ end was amplified by PCR and then fused at the N-terminus with mCherry carrying FseI recognition site at its 5’end . The fragment mCherry-CRZ1 and mCherry-ZNF2 was digested with FseI and PacI and then ligated into the PGPD1 vector or the PCTR4 vector . The construct of the mCherry tagged protein controlled by the GPD1 or the CTR4 promoter ( PGPD1-mCherry-CRZ1 and PCTR4-mCherry-ZNF2 ) were then introduced into the recipient strains by biolistic transformation as described previously [99] . The N-terminal tagged Crz1 and Znf2 are functional based on the observation that they could restore the filamentation defect observed in the corresponding gene deletion mutants . Colony morphology was examined with a SZX16 stereoscope ( Olympus ) . Colony images were captured with a GO-21camera and acquired using the QIMAGINE software . To determine the subcellular localization of mCherry-Crz1 or mCherry-Znf2 , cells were observed with a Zeiss M2 epi-fluorescence microscope and images were acquired with the AxioCam MRm camera and processed with the software Zen 11 ( Carl Zeiss Microscopy ) . The filter used for visualizing mCherry was the FL filter set 43 HE cy3 ( Carl Zeiss Microscopy ) . GFP was visualized using the filter FL filter set 38 HE GFP ( Carl Zeiss Microscopy ) . To visualize the nuclei , cells were fixed in a fixer solution ( 3 . 7% formaldehyde; 1X PBS; 1% Triton X ) for 10 min and then stained with DAPI ( 0 . 4 μg/ml ) for 15 min . The filter used to visualizing DAPI was FL Filter Set 49 DAPI ( Carl Zeiss Microscopy ) . To examine the effect of temperature on Crz1 localization , cells of the tested strains were cultured in the YPD medium at 22°C , 30°C , or 37°C overnight . To test the effects of different conditions or mutations on the subcellular localization of mCherry tagged Crz1 , Cryptococcus cells were grown in liquid media at 22°C for 10 hours . To examine the impact of glucosamine on Crz1’s subcellular localization , cells were cultured in the YP-glucosamine ( 2% ) liquid medium at 22°C for 10 hrs . To examine the impact of calcium or salt on the localization of Crz1 , cells were cultured first in YPD at 22°C , centrifuged , washed with PBS , and then suspended in 100 mM CaCl2 or 1 . 5 M NaCl for 10–30 minutes . To test the impact on Crz1’s subcellular localization by the deletion of SSK1 , SSK2 , PBS2 , CNA1 , CNB1 or CBP1 , the corresponding Cryptococcus strains were cultured in either YPD or glucosamine medium at 22°C for 10 hours . To quantify the percentage of cells with Crz1 localized to the nucleus , the numbers of cells with Crz1 in the nucleus and the total cells with fluorescence signals were determined and the ratio was calculated in three replicated samples . The data were used to calculate the mean value of the population with nuclear localization and the standard errors . The overnight culture of wild-type strain H99 was inoculated in fresh YPD liquid medium ( 250 ml ) and incubated at 30°C until the culture reached the optical density of approximately 0 . 9–1 . 0 at 600 nm ( OD600 ) . Cells were harvested by centrifugation , washed two times in PBS , and resuspended in YPD medium containing 1 M NaCl or in YP medium containing 2% glucosamine . At each designated time point , an aliquot of 50 ml of the culture was mixed with an equal volume of ice-cold stop solution ( 0 . 9% NaCl , 1 mM NaN3 , 10 mM EDTA , 50 mM NaF ) . The cells were then collected by centrifugation and resuspended in lysis buffer [50 mM Tris-Cl ( pH 7 . 5 ) , 1% sodium deoxycholate , 5 mM sodium pyrophosphate , 0 . 2 mM sodium orthovanadate , 50 mM NaF , 0 . 1% SDS , 1% Triton X-100 , 0 . 5 mM phenylmethylsulfonyl fluoride , and 2 . 5× protease inhibitor cocktail solution ( Calbiochem ) ] . The resuspended cells were disrupted using a bead-beater for 6 cycles ( 30 sec bead beating with 2 min rest intervals ) . Protein concentrations were determined with the Pierce BCA protein assay kit ( Thermo Fisher Scientific ) . A total of 5 μg of proteins were loaded into 10% SDS-polyacrylamide gel and analyzed by western blot using a primary antibody of rabbit P-p38 MAPK specific antibody ( Cell Signalling Technology ) to detect phosphorylated Hog1 and polyclonal anti-Hog1 antibody ( Santa Cruz ) for the detection of Hog1 as a loading control . Anti-rabbit IgG horseradish peroxidase-conjugated antibody ( Santa Cruz ) was used as a secondary antibody . The blot was developed using the ECL western blotting detection system according to the instruction of the manufacture ( Bio-Rad ) . Statistical significance of different groups in terms of Crz1 localization was assessed by the t-test . The statistical analyses were performed using the Graphpad Prism 5 program , with p values lower than 0 . 05 considered statistically significant .
|
Cryptococcal meningitis claims half a million lives each year . There is no clinically available vaccine and the current antifungal therapies have serious limitations . Thus identifying cryptococcal specific programs that can be targeted for antifungal or vaccine development is of great value . We have shown previously that switching from the yeast to the hypha form drastically attenuates/abolishes cryptococcal virulence . Cryptococcal cells in the filamentous form also trigger host immune responses that can protect the host from a subsequent lethal challenge . However , self-filamentation is rarely observed in serotype A isolates that are responsible for the vast majority of cryptococcosis cases . In this study , we found that glucosamine stimulated self-filamentation in genetically distinct strains of the Cryptococcus species complex , including the most commonly used serotype A reference strain H99 . We demonstrated that filamentation elicited by glucosamine did not depend on the pheromone pathway , but it requires the calcineurin transcription factor Crz1 . Glucosamine promotes nuclear translocation of Crz1 , which is positively controlled by the phosphatase calcineurin and is suppressed by the HOG pathway . These findings raise the possibility of manipulating genetic pathways controlling fungal morphogenesis against diseases caused by the Cryptococcus species complex .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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] |
2017
|
Glucosamine stimulates pheromone-independent dimorphic transition in Cryptococcus neoformans by promoting Crz1 nuclear translocation
|
Mammalian genomes are pervasively transcribed outside mapped protein-coding genes . One class of extragenic transcription products is represented by long non-coding RNAs ( lncRNAs ) , some of which result from Pol_II transcription of bona-fide RNA genes . Whether all lncRNAs described insofar are products of RNA genes , however , is still unclear . Here we have characterized transcription sites located outside protein-coding genes in a highly regulated response , macrophage activation by endotoxin . Using chromatin signatures , we could unambiguously classify extragenic Pol_II binding sites as belonging to either canonical RNA genes or transcribed enhancers . Unexpectedly , 70% of extragenic Pol_II peaks were associated with genomic regions with a canonical chromatin signature of enhancers . Enhancer-associated extragenic transcription was frequently adjacent to inducible inflammatory genes , was regulated in response to endotoxin stimulation , and generated very low abundance transcripts . Moreover , transcribed enhancers were under purifying selection and contained binding sites for inflammatory transcription factors , thus suggesting their functionality . These data demonstrate that a large fraction of extragenic Pol_II transcription sites can be ascribed to cis-regulatory genomic regions . Discrimination between lncRNAs generated by canonical RNA genes and products of transcribed enhancers will provide a framework for experimental approaches to lncRNAs and help complete the annotation of mammalian genomes .
A most striking finding of modern genomic biology has been the identification of a large amount of transcription that occurs outside mapped protein-coding genes and generates a heterogeneous spectrum of transcripts [1] , [2] , which may in principle exert broad regulatory or effector functions [3]–[5] . These data imply that the amount of information contained in the complex genomes of eukaryotes , and higher eukaryotes in particular , is much higher than the classical linear models of genomic organization can accommodate [6] . The abundance of non-coding transcription also generates novel conceptual and experimental challenges . Probably the most outstanding and urgent issues are ( i ) to define how many , and which , of the transcriptional events occurring outside protein-coding genes are functional and regulated ( as opposed to those that represent noise ) [7] , [8]; ( ii ) to discriminate if functionality is conveyed by the transcript , by the act of transcription , or both; ( iii ) to classify functional transcription sites as canonical RNA genes or regulatory sequences undergoing transcription , like enhancers and locus control regions ( LCRs ) , that in anecdotal cases were shown to be transcribed and to generate ncRNAs [9]–[13] . Regarding functionality , the two extreme views are that most of this extragenic non-coding transcription merely represents noise , namely the consequence of unscheduled but productive collisions of RNA polymerases with random genomic regions , and that most of the products of non-coding transcription are functional RNA molecules exerting downstream functions [3] , [7] . Examples of transcriptional noise may be represented both by the recently described “ripples” of transcription extending from one protein coding-gene into the adjacent genomic regions [14] and by the spurious intragenic transcription initiation events , which in yeast seem to be actively suppressed [15] . In several cases , including the Xist , Air , and Kcnq1ot1 ncRNAs [12] , [16]–[23] , specific functions have been ascribed to selected lncRNAs on the basis of loss- or gain-of-function experiments . Evidence for functionality of lncRNAs as a class also stems from evolutionary analyses indicating that purifying selection has acted on both the promoters and the internal sequences of lncRNA genes to eliminate nucleotide substitutions , insertions and deletions [3] , [8] , [24] , [25] . Two aspects of these evolutionary signatures of functionality deserve a more detailed analysis . First , the overall level of conservation , albeit significant , is comparatively low , with point mutations occurring with a frequency about 10-fold higher in lncRNA sequences as compared to protein-coding genes , although lncRNA splice sites tend to be conserved [24] , [25] . Second , conservation was found to be much higher at promoters than within the transcript sequences [24] , [26] , which may indicate either the stronger sequence constraints of regulatory regions as compared to the ncRNA products or that at least in some cases the target of purifying selection may be represented by the act of transcription rather than by its products . The concept that transcription has roles other than generating functional products mainly stems from the analysis of cis-regulatory elements like LCRs and enhancers . Unidirectional transcription of the β-globin LCR by RNA Pol_II [9] is required to generate and maintain an open chromatin domain [10] . Similarly , the switch of polycomb group response elements ( PRE ) from a repressed to an activated state in Drosophila requires intergenic transcription through the PRE , indicating that in some cases transcription may provide an anti-silencing mechanism [27] . Additional examples of non-coding transcription correlating with ( and causing ) locus activation were described in the LCR of the major histocompatibility complex II locus [28] , in the T cell receptor locus [11] , and upstream of the lysozyme gene in activated macrophages [13] . Non-coding transcription occurring close to protein-coding genes also has the potential to cause gene repression . Transcription of the non-coding gene SRG1 through the promoter of SER3 in yeast interferes with binding of transcription factors and subsequent activation , thus providing a paradigmatic example of transcriptional interference mediated by non-coding transcription [29] . Similarly , the Ubx gene in Drosophila is repressed by non-coding transcription elongating from the upstream bxd locus , which results in complementary and non-overlapping patterns of expression of Ubx mRNA and bxd ncRNAs [30] . In some ( but not all ) cases described above , formal evidence was provided that the act of transcription per se ( rather than the transcripts ) mediates downstream effects . For instance , intergenic transcription extending in the yeast PHO5 promoter is required for nucleosome eviction and gene activation; however , increasing the level of the unstable lncRNA generated in this region didn't affect gene activation [31] . In other cases the lncRNA generated by extragenic transcription was found to impart regulation . For instance , nascent ncRNAs were shown to act as platforms for the recruitment of an RNA-binding transcriptional regulator upstream of the CCND1 gene [20] , and the Evf2 ncRNA ( derived from an ultraconserved regulatory region ) was shown to act in trans to coactivate the homeodomain TF Dlx-2 [12] . Mechanistically , transcriptional elongation causes a broad spectrum of effects to the underlying chromatin template , including chromatin remodeling , nucleosome eviction , and changes in the acetylation and methylation state of histone tails [32] , [33] , effects that are all due to the association of multiple enzymatic activities with the elongating Pol_II complex [34] , [35] . Direct biochemical and genetic evidence supporting this type of mechanism comes from a recent time-resolved analysis in S . Pombe: transiently inducible non-coding Pol_II transcription upstream of the fbp1 locus caused a wave of chromatin remodeling preceding , and required for , binding of activating transcription factors to cognate sites in the fbp1 promoter [36] . However , the possible role of the nascent , very low abundance ncRNAs generated by transcription upstream of fbp1 was not directly addressed . In spite of all these observations , it is still unclear to what extent each of these reports represents an anecdotal description of uncommon gene regulatory mechanisms or conversely a paradigmatic example of a more general contribution of non-coding transcription to gene control . Moreover , the extent to which transcription occurring outside protein-coding genes indicates underlying RNA genes rather than Pol_II elongation along distant cis-regulatory regions ( like enhancers and LCRs ) is completely unknown . Here we took advantage of a dataset of extragenic Pol_II sites in a model of highly regulated gene expression ( endotoxin-stimulated primary macrophages ) . Using chromatin signatures we discriminated between transcribed enhancers and transcription start sites ( TSS ) of RNA genes . Remarkably , 70% of extragenic transcription sites ( which were frequently up- or down-regulated by endotoxin stimulation ) corresponded to genomic regions with an enhancer-type chromatin signature . These Pol_II peaks overlapped with annotated lncRNAs , were associated with binding sites for inflammatory transcription factors , and displayed enhancer activity in reporter assays . We also identified about 700 extragenic Pol_II clusters with a typical signature of active TSS and highly enriched for CpG islands , thus likely representing the 5′ end of bona fide RNA-coding genes . Overall , enhancers overlap a sizeable fraction of extragenic transcription sites in higher eukaryotes .
We first determined the genomic distribution of RNA Pol_II in unstimulated and activated mouse macrophages ( stimulated for 2 h with LPS in the presence of gamma interferon , γIFN ) . These ChIP-Sequencing datasets ( described in [37] ) were generated with an antibody recognizing all isoforms of the large RNA Pol_II subunit , Rbp1 , irrespective of their phosphorylation state . Therefore , they provide a snapshot of global Pol_II distribution over the mouse genome . We first browsed genomic regions containing genes regulated by LPS stimulation , like cytokine and chemokine genes , to identify sites of extragenic transcription . Figure 1A shows an example of extragenic Pol_II sites induced by LPS stimulation and located upstream of the inflammatory chemokine gene Ccl5 . Upstream Pol_II peaks are extremely broad , covering about 20 kb of extragenic sequence with no annotation of known or predicted exons; moreover their height is much lower than that found inside the coding region . Upstream Pol_II signals do not seem to be continuous ( with three or four distinct clusters ) and stop just upstream of the Ccl5 TSS . Upstream of another chemokine gene , Cxcl11 ( Figure S1 ) , two discrete inducible peaks can be observed , covering an area of about 10 kb . Although these peaks overlap a gene ( Art3 ) that extends in antisense orientation over Cxcl11 ( and the closely spaced Cxcl10 ) , they cannot be ascribed to the activity of Art3 , which is very poor in these cells ( as indicated by the very small amount of Pol_II loaded on its TSS ) . Intergenic Pol_II ( with no continuity with the Pol_II signals tracking from the 3′ of Cxcl11 ) can also be detected in the space separating the 3′ of Cxcl11 from the 5′ of Cxcl10 . Other examples are shown in Figure S1 . To determine if Pol_II is actively transcribing these extragenic regions , we also generated ChIP-Seq datasets using an antibody specific for an elongating Pol_II isoform ( phosphorylated at Ser5 of the carboxy-terminal domain of Rbp1 ) [38] . Ser5 profiles confirmed that Pol_II binding upstream of Ccl5 reflects active transcription ( Figure 1B ) . To start characterizing the properties of the extragenic transcription described above , we first analyzed kinetics of induction of the corresponding ncRNA relative to that of the downstream coding gene . We carried out quantitative RT-PCR with primers designed in regions contained within the extragenic Pol_II peaks . In the case of Ccl5 we explored the three regions of extragenic transcription ( named −1 , −2 , and −3 ) indicated in Figure 1A . Importantly , the Q-PCR primers were designed at a fixed distance in order to generate products of 200 nucleotides . Therefore , a positive signal implies the existence of RNA species of at least 200 nt . The kinetics of activation of these regions , as evaluated by the behavior of the corresponding transcripts ( Figure 2A ) , were very similar with each other , appearing already at 30′ after stimulation and reaching maximal levels between 60 and 90 min . At Cxcl11 the two upstream transcripts tested appeared even faster , peaking between 30 and 60 min , to be then rapidly downregulated ( Figure 2B ) . In both cases , however , kinetics of induction of upstream extragenic transcription preceded the appearance of the mature mRNA generated from the downstream coding genes , a concept also supported by the analysis of the nascent transcripts ( Figure S2 ) . Moreover , extragenic transcription was downregulated when the coding gene reached its maximal level of expression , a result particularly obvious at Cxcl11 . This type of behavior was not specific to these two genes , as it could be detected at several other genes associated with inducible upstream extragenic transcription ( Figure 2C ) . Therefore , extragenic transcription associated with inducible gene expression at these loci displays a clear temporal pattern in which upstream ( presumably non-coding ) transcription precedes the induction of the downstream protein-coding gene . This kinetic behavior is reminiscent of the relative temporal profiles of non-coding versus coding transcription observed in other systems . At the fbp1 gene in S . Pombe , a rapidly induced , low-level upstream transcription ( which is required for chromatin opening at the locus ) precedes downstream gene activation and is turned off when the gene is activated [36] . Importantly , all the ncRNAs we detected in this analysis accumulated at very low levels , usually hundreds of folds less than the adjacent coding genes . This may reflect the combination of a low transcription rate ( indicated by both the low intensity of both the Pol_II peaks and the nascent transcripts shown in Figure S2 ) and a high instability of the final product ( see below ) . Detailed structural characterization of these inducible extragenic transcripts is hindered by their very low abundance . Priming the reverse reaction with oligo-dT indicates that transcripts generated upstream of Ccl5 are poly-adenylated ( Figure 3A ) . Moreover , they can be detected exclusively in the nuclear compartment ( Figure 3B ) . Priming the cDNA synthesis with antisense primers located upstream of the 5′ of Ccl5 showed that upstream transcription generates long unspliced RNAs extending for a few kilobases ( Figure 3C ) . However , using the same cDNAs we couldn't obtain Q-PCR signals in peaks further upstream ( indicated as −2 and −3 in Figure 1A ) ( unpublished data ) . cDNAs primed by multiple oligonucleotides on the opposite strand didn't generate any Q-PCR product ( unpublished data ) , indicating that transcription is strand-specific , occurring on the upper strand toward Ccl5 , and as such unlikely to reflect random transcriptional events occurring at open chromatin . Finally we measured the stability of these transcripts using an actinomycinD chase . In comparison to both the mRNAs generated by the associated protein-coding genes and some known lncRNAs ( like Xist and Neat ) , the upstream non-coding transcripts were very unstable , being reduced by 80% to 90% after a 30 min actinomycinD treatment ( indicating a half-life lower than 7 . 5 min ) ( Figure 3D and Figure S3 ) . High instability of a subset of lncRNAs both in yeast and mammals mainly depends on degradation by the nuclear exosome [39] , [40] and often results in the generation of more stable short RNA products [41] , which in principle might be responsible for downstream functional effects . Another interesting property of some of the upstream transcripts is that , unlike mRNAs , they are poorly sensitive to DRB treatment ( Figure 3E ) . DRB is an inhibitor of Cdk9 , the catalytic subunit of the elongation factor pTEFb [42] . Cdk9 acts on multiple substrates to promote Pol_II entry into the elongation phase and cotranscriptional mRNA processing . The previous finding that up to 40% of nuclear RNA synthesis is unaffected by DRB treatment , as opposed to the 95% reduction of cytoplasmic polyadenylated transcripts [43] , may indirectly suggest that at least part of extragenic transcription is subjected to control mechanisms different from those acting at protein coding genes , and specifically that P-TEFb may not be required for Pol_II activity at some of these regions . Browsing through the data indicated some major challenges towards a systematic and correct identification of extragenic Pol_II peaks . The most obvious one was represented by the extension of elongating Pol_II molecules several kilobases beyond the end of annotated protein coding genes , namely in regions that by definition are extragenic . This is most likely due to the lack of specific and strong termination signals for RNA Pol_II . Moreover , alternative TSSs located upstream of the annotated ones contribute to create ambiguity in extragenic Pol_II peak annotation . To systematically annotate sites of extragenic transcription , we first filtered out all Pol_II signals overlapping UCSC known genes as well as peaks within 10 kb from the 3′ end of annotated genes ( which after several tests proved to be an optimal length to eliminate most signals due to Pol_II tracking from the upstream gene ) . It is important to stress that because of this design , our analysis does not take into account gene boundaries , which represent a major source of long and short non-coding RNAs [40] , [41] , [44]–[47] . The initial list was eventually curated for additional filtering ( mainly to eliminate Pol_II signals showing continuity with upstream genes ) , leading to 4 , 588 high-confidence extragenic Pol_II peaks . Using a statistical approach for ChIP-Seq data analysis [48] we classified these peaks as constitutive ( 895 ) , inducible ( 1 , 482 ) , or repressed ( 2 , 211 ) in response to stimulation ( Figure 4A and Table S1 ) . Chromatin signatures generated by specific combinations of post-translational modifications of core histone tails are powerful and sensitive indicators of functionality [49]–[51] . A simple , yet informative combination of modifications includes the mono-methylation of H3K4 ( H3K4me1 ) and the tri-methylation of the same residue ( H3K4me3 ) . TSSs of genes that are either active or poised for activity are characterized by high levels of H3K4me3 ( peaking just downstream of the TSS and confined to a few nucleosomes ) , flanked on both sides by regions enriched for H3K4me1 . Conversely , enhancers display high levels of H3K4me1 , usually distributed over several kilobases , associated with low or no H3K4me3 ( H3K4me1hi/H3K4me3lo domains ) [52] , [53] . Enhancers are also frequently bound by the histone acetyltransferase p300 [52] . In order to assign the extragenic Pol_II clusters in our dataset to either TSSs of lncRNA genes or to enhancers , we used a machine-learning algorithm ( Text S1 ) . The algorithm was instructed to discriminate enhancers from promoters using the H3K4me3/H3K4me1 chromatin profiles at 556 informative ( unambiguous ) extragenic p300 peaks ( described in [54] ) and the H3K4me3/H3K4me1 profiles at an identical number of promoters/TSSs with a broad range of Pol_II levels . This approach was validated by multiple tests ( see Text S1 ) including its ability to properly classify ChIP-Seq peaks of the macrophage TF PU . 1 , which we found to be strongly but not exclusively enriched in enhancers [54]: PU . 1 peaks that were classified as promoters/TSSs using this algorithm overlapped annotated TSSs of UCSC known genes in 67% of cases , while PU . 1 peaks classified as enhancers overlapped annotated TSS only in 7% of cases ( and in most cases visual inspection confirmed that these TSS did not show a typical signature of promoters ) . We thus applied this machine-learning algorithm to the dataset of 4 , 588 extragenic Pol_II peaks described above . We found that 3 , 227/4 , 588 peaks were contained in regions with a chromatin signature of enhancers , 1 , 004 were in regions with a signature of active or poised TSSs , while 357 were associated with regions with a non-predictive signature . Peaks were then clustered ( see Methods ) and then filtered against Ensembl protein coding genes to definitively discard regions with protein-coding potential . The final dataset consisted of 3 , 216 Pol_II clusters , including 2 , 236 enhancers ( 69% ) , 779 promoters ( 24% ) , and 201 unpredictable regions ( 7% ) ( Figure 4B and Table S4 ) . Chromatin signatures at the enhancer and promoter groups are shown in Figure 4C , and examples of predicted enhancers and promoters associated with extragenic Pol_II clusters are shown in Figure 4D . The chromatin signature at the region upstream of Ccl5 is also compatible with its enhancer activity ( Figure S4 ) . If these predictions are correct , an obvious expectation is that the group associated with the promoter/TSS signature should be enriched for CpG islands . This was indeed the case: 165/779 promoters ( 21 . 2% ) were associated with an underlying CpG island ( p<1e-3 ) as compared to only 11/2 , 236 enhancer clusters ( 0 . 5% , which is similar to what was found in random sets of genomic sequences with similar composition ) ( Figure 4E ) . The association between putative ncRNA genes and CpG islands is clearly much lower than observed at protein-coding genes ( 72% ) [55]; however , our results are similar to those reported by Ponjavic et al . for ncRNA genes expressed in mouse development , which were associated with CpG islands in about 30% of cases [56] . The TSSs of annotated , bona-fide RNA genes ( like Neat1 , Malat , and Xist ) [2] have chromatin features analogous to those of protein-coding genes and perfectly fitting the pattern of our promoters/TSSs group ( Figure S5 and unpublished data ) . This is in keeping with the notion that lncRNA genes can be retrieved using the same H3K4me3/H3K36me3 chromatin signature that was originally described at active protein coding genes [25] . We next investigated the relationship between the transcriptional activity of predicted enhancers and that of the associated protein-coding genes . First , we assigned predicted transcribed enhancers to adjacent coding genes if distant from them less than 20 kb . We considered this restrictive criterion essential to limit incorrect or arbitrary matches . Enhancers whose association with Pol_II was induced or increased by stimulation were strongly associated with inducible genes ( p<1e-7 when compared to the expected fraction ) , while association with constitutive and repressed genes was underrepresented in a statistically significant manner ( Figure 4F and Table S5 ) . In a specular manner , repressed enhancers were associated with repressed genes , albeit at low statistical significance ( Figure 4F ) . It should be stressed that repressed enhancers are also associated with a large number of genes that are induced by stimulation . Although from a statistical point of view this group of inducible genes is underrepresented as compared to what is expected , the possibility should not be discounted that transcriptional downregulation of an enhancer may be involved in the activation of the associated gene , possibly by relieving transcriptional interference [29] . In the cases shown in Figure 2 we could detect and measure low-abundance long RNAs ( ≥200 nt ) generated at regions of extragenic Pol_II binding . However , Pol_II recruitment to chromatin is not necessarily followed by elongation [57] , [58] . To address this crucial issue , we carried out several complementary analyses and experiments . First , we analyzed the overlap between extragenic Pol_II sites and annotated ncRNAs datasets . We used two different catalogues: a “macroRNA” dataset ( 2 , 168 ncRNAs ) generated by the FANTOM consortium by massive cDNA sequencing [26] and then filtered to eliminate RNAs overlapping all current protein-coding gene annotations [24] , [59] , and a dataset of large intervening ncRNAs ( 1 , 408 “lincRNAs” ) identified by the H3K4me3/H3K36me3 chromatin signatures characteristic of bona fide active genes [25] and then filtered against the Ensemble protein-coding genes ( Table S2 ) . These two catalogues show little overlap , suggesting that each of them includes only a small fraction of a presumably much larger ncRNA repertoire [59] . 26/2 , 236 predicted enhancers and 21/779 promoters/TSSs overlapped annotated macroRNAs ( albeit low , the overlap was statistically significant ) ( Table S3 ) . LincRNAs were associated with the promoter group ( 122/779; 15 . 6% ) and , to a lower extent , to the enhancer group ( 167/2 , 236; 7 . 4% ) ( Table S3 ) . As lincRNAs were identified on the basis of an H3K4me3/H3K36me3 chromatin signature that distinguishes active genes , the overlap with the enhancer group may appear surprising . However , visual inspection of these enhancers was consistent with the notion that they represent regulatory regions located within these extended H3K4me3/H3K36me3 domains ( see Figure S6 ) . Second , using a database of CAGE tags generated from the FANTOM consortium [60] , we found that the transcriptional potential of 72% of regions in the promoter group and 53% in the enhancer group was supported by overlapping CAGE tags . In interpreting these data it should be considered that the lncRNAs generated at the β-globin LCR do not contain a CAP at their 5′ end [61] , which implies that a fraction of the transcripts generated at regulatory regions is not represented in CAGE tags libraries . Interestingly , the median distance between multiple CAGE tags is significantly higher in enhancers than in promoters ( Figure 5A ) . These data confirm the transcriptional potential of predicted enhancers and suggest that while TSSs are tightly clustered in the promoter group , they are distributed over broader distances in the enhancer group ( presumably generating primary transcripts with heterogeneous 5′ ends ) . Third , we generated ChIP-Seq datasets in untreated and LPS-treated macrophages using an antibody that recognizes the large Pol_II subunit Rbp1 only when phosphorylated at Ser5 of its C-terminal domain ( CTD ) . Ser5 phosphorylation by TFIIH occurs at the transition to transcription initiation and is maintained throughout the length of transcribed genes to be then removed by a phosphatase at the very 3′ end [38] . Ser5-P was extensively associated with both predicted enhancers and promoters in our datasets ( Figure 5B , Table S6 ) . Median Ser5 peak length is 479 bp , with a minimum of 110 bp and a maximum of 7341 bp , indirectly suggesting that in most cases long ( >200 nt ) primary transcripts are generated . This result confirms that , independently of the final abundance of the transcripts , enhancers associated with Pol_II are actively transcribed . Similar results were obtained for promoters ( Figure S7 ) . Fourth , we analyzed by quantitative RT-PCR a representative set of 100 predicted enhancers within the whole range of p values associated with the corresponding Pol_II peaks ( as in Table S1 ) . Primers were designed to generate 200 nt amplicons . 96/100 tested regions generated detectable transcripts ( Table S7 ) , indirectly indicating that the vast majority of extragenic Pol_II peaks likely generate transcripts . Due to their very low abundance , a comprehensive analysis of extragenic ncRNAs and their detailed structural characterization present obvious difficulties . RNA sequencing is a powerful approach for detection of potentially all RNA species in a cell , although low abundance transcripts can be identified only at very high sequencing depth . As an initial step toward characterization of enhancer-associated transcripts , we generated an RNA-Seq dataset in untreated macrophages using total nuclear RNAs . At the level of sequencing depth we reached ( 11 . 5 million aligned tags from four Solexa GAII lanes ) we could detect 225 , 439 transcripts corresponding to 13 , 702 RefSeq genes and 28 , 247 UCSC known genes . We found RNA-Seq tags overlapping 193/484 promoters and 369/1 , 660 enhancers active in untreated macrophages ( corresponding to the constitutive and repressed groups; p<1e-3 compared to random sets of intergenic genomic sequences ) . In most cases , however , low density of tags precluded the identification of well-defined transcripts . Importantly , the extragenic regions associated with RNA-Seq tags displayed median Pol_II signals about 1 . 5 orders of magnitude higher than the regions for which transcripts could not be detected at this sequencing depth ( Figure 5C ) . Therefore , only the transcripts produced at the extragenic regions with high transcriptional activity could be detected ( Table S4 ) . Nevertheless , these data further confirm that Pol_II-bound extragenic regions are in general subjected to active transcription . While a large fraction of extragenic transcription sites bear an enhancer-associated chromatin signature , this doesn't demonstrate that these regions have functional properties of enhancers . We first searched the predicted enhancers for evolutionary signatures of functionality and specifically for evidence of purifying selection . We used phastCons scores in placental mammals [62] to measure the degree of conservation in the three groups of extragenic Pol_II clusters . Both promoters and enhancers were strongly conserved , with overall higher scores in the promoter group ( Figure 6A ) . In both groups conservation was statistically significant as compared to matched random sequence sets ( Figure 6B ) . Conversely , the group of Pol_II clusters with a non-informative chromatin signature did not significantly deviate from random sets . Sequence conservation in both the enhancer and the promoter group was stronger in the central regions ( and precisely in the sequences just flanking the summit of the Pol_II peaks ) and it was progressively diluted moving outwards . We next cloned some of these predicted enhancer sequences in a plasmid bearing a minimal promoter driving luciferase expression and tested their ability to increase reporter gene activity . All the sequences tested increased basal expression and some provided responsiveness to LPS stimulation ( Figure 6C ) . The first sequence from the left was also assayed for orientation-independence of enhancer activity ( Figure 6C ) . As additional evidence that these regions are in fact bona fide enhancers , we tested their ability to fold onto the neighboring promoter using chromosome conformation capture ( 3C ) [63] . The transcribed regions upstream of Ccl5 and Cxcl11 were in fact both associated with the regions surrounding the respective TSS ( Figure S8 ) . Association was not dependent on stimulation as it could be found also in basal conditions . In fact , stimulation reduced to a various extent the degree of looping . Finally , we evaluated the degree of overlap between extragenic Pol_II and binding of the transcription factor PU . 1 , which ( in addition to being recruited to active promoters ) is very extensively associated with enhancers in macrophages [54] . Considering a search space of ±500 nt surrounding ChIP-Seq PU . 1 peaks , we found that 84 . 4% of enhancer-type extragenic Pol_II clusters were associated with PU . 1 binding ( Figure 6D; see Figures S4 and S6 for some examples ) . PU . 1 association with promoter/TSS-type transcribed regions was also very frequent ( 69 . 3% ) , while Pol_II peaks with a non-predictive chromatin signature were associated with PU . 1 only in 33 . 8% of cases . Such a substantial association between extragenic Pol_II and binding of a sequence-specific TF ( 72% overlap considering the entire dataset ) strongly argues against the notion that this extensive transcriptional activity is mere noise and conversely confirms its nature as a regulated process . Enhancer functionality depends on the transcription factor binding sites ( TFBS ) contained in their sequence . TFs activated by stimulation with LPS+IFNγ include NF-kB/Rel family members [64] , IRFs ( interferon regulatory factors ) [65] , and STAT1 [66] . Moreover , the hematopoietic Ets family member PU . 1 , which is constitutively expressed at highest levels in macrophages , is highly enriched in enhancers , where it provides context dependence to responses driven by inflammatory TFs [54] . We therefore searched our dataset of 2 , 236 predicted enhancers associated with extragenic Pol_II for enriched TFBSs . To this aim , we first assembled a library of 338 position weight matrices ( PWMs ) by combining the DNA binding motifs in the Jaspar database [67] and those in a recently reported set of PWMs for 104 mouse transcription factors [68] . Then we divided the enhancers in three groups based on Pol_II behavior ( constitutive , inducible , and repressed ) and used a statistical approach [69] to score TFBS enrichment in each group relative to two background sets ( namely the whole mouse chr 19 and a set of all 5 kb sequences located upstream of the TSSs of mouse RefSeq genes ) . In the inducible group we found a strong enrichment for IRFs and STAT1 ( which bind related sites and were recognized by five distinct PWMs ) , as well as for NF-kB/Rel ( identified by four PWMs ) ( Figure 7A and Table S8 ) . Moreover , the dataset was strongly enriched for PU . 1/Spi1 PWMs , which is in keeping with its association with enhancers [54] . The constitutive group , in addition to a strong enrichment for PU . 1/Spi1 , showed a comparatively lower but anyway significant enrichment for IRF/STAT1 and NF-kB/Rel PWMs ( Table S8 ) . In this regard , it should be noticed that some of the enhancers that we define as “constitutive , ” in fact show LPS-induced increases in Pol_II levels that do not reach the threshold we set for the inclusion among the inducible peaks . Remarkably , the group of putative enhancers repressed by stimulation was strongly enriched for PU . 1/Spi1 but not for any of the PWMs for the inducible , inflammatory TFs associated with the other two groups ( Table S8 ) . Therefore the enhancers whose association with Pol_II is reduced by stimulation appear to represent a distinct group with a completely different TFBS composition . Importantly , also the group of the induced promoters ( and to a lesser extent the one including the constitutive promoters ) was enriched for binding sites for inflammatory TFs ( Table S8 ) , indicating that the TFs driving the inflammatory gene expression program also control many canonical RNA genes . We next evaluated if the identified TFBSs are functional . Some of the inducible Pol_II peaks in the region upstream of Ccl5 scored positively for IRF3 ( as well as other IRFs ) and NF-kB , which are known to coregulate Ccl5 expression [70] . Blocking IRF3 and NF-kB activity with specific mutants in stable Raw264 . 7 macrophage cell lines ( kindly provided by G . Cheng , UCLA ) blocked not only the induction of the Ccl5 mRNA but also the appearance of the upstream non-coding transcripts ( Figure 7B ) . Moreover , the NF-kB subunit p65/RelA was recruited to the Ccl5 upstream region ( Figure 7C ) , thus further supporting the functionality of the identified sites . Interestingly , maximal p65 recruitment to this region preceded recruitment to the NF-kB binding sites contained in the Ccl5 promoter , which is in keeping with the faster kinetics of induction of upstream transcription as compared to that of the Ccl5 mRNA ( as shown in Figure 2 ) . As we could detect thousands of enhancer-associated extragenic Pol_II peaks with distinct behaviors , some degree of functional heterogeneity is expected . Moreover , definitive understanding of the function of each extragenic transcription site would require dedicated genetic approaches to interfere with Pol_II loading and/or elongation ( like the knock-in of transcriptional terminator sequences; see for instance [11] , [36] ) . Attempts to deplete ncRNA generated at enhancers by RNAi were not successful , which likely reflects their constitutive instability ( see Discussion ) . We tried however to get an initial glimpse into the functional impact of transcription through enhancers in this system . One model supported by experimental data is that extragenic transcription leads to the repeated passage of several Pol_II-associated enzymes , including Swi/Snf remodeling complexes [71] , [72] and histone acetyltransferases [35] , through chromatin regions , thus leading to extensive remodeling and changes in accessibility [32] . We first found that macrophage activation is associated with a domain-wide increase in acetylation at the transcribed regions upstream of Ccl5 ( Figure 8A and unpublished data ) . Domain-wide hyperacetylation was strongly reduced by treatment with actinomycinD but not with the protein synthesis inhibitor cycloheximide ( CHX ) . Importantly , Ccl5 is a primary response gene and as such it is not sensitive to CHX treatment [73] . Therefore , while new protein synthesis does not impact on acetylation of the locus , new transcription is required for maximal acetylation both at the TSS and at upstream regions . ActD ( but not CHX ) treatment also prevented recruitment of Pol_II at the Ccl5 TSS ( Figure 8B , left ) . Conversely , at a secondary gene ( interleukin 6 , IL-6 ) , both CHX and ActD completely blocked Pol_II recruitment ( Figure 8B , left ) . The effects of ActD on Pol_II recruitment to TSSs were not general , as they could not be detected at two other genes tested ( Figure 8B , right ) . Therefore , with all the due cautions required in experiments with global inhibitors , it seems that the act ( or the products ) of transcription ( rather than the induction of new protein products ) is involved both in acetylation through the Ccl5 locus and in gene induction . A similar behavior was found at the Cxcl11 upstream regions ( Figure 8C ) . Here we could detect a high basal level of acetylation in the regions corresponding to the extragenic Pol_II peaks . Acetylation was strongly increased by stimulation and returned to basal levels upon ActD ( but not CHX ) treatment , thus indicating that also in this case extragenic transcription ( or its products ) may be involved in controlling the chromatin state of the locus .
The main finding of this study is that RNA Pol_II association with , and productive transcription of , a subset of cis-regulatory regions accounts for a sizeable fraction of transcription sites located outside of coding gene borders . It is important to notice that the design of our study—which is based on the analysis of Pol_II occupancy in regions not overlapping annotated protein-coding genes—implies that gene boundaries , which contribute in a substantial manner to the repertoire of short and long ncRNAs in mammalian cells [40] , [41] , [44]–[47] , were not taken into consideration . The concept that enhancers and LCRs in some cases undergo transcription was previously demonstrated at individual loci in various experimental models [9]–[11] , [29] , [30] , [36] . Our data demonstrate on a genomic scale that this is a common occurrence . However , based on our data on enhancers in this specific system [54] , as well as reports in other models [53] , it seems clear that non-transcribed enhancers ( in the order of magnitude of dozens of thousands in every given cell type ) greatly outnumber the transcribed ones , which raises some obvious questions . First , can enhancers be classified on the basis of being transcribed or not , and do Pol_II-transcribed enhancers represent a functionally and mechanistically homogeneous group ? A simple model , compatible with a large body of experimental data , is that functionality of transcribed enhancers and LCRs indeed depends on the directional movement of Pol_II along their sequence [32] . Large chromatin domains often undergo regulated and extensive modifications ( like acetylation and reduction of nucleosomal density ) controlling their accessibility and functionality: in such cases it is difficult to imagine how chromatin-modifying enzymes recruited to discrete sites by association with sequence-specific TFs can promote such large scale changes . Conversely , loading the same enzymes onto elongating Pol_II complexes provides a regulated and specific way to catalyze rapid changes across extended regions , thus establishing transcriptional competence ( discussed in [32] ) . An example of a specific effect of the transcription process itself , in which the ncRNA product apparently has no direct role , is provided by the PHO5 gene in yeast , whose activation requires nucleosome eviction stimulated by non-coding transcription across its promoter [31] . When the level of the ensuing ncRNA was artificially increased ( by either overexpression or by inactivation of the nuclear exosome ) , no consequences on nucleosome depletion were found [31] . In other cases it was shown that the ncRNAs generated from regulatory regions is functional [8] , either by controlling the deposition of epigenetic modifications [21] or by promoting the recruitment [20] or stimulating the activity [12] of transcriptional activators . In some of these cases , it is implicit that the ncRNAs would act at the production site , possibly when still associated with elongating Pol_II . This model may well apply to ncRNAs generated at enhancers , whose function may relate to the control of local chromatin features . Overall , the role of ncRNA transcripts versus transcription in conveying regulatory information likely varies depending on the regulatory region considered , and ad hoc experiments will be required to understand the relative frequency of the two groups of mechanisms . For those enhancers whose associated transcripts will be demonstrated to be functional ( as in [20] ) , their distinction from canonical RNA genes may appear conceptually subtle and in the end rely exclusively on their distinct chromatin signature . However , we believe that an additional important aspect should be considered in distinguishing enhancers that generate functional RNAs from canonical RNA genes: the local and temporally restricted cis-regulatory role of the enhancer-associated ncRNA ( temporal restriction being related to the rapid degradation of these transcripts after they are synthesized ) . On the contrary , ncRNAs generated from canonical RNA genes in most cases act at a distance from the production site ( e . g . Neat1 ) [2]; even when acting in cis , as in the case of Xist and Air , they coat ( and functionally affect ) broad chromosomal regions , thus in fact exerting an activity that extends far beyond the borders of their site of synthesis . In this context , it appears very relevant to bring into focus the conceptual and technical problems related to the mechanistic dissection of the ncRNAs generated at regulatory regions . Assessing the functionality of these ncRNAs will require that their specific elimination or depletion be dissociated from any effect on the underlying transcription . Therefore , knocking-in transcriptional terminators to interfere with Pol_II elongation ( see for instance [11] ) is in fact non-informative in this regard . Depletion of ncRNAs by RNAi efficiently works when applied to stable transcripts encoded by RNA genes [19] . However , enhancer-generated transcripts are very unstable , possibly due to a constitutive surveillance by the nuclear exosome [39] , [40] , leading to their complete degradation or the generation of short RNAs [41] . Moreover , the role of nascent ncRNAs in targeting to chromatin specific regulators with RNA binding modules ( as suggested in [20] ) may be limited to a very short window of opportunity during which proximity to chromatin is maintained , namely the time of Pol_II passage over a specific genomic region . Low level of expression of these ncRNAs ( see Figure 2 ) may reflect the restriction of their activity to the genomic regions where they are synthesized . For both reasons , reducing their levels by RNA interference ( before they are degraded or before they exert a local and transient functional activity ) may not be feasible , at least using simple tools . On the other hand , for those ncRNAs acting at their site of production , overexpressing them cannot recapitulate their normal function . A second outstanding question pertains to the identity of the determinants of enhancer association with RNA Pol_II factories and of the molecular mechanisms controlling transcriptional initiation at these regulatory regions . It seems clear that in some cases Pol_II can be loaded at multiple positions along the enhancer/LCR [61] , a result in keeping with the presence of multiple distant CAGE tag clusters at enhancer regions in our dataset ( Figure 5C ) . Still , the directionality of transcription ( see also Figure 3C ) implies a tight control upon formation of the preinitiation complex and rules out the possibility that transcription is a mere consequence of random Pol_II collisions with accessible loci . A third related question is whether enhancer-associated transcription is mechanistically different from transcription of protein- and RNA-coding genes . This possibility is supported by several observations , including the resistance to the general elongation inhibitor DRB of part of nuclear transcription ( [43] and our own data ) and , as discussed above , the fact that enhancer associated transcription often initiates at multiple points along the sequence of the enhancer [61] , as if rules for initiation were less stringent at enhancers as compared to protein and RNA genes . One important aspect of extragenic transcription , and particularly the fraction not associated with putative RNA-coding genes bearing a promoter signature at their 5′ end , is that it should be unambiguously distinguished from the transcriptional noise that may arise from spontaneous collisions of the Pol_II transcriptional machinery with some genomic sequences [7] , [8] . A form of noise that has been recently described is represented by waves of transcription extending from highly active immediate early genes ( IEGs ) into neighboring sequences , including genes and intergenic regions [14] . This “ripple effect” is somehow similar to the inducible extragenic transcription we show here , and therefore it deserves a careful analysis . The interpretation of the authors [14] is that ripples start from IEGs and extend into the adjacent regions: because of this behavior these Pol_II waves should be considered noise , and specifically the downstream consequence of a strong gene activation that cannot be confined to the limits of the gene itself . It should be noticed that in the system used by Ebisuya et al . , namely growth factor stimulation of fibroblasts , IEG induction is extremely fast , with Pol_II peaking in several cases at 10 min after stimulation . Therefore , this system offers limited possibilities to identify complex temporal sequences in the activation of upstream extragenic regions versus associated coding genes . Conversely , the system used in this study has the advantage that genes are induced in a kinetically complex fashion [73] , [74] , in some cases relatively long after the initial stimulation . At genes like Ccl5 and Cxcl11 , as well as at several others ( see Figure 2 ) , this kinetic behavior allowed us to identify a recurring temporal pattern in which upstream non-coding transcription not only precedes the induction of the neighboring protein-coding gene but also peaks when the activity of the associated coding gene is hardly detectable ( which is similar to what was described at the inducible fbp1 gene in yeast ) [36] . Conversely , a ripple effect should parallel RNA Pol_II activity at the associated coding gene and reach maximal levels when the coding gene is at its peak of activity . A second expected feature of a ripple effect is that extragenic waves of Pol_II should show continuity with Pol_II elongating from the inducible coding gene . In our dataset , this is an unusual occurrence ( see Figures 1 and 2 and Figure S1 for examples ) . The direct evidence arguing against the possibility that extragenic Pol_II reflects transcriptional noise comes from four groups of data we obtained in this study: ( 1 ) the presence of an enhancer-associated chromatin signature [52] , ( 2 ) the enrichment for inflammatory TFBSs like NF-kB and the IRFs , ( 3 ) the functionality of some tested regions in heterologous reporter assays , and most importantly , ( 4 ) the very extensive overlap between sites of extragenic transcription and binding sites for the TF PU . 1 , which is required for macrophage differentiation [75]–[77] and function [78] , [79] , and very extensively marks enhancers [54] . The only group of extragenic Pol_II peaks ( about 8% of the peaks in the dataset ) that in principle may represent noise ( although it is not possible to formally demonstrate it with our analysis ) is the one consisting of regions without an informative chromatin signature: in fact , this group shows levels of sequence conservation that are not significantly different from those of random sequences ( see Figure 6B ) . Overall , we can safely conclude that transcribed extragenic regions with an enhancer-associated chromatin signature represent in most cases sites of highly regulated Pol_II recruitment and elongation , possibly relevant for their function as enhancers . An additional aspect worthy of attention is that at least in this system extragenic Pol_II peaks are more frequently repressed than induced by stimulation . While in many cases repression correlated with downregulation of the associated genes , in several others it correlated with gene activation of a neighboring gene . A reasonable hypothesis in this case is that , similar to what was described in other models [29] , [30] , extragenic transcription extending into neighboring genes may interfere with their activity: therefore gene induction can occur only when transcription from adjacent extragenic regions is switched off . In conclusion , our study demonstrates that the pervasive transcription occurring in mammalian genomes [1] is contributed not only by RNA-coding genes but also by a large number of enhancers associated with constitutive or regulated Pol_II transcriptional activity . These data are relevant for functional genomic annotations and at the same time indicate that Pol_II-dependent transcription is integral to the activity of a fraction of functional cis-regulatory elements .
Bone marrow cells isolated from female Fvb/Hsd mice were plated in 10 cm plates in 5 ml of BM-medium ( high glucose DMEM supplemented with 20% low-endotoxin fetal bovine serum , 30% L929-conditioned medium , 1% glutamine , 1% Pen/Strep , 0 . 5% Sodium Pyruvate , 0 . 1% β-mercaptoethanol ) . Cultures were fed with 2 . 5 ml of fresh medium every 2 d . Stimulations were carried out at day 7 . Raw264 . 7 were cultured in high glucose DMEM containing 10% low endotoxin FCS . Clones stably expressing dominant negative IRF3 and IkBα super-repressor were a gift of G . Cheng ( UCLA ) [70] . ActinomycinD , cyclohexymide , and DRB were from Sigma and were used at a final concentration of 5 µg/ml , 10 µg/ml , and 50 µg/ml , respectively . The anti-p65 antibody used in the ChIP in Figure 5C was from Santa Cruz ( sc-372 ) . The anti-acetylH3K9 antibody used in Figure 6 is from Millipore ( #07-352 ) . The RNA Pol_II and H3K4me3 ChIP-Sequencing datasets are described in [37] . The H3K4me1 and PU . 1 datasets are described in [54] . Briefly , the RNA Pol_II ChIP-Seq experiment was carried out in unstimulated and LPS+γIFN-stimulated ( 2 h ) macrophages using an antibody recognizing all isoforms of the large Pol_II subunit , Rbp1 ( Santa Cruz sc-899 ) . The Ser5-Pol II ChIP-Seq datasets were generated using the Ab5131 antibody from Abcam , which recognizes the RNA Pol_II CTD repeat YSPT ( phospho ) SPS . The H3K4me3 , H3K4me1 , and PU . 1 datasets used in this study were all obtained in unstimulated cells , and antibodies were from Abcam ( H3K4me3 , Ab8580; H3K4me1 , Ab8895 ) or Santa Cruz ( PU . 1 sc-352 ) . Datasets are available for download from NCBI's Gene Expression Omnibus ( GEO , http://www . ncbi . nlm . nih . gov/geo ) , accession numbers GSE17631 , GSE19553 , GSE19991 . Nuclei were isolated as described in the section below and total nuclear RNA was extracted using Trizol . After quality control , RNA was processed following the same standard Solexa protocol recommended for mRNA sequencing . The dataset is available for download from GEO , accession number GSE20370 . RNA was extracted from macrophages using Trizol ( Invitrogen ) and reverse transcribed with random hexamers . In some experiments oligo-dT or gene specific oligonucleotides were used to prime the reverse transcription , as indicated in the text . For isolation of nascent transcripts , cells were lysed in HB buffer ( 10% glycerol , 60 mM KCl , 15 mM NaCl , 1 . 5 mM HEPES pH 7 . 9 , 0 . 5 mM EDTA ) containing 0 . 3 M sucrose and 0 . 8% NP40 . Nuclei were then pelleted through a 0 . 9 M sucrose cushion in HB buffer and then resuspended in 100 µl of NRB ( 75 mM NaCl , 20 mM Tris-HCl pH 7 . 5 , 0 . 5 mM EDTA , 50% glycerol , 100 µg/ml yeast tRNA ) ; lysis was carried out by addition of 750 µl of NLB ( 0 . 3 M NaCl , 20 mM HEPES pH 7 . 6 , 0 . 2 mM EDTA , 7 . 5 mM MgCl2 , 1 M urea , 1% NP-40 , 100 µg/ml yeast tRNA ) . Chromatin was then pelleted in microfuge at 4°C and nascent transcripts extracted in Trizol . As control of the lack of genomic DNA contamination , Q-PCR was also carried out on RNA that was not reverse-transcribed . The sequences of the primers used are in Tables S7 and S9 . Computational procedures , including the machine-learning algorithm used to classify enhancers and promoters , are described in detail in Text S1 . RAW264 . 7 cells were transiently transfected in a 24-well format with 0 . 8 µg of empty vector ( pGL3-promoter vector , Promega ) or vectors containing the specified genomic regions ( Table S10 ) with Lipofectamine 2000 ( Invitrogen ) according to the manufacturer's protocol . Twenty-four h after transfection , cells were treated with LPS ( 10 ng/ml ) and luciferase assay ( Bright-Glo , Promega ) was performed 16 h after treatment . Values are expressed as fold increase in luciferase counts over the empty vector for each cell line .
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Mammalian genomes contain vast intergenic regions that are extensively transcribed and generate various types of short and long non-coding RNAs ( ncRNAs ) . Although in some cases specific functions have been assigned to intergenic transcripts , the functional significance of this transcriptional output remains largely unknown , and the possibility exists that part of this transcription reflects noise generated by random collisions of the transcriptional machinery with the genome to generate meaningless transcription . In this study we used chromatin signatures to characterize extragenic transcription sites targeted by RNA Polymerase II ( RNA Pol II ) in a highly regulated response—endotoxin activation of macrophages . We found that a significant portion of extragenic transcription sites are associated with the chromatin signature characteristic of enhancers . Consistent with their chromatin signature , we found that these extragenic transcription sites are under purifying selection and contain binding sites for inflammatory transcription factors , as well as for PU . 1 , a hematopoietic transcription factor that marks enhancers in macrophages . Moreover , much of this extragenic transcription is regulated by stimulation . We also identified hundreds of transcribed regions with a signature of canonical RNA genes . Our data indicate that extragenic transcription sites can be efficiently classified using chromatin signatures , which will be relevant for functional annotation of mammalian genomes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"biology/histone",
"modification",
"cell",
"biology/leukocyte",
"signaling",
"and",
"gene",
"expression",
"immunology/innate",
"immunity",
"genetics",
"and",
"genomics/epigenetics",
"cell",
"biology/gene",
"expression"
] |
2010
|
A Large Fraction of Extragenic RNA Pol II Transcription Sites Overlap Enhancers
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The presence of poor quality medicines in the market is a global threat on public health , especially in developing countries . Therefore , we assessed the quality of two commonly used anthelminthic drugs [mebendazole ( MEB ) and albendazole ( ALB ) ] and one antiprotozoal drug [tinidazole ( TNZ ) ] in Ethiopia . A multilevel stratified random sampling , with as strata the different levels of supply chain system in Ethiopia , geographic areas and government/privately owned medicines outlets , was used to collect the drug samples using mystery shoppers . The three drugs ( 106 samples ) were collected from 38 drug outlets ( government/privately owned ) in 7 major cities in Ethiopia between January and March 2012 . All samples underwent visual and physical inspection for labeling and packaging before physico-chemical quality testing and evaluated based on individual monographs in Pharmacopoeias for identification , assay/content , dosage uniformity , dissolution , disintegration and friability . In addition , quality risk was analyzed using failure mode effect analysis ( FMEA ) and a risk priority number ( RPN ) was assigned to each quality attribute . A clinically rationalized desirability function was applied in quantification of the overall quality of each medicine . Overall , 45 . 3% ( 48/106 ) of the tested samples were substandard , i . e . not meeting the pharmacopoeial quality specifications claimed by their manufacturers . Assay was the quality attribute most often out-of-specification , with 29 . 2% ( 31/106 ) failure of the total samples . The highest failure was observed for MEB ( 19/42 , 45 . 2% ) , followed by TNZ ( 10/39 , 25 . 6% ) and ALB ( 2/25 , 8 . 0% ) . The risk analysis showed that assay ( RPN = 512 ) is the most critical quality attribute , followed by dissolution ( RPN = 336 ) . Based on Derringer's desirability function , samples were classified into excellent ( 14/106 , 13% ) , good ( 24/106 , 23% ) , acceptable ( 38/106 , 36%% ) , low ( 29/106 , 27% ) and bad ( 1/106 , 1% ) quality . This study evidenced that there is a relatively high prevalence of poor quality MEB , ALB and TNZ in Ethiopia: up to 45% if pharmacopoeial acceptance criteria are used in the traditional , dichotomous approach , and 28% if the new risk-based desirability approach was applied . The study identified assay as the most critical quality attributes . The country of origin was the most significant factor determining poor quality status of the investigated medicines in Ethiopia .
Intestinal parasites are a diverse group of organisms that include single-celled protozoans and multi-cellular intestinal helminths that affect the gastro-intestinal tract of humans and other animals [1] . Soil-transmitted helminthiasis is caused primarily by four species of nematodes , i . e . Ascaris lumbricoides ( roundworm ) , Trichuris trichiura ( whipworm ) , and Ancylostoma duodenale and Necator americanus ( hookworms ) that parasitize human gastrointestinal tract [2] . These major human soil-transmitted helminths ( STH ) have significant impact on human health in many parts of the world , particularly in developing countries [3] . If not treated early and efficacious , they may lead to malnutrition , chronic diarrhea , anemia , and other public health problems that can impair physical and intellectual development in children [4]–[6] . Currently , four drugs are recommended by the World Health Organization ( WHO ) for STH: MEB , ALB , levamisole and pyrantel pamoate [7] , [8] . MEB and ALB are increasingly deployed in mass drug administration programs [8] which require a single drug administration to all subjects without prior diagnosis or checking for contra-indications . For this reason , the two benzimidazole 2-carbamates MEB and ALB ( chemical structures presented in S1 Supporting information ) are preferred over levamisole and pyrantel pamoate , which require weight-based dosing and which are also intrinsically less potent . Literature reports indicate that TNZ , a 5-nitroimidazole compound ( S1-1 Supporting information ) , also has some anthelmintic efficacy [9] , although it is therapeutically mainly used against protozoan infections and infections caused by anaerobic bacteria in humans . As such TNZ is often used by the same patients treated with STH drugs [10] , [11] . Effective treatment and prevention strategies for these neglected tropical diseases can be delivered cheaply , but reports of treatment failure are frequent in developing countries most likely because of poor quality medicines , which includes spurious/falsely labeled/falsified/counterfeit ( SFFC ) medicines , chemical and/or physicochemical instability , inappropriate storage and transport , and poor quality control during manufacturing and importing medicines [12] . SFFC medicines are medicines that are deliberately and fraudulently mislabeled with respect to identity and/or source and include products with the correct ingredients or with wrong ingredients , without active ingredients , with insufficient or too much active ingredient , or with fake packaging [13] . Substandard medicines , i . e . not having the appropriate quality ( which is expected to be equivalent to the regulatory quality ) , may be SFFC but also approved medicines . In a quality survey in Nigeria , 48% of the samples of different categories of medicines were found to be outside the British Pharmacopoeia ( BP ) limits for active pharmaceutical ingredient ( API ) assay . Some medicines were even lacking the active ingredient [14] . The use of substandard medicines may result in therapeutic failure , resistance development , and occurrence of serious adverse events or even death due to excessive dose or the presence of toxic impurities [15]–[17] . A study conducted in sub-Saharan Africa in 2010 on the quality of selected anti-malarial medicines reported 64% overall quality failure in Nigeria , from which one artemisinin-based anti-malarial drug sample did not contain any of artemether API [18] . The presence of substandard and SFFC medicines not only poses threats to the individual users in terms of the health and side effects experienced , but also to the public and government in terms of trade relations and economic implications [19] . Hence , like many other public health problems , the issue of the presence of these substandard and SFFC medicines for public consumption should receive careful attention in developing countries [16] . Finished pharmaceutical products ( FPPs ) are tested for quality by assessing whether they meet pharmacopoeial or any other approved specifications . If not , they are discarded as non-conforming . This is a dichotomous decision without differentiation of the seriousness of failure and/or importance of quality attributes towards clinical use for the patient [20] . The evaluation of quality of any product poses thus a common problem due to a multiplicity of measures which must be balanced one against the other . Even when the quality attributes are precisely measurable , a serious challenge exists in combining the individual measurements into one index representing the total quality [21] . Such balance problems can be solved by using a Derringer's desirability function [22] . In general , this study was carried out to assess the pharmacopoeial quality of three medicines ( MEB , ALB and TNZ ) circulating in Ethiopia . The quality in terms of quality attributes like assay/content , dosage uniformity , dissolution , disintegration and friability was evaluated . The criticality of the quality attributes was assessed using FMEA risk-based analysis and Derringer's desirability function was applied to obtain one global quality index for each sample investigated .
MEB USP working standard [Cadila Pharmaceuticals ( Ethiopia ) ] , ALB reference standard [Greenfield Pharmaceuticals ( China ) ] and TNZ reference standard [Greenfield Pharmaceuticals ( China ) ] were kindly donated from Food , Medicine and Health-care Administration and Control Authority ( FMHACA ) of Ethiopia and used as received . Purified ultra pure water was obtained by water purification system ( Thermofischer Scientific , USA , 18 . 2 MΩ . cm at 25°C ) . All other chemicals used in this study were analytical grade and used as received . The sampling strategy was defined following the Medicine Quality Assessment Reporting Guidelines ( MEDQUARG ) as proposed by Newton PN et al . , 2009 [23] based on the questions: “Are there medicines of poor quality in the formal distribution outlets in Ethiopia ? If there are , what is the prevalence of these poor quality medicines ? ” Moreover , since there is a possible influence of origin and distribution conditions on medicines quality as received by the patient , we included the different formal outlets that are in practice used by patients in Ethiopia . So , we also looked at the following question: “Is there a difference in quality of medicines ( 1 ) among the different levels of medicines outlets ? ( 2 ) across different geographic areas of the country ? ( 3 ) among the two national economies: government and privately owned medicines outlets and ( 4 ) among the different countries of origin” . Therefore , in function of the questions , sampling units were defined to be the medicines sold from the drug retail outlets of the formal supply chain in the country , the different levels of the supply chain system in Ethiopia ( drug stores incl . health centers , pharmacies incl . hospital pharmacies , wholesalers ) , the geographic areas , government/privately owned medicines outlets and country of origin . Based on the sampling strategy , 106 drug samples were collected between January and March 2012 through multilevel stratified random sampling from all the levels of the supply chain system of the country ( n = 3 ) covering all types of government and privately owned drug outlets ( n = 2 ) . All available drug samples of the three study medicines were collected from each of the selected drug outlet . Through proportional allocation to each stratum of the supply chain , 59 . 4% ( n = 63 ) of the drug samples were collected from drug stores; 36 . 8% ( n = 39 ) were from pharmacies while the remaining 3 . 8% ( n = 4 ) samples were from wholesales . 17 . 9% ( n = 7 ) of pharmacy collected drug samples were obtained from hospitals , while four of the drug samples collected from drug stores was from health centers . Depending on the geographic locations and drug markets , the samples were collected from 7 major cities of the country: Addis Ababa , Hawasa ( and its region including Arbaminch and Shashemene ) , Jimma , Assosa ( and its region including Nekemte ) , Adama , Mekele and Bahirdar; which represent all four directions starting from Addis Ababa , the major central commercial center . All samples were tablet formulations and purchased anonymously by mystery shoppers from local area who were trained before . The mystery shoppers stated , if needed , that they were a travelling five member family and the family head , a man of 35 years old , abruptly caught a stomach ache ( ‘kurtet’ in Amharic ) due to worm infestations and requested the dispenser at the medicine outlet for some mebendazole ( for ‘kurtet’ ) and albendazole tablets ( for ascariasis ) as he used both medicines from his past experiences . At the same time , the family's 18 years old son was suffering from diarrhea and thus requested the dispenser for any medicines which could be given for him describing that he was taking tinidazole tablets two months ago for similar symptoms . Since the travelling family was in a worry of coming up with shortage of the medicines while travelling they requested a sufficient quantity of tablets of the medicines . The mystery shoppers were blinded about the purpose of the study and only instructed to purchase medicines in their original primary packaging as supplied by the manufacturer . For the purpose of this study , the relevant information of all collected samples was recorded on a standard form as soon as leaving the drug outlet and entered into database . The information included the level of the drug outlet , place/city of collection , name of the active pharmaceutical ingredient , the country of origin , manufacturing company , expiry date , manufacturing date , batch/lot number , and labeled dose ( strength ) of the active ingredient . Medicines purchased from a specific outlet , labeled with a specific generic name or brand name , strength , number of units per strip/package , batch number , country of origin , manufacturing and expiry dates were considered as one sample . Since the mystery shoppers stated that they were a travelling five member family , they were able to buy enough units per sample . For MEB , 50 tablets per sample were purchased while for ALB and TNZ , a sample contained 100 tablets . The samples were stored at ambient temperature ( 20°C to 25°C ) until tested , with a storage period of maximally 3 months before testing , and none of samples had expired at the time of testing . The quality control laboratory tests were performed in Jimma University Laboratory of Drug Quality ( JuLaDQ ) , Jimma , Ethiopia . JuLaDQ follows a quality system extended from its collaborating laboratory , Laboratory of Drug Quality and Registration ( DruQuaR ) of Ghent University , 9000-Ghent , Belgium . The laboratory tests were carried out according to the general and individual monographs specified in different Pharmacopoeias , as indicated in S1-2 supporting information . Instrument performance and system suitability tests were successfully performed for the analytical instruments and HPLC methods , respectively . For any drug product , identification of the active pharmaceutical ingredient ( API ) is a critical quality attribute . The three drugs ( ALB , MEB and TNZ ) belong to biopharmaceutical classification system ( BCS ) class II , with low aqueous solubility and high permeability [24] , [25] . Moreover , disintegration is an integral part of and/or pre-requisite for dissolution of immediate release dosage forms [26] . Therefore , quality attributes based upon which the products were evaluated were defined to be identification , assay/content , dissolution , dosage uniformity , disintegration and friability tests . Quality failure was defined as a sample failing any single test of the aforementioned tests for which it was evaluated . Details of the laboratory test methods used to evaluate the study medicines are presented in S1-3 supporting information . Risk analysis is a general quality tool which has its roots in engineering [27] , but is now becoming a well-established tool in the pharmaceutical field as well . As such , ICH has devoted a separate guideline ( Q9 ) to quality risk management , which is being embraced by pharmaceutical authorities [28] . Risk analysis , i . e . the estimation of the risk associated with the identified hazards , is an important part of this global risk management . Several quality risk management tools like FMEA ( Failure Mode Effects Analysis ) are available , as mentioned by ICH in Q9 . Therefore , FMEA was used to evaluate the criticality of product quality attributes in this study . Criticality was evaluated using RPN , based on evaluations about the probability of occurrence of the failure ( O ) , the severity of the failure ( S ) and the probability of not detecting the failure ( D ) . These judgments are converted into numerical values using descriptive scales and finally combined in the RPN [29] by means of Equation ( 1 ) : ( 1 ) Used scales for severity , occurrence and detectability of failure are presented in Tables 1 to 3 [30] . For severity ratings , five pharmaceutical experts in Belgium ( 4 ) and Ethiopia ( 1 ) ( S1-4 supporting information ) were assigned to score it and the median score was taken . For occurrence , literature was reviewed for the three products ( MEB , ALB and TNZ ) in Africa and for other drugs in Ethiopia as there was no previous quality study conducted for these three products in Ethiopia . In Nigeria , 48% of MEB samples contained amounts of active ingredient outside the appropriate assay limits [31] . Assay based pharmaceutical quality assessment in Kenya reported very poor quality for majority of marketed anthelmintic preparations [32] . Therefore , the highest occurrence score of 8 was assigned for assay . Studies conducted in Ethiopia indicated that the occurrence of failure of identification , disintegration and friability tests are very low making the scores assigned to each of these failures to be 1 [18] , [33] , [34] . Since 19 . 1% ( 8/42 ) of our MEB samples did not meet the pharmacopoeial acceptance criteria for dosage form uniformity , the probability of occurrence of this failure is moderately high and thus a score of 6 was assigned for its occurrence . For scoring the detectability , the scaling ranged from the low score assigned to the easiest detection to the highest score for the more difficult detection method . Friability can be detected through simple visual/weighing observation; hence , a score of 1 was assigned to its detectability . On the other hand , assay and dissolution studies involve quantitative tests , requiring fully equipped laboratory system and trained personnel . Therefore , detectability was scored to be 8 for each of these failure modes . Since identification requires field tests like color reactions and/or TLC , a score of 5 was assigned to detectability of identity failures . Desirability function , just like risk analysis , is a quality tool first proposed by Harrington in 1965 for use in the optimization of quality of manufactured products . The approach has basic foundation in engineering [35] , [36] and is widely adopted in the manufacturing industry . The central idea of a desirability function is to create one ball-mark figure . which is a composite number reflecting different response . This is done by mapping the value of each property/response onto a unit-less score in the range from zero to one based on the appropriateness ( or desirability ) of the property/response . Therefore , Derringer's desirability function was applied for the assessment of the quality of the three pharmaceutical products ( MEB , ALB and TNZ ) . The desirability function can be used to combine multiple responses into one response called the “overall desirability function” D , ranging between a value of 0 ( one or more product characteristics are completely unacceptable ) to 1 ( all product characteristics are on target ) . This overall desirability function D is obtained from the geometric mean of the individual desirabilities ( di ) which provide a way to assess the quality of one property . The formula to calculate the overall D-value is presented in Equation 2: ( 2 ) In this equation , pi was the weight or relative importance assigned to the response . For this study , n equals 4 since four characteristics were considered in the global evaluation of ALB and TNZ , while n = 3 for MEB since dissolution study was not performed . The advantage of calculating the geometric mean is that when one of the criteria has an unacceptable value , the overall product will be unacceptable as well . The highest global desirability value represents the product with the highest quality . Individual desirability functions were defined for each of the quality attributes , based on a psychophysical scale and the results obtained from the FMEA quality assessment . Desirability function possessing values in the range ( 0–1 ) classifies the conversion of the quantity value of a specific quality indicator into the assessment of the desirability ( preference ) of a certain condition of evaluated subject ( pharmacopoeial quality of the three medicines ) . Among the specific ways to implement the desirability function for the corresponding estimation , a psychophysical scale of Harrington is chosen providing universal application . The scale served to establish the correspondence between physical and psychological parameters . All the numeric desirability values ( 0–1 ) of the measured parameters/quality attributes are regarded as physical parameters , while a purely subjective assessment of a researcher ( e . g . excellent , good , acceptable , low , bad ) to express degree of satisfaction are regarded as psychological parameters . A rough estimation constructs a five–interval quality scale ( Table 4 ) [37] . For assay and dissolution , a two-sided desirability function was used where it becomes zero at the lowest and upper limit . For identity and dosage form uniformity , a one-sided desirability function was used . Absence of API is assumed to be clinically completely undesirable and thus this point was assigned d = 0 where as 100%lc was assigned d = 1 ( i . e . optimal desirability ) . Since the pharmacopoeial specification for assay is 90–110%lc for all the three products and the pychophysical Harrington's scale of quality specifies desirability range from about 0 . 7 to 1 . 0 to be good , d = 0 . 7 was assigned for assay values of 90 and 110%lc . Moreover , d = 0 . 3 was assigned for both 70% and 130%lc , while d = 0 . 01 was assigned to 50% and 150%lc . The individual desirability function for assay was then defined as different linear sections of different slopes in the range of 100%lc to 90%lc ( slope = 0 . 03 ) , 90%lc to 70%lc ( slope = 0 . 02 ) and from 70%lc to 50%lc ( slope = 0 . 01 ) . Similar but negative slopes were used for assay values greater than 100%lc , mirroring the under-dosing profile . For dissolution , %drug release was considered . According to USP acceptance criteria ( S1-2 supporting information ) , ALB should release 80% within 30 minutes , while TNZ should release 75% within 120 minutes . However , BP sets acceptance criteria for both drugs at 70% . Therefore , d = 1 was assigned for 100% drug release , while d = 0 . 7 was assigned for the average 75% and 125% drug release for both ALB and TNZ . Moreover , d = 0 . 3 was assigned for both 50% and 150% drug release , while d = 0 . 01 was assigned to 40% and 160% drug release . For dosage uniformity , the relative standard deviation ( RSD ) was considered as response . According to Ph . Eur . ( 2012 ) , RSD should be not more than 2%; and thus d = 1 was assigned for RSD = 0% while d = 0 . 7 for RSD = 2% . Following Harrington's scale , d = 0 . 3 was assigned for RSD of 6% and d = 0 . 01 for RSD of 15%; while for RSD = 25% , d was assigned to be 0 . For identity , d = 1 . 0 was assigned for those complying with pharmacopoeial specifications for identity and d = 0 for those which do not comply . Data entry and analysis was carried out using Statistical Package for Social Sciences software ( version 16 . 0 for windows; SPSS ) . The assay was carried out in triplicate and data were expressed as mean values . The Fisher exact test was used to test the association of the binary quality attributes with the country of origin ( 5 origins ) , collection sites ( 7 cities ) and drug outlets ( 3 types ) . A more detailed statistical data analysis , based on the fixed effects model with different response variables ( product quality attributes ) and different categorical covariates derived from our sampling strategy questions was done . FMEA was used to assess the criticality of the quality risks associated with each quality attribute and Derringer's desirability function was applied to evaluate quality of the products .
A total of one hundred and six samples of MEB , ALB and TNZ were collected between January and March 2012 in seven major cities that represent most parts of the country considering pharmaceutical market and geographic areas . The samples had been collected from 38 premises ( wholesales , pharmacies and drug stores ) . Of these , 42 samples were MBZ , 25 samples were ALB and 39 were TNZ samples . The origin ( place of manufacturing ) of samples was domestic and foreign ( China , India , Korea , and Cyprus ) . Domestic products constituted 45 . 3% ( 48/106 ) , followed by Indian products with 26 . 5% ( 28/106 ) . All samples had the intended active ingredient as demonstrated by the positive identification tests . No gross mislabeling ( incorrect , inadequate or incomplete identification ) was observed for the samples . However , the quantitative laboratory experiments indicated that 45 . 3% ( 48/106 ) of the samples did not meet the expected pharmacopoeial quality specifications: 45 . 2% ( 19/42 ) MEB , 48 . 0% ( 12/25 ) ALB and 43 . 6% ( 17/39 ) TNZ samples . The results of the different quality control tests of the samples are presented in Table 5 and are detailed below . The results of the RPN values after scores assigned for severity , occurrence and detectability of the failure mode are presented in Table 7 . In the quality attributes subjected to FMEA , a total of 5 failure modes with RPN scores ranging from 2 to 512 were identified . Risk analysis showed that assay ( RPN = 512 ) is the most critical quality attribute followed by dissolution ( RPN = 336 ) and dosage uniformity ( RPN 144 ) . Friability was found to be the quality attribute of the least concern according to FMEA analysis applied to product quality assessment . The results of individual desirability values di and the overall desirability D are presented in the S2 supporting information . The individual desirability values assigned to the different segments were fitted to the segmented linear model as indicated in Fig . 4 . For each medicine analyzed for the retained 4 quality attributes ( assay , dissolution , dosage form uniformity and identity ) , a global D was finally calculated using the above mentioned d-functions and evaluated using the psychophysical Harrington's scale of quality as presented in Table 4 . According to this scale , it was revealed that 13 . 2% ( 14/106 ) of the products were excellent , while 22 . 6% ( 24/106 ) were good and 35 . 8% ( 38/106 ) were of acceptable quality . Thirty products ( 28 . 3% ) were found to be of unacceptable quality ( low and bad ) . Moreover , the distribution of the D-values among the investigated products is presented in Fig . 5 .
In general , the prevalence of poor quality medicines was the highest for ALB tablets ( 48 . 0% , 95% CI: 28 . 4 to 67 . 6 ) , followed by MEB ( 45 . 2% , 95% CI: 30 . 2 to 60 . 3 ) and TNZ ( 43 . 6% , 95% CI: 28 . 0 to 59 . 2 ) tablets ( Table 5 ) . Overall , 45% ( 48/106 ) of the analyzed drug samples failed to meet the official tolerance limits for assay , dissolution , friability and uniformity of dose . A similar survey conducted on anti-malarial drugs in Senegal , Madagascar and Uganda identified 44% , 30% , and 26% substandard anti-malarial drugs , respectively [41] . Assay and dissolution profile study for anti-malarial samples conducted in south-east Nigeria reported 37% substandard medicines [42] . Assay based pharmaceutical quality assessment in Kenya reported that many anthelmintic preparations marketed in Kenya were of very poor quality [32] . The probable causes for the presence of poor quality medicines in developing countries like Ethiopia might be due to poor storage conditions , insufficient quality assurance , poor compliance with good manufacturing practice standards , lack of scientific expertise in manufacturing sector , limited technical capacity and insufficiently well developed regulatory system to evaluate and take action to solve the problems related to drug quality [43] . From those drug samples collected from pharmacy , about 51 . 1% ( 24/46 ) failed while 46 . 9% ( 23/55 ) and 20 . 0% ( 1/5 ) were the failure rates for those collected from drug store and wholesale , respectively . Even though the sample size was small to generalize , there was significant difference in the pharmacopoeial quality parameter of medicines between the country of origin ( P<0 . 05 ) but there was no significant association for place of collection and outlets , P>0 . 05 as presented in Table 8 and Fig . 1 . Regarding the collection areas , a high failure rate was observed for samples collected from Addis Ababa , Jimma and Adama areas . Since these areas are commercial centers due to their geographic location , it requires special attention by the regulatory offices to control the circulation of these anthelmintic medicines to combat poor quality medicines circulation . All analyzed samples contained the intended active ingredient . Even though a single case of API-absent medicine is unacceptable , the finding of this study was good as compared to other studies , e . g . in Cambodia ( 4 . 2% ) [44] . However , 29 . 2% ( 31/106 ) of the samples did not comply with the pharmacopoeial acceptance criteria for assay . Of the MEB samples , 45 . 2% were found to be of poor quality with respect to assay as per the official tolerance limit . This result is in agreement with the study conducted in Nigeria's pharmacies in which 48% samples of MEB did not comply with set pharmacopoeial limits [31] . On the other hand , ALB samples showed relatively better compliance but still unacceptable as 8 . 0% did not meet the official acceptance limit for assay . In general , from those drug samples which failed assay test , 19 . 4% ( 6/31 ) were under-dosed . One of the contributing factors for the development of drug resistance is under-dosing due to poor quality medicines [45] . Uniformity of dosage unit is defined as the degree of uniformity in the amount of active substance among individual dosage units . Content uniformity depends on a number of formulations and manufacturing processes , hence it is obviously unrealistic to presume that every unit contains exactly the same amount of the active ingredient as indicated on the label . Therefore , pharmacopoeial standards and specifications have been established to provide generic limits for allowable variations for the active ingredients in single dosage units considering fitness-for-use and production capability considerations [46] . It was previously reported that ( single dose ) ALB is more efficacious against hookworm than ( triple dose ) MEB [47] , which may partly be explained by our quality results revealing that all ALB and TNZ samples fulfill the acceptance criteria for dosage uniformity while 19 . 1% ( 8/42 ) of MEB samples did not meet these pharmacopoeial acceptance criteria . Friability test is conducted to check whether the weight loss during handling is within 1 . 0% loss specification limit . As indicated in Table 5 , 5 ALB , and 3 MEB and 3 TNZ tablet samples failed the pharmacopoeial acceptance criteria of friability . The percent weight loss for all the drug samples failing the specification criteria ranges between 2 . 2 to 6 . 0% , where the largest weight loss was registered from a MEB tablet sample . Taking into consideration the single dose regimen and the already substandard drugs with content less than 90%lc , this maximum weight loss from friability study by MEB sample could further pose more risk of drug resistance leading to treatment failure than the other two drugs , ALB and TNZ . In the present study , since all the drug samples tested for disintegration have met the pharmacopoeial acceptance criteria , there is no risk associated with disintegration as a quality attribute . However , 42% of the ALB samples and 18% of the TNZ samples which were tested for dissolution have been found to be out of the pharmacopoeial specification limit . For low solubility drugs , raw material and process variables could have impact on clinical safety and efficacy through their effects on dissolution . Therefore , the risk of clinical failure is higher for ALB than TNZ as more delayed dissolution was observed , which could be due to changes in the drug substance particle size , failure to control granulation , and increased level of binder in the formulation [48] . The information available on the effectiveness of various BZs derivatives ( e . g . ALB and MEB ) is somewhat inconsistent [49] , [50] . Thus the observations of different therapeutic outcomes have been to some extent attributed to the different polymorphs with different dissolution rates and anthelmintic activities . Solid-state properties play crucial role in dissolution rate and solubility , especially when different polymorphs are involved affecting the in-vivo performance of the drugs [51]–[54] . For example , MEB exists as polymorphs and solvates in the solid state . Of particular importance is the difference in the physicochemical properties of the three known polymorphs A , B , and C . The polymorphic forms of MEB display significant differences in solubility and therapeutic efficacy and form C is preferred clinically due to its optimal bioavailability and reduced toxicity . This is important because polymorph A has no anthelmintic activity alone or when present above 30% in polymorphic mixtures . Literatures indicate that at temperatures typically found in countries located in ICH climatic zones III ( hot and dry ) and IV ( hot and humid ) trace amounts of form A in tablets significantly accelerate the transformation of the clinically active polymorph C to form A . This transformation significantly reduces the shelf lives and the dissolution rates of these tablets [55] . ALB also exhibits some polymorphic forms by forming solvated crystals . Each of these crystals , including the un-solvated form , may exhibit all the aspects of polymorphism . However , solid state characterization of ALB indicated that both forms are physically quite stable [51] . A literature report indicated that TNZ also exhibits crystal polymorphism [56] . Regarding the use of ALB or MEB , specific attention should be given to the dose appropriate for infants ( 12 months and less ) . Apart from the likelihood of both prevalence and intensity being relatively low in infants in areas where soil-transmitted helminthiasis is endemic , there are questions of efficacy and safety when using an anthelmintic drug in very young children [57] . Some studies reveal that the no observed effect level/no observed adverse effect level ( NOEL/NOAEL ) for ALB is 7 mg/kg/day and that of MEB was found to be 7 . 8 and 8 . 4 mg/kg/day in males and females , respectively in experimental animals [58] . Taking the studied ALB tablets , it is possible to assess the associated risk due to the overdosed assay values . The standard treatment guideline for Ethiopia recommends 400 mg tablet as a single dose for treatment of different helminths infections [58] . Assuming an average body weight of 70 kg ( body mass index: 23 and height: 175 cm ) , the NOEL/NOAEL value for ALB can be calculated to be 490 mg per day ( taking a safety factor of 1 ) , equivalent with 122 . 5%lc for a 400 mg tablet . All the assay values for ALB drug products were found to be less than or equal to 111 . 0%lc , indicating absence of clinically significant risk for the ALB overdosed formulation related to adverse effects . For MEB , since the treatment guideline recommends 200 mg per day [59] and the NOEL/NOAEL value is much higher , the over-dose in the assay values is not a direct clinical concern related to adverse effects . The assay distribution of the analyzed TNZ samples was found to be from 86 . 1 to 120 . 6%lc . Considering the 2 g single dose regimen of TNZ for treatment of giardiasis and the high level NOEL/NOAEL value of 150 mg/kg together with the relative clinical safety of TNZ , the over-dose in the assay values is also not a direct clinical concern related to the adverse effects . Under-dosing , which could be caused by degradation due to inappropriate storage conditions , might pose toxicity risks due to the degradant impurities . It can be one of the risk factors for the development of anthelmintic resistance . Sub-optimal regimens are the rule in human treatment: anthelmintics are administered in single doses that never achieve 100% efficacy . Taking into account the limited efficacy of single dose anthelmintic treatments , the currently recommended regimens could constitute a significant contributing factor to the development of anthelmintic resistance in STH [60] . In addition to the single dose regimen , the substandard drugs with content less than 90%lc , could further exacerbate the problem of drug resistance leading to treatment failure . Therefore , the risk of development of drug resistance to MEB is higher than the other two drugs , ALB and TNZ since four of the six under-dosed substandard drug samples were MEB . FMEA is a well-known assessment tool used to identify the critical components most likely to cause failures and to enhance system reliability , through the development of suitable corrective and preventive actions ( CAPAs ) [61] . Typically , the criticality is evaluated either with the criticality number ( CN ) , or with the risk priority number ( RPN ) . Although the CN is considered more consistent and accurate , the RPN approach is generally preferred , especially for its easiness of use [62] , where the higher RPN values indicate the criticality of the quality attribute . Optimizing parameters is a critical issue during the development of any method and/or product . A special set of functions called desirability functions have been used in optimizing methods [63] , [64] and products characteristics [65] , [66]; but the application of such desirability functions for the assessment of the quality of pharmaceutical products is new . The overall desirability function D is obtained from the individual desirabilities ( di ) using Equation 2 . It can provide a way to assess the quality according to one property , the overall D-value . By mapping all properties onto a desirability scale between 0 and 1 , the individual desirability scores due to multiple properties may be easily combined as a geometric mean even if the properties have different scales or units of measurement [67] . In the calculation of the overall D-value using Equation 2 , pi = 3 was used for assay since quality risk associated to it was found to be more important ( RPN = 512 ) . Similarly , pi = 2 was used for dissolution since the risk associated with dissolution was of more concern ( RPN = 336 ) than others . For each of identity and dosage uniformity , pi = 1 was assigned . The risk assessment revealed that friability was not critically important with calculated RPN value of only 2 and thus was not considered for the desirability study . The risk analysis conducted indicated that the failure effects due to the failure modes ( non-complying quality attributes ) was found to be almost similar for the three products analyzed . For example , for all , the over-dose in the assay values was evaluated to be not a direct clinical concern related to the adverse effects . Moreover , since all the three drugs are in BCS class II [26] , dissolution is equally a concern . Therefore , the same Derringer's desirability function was applied to all the drug products . In general , comparing the two quality evaluation approaches , it is reported that 29 . 2% of the samples were of poor quality when using the pharmacopoeial method of quality evaluation , while it is 28 . 3% using the new innovative risk-based desirability function approach . Even though it seems that there is no discrepancy between the results of the conventional and D-function approach , we still want to argue that the D-approach provides more weight to the clinically more critical quality attributes and thus fit-for-purpose in resource-limited economies . Resources could thus be prioritized and reliable decisions can be made on the available data using only the clinically more critical quality attributes ( assay and dissolution ) than the less critical ones ( friability and disintegration tests ) . Moreover , the new QbD and risk-based approach will less heavily penalize marginal out-of-specification medicines , and therefore , we believe it is especially important for poor-resource countries . In conclusion , this study indicated that all sampled products ( MEB , ALB and TNZ ) did contain the stated active ingredient , but poor quality products were identified in all three medicines and collection sites in the country due to non-compliant assays , inadequate drug release of required dose or toxicity concerns due to over-dosage of some of the medicines containing higher level of active ingredient . Over-dose in the assay values of the three studied drugs is not a direct clinical concern related to adverse effects where as under-dosing constituted one of the risk factors for the development of resistance . The study further identified the most critical quality attributes in product quality assessment using FMEA risk-based quality evaluation of the three drugs where assay was found to be the most critical quality attribute with highest RPN . Moreover , it was revealed that Derringer's desirability function can be applied to pharmaceutical quality assessment using Psychophysical Harrington's scale of quality where products could be classified into excellent , good , acceptable , low and bad quality . Our study suggests policy strategies of containing the problems related to poor quality medicines using this proactive risk-based and desirability function approaches in nation-wide surveillance of the quality of medicines circulating in their respective markets . Furthermore , other possible strategies for containing the problem of these poor quality medicines are e . g . :
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Access to medicines of good quality improves the chances of successful treatment for individual patients and promotes better outcomes for public health in general . At present , the prevailing strategy for improving access to medicines for neglected tropical diseases ( NTDs ) is drug donation programs . However , the presence of poor quality medicines in the market is a global threat on public health , especially in developing countries by critically risking efforts of treatment and control of diseases in general and the NTDs in particular . Conventionally , medicine quality has been ignored in NTDs , though scattered reports show that serious problems exist . Therefore , we assessed the quality of two commonly used anthelminthic drugs ( MEB and ALB ) and one antiprotozoal drug ( TNZ ) in Ethiopia . The analytical results were converted into conclusions using two systems: the traditional dichotomous pharmacopoeial specification-compliance based approach and the risk-based Taguchi quantitative desirability approach . Overall , the results showed high prevalence of poor quality of the three medicines , mainly determined by the country of origin . We conclude that risk-based regulatory quality control procedures should be based on identification of the most critical quality attribute and apply desirability functions to quantify and classify the quality of medicines .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"biology",
"and",
"life",
"sciences",
"medicine",
"and",
"health",
"sciences"
] |
2014
|
Quality of Medicines Commonly Used in the Treatment of Soil Transmitted Helminths and Giardia in Ethiopia: A Nationwide Survey
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The human and chimpanzee X chromosomes are less divergent than expected based on autosomal divergence . We study incomplete lineage sorting patterns between humans , chimpanzees and gorillas to show that this low divergence can be entirely explained by megabase-sized regions comprising one-third of the X chromosome , where polymorphism in the human-chimpanzee ancestral species was severely reduced . We show that background selection can explain at most 10% of this reduction of diversity in the ancestor . Instead , we show that several strong selective sweeps in the ancestral species can explain it . We also report evidence of population specific sweeps in extant humans that overlap the regions of low diversity in the ancestral species . These regions further correspond to chromosomal sections shown to be devoid of Neanderthal introgression into modern humans . This suggests that the same X-linked regions that undergo selective sweeps are among the first to form reproductive barriers between diverging species . We hypothesize that meiotic drive is the underlying mechanism causing these two observations .
Despite constituting only 5–6% of the human genome , the human X chromosome is important for elucidating evolutionary mechanisms . Because of its particular inheritance pattern and its cosegregation with the very different Y chromosome , evolutionary forces may act upon it in different ways than on the autosomes [1 , 2] . Thus contrasting the evolution of the X chromosome with that of the autosomes provides clues to the relative importance of different evolutionary forces . Hemizygosity of males implies that there are fewer X chromosomes than autosomes in a population ( 3/4 for even sex ratios ) . Thus , genetic drift is expected to be relatively stronger on the X chromosome . New variants with recessive fitness effects will also be selected for or against more efficiently on the X chromosome , where they are always exposed in males , than on the autosomes , potentially overriding the increased genetic drift . Empirical studies have shown that nucleotide diversity is more reduced around genes on the X chromosome than on the autosomes [3–5] . This has been interpreted as the result of more efficient selection on coding variants on the X chromosome , which affects linked positions around the genes . However , no distinction is made here between linked effects of positive selection ( genetic hitchhiking [6] ) and linked effect of selection against deleterious mutations ( background selection [7] ) . For recessive variants , hitchhiking is expected to be more wide ranging for X chromosomes , whereas a different distribution of fitness effects of deleterious variants on the X is needed to cause stronger background selection on the X . Contrasting non-synonymous and synonymous substitutions with non-synonymous and synonymous polymorphisms , several recent studies have reported evidence for more positive selection on protein changes on the X chromosome in both primates and rodents [8–11] . Whether this is due to hemizygosity , different gene content of the X chromosome , antagonistic selection between sexes being more prevalent on the X chromosome , or some fourth reason is not known . A separate observation is that the X chromosome in most investigated species is disproportionately involved with speciation , as it ( i ) contributes disproportionately to hybrid incompatibility ( the large X effect ) and ( ii ) together with the Y chromosome is responsible for stronger hybrid depression in males than in females ( Haldane’s rule ) . We refer to Laurie ( 1997 ) [12] and Schilthuizen , Giesbers and Beukeboom ( 2011 ) [13] for several non-exclusive hypotheses for the underlying genetic mechanisms leading to Haldane’s rule . Recent introgression from Neanderthals into modern humans was recently reported to be far less common on the X chromosome than on the autosomes . This can be interpreted as evidence for emerging incompatibilities between the two species preferentially residing on the X chromosome [14] . It has been suggested that incompatibilities can accrue due to genetic conflicts between the X and the Y [15–19] and some hybrid incompatibility factors in Drosophila do show evidence of causing meiotic drive [20] . We , and others , have previously reported that the X chromosome shows much less divergence between humans and chimpanzees than expected from autosomal divergence [21–23] . This observation is not based on the nucleotide divergence of the X chromosome versus the autosomes—which will be affected by a difference in mutation rate—but on estimating the effective population size of the ancestral species from the proportion of discordant gene trees . Because the speciation event between human and chimpanzee and the speciation event between the human-chimpanzee ancestor and the gorilla occurred close in time , around 30% of the autosomal genome shows a gene tree different from the species tree—a phenomenon called incomplete lineage sorting ( ILS ) . The expected amount of ILS depends on the difference between the two speciation times and the effective population size in the human-chimpanzee ancestor . For estimates of the two speciation times in question [24] , and assuming that the effective population size of the X chromosome is three quarters of that of the autosomes , the X chromosome is expected to show 24% of ILS . The observed mean amount of ILS , however , is around 15% . We recently reported that certain regions of the X chromosome in different great ape species often experience what looks like very strong selective sweeps [18] . Here we study the amount of incomplete lineage sorting between human , chimpanzee and gorilla along the X chromosome . We observe a striking pattern of mega-base sized regions with extremely low amounts of ILS , interspersed with regions with the amount of ILS expected from the effective population size of the X chromosome ( that is , three quarters that of the autosomes ) . We show that the most plausible explanation is several strong selective sweeps in the ancestral species to humans and chimpanzees . The low-ILS regions overlap strongly with regions devoid of Neanderthal ancestry in the human genome , which suggests that selection in these regions may create reproductive barriers . We propose that the underlying mechanism is meiotic drive resulting from genetic conflict between the sex chromosomes , and that this is caused by testis expressed ampliconic genes found only on sex chromosomes and enriched in the regions where we find signatures of selective sweeps .
To explore the pattern of human-chimpanzee divergence across the full X chromosome we performed a detailed analysis of the aligned genomes of human , chimpanzee , gorilla and orangutan [21] . Using the coalescent hidden Markov model ( CoalHMM ) approach [25] , we fitted a model of speciation by isolation , with constant but distinct ancestral effective population sizes for the human-chimpanzee ( HC ) and the human-chimpanzee-gorilla ( HCG ) ancestors . The parameters of the model are ( i ) two speciation times τHC and τHCG for human vs . chimpanzee and for HC vs . gorilla , respectively , ( ii ) two ancestral population sizes θHC and θHCG for the HC and HCG ancestral populations , respectively , as well as the recombination rate r assumed to be constant along both the alignment and phylogeny . An additional parameter is used to account for the divergence with the outgroup sequence . The speciation time , effective population size and recombination rate parameters are scaled according to 2 . Ne . u . g , 2 . Ne . u and u , respectively , where u is the mutation rate per generation , g the generation time and Ne the population size of a reference extant species [22 , 25] . Extant population sizes are not parameters of the model , and only serve for the purpose of scaling parameters . To account for putative variation of parameters along the genome alignment , we estimated demographic parameters in non-overlapping 1 Mb windows . We inferred the proportion of ILS using posterior decoding averaged over each of these 1Mb windows . The expected proportion of ILS in a 3-species alignment is given by the formula: Pr ( ILS ) =23×exp ( −Δτθ ) where Δτ is the difference in speciation times and θ is the ancestral effective population size of the two most closely related species [26 , 24] ( see also [27] ) . Estimates of these parameters from the gorilla genome consortium are Δτ = 0 . 002468 and θ = 0 . 003232 [21] . From these parameters , the expected mean proportion of ILS is 31 . 06% . The observed distribution of ILS proportions on autosomes follows a negatively skewed normal distribution , with a mean of 30 . 58% ( Figs 1A and S1 for individual chromosome distributions ) . Assuming that the ancestral effective population size of the X chromosome , θX , is three quarters that of the ancestral effective population size of the autosomes , the expected amount of ILS on the X chromosome should be 24 . 08% . The distribution of ILS proportions on the X chromosome is bimodal ( Fig 1B ) and in stark contrast to the distribution on the autosomes ( see also S1 Fig for a breakdown on individual autosomes ) . One mode represents 63% of the alignment , with a mean proportion of ILS of 21% , close to the expectation of 24% ( the 99% confidence interval of the high ILS mode is [17 . 6% , 24 . 5%] , estimated using parametric bootstrap ) . The second mode is estimated to represent 37% of the alignment and shows a mean proportion of ILS below 5% . The regions exhibiting low ILS form 8 major segments spread across the X chromosome ( Table 1 and Fig 2A ) and cover 29 Mb out of a total alignment length of 84 Mb . Region X5 is split in two by the centromeric region , where alignment data are missing . Regions with comparatively low amount of ILS have a higher frequency of genealogy where the human and chimpanzee coalesce within the HC ancestor , while in ILS genealogies , the human and chimpanzee lineages coalesce further back in time , within the HCG ancestor . As a result , low-ILS regions display a lower divergence compared to the rest of the genome . These results are two-fold: ( i ) they demonstrate that one third of the X chromosome explains the previously reported low divergence of the chromosome , as the remaining two thirds display a divergence compatible with the expectation under a simple model of divergence with an ancestral effective population size equal to three quarters that of the autosomes and ( ii ) that unique evolutionary forces have shaped the ancestral diversity in the low-ILS regions . In Scally et al . [21] , we independently estimated parameters in non-overlapping windows of 1 Mb , allowing for parameters to vary across the genome . To test whether inference of very low proportions of ILS could result from incorrect parameter estimation , we compared the inferred amount of ILS under alternative parameterizations with that inferred using fixed parameters ( either fixing all parameters or fixing speciation time parameters only ) along the genome . These alternative parameterizations result in very similar estimates of ILS ( S2 Fig and corresponding UCSC genome browser tracks at http://bioweb . me/HCGILSsupp/UCSCTracks/ ) . We addressed the possibility that our observation is due to a lower power to detect ILS in the identified regions resulting from reduced mutation rate . We counted the number of informative sites supporting each of the three alternative topologies connecting humans , chimpanzees and gorillas in non-overlapping 100 kb windows along the alignment . If the reduction of ILS is due to a lower mutation rate in these regions , we expect to observe a reduction of the amount of parsimony-informative sites supporting all three topologies . While the total frequency of parsimony-informative sites is significantly lower in the low-ILS regions compared with the rest of the genome ( 0 . 00270 vs . 0 . 00276 , Fisher's exact test p-value = 1 . 34e-05 ) , there is a highly significant excess of sites supporting the species topology ( 0 . 00229 vs . 0 . 00210 , Fisher's exact test p-value < 2 . 2e-16 ) and deficit of sites in these regions supporting ILS topologies ( 0 . 00042 vs . 0 . 00066 , Fisher's exact test p-value < 2 . 2e-16 , Fig 2B and 2C ) , which suggests that the observed reduction of ILS is not the result of a lower mutation rate . We computed the ratio of human-chimpanzee divergence to human-gorilla divergence and human-orangutan divergence in 100 kb windows . Assuming a constant mutation rate across the phylogeny and constant ancestral effective population sizes along the genome , these ratios should be on average identical between regions from the genome . In regions with reduced ILS , however , this ratio is expected to be lower because of a more recent human-chimpanzee divergence . In agreement with this latter hypothesis , we observe a significant lower ratio of divergences in low-ILS regions ( Fig 2D ) . A lower mutation rate in these regions would explain this pattern only if the reduction is restricted to the human-chimpanzee lineage . Deleterious mutations are continuously pruned from the population through purifying selection , reducing the diversity of linked sequences . Such background selection potentially plays an important role in shaping genetic diversity across the genome [28] . The strength of background selection increases with the mutation rate , with density of functional sites , with decreasing selection coefficient against deleterious mutations , and with decreasing recombination rate [29] . Low-ILS regions display both a 0 . 6-fold lower recombination rate compared to the rest of the chromosome ( 1 . 01 cM/Mb versus 1 . 62 cM/Mb , Wilcoxon test p-value = 2 . 2e-07 ) as well as a two-fold higher gene density—a proxy for the proportion of functional sites ( 3 . 1% exonic sites versus 1 . 5% on average , Wilcoxon test p-value < 2 . 2e-16 ) . Background selection is therefore both expected to be more common ( by a factor of ~2 . 1 due to more functional sites ) and to affect larger regions ( by a factor of ~1 . 8 due to less recombination ) in the low-ILS regions . To estimate extent to which this may explain our observations , we used standard analytical results that estimate the combined effect of multiple sites under purifying selection ( see Material and Methods ) . Even if we assume that the proportions of functional sites in the candidate regions is two times higher than the observed number of exon base pairs , and that all mutations at these sites are deleterious with a selection coefficient that maximizes the effect of background selection , the expected proportion of ILS should only be reduced by approximately 10% relative to the level found on the remaining X chromosome ( 19% ILS compared to 21% ILS ) . To explain the observed reductions in ILS by background selection alone , unrealistic differences of functional site densities are required ( e . g . 50% inside identified regions and 10% outside , see Figs 3 and S2 ) . As a further line of evidence , we computed the maximal expected reduction of ILS based on the observed density of exonic sites and average recombination rate ( see Methods ) . We find that only 79 of 252 analyzable windows ( 31% ) could be explained by the action of background selection only , an observation incompatible with the hypothesis that background selection is the sole responsible for the widespread reduction of ILS along the X chromosome . Finally , recombination rate is lower in males than in females . As X chromosomes spend 2/3 of their time in highly recombining females while autosomes spend only half , background selection is expected to be weaker on the X chromosome than on the autosomes . Consequently , in Drosophila where males do not recombine , X chromosomes display a higher than expected diversity [30] . The fact that we do not observe large regions devoid of ILS on the autosomes further argues against background selection as the major force creating the observed large regions with reduced ILS on the X chromosome . Adaptive evolution may also remove linked variation during the process of fixing beneficial variants . In the human-chimpanzee ancestor , such selective sweeps will have abolished ILS at the locus under selection and reduced the proportion of ILS in a larger flanking region . Several sweeps in the same region can thus result in a strong reduction of ILS on a mega-base scale . We simulated selective sweeps in the human-chimpanzee ancestor using a rejection sampling method ( see Material and Methods ) . A single sweep is only expected to reduce ILS to less than 5% on a mega-base wide region if selection coefficients are unrealistically high ( s > 0 . 2 ) , suggesting that several sweeps have contributed to the large-scale depletions of ILS ( Figs 4 and S4 ) . If the low-ILS regions are indeed subject to recurrent sweeps , they are expected to also show reduced diversity in human populations . We therefore investigated the patterns of nucleotide diversity in the data of the 1000 Genomes Project [31] . We computed the nucleotide diversity in 100 kb non-overlapping windows along the X chromosome and compared windows within and outside low-ILS regions . Fig 5 summarizes the results for the CEU , JPT and YRI populations ( results for all populations are shown in S5 Fig ) . We find that diversity is significantly reduced in all low-ILS regions compared with the chromosome average ( Table 2 ) , and this reduction is on average significantly greater in the Asian and European populations than in the African population ( analysis of variance , see Material and Methods ) . This global difference in magnitude could be explained by phenomena such as sex-biased demography or generation time and population structure during the migration out of Africa [32] . We also compared the eight low-ILS regions separately , and reported differences between regions ( Table 3 ) . Plotting population specific diversity across the X chromosome revealed several cases of large-scale depletions of diversity in both Europeans and East Asians . While these depletions affect similar regions , their width differs between populations . This finding suggests that strong sweeps in these regions occurred independently in the European and East Asian population after their divergence less than 100 , 000 years ago .
Using a complete genome alignment of human , chimpanzee , gorilla and orangutan , we report that the human-chimpanzee divergence along the X chromosome is a mosaic of two types of regions: two thirds of the X chromosome display a divergence compatible with the expectation of an ancestral effective population size of the X equal to three quarters that of the autosome , while one third of the X chromosome shows an extremely reduced divergence , and is virtually devoid of incomplete lineage sorting . We have demonstrated that such diversity deserts cannot be accounted for by background selection alone , but must result from recurrent selective sweeps . We recently reported dramatic reductions in X chromosome diversity in other great ape species that almost exclusively affect areas of the low-ILS regions [18] ( see S6 Fig ) . If the low-ILS regions evolve rapidly through selective sweeps , they could be among the first to accumulate hybrid incompatibility between diverging populations . Recently , the X chromosome was reported to exhibit many more regions devoid of Neanderthal introgression into modern humans than the autosomes . This suggests an association of negative selection driven by hybrid incompatibility with these X-linked regions [14] . We find a striking correspondence between regions of low ILS and the regions devoid of Neanderthal introgression for European populations ( p-value = 0 . 00021 , permutation test ) and a marginally significant association with the more introgressed Asian populations ( p-value = 0 . 06721 , Fig 5 ) . Taken together , these findings show that the regions on the X chromosome that contributed to hybrid incompatibility in the secondary contact between humans and Neanderthals have been affected by recurrent , strong selective sweeps in humans and other great apes . The occurrence of a secondary contact between initially diverged populations , one of which diverged into modern chimpanzees and the other admixed with the second to form the ancestral human lineage—the complex speciation scenario of Patterson et al . [23]–is also compatible with our observations: if these regions evolved to be incompatible , the lineages within the regions only came from the ancestral population related to chimpanzees while lineages outside the regions come from both ancestral populations , so that we would also expect to see reduced ILS within the regions and not outside the regions . However , such a complex speciation scenario does not explain the observed large-scale reductions of diversity in extant species . Conversely , a scenario consisting only of recurrent sweeps would explain both the divergence patterns along the human and chimpanzee X chromosomes and the reduction of extant diversity , without the need for secondary introgression . To explain the occurrence of recurrent selective sweeps in the lineage of great apes , we propose a hypothesis that may account for the generality of our findings: Deserts of diversity may arise via meiotic drive , through which fixation of variants that cause preferential transmission of either the X or Y chromosome produces temporary sex ratio distortions [17] . When such distortions are established , mutations conferring a more even sex ratio will be under positive selection . Potential candidates involved in such meiotic drive are ampliconic regions , which contain multiple copies of genes that are specifically expressed in the testis . These genes are postmeiotically expressed in mice , and a recent report suggests that the Y chromosome harbors similar regions [33] . Fourteen of the regions identified in humans [34] are included in our alignment , 11 of which are located in low-ILS regions ( Figs 2 and 5 ) , representing a significant enrichment ( p-value = 0 . 01427 , permutation test ) , a result which is even more significant when regions in the centromeric region are included ( p-value = 0 . 00642 ) . Whatever the underlying mechanism , our observations demonstrate that the evolution of X chromosomes in the human chimpanzee ancestor , and in great apes in general [18] , is driven by strong selective forces . The striking overlap between the low-ILS regions we have identified and the Neanderthal introgression deserts identified by Sankararaman et al . [14] further hints that these forces could be driving speciation .
The Enredo/Pecan/Ortheus genome alignment of the five species human , chimpanzee , gorilla , orangutan and macaque from Scally et al . [21] was used as input . In order to remove badly sequenced and / or ambiguously alignment regions , we filtered the input 5-species alignments using the MafFilter program [35] . We sequentially applied several filters to remove regions with low sequence quality score and high density of gaps . Details on the filters used can be found in the supplementary material of Scally et al . [21] The divergence of two genomes depends on both the mutation rate and underlying demographic scenario . With a constant mutation rate u and simple demography ( constant sized panmictic population evolving neutrally ) , the time to the most recent common ancestor of two sequences sampled from different species is given by a constant species divergence , τ = T . u , and an ancestral coalescence time following an exponential distribution with mean θ = 2 . NeA . u , where T is the number of generations since species divergence and NeA is the ancestral effective population size [22 , 36] . For species undergoing recombination , a single individual genome is a mosaic of segments with distinct histories , and therefore displays a range of divergence times [22 , 23 , 37] . When two speciation events separating three species follow shortly after each other , this variation of genealogy can lead to incomplete lineage sorting ( ILS ) , where the topology of gene trees do not correspond to that of the species tree [22 , 26] . Reconstructing the distribution of divergence along the genome and the patterns of ILS allows inference of speciation times and ancestral population sizes . We used the CoalHMM framework to infer patterns of ILS along the X chromosome . Model fitting was performed as described in [21] . ILS was estimated using posterior decoding of the hidden Markov model as the proportions of sites in the alignment which supported one of the ( HG ) , C or ( CG ) , H topologies . All parameter estimates can be visualized in the UCSC genome browser using tracks available at http://bioweb . me/HCGILSsupp/ . For the autosomal distribution of ILS , we fitted a skewed normal distribution ( R package 'sn' [38] ) using the fitdistr function from the MASS package for R . For the X chromosome ILS distribution , we fitted a mixture of gamma and Gaussian distributions . The mixed distribution follows a normal density with probability p , and a gamma density with probability 1-p . In addition to p , the mixed distribution has four parameters: the mean and standard deviation of the Gaussian component , and the shape and rate of the gamma component . The L-BFGS-B optimization method was used to account for parameter constraints . Resulting parameter estimates are 0 . 209 for the mean of the Gaussian component , 0 . 066 for the standard deviation of the Gaussian component , 4 . 139 for the alpha parameter ( shape ) of the gamma component , 83 . 369 for the beta parameter ( rate ) of the gamma component , and p = 0 . 632 . The mean of the gamma component is alpha / beta = 0 . 0497 , that is , less than 5% ILS . We compared the resulting fit with a mixture of skewed normal distributions , which has two extra parameters compared to a Gamma-Gaussian mixture , and found that the skew of the higher mode is very close to zero , while the Gamma distribution offered a better fit of the lower mode . We used a parametric bootstrap approach to estimate the confidence interval of the proportion of ILS for the mean of the normal component of the mixed distribution . We generated a thousand pseudo-replicates by sampling from the estimated distribution , and we re-estimated all parameters from each replicate in order to obtain their distribution . Replicates where optimization failed were discarded ( 40 out of 1000 ) . In order to characterize the patterns of ILS at a finer scale , we computed ILS in 100 kb windows sliding by 20 kb along the posterior decoding of the alignment . To exhibit regions devoid of ILS , we selected contiguous windows with no more than 10% of ILS each . Eight of these regions were greater than 1 Mb in size , and their resulting amount of ILS is less than 5% on average ( Table 1 ) . The coordinates of these regions were then translated according to the human hg19 genome sequence . These data are available as a GFF file for visualization in the UCSC genome browser at http://bioweb . me/HCGILSsupp/ . Background selection reduces diversity by a process in which deleterious mutations are continuously pruned from the population . The strength of background selection in a genomic region is determined by the rate at which deleterious mutations occur , U , the recombination rate of the locus , R , and the strength of negative selection on mutants , s . We consider the diversity measure , π ( the pairwise differences between genes ) which in a randomly mating population is linearly related to the effective population size . If π0 denotes diversity in the absence of selection and π the diversity in a region subject to background selection , then the expected reduction in diversity is given by ππ0=exp ( −Us+R ) ( 1 ) ( see Durrett [39] equation ( 6 . 24 ) ) The rates U and R are both functions of the locus length ( U = uL and R = rL ) where r denotes the per-nucleotide-pair recombination rate , u the per-nucleotide deleterious rate , and L the length of the locus . To investigate if background selection can explain the observed reductions in ILS we must compute the expected reduction in diversity in the low-ILS regions relative to the reduction in the remaining chromosome . A larger reduction in low-ILS regions may be caused by weaker negative selection , higher mutation rate , lower recombination rate , and larger proportion of functional sites at which mutation is deleterious . To model the variation of these parameters inside and outside low-ILS regions we simply add a factor to each relevant variable . The relative reduction can thus be expressed as: πlow−ILSπgenome=exp ( Us+R ) exp ( fu . Ufs . s+fR . R ) ( 2 ) The recombination rate , R , and the factor , fR , can be obtained from the deCODE recombination map [40] . We computed the average deCODE recombination rate , as well as the proportion of sites in exons ( as a measure of selective constraint ) in non-overlapping 100 kb along the human X chromosome . The recombination rate average outside the low ILS regions is 1 . 62 cM/Mb and the recombination rate inside the regions is 1 . 01 cM/Mb which gives us fR = 0 . 6 . For the remaining parameters , s and U , we need to identify realistic values outside the low-ILS regions . Background selection is stronger when selection is weak , but the equation is not valid for very small selection values where selection is nearly neutral . Once s approaches 1/Ne , we do not expect any background selection . Most stimates of effective population sizes , Ne , in great apes are on the order 10 , 000–100 , 000 and this puts a lower limit on relevant values of s at 10−4–10−5 . To conservatively estimate the largest possible effect of background selection we explore this range of selection coefficients: s = 10−4 and s = 10−5 and allow the selection inside the low ILS regions to be one tenth ( fs = 0 . 1 ) of that outside . For U values outside low-ILS regions we assume the mean human mutation rate , estimated to be 1 . 2·10−8 per generation [41] . To obtain the rate of deleterious mutation we must multiply this with the proportion of sites subject to weak negative selection , d . Although this proportion is subject to much controversy it is generally believed to be between 3% and 10% [42] . However , as explained below we explore values up to 100% inside the low-ILS regions . We assessed the relative diversity for combinations of s and d values ( S3 Fig ) . Each cell represents a combination of parameter values for s , d , fU and fs . The reduction of diversity Δπ translates into reduction of ILS , ΔILS ( Fig 3 ) . Assuming the time between speciation events , the generation time and population size reported in Scally et al . [21] ( ΔT = 2 , 250 , 000 years , g = 20 ) ILS is given by ILS=23exp ( −ΔT/g3/4×π ) ( 3 ) and the relative ILS is given by ILSILS0=exp ( ΔT/g3/4 ( 1π0−1π ) ) . ( 4 ) For the most extreme parameter values , we see a relative reduction in ILS of nearly 100% . In these cases , however , 100% of the nucleotides within low-ILS regions are under selection . In the cases where 25% of the nucleotides in the low-ILS regions are under selection compared to 5% outside ( fU = 5 , d = 0 . 05 ) , the regions retain more than half of the diversity seen outside the regions . We further computed the expected reduction of ILS due to background selection in 100 kb windows located in low-ILS regions using ( eq 4 ) . For each window , we computed the frequency of sites in exons and the average deCODE recombination rate . We further assumed a selection coefficient s = 10−5 and allow the selection inside the low ILS regions to be one tenth ( fs = 0 . 1 ) . Out of 285 windows located in low-ILS regions , we could estimate the maximal reduction of ILS due to background selection in 252 windows for which a deCODE recombination estimate was available . In 79 of these windows only the expected reduction matched the observed one of 0 . 20 . To assess how hard and soft sweeps in the human-chimpanzee ancestor can have reduced the proportion of ILS we simulated sweeps for different combinations of selection coefficients , s , and frequencies of the selected variant at the onset of selection , f . Frequency trajectories of selected variants are obtained using rejection sampling to obtain trajectories that fix in the population . Trajectories used to simulate hard sweeps begin at one and proceed to fixation at 2N * 3/4 by repeated binomial sampling with probability parameter Nmut/ ( Nmut + ( N − Nmut ) ( 1-s ) ) , where Nmut is the number of selected variants in the previous generation . We use a human-chimpanzee speciation time of 3 . 7 Myr , a human-gorilla speciation time of 5 . 95 Myr , a human-chimpanzee effective population size of 73 , 200 as reported in [21] , assuming a mutation rate of 1e-9 and a generation time of 20 years . Trajectories used to simulate soft sweeps are constructed by joining two trajectories . If f is the frequency of the variant at the onset of selection F = f * 2N * 3/4 is the number of variants . We first sample a trajectory that represents the time before the onset of selection . This trajectory is required to reach F at least once before it fixes or is lost , and is truncated randomly at one of the points where it passes the value F . The truncated trajectory is then appended with a trajectory under selection that begins at F and proceeds to fixation . In each simulation we consider a sample of two sequences that represent 10 cM . As the effect of the sweep is symmetric we only simulate one side of the sweep . We then simulate backwards in the Wright-Fisher process with recombination allowing at most one recombination event per generation per lineage but allowing mergers of multiple lineages expected to occur in strong sweeps . The simulation proceeds until all sequence segments have found a most recent common ancestor ( TMRCA ) . For each combination of parameters s and f we perform 1 , 000 simulations and the mean TMRCA is computed in bins of 10 kb . In each simulation individual sequence segments are called as ILS with probability 2/3 if the TMRCA exceeds the time between the speciation events . The width of the region showing less than 5% ILS is then computed for each simulation . In Figs 4 and S3 a recombination rate of 1 cM/Mb is assumed to translate to physical length . We computed the nucleotide diversity in 100 kb non-overlapping windows along the X chromosome for the 14 populations from the 1 , 000 genomes project . The windows in each low-ILS region were compared to windows outside the regions using a Wilcoxon test with correction for multiple testing [43] ( Table 2 ) . We computed the relative nucleotide diversity in the 1 , 298 windows located in low-ILS regions by dividing by the average of the rest of the X chromosome . Each population was further categorized according to its origin , Africa , America , Asia or Europe [31] . A linear model was fitted after Box-Cox transformation: BoxCox[RelativeDiversity] ~ ( Region / Window ) * ( PopulationGroup / Population ) where Window is the position of the window on the X chromosome , and is therefore nested in the ( low-ILS ) Region factor . Analysis of variance reeals a highly significant effect of the factors Region and Window ( p-values < 2e-16 ) , PopulationGroup ( p-value < 2e-16 ) and their interactions ( p-value < 2e-16 ) . The nested factor Population however was not significant , showing that the patterns of relative diversity within low-ILS regions are similar between populations within groups . A Tukey's Honest Significance Difference test ( as implemented in the R package 'agricolae' ) was performed on the fitted model and further revealed that European and Asian diversity are not significantly different , while they are different from African and American diversity . In order to test the association of low-ILS regions with other genomic features , we developed a Monte-Carlo simulation procedure . In such a test , we wanted to compare a set of "reference" intervals with a set of "query" intervals . The null hypothesis is that the query intervals are independent of the reference intervals . We use the size of the overlap of the two sets of intervals as a statistic . During the randomization procedure , the set of query intervals is shuffled , so that each interval is conserved in length , only the relative order and positions of intervals are changed . Intervals are not allowed to overlap , so that the size of the query set is constant through simulations and identical to the observed one . The distance between two intervals is however allowed to be zero . For each simulation , the size of the overlap with the reference set of intervals is computed . A p-value is calculated by counting the number of simulations with an overlap at least equal to the observed one . In order to randomize intervals , we developed the following procedure: 1 ) compute the total size S of the chromosome not included in any interval of the query set; 2 ) draw n breakpoints uniformly between 0 and S , where n in the number of intervals in the query set; 3 ) insert randomly one query interval at each breakpoint . This procedure has the advantage that it keeps the structure of the reference set , so that the putative auto-correlation of reference intervals along the genome is accounted for . The 'intervals' R package was used for handling intervals and computing their overlap , and 100 , 000 randomizations were performed for each test . We applied the randomization test to the two sets of Neanderthal introgression free regions for European and Asian populations , as well as for the ampliconic regions . The coordinates of ampliconic regions tested in [34] were translated to hg19 using the liftOver utility from UCSC . Fourteen regions were included in our alignment . For all tests , the set of low-ILS regions was used as a query set . For ampliconic regions , we performed a second test where ampliconic regions located close to the centromere and not included in our alignment were discarded .
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Because the speciation events that led to human , chimpanzee and gorilla were close in time , the genetic relationship of these species varies along the genome . While human and chimpanzee are the closest related species , in 15% of the genome , human and gorilla are more closely related , and in another 15% of the genome the chimpanzee and gorilla are more closely related—a phenomenon called incomplete lineage sorting ( ILS ) . The amount and distribution of ILS can be predicted using population genetics theory and is affected by demography and selection in the ancestral populations . It was previously reported that the X chromosome , in contrast to autosomes , has less than the expected level of ILS . Using a full genome alignment of the X chromosome , we show that this low level of ILS affects only one third of the chromosome . Regions with low level of ILS also show reduced diversity in the extant populations of human and great apes and coincide with regions devoid of Neanderthal introgression . We propose that these regions are targets of selection and that they played a role in the formation of reproductive barriers .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Strong Selective Sweeps on the X Chromosome in the Human-Chimpanzee Ancestor Explain Its Low Divergence
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The global transcriptional regulator , CodY , binds strongly to the regulatory region of the braB gene , which encodes a Bacillus subtilis branched-chain amino acid ( BCAA ) permease . However , under conditions that maximize CodY activity , braB expression was similar in wild-type and codY null mutant cells . Nonetheless , expression from the braB promoter was significantly elevated in cells containing partially active mutant versions of CodY or in wild-type cells under growth conditions leading to intermediate levels of CodY activity . This novel pattern of regulation was shown to be due to two opposing mechanisms , negative and positive , by which CodY affects braB expression . A strong CodY-binding site located downstream of the transcription start point conferred negative regulation by direct interaction with CodY . Additionally , sequences upstream and downstream of the promoter were required for repression by a second pleiotropic B . subtilis regulator , ScoC , whose own expression is repressed by CodY . ScoC-mediated repression of braB in codY null mutants cells was as efficient as direct , CodY-mediated repression in wild-type cells under conditions of high CodY activity . However , under conditions of reduced CodY activity , CodY-mediated repression was relieved to a greater extent than ScoC-mediated repression was increased , leading to elevated braB expression . We conclude that restricting increased expression of braB to conditions of moderate nutrient limitation is the raison d’être of the feed-forward regulatory loop formed by CodY and ScoC at the braB promoter . The increase in BraB expression only at intermediate activities of CodY may facilitate the uptake of BCAA when they are not in excess but prevent unneeded BraB synthesis when other BCAA transporters are active .
BraB is one of three permeases demonstrated to be involved in the uptake of branched-chain amino acids ( BCAA ) in Bacillus subtilis [1] . Given the important role of BCAA in cell metabolism , it is not surprising that the synthesis of the permeases is strictly regulated and coordinated . The most efficient BCAA permease , BcaP , is subject to very strong transcriptional repression by CodY [2] , a global regulator in B . subtilis and other Gram-positive bacteria [3 , 4] . A second permease , BrnQ , is subject to strong repression by AzlB , a member of the AsnC/Lrp family of transcriptional regulators , in response to an as yet unidentified signal [5] . The regulation of BraB synthesis has not been previously determined . A fragment containing the regulatory region between the divergently transcribed iscSB ( formerly nifZ ) and braB genes was found to bind CodY strongly in vivo in a ChIP-to-chip experiment [6] . Moreover , a strong CodY-binding site in the iscSB-braB intergenic region was also detected in vitro during the global characterization of CodY-binding sites by IDAP-Seq [7] . The latter site is well-placed to serve as a potential site of regulation of braB . However , transcription of neither braB nor iscSB was altered >2 . 0-fold by a null mutation in codY , as detected in DNA microarray or RNA-Seq experiments [6 , 8] ( http://www . genome . jp/kegg/expression/ ) . CodY controls directly or indirectly the transcription of more than 200 B . subtilis genes [7 , 8] . The DNA-binding affinity of CodY from B . subtilis and many other species is increased by interaction with two types of ligands , the BCAA [isoleucine , leucine , and valine ( ILV ) ] [9–11] and GTP [6 , 11–14] . Thus , given the presence of CodY-binding sites in the putative braB regulatory region and the influence of BCAA on CodY activity , it was surprising that expression of braB was only minimally affected by a codY null mutation . We describe here a detailed analysis of the mechanisms by which CodY regulates braB . The braB gene proved to be directly repressed by two proteins , CodY and another pleiotropic regulator , ScoC ( formerly known as hpr or catA ) [15–18] . Because CodY also represses scoC [19] , CodY and ScoC form a feed-forward regulatory loop [20 , 21] in which CodY acts an indirect positive regulator of braB . The opposing effects of fully active CodY balance each other; as a result , braB derepression could only be observed at intermediate levels of CodY activity or when both regulators are inactive . These findings emphasize that the phenotypes caused by null mutations in global regulatory protein genes can be misleading .
The unexpected absence of an effect of a codY null mutation on expression of a gene with a strong CodY-binding site in its putative regulatory region led us to analyze braB transcription in more detail . A primer extension experiment established that the 5’ end of the braB mRNA is located 72 bp upstream of the initiation codon . The sequences TTGACT and TATAAT , with one and no mismatches to the –35 and –10 regions of σA-dependent promoters , respectively , and a 16-bp spacer region , can be identified upstream of the 5’ end location , suggesting that this position does in fact correspond to the transcription start point ( Fig 1A ) . ( Since B . subtilis σA-dependent promoters rarely have a 16-bp spacer , our assignment of the -10 and -35 regions may be off by 1 or 2 bp . ) A mutation , T ( -29 ) C , located immediately downstream of the likely -35 region , reduced expression of a braB-lacZ fusion 6-fold ( 1 . 97±0 . 35 Miller units , see below ) , consistent with our assignment of the promoter . DNase I footprinting experiments showed that CodY protected two sites , I and II , within the 194-bp iscSB-braB intergenic region from positions -62 to -47 and +11 to +50 of the template DNA strand with respect to the braB transcription start point , respectively ( Figs 1A , 2B and 2C ) . Site II is much stronger than site I . Binding to an additional very weak site , III , from positions –143 to –124 , which is within the upstream iscSB gene , was observed only at high concentrations of CodY ( ≥200 nM ) ( Fig 2B and 2C ) . The results of the footprinting experiments are consistent with the identification of a strong CodY-binding site in this area by ChIP-to-chip experiments [6] . Moreover , they confirmed and extended the results of the in vitro IDAP-Seq experiments , which identified a strong core binding site from positions +29 to +43 , a much weaker core site , which ends at position -45 , and an additional , very weak core site , ending at position -116 and detected only at a very high CodY concentration ( 1 μM ) ( core sites only include positions that are essential for CodY binding; the beginning positions of the two upstream core sites could not be determined due to limitations of the IDAP-Seq procedure ) [7] . The braB regulatory region contains five 15-bp motifs , which resemble the 15-bp CodY-binding consensus sequence , AATTTTCWGAAAATT [22–24] ( we use the terms “site” and “motif” to describe an experimentally determined location of CodY binding and a 15-bp sequence that is similar to the consensus motif , respectively ) . Site I of the braB gene overlaps CodY-binding motif 1 , located between positions -64 and -50 , that has 4 mismatches with respect to the CodY-binding consensus ( Fig 1A and Table 1 ) . The strong site II overlaps two adjoining versions of the 15-bp sequence , motifs 2 and 3 , located between positions +14 and +43 , each of which has three mismatches with respect to the consensus motif . Another 15-bp sequence , motif 4 , with four mismatches is located from positions +40 to +54 and overlaps motif 3 by 4 bp . Site III overlaps CodY-binding motif 5 , with 5 mismatches , located from positions -141 to -127 ( Fig 1A and Table 1 ) . Binding of CodY to upstream braB sites occurred independently of the presence of the downstream site and vice versa ( Figs 1 and 3; see below for generation of the truncated fragments ) , similar to the case for other genes containing multiple CodY-binding sites within their regulatory regions [2 , 25 , 26] . In gel-shift experiments , CodY bound to DNA fragments containing only sites III and I ( braB156 ) or only site II ( braB144 ) with apparent dissociation constants ( KD ) of ∼75 nM and ∼4 nM , respectively , compared with ∼3 nM for the full-length fragment , braB242 ( Fig 4A , 4B and 4D ) ( KD reflects the CodY concentration needed to shift 50% of DNA fragments under conditions of vast CodY excess over DNA ) . Complexes with lower mobility were formed at higher concentrations of CodY for all fragments , indicating apparent changes in stoichiometry of CodY binding ( Fig 4 ) . We constructed a transcriptional fusion ( braB242-lacZ ) containing a 242-bp fragment that includes the entire iscSB-braB intergenic region ( Fig 1 ) . Under conditions of maximal CodY activity , in cells grown in TSS glucose–ammonium medium supplemented with ILV and a mixture of 13 other amino acids ( referred to here as the 16 aa-containing medium ) , fusion expression in a codY null mutant strain was very similar ( 1 . 3-fold less ) to that in the wild-type strain ( Table 2 , strains BB3076 and BB3079 ) . Consistent with the lacZ fusion results , only very weak , positive regulation ( 1 . 6- to 1 . 9-fold ) in amino acid-containing medium was detected in microarray or RNA-Seq experiments by comparing wild-type and codY null mutant strains [6 , 8] . The activity of CodY is reduced to intermediate levels when some amino acids are removed from the medium and decreases strongly in the absence of all amino acid supplements [2 , 27] . Expression of the braB242-lacZ fusion in the wild-type strain in the absence of any amino acids or in the presence of ILV only was very similar to that in the presence of 16 aa ( 11 . 3 to 14 . 7 MU versus 12 . 2 MU ) . Unexpectedly , almost 3-fold higher activity was found in 13 aa-containing medium ( i . e . , in the absence of ILV ) , indicating that CodY , at an intermediate level of activity , may serve as a positive regulator of braB ( Table 2 ) . To test whether expression of the braB gene indeed responds differentially to varying levels of CodY activity in vivo we made use of a previously constructed set of mutant forms of CodY that have different levels of residual responsiveness to ILV . Most of these proteins have alterations in amino acids that form the ILV-binding pocket; they are expressed at wild-type levels and have undiminished activity in effector-independent DNA binding [2 , 8 , 28] . Since the population of CodY molecules in the cell is in equilibrium between the liganded and unliganded forms of the protein , the unliganded fraction of the population of a mutant protein that has lower affinity for ILV will be greater than for the wild-type protein at a given intracellular ILV concentration . That is , a mutant strain containing a form of CodY that has low affinity for ILV behaves functionally equivalently to the wild-type strain that has a low intracellular pool of ILV . The analysis of Dataset S1 of Ref . ( 8 ) indicates that expression of braB determined by RNA-Seq experiments was up to 4 . 1-fold higher in three strains containing partially active versions of CodY , F71Y , R61K , or R61H ( Fig 5A ) . The results of the RNA-Seq experiments were confirmed and extended by real-time RT-PCR and by analyzing expression of the braB242-lacZ transcriptional fusion in a larger collection of partial codY mutants ( Fig 5B and 5C ) . The up-and-down expression pattern of the braB fusion , in which maximal activity was seen in mutants with intermediate levels of CodY activity , was in drastic contrast to the plateau-reaching expression pattern of the previously characterized CodY-repressed bcaP283-lacZ fusion ( Fig 5F ) [2] and all other CodY-regulated genes [2] . A braB242-gfp translational fusion was introduced into the wild-type strain , the codY null mutant , and a codY point mutant ( R61K ) strain with intermediate residual activity . In all cases , the level of braB expression was rather similar across the cell population ( Fig 6 ) , eliminating the possibility that a bistable expression pattern could explain our results . As expected , the codY ( R61K ) mutant strain had elevated expression compared to the wild-type and codY null mutant strains . We initially hypothesized that the very unusual pattern of braB regulation observed results from CodY binding independently to negative and positive regulatory sites within the braB regulatory region . If so , the positive and negative effects of fully active CodY might balance each other , but , at intermediate levels of CodY activity , positive regulation might dominate . The CodY-binding sites I and II are located upstream and downstream of the braB promoter in positions appropriate for positive and negative regulation , respectively . To determine their independent effects , we created additional lacZ fusions containing truncated versions of the braB regulatory region lacking the upstream CodY-binding site III ( braB184-lacZ and braB162-lacZ ) or sites III and I ( braB144-lacZ ) or the downstream site II ( braB156-lacZ ) ( Fig 1B ) . ( Note that the braB242- , braB184- , braB162- and braB144-lacZ fusions have the identical junction with lacZ; their levels of activity can be directly compared . However , other fusions , such as braB156-lacZ , have different junctions; their activities in wild-type cells can only be compared to the activity of the same fusion in mutant strains or other fusions with a similar junction . ) Surprisingly , deletion of the upstream binding sites III and I did not cause any significant decrease in braB expression in wild-type cells ( Table 2; compare strains BB3076 , BB3719 , BB3811 , and BB3122 ) , implying that these are not sites of positive regulation . On the other hand , the braB162-lacZ and braB144-lacZ fusions , but not the braB184-lacZ fusion , were derepressed 12-fold when codY was inactivated ( Table 2 ) , suggesting that braB expression is subject to negative regulation by CodY bound to the remaining downstream site II . If so , this regulation must be masked in other fusions by the action of a second repressor that binds to the sequence located between the 5’ ends of braB184-lacZ and braB162-lacZ . Interestingly , no up-and-down expression pattern in mutants with different levels of CodY activity was observed for the braB144-lacZ fusion , which lacks the putative binding site for the predicted second regulator ( Fig 5E ) , suggesting that the latter is responsible for the unusual regulation . As expected from this new model , the braB156-lacZ fusion , which lacks the downstream CodY-binding site II , was not subject to regulation by CodY ( Table 2 ) . To confirm that site I is not involved in braB regulation and to quantify more directly the contribution of site II , we changed the very highly conserved G9 and A10 residues of CodY-binding motifs 1 , 2 , and 3 to CC ( the p1 , p2 , and p3 mutations , respectively ) ( Fig 1 and Table 1 ) . The p1 mutation in site I reduced ~10-fold the affinity of CodY for a fragment containing sites III and I , indicating that site I is the major contributor for CodY binding to this fragment ( Fig 4B and 4C ) . However , as expected from our deletion analysis , the p1 mutation did not affect expression of the braB242-lacZ fusion ( Table 3 , strains BB3731 and BB3076 ) . Thus , as noted previously , many CodY-binding sites have no physiological significance either because they are not positioned appropriately for regulation or because binding is too weak [7] . The p3 mutation reduced the affinity of CodY for site II ≥10-fold ( Fig 4E ) . The p2 mutation did not affect binding of CodY to site II , but further decreased the ability of CodY to interact with this site if it already contained the p3 mutation ( Fig 4F and 4G ) . Footprinting experiments showed that each mutation affected CodY binding to the region of site II , which corresponded to the respective motif ( Fig 3B ) . Taking together the gel-shift and footprinting results , we conclude that interaction of CodY with motif 2 is weaker than with motif 3 and is partly dependent on simultaneous interaction of CodY with motif 3 ( see below for the effect of p2 on braB regulation ) . The p3 mutation increased expression of the braB242-lacZ fusion 8-fold consistent with relief from CodY-mediated repression ( Table 3 , strains BB3729 and BB3076 ) . However , expression of the braB242p3-lacZ fusion was substantially reduced in a codY null mutant strain apparently due to repression by the second regulator ( Table 3 , strain BB3735 ) . This result suggests strongly that the second regulator is active in codY mutant cells , but not in wild-type cells , i . e . , its activity or expression is under negative CodY control . Paradoxically , this indicates that our initial hypothesis that braB regulation is subject to simultaneous positive and negative regulation by CodY was likely to be correct , though positive regulation appears to be indirect and mediated through regulation of the second repressor . CodY is known to regulate the expression of a small number of other regulatory proteins , including ScoC [6–8 , 19 , 29 , 30] . ScoC is a repressor of multiple genes , including those encoding extracellular proteases and oligopeptide permeases , and is also involved in the regulation of sporulation [15–19 , 31–33] . Though microarray experiments did not identify braB as a ScoC target [15] , we decided to test whether ScoC is the second regulator of braB expression . No effect of a single scoC null mutation on expression of the braB242-lacZ fusion in TSS + 16 aa was detected ( Table 2 , strain BB3847 ) . However , in a double codY scoC null mutant , expression of the fusion was 11- to 12-fold higher than in the wild-type strain or in scoC or codY single mutants ( Table 2 , strain BB3835 ) , indicating that both CodY and ScoC contribute to negative regulation of braB but these effects cannot be dissected if either one of the regulators is active . Expression of the same fusion in a double null mutant in TSS + 13 aa medium was very similar ( Table 2 ) , indicating that our original observation of higher braB expression under these growth conditions in a wild-type strain was indeed due to reduced CodY activity and its effect on ScoC expression . As expected , in the absence of ScoC , the up-and-down expression pattern of the braB242-lacZ fusion in strains with different CodY activity was replaced by a plateau-reaching pattern , resembling that of the bcaP283-lacZ fusion , which is not subject to ScoC-mediated regulation ( Fig 5D and 5F ) . Expression of the scoC561-lacZ fusion in strains with different CodY activity also followed a plateau-reaching pattern , characteristic for most genes regulated by CodY , and did not correlate with expression from the braB promoter ( Fig 5G ) . In DNase I footprinting experiments , ScoC protected two sites , I and II , within the iscSB-braB intergenic region from positions -79 to -68 and +43 to +57 of the template DNA strand with respect to the braB transcription start point , respectively ( Figs 1A and 7 ) . A short , weakly protected region , site III ( possibly a part of site II ) , was also detected from positions +16 to +20 . Binding of ScoC to the downstream sites II and III was independent of the presence of the upstream site I on the same DNA fragment ( Fig 7 ) . The downstream CodY- and ScoC-binding sites partly overlap ( Fig 1A ) . To address the possibility that CodY and ScoC compete for binding at this location , we analyzed interaction of these proteins with a short , 64-bp braB fragment , containing CodY-binding site II and ScoC-binding sites II and III ( Fig 1B ) . In accord with the results described above , ScoC bound this fragment in gel shift experiments less efficiently ( KD≈150 nM ) than did CodY ( KD≈5 nM ) ( Fig 8A and 8B ) . Nevertheless , ScoC , in a concentration-dependent manner , was able to replace CodY efficiently in a preformed braB-CodY complex as evidenced by formation of ScoC-specific complexes with higher mobility and the decrease in the amount of braB-CodY complexes with lower mobility ( Fig 8C ) . The CodY-mediated displacement of ScoC from the preformed braB-ScoC complex cannot be recognized confidently because of the low mobility of CodY-specific complexes ( complexes containing both proteins would have a similar low mobility ) . However , by comparing and Fig 8B and 8D , it is clear that CodY bound much less efficiently to preformed braB-ScoC complexes than to free braB DNA , confirming competition between the two proteins for binding . A similar competition between CodY and ScoC was previously detected at the oppA promoter [19] . Another ScoC-binding site , site IV , was detected further upstream within the divergent iscSB gene ( Figs 1A and 7 ) . This site was not present in the braB184-lacZ fusion and therefore was not involved in the regulation described . No consensus ScoC-binding motifs , AATAnTATT [18] , with ≤2 mismatches were detected within any of the braB binding sites . The locations of ScoC-binding sites I and II ( Figs 1 and 7 ) correspond well to the binding sites for the predicted second regulator of braB determined by deletion analysis ( Table 2 ) . That is , expression of the braB162-lacZ and braB144-lacZ fusions , which lack the upstream ScoC-binding sites , was not affected by a scoC mutation even if the latter was present together with a codY mutation ( Table 2 ) . On the other hand , expression of the slightly longer braB184-lacZ fusion , which includes an intact ScoC-binding site I , as well as the downstream site II , was subject to full ScoC repression ( as revealed in a double codY scoC mutant ) ( Table 2 ) . Expression of the braB156-lacZ and braB181-lacZ fusions , which carry the upstream ScoC binding site but lack the downstream site II , was also not affected by a scoC mutation ( Table 2 ) . A requirement for interaction with two ( or more ) binding sites within the same regulatory region appears to be a common theme for ScoC-mediated repression [18 , 19 , 34–36] . The lack of both ScoC- and CodY-mediated regulation explains why the braB76-lacZ , braB156-lacZ , and braB181-lacZ fusions are expressed at the same level in wild-type cells and in codY null mutant cells ( Table 2 ) . On the other hand , the braB242p3-lacZ fusion , which lost direct CodY-mediated regulation , is still subject to repression by increased levels of ScoC accumulated in a codY null mutant strain ( Table 3 ) . Interestingly , the p2 mutation , designed to reduce binding of CodY to motif 2 of site II , in fact may affect ScoC interaction with the braB regulatory region . Indeed , the p2 mutation did not affect expression of the braB242-lacZ fusion in a wild-type strain , in which scoC is repressed , but did so in codY null mutant cells , in which ScoC is expressed ( Table 3 , strains BB3730 and BB3736 ) ; the p2 mutation is located 1 bp downstream of ScoC-binding site III ( Fig 1 ) . The expression levels of different fusions and locations of the ScoC-binding sites confirmed that ScoC is the predicted second repressor of braB . As noted above , deleting of one of the ScoC-binding sites resulted in a plateau-reaching expression pattern of the braB144-lacZ fusion in strains with different CodY activity ( Fig 5E ) .
Although previous analysis did not detect any significant regulation of braB by CodY , we now know that braB is subject to complex CodY-mediated regulation by which the protein acts both as a direct repressor and as an indirect positive regulator . The positive effect of CodY is mediated by its repression of the gene encoding a second repressor of braB , ScoC . As a result , braB expression only escapes repression under conditions ( e . g . , during growth in a medium containing multiple amino acids but lacking ILV ) in which CodY activity is limited enough to prevent repression of braB , but high enough to maintain sufficient repression of scoC ( Fig 9 ) . Our previously described repression of scoC by CodY , coupled with ScoC autorepression [19] , keeps the level of ScoC relatively low when cells are growing rapidly . Thus , CodY and ScoC are never fully active or inactive simultaneously . When CodY is inactive , the ScoC level is high enough to repress its target genes , including braB . When CodY is fully active , the ScoC level is insufficient for repression , but CodY is able to repress braB to the same level as fully active ScoC . Because we observe higher expression of braB under conditions of partial CodY activity , we suspect that as CodY activity declines , its binding to the braB regulatory region decreases more rapidly than does its binding to the scoC regulatory region . Alternatively , the affinity of ScoC for its braB binding site might be low enough that ScoC needs to reach a relatively high concentration in order to be effective; by the time this happens , CodY-mediated repression of braB is already very low . In addition , it is possible that the competition between more strongly binding CodY and more weakly binding ScoC for interaction with the same region of the braB regulatory region ( at the downstream sites for each protein ) may contribute to the differential response of braB expression to varying levels of CodY activity . As a result , even relatively small losses in activity of CodY , such as in CodY ( F71A ) , allow neither efficient direct repression by CodY nor sufficient derepression of ScoC , which would compensate for the loss of CodY-mediated repression . The novelty of braB regulation reinforces the view that important mechanisms of gene regulation can be missed by using regulatory protein null mutants as the only means of genetic analysis . A null mutant has no activity in any environment and at any stage of its life cycle , but in wild-type cells regulatory proteins are rarely , if ever , totally inactive . What normally varies is the fraction of the population of the regulator that is in the active state . Furthermore , interpreting the phenotype of a null mutant usually assumes that a regulatory protein is either only a positive regulator or only a negative regulator of its target gene ( s ) . Studying the behavior of genes at different levels of a regulator’s activity has the potential to reveal more complex mechanisms in detail . It should be noted that , although the complex pattern of braB regulation is very interesting , it is not common . Combined repression by CodY and ScoC has also been observed for the B . subtilis opp operon and scoC gene itself . However , in case of opp , ScoC-mediated repression was more efficient than CodY-mediated repression and was detected , although at a reduced level , even in codY+ cells [19] . The opposite was true for expression of the scoC gene , whose regulation by CodY was detected even in scoC+ cells [19] . Among CodY-regulated genes , only the braB gene has shown the described up-and-down pattern , i . e . , expression was maximal at the intermediate levels of CodY activity [8] . It remains unknown whether additional regulatory inputs , e . g . , through SalA-mediated regulation of scoC expression [37] , affect interaction between ScoC and CodY . We have recently characterized three permeases , BcaP , BraB , and BrnQ , involved in the BCAA uptake in B . subtilis cells [1] . The roles of different BCAA permeases in amino acid uptake under different growth conditions should reflect their levels of expression . The bcaP ( yhdG ) gene encodes the most efficient permease for isoleucine and valine and is one of the genes most highly repressed by CodY; expression of the bcaP gene is virtually abolished in amino acid-rich media [2 , 6] . It is very likely that higher activity of BraB is not needed during strong nutrient limitation when CodY activity is very low , because BcaP is fully derepressed . It is also likely that when bcaP and braB are repressed by highly active CodY , the residual activity of BraB , together with BrnQ , is sufficient for the uptake of high concentrations of BCAA . However , the increase in BraB expression at partial activities of CodY may facilitate the uptake of intermediate concentrations of BCAA . It is not uncommon for two regulators to control expression of the same gene in such a way that the lack of one regulator is fully compensated for by the increased activity of the other regulator and , as a result , no regulatory effect is observed in single null mutant strains . However , when such regulators act independently and do not form a feed-forward regulatory loop , the full compensatory effect should also be observed at intermediate activities of the regulator . The peculiarity of braB regulation is that the full compensatory effect of ScoC is seen only when CodY has very low or no activity . The feed-forward regulatory loop formed by CodY and ScoC at the braB promoter , known as a type-2 incoherent loop , is an arrangement in which two regulatory proteins repress the same target gene and one of the regulators represses expression of the other [20 , 21] . This regulatory mechanism may have evolved specifically to achieve higher expression of the braB gene at intermediate activities of CodY . Genes that are regulated by a single repressor are also expressed at a higher level when activity of the repressor is reduced . However , expression of such genes reaches its maximum only when the repressor is completely inactive; the regulatory mechanism of braB avoids this scenario .
The B . subtilis strains constructed and used in this study were all derivatives of strain SMY [38] and are described in Table 4 or in the text . Escherichia coli strain JM107 [39] was used for isolation of plasmids . Bacterial growth in DS nutrient broth or TSS 0 . 5% ( w/v ) glucose-0 . 2% ( w/v ) NH4Cl minimal medium was as described [2] . The TSS medium was supplemented as indicated with a mixture of 16 amino acids [40] . This mixture contained all amino acids commonly found in proteins ( all concentrations in μg/ml ) except for glutamine , asparagine , histidine , and tyrosine: glutamate-Na , 800; aspartate-K , 665; serine , 525; alanine , 445; arginine-HCl , 400; glycine , 375; isoleucine , leucine , and valine , 200 each; methionine , 160; tryptophan , 150; proline , threonine , phenylalanine , and lysine , 100 each; cysteine , 40 . In some experiments , ILV were omitted from the amino acid-containing medium . Methods for common DNA manipulations , transformation , primer extension , and sequence analysis were as previously described [24 , 41] . All oligonucleotides used in this work are described in Table 5 . Chromosomal DNA of B . subtilis strain SMY or plasmids constructed in this work were used as templates for PCR . All cloned PCR-generated fragments were verified by sequencing . Plasmid pBB1593 ( braB242-lacZ ) was created by cloning the XbaI- and HindIII-treated PCR product in an integrative plasmid pHK23 ( erm ) [24] . The 0 . 24-kb braB PCR product , containing the entire braB regulatory region , was synthesized with oBB417 and oBB418 as primers . Plasmids pBB1596 ( braB144-lacZ ) or pBB1772 ( braB184-lacZ ) , containing the braB regulatory region truncated from the 5’ end , were constructed in a similar way using oBB422 or oBB645 , respectively , instead of oBB417 . Plasmids pBB1597 ( braB156-lacZ ) and pBB1807 ( braB181-lacZ ) , containing the braB regulatory region truncated from the 3’ end , were created as pBB1593 but using oBB423 or oBB688 , respectively , instead of oBB418 . Plasmids pBB1803 ( braB162-lacZ ) , pBB1804 ( braB76-lacZ ) , and pBB1808 ( braB101-lacZ ) , in which the braB regulatory region was additionally truncated at the 5’ end , were constructed as pBB1593 , pBB1597 , and pBB1807 , respectively , but using the ApoI and HindIII-digested PCR products that were cloned in pHK23 , treated with EcoRI and HindIII . B . subtilis strains carrying various lacZ fusions at the amyE locus ( Table 4 ) were isolated after transforming strain BB2511 ( amyE::spc lacA ) with the appropriate plasmids , by selecting for resistance to erythromycin , conferred by the plasmids , and screening for loss of the spectinomycin-resistance marker , which indicated a double crossover , homologous recombination event . Strain BB2511 and all its derivatives have very low endogenous β-galactosidase activity due to a null mutation in the lacA gene [42] . Plasmids pBB1773 ( braB242p3-lacZ ) , pBB1774 ( braB242p2-lacZ ) , and pBB1775 ( braB242p1-lacZ ) , containing 2-bp substitution mutations in CodY-binding motifs , were constructed as described for pBB1593 using fragments generated by two-step overlapping PCR . In the first step , a product containing the 5’ part of the braB regulatory region was synthesized by using oligonucleotide oBB417 as the forward primer and mutagenic oligonucleotide oBB641 , or oBB643 , or oBB646 as the reverse primer . A product containing the 3’ part of the braB regulatory region was synthesized by using mutagenic oligonucleotides oBB642 , or oBB644 , or oBB647 as the forward primer and oligonucleotide oBB418 as the reverse primer . The PCR products were used in a second , splicing step of PCR mutagenesis as overlapping templates to generate a modified fragment containing the entire braB regulatory region; oligonucleotides oBB417 and oBB418 served as the forward and reverse PCR primers , respectively . Plasmid pBB1776 ( braB242p2/p3-lacZ ) , pBB1801 ( braB242p1/p3-lacZ ) , and pBB1802 ( braB242p1/p2-lacZ ) , containing two mutations , each , were constructed in a similar way , but using a plasmid , containing one of the mutations , pBB1773 or pBB1774 , as template for PCR . Truncated plasmids , containing mutations in the braB regulatory region , were constructed in the same way as plasmids without mutations . A conversion plasmid for replacing the aphA3 marker for the erm marker , originating from Tn917 , was constructed by cloning the 1 . 5-kb SmaI-StuI fragment of pDG782 [43] into the SnaBI site of pJPM8 [44] . In the resulting plasmid , pBB1560 , the aphA3 gene of pDG782 , conferring resistance to kanamycin or neomycin , is flanked by 5’ and 3’ parts of the erm cassette of pJPM8; the orientation of the aphA gene coincides with that of erm . The PCR products containing the regulatory region of the braB gene were synthesized using braB-specific oliginucleotides or vector-specific oligonucleotides oBB67 or oBB358 and oBB102 , as the forward and reverse primers , respectively . The reverse primer for each PCR reaction ( which would prime synthesis of the template strand of the PCR product ) was labeled using T4 polynucleotide kinase and [γ-32P]-ATP . oBB67 and oBB358 start 96 bp or 12 bp upstream of the XbaI site ( and 112 bp or 28 bp upstream of the EcoRI site ) used for cloning , respectively , and oBB102 starts 36 bp downstream of the HindIII site that serves as a junction between the promoters and the lacZ part of the braB fusion . The procedures for gel shift and DNase I footprinting experiments were as described [19] . A 0 . 24-kb braB PCR product , containing the entire braB regulatory region , was synthesized with oBB724 and oBB725 as primers . Plasmid pBB1845 ( braB242-gfp ) was created by cloning the EcoRI- and SalI-treated PCR product between the EcoRI and XhoI sites of an integrative plasmid pMMB759 ( tet ) , containing a gene encoding a monomeric version ( A206K ) of GFPmut2 [45] . The braB insert within pBB1845 was identical to the insert in pBB1593 ( braB242-lacZ ) . B . subtilis strain BB4082 carrying the braB242-gfp fusion at the lacA locus was isolated after transforming strain BB2263 ( lacA::spc ) with pBB1845 , by selecting for resistance to tetracycline , conferred by the plasmid , and screening for loss of the spectinomycin-resistance marker , which indicated a double crossover , homologous recombination event . Cells , containing the braB242-gfp fusion , were grown until mid- to late-exponential phase in TSS + 16 aa medium , centrifuged and resuspended in TSS medium at OD600≈3 . The images were collected at a 1 , 500 ms exposure time using the 100x ( 1 . 3 N . A . ) objective of the Zeiss Axio Observer . Z1 fluorescent microscope ( Zeiss ) with the Colibri . 2 LED light source , and the ORCA-R2 digital charge-coupled device camera ( C10600 , Hamamatsu ) . Zen Pro 2012 software ( Zeiss ) was used to acquire , view , and analyze the images . CodY-His5 and His6-ScoC were purified to near homogeneity as described previously [19 , 24] . β-Galactosidase specific activity was determined as described previously [46] . RNA isolation , DNA depletion , and cDNA synthesis were performed as previously described [14] . Quantitative , real-time RT-PCR was used to measure steady state braB transcript abundance during exponential growth using oligonucleotides oSRB339 and oSRB340 as described [14] , except that we used B . subtilis strain SMY chromosomal DNA to generate the standard curve . rpoC transcript was used to normalize mRNA abundance .
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Expression of Bacillus subtilis BraB , a branched-chain amino acid permease , is under both negative and positive control by a global transcriptional regulator CodY . The negative control is direct and the positive control is indirect and mediated by another B . subtilis pleiotropic transcriptional regulator , ScoC , which , in turn , is repressed by CodY . Thus , CodY and ScoC form a feed-forward regulatory loop at the braB promoter . In a very unusual manner , the interaction of CodY and ScoC results in high braB expression only at intermediate CodY activities; braB expression remains low both at high and low CodY activities . The novel regulation of braB shows that important , novel regulatory phenomena can be missed by analyzing null mutants in regulatory genes but revealed by using mutants with partial activity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Intermediate Levels of Bacillus subtilis CodY Activity Are Required for Derepression of the Branched-Chain Amino Acid Permease, BraB
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Environmental factors and host genetics interact to control the gut microbiota , which may have a role in the development of obesity and insulin resistance . TLR2-deficient mice , under germ-free conditions , are protected from diet-induced insulin resistance . It is possible that the presence of gut microbiota could reverse the phenotype of an animal , inducing insulin resistance in an animal genetically determined to have increased insulin sensitivity , such as the TLR2 KO mice . In the present study , we investigated the influence of gut microbiota on metabolic parameters , glucose tolerance , insulin sensitivity , and signaling of TLR2-deficient mice . We investigated the gut microbiota ( by metagenomics ) , the metabolic characteristics , and insulin signaling in TLR2 knockout ( KO ) mice in a non-germ free facility . Results showed that the loss of TLR2 in conventionalized mice results in a phenotype reminiscent of metabolic syndrome , characterized by differences in the gut microbiota , with a 3-fold increase in Firmicutes and a slight increase in Bacteroidetes compared with controls . These changes in gut microbiota were accompanied by an increase in LPS absorption , subclinical inflammation , insulin resistance , glucose intolerance , and later , obesity . In addition , this sequence of events was reproduced in WT mice by microbiota transplantation and was also reversed by antibiotics . At the molecular level the mechanism was unique , with activation of TLR4 associated with ER stress and JNK activation , but no activation of the IKKβ-IκB-NFκB pathway . Our data also showed that in TLR2 KO mice there was a reduction in regulatory T cell in visceral fat , suggesting that this modulation may also contribute to the insulin resistance of these animals . Our results emphasize the role of microbiota in the complex network of molecular and cellular interactions that link genotype to phenotype and have potential implications for common human disorders involving obesity , diabetes , and even other immunological disorders .
The recent epidemics of obesity and type 2 diabetes mellitus ( T2DM ) in the past 20 years have stimulated researchers to investigate the mechanisms that are responsible for the development of these diseases . The general view is that obesity and T2DM have a genetic background and are strongly influenced by the environment and that insulin resistance is an early alteration in these diseases [1]–[5] . In addition , studies over the past 10 years have also shown that subclinical inflammation has an important role in the molecular mechanism of insulin resistance in obesity and T2DM [6]–[10] . During the past five years , an increasing body of literature has suggested other components of the mechanisms of these diseases that lie between the genetic and the environment factors , where the gut microbiota are now considered to make an important contribution to these mechanisms [11]–[16] . Then , it is now clear that environmental factors and host genetics interact to control the gut microbiota , which may have a role in the development of obesity and insulin resistance [17] . Metagenomic studies demonstrated that the proportion of Firmicutes is higher in obese animals and in humans , compared with lean controls , and this correlates with a higher number of genes encoding enzymes that break down otherwise indigestible dietary polysaccharides , with more fermentation end products and fewer calories remaining in the faeces of obese individuals [18] , [19] . Another mechanism by which the microbiome may contribute to metabolic disorders is by triggering systemic inflammation [20] . The immune system coevolves with the microbiota during postnatal life , which allows the host and microbiota to coexist in a mutually beneficial relationship [21] , [22] . In particular , the innate immune system has emerged as a key regulator of the gut microbiota . Innate immune recognition of microbe-associated molecular patterns is executed by families of pattern-recognition molecules with a special role for Toll-like receptors ( TLRs ) [23] , [24] . Recent findings indicate that TLRs , which are up-regulated in the affected tissue of most inflammatory disorders , can mediate crosstalk between the immune systems and whole body metabolism [23] . It has been demonstrated that TLR4 , a sensor for lipopolysaccharides on Gram-negative bacteria , is involved in the induction of proinflammatory cytokine expression in macrophages , adipocytes , and liver [13] , [25] . We and others have demonstrated that TLR4 genetically deficient mice or mice with an inactivating mutation for this receptor are substantially protected from obesity-induced insulin resistance [26] , [27] . Similarly , TLR2 genetically deficient mice are protected from high-fat-induced insulin resistance [28] , [29] . On the other hand , TLR5-deficient mice exhibit hyperphagia and develop hallmark features of metabolic syndrome , including hyperlipidemia , hypertension , insulin resistance , and increased adiposity [30] , and these alterations are the consequence of alterations in the gut microbiota . It is important to emphasize that the studies that investigated TLR4- and TLR5-deficient mice were performed without germ-free conditions [26] , [27] , [30] , suggesting that the microbiota have an important influence on TLR5-deficient mice phenotype , inducing obesity and insulin resistance; however , in the TLR4-deficient mice , the microbiota do not have a role in these phenomena because these animals are protected from diet-induced insulin resistance , independently of germ-free conditions [26] , [27] . Taken together , these findings suggest that the interaction of the innate immune system with gut microbiota may determine the insulin sensitivity of an animal and that TLRs may have different roles in this process . Since in most studies with TLR2-deficient mice the microbiota were not investigated , we cannot predict the influence of microbiota in the protection or in the development of insulin resistance in these mice . It is possible that the presence of a diverse gut microbiota could completely reverse the phenotype of an animal , inducing insulin resistance in an animal genetically determined to have increased insulin sensitivity , such as the TLR2 KO mice . The aim of the present study was to investigate the influence of gut microbiota in metabolic parameters , glucose tolerance , insulin sensitivity , and signaling of TLR2-deficient mice .
TLR2 KO mice did not present any difference in weight gain , compared with their controls up until 12 wk . However , after 12 wk , TLR2 KO mice were heavier than their controls ( p<0 . 05; Figure 1A ) . No significant differences were observed with regard to food intake between the groups after either 8 or 16 wk ( Figure 1B ) . The food intake was also normalized for body weight and no difference was observed between groups at 8 wk old ( WT = 0 . 22±0 . 035 g/g animal/day; TLR2 KO = 0 . 21±0 . 021 g/g animal/day ) . However , after 16 wk , TLR2 KO mice presented increased epididymal fat weight ( Figure 1C ) . After 12 wk , the amount of adipose tissue is visually increased in TLR2 KO mice ( Figure 1D ) . It is interesting that TLR2 KO mice at 8 wk old had decreased glucose tolerance compared to their controls ( p<0 . 05; Figure 1E ) , but no difference was observed in fasting serum insulin between the groups ( Figure 1F ) . We next submitted these animals to a hyperinsulinemic euglycemic clamp to investigate insulin sensitivity; results showed that TLR2 KO mice presented a significant decrease in the rate of glucose uptake under high insulin stimulus ( 50% of control , p<0 . 05; Figure 1G ) , indicating a clear situation of insulin resistance . We next analyzed the oxygen consumption from both groups and observed that TLR2 KO mice presented decreased oxygen consumption ( Figure 1H ) , suggesting decreased energy expenditure when compared with their controls . However , the respiratory exchange ratio , approximately 0 . 85 , was similar between the groups ( Figure 1I ) . As the oxygen consumption was decreased in TLR2 KO mice , we evaluated a marker of thermogenesis in the brown adipose tissue of both groups . The expression of the thermogenesis-inducing protein , UCP1 , was significantly decreased in TLR2 KO mice ( Figure 1J ) , suggesting reduced energy expenditure in these animals , in accordance with the reduced oxygen consumption observed . In order to characterize the gut microbiota of TLR2 KO mice , we pyrosequenced the 16S ribosomal RNA ( rRNA ) from the stools of these animals . TLR2 KO mice presented a different gut microbiota , compared with their controls . The major difference concerns the proportion of Firmicutes , which was approximately 47 . 92% in TLR2 KO mice , while the controls presented a proportion of 13 . 95% . Moreover , TLR2 KO mice presented 47 . 92% Bacteroidetes and 1 . 04% Proteobacteria , while their controls presented approximately 42 . 63% and 39 . 53% , respectively ( Figure 2A , B ) . TLR2 KO mice presented other differences in regards to classes and families and these results are presented in the Supporting Information section ( Figures S1 and S2 ) . However , it is important to notice that we have observed different proportions of these phyla between TLR2 KO mice and their controls in different ages of mice . From 4-wk-old to 1-y-old mice , we observed increased proportion of Firmicutes in TLR2 KO mice compared with the controls . We have also observed a tendency of decreasing the proportion of Bacteroidetes progressively as TLR2 KO mice get older ( Figures S3 , S4 , S5 ) . Next , we determined the serum concentration of IL-6 and TNF-α in both groups of animals and observed that TLR2 KO mice presented reduced levels of these cytokines compared with their controls ( Figure 3A , B ) . We also investigated the serum concentrations of leptin , adiponectin , and LPS . No significant difference was observed between the groups with regard to leptin and adiponectin ( Figure 3C , D ) . On the other hand , LPS serum concentration was increased in TLR2 KO mice ( Figure 3E ) . As TLR2 KO mice presented increased serum LPS levels , and this alteration was previously described in an animal model of obesity in which there was a reduced proportion of Bifidobacterium [31] , we investigated the proportion of this group of bacteria . We observed that TLR2 KO mice presented a decrease in Bifidobacterium proportion compared with WT ( Figure 3F ) . In order to unravel the mechanism by which the insulin resistance occurs in the TLR2 KO mice , we studied important pathways involved in this phenomenon: phosphorylation of JNK , activation of ER stress , serine phosphorylation of the insulin receptor substrate ( IRS ) -1 , and expression of IκB-α , which is involved in the inhibition of the IKK/NFκB pathway activation . TLR2 KO mice presented increased phosphorylation of JNK in muscle , liver , and adipose tissue compared with controls ( Figure 4A–C ) . Since the activation of ER stress leads to the phosphorylation of JNK , the increased phosphorylation of this protein in TLR2 KO mice could be due to this event . In fact , the phosphorylation of PERK was increased in the liver and adipose tissue of the KO mice , suggesting increased ER stress activation at least in these two tissues ( Figure 4D–F ) . Next , we studied the inhibitory serine phosphorylation of IRS-1 in muscle , liver , and adipose tissue of TLR2 KO mice and observed that this phosphorylation was increased , compared with the controls , suggesting impairment of insulin signaling ( Figure 4G–I ) . Since increased serum concentration of LPS , a TLR4 ligand , was observed in TLR2 KO mice , we investigated the activation of TLR4 in the muscle , liver , and adipose tissue of these mice . An increased activation of this receptor was observed in all tissues studied ( Figure 4J–L ) , suggesting that , in the absence of TLR2 , a compensatory action may lead to increased activation of TLR4 , which may also contribute to the development of insulin resistance in TLR2 KO mice . Then , we studied the activation of IKK/NFκB pathway , indirectly , by the expression of IκB-α . Curiously , the expression of IκB-α was increased in the muscle , liver , and white adipose tissue of TLR2 KO mice , compared with controls , suggesting a decreased activation of IKK/NFκB pathway ( Figure 4M–O ) . In order to confirm this result , we studied the activation of NFκB and observed that this was decreased in all tissues studied from TLR2 KO mice ( Figure 4P–R ) . The insulin-induced tyrosine phosphorylation of the insulin receptor ( IR ) ( Figure S6A–C ) and of the insulin receptor substrate ( IRS ) -1 ( Figure 5A–C ) , as well as the insulin-induced serine phosphorylation of AKT , was decreased in the muscle , liver , and adipose tissue of TLR2 KO mice ( Figure 5D–F ) , compared with their controls , suggesting reduced insulin signaling in these tissues . Other proteins that are important in the modulation of insulin action were also investigated . Our data showed that the phosphorylation of AMPK ( Figure S6D–F ) and the expression of PGC-1α ( Figure S6G–I ) were similar between controls and TLR2 KO mice in the three tissues investigated . As an increased phosphorylation of JNK was observed in TLR2 KO mice , we prevented the activation of this protein with a pharmacological inhibitor , SP600125 , by treating mice with daily i . p . injections for 5 d . Subsequently , we observed an increased glucose uptake in these animals , suggesting that the activation of JNK is indeed relevant to the development of insulin resistance ( Figure 6A ) . We also observed increased insulin-induced serine phosphorylation of AKT in the liver ( Figure 6B ) , muscle , and white adipose tissue ( unpublished data ) of TLR2 KO mice after this treatment , suggesting increased insulin signaling , as well , associated with a reduction in JNK phosphorylation in the liver of TLR2 KO mice ( Figure 6D ) . An increased activation of ER stress leads to the activation of JNK [32] , [33] . Therefore , we studied whether preventing the activation of this phenomenon could improve the insulin sensitivity and signaling . For this purpose , we treated mice with a pharmacological inhibitor of ER stress , 4-phenyl butyric acid ( PBA ) , using i . p . daily injections for 10 d . This treatment was found to lead to an increased glucose uptake in TLR2 KO mice ( Figure 6A ) and increased insulin-induced serine phosphorylation of AKT in the liver ( Figure 6B ) , muscle , and white adipose tissue ( unpublished data ) , suggesting an improvement in the insulin signaling as well . After this treatment , we also investigated the phosphorylation of JNK and observed a reduction in the liver ( Figure 6D ) of TLR2 KO mice . Results suggest that both the activation of ER stress and the activation of JNK are important contributors to the development of the phenotype observed in TLR2 KO mice . Since TLR4 was more activated in the tissues of TLR2 KO mice , possibly constituting one of the mechanisms responsible for the development of insulin resistance , we inhibited its expression using a TLR4 antisense oligonucleotide ( ASO; with two daily i . p . injections ) for 5 d . After TLR4 ASO treatment , TLR2 KO mice were found to present a significantly increased glucose uptake during the euglycemic hyperinsulinemic clamp compared with their controls ( Figure 6A ) . The insulin signaling was also increased , with increased insulin-induced serine phosphorylation of AKT in the liver ( Figure 6C ) , muscle , and white adipose tissue ( unpublished data ) of TLR2 KO mice . After this treatment , decreased phosphorylation of JNK was observed in the liver ( Figure 6D ) of TLR2 KO mice . Using another method to inhibit TLR4 signaling , a pharmacological inhibitor of TLR4 , TAK-242 , was administered daily by gavage during 5 d and confirmed the results seen with the TLR4 ASO treatment . The insulin sensitivity was increased in TLR2 KO-treated animals ( Figure 6A ) , and the insulin-induced serine phosphorylation of AKT was also increased in the liver ( Figure 6C ) of these animals , suggesting an improvement in insulin signaling . The phosphorylation of JNK was decreased in the liver ( Figure 6D ) of TLR2 KO treated mice . As the serum LPS levels were increased in TLR2 KO mice , and the changes in microbiota may not account for this increase , we tested whether the LPS absorption was also increased in these animals . For this purpose , we administered LPS orally to TLR2 KO mice and wild type mice and determined the circulating LPS levels 1 h later . We observed that all animals presented increased serum LPS concentration after the LPS administration . However , TLR2 KO mice presented a higher increase in serum LPS concentration after the treatment , compared with the wild type mice ( Figure 6E ) . As this result suggested that TLR2 KO mice presented increased gut permeability , we investigated the expression of an important tight-junction protein of the ileum of these mice , zonula occludens ( ZO ) -1 , and observed that it was indeed decreased , compared with the control mice ( Figure 6F ) . Reduction of ZO-1 expression in TLR2 KO mice was also observed in other parts of the small intestine and in the colon ( unpublished data ) . Previous data showed that TLR2 KO mice have a decreased number of regulatory T cells in the circulation compared with control mice [34] . This can have a role in the modulation of intestinal barrier and also in insulin resistance . We next investigated the frequency of Foxp3+ CD4+ T regulatory cells in mesenteric adipose tissue . We observed that the frequency of these cells was decreased in TLR2 KO mice ( Figure 6H ) , compared with the wild type mice ( Figure 6G ) . As the gut microbiota from TLR2 KO mice was shown to differ from that of controls , we treated both groups with a mixture of antibiotics ( ampicillin , neomycin , and metronidazole ) in their drinking water for 20 d . Moreover , we characterized the gut microbiota of TLR2 KO mice using culture-based microbial analysis of cecal contents after the antibiotics treatment and the results showed that aerobic bacteria were almost suppressed , while anaerobic bacteria decreased its abundance to 40% compared to the control group ( Figure S7A ) . After the treatment with the mixture of antibiotics , we also observed changes in the relative abundance of three phyla of bacteria . The abundance of Bacteroidetes was reduced from 47 . 92% to 19 . 78% and Firmicutes abundance decreased from 47 . 92% to 11 . 76% in the TLR2 KO mice , while Proteobacteria abundance increased from 1 . 04% to 67 . 38% in these mice ( Figure S7B , C ) . TLR2 KO treated mice presented other differences in regards to classes and families and these results are presented in the Supporting Information section ( Figures S8 and S9 ) . When TLR2 KO mice were treated with metronidazole , neomycin , and ampicillin individually , and not as an antibiotics mixture , we observed that ampicillin was the most effective one to exterminate more bacteria diversity . When treated with metronidazole , TLR2 KO mice presented 46 . 51% of Proteobacteria , 10 . 69% of Firmicutes , and 42 . 32% of Bacteroidetes . When treated with neomycin , TLR2 KO mice presented 44 . 18% of Firmicutes and 55 . 81% of Bacteroidetes . When treated with ampicillin , almost 100% of the sequenced bacteria left corresponded to Proteobacteria ( Figure S10A–C ) . Since the treatment with ampicillin or metronidazole normalized glucose tolerance in TLR2 KO mice , and neomycin only mildly improved glucose tolerance in these mice ( unpublished data ) , we can speculate that the changes in microbiota induced by ampicillin or metronidazole are more relevant than the changes induced by neomycin , although no specific genera of bacteria can be implicated in this response . However , in accordance with previous data on obese mice , the decrease in the proportion of the phylum Firmicutes , as observed in the groups that received ampicillin or metronidazole , correlates with the improvement in glucose tolerance . TLR2 KO mice presented decreased epididymal fat pad and visceral adipose tissue weight after the treatment with antibiotics compared with non-treated TLR2 KO , while no difference was observed in the treated and non-treated control animals ( Figure 7A , B ) . TLR2 KO mice also presented increased glucose tolerance ( Figure 7C ) and increased oxygen consumption ( Figure 7D ) after the treatment compared with non-treated TLR2 KO mice , but no significant difference was observed between treated and non-treated control mice . With regard to insulin sensitivity and signaling , we observed an improvement in insulin-induced glucose uptake , using the euglycemic hyperinsulinemic clamp , in TLR2 KO mice after antibiotics treatment ( Figure 7E ) , with no difference in the treated and non-treated control mice . After the treatment , we also observed an increase in the UCP-1 expression in the brown adipose tissue of TLR2 KO mice , supporting the increased oxygen consumption observed in this condition ( Figure 7F ) . We also observed an increased insulin-induced serine phosphorylation of AKT in the liver ( Figure 7G ) , muscle , and white adipose tissue ( unpublished data ) of TLR2 KO mice after the treatment . Moreover , there was a decreased phosphorylation of JNK in the liver ( Figure 7H ) , muscle , and white adipose tissue ( unpublished data ) of the knockout mice after the treatment . The antibiotics treatment also led to an increased expression of ZO-1 in TLR2 KO mice , with no difference in the treated and non-treated control mice ( Figure 7I ) . These data suggest that , in TLR2 KO mice , the reduction in their gut microbiota associated with qualitative changes in composition , induced by antibiotics , was able to reverse the insulin resistance of these animals . In order to investigate whether the gut microbiota was responsible for triggering all the alterations seen in TLR2 KO mice , we transplanted the cecal microbiota from TKR2 KO mice and from WT mice in 4-wk-old-Bacillus-associated WT mice , which contain few species of the genus Bacillus , without any other genera , as obtained by 16S rRNA pyrosequencing , in the following proportion: Bacillus simplex ( 0 . 68% ) , Bacillus sp ( 1 . 1% ) , Bacillus sp Kaza-34 ( 6 . 28% ) , and uncultured Bacillus ( 91 . 96% ) . There was a non-significant increase in the epididymal adipose tissue fat pad weight , in the total body weight gain , in the fasting blood glucose , and in the oxygen consumption in Bacillus-associated mice transplanted with WT microbiota ( Figure 8A , C , D , G ) . However , in Bacillus-associated mice transplanted with TLR2 KO microbiota , we observed a marked increase in the epididymal fat pad and visceral adipose tissue weight ( Figure 8A , B ) ; in the body weight gain ( Figure 8C ) , with a trend towards increased fasting blood glucose ( Figure 8D ) , as well as a decrease in the glucose tolerance ( Figure 8E , F ) ; in the oxygen consumption ( Figure 8G ) ; and in the insulin sensitivity , obtained by euglycemic hyperinsulinemic clamp , compared with those transplanted with WT microbiota ( Figure 8H ) 8 wk after the transplantation ( p<0 . 05 ) . Bacillus-associated mice transplanted with WT microbiota also presented decreased insulin sensitivity compared with the non-transplanted mice ( p<0 . 05 ) . Bacillus-associated WT mice transplanted with TLR2 KO or with WT microbiota also showed decreased expression of UCP-1 in the brown adipose tissue compared with the non-transplanted mice . Bacillus-associated mice transplanted with TLR2 KO microbiota showed marked decrease in UCP-1 expression compared with those transplanted with WT microbiota ( Figure 8I ) . Moreover , these animals had decreased insulin signaling , as seen by the reduction in serine phosphorylation of AKT in liver , compared to mice transplanted with WT microbiota ( Figure 8J ) . In the mice transplanted with TLR2 KO microbiota , there was increased phosphorylation of JNK in liver ( Figure 8K ) , muscle , and white adipose tissue ( unpublished data ) compared with the mice transplanted with WT microbiota . The experiments described above had also been performed in few germ-free mice , but with very similar results ( unpublished data ) . Eight weeks after the transplantation , the expression of ZO-1 was evaluated in the 12-wk-old mice . We observed that it was decreased in mice transplanted with TLR2 KO microbiota compared to those transplanted with WT microbiota ( Figure 8L ) . The same data were observed in other parts of the small intestine and in the colon ( unpublished data ) . We also investigated the frequency of CD4+Foxp3+ regulatory T cells in these animals and observed that they were decreased in mesenteric adipose tissue in mice transplanted with TLR2 KO microbiota ( Figure 8O ) compared with the mice transplanted with WT microbiota ( Figure 8N ) and non-transplanted Bacillus-associated mice ( Figure 8M ) . In summary , as expected , the transplantation of a wild-type microbiota in Bacillus-associated mice resulted in a moderate increase in adipose visceral fat and in a mild decrease in glucose tolerance . However , the effect of the transplantation of TLR2 KO microbiota in Bacillus-associated mice induced marked changes , and clearly indicates deleterious effects of this TLR2 KO microbiota on body weight and glucose metabolism . Next , we investigated the effect of high-fat diet ( HFD ) on metabolic parameters of TLR2 KO mice . The results showed that at 8 wk old TLR2 KO mice on a HFD presented increased body weight ( Figure 9A ) , similar food intake ( WT = 7 . 3 g per day , TLR2 KO = 6 . 1 g per day; WT = 0 . 25±0 . 055 g/g animal/day; TLR2 KO = 0 . 19±0 . 046 g/g animal/day ) ( Figure 9B ) , increased epididymal fat weight ( Figure 9C ) , reduced glucose tolerance ( Figure 9D ) , increased fasting serum insulin ( Figure 9E ) , and reduced glucose uptake ( Figure 9F ) compared to the controls . The oxygen consumption of both groups was compared and TLR2 KO mice were seen to present decreased oxygen consumption ( Figure 9G ) , suggesting decreased energy expenditure compared to the controls . However , the respiratory exchange ratio was similar in both groups , being around 0 . 75 ( Figure 9H ) . In accordance with the reduced oxygen consumption observed , the expression of UCP1 was significantly decreased in TLR2 KO mice ( Figure 9I ) . Similarly , insulin signaling was reduced in TLR2 KO mice fed on the HFD . The insulin-induced serine phosphorylation of AKT was reduced in the muscle , liver , and white adipose tissue of TLR2 KO mice , compared with controls ( Figure 10A–C ) . Moreover , the phosphorylation of JNK was increased in all tissues studied of the TLR2 KO mice ( Figure 10D–F ) , while the expression of IκB-α was increased ( Figure 10G–I ) , suggesting that the IKK/NFκB pathway is decreased in TLR2 KO mice on a HFD , as observed for mice on a standard chow . These results suggest that the metabolic phenotype of the TLR2 KO mice characterized by insulin resistance is aggravated by HFD , which leads to the development of diabetes , as demonstrated by fasting blood glucose and glucose tolerance tests .
It is now considered that environmental factors and host genetics interact to control the acquisition and stability of gut microbiota . In turn , environment , host genetics , and microbiota interact to maintain the homeostasis of gut , weight control , and insulin sensitivity [17] . Clearly , the modification of one or more of these three components may trigger the development of insulin resistance and obesity . The results of the present study demonstrated that TLR2 KO mice in conventionalized conditions in our breeding center have insulin resistance and glucose intolerance associated with alterations in the composition of the gut microbiota , which displayed an increase in the relative abundance of Firmicutes and Bacteroidetes and decreased relative abundance of Proteobacteria , compared to their controls . The insulin resistance of TLR2 KO mice was accompanied by a down-modulation of insulin-induced insulin signaling in the liver , muscle , and adipose tissue , associated with an increase in endoplasmic reticulum stress . These metabolic alterations were characterized in 8-wk-old TLR2 KO mice , when they had similar body weights to the control animals . As demonstrated in other animal models [35] , [36] , this insulin resistance precedes the development of obesity , and an augmentation in body weight compared to controls is observed after the 12th wk of age . However , previous studies [28] , [37] have reported that TLR2 KO mice present decreased body weight and adiposity , are protected against insulin resistance , and gain less weight on a HFD than control mice and are also protected against related comorbidities [38] , [39] . We believe that the main difference between these studies and our study may be related to gut microbiota . It should be taken into consideration that although the animals have the same genetic deficiency they were bred in different rooms and fed with food from different sources , which can certainly have a role in the establishment and maintenance of gut microbiota . Although in most of the previous studies the gut microbiota was not investigated , we can suggest that TLR2 deficiency associated with different environmental conditions can induce different phenotypes , probably induced by different microbiotas . Kellermayer et al . have shown that the proportion of Firmicutes found in TLR2 KO mice was lower than in WT , while the proportion of Bacteroidetes was increased [40] . In our study , we show that TLR2 KO mice present the opposite , with increased proportion of Firmicutes and decreased proportion of Bacteroidetes , compared with the WT . This way , it is possible that in the other published studies the proportions of this phyla might be different , compared with the proportions we have found , which might influence differently the phenotype observed . These results reinforce the importance of environment and of the innate immune system as key regulators of gut microbiota and suggest that a genetic condition , which by itself can prevent insulin resistance in some conditions , can also overcome the protective effect on insulin resistance in other environmental conditions inducing more weight gain , probably due to differences in the microbiota . In addition , these findings may help explain differences in the metabolic behavior of the same animal , when analyzed in distinct environments , and can contribute to explaining differences in metabolic behavior between animals with the same background or with the same genetic alteration . The mechanisms by which the TLR2 KO mice presented insulin resistance and , later , obesity were also investigated . The gut microbiota of the TLR2 KO animal have some similarities to those found in obese animals and humans , with an increase in Firmicutes [41] , [42] . This type of microbiota is usually associated with an increased capacity for energy harvesting from the diet [19] . This might contribute to explaining the obesity observed , but does not explain why these animals are clearly insulin resistant many weeks before they start to gain more weight than their controls . In addition , it was demonstrated that germ-free ( that gain less weight on HFD ) and conventionalized mice have similar energy contents in their feces , suggesting that other mechanisms may have an important role in gut microbiota-induced insulin resistance and obesity [43] . Additionally some studies suggest that the gut microbiota can contribute to obesity by inducing a reduction in fat oxidation and an increase in fat storage [43] , associated with a relative reduction in the expression of PGC1 alpha and in AMPK phosphorylation . This mechanism is less probable in our animal model , because the RQ of TLR2 KO mice was identical to that of control mice , showing that they were oxidizing fat in the same proportion of controls , and also the tissue levels of PGC1alpha and also the phosphorylation of AMPK were similar in liver and muscle of controls and TLR2 KO mice . Another possible mechanism that could induce insulin resistance in obesity is the increased level of LPS , which is observed in HFD mice [11] , [44] . Notably , although TLR2 KO mice were fed on standard rodent chow , they presented higher circulating levels of LPS . Since the microbiota of these mice had a predominance of Firmicutes , which are gram-positive , and do not have LPS in the outer membrane , the increase of LPS circulating levels is certainly not the consequence of a microbiota that produces more LPS . However , the microbiota observed in obesity and also in TLR2 KO mice may increase gut permeability and LPS absorption [45]–[47] . Importantly , as observed in obese animals , which present a significant reduction in Bifidobacteria [48] , [49] , in the microbiota of lean TLR2 KO mice this genera was reduced compared with the controls . In this regard , the supplementation of Bifidobacteria has been linked to an improvement in the gut barrier function and to reduced levels of LPS [31] , [50] , [51] . In order to prove that the increased circulating LPS levels of TLR2 KO mice were related to gut permeability , we administered LPS orally to these mice and observed that , in addition to higher basal LPS levels , these animals also showed a higher peak of LPS 1 h after oral gavage of this lipopolysaccharide . Previous data showed that TLR2 regulates tight junction ( TJ ) -associated intestinal epithelial barrier integrity and that TLR2 deficiency predisposes to alterations of TJ-modulated barrier function leading to perpetuation of mucosal inflammation [52] , [53] . In this regard , our data also demonstrated that , in TLR2 KO mice , there is a reduction in ZO-1 in the small intestine and in the colon , reinforcing that there are alterations in epithelial integrity and gut permeability in these mice . Taken together , these results suggest that the interactions between the predisposition of TLR2 KO mice to alterations in barrier function and the microbiota may have an important role in the increased circulating LPS levels observed in these mice . In accordance with alterations in gut permeability , Kellermayer et al . recently investigated the epigenomic and metagenomic consequences of Tlr2 deficiency in the colonic mucosa of mice in order to understand the biological pathways that shape the interface between the gut microbiota and the mammalian host . The results showed epigenomic and transcriptomic modifications associated with alterations in mucosal microbial composition and the abundance of many bacterial species were found to differ between WT and TLR2 KO animals . The expression of genes involved in the immune system was modified in the colonic mucosa of TLR2 KO mice , which correlated with DNA methylation changes . This pioneer study demonstrates that significant microbiota shifts associate with epithelial epigenetic changes influenced by the host genome [54] . In order to confirm that gut microbiota was inducing insulin resistance in TLR2 KO mice , we treated these mice with antibiotics for 15 d and showed that this treatment dramatically reduced the gut microbiota and also changed its composition . In parallel , there was an improvement in insulin action , characterized by an increased glucose infusion rate during the glucose clamp , and also an improvement in insulin signaling in the liver , muscle , and adipose tissue . In the TLR2 KO mice treated with antibiotics , we also observed a marked reduction in LPS levels . When we performed gut microbiota transplantation of TLR2 KO mice to Bacillus-associated WT mice , which are colonized only by the genus Bacillus and are capable of receiving a different microbiota from other mice , the complex composition of the transferred organism was preserved . The transplanted TLR2 KO mice microbiota conferred more weight gain , glucose intolerance , and reduced insulin sensitivity and signaling , associated with increased LPS circulating levels . These data reinforce the hypothesis that the TLR2 KO mice microbiota are able to induce changes in the gut permeability , in turn increasing serum LPS levels , associated with insulin resistance . The increase in LPS may induce insulin resistance by counteracting insulin signaling , as previously demonstrated [11] , [55] , [56] . However , the insulin resistance observed in TLR2 KO mice has unique characteristics . There was activation of TLR4 in the liver , muscle , and adipose tissue , associated with ER stress and JNK activation , but no activation of the IKKβ-IκB-NFκB pathway . It was previously described that there is cooperation between TLR4 and TLR2 signaling . This cooperation is evident when LPS is injected in TLR2 KO mice . After the first bolus of LPS , TLR2 KO mice show a robust signal for genes encoding innate immune proteins in the brain . However , the second LPS infusion failed to trigger TNFalpha in TLR2 KO mice . These results indicate that TLR2 is involved in the second wave of TNFalpha expression after LPS and that there is an elegant cooperation between TLR2 and TLR4 [57] . Our results extended these data by showing that the chronic elevation in LPS levels in TLR2 KO mice was not able to increase IKK/IkB/NF-kB pathway and TNFalpha and IL-6 production , but induced an increase in JNK activation in liver , muscle , and adipose tissue of these mice . These data suggest that chronic activation of TLR4 by low doses of LPS is sufficient to increase JNK activation , but the activation of IKK/IkB/NF-kB pathway may also depend on the cooperation between TLR2 and TLR4 . The absence of activation of the NFκB pathway and the reduced levels of TNFα and IL-6 make the insulin resistance of TLR2 KO mice different from that observed in DIO mice or in ob/ob mice . We can , thus , suggest that the increase in LPS circulating levels caused activation of TLR4 , induced ER stress and JNK activation accompanied by increased IRS-1 serine 307 phosphorylation in the liver , muscle , and adipose tissue , leading to a reduction in insulin sensitivity and signaling and conferring the phenotype observed in the TLR2 KO mice . Phosphorylation of IRS-1 on serine residues interferes with the subsequent insulin-stimulated tyrosine phosphorylation of IRS-1 by IR [58] and IRS-1 can also mediate inhibition of the insulin receptor tyrosine kinase activity [55] , and also with downstream signaling as Akt phosphorylation . This insulin signaling pathway is crucial for the metabolic effects of insulin on glucose metabolism [59] . The pharmacological or genetic blockage of TLR4 , of ER stress , or of JNK improved action and signaling of insulin in TLR2 KO mice , confirming that this sequence of events has an important role in the insulin resistance of these animals . Regulatory T cells , a small subset of T lymphocytes , are thought to be one of the body's most important defenses against inappropriate immune responses [60] , [61] and can influence the activities of cells of the innate immune system [62]–[64] . Previous data showed that regulatory T cells were highly enriched in the abdominal fat of control mice and reduced at this site in animal models of obesity . This reduction in obesity of regulatory T cells influenced the inflammatory state of adipose tissue and certainly contributes to insulin resistance . Our data showing that in TLR2 KO mice there is a reduction in regulatory T cell in visceral adipose tissue may suggest that this modulation may also contribute to the insulin resistance observed in these animals . The development of obesity and insulin resistance in humans is thought to be promoted by a HFD . Feeding TLR2 KO mice with a HFD for 8 wk caused a marked increase in body weight and in fasting plasma glucose , with levels of over 400 mg/dl at 2 h during the glucose tolerance test , demonstrating that these animals developed not only a more severe form of insulin resistance but also diabetes . The alterations in insulin signaling in tissues also showed a marked down-regulation , in parallel with a higher activation of JNK compared to their controls on HFD . Interestingly , the absence of activation of the IKKβ-IκB-NFκB pathway , described in TLR2 KO mice on standard rodent chow , was also observed when these mice were on HFD . These results demonstrate that the insulin resistance , and later the increase in body weight observed in TLR2 KO mice , is exacerbated by HFD . A recent report demonstrated that genetically deficient TLR5 mice exhibit hyperphagia , hyperlipidemia , insulin resistance , and increased adiposity [30] . These metabolic alterations correlated with changes in the composition of the gut microbiota . Our model , although showing similar features , presented different aspects that may suggest that different mechanisms may be operating in TLR5 or TLR2 KO mice . First , TLR2 KO mice did not present hyperphagia , and the difference in body weight starts only when these animals are 16 wk old . In the TLR5 KO mice , the insulin resistance is not dependent on TLR4 , but in TLR2 KO mice there is an increase in circulating LPS and activation of TLR4 . It is possible that these differences not only represent differences in genetic defects but also differences in gut microbiota between these mice . In conclusion , we may suggest that the loss of TLR2 in conventionalized mice results in a reminiscent phenotype of metabolic syndrome , characterized by a clear difference in the gut microbiota , which induces insulin resistance , subclinical inflammation associated with ER stress , glucose intolerance , and later obesity , which is reproduced in WT by microbiota transplantation and can be reversed using antibiotics . Our results emphasize the role of microbiota in the complex network of molecular and cellular interactions that bridge genotype to phenotype and have potential implications for a wide array of common human disorders involving obesity , diabetes , and even other immunological disorders .
Human recombinant insulin was from Eli Lilly ( Indianapolis , Indiana , USA ) . Reagents for SDS-PAGE and immunoblotting were from Bio-Rad . HEPES , phenylmethylsulfonyl fluoride , aprotinin , dithiothreitol , Triton X-100 , Tween 20 , glycerol , and bovine serum albumin ( fraction V ) were from Sigma . Protein A-Sepharose 6MB was from GE Healthcare , and nitrocellulose paper ( BA85 , 0 . 2 µm ) was from Amersham Biosciences . The reagents for the chemiluminescence labeling of proteins in blots were from Amersham Biosciences . Sense and antisense oligonucleotides specific for TLR4 ( sense , 5′-C TGA AAA AGC ATT CCC ACC T-3′ and antisense , 5′-A GGT GGG AAT GCT TTT TCA G-3′ ) were produced by Invitrogen Corp . ( Carlsbad , CA ) . Antibodies against β-actin ( mouse monoclonal , sc-8432 ) , TLR4 ( rabbit polyclonal , sc-30002 ) , phospho [Ser307]-IRS-1 ( rabbit polyclonal , sc-33956 ) , phospho [Tyr941] ( goat polyclonal , sc-17199 ) , IRS-1 ( rabbit polyclonal , sc-559 ) , phospho [Ser 473]-AKT ( rabbit polyclonal , sc-33437 ) , AKT1 ( goat polyclonal , sc-1618 ) , phospho [Thr 981]-PERK ( rabbit polyclonal , sc-32577 ) , PERK ( goat polyclonal , sc-9477 ) , phospho-JNK ( mouse monoclonal , sc-6254 ) , JNK1 ( mouse monoclonal , sc-1648 ) , phospho[Tyr1162/1163]-Insulin Receptor ( rabbit polyclonal , sc-25103 ) , Insulin Receptor β ( goat polyclonal , sc-31369 ) , UCP1 ( goat polyclonal sc-6529 ) , and MyD88 ( goat polyclonal , sc-8197 ) were from Santa Cruz Biotechnology , Inc . ( Santa Cruz , CA ) . Antibody against ZO-1 was from Abcam ( AB96594 ) ( Cambridge , MA ) . Antibodies against phospho [Thr172]-AMPKα ( rabbit polyclonal , #2531 ) , AMPKα ( rabbit polyclonal , #2532 ) , and IκB-α ( rabbit polyclonal , #9242 ) were from Cell Signaling Technology ( Beverly , Massachusetts , USA ) . TLR2-deficient mice , also called TLR2 knockout ( KO ) mice , were obtained by Dr . Akira [65] and were kindly provided by Dr . Ricardo Gazzinelli [66] and maintained on a C57BL/6J genetic background . Studies were carried out using male TLR2 KO mice that were age matched with C57BL/6J and obtained from the University of Campinas Breeding Center . C57BL/6J and the TLR2 KO mice have the same origin and have been raised in the same institution ( UNICAMP ) and in the same room , at University of Campinas Breeding Center . The C57BL/6J strain was generated by backcrossing mice carrying the TLR2 KO mutation 10 times to C57BL/6J inbred mice [67] . TLR2-deficient mice are viable and fertile . The control and the knockout mice used for the experiments were littermates , obtained from a heterozygote × heterozygote cross , from the same mother , from the same cage , in order to have standard conditions for all animals . The investigation was approved by the ethics committee and followed the university guidelines for the use of animals in experimental studies , and experiments conform to the Guide for the Care and Use of Laboratory Animals , published by the U . S . National Institutes of Health ( NIH publication no . 85–23 revised 1996 ) . The animals were maintained on 12 h/12 h artificial light-dark cycles and housed in individual cages . Mice were randomly divided into two groups: control , fed on standard rodent chow ( 3 . 948 kcal/Kg−1 ) , and HFD , fed on a rich-fat chow ( 5 . 358 kcal/Kg−1 ) ad libitum for 16 wk . The mice were bred under specific pathogen-free conditions at the Central Breeding Center of the University of Campinas . Mice were fasted for 5 h , at which time blood was collected by the retrobulbar intraorbital capillary plexus . Hemolysis-free serum was generated by the centrifugation of blood using serum separator tubes ( Becton Dickinson , Franklin Lakes , New Jersey ) . Serum insulin , cytokines , leptin , and adiponectin were analyzed by ELISA kits purchased from Linco Research Inc ( St . Charles , Missouri ) . NF-κB p50 activation was determined in nuclear extracts from muscle and adipose tissue by ELISA ( 89858; Pierce Biotechnology ) , according to the recommendations of the manufacturer . Serum LPS concentration was determined using a kit based on a Limulus amebocyte extract ( LAL kit endpoint-QCL1000; Cambrex BioScience , Walkersville , Maryland ) , where samples were diluted 1/40 to 1/100 and heated for 10 min at 70°C . Internal control of recovery calculation was included in the assessment . After 6 h fasting , mice were anesthetized by an i . p . injection of sodium amobarbital ( 15 mg/kg body weight ) , and the experiments were initiated after the loss of corneal and pedal reflexes . After collection of an unchallenged sample ( time 0 ) , a solution of 20% glucose ( 2 . 0 g/kg body weight ) was administered into the peritoneal cavity . Blood samples were collected from the tail at 30 , 60 , 90 , and 120 min for determination of glucose and insulin concentrations [68] . After a 6-h fast , a prime continuous ( 3 . 0 mU·kg−1·min−1 ) infusion of regular insulin was administered in the groups of mice for 2 h from time 0 , to raise plasma insulin and maintain it at a steady-state plateau ( 90–120 min ) . A variable glucose infusion ( 10% ) was started 5 min after the beginning of the experiment and was corrected , if necessary , to maintain euglycaemia between 5 and 6 . 1 mmol/l [69] . Blood samples for determination of plasma glucose were obtained at 5-min intervals throughout the study . Oxygen consumption/carbon dioxide production and respiratory exchange ratio ( RER ) were measured in fed animals through an indirect open circuit calorimeter ( Oxymax Deluxe System; Columbus Instruments , Columbus , Ohio ) , as described previously [70] . Standard chow or HFD was given and food intake was determined by measuring the difference between the weight of chow given and the weight of chow at the end of a 24-h period . This procedure was performed during 5 d , with 8-wk-old mice , using metabolic cages for a single mouse ( Tecniplast , Italy ) , obtaining an average of food intake per cage per day . This average was also normalized for body weight . PBA is a chemical chaperone and evidence suggests that it relieves endoplasmic reticulum stress [71] . For acclimation , mice received 100 µl phosphate buffered saline ( PBS ) twice daily ( 8 a . m . and 6 p . m . ) , by gavage , for 3 d . Following the acclimation period , PBA was administered twice daily in two divided doses ( 500 mg/kg at 8 a . m . and at 6 p . m . , total 1 g/kg/day ) by gavage for 10 d . Control groups received the same volume of vehicle instead of PBA at the same treatment points [33] . SP600125 , a potent and selective inhibitor of JNK , was dissolved in a 7% ( in PBS ) Solutol HS-15 solution and administered intraperitoneally ( 30 mg/kg/day ) for 5 d [72] . In order to inhibit the expression of TLR4 , two methods were used: pharmacological inhibition , using 2 . 4 mg/kg/day ethyl ( 6R ) -6-[N- ( 2-chloro-4-fluorophenyl ) sulfamoyl]cyclohex-1-ene-1-carboxylate ( TAK-242 ) ( synthesized at the Chemistry Institute of the University of Campinas ) [73] , administered daily by gavage during 5 d , and 4 nmol TLR2 antisense oligonucleotide ( ASO ) inhibition , composed by 5′-AGGTGGGAATGCTTTTTCAG-3′ ( sense ) and 5′-CTGAAAAAGCATTCCCACCT-3′ ( antisense ) , administered by two daily i . p . injections during 5 d , produced by Invitrogen Corp . ( Carlsbad , California , USA ) . An LPS tolerance test was performed as follows: Fasted mice were gavaged with LPS ( 300 µg/kg ) diluted in water ( 100 µL ) or with water ( 100 µL ) . Blood was collected from the cava vein 60 min after gavage . Plasma was separated and frozen [11] . The cells were obtained from the adipose tissue and analyzed by flow cytometry . For the determination of the frequency of putative regulatory T cells , the adipose tissue mononuclear cells were stained for the surface marker CD4 ( Percp ) and after for intracellular transcription factor Foxp3 using APC anti-mouse/rat Foxp3 staining ( eBioscience , San Diego , California ) . The cells were acquired in the FACS Calibur Flow cytometer ( BD ) and analyzed with FlowJo software . Four-week-old WT and TLR2 KO mice were placed on broad spectrum antibiotics ( 1 . 0 g/L ampicillin , 1 . 0 g/L metronidazole , and 0 . 5 g/L neomycin ) in drinking water for 20 d . During this period mice were monitored for food intake and stool microbiota sequencing . Total aerobic and anaerobic bacteria were enumerated in selective media and incubation conditions according to Schumann et al . [74] . In brief , cecal samples were diluted in Ringer medium , and total aerobic and anaerobic bacteria were investigated by plating onto nonselective media: TSS medium ( Biomerieux , Lyon , France ) for 24 to 48 h at 37°C in aerobic and anaerobic conditions . Bacterial numbers were expressed as colony forming units ( CFU ) /mg cecal content [75] . Faeces samples were collected in metabolic cages with separated waste collectors , frozen in liquid nitrogen , and kept at −80°C until use . DNA was then extracted using the QIAamp DNA Stool Mini Kit ( Qiagen , Hilden , Germany ) and quantified . Libraries were synthesized from 500 ng of total DNA following the Rapid Library Preparation Kit ( Roche Applied Science , Mannheim , Germany ) instructions . These libraries were analyzed in a Bioanalyzer with a High Sensitive DNA Kit ( Agilent Technologies Inc . , Santa Clara , California , USA ) , and equimolar pools were made , titrated , and submitted to large volume PCR , following the manufacturer's instructions ( Roche Applied Science , Mannheim , Germany ) . Subsequently , samples were sequenced in GS FLX Titanium , using a GS FLX Titanium PicoTiterPlate Kit combined with a GS FLX Titanium Sequencing Kit XLR70 ( Roche Applied Science , Mannheim , Germany ) . The data obtained from the sequencing were submitted to the MG-RAST server and compared by phylum prevalence among groups [76] . Cecal contents were pooled from 3 TLR2 KO mice and age- and gender-matched WT littermates . Cecal extracts were suspended in PBS ( 2 . 5 ml per cecum ) and were administered ( 0 . 1 ml per mouse ) immediately to sterilely packed , 4-wk-old , Bacillus-associated , WT mice that were obtained from the Central Breeding Center of the State University of Campinas . Transplanted mice were maintained in sterile cages and monitored for body weight [30] . Mice were anesthetized by intraperitoneal injection of sodium thiopental and used 10–15 min later—i . e . , as soon as anesthesia was assured by the loss of pedal and corneal reflexes . In some experiments , 3 or 5 min after insulin injection ( 3 . 8 units/kg , intraperitoneally ) , liver or muscle and white adipose tissue were removed , respectively , and homogenized immediately in extraction buffer at 4°C ( 1% Triton X-100 , 100 mm Tris-HCl ( pH 7 . 4 ) , 100 mm sodium pyrophosphate , 100 mm sodium fluoride , 10 mm EDTA , 10 mm sodium orthovanadate , 2 . 0 mm phenylmethylsulfonyl fluoride , and 0 . 1 mg of aprotinin/ml ) with a Polytron PTA 20 S generator ( model PT 10/35; Brinkmann Instruments ) . Insoluble material was removed by centrifugation for 30 min at 9 , 000×g in a 70 Ti rotor ( Beckman , Fullerton , California ) at 4°C . The protein concentrations of the supernatants were determined by the Bradford dye binding method . Aliquots of the resulting supernatants containing 1 . 0 mg of total protein were used for immunoprecipitation with antibodies against MyD88 overnight at 4°C , followed by SDS-PAGE , transfer to nitrocellulose membranes , and blotting with anti-TLR4 . In direct immunoblot experiments , 0 . 2 mg of protein extracts were separated by SDS-PAGE , transferred to nitrocellulose membranes , and blotted with anti-UCP1 , anti-phospho-JNK , anti-IκBα , anti-phospho-PERK , anti-phospho-AKT , anti-phospho [Ser307]-IRS-1 , anti-phospho [Tyr941]-IRS-1 ( Tyr ) , anti-phospho-IR , anti-ZO-1 , anti-PGC-1α , anti-phospho [Thr171]-AMPK , and anti-IκB-α . The homogeneity of gel loading was always evaluated by blotting the membranes with antibodies against β-actin , IRS-1 , AKT , IR , JNK , PERK , and AMPK as appropriate . Specific protein bands present on the blots were quantified by densitometry . Mean ± S . E . values obtained from densitometric scans and from the other experiments were compared utilizing Student's t test for paired samples or by repeat-measure analysis of variance ( one-way or two-way analysis of variance ) followed by post hoc analysis of significance ( Bonferroni test ) when appropriate . When analyzing non-linear parameters , we used Mann-Whitney test . A p<0 . 05 was accepted as statistically significant .
|
An intricate interaction between genetic and environmental factors influences the development of obesity and diabetes . Previous studies have shown that mice lacking an important receptor of the innate immune system , Toll-like Receptor 2 ( TLR2 ) , are protected from insulin resistance . Given that the innate immune system has emerged as a key regulator of the gut microbiota , we undertook to investigate in this study whether the gut microbiota have a role in modulating the response to insulin . By rearing these TLR2 mutant mice in conventional facilities ( as opposed to “germ-free” conditions ) we figured that they would develop an altered gut microbiota . In contrast to previous studies , our results show that these TLR2 mutant mice now develop a diseased phenotype reminiscent of metabolic syndrome , including weight gain , and end up with gut microbiota similar to that found in obese mice and humans . These mice could be rescued by treatment with broad-spectrum antibiotics , which decimated the microbiota . Conversely , transplantation of the gut microbiota from these mice to wild-type mice induced weight gain and the metabolic syndrome phenotype . Our results indicate that the gut microbiota per se can subvert a genetically predetermined condition previously described as being protective towards obesity and insulin resistance into a phenotype associated with weight gain and its complications , such as glucose intolerance and diabetes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"model",
"organisms",
"endocrinology",
"immunology",
"biology",
"microbiology",
"diabetes",
"and",
"endocrinology",
"genetics",
"and",
"genomics",
"metabolic",
"disorders"
] |
2011
|
Gut Microbiota Is a Key Modulator of Insulin Resistance in TLR 2 Knockout Mice
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Certain Major Histocompatibility-I ( MHC-I ) types are associated with superior immune containment of HIV-1 infection by CD8+ cytotoxic T lymphocytes ( CTLs ) , but the mechanisms mediating this containment are difficult to elucidate in vivo . Here we provide controlled assessments of fitness landscapes and CTL-imposed constraints for immunodominant epitopes presented by two protective ( B*57 and B*27 ) and one non-protective ( A*02 ) MHC-I types . Libraries of HIV-1 with saturation mutagenesis of CTL epitopes are propagated with and without CTL selective pressure to define the fitness landscapes for epitope mutation and escape from CTLs via deep sequencing . Immunodominant B*57- and B*27- present epitopes are highly limited in options for fit mutations , with most viable variants recognizable by CTLs , whereas an immunodominant A*02 epitope-presented is highly permissive for mutation , with many options for CTL evasion without loss of viability . Generally , options for evasion overlap considerably between CTL clones despite highly distinct T cell receptors . Finally , patterns of variant recognition suggest population-wide CTL selection for the A*02-presented epitope . Overall , these findings indicate that these protective MHC-I types yield CTL targeting of highly constrained epitopes , and underscore the importance of blocking public escape pathways for CTL-based interventions against HIV-1 .
HIV-1-specific CD8+ cytotoxic T-lymphocytes ( CTLs ) play a significant protective role in the pathogenesis of HIV-1 infection [1–3] , but ultimately fail to prevent disease progression in most persons . Myriad failure mechanisms have been proposed , but given the remarkable mutation rate and sequence plasticity of HIV-1 [4 , 5] , the major factor is viral epitope escape mutation resulting in a cascade of viral persistence , CTL exhaustion , dysfunction , and senescence in chronic infection [6] . Indeed , evasion of CTLs is the major determinant of viral evolution in vivo [7–10] . Moreover , the major histocompatibility complex class I ( MHC-I ) locus is the best defined genetic determinant of disease progression rate in genome-wide association [11–13] and epidemiologic studies [14 , 15] , indicating that MHC-I-associated properties of CTLs are important determinants of their efficacy . Several studies of persons with “protective” MHC-I types who contain viremia without treatment have shown limited variation in targeted epitopes . Some have suggested that these are limited escape mutations with high fitness costs , based on examination of a few epitope variants observed in vivo [16–24] . However , the generality and mechanisms behind this observation are unclear , and the contributions of viral versus immune constraints for HIV-1 escape from CTLs are incompletely understood . Properties of the targeted epitope could be important; HIV-1 sequence plasticity is not uniform and epitopes likely vary in their constraints for mutation [25] . Alternatively , properties of the CTLs could differ; it has been proposed that the T cell receptors ( TCRs ) associated with protective MHC-I types either have greater cross-reactivity for epitope mutants and thus better limit possibilities for escape [26–28] , or rather are better matched to common epitope variants [29] . Thus it is unresolved whether the limited escape is due to properties of the epitopes versus CTLs . Finally , CTL responses against a given epitope are generally comprised of multiple clones with differing TCRs [30 , 31] . Because individual clones recognizing the same epitope can vary in the recognition of different variants [32–34] , it has been proposed that clonal breadth may be important for preventing escape [30] , but protective MHC-I types do not appear to yield greater TCR breadth overall [31] . This suggests qualitative differences in the composition or function of TCRs , and it is unclear to what degree the constraints for HIV-1 to escape CTLs are shared ( “public escape” ) versus specific for each clone ( “private escape” ) . Such issues are difficult to address in vivo , where the CTL response is polyclonal , the starting sequences of HIV-1 are typically undefined , and it is impossible to normalize selective pressure between epitopes . Here we assess the effect of HIV-1-specific CTLs on the fitness landscape of viral epitope mutation at clonal resolution . Libraries of HIV-1 epitope mutants are propagated under selective pressure to define the options for immune escape for multiple CTL clones associated with protective and non-protective MHC-I types , addressing these issues with an experimentally controlled approach to reveal CTL escape pathways for HIV-1 .
Saturation mutagenesis was applied to three immunodominant HIV-1 epitopes in Gag ( Table 1 ) : SLYNTVATL ( SL9 , Gag 77–85 , A*02-restricted ) , KAFSPEVIPMF ( KF11 , Gag 162–172 , B*57-restricted ) , and KRWIILGLNK ( KK10 , Gag 263–272 , B*27-restricted ) . Degenerate nucleotide DNA synthesis was utilized for each codon encoding the epitope and its flanking amino acids , as well as every combination of two codons , followed by substitution into the whole proviral genome of HIV-1 strain NL4-3 ( Fig 1 ) . The resulting plasmid libraries were found by deep sequencing to contain a full representation ( 100% for each epitope ) of single amino acid variants and partial representation of double amino acid variants ( 38 to 43% ) achieving the threshold frequency of 2 . 5x10-5 that was considered adequate for detectable virus production after transfection ( Table 2 ) . As expected , the consensus epitope sequence was overrepresented in each library because consensus amino acids were included in every degenerate codon ( Fig 2A ) . These proviral DNA libraries were transfected into producer cells to yield starting virus libraries after a week of expansion . Deep sequencing of viral RNA in these libraries again demonstrated that the consensus epitope variant was predominant , but also demonstrated that a minority of the adequately represented variants in the plasmid library persisted as replication-competent variants ( Table 2 and Fig 2A ) , suggesting that most epitope mutations were deleterious ( 36 . 4 to 86 . 6% of single codon mutants , 99 . 12 to 99 . 97% of double codon mutants ) . Epitope variants with a threshold frequency <10−4 in two experimental replicates of virus libraries were considered to be nonviable , because they tended to decay if present in only one library , indicating insufficient replicative capacity . To investigate the properties of the epitope variants , each library was passaged in the absence and presence of CTLs ( Table 1 ) that had been confirmed to have antiviral activity in virus suppression assays ( S1 Fig ) . Epitope sequences were obtained by deep sequencing at baseline and after each of two serial passages of one week each ( S2 Fig ) . Significant shifts in the frequencies of epitope variants within a library occurred in the absence of CTLs , reflecting replicative capacity differences between variants ( Fig 2A ) . Library propagation with the addition of epitope-targeted CTLs yielded distinctly different profiles of epitope variants , indicating superimposed selective pressure by the CTLs ( Fig 2B ) . Control CTLs targeting an irrelevant epitope did not induce a profile distinct from passaging without CTLs , and the magnitude of the epitope-specific CTL-induced change was dose-dependent ( Fig 2C ) . The small minority of variants containing stop codons that achieved the detectable threshold in the initial virus libraries generally showed sharply decaying frequencies ( S3 Fig ) , confirming the reflection of replicative capacity . The outcome for each epitope variant was quantified as a relative enrichment value ( RE ) compared to the subtype B consensus epitope sequence , calculated as the log10 transformed ratio of frequencies normalized to subtype B consensus variant ( Fig 2D ) in the absence or presence of CTLs ( RE-CTL and RE+CTL respectively ) . Thus RE-CTL values reflected intrinsic replicative capacity relative to the consensus variant , with values <0 and >0 indicating variants replicating less and more efficiently ( relative to the consensus variant ) respectively . RE+CTL values reflected the impact of CTL selection relative to the consensus variant , independently of replicative capacity ( e . g . a variant with RE-CTL<0 and RE+CTL>0 indicates that it replicates less efficiently and is less suppressed by CTL than consensus ) . The REs between experimental replicates were highly correlated ( Fig 2E and 2F ) , demonstrating the robustness of this measurement . Two separately produced virus libraries were utilized for all further determinations of RE-CTL and RE+CTL values , which were calculated as averages of quadruplicates ( duplicate virus libraries each passaged in duplicate without CTLs ) and duplicates ( duplicate virus libraries each passaged singly with CTLs ) respectively . The impacts of mutations at each epitope amino acid position were evaluated by examining the subsets of single codon mutants in each library ( Figs 3–5 ) . Passaging in the absence of CTLs revealed the effects of point mutations on intrinsic viral replication . For each epitope , most mutations had negative effects on replication ( RE-CTL<0 ) . However , each epitope also demonstrated mutations that were tolerated or advantageous ( RE-CTL≥0 ) . For SL9 , substitutions at multiple positions yielded enrichment , particularly at residues -1 , 5 , and 8 of the epitope ( Fig 3 “No CTL” panel ) . KF11 appeared to have fewer tolerated mutations ( Fig 4 “No CTL” panel ) , mostly at residues 2 and 4 , while KK10 ( Fig 5 “No CTL” panel ) had several tolerated mutations mostly at residues 2 , 5 , and 6 . Evaluation of these epitopes under additional CTL selection also demonstrated patterns of epitope enrichment relative to the consensus epitope sequences ( RE+CTL >0 ) . The addition of CTL generally appeared to augment enrichment of epitope variants with intrinsic growth advantages in the absence of CTLs ( Figs 3–5 ) , although there were also some intrinsically disadvantageous variants that gained enrichment with the addition of CTLs . Conversely , some intrinsically advantageous variants were selected against with the addition of CTLs , particularly those with substitutions at the -1 position of the SL9 epitope . The net effect of CTL selection ( ΔRE = RE+CTL- RE-CTL ) was examined for each epitope variant ( Fig 6 ) . The relevance of this value to identify potential CTL escape variants was confirmed by generating HIV-1 clones corresponding to library variants with defined ΔRE values , and testing their susceptibility to inhibition of replication by CTLs ( Fig 7 ) . Thus this parameter showed that many single substitutions conferred benefits against CTL selection ( Fig 6 ) . A major exception was the N-terminal flanking amino acid of the SL9 epitope ( position -1 ) , where most substitutions increased susceptibility to CTLs . Overall , these data demonstrate epitope-specific constraints for mutation and evasion of CTLs . Quantitative analyses were extended to all epitope variants in the libraries , including double amino acid mutants ( Table 1 ) , to compare epitopes . First examining RE-CTL ( Fig 8 , S4–S6 Figs first columns ) , the SL9 library yielded more variants with neutral to moderately decreased replication capacity ( RE-CTL≥0 or RE-CTL≥-0 . 5 ) compared to KF11 and KK10 , whereas KF11 and KK10 were similar ( Fig 9 ) . There were 30 and 59 ( 0 . 34% and 0 . 68% of all single and double SL9 mutants adequately represented in the plasmid library ) SL9 variants with RE-CTL ≥0 and -0 . 5 respectively , compared to 2 and 17 ( 0 . 018% and 0 . 031% ) and 3 and 16 ( 0 . 16% and 0 . 16% ) of KF11 and KK10 epitopes reaching those thresholds ( Fig 9 top ) . The distributions of measurements showed increasing numbers of lower RE-CTL variants , consistent with insufficient replicative capacity for the variants in the plasmid library that were not detected in the virus library ( Fig 9 bottom ) . Comparing susceptibilities of epitope variants to CTLs ( ΔRE ) , many variants had neutral to enriched effects under CTL selection ( Fig 8 ) . Across all variants in the virus libraries ( excluding variants with mutations in epitope flanking residues , to isolate effects of changes in CTL epitope recognition from epitope processing ) , this parameter displayed a range of values that was normally distributed ( Fig 10 ) . The mean ΔRE value across all SL9-specific CTLs was similar to KF11- and KK10- specific CTLs ( 1 . 31 versus 1 . 54 and 1 . 32 respectively ) , although the percentages of variants with at least 5-fold advantage under CTL selection ( ΔRE≥0 . 7 ) was significantly higher ( 92 . 9% versus 78 . 1% and 74 . 8% respectively , Fig 10 top ) . Over the range of 2-to 10-fold relative enrichment with CTLs , a stable profile of selected variants was observed ( S4-S6 third columns ) , and thus 5-fold selection ( ΔRE≥0 . 7 ) was chosen as a definition of potential escape . Finally , considering the numbers of potential escape variants under this definition with at least moderate replicative capacity ( RE-CTL≥-0 . 5 ) as viable options for escape , SL9 had significantly more options than KF11 or KK10 . Using mean values across CTLs , SL9 had 19 variants ( 0 . 22% of variants in the virus library ) compared to 4 ( 0 . 036% ) and 3 ( 0 . 031% ) variants for KF11 and KK10 respectively meeting these criteria ( Fig 11 ) . In summary , the SL9 epitope offers more options for viable mutations than KF11 or KK10 , and average CTL coverage of those mutations is similar or perhaps modestly decreased for SL9 compared to KF11 and KK10 ( Fig 12 ) .
This study addresses the fitness landscape for mutational variation of three HIV-1 epitopes and the restrictions imposed by CTLs . While in vivo observations have revealed the effects of CTL on viral evolution to escape , our data dissect this process in greater detail , resolving the interaction at the level of individual CTL clones and defined starting virus quasispecies populations . For each epitope , the effect of every single amino acid polymorphism ( as well as about a third of all double amino acid polymorphisms ) versus the subtype B consensus sequence is assessed by frequency change as a reflection of fitness during serial passaging , as well as the impact of clonal CTL selection on these variants . Two epitopes presented by protective MHC-I types B*57 ( KF11 ) and B*27 ( KK10 ) and an epitope presented by the non-protective type A*02 ( SL9 ) are examined in detail . The quantities of mutation options in the absence of CTL selection markedly differ between these epitopes . The SL9 epitope exhibits many variants with similar or higher fitness compared to consensus , whereas KF11 and KK10 epitopes appear to have very few . This finding indicates that the SL9 epitope is much less constrained for mutation than KF11 and KK10 , suggesting that HIV-1 generally has fewer options for mutational escape in KF11 and KK10 ( Gag p24 ) than SL9 ( Gag p17 ) epitopes . This result agrees with prior observations that: efficient immune containment of HIV-1 corresponds to CTL targeting of p24 [35] , that immunodominance of p24 targeting is commonly associated with protective MHC-I types ( including B*57 and B*27 ) [36 , 37] , and that p24 is highly conserved overall [25] . However , studies delineating associations of particular CTL responses with immune containment of HIV-1 demonstrate that protective epitope targeting is not limited to p24 [37 , 38] , suggesting that sequence constraint at the level of the individual epitope overrides the particular source protein in importance for escape and thus CTL efficacy . The epitope variants that were enriched under CTL selection further illuminate the constraints for escape mutation . For SL9 , there are several highly CTL-enriched variants with intrinsic fitness near the consensus epitope . In contrast , KF11 and KK10 both exhibit few CTL-enriched variants with preserved fitness , in agreement with prior studies showing that CTL escape mutations for these epitopes require high fitness costs [16–19 , 21–24] . Moreover , the variants enriched by CTL selection recapitulate several previously reported escape variants in vivo , such as Y79F in SL9 [16] and A163G in KF11 [18] , although some other reported escape variants such as KF11 A163G/S165N [18] were present in the initial plasmid library but appeared replication incompetent . As a whole , these data support the concept that protective MHC-I types such as B*27 and B*57 are beneficial through generating CTL responses against epitopes for which escape occurs only at a high fitness cost to HIV-1 . Regarding the alternative hypothesis that protective MHC-I types yield TCRs with greater promiscuity for epitope variation [26–28] , our findings do not provide definitive evidence . While KF11- and KK10- specific CTLs do appear to recognize more variants on average than SL9-specific CTLs , the average impacts of CTLs on epitope variants do not vary significantly between epitopes . However , these measurements are limited to CTL interactions only with viable variants , and are thus not a comprehensive evaluation of promiscuity across all epitope variation . Within the subset of viable mutants , there is no clear difference in coverage by CTLs across the three epitopes , and the findings are consistent with a study suggesting that better immune containment of HIV-1 is mediated by CTL responses that are more focused on viable epitope variants despite recognizing fewer epitope variants overall [29] . An unexpected finding is that CTL recognition of SL9 is enhanced by various substitutions at the N-terminus flanking amino acid . This suggests that these substitutions increase epitope presentation compared to the consensus sequence . Although the influence of various mutations within the SL9 epitope reducing its proteasomal processing and presentation have been demonstrated [39] , the impairment of processing associated with the N-terminus flanking residue in the consensus sequence has not been reported . Given the high prevalence of A*02 and the capacity of other MHC-I types such as B*40 to present the SL9 epitope , it is plausible that the consensus sequence represents escape adaptation across the human population . Also unexpected is the observation that several SL9 epitope variants had apparently higher fitness than the consensus sequence . Both these findings support the proposal that HIV-1 can accumulate escape mutations in the consensus sequence for circulating strains , as has been suggested specifically for SL9 [40] and more generally across the HIV-1 genome [7 , 8] . We previously reported the differential ability of CTL clones targeting the same epitope to cross-recognize escape variants [32–34] . Here we confirm such differences between clones , but find that the overall options for escape are strikingly similar even between TCRs with entirely different variable chains . For each epitope , the amino acid substitutions resulting in CTL evasion follow stereotypic patterns mostly sparing the main MHC-I anchor-binding residues . Although such substitutions could affect proteasomal processing , epitope stability , or MHC-I binding , this suggests shared mutational pathways for ablating binding of sequence-distinct TCRs , and that these “public escape” pathways may predominate for these epitopes , consistent with prior population-based studies of HIV-1 escape “footprints” in vivo [8 , 41] . Several caveats must be considered for the interpretation of our data . Our libraries provide complete coverage for single amino acid polymorphisms in the epitopes , but incomplete coverage for double amino acid polymorphisms , and no coverage for three or more changes . However , most reported escape mutations are single or double polymorphisms compared to consensus , and our data show sharply decreased viability for double mutants compared to single mutants , suggesting that very few triple mutants would be viable . The RE values for epitope variants are semiquantitative reflections of HIV-1 fitness , given the saturating conditions for viral growth that can exaggerate the competitive advantage of the most fit variants . Moreover , the selective pressure exerted by CTLs is dependent on the experimental conditions , i . e . the number of added cells and functional activity of the cells . While these parameters are kept as constant as possible between experiments , there is biologic variability that is difficult to control entirely; thus setting RE values based on consensus sequence epitopes provides a frame of reference for comparisons between different experiments and SL9 , KF11 , and KK10 epitopes , because HIV-1 with consensus sequences in all three epitopes is shared between all libraries . Finally , fitness costs for sequence polymorphisms can vary considerably in different genomic contexts , and our results in HIV-1 strain NL4-3 using single epitope targeting may not reflect the outcome for different virus with CTL pressure on multiple epitopes simultaneously . Related to this point is the inability to assess for compensatory mutations . However , the general patterns we observe are striking , and provide insight into the overall levels of constraints for these epitopes . In summary , our findings indicate that two immunodominant epitopes associated with protective MHC-I types have highly restricted fitness landscapes for mutation compared to one that is not associated with protection , and that this allows very limited options for escape from CTLs . Additionally , most escape pathways appear to be public and shared between different clones recognizing these epitopes . These results have implications for harnessing CTL responses as vaccines and/or immunotherapies . An early attempt at therapeutic adoptive transfer of CTLs resulted in rapid viral escape [42] , and analysis of the failed Step trial demonstrated a “sieve” effect in infected individuals , reflecting viral escape from vaccine-induced CTLs [43] . Thus , a successful CTL-based approach will require understanding of the constraints for escape and strategies to block HIV-1 escape routes through reducing HIV-1 options for mutational escape and/or increasing CTL coverage of mutation options .
Double-stranded DNA spanning the Gag epitope regions of interest were commercially synthesized ( gBlock , Integrated DNA Technologies , Coralville , IA ) using NNK degenerate codons ( where “N” is any nucleotide , and “K” is guanine or thymidine ) at each single or double codon position for the epitope and its flanking codons . These gBlock DNA fragments were then PCR amplified using primers 5’-ATCTCTAGCAGTGGCGCCC-3’ with 5’-TTTGGCTGACCTGGCTGTTG-3’ for the fragment containing the SLYNTVATL ( Gag 77–85 , SL9 ) epitope , and 5’-AGACACCAAGGAAGCCTTAGATAAGA-3’ with 5’-TACCTCTTGTGAAGCTTGCTCG-3’ for the fragments containing the KAFSPEVIPMF ( Gag 162–172 , KF11 ) and KRWIILGLNK ( Gag 263–272 , KK10 ) epitopes . These primer sequences corresponded to the start and end sequences of the synthesized DNA fragments . A modified HIV-1 NL4-3 provirus plasmid was created to reduce LTR-driven recombination during cloning , with 5’ U3 and 3’ U5 regions of the HIV LTR removed ( to reduce LTR homology ) , flanked by the CMV immediate-early promoter and the BGH polyA sequence ( Fig 1 ) . Additionally , this vector was modified to delete the synthesized epitope regions except the first and last 15 nucleotides; the junction of the deleted regions were modified to have blunt cutting restriction enzyme sites: SfoI for the region containing SL9 , AfeI for the region containing KF11 and KK10 . After linearizing each plasmid vector with the appropriate enzyme , the PCR-amplified gBlock DNA fragments were inserted via the 15 nucleotide homology by “Infusion” ( Clontech , Mountain View , CA ) to created whole genome plasmid libraries . The resulting plasmids were then transformed into Stellar chemocompetent E . coli ( Clontech , Mountain View , CA ) , plated onto 100mm LB/ampicillin plates at ~2x104 colonies/plate and grown for 24 hours at 30°C . Colonies were collected by washing the bacteria from the plates with Luria broth with ampicillin . The plasmid DNA isolated from these bacteria served as the initial “plasmid libraries” for each epitope . The plasmid libraries of each epitope were lipofected into two T75 flasks of 70% confluent HEK 293T cells ( obtained from Dr . Irvin S . Y . Chen , University of California , Los Angeles ) using 20μg DNA with BioT lipofection reagent ( Bioland Scientific , Paramount , CA ) . After 24 hours the media was removed , and 107 T1 cells [44] ( obtained from Dr . Bruce D . Walker , Harvard University ) in 20mL RPMI 1640 medium supplemented with 10% FCS , L-glutamine , HEPES , and penicillin-streptomycin ( R10 ) were added to each flask to promote cell-cell infection of the T1 cells . After 24 hours , the nonadherent cells were removed and transferred to a new flask . These cells were then cultured for 6 to 8 days in R10 media until at least 50% of the cells were infected with HIV-1 ( determined by expression of p24 antigen in the cells by intracellular staining and flow cytometry ) . The supernatant was then filtered through a 0 . 45 micron filter and cryopreserved to be utilized as the “starting virus library . ” All virus libraries were produced in duplicate , and all experiments utilized both libraries in parallel , with duplicates for cultures without CTLs ( two replicates for each library , four total ) and singles for cultures with CTLs ( one replicate for each library , two total ) . Cell lines utilized for passaging of HIV-1 included T1[44] ( expressing A*02 for the SL9 library and A*02-restricted CTLs ) , 1CC4 . 14 cells ( expressing B*57 for the KF11 library and B*57-restricted CTLs , previously produced in our laboratory [45] ) , and Subject 00076 EBV-transformed B-cells ( previously produced in our laboratory from PBMC ) that were transduced with human CD4 ( expressing B*27 for the KK10 library and B*27-restricted CTLs ) . CTL clones ( Table 1 ) were previously isolated from chronically HIV-1-infected persons and maintained as previously described [46–48] from blood obtained with written informed consent under a University of California , Los Angeles Institutional Review Board-approved protocol , with the exception of 68A62 provided by Dr . Bruce D . Walker ( Harvard University ) . In brief , peripheral blood mononuclear cells ( PBMCs ) were enriched for the CTLs of interest by culture with the appropriate epitope , followed by cloning at limiting dilution . Some experiments utilized KK10-specific CTLs previously produced by stable lentiviral transduction of allogeneic CD8+ T-cells with a KK10-specific T cell receptor ( TCR ) sequence identified by quantitative spectratyping [31] ( TCR5 ) that had been cloned into a lentiviral vector as previously described [34] . CTLs were maintained by periodic stimulation with 200ng/mL of the monoclonal anti-CD3 12F6 antibody [49] with irradiated allogeneic PBMCs ( obtained anonymously through the UCLA AIDS Institute Virology Core Facility ) in R10 media supplemented with recombinant human interleukin-2 ( NIH AIDS Reference and Reagent Repository ) at 50IU/mL ( R10-50 ) . For the CTL clones , TCR beta variable ( BV ) chain sequences were determined after RNA isolation using Trizol reagent ( ThermoFisher Scientific , Waltham , MA ) , amplification and cloning of the BV gene using the SMARTER 5’ RACE kit ( Clontech , Mountain View , CA ) with a constant region primer ( 5’-CTTCTGATGGCTCAAACAC-3’ ) , and sequencing using the same primer . 5x106 permissive cells ( 106 cells for the SL9 library passaged with the 1 . 9 CTL ) were infected with the starting virus library , yielding about 10–20% infected cells after 72–96 hours ( determined by intracellular staining for p24 ) . The cells were then washed twice and resuspended at 5x105 cells/mL in R10-50 . CTLs were added at effector:target ratios of 1:8 ( except 1:2 for the SL9 library with CTL 1 . 9 ) , with parallel no-CTL controls . These cultures were fed every 3 days by removing and replacing half of the media . After 7 days the supernatant was filtered through a 0 . 45 micron filter and cryopreserved; virus in the supernatant was quantified via p24 ELISA ( Xpress Bio , Frederick , MD ) . This virus was utilized to infect cells for a second passage in the same manner using 5x103 pg p24 per 106 target cells ( 103 pg p24 per 106 target cells for the KK10 library ) , followed by collection and cryopreservation as before . All passaging with CTLs was performed with duplicate virus libraries , and passaging without CTLs was done in quadruplicate ( 2 replicates for each virus library ) . The passaged virus supernatant was treated with DNAse I ( New England Biolabs , Ipswich , MA ) to remove residual plasmid DNA . HIV-1 RNA was isolated with the QIAmp viral RNA mini kit ( Qiagen , Hilden , Germany ) , and reverse-transcribed with the high capacity cDNA reverse transcription kit ( ThermoFisher Scientific , Waltham , MA ) and quantified by real-time PCR with ssoFast EvaGreen supermix on a CFX96 ( Bio-Rad , Hercules , CA ) with gag-specific primers ( 5’-ATCTCTAGCAGTGGCGCCC-3’ and 5’-TTTGGCTGACCTGGCTGTTG-3’ ) compared to NL4-3 plasmid standard to ensure ≥5x105 copies/μL of cDNA per specimen . This cDNA and the starting plasmid libraries were prepared for deep sequencing by PCR amplification using primers tagged with 6 base-pair customized barcodes . The gene specific portions of the primers were: Deep sequencing was performed with Hiseq PE150 sequencing ( Illumina , San Diego , CA ) . The sequence data were parsed using the SeqIO function of open source BioPython software ( http://biopython . org/ ) . Sequences from different samples were de-multiplexed by the barcodes and mapped to the corresponding region in the HIV-1 genome . Since both forward and reverse reads covered the mutated region , paired reads were used to compensate for sequencing errors . A polymorphism was accepted as valid only if observed in both reads and with a quality score ≥30 . Further filtering for errors was done by comparison to control deep sequencing of the index NL4-3 plasmid; variants present at a frequency <10−4 were only accepted if their frequencies in duplicate virus libraries exceeded 10-fold the observed frequency of the variant in the control plasmid sequences ( due to background error ) . The sequencing depth was >6x105 and >4x106 for the virus and plasmid libraries respectively . All the data processing and analysis was performed with customized python scripts , which are available upon request . Variants above threshold in initial virus libraries whose frequencies decayed to 0 after passaging were assigned a frequency of 10−6 for calculation of RE values . All sequences have been uploaded to GenBank ( PRJNA394927 ) . Site-directed mutagenesis was performed with the Q5 mutagenesis kit ( New England Biolabs , Ipswich , MA ) on the modified pNL4-3 vector described above , which had been further modified to contain the M20A mutation that ablates Nef-mediated MHC-I downregulation [50 , 51] . The SL9 epitope was modified to create variants SLYNAVAVL ( codon 4 = GCT , codon 7 = GTG ) , SLYNTVACL ( codon 8 = TGT ) , SLYITVATL ( codon 4 = ATA ) , SLYNCVACL ( codon 5 = TGT , codon 8 = TGT ) , SLYCTVATL ( codon 4 = TGT ) , and the resulting plasmids were lipofected into HEK 293T cells as above to produce virus . Evaluation of HIV-1 susceptibility to CTL suppression was performed as previously described [32 , 48] . Briefly , T1 cells [44] were infected with 500pg p24/106 cells of the indicated viruses , and 5x104 infected cells with 5x104 CTL ( S2 Fig ) or 1 . 25x104 CTL ( Fig 9 ) were cultured in 200μL R10-50 U/ml IL-2 in a 96 well flat-bottom plate , with monitoring of supernatant p24 antigen by ELISA ( Xpress Bio , Frederick , MD ) . Comparisons for correlations of replicate experiments and selection of epitope variants by different CTL clones were performed using Spearman rank correlation . Comparisons of means of two groups were performed using Student’s t-test . Comparisons of frequencies between two groups were performed using Fisher’s exact test .
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Certain MHC class I types are associated with superior immune containment of HIV-1 , underscoring the importance of CD8+ cytotoxic T lymphocytes ( CTLs ) . Epitope escape mutations for these types is limited , indicating reduced immune evasion . Two proposed mechanisms are: 1 ) CTL targeting of highly sequence-constrained epitopes , or 2 ) more promiscuous CTLs for epitope variation . However , the in vivo complexity of undefined starting virus , multiple targeted epitopes , polyclonal CTL responses against each epitope , and post-hoc evaluation of the interaction renders examination of mechanisms difficult . Here we approach this question with controlled prospective in vitro experiments using saturation mutagenesis of epitopes in clonal HIV-1 , propagated in the absence or presence of CTL clones to define the options for epitope mutation and immune evasion by deep sequencing . We find that two immunodominant epitopes presented by protective MHC types are highly mutation-constrained compared to one presented by a non-protective MHC type , whereas CTL promiscuity for epitope variation is not appreciably different . These results suggest that these protective MHC types are associated with limited HIV-1 escape predominately due to intrinsic constraints on epitope mutation , and underscore the importance of focusing the CTL response on highly conserved epitopes for immunotherapies and vaccines .
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2017
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HIV-1 epitopes presented by MHC class I types associated with superior immune containment of viremia have highly constrained fitness landscapes
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Chromatin insulators block the action of transcriptional enhancers when interposed between an enhancer and a promoter . In this study , we examined the role of chromatin loops formed by two unrelated insulators , gypsy and Fab-7 , in their enhancer-blocking activity . To test for this activity , we selected the white reporter gene that is activated by the eye-specific enhancer . The results showed that one copy of the gypsy or Fab-7 insulator failed to block the eye enhancer in most of genomic sites , whereas a chromatin loop formed by two gypsy insulators flanking either the eye enhancer or the reporter completely blocked white stimulation by the enhancer . However , strong enhancer blocking was achieved due not only to chromatin loop formation but also to the direct interaction of the gypsy insulator with the eye enhancer , which was confirmed by the 3C assay . In particular , it was observed that Mod ( mdg4 ) -67 . 2 , a component of the gypsy insulator , interacted with the Zeste protein , which is critical for the eye enhancer–white promoter communication . These results suggest that efficient enhancer blocking depends on the combination of two factors: chromatin loop formation by paired insulators , which generates physical constraints for enhancer–promoter communication , and the direct interaction of proteins recruited to an insulator and to the enhancer–promoter pair .
The complexity of regulatory systems in higher eukaryotes , featuring many distantly located enhancers that nonetheless properly activate the target , has promoted the hypothesis that the action of enhancers should be restricted by elements called insulators . Initially , insulators were regarded as genomic regulatory elements ( nucleoprotein complexes ) that have two characteristic properties: they can block the action of an enhancer on a promoter when interposed between them and can protect the transgenes they flank from chromosomal position effects ( for reviews , see [1]–[7] ) . However , recent results of studies on insulators in transgenic Drosophila lines [8]–[12] , genome-wide identification of biding sites for insulator proteins by ChIP-on-ChIP and ChIP-seq [13]–[17] , analysis of locus architecture by different variants of chromosome conformation capture technology [18]–[19] , and genome-wide analysis of interaction between CTCF sites by paired-end tag ( PET ) approach , ChIA-PET [20] , and Hi-C technique [21] , [22] suggest that insulators are mainly involved in organization of long-distance specific interactions between remote genome regions such as enhancers and promoters , different promoters , or multiple regulatory elements . Well-characterized insulators in Drosophila include the scs and scs' sequences from the 87A heat shock locus [23] , [24]; the Mcp , Fab-7 and Fab-8 insulators from the Abd-B regulatory region [25]–[29]; the SF1 insulator from the Antennapedia complex [30]; the IdefixU3 insulator [31]; the Wari insulator located at the 3′ side of the white gene [10]; and the insulator sequences associated with the Su ( Hw ) protein [32]–[36] . Today , there are two basic models explaining how insulators block the activity of enhancers [1] , [2] , [4] , [6] , [37] . The decoy model suggests that the insulator complex binds to an enhancer or a promoter complex to neutralize it or traps its vital component ( s ) . The alternative model suggests that the interaction between insulators results in the formation of chromatin loops that constrain interaction between an enhancer and a promoter located on the opposite sides of the insulator . The latter model is indirectly supported by the ability of the insulators to specifically interact over large distances [11] , [12] , [20]–[22] , [38]–[40] . However , there are only a few pieces of indirect experimental evidence supporting the model that a loop formed by interacting insulators is essential for enhancer blocking [41]–[46] . Bondarenko et al . ( 2003 ) used a bacterial enhancer–promoter pair and a pair of lac operators ( lacO ) that mimicked eukaryotic insulators [42] , [43] . It was shown in an in vitro transcription assay that the enhancer action was blocked when the interacting lacO copies formed two closed loops , one with the enhancer and the other with the promoter . This finding suggests that if DNA looping alone is sufficient to suppress the enhancer activity in an in vitro model system , it may as well play an important role in eukaryotic cells . Ameres et al . ( 2005 ) examined the expression of a reporter gene in HeLa cells transfected with the plasmid in which the SV40 enhancer was placed downstream of the reporter gene [41] . The SV40 enhancer was flanked by two boxes , each consisting of seven repeats of the tetR element . When the chimeric protein consisting of the tetR protein and a dimerization domain bound to tetR elements , the dimerized proteins formed a 344-bp chromatin loop containing the SV40 enhancer . As a result , this enhancer was blocked , with consequent reduction of the reporter gene expression , which suggested a role for the loop in preventing the interaction between the SV40 enhancer and the promoter . However , alternative mechanisms of SV40 enhancer blocking cannot be excluded . For example , the small chromatin loop may interfere with proper binding of transcription factors to the enhancer . In the study by Hou et al . ( 2008 ) , a CTCF-dependent insulator ectopically inserted between the beta-globin locus control region ( LCR ) and downstream genes was found to function as an enhancer blocker and form an aberrant loop with the endogenous CTCF region located upstream of the LCR [46] . However , these authors did not perform experiments to show that the ectopic insulator could not block LCR in the absence of the loop formation with the endogenous insulator and , therefore , failed to obtain direct evidence for the role of chromatin loop formation in enhancer blocking . For this reason , we established a Drosophila transgenic model system in order to test whether isolation of either an enhancer or the reporter white gene in a loop formed by gypsy or Fab-7 insulators can block the enhancer–promoter communication . The gypsy insulator , the strongest and the best studied in Drosophila , contains 12 consecutive degenerate direct repeats of the binding motif for the zinc-finger protein Su ( Hw ) , which is indispensable for the insulator function [32] , [33] . The Su ( Hw ) protein also associates with hundreds of non-gypsy regions that do not contain clustered Su ( Hw ) -binding sites , with the vast majority of them carrying a single copy of the corresponding sequence [13] , [14] , [36] . Su ( Hw ) interacts with three other components of the gypsy insulator , Mod ( mdg4 ) -67 . 2 [47] , [48] , CP190 [49] , and E ( y ) 2 [50] . Based on the results of genetic interactions , it has been suggested that Mod ( mdg4 ) -67 . 2 and CP190 are essential for the enhancer-blocking activity of Su ( Hw ) insulators . Mod ( mdg4 ) -67 . 2 interacts with Su ( Hw ) through its carboxy-terminal domain [47] , [48] , [51] . The BTB domain is located at the N-terminus of Mod ( mdg4 ) -67 . 2 and mediates homo-multimerization [52] . There are many evidences of functional distant interactions between the gypsy insulators [9] , [11] , [53] , which were recently confirmed by the 3C method [54] . Among insulators found in the regulatory region of Abd-B , the best characterized is Fab-7 located between the iab-6 and iab-7 cis-regulatory domains [55] . Mutations that inactivate Fab-7 lead to the fusion of the iab-6 and iab-7 domains , and this disrupts the specification of PS11 [56]–[58] . Previously we found that interaction between paired two copies of the Fab-7 insulator can support long-distance enhancer-promoter interactions [59] . It was also found that Fab-7 insulators , similar to gypsy insulators [11] , can support interactions across several megabases [12] . In previous studies [9] , [28] , [33] , [45] , [59] , [60]–[62] , the activities of gypsy and Fab-7 insulators were mainly tested in transgenic lines with the yellow and white genes as reporters that allowed changes in gene expression to be assayed by simple phenotypic analysis . It was found that one copy of the gypsy insulator completely blocked the communication between the yellow enhancers and the promoter [33] , [60] , [62] . Genetic studies on transgenic lines carrying different mutations in the yellow promoter suggested that the gypsy insulator directly interacted with the yellow promoter [63] . Placing the yellow enhancers in a 10-kb chromatin loop formed by the gypsy insulators led to neutralization of enhancer blocking [62] , providing evidence against the role of a chromatin loop formed by the insulators in enhancer blocking . Therefore , we used the white model system to test the role of the chromatin loop in the enhancer-blocking activity of the gypsy and Fab-7 insulators . Recently we found that the white gene contains the insulator , named Wari , located downstream of the polyadenylation signal [10] . This insulator can interact equally well with another copy of Wari and with unrelated Su ( Hw ) insulators [9] , [10] . It was shown that the interaction between the Wari and gypsy insulators strongly improves enhancer blocking . To test whether a single copy of gypsy or Fab-7 insulator can block the eye enhancer , we deleted the Wari insulator from the white gene . We found that one copy of the gypsy or Fab-7 insulator failed to affect the eye enhancer activity in most of the transgenic lines tested . At the same time , the insertion of two gypsy insulators on both sides of the eye enhancer or the white gene completely blocked the enhancer–promoter communication in all transgenic lines . In contrast , flanking the eye enhancer by Fab-7 insulators only slightly contributed to the blocking of enhancer–promoter communication . Such a difference in the ability to block the eye enhancer between the pairs of gypsy and Fab-7 insulators is explained by the finding that the gypsy insulator can directly interact with the enhancer . In particular , Mod ( mdg4 ) -67 . 2 interacts with Zeste and can interfere with its activity in supporting enhancer–promoter communication at the white gene .
To establish a model system for testing the role of a chromatin loop in insulation , we used the white reporter gene that is stimulated by a tissue-specific enhancer in the eyes . The level of eye pigmentation is a sensitive indicator of the amount of white transcription . To test the enhancer-blocking activity of one copy of the gypsy insulator in different genomic positions , we deleted the Wari insulator from the white gene ( WΔ ) . The gypsy insulator flanked by lox sites was inserted between the eye enhancer flanked by frt sites and the white gene ( Figure 1A ) . Parentheses in construct designations and short downward arrows in the schemes indicate the elements flanked by lox or frt sites for in vivo excision by crossing , as outlined in Materials and Methods . Such excisions are denoted by “Δ” in the primary ( expression ) data . Comparing eye phenotypes in the transgenic lines before and after deletion of either the eye enhancer or the gypsy insulator allowed estimation of their contribution to white expression . Since it was shown [64] that the eye enhancer can initiate transcription in the direct orientation , we inserted the eye enhancer in either direct ( Ee ) or reverse orientation ( EeR ) . In Figure 1 , we combined the results obtained with transgenic lines carrying both constructs , because they displayed a similar range of phenotypes . In 22 out of 34 transgenic lines , males heterozygous for the construct had high levels of eye pigmentation ( from brown to brown-red ) that decreased significantly after deletion of the eye enhancer ( Figures 1A , 2A ) . These results suggest that the eye enhancer can stimulate white expression across the gypsy insulator . Moreover , deletion of the gypsy insulator slightly changed eye pigmentation in only 3 out of 22 tested lines , indicating that gypsy failed to block the eye enhancer . To test for the functional role of Zeste in white stimulation by the eye enhancer , we crossed the transgenic lines into the background of zv77h , a null mutation of the zeste gene [65] . As a result , we observed that zv77h strongly reduced eye pigmentation to the same level as did the deletion of the eye enhancer ( Figure 1A ) , indicating that Zeste is critical for the eye enhancer activity . After deletion of the gypsy insulator , zv77h still reduced white expression in transgenic lines ( Figure S1A ) . However , zv77h did not influence white phenotypes in transgenic lines carrying derivative constructs with the deleted eye enhancer . These results suggest that Zeste is essential for the eye enhancer activity even when it is located close to the white promoter . Four transgenic lines had yellow eyes that did not change in color after deletion of the gypsy insulator , indicating that the eye enhancer was inactive in these lines ( data not shown ) . In the remaining eight lines ( Figure 1B ) , the deletion of gypsy led to change in eye color from yellow to brown , suggesting that this insulator could effectively block the enhancer–promoter communication . Thus , the gypsy insulator proved to block the eye enhancer in only 8 out of 30 transgenic lines ( 27% ) . Next , we examined eye color in flies of 19 homozygous transgenic lines in which the gypsy insulator failed to block the eye enhancer ( Figure 1C ) . Unexpectedly , we found that flies carrying the homozygous transgene had lighter eyes than flies with the heterozygous transgene ( Figures 1C , 2B ) . The deletion of the eye enhancer did not change eye pigmentation in lines homozygous for the transgene , indicating that the gypsy insulator completely blocked the eye enhancer in these lines . Taken together , these results suggest that one copy of the gypsy insulator failed to block the eye enhancer–white promoter communication in more than 70% of the transgenic lines . However , pairing between the gypsy insulators located on homologous chromosomes restricted the eye enhancer activity . As a second model insulator , we selected the well-described Fab-7 insulator that supports specific long-distance interactions [12] . As shown in previous experiments with transgenic lines carrying constructs with the 1 . 2-kb Fab-7 insulator inserted between the eye enhancer and the white promoter , flies in approximately half of these lines had relatively light eyes indicating effective blocking of the eye enhancer by the Fab-7 insulator [25] , [61] . However , it was not proved that the eye enhancer was functional in these lines , since the Wari insulator located at the 3′ end of the white gene could also improve the activity of Fab-7 . For these reasons , we again tested the enhancer-blocking ability of one Fab-7 copy in the construct where it was flanked by lox sites and inserted between the eye enhancer ( flanked by frt sites ) and the white gene ( Figure 3 ) . In 14 transgenic lines , flies had extensive eye pigmentation ranging from dark orange to brown ( Figure 3A ) . Deletion of the Fab-7 insulator resulted in a slight enhancement of pigmentation in 9 out of 14 transgenic lines , suggesting that Fab-7 functioned as a weak enhancer blocker in these transgenic lines . As in the case of transgenic lines carrying the gypsy insulator , the deletion of the eye enhancer and crossing into the zv77h mutant background reduced eye pigmentation in flies to the same extent ( Figure 3A , S1B ) , suggesting a key role for Zeste in the eye enhancer activity and its ability to bypass the Fab-7 insulator . Flies of the remaining 10 transgenic lines had yellow eyes . When the Fab-7 insulator was deleted , eye pigmentation was restored in only four lines ( Figure 3B ) , suggesting that the eye enhancer in other six lines was inactive ( data not shown ) . Thus , the Fab-7 insulator could effectively block the eye enhancer in only 4 out of 18 transgenic lines ( 22% ) carrying the transgene with the functional eye enhancer . We then examined eye pigmentation in transgenic lines homozygous for the construct ( Figure 3C ) in which the Fab-7 insulator displayed a weak enhancer-blocking activity . In all these lines , flies had darker eye color , compared to the lines heterozygous for the construct , suggesting that the Fab-7 insulator failed to effectively block the eye enhancer when the construct was in the homozygous state . Taken together , these results indicate that the Fab-7 insulator can only weakly affect the activity of the eye enhancer in most of genomic positions . Both Fab-7 and gypsy insulators can effectively block the eye enhancer in approximately one-fourth of transgenic lines . In contrast to the gypsy insulator , the pairing of two Fab-7 insulators located on homologous chromosomes failed to improve the eye enhancer blocking . Since one copy of the gypsy or Fab-7 insulator in most insertion sites of the transgenes failed to block the eye enhancer , we decided to test if the eye enhancer placed between two insulators would improve the efficiency of blocking . For this purpose , we made constructs where the eye enhancer was flanked by a pair of gypsy insulators ( Figure 4A ) or Fab-7 insulators ( Figure 4B ) inserted in opposite orientations . In both cases , the insulator located upstream of the eye enhancer was flanked by lox sites , which allowed us to assess its role in blocking the eye enhancer . In all 11 transgenic lines carrying the construct with two gypsy insulators ( Figure 4A ) , flies had eye pigmentation in the range from pale yellow to orange , indicating that the eye enhancer activity was strongly suppressed . Deletion of the eye enhancer resulted in a slight reduction of pigmentation in only 2 out of 11 lines , providing evidence that the eye enhancer was strongly blocked in all these lines . Deletion of the upstream insulator restored eye pigmentation in 10 transgenic lines ( Figure 4A ) , while subsequent deletion of the eye enhancer reduced it to the initial level ( data not shown ) . These results showed that two copies of the gypsy insulator flanking the eye enhancer completely blocked its activity . In transgenic lines carrying the construct with two Fab-7 insulators ( Figure 4B ) , we observed a wide range of eye phenotypes , from brown-red to yellow . Deletion of the eye enhancer resulted in reduction of pigmentation in 10 out of 11 transgenic lines , showing that two Fab-7 insulators failed to effectively block the eye enhancer . However , the deletion of the upstream insulator provided for slight intensification of eye pigmentation in 8 out of 11 transgenic lines , suggesting that the interaction between the Fab-7 insulators could contribute to enhancer blocking . To test whether two different insulators can cooperate in blocking the eye enhancer , we made the construct that contained one Fab-7 insulator flanked by lox sites inserted upstream of the eye enhancer and one gypsy insulator placed between the eye enhancer flanked by frt sites and the promoter ( Figure 4C ) . We obtained eight transgenic lines that displayed high levels of eye pigmentation . Deletion of the eye enhancer strongly reduced eye pigmentation , while deletion of the Fab-7 insulator did not have any effect on eye color in any of the lines tested . This is evidence that the Fab-7 and gypsy insulators do not functionally interact in blocking the eye enhancer . In transgenic lines described in Figure 4 , the eye enhancer was tightly flanked by the gypsy insulators . Therefore , the putative insulator loop was probably quite small , and chromatin could be wound into a “tight knot” with consequent conformational and/or steric hindrances to the enhancer function . To test whether an increase in the distance between the gypsy insulators flanking the eye enhancer can restore enhancer–promoter communication at the white gene , we inserted the frt-flanked eye enhancer in the center of a 4 . 3-kb fragment bordered by the gypsy insulators in either the opposite ( Figure 5A ) or the same orientation ( Figure 5B ) . The upstream gypsy insulator was flanked by lox sites . In both series of transgenic lines , flies had eye pigmentation in a dark-yellow to pale-yellow range . The deletion of the eye enhancer slightly reduced eye pigmentation in 7 out of 13 transgenic lines . The deletion of the upstream gypsy insulator restored eye pigmentation in all 13 transgenic lines , indicating that one copy of the gypsy insulator failed to block the eye enhancer . These results showed that a 5 . 2-kb loop ( 4 . 3-kb DNA fragment and 0 . 9-kb frt-flanked eye enhancer ) formed by the gypsy insulators allowed blocking of the eye enhancer located in the center of the loop . Importantly , the enhancer blocking did not depend on the relative orientation of the gypsy insulators . In all the above constructs , the white promoter was located in close proximity to one of the insulators that formed the chromatin loop around the eye enhancer . In such configuration of the regulatory elements , the promoter might be unable to interact with the eye enhancer due to steric hindrances . To test for the role of distance between the white promoter and the insulator loop in this process , we modified the construct shown in Figure 5A by inserting an additional 2-kb DNA fragment between the gypsy insulator and the white promoter ( Figure 5C ) . Once again , the interacting gypsy insulators effectively blocked the eye enhancer in most of transgenic lines , while one copy of the insulator had no enhancer-blocking activity . In the next series of experiments , we tried to test whether the relative orientation of the gypsy insulators is important for the enhancer blocking when the eye enhancer is located in close proximity to the insulator inside the loop . For this purpose , we inserted the frt-flanked eye enhancer in close proximity to the upstream gypsy insulator . As a result , the eye enhancer was inside the 5 . 5-kb loop formed by the insulators located in either the opposite ( Figure 5D ) or the same orientation ( Figure 5E ) . In lines carrying the transgene with gypsy insulators in opposite orientations , flies had eye pigmentation ranging from dark yellow to pale yellow , which remained unchanged after the deletion of the eye enhancer . Thus , in such configuration of regulatory elements , gypsy insulators completely blocked the eye enhancer . The deletion of the proximal gypsy insulator restored the enhancer activity , suggesting that the interaction between the insulators was critical for blocking the eye enhancer . Interestingly , when the insulators were in the same orientation , flies had brown eyes and the deletion of the enhancer strongly reduced eye pigmentation , which was indicative of a role for the enhancer in stimulating of the white expression . The deletion of the proximal insulator also slightly reduced eye pigmentation in half of the transgenic lines . Thus , the gypsy insulators located in the same orientation allow the interaction between the eye enhancer located within the loop and the white promoter located outside the loop . Next , we tested if placing gypsy insulators on both sides of the white gene would also lead to the improvement of enhancer-blocking activity . In two constructs , one frt-flanked gypsy insulator was inserted downstream of the white gene , and the other , lox-flanked gypsy insulator was inserted between the eye enhancer and the promoter in either the opposite ( Figure 6A ) or the same orientation ( Figure 6B ) . As a result , the eye enhancer was located upstream of the chromatin domain formed by two gypsy insulators bordering the white gene . When the gypsy insulators were in opposite orientations , the activity of the eye enhancer was almost completely blocked ( Figure 6A ) : flies in 11 transgenic lines had eye pigmentation in the range from orange to yellow . As Zeste is essential for the eye enhancer activity , we regarded the eye phenotype in the zv77h background as resulting from deletion of the eye enhancer . Crossing transgenic lines into the mutant zv77h background only weakly diminished eye pigmentation in 5 out of 11 lines ( Figure 6A ) , confirming that the eye enhancer was inactive . At the same time , the deletion of the downstream gypsy insulator restored white expression , suggesting that the interaction between the gypsy insulators is critical for enhancer blocking . When the gypsy insulators were inserted in the same orientation ( Figure 6B ) , flies displayed higher levels of eye pigmentation . Crossing the transgenes into the zv77h background significantly reduced eye pigmentation in all test lines , indicating that the eye enhancer was partially active . Therefore , when the gypsy insulators had the same orientation , their enhancer-blocking potential was reduced . To test if the distance between the chromatin domain formed by insulators bordering the white gene and the eye enhancer is important for insulation , we inserted a 4 . 6-kb DNA fragment between the eye enhancer and the proximal gypsy insulator ( Figure 6C ) . The gypsy insulators were placed in opposite orientations . In 14 transgenic lines , flies displayed a wide range of eye colors , from brown-red to yellow . The zv77h mutation reduced eye pigmentation in 11 out of 14 lines; i . e . , the eye enhancer could stimulate transcription in most of the lines . Thus , an increase in the distance between the eye enhancer and the chromatin domain formed by the gypsy insulators diminished the insulating effect of the loop . Next , we inserted the 4 . 6-kb fragment between the gypsy insulator and the white promoter ( Figure 6D ) so that the eye enhancer was near the upstream insulator . As a result , the white promoter was in the center of the chromatin domain formed by the gypsy insulators inserted in opposite orientations . Once again , we found that the eye enhancer was partially active in all transgenic lines , indicating that the distance between the enhancer or promoter and the gypsy insulator is important for blocking activity . Comparisons of eye phenotypes in all derivative transgenic lines before and after deletion of the gypsy insulator located on the 3′ side of the white gene ( Figures 6A , 6C , 6D ) showed that the downstream gypsy insulator effectively stimulated white expression . Thus , the gypsy insulator placed at the end of the white gene can potentiate the white promoter activity . According to FlyBase data , the su ( Hw ) gene is weakly expressed in the eyes of adult flies . Therefore , the level of the Su ( Hw ) protein is also low , which may account for the inability of a single copy of the gypsy insulator to block the eye enhancer . To test this possibility , we produced three transgenic lines carrying the su ( Hw ) gene under control of the hsp70 promoter ( Figure S2A ) . The elevated level of Su ( Hw ) had no effect on eye pigmentation in transgenic lines with either one or two gypsy insulators ( Figures 7 , S3 ) suggesting that the concentration of this protein is not critical for eye enhancer blocking by the gypsy insulator . Further stimulation of Su ( Hw ) expression by heat shock ( Figure 7 , S3 ) also had no effect on eye pigmentation in any of transgenic lines . Thus , overexpression of Su ( Hw ) failed to provide for eye enhancer blocking by one copy of the gypsy insulator . Next , we examined the role of Su ( Hw ) and Mod ( mdg4 ) -67 . 2 proteins in blocking the eye enhancer by two copies of the gypsy insulator . To test Su ( Hw ) , we used the su ( Hw ) v/su ( Hw ) 2 combination of mutations that significantly reduced the amount of the Su ( Hw ) protein [66] , [67] . In the su ( Hw ) v/su ( Hw ) 2 background , eye pigmentation was restored to the same level as after deletion of the gypsy insulator , indicating that Su ( Hw ) is critical for insulation ( data not shown ) . In the mod ( mdg4 ) u1 and mod ( mdg4 ) T6 mutations , the truncated Mod ( mdg4 ) -67 . 2 with deleted C-terminal domain partially lost its functional activity [47] , [68] . Both mutations significantly but not completely restored eye pigmentation in the transgenic lines carrying two copies of the gypsy insulator ( Figures 7 , S3 ) , suggesting a role for Mod ( mdg4 ) -67 . 2 in blocking the eye enhancer . To further test the role of the insulator proteins , we examined the effect of the mod ( mdg4 ) u1 mutation in combination with overexpression of the Su ( Hw ) protein ( Figure 7 , S3 ) . The hsp70su ( Hw ) transgene did not affect white expression in the mod ( mgd4 ) mutant background in any of transgenic lines , suggesting that a moderate increase in the amount of Su ( Hw ) is insufficient for counterbalancing Mod ( mdg4 ) -67 . 2 inactivation . However , strong overexpression of Su ( Hw ) after induction by heat shock proved to partially restore enhancer blocking by paired gypsy insulators suppressed by the mod ( mdg4 ) u1 and mod ( mdg4 ) T6 mutations ( Figure 7 , S3 ) . Previously it was found that , in the mod ( mdg4 ) mutant background , the gypsy insulator directly repressed the yellow promoter in pupae [68] and the white promoter in embryos [69] . Therefore , overexpression of Su ( Hw ) in the mod ( mdg4 ) mutant background could possibly lead to direct repression of the white promoter . However , induction of Su ( Hw ) expression by heat shock had no effect on eye pigmentation in flies carrying one copy of the gypsy insulator in the mod ( mdg4 ) mutant background ( Figure 7 ) . Thus , a high level of Su ( Hw ) did not induce direct repression of the white promoter . These results suggest that Mod ( mdg4 ) -67 . 2 is required for blocking the eye enhancer by paired gypsy insulator and that overexpression of Su ( Hw ) can partially compensate for inactivation of Mod ( mdg4 ) -67 . 2 in the mod ( mdg4 ) mutations . Interestingly , a similar result was obtained with the ct6 mutation , a classical model for testing the activity of the gypsy insulator . In the ct6 allele , a gypsy element is inserted close to and completely blocks a wing margin enhancer located about 85 kb upstream of the cut promoter [47] , producing a cut wing phenotype ( Figure S2B ) . The mod ( mdg4 ) u1 mutation almost completely suppressed the ct6 mutant phenotype , suggesting that Mod ( mdg4 ) -67 . 2 is essential for blocking the wing enhancer . Overexpression of Su ( Hw ) in the mod ( mdg4 ) mutant background partially rescued the blocking of the wing enhancer , resulting in an intermediate cut wing phenotype . Thus , a high level of Su ( Hw ) can partially counterbalance the negative influence of the mod ( mdg4 ) u1 mutation on insulation . This is also supported by the previous observation that overexpression of Su ( Hw ) partially restored y2 expression in bristles that was repressed in the mod ( mdg4 ) u1 background [70] . Finally , we tested whether Mod ( mdg ) -67 . 2 is essential for the ability of the distal gypsy insulator located on the 3′ side of the gene to stimulate white expression . We examined six transgenic lines ( described in Figure 6 ) carrying only the distal gypsy insulator and the eye enhancer located either close to the white promoter ( Figure S4 A ) or at a distance of 4 kb from it ( Figure S4 B , C ) . The results showed that deletion of the gypsy insulator reduced white expression in all cases , while its reduction in mod ( mdg4 ) u1 mutants was relatively weak . Thus , Mod ( mdg4 ) -67 . 2 proved to be essential but not critical for the ability of the gypsy insulator to stimulate white expression from a distance . The ability of the gypsy insulator located on the 3′ side of the white gene to stimulate its expression suggested that the insulator may directly interact with the white regulatory regions . To test this possibility , we used the 3C assay to examine long-distance interactions in pupae from a transgenic line homozygous for the construct shown in Figure 6D . In this transgene ( Figure 8A ) , the eye enhancer was isolated by the proximal gypsy insulator located at 4 . 6 kb from the white promoter , while the distal gypsy insulator was inserted downstream of the white gene . We examined the original transgenic line and two derivatives carrying either the distal gypsy insulator or no insulators . We also tested the role of the mod ( mdg4 ) u1 mutation in the interaction between the distal gypsy insulator and the regulatory regions of the white gene . Using anchors at the eye enhancer , the white promoter , and the distal gypsy insulator , we observed strong interaction between the gypsy insulators ( Figure 8B ) . The interaction between the eye enhancer and the promoter was reduced , correlating with the low level of white expression in the transgenic line carrying two copies of the gypsy insulator . After deletion of the proximal gypsy copy , the enhancer-promoter interaction was considerably increased , and this was accompanied by stimulation of white expression by the enhancer in the derivative transgenic line . Thus , the interaction between two gypsy insulators partially blocked the eye enhancer . When the distal insulator alone was present in the transgene , this insulator interacted with the promoter and the eye enhancer . The mod ( mdg4 ) u1 mutation did not significantly affect these interactions . In the absence of distal gypsy insulators , no interactions were found between the regulatory regions and the 3′ side of the white gene , confirming that the gypsy insulator is essential for the observed contacts . In correlation with the observed interactions , deletion of the distal insulator , but not introduction of the mod ( mdg4 ) u1 mutation , strongly reduced white expression . These results support the model that the gypsy insulator directly interacts with the white enhancer/promoter and stimulates its basal activity . To further verify the ability of the gypsy insulator to interact with the white regulatory elements , we used chromatin immunoprecipitation ( ChIP ) assay to analyze binding of the insulator proteins Su ( Hw ) , E ( y ) 2 , CP190 , and Mod ( mdg4 ) -67 . 2 to several sites in the promoter , enhancer and insulator regions in pupae from the same transgenic line and its derivatives ( Figure 9 ) . In the derivative transgenic line carrying the construct without gypsy insulators , the insulator proteins were not detected on the enhancer and promoter of the white gene . The recruitment of these proteins to the transgene only by the gypsy insulator allowed us to test interaction of the gypsy insulator with the white regulatory regions . It could be expected that the insulator proteins would be detected on the enhancer or/and promoter if these regulatory elements directly interacted with the insulator . The insulator proteins proved to bind to the insulators and the eye enhancer in the transgenic line carrying two copies of the gypsy insulator , but no binding took place after both copies were deleted . Therefore , pairing of the gypsy insulators did not preclude their interaction with the eye enhancer . When the proximal insulator alone was deleted , we still detected the insulator proteins binding to the eye enhancer ( Figure 9 ) . Taken together , these results strongly suggest that one or two copies of the gypsy insulator can directly interact with the eye enhancer . In the mod ( mdg4 ) u1 background ( Figure 9 ) , the E ( y ) 2 and CP190 proteins continued to bind to the eye enhancer region , confirming the 3C results that the gypsy insulator interacts with the eye enhancer in the absence of Mod ( mdg4 ) -67 . 2 protein . This finding correlates with data on the ability of the gypsy insulator to stimulate transcription in the mod ( mdg4 ) u1 background . As shown previously , Zeste has binding sites in the enhancer and promoter regions of the white gene [64] , and these sites in the promoter region are essential for the insulator bypass by the eye enhancer [71] . Using the ChIP assay , we analyzed the binding of Zeste to the eye enhancer and the promoter of the white gene in pupae from the transgenic line homozygous for the construct and compared its binding to the white promoter before and after deletion of the eye enhancer ( Figure 10A ) . As expected , Zeste bound to the eye enhancer and the white promoter in the initial line , but only traces of this protein were detected on the white promoter in the derivative transgenic line with the deleted eye enhancer . Thus , Zeste was found to be recruited to the white promoter only in the presence of the eye enhancer . In agreement with this finding , inactivation of Zeste in the zv77h mutants did not affect white expression in absence of the eye enhancer ( Figure S1 ) . Zeste was not detected on the white promoter in the transgenic line carrying the gypsy insulator between the eye enhancer and the promoter , in agreement with data on the blocking of the eye enhancer by one copy of the gypsy insulator in transgenic lines homozygous for the construct ( Figure 10A ) . Next , we examined the binding of Zeste to the transgenic line and its derivatives described in Figures 8 and 9 . In the presence of two gypsy insulators , the enrichment of enhancer and promoter sequences upon ChIP with anti-Zeste antibodies was considerably reduced ( Figure 10B ) . However , we unexpectedly observed strong Zeste binding to the distal insulator . Similar results were obtained with two additional transgenic lines in which the white gene was flanked by the gypsy insulators ( Figure S5 ) . For both transgenic lines , we observed strong enrichment of sequences related to the distal gypsy insulator upon ChIP with anti-Zeste antibodies . At the same time , binding of Zeste to the promoter and the enhancer was considerably reduced . After deletion of the proximal gypsy insulator ( Figure 10B ) , the level of Zeste at the white enhancer and promoter was still low , but this protein was detected at the reference sequence near the eye enhancer . After deletion of both gypsy insulator , Zeste was again detected by ChIP on the eye enhancer and the promoter . To confirm these results , we analyzed the enrichment of the enhancer and promoter regions by ChIP with anti-Zeste antibodies in several additional transgenic lines . The enrichment of enhancer sequences was considerably reduced in all transgenic lines carrying one or two copies of the gypsy insulator near the eye enhancer ( Figures 10A , 10B , S5 ) , but it returned to the reference level after the insulators were deleted . In experiments with pupae carrying the transgene with the eye enhancer flanked by two Fab-7 insulators , the enrichment of the enhancer sequences upon ChIP with anti-Zeste antibodies was even higher than in experiments with the control transgenic line carrying only the eye enhancer or the derivative transgenic line with the deleted upstream Fab-7 insulator ( Figure 10C ) . Thus , proteins bound to the Fab-7 insulator did not interfere with Zeste . The plausible explanation of these results is that the proteins bound to the gypsy insulators interacted with Zeste and partially masked the epitopes recognized by the antibodies in ChIP . As a result , Zeste was not detected at the eye enhancer and promoter but was found at the distal gypsy insulator . To test this possibility , we examined the interaction of insulator proteins with Zeste in the yeast two-hybrid assay ( Figure 11A ) . The results showed that Zeste interacted with Mod ( mdg4 ) -67 . 2 but not with the Su ( Hw ) and CP190 proteins . It is noteworthy that both BTB and C-terminal domains of Mod ( mdg4 ) -67 . 2 are essential for the interaction with Zeste . We also demonstrated co-immunoprecipitation between Zeste and Mod ( mgd4 ) -67 . 2 in embryonic extracts ( Figure 11B ) . To confirm the role of Mod ( mdg4 ) -67 . 2 in the interaction with Zeste , we performed ChIP experiments with pupae from two transgenic lines carrying a pair of gypsy insulators inserted on both sides of the eye enhancer in the mod ( mdg4 ) u1 mutant background ( Figure 11C ) . In both transgenic lines , partial inactivation of Mod ( mdg4 ) -67 . 2 restored enrichment with the eye enhancer sequence upon ChIP with the anti-Zeste antibodies . At the same time , the mod ( mdg4 ) u1 mutation did not affect Zeste expression in pupae ( Figure S6 ) . Taken together these results confirm the role of Mod ( mdg4 ) -67 . 2–Zeste interaction in pairing of the eye enhancer with the gypsy insulator . Finally , we used ChIP with anti-Zeste antibodies to test whether Mod ( mdg4 ) -67 . 2 affected Zeste binding to the eye enhancer in transgenic lines carrying the gypsy insulator on the 3′ side of the white gene . The eye enhancer in these lines was located either close to the white promoter ( Figure S7A ) or 4 . 6 kb upstream of it ( Figure S7B , C ) . We unexpectedly observed only a relatively weak enrichment of the enhancer sequence in the presence of the gypsy insulator , but its deletion or introduction of the mod ( mdg4 ) u1 mutation restored the normal level its enrichment in all transgenic lines . These results provide additional evidence for the direct interaction between Mod ( mdg4 ) -67 . 2 and Zeste and the long-distance interaction of the insulator complex formed on gypsy sequences with the regulatory elements of the white gene .
In this study , we have examined two Drosophila insulators in the model system containing the eye enhancer and the white reporter gene lacking the endogenous Wari insulator that improves the enhancer-blocking activity of the gypsy insulator [10] . The results show that one copy of the gypsy or Fab-7 insulator fails to disrupt communication between the eye enhancer and the white promoter in the major part of transgenic lines , with the eye enhancer blocking being effective in only 22–28% of these lines . A plausible explanation to such a position-dependent enhancer blocking activity of the insulators is that there is only a minor part of genomic sites where an endogenous insulator and a transgenic insulator can form a loop that results in isolation of the eye enhancer . Alternatively , the strength of insulation depends on the functional activity of the eye enhancer , which depends on the site of transgene insertion [71] . We demonstrated the role of a putative chromatin loop formed by the gypsy insulators in blocking the eye enhancer . In general , two gypsy insulators flanking either the eye enhancer or the white gene effectively block the enhancer–promoter communication . However , two Fab-7 insulators fail to effectively block the eye enhancer activity , which is unlikely to be explained by their inability to form a loop around the eye enhancer . As shown previously , the interaction between the Fab-7 insulators can support long-distance enhancer–promoter communication [59] and higher-order organization of PcG targets in the nucleus [12] . Thus , the Fab-7 insulators should form a chromatin loop including the eye enhancer that fails to block the enhancer–promoter communication . Taken together , these results suggest that the chromatin loop formed by the interacting insulators is not sufficient for blocking enhancer–promoter communication coordinated by Zeste ( Figure 12A ) . It appears that insulator complexes do not function as a neutral barrier and that specific interactions between insulator proteins and proteins bound either to an enhancer or to a promoter are essential for the effectiveness of blocking . Here we have found that the proteins bound to the gypsy insulator effectively interact with the enhancer of the white gene . The gypsy insulator recruits Mod ( mdg4 ) -67 . 2 that directly interacts with the Zeste protein . It appears that the BTB and C-terminal domains of Mod ( mdg4 ) -67 . 2 are required for interaction with Zeste . Mod ( mdg4 ) -67 . 2 can oligomerize through its BTB and second dimerization domain [51] , [52] , and this can help it to effectively interfere with the activity of Zeste , which also forms oligomers . Zeste is critical for the long-distance interaction between the white promoter and the eye enhancer [64] , [71] . Thus , Mod ( mdg4 ) -67 . 2 may interfere with the ability of Zeste to bring together remote regulatory elements . As the inactivation of Mod ( mdg4 ) -67 . 2 does not completely disrupt the enhancer blocking , it seems likely that other insulator proteins also contribute to interactions with proteins bound to the eye enhancer and , therefore , may interfere with the enhancer–promoter communication . The results presented above provide the basis for the model that two different mechanisms are involved in blocking the eye enhancer by the gypsy insulators: ( 1 ) a chromatin loop physically interferes with the ability of the protein complexes bound to the eye enhancer and promoter to interact with each other , and ( 2 ) direct interactions between insulator proteins and enhancer/promoter proteins interfere with the ability of an enhancer to properly stimulate a promoter . In particular , Mod ( mdg4 ) -67 . 2 partially blocks the activity of Zeste via a direct protein–protein interaction ( Figure 12B ) . These mechanisms function cooperatively , which ensures a strong blocking of the eye enhancer by the paired gypsy insulators . It seems likely that the protein complex bound to the Fab-7 insulator does not interfere with the activity of Zeste . As a result , a loop formed by the Fab-7 insulators only weakly affects communication between the eye enhancer and the white promoter . As in the case of the insulator-mediated chromatin loop , the pairing of the gypsy insulators located on the homologous chromosomes may physically interfere with the enhancer–promoter communication . Thus , homologous pairing might be one of possible mechanisms contributing to the enhancer blocking activity of insulators . Supposedly , such mechanism may account for dosage compensation of some X-chromosomal genes that contain insulators between enhancers and promoters . Since paring between insulators can occur only in females , which have two X chromosomes , such insulators should block enhancers more effectively in females than in males . Previously , we have used the Flp-recombination assay [53] to demonstrate that the pairing between gypsy insulators strongly depends on their relative orientation . According to our model , the orientation-dependent effect is explained by the involvement of at least two insulator-bound proteins in specific protein-protein interactions . Here we have found that when the eye enhancer is in the center of the loop , the relative orientation of the gypsy insulators is not critical for the efficient blocking of the eye enhancer . The opposite situation is observed when the eye enhancer is in close proximity to the upstream gypsy insulator ( Figure 12C , D ) . In this configuration of the regulatory elements , the gypsy insulators located in the same orientation bring together the eye enhancer located within the loop and the white promoter located outside the loop . Therefore , the position of an enhancer relative to gypsy insulators within the loop appears to be critical for the functional outcome of loop formation . The results of our study also support the model that insulators specifically interact with enhancers and promoters and potentiate their activity [72]–[74] . As shown previously , the endogenous Su ( Hw ) -binding region ( 1A2 ) placed at the 3′ end of the white gene in the transgenic line can interact with the promoter and stimulate its activity [74] . Here , we have found that the gypsy insulator located on the 3′ side of the white gene stimulates white expression by interacting with the enhancer ( Figure 12D ) . The observed interaction between Mod ( mdg4 ) -67 . 2 and Zeste is important but not critical for distant pairing of the eye enhancer and the gypsy insulator . Further study is required to identify additional transcription factors bound to the insulators , enhancers , and promoters that are involved in such interactions .
Flies were maintained at 25°C on standard medium . The construct , together with a P element containing defective inverted repeats ( P25 . 7wc ) that was used as a transposase source , were injected into yacw1118 preblastoderm embryos [75] . The resulting flies were crossed with yacw1118 flies , and the transgenic progeny were identified by their eye pigmentation under a Stemi 2000 stereomicroscope ( Carl Zeiss , Germany ) . The transformed lines were tested for transposon integrity and copy number by Southern blot hybridization . Only single-copy transformants were included in the study . The lines with DNA fragment excisions were obtained by crossing the transposon-bearing flies with the Flp ( w1118; S2CyO , hsFLP , ISA/Sco; + ) or Cre ( y1w1; Cyo , P[w+ , cre]/Sco; + ) recombinase-expressing lines [76] , [77] . The Cre recombinase induces 100% excisions in the next generation . A high level of Flp recombinase was produced by heat shock treatment ( 2 hours daily ) during the first 3 days after hatching . All excisions were confirmed by PCR analysis . To inactivate the Zeste protein , we used the null zv77h mutation ( zv77h w67c23 , Bloomington Stock Center #1385 ) , which contains a 314-bp deletion including RNA leader sequences and the AUG initiation codon of zeste [65] . To inactivate Mod ( mdg4 ) -67 . 2 , we used the mod ( mdg4 ) u1 and mod ( mdg4 ) T6 mutations associated with the deletion of the C-terminal protein domain interacting with Su ( Hw ) [48] , [68] . Generation of transgenic lines and introduction of zv77h , mod ( mdg4 ) mutations , su ( Hw ) 2/su ( Hw ) v mutations , and hsp70su ( Hw ) construct were described previously [51] , [68] , [71] . To express the su ( Hw ) gene regulated by the hsp70 promoter , flies carrying the construct were heat-shocked for 2 h every day during the period from the second instar larva to middle pupa stages . To estimate the levels of white expression , we visually determined the degree of pigmentation in the eyes ( white ) of 1- to 3-day-old males developing at 25°C , with reference to standard color scales . All flies were scored independently by two observers . On the nine-grade scale for white , red ( R ) eyes corresponded to the wild type and white ( W ) eyes to the total loss of white expression; intermediate pigmentation levels , in order of decreasing gene expression , were brownish red ( BrR ) , brown ( Br ) , dark orange ( dOr ) , orange ( Or ) , dark yellow ( dY ) , yellow ( Y ) and pale yellow ( pY ) . The constructs were based on the CaSpeR vector [78] . The pCaSpew15 ( +RI ) plasmid was constructed by inserting an additional EcoRI site at +3190 of the mini-white gene ( white ) in the pCaSpew15 plasmid . The Wari insulator located on the 3′ side of the white gene was deleted from pCaSpew15 ( +RI ) to produce plasmid pCaSpeRΔ700 ( CΔ ) . The 777-bp white regulatory sequences containing the testis and eye enhancers from −967 to −1745 relative to the transcription start site ( E ) were cloned between two frt sites ( frt ( E ) ) . Insulator fragments ( I ) were obtained by PCR amplification . The 340-bp fragment containing the Su ( Hw ) -binding region ( Gy ) was PCR amplified from the gypsy retrotransposon . The 0 . 858-kb Fab-7 fragment ( F7 ) was cloned by PCR amplification between primers 5′-GATTTCAAGCTGTGTGGCGGGG-3′ and 5′-CGTGAGCGACCGAAACTC-3′ . The products after sequencing were subcloned in pSK plasmid , or between lox ( lox ( I ) ) or frt sites ( frt ( I ) ) . ( E ) ( I ) W: The frt ( E ) fragment was inserted in front of the white promoter into the CΔ plasmid digested with XbaI ( ( E ) W ) . The lox ( F7 ) or lox ( Gy ) fragment was cloned in the middle of the 480-bp PvuI–PvuI fragment from lacZ cDNA digested with EcoRV . The resulting DNA fragment was inserted into the ( E ) W digested with BamHI . In the plasmid , the insulators were at −695 relative to the white transcription start site . ( I ) ( E ) IRW: The lox ( F7 ) or lox ( Gy ) fragment was cloned into the ( E ) W plasmid digested with PstI just upstream of the eye enhancer ( ( I ) ( E ) W ) . The second insulator fragment was cloned in the middle of 480-bp lacZ spacer ( Isp ) and then was inserted into the ( I ) ( E ) W plasmid between the eye enhancer and promoter at −345 relative to the white transcription start site . As a result , the proximal insulator was located at 585 bp from the transcription start site . E ( Gy ) W ( Gy ) and E ( Gy ) RW ( Gy ) : The eye enhancer was cloned into the CΔ plasmid digested with XbaI ( EW ) . The lox ( Gy ) fragment was cloned in two orientations in the EW plasmid between the eye enhancer and the promoter at −482 relative to the white transcription start site . The frt ( Gy ) fragment was cloned into the EW plasmid digested with EcoRI in direct orientation relative to the white gene . E4 , 6 ( Gy ) RW ( Gy ) : The frt ( Gy ) fragment was cloned in EW digested with EcoRI ( EW ( Gy ) ) . The 4 . 6-kb BamHI-BglII fragment of the yellow gene was cloned in the EW ( Gy ) plasmid digested with BamHI between the eye enhancer and the promoter ( E4 , 6W ( Gy ) ) . The lox ( Gy ) fragment was cloned in reverse orientation in the E4 , 6W plasmid digested with BglII between the eye enhancer and the promoter at −482 relative to the white transcription start site . E ( Gy ) R4 , 6W ( Gy ) : This construct was made like E4 , 6 ( Gy ) RW ( Gy ) , but the lox ( Gy ) fragment was cloned in reverse orientation in E4 , 6W ( Gy ) restricted with BamHI between the white enhancer and promoter in position −5096 relative to the white transcription start site . ( Gy ) E4 , 6 ( Gy ) W and ( Gy ) RE 4 , 6 ( Gy ) W: The 4 . 6-kb BamHI–BglII fragment of the yellow coding region was cloned in EW digested with BamHI upstream of the white promoter ( E4 , 6W ) . The lox ( Gy ) fragment was cloned in both orientations into the E4 , 6W plasmid digested with PstI upstream of the eye enhancer . The frt ( Gy ) fragment was cloned in direct orientation in position −462 relative to the white transcription start site ( considering one frt site ) . ( Gy ) 2 , 1 ( E ) 2 , 2GyW and ( Gy ) R2 , 1 ( E ) 2 , 2GyW: The 4 . 6-kb BamHI–BglII fragment of the yellow coding region was cloned in CΔ digested with BamHI in front of the white promoter ( 4 , 6W ) . The lacZ gene was cloned in pBluSK; frt ( E ) was cloned approximately in the center of the lacZ gene digested with Eco47III ( 1 , 4frt ( E ) 2 , 1pSK ) . The KpnI–BamHI fragment was then cloned in the 4 , 6W plasmid digested with KpnI–BglII ( 1 , 8frt ( E ) 2 , 4W ) . The Gysp fragment was inserted in the 1 , 4frt ( E ) 2 , 4W plasmid digested with SmaI in front of the white promoter ( 1 , 8frt ( E ) 2 , 2GyW ) . The lox ( Gy ) fragment was inserted into the 1 , 8frt ( E ) 2 , 2GyW plasmid digested with XbaI in both orientations . ( Gy ) 2 , 1 ( E ) 2 , 2Gy2 , 0W: The construct was made like ( Gy ) 2 , 1 ( E ) 2 , 2GyW , but the 1460-bp EcoRI–BglII yellow fragment was inserted between proximal Gy and white promoter . As a result , the distance between the proximal gypsy insulator and white transcription start site was 2055 bp . Chromatin was prepared from mid-late pupae . A 500-mg sample was ground in a mortar in liquid nitrogen and resuspended in 10 mL of buffer A ( 15 mM HEPES-KOH , pH 7 . 6; 60 mM KCl , 15 mM NaCl , 13 mM EDTA , 0 . 1 mM EGTA , 0 . 15 mM spermine , 0 . 5 mM spermidine , 0 . 5% NP-40 , 0 . 5 mM DTT ) supplemented with 0 . 5 mM PMSF and Complete ( EDTA-free ) Protease Inhibitor Cocktail V ( Calbiochem , United States ) . The suspension was then homogenized in a Dounce homogenizer with pestle B and filtered through Nylon Cell Strainer ( BD Biosciences , United States ) . The homogenate was transferred to 3 mL of buffer A with 10% sucrose ( AS ) , and the nuclei were pelleted by centrifugation at 4 000 g , 4°C for 5 min . The pellet was resuspended in 5 mL of buffer A , homogenized again in a Dounce homogenizer , and transferred to 1 . 5 mL of buffer AS to collect the nuclei by centrifugation . The nuclear pellet was resuspended in wash buffer ( 15 mM HEPES-KOH , pH 7 . 6; 60 mM KCl , 15 mM NaCl , 1 mM EDTA , 0 . 1 mM EGTA , 0 . 1% NP-40 , protease inhibitors ) and cross-linked with 1% formaldehyde for 15 min at room temperature . Cross-linking was stopped by adding glycine to a final concentration of 125 mM . The nuclei were washed with three 10-mL portions of wash buffer and resuspended in 1 . 5 mL of nuclei lysis buffer ( 15 mM HEPES , pH 7 . 6; 140 mM NaCl , 1 mM EDTA , 0 . 1 mM EGTA , 1% Triton X-100 , 0 . 5 mM DTT , 0 . 1% sodium deoxycholate , 0 . 1% SDS , protease inhibitors ) . The suspension was sonicated on ice with a Branson Sonifier 150 ( Branson Instruments , United States ) for 5×20 sec at 1-min intervals . Debris was removed by centrifugation at 14000 g , 4°C for 10 min , and chromatin was pre-cleared in Protein G agarose ( Pierce , Unites States ) blocked with BSA and salmon sperm DNA . Aliquots of such pre-cleared chromatin were used as the input samples . These samples were incubated overnight , at 4°C , with rat antibodies against Zeste ( 1∶100 ) , Su ( Hw ) ( 1∶500 ) , and CP190 ( 1∶500 ) ; rabbit antibodies against Mod ( mdg4 ) -67 . 2 ( 1∶500 ) and Ey2 ( 1∶200 ) ; and nonspecific IgG purified from rat and rabbit preimmune sera . Chromatin–antibody complexes were collected using blocked protein G ( for rat probes ) or A ( for rabbit probes ) agarose at 4°C over 5 h . After several rounds of washing with lysis buffer ( as such and with 500 mM NaCl ) , LiCl buffer ( 20 mM Tris-HCl , pH 8; 250 mM LiCl , 1 mM EDTA , 0 . 5% NP-40 , 0 . 5% sodium deoxycholate , protease inhibitors ) , and TE buffer , the DNA was eluted with elution buffer ( 50 mM Tris-HCl , pH 8; 1 mM EDTA , 1% SDS ) , the cross-links were reversed , and the precipitated DNA was extracted by the phenol–chloroform method . The enrichment of specific DNA fragments was analyzed by real-time PCR , using a StepOne Plus Thermal Cycler ( Applied Biosystems , United States ) . Relative enrichment was calculated as a percentage of the input normalized to a control positive genomic site ( region 62D for Su ( Hw ) , Cp190 , Mod ( mdg4 ) -67 . 2 , and EY2 [36] , and PRE from the Ubx gene for Zeste [79] . The primers used for PCR in ChIP experiments for genome fragments are shown in Table S1 . The 3C assay was performed as described [54] , with minimal modifications . The nuclear pellet prepared from 50-mg sample of pupae ( see Chromatin Immunoprecipitation ) was washed with wash buffer and resuspended in 5 mL of nucleus preparation ( NP ) buffer ( 15 mM HEPES , pH 7 . 6; 60 mM KCl , 15 mM NaCl , 4 mM MgCl2 , 0 . 1% Triton X-100 , 0 . 5 mM DTT , 1× Complete ( EDTA-free ) Protease Inhibitor Cocktail V ( Calbiochem , United States ) , and 2% formaldehyde ) . The suspension was incubated with slow agitation for a total cross-linking time of 30 min at 25°C . Fixation was stopped by adding 2 . 5 M glycine to a concentration of 0 . 125 M , and the sample was cooled on ice for at least 5 min and centrifuged at 2500 g , 4°C for 5 min . The pellet was washed with two portions of cold NP buffer and one portion of cold 1 . 25× NEBuffer 3 ( 62 . 5 mM Tris-HCl , pH 8 . 0; 125 mM NaCl , 12 . 5 mM MgCl2 , 1 . 25 mM DTT ) ( New England Biolabs , United States ) and resuspended in 300 µL of 1 . 25× NEBuffer 3 . The suspension was supplemented with 4 . 5 µL of 20% SDS and incubated at the thermoshaker at 37°C and 1400 rpm for 1 h; then 34 µL of 20% Triton X-100 was added , and the sample was incubated again at 37°C and 1400 rpm for 1 h . At this stage , a 30-µL aliquot of the sample was taken to be used as the undigested control . Thereafter , 1500 units of DpnII ( New England Biolabs ) was added , and the sample was incubated at 37°C and 1400 rpm for 10 h and then at 65°C and 1400 rpm for 20 min to inactivate DpnII . At this stage , a 30-µL aliquot of the sample was taken to be used as the digested control . Another 170-µL aliquot was cooled , mixed with 250 µL of 1 . 7× ligation buffer ( 1 . 7× T4 DNA ligase buffer with 100 units/mL of T4 DNA Ligase , New England Biolabs ) , and incubated with slow agitation at 25°C for 5 h . At this stage , a 75-µL aliquot of the sample was taken to be used as the ligation control . Cross-links were reversed overnight at 65°C and 1400 rpm . The sample was incubated with 4 µL of 10 mg/mL RNase A at 37°C and 1400 rpm for 1 h and then with 11 µL of 20 mg/mL Proteinase K at 56°C and 1400 rpm for 4 h . The sample was extracted with 10 mM Tris-HCl ( pH 8 . 0 ) saturated phenol , 10 mM Tris-HCl ( pH 8 . 0 ) saturated phenol/chloroform/isoamyl alcohol , and chloroform/isoamyl alcohol . At the each step , the mixture was centrifuged at 10 000 g and room temperature for 10 min . The extracted solution was mixed with 0 . 1 volume of 3 M AcNa ( pH 5 . 2 ) containing 35 µg glycogen and 2 volumes of 96% ethanol and incubated overnight at −80°C . DNA was precipitated by centrifugation at 20 000 g for 90 min at 4°C . The DNA pellet was washed with 70% ethanol , air dried , and resuspended in 100 µL of 10 mM Tris-HCl ( pH 7 . 5 ) . Sample DNA concentrations were adjusted to 10 ng/µL with 10 mM Tris-HCl ( pH 8 . 0 ) . All control procedures and quantitative analyses were performed as described [80] . As a control template containing all ligation products in equal amounts , we used a BAC clone ( BACR06H06 , RPCI-98 , Roswell Park Cancer Institute Drosophila melanogaster BAC library ) covering the site of transgenic construct integration into the genome , which was mixed in equimolar amounts with plasmid DNA construct digested with DpnII ( New England Biolabs ) at a concentration of 10 U/µg DNA . Digested DNA was purified by phenol/chloroform extraction and ethanol precipitation as described above , ligated with T4 DNA ligase at 25°C for 5 h , and purified again in the same way . Primers were designed so as to flank DpnII restriction sites in the transgenic construct . TaqMan Probes were designed with 5′FAM reporter dye and 3′BHG1 quencher dye . To normalize for the PCR efficiency of different primer pair/probe combinations , standard curves for each combination were generated using the BAC control template . Interaction frequencies were calculated on the basis of Ct values of each sample relative to the standard curve for the given primer pair/probe combination . The primers and probes used in the study are listed in Table S1 . All real-time PCR reactions were carried out in a StepOne Plus system ( Applied Biosystems ) according to the manufacturer's instructions , in four replications each . Amplification involved initial denaturation at 95°C for 15 min followed by 50 cycles of 95°C for 15 s and 60°C for 60 s . To compare interaction frequencies , the normalization procedure was performed: the amount of test ligation product was divided by the amount of reference product to give a “relative interaction frequency . ” Loading adjustment was performed as described [81] to provide for normalization and subsequent comparison of probes from different transgenic flies . A sample of 20 flies was ground with a homogenizer in extraction buffer ( 20 mM HEPES , pH 7 . 5; 100 mM KCl , 5% glycerol , 10 mM EDTA , 0 . 1% Triton X-100 , 1 mM DTT , 0 . 5 mM PMSF , 20 mg/mL aprotinin , 5 mg/mL leupeptin , 5 mg/mL pepstatin A ) , 10 µL per fly ) . Debris was removed by centrifugation at 12 000 g , 4°C for 10 min , and the appropriate amount of 5× SDS loading was added directly to the homogenate . The sample was boiled for 5 min , centrifuged at 12 000 g for 5 min , and loaded onto SDS-PAGE . Mouse anti-lamin antibody ADL67 . 10 ( working dilution for Western blotting , 1∶1000 ) was from the Hybridoma Bank at the University of Iowa . Rabbit anti-Mod ( mdg4 ) -67 . 2 antibody ( 1∶5000 ) was a gift from A . Golovnin . Rat antibodies against α-Su ( Hw ) ( 1∶1000 ) , α-CP190 ( 1∶1000 ) , α-Zeste ( 1∶200 ) , and E ( y ) 2 ( 1∶200 ) were raised in our laboratory and affinity purified . To this end , protein fragments 6× His-Su ( Hw ) ( aa 1–150 ) , CP190 ( aa 308–1096 ) , Zeste ( aa 1–175 ) and EY2 were expressed in BL21 cells , affinity purified on Ni-NTA agarose ( Invitrogen ) according to the manufacturer's protocol , and injected into rats following the standard immunization procedure . Total RNA was isolated using the TRI reagent ( Molecular Research Center , United States ) according to the manufacturer's instructions . RNA was treated with two units of Turbo DNase I ( Ambion ) for 30 min at 37°C to eliminate genomic DNA . The synthesis of cDNA was performed using 2 µg of RNA , ArrayScript reverse transcriptase ( Ambion ) and oligo ( dT ) as a primer . The amounts of specific cDNA fragments were quantified by real-time PCR . At least three independent experiments with each primer set were performed for three independent RNA samples . Relative levels of mRNA expression were calculated in the linear amplification range by calibration to a standard curve of genomic DNA to account for differences in primer efficiencies . Individual expression values were normalized with reference to rpl32 mRNA . The nuclear extracts were obtained from S2 cells , and the protein complexes were immunoprecipitated from the extracts . For this purpose , 1×108 S2 cells were washed twice in 10 mL of ice cold PBS , resuspended in 10 mL of ice cold IP-Sucrose buffer ( 10 mM Tris , pH 7 . 5; 10 mM NaCl , 10 mM MgCl2 , 1 mM EDTA , 1 mM EGTA , 1 mM DTT , 250 mM sucrose , 0 . 5 mM PMSF ) with Complete ( EDTA-free ) Protease Inhibitor Cocktail V ( Calbiochem , United States ) , incubated on ice for 10 min , and homogenized with a Dounce loose pestle ( 20 strokes ) . The nuclei were then pelleted by centrifugation at 3000 g , 4°C for 10 min . The pellet was resuspended in 500 µL of ice cold IP-10 buffer ( 10 mM Tris , pH 7 . 5; 10 mM NaCl , 10 mM MgCl2 , 1 mM EDTA , 1 mM EGTA , 1 mM DTT; , 0 . 1% NP-40 , 10% glycerol , 0 . 5 mM PMSF , and Complete Protease Inhibitor Cocktail V ) , homogenized with a Dounce tight pestle ( 20 strokes ) , and mixed with an equal volume of IP-850 buffer ( 10 mM Tris , pH 7 . 5; 850 mM NaCl , 10 mM MgCl2 , 1 mM EDTA , 1 mM EGTA , 1 mM DTT , 0 . 1% NP-40 , 10% glycerol , 0 . 5 mM PMSF , and Complete Protease Inhibitor Cocktail V ) . The suspension was incubated on ice for 10 minutes and then centrifuged at 20 000 rpm , 4°C , for 10 min . The supernatant fluid ( the nuclear fraction ) was collected for immunoprecipitation experiments . Rat antibodies against α-Su ( Hw ) ( 1∶200 ) and α-Zeste ( 1∶100 ) were conjugated with Protein G agarose , and rabbit antibodies against α-Mod ( mdg4 ) -67 . 2 ( 1∶500 ) , with Protein A agarose beads ( Pierce ) ; in respective control experiments , rat or rabbit preimmune serum was used . An aliquot of an antibody was mixed with 30 µL of agarose beads equilibrated in IP buffer with 150 mM NaCl ( IP-150 ) and incubated on a rotator at 4°C for 3 h . The beads were then washed with IP-150 , blocked with 1% BSA for 30 min under the same conditions , and washed with two portions of IP-150 . The nuclear extract was adjusted to 150 mM NaCl , and its 1 ml containing approximately 1 mg of total protein was mixed with 30 µL of “fresh” agarose beads equilibrated in IP-150 and incubated at 4°C for 1 h for pre-clearing the sample . The beads were pelleted , and the supernatant fluid was transferred to a new tube and mixed with antibody-conjugated beads . The samples were incubated on a rotary mixer at 4°C for 3 h , and the beads were washed with three portions of IP buffer with 300 mM NaCl , one portion of IP buffer with 500 mM NaCl , and one portion of IP buffer with 150 mM NaCl . After the last washing step , the beads were resuspended in SDS–PAGE loading buffer , boiled , and analyzed by Western blotting . Proteins were detected using the SuperSignal West Fempto substrate ( Pierce ) . Yeast two-hybrid assay was carried out using yeast strain pJ69-4A , with plasmids and protocols from Clontech . For growth assays , plasmids were transformed into yeast strain pJ69-4A by the lithium acetate method , as described by the manufacturer , and plated on media without tryptophan and leucine . After 2 days of growth at 30°C , the cells were plated on selective media without tryptophan , leucine , histidine and adenine , and their growth was compared after 2–3 days . Liquid culture β-galactosidase assay was performed according to the yeast protocols handbook ( Clontech ) . Each assay was repeated twice .
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The mechanism underlying enhancer blocking by insulators is unclear . Current models suggest that insulator proteins block enhancers either by formation of chromatin loops or by direct interaction with protein complexes bound to the enhancers and promoters . Here , we tested the role of a chromatin loop in blocking the activity of two Drosophila insulators , gypsy and Fab-7 . Both insulators failed to effectively block the interaction between the eye enhancer and the white promoter at most of genomic sites . Insertion of an additional gypsy copy either upstream of the eye enhancer or downstream from the white gene led to complete blocking of the enhancer–promoter communication . In contrast , flanking of the eye enhancer by Fab-7 insulators only weakly improved enhancer blocking . Such a difference in enhancer blocking may be explained by finding that Mod ( mdg4 ) -67 . 2 , a component of gypsy insulator , directly interacts with the Zeste protein , which is critical for enhancer–promoter communication in the white gene .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"gene",
"expression",
"genetics",
"epigenetics",
"biology",
"molecular",
"cell",
"biology"
] |
2013
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Effective Blocking of the White Enhancer Requires Cooperation between Two Main Mechanisms Suggested for the Insulator Function
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Human tuberculosis ( TB ) in West Africa is not only caused by M . tuberculosis but also by bacteria of the two lineages of M . africanum . For instance , in The Gambia , 40% of TB is due to infections with M . africanum West African 2 . This bacterial lineage is associated with HIV infection , reduced ESAT-6 immunogenicity and slower progression to active disease . Although these characteristics suggest an attenuated phenotype of M . africanum , no underlying mechanism has been described . From the first descriptions of M . africanum in the literature in 1969 , the time to a positive culture of M . africanum on solid medium was known to be longer than the time to a positive culture of M . tuberculosis . However , the delayed growth of M . africanum , which may correlate with the less virulent phenotype in the human host , has not previously been studied in detail . We compared the growth rates of M . tuberculosis and M . africanum isolates from The Gambia in two liquid culture systems . M . africanum grows significantly slower than M . tuberculosis , not only when grown directly from sputa , but also in growth experiments under defined laboratory conditions . We also sequenced four M . africanum isolates and compared their whole genomes with the published M . tuberculosis H37Rv genome . M . africanum strains have several non-synonymous SNPs or frameshift mutations in genes that were previously associated with growth-attenuation . M . africanum strains also have a higher mutation frequency in genes crucial for transport of sulphur , ions and lipids/fatty acids across the cell membrane into the bacterial cell . Surprisingly , 5 of 7 operons , recently described as essential for intracellular survival of H37Rv in the host macrophage , showed at least one non-synonymously mutated gene in M . africanum . The altered growth behaviour of M . africanum might indicate a different survival strategy within host cells .
Mycobacterium africanum , a member of the Mycobacterium tuberculosis complex , was first described in 1968 in Dakar , Senegal [1] . Infections with M . africanum are generally geographically restricted to human populations in West Africa , and are not well understood [2] . Molecular techniques have since refined the classification of the sub-species M . africanum into M . africanum West African 1 , common around the Gulf of Guinea , and M . africanum West African 2 , mainly found in Western West Africa [3] , [4] . Although up to 30–40% of all human tuberculosis in West Africa is caused by either of the two M . africanum lineages [2] , basic research on these clinically important mycobacteria was neglected to date . However , an improved understanding of the biology of this mycobacterial lineage will also give clues about genetic functions in the closely related M . tuberculosis . The biochemical characteristics of M . africanum vary; at times , they resemble those of M . bovis , and , at times , M . tuberculosis [5] . Clinically and epidemiologically , M . africanum behaves very differently from M . tuberculosis . For instance , studies from The Gambia showed that M . africanum West African 2 is associated with HIV infection [2] , reduced ESAT-6 immunogenicity [6] and a slower progression to active disease [7] . These features suggest an overall attenuation of the bacterium , yet no underlying mechanism has been identified to date . From the first descriptions of M . africanum , the time to detection on solid medium was known to be longer for M . africanum compared to M . tuberculosis . This delayed growth , which may explain the reduced virulence of M . africanum , has not previously been investigated . We determined the bacterial growth rate of molecularly characterized lineages of the M . tuberculosis complex collected from The Gambia in two liquid culture systems , both directly from sputum and in carefully controlled growth experiments . M . africanum West-African 2 ( from now on referred to as M . africanum ) grows significantly slower than M . tuberculosis in all of the culture systems we used . By comparisons of genetic sequence data , M . africanum strains have several mutations in genes that were previously associated with growth-attenuation in M . tuberculosis H37Rv . This high mutation frequency was also observed in functional groups of molecular membrane transport systems that translocate macromolecules and nutrients across the cell membrane into the bacterial cell . We conclude that the altered growth behaviour of M . africanum may be a different survival strategy within the host .
In the context of several TB cohort studies , we collected clinical isolates from patients with smear positive pulmonary TB . Each TB patient submitted up to three sputum samples . Sputum was decontaminated using NALC-NaOH and inoculated into either BACTEC MYCO/F-Sputa vials ( for the BACTEC 9000 , BD ) and/or BACTEC MGIT 960 Tubes supplemented with PANTA ( for BACTEC MGIT 960 , BD ) . The tubes were incubated at 37°C and the “Time to Positivity” ( manufacturer-set threshold: 75 Growth Units ) was recorded in days . Tubes were incubated for a maximum of 42 days . In a second experiment we compared the growth rates of M . tuberculosis laboratory strain Mt14323 [8] and clinical M . africanum isolate ITM 080552 in a controlled laboratory setting , using defined inocula . For each strain , a standardized inoculum was prepared from a fresh subculture of 21 days with a turbidity of McFarland N° 0 . 5 . The OD492 nm and OD595 nm were measured and the bacterial suspension was adjusted to OD = 0 . 01–0 . 03 . A dilution of 1∶10 was prepared in distilled water . From this dilution we made a half logarithmic dilution series and 100 µl of each dilution was inoculated in triplicate into MGIT960 . To estimate colony-forming-units ( CFU ) each inoculum was plated on 7H11 plates . Growth curves were monitored using the BD Epicentre software , data were extracted , and the length of the lag phase or “Time to Positivity” for each strain to reach the “positivity threshold” of 75 growth units ( GU ) was measured . Furthermore , the actual growth rate or doubling time was determined as the time needed for a strain to grow from 5000GU to 10000GU . We used the non-parametric Wilcoxon rank sum test to compare the median time to positivity for M . tuberculosis versus M . africanum . The samples used are all from the MRC strain collection , which comprises strains from various studies that were conducted over the last years . All these studies obtained ethical approval , informed consent from patients and samples were anonymized . Genotyping was done using spoligotype analysis [9] and PCR for Large Sequence Polymorphisms [4] on the mycobacterial DNA from one isolate from each patient , with the assumption that all samples from the same patient contained the same mycobacterial isolate . Isolates were grouped in phylogenetically distinct lineages within the M . tuberculosis complex , as previously defined [4] . We sequenced the genomes of four M . africanum West Africa 2 isolates , three that originated from The Gambia and one publicly available strain from Senegal . We re-sequenced the published strain GM041982 [10] , two randomly selected Gambian isolates 03/03910 and 03/030671 from MRC's strain collection , and strain ATCC 35711 . We compared selected genes from the M . tuberculosis H37Rv genome [11] with their respective homologues of the sequenced M . africanum strains . We only considered single nucleotide polymorphisms ( SNPs ) or deletion/insertion polymorphism ( DIPs ) that were common to all four M . africanum strains . Genes that were only mutated in some of the sequenced strains were considered to be uncommon to M . africanum and were considered wildtype genes . The analysed set of genes which is responsible for attenuated growth in vitro was extracted from a previous publication [12] . Genes and operons essential for in vivo growth within macrophages were recently published [13] . Genes encoding nutrient and macromolecule transport mechanisms were identified from the literature [14]–[17] and by a NCBI PubMed search . Additionally , genes annotated as transporters were identified in the TubercuList database ( http://tuberculist . epfl . ch/ ) . In both searches , genes encoding transport proteins with unknown substrate specificity or annotated drug/antibiotic efflux pumps were excluded from the analysis . To compare the proportion of genes carrying non-synonymous SNPs between groups the Fisher's exact test was conducted and the results were considered significant at the level of p≤0 . 05 , assuming the likelihood of a Type I error was α = 0 . 05 . To understand whether an amino acid substitution affected protein function in M . africanum , we conducted an analysis using the SIFT ( “Sorting Intolerant from Tolerant” ) Sequence algorithm ( http://sift . bii . a-star . edu . sg/www/SIFT_seq_submit2 . html ) [18] . The parameters were used at their default setting and the gene sequences and respective substitutions were analysed using the “UniProt-SwissProt+TrEMBL 2010_09 database” as reference .
Among isolates from 552 TB cases the prevalence of M . africanum West Africa 2 ( n = 223 ) was 40% , consistent with previously reported results [19] . From these 552 patients a total of 1333 positive cultures ( M . tuberculosis n = 823 , M . africanum n = 510 ) were obtained and analysed in the study ( Figure 1 ) . In both liquid culture systems , the median time to culture positivity was significantly shorter for M . tuberculosis ( Bactec 9000: 13d , MGIT960: 9d ) relative to M . africanum ( Bactec 9000: 21d , MGIT960: 15d ) ( see Table 1 and Fig . 2 ) . To further compare the growth dynamics of the two lineages , we inoculated 5 . 9×103 CFU/ml and 8 . 3×103 CFU/ml of M . tuberculosis and M . africanum , respectively , into MGIT tubes and incubated them at 37°C . The lag phase or “time to positivity” was 175 . 2 hours ( 7 . 30 days ) for M . tuberculosis strain Mt14323 and 213 . 00 hours ( 8 . 88 days ) for M . africanum strain ITM 080552 . Furthermore , we determined the doubling time of M . tuberculosis to be 20 . 16 h , in contrast to the doubling time of 24 . 12 h for M . africanum ( see Figure 3 ) .
M . africanum grows slower than M . tuberculosis , with delayed culture positivity ( by 4–6 days ) when grown from sputum in modern liquid culture systems . Although these liquid culture systems are only indirectly measuring growth by detecting oxygen or radioactive precursor consumption as a proxy for growth , they are well suited to compare the growth behaviour of different bacterial isolates . The observed growth differences between the two lineages were further emphasized by a survival analysis which was adjusted for smear grade , and we estimated a Hazard ratio ( HR ) = 0 . 40 ( 95%CI 0 . 35–0 . 47 , p<0 . 0005 ) . Consistent with this diagnostic observation , we determined a longer doubling time of M . africanum in growth experiments in which the inoculum was carefully standardized by CFU . One limitation of this approach is that it is not clear whether cording differs between M . africanum and M . tuberculosis , which could potentially impact on CFU standardization . Also testing a wider range of isolates together with comparative genomics could enhance the power of the in vitro experiments in the future . However , both our findings on growth from sputum and standardized inoculum are consistent with other studies , as the measured doubling times for both of the lineages were in the same range as previously described by Bold et al . [20] . Our results also reproduced the initial observations from Senegal in 1968 , when isolates , identified as M . africanum by biochemical methods , yielded growth on solid media later than M . tuberculosis isolates [1] . Therefore , Castets in 1979 recommended incubation for 90 days for the detection of M . africanum on solid media [21] . However , with the advent of modern automated liquid culture systems , the original recommendations need to be adjusted and redefined . For the current study , we incubated all samples for 42 days , as recommended by the manufacturer of the liquid culture systems . Although 42 days are sufficient to detect M . tuberculosis strains , the frequency distributions indicated that for M . africanum strains a longer incubation period might be advisable . For instance , in the Bactec 9000 , only seven cultures turned positive on the very last day of the incubation period . For this reason , the Bactec MGIT960 seems preferable and better suited for the cultivation of M . africanum , as overall incubation times decreased and detection of culture positivity can be achieved faster . Therefore to evaluate the potential of these liquid cultures systems as diagnostic tools for M . africanum detection , further long-term growth studies need to determine the maximum M . africanum - specific incubation times . Such studies could also evaluate whether the currently applied incubation protocols resulted in an underestimation of M . africanum prevalence or failed to detect mixed infections . The selective advantage of the growth delay of M . africanum is not clear . However comparison of the genome sequences of M . tuberculosis H37Rv and M . africanum give some clues to the underlying mechanisms . First , we investigated a group of genes , each of which has already been described to result in in vitro growth-attenuation of M . tuberculosis H37Rv upon transposon ( TraSH ) inactivation [12] . Of 42 growth-attenuating genes , 12 genes contained non-synonymous mutations or were pseudogenes due to frameshift mutations in M . africanum . In particular , 4 gene products ( Rv2112c/MAF_21240 , Rv0862c/MAF_08710 , AceE , RecA ) were predicted by SIFT analysis to be affected in their protein function . These four proteins are the most likely candidates responsible for the observed growth attenuation in M . africanum . A fifth identified gene , glpK , is a pseudogene in GM041182 or with non-synonymous mutations in the other 3 strains , yet SIFT analysis identified the amino acid substitution to be tolerated by the bacteria . Therefore the Glpk protein is most likely functional in 3 out of 4 M . africanum strains and is not a common cause for the observed , slower growth . We further hypothesized that a reduction and/or deficiency of molecular membrane transporters could limit growth of M . africanum . For instance , the knock-out of outer membrane Msp porins and a subsequent reduced sugar and phosphate uptake led to a slower growth rate of M . smegmatis [22] . Similarly it was previously suggested that the slower growth of M . tuberculosis , when compared to the fast-growing Mycobacterium smegmatis , could be due to the loss of several sugar transporters [17] . Therefore we aimed to determine the status of known transport mechanisms in the sequenced M . africanum genomes and M . tuberculosis H37Rv . We identified 132 membrane transporter genes common to the two mycobacterial lineages . In M . africanum , there were significantly more genes encoding sulphur/sulphate- , lipid/fatty-acid , and ion-transporter with non-synonymous or frameshift mutation than in essential genes . Although sulphur is an essential nutrient for mycobacterial survival and virulence ( for review see [16] , [23] ) , we identified ( protein function affecting , SIFT ) mutations in the cysTWA/subI ABC-transporter of M . africanum . Since subI-knock out mutants of M . bovis were restricted in their sulphate uptake [24] , it is conceivable that M . africanum strains are similarly impaired in their import of sulphur . Although there was speculation that Rv1739c , another predicted sulphate transporter [25] could compensate for the loss of the cysTWA/subI transport system [26] , it is unlikely because this protein is likewise potentially inhibited in its protein function due to a SNP mutation . Whether the hypothetical sulphate-transporter Rv1707 , which carries a tolerated amino acid change in M . africanum , is a functional sulphate transport mechanism still has to be experimentally confirmed . A second group of highly mutated M . africanum genes encode for ion transport mechanisms . Unfortunately , the knowledge about this important group of proteins is still scarce [14] . However , KefB , a potassium/proton antiporter that controls the early acidification of the phagosome , was mutated and impaired in its protein function ( SIFT ) in M . africanum [27] . Also , knock-out mutants of the Mg2+-transporter MgtC , which is potentially affected in its protein function ( SIFT ) in M . africanum , had impaired growth under certain in vitro conditions [28] . Another group of heavy-metal ion transporter genes in M . tuberculosis , ctpA-ctpV , are very different in M . africanum . For instance ctpV , one of the best studied members of this family , yet with a tolerated ( SIFT ) mutation in M . africanum , is key for mycobacterial copper homeostasis and virulence [29] . Similarly , the iron-specific ABC-transporter IrtAB , in which the IrtA subunit has an intolerable amino acid substitution ( SIFT ) in M . africanum , is not only crucial for survival in iron-deficient conditions , but is also required to effectively establish infection in the experimental murine host [30] . Most importantly , all the above mentioned ion transporter genes have one thing in common: they were found to play key roles in the intracellular survival of the bacteria within the phagolysosome of macrophages [27]–[30] . It is surprising that genes important for this crucial step of mycobacterial pathogenesis were among the least conserved in M . africanum , which could indicate that M . africanum might pursue a different intracellular survival strategy than M . tuberculosis to cope with the harsh environment within a phagolysosome . Therefore we investigated 7 putative operons that were previously described as essential for the intracellular survival of M . tuberculosis H37Rv in macrophages [13] . Components of the sugar transport system ( sugA/B/C/lpqY ) were hyperconserved among the M . africanum isolates . Similarly , with the exception of pstA1 , all M . africanum genes encoding phosphorous transporters were conserved amongst the sequenced isolates . Our results suggest that both pathways are equally important for M . tuberculosis and M . africanum . However , we found a remarkable difference between the two lineages . In the course of host macrophage infection , lipids become increasingly more important and replace carbohydrates as the major carbon source [17] , and 3 operons of lipid metabolism ( Rv3540c–Rv3545c , Rv3550–Rv3552 , Rv3569c–Rv3570c ) were described to be required for mycobacterial survival [13] . Interestingly , all of these operons have at least one mutated gene in M . africanum . Consistently , 8/14 mmpL genes , that likely transport lipids/fatty acids across the membrane , have altered amino acid sequences , including a frameshift mutation in mmpL13a that results in a pseudogene . Of note , mmpL3 , which is one of 5 conserved mmpL genes in the obligate intracellular M . leprae , was shown to be the only gene of this family to be essential for viability of M . tuberculosis [31] . However , mmpl3 is potentially affected in its protein function ( SIFT analysis ) in M . africanum . Other genes , essential for survival in macrophages , belong to a putative operon spanning from Rv3864 to Rv3878 [13] , a genetic region that partially includes the RD1 locus encoding the ESX-1 secretion system and its virulence genes such as esxA ( encoding ESAT-6 ) and esxB ( encoding CFP-10 ) . One of the genes , Rv3864 ( espE ) , had a non-synonymous SNP in one M . africanum isolate and a frameshift mutation in the remaining three sequenced M . africanum strains . This is interesting as Rv3864 was associated with virulence , yet it is assumed that a loss of the gene can be compensated by its homologue Rv3616 ( espA ) [32] . However , as the Rv3616 ( espA ) homologue has a non-synonymous mutation in M . africanum as well ( data not shown ) , it is possible that none of these genes is functional in M . africanum . This is supported by the previous finding that certain M . africanum isolates were less likely to induce an ESAT-6 dependent IFN-γ host response and it was speculated that this was due to an ESAT-6 secretion impairment [6] . Combining the finding that the Rv3864/espE homologue in M . marinum is required for secretion of CFP-10 [32] , and CFP-10 contains the secretion signal of the ESAT-6/CFP-10 dimer [33] , an inactive Rv3864/espE could therefore be the missing genetic link to explain the reduced ESAT-6 secretion of M . africanum . Finally , the seventh operon under study , Rv0169–Rv0178 , is essential for entry into the mammalian cell and intracellular survival , yet several members are highly mutated in M . africanum . Interestingly , the overall regulator of this operon , Rv0165 ( mce1R ) , has a frameshift mutation in M . africanum ( data not shown ) and recent studies suggest that Mce1R is part of a global genome-wide regulatory network which control cell growth [34] . In the present study we found that M . africanum strains are impaired in their capacity to grow . We identified several potential gene candidates and functional protein groups that might contribute to the observed growth defect . To unambiguously confirm causality , complementation experiments in which the M . africanum mutant genes are replaced by wildtype H37Rv genes will have to be conducted . However , our genomic analysis suggests that the underlying genetic reason for the growth defect is rather complex . The growth attenuation might be a redundant result due to the loss of multiple genes . Moreover , large scale genomic analyses on additional M , africanum genomes will have to be conducted to confirm which of the described SNPs are really specific to all members of the M . africanum West Africa 2 lineage . Taken together , from a genetic and phenotypic point of view M . africanum appears to be distinct from M . tuberculosis . M . africanum may have a modified , yet unknown , survival strategy of the bacterium within the human host . Future research on the lifestyle of M . africanum may lead to an improved understanding of growth promoting factors in M . tuberculosis and may ultimately reveal new strategies to interrupt bacterial growth and replication within the human host .
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Mycobacterium tuberculosis and Mycobacterium africanum are the two major lineages within the M . tuberculosis complex that cause human tuberculosis in West Africa . Despite being closely related , the outcome after infection differs between these two pathogens . Although M . africanum has not yet been studied to the same extent as M . tuberculosis , M . africanum is less likely to stimulate the host immune system or to progress to active disease . We hypothesized that this somewhat attenuated phenotype is due to the slower growth of M . africanum within the host . Therefore , we analysed clinical isolates from 522 patients with tuberculosis in The Gambia . M . africanum West Africa 2 strains grew more slowly than M . tuberculosis . We sequenced four M . africanum strains and identified several candidate genes that may cause the growth-attenuation of the bacteria . Describing the fundamental genomic and phenotypic differences between M . tuberculosis and M . africanum will enable us to better understand the virulence mechanisms that make M . tuberculosis one of the most successful bacterial pathogens , and to discover potential strategies to interfere with mycobacterial pathogenicity .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"bacteriology",
"microbial",
"metabolism",
"microbial",
"mutation",
"microbial",
"physiology",
"microbial",
"pathogens",
"biology",
"microbiology",
"bacterial",
"biochemistry"
] |
2013
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Deciphering the Growth Behaviour of Mycobacterium africanum
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Recent years have seen the greatest ecological disturbances of our times , with global human expansion , species and habitat loss , climate change , and the emergence of new and previously-known infectious diseases . Biodiversity loss affects infectious disease risk by disrupting normal relationships between hosts and pathogens . Mosquito-borne pathogens respond to changing dynamics on multiple transmission levels and appear to increase in disturbed systems , yet current knowledge of mosquito diversity and the relative abundance of vectors as a function of habitat change is limited . We characterize mosquito communities across habitats with differing levels of anthropogenic ecological disturbance in central Thailand . During the 2008 rainy season , adult mosquito collections from 24 sites , representing 6 habitat types ranging from forest to urban , yielded 62 , 126 intact female mosquitoes ( 83 , 325 total mosquitoes ) that were assigned to 109 taxa . Female mosquito abundance was highest in rice fields and lowest in forests . Diversity indices and rarefied species richness estimates indicate the mosquito fauna was more diverse in rural and less diverse in rice field habitats , while extrapolated estimates of true richness ( Chao1 and ACE ) indicated higher diversity in the forest and fragmented forest habitats and lower diversity in the urban . Culex sp . ( Vishnui subgroup ) was the most common taxon found overall and the most frequent in fragmented forest , rice field , rural , and suburban habitats . The distributions of species of medical importance differed significantly across habitat types and were always lowest in the intact , forest habitat . The relative abundance of key vector species , Aedes aegypti and Culex quinquefasciatus , was negatively correlated with diversity , suggesting that direct species interactions and/or habitat-mediated factors differentially affecting invasive disease vectors may be important mechanisms linking biodiversity loss to human health . Our results are an important first step for understanding the dynamics of mosquito vector distributions under changing environmental features across landscapes of Thailand .
Our expanding and increasingly globalized human population has seen the emergence of new infectious diseases such as SARS and the resurgence of familiar diseases such as dengue and influenza to epidemic proportions . At the same time , our environment has experienced substantial ecological disturbance due to habitat destruction , invasive species and climate change , with dramatic losses of native species and ecosystems . Biodiversity , or the variety of life forms and functions in nature [1] , affects the stability and long-term health of communities by virtue of rich and life-sustaining networks of ecological and evolutionary interactions . Changes in biodiversity have the potential to affect the risk of infectious diseases in a system by disrupting normal relationships between hosts and pathogens . Bonds et al . [2] report that biodiversity loss is an important factor in the increase of vector-borne and parasitic diseases , which in turn have negative economic and human health impacts . This has been demonstrated experimentally with reduced infection intensities of the human parasite Schistsoma mansoni in diverse snail communities [3] . Anthropogenic changes specifically have been linked to the recent emergence of certain infectious diseases [4] , [5] . For example , in Malaysia the emergence of Nipah virus has been linked to agricultural intensification [6] . In Australia , urban habituation increased the number of fruit bats in contact with humans and domestic animals , resulting in the emergence of Hendra virus [7] . In the eastern United States , forest fragmentation and urbanization led to reduced host diversity , allowing disease-competent rodent hosts to dominate the community , contributing to the emergence of Lyme disease [8] . Thus , in these and many other cases , anthropogenic environmental changes disturb ecological relationships in communities and consequently affect the distribution and relative abundance , or biodiversity , of organisms involved in disease transmission . The mechanisms by which anthropogenic habitat change can lead to biodiversity loss include changes in the relative abundance of species already present in a community , the introduction of new species , or both , where changes in all species may be brought about by direct or indirect interactions ( e . g . , competition , predation or a change in resources , respectively ) . The specific mechanisms by which biodiversity change affects infectious disease distribution depend on the biology of the pathogen and could include the loss of alternate and less competent hosts ( the dilution effect [9] , [10] ) , the breakdown of ecological barriers that normally check transmission including cross-species transmission to new hosts , or the generation of new ecological niches in which certain pathogens can flourish ( reviewed in [9] ) . The introduction of invasives in particular can directly , through the introduction of specific pathogens and their role as an optimal niche , or indirectly , through disrupting other species' ecological relationships , contribute to changing infectious disease distribution . For a vector-borne disease system , which integrates multiple trophic levels across communities , biodiversity change may involve the shifting of overall vector community feeding patterns [8] , [11] , [12] , vector distribution , density , activity and longevity , thereby altering host exposure to vector populations , and hence disease risk [13] , [14] , [15] , [16] , [17] . The introduction of human-adapted vectors can both introduce new human pathogens as well as reduce the relative abundance of other species , or their relationships to hosts , leading to biodiversity loss and changes in infectious disease distribution . In this study , we assess variation in the biodiversity of mosquito communities that include many types of vectors and potential pathogens across habitats differentially impacted by humans , to address when and how these mosquito communities change , as an important first step in identifying potential mechanisms by which this change might affect host-vector interactions and ultimately vector-borne disease risk . Biodiversity of mosquito communities may change across landscapes through multiple mechanisms , including changes in habitat affecting species relative abundance and the invasion of new species . Invasive species could directly impact biodiversity measures through their own numbers and/or via direct competition with endemics or indirectly through habitat changes that affect both endemics and invasives . Here we measure biodiversity using several diversity indices , and examine its variation across habitat types , as well as relative to the abundance of specific invasive and/or medically important species . As an initial step to assess the impacts of biodiversity change on vector-borne diseases , we also examine the relative abundance of medically important species against habitat type and biodiversity change . To examine patterns of mosquito diversity change we take advantage of Thailand's diversity of mosquitoes and habitat types , from highly developed to largely untouched , as well as local expertise in mosquito taxonomy and ecology . In Thailand , many mosquito borne pathogens persist despite intense eradication efforts . For example , the average number of dengue cases reported by the Department of Disease Control from 2002 to 2011 was 76 , 625 . 55 cases per year ( SD = 32 , 983 . 48 ) ( http://www . ddc . moph . go . th/ ) , and its true disease burden is largely underestimated [18] . Chikungunya , transmitted by the same vectors as dengue , has long been ignored until recently with the advent of major outbreaks in South East Asia , including Thailand in 2008 , [19] , [20] . Japanese encephalitis ( JE ) virus , despite a vaccine program initiated in 2000 , remains an important cause of encephalitis in Thailand , responsible for an estimated 15% of hospitalized encephalitis cases [21] . Malaria is also still one of the most important infectious diseases in Thailand: in spite of decades of successful control programs and dramatic reductions in the numbers of cases in most major cities , malaria remains prevalent in undeveloped rural villages and mountainous areas of Thailand [22] . Mosquito communities and the vertebrates they feed upon are an important factor in the distribution of these and other infectious diseases , yet their composition is poorly known in most areas . Studies on mosquito communities in Thailand have mainly focused on either medically important genera such as Aedes spp . [23] , [24] , [25] or specific habitats such as rice fields [26] , [27] , swamp forests [28] , and rural villages [25] . A thorough literature search did not reveal any studies that have investigated the diversity of mosquito communities , the relative abundances of vectors , and their vertebrate communities across habitats in Thailand . In this study , we describe mosquito community diversity specifically across habitat types experiencing different levels of anthropogenic ecological pressures in central Thailand . We explore the relationship between mosquito vector relative abundance and the ecological characteristics of habitats . Ultimately , mosquito and host distribution and diversity can affect vector behaviour and vector-borne disease risk . Understanding vector community dynamics in the face of anthropogenic changes could form the basis for understanding the emergence and persistence of mosquito borne diseases .
We studied six habitat types ( forest , fragmented forest , rice field , rural , suburban , and urban ) along a forest-agro-urban landscape gradient ( Figure 1 ) in Nakhon Nayok province , central Thailand . Nakhon Nayok served as a suitable area for developing the gradient of habitats since the north end of the province is a part of Khao Yai National Park and the Sankambeng Range while the center of the province is a flat river plain formed by the Nakhon Nayok River and includes agricultural activities as well as more densely populated sections . The habitat types were identified along the landscape gradient first by distant imaging ( Google Earth , http://www . google . com/earth/index . html ) and later by direct observation . Selection criteria included the presence of human settlement , degree of human activity , degree of agricultural activity , and the amount of trash or clutter ( Table 1 ) . Within each habitat type , four sites were selected as replicates based on their similarities under these criteria ( Table 1 , Figure 2 ) . We trapped each site using the same combination of adult mosquito traps , designed to maximize the breadth of species encountered ( see below ) . For each trapping session , defined as the deployment of all traps at the same site on a given date , sites were characterized for the following variables in order to quantify the level of human activity: intensity of human settlement ( number of houses in the site ) , intensity of agricultural practice ( estimated percentage of site area allotted for agricultural purposes ) , amount of traffic ( numbers of cars and people passing by the site in 30 minutes near noon on a weekday ) , type of vegetation , estimated percentage of site covered by vegetation , estimated amount of trash and clutter found in the site but outside of the houses ( three categories: low , medium , or high ) , and description of surrounding habitat ( within a 100 meters radius ) . Other variables that may affect trap performance such as the distance of light traps from other closest artificial light sources , positioning and height of all traps , and shade cast above the traps were also collected . All environmental variables were described independently by the same two observers for all sites . Mosquito collections were conducted during the rainy season of 2008 ( Table 1 ) . Four types of adult mosquito traps were used in order to maximize the variety of mosquitoes captured: the BG sentinel targets resting adults near human habitations , the Mosquito Magnet© targets host-seeking females and their attendant males , the CDC UV light trap targets nocturnally active mosquitoes of both genders , and the CDC backpack aspirator uses direct suction and was applied to potential roosts . The area trapped at each site was approximately 1 ha . The number and placement configuration of the traps , as well as the duration of sampling , were kept constant across all sites . At each site , two Mosquito Magnets© were placed in desirable locations 50 meters apart , four CDC UV light traps and four BG sentinel traps were placed 10–20 meters from each magnet . All traps were at least 10 meters away from each other . CDC UV light traps were hung outdoors from tree branches or other structures and situated 1 . 5 to 2 meters from the ground . If a residence was present at the site , permission was requested verbally to use the BG sentinel trap and aspirator in and around the dwelling . All residents readily gave permission and were enthusiastic to have mosquitoes removed from their vicinity . No data on humans or identifiable data to link individuals with survey results were collected , including locality data , which refers to a centralized location unlinked to a specific residence . Mosquitoes were collected for 24 hours per trapping session , with different trapping regimes for day and night . Day trapping , from 10 am to 6 pm , consisted of eight BG sentinels , two Mosquito Magnets© , and three sessions of 3–10 minute long aspirations . Night trapping , from 6 pm to 10 am , consisted of eight BG sentinels , two Mosquito Magnets© , and eight UV light traps . Thus the only difference between day and night trapping regimes was the replacement of aspiration sessions in the day with UV light traps at night . The timing of the trapping sessions at replicate sites was designed so that mosquitoes from at least two sites of the same habitat type were collected one day apart ( Table 1 ) . The trap contents were collected in the evening and in the morning and transported on ice to the field base where mosquitoes were separated on a chill table from other insects and stored at −20°C . Mosquito samples were then transported to the laboratory at Mahidol University , where males were separated out and female mosquitoes were identified using available morphological keys [29] , [30] , [31] , [32] , [33] , [34] with assistance from Thai mosquito expert Dr . Rampa Rattanarithikul . Dr . Rampa trained two graduate students ( PT and AG ) and one technician to assist with the identification process , which took several months at 24–32 person hours per day . Identification keys used followed the taxonomic nomenclature of Knight and Stone [35] and supplements to that publication . However , in the last 10 years there have been major revisions of tribe Aedini ( Neveu-Lemaire ) , including the formal recognition of 80 genera within the tribe [36] . Although the identification keys reflect this reclassification , we maintained usage of the traditional taxonomic names: our diversity indices remain the same using either taxonomic scheme , and the use of traditional nomenclature should avoid confusion and communication difficulties among researchers and the general public . When species identification was not possible , specimens were grouped together in higher taxonomic categories ( genus or family ) . Males , partial specimens or those in bad condition such that they were unidentifiable were excluded from further analysis . Three to twenty specimens were pinned and archived as vouchered reference specimens for each taxon identified . The archived specimens are housed at the Center of Excellence for Vectors and Vector-borne Diseases , Mahidol University , Thailand . Statistical analyses were performed in R version 2 . 13 . 0 ( 2011 , The R Foundation for Statistical Computing , http://www . R-project . org ) . Total abundances of male and female mosquitoes and the mean numbers of mosquitoes captured indoors and outdoors per trap were calculated across all sites and averaged for each habitat type . To test for habitat effects , the Kruskal-Wallis one-way analysis of variance by ranks was used to compare the average abundances between habitat types . The differences between the mean numbers of mosquitoes captured indoors and outdoors per trap in each habitat were compared using the Wilcoxon-Mann-Whitney rank sum test . Mosquito diversity between habitat types was assessed by combining measures of species richness ( number of species or taxa ) and heterogeneity ( number of species and their relative abundance ) . Because of the differences in numbers of mosquitoes collected at each site , species richness cannot be compared directly across habitat types . We used two strategies to correct for unequal sample size: 1 ) individual-based rarefaction , which allows the calculation of species richness for a given number of sampled individuals ( species density or SD ) , and 2 ) non-parametric extrapolation-based estimation , which extrapolates species accumulation curves and estimates ‘true’ species richness based on the number of rare species in the sample . Rarefaction-based estimates and their 95% confidence intervals ( CIs ) for all sites were computed using the function rarefy in R ( ‘vegan’ package ) . Individual-based rarefaction curves for all sites were constructed from software EstimateS . Two estimators of the ‘true’ number of species in each site , Chao1 and ACE ( Abundance-base Coverage Estimator ) , were calculated using the command estimateR in the ‘vegan’ package . Shannon and Simpson diversity indices were used as a measure of community heterogeneity . As a first step to assessing the impacts of biodiversity change specifically on vector-borne diseases , we examine the relative abundance of specific species that act as disease vectors against habitat type and biodiversity change . Average abundance of important vector species , including invasives , was characterized for each habitat type , and statistically assessed for significance using ANOVA . Correlation analysis ( Pearson's test ) was used to assess the significance of the relationship between the proportion of a given species , and its abundance , log-transformed , relative to mosquito community diversity indices ( Chao1 and ACE ) . For all statistical analysis , significance was considered if p<0 . 05 . Biodiversity of mosquito communities may change across landscapes through multiple mechanisms , including changes in habitat affecting species relative abundance and the invasion of new species . Invasive species could directly impact biodiversity measures through their own numbers and/or by interacting with endemics via direct competition or indirectly through habitat changes that disproportionately affect both endemics and invasives ( e . g . the presence of insecticide to which invasive species have greater resistance ) . To determine to what degree biodiversity change in mosquito communities is 1 ) a direct result of the addition of invasives ( addition of invasive species drives the value of biodiversity indices ) , or 2 ) a result of invasive/endemic species interactions or habitat change , we compare biodiversity index ACE to the relative abundance of specific invasive and/or medically important species using indices generated on all species at a given site , and indices generated without the specific invasive included . We call the latter the residual biodiversity index . If variation in residual biodiversity index ACE is correlated with a given vector's abundance , this suggests that the species is either interacting with the other species directly ( e . g . competition ) and/or its abundance reflects habitat changes that differentially affect all species above and beyond the impact of their numbers on the generation of biodiversity measures ( e . g . mechanism 2 above ) .
Mosquitoes were collected along a forest-agro-urban landscape gradient in Nakhon Nayok province , central Thailand ( Figure 2 ) . The latitude and longitude for 24 sites representing six habitat types , and their habitat characteristics , are listed in Table 1 . Forest sites were characterized by primary growth and no human impacts and were situated along the border of Khao Yai National Park and at least 7 km from human settlements . No agricultural lands and domestic vertebrates were present in these sites or nearby . Fragmented forest sites were situated on the edge of a secondary forest patch fragmented from the National Park where human settlements were sparse ( 1–2 houses within each site ) and where small scale , mixed , and non-irrigated agriculture was practiced . Most farmers in these sites either used water buffalo for pulling farming equipment or as a status symbol . The rice field sites were in the lowland closer to the Nakhon Nayok River to facilitate irrigated rice agriculture . Here , the use of water buffalo was replaced by industrialized machinery . Large continuous rice fields and small orchards could be found surrounding the farmers' houses . The rural , suburban , and urban sites were distributed based on the distance from the center of town . The urban sites were near the center of Nakhon Nayok town where agricultural settings and large natural vegetation patches were absent . In the urban sites , the numbers of houses ( average 25 . 5±4 . 65 houses per site ) , human traffic ( average 52 . 5±27 . 6 people walked into/past the site during the 30-minute observation period ) , car traffic ( average 248 . 75±213 . 76 automobiles were driven into/past the site during the observation period ) , amount of trash and clutter ( categorized as medium/high or high ) was highest . Vegetation patches and/or landscaping was found around some houses and in empty lots . Suburban sites were 1–3 km from the town center . Houses were arranged in rows or clusters along the main paved street with an average of 11 . 75 houses per site ( ±4 . 35 ) . Average number of people ( 17 . 75 , ±15 . 02 ) and automobile ( 49 , ±29 . 72 ) traffic in the site were in between the urban and rural sites . The amount of trash and clutter in the suburban sites was either medium or high . There were small patches of active rice fields and empty vegetated lots surrounded the sites . Rural sites were between 7 to 15 km from the town center . Houses were arranged in clusters with an average of 8 . 25 houses per site ( ±2 . 98 ) . The house clusters were situated next to either agricultural land such as rice fields and orchards or secondary forest . Rural sites had the lowest amount of trash and clutter ( categorized as low or low/medium ) and lowest human and car traffic of all the human residential sites ( 3 . 25 , ±2 . 22 and 5 . 00 , ±3 . 74 , respectively ) . All sites were situated at least 0 . 5 km away from each other . A total of 83 , 325 mosquitoes was collected over the six-week period from 24 sites representing the six habitat types described . The total numbers of mosquitoes caught were significantly different among habitats ( Kruskal-Wallis , chi-squared = 13 . 2 , df = 5 , P = 0 . 0213 ) . The highest number of female mosquitoes was caught in the rice field habitat and the average abundance was 8 , 922 mosquitoes caught within 24 hours per site ( sd = 2402 , number of sites = 3 ) . The lowest number of female mosquitoes was caught in the forest habitat ( 1 , 402 mosquitoes per site , sd = 582 , number of sites = 4 ) . The average abundance of male and female mosquitoes in each habitat type is shown in Figure 3 . Out of all mosquitoes captured , 62 , 126 female mosquitoes could be morphologically identified into 109 taxa spanning 15 genera . Of these , 27 , 013 individuals were further classified into 68 species . The remaining 35 , 113 individuals were only identified to genus , subgenus , group , or subgroup either because specimens were damaged in the trap and/or they belonged to cryptic species complexes . Mosquito identifications were overseen and verified by expert SE Asian taxonomist Dr . Rampa . Taxa and their abundance by habitat over the trapping period are listed in Table S1 . The most dominant taxa overall were the Culex ( Culex ) spp . of the Vishnui subgroup ( n = 28 , 967 or 46 . 63% of all identifiable female mosquitoes ) , Cx . ( Cux . ) gelidus ( Theobald ) ( n = 6 , 246 or 10 . 05% ) , Cx . ( Oculeomyia ) sinensis ( Theobald ) ( n = 4 , 261 or 6 . 86% ) , and Cx . ( Cux . ) quinquefasciatus ( Say ) ( n = 3 , 535 , or 5 . 69% ) . The Culex spp . of the Vishnui subgroup was also the most dominant taxon in the fragmented forest habitat ( n = 4 , 238 or 53 . 77% from fragmented forest ) , rice field habitat ( n = 17 , 853 or 65 . 92% ) , rural habitat ( n = 2 , 659 or 34 . 01% ) , and suburban habitat ( n = 3 , 011 or 34 . 77% ) . The most abundant taxon for the forest habitat was Uranotaenia spp . ( n = 2 , 694 or 56 . 68% ) and for the urban habitat , Cx . quinquefasciatus ( n = 1 , 839 or 31 . 01% ) . Except for the forest habitat where there was no indoor trapping , the only two habitats in which the number of mosquitoes caught outdoors per trap was not significantly higher than the number of mosquitoes caught indoors per trap were the suburban and urban habitats ( Wilcoxon-Mann-Whitney test; Figure 4 ) . The average number of mosquitoes caught outdoors per trap was highest in the rice field habitat . The highest rate of indoor trapping was found in the urban habitat . The most abundant species indoors was Aedes ( Stegomyia ) aegypti ( Linnaeus ) in the rural ( n = 210 or 39 . 85% of all mosquitoes collected indoors in the rural habitat ) , and rice field habitats ( n = 31 , 44 . 29% ) , Culex spp . of the Vishnui subgroup in the fragmented forest ( n = 20 , 28 . 17% ) , and Cx . quinquefasciatus in the suburban ( n = 977 , 81 . 48% ) and urban habitats ( n = 851 , 67 . 43% ) . The average numbers of taxa identified ( N ) , Shannon diversity ( H ) , Simpson diversity ( D ) , Chao1 , and ACE indices generally varied significantly across habitat type ( Table 2 ) . The average diversity indices for the six habitat types ranged from 1 . 21 to 2 . 30 for Shannon index and from 0 . 51 to 0 . 82 for Simpson index . Both indices were highest in the rural habitat and lowest in the rice field habitat . Analysis of variance ( ANOVA ) tests revealed significant differences in both diversity indices among the six habitat types ( F = 5 . 68 , df = 5 , P = 0 . 0029 for Shannon index and F = 4 . 50 , df = 5 , P = 0 . 0086 for Simpson index ) . Tukey multiple comparisons of means revealed significant differences in Shannon indices between rural-forest ( P = 0 . 0158 ) , rural-fragmented forest ( P = 0 . 0452 ) , rural-rice field ( P = 0 . 0030 ) , and urban-rice field ( P = 0 . 0416 ) and in Simpson indices between rural-rice field ( P = 0 . 0177 ) . Number of taxa identified was highest in the fragmented forest habitat ( average number of taxa = 34 . 25 ) , and lowest in the forest habitat ( average number of taxa = 26 . 25 ) . The forest site also yielded the least number of individuals , was the most difficult to sample , and this probably accounted for the lower number of species . To correct for unequal sample sizes among sites , numbers of species were rarefied to a constant number of individuals . At an equal sample size of 614 , expected number of species or species density ( SD ) was highest in the rural habitat ( SD = 28 . 37 , 95% CI = 25 . 20 to 31 . 53 ) and lowest in the rice field habitat ( SD = 18 . 20 , 95% CI = 14 . 82 to 21 . 57 ) . Individual-based rarefaction curves were constructed to determine whether the number of mosquitoes collected reached the point where species richness is saturated ( Figure 5 ) . Overall , the shape of the rarefaction curves for most sites indicated that more individuals needed to be collected for the curves to reach their plateau . To estimate the number of rare and undetected species and add them to the observed richness , abundance-based extrapolated richness estimates such as Chao1 and ACE were calculated ( Table 2 ) . In contrast to some of the other indices , the highest richness was found in the fragmented forest habitat according to Chao1 , and in the forest habitat according to ACE , followed by the rural , rice field , and suburban habitats . The lowest richness was in the urban habitat according to both estimates . However , the differences in Chao1 and ACE across sites were not significantly different based on ANOVA ( F = 0 . 551 , df = 5 , P = 0 . 736 for Chao1 and F = 0 . 830 , df = 5 , P = 0 . 545 ) . Because of the known presence of several vector-borne infectious diseases in the study area , the average abundance of important mosquito vectors was compared across habitats ( Table 3 ) . Ae . aegypti and Ae . ( Stg . ) albopictus ( Skuse ) , vectors for dengue , chikungunya , and yellow fever virus , were most abundant in the urban and rural habitats , respectively . Malaria vectors , Anopheles spp . , including the Barbirostris group , Hyrcanus group , Pyretophorus series , Neocellia series , and Neomyzomyia series , were most abundant in the rural habitat . Mansonia spp . , which transmits Brugia malayi , an agent of Malayan filariasis , was most abundant in the rice field habitat . In Thailand , Bancroftian filariasis , filarial infection with the nematode Wuchereria bancrofti , is principally transmitted by Cx . quinquefasciatus . This species was found mainly in urban and suburban areas . Other possible vectors of Bancroftian filariasis include Cx . ( Ocu . ) bitaeniorhyncus ( Giles ) , which was distributed mainly in the rice field habitat , and Armigeres ( Armigeres ) subalbatus ( Coquillett ) , which was distributed relatively evenly across all habitat types . The principal vector of JE is Culex spp . of the Vishnui subgroup , which was abundant in the rice field habitat . The other possible vectors for JE include Cx . ( Cux . ) fuscocephala ( Theobald ) , abundant in rural and fragmented forest areas , and Cx . gelidus , abundant in rice field and suburban areas . For all vectors whose abundance differed significantly across sites , their abundance was lowest in intact , forest sites ( Table 3 ) . Correlation analysis revealed a significant negative correlation between ACE index and the fraction of Aedes aegypti in the total number of mosquitoes ( r = −0 . 46 , t = −2 . 35 , df = 21 , P = 0 . 0287 ) and of Culex quinquefasciatus ( r = −0 . 49 , t = −2 . 55 , df = 21 , P = 0 . 0185 ) . This relationship was not observed with the other medically important species . To determine to what extent these relationships reflect the numerical influence of Ae . aegypti or Cx . quinquefasciatus on the statistical value of the ACE index versus inherent properties of species or habitat interactions , we examined the correlation between vector relative abundance at sites where present and the ‘residual’ diversity index calculated without that species included and found a significant negative correlation between the ‘residual’ ACE index and Aedes aegypti relative abundance ( r = −0 . 52 , t = −2 . 77 , df = 21 , p-value = 0 . 0115 ) and Culex quinquefasciatus relative abundance ( r = −0 . 47 , t = −2 . 44 , df = 21 , p-value = 0 . 0238 ) . These results suggest that these vector species are found in truly reduced diversity environments , whose index is not simply a statistical consequence of the given invasive vector's abundance .
With recent global expansions of humans and vectors , new and recurring infectious diseases have emerged , often in epidemic proportions , and in some cases have been correlated with changes in the biodiversity of affected communities [2] . Biodiversity can increase the resilience of communities to change [37] . A hallmark of disturbed ecosystems includes the emergence of infectious diseases , which has also been correlated with biodiversity loss [2] , [38] . Here we examined the relationship between mosquito diversity and habitat modification by humans across a range of sites , from primary forest to urban centers , in Central Thailand . The mosquito communities sampled included several important vectors of infectious diseases such as dengue , chikungunya , yellow fever , filariasis , and malaria . We showed that both the diversity of mosquito communities and the relative abundance of disease vectors varied by habitat , with the lowest diversity and highest abundance of certain vectors occurring in urban environments , whereas other vectors were most abundant in different habitats depending on their biology . In all cases , vectors of disease were lowest in intact forest habitats . We combined both a unique field design and analytical approach to explore the relationship between habitat degradation and mosquito biodiversity . We fully characterized 24 sites to represent six habitat types that varied by degree of human impact , from least to most: forest , forest fragment , agricultural , rural , suburban , and urban . Our analytical approach applied a combination of standard diversity indices as well as estimates of true richness that take into account sample size variation . To date , most studies on mosquito communities have compared the numbers of species found in communities without considering 1 ) the differences in the numbers of samples collected , and 2 ) whether sampling was sufficient to capture most species , such that species accumulation curves reached their asymptotes [39] , [40] , [41] , [42] , [43] . The number of individuals that must be sampled to reach this asymptote can be prohibitively large especially in the tropics , where species diversity is high and most species are rare [44] . Consequently , collecting enough samples is often difficult or technically impossible , and using true richness estimators is preferred . Our results indicated that estimates of true richness ( Chao1 and ACE ) can differ greatly from standard diversity indices ( species abundance , Shannon and Simpson ) . Over the six habitat types surveyed in Central Thailand , we collected 83 , 325 mosquitoes , of which 62 , 126 were females and identifiable into 109 taxa including 15 genera and 68 species . Mosquito diversity varied greatly by habitat . According to the true richness estimators , the least diverse habitats were the urban , followed by suburban , rice field , and rural . The most diverse habitats were the forest and the fragmented forest . Forest estimates of diversity are conservative and probably underestimate the diversity more than other sites . Common forest species , such as the genus Uranotaenia , are particularly small and difficult to identify . In addition , compared to other habitat types , the adverse conditions of the forest habitat such as more rain and humidity differentially degrades mosquito condition , possibly affecting mosquito estimates of richness compared with other sites . Fewer individuals were also collected at forest sites , suggesting a combination of lower abundance and that the habitat may be harder to survey with our trap sets , possibly due to the abundance of alternative microniches . Even under these less than ideal conditions , richness estimators still indicated that urban/suburban habitats are less diverse in terms of mosquitoes than forest/fragmented forest habitats . Other studies that have compared mosquito communities across human-modified landscapes focused on urban , semi-urban , and rural environments also showed that urban environments are the least diverse [39] , [45] , [46] , [47] . The mechanisms underlying this pattern are not well understood , but some have suggested the positive effect of habitat diversity on mosquito species diversity [48] , [49] , or that increased stress and pollution in urban habitats favor certain invasive genera such as Culex , which is more adaptable to a variety of habitats and may competitively exclude other species [50] , [51] , [52] . We suspect that the urban environments in our study may have had fewer kinds of aquatic habitats that different female mosquitoes could exploit , thus favoring human-adapted mosquitoes such as Aedes aegypti and Culex quinquefasciatus . In addition , the contamination of pesticide in households and agricultural land may alter natural aquatic community composition , influence larval mosquito abundance , and favor species that are more resistant to chemicals [53] , [54] . Agricultural environments characterized by monoculture are similarly niche-poor and liable to suffer biodiversity loss . We observed that the rice field habitat , where irrigated and intensive rice cultivation is practiced , was less diverse than the rural and fragmented forest habitats , where small , non-irrigated , and mixed agriculture is practiced . A study in Kenya [40] comparing mosquito communities between planned , unplanned , and non-irrigated riceland agroecosystems found the highest diversity in non-irrigated agroecosystems and this was linked to higher habitat diversity in this environment . In our study , we observed that there were more types of aquatic habitat in rural and fragmented forest environments than in rice fields . In fact , the rural and fragmented forest habitats are ecotones , transition zones between two or more adjacent ecological systems [55] , and as such should include an elevated number of species , the combined set of species from different adjacent and partially overlapping habitats . Ecotones have been shown to play a role in a number of important emerging infectious diseases [56] . The distribution of medically important mosquito species differed across habitats , correlated with biodiversity changes , and may have important implications for disease transmission in Thailand . Our results show that Culex quinquefasciatus was most abundant in the urban habitat both indoors and outdoors . Cx . quinquefasciatus uses dirty and polluted urban aquatic sources as larval habitat [50] , [51] , which are particularly associated with human habitation [57] . Culex spp . of the Vishnui subgroup , which includes the morphologically cryptic Cx . ( Cux . ) vishnui ( Theobald ) , Cx . ( Cux . ) tritaeniorhynchus ( Giles ) , and Cx . ( Cux . ) pseudovishnui ( Colless ) , were found in all habitats but were most abundant in rice fields . In Thailand , Cx . quinquefasciatus and the Vishnui subgroup are the main vectors of filariasis and Japanese encephalitis , respectively [58] , [59] , [60] . Cx . gelidus , Cx . bitaeniorhynchus , and Mansonia spp . , also vectors of filariasis and/or JE , were also most abundant in rice fields . Anopheles spp . , vectors of malaria-causing Plasmodium spp . , were most abundant in rural sites . Ae . aegypti is highly anthropophilic and prefers to feed on human blood [61] and consequently was primarily collected in urban sites , occasionally in suburban and rural sites , and rarely or never in the other habitats . Ae . albopictus , on the other hand , was collected primarily from rural and fragmented forest habitats , and occasionally in other habitats . These findings concur with others showing that the average number of Ae . aegypti was higher in urban than rural areas , whereas the opposite was found for Ae . albopictus [62] , [63] , [64] . Ae . aegypti and Ae . albopictus are important disease vectors of dengue virus , in which the former has mostly been associated with epidemic transmission [65] . All vectors were least abundant in the forest sites . Furthermore , Ae . aegypti and Cx . quinquefasciatus relative abundances were both negatively correlated with biodiversity measures across sites . The negative relationship between mosquito abundance and site diversity for Ae . aegypti and Cx . quinquefasciatus was observed relative to both raw ACE diversity indices and the ‘residual’ indices , those derived with the direct numerical influence of each vector species removed . The relationship between vector abundance and residual diversity is a novel presentation of the data and suggests a negative interaction between these vectors and other species . Such interactions could include competition , as documented between Ae . aegypti and Ae . albopictus [66] , [67] , [68] , [69] , and Cx . quinquefasciatus and other Culex spp . [70] , or that the community may be responding to a third variable affecting abundance and/or biodiversity , for example , differential habitat suitability or insecticide resistance , that disproportionally favors invasive species over native species . Although this pattern is suggestive of a potential ameliorating effect of biodiversity on human health , further studies are necessary to distinguish the causal links underlying this pattern of biodiversity change . Evidence for the importance of biodiversity on infectious diseases in human populations is growing , yet mechanisms such as the ecological role of vectors and host communities are still controversial [3] , [71] . In this study we examined patterns of mosquito community change across a range of anthropogenically-modified habitats as a first step towards identifying potential mechanisms by which vector-borne disease distribution might be affected . The result is a documentation of biodiversity change in a group seldom considered for the full breadth of its diversity . Previous mosquito studies in Thailand have been restricted to only a few habitats or important vector species , thus current knowledge of mosquito community diversity and the relative abundance of disease vectors across habitats is limited . Our patterns suggest multiple mechanisms might link biodiversity loss with human health risk across Central Thailand , including the direct invasion of specific disease-bearing vectors and their interactions with other mosquito species . Competitive interactions between key invasive vectors and other mosquitoes , such as between Ae . aegypti and Ae . albopictus , and Cx . quinquefasciatus and other Culex spp . , may provide an opportunity to control the impact of anthropogenic change on invasive vector abundance .
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Biodiversity affects the long-term health of a community by virtue of the many interactions constituent organisms depend upon . Mosquito-borne diseases are particularly likely to respond to changes on multiple transmission levels that span mosquito and vertebrate host communities . We characterized mosquito communities across habitats with differing levels of anthropogenic degradation in central Thailand . During the 2008 rainy season , 83 , 325 adult mosquitoes were collected from 24 sites , representing 6 habitat types ranging from forest to urban , of which 62 , 126 females were assigned to 109 taxa . Extrapolated estimates of true richness ( Chao1 and ACE ) indicated higher diversity of mosquito communities in forest/fragmented forests and lower diversity in urban habitats . Species of medical importance differed significantly across habitats and were always lowest in forest . The relative abundance of vectors Aedes aegypti and Culex quinquefasciatus was negatively correlated with biodiversity , suggesting that direct species interactions and/or habitat-mediated factors differentially affecting invasive vectors may be important mechanisms linking biodiversity loss to human health . Our results represent an important first step towards understanding the distributions of mosquitoes including disease vectors under changing environmental features .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
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Mosquito Vector Diversity across Habitats in Central Thailand Endemic for Dengue and Other Arthropod-Borne Diseases
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Entamoeba histolytica is the pathogenic amoeba responsible for amoebiasis , an infectious disease targeting human tissues . Amoebiasis arises when virulent trophozoites start to destroy the muco-epithelial barrier by first crossing the mucus , then killing host cells , triggering inflammation and subsequently causing dysentery . The main goal of this study was to analyse pathophysiology and gene expression changes related to virulent ( i . e . HM1:IMSS ) and non-virulent ( i . e . Rahman ) strains when they are in contact with the human colon . Transcriptome comparisons between the two strains , both in culture conditions and upon contact with human colon explants , provide a global view of gene expression changes that might contribute to the observed phenotypic differences . The most remarkable feature of the virulent phenotype resides in the up-regulation of genes implicated in carbohydrate metabolism and processing of glycosylated residues . Consequently , inhibition of gene expression by RNA interference of a glycoside hydrolase ( β-amylase absent from humans ) abolishes mucus depletion and tissue invasion by HM1:IMSS . In summary , our data suggest a potential role of carbohydrate metabolism in colon invasion by virulent E . histolytica .
In the human colon , mucus acts as a lubricant facilitating the passage of digestive content , protects the underlying epithelium from mechanical stress , and provides a protective barrier against pathogens . Mucin 2 ( MUC2 ) is the major component of the mucus layer . MUC2 is a heavily glycosylated protein , containing more than 100 different glycan chain variants which are responsible for approximately 80% of the MUC2 mass [1] . The extensive glycosylation of MUC2 provides protection to resist proteolytic activities . The MUC2-related glycans also represent a potential carbon source for microbiota nutrition , mainly in the distal colon where the availability of free carbohydrates is limited . For instance , intestinal commensal bacteria express genes involved in the biodegradation of complex sugars and glycans present in dietary fibers [2] or genes important for degrading the endogenous pool of host glycans , the last offering a permanent nutrient source for the gut microbiota [3] . During infection , pathogens and resident microbiota compete for nutritional metabolites present in the intestinal lumen and therefore changes in carbon availability may alter the equilibrium in the colon ecosystem contributing to the susceptibility to infection . Entamoeba histolytica is a protozoan parasite residing in the human colon where it feeds on bacteria . In some cases , trophozoites invade the tissue leading to intestinal amoebiasis and , in rare cases , to hepatic amoebiasis . E . histolytica infection is a persistent and worldwide disease that is the third leading cause of mortality due to a protozoan [4] . Most infections with this parasite are asymptomatic since only ∼20% of the cases develop intestinal amoebiasis , which are characterized by colonic mucosa invasion and tissue destruction . Trophozoites have been isolated from symptomatic and asymptomatic patients . E . histolytica HM1:IMSS , isolated from a patient suffering from amoebic dysentery , is a virulent strain routinely used to reproduce the main features of intestinal [5] and hepatic amoebiasis [6] in experimental models . Another strain , E . histolytica Rahman , was isolated from an asymptomatic carrier , it is unable to growth in animals due to its inherent phenotype and is classically referred as a non-virulent strain [7] . Analysis of the 5 . 8S rRNA sequences indicates that Rahman belongs to E . histolytica species [8] , nonetheless , the Rahman strain presents a reduced cytotoxicity towards epithelial cells in vitro [9] , does not form liver abscesses in animal models [10] , exhibits defects in phagocytosis , and shows significantly reduced virulence in a human intestinal xenograft model of amoebic colitis [11] . A genomic hybridization study comparing HM1:IMSS and Rahman strains revealed that only 5 out of 1817 genes studied are significantly divergent [12] . At the protein level , important differences in Rahman have nevertheless been described including a truncated glycan chain of the proteophosphoglycan coating the surface [13] , a decreased level of both peroxiredoxin [11] and the light subunit of the Gal/GalNAc lectin [9] . Several studies have also attempted to identify genes whose expression correlates with a virulent phenotype by comparing the transcriptomes of both strains under culture conditions [14] , [15] . Although they highlighted changes in multiple pathways during parasite axenic growth , no clear explanation has been given to account for their differences in virulence . To gain insights into the molecular basis of phenotypic differences between E . histolytica HM1:IMSS and Rahman during their interaction with the intestine , we took advantage of the human colon ex vivo model of amoebiasis [5] . Their interaction with the human colon explants was investigated by analysing the morphological changes of the mucosa architecture . We then performed a gene expression analysis for each strain and made comparisons between their transcriptomes . We identified ( i ) genes that are constitutively expressed in each strain in the two different environments ( i . e . in axenic culture or human colon explants ) , ( ii ) transcripts specifically upregulated in each strain upon contact with human colon explants , and ( iii ) transcripts commonly modulated in both strains upon contact with human colon explants . Genes encoding glycolytic enzymes , carbohydrate catabolism enzymes , and genes characterized as virulent factors were identified and exclusively upregulated in HM1:IMSS upon contact with human colon explants . In particular , one of the most upregulated genes in HM1:IMSS is β-amylase , a glycoside hydrolase absent in humans . The potential role of β-amylase in colon invasion was further investigated by knocking down of its encoding gene using double-stranded RNA ( dsRNA ) . Parasites treated with dsRNA were unable to deplete the mucus and subsequently invade the human colon explants . Altogether , our data provides a novel view of how E . histolytica crosses the intestinal barrier and suggests new avenues to understand amoebic pathogenicity .
To investigate the phenotypic differences between the virulent HM1:IMSS and non-virulent Rahman strains during their interactions with the human colon explants , we monitored the ex-vivo invasion of human colon explants from six patients . After 1 or 7 hours ( h ) of incubation with trophozoites , colon fragments were fixed for histological analysis and longitudinal sections of the tissues were prepared and examined for mucus integrity ( Figure 1A ) and tissue invasion ( Figure 1B ) . After 1 h of incubation , the protective mucus layer remains intact in all three conditions ( Figure 1A ) . After 7 h of incubation , we observed tissue penetration by the HM1:IMSS trophozoites with a strong depletion of the mucus layer ( Figure 1A ) . Trophozoites were then localized by immunostaining for the Gal/GalNAc lectin ( Figure 1B ) . The HM1:IMSS trophozoites degraded the intestinal epithelium and penetrated into the mucosa as described previously [5] , [16] . In contrast to these findings , after 7 h of incubation with the Rahman strain , trophozoites were still at the surface of the explant , no penetration of the mucosa was observed , and the tissue structure remains intact ( Figure 1B ) . We utilized video microscopy to monitor Rahman trophozoites on the explants to ensure that they were still viable during the incubation ( Video S1 ) . To identify gene expression specifically modulated in HM1:IMSS and Rahman strains upon contact with the human colon explants , total RNA was purified from trophozoites in axenic culture and after contact for 1 h with the human colon explants , a time where virulent trophozoites begin to penetrate through the mucus layer of the colonic tissue [5] . The experiment was conducted on six independent human colon explants from six patients and therefore six biological replicates . Each explant was cut into three pieces , the first was incubated with HM1:IMSS trophozoites , the second was incubated with Rahman trophozoites and the third was incubated without trophozoites ( as a control for pathophysiology ) . RNA was purified from the amoebic samples in contact with the tissue as well as from amoebic samples growing in in vitro culture . As a control , RNA was also purified from trophozoites of both strains incubated in Krebs buffer only ( the medium for incubation with the human colon explants ) . RNA was then reverse-transcribed and hybridized with whole genome cDNA microarrays ( EH-IP2008 , Agilent technologies ) as described previously [17] . A total of 54 hybridizations were performed . For transcriptome data analysis we adopted a step-by-step strategy . First , we performed pairwise comparisons ( Figure 2A ) with statistics computed for each gene and each condition to identify the transcriptome differences between HM1:IMSS and Rahman strains under axenic culture ( Comparison 1 ) and upon contact with the human colon explants ( Comparison 4 ) . The pairwise comparisons also identified transcriptome responses specific for each strain when comparing the human colon explant to the respective profiles in axenic culture ( Comparisons 2 and 3 ) . The genes that were commonly modulated in TYI or Krebs buffer only were eliminated from the analysis ( Table S1 and S2 ) . In the second part of the analysis , we used a nested statistical approach ( Limma package [18] ) where values were tested across the comparisons as indicated in Figure 2B . The gene expression profile of ubiquitously expressed genes for HM1:IMSS was defined by the genes upregulated during both in axenic culture and upon contact with the human colon explants , compared to Rahman ( Conditions ( A/C+B/D ) ) . Notice that in this analysis genes downregulated in HM1:IMSS were also considered since these genes became upregulated in Rahman . Similarly , the gene expression profile of ubiquitously expressed genes for Rahman was obtained by detecting genes upregulated in axenic culture and upon contact with the human colon explants compared to HM1:IMSS ( Conditions ( C/A+D/B ) ) . Since both strains bind to the mucus , we searched for a gene expression profile reflecting their common responses the mucus contact by comparing the upregulated genes shared by both strains upon contact with the human colon explants compared to axenic culture ( Figure 2B , Conditions ( D/C+B/A ) ) . Furthermore , we established the gene expression profile of HM1:IMSS genes specifically expressed during mucus contact , composed of upregulated genes in HM1:IMSS upon contact with the human colon explants compared ( i ) to the axenic culture and ( ii ) to Rahman upon contact with the human colon explants . We removed genes upregulated in HM1:IMSS in axenic culture compared to both Rahman in axenic culture and Rahman upon contact with the human colon explants ( Conditions ( B/A+B/D ) − ( A/C and D/C ) ) . Analogously , we obtained the gene expression profile of Rahman genes specifically expressed during mucus contact , composed of upregulated genes in the Rahman upon contact with the human colon explants as compared ( i ) to the axenic culture and ( ii ) to HM1:IMSS upon contact with the human colon explants . We removed genes that were upregulated in HM1:IMSS in the axenic culture and upon contact with the human colon explants ( Conditions ( D/C+D/B ) − ( C/A and B/A ) ) . Overall the combined analysis established the gene expression profiles characteristic of the virulent and non-virulent phenotypes . Statistical evaluation by Principal Component Analysis ( PCA ) of the expression data showed that each comparison segregates as a distinct pool ( Figure 3 ) indicating that ( i ) the biological replicates within each comparison showed similar gene expression patterns and ( ii ) the differences between the comparisons were higher than the individual variability , thereby validating our experimental settings . A stringent statistical threshold for the microarray data analysis was used , detecting a total of 614 genes with significantly modulated expression ( Fold Change ( FC ) ≥2 , Bonferroni adjusted p value≤0 . 05 ) ( Figure 4 ) . Eighty-one upregulated and 59 downregulated transcripts were different between HM1:IMSS and Rahman in axenic culture ( Comparison 1 ) ( Figure 2B , Table S3 ) . Upon contact with the human colon explants ( Comparison 2 ) , 63 genes were upregulated and 56 were downregulated in HM1:IMSS , compared to axenic culture ( Table S4 ) . Comparison 3 indicates that 75 genes are upregulated and 95 downregulated in Rahman upon contact with the human colon explants , compared to axenic culture . Following mucus contact an additional 77 genes are upregulated in Rahman compared to HM1:IMSS ( Table S5 ) . Finally , the comparison between HM1:IMSS and Rahman upon contact with the human colon explants ( Comparison 4 ) , reveals 133 genes upregulated and 53 genes downregulated ( Table S6 ) . The 614 modulated genes were then manually classified into functional categories based on the gene annotation in AmoebaDB . These categories include , adhesion - cell surface molecules , translation - protein maturation , stress response , DNA-RNA regulation , cell signalling , nucleic acid metabolism , subcellular trafficking , oxidoreduction activities , proteolysis , carbohydrate metabolism , lipid metabolism , and cytoskeleton ( Table 1 ) . Genes ubiquitously expressed in Rahman strain ( n = 17 , Table 2 ) , were defined as those transcripts upregulated in both axenic culture and upon contact with human explants compared to that of HM1:IMSS . In particular , two α-1 , 3-mannosyltransferases ( ALG2 ) and two Cysteine protease , CP-A8 and CP-A3 were included . CP-A3 has already been associated with non-virulent phenotypes as it is upregulated in the Rahman strain [11] and the non-virulent species , E . dispar [19] . Five genes encoding enzymes involved in lipid biogenesis were also present , including three lecithin:cholesterol acyltransferases that convert free cholesterol into cholesteryl ester , a START lipid binding domain containing protein , and a 1-O-acylceramide synthase . One gene belongs to the cytoskeleton functional category , coronin , as well as 2 genes encoding signalling molecules , were characteristic for the Rahman strain . We identified 37 genes in Rahman trophozoites specifically upregulated only upon contact with the human colon explants ( Table 3 ) . The largest functional group is composed of factors involved in cell signalling such as several genes encoding phosphatases , kinases , a guanine exchange factor , a GTPase , and calcium binding protein 1 ( CaBP1 ) . The cysteine protease , CP-A4 , is specific to this profile . Five genes encoding proteins that regulate lipid metabolism were found , including a gene encoding a long-chain-fatty-acid-CoA ligase ( also called Fatty acyl-CoA synthetase ) which catalyses the formation of fatty acyl-CoA , a substrate for β-oxidation and phospholipid biosynthesis [20] and another allele of lecithin:cholesterol acyltransferase . We also observed an increased expression of genes encoding proteins involved in DNA-RNA regulation , including a DNA repair and recombination protein , a DNA-directed RNA polymerase II , Piwi , a 5′-3′ exonuclease , and 1 RNA binding protein . Upon contact with the human colon explants , the response common to both E . histolytica strains , was defined by 13 genes ( Table 4 ) . The adhesion-cell surface molecules class includes the intermediate subunit 2 of the Gal/GalNAc lectin ( Igl-2 ) and 1 newly identified protein containing a fibrinogen-binding domain ( EHI_098440 ) . Two cysteine protease-encoding genes were also identified for both strains as being upregulated . CP-A7 and an unannotated CP ( EHI_010850 ) belonging to the peptidase C1A subfamily . Concerning energy metabolism , a gene implicated in lipid metabolism ( long chain fatty acid CoA ligase ) and 2 genes involved in carbohydrate metabolism were found ( α-amylase and UDP-glucose 4-epimerase ) . A member of the Myb transcription factor family ( EHI_008130 ) and several transcripts encoding signalling molecules were also found . The specific signature of HM1:IMSS in axenic culture and upon contact with the human colon explants is characterized by 39 transcripts ( Table 5 ) . This signature includes several surface associated proteins [21] namely the Gal/GalNAc lectin light subunits Lgl-1 and Lgl-5 , the lysine- and glutamic acid- rich protein 1 ( KERP1 ) , the serine/threonine/isoleucine-rich protein ( STIRP ) , and the cysteine protease CP-A5 . The presence of CP-A5 is important to highlight since its activity is necessary for invasion of the human colon [5] , [16] . The fact that we found well-known virulence factors associated with the HM1:IMSS gene signature confirms the relevance of the integrated analysis performed here . Genes encoding proteins were identified to be important for the amoebic stress response and include heat shock proteins-70 ( HSP-70 ) and HSP-101 , a calcium binding protein involved in signalling , two calmodulins , and several GTPases from the AIG protein family . Three proteases-encoding genes also characterized the gene expression profile specific for the HM1:IMSS strain , namely an unannotated Cysteine protease containing a C1-A peptidase domain and a metalloprotease MP-1 . Several genes implicated in carbohydrate metabolism , including 5 genes encoding glycolytic enzymes , phosphofructokinase , fructose 1–6 aldolase , and aldose reductase , and two genes encoding β-amylase were found . HM1:IMSS trophozoites specifically upregulate 40 genes upon contact with human colon explants ( Table 6 and 7 ) and two points are worth to notice in particular . First , it is the upregulation of 6 genes encoding proteins annotated as regulators of nonsense transcripts . They all contain a RNA helicase domain belonging to the super family 1 ( SF1 ) . This RNA helicase domain promotes structural transitions of RNA or RNA-protein complexes . We further found Myb 13 ( EHI_053000 ) that belongs to the MybR2R3 family of transcription factors and which has been reported to bind a DNA consensus Myb recognition element in vitro [22] . Second , it is the upregulation of proteins involved in signalling , including a phosphatase , a kinase , 2 Rab GTPases , 3 Ras GTPases , and a cyclin . Genes linked to the stress response were identified and include 2 HSP-70 genes and 2 ubiquitin genes . Furthermore , the 2 genes implicated in sugar catabolism were also upregulated and they encode a starch binding protein ( EHI_074010 ) and another allele of β-amylase ( EHI_035700 ) respectively . The 5 profiles established above were combined to depict the transcriptomic landscape associated with HM1:IMSS ( virulent and intestinal invasive ) and the Rahman strain ( non-virulent and intestinal non-invasive ) phenotypes of E . histolytica . We highlighted in Figure 5 the well-known virulent factors and the metabolic pathways herewith identified . The specific signature for the mucus-invading HM1:IMSS strain is composed of the following 3 profiles: the HM1:IMSS ubiquitously expressed genes ( common to culture and mucus ) , the common gene expression profile of both strains in response to mucus contact , and the gene expression profile induce in response to colon invasion ( exclusive to HM1:IMSS and inherent to mucus invasion ) . Thus the virulent phenotype of E . histolytica associated with HM1:IMSS is characterized by the expression of genes involved in adhesion ( Lgl-1 , Lgl-5 , Igl-2 , KERP1 , STIRP , putative fibrinogen binding protein ) , proteolytic activities ( MP-1 , CP-A5 , CP ) , and carbohydrate metabolism ( phosphofructokinase , aldose reductase , fructose aldolase , β-amylase , α-amylase , UDP-glucose isomerase , triosephosphate isomerase , glucose-4-epimerase , 4-α-glucanotransferase , and oligosaccharide-glycosyltransferase ) . The specific signature associated to the non-virulent Rahman strain consists of the ubiquitously expressed gene profile ( common to culture and mucus ) in addition to the common gene expression profile in response to mucus contact and the gene expression profile specifically induce in Rahman in response to colon contact ( exclusive to Rahman and inherent to mucus contact ) . Thus the non-virulent phenotype of E . histolytica associated to Rahman strain is characterized by the independence from adhesion molecules , the activation of genes encoding proteases activities ( CP-A3 and CP-A8 ) distinct from virulent trophozoites , and the importance of lipid metabolism ( lecithin: cholesterol acyl-transferase , START protein , 1-O-acylceramide synthase , fatty acid elongase , long chain fatty acid-CoA synthase , serine palmitoytransferase , and Niemann-Pick C1 protein ) . Since the Rahman strain expresses this particular set of genes , we conclude that it does not favour colonic mucosa invasion . A striking result of this study is the discovery of specific distinction concerning energy metabolism pathways activated by non-virulent and virulent strains when they are in contact with the mucus layer . Rahman strain is characterized by an increased expression of genes related to lipid metabolism ( Table 2 and 3 ) , whereas HM1:IMSS strain is characterized by upregulation of genes encoding proteins involved in carbohydrate metabolism ( Table 5 and 7 ) . To test for functional enrichment in genes upregulated in HM-1:IMSS versus Rahman strains during colon invasion , we performed a hyper-geometric test for gene ontology enrichment [23] and gene set enrichment analysis [24] for KEGG pathway [25] . In the hyper-geometric test , carbohydrate catabolic process ( GO:0016052 ) , among other carbohydrate metabolism related gene ontology terms , was significantly enriched ( Table S7 ) . Moreover , in gene set enrichment analysis for KEGG pathway , Glycolysis/Gluconeogenesis ( ehi00010 ) and , Fructose and mannose metabolism ( ehi00051 ) were also significantly enriched ( Table S8 ) . The results from gene enrichment tests prompt us to take a closer look at the carbohydrate metabolism genes that are significantly upregulated in the HM1:IMSS strain ( without fold-change cut-off ) , 39 additional genes involved in carbohydrate metabolism were found and listed in Table S9 . In particular we identified genes encoding enzymes that are potentially involved in carbohydrate retrieval from MUC2: β-galactosidase ( EHI_170020 ) and β-N-acetylhexosaminidase ( EHI_148130 ) and 3 genes involved in the production of glucose-1-phosphate - glycogen phosphorylase ( EHI_110120 ) , 2 other alleles of β-amylase ( EHI_098200 , EHI_148800 ) , and UDP-glucose pyrophosphorylase ( EHI_000440 ) . Glycogen phosphorylase catalyses the rate-limiting step in glycogen degradation by releasing glucose-1-phosphate from the terminal α-1 , 4-glycosidic bond , β-amylase releases maltose from the polysaccharide chain by hydrolysis of α-1 , 4-glucan linkages , and UDP-glucose pyrophosphorylase that catalyses the formation of glucose-1-phosphate and UDP from UDP-glucose . In addition , among the 11 enzymes involved in the glycolytic pathway , 7 were specifically induced in the HM1:IMSS strain: phosphoglucomutase , aldose reductase , glucose-6-phosphate isomerase , phosphofructokinase , fructose-1 , 6-biphosphate aldolase , triosephosphate isomerise , and phosphoglycerate mutase ( Table S9 and Table 5 ) . A global view of potential activities of these metabolic enzymes accounting for MUC2 degradation by HM1:IMSS during the invasive process is presented in Figure 6 . Based on the sharp increase in β-amylase transcript level ( EHI_192590 , fold change up to 25 ) in HM1:IMSS strain and the enrichment of carbohydrate metabolism genes , we opted to further investigate the role of β-amylase during human mucus invasion . The predicted 3D structure of E . histolytica β-amylase using LOMETS software [26] reveals a strong structural homology to the crystal structure of Glycine max β-amylase . Analysis of the β-amylase amino acid sequence by BLAST reveals similarity ( 42% pairwise identity , E value = 9e−119 ) with β-amylase of G . max . Importantly , two glutamic acids residues ( E185 and E378 ) involved in the catalytic activity are present at the homologous position in the E . histolytica enzyme ( Figure 7A ) . An additional trans-membrane domain was predicted for E . histolytica β-amylase ( Figure 7B ) . Based on significant protein homologies with β-amylase from plants , we took advantage of an existing antibody against β-amylase , which recognizes these enzymes . The specificity of this commercial antibody was confirmed by expressing the amoebic β-amylase encoding gene ( EHI_192590 ) in bacteria and western blot analysis ( Figure S1 ) . We observed that the protein is localized both on the cell surface and at focused locations in cytoplasm by using immunofluorescence on trophozoites ( Figure 7 C ) . Entamoeba histolytica possesses 8 copies of the β-amylase encoding gene ( EHI_009020 , EHI_035700 , EHI_049700 , EHI_148800 , EHI_058340 , EHI_118440 , EHI_192590 , EHI_098200 ) whose protein lengths range from 436 to 444 amino acids . We confirmed this information by taking advantage of RNA-Seq analysis recently performed in our laboratory [27] that the most highly expressed β-amylase genes in HM1:IMSS were EHI_192590 , EHI_098200 , and EHI118440 ( Figure S2 ) . In our microarray experiments , EHI_192590 is the most upregulated compared to Rahman ( mucus and culture conditions ) and in addition EHI_035700 is only overexpressed in HM1:IMSS during colon invasion . Levels of expression were very low in the Rahman strain and we confirmed by western blot that β-amylases were indeed present in cultured HM1:IMSS strain and highly reduced in Rahman ( 7 . 7 fold decrease at the protein level , Figure 7D ) . In order to gain insights into the role of β-amylase during mucus invasion , we knock down the expression of β-amylase in the HM1:IMSS strain using a dsRNA-based RNA interference approach [28] . We designed a specific dsRNA targeting the transcripts of all 8 copies ( see material and methods ) . Total protein extracts were analysed using western blot after 24 h and 48 h of incubation with the specific β-amylase dsRNA or a control dsRNA ( i . e GFP dsRNA ) . After 48 h of incubation , the β-amylase quantity was decreased by 75 . 5% ( SEM ± 4 . 6%; n = 3 ) in comparison to the control ( GFP dsRNA-treated trophozoites ) without impacting the growth of theses trophozoites ( Figure 8A ) . The viability of dsRNA treated trophozoites was determined upon an hour incubation in Krebs buffer by trypan bleu exclusion test ( percentage of cell death was for dsGFP = 11 . 2±3 . 4 sd , and for ds β-amylase = 10 . 8±2 . 9 sd . , n = 3 ) . HM1:IMSS trophozoites with reduced levels of β-amylase were then challenged for human colon invasion . We observed by histological analysis that after 7 h of incubation , tissue invasion by β-amylase dsRNA-treated trophozoites was abolished , while these trophozoites were still associated with the mucus layer ( Figure 8B and 8C ) . In contrast , GFP dsRNA treated parasites ( used as a control ) depleted the mucus layer and penetrated the lamina propria , as wild-type HMI:IMSS trophozoites . Measurement of the mucus thickness after 7 h incubation in the presence of β-amylase dsRNA-treated trophozoites ( 133 . 7 µm SEM ± 2 . 33 µm ) was comparable to the mucus thickness of the tissue control incubated without trophozoites ( 132 . 7 µm SEM ± 3 . 29 µm ) . However , in the presence of GFP dsRNA treated parasites the mean thickness of the mucus layer was significantly decreased to 13 . 58 µm ( SEM ± 1 . 35 µm , p<0 . 0001 ) .
E . histolytica colonizes the human gut mainly as a parasite . Only 1 in 5 infections leads to disease [29] . The classical view of amoebic infection outcome is that the virulence of E . histolytica is the consequence of the interactions between host , parasite , and environmental factors . Although the evidence supporting the phenotypic conversion of a strain from non-virulent to virulent is currently lacking , it is admitted that a latent period between infection and disease is due to parasite adaptation to the host via modifications in gene expression [30] . However , E . histolytica strains isolated from healthy asymptomatic carriers do not reproduce infection in animals implying that there is an unidentified mechanism regulating gene expression in addition to adaptation . Using the human colon explant model [5] , we compared the transcriptome modulation upon mucus contact of E . histolytica strains isolated from asymptomatic ( Rahman ) or symptomatic ( HM1:IMSS ) patients . Notice that only one representative virulent strain ( HM1-IMSS ) and only one representative non-virulent strain ( Rahman ) were compared in this study . Trophozoites from these isolates has been in culture for decades and likely may harbour differences unrelated to virulence , however these represent the best characterized amoebic isolates from genomics and biological point of views . Indeed , non-virulent Rahman trophozoites bound to the mucus but neither depleted the protective barrier nor invaded and destroyed the tissue , in contrast to HM1:IMSS virulent trophozoites . The transcriptome analysis identified genes: ( i ) ubiquitously expressed in each strain , ( ii ) common to the 2 strains interacting with human mucus , and ( iii ) specifically expressed in response to colon contact . The transcriptome of amoebae able to invade the mucus was characterized by several virulence factors ( the Gal/GalNAc lectin , STIRP , KERP1 and CP-A5 ) already described for their participation in the pathological process or over-expressed in virulent amoebic strains [21] . Also identified were proteins such as the SHAQKYF ( Myb 13 ) transcription factor regulating the expression of genes related to signal transduction , vesicular transport , heat shock response and virulence [22] as well as transcripts linked to stress responses and to signalling pathways , including the GTPase AIG1 known to be expressed during colonization of the mouse intestine [31] and in pathogenic E . histolytica [32] . Besides the involvement of the above cited virulence factors , the remarkable feature of colon explant invasion concerns the changes in expression of genes encoding enzymes involved in the carbohydrate metabolism . In addition to several enzymes implicated in the production of glucose-1-phosphate , upregulation of genes encoding the majority of enzymes involved in glycolysis was characteristic to mucus depletion . Therefore , we hypothesized that carbohydrate metabolism might play a role in sustaining the invasive behaviour of the virulent strain during intestinal invasion . Indeed when accessibility of polysaccharides in the lumen is decreased and glucose levels are low , virulent E . histolytica might be able to adapts its transcriptome to proficiently utilized host mucus glycans as its carbon source . Here we proposed a sequential mode of MUC2 degradation , involving the release of oligosaccharides from MUC2 by glycosidases ( e . g . beta-galactosidase and beta-N-acetylhexosaminidase , upregulated in virulent strain during colon invasion ) , and followed by cleavage of the exposed protein backbone by proteases ( Figure 6 and Figure 9 ) . We speculate β-amylase might play a role in breaking down the already released oligosaccharides into sugars as carbon sources for energy production . Thus , the reduced β-amylase activity in the dsRNA treated strain might hamper the utilization of MUC2 as the carbon source for glycolysis . The upregulation of multiple genes in the glycolytic pathway in the virulent strain during colon invasion correlates with this speculation and we interpret the upregulation of these genes in the virulent strain as the consequence of utilization of MUC2 as the carbon source . This hypothesis supports our previous findings showing that E . histolytica virulence increased when in the presence of a low glucose environment [33] . This scenario also fits well with previous findings indicating that E . histolytica depletes colonic mucin oligosaccharide side chains by using a glycosidase activity [34] . Following the breakdown of MUC2 oligosaccharides , the protein backbone is no longer protected and may be degraded by specific amoebic proteases as has been previously demonstrated [35] , [36] . In this work we highlighted β-amylase , because it is a protein absent from the mammalian kingdom proteome and is strongly overexpressed ( 25-fold ) in HM1:IMSS strain . The enzyme β-amylase acts on the α-1 , 4 glycosidic bonds and catalyses the breakdown of starch into maltose ( a glucose dimer ) . Using a dsRNA-based strategy , we decreased β-amylase protein levels in HM1:IMSS strain , and resulted in reduced mucus layer depletion and mucosa invasion . The β-amylase activity and its substrate in the invasive process have yet to be determined . The fact that β-amylase does not exist in the human genome makes this enzyme a potential therapeutic target to inhibit amoebic intestinal invasion . Entamoeba histolytica typically feeds on bacteria in the intestinal lumen . Microbial inhabitants of the gut , which can also have an influence on metabolic processes , such as energy extraction from food and host mucus glycan , can be considered as an environmental factor that contributes to amoebic maintenance in the colon lumen and further in the pathology . Our hypothesis ( Figure 9 ) is in line with findings obtained from bacteria resident in the mucus layer , in which they are capable to adapt their gene expression to gut diet content . For example gene expression profiling of Bacteroides thetaiotaomicron , revealed that rich polysaccharide diets are associated with a selective upregulation of glycoside hydrolases ( e . g . xylanases , arabinosidases , and pectate lyase ) . These bacteria also upregulate genes encoding enzymes involved in delivering glucose to the glycolytic pathway [3] . When these bacteria are in the presence of a unique glucose diet devoid of polysaccharides , the induction of a different subset of glycoside hydrolases is activated including enzymes necessary for retrieving carbohydrates from mucus glycans , as well as enzymes that increase accessibility to host glycans [3] . We proposed that the ability of virulent E . histolytica trophozoites to exploit carbohydrate resources derived from the human mucus might be one of the factors powering intestinal amoebiasis .
Healthy segments of human colon were obtained from patients undergoing colon surgery . Patient-written informed consent was obtained at Foch Hospital and the data were analysed anonymously at the Pasteur Institute . Tissues were processed according to the French Government guidelines for research on human tissues and the French Bioethics Act , with the authorization from the “comité de protection des personnes , Ile de France VII” and the “Institut Pasteur Recherche Biomedicale” investigational review board ( RBM . /2009 . 50 ) . RNAseIII-deficient Escherichia coli strain HT115 ( rnc14::ΔTn10 ) was grown in LB-broth containing ampicillin ( 100 µg/ml ) and tetracycline ( 10 µg/ml ) . Entamoeba histolytica HM1:IMSS is a virulent strain and E . histolytica Rahman is a non-virulent strain [7] . The HM1-IMSS strain was isolated in 1967 from a colonic biopsy of rectal ulcer from adult human male with amebic dysentery , Mexico City , Mexico . The HM1-IMSS was deposited in the American strain collection ( ATCC® 30459™ ) and it is a gift of Professor Ruy Perez Tamayo ( UNAM , Mexico ) . To maintain virulence , the HM1-IMSS strain has been passed through the liver of hamsters ( Male Syrian golden hamsters Mesocricetus auratus ) ( roughly 174 passages since isolation until experiments were done ) . The procedure applied for animal infection was previously described [6] , trophozoites were isolated from the liver abscesses after 7 days of intraportal inoculation ( 4 animals ) , mixed and further growth in axenic conditions . The Rahman strain is non-virulent [7] and is unable to growth in animals due to its inherent phenotype . The Rahman strain has been maintained in axenic culture since isolation in 1978 ( with undetermined periods of frozen preservation ) and it is a gift of Professor David Mirelman ( Weizmann Institute , Israel ) . Trophozoites of both strains were grown axenically in TYI-S-33 medium at 37°C [37] and harvested during the exponential growth phase . Previous experimental published conditions were used for handling human colon pieces [5] . Briefly , 1 . 6×105 trophozoites were added to the luminal face of the colon and incubated in Krebs buffer at 37°C for 1 and 7 h . After 1 h of incubation , mucus interacting trophozoites were collected by pipetting the mucus layer and 1 ml of Trizol was added . After 7 h of incubation , tissue fragments were fixed either in Carnoy fixative or in PFA ( 4% ) and included into paraffin . PFA-fixed tissue sections were immunostained with a 1∶200 diluted rabbit antibody recognizing the Gal/GalNAc lectin [5] Sections from Carnoy-fixed tissue were stained with Alcian blue to visualize the mucus layer [38] . For each experiment , a representative histology image was taken . For the measurement of mucus layer thickness , transverse sections were stained with Alcian blue stain . Light microscope images ( NIKON , Eclipse E800 ) were analysed with ACT-1 software ( NIKON ) . The mucus layer thickness was measured at three points of twenty different sections for three different patients ( 60 measurements for each condition ) . The mean of these measurements was considered as the mucus thickness for each condition . Statistical analysis was performed using GraphPad Prism software version 5 . 0b ( GraphPad Software Inc ) . An unpaired , two-tailed student T-test was performed . Differences being considered as significant if P<0 . 05 . Data are expressed as mean ± SEM . Entamoeba histolytica HM1:IMSS or Rahman trophozoites ( 1 . 6×105 ) grown in axenic culture were lysed with Trizol reagent ( Invitrogen ) , and total RNA isolated according to the manufacturer's protocol . RNA from mucus-interacting trophozoites was purified by gently scratching-off the mucus layer containing the trophozoites after 1 h of incubation . Trizol was added to the samples and RNA purification was performed . RNA was analysed for integrity and the concentration determined by capillary electrophoresis using the Agilent Bioanalyzer 2100 RNA nanochip Assay ( Agilent Technologies ) . RNA from mucus-interacting trophozoites showed a mixture of amoebic and human RNA ( up to 30% ) . Thus RNA isolated from human epithelial cells was used as a control to evaluate potential cross-hybridization of human transcripts in the subsequent experiments . Agilent microarrays EH-IP2008 , scanning the entire amoebic genome , were used as previously described [17] . Six biological replicates were performed with amoebic strains grown in culture or incubated with the colon explants . Dye swap hybridizations were performed for the six biological replicates leading to a total of 12 hybridizations for each of the four conditions: Rahman in colon vs culture , HM1:IMSS in colon vs culture , Rahman vs HM1:IMSS in the colon , and Rahman vs HM1:IMSS in culture . In addition , one technical replicate was performed for one of the biological replicates and two self-self hybridizations were conducted . The resulting fluorescence signals were used to tune the scanner for the set of arrays . Probes cross-hybridizing to human RNA were identified and removed from the analysis ( data not show ) . In addition , since prior to colon mucus contact the parasites were incubated in Krebs buffer we also determined gene expression changes in Krebs buffer; the modulated genes in each strain were removed before the analysis ( Data in Table S1 and S2 ) . The experiment finally yielded 54 competitive hybridizations . The whole data set was submitted to the ArrayExpress database ( Accession number: E-MTAB-1201 ) . A Principal Component Analysis of the whole microarray dataset was first carried out with Partek ( http://www . partek . com/software ) on the raw data . Microarray data statistical analyses were carried out with the R software ( http://www . R-project . org ) and Bioconductor packages ( http://www . bioconductor . org ) . Our experiment follows a multifactorial design that includes two strains ( HM1:IMSS and Rahman ) in two different growth conditions ( colon and culture ) . Linear models are well suited for the analysis of such designs , since they allow a global analysis of the whole dataset . Global effects , such as strain or growth condition effects can be measured , as well as differences between particular pairs of combinations of factors called contrasts , for example , the difference between Rahman and HM1:IMSS in colon condition . As Limma implements linear models for microarray data analysis , it was chosen for the present study ( Limma package [19] ) . A Loess normalization was first performed on the 48 microarrays in order to render expression ratios comparable . The full experimental design was described through a design matrix ( as explained in the Limma vignette ) which is a binary matrix composed of ( 0 , 1 , −1 ) used by the linear model . The matrix makes a formal correspondence between arrays and pairs of conditions that have been hybridized . Then , a contrast matrix was created . It contains the list of comparisons that we wish to test with the linear model , namely HM1:IMSS – colon vs culture , Rahman – colon vs culture , HM1:IMSS vs Rahman – colon , HM1:IMSS vs Rahman – culture , HM1:IMSS vs Rahman , and colon vs culture . The moderated t-test associated with the empirical Bayes method ( 33was first applied to the hybridization value of each probe and the resulting p-values were further adjusted using a Bonferroni correction [39] ) . Finally , a median log-ratio was computed taking all probes in consideration in the case of genes represented by more than one probe on the array . An equivalent analysis was performed on a gene basis using the same design and contrast matrices and the same p-value adjustment . Only genes with an adjusted p-value lower than 0 . 05 and a fold change higher than 2 were considered for further analysis . Notice that according to this microarray analysis , upregulated and downregulated genes were taken into consideration . Thus the final fold changes values correspond to the ratio of changes between the two strains ( i . e numbers from HM1:IMSS versus numbers from Rahman and vice versa ) . In other words genes appearing upregulated for HMI:IMSS strain are down regulated for Rahman strain counterpart and conversely genes upregulated for Rahman are downregulated for HM1:IMSS . Gene ontology and KEGG pathway annotations were retrieved from AmoebaDB v3 . 0 [40] and KEGG database [25] . To test for gene ontology enrichment , genes that are significantly upregulated ( FDR<0 . 05 , without fold-change cut-off ) in HM1:IMSS comparing with Rahman during colon invasion were used as the foreground to test against the whole gene background using the hyper-geometric test implemented in FUNC package [23] . To test for KEGG pathway enrichment , the moderated fold-change of all genes in HM1:IMSS versus Rahman during colon invasion was used as the input into GSEA package [24] . Statistical significance was determined according to the default false discovery rates of the packages ( 5% in FUNC and 25% in GSEA ) . The structure of β-amylase from E . histolytica ( Accession number EHI_192590 ) was predicted using LOMETS [41] which identifies β-amylase from Glycine max ( Accession number: BMY1; 547931 BMY1 ) as the best-hit template ( Z score = 102 , 377 ) . Protein domains were identified with SMART and defined EHI_192590 as a member of glycoside hydrolase family 14 which comprises β-amylase ( EC 3 . 2 . 1 . 2 ) . The amino acid sequence of the full-length homolog in Entamoeba was aligned by CLUSTALW software with β-amylase from G . max . The N-terminal tail of E . histolytica β-amylase was predicted as a transmembrane domain using TMHMM plugin of the Geneious software . To construct the dsRNA expression vectors , DNA fragments of the E . histolytica β-amylase gene ( position +694 to position +1187 , GenBank Accession number: EHI_192590 ) and the entire green fluorescent protein ( GFP ) coding sequence ( GenBank Accession number: U73901 ) were amplified by PCR and subcloned into the TA-cloning vector pCR2 . 1-TOPO ( Invitrogen ) . DNA inserts were excised from these constructs with restriction enzymes ( KpnI and BamHI for GFP; KpnI and Bgl II for β-amylase ) and cloned into the MCS of the L4440 plasmid vector that is bidirectionally flanked by T7 promoters . The resulting plasmids construct L4440-β-amylase and L4440-GFP were verified by restriction analysis and DNA sequencing . To purify dsRNA and perform soaking experiments we followed the procedure described previously [28] . A polyclonal rabbit anti-β-amylase antibody raised against the full-length β-amylase of Ipomoea batatas ( sweet potato ) was purchased from Abcam ( ab6617 ) . The specificity of this antibody was assessed by expression of amoebic β-amylase encoding gene ( EHI_192590 ) in Escherichia coli ( BL 21 strain ) . To this end the gene was amplified from the amoebic genome ( forward primer: TACCATGGATGTTATTAACACTATGTTTTATATCAATAGC; reverse primer: ATCTCGAGTCTCATTGAATTAACAAATGAACAA ) and cloned in MCS of pET28 vector . The insert was verified by DNA sequencing and upon expression in bacteria the recombinant protein was identified by western blot ( Figure S2 ) . For western blot analysis of amoebic extracts , the loaded protein amounts were normalized using an anti-actin C4 monoclonal antibody ( ref: 08691001 , MP Biomedicals ) and secondary HRP-antibodies ( MP Biomedicals ) were used . Trophozoites submitted to dsRNA soaking experiments were collected to prepare crude extracts as previously described [42] . Crude extracts ( 4×104 cells/lane ) were resolved by SDS-PAGE , transferred to PVDF membranes and incubated with specific antibodies and ECL Plus reagent ( GE Healthcare Bio-sciences ) for chemiluminescence detection . Semi-quantitative analysis of light emission from probed nitrocellulose membranes was carried out from scanned autoradiographs using Quantity one software ( BioRad ) and protein abundance was normalized with actin values . Trophozoites were grown axenically in TYI-S-33 medium at 37°C and then centrifuged for 5 min at 550× g during the exponential growth phase . The pellet was fixed in 4% paraformaldehyde at 37°C for 15 min and permeabilized or not with Triton X-100 . Cells were incubated in 1% PBS/BSA to avoid non-specific labelling . The primary antibody againstβ-amylase 1/1000 ( Abcam® ( ab6617 ) ) was then deposited onto the coverslip and incubated in a humid chamber for 2 h at 37°C . The coverslips were washed in 1% PBS/BSA and the secondary antibody coupled to Alexa-568 ( Molecular Probes , Invitrogen ) 1/200 was added to the coverslip and incubated in a humid chamber for 30 min at 37°C . The coverslips were washed and the slides were then mounted using VectaShield mounting medium , sealed and conserved at 4°C until confocal microscopy analysis . The slides were analysed using a Zeiss LSM 710 Confocal Microscope and LSM software .
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Entamoeba histolytica is an intestinal parasite which displays diverse phenotypes with respect to pathogenesis in the human colon . Trophozoites can remain as commensal , without causing evident intestinal damage , or they can destroy the colonic mucosa leading to amoebiasis . Using human colon explants and transcriptome analysis , we investigated the gene expression profile of two E . histolytica strains ( virulent and non-virulent ) during their contact with the intestinal mucus to gain insights into the molecular basis responsible for amoebic divergent phenotypes . Our results suggest that the virulent E . histolytica , when in contact with the intestinal barrier , specifically increases the rate of gene transcription for enzymes necessary to exploits the carbohydrate resources present in the human colon . Using RNA interference methodologies to knockdown gene expression , our data revealed the potential role of amoebic β-amylase ( a glycosydase ) in colon invasion and mucus depletion . Our data implies that the ability of an E . histolytica strain to exploit the carbohydrate resources might affect its ability to invasion the intestine .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Identification of the Virulence Landscape Essential for Entamoeba histolytica Invasion of the Human Colon
|
Monkeypox ( MPX ) is a zoonotic disease endemic in Central and West Africa and is caused by Monkeypox virus ( MPXV ) , the most virulent Orthopoxvirus affecting humans since the eradication of Variola virus ( VARV ) . Many aspects of the MPXV transmission cycle , including the natural host of the virus , remain unknown . African rope squirrels ( Funisciurus spp . ) are considered potential reservoirs of MPXV , as serosurveillance data in Central Africa has confirmed the circulation of the virus in these rodent species [1 , 2] . In order to understand the tissue tropism and clinical signs associated with infection with MPXV in these species , wild-caught rope squirrels were experimentally infected via intranasal and intradermal exposure with a recombinant MPXV strain from Central Africa engineered to express the luciferase gene . After infection , we monitored viral replication and shedding via in vivo bioluminescent imaging , viral culture and real time PCR . MPXV infection in African rope squirrels caused mortality and moderate to severe morbidity , with clinical signs including pox lesions in the skin , eyes , mouth and nose , dyspnea , and profuse nasal discharge . Both intranasal and intradermal exposures induced high levels of viremia , fast systemic spread , and long periods of viral shedding . Shedding and luminescence peaked at day 6 post infection and was still detectable after 15 days . Interestingly , one sentinel animal , housed in the same room but in a separate cage , also developed severe MPX disease and was euthanized . This study indicates that MPXV causes significant pathology in African rope squirrels and infected rope squirrels shed large quantities of virus , supporting their role as a potential source of MPXV transmission to humans and other animals in endemic MPX regions .
Monkeypox virus ( MPXV ) is a zoonotic Orthopoxvirus ( OPXV ) endemic to West and Central Africa . MPXV causes monkeypox disease ( MPX ) in humans , characterized by fever , maculopapular rash , and lymphadenopathy [3] , with a mortality rate of approximately 10% in the more pathogenic Central African strains of the virus [4] . Since the 1980’s , this disease has mostly been confined to Central Africa , although serological evidence suggests it may also circulate in West Africa [5 , 6] . Recent studies have suggested an increase in human cases in Central Africa , suggesting that this may be an emerging infectious disease [1 , 7] . The increasing incidence of MPX may be due to waning worldwide OPXV immunity . Smallpox vaccination campaigns , which also provided protection against MPXV , largely ceased in the 1980’s [7 , 8] . Future use of the smallpox vaccine for prevention of MPX is restricted by the large number of side effects of vaccination , especially in immunocompromised populations [9 , 10 , 11] . Humans become infected with MPXV through contact with infected animals or humans , and no effective treatment for MPX is available [1 , 12] . Therefore , the most feasible public health intervention to protect humans from MPX is to identify and avoid exposure to reservoir species [1] . Although MPX is a zoonosis , the identity of the reservoir host ( s ) remains unknown [4 , 13]; some evidence suggests native African rodents play a role [14] . The virus was first isolated from a captive cynomolgus macaque ( Macaca fascicularis ) in 1958 and identified in human cases in 1971 [15] . The first of only two isolates of MPXV from a wild animal was found in a moribund rope squirrel ( Funisciurus anerythrus ) in Zaire ( now Democratic Republic of Congo ( DRC ) [16] . Shortly after , serological evidence for infection was found in rope squirrels , sun squirrels ( Heliosciurus rufobrachium ) and non-human primates in DRC , however seroprevalence was highest in rope squirrels ( 24 . 7% ) [2] . Most recently , seropositive rope squirrels were found in the area of a human MPX outbreak in DRC [1] . The second isolation of MPXV from a wild animal occurred in Cote d’Ivoire in 2012 , from a dead sooty mangabey ( Cercocebus atys ) [17] . In West Africa , evidence of OPXV infection has been found in several species: Gambian pouched rats ( Cricetomys gambianus ) , African dormice ( Graphiurus sp . ) , rope squirrels ( Funisciurus sp . ) , sun squirrels ( Heliosciurus sp . ) , and ground squirrels ( Xerus sp . ) [5] . An outbreak of MPX in the US in 2003 was associated with the importation of pouched rats , rope squirrels , and African dormice from Ghana [18] . Thus , there is evidence that African rope squirrels , and perhaps pouched rats , may be important for MPXV maintenance in nature . Understanding the pathology and viral shedding in potential reservoir species provides valuable information needed to estimate the risks for humans in contact with these species . To date , very little experimental work with MPXV has been completed in suspected host species due to the logistical barriers of transporting live animals to institutions with BSL3 laboratories . Similarly , despite years of field surveillance , very few animals have been found to be clinically ill with MPXV infection , increasing the complexity of studying the natural history of the virus within free-living animals . Here , we use in vivo bioluminescent imaging ( BLI ) to study MPXV infection in laboratory and wild-caught animals [19 , 20 , 21] , allowing us to characterize the distribution and amount of MPXV replication in live animals in real time . This approach has several advantages for studying the pathogenesis of MPXV in potential reservoir species [19] . First , BLI reduces the number of animals used , as serial sacrifice at successive time points is not needed [22 , 23 , 24 , 25 , 26 , 27 , 28] . Secondly , compared to inbred laboratory models , wild animals are more variable genetically and may demonstrate greater variation in response to infection . Following individual animals through time can illuminate these differences [29] . Finally , viral infection may occur in reservoir species without clinical signs , but BLI can detect viral replication , including in sites that are not commonly sampled during timed sacrifice studies , such as skin , lymph nodes , or ovaries [28 , 29] .
This work was approved by the Animal Care and Use Committee of the National Wildlife Health Center , protocol number EP090616A6 . All work with live animals meets the guidelines set forth in the Guide for the Care and Use of Animals and the Animal Welfare Act and regulations [30] , and was performed under an assurance ( A4492-01 ) from the Office of Laboratory Animal Welfare ( OLAW ) within the U . S . Public Health Service . Anesthesia was performed using isoflurane inhalant anesthetic , with or without injectable dexmedetomidine . Euthanasia was performed by CO2 asphyxiation , while under isoflurane anesthesia . Nine rope squirrels ( Funisciurus anerythrus ) were captured in Kinshasa and surrounding areas and were housed at the University of Kinshasa Biology Department for quarantine and testing . Animals were tested for the presence of OPX antibodies at the Congolese National Veterinary Laboratory , as described below . After confirmation that all rope squirrels were negative for OPX antibodies , they were examined , treated with topical fipronil ( Frontline spray , Merial , Duluth , GA ) , and then exported to the United States ( US ) with permission of the Democratic Republic of Congo Ministry of Agriculture and Environment and the Centers for Disease Control and Prevention . After arrival in the US , rope squirrels were transported and housed at the USGS National Wildlife Health Center ( NWHC , Madison , WI ) in the select-agent registered ABSL3 animal facility . Rope squirrels were quarantined for one month , during which time they were treated with praziquantel ( Wedgewood Pharmacy , Swedesboro , NJ ) and two doses of selamectin ( Zoetis , Florham Park , NJ ) for external and internal parasites . For the infection study , all animals were individually housed in polysulfide rat cages with filter-topped lids inside a hepa- filtered cabinet . Following the infection study , 400 base pairs of the cytochrome b gene of all animals were sequenced as described in S1 Text . No sequences from Funscisiurus spp . were previously listed in GenBank , and these sequences have been deposited . Analysis of these sequences revealed polymorphisms , which are also described in S1 Text . Animals were infected with Central African MPXV that expresses firefly luciferase ( MPXV/Congo/Luc+ ) . The production and comparison of this virus to wild type MPXV were previously described [31] . This recombinant virus was produced in the laboratory of Dr . Tonie Rocke at the National Wildlife Health Center and the original parental strain ( MPXV-2003-Congo-358 ) was isolated in the Republic of Congo and kindly provided by Dr . Inger Damon at the Centers for Disease Control and Prevention . All work with recombinant Monkeypox viruses was approved by the NWHC Biosafety Committee . Four animals were infected intranasally ( IN ) by inoculating 5 μL in each nostril of phosphate buffered saline ( PBS ) containing 1 x 106 plaque forming units ( PFU ) of MPXV/Congo/Luc+ . Four animals were infected intradermally ( ID ) by placing 10 μL of 1 x 106 PFUs of virus diluted in PBS onto a shaved area of skin in the dorsal midscapular area and then piercing into the dermis with a 26g needle 10 times , through the droplet . A sentinel animal received 10 μL of PBS IN and was housed in the same cabinet as the other squirrels . After infection , animals were observed twice daily for clinical signs . Squirrels were imaged and sampled on days 3 , 6 , 8 , 11 , 13 , 15 , 18 , 22 , 25 , and 27 post infection ( pi ) . Before in vivo imaging , animals were sedated with intramuscular dexmedetomidine ( Zoetis , Florham Park , NJ ) and anesthetized with isoflurane ( Phoenix Pharmaceutical , St . Joseph , MO ) . After day 11 pi , dexmedetomidine was discontinued and anesthesia consisted of isoflurane alone . Animals were injected with 150 mg/kg of luciferase substrate ( GoldBio , St . Louis , MO ) intraperitoneally before being imaged using the IVIS 200 series in vivo imager ( Perkin Elmer , Waltham , MA ) . Images were collected 30 minutes after injection ( determined to be the time of peak luciferase distribution ) using Living Image version 4 . 2 software ( Perkin Elmer , Waltham , MA ) . Both dorsal and ventral images were collected , with two animals imaged together . Region of interest ( ROI ) analysis was performed with hand-drawn ROIs that encompassed the entire animal within the field of view . The tail was not visible in all views . Total luminescence was estimated as mean average radiance [p/s/cm2/sr] of ventral and dorsal views of individual animals and plotted with the time of infection [29] . During anesthesia for imaging , animals were also weighed , examined for lesions , bled , and sterile polyester swabs were used to sample oral , nasal , rectal , and ocular mucosal surfaces . All swabs were placed into tubes containing 400 μL of Dulbecco’s Modified Eagle’s Medium ( DMEM ) supplemented with 1 μg/L amphotericin B , 100 U/mL penicillin , 100 μg/mL streptomycin , and 0 . 05 mg/mL gentamycin ( Life Technologies , Grand Island , NY , USA ) . Blood was collected from the facial vein and placed into serum separator tubes ( Sarstedt , Nümbrecht , Germany ) . Serum separator tubes were centrifuged at 2000 rpm for 10 minutes at room temperature . All swabs and sera samples were frozen at -80°C until later processing . Animals were euthanized if they lost more than 25% body weight , were unable to eat or drink , or had difficulty breathing . Euthanasia was performed by CO2 asphyxiation following isoflurane anesthesia . Carcasses were temporarily stored at 4°C and necropsied within 24 hours . Skin , tongue , superficial cervical lymph node , salivary gland , heart , lung , spleen , liver , intestine , bladder and ovary or testis tissue samples were collected and stored at -80°C until further processing . Sections of these tissues were also fixed in 10% formalin for histopathological analysis . Tissue pieces ( 25–60 mg ) were homogenized in PBS , supplemented with 1% fetal bovine serum ( FBS ) , using a Buller Blender Storm bead homogenizer with stainless steel beads ( Next Advance , Averill Park , NY , USA ) . PBS with 1% FBS was added to the slurry to make a final concentration of 10% ( wgt/vol ) . 200 μL of the tissue slurry was used for DNA extraction with the Zymo tissue g-DNA kit ( Zymo Research , Irvine , CA ) . 200 μL of whole blood was used for DNA extraction with the Zymo g-DNA kit ( Zymo Research , Irvine , CA ) . The quality of each DNA extraction was confirmed using a Nanodrop 2000 spectrophotometer ( Nanodrop products , Wilmington , DE ) . Viral DNA in blood and tissues was quantified using real-time PCR to detect the E9L gene of orthopoxviruses using primers described by Li et al . [32] . SYBR Green PCR Master Mix ( Applied Biosystems , Carlsbad , California , USA ) was used in an iQ5 Real-Time PCR Detection System ( Bio-Rad , Hercules , California , USA ) . DNA standards containing 4 . 15 ng to 4 . 15 x10-7ng were made by extracting total DNA from purified MPXV-Congo/Luc+ as described above , and creating eight 10-fold serial dilutions of the purified DNA in molecular grade water . Standards were aliquoted , and each aliquot was used no more than 5 times to prevent degradation of standards from repeated freeze-thaw cycles . Using these standards , assays were sensitive enough to detect approximately 2042 . 49 viral genomes in 0 . 1 mL . Standard curves , DNA concentration , and Ct value were calculated using the iQ5 Optical System Software , Version 2 . 1 . 97 . 1001 ( Bio-Rad ) . The cutoff for fluorescence signal was determined by calculating the average and standard deviation of Ct values for standards across all PCRs . Samples with Ct values that were less than or equal to 2 standard deviations of the mean of the lowest concentration detectable within 40 cycles were considered positive . DNA quantities were used to calculate the number of viral genomes in the samples . Virus in swabs was detected and measured using a TCID50 assay and converted to PFU/mL as described elsewhere [21 , 29] . Briefly , 96 well-plates were seeded with Vero cells ( ATCC CCL-81 , Manassas , VA ) to approximately 90% confluence . Swabs were sonicated and vortexed in the viral transport medium ( described in sample collection ) after thawing . Swab medium was diluted in DMEM with 1% FBS and antibiotics ( 1 μg/L amphotericin B , 100 U/mL penicillin , 100 μg/mL streptomycin , and 0 . 05 mg/mL gentamycin ( Life Technologies , Grand Island , NY , USA ) ) . 100 μL of eight 10-fold serial dilutions were added to each well in a 96 well plate . Plates were incubated at 37°C with 5% CO2 for 3 days before being fixed with 0 . 1% crystal violet in 10% formalin . After fixation and staining , viral plaques were counted and titer was determined with a Reed and Muench calculator [21 , 29] . Antibodies for MPXV were detected by ELISA , using the methods outlined in Hutson et al [33] . Plates were coated with the lysate of Vaccinia virus-infected Vero cell lysate on one half and with lysate of uninfected Vero cells on the other half . Three serum samples were added in duplicate , to each side of the plate , in concentrations of 1:50 to 1:4800 . Serum from a vaccinia-vaccinated human and a Gambian pouched rat that survived MPXV infection served as positive controls and the negative control was uninfected Gambian pouched rat serum from previous studies . A 1:20 , 000 dilution of the secondary antibody , an A/G conjugate was used , as well as SureBlue peroxidase substrate ( KPL #52-00-01 , Kirkegaard and Perry Laboratories , Washington DC , USA ) . After developing , plates were read at 450nm on a spectrophotometer ( EL 800 Universal Microplate Reader , Bio-Tek Instruments Inc . , Winooski , VT , USA ) . A cut-off value for each plate was calculated as two standard deviations above the mean of the optical density of the uninfected lysate side of the plate . Pre-existing MPXV-specific antibodies were also detected by serum neutralization assay [21] . Briefly , Vero cells were cultured on 96-well plates . Cells were infected with 100 PFU per well of MPXV/Congo/Luc+ . 100 μL of 6 serial 2-fold dilutions of rope squirrel serum ( 1:20 , 1:40 , 1:80 , 1:160 , 1:340 , 1:680 , and 1:1320 ) were added to the wells . After 24 hours , luciferase expression was detected using the Steadylite Plus luciferase detection kit ( PerkinElmer , Waltham , MA ) with the VICTOR Light 1420 plate luminometer ( PerkinElmer , Waltham , MA ) . Inhibition of viral infection was detected by a 50% reduction in luminescence detection compared to infected cells without serum . Positive and negative controls were the same as for the ELISA , described above . Formalin-fixed tissues were sectioned and stained with routine hematoxylin and eosin ( H&E ) stains . Selected tissue sections were studied immunohistochemically by using an anti-vaccinia virus HRP rabbit polyclonal antibody ( Thermo Fisher , Waltham , MA , USA ) at a dilution of 1:200 . The primary antibody was incubated with the section at 4°C overnight . A DAB substrate kit ( Sigma Aldrich , St . Louis , MO ) was used to detect the primary antibody , and Hematoxylin QS Vector ( Vector Laboratories , Burlingame , CA ) was used as a counterstain . Tissue sections from an uninfected rope squirrel were used as negative controls . H&E and IHC slides were reviewed by two pathologists ( MR and AM ) . Luminescence and shedding via oral , nasal , rectal , and ocular routes were assessed with a generalized linear model ( log link function ) using JMP 10 ( SAS Institute Inc . , 2012 , Cary , NC ) , with days pi and route of infection ( IN or ID ) as the fixed effects .
All rope squirrels displayed moderate to severe morbidity following infection by either route . Mortality was 75% in the IN group and 50% ID group , although small group size prevented meaningful comparison of the mortality rate by route . In the ID-infected group ( RS12 , RS15 , RS17 and RS18 ) , the primary skin lesions were visible beginning at day 3 pi . Typical poxviral lesions in the skin and in the oral cavity were evident by day 6 pi . One animal ( RS12 ) died on day 8 pi during anesthesia , after one day of increased respiratory rate . Corneal lesions developed in one animal ( RS17 ) on day 11 and both remaining animals displayed skin lesions , oral lesions , and nasal discharge . On day 22 pi , RS15 died . The remaining two squirrels ( RS17 and RS18 ) survived the infection until the end of the study , day 27 pi , although skin lesions , respiratory disease , and ocular lesions were still present . Skin lesions progressed from papules , to pustules , then crusts , as described in other animal models such as prairie dogs and cynomolgus monkeys [34 , 35] . Fig 1 shows the disease progression of the ID group . In the IN-inoculated group ( RS11 , RS13 , RS16 , and RS19 ) , ocular lesions appeared in one animal ( RS13 ) on day 6 pi . Oral lesions were observed on day 8 pi , and three of four squirrels displayed severe respiratory disease beginning on day 9 pi with increased respiratory rate and nasal discharge . Some animals stopped breathing during anesthesia on several occasions and manual chest compressions were used to re-establish respirations . RS19 was euthanized on day 11 pi due to respiratory distress . Classic poxvirus type skin lesions , as described above , were visible in RS13 and RS16 between days 11 and 13 pi . On day 13 , RS13 was found dead , and RS16 was euthanized immediately after imaging , due to respiratory distress . RS11 survived infection and the ocular lesions resolved in this animal by day 25 pi . Fig 2 shows the progression of disease of the IN group . The sentinel animal , RS14 , showed clinical signs of MPXV infection , including increased respirations , nasal discharge , and oral lesions beginning after day 13 pi . On day 18 , it was euthanized due to respiratory distress , weight loss , and severe lethargy . A table describing the clinical signs in each individual animal is available in the supplementary material ( S1 Table ) . Luminescence overlays from the ventral view are shown for animals in the ID group in Figs 3 and 4 , and for the IN group in Figs 5 and 6 . Luminescence , indicative of viral replication , was present in the oral and nasal areas of both IN and ID groups by day 3 pi , the first time point of BLI ( Figs 3 and 5 ) . In the ID group , this location represents a secondary site of replication . The site of inoculation on the dorsal scapular area also had strong luminescence on days 3 to 11 pi ( see data in S2 Table ) . In the ID group , luminescence peaked at day 6 pi and a second peak occurred on day 13 pi ( Fig 7 ) . In this group , the luminescence dropped down to background levels by day 20 pi ( Figs 4 and 7 ) . In the IN group , luminescence was visible in sites distal to the inoculation on day 6 pi ( Fig 5 ) . Luminescence increased more gradually and peaked on day 12 pi ( Fig 7 ) . After day 13 pi , three of four animals ( 75% ) in the IN group had died or were euthanized , and the luminescence of the remaining animal , RS11 , quickly decreased to background levels by day 18 pi ( Figs 6 and 7 ) . Overall , luminescence was slightly higher ( P = 0 . 02 ) in the IN group than the ID group . The sentinel animal did not increase above background luminescence levels ( Fig 7 ) , which was surprising . Analysis of the virus infecting the sentinel animal proved to be a non-recombinant virus of the same parental strain . Follow-up studies on the MPXV/Congo/luc stocks discovered that they were incompletely purified and non-recombinant virus was present . Details of this analysis and conclusions are described in S4 . All infected animals in both ID and IN groups shed high amounts of virus ( Figs 8 and 9 ) . The highest concentration of virus was detected in oral secretions . For the ID group , shedding peaked on days 8–11 pi ( Fig 8 ) . For the IN group , the highest shedding was detected on days 11–13 pi ( Fig 9 ) . For both groups , shedding decreased after day 13 , although one animal ( RS17 ) had persistent ocular lesions and shed high amounts of virus in ocular secretions . Animals shed live MPXV as long as day 25 pi and as early as day 3 pi , even before the onset of clinical signs . Shedding via any route ( oral , nasal , rectal , or ocular ) was not significantly different ( P>0 . 05 ) via route of infection ( ID or IN ) . Although there was no luminescence detected from the sentinel ( RS14 ) , it did shed live MPXV ( Fig 10 ) beginning on day 13 pi , consistent with the appearance of clinical signs around that time , and continuing until the time of euthanasia . The virus isolated was confirmed as MPXV by the MPXV-specific primers described by Li et . al . [32] . The lack of luminescence of this isolate was confirmed by the plate luminescence assay ( S2 Text ) . PCR was also used to confirm that the MPXV isolated from this animal was from the inoculum used to infect the other animals; however it did not contain any of the GPT/luc cassette ( S2 Text ) . The first whole blood sample was collected on day 8 pi . Viremia , as estimated by real-time PCR detection of viral genomes , is shown in Table 1 . Viremia was detected after day 8 pi in three of four animals in each group . All samples from RS12 , which were of small volume , were used for serology . The detection of viremia by real time PCR seems to show a cyclical pattern of viremia , although true viremia was not confirmed by culture of live virus from blood . RS14 , the sentinel , had detectable viremia on days 15 and 18 pi , although clinical signs and viral shedding were already present by day 13 pi . The pattern of viral distribution in tissues was highly variable ( Table 2 ) . The highest number of viral genomes was detected in primary skin lesions ( ID group only ) , lips , and tongues ( IN and ID groups ) . Many of the tissues of the three surviving animals ( RS11 , RS17 , and RS18 ) were negative , although each of them contained viral DNA in one or more tissues at the end of the study . All animals were sero-negative prior to the study , as measured by serum neutralization assays ( Fig 11 ) . Serum from RS14 and RS19 diluted to 1:40 displayed a slight reduction in viral neutralization . This was a high concentration of the serum and was not considered a positive anti-MPXV antibody titer . Antibody titers , as measured by ELISA , began to rise around day 6 pi ( Fig 12 ) . RS12 , RS19 , and RS13 did not have detectable antibody titers on the days of their death ( days 8 , 11 , and 13 pi , respectively ) . RS15 and RS16 had titers of 1:2400 upon their death on days 22 and 13 , respectively . The surviving animals in both groups had titers of 1:4800 , the highest dilution tested . Histopathologically , typical MPXV infection lesions were present in the skin , the oral cavity , and the lungs . Table 3 shows the distribution and frequency of microscopic lesions . In the ID-infected group ( RS12 , RS15 , RS17 and RS18 ) , cutaneous lesions were characterized by epithelial hyperplasia and necrosis , most prominently in the stratum basal and deep stratum spinosum . In two animals ( RS17 and RS15 ) , small foci of epithelial hyperplasia were evident with formation of intraepithelial pustules and multiple dermal foci of lymphoplasmacytic inflammation that was predominantly perivascular . Cutaneous lesions were severe in RS12 and RS15 characterized by epidermal spongiosis , acanthosis , acantholysis , and occasional cell necrosis , predominantly in the basal layer , leading to ulceration . Superficial dermal inflammation was composed of neutrophils , macrophages , and necrotic debris . Surrounding vessels exhibited reactive endothelium and walls with edema , fibrin ( fibrinoid change ) , and acute inflammatory infiltrates ( RS12 ) . In contrast , in the sections of the IN-infected group ( RS13 , RS16 and RS19 ) , cutaneous lesions were characterized only by epithelial hyperplasia and multiple dermal foci of perivascular lymphoplasmacytic inflammation . Cutaneous lesions were not evident in the skin sections collected from RS11 . Lesions in the oral cavity were only present in RS13 , RS16 and RS14 . Oral epithelium , including tongue and labial mucosa , were similar morphologically to those in the skin and were characterized by epithelial hyperplasia , intracellular edema ( ballooning degeneration ) , necrosis and ulceration with mixed inflammation in the adjacent submucosa . Scattered and poorly defined eosinophilic intracytoplasmic inclusion bodies were observed in epidermal and oral epithelium . In the respiratory tract , all animals except RS11 exhibited mild lymphoplasmacytic interstitial pneumonia with hyperplasia of the peribronchial associated lymphoid tissue . The alveolar septae were mildly thickened and peripheral alveoli were often flooded with edema and foamy alveolar macrophages . RS15 , RS12 , RS14 and RS19 also presented moderate to severe alveolar histiocytosis with presence of numerous multinucleated giant cells . In addition to epidermal and pulmonary lesions that are typical in other species infected with MPXV , renal lesions were common in this study . Many animals ( RS12 , RS14 RS16 , RS17 , RS18 and RS19 ) presented widespread renal tubular degeneration and multiple foci of perivascular lymphoid and plasmacytic inflammation in cortex and renal pelvis . Lesions in the heart included multifocal lymphoid and plasmacytic pericarditis , myocarditis and endocarditis , observed in RS15 , RS16 , RS17 and RS18 . Atrium sections exhibited multiple areas of perivascular lymphoplasmacytic infiltrate , as well as in the adjacent epicardial adipose tissue . Lymphocytic gastritis and esophagitis were observed in RS15 and RS14 . Lastly , a focus of necrotizing lymphadenitis of the superficial cervical lymph node occurred in one animal ( RS12 ) , accompanied by proliferation of fibroblasts and macrophages . No lesions attributable to MPXV were seen in liver , spleen , urinary bladder , mammary gland , parathyroid gland , adrenal gland , reproductive organs , small and large intestine , gallbladder , pancreas or brain . Immunohistochemically , MPXV antigen was only detected in skin and tongue lesions . There were areas corresponding to the epithelial lesions with numerous intralesional cells ( stratum basale and spinosum epithelial cells ) with strong positive cytoplasmic staining . Positive immunostaining in the skin was observed in the sentinel animal , RS14 . No positive staining was observed in control sections .
In conclusion , rope squirrels are very susceptible to MPXV infection , displaying high morbidity and moderate mortality . During clinical infection , rope squirrels shed large quantities of virus , which supports their potential role in the epidemiology of MPXV in Central Africa .
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Monkeypox virus ( MPXV ) is a virus closely related to Variola virus , the cause of smallpox , and it causes a similar , though less severe , disease in humans in Central and West Africa . This disease is particularly dangerous for children and people with compromised immune systems and the mortality rate is estimated to be about 10% in the more pathogenic Central African strains . Unlike Variola virus , MPXV is primarily transmitted to humans from animals , but it is not known which species commonly carry and spread this virus . Rope squirrels in the genus Funisciurus have previously been linked to the virus in Central Africa . In this study , rope squirrels were experimentally infected with MPXV and subsequently monitored for signs of disease , as well as the amount of virus they shed in their bodily fluids . The results of this study showed that rope squirrels became moderately ill but not all died . They shed large amounts of virus in oral , nasal , rectal , and ocular secretions . This information helps public health doctors and epidemiologists understand the potential risks of contacting rope squirrels for local populations in Africa and will help disease ecologists understand how MPXV is maintained and transmitted between animals and to humans .
|
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2017
|
Characterization of Monkeypox virus infection in African rope squirrels (Funisciurus sp.)
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The canary pox vector and gp120 vaccine ( ALVAC-HIV and AIDSVAX B/E gp120 ) in the RV144 HIV-1 vaccine trial conferred an estimated 31% vaccine efficacy . Although the vaccine Env AE . A244 gp120 is antigenic for the unmutated common ancestor of V1V2 broadly neutralizing antibody ( bnAbs ) , no plasma bnAb activity was induced . The RV305 ( NCT01435135 ) HIV-1 clinical trial was a placebo-controlled randomized double-blinded study that assessed the safety and efficacy of vaccine boosting on B cell repertoires . HIV-1-uninfected RV144 vaccine recipients were reimmunized 6–8 years later with AIDSVAX B/E gp120 alone , ALVAC-HIV alone , or a combination of ALVAC-HIV and AIDSVAX B/E gp120 in the RV305 trial . Env-specific post-RV144 and RV305 boost memory B cell VH mutation frequencies increased from 2 . 9% post-RV144 to 6 . 7% post-RV305 . The vaccine was well tolerated with no adverse events reports . While post-boost plasma did not have bnAb activity , the vaccine boosts expanded a pool of envelope CD4 binding site ( bs ) -reactive memory B cells with long third heavy chain complementarity determining regions ( HCDR3 ) whose germline precursors and affinity matured B cell clonal lineage members neutralized the HIV-1 CRF01 AE tier 2 ( difficult to neutralize ) primary isolate , CNE8 . Electron microscopy of two of these antibodies bound with near-native gp140 trimers showed that they recognized an open conformation of the Env trimer . Although late boosting of RV144 vaccinees expanded a novel pool of neutralizing B cell clonal lineages , we hypothesize that boosts with stably closed trimers would be necessary to elicit antibodies with greater breadth of tier 2 HIV-1 strains . Trial Registration: ClinicalTrials . gov NCT01435135
Six HIV-1 vaccine efficacy trials have been performed [1–5] , of which only one , the ALVAC-HIV and AIDSVAX B/E prime-boost RV144 trial , showed vaccine protection , with estimated vaccine efficacies of 60% at 12 months [6] and 31% at 42 months [7] . Plasma IgG antibodies binding to HIV-1 envelope variable region 2 ( V2 ) and low Env IgA binding levels were immune correlates of decreased transmission risk [8] . V2-specific antibodies isolated from RV144 bound tier 2 HIV-1 infected CD4 T cells and mediated antibody dependent cellular cytotoxicity ( ADCC ) [9] . While no broadly neutralizing antibodies ( bnAbs ) were induced in RV144 [8 , 10] the induction of bnAbs remains a prime goal of HIV vaccine development , since passive administration of bnAbs has repeatedly shown to protect against simian HIV-1 ( SHIV ) chimeric virus challenge [11–15] . BnAbs develop in approximately 50% of HIV-1 infected individuals , but these arise only after several years of infection [16 , 17] . One hypothesis to explain why HIV-1 bnAbs have been difficult to induce by vaccination is that these antibodies have one or more unusual characteristic—long HCDR3 regions , autoreactivity with host antigens , and/or extensive somatic mutations—all traits of antibodies controlled by host tolerance control mechanisms [18–22] . A result of tolerance control of bnAbs is that bnAb precursors may be reduced in frequency in the pre-vaccination B cell repertoire; they may also be at a competitive disadvantage with other more dominant precursor B cell pools . For these reasons , inducing bnAbs may require an extensive vaccination-regimen . Here we sought to determine if a pool of subdominant B cells , such as those that produce long HCDR3 CD4 bs bnAbs , may be expanded when an Env immunogen that binds bnAb UCAs is included in a boosting regimen . In the RV305 clinical trial , RV144 vaccine-recipients who had previously received the initial ALVAC-HIV + AIDSVAX B/E gp120 immunization regimen ( 0 , 1 , 3 , 6 months ) and remained HIV-1- uninfected were boosted with ALVAC-HIV , AIDSVAX B/E gp120 , or ALVAC-HIV + AIDSVAX B/E gp120 6–8 years later ( S1 Fig ) . We found that boosting of RV144 vaccinees led to an increased frequency of memory B cells producing envelope-specific antibodies with long HCDR3s . Several of the mature antibodies and inferred unmutated common ancestors ( UCA ) neutralized both neutralization sensitive HIV-1 isolates ( tier 1 ) and a difficult-to-neutralize ( tier 2 ) HIV-1 CRF01 AE isolate , CNE8 .
After two boosts ( 6-month interval ) with the same immunogens 6–8 years after the completion of the RV144 primary immunizations ( S1 Fig ) , plasma neutralizing antibody ( nAb ) responses were assayed in the A3R5 pseudovirus neutralization assay [23] against a panel of 11 CRF01 AE isolates ( S2A Fig ) . Previous work has shown that neutralization of neutralization resistant ( tier 2 ) HIV-1 isolates by antibodies is more readily detected in the A3R5 cell based assay than in the TZM-bl cell based assay [23] . Here the A3R5 cell based assay was used to search for vaccinees who had robust antibody responses to Env . We selected four vaccinees for study who had high magnitude and breadth of neutralization . Two were from RV305 Group 1 who received ALVAC-HIV plus AIDSVAX B/E gp120 boosts ( 3043 , 3070 ) , and two were from RV305 Group 2 who received only AIDSVAX B/E gp120 boosts ( 3064 , 3053 ) ( S2B Fig ) . In all four vaccinees , the RV305 boosts increased both autologous ( AE . A244gp120 ) and heterologous ( B . 6240gp120 ) plasma IgG-gp120 binding responses , to levels higher than those observed after the initial RV144 regimen ( S3A Fig ) The RV305 boosts also increased the magnitude of B . MN and AE . 92TH023 neutralization in the TZM-bl neutralization assay by plasma from all four vaccinees , but there was no plasma tier 2 neutralizing activity seen ( S3B Fig ) . We isolated AE . A244 gp120 Env-specific post-RV305 boost memory B cells from the four vaccinees- 3043 , 3070 , 3064 and 3053 ( S4 Fig ) and from the same vaccinees post-RV144 samples for three of the four vaccinees for whom PBMCs were available . Comparison of the gp120-reactive mAbs from post-RV144 ( n = 184 mAbs ) with the gp120-reactive mAbs from post-RV305 ( n = 242 mAbs ) showed that the mean VH nucleotide mutation frequency increased over 2-fold in each vaccinee from a mean of 3 . 1% to 6 . 9% ( Fig 1A and 1C ) . Mobilizing and expanding the pool of long HCDR3 antibodies will be critical for the eventual induction of V2-glycan , V3-glycan , or HCDR3-loop binding bnAbs since many of these bnAbs have HCDR3s longer than 22 amino acids ( aa ) [24–28] . A meta-analysis of antibodies isolated from post-RV144 studies found that the frequency of Env-reactive B cells with HCDR3s ≥ 22 aa was 2 . 1% . An analysis of the post-RV305 antibodies indicated that the frequency of Env-reactive B cells with HCDR3s ≥ 22 aa was 20 . 7% ( S5 Fig ) . To confirm that the increased frequency of Env-reactive long HCDR3 mAbs was related to late boosting , we analyzed the B cell repertoires of three of the four vaccinees ( 3043 , 3053 and 3064 ) for whom blood samples were available both 2 weeks after the initial RV144 immunization and 2 weeks after the RV305 immunizations . The average frequency of Env-reactive long HCDR3 antibodies within the same vaccinees increased from 7 . 6% to 20 . 7% . ( Fig 1B and 1D ) . The HCDR3 length is dictated primarily by V ( D ) J recombination and can be diversified through secondary means: VH replacement , D-D fusion , insertions , N-nucleotide addition and P-nucelotide addition . Long HCDR3 antibodies have been shown to be biased towards DH2 , DH3 gene and JH6 gene segment usage [29] . Coinciding with this observation 72% of the Env-reactive long HCDR3 antibodies isolated post-RV305 and utilized DH2 or DH3 and 58% used JH6 ( S1 Table ) . To determine if this phenomenon was unique to B cell repertoires from late boosting of RV144 vaccinees , we compared these data with the frequency of Env-reactive long HCDR3 found in other HIV-1 Env based immunization regimens . In the GSK PRO HIV-002 human clinical trial , vaccine-recipients received gp120 immunizations in AS01B adjuvant , and the frequency of Env-reactive mAbs with long HCDR3s was 6 . 9% ( n = 58 ) [30] . In the DNA prime Ad5 boost HIV-1 vaccine regimen used in the HVTN 505 efficacy trial , the frequency of gp140-reactive mAbs with long HCDR3s was 4 . 1% [31] ( S2 Table p< 0 . 05 compared to RV305 boost data; Fisher’s Exact Test ) . These data suggested that other immunization regimens without boosting did not expand memory B cell pools with long HCDR3s to the extent achieved with the RV305 boosts . In vaccinee 3053 , seven gp120-reactive B cell clonal lineages were present after the initial RV144 vaccine regimen that persisted and had expanded after boosting 6–8 years later in RV305 , one of which , DH678 , had a long HCDR3 . In vaccinee 3043 nine gp120-reactive B cell clonal lineages were identified after RV144 that were also represented in the samples taken after the RV305 boosts . The antibodies in two of these lineages , DH686 and DH576 , had long HCDR3s ( S6 Fig ) . These data demonstrate that memory B cells producing antibodies with long HCDR3s were induced by the initial RV144 regimen and could be expanded with boosting 6–8 years later . All antibodies isolated were assayed by ELISA as transient transfection supernatants and we selected twenty-seven Env-binding antibodies derived from blood memory B cells post-RV305 boosts based on HCDR3-length ( ≥ 22 aa ) as a representative set of antibodies for characterization ( S3 Table ) . Nine of the 27 mAbs neutralized the neutralization sensitive ( tier-1 ) virus AE . 92TH023 in the TZM-bl neutralization assay [23 , 32 , 33] ( S4 Table ) . The epitopes of these nine neutralizing mAbs with long HCDR3s were then mapped by ELISA for activity in blocking soluble ( s ) CD4 binding to Env and for binding to mutant Envs . All 9 long-HCDR3 antibodies that neutralized HIV-1 blocked sCD4 binding by ≥70% ( Fig 2A ) and also blocked binding of CD4 bs bnAbs VRC01 and CH31 ( S7 Fig ) . Env mutations I371 , P363 , R476 and D368 generally reduce binding by CD4bs Abs [34] . When assayed with Δ371I/P363N and D368R CD4bs Env mutants , binding of three neutralizing mAbs ( DH576 , DH576 . 2 , and DH577 ) was measurably lower compared to wild-type Env ( Fig 2B ) . Seven of nine long HCDR3 sCD4 blocking mAbs ( Fig 2A ) bound to B . YU2gp120 . The binding epitopes of these seven mAbs were mapped by yeast display using B . YU2gp120 core ( ΔV1 , V2 , V3 loops ) and B . YU2gp120 cores with mutations that reduce binding by known CD4bs Abs [35] . In contrast to epitope mapping on A244gp120 Env binding of six of seven mAbs were D368R sensitive ( Fig 2C ) . The four Abs not sensitive to the D368R mutation in A244gp120 likely have a higher affinity for the A244gp120 protein then YU2gp120 and their epitope is less dependent on Env D368 . Abs DH576 and DH576 . 2 shared with the CD4bs bnAb B12 sensitivity to 3 CD4bs-critical mutations ( D368R , R419G , T455E ) and 2 of 3 additional mutation sensitivities ( K282V and I467K ) [36 , 37] suggesting these vaccine-induced CD4bs mAbs have a specificity more similar to that of B12 than to that of the non-bnAb CD4bs mAb , B6 which is not sensitive to D368R , R419G and T455E mutations ( Fig 2C ) . In the TZM-bl cell assay , all neutralizing CD4bs mAbs neutralized not only AE . 92TH023 but also the heterologous tier 2 CRF01 isolate AE . CNE8 isolate . DH583 was the broadest neutralizing antibody , also neutralizing the tier 1 viruses B . SF162 , B . MN , and the tier 1B ( intermediate neutralization sensitivity ) primary isolate C . 6644 ( Fig 2D ) . Long HCDR3 neutralizing mAbs were assayed against four additional tier 2 CRF01 AE isolates but showed no additional neutralization breadth ( S5 Table ) . In RV144 , infection risk correlated inversely with V1V2 antibody responses [8] . Two V1V2 binding antibodies , CH58 and CH59 , neutralized the autologous tier 1 isolate AE . 92TH023 in the TZM-bl neutralization assay and also mediated ADCC against tier 2 virus infected cells [9] . To determine whether the long HCDR3 CD4bs mAbs isolated after the RV305 boosts also mediated ADCC , the Abs were expressed in an IgG1 backbone optimized for FcγRIIIa binding[38] and assayed for ADCC against virus-infected cells . DH583 mediated ADCC against B . WITO and C . 1086C virus infected cells , with an endpoint concentration of approximately 0 . 1μg/ml and overall ADCC activity , as evaluated by positive area under the dilution curve , similar to that observed for the CD4bs bnAb CH31 . The other eight long HCDR3 CD4 bs mAbs had little to no ADCC activity against any of the isolates tested ( S8 Fig ) . The most heavily mutated member of the long HCDR3 CD4bs DH576 B cell clonal lineage was DH576 . 2 ( VH nucleotide mutations of 10 . 33% ) , but the additional mutations did not broaden or strengthen HIV-1 tier 2 CRF01 AE . CNE8 neutralization ( Fig 2 ) with respect to neutralization by less mutated lineage members such as DH576 ( VH mutations of 7 . 33% ) ( S3 Table ) . To determine the effects of affinity maturation , we assayed the UCA , IAs and three naturally occurring DH576 clonal lineage mAbs for neutralization of the autologous tier 1 virus AE . 92TH023 and the heterologous tier 2 virus CRF01 AE . CNE8 . The DH576 UCA neutralized both the tier 1 HIV-1 AE . 92TH023 and the tier 2 HIV-1 CRF01 AE . CNE8 . As affinity maturation progressed , there was a difference in the ratio of neutralization potencies for tier 1 and tier 2 viruses . Affinity maturation increased DH576 ineage neutralization potency ( IC50 ) against the tier 1 AE . 92TH023 by over 3 logs , but increased its potency ( IC50 ) against the tier 2 AE . CNE8 by less than 1 log ( Fig 3 ) . These data can be explained in part as follows . The UCA of DH576 had a higher affinity for AE . CNE8gp120 than did the UCA for AE . A244gp120 ( nearly identical in sequence to AE . 92TH023 ) . Binding assays to the two gp120s showed affinity maturation of < 1 log to AE . CNE8gp120 while there was > 2 log increase in affinity maturartion for AE . A244gp120 ( S6 Table ) . To determine whether neutralization of HIV by a UCA was a common property of HCDR3-loop CD4 bs binding mAbs , we assayed the UCAs of the other vaccine-induced CD4bs mAbs and found that 3 of 8 nAb UCAs neutralized both AE . 92TH023 and AE . CNE8 ( S7 Table ) . These data indicated that the vaccination regimens in both RV144 and RV305 trials could elicit long HCDR3 CD4bs mAbs , whose germline genes could mediate tier 2 neutralization of HIV-1 AE . CNE8 . Progression from sporadic tier 2 neutralization to increased tier 2 virus neutralization breadth depends upon the epitope specificity [39] and the precise footprint of the Ab on Env [26] . We analyzed by negative stain electron microscopy ( EM ) a CH505 SOSIP . 664 trimer bound with DH576 . A 3D reconstruction showed DH576 bound to an open trimer—that is , to Env in a conformation related to the one stabilized by CD4 binding ( Fig 4A , S9 Fig ) . A top view of the complex suggested that the DH576 footprint might resemble those of bnAbs B12 and CH103 ( Fig 4B ) . The bnAbs CH103 , CH235 , CH31 , VRC01 , and PGV04 , as well as CD4 itself , project away from the center of the trimer , avoiding interference with adjacent gp120 subunits in the closed trimer conformation , whereas DH576 may require the open form in order to avoid overlap . The DH576 Fab has an orientation with respect to Env quite similar to that of the B12 Fab , but turned by ~90° about its long axis ( Fig 4B ) . The CD4bs bnAb B12 interaction with gp120 depends upon an aromatic residue at the apex of the HCDR3 loop , aromatic residues around the base of the HCDR3 region , a tyrosine at the apex of the HCDR2 loop and positively charged amino acids in the LCDR1[40] . An alignment of the DH576 inferred UCA and naturally occurring clonal lineage members with the B12 heavy sequence showed that , like B12 , the DH576 clonal lineage contained an aromatic residue at the apex of the HCDR3 loop , aromatic residues around the base of the HCDR3 and a tyrosine in the HCDR2 loop ( S10 Fig ) . The HCDR3s of B12 and DH576 protrude at different angles and when DH576 is superimposed on the B12-gp120 complex , the HCDR3 of DH576 sterically clashes with gp120 . Thus it is not suprising that DH576 rotates by approximately ~90° when it binds to gp120 ( S10 Fig ) . Negative stain EM of 92Br SOSIP . 664 with DH583 , the broadest mAb identified , showed that DH583 also binds an open form of the trimer , even though this trimer is stable in the closed form ( S11 Fig ) . These observations suggest that antibodies elicited in the RV305 trial bind epitopes generally shielded in closed trimers , consistent with the use of gp120 ( rather than a closed Env trimer ) as a principal component of the original , RV144 vaccine .
In this paper we demonstrate that late ( 6–8 year ) boosting of RV144 vaccinees with ALVAC-HIV and AIDSVAX gp120 B/E increased the VH chain gene mutation frequency and expanded clonal lineages of CD4bs antibodies with long HCDR3 regions . Increased somatic hypermutation and affinity maturation by repetitive immunization with a gp120-protein has previously been reported in humans and non-human primates [30 , 41] . In this study the boosting of RV144 vaccinees occurred several years later suggesting that in spite of the rapid waning in plasma IgG seen in the RV144 vaccine trial , long lived memory B cells were induced that could be recalled with subsequent boosting . The observation that three CD4bs clonal lineage UCAs could neutralize tier 2 CRF01 AE AE . CNE8 raised the hypothesis that the AE . A244 gp120 Env in the boost selectively stimulated expansion of a pool of pre-existing tier 2 neutralizing clonal lineages . An antibody HCDR3 arises from recombination of immunoglobulin heavy variable ( VH ) , diversity ( DH ) , and joining ( JH ) genes; its overall length is determined by gene usage [20 , 29 , 42] , D-D fusion [25 , 42 , 43] , N nucleotide additions [22 , 42] , or VH gene replacement [44 , 45] . While B cells that give rise to long HCDR3 antibodies frequently undergo productive gene rearrangement [42] , they can experience negative selection during B cell development because of autoreactivity or polyreactivity [21 , 22] . Thus , in uninfected individuals , only approximately 4% of the naïve repertoire consists of long HCDR3 antibodies , and this population contracts by ~ 50% due to negative selection in the bone marrow at the first immune tolerance checkpoint [22 , 29] . Virus neutralization by a fully reverted , inferred UCA has been reported for V1V2 and CD4bs bnAbs [25 , 46–49] that came from HIV-1 chronically infected individuals . Pancera et al [49] and Bonsignori et al [25] found that V1V2 bnAb UCAs of PG16 and CH01 could neutralize several primary HIV strains . Both UCAs neutralized clade C ZM233 , clade A AQ23 and clade B WITO[25 , 49] . More recently Gorman et al [47] and Andrabi et al [48] have shown that the combining sites of multiple V1V2 bnAbs share binding motifs , and their UCAs frequently neutralize the same HIV-1 primary isolates , suggesting that these primary isolate Envs might be candidates for use as immunogens . The fundamental question raised is whether the CD4bs B cell clonal lineages primed by RV144 and expanded with the repetitive boost of the same vaccine can , with continued boosting , affinity mature into bnAbs . The epitopes of the vaccine-induced CD4bs mAbs described here appear to overlap those of other CD4bs antibodies and that of bnAb B12 in particular . Electron microscopy of negatively stained complexes showed that the vaccine-induced mAbs DH576 and DH583 bound an open form of Env , consistent with a gp120 being used in the vaccine-regimen , and the images were consistent with the CD4bs epitope mapping . Seven of the nine long HCDR3 CD4bs mAbs characterized here had the same VH3 gene usage as the CD4 bs bnAbs CH98 [36] and HJ16[50]; one of the nine used VH1-69 ( S3 Table ) , like VRC13 [26] . One mAb also used a VL κ4–1 like HJ16 and six of the nine long HCDR3 CD4 bs mAbs used either a VL κ3–20 or VL κ1–33 , which are VL chain genes used by the CD4bs bnAbs B12 , VRC01 , VRC-PGV04 , VRC30-34 , 3BNC117 , 3BNC60 , NIH45-46 , 12A12 , 12A21 and 8ANC131 ( reviewed in [16] ) . Nonethless , after 6–8 years and 4 boosts , the induced mAbs neutralized only 1 of 40 tier 2 viruses that were assayed with DH583 and DH576 . Moreover , the neutralizing IC50 of the DH576 clonal lineage for CRF01 AE . CNE8 changed only marginally during affinity maturation , strongly suggesting that theAE . A244gp120 , although it could bind to the UCA , did not select clonal lineage members that could undergo affinity maturation and exhibit greater breadth . Rather it was only neutralization of the tier 1 virus AE . 92TH023 for which vaccine boosting led to a 3 log increase in IC50 . Thus , it is likely that AE . A244 gp120 selected antibody responses that neutralized viruses with an “open” Env conformation , consistent with known conformational properties of the free gp120 fragment . As previously shown in non-human primates antibodies that exclusively bind an open Env sterically clash with Env variable regions leaving little chance of maturing to a bnAb [51 , 52] . We do not yet know whether a de novo series of prime-boost immunizations with stable , closed trimer as proposed by others [51 , 53 , 54] would engage the UCAs of long HCDR3 antibodies such as DH576 and induce affinity maturation to neutralization breadth . In general , Envs of tier 1 viruses open readily , while those of tier 2 viruses do not . The Env of CRF01 AE . CNE8 apparently opens readily enough to bind the antibodies we have characterized , but most other tier 2 Envs do not . The boosts that expanded the pool of long HCDR3 mAbs occurred several years after the completion of the RV144 trial . We do not know what effect the interval between boosting has on the vaccine-induced antibody repertoire . In the RV306 HIV-1 clinical trial ( NCT01931358 ) , vaccine-recipients received the same ALVAC-HIV and AIDSVAX B/E prime-boost regimen and were boosted again with a shorter rest period . Characterization of the Env-reactive mAb repertoire in these vaccine-recipients may provide some insight into whether the length of the rest period necessary for expansion of long HCDR3 mAbs . In summary , study of the B cell repertoires of memory B cells induced by the RV305 trial vaccine-regimen has defined a set of CD4bs-reactive B cell clonal lineages that were initiated by the RV144 vaccine-regimen and expanded after late boosting with the ALVAC-HIV and AIDSVAX B/E immunogens . These antibodies derived from UCAs with some degree of tier 2 virus neutralization capability .
The RV305 clinical trial ( NCT01435135 ) received approvals from Walter Reed Army Institute of Research , Thai Ministry of Public Health , Royal Thai Army Medical Department , Faculty of Tropical Medicine , Mahidol University , Chulalongkorn University Faculty of Medicine , and Siriraj Hospital . Written informed consent was obtained from all clinical trial participants . The Duke University Health System Institutional Review Board approved all human specimen handling . The RV305 clinical trial ( NCT01435135 ) was a randomized double blinded placebo-controlled boosting of 162 RV144 clinical trial participants ( NCT00223080 ) that occurred in Thailand . The RV305 clinical trial was sponsored by the U . S . Army Office of the Surgeon General and conducted in collaboration with the U . S . Army Medical Research and Materiel Command and the Thailand Ministry of Public Health . The primary objective was to characterize the cellular and humoral immune response after boosting and to evaluate the safety and tolerability of late and repetitive boosting with the ALVAC-HIV ( vCP1521 ) and AIDSVAX B/E immunogens . Six-eight years after the conclusion of RV144 , RV305 volunteers were randomized into three groups and boosted two times with a six month interval with either AIDSVAX B/E + ALVAV-HIV ( vCP1521 ) , AIDSVAX B/E or ALVAC-HIV ( vCP1521 ) or a placebo . After commencement no changes were made to the vaccine-regimen . All HIV-1 uninfected RV144 participants that had completed the full RV144 vaccine-regimen , were at low risk for HIV-1 infection based on self-reported behavioral habits , able to pass a Test of Understanding , gave written consent and were in general good health were eligible . Female volunteers had to be on adequate birth control 45 days prior to the first inject and consent to remaining on birth control . For safety reasons women that were pregnant , nursing or planning on becoming pregnant were excluded . Volunteers with a conflict of interest , psychological or medical conditions , or those unable to complete a Test of Understanding were excluded . Vaccine safety was measured by self-reporting on a diary card local and systemic reactions for three days post-vaccination . All adverse events and serious adverse events were recorded throughout the trial and up to three months post final boost . Peripheral blood mononuclear cells ( PBMCs ) were stained with Aqua vital dye ( ( AqVd ) Invitrogen ) , IgM-FITC , IgD-PE , CD3 -PECy5 , CD14-BV605 , CD16-BV570 , CD235a-PECy5 , CD27-PECy7 , CD38-APC-AF700 , CD19-APCCy7 , along with AF647 and BV421 conjugated antigens . Viable antigen-specific B cells ( AqVd-CD14-CD16-CD3-CD235a-CD19+IgD-CD38all , AF647 and BV421 double positive ) were single-cell sorted with a BD FACSAria II- SORP ( BD Biosciences , Mountain View , CA ) into 96 well PCR plates and stored at -80°C . Immunoglobulin variable heavy and light chain variable regions ( VH and VL ) were RT-PCR amplified using AmpliTaq360 Master Mix ( Applied Biosystems ) with conditions previously described [55] . PCR products were purified ( Qiagen , Valencia , CA ) and sequenced with a BigDye Sequencing kit ( Applied Biosystems ) on an ABI 3700 sequencer . VH and VL chain gene rearrangements , clonal relatedness , UCA and intermediate ancestor ( IA ) inferences were made using Cloanalyst [56] . PCR-amplifed sequences were transiently expressed as previously described [55] . Briefly , linear expression cassettes were constructed by placing the PCR-amplified VH and VL chain genes under the control of a CMV promoter along with an IgG constant region and poly A signal sequence . These linear expression cassettes were then co-transfected into 293T cells and after three days the cell culture supernatants were harvested and concentrated . For large scale expression , the VHDHJH and VLJL genes were synthesized ( VH chain in the IgG1 4A backbone ) and transformed into DH5α cells ( GeneScript , Piscataway , NJ ) . Plasmids were expressed in Luria Broth , purified ( Qiagen , Valencia , CA ) and ~ 5x106 293i cells were transfected with 1 mg of Ig ( VH ) and light ( VL ) chain genes using poly-ethylenimine ( PEI ) or with 0 . 4mgs of heavy- and light chain-gene using ExpiFectamine™ ( Life Technologies , Carlsbad , CA ) following the manufacturers protocol . After five days mAbs were concentrated , purified from the cell culture supernatant by an overnight incubation with Protein A beads and buffer exchanged into PBS . High affinity 384-well microplates ( Costar 3700 ) were coated overnight at 4°C with 30ng/well of protein in 0 . 1% Sodium Biocarbonate . For binding , a direct ELISA was performed in which monoclonal antibodies ( mAbs ) beginning at 100ug/mL were diluted 3-fold in blocking buffer and added to the plates for 1 hour . Antibody binding was detected using IgG-HRP ( Rockland , Limerick , PA ) diluted 1:10 , 000 in azide-free blocking buffer . For the blocking ELISA , mAbs of interest were diluted and added to the plate for one hour . Plates were washed and a biotinylated mAb was added for one hour . Blocking was evaluated by adding streptavidin-HRP . The direct binding and blocking ELISAs were developed using SureBlue Reserve TMB One Component microwell peroxidase substrate ( catalog no . 53-00-03; KPL ) and the reactions were stopped with 0 . 1% HCL . Plates were read on a plate reader ( Molecular Devices ) at 450 nm . Palivizumab ( Synagis ) ( MedImmune , LLC; Gaithersburg , MD ) was used as a negative control . The plasma was screened with the binding Ab multiplex assay ( BAMA ) as previously described . The antibody B12 was a gift from QBI and the Vaccine Research Program , Division of AIDS , NIAID contract # HSN272201100023C . Neutralization assays were performed in both TZM-bl and A3R5 cell lines as previously described [23 , 32] . Data were reported as ID50 titers for plasma and IC50 titers for mAbs . Purified mAbs were epitope mapped on B . YU2gp120 core proteins ( ΔV1 , V2 , V3 loops ) displayed on S . cerevisiae as previously described [35 , 36] . Briefly , mAbs that bound B . YU2gp120 core protein were assayed for binding to 31 different B . YU2gp120 core proteins with point-mutations and the wild type protein . Antigen-specific recognition was confirmed by the observation that mAbs did not show binding to non-displaying S . cerevisiae . Data was recorded as the percent binding to a mutant relative to the wild type core proteins . The B12 binding data in Figure 3 are from [36] . Surface plasmon resonance was performed on a BIAcore 4000 instrument . The purified recombinant mAb was immobilized to a CM5 sensor chip and envelope binding was measured in real time with continuous flow of PBS ( 150mM NaCL , 0 . 005% surfactant P20 [pH 7 . 4] at 10–30 μl/min . Data was analyzed with BIAevaluation 4 . 1 software ( BIAcore ) . ADCC mediated by the mAbs was assessed according to previously published procedures [57 , 58] . Briefly , HIV-1 reporter virus used was a replication-competent infectious molecular clone ( IMC ) designed to encode the HIV-1 env genes in cis within an isogenic backbone that also expresses the Renilla luciferase reporter gene and preserves all viral open reading frames [59] . CEM . NKRCCR5 cells ( NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH: CEM . NKR CCR5+ Cells from Dr . Alexandra Trkola [60] were infected with HIV-1 IMCs encoding the subtype AE CM235 ( accession number AF259954 ) , B WITO ( accession number JN944948 ) , and C Ce1086 . c ( accession number FJ444395 ) env genes within an NL4-3 backbone [59] . Whole PBMC from an HIV-seronegative donor with the heterozygous 158F/V genotype for Fc-gamma receptor IIIa were used as effector cells at an effector cell to target cell ( E:T ) ratio of 30:1 . MAb A32 ( James Robinson; Tulane University , New Orleans , LA ) , Palivizumab ( MedImmune , LLC; Gaithersburg , MD; used as negative control ) and vaccine induced mAbs were tested at a final concentration range of 10–0 . 039μg/ml using 4-fold serial dilutions . All the conditions were evaluated after 6 hour incubation at 37°C and 5%CO2 . The ADCC activity was reported as % specific killing calculated as [ ( RLU in control well − RLU in test well ) / RLU of control well] ×100 . The results were considered positive if ADCC activity was ≥15% specific killing . ADCC activities are reported either as the endpoint concentration ( EC ) , defined as the mAb concentration that intersects the positive cutoff of 15% specific killing , or as positive area under the curve ( pAUC ) , calculated by the trapezoidal rule using the values ≥15% specific killing . To generate the autologous HIV-1 CH505 SOSIP . 664 and clade B 92Br SOSIP . 664 expression constructs we followed established SOSIP design parameters [61] . Briefly , the SOSIP . 664 trimer was engineered with a disulfide linkage between gp120 and gp41 by introducing A501C and T605C mutations ( HxB2 numbering system ) that covalently links the two subunits of the heterodimer [61] . The I559P mutation was included in the heptad repeat region 1 ( HR1 ) of gp41 for trimer stabilization , and a deletion of part of the hydrophobic membrane proximal external region ( MPER ) , in this case residues 664–681 of the Env ectodomain [61] . The furin cleavage site between gp120 and gp41 ( 508REKR511 ) was altered to 506RRRRRR511 to enhance cleavage [61] . The resulting , codon-optimized CH505 SOSIP . 664 env gene was obtained from GenScript ( Piscataway , NJ ) and cloned into pVRC-8400 using Nhe1 and NotI restriction sites and the tissue plasminogen activator signal sequence . Fabs were expressed by transient transfection of HEK 293F suspension cells , using linear PEI following the manufacturer’s suggested protocol . After 5 d , supernatants were clarified by centrifugation and diluted twofold with 1x PBS buffer , and the protein isolated from the diluted spernatant using CaptureSelect LC-Kappa ( Hu ) affinity matrix ( Thermo Fisher Scientific , Waltham , MA ) , according to manufacturer’s protocols . Fractions containing the protein of interest were pooled , concentrated , and further purified by gel filtration chromatography using a Superdex 200 analytical column ( GE Healthcare Life Sciences , Pittsburgh , PA ) in a buffer of 2 . 5mM Tris , pH 7 . 5 , 350mM NaCl , and 0 . 02% sodium azide . Each SOSIP . 664 construct was transfected into 293F cells together with a plasmid encoding the cellular protease , furin , at a 4:1 Env:furin ratio . The cells were allowed to express the soluble trimer for 5–7 days . Culture supernatants were collected , cells removed by centrifugation at 3800 x g for 20 min , and the supernatant filtered with a 0 . 2 μm pore size filter . The soluble SOSIP was purified by flowing the filtered supernatant over a lectin ( Galanthus nivalis ) affinity chromatography column overnight at 4°C . The lectin column was washed with 1x PBS , followed with 1x PBS supplemented with 0 . 5 M NaCl , and proteins were eluted with 1 M methyl-α-D-mannopyranoside dissolved in 1x PBS . The eluate was concentrated and loaded for further purification onto a Superdex 200 10/300 GL column ( GE Healthcare Life Sciences , Pittsburgh , PA ) prequilibrated in a buffer of 5 mM Hepes , pH 7 . 5 , 150 mM NaCl and 0 . 02% sodium azide for analysis by EM . Purified SOSIP . 664 trimer was incubated with a five molar excess of Fab at 4°C for 1 hour . A 3 μL aliquot containing ~0 . 01 mg/ml of the complex was applied for 30 s onto a carbon coated 400 Cu mesh grid that had been glow discharged at 20 mA for 30 s , followed by negative staining with 2% uranyl formate for 20 s . Samples were imaged using a FEI Tecnai T12 microscope operating at 120kV , at a magnification of 52 , 000x , resulting in a pixel size of 2 . 13 Å at the specimen plane . Images were acquired with a Gatan 2K CCD camera using a nominal defocus of 1500 nm at 10° tilt increments , up to 50° . The tilts provided additional particle orientations to improve the image reconstructions . Particles were picked semi-automatically using EMAN2 [62] and put into a particle stack . Initial , reference-free , two-dimensional ( 2D ) class averages were calculated and particles corresponding to complexes ( with one , two , or three Fabs bound ) were selected into a substack for determination of an initial model for the DH576: CH505 SOSIP . 664 complex . The initial model was calculated in EMAN2 , imposing 3-fold symmetry , and subsequent refinement in EMAN2 also imposed 3-fold symmetry . In total , 22 , 929 particles were included in the final reconstruction . The resolution of the final model was determined using a Fourier Shell Correlation ( FSC ) cut-off of 0 . 5 . The cryo-ET structure of b12-bound gp120 trimer ( PDB ID: 3DNL ) [63] and an Fab model were manually docked into the EM density and refined with the UCSF Chimera ‘Fit in map’ function [64] . The gp120 subunit of crystal structures with different Fabs were superposed on other gp120 cores from the PDB by least-squares fitting in Coot [65] The DH576 Fab was crystallized at 10–15 mg/mL . Crystals were grown in 96-well format using hanging drop vapor diffusion and appeared after 24–48 h at 20°C . Fab crystals were obtained in the following conditions: 20% PEG 4000 , 100mM Hepes , pH 7 . 0 , 1M NaCl . Crystals were harvested and cryoprotected by the addition of 20–25% glycerol to the reservoir solution and then flash-cooled in liquid nitrogen . Diffraction data were obtained at 100 K from beam line 24-ID-C at the Advanced Photon Source using a single wavelength . Datasets from individual crystals were processed with HKL2000[66] . Molecular replacement calculations for the free Fab were carried out with PHASER[67] , using the variable domains of PGT135 [Protein Data Bank ( PDB ) ID 4JM2] and the constant domains of VRC01 from the VRC01/gp120 complex [Protein Data Bank ( PDB ) ID 4LSS] as the starting models for molecular replacement . Refinement was carried out with PHENIX[68] , and all model modifications were carried out with Coot[65] . During refinement , maps were generated from combinations of positional , group B-factor , and TLS ( translation/libration/screw ) refinement algorithms . Secondary-structure restraints were included at all stages for all Fabs . Structure validations were performed periodically during refinement using the MolProbity server[69] . The final refinement statistics are summarized in ( S8 Table ) . All statistical analysis was performed in SAS by the Duke Human Vaccine Institute statistical team . The statistical test and p value are recorded where used . The EM reconstruction has been deposited in the Electron Microscopy Data Bank as EMD-8573 . The crystal structure of DH576 has been deposited in the Protein Data Bank as PDB ID5UIX . The VH and VL chain genes described have been submitted to Genbank with accessioning numbers KY499910-KY499949 .
|
Developing a successful HIV-1 vaccine remains a high global health priority . Several HIV-1 vaccine trials have been performed with only the RV144 vaccine trial showing vaccine efficacy , albeit modest . No broadly neutralizing antibody activity was identified in RV144 and inducing sterilizing immunity against a complex pathogen like HIV-1 remains a major challenge . Here we characterize the B cell responses after RV144 vaccine-recipients received two additional boosts severals years after the conclusion of the RV144 vaccine trial . Delayed and repetitive boosting of RV144 vaccine-recipients was capable of increasing somatic hypermutation of the Env-reactive antibodies and expanding subdominant pools of neutralizing B cell clonal lineages . These data are pertinent to HIV-1 vaccine-regimen design .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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"blood",
"cells",
"viral",
"vaccines",
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"chemical",
"characterization",
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"enzyme-linked",
"immunoassays",
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"medicine",
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"cells",
"pathogens",
"immunology",
"microbiology",
"retroviruses",
"viruses",
"immunodeficiency",
"viruses",
"vaccines",
"rna",
"viruses",
"infectious",
"disease",
"control",
"antibodies",
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"organisms"
] |
2017
|
Boosting of HIV envelope CD4 binding site antibodies with long variable heavy third complementarity determining region in the randomized double blind RV305 HIV-1 vaccine trial
|
Kaposi sarcoma-associated herpesvirus ( KSHV ) is linked with the development of Kaposi sarcoma and the B lymphocyte disorders primary effusion lymphoma ( PEL ) and multi-centric Castleman disease . T cell immunity limits KSHV infection and disease , however the virus employs multiple mechanisms to inhibit efficient control by these effectors . Thus KSHV-specific CD4+ T cells poorly recognize most PEL cells and even where they can , they are unable to kill them . To make KSHV-infected cells more sensitive to T cell control we treated PEL cells with the thymidine analogue azidothymidine ( AZT ) , which sensitizes PEL lines to Fas-ligand and TRAIL challenge; effector mechanisms which T cells use . PELs co-cultured with KSHV-specific CD4+ T cells in the absence of AZT showed no control of PEL outgrowth . However in the presence of AZT PEL outgrowth was controlled in an MHC-restricted manner . To investigate how AZT sensitizes PELs to immune control we first examined BJAB cells transduced with individual KSHV-latent genes for their ability to resist apoptosis mediated by stimuli delivered through Fas and TRAIL receptors . This showed that in addition to the previously described vFLIP protein , expression of vIRF3 also inhibited apoptosis delivered by these stimuli . Importantly vIRF3 mediated protection from these apoptotic stimuli was inhibited in the presence of AZT as was a second vIRF3 associated phenotype , the downregulation of surface MHC class II . Although both vFLIP and vIRF3 are expressed in PELs , we propose that inhibiting vIRF3 function with AZT may be sufficient to restore T cell control of these tumor cells .
Kaposi sarcoma-associated herpesvirus ( KSHV ) is an oncogenic human γ-herpesvirus which infects endothelial cells and establishes a latent infection in B lymphocytes . It is associated with the endothelial cell malignancy Kaposi sarcoma ( KS ) and the B lymphocyte disorders primary effusion lymphoma ( PEL ) and multi-centric Castleman disease ( MCD ) [1] . The immune response is important in controlling infection and disease caused by KSHV as seen by the higher frequency of KSHV-associated disease found in immunosuppressed patients such as HIV or transplant patients[2] . Restoration of immune competence in KS patients can lead to resolution of this malignancy [3 , 4] , implying an important role for the cellular immune response in the control of KSHV-infection and disease . To control KSHV-malignancies the cellular immune response must overcome immune evasion mechanisms employed by the virus . These include the production of a restricted repertoire of proteins , limiting the range of immune targets but allowing establishment of a predominantly latent non-virus productive infection . Proteins expressed within KSHV malignancies include the genome maintenance protein LANA , a cyclin D homologue vCyclin , an NF-κB activator with pro-survival function vFLIP , and the Kaposin proteins of which the best characterized , Kaposin B , functions to stabilize cytokine mRNAs ( for review see Schulz and Cesarman[5] ) . Some of these genes show intrinsic features which likely minimize exposure to CD8+ T cells by restricting synthesis of their encoded protein , reducing the supply of defective ribosomal products ( DRiPs ) that are thought to be the source of CD8+ T cell peptide-epitopes[6] . Firstly vFLIP utilizes inefficient codons resulting in the production of unstable mRNA and low levels of protein expression[7] . Secondly LANA encodes extensive repeat sequences which restrict translation and proteasomal mediated destruction[8] , minimizing epitope presentation from this protein to CD8+ T cells[9] . KSHV B cell pathologies additionally express an interleukin-6 homologue vIL-6 , and the multifunctional protein vIRF3 . Amongst other functions , vIRF3 can inhibit p53 and IRF5 function[10 , 11] as well as decrease surface MHC class II expression through inhibiting the promoter of the class II transcriptional transactivator CIITA[12] . Additionally , infected B cells express the ubiquitin ligases K3 and K5 , which induce endocytosis of surface MHC class I and co-stimulatory molecules such as ICAM and CD86[13 , 14] . These multiple layers of immune evasion mechanisms represent a challenge for T cell mediated control of infected cells . Studies using CD8+ T cells to probe recognition of PELs expressing reporter antigens have shown that they were unable to recognize these targets[15] . Recognition of PELs by LANA-specific CD4+ T cells , which would be less affected by the restricted production of this protein as CD4 epitope generation is not reliant on the DRiP pathway , is also mostly poor[16] . This is likely due to vIRF3 expression as PELs which either constitutively[16] or transiently[17] express reduced levels of vIRF3 have increased levels of surface MHC class II and can be recognized by the T cells . However in these cases or when vIRF3 function is bypassed and expression of class II is restored to allow recognition of PELs by LANA-specific CD4+ T cells , these effectors are not able to kill PELs despite killing other target cell types in parallel assays[16] . Consistent with this finding is that PEL lines are highly resistant to cell death induced by effector mechanisms that T cells employ , namely stimulation via the extrinsic apoptotic pathways through Fas or TNF-related apoptosis inducing ligand ( TRAIL ) receptors[18 , 19] . The finding that knock down of vFLIP induced sensitivity to Fas ligation indicates that this viral protein can interfere with this apoptotic pathway[18] , however vFLIP is thought to be expressed at low levels within infected cells[7]; whether other viral proteins contribute to this inhibition is unclear . An understanding of whether other latent proteins inhibit pathways induced by extrinsic apoptotic stimuli would allow the rational design of interventions to inhibit these functions and restore sensitivity to T cell effector mechanisms . Nevertheless some chemotherapeutic approaches to increase sensitivity of PEL cells to immune clearance have been developed . In particular , treating PEL cell lines with the thymidine analogue azidothymidine ( AZT ) renders them sensitive to Fas-ligand or TRAIL mediated killing [19 , 20] . Additionally AZT has been used therapeutically in combination with IFNα to induce TRAIL expression in PELs , resulting in their apoptosis [21] . AZT treatment of PELs induces some cleavage of caspase 3 [19] and its mechanism of action has been linked to reducing nuclear translocation of NF-κB . Specifically the mono-phosphorylated form of AZT has been found to inhibit in vitro IKKβ-mediated phosphorylation of the NF-κB regulator IκBα [21] . However compared to other NF-kB inhibitors AZT has a more subtle effect on PELs , not obviously altering growth or apoptosis , while classic inhibitors such as BAY 11–7082 or Bortezomib rapidly induce apoptosis[22 , 23] . As T cells are likely to express Fas-ligand or TRAIL , we determined whether KSHV-specific CD4+ T cells could inhibit the outgrowth of MHC matched AZT-treated PEL lines and found that this treatment induced sensitivity of PELs to T cell control . We also identified that in addition to vFLIP , expression of vIRF3 prevented apoptosis induced by extrinsic pathways and that AZT treatment inhibited this vIRF3 mediated protection .
We have previously found that KSHV-specific CD4+ T cells poorly recognize most MHC-matched PEL cell lines , as few PEL cells only transiently express surface MHC class II at any one time [17] . Engineering PELs to express surface MHC class II allowed recognition but not killing by the T cells , despite these effectors killing other targets in parallel assays [16] . Culturing PELs in the thymidine analogue azidothymidine ( AZT ) induces sensitivity to apoptosis mediated by Fas-ligand and TRAIL [19 , 20] and as these can be expressed by T cells , we asked whether AZT would sensitize PELs to control by KSHV-specific CD4+ T cells . We initially determined whether a panel of established KSHV-specific CD4+ T cell clones [16] ( Table 1 ) expressed Fas-ligand and TRAIL transcripts by qRT-PCR analysis after stimulation with autologous target cells sensitized with their cognate peptide-epitope . Transcript expression was estimated and the levels detected shown in Table 1 , expressed relative to those seen in peripheral blood mononuclear cells ( PBMC ) activated with phorbol myristate acetate and ionomycin to induce transcription of these genes[24 , 25] . Compared to activated PBMC , the clones expressed Fas-ligand or TRAIL transcripts in most cases at similar levels to activated PBMC . We next developed an in vitro assay to test the ability of KSHV-specific CD4+ T cells to control outgrowth of MHC-matched AZT-treated PEL lines . Here T cells were co-cultured with PELs for ten days so all PELs would likely transiently express MHC class II at some time during the assay , allowing T cell recognition . PELs were grown in 10 μg/ml AZT , which did not lead to any consistent changes in latent gene expression across the PELs as judged by qRT-PCR analysis ( S1A Fig ) . Additionally little if any inhibition of proliferation of PELs was seen when cultured in the presence of AZT ( S1B Fig ) as previously described [19] . Doubling dilutions from 10 000 PELs were seeded as triplicate microcultures in 96 well plates and 10 000 MHC-matched KSHV-specific CD4+ T cells were added to each replicate . To act as controls PELs were either sensitized with the T cells cognate peptide-epitope , or challenged with MHC-mismatched T cells to assess non-specific inhibition of PEL growth . Additionally , dilutions of PELs were seeded in the absence of T cells to monitor any growth inhibition from AZT . Identity of the outgrowing cells was determined by flow cytometry analysis , staining for CD138 and CD4 expression as markers of PELs or T cells respectively . Fig 1 shows representative results of an assay using the PEL VG-1 and DP-1 restricted , LANA-encoded LAPSTLRSLRKRRLS-specific CD4+ T cells; T cells are subsequently identified by the first three amino acids of their cognate peptide-epitope . Microscopic images presented in Fig 1A show representative results of assays conducted and indicate that there was substantial growth of cells under some conditions but not others . Thus PELs cultured in the absence of T cells showed little or no outgrowth inhibition in the presence or absence of AZT . Co-cultures of PELs with T cells showed that in the absence of AZT there was substantial cell growth; it would seem unlikely that the cells proliferating here would be the T cell clones as these require high levels of cytokines to proliferate . Indeed flow cytometry analysis of these populations presented in Fig 1B ( left hand panels ) confirmed that they were dominated by CD138 positive PEL cells and that no additional control of outgrowth was seen when PELs had been sensitized with epitope-peptide . Parallel assays conducted in the presence of 10 μg/ml AZT , as shown in Fig 1A ( right hand panels ) , showed cell outgrowth when MHC-mismatched T cells were co-cultured with PELs . However no obvious outgrowth of cells was seen in co-cultures of VG-1 with MHC-matched LAP-specific T cells in the presence of AZT; furthermore flow cytometry analysis confirmed that only CD4+ T cells could be detected in these wells ( Fig 1B right hand panels ) . Importantly , PEL outgrowth was controlled even when they were not sensitized with the peptide-epitope , implying there was sufficient recognition of the endogenously processed and presented epitope to allow control . Fig 2 shows averaged results of outgrowth assays graphed as the number PEL cells seeded required to outgrow T cells . Here the PEL cell lines VG-1 , BCBL-1 and JSC-1 were assayed on at least two occasions with a total of five different MHC-matched CD4+ T cell clones specific for vCyclin or LANA epitopes . In all cases , assays conducted without AZT showed no T cell control of PEL outgrowth , regardless of whether the PELs had been pre-sensitized with the T cell’s peptide-epitope . However parallel assays using MHC-matched T cell clones showed that in the presence of 10 μg/ml AZT , PEL growth could be inhibited and in some cases completely controlled . By contrast , culturing MHC-mismatched T cell clones with AZT treated JSC-1 showed some control of these PELs while VG-1 or BCBL-1 showed little or no growth inhibition when cultured with mismatched clones indicating the specificity of T cell control . Of note , the inhibition of outgrowth appeared relatively slow . Control of PEL outgrowth took several days , even in the case of BCBL-1 where all cells expressed surface class II . Thus VG-1 cells cultured with AZT assayed versus LAP- or LAP/LRS-specific clones were ultimately completely controlled by these effectors , while TFQ-specific cells showed increased control over background . LRS-specific clones showed the lowest increase in control of non-peptide sensitized VG-1 cells with AZT compared to the MHC-mismatch cells which may reflect their lower functional avidity ( 10−6 M; Table 1 ) however sensitizing the PELs with LRS peptide increased control by these T cells . Similar results were observed using the BCBL-1 and JSC-1 PELs , assayed against appropriately matched T cells . Additionally we confirmed that T cells pre-treated with AZT showed equivalent control in outgrowth assays ( S2 Fig ) . These findings indicate that most KSHV-specific T cells recognize sufficient epitope derived from the endogenously expressed protein to induce effector function in these longer term assays and that AZT sensitizes PEL cells to T cell control . To better understand the mechanism of T cell control in the outgrowth assays we examined the PELs for expression of apoptotic cell death markers during the assays . Here BCBL-1 cells were cultured either with or without AZT and parallel cultures challenged with DQ6-matched TFQ-specific or MHC-mismatched T cells . After five days PELs were stained with Annexin V to detect phosphatidylserine as a marker of apoptosis and propidium iodide ( PI ) to measure membrane integrity . Fig 3 shows representative flow cytometry plots of one of three assays and the percentage dead cells was estimated as the combined percentage of cells expressing Annexin V and/or PI . BCBL-1 cells cultured without T cells showed 6 . 2% dead cells and parallel cultures in AZT containing media showed a small increase to 10 . 8% . BCBL-1 cultured with MHC-mismatched T cells showed 8 . 1% dead cells while parallel assays conducted with AZT showed an increase to 16 . 2% . Assaying the MHC-matched TFQ-specific T cells against non-treated BCBL-1 showed some increase compared to the controls with 16 . 4% dead cells . However PELs cultured in AZT used in parallel assays with these effectors showed increased cell death with 47 . 3% dead cells . These studies indicate that the induction of apoptosis in the PELs correlates with the control of outgrowth of these cells . We next sought to understand how AZT sensitized PELs to T cell control . The ability of KSHV-specific CD4+ T cells to kill targets such as Epstein-Barr virus ( EBV ) transformed B cells but not PELs [16] led us to hypothesize that a KSHV expressed gene was responsible for the protective effect and AZT was influencing its function . vFLIP has been shown to have anti-apoptotic function , however its low level of expression in PELs led us to examine whether other latent proteins may protect from apoptosis mediated by T cell effector mechanisms such as Fas or TRAIL receptor stimulation . Here genes encoding the PEL expressed proteins LANA , vCyclin , vFLIP , Kaposin B , vIRF3 and vIL-6 or a control empty construct were cloned into lentiviral vectors which express transgenes in a tetracycline regulated manner and BJAB cells transduced with the individual lentivirus constructs . Transgene expressing cells were then assessed for sensitivity to apoptotic killing through Fas-ligand and TRAIL receptor engagement . BJAB cells were used as they are of B cell origin and sensitive to killing through these pathways . We first confirmed transgene expression in the BJAB cells after induction for 24 hours by western blot analysis of lysates from the cells ( Fig 4A ) . LANA was expressed but showed a reduction in molecular weight compared to BCBL-1 . Nucleotide sequence analysis indicated this construct was the same as the KSHV-BAC36 sequence from which it was derived , which in turn was established from BCBL-1 . Why it has an apparent lower molecular weight in BJAB cells is unclear but may relate to differences in post translation modifications of proteins by BJAB compared to BCBL-1 . vCyclin and vFLIP were expressed at markedly higher levels in transduced BJAB cells compared to BCBL-1 , while vIRF3 expression was increased compared to BCBL-1 . We were unable to detect protein expression of Kaposin B so no further analysis was conducted with these cells . vIL-6 protein could not be detected either in the BJAB or BCBL-1 cells , however using a previously validated qRT-PCR assay[14] we detected vIL-6 transcript at levels similar to those seen in BCBL-1 ( Fig 4B ) . We next induced expression of the KSHV transgenes in the BJAB cells for 24 hours then challenged these with agonistic anti-Fas antibody or recombinant TRAIL for 48 hours , as used previously [19] . Cell death was measured as the combined percentage of cells binding Annexin V and/or staining with PI . Fig 5A shows representative FACS profiles of induced BJAB cells either unchallenged or exposed to anti-Fas antibody . Control cells showed induction of cell death upon challenge , whilst vFLIP or vIRF3 expressing BJAB showed attenuation of death . Fig 5B and 5C show averaged results from at least five independent assays of the transgene expressing cells challenged with two concentrations of anti-Fas or TRAIL . For each concentration of the apoptotic stimulus used , significant differences in mean values across the groups were tested for by one way ANOVA analysis and significant differences between groups identified by Tukey’s Honest Significant Difference test . Anti-Fas antibody induced high levels of death in control transgene expressing BJAB cells , while those expressing vIL-6 , LANA and vCyclin showed no significant difference in the percentage of dead cells compared to control cells when challenged with 0 . 15 μg/ml or 0 . 075 μg/ml of anti-Fas antibody ( p≥0 . 2248 and p≥0 . 1642 respectively ) . However BJABs expressing vFLIP or vIRF3 showed significantly reduced frequencies of dead cells after challenge with either concentration of anti-Fas antibody compared to control BJAB ( vFLIP p = 5 . 6x10-4 and p = 4x10-7 or vIRF3 p = 2 . 6x10-4 and p = 4 . 1x10-6 for each concentration of anti-Fas ) . Similar challenge of control transgene expressing BJAB cells with either 0 . 1 μg/ml or 0 . 05 μg/ml TRAIL induced cell death while expression of vCyclin or vIL-6 gave no protection and LANA showed some decrease , although none of these values were significantly different to control cell death ( p≥0 . 6164 and p≥0 . 3182 respectively for each concentration ) . However significant inhibition of cell death was again seen in vFLIP or vIRF3 expressing BJAB after challenge with either concentration of TRAIL compared to the control cells ( vFLIP p<1x10-7 and p = 2x10-4 or vIRF3 p = 5x10-7 and p = 0 . 0027 for each concentration of TRAIL ) . These findings suggest that in addition to vFLIP , vIRF3 has the potential to inhibit apoptosis induced through Fas and TRAIL pathways . We next determined whether AZT treatment of vFLIP or vIRF3 expressing BJAB cells restored sensitivity to anti-Fas or TRAIL challenge . Here control , vIRF3 and vFLIP BJAB cells were grown in media containing 10 μg/ml AZT for at least seven days , which did not obviously affect their growth ( S3 Fig ) , and transgene expressing cells challenged with anti-Fas or TRAIL as before . Fig 6 shows data from at least seven independent assays; note a second more potent batch of anti-Fas antibody was used which induced higher levels of apoptosis . Compared to untreated control BJABs , AZT treated control BJABs showed non-significant increases in the percentage of dead cells when challenged with either concentration of anti-Fas ( p = 0 . 99 and p = 0 . 99 ) or TRAIL ( p = 0 . 94 and p = 0 . 41 ) . vFLIP expressing BJABs as before showed protection upon challenge with these stimuli compared to control cells , while comparison of untreated vFLIP expressing to vFLIP expressing cells cultured in AZT showed small non-significant increases in the percentage of dead cells upon challenge with both concentrations of anti-Fas ( p = 0 . 98 and p = 0 . 99 ) or TRAIL ( p = 0 . 28 and p = 0 . 91 ) . BJAB expressing vIRF3 again showed protection from challenge with anti-Fas or TRAIL compared to control transgene expressing cells . However parallel cultures of vIRF3-expressing BJABs treated with AZT had significantly more dead cells compared to non-treated vIRF3 expressing cells when challenged with both concentrations of anti-Fas ( p = 0 . 0034 and p = 0 . 0014 ) or TRAIL ( p<1x10-7 and p = 0 . 0027 ) . These results indicate that in this system , AZT inhibits vIRF3 mediated protection from anti-Fas and TRAIL challenge . To probe the effectiveness of AZT inhibition of vIRF3 protection from apoptotic challenge , we titrated the concentration of AZT that the vIRF3 and control transgene expressing BJAB cells were cultured in and then challenged these cells with anti-Fas antibody . Fig 7 shows compiled results of three assays measuring the percentage of dead cells 48 hours after challenge . As before , vIRF3 expressing cells treated with 10 μg/ml AZT lost their protection from apoptosis induction as did those treated with 5 μg/ml AZT . However concentrations of AZT at 2 μg/ml or less gave no inhibition of vIRF3 protection . The ability of AZT to inhibit vIRF3 protective function led us to examine whether AZT treatment altered expression of this protein in the PEL lines . Here lysates from PELs which had been used in outgrowth assays and either treated with 10 μg/ml AZT or not were made and subjected to western blot analysis for vIRF3 levels . Fig 8A displays one representative blot of three , showing vIRF3 or actin levels in BCBL-1 , JSC-1 and VG-1 cells while Fig 8B shows compiled fold change in vIRF3 expression from the three assays . AZT treatment of BCBL-1 and VG-1 induced small if any changes in vIRF3 levels while some decreased expression was seen in AZT treated JSC-1 cells . These results indicate that AZT treatment can reduce vIRF3 protein levels in some PEL lines but not others , suggesting its mechanism of action may be through reduction of vIRF3 levels in some lines but potentially through inhibiting vIRF3 function in others . We finally investigated whether AZT interfered with other vIRF3 functions by examining a second phenotype associated with its expression , namely modulation of surface MHC class II expression on PEL cells [12] . As shown in Fig 9 treatment of BCBL-1 with AZT , which constitutively expresses high levels of surface MHC class II [16] , did not alter expression . However the PELs JSC-1 and VG-1 , which constitutively express low levels of surface class II , when treated with AZT showed increased proportions of cells expressing MHC class II . These studies are consistent with AZT treatment interfering with vIRF3 function .
This work was prompted by our observations that although KSHV-specific CD4+ T cells could recognize PELs expressing MHC class II [17] or PELs engineered to express surface class II , they were unable to control them despite killing other cell types [16] . To make PELs more sensitive to T cell control , we treated PELs with AZT and found that LANA- and vCyclin-specific CD4+ T cells inhibited or completely controlled the outgrowth of PELs in co-culture assays . The inhibition observed in these assays was equivalent or more efficient than that seen in similar assays using CD4+ T cells specific for latent epitopes from the related human γ-herpesvirus EBV to control outgrowth of EBV-transformed B cells [26] . To identify how AZT induced sensitivity to T cell control we hypothesized that it was interfering with pro-survival KSHV-gene functions which inhibit cell death pathways initiated by T cell recognition , namely those of the extrinsic apoptotic pathway . Such KSHV gene products would include vFLIP which can inhibit killing via Fas [27 , 28] and a second receptor mediated killing mechanism , TNF-α [29] . Consistent with these studies we found that expression of vFLIP in BJAB cells inhibited killing by anti-Fas antibody and TRAIL . Unexpectedly we found that expression of vIRF3 also inhibited apoptosis induced by these two mechanisms . vIRF3 is a polyfunctional protein which subverts numerous host pathways , some of which are linked to apoptosis . Thus knockdown of vIRF3 in PELs induces growth arrest , caspase 3 activation and apoptosis [30] . vIRF3 has further been reported to bind and inhibit p53 function [10] , and bind to the forkhead box class O ( FoxO ) 3a transcription factor and its regulatory 14-3-3 proteins . This causes retention of FoxO factors in the cytoplasm , preventing their ability to induce expression of proapoptotic proteins such as Bim [31] . However these mechanisms would seem unlikely to intersect with the extrinsic apoptotic pathways triggered in our study . vIRF3 has also been shown to interfere with apoptosis induced by overexpression of the interferon induced dsRNA-activated protein kinase R [32] , which is known to require the FADD/caspase pathway for apoptosis induction . However it is not thought that interactions through receptors such as Fas are responsible for cell death induction in this situation [33] . Our study suggests that vIRF3 inhibits the extrinsic apoptotic pathway using a different mechanism to those so far described . The ability of vFLIP and vIRF3 to protect BJAB cells from extrinsic apoptotic stimuli led us to examine whether AZT treatment restored sensitivity to these stimuli and we found that while vFLIP expressing BJAB remained resistant , vIRF3 expressing cells became sensitive . This result was surprising as vFLIPs pro-survival function is thought to be mediated through its ability to activate NF-κB through the canonical pathway by interacting with the IκB kinase ( IKK ) complex [34] and AZT is believed to inhibit IKK mediated phosphorylation of IκB [21] . One may have predicted then that AZT would affect vFLIP function , however vFLIP can also activate the alternative NF-κB pathway [35] and whether AZT can inhibit this pathway is unknown . Regardless of this , the current experiments indicate that AZT can affect pathways manipulated by KSHV in addition to NF-κB . The inhibition of vIRF3 mediated protection from apoptosis by AZT suggests that either AZT alters a cellular pathway that vIRF3 interacts with , or AZT may act more directly on vIRF3 itself . Support for the latter possibility comes from our observations that culturing the low class II expressing JSC-1 and VG-1 PELs in AZT increased surface MHC class II expression on a subset of cells . This finding is consistent with the drug inhibiting vIRF3 function , preventing repression of CIITA expression , restoring some degree of MHC class II transcription [12] . Additionally in this context , we believe it is unlikely that increased expression of surface class II itself is sufficient for T cell control of PEL outgrowth as BCBL-1 cells express relatively high levels of surface class II and can be recognized by CD4+ T cells [16] , yet in the absence of AZT are not controlled by the T cells . Furthermore we have previously found that inducing expression of CIITA in PELs , which consequently upregulates surface class II expression , allows recognition by KSHV-specific CD4+ T cells but not killing of these tumor cells [16] . Although we have demonstrated a role for AZT in inhibiting vIRF3 function , we can not exclude that this drug also alters function of the CD4+ T cell clones used in our assays . However murine T cells treated with similar concentrations of AZT maintain their cytotoxic function when cultured in AZT containing medium [36] . Similarly human PBMC maintained in lower concentrations of AZT compared to the present study also show no obvious difference in cytokine secretion after stimulation with phytohaemagglutanin or anti-CD3 [37] . Nevertheless AZT treatment of PBMC is known to be toxic to mitochondria within T cells or induce apoptosis in these cells [38] , and may reduce their proliferative potential after stimulation with anti-CD3 [39] . However in the outgrowth assays conducted here we would expect to see little proliferation of the T cells in the cytokine poor environment of these experiments . The protection afforded by vFLIP and vIRF3 in BJAB cells upon anti-Fas or TRAIL challenge suggests both may contribute to protection from such challenges in PELs and potentially , inhibition of one may be compensated for by the other . Assigning the contribution of either viral product to protection from apoptotic stimuli may be assessed through using CRISPR/Cas9 mediated genome editing to establish PEL lines lacking either of these genes , as used to manipulate other herpesvirus infected cells [40] . However knock down of either vFLIP or vIRF3 expression in PELs sensitizes or induces apoptosis in these cells , so establishing lines deficient in either of these may not be possible [18 , 30] . Such a question may be best addressed by making viruses lacking either or both of these genes , using these to infect B cell lines which are known to support virus expression of vIRF3 , such as BJAB or primary B cells [14 , 41] , and then assess apoptotic sensitivity of these cells . Nevertheless , inhibiting function of either of these may be sufficient then to allow improved control by immune effectors . Thus even though AZT only inhibited vIRF3 function in the BJAB model , inhibition mediated by AZT over vIRF3 in PELs may be sufficient to induce their sensitization to CD4+ T cell control . Recently AZT has been used successfully with valganciclovir in an MCD setting where these prodrugs are thought to be phosphorylated to toxic compounds by KSHVs lytic cycle expressed thymidine kinase ( ORF21 ) and phosphotransferase ( ORF36 ) respectively[42] . Infected B lymphocytes within MCD lesions also express vIRF3 [10] and potentially AZT may be inhibiting this proteins function , allowing better targeting by KSHV-specific CD4+ T cells . Important in this context is that MCD patients , who are usually co-infected with HIV , have relatively preserved immune competence in terms of CD4+ T cell numbers [42 , 43] , although no studies have so far examined KSHV-specific CD4+ T cell immunity in this patient group . Correlating KSHV-specific CD4+ T cell responses with patient outcome upon AZT treatment would help give an understanding of whether this drug and immune effector combination is capable of controlling KSHV-infected cells in this group . Additionally , little is known as to when vIRF3 is expressed in vivo , outside of cases of MCD and PEL . However as the gene appears to be expressed in infected B lymphocytes [13 , 14 , 41] it would seem likely that it is expressed during the establishment and maintenance of the latent KSHV reservoir in B lymphocytes . Potentially then , AZT treatment may help make infected B lymphocytes within the latent reservoir more susceptible to T cell control . However our studies using the BJAB-vIRF3 transduced cells indicates that a concentration of 5 μg/ml AZT is required to inhibit vIRF3 function which would likely exceed plasma levels attained after a standard oral dose of 200 mg of AZT that results in plasma AZT levels of 0 . 9–2 . 9 μg/ml [44] . To attain sufficient levels it may be necessary to use high doses like those used in the above MCD setting of multiple oral doses of 600 mg of AZT [42] . Nevertheless our studies suggest that interfering with vIRF3 function may help restore the virus-host balance .
CD4+ T cell clones were established in a previous study [16] and their characteristics are shown in Table 1 . An additional specificity was included which recognized the HLA-DQ*0601 presented vCyclin encoded peptide TFQQSLTSHMRKLLG . PEL cells were maintained in RPMI-1640 ( Sigma-Aldrich ) with 10% fetal calf serum ( FCS ) ( LifeTechnologies ) . PELs used in outgrowth assays were VG-1 ( a kind gift from Prof Christian Brander , IrsiCaixa AIDS Research Institute–HIVACAT , Barcelona , Spain [45] ) , BCBL-1 ( NIH AIDS Reagent Program , catalogue number 3233 , [46] ) and JSC-1 ( American Type Culture Collection , catalogue number CRL-2769 , [47] ) . Outgrowth assays were performed by seeding doubling dilutions of triplicate PEL cell cultures in 96 well plates , ranging from concentrations of 10 000 to 1250 cells per well . PEL cultures had 10 000 CD4+ T cells added and assays were conducted in the presence or absence of 10 μg/ml AZT ( Sigma-Aldrich ) . Cultures were incubated for 10 days , after which cell outgrowth identity was determined by staining cells with antibodies specific to CD138 or CD4 ( Biolegend ) . Stained cells were analyzed on an Accuri C6 flow cytometer ( BD Biosciences ) and data analyzed using Flowjo ( Treestar ) . Outgrowth assays were scored as the minimal number of PELs seeded to overgrow T cells , as defined by there being an equal or greater frequency of PEL cells compared to T cells at the end of the assay . Surface MHC class II expression on PEL lines was measured by staining with antibodies specific to HLA-DR ( L243 , Biolegend ) and analyzing by flow cytometry . CD4+ T cell clones were activated by stimulation with peptide-epitope sensitized Epstein-Barr virus-transformed B cells for five hours , RNA extracted and cDNA synthesized as described[14] . Fas-ligand and TRAIL transcript levels were assessed by Taqman Gene Expression Assays ( ThermoFisher ) on 5 ng of cDNA as described by the manufacturer . Relative levels of transcripts were determined by comparing to dilutions of cDNA prepared from peripheral blood mononuclear cells ( PBMC ) activated with phorbol myristate acetate and ionomycin for 5 hours[24 , 25] . KSHV transcript levels were estimated on RNA extracted from PELs using validated qRT-PCR assays as previously described [14] . PBMC were collected from healthy donors after obtaining written informed consent to donate samples and experiments were approved by the South Birmingham Research Ethics Committee ( approval reference number 06/Q2707/300 ) . The KSHV latent genes vCyclin , vFLIP , Kaposin B , vIRF3 and vIL-6 were PCR amplified from cDNA extracted from the BCBL-1 PEL , additionally Kaposin B was epitope tagged with the influenza HA epitope . Sequence encoding LANA was excised from the BCBL-1 derived BAC36 construct [48] and these genes and a control vector lacking an insert cloned individually into pENTR1a ( ThermoFisher ) . Constructs were recombined into the lentiviral vector pInducer 20[49] , which expresses transgenes under the control of a tetracycline regulated promoter and the resulting plasmid co-transfected with standard plasmids encoding lentiviral packaging and envelope genes into 293T cells ( American Type Culture Collection , catalogue number CRL-3216 ) . After 48 hours , supernatants were used to infect BJAB cells ( a kind gift from Prof George Klein [50] ) and transduced BJABs selected by culturing in RPMI-1640 10% FCS supplemented with 200 μg/ml G418 . Transgene expression was assessed by inducing expression with 2 μg/ml doxycycline for 24 hours , then lysing cells for either western blot or qRT-PCR analysis as previously described[14 , 16] . Blots were probed with antibodies specific to LANA ( clone LN-53 , Advanced Biotechnologies ) , vCyclin ( 94B , Abcam ) , vFLIP ( 4C1 ) [51] , HA ( 3F10 , Roche ) , vIRF3 ( CM-A807 , Abnova ) , vIL-6 ( Abbiotec ) , and actin ( AC-74 , Sigma ) and these detected using an appropriate anti-species HRP-conjugated antibody , followed by ECL detection ( GE Healthcare ) . Transgene expression in BJAB cells was induced for 24 hours by addition of 2 μg/ml doxycycline , after which 150 000 cells were incubated in 100 μl of media with the indicated concentrations of either an agonistic anti-Fas antibody ( CH11 , Beckman-Coulter ) or recombinant TRAIL ( Peprotech ) for 48 hours . Cell death was estimated using an Annexin V-propidium iodide apoptosis detection kit ( eBioscience ) and cells analyzed by flow cytometry . Statistical comparisons between levels of cell death in apoptosis assays were performed using the R statistical program ( v 3 . 0 . 2 , R Development Core Team , http://www . R-project . org ) . So that parametric statistical tests could be used we first assessed whether the data satisfied the assumptions for use of these statistics namely that it was normally distributed using the Shapiro-Wilk test , and was homoscedastic using Levene’s test . To identify whether there were variation in mean levels of cell death between groups , these were tested by one way ANOVA and groups which differed were identified by post-hoc testing with Tukey’s Honest Significant Difference test .
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Kaposi sarcoma-associated herpesvirus ( KSHV ) can cause disease in humans in the form of B lymphocyte disorders such as primary effusion lymphoma ( PEL ) and multicentric Castleman disease . Where tested , these are highly resistant to immune control by KSHV-specific T cells . To investigate how such KSHV-infected cells can be made more sensitive to T cell control we treated PEL lines with azidothymidine ( AZT ) , which has been shown to induce sensitivity in such lines to the mechanisms which T cells use to kill targets . We found this allowed the T cells to control in vitro lymphoma growth . The ability of the T cells to control PEL cell growth was found to correlate with AZT mediated inhibition of function of the KSHV protein vIRF3 which we show has the ability to protect cells from killing by immune effector mechanisms . These studies suggest that the therapeutic drug AZT may be of use to tip the virus host balance away from the virus by interfering with this immune evasion and pro-survival protein , potentially allowing better control by the host .
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2016
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Azidothymidine Sensitizes Primary Effusion Lymphoma Cells to Kaposi Sarcoma-Associated Herpesvirus-Specific CD4+ T Cell Control and Inhibits vIRF3 Function
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Microorganisms exist almost exclusively in interactive multispecies communities , but genetic determinants of the fitness of interacting bacteria , and accessible adaptive pathways , remain uncharacterized . Here , using a two-species system , we studied the antagonism of Pseudomonas aeruginosa against Escherichia coli . Our unbiased genome-scale approach enabled us to identify multiple factors that explained the entire antagonism observed . We discovered both forms of ecological competition–sequestration of iron led to exploitative competition , while phenazine exposure engendered interference competition . We used laboratory evolution to discover adaptive evolutionary trajectories in our system . In the presence of P . aeruginosa toxins , E . coli populations showed parallel molecular evolution and adaptive convergence at the gene-level . The multiple resistance pathways discovered provide novel insights into mechanisms of toxin entry and activity . Our study reveals the molecular complexity of a simple two-species interaction , an important first-step in the application of systems biology to detailed molecular dissection of interactions within native microbiomes .
Microorganisms are typically found in complex communities such as those in the soil , aquatic environments , and the microbiome [1] , and interactions between microbial species can critically impact their survival and evolutionary trajectories [1 , 2] . Current knowledge suggests that competition plays an important role in interspecies microbial interactions [3 , 4] . This includes both exploitative competition , where species compete for limited nutrients , as well as interference competition , where species directly antagonize each other [5] . However , such ecological processes are understudied and poorly characterized in microbial systems [2] . Previous studies have identified molecules produced by bacteria that may affect the behavior or fitness of other species . Such molecules could be beneficial to the target species [6] , but a wide variety of them have been shown to be antagonistic in nature [7–9] . In most cases , such studies have looked at a single molecule or class of molecules , and the potential effects these could have on exogenous bacteria . However , the entire breadth of interactions that actually determines fitness in a specific multispecies system has rarely been identified and characterized at the molecular level . As communities are established , bacteria evolve in response to the biotic and abiotic challenges present . Although adaptation to various physicochemical stresses has been widely studied ( for example [10] ) , the mechanisms that underlie adaptation to interspecies competition remain largely unknown . The immediate cellular effect of toxic exoproducts on target bacteria has been described for some interactions , but how target populations can evolve upon such exposure to combat the antimicrobials has not been studied . Here we systematically dissect interactions in a two-species bacterial system containing P . aeruginosa and E . coli . P . aeruginosa , an opportunistic pathogen , is frequently found in multi-species infections [11 , 12] and is capable of interacting with other microorganisms via a variety of antimicrobial molecules [8] . The other interacting partner , E . coli , is a commensal and the best studied bacterial species , which we utilized as a model target organism . Our genome-scale analyses of this two-species bacterial system identified both interference and exploitative competition , mediated by multiple molecules in the antagonistic species , which explained all of the observed competition . We also discovered several diverse genetic determinants of resistance in the target species , gaining insights into the properties of adaptive trajectories in the face of interspecies competition .
We studied a bi-species system containing P . aeruginosa and E . coli in planktonic culture ( using media conditions in which they have very similar growth rates ) . We tested whether any interactions are seen between these species , by measuring the relative fitness of wild-type ( WT ) E . coli and P . aeruginosa in direct competition with each other . E . coli cells were found to have a relative fitness of 0 . 2 ( ± 0 . 09 ) , which is significantly less than 1 , revealing that P . aeruginosa was inhibiting E . coli growth . Further , E . coli cells showed substantial reduction in growth upon exposure to P . aeruginosa spent media for a few hours ( Fig 1 ) , indicating that , at least part of the P . aeruginosa antagonism was mediated by secreted molecules . Each of the conditions in this experiment had the same volume of fresh media ( 50% ) to enable comparison across conditions , and the remainder was made up of the appropriate volume of spent media added to the media salts base . E . coli spent media did not have a significant effect on P . aeruginosa growth ( S1 Fig ) . We determined the global transcriptional response of E . coli to P . aeruginosa spent media , and identified the Gene Ontology ( GO ) annotations enriched ( and depleted ) across the full range of change in gene expression , using iPAGE , a mutual-information based pathway analysis tool [13] . One of the most strongly induced pathways was iron transport ( Fig 2 ) . P . aeruginosa is known to secrete two siderophores , pyoverdine and pyochelin , that chelate iron and transport it inside the cell [14] . Although these molecules are thought to have evolved primarily for iron acquisition by the producer , they may also limit iron-availability for other microbial species within the community . P . aeruginosa spent media also induced several amino acid biosynthesis pathways , while repressing genes involved in core cellular processes such as ribosomal translation , nucleotide biosynthesis , ATP synthesis , and the electron transport chain ( Fig 2 ) . These changes are similar to those produced during the stringent response , which is known to be induced by iron starvation [15 , 16] . Additionally , genes involved in ciliary or flagellar motility are also upregulated , which may represent an adaptive response to enable migration away from competition or to niches with higher iron availability . Mass spectrometry ( both MALDI and ESI ) on whole spent media from WT P . aeruginosa revealed a major component that had an m/z of 1335 Daltons ( S2 Fig ) , which matches the molecular mass of pyoverdine [17 , 18] . We also fractionated the spent media by reverse-phase HPLC using an acetonitrile-water gradient , and tested the fractions for growth-inhibitory activity against E . coli . While some bioactive fractions showed a complex mass-spectrometry profile making it difficult to identify the active component , the main component in one of the active fractions also had an m/z of 1335 Daltons ( S3 Fig ) . Consistent with the iron-sequestration mechanism of competition , the addition of surplus iron partially alleviated the growth inhibition of E . coli by P . aeruginosa spent media in a concentration-dependent manner ( Fig 3A ) . Iron supplementation also caused a marginal 1 . 4-fold increase in growth under control conditions ( S4 Fig ) , with the effect saturating at 10μM ferric citrate supplementation . This mild iron limitation in the growth media does not account for the up to 27-fold increase in growth caused by iron supplementation in the presence of P . aeruginosa spent media , indicating that the spent media was causing the significant iron limitation seen . Further , deletion of genes encoding key enzymes in the pyoverdine ( pvdJ ) and pyochelin ( pchE ) biosynthetic pathways , singly and in combination , caused significantly lower growth inhibition of E . coli than the WT ( Fig 3B ) . E . coli also had higher relative fitness in competition with a P . aeruginosa siderophore double mutant , compared to WT ( Fig 4 ) , confirming that iron-limitation by the P . aeruginosa siderophores pyoverdine and pyochelin engenders exploitative competition and inhibits the growth of E . coli , in our system . One of the major P . aeruginosa quorum sensing molecules , PQS ( Pseudomonas Quinolone Signal ) , is also known to chelate ferric ions [19 , 20] . We tested a deletion mutant for a gene encoding a key enzyme in the PQS biosynthesis pathway ( pqsA ) . Spent media from the ΔpqsA mutant as well as a ΔpvdJ ΔpchE ΔpqsA mutant caused significantly lower growth inhibition of E . coli compared to the WT ( Fig 5 ) , and E . coli had higher relative fitness in competition with these mutants as compared to WT P . aeruginosa ( Fig 4 ) demonstrating that the PQS pathway is also involved in the antagonism . PQS not only chelates iron , but also induces a wide range of virulence factors such as hydrogen cyanide , rhamnolipids , lectin , and phenazines [21] , via the PQS-response protein PqsE . A ΔpqsE mutant was deficient in inhibiting E . coli growth ( Fig 5 ) , indicating that the PQS pathway molecules inhibited E . coli growth indirectly , likely through the expression of one or more virulence factors . To identify these factor ( s ) , we determined the transcriptional response of E . coli to WT P . aeruginosa spent media supplemented with ferric citrate ( to eliminate the effect of iron-limitation ) , and analyzed the results using iPAGE [13] . The ‘transcription factor regulon’ module in iPAGE [22] identified the SoxRS regulon as being enriched in the upregulated genes ( Fig 6 ) . Interestingly , soxS has recently been shown to be upregulated in E . coli in response to several biotic stresses including a Vibrio cholera strain known to kill E . coli , the P1vir bacteriophage , and the antimicrobial peptide Polymyxin B , likely to protect against reactive oxygen species generated due to these stresses [23] . The P . aeruginosa secondary metabolite pyocyanin ( the terminal phenazine molecule ) is known to upregulate the soxS-response in P . aeruginosa [24] . Further , phenazines are known to be PQS-induced , and to have antimicrobial properties , likely due to the production of reactive oxygen species or inhibition of bacterial respiration [25–27] . We thus hypothesized that the phenazine pathway was responsible for the PQS-mediated growth inhibition of E . coli . Spent media from a mutant lacking both copies of the phenazine biosynthesis operon ( Δphz1/2 ) , as well as a ΔpvdJ ΔpchE Δphz1/2 mutant , showed significantly reduced E . coli growth inhibition ( Fig 7A ) , and E . coli had higher relative fitness in competition with these mutants ( Fig 4 ) , compared to the WT . Further , pyocyanin , the terminal phenazine molecule , caused concentration-dependent growth inhibition of E . coli ( Fig 7B ) , confirming that phenazine molecules directly inhibit E . coli growth via interference competition . The concentration of pyocyanin used in our experiments ( 25–100μM ) covers the range of measured pyocyanin concentrations in the growth media of PA14 ( 30–60μM ) in both minimal media as well as LB [24] . Importantly , E . coli had a relative fitness of almost 1 in competition with P . aeruginosa strains deficient for both siderophores and phenazines ( Fig 4 ) , demonstrating that these molecules account for the entirety of the measurable P . aeruginosa antagonism against E . coli . Thus , using unbiased genomic level approaches , we have identified pathways by which P . aeruginosa inhibits the growth of E . coli in the conditions under study , via both exploitative and interference competition . P . aeruginosa limits iron availability in the environment , thereby shutting down most core cellular processes in E . coli cells . The phenazine molecules further limit E . coli growth possibly by inhibiting cellular respiration [25 , 26] , and inducing the production of reactive oxygen radicals in the E . coli cells that are still able to respire aerobically [27] . Despite identification of individual molecules that can mediate competitive interspecies interactions , the mechanisms of adaptation to such competition and the attributes of the adaptive solutions have remained largely unstudied . To identify pathways by which E . coli can resist P . aeruginosa antimicrobials , we carried out laboratory evolution of E . coli in the presence of either WT spent media , ΔpvdJ spent media , or pyocyanin . We exposed WT E . coli to increasing concentrations of the spent media or pyocyanin , and performed 7–19 daily transfers into the selective media ( the transfers were stopped when the cultures did not show significantly improved growth under the selective condition for two consecutive days ) . We then carried out whole genome sequencing on 2 or 3 clones each from 2 or 3 populations evolved under each condition . All the evolved clones had between 1–9 mutations , with recurring mutations in mprA in the spent-media selected clones , and in fpr and ompC in the pyocyanin-selected clones ( S1 Table ) . The transcriptional repressor mprA negatively regulates the expression of the multidrug resistance ( MDR ) pump EmrAB [28] , and is also predicted to regulate the MDR pump AcrAB [29] . We identified mutations in mprA in all sequenced clones from 5 independent populations selected against either WT P . aeruginosa or ΔpvdJ spent media ( S1 Table ) . While the mutations were identical within a population , only 2 populations showed a common mutation–a single base-pair deletion at position 446 of the gene ( henceforth referred to as mprA* ) . Thus , while there is little parallelism at the level of individual mutations , adaptive convergence is extensive at the gene level , a phenomenon seen previously in E . coli [10 , 30] . All the mutations identified in mprA were either non-synonymous or resulted in a frameshift mutation ( S1 Table ) in different parts of the gene , indicative of a hypomorphic ( as opposed to a hyper- or neo-morphic ) phenotype . We transferred the mprA* allele to the WT parental background and tested it along with an mprA deletion mutant against WT P . aeruginosa spent media . Surprisingly , although the mprA* substituted strain showed significant resistance to WT P . aeruginosa spent media , the ΔmprA mutant did not show either increased resistance or sensitivity ( Fig 8A ) . This suggested that either there is some compensatory regulation in the ΔmprA mutant , or the adaptive alleles are neomorphic , and not simple hypomorphs . The mprA mutants ( as well as the fpr and ompC mutants described below ) did not show a significant difference compared to the WT under control conditions ( S5 Fig ) . Interestingly , mprA was repressed 2 . 5-fold in E . coli exposed to P . aeruginosa spent media supplemented with ferric citrate ( in the transcriptional response measurements described above ) , which might be an adaptive response to exposure to antimicrobials , resulting in the upregulation of efflux pumps . Parallel evolution at the gene level was also seen in the two independent populations selected against pyocyanin–both had mutations in the fpr and ompC genes ( S1 Table ) . The fpr gene codes for the flavodoxin NADP+ reductase enzyme that transfers electrons between flavodoxin and NADPH , and is required for the activation of anaerobic ribonucleoside reductase , pyruvate-formate lyase and methionine synthase [31] . We identified multiple alleles of fpr in our strains ( S1 Table ) , and further studied both the synonymous mutation common in population pyo1 ( henceforth called fpr1* ) as well as the non-synonymous mutation common in population pyo2 ( henceforth called fpr2* ) . We transferred both alleles to the parental background , and tested them along with an fpr deletion strain . Interestingly , both fpr1* and fpr2* , but not Δfpr , showed increased resistance to pyocyanin ( Fig 8B ) , confirming that even the fpr1* synonymous mutation had a significant phenotypic effect . Further , the Δfpr mutant showed increased sensitivity to pyocyanin at lower concentrations , compared to the WT parental strain ( Fig 8C ) . Thus , it is likely that both the synonymous fpr1* and the non-synonymous fpr2* are hypermorphic alleles , and an increase in Fpr activity can lead to pyocyanin resistance . Pyocyanin inhibits respiration in target cells [26] , which could lead to the induction of metabolic pathways that normally function under anaerobiosis , and our results suggest that Fpr activity is a limiting step for growth under these conditions . The expression of fpr is induced 30-fold in E . coli exposed to P . aeruginosa spent media supplemented with ferric citrate ( in the transcriptional response measurements described above ) , which is likely a response by E . coli cells to the perceived anaerobic conditions created by pyocyanin exposure . The ompC gene codes for one of the two main porins in E . coli which allow for the influx of mostly hydrophilic small molecules across the outer membrane [32] . We identified 2 different alleles of ompC in the 2 independent populations evolved in the presence of pyocyanin . The allele in the pyo1 population ( henceforth referred to as ompC* ) results in an early stop at position 54 , while the allele in the pyo2 population has a frameshift that also leads to an early stop after 6 additional amino acids . Both the ompC* allele and an ompC deletion , in the parental background , provided significant resistance against pyocyanin , with the deletion showing approximately 2-fold higher resistance ( Fig 8D ) , suggesting that the ompC* allele is a hypomorph . Thus , it is likely that pyocyanin enters target E . coli cells via the OmpC porin , and modulation of this protein can lead to pyocyanin resistance . Additionally , a double mutant carrying both the ompC* and fpr2* alleles had significantly higher resistance to pyocyanin than either of the single mutants or the ΔompC mutant ( Fig 8D ) . This indicates that some pyocyanin can enter the cell even in the absence of OmpC , and increased Fpr activity can provide further resistance .
Our study of a P . aeruginosa–E . coli two-species system utilized genome-scale methods to identify the pathways and molecules that underlie all the various components of the observed P . aeruginosa antagonism against E . coli . Specific molecules that could have an effect on exogenous species under certain conditions have been identified previously , and these include siderophores [33] and phenazine molecules [25] , among various others . However , here we used unbiased , agnostic methods such as HPLC and mass-spectrometry based identification of bioactive molecules , as well as measurement and computational analyses of transcriptional responses , to comprehensively characterize the specific competitive interactions present in our bi-species system under the particular conditions of our study . Such approaches can be easily carried out in less well-characterized bacterial species , thus accelerating research into the study of other basic and biomedically relevant bacterial interactions . Furthermore , the use of E . coli as a model “target” organism can also aid in the discovery of molecules underlying interspecies interactions , the immediate molecular responses elicited in target bacteria , as well as potential pathways of adaptation to such interactions . P . aeruginosa is known to produce a wide variety of small antimicrobial molecules [8] , and our results underscore the multi-pronged mode of its microbial antagonism . The combination of both interference and exploitative competition seen in a single interaction suggests that P . aeruginosa encounters other microbial species frequently in its natural habitats , and has evolved a variety of strategies to compete with this microbial diversity . In our system , sequestration of iron limits the ability of other species to carry out basic cellular processes including respiration and DNA synthesis . The target cells that are still able to grow are further exposed to phenazine molecules , which are thought to target the electron transport chain and inhibit respiration [25 , 26 , 34] . Lastly , the subset of target cells that are still able to carry out aerobic respiration under these conditions are then likely to be subjected to cellular toxicity due to the production of reactive oxygen radicals by the phenazine molecules [27] . Iron is a scarce resource in many environments , and competition for iron is likely to be crucial in most communities . The pyoverdine biosynthesis locus is the most divergent alignable locus in the P . aeruginosa genome [35] , likely due to evolutionary pressure to counter siderophore piracy by “cheater” strains , as well as for protection against the pyocin S3 [36] . Our results suggest that it plays a role in interspecies competition as well . Regulation of pyoverdine production is dependent on iron levels , although other factors also modulate this regulation [37 , 38] . Since P . aeruginosa is known to detect and respond to the presence of other species [39] , and competition is thought to have shaped bacterial regulatory networks [4] , it is an intriguing possibility that the induction of siderophore production may be dependent on sensing foreign species , to inhibit niche invasion . Quorum-sensing pathways and molecules were originally thought to regulate population behavior within a species , but more recently , these molecules have been shown to have other functions , including the modulation of behaviors of exogenous species by quorum-sensing interference [40] , or the regulation of antimicrobial production [41] . P . aeruginosa has multiple quorum-sensing pathways , and our results show that the PQS-pathway is important for interference competition by inducing the production of antimicrobial molecules such as the phenazines . Interestingly , while mutations in the lasR quorum-sensing pathway are frequently found in P . aeruginosa isolates from chronic infections [42] , PQS pathway mutants have not been seen , and isolates from cystic fibrosis infections may even over-produce PQS-pathway molecules [43–45] . Thus , despite the complex interconnected quorum-sensing pathways in P . aeruginosa , there might be a separation of functions between the PQS and the classical homoserine lactone pathways , based on their roles in virulence and microbial antagonism . While antagonistic molecules produced by various species have been identified in competitive interactions , we have limited knowledge about adaptation to such competition . Our laboratory evolution experiments identified adaptive mutations in mprA , fpr and ompC , demonstrating that there are multiple pathways to combat interspecies competition . Additionally , even though we evolved only a few populations in each specific condition , we still observed parallel evolution and adaptive convergence at the gene level , with multiple independent mutations in each of the above three genes , supporting the idea that these are critical genetic determinants of resistance to P . aeruginosa antimicrobials . Laboratory evolution also revealed the OmpC porin to be a major means of pyocyanin entry into target cells , suggesting that this antimicrobial pathway takes advantage of endogenous membrane permeability routes to enter the cell . The Fpr protein is known to be important for activation of the anaerobic ribonucleoside reductase , pyruvate-formate lyase , and methionine synthase [31] , and ribonucleoside reductase is the rate-limiting step for DNA synthesis [46] . The modulation of pyocyanin resistance by Fpr suggests that by inhibiting respiration [26] , pyocyanin likely causes cellular metabolism to shift to anaerobic pathways , and the activation of anaerobic ribonucleoside reductase underlies the role of Fpr in acquisition of pyocyanin resistance . Mutants lacking Fpr have also been shown to have increased sensitivity to paraquat , which is a redox-cycling drug , and mutants that overproduce this protein are resistant to this drug [31] , further supporting the notion that Fpr activity is rate limiting for anaerobic growth . The MprA transcriptional repressor is known to negatively regulate the transcription of the MDR pumps [28 , 29] , and non-synonymous mutations in this gene have been shown to confer resistance against compounds such as thiolactomycin and CCCP [28] . Thus a loss-of-function mutation in mprA could likely result in resistance to the P . aeruginosa antimicrobials . However , the lack of a phenotype seen in the ΔmprA mutant implies that the mutations we identified in mprA may be neomorphic despite the fact that there are 4 different mutations found in different parts of the protein . Two independent populations in our selections showed the same identical mprA* mutation–a deletion of a single base pair at position 446 which causes a frameshift mutation resulting in a novel 26 amino-acid C-terminus . Interestingly , an almost identical C-terminus ( with a difference of only 2 amino acids between the two proteins , both of which are positive matches ) was identified previously in a pathogenic isolate ECA-0157 from clinical bovine mastitis [47] , raising the possibility that the adaptive pathways we identified may be relevant for interspecies interactions seen in natural niches . Our results clearly show that even bi-species microbial interactions can be complex , including both exploitative and interference competition , and involving multiple genetic determinants and mechanisms . We provide a framework for identifying the actual fitness-determining interactions under any condition , and demonstrate the utility of applying systems-biology approaches to such problems . This framework can be expanded and applied to the study of bacterial interactions in diverse settings , including competitive and cooperative interactions within healthy and diseased states of the human microbiome , as well as polymicrobial infections . Approaches similar to those presented here can also help elucidate how stable microbial communities are formed and maintained , and how community structure can be manipulated .
All strains used in this work are described in S2 Table . For all experiments in liquid media , bacterial strains were grown in modified M63 media [48] ( 13 . 6g/L KH2PO4 , 2g/L ( NH4 ) 2SO4 , 2μM ferric citrate , 1mM MgSO4; pH adjusted to 7 . 0 with KOH ) supplemented with 0 . 3% glucose and 5g/L casamino acids , at 37°C , and shaken at 250rpm . For the P . aeruginosa–E . coli competition assays , the bacterial mixtures were plated on M9 + 0 . 5% lactose plates to select for E . coli , and on M9 + 10mM sodium citrate plates to select for P . aeruginosa . M9 media [49] contained 12 . 8g/L Na2HPO4 . 7H2O , 3g/L KH2PO4 , 1g/L NH4Cl , 0 . 5g/L NaCl , 0 . 1mM CaCl2 and 2mM MgSO4 . For the spent media resistance assays , cells were plated on LB plates . Strains were grown in LB liquid media ( 10g/L Bacto-tryptone , 5g/L yeast extract , 10g/L NaCl ) or on LB plates for routine cloning and strain construction . Salt-free LB + sucrose plates contained 10g/L Bacto-tryptone , 5g/L yeast extract and 10% v/v sucrose . All plates contained 15g/L agar . The antibiotic concentrations used are listed in S3 Table . For all bi-parental conjugations , the donor and recipient cells were grown overnight shaking at 250rpm at 37°C in LB ( with the appropriate antibiotic , if required ) ; 0 . 5ml of each overnight culture was used per conjugation . The overnight cultures were washed twice with PBS , and resuspended in 1/10th the original volume of 100mM MgSO4 . Multiple mating spots from a 1:1 mixture of the two parental strains were placed on LB plates , and incubated at 37°C for 3–4 hours . Cells were scraped off , collected in PBS , and plated on the appropriate selection plates . We generated all single and multiple in-frame gene deletion mutants except for the phenazine deletion mutants ( in P . aeruginosa strain PA14 ) using the Gateway-compatible vector pEX18ApGW [50] , similar to that described in [50] . We amplified a FRT-site flanked Gentamycin resistance cassette ( GmR ) by PCR from a pPS856 plasmid template [50] . We also amplified ~600bp fragments flanking the gene of interest by PCR ( all primer sequences for the deletion constructs are listed in S4 Table ) , and then carried out PCR overlap extension between these 3 fragments to generate the mutant cassette . This cassette was cloned into the Gateway Entry vector PCR8/GW/TOPO ( Invitrogen ) by TA cloning , and transferred to the pEX18ApGW plasmid via an LR reaction using the LR Clonase II Enzyme mix ( Invitrogen ) . The cloned fragments were verified at each stage by sequencing . The final knockout plasmid was transformed into the conjugative S17-1 λ-pir E . coli strain , and then transferred to the parental P . aeruginosa strain using bi-parental conjugation , followed by selection on LB + irgasan + gentamicin plates . Individual conjugant colonies were streaked on salt-free LB + sucrose plates , and sucrose-resistant colonies were streaked out on LB + gentamicin and LB + carbenicillin plates . Gentamicin-resistant carbenicillin-sensitive clones were verified for the gene knockout by sequencing the target locus . To remove the gentamicin-resistance cassette , the pFLP2 plasmid expressing the Flp recombinase [51] was transferred to these knockout strains via a bi-parental conjugation with a pFLP2 carrying E . coli S17-1 λ-pir strain , followed by selection on LB + irgasan + carbenicillin plates . Individual conjugant colonies were streaked on salt-free LB + sucrose plates , and sucrose-resistant colonies were streaked out on LB , LB + gentamicin , and LB + carbenicillin plates . Gentamicin- and carbenicillin-sensitive clones were verified for proper recombination by sequencing the target locus . The phenazine mutants were generated in strain PA14 using the pΔphzA1-G1 and pΔphzA2-G2 knockout plasmids [24] . These plasmids were transformed individually into E . coli S17-1 λ-pir , and pΔphzA1-G1 was mobilized into the parental strain using bi-parental conjugation , followed by selection on LB + irgasan + gentamycin plates . Individual conjugant colonies were streaked on salt free LB + sucrose plates , to resolve merodiploids , and sucrose-resistant clones were verified for the phzA1-G1 knockout by sequencing the target locus . Subsequently , a phzA2-G2 deletion was similarly generated in the phzA1-G1 mutants to obtain a phenazine deletion mutant . All single mutants ( as well as the ΔphzA1-G1 ΔphzA2-G2 mutant ) were generated in the PA14 strain . For the multiple gene knockouts , the above protocol was followed multiple times in succession for each gene deletion . Overnight cultures of P . aeruginosa or E . coli strains were diluted 1:100 in fresh media , shaken at 37°C at 250rpm for 22h , and then centrifuged at 5000g for 20 minutes . The supernatant was passed through a 0 . 22μm filter , aliquoted if required , and stored at -20°C . To determine the time-course of the response of E . coli to WT P . aeruginosa spent media , an overnight culture of WT E . coli was diluted 1:250 in fresh media , and grown for 1 . 5h shaking at 250rpm at 37°C . 500μl of this culture was added to either 500μl of spent media , 200μl spent media + 300μl of 1X M63 salts , 100μl spent media + 400μl of 1X M63 salts , or 500μl 1X M63 salts ( for the control ) . Appropriate dilutions of the starting culture in 1X PBS were plated on LB plates , and the cultures were grown shaking at 250rpm at 37°C . Aliquots were removed from these cultures at the appropriate time-points , diluted appropriately in 1X PBS , and plated on LB plates . Samples were diluted and plated in triplicate , and the plate counts were averaged across the replicates . The time-course of the response of P . aeruginosa to WT E . coli spent media was determined similarly–an overnight culture of WT P . aeruginosa was diluted 1:250 in fresh media , and grown for 1 . 5h shaking at 250rpm at 37°C . 500μl of this culture was added to either 500μl of spent media , 200μl spent media + 300μl of 1X M63 salts , or 500μl 1X M63 salts ( for the control ) . Appropriate dilutions of the starting culture in 1X PBS were plated on LB plates , and the cultures were grown shaking at 250rpm at 37°C . Aliquots were removed from these cultures at the appropriate time-points , diluted appropriately in 1X PBS , and plated on LB plates . To measure the resistance of E . coli to P . aeruginosa spent media , an overnight culture of E . coli was diluted 1:250 in fresh media , and grown for 1 . 5h shaking at 250rpm at 37°C . 500μl of this culture was added to either 500μl of spent media , 200μl spent media + 300μl of 1X M63 salts , 500μl 1X M63 salts + appropriate volumes of 20mM pyocyanin , or 500μl 1X M63 salts ( for the control ) , and the cultures were grown for 16h shaking at 250rpm at 37°C . Thus , all samples had only 50% of fresh media with the rest being made up of spent media + 1X M63 salts ( without glucose or casamino acids ) , to enable comparison between the samples . The cultures were diluted in 1X PBS and plated on LB plates before and after growth in the presence of spent media or pyocyanin to obtain the fold change in cell-density . For the E . coli–P . aeruginosa competitions , overnight cultures of the competing strains were diluted 1:250 in fresh media , shaken at 250rpm at 37°C for 90 minutes , and then mixed at a 1:1 ratio . Appropriate dilutions of the strains in PBS were plated on M9 + lac and M9 + citrate plates as selective conditions for E . coli and P . aeruginosa respectively . Appropriate dilutions were also plated after 20 hours of shaking at 250rpm at 37°C . Samples were diluted and plated in triplicate , and the plate counts were averaged across the replicates . The mean cell densities for each competitor were used to calculate the effective growth rate m ( the realized Malthusian parameter ) as the number of doublings over the duration of the competition [52 , 53]: mStrain=log2 ( Nf/Ni ) /t where Ni and Nf are the initial and final cell densities , and t is the duration of the competition . The relative fitness of strain A to its competitor strain B was then calculated as the ratio of their effective growth rates ( mA / mB ) . To measure the transcriptional response of E . coli to P . aeruginosa spent media , we diluted an overnight culture of the E . coli MG1655 into 40ml of media , to a final A600 of 0 . 05 ( ~130-fold dilution ) . The cultures were incubated shaking at 250rpm at 37°C for 2 hours . We added 10ml of WT P . aeruginosa spent media to the flask , and immediately removed 2 . 5ml of the mixture for the 0 minute time-point . Subsequently , we removed a similar aliquot after 20 minutes of shaking at 250rpm at 37°C . Two replicates were performed for this experiment . We added each aliquot immediately to 5ml of the RNAprotect Bacteria Reagent ( Qiagen ) , incubated at room temperature for 5 minutes , and then centrifuged at 5000g for 10 minutes . We discarded the supernatant , and stored the pellets at -80°C . We isolated RNA from these samples using the Total RNA Purification Kit ( Norgen ) , as per the manufacturer’s protocol for bacteria . To label the RNA , we first polyadenlyated it , by combining 25μl of the undiluted RNA with 5μl 10X Poly ( A ) Polymerase Reaction Buffer ( New England Biolabs ) , 5μl 10 mM ATP , and 1μl ( 5 U ) E . coli Poly ( A ) polymerase ( New England Biolabs ) in a total volume of 50μl , and incubating at 37°C for 30 minutes , followed by a 20 minute incubation at 65°C to inactivate the enzyme . We cleaned the samples using the RNeasy Mini Kit from Qiagen , and then labeled 300ng of the 0 minute RNA with cyanine 3-CTP , and 300ng of the 20 minute sample with cyanine 5-CTP using the Low Input Quick Amp Labeling Kit ( Agilent ) . We hybridized the two samples on custom tiling arrays from Agilent ( Design ID 024568 ) [52] , according to the manufacturer’s protocol . To measure the transcriptional response of E . coli to P . aeruginosa spent media in the presence of iron , we diluted overnight cultures of the E . coli MG1655 into 24 . 75ml of media , to a final A600 of 0 . 05 ( ~130-fold dilution ) . The cultures were incubated shaking at 250rpm at 37°C for 105 minutes . We added 20 . 25ml of WT P . aeruginosa spent media and 100μM ferric citrate to the flask , and immediately removed 2ml of the mixture for the 0 minute time-point . Subsequently , we removed a similar aliquot after 20 minutes of shaking at 250rpm at 37°C , and processed the aliquots as above . Two replicates were performed for the experiment . The fluorescence intensities were extracted using the Agilent Feature Extraction Software Version 9 . 5 , using the protocol GE2-v5_95_Feb07 without spike-in controls . The probes were filtered using the IsFound , IsFeatNonUnif , IsBGNonUnif , ISFeatPopnOL , and IsBGPopnOL flags , and discarded if the first flag had a value of 0 , or any of the others had a value of 1 . We used the ‘LogRatio’ value for each probe , and all probes which were on the sense strand of the coding region of a gene were assigned to the gene . The values were averaged across all probes for a gene , and across the two biological replicates for each experiment . We ran iPAGE [13] in continuous mode with various numbers of bins , which did not significantly change the categories identified . The ‘GO annotation’ module was used for the data shown in Fig 2 , and the ‘Transcription factor regulon’ module was used for the data shown in Fig 6 . Whole spent media from WT P . aeruginosa was analyzed using both MALDI-TOF and ESI static nanospray mass spectrometry . The main component seen in the spent media was the same in both spectra , and had a mass of 1335 Daltons . The mass spectrometry analysis was performed at the Protein Core Facility at Columbia University . For the analysis of active fractions of P . aeruginosa spent media , spent media from WT P . aeruginosa was fractionated by HPLC-MS using a C18 reverse-phase column in a linear 5%–95% acetonitrile-water gradient , with a flow-rate of 1 ml/minute for 90 minutes . Fractions were collected every 2 minutes for a total of 45 fractions . The fractions were dried using a Savant DNA120 concentrator and resuspended in 200μl water . The growth of E . coli cells was then tested against 20% ( v/v ) of the resuspended fractions in 100μl media in a 96-well plate , starting from a 1:100 dilution of an overnight culture of E . coli . A sample with no spent media fractions was used as the control . The media was covered with 100μl mineral oil to prevent evaporation . The plate was shaken continuously without the lid at the ‘medium’ setting at 37°C for 22 hours in a Biotek Synergy MX plate reader . The absorbance at 600nm was read , and fractions which inhibited the fold-change in absorbance more than 10-fold compared to the control were identified . Three consecutive active fractions had a mass-spectrometry profile with the same single peak ( shown in S2 Fig ) . The HPLC-MS was performed at the Princeton Proteomics and Mass Spectrometry Core Facility . WT E . coli cells were grown shaking at 250 rpm at 37°C , in the presence of increasing concentrations of the selective agent ( WT P . aeruginosa spent media , ΔpvdJ spent media , or pyocyanin ) , with a daily 50–100 fold dilution into 1 ml fresh media in snap-cap tubes containing the selective agent . 7 daily transfers were carried out for the WT P . aeruginosa spent media ( concentration increasing from 12 . 5–35% ( v/v ) ) , 15 for the ΔpvdJ spent media ( concentration increasing from 30–70% ( v/v ) ) , and 19 for pyocyanin ( concentration increasing from 75–800 μM ) . Following the selections , 2 populations each evolved in the presence of WT spent media and pyocyanin , and 3 populations evolved in the presence of ΔpvdJ spent media were streaked out to obtain individual clones and 2–3 individual clones were analyzed by whole-genome sequencing . We prepared genomic DNA from the evolved clones using the Qiagen DNeasy Blood and Tissue kit , and prepared indexed paired-end libraries from the DNA using the Illumina Nextera XT DNA Library Preparation kit . The samples were pooled and sequenced on a NextSeq 500 ( Illumina ) for 150 cycles . The bcl2fastq package from Illumina was used to demultiplex the data and obtain FASTQ files for each sample . The Illumina adapters were removed using cutadapt [54] and the sequences were trimmed to remove poor quality bases at the ends using trimmomatic [55] . The sequences from each sample were then analyzed using the default settings of breseq-0 . 26 [56] , to identify the mutations in the evolved strain compared to the parental MG1655 background . The samples had an average of 20–45x coverage over the genome . The breseq-0 . 26 pipeline identifies any variants between the given sequence and a reference genome ( in this case Genbank Accession NC_000913 . 2 ) . We only focused on the high-confidence mutations , and do not report the marginal predictions . The parental strain used also has mutations compared to the reference genome ( listed in S5 Table ) , some of which have been previously reported [57] . The mutations identified in the evolved clones compared to the ancestral genome are listed in S1 Table . We generated all single and multiple allele-replacements ( in the parental E . coli MG1655 strain ) using the pKOV plasmid [58 , 59] . We amplified the evolved allele from the appropriate strain , including ~500bp flanking the mutation on both sides , using primers that had 20–25bp overlap with the ends of the pKOV plasmid linearized with BamHI/NotI ( all primer sequences for the constructs are listed in S6 Table ) . The pKOV plasmid was digested with BamHI and NotI ( New England Biolabs ) and the 5 . 6kb fragment was purified using the Zymoclean Gel DNA Recovery Kit . The mutant allele was then cloned into pKOV using Gibson Assembly , and the cloned fragment verified by sequencing . The allele replacement was carried out similar to the original protocol [59] . The allele-replacement plasmid was transformed into the appropriate strain , followed by selection on LB + chloramphenicol plates at 42°C to obtain integrants . Individual colonies were re-streaked out on LB + chloramphenicol plates at 42°C to reduce the background of non-integrants . Chloramphenicol-resistant clones were streaked out on salt-free LB + sucrose plates at 30°C to resolve the integration and individual sucrose-resistant colonies were tested for the allele-replacement by PCR with mismatched primers [60] . Strains with the appropriate allele replacement were streaked out on LB + chloramphenicol plates at 30°C and chloramphenicol sensitive clones were verified for the allele replacement by sequencing the target locus . All single mutants were generated in the WT E . coli strain . For the multiple allele replacements , the above protocol was followed multiple times in succession for each allele . The single gene deletions were obtained from the Keio collection [61] and transferred to the WT E . coli MG1655 background using P1 vir transduction [49] , followed by selection on LB + Kanamycin plates . Kanamycin-resistant clones were tested for the appropriate mutation by PCR , and then cured of the kanamycin resistance cassette by transforming with the plasmid pcp20 [62] , and selecting on LB + Ampicillin plates at 30°C . Ampicillin resistant clones were streaked out on LB plates and incubated at 42°C for 24 hours , and then streaked out on LB , LB + Ampicillin and LB + Kanamycin plates . Ampicillin- and kanamycin-sensitive clones were verified for the deletion by sequencing the target locus . The microarray data have been deposited in the Gene Expression Omnibus ( GEO ) with the accession number GSE72283 . The whole genome sequencing data have been deposited in the Sequence Read Archive , associated with the BioProject PRJNA292975 .
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Bacteria commonly exist in nature as part of large multispecies communities , and their behavior is affected by the surrounding species via secreted molecules or physical contact . Such interactions are poorly understood , and the pathways that actually affect bacterial growth and behavior in any multispecies system have rarely been studied . In this study , we show that the opportunistic pathogen Pseudomonas aeruginosa inhibits the growth of the commensal Escherichia coli , and we use unbiased genome-scale methods to identify the mediators . We find that P . aeruginosa iron-chelating molecules and redox-active phenazines account for all of the E . coli growth inhibition seen in our system . We also evolve E . coli in the presence of the P . aeruginosa antimicrobials and identify multiple pathways that lead to resistance , gaining novel insights into the mechanism of action of these antimicrobial molecules . Thus , our study demonstrates the complexity of even simple two-species bacterial systems and lays down a framework for studying such interactions at the molecular level .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Multifactorial Competition and Resistance in a Two-Species Bacterial System
|
Graded Sonic hedgehog ( Shh ) signaling governs vertebrate limb skeletal patterning along the anteroposterior ( AP ) axis by regulating the activity of bifunctional Gli transcriptional regulators . The genetic networks involved in this patterning are well defined , however , the epigenetic control of the process by chromatin remodelers remains unknown . Here , we report that the SWI/SNF chromatin remodeling complex is essential for Shh-driven limb AP patterning . Specific inactivation of Srg3/mBaf155 , a core subunit of the remodeling complex , in developing limb buds hampered the transcriptional upregulation of Shh/Gli target genes , including the Shh receptor Ptch1 and its downstream effector Gli1 in the posterior limb bud . In addition , Srg3 deficiency induced ectopic activation of the Hedgehog ( Hh ) pathway in the anterior mesenchyme , resulting in loss of progressive asymmetry . These defects in the Hh pathway accompanied aberrant BMP activity and disruption of chondrogenic differentiation in zeugopod and autopod primordia . Notably , our data revealed that dual control of the Hh pathway by the SWI/SNF complex is essential for spatiotemporal transcriptional regulation of the BMP antagonist Gremlin1 , which affects the onset of chondrogenesis . This study uncovers the bifunctional role of the SWI/SNF complex in the Hh pathway to determine the fate of AP skeletal progenitors .
Vertebrate limb anteroposterior ( AP ) patterning is controlled by a diffusible morphogen , Sonic hedgehog ( Shh ) , that is produced from the posteriorly located zone of polarizing activity ( ZPA ) [1] . Cell fate marking studies on mouse limb buds have revealed that Shh signaling regulates identities of limb skeletal elements , such as the ulna and digits 2 to 5 , depending on the signal concentration and time of exposure to that signal [2–4] . During limb bud outgrowth , Shh promotes FGF signaling in the apical ectodermal ridge ( AER ) by mediating the BMP antagonist Gremlin1 ( Grem1 ) that maintains low BMP activity [5] . In vertebrates , binding of Shh to its receptor Patched1 ( Ptch1 ) enables the signal transduction through derepression of signal transducer Smoothened , allowing Gli transcription factors ( Gli1−3 ) to function as activators ( GliA ) [6] . The transcriptional upregulation of Ptch1 serves as a sensitive readout of Shh activity and is required for sequestering diffusible ligands to restrain their spread within the target range [7 , 8] . Notably , the spatiotemporal regulation of Ptch1 expression is important to prevent aberrant activation of Hedgehog ( Hh ) signaling , indicating that Ptch1 functions as a negative regulator of Hh signaling [9 , 10] . Meanwhile , the full-length activators Gli2A and Gli3A contribute to the activation of Shh target genes such as Gli1 , which might act as an indicator of the Shh signaling range in limb development [11–13] . The absence of Shh signaling allows proteolytic processing of bifunctional Gli2 and Gli3 to form the truncated repressors Gli2R and Gli3R ( GliR ) [14 , 15] . Gli3 functions as a major regulator of AP digit patterning , whereas Gli2 has compensatory roles of Gli3 activity [4 , 16–18] . During early limb bud development , Gli3 is required to establish AP polarity through mutual antagonism with Hand2 and is involved in the formation of two signaling centers , the ZPA and AER , by restraining GliA activity [10 , 19–21] . In addition , constitutive Gli3 expression during anterior digit patterning is mediated by repressing cell-cycle genes implicated in the proliferative expansion of Shh-dependent mesenchymal progenitors and by terminating Grem1 expression to initiate chondrogenic differentiation [22 , 23] . Despite recent progress in identifying networks of trans-acting regulators interacting with multiple cis-regulatory modules ( CRM ) that orchestrate limb development , epigenetic control of the developmental process , especially the role of chromatin remodelers , remains poorly understood . The mammalian SWI/SNF chromatin remodeling complex is an ATP-dependent chromatin remodeler that uses the energy of ATP hydrolysis to alter nucleosomal structure [24] . The SWI/SNF complex is a multisubunit complex including core factors such as ATPase Brg1 , tumor suppressor Snf5 , and scaffolding subunit Srg3/mBaf155 ( hereafter referred to as Srg3 ) [25] . In differentiation pathways , SWI/SNF complexes cooperate with histone-modifying factors and transcriptional regulators to mediate both transcriptional activation and repression in response to extracellular stimuli [26] . Here , we show that the SWI/SNF complex is essential for limb AP skeletal patterning . Specific inactivation of limb mesenchymal Srg3 , resulting in defects in SWI/SNF complex activity [27] , fails to upregulate posterior Shh/Gli target gene expression and induces the ectopic activation of target genes in the anterior limb bud after intact establishment of the ZPA . The SWI/SNF complex-mediated modulation of Shh responsiveness and repression of the ectopic Hh pathway determine the AP identities of limb progenitors and regulate the spatiotemporal expression of Grem1 . Thus , bifunctional action of the SWI/SNF complex in the Hh pathway is essential to pattern AP limb skeletal elements .
To study the specific function of the SWI/SNF complex in developing limb buds , we used a conditional loss-of-function allele of Srg3 ( Srg3f/f ) [28] and a Prx1Cre transgene encoding a Cre recombinase that is activated in the early limb bud mesenchyme [29] . Prx1Cre-mediated inactivation of Srg3 in the limb bud mesenchyme was confirmed by measuring the expression of the transcript and protein in control and Srg3f/f;Prx1Cre ( hereafter shortened as Srg3 CKO ) limb buds . Whole-mount RNA in situ hybridization showed the specific clearance of Srg3 transcripts throughout the mesenchyme and western blot analysis confirmed the downregulation of Srg3 proteins with a time lapse between the fore- and hindlimb buds ( S1A and S1B Fig ) . In addition , the downregulation of Brg1 observed in Srg3 CKO limb buds revealed the structural function of Srg3 that stabilizes the SWI/SNF complex ( S1B Fig ) [27] . Skeletal analysis of Srg3 CKO limbs at birth ( P0 ) revealed the requirement of Srg3 for limb development ( Fig 1 ) . In Srg3 CKO forelimbs , the scapula developed poorly with bifurcated or enlarged foramen , aplastic clavicle , stylopod ( humerus ) lacking deltoid tuberosity , and radial agenesis were observed ( Fig 1A and 1B ) . In Srg3 CKO hindlimbs , the proximal skeletons ( pelvic girdle and femur ) were retained normally , whereas zeugopod elements ( tibia and fibula ) were shortened to a similar extent ( Fig 1C and 1D and S1C Fig ) . Both Srg3 CKO fore- and hindlimbs had rudimentary digits that were connected by ossified tissues in the anterior digital tips ( syndactyly ) and exhibited more severe ossification defects in anterior digits than those in posterior digits ( Fig 1B and 1D and S1D Fig ) . Unlike predominant preaxial polydactyly in Srg3 CKO hindlimbs , digit number was variable in Srg3 CKO forelimbs ( 4 or less , 28%; 5 , 34%; 6 or more , 38% , n = 84 ) ( Fig 1E ) . The discrepancy in severity between fore- and hindlimbs lacking Srg3 is a likely consequence of Srg3 deficiency mediated by the onset timing of Prx1Cre activity , which is first activated in the prospective forelimb bud prior to hindlimb budding [29] . Taken together , the malformation of zeugopod elements and variable digit numbers observed in Srg3-deficienct limbs suggest that mesenchymal Srg3 is involved in AP limb skeletal patterning . Given that limb bud development requires formation of the ZPA and AER [5] , we first analyzed the formation of ZPA and AER signaling centers at early stages . In E10 Srg3 CKO forelimb buds , ZPA-Shh expression levels was similar with control expression levels ( n = 8 limb buds analyzed ) , whereas AER-Fgf8 expression was slightly reduced in Srg3 CKO forelimb buds relative to controls ( n = 6 ) ( S2A Fig ) . Although Srg3 inactivation did not significantly alter the formation of signaling centers , subtle changes in the AER suggest that the SWI/SNF complex functions in initial limb development . To understand the mechanism underlying Srg3-mediated limb AP patterning controlled by the counteraction of Shh and Gli3 [16 , 17] , we examined the expression of Shh/Gli target genes . In Srg3 CKO forelimb buds , the expression domains of Gli1 and Ptch1 were normal up to at least E10 ( Gli1 , n = 12; Ptch1 , n = 8 ) , but were ectopically activated at E10 . 25 and at E10 . 75 , respectively , in the anterior mesenchyme ( Gli1 and Ptch1 , n = 6 ) ( Fig 2A and 2B ) . In addition , Gli1 and Ptch1 expression was activated in a graded manner along the AP axis in control forelimb buds , whereas their expression domains including ectopic regions were confined to the distal region in Srg3 CKO forelimb buds over time ( Fig 2A and 2B; Gli1 , n = 5; Ptch1 , n = 6 ) . Importantly , Ptch1 transcripts were not detected in the core mesenchyme of Srg3 CKO forelimb buds ( Fig 2B ) . Gli1 was ectopically activated from around E11 in Srg3 CKO hindlimb buds , but its expression was comparable to control hindlimb buds in the posterior region ( S2B Fig ) . These data suggest that Srg3 both activates and represses Shh/Gli target gene expression in distinct regions . To define whether bifunctional action of Srg3 in the Hh pathway affects the interlinked signaling between the ZPA and the AER [30] , we examined the expression pattern of epithelial-mesenchymal signaling genes during limb bud outgrowth . In Srg3 CKO forelimb buds , the size of the Shh expression domain was subtly reduced ( Fig 2C; n = 6 ) . Grem1 expression expanded anteriorly ( n = 7 ) , whereas Bmp4 expression was reduced in the anterior and posterior mesenchyme of Srg3 CKO forelimb buds ( n = 4 ) ( Fig 2D and 2E ) . AER-Fgf4 expression shifted anteriorly in Srg3 CKO forelimb buds ( Fig 2F; n = 6 ) . Taken together , these data suggest that distinct Hh pathways established by Srg3 deficiency differentially impacted epithelial-mesenchymal signaling in the anterior and posterior mesenchyme . The polarization of nascent limb mesenchyme and establishment of the ZPA are controlled by antagonistic interactions between Hand2 and Gli3 in the posterior and anterior regions , respectively [19–21] . To assess whether Srg3 deficiency in the limb bud mesenchyme affects AP polarity at the prepatterning stage , we examined the expression domains of Hand2 and Gli3 at E9 . 5 . Consistent with the formation of an intact ZPA up to at least E10 ( S2A Fig ) , the expression domains of these positional markers remained comparable to controls in Srg3 CKO forelimb buds ( Fig 2G and 2H; Hand2 , n = 8; Gli3 , n = 9 ) . During limb bud outgrowth , the distribution of posterior markers Hand2 and Hoxd13 was more posteriorly restricted or reduced in Srg3 CKO forelimb buds than in control limb buds , whereas their expression was activated in the anterior region at E10 . 75 ( Fig 2I and 2J; Hand2 , n = 7; Hoxd13 , n = 5 ) . By contrast , the expression domains of anterior markers Alx4 and Pax9 exhibited progressive decreases in Srg3 CKO forelimb buds ( Fig 2K and S3A Figs; Alx4 , n = 6; Pax9 , n = 6 ) [31] . Consistently , the expression of anterior markers was mildly downregulated in Srg3 CKO hindlimb buds ( S3B Fig ) , suggesting that the loss of anterior identity in Srg3-deficient limb buds correlates with the timing of Srg3 inactivation . Taken together , these data indicate that Srg3 deficiency progressively decreased the AP identities of limb progenitors , leading to a disruption of asymmetry after early specification of the AP axis . Inactivation of Srg3 in the limb bud mesenchyme caused progressive alterations in Shh/Gli target gene expression and in AP identity ( Fig 2 ) . To gain further insights into the regulation of Shh/Gli target genes by the SWI/SNF complex , we reexamined the distribution of epithelial-mesenchymal signaling genes at subsequent stages . In Srg3 CKO limb buds , Shh expression was ectopically induced in the anterior margin and subsequently expanded along the distal margin ( Fig 3A and S4A Fig; n = 6 per stage ) . Ectopic Shh signaling reduced Gli3R protein levels by inhibiting Gli3 processing in the anterior mesenchyme of Srg3 CKO forelimb buds ( Fig 3B ) . To test whether the SWI/SNF complex is directly implicated in repressing Shh through the regulation of limb-specific Shh enhancer ZRS ( ZPA regulatory sequence ) , which is responsible for localized expression of Shh [19 , 32] , we performed a chromatin immunoprecipitation ( ChIP ) assay . We did not observe the enrichment of Srg3 at any regions on the ZRS ( S4B Fig ) . This suggests that ectopic Shh expression is indirectly induced in Srg3 CKO limb buds . After ectopic Shh expression was activated , the anteriorly expanded domain of Grem1 at E10 . 5 was divided into two parts: the anterior domain and the posterior domain ( Figs 2D and 3C; n = 7 ) . In E11 . 5 Srg3 CKO forelimb buds , the derepressed expression of Grem1 in the anterior was remarkably reduced in the distal mesenchyme , whereas its posterior domain was distally shifted ( Fig 3C ) . As the posterior domain of Grem1 closer to the AER reflects loss of FGF signaling repressing Grem1 [33] , we assessed AER-Fgf8 expression and found the thinning and posterior loss of AER together with ectopic upregulation in the anterior end ( Fig 3D; n = 6 ) . Hoxd13 expression was also anteriorly expanded and confined to the distal mesenchyme in Srg3 CKO forelimb buds ( Fig 3E; n = 6 ) . Likewise , the expression of Grem1 , Fgf8 and Hoxd13 was ectopically upregulated in the anterior margin of Srg3 CKO hindlimb buds ( S4C–S4E Fig ) . Particularly , distalization of Grem1 and Hoxd13 expression domains was also observed in Srg3 CKO hindlimb buds ( S4C and S4E Fig ) . These data reveal that low Shh response and anterior Hh pathway activity by Srg3 deficiency distalized epithelial-mesenchymal signaling and expanded the anterior digit progenitors . To verify whether Srg3 directly regulates the expression of Shh/Gli target genes in developing limbs , we examined the effects of Srg3 deficiency by transducing a Cre-expressing retroviral vector into Srg3f/f mouse embryonic fibroblasts ( MEFs ) . We focused our analyses on the transcriptional regulation of Shh/Gli target genes Gli1 and Ptch1 . Quantitative real-time PCR ( qPCR ) showed that Srg3-deficient MEFs expressed higher levels of Gli1 and Ptch1 , suggesting that the SWI/SNF complex represses Shh/Gli target genes ( Fig 4A ) . To exclude the possibility that the SWI/SNF complex indirectly represses Shh/Gli target genes by other factors in the MEFs , we treated Srg3-deficient MEFs with the Hh pathway inhibitor cyclopamine [34] . Although cyclopamine reduced Gli1 and Ptch1 expression in control and Srg3-deficient MEFs , Srg3-deficient MEFs expressed higher levels of Gli1 and Ptch1 than control MEFs ( Fig 4A ) . This indicates that the Srg3-containing SWI/SNF complex represses Shh/Gli target genes in , at least , a Hh-free condition . Thus , this finding could corroborate the derepression of Shh/Gli target genes in the anterior mesenchyme of Srg3 CKO limb buds . Next , we examined whether Srg3 is involved in the activation of Gli1 and Ptch1 expression upon Shh stimulation . In the presence of Shh-conditioned medium , Srg3-deficient MEFs displayed severely reduced activation levels of Gli1 ( 101- vs . 16 . 1-fold ) and Ptch1 ( 7 . 79- vs . 2 . 96-fold ) , relative to controls ( Fig 4B ) . These data suggest that Srg3 is required for responses to Shh , supporting findings that the distribution of Shh/Gli target genes was confined to the distal-posterior mesenchyme in Srg3 CKO forelimb buds . During limb development , Gli1 expression requires both transcription factors Gli2 and Gli3 [11] , and Gli proteins regulate the expression of Ptch1 [23 , 35] . We asked whether the bifunctional action of Srg3 requires an interaction with Gli2 and Gli3 to regulate Gli1 and Ptch1 expression in developing limbs . Reciprocal coimmunoprecipitation of Srg3 with Gli2 and Gli3 from E11 . 5 limb bud lysates revealed that Srg3 formed a complex with endogenous Gli2 , Gli3FL , and Gli3R ( Fig 4C ) . Using previously reported Gli-binding sites [23] , we assessed Brg1 and Srg3 occupancy at the regulatory regions of Gli1 and Ptch1 by performing chromatin immunoprecipitation followed by qPCR ( ChIP−qPCR ) in E11 . 5 limb bud extracts . ChIP−qPCR analysis showed that both Brg1 and Srg3 proteins were enriched at the promoter regions of Gli1 and Ptch1 around Gli-binding regions in control limb buds , whereas their occupancies were considerably diminished in Srg3 CKO limb buds ( Fig 4D ) . Furthermore , Brg1 and Srg3 were also enriched near the limb specific enhancer of Ptch1 , which might be required for sensing graded Shh activity ( Fig 4D , region p5 ) [35] . We next investigated whether loss of Srg3 affects the recruitment of Gli2 and Gli3 proteins to the regulatory region of Shh target genes . The occupancy of Gli2 and Gli3 proteins was not significantly changed at the regulatory regions of Gli1 and Ptch1 in Srg3-deficient MEFs ( Fig 4E ) . However , we found that Gli2 occupancy of Gli-binding sites was increased and the occupancy of Gli3 was reduced in E11 . 5 Srg3 CKO limb buds at the promoter regions of Gli1 and Ptch1 relative to controls ( Fig 4F , regions g1−g2 and p1−p4 ) . These data indicate that Gli proteins bound to the Gli-binding sites were not affected by Srg3 deficiency and suggest that their enrichment was differentially influenced by ectopic Shh activity in E11 . 5 Srg3 CKO limb buds . By contrast , we also found the decreased occupancy of Gli2 and Gli3 proteins near the limb specific enhancer of Ptch1 in E11 . 5 Srg3 CKO limb buds ( Fig 4F , region p5 ) , suggesting that GliA contributed by ectopic Shh signals might have no significant effect on this region . We hypothesized that Srg3 deficiency affects histone modification at the promoter regions of Gli1 and Ptch1 because the expression domains of Gli1 and Ptch1 were not expanded throughout Srg3 CKO forelimb buds at E11 . 5 , despite the high GliA and the low GliR condition . Indeed , SWI/SNF complexes functionally interact with histone modifying proteins [36–38] . Furthermore , Shh signaling induces a loss of a repressive mark , trimethylation of histone 3 at lysine 27 ( H3K27me3 ) , by switching histone modifiers from methyltransferase Ezh2 to demethylase Jmjd3 [39] . To test our hypothesis , we compared H3K27me3 enrichment at the promoter regions of Gli1 and Ptch1 upon Srg3 deficiency against a previously reported distribution of H3K27me3 in MEFs [40] . Although Ezh2 and Suz12 , components of the Polycomb repressive complex 2 ( PRC2 ) , were immunoprecipitated with both Gli2 and Gli3 in developing limbs ( S5A Fig ) , there was no global change in H3K27me3 levels in Srg3-deficient MEFs or in the anterior and posterior mesenchyme of Srg3 CKO limb buds ( S5B and S5C Fig ) . At the enriched regions of H3K27me3 on Gli1 and Ptch1 promoters , however , Srg3 deficiency resulted in decreased H3K27me3 level in a basal condition ( Fig 4G , regions g1−g2 and p1−p2 ) . Upon Shh stimulation , on the contrary , H3K27me3 levels at these regions in Srg3-deficient MEFs were significantly higher than in controls ( Fig 4H ) . Taken together , these data suggest that Srg3-containing SWI/SNF complexes contribute to the activation and repression of Shh target genes through changes in the chromatin status of Gli binding regions . Posterior Shh signaling establishes limb skeletal structures including posterior zeugopod elements ( ulna/fibula ) and digits 2 to 5 [2 , 4] . By contrast , loss of Gli3R or ectopic Shh signaling is detrimental to the formation of anterior skeletal structures [17 , 31 , 41] . To determine the role of bifunctional Srg3 in skeletal patterning , we assessed BMP activity , which promotes chondrogenesis at late stages [42] . In the anterior mesenchyme of Srg3 CKO forelimb buds , Msx2 expression , which marks BMP activity [30] , was reduced at E10 . 75 and greatly abolished in the proximal region excluding the distal mesenchyme after ectopic Shh was induced ( Fig 5A; n = 8 per stage ) . By contrast , posterior BMP activity remained low in Srg3 CKO forelimb buds ( Fig 5A ) . Among Bmp ligands , the expression of both Bmp2 and Bmp4 , but not Bmp7 , was diminished in the posterior mesenchyme of Srg3 CKO forelimb buds at E11 . 75 ( Fig 5B and S6 Fig; n = 6 per gene ) . Concurrent downregulation of Bmp2 and Bmp4 , which are required to form the ulna and posterior digits 4 and 5 [43] , could be causally implicated in the hypoplastic posterior skeletal elements of Srg3 CKO forelimbs ( Fig 1 ) . These results indicate that Srg3 deficiency disrupted BMP activities in the anterior and posterior mesenchyme . We next examined the distribution of Sox9 , which marks the condensation of chondrogenic progenitors [44] . Consistent with partially developed proximal skeletal elements , Sox9 expression was diminished in the stylopod primordia of Srg3 CKO forelimb buds prior to E10 . 75 ( Fig 5C , left panel; n = 6 ) . The expression of Hoxa9 , Hoxd9 , and Hoxd10 of paralogous hox groups Hox9 and Hox10 , which control the formation of proximal skeletal elements [45 , 46] , was reduced in the proximal region of Srg3 CKO forelimb buds ( S7A Fig; n = 4 per gene ) . The expression of Irx3 and Irx5 , which are essential for patterning proximal and anterior skeletal structures [31] , was also downregulated in the proximal anterior region of Srg3 CKO forelimb buds ( S7B Fig; n = 4 per gene ) . These data suggest that the Srg3-containing complexes might be required to pattern proximal skeletons . As limb bud outgrowth distally proceeds , Sox9-expressing progenitors were also decreased in the zeugopod and autopod primordia of Srg3 CKO forelimb buds ( Fig 5C , middle and right panel; n = 8 ) . Particularly , Sox9-expressing autopod progenitors in Srg3 CKO forelimb buds did not initiate mesenchymal condensation . Furthermore , Hoxa13 , which delineates the presumptive autopod territories [47] , was distalized and relatively enhanced in the anterior region ( Fig 5D; n = 5 ) . Taken together , deficiency of mesenchymal Srg3 progressively resulted in the loss of Sox9-positive progenitors in zeugopod and autopod primordia and this loss was paralleled by alterations in BMP activity . We tested whether ectopic Shh activity impacts anterior zeugopod development , as demonstrated by the absence of the radius and hypoplastic tibia in Srg3 CKO limbs . We introduced a single conditional allele of Twist1 ( Twist1f/+ ) , which represses Shh expression in the anterior mesenchyme [41] , into the Srg3 CKO background . Twist1f/+;Prx1Cre forelimbs were phenotypically similar to Srg3 CKO forelimbs , except for more severe defects in the scapula ( n = 13/13 ) ( compare S8A Fig with Fig 1B ) . However , Twist1f/+;Srg3f/f;Prx1Cre hindlimbs displayed ossification defects and syndactyly in the anterior autopods similar to those of Srg3 CKO hindlimbs ( Fig 1D and S8B Fig , arrow ) , but Twist1f/+;Srg3f/f;Prx1Cre hindlimbs exhibited a complete absence of tibia ( S8B Fig , arrowhead ) . Consistent with this skeletal phenotype , Sox9-positive progenitors of the tibia primordia were reduced in Twist1f/+;Srg3f/f;Prx1Cre hindlimbs relative to Twist1f/+;Prx1Cre hindlimbs ( S8C Fig; n = 6 ) . In addition , ectopic expression of Gli1 in Twist1f/+;Srg3f/f;Prx1Cre hindlimb buds was activated earlier than in Twist1f/+;Prx1Cre and Srg3 CKO hindlimb buds ( compare S8D Fig with S2B Fig; n = 7 ) . However , early activation of ectopic Shh expression was not detected in Twist1f/+;Srg3f/f;Prx1Cre hindlimb buds ( S8E Fig; n = 6 ) . These data suggest that anterior zeugopod development might be affected by SWI/SNF complex-mediated epigenetic changes including the ectopic Hh pathway and not simply because of the Shh ligand-dependent pathway . This finding supports the idea that the fate of anterior skeletal progenitors is progressively determined . The low to high transition of BMP activity by the timely termination of Grem1 expression is required to initiate condensation and chondrogenic differentiation of proliferative digit progenitors [30 , 43] . To determine the effect of bifunctional action of the SWI/SNF complex on chondrogenic differentiation , Grem1 expression and BMP activity were analyzed at later stages . By E11 . 75 , Grem1 expression began to be downregulated throughout the entire mesenchyme of control forelimb buds , but its decline was not observed and its separated domains became closer than those at E11 . 5 in Srg3 CKO forelimb buds ( compare Fig 6A left panel with Fig 3C , black brackets; n = 8 ) . At E12 . 5 , Grem1 expression was cleared from the presumptive digit territories and confined to the interdigital mesenchyme in control forelimbs , but these spatial pattern changes were not observed in the autopods of Srg3 CKO forelimbs ( Fig 6A , right panel; n = 6 ) . Although Msx2 expression in the anterior margin of Srg3 CKO forelimb autopods was comparable to that in controls , it was undetectable in the interdigital mesenchyme ( Fig 6B; n = 8 ) . By contrast , the increased expression of Grem1 and low BMP activity were observed in the anterior region of Srg3 CKO hindlimb autopods ( S9A Fig ) . We next examined whether delayed temporal kinetics of Grem1 in Srg3 CKO autopods is correlated with chondrogenesis of digit primordia and with digit separation . In Srg3 CKO autopods , the distributions of Sox9 and its target gene Col2a1 revealed delayed mesenchymal condensations , and anterior digit progenitors were relatively less condensed than posterior ones ( Fig 6C and S9B Fig; n = 8 per gene ) . In addition , the comparison of Col2a1 distributions in Srg3 CKO fore- and hindlimb autopods revealed that both the extent of Grem1 propagation and its anterior upregulation caused the sequential onset of chondrogenesis in the posterior and anterior autopods . At this stage , Lysotracker Red staining in Srg3 CKO forelimb autopods showed increases of apoptotic cells in the distal mesenchyme underlying the AER , likely as a consequence of growth defects ( Fig 6D , left panel; n = 6 per stage ) . Furthermore , the reduction of cell death in the interdigital mesenchyme , resulting in soft tissue syndactyly , was observed in the anterior autopods of Srg3 CKO forelimbs at E13 . 5 and hindlimbs at E12 . 5 ( Fig 6D , right panel and S9C Fig ) . Taken together , these data indicate that the spatiotemporal regulation of Grem1 by the SWI/SNF complex is involved in digit determinative processes as well as in cell survival of expanding autopod progenitors .
Our genetic analysis has shown that the SWI/SNF complex is required to modulate Shh responsiveness and repress the ectopic Hh pathway . Although specification of the AP limb bud axis is not affected by conditional inactivation of Srg3 in the limb bud mesenchyme , Srg3 CKO posterior progenitors fail to respond to graded Shh activity , leading to the redistribution of epithelial-mesenchymal signaling to the distal region . In parallel , loss of Srg3 causes the activation of ligand-independent and subsequent ligand-dependent Hh pathway in the anterior mesenchyme , resulting in the loss of anterior identity over time . Our analysis also reveals the dual requirement of the SWI/SNF complex in the Hh pathway for spatiotemporal regulation of Grem1 . Posterior limb skeletal elements are patterned depending on Shh signaling [2 , 4] . By contrast , recent reports have shown that formation of proximal and anterior limb skeletons is inhibited by early Hh activity prior to establishment of the ZPA and by activation of the anterior Hh pathway during limb patterning [10 , 31] . Skeletal phenotypes in Srg3 CKO forelimbs suggest that the Srg3-containing SWI/SNF complex is required for these distinct responses to Hh signaling . It has been known that SWI/SNF complexes and Polycomb group ( PcG ) proteins have antagonistic functions in repressing differentiation-related genes of embryonic stem cells [38] . In anterior limb buds , however , the SWI/SNF complexes appear to function synergistically with PcG proteins to repress the basal expression of Shh target genes . Consistent with our findings , deletion of H3K27 methyltransferase Ezh2 , a catalytic subunit of PRC2 , leads to ectopic expression of Shh target genes in anterior limb buds as well as derepression of Shh target genes in MEFs [39 , 48] . Given that the PRC2 interacts with Gli proteins in developing limbs , PRC2 complexes are also likely to be involved in Gli-mediated repression of Shh target genes in anterior limb buds . In addition to the repressive function in the anterior limb bud , it is assumed that the SWI/SNF complexes also act cooperatively with H3K27 demethylases in activating Shh-induced target genes . It has been demonstrated that the SWI/SNF complexes functionally interact with H3K27 demethylases such as Jmjd3 and Utx in various tissues such as developing lungs and hearts [36 , 37] . Particularly , a recent report showed changes in the epigenetic environment by switching Ezh2-PRC2 to Jmjd3 for Shh-induced target gene activation [39] . This implies that cooperative action between the SWI/SNF complex and Jmjd3 might be required for Shh target gene activation during limb development . Previous studies regarding SWI/SNF components have demonstrated that Snf5 deficiency leads to ectopic expression of Gli1 in developing limbs [49] , and ATPase Brg1 is involved in the regulation of Shh target genes in an ATPase activity-independent manner during neural development [50] . However , we have presented genetic evidence showing bifunctional action of the SWI/SNF complex in distinct territories of limb bud mesenchyme . We do not exclude the possibility that the SWI/SNF complex acts cooperatively with other chromatin regulators such as histone deacetylase ( HDAC ) that is associated with Shh/Gli pathway in developing limbs [50 , 51] . In addition , the phenotypes observed in Srg3 CKO limbs raise the possibility that the SWI/SNF complex likely controls the expression of other transcriptional regulators not specific to the Shh signaling pathway , such as Bmp and Hox genes . Further studies , including genome wide mapping of a H3K27Ac enhancer mark from the anterior and posterior limb buds of Srg3 CKO embryos , will help to elucidate the distinct regulatory functions of the SWI/SNF complex in chondrogenic differentiation and proximal patterning . In Srg3 CKO forelimbs , one notable phenotype is the formation of variable digits , unlike polydactyly in hindlimbs . Concomitant deletion of Gli2 and Gli3 completely eliminates Gli1 expression but does not lead to digit loss in developing limbs [4 , 11 , 18] . Prx1Cre-mediated early deletion of Ptch1 , however , causes oligodactyly and is accompanied by activation of the Hh pathway , whereas late Ptch1 depletion causes polydactyly [9 , 10] . Importantly , we have uncovered the requirement of the SWI/SNF complex for robust expression of Ptch1 . Thus , the core mesenchymal deficiency of Ptch1 expression , resulting from its posterior restriction , may lead to reduced Shh activity sensing and restrain posterior digit formation in Srg3 CKO forelimbs . In Srg3 CKO forelimb buds , the reduced sensing of Shh causes distalization of epithelial-mesenchymal signaling and Hoxa13/Hoxd13-positive presumptive autopod regions , markedly similar to limb buds conditionally lacking Ptch1 [9 , 35] . Recent studies on the mammal species with two to four digits might support variable digit patterning by altered Ptch1 expression observed in Srg3 CKO forelimb buds [35 , 52] . We assume that the extent of digit loss might be dependent on the integrity of the SWI/SNF complex controlled by Srg3 . Meanwhile , ectopic Shh expression was induced in Srg3 CKO limb buds , although there is no enrichment for Srg3 on the ZRS . It has been shown that ectopic expression of Hoxd13 and Hand2 leads to misexpression of Shh in anterior limb buds [53–55] . These molecular changes observed in Srg3 CKO limb buds may result in ectopic expression of Shh , causing preaxial polydactyly . Taken together , variable digit patterning in Srg3 CKO forelimbs appears to occur through combinatorial actions of altered Ptch1 expression and ectopic anterior Hh activity . Both the proximal and distal BMP activities in the anterior mesenchyme of Srg3 CKO forelimb buds are distinct from those of Gli3-deficient limb buds [22] . The comparison of anterior zeugopod development and digit numbers between Srg3 CKO fore- and hindlimbs showed that the dose and exposed duration of ectopic Hh activity negatively impact the differentiation of anterior prechondrogenic progenitors . Our data and previous reports have demonstrated that the expansion of Hh signaling has an inhibitory effect on the formation of proximal and anterior skeletal elements [10 , 31 , 41] . In this regard , the proliferative expansion of anterior progenitors negatively controlled by Gli3 might require time to ensure a sufficient population such as both Irx3- and Irx5-positive early progenitors [22 , 31] . Particularly , the genetic interaction between Srg3 and Twist1 showed synergism in limb skeletal formation such as in anterior zeugopod development . Twist1 not only functions as a Shh repressor but also controls the onset of osteoblast differentiation [41 , 56] . It is possible that the repressive roles of Twist1 in developmental processes might contribute to recruit chromatin regulators such as the PcG , for example , in promoting the epithelial-mesenchymal transition and in suppressing mesenchymal stem cell senescence [57 , 58] . The functional interaction of the SWI/SNF complex with transcriptional regulators acting either as activators or as repressors , which can recruit enzymes that modify active or repressive histone marks , may reveal synergistic and antagonistic actions of gene regulation at the chromatin level . Derepression is one of the regulatory mechanisms underlying limb bud patterning . Our data highlight the sustained requirement of the SWI/SNF complex for transcriptional regulation of Grem1 , a major Gli target gene controlled by derepression [23] . The expression of Grem1 in the limb bud is severely reduced in Shh−/− mutants and symmetrically expanded in both Gli3−/− and Shh−/−;Gli3−/− mutants [16 , 17 , 59] . Compared with previous observations , Grem1 expression in Srg3 CKO forelimb buds is dynamically redistributed , possibly a consequence of the reconstitution of the GliA/GliR gradient by low Shh responsiveness and ectopic Shh activity . Consistently , it has recently been suggested that limb-specific enhancers integrated by multiple posterior GliA- and anterior GliR-dependent CRMs regulate the transcriptional activity of Grem1 [60] . Furthermore , the combined region of Grem1 expression domains in Srg3 CKO forelimb buds indicates that the definitive digit identity in this region could be progressively determined by altered Hh activity ( Fig 6 ) . Thus , our analysis suggests that bifunctional action of the SWI/SNF complex in the Hh pathway is essential for spatiotemporal regulation of Grem1 that mediates AP skeletal patterning elicited by GliA and GliR functions [18 , 22] . We have demonstrated that the SWI/SNF complex plays decisive roles in conferring graded Shh signaling upon developing limb progenitor cells . The SWI/SNF complex influences the progression of interlinked morphogen signaling pathways by modulating Shh responsiveness in the posterior limb bud and by repressing the Hh pathway in Shh-free regions . Our study showing the effects of epigenetic regulation by the SWI/SNF chromatin remodeling complex on limb patterning provides insights into deciphering developmental processes directed by morphogen gradients .
All experiments with animals were performed according to the guidelines established by the Seoul National University Institutional Animal Care and Use Committees ( SNUIACUC ) . SNUIACUC approved this study ( approval number: SNU-130503-2 ) . CO2 gas was used for animal euthanasia . Generation of mice carrying a conditional allele of Srg3 ( Srg3f/f ) was previously described [28] . Srg3f/f , Prx1Cre [29] , and Twist1f/f mice [41] were bred and maintained on a C57BL/6J genetic background . For all experiments , Srg3+/+;Prx1Cre and Srg3f/+;Prx1Cre mice and embryos harboring a Prx1Cre transgene were used as wild-type controls . The transcript distributions were assessed by whole-mount in situ hybridization according to the standard procedures as described [61] with the following minor modifications: embryos were permeabilized in proteinase K ( 10 μg/ml ) in PBST at room temperature for 11 min ( E9 . 5−E10 . 5 ) , 14 min ( E10 . 5−E11 . 5 ) or 17 min ( E11 . 5−E12 . 5 ) for analysis of limb mesenchyme and briefly for 3 min regardless of age for analysis of AER . All probes were linearized with the appropriate restriction enzyme and labeled using digoxigenin RNA labeling mix ( Roche ) with the appropriate polymerase ( T7 , T3 or SP6 ) . Shh , Gli1 , Bmp2 , Bmp4 and Bmp7 probes were kindly provided by Y . Kong ( Seoul National University ) . Fgf4 ( Addgene plasmid #22085 ) [62] and Fgf8 ( Addgene plasmid #22088 ) [63] probes were gifts from G . Martin . Hoxa9 , Hoxd9 and Hoxd10 probes were generously provided by D . Wellik and Irx3 and Irx5 probes were provided by C . Hui . Other probes were amplified by PCR from cDNA fragments encompassing at least two exons ( about 400−600 bp ) of target genes and cloned into pGEM-T vectors ( Promega ) . All representative expression patterns were obtained by analyzing at least three independent embryos per probe . Skeletal preparations and detection of apoptotic cells were performed as previously described [19 , 30] . For analysis of skeletal structures , samples were collected at E14 . 5 and P0 and cartilages and bones were stained with Alcian Blue and Alizarin Red , respectively . Distribution of apoptotic cells in whole limb buds was analyzed using Lysotracker Red ( Molecular Probes L-7528 , Invitrogen ) . Primary Mouse Embryonic Fibroblasts ( MEFs ) prepared from E13 . 5 Srg3f/f embryos , HEK293T , and Phoenix-eco cells were grown in DMEM medium ( WelGENE ) supplemented with 10% fetal bovine serum ( FBS ) . For generation of Srg3-deficient MEFs , Phoenix-eco packaging cells were transfected with retroviral vectors expressing GFP alone ( Empty ) as a control or Cre-recombinase ( Cre ) by calcium phosphate method and their retroviral supernatants were harvested 2 d after transfection . MEFs were infected with the retroviral supernatant by spin infection for 90 min at 2500 rpm in the presence of 8 μg/ml polybrene . For inhibition of Hh signaling , MEFs were treated with 5 μM cyclopamine dissolved in ethanol vehicle for 24 h . For Shh stimulation , HEK293T cells were transiently transfected with ShhN expressing vector ( kindly provided by M . Kang , Korea University Guro Hospital ) . Shh conditioned medium produced from transfected HEK293T cells was replaced with DMEM containing 2% FBS 24 h before harvesting and filtering of medium , and then added to MEFs for 24 h . Shh stimulated or cyclopamine treated MEFs were harvested for qPCR . IP and western blotting were performed as previously described [19 , 28] . Limb bud lysates were immunoprecipitated or detected with following antibodies: Gli2 ( R&D systems ) , Gli3 ( R&D systems ) , α-tubulin ( Sigma ) , Ezh2 ( BD transduction ) , Suz12 ( Cell signaling ) , H3K27me3 ( Millipore ) , Histone H3 ( Abcam ) , and rabbit polyclonal IgG ( Millipore ) . Antisera for Brg1 and Srg3 were raised from rabbits in our laboratory . The band density of Gli3R level was quantified using ImageJ software ( NIH ) and normalized to α-tubulin as a loading control . E11 . 5 control and Srg3f/f;Prx1Cre limb buds were dissected in cold PBS and minced with a douncer and MEFs were trypsinized . Dissociated tissues and MEFs were crosslinked in 1% formaldehyde ( Sigma ) for 10 min on a rotator at RT and were lysed for 10 min on ice with SDS lysis buffer ( 1% SDS , 50mM Tris-Cl ( pH 8 . 1 ) , 10mM EDTA ) . Lysates were sonicated to an average length of 200–500 bp using a Bioruptor sonicator and diluted 10-folds in dilution buffer ( 20mM Tris-Cl ( pH 8 . 1 ) , 150mM NaCl , 1% Triton X-100 , 2mM EDTA ) . To reduce nonspecific background , samples were precleared for minimally 1 h with salmon-sperm DNA/Protein-A or G agarose ( 50% slurry , Millipore ) . Precleared lysates were incubated overnight on a rotator at 4°C with anti-Brg1 , anti-Srg3 , anti-H3K27me3 ( Millipore ) , anti-Gli2 ( abcam ) , anti-Gli3 ( R&D systems ) or with isotype-control anti-rabbit IgG ( Millipore ) as a negative control . Washing , elution and reverse-crosslinking of DNA-immunocomplexes and DNA purification were enriched as previously described [28] . Purified DNA was analyzed by qPCR with the following primers: g1 forward: 5’-CCGGCACCCCCTCTCTAG-3’ , g1 reverse: 5’- GGCTCTTCCCGCTCACTTC-3’ , g2 forward: 5’-TTGCTCCCCGCTCTGAATC-3’ , g2 reverse: 5’-CTTGATGCTGTTCCCAAAGCT-3’ , p1 forward: 5’-AGGACACAATGCACCTGAGG-3’ p1 reverse: 5’-AGGTCTTGTGGGTGCCTCTA-3’ p2 forward: 5’-TAGTGGCGAGAATGACAGCG-3’ p2 reverse: 5’-TTTCTCCCTACCAACCGCAG-3’ , p3 forward: 5’-ACACACTGGCGCACTATCCA-3’ , p3 reverse: 5’-CCTCAAGCTGCAGCAAATACTG-3’ , p4 forward: 5’-GAATGGGAGAGGGAGGAAAGAT-3’ , p4 reverse: 5’-GCGGGAGCTCAGTTAGGAAA-3’ , p5 forward: 5’-TCTTCCAGCATGCTTACCTCTTT-3’ , p5 reverse: 5’-GCTTGGCCGCTGTAATCAAA-3’ .
|
Anteroposterior ( AP ) limb skeletal patterning is directed by morphogen Sonic hedgehog ( Shh ) signaling . Modulation of Shh responsiveness and repression of Shh pathway activity in distinct limb bud regions are essential for proper limb skeletal formation . Although the genetic networks involved in these processes have been identified , epigenetic control by chromatin remodeler remains unknown . We have unraveled the function of the SWI/SNF chromatin remodeling complex in Shh signaling during limb patterning . The complex activates the responses of the posterior limb progenitors to Shh , however , it represses the signaling in the anterior limb progenitors . Here we provide genetic evidence for the dual requirement of the SWI/SNF complex in Shh signaling to pattern AP limb skeletal elements .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"gene",
"regulation",
"dna",
"transcription",
"developmental",
"biology",
"regulator",
"genes",
"gene",
"types",
"epigenetics",
"chromatin",
"embryology",
"limb",
"buds",
"chromosome",
"biology",
"gene",
"expression",
"hedgehog",
"signaling",
"signal",
"transduction",
"cell",
"biology",
"genetics",
"biology",
"and",
"life",
"sciences",
"cell",
"signaling",
"bmp",
"signaling"
] |
2016
|
Anteroposterior Limb Skeletal Patterning Requires the Bifunctional Action of SWI/SNF Chromatin Remodeling Complex in Hedgehog Pathway
|
Coronary heart disease ( CHD ) is the leading cause of mortality in African Americans . To identify common genetic polymorphisms associated with CHD and its risk factors ( LDL- and HDL-cholesterol ( LDL-C and HDL-C ) , hypertension , smoking , and type-2 diabetes ) in individuals of African ancestry , we performed a genome-wide association study ( GWAS ) in 8 , 090 African Americans from five population-based cohorts . We replicated 17 loci previously associated with CHD or its risk factors in Caucasians . For five of these regions ( CHD: CDKN2A/CDKN2B; HDL-C: FADS1-3 , PLTP , LPL , and ABCA1 ) , we could leverage the distinct linkage disequilibrium ( LD ) patterns in African Americans to identify DNA polymorphisms more strongly associated with the phenotypes than the previously reported index SNPs found in Caucasian populations . We also developed a new approach for association testing in admixed populations that uses allelic and local ancestry variation . Using this method , we discovered several loci that would have been missed using the basic allelic and global ancestry information only . Our conclusions suggest that no major loci uniquely explain the high prevalence of CHD in African Americans . Our project has developed resources and methods that address both admixture- and SNP-association to maximize power for genetic discovery in even larger African-American consortia .
Coronary heart disease ( CHD ) is the leading cause of mortality in African-American men and women [1] . The risk factors for CHD in African Americans are similar to those reported in Caucasians , but their relative impact varies between the two ethnic groups . Multiple studies have reported that smoking , type-2 diabetes ( T2D ) , hypertension , and LDL- and HDL-cholesterol ( LDL-C and HDL-C ) are significant independent risk factors for CHD in African Americans [2]–[5] . In general , hypertension and LDL-C have a larger and smaller impact on CHD risk , respectively , in African Americans compared with Caucasians [3] . There is also extensive evidence of the role of genetic factors in the familial aggregation of CHD and its predictors in African Americans [6] . However , the underlying genes remain largely unknown . Recent advances in genome-wide association studies ( GWAS ) have made spectacular advances in identifying genes contributing to numerous common chronic diseases in Europeans and European Americans [7] . There are multiple loci convincingly associated with CHD risk in Caucasians , including many genes involved in lipid metabolism , as well as novel chromosomal regions that do not appear to contribute to risk through traditional risk factors [7]–[14] . However , there have been no large-scale GWAS for CHD and its risk factors in African Americans . GWAS in African Americans is important because new genes may be identified as a result of genetic variation private to populations of African-descent , differences in allele frequencies and in patterns of linkage disequilibrium ( LD ) , differences in the relative impact of risk factors to disease , or differences in gene-environment interactions . Here we report a large ( and for most phenotypes first ) GWAS for CHD , type-2 diabetes ( T2D ) , hypertension , LDL-C and HDL-C , and smoking in 8 , 090 African Americans as part of the National Heart , Lung , and Blood Institute ( NHLBI ) -sponsored Candidate gene Association Resource ( CARe ) Project [15] .
We genotyped 909 , 622 single nucleotide polymorphisms in 9 , 119 African Americans from the ARIC ( N = 3 , 269 ) , CARDIA ( N = 1 , 209 ) , CFS ( N = 704 ) , JHS ( N = 2 , 200 ) , and MESA ( N = 1 , 737 ) population-based cohorts , on the Affymetrix Genome-Wide Human SNP Array 6 . 0 platform . Genotypes were called using Birdseed v1 . 33 [16] , and stringent quality-control filters were applied ( Tables S1 and S2 ) . For samples that passed quality control ( N = 8 , 100 ) , principal component analysis ( PCA ) using EIGENSTRAT [17] revealed only ten population outliers across all cohorts; these samples were also excluded from the analysis ( Text S1 and Figure S1 ) . Overall , a total of 8 , 090 African Americans with very high genotype quality ( average genotype success rate of 99 . 65% ) were available for analysis . The demographics of these participants by cohort are shown in Table 1 . To increase our coverage of common genetic variation and statistical power , and to facilitate comparisons across genotyping platforms , we imputed genotypes in the CARe African-American populations using MACH taking into account the admixed nature of the population ( Text S1 ) [18] , [19] . For all cohorts except CFS , single marker genetic association tests were performed by study using PLINK v1 . 06 [20] under an additive genetic model . We used linear regression for quantitative traits ( HDL-C , LDL-C , and smoking ) and logistic regression for dichotomous phenotypes ( CHD , hypertension , and T2D ) . For CFS , family structure was modeled using linear mixed effects ( LME ) models and generalized estimating equations ( GEE ) for quantitative and dichotomous phenotypes , respectively [21] . For all analyses , the first ten principal components were used as covariates to account for global admixture and population stratification . A detailed description of the analysis methods and the phenotypic definitions used can be found in Text S1 . Power calculations for the different phenotypes analyzed are summarized in Table S3; we have excellent power to find strong signals , but low to modest power for variants with weak phenotypic effects . The inflation factors ( λs ) observed were all near unity ( Table S4 ) , suggesting that most confounders , including population stratification , were well-controlled . We applied genomic control to the individual cohorts' results and combined them using the inverse variance meta-analysis method [22] . Inflation factors of the meta-analysis results were modest and were again scaled using genomic control ( Table S4 ) . Quantile-quantile ( QQ ) plots of the six different meta-analyses after double genomic control corrections show that the test statistics follow the null expectations , except for the HDL-C and LDL-C meta-analyses , which show an upward departure from the null distributions at the lowest P-values ( Figure 1 ) . This departure is caused by known genetic variants with large effects on lipid levels ( Figure S2 ) . The main goal of this study was to identify new genetic risk factors for CHD and its predictors in African Americans . For five traits analyzed ( we could not identify African-American replication cohorts for smoking ) , we identified SNPs with the strongest evidence of association in the CARe meta-analysis – SNPs were selected after accounting for LD to limit association signals redundancy – and sought replication using in silico data or direct genotyping in independent African-American cohorts ( Table 1 ) . Combined results from a meta-analysis of the CARe and replication data are presented in Tables S5 , S6 , S7 , S8 , S9 and summarized in Table 2 . We identified one novel locus that reached the generally accepted level for genome-wide significance ( P≤5×10−8 ) : SNP rs7801190 in the potassium/chloride transporter gene SLC12A9 and hypertension ( OR = 1 . 31 , combined P = 3 . 4×10−8 ) . Despite reaching genome-wide significance , we are cautious in highlighting this association because it was identified using imputed genotypes ( imputation quality r2_hat = 0 . 70 ) and the replication result , also obtained by imputation , was not statistically significant ( P = 0 . 29 ) . Indeed , when we directly assessed the quality of the imputation by directly genotyping rs7801190 in ARIC African-American samples ( N = 2 , 572 ) , we failed to validate the observed association with hypertension . This result suggests that the association between rs7801190 and hypertension status observed in the CARe African-American datasets is likely due to chance . To validate our phenotype modeling and analytical strategy , we sought to replicate in the CARe meta-analyses genetic associations previously reported in populations of European ancestry . We retrieved all index SNPs associated at genome-wide significance level with CHD , T2D , hypertension , HDL-C , LDL-C , and smoking in Caucasians as well as their proxy SNPs ( defined as markers with an r2≥0 . 5 with the index SNPs in HapMap samples of European ancestry ( CEU ) ) ( Table S10 ) [23] . We then determined whether there was also evidence of association for the same signals in this large sample of African Americans . We detected modest to strong evidence of replication for one locus associated with CHD , one locus with T2D , nine with HDL-C , and six with LDL-C ( Table 3 and Table S11 ) . We did not replicate signals associated with smoking or hypertension . Furthermore , the top ten associated SNPs in a recent hypertension GWAS performed in African Americans [24] were not associated with hypertension in the CARe meta-analysis ( different direction of effect and/or P>0 . 05 ) . Since these hypertension association signals did not replicate in the original publication , non-replication here may result from their being falsely positive in the original report . Although replication of some of the above loci in African-derived populations had been reported previously [25] , for most of them , the CARe results represent the first replication in populations of African ancestry . Taking advantage of the LD patterns in African Americans ( LD breakdown over shorter distances compared with Caucasians ) , we assessed whether we could fine-map some of the associations previously reported in Caucasians . For this , we evaluated SNPs that were correlated with the index SNP in HapMap CEU ( r2≥0 . 5 ) , but largely uncorrelated with it in HapMap samples of African descent ( YRI ) ( r2≤0 . 1 ) . In most cases , the same signals were responsible for the associations in Caucasians and African Americans ( Table 3 and Table S11 ) . However , we found five examples where the predominant association signals were at SNPs strongly correlated with the index SNPs in HapMap CEU but weakly or not correlated with the index SNPs in HapMap YRI: the CDKN2A/CDKN2B locus for CHD and the FADS1-3 , PLTP , LPL , and ABCA1 loci for HDL-C ( Table S12 ) . Using available genetic association results for myocardial infarction [10] and HDL-C [26] in Caucasians , we illustrate in Figure 2 and Figure S3 how our results in African Americans can help refine association signals . For instance , for the FADS locus , the index SNP in Caucasians ( rs174547 ) is in strong LD with the top SNP in the CARe African-American meta-analysis ( rs1535 ) in HapMap CEU ( r2 = 1 ) but not in HapMap YRI ( r2 = 0 . 09 ) . The region of strong LD around rs174547 in HapMap CEU is 113 kb wide and includes the three FADS genes , whereas rs1535 , located in an intron of FADS2 , is in strong LD with no other markers in HapMap YRI ( Figure 2 ) . Comparison of association signals regionally in African Americans and European-derived individuals can thus be useful in two ways: ( 1 ) they may suggest smaller chromosomal regions for re-sequencing experiments to attempt to identify causal variant ( s ) that underlie shared signals between African- and European-derived chromosomes or ( 2 ) they may indicate that the index SNPs for African and European populations are linked to distinct causal variants . A third potentially interesting result from trans-ethnic comparison of association results is the identification of ethnic-specific association signals . For instance , at the ABCA1 locus , three SNPs in LD ( rs4743763 , rs4149310 , and rs2515629 ) are associated with HDL-C in CARe African Americans ( P<1×10−5 ) , but not in Caucasians ( Figure S3D ) . The optimal analytical strategy for GWAS in recently admixed populations has not been established . In African Americans , an ideal test statistic would incorporate both genotype information as traditionally used in GWAS , but also , at each locus , the probability that a given individual has zero , one , or two copies of a European ( or African ) chromosomal segment . This method would be particularly informative in a case where , for example , the causal allele is not in LD with any markers on the genotyping array , but is at higher frequency on one ancestral background . To explore the benefits of such a statistical framework , we designed and applied a novel method that combines evidence of association from genotypes and local ancestry estimates; the method is described in details in Text S1 . Briefly , we use a panel of ancestry informative markers across the genome and a new implementation of the software ANCESTRYMAP [27] to estimate , for each of the CARe African Americans genotyped , the probabilistic proportion of European ancestry ( 0–100% ) at the locus for each of the ∼900 , 000 SNPs genotyped on the Affy6 . 0 platform . For each SNP , we can then compute association between the phenotype and both the SNP genotype and the SNP estimate of local ancestry to generate a combined score that summarizes allelic variation and admixture . This method was used to produce the association data presented in Table 4 . Our method to assess combined SNP- and ancestry-association was tested explicitly on CHD and its risk factors in the CARe African-American samples ( Figures S4 , S5 ) . For each SNP , we compared the test statistic obtained using the SNP-alone or the SNP+admixture information ( in both methods , global ancestry is included as a covariate ) , focusing on markers that would not have been prioritized for follow-up replication when considering only SNP genotype association results ( Figure S6 ) . Across the six phenotypes , we identified 12 SNPs outside the previously known loci with a P≤1×10−6 in this SNP+admixture test statistic ( Table 4 ) . Most of these SNPs have a large allele frequency difference between the HapMap CEU and YRI individuals , suggesting that local ancestry might confound simple SNP association testing . For instance , the frequency of the C-allele at rs8078633 near the APPBP2 gene is 100% and 18% in CEU and YRI , respectively . The association between this SNP and HDL-C levels is weak when considering only allelic variation ( P = 0 . 98 ) but becomes highly significant when evidence from the genotype and the estimate of local ancestry is combined ( P = 3 . 6×10−7 ) ( Table 4 ) . This composite approach also identified a SNP near the phospholipase B1 gene ( PLB1 ) that is strongly associated with LDL-C levels ( P = 4 . 1×10−8 ) , but that would not have been noticed using traditional genotype-only association testing ( P = 0 . 23 ) ( Table 4 ) . As more large-scale GWAS in individuals of African ancestry are completed , it will be important to replicate these results .
Most large-scale genetic efforts to identify risk factors for CHD have focused so far on populations of European ancestry . Given the prevalence of the disease in African Americans , and the development of better genotyping platforms that more completely survey common genetic variation in African-derived genomes [16] , it is now both pertinent and timely to investigate the genetics of CHD in populations of African ancestry . The CARe Project was launched four years ago with the specific goal to create a resource for association studies of various heart- , lung- , and blood-related phenotypes across different ethnic groups [15] . In this article , we present results from the largest GWAS to date for CHD and its risk factors in African Americans . Despite being the largest , the size of our GWAS is modest compared to that of some European-derived consortia . As a consequence , we had limited discovery power and did not identify novel loci specifically associated with CHD or its risk factors that reach genome-wide significance in our African-American dataset . We also attempted to replicate in the CARe African-American participants genetic associations to CHD and its risk factors previously identified in Caucasians . We could replicate 17 of those associations; for many of them , this was the first replication in a non-European-derived population ( Table 3 ) . For five of these 17 associations , we showed how cross-ethnic comparisons of genetic association results may help refine genomic intervals carrying causal alleles ( Figure 2 and Figure S3 ) . There were , however , a large number of loci originally found in Caucasians that were not replicated in the CARe meta-analyses presented in this manuscript ( Table S11 ) . Because our sample size was relatively modest , that we used stringent statistical thresholds to declare replication in order to control our false positive rate , and that effect sizes could be weaker for given loci across different ethnic groups , our limited power probably explains why many loci did not replicate in the CARe African Americans . Alternatively , some of these non-replications could be explained by the absence of variants within these loci associated with these traits in African Americans . Our data does not allow us to distinguish these two possibilities , and larger replication studies in African-American cohorts will be needed to draw informative conclusions . Taken together , our results suggest that CHD risk in African Americans is not influenced by loci with major phenotypic effect on disease risk , but rather by multiple variants of weak effect , as we have observed for CHD and other traits in Caucasians . Because opportunities for replication and meta-analysis with other African-American cohorts are evolving rapidly , the CARe dataset is an outstanding public resource that provides a strong base for discovery of genetic contributors to CHD in non-European-derived populations .
All participants gave informed written consent . The CARe project is approved by the ethic committees of the participating studies and of the Massachusetts Institute of Technology . African-American participants for the GWAS were drawn from five population-based studies: Atherosclerosis Risk in Communities ( ARIC; N = 3 , 269 ) , Coronary Artery Risk Development in young Adults ( CARDIA; N = 1 , 209 ) , Cleveland Family Study ( CFS; N = 704 ) , Jackson Heart Study ( JHS; N = 2 , 200 ) , and Multi-Ethnic Study of Atherosclerosis ( MESA; N = 1 , 737 ) . Although longitudinal data is available for most participants , only information collected at recruitment was considered in this GWAS . Replication results for top SNP associations were obtained using in silico or de novo genotyping from four African-American and African-Caribbean population-based cohorts ( Health , Aging , and Body Composition Study ( Health ABC; N = 1 , 119 ) , National Health and Nutrition Examination Survey III ( NHANES III , N = 1 , 720 ) , Jamaica Spanish Town ( SPT , N = 1 , 746 ) and Jamaica GXE ( N = 969 ) , one nested case-control panel from the population-based Multiethnic Cohort ( MEC , N = 2 , 184 ) , and two case-control panels ( Cleveland Clinic , N = 620 , and PennCATH , N = 491 ) . A detailed description of all cohorts and phenotype definitions used in this study is provided in Text S1 . All discovery samples ( GWAS ) were genotyped on the Affymetrix Genome-Wide Human SNP array 6 . 0 according to the manufacturer's protocol . For replication , the MEC samples were genotyped by Taqman , and the NHANES III , Jamaica SPT , Jamaica GXE , Cleveland , and UPENN samples were genotyped using Illumina's Oligos Pool All ( OPA ) technology . The Health ABC samples were genotyped on the Illumina Human1M-Duo BeadChip array as part of an independent GWAS; SNP results for the replication of the CARe findings were extracted and analyzed . Several quality control ( QC ) filters were applied to the genome-wide genotype data: DNA concordance checks; sample and SNP genotyping success rate ( >95% , minor allele frequency ≥1% ) ; sample heterozygosity rate , identity-by-descent analysis to identify population outliers ( Figure S1 ) , problematic samples , and cryptic relatedness; Mendel errors rate in CFS and JHS , and SNP association with chemistry plates . For replication , SNPs and samples with genotyping success rate <90% were excluded . Because of the admixed nature of the participants , SNPs were not removed solely because they departed from Hardy-Weinberg equilibrium . A detailed description of the quality control checks applied to the discovery ( GWAS ) and replication genotyping data can be found in Text S1 . To increase coverage and facilitate comparison with other datasets , we imputed genotype data using MACH v1 . 0 . 16 [19] . We built a panel of reference haplotypes using HapMap phase II CEU and YRI data , and imputed SNP genotypes using all Affymetrix 6 . 0 SNPs that passed the QC steps described above . Using and independent dataset of ∼12 , 000 SNPs genotyped on the same DNA but with a different platform , we estimated an allelic concordance rate of 95 . 6% ( Text S1 ) . SNP-only based genetic association analysis of quantitative ( HDL-C , LDL-C , smoking ) and dichotomous ( coronary heart disease , type-2 diabetes , hypertension ) traits were carried out using linear and logistic statistical framework , respectively , in PLINK ( unrelated cohorts: ARIC , CARDIA , JHS , MESA , UPENN , Cleveland , MEC , NHANES III , and Health ABC ) or using R scripts that model family structure ( related cohort: CFS ) [28] . For the cohorts with genome-wide genotyping data available , the first ten principal components were included in each analysis to account for population stratification and admixture . The method to estimate local ancestry was implemented in ANCESTRYMAP and is described in details in Text S1 . To combine allelic and local ancestry information ( Table 4 ) , we calculated a chi-square statistic with two degrees-of-freedom . Association results were combined across cohorts using an inverse variance meta-analysis approach as implemented in metal . CARe: http://www . broadinstitute . org/gen_analysis/care/index . php/Main_Page; MACH: http://www . sph . umich . edu/csg/abecasis/MACH; METAL: http://www . sph . umich . edu/csg/abecasis/Metal/index . html; PLINK: http://pngu . mgh . harvard . edu/~purcell/plink .
|
To date , most large-scale genome-wide association studies ( GWAS ) carried out to identify risk factors for complex human diseases and traits have focused on population of European ancestry . It is currently unknown whether the same loci associated with complex diseases and traits in Caucasians will replicate in population of African ancestry . Here , we conducted a large GWAS to identify common DNA polymorphisms associated with coronary heart disease ( CHD ) and its risk factors ( type-2 diabetes , hypertension , smoking status , and LDL- and HDL-cholesterol ) in 8 , 090 African Americans as part of the NHLBI Candidate gene Association Resource ( CARe ) Project . We replicated 17 associations previously reported in Caucasians , suggesting that the same loci carry common DNA sequence variants associated with CHD and its risk factors in Caucasians and African Americans . At five of these 17 loci , we used the different patterns of linkage disequilibrium between populations of European and African ancestry to identify DNA sequence variants more strongly associated with phenotypes than the index SNPs found in Caucasians , suggesting smaller genomic intervals to search for causal alleles . We also used the CARe data to develop new statistical methods to perform association studies in admixed populations . The CARe Project data represent an extraordinary resource to expand our understanding of the genetics of complex diseases and traits in non-European-derived populations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/complex",
"traits",
"cardiovascular",
"disorders/hypertension",
"genetics",
"and",
"genomics/population",
"genetics",
"cardiovascular",
"disorders/coronary",
"artery",
"disease",
"cardiovascular",
"disorders/myocardial",
"infarction",
"diabetes",
"and",
"endocrinology/type",
"2",
"diabetes"
] |
2011
|
Genome-Wide Association Study of Coronary Heart Disease and Its Risk Factors in 8,090 African Americans: The NHLBI CARe Project
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Schistosomiasis has been reported in 78 endemic countries and affects 240 million people worldwide . The digenetic parasite Schistosoma mansoni needs fresh water to compete its life cycle . There , it is susceptible to soluble compounds that can affect directly and/or indirectly the parasite’s biology . The cercariae stage is one of the key points in which the parasite is vulnerable to different soluble compounds that can significantly alter the parasite’s life cycle . Molluscicides are recommended by the World Health Organization for the control of schistosomiasis transmission and Euphorbia milii latex is effective against snails intermediate hosts . We used parasitological tools and electron microscopy to verify the effects of cercariae exposure to natural molluscicide ( Euphorbia milii latex ) on morphology , physiology and fitness of adult parasite worms . In order to generate insights into key metabolic pathways that lead to the observed phenotypes we used comparative transcriptomics and proteomics . We describe here that the effect of latex on the adult is not due to direct toxicity but it triggers an early change in developmental trajectory and perturbs cell memory , mobility , energy metabolism and other key pathways . We conclude that latex has not only an effect on the vector but applies also long lasting schistosomastatic action . We believe that these results are of interest not only to parasitologists since it shows that natural compounds , presumably without side effects , can have an impact that occurred unexpectedly on developmental processes . Such collateral damage is in this case positive , since it impacts the true target of the treatment campaign . This type of treatment could also provide a rational for the control of other pests . Our results will contribute to enforce the use of E . milii latex in Brazil and other endemic countries as cheap alternative or complement to mass drug treatment with Praziquantel , the only available drug to cure the patients ( without preventing re-infection ) .
Schistosomiasis is a chronic parasitic disease caused by trematodes of the Schistosoma genus . It has been reported in 78 endemic countries and affects more than 240 million people worldwide [1] . If left untreated , it is associated with fibrosis , abdominal pain , diarrhea and anemia , resulting in disabling patient symptoms [2] . The main symptoms of this disease are caused by the body's reaction to the parasites' eggs . In the case of intestinal schistosomiasis , caused by Schistosoma mansoni , the liver is the main target that can undergo enlargement frequently associated with an accumulation of fluid in the peritoneal cavity and hypertension of the abdominal blood vessels [2] . Controlling or preventing morbidity in patients has not been a very successful strategy to limit schistosomiasis transmission in high-risk areas . Optimal disease prevention can occur only when parasite infection and/or reinfection is effectively impeded [3] . In this sense , the World Health Organization ( WHO ) published a report of the WHO Strategic and Technical Advisory Group for Neglected Tropical Diseases ( NTD ) [4] . It addresses schistosomiasis management through the ecological control of population of the intermediate host of the parasite , snails from the Biomphalaria genus [4] . Molluscicides have been the primary method used for controlling schistosomiasis transmission . They can be divided into two classes: chemical and phytochemical compounds [5] . Among the chemical compounds , niclosamide is recommended by the WHO as the only chemical molluscicide to be used for snail control despite reported cases of resistance in mollusks after two decades of repeated use [6] . Many plants were tested as a source of potential phytochemical molluscicides [7 , 8 , 9] . Euphorbia milii var . hislopii was described as the most promising plant molluscicide [10] . Its latex exhibits molluscicidal activity at doses under 0 . 5 ppm in laboratory condition , it can be easily cultivated in endemic areas , it is biodegradable and it has been proved to be less damaging to non-target organisms than niclosamide , meeting the requirements of WHO for use as a natural molluscicide [2 , 11] . A field study in Brazil was conducted resulting in disappearance of B . glabrata up to 14 month after two applications of 12 mg . L-1 of E . milii latex [12] . However , the use of a lower dose has been proposed as a promising method to control schistosomiasis transmission by the selective control of infected snails [13] . Although snails are very sensitive to latex , the effect of latex on cercaria survival and penetration are concentration-dependent and time-dependent , with no effect observed for exposure up to 8 mg . L-1 for 2 hours [14 , 15] . Cercariae of digenetic trematodes have complex structure characterized by a sequence of remarkable morphological and biochemical transitions between the aquatic environments to mammalian hosts [16] . They must swim to find their specific hosts before energy resources are exhausted . However , many soluble compounds and different forms of pollution can disrupt their interaction with the next host . The acquisition of soluble macromolecules through the tegument or by ingestion can induce changes in the gene expression in cercaria , which can affect its growth and development in its definitive host [17] . After infection , the success of host-parasite relationship depends , among other factors , on the expression , interaction and modulation of genes and proteins for the co-existence of both organisms [18] . In adult parasites , the transcriptome shows intense expression of genes mostly linked to the escape from the host immune system , to motility and to energy metabolism [16] . Besides that , the influence of environmental factors inside the host can also produce changes in the parasite gene expression and these could be mitotically heritable from schistosomula to adult worms [19] . Environmental factors can therefore affect some key genes and proteins that have consequences on parasite fitness , ultimately leading to a modification of disease dynamic and morbidity , which can make them good therapeutic targets for schistosomiasis control . In the present work , the effect of E . milii latex on S . mansoni was investigated through a integrative multidisciplinary approach . After exposure of cercaria to latex at a low dose of 1 . 4 mg . L-1 in water , different parameters were measured in worms at their adult stage such as development in the murine host , morphology by scanning electron microscopy and granuloma reaction in the liver . Two month after the transient pulse treatment of cercariae with latex we found striking changes in phenotype and fitness of adult parasites . In order to provide insights into key metabolic pathways that could explain the observed phenotypes , both comparative transcriptomics and proteomics were used . These two approaches revealed effects on transcripts and proteins involved in parasite mobility , energy metabolism and other key pathways . We conclude that latex has a long lasting schistosomastatic effect and we hypothesize about the mechanisms that could be the bases of this effect .
This research was approved by the Animal Ethics Committee of the Oswaldo Cruz Foundation ( CEUA-FIOCRUZ LW-07/13 ) in agreement with the guidelines of the Brazilian College for Animal Experiments ( COBEA ) . At IHPE , housing , feeding and animal care followed the national ethical standards established in the writ of February 1st , 2013 ( NOR: AGRG1238753A ) . The French Ministère de l’Agriculture et de la Pêche and the French Ministère de l’Education Nationale de la Recherche et de la Technologie provided permit A66040 to the laboratory for animal experiments and certificate to the experimenters ( authorization 007083 , decree 87–848 ) . The E . milii var . hislopii latex was collected at Ilha do Governador district ( 22°48´09´´S/43°12´35´´W ) , Rio de Janeiro , Brazil . The sample was pre-frozen in dry ice and absolute ethanol and subsequently lyophilized at -52°C on 8 x10-1 mBar for three 12-hour cycles in a Modulyo 4K Freeze Dryer with an acrylic chamber ( Edwards High Vacuum Int . , UK ) . The lyophilized pellet obtained with this process was diluted in distilled water and homogenized by sonication for 20 min . The dose of the powdered lyophilized latex of E . milii used to expose cercariae was 1 . 4 mg . L-1 , described by [20] as LC50 for the intermediate host Biomphalaria glabrata . The S . mansoni LE strain , originally sampled in Brazil was used in this study . Cercariae were collected from intermediate host Biomphalaria glabrata 30 days after infection by pipetting from spring water and sedimentation on ice and separated into two groups . The first group was exposed to a solution of E . milii lyophilized latex in distilled water ( 1 . 4 mg . L-1 ) for one hour . The second group ( control ) was kept in water for the same time period . Female mice were chosen as definitive host to avoid the effect of testosterone level in the parasite development [21] . We infected 57 female 4 weeks-old Swiss-Webster mice ( weight mean: 18g ) with 150 exposed cercariae per mouse ( total of 8 , 550 exposed cercariae ) and another 18 mice were infected with 150 mock treated cercariae per mouse ( total of 2 , 700 control cercariae ) , all using standard percutaneous inoculation and mixed sexes [22] . Water and food were given ad libitum . Finally , parasites couples were recovered at 65 days post-infection by perfusion . Females and males were manually separated and counted . They were stored at room temperature in 70% ethanol for parasitological analysis and electronic microscopy and at -80°C for transcriptomic and proteomic analysis . The intensity of infection and the reproduction of parasite female were measured by the Kato-Katz technique . This approach allows quantifying the number of eggs per gram of stool ( EPG ) . For that , three slides in three different days were performed for each experimental group , which corresponds to the feces collected in a two-hour period between 10:00 and 12:00 a . m . The parasitological results were expressed as the mean number ± standard deviation and they were analyzed with Student's t test ( α = 5% ) performed using the R program ( version 3 . 3 . 2 , R Development Core Team , 2012 ) . The graphics were constructed using GraphPad Prism software ( GraphPad V . 4 . 00 , Prism , GraphPad , vol . 3 . 02 , Prism Inc . ) . The liver is the most important tissue for schistosomiasis disease considering that a high number of eggs and large granulomas are dangerous for liver healthy . To measure the intensity of hepatic granuloma inflammation , we fixed the entire liver in Milloning ( 37–40%formaldehyde , NaH2PO4 , NaOH , sucrose , pH 7 . 2–7 . 4 ) for subsequent histological examination in the same organ region . After successive washes with 70% ethanol to completely remove the fixative , tissues were dehydrated in an ethanol series from 70% to absolute alcohol . After this stage , the samples were cleared in xylene and embedded in histological paraffin melted at 60°C . Subsequently , they were embedded in paraffin , cut with a rotary microtome ( Leica RM2125RT model , Nussloch—Germany ) , yielding sections of 5 μm thickness . The cuts intended for histopathology were stained with hematoxylin / eosin ( HE ) to visualize the granulomas and measure their size . We measured the diameter of the hepatic granulomas by Olympus CX31 microscope with 10X objective . On average , 35 granulomas were measured in each group analyzed . Statistical analyses were performed with Student's t test ( α = 5% ) using the R program ( version 3 . 3 . 2 , R Development Core Team , 2012 ) . To explore the ultrastructure of adult parasites resulting from latex-exposed or unexposed cercariae , we used scanning electron microscopy ( SEM ) . To perform the SEM , the parasites were fixed in 2 . 5% glutaraldehyde in 0 . 1 M cacodylate buffer ( pH 7 . 2 ) . They were then washed in PBS ( pH 7 . 2 ) and post-fixed in 1% osmium tetroxide in 0 . 1 M cacodylate buffer for 1 hour at room temperature in darkness . The dehydration step was performed in increasing concentrations of acetone before the critical point using CO2 . The specimens were sputtered with gold and observed in a JEOL JSM 6390 at the Plataforma de Microscopia Eletrônica of Fiocruz , Brazil . To investigate latex-induced gene expression modifications , we performed RNA-Seq on female and male adult worms from each experimental group as described by [19] . Two biological replicates from each group were analyzed and each replicate consisted in 50 worms crushed in liquid nitrogen for 5 minutes . Total RNA was extracted by following TRIzol-Choroform extraction procedure [19] . RNA was purified by using PureLink RNA Mini kit ( Ambion ) following the manufacturer’s protocol and then eluted in 30 μL RNAsecure ( Ambion ) . Each sample was then treated with TURBODNase ( TURBODNA-free , Ambion ) , RNA was purified by using columns from RNeasy mini kit ( QIAGEN ) and eluted in 30 μL RNase-free water . Quality and concentration of RNA were assessed by spectrophotometry with the Agilent 2100 Bioanalyzer system . Total RNA was quantified by NanoDrop Spectrophotometer ND-1000 ( NanoDrop Technologies , Inc . ) and its integrity was assessed by 2100 Bioanalyzer ( Agilent Technologies ) . Libraries were generated from 250 ng of total RNA using the TruSeq stranded mRNA Sample Preparation Kit ( Illumina ) , as per the manufacturer’s recommendations . Libraries were quantified using the Quant-iT PicoGreen dsDNA Assay Kit ( Life Technologies ) and the Kapa Illumina GA with Revised Primers-SYBR Fast Universal kit ( Kapa Biosystems ) . Average size fragment was determined using a LabChip GX ( PerkinElmer ) instrument . The libraries were normalized , denatured in 0 . 05N NaOH and then were diluted to 8pM using HT1 buffer . The clustering was done on a Illumina cBot and the flowcell was ran on a HiSeq 2000 for 2x100 cycles following the manufacturer's instructions . A phiX library was used as a control and mixed with libraries at 1% level . The Illumina control software was HCS 2 . 2 . 58 , the real-time analysis program was RTA v . 1 . 18 . 63 . Program bcl2fastq v1 . 8 . 4 was then used to demultiplex samples and generate fastq reads . For the analysis of global transcription mate-pair ended reads were aligned with STAR 2 . 4 . 0d [23] to the reference genome sma_v5 . 2 . chr . fasta without gene model file for splice junctions . SAM attribute XS was added for and all non-canonical junctions were removed for downstream processing with cufflinks . For all other parameters ( seed , alignment , and chimeric alignment ) default values were used . For analysis of transcription from repetitive sequences paired-end reads were aligned using STAR with RepBasePerpignanSma52 . fasta as reference repetome [24] , no gene model file for splice junctions and length of the genomic sequence around annotated junctions set to 100 . All other parameters were left as default . Intron-exon structures were reconstructed with cufflinks v2 . 2 . 1 using Max Intron Length 300000 , Min Isoform Fraction 0 . 1 , Pre MRNA Fraction 0 . 15 , without reference annotation , bias correction or multi-read correction [25] . Cufflinks effective length correction was used and for all other parameters default values . Cuffmerge 2 . 2 . 1 . 0 was used to combine cufflinks output files first separated by sex and then as a combined male and female transcription annotation in gtf format . BAM files were name sorted and Htseq-count version 0 . 6 . 0 in the Union mode , unstranded and Minimum alignment quality 10 Feature type exon and ID Attribute gene_id was used to obtain hit counts for every gene in the gtf combined cuffmerge file on both sexes [26] . For repeats , STAR alignements were converted into read counts with the galaxy tool sam2counts_edger version 1 . 0 . 0 . ‘ = ‘ and ‘#’ were replaced in the sequence names for further processing . Differentially expressed genes and repeats were identified using DESeq2 version 1 . 8 . 2 ( fit type parametric ) under R version 3 . 2 . 1 . Bonferroni adjusted -log10 ( P ) ≥ 5 were considered as significant [27] . All analyses were done on the Galaxy instance of the IHPE ( http://bioinfo . univ-perp . fr ) [28] . To investigate latex-induced protein expression modifications , protein profiles were analyzed by 2D gel electrophoresis . Total proteins were extracted by UTC denaturing solution ( 7M urea , 2M thiourea , 30mM tris pH 8 . 5 and 4% CHAPS ) of 5 adult male or 8 adult female parasites . Five biological replicates were processed for each sex and each group . Protein concentration of each sample was quantified using 2D Quant kit ( GE Healthcare life sciences ) and they were stored at -80°C until use . The samples were subjected to 2D gel electrophoresis using 100 μg of protein and a 17 cm ReadyStrip IPG Strips with a non-linear 3–10 pH gradient ( BioRad ) . Isoelectric focusing was initiated immediately after sample loading on the strip by 5 h of passive rehydration followed by 14 h of active rehydration ( 50 V ) . Rehydration and focusing were both performed on a Protean IEF Cell system ( Bio-Rad ) at 20°C following a four-step program: 50 V for 1 h , 250 V for 1 h , 8 , 000 V for 1 h and a final step at 8 , 000 V for a total of 90 , 000 V . h with a slow ramping voltage ( quadratically increasing voltage ) at each step . After isoelectric focusing , the strips were reduced twice in equilibration buffer ( 6 M urea , 0 . 075 M Tris HCl ( pH 8 . 8 ) , 29 . 3% glycerol , 2% SDS , and 0 . 002% bromophenol blue ) containing 2% dithiothreitol ( DTT ) and alkylated once in equilibration buffer containing 5% iodoacetamide . For the second dimension , the strips were placed on a 12%/0 . 32% acrylamide/piperazine diacrylamide gel run at 25 mA/gel for 30 min followed by 75 mA/gel for 8 h using a Protean II XL system ( Bio-Rad ) . Protein standards were loaded with whatman paper impregnated with 3 μL of Unstained Precision Plus Protein Standards ( Bio-Rad ) on the left part of the gels . Gels were stained using regular silver staining , and comparative analysis of digitized proteome maps was performed using the PDQuest 7 . 4 . 0 image analysis software ( Bio-Rad ) . Only spots whose abundance was significantly different between exposed and control conditions at a significance level of p< 0 . 05 based on one-way ANOVA analysis ( assuming equal variance ) and a ratio above 1 . 5 were considered . Selected spots were manually excised from MS-compatible silver stained gels using a Onetouch Plus Spot Picker Disposable ( Harvard Apparatus ) equipped with specific 1 . 5 mm methanol-washed tips . Gel plugs were first destained following a potassium ferricyanide/sodium thiosulfate procedure and then , proteins were trypsin-digested overnight at 30°C . Peptides were recovered after three washes a solution of formic acid ( 1% ) and acetonitrile ( 50% ) . Peptides were lyophilized and sent to the PISSARO proteomic platform ( University of Rouen , France ) for identification with a nano-LC1200 system coupled to a Q-TOF 6550 mass spectrometer equipped with a nanospray source and an HPLC-chip cube interface ( Agilent Technologies ) . For protein identification , peak lists were extracted ( merge MSn scans with the same precursor at +/- 30 s retention time window and +/- 50 ppm mass tolerance ) and compared with S . mansoni genome as reference database by using the PEAKS studio 7 . 5 proteomics workbench ( Bioinformatics Solutions Inc . , build 20150615 ) . Only significant hits with a false discovery rate ( FDR ≤ 1 ) for peptide and protein cut off ( -logP ≥ 20 and unique peptides ≥ 2 ) were considered . To improve our understanding of the changes caused by latex exposure in both sexes , a pathway analysis combining transcripts and proteins differentially abundant for each sex and each exposure group was conducted . First of all , the biological functions of the transcripts/proteins differentially expressed in female and in male between control and exposed groups were annotated by Gene Ontology Annotation and AmiGO2 . We focused our analysis on biological processes only and the number of GO terms in each category was analyzed for each parasite sex . To obtain a more synthetic overview , we used the highest hierarchical level as categories . Additionally , to gain insight into predicted protein-protein interactions and affected pathways on female and male , enrichment analysis using String Database were performed by focusing on pathways containing genes and/or proteins that were under-expressed or over-expressed [29] . Only the highest confidence interactions ( i . e . , interactions with a score larger than 0 . 9 ) were considered , including direct ( physical ) and indirect ( functional ) associations derived from four different sources: “genomic context” ( associations based on physical location of genes on genome that indicates that encoded proteins participates in the same metabolic pathway ) , “high throughput” ( associations based on the function of proteins ) , “co-expression” ( associations based on simultaneous expressions of different genes in the same organism ) and “previous knowledge” ( associations from previous annotations in other databases ) .
In the exposed group , the number of adult parasites and the number of recovered couples were 37 . 7% and 46 . 3% lower compared to control parasites , respectively ( Table 1 ) . Sex ratio was changed with an increased proportion of S . mansoni males in the control population when the number of parasites increases ( Fig 1A ) but not in the latex-exposed group ( Fig 1B ) . The quantity of eggs per female was 36 . 7% lower in the exposed group with a mean of 0 . 79 and 0 . 17 eggs per female in control and exposed groups , respectively ( Student’s t test , p < 0 . 001 ) ( Table 1 ) . The significant reduction of the number of adult parasites ( Student’s t test , p < 0 . 01 ) associated with a significant decreased number of eggs released per female caused a 89 . 2% reduction of the number of eggs recovered from mice ( Student’s t test , p < 0 . 01 ) ( Table 1 ) . Periovular granulomas were observed isolated and sparsely distributed in the hepatic parenchyma , sometimes forming clusters , but it was not possible to observe differences between groups . Moreover , in many of these granulomas a deposition of small amounts of collagen was noticed in many of these granulomas . Intense eosinophilic infiltration also occurred in medium and large portal spaces and sometimes in the central portion of the granulomas ( Fig 2 ) . The diameter of the hepatic granulomas in definitive hosts infected with cercariae exposed to latex was 10% smaller than those of control group ( Student’s t test , p = 0 . 01 ) ( Table 1 ) . The ultrastructure of the mid-dorsal tegument of S . mansoni males exposed to latex exhibited pronounced changes as compared to control ones , with an important loss of pattern in the distribution of tubercles and spines on the mid-dorsal surface ( Fig 3A–3D ) . In some areas , wrinkles and complete absence of tubercles and spines were observed ( Fig 3C and 3D ) . The ultrastructure of the gynecophoral channel of male worms from exposed group revealed a complete loss of spines in the area as compared to control ones ( Fig 3E and 3F ) . The females from exposed group exhibited remarkable wrinkles both in the tegument and suckers as compared to undamaged females from control ( Fig 4 ) . In total , the transcriptome sequencing of the two experimental groups yielded 1 , 080 , 386 , 261 Illumina single reads and 981 , 363 , 482 of them ( 90 . 8% ) were mapped to the S . mansoni reference genome ( v5 . 2 ) . These mapped reads in female samples were reconstructed into 6 , 654 genes ( XLOC ) ans 9 , 598 unique transcripts ( TCONs ) ( S1 Table ) . In male samples , they were 16 , 192 unique genes and 33 , 396 transcripts , respectively ( S1 Table ) . Quantification of read abundance and DEseq2 analysis of differential gene expression between sexes and experimental groups ( adjusted P-value < 0 . 05 ) identified 24 genes that were under-expressed in female adults from the exposed group as compared to those from control group ( S2 Table ) . Conversely , 15 genes were differentially expressed between exposed and control groups in male adult worms , with 11 upregulated in exposed group ( S2 Table ) . No significant differences in mRNA levels were detected for repeats . Among the differently expressed genes , several transcripts related to cell cycle control ( Putative meiosis-specific nuclear structural protein 1 , Putative microfibril-associated protein ) , neoblast formation ( Bruno-like rna binding protein ) and energy metabolism ( Putative ATPase class VI and type 11c , Putative ribosome biogenesis protein BMS1 , Ceramidase ) were found . A total of 926 and 757 spots were identified in protein gels and included in the analysis from adult males and females , respectively ( Fig 5 ) . Seventeen spots were found differentially expressed ( from -4 . 17 to 7 . 14 fold ) between females from exposed and control groups , of which 16 provided significant matches against S . mansoni genome ( S3 Table ) . For males , 31 spots were differentially expressed between conditions ( from -12 . 50 to 21 . 98 fold ) and proper protein identification was obtained for 29 of them ( S3 Table ) . We detected several proteins among male and female parasites associated with the schistosome muscle layer , such as the troponin T , tropomyosin , putative 22 . 6kDa tegument-associated antigen , antigen Sm21 . 7 and putative calcineurin . Additionally , our proteomic data showed three spots of putative hsp70-interacting protein ( Smp_062420 . 1 ) that were detected only in male worms derived from latex exposed cercariae . To identify Gene Ontology ( GO ) categories of genes and proteins differentially expressed between latex-exposed and control groups for male and female adult worms , enrichment analysis was performed . The main biological process categories specifically affected in females were “cytoskeleton/actin organization” ( containing the terms “sequestration of actin monomers” and “Arp2/3 complex-mediated actin nucleation” ) and “apoptotic process” ( containing the term “positive regulation of apoptotic process” ) . Categories that were specifically affected in male worms were iron metabolism ( containing the terms “intracellular sequestering of iron ion” and “iron ion transport” ) and “cell division and organization” ( containing the terms “mitotic spindle organization” , “phospholipid translocation” and “microtubule-based process” ) ( S4 Table ) . Four categories were affected in both males and females which are “RNA processes” , “amino acid metabolism” , “energy process” and “muscle contraction” . Among them , the terms “regulated of muscle contraction” , “glycolytic process” and one step of biogenesis of RNA were affected in both sexes ( S4 Table ) . Female worms displayed less terms of “RNA processes” and “amino acid metabolism” than males ( S4 Table ) . Male worms had 6 terms in the “amino acid metabolism” category and 3 in RNA processes while there was only two for both categories in females . Male worms showed several changes in “amino acid metabolism” and among the GO terms involved in “translational frameshifting” , “positive regulation of translational termination” , “positive regulation of translational elongation” , “protein import into mitochondrial matrix” , “protein folding” and “translational initiation” ( S4 Table ) . The anabolic metabolism of female adult parasites displayed reduction on “spliceosome” ( observed gene count: 18 ) and “biosynthesis of amino acids” ( observed gene count: 8 ) pathways . In the meantime , males displayed reduction on “ribosome biogenesis in eukaryotes” ( observed gene count: 21 ) , “biosynthesis of amino acids” ( observed gene count: 5 ) , “oxidative phosphorylation ( observed gene count: 4 ) and “proteasome” ( observed gene count: 22 ) , but “basal transcription factors” ( observed gene count: 13 ) , “ribosome biogenesis in eukaryotes” ( observed gene count: 16 ) and “protein processing in endoplasmic reticulum” ( observed gene count: 7 ) pathways were increased ( Fig 6; S5 Table ) . The catabolic process exhibited similar patterns of reduction in both sex , especially for “glycolysis/gluconeogenesis” , “carbon metabolism” , “fructose and mannose metabolism” , “metabolic pathways” and “microbial metabolism in diverse environments” pathways . Additionally , some affected pathways were sex-specific , such as “FoxO signaling” pathway ( observed gene count: 3 ) in male parasite ( Fig 6; S5 Table ) .
We describe here the influence of a water-soluble compound used as molluscicide on the development of adult parasite worm . Molluscicides are recommended by the World Health Organization for the development of effective and practical measures for the control of schistosomiasis transmission through the elimination of snails [30] . In the snail intermediate hosts , low doses of E . milii latex affects carbohydrate metabolism and nitrogen products [11] . However , it does not impact S . mansoni cercariae survival and infection success [15] . Here we show that transient exposure of cercariae to latex decreases worm fitness and lead to severe phenotypic changes that can be observed sixty days after the treatment . The input of soluble macromolecules and/or environmental factors in the free larval stages must result in changes in gene expression which modify the phenotype and thus affect the parasite’s biology in ways that is stable over the parasite’s life span [17 , 19 , 31] . Our integrated transcriptomics , proteomics and ultrastructural analysis converge towards a scenario in which a short latex contact at larval stage has a lasting schistosomastatic action affecting the development into adult worms . Several studies reported that congruency between omics data and phenotypic features are difficult when analyzed independently [32 , 33] . Transcriptome analysis gives insights into the dynamic expression of genes while proteomic takes into account all post-transcriptional and post-translational events to quantify proteins abundance , which is influenced by proteins half-life . This is why it is often difficult to predict the abundance of proteins based only on the patterns of gene expression , and vice-versa . Functional annotations are indicated for integrative approaches using heterogeneous datasets to improve the understanding of a determined biological event . In this sense we focused our analysis on GO annotations of transcripts/proteins differentially expressed for each sex and for each exposure group to improve the understanding of similarities in anabolic and catabolic pathways . Energy metabolism is particularly interesting to understand this model , especially in helminths parasites that inhabit or encounter hypoxic or anoxic habitats as Schistosomes . During the aquatic environment , cercariae presents intense aerobic metabolism in the tail when it is seeking the definitive host however schistosomulum and adult worms exhibits anaerobic metabolism inside the definitive host [34] . In the present study , several steps in energy metabolism had decreased in both sexes besides modulate catabolic pathways in different metabolic chokepoints such as glyceraldehyde-3-phosphate dehydrogenase ( Smp_056970 . 3 ) , phosphoglycerate kinase ( Smp_018890 ) and enolase ( Smp_024110 ) . The latex effect on metabolic chokepoints could be critical in the energy required for Schistosoma maturation since in these metabolic steps are expected that the inhibition of an enzyme that consumes a unique substrate or that produces a unique product can potentially toxic or cripple to essential cell functions . In addition , other important chokepoint enzyme was detected in the fatty acid biosynthetic process: 3 oxoacyl ( acyl carrier protein ) reductase ( Smp_042680 ) . The inability to generate long chain fatty acids or 'de novo' cholesterol by Schistosoma adult worms [35] requires the parasite to incorporate lipids from the host and although also involved in energy metabolism as the previous chokepoints fatty acids has an important role in female egg production . Thus , it is believed that chokepoint enzymes might be vital to the parasite and are consequently potential drug targets . It should be noted that the impairment of lipid incorporation is the basis of the single vaccine currently in clinical phase for schistosomiasis [36] . Moreover , the under-expression of other catabolic pathways ( carbon metabolism , fructose and mannose metabolism , pentose phosphate pathway , glutathione metabolism ) and anabolic pathways ( spliceosome , cytoplasmic ribosomes , foxO signaling , oxidative phosphorylation , biosynthesis of amino acids and proteasome ) forced a compensatory mechanism in an attempt to maintain the parasite homeostasis ( basal transcription factors , RER associated ribosomes , protein processing in RER/Golgi ) . The effect in the energy availability generated strong consequences in the parasite’s development and maintenance inside the mammalian host [37] . The parasite development undergoes in functional terms related to muscle contraction , cell cycle and tegument renewed in both genres but mainly males showed morphological malformations of spines and tubercles and a complete loss of spines inside gynecophoral channel . The S . mansoni tegument is a multifunctional structure and presents vital importance for adult parasites [38] . Due to its interface with the definitive host , this structure is intimately associated to the immune system of the host . The parasite's tegument presents a constant renewal process in the outer syncytium zone [39 , 40] and when exposed to anthelmintic drugs ( Hicantone , Oxamniquine , Praziquantel ) some local deformation such as wrinkling , erosion and loss of tubers are observed [39 , 41] . Recent studies suggested that these parasites can use distinct populations of neoblast-like cells to in response to a variety of external stimuli [42] . Indeed , in the present work , we observed different genes/proteins and pathways related to the tegument and notably involved in tegument renewal , including bruno-like RNA binding protein [43] . Our hypothesis is that latex affects several steps of energy metabolism , including catabolic and anabolic pathways , amino acid metabolism , cell cycle and motor activities . As observed with snails , in our model the effects started at the cercariae stage and were mitotically heritable directing significant changes in phenotype and fitness over the parasite’s life span . Furthermore , the significantly reduce on reproductive activity of adult parasites , size of hepatic granuloma and the effect on sexual proportion are important factors to morbidity , transmission and maintenance of the schistosomiasis in a specific area [44 , 45 , 46 , 47] . In summary , 60 days post exposure of cercariae to latex , it is possible to detect in the adults its impact on gene expression and protein abundance , and on the adult parasite’s phenotype and fitness . We believe that this particularly interesting illustration of the G x E concept [48] that we have recently extended towards a systems view of environment and genetic and non-genetic inheritance [49] . In our case , the effect of latex on the adult is not a direct toxicity but must trigger an early change in developmental trajectory and/or perturbations in cell memory ( S1 Fig ) . Bona fide candidate for such cellular memory are circulating neoblasts . Based on the strong differences in tegumental transcripts and proteins , morphological deformations in the adult worm’s teguments , cytoskeleton/mobility differences , combined with the fact that the majority of neoblasts commit to tegument renewal [50] , we believe that neoblasts are candidates for the lasting latex effect . Damage of neoblast commitment or their migration capacity would lead to inefficient tegument renewal . Since correct formation of the gynecophoral canal and muscle contractions are needed to maintain the couples , we can infer that fragility associated to the absence of spines inside of gynecophoral channel can alter the male's ability to physically maintain the female and exchange biochemical signals necessary for female maturation . This would affect the reproductive act , and cell mobility might also alter the migratory capacity necessary for oviposition , thus leading to the low number of eggs observed . Less eggs would require less energy production , which could explain the decrease in catabolic pathway associated RNAs and proteins . The biological cycle of digenetic parasites has one of the key points of interruption the vulnerability of larval forms in limnic environments . In these environments , the larvae are affected by different soluble compounds that can significantly alter the life cycle . To date , there are few studies evaluating new molluscicides from plant extracts and its interference in the cycle of S . mansoni . The present study shows the double impact of molluscicide E . milii affecting the morphology , physiology and fitness of adult parasite worms . In this scenario , the effects of E . milii latex differ partially on both genera in account of biological differences between them . Adult male parasite exhibits high activity in genes involved with the regulation of transmembrane transport and muscular layer . At this stage the worms' tegument is exposed to host immune cells which requires a constant renewal while it is involved in the input of molecules . On the other hand female parasite are located within the gynechephoral channel with a partial contact with host immune response . At adult stage , female worms' demand high abundance of transcripts to energy production to the production of hundreds of eggs daily . Although the same stress factor was performed on both sexes , the impact on functional annotation changed between male and female . This study opens perspectives for a new concept in the control of schistosomiasis in endemic areas using water soluble products at lower concentrations for snails , which may also reduce parasitic load in the final host . This indirect action is inexpensive , ecological and efficient and may in the future help the antihelminthic therapy currently recommended by the WHO , increasing the use of E . milii latex from molluscicide to schistosomiastatic .
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Intestinal schistosomiasis is among the most important parasitic disease caused by helminthes , affecting 67 million people worldwide . Vector and intermediate host of the parasitic worm are fresh water snails . WHO recommends use of molluscicides for control of local transmission . Among those , natural plant extracts such as Euphorbia milii latex have attracted particular attention since they are sustainable and cheap . We had anecdotic evidence that E . milii latex also impacts infection outcome if treated snails were infected with S . mansoni . We show here that transient exposure of the human dwelling larvae ( cercariae ) to the latex at doses that do not affect its infectivity has effects 60 days later on the morphology , physiology and fitness of the adult parasite worms . In order to generate insights into key metabolic pathways that lead to the observed phenotypes we used comparative transcriptomics and proteomics . We show that the effect of latex on the adult is not due to direct toxicity but it triggers an early change in developmental trajectory and perturbs cell memory , mobility , energy metabolism and other key pathways . We conclude that latex has not only an effect on the vector but applies also long lasting schistosomastatic action . The present work might also provide insights on targets with implications for developing new interventions for schistosomiasis control .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
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2017
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Double impact: natural molluscicide for schistosomiasis vector control also impedes development of Schistosoma mansoni cercariae into adult parasites
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The cytosol is the major environment in all bacterial cells . The true physical and dynamical nature of the cytosol solution is not fully understood and here a modeling approach is applied . Using recent and detailed data on metabolite concentrations , we have created a molecular mechanical model of the prokaryotic cytosol environment of Escherichia coli , containing proteins , metabolites and monatomic ions . We use 200 ns molecular dynamics simulations to compute diffusion rates , the extent of contact between molecules and dielectric constants . Large metabolites spend ∼80% of their time in contact with other molecules while small metabolites vary with some only spending 20% of time in contact . Large non-covalently interacting metabolite structures mediated by hydrogen-bonds , ionic and π stacking interactions are common and often associate with proteins . Mg2+ ions were prominent in NIMS and almost absent free in solution . Κ+ is generally not involved in NIMSs and populates the solvent fairly uniformly , hence its important role as an osmolyte . In simulations containing ubiquitin , to represent a protein component , metabolite diffusion was reduced owing to long lasting protein-metabolite interactions . Hence , it is likely that with larger proteins metabolites would diffuse even more slowly . The dielectric constant of these simulations was found to differ from that of pure water only through a large contribution from ubiquitin as metabolite and monatomic ion effects cancel . These findings suggest regions of influence specific to particular proteins affecting metabolite diffusion and electrostatics . Also some proteins may have a higher propensity for associations with metabolites owing to their larger electrostatic fields . We hope that future studies may be able to accurately predict how binding interactions differ in the cytosol relative to dilute aqueous solution .
The composition of metabolites , ions and proteins , and processes such as metabolism and signalling which take place in the E . coli cytosol are largely well defined [1] , [2] . However , the structure and dynamical nature of the cytosol solution is less well understood whether on the local or cytosol-wide levels . Current perception of the cytosol solution is often of a unstructured mixture with behaviour that differs quantitatively but not qualitatively from an ideal solution . Alternatively , there are theoretical descriptions of a cytosol organised into functionally specific regions and even separated protein and small molecule regions linked by metabolite transit pathways [3] , [4] . Cytosolic metabolites are extremely varied , but a large majority of these molecules are negatively charged . Assumed electro-neutrality is maintained by a large concentration of potassium ions and to a lesser degree by magnesium and poly-amines such as putrescine and spermidine . The large amount of charge in the cytosol suggests that electrostatics is a dominant force . However , the Debye length at physiological ionic strength is very short ( less than 1 nm ) [3] , [4] . This electrostatic screening is probably essential for the observed extent of macromolecular crowding [5] . The charge distribution and dynamics of the solution also determines the dielectric constant ( ) , which is reduced by increasing concentration of monatomic ions [6] , [7] while Zwitterionic metabolites are thought to increase [8]–[10] . The effect of proteins seems to vary , with some studies suggesting an increment [11]–[13] and others a decrement [14] , [15] . Experimental data on of the cytosol is sparse but in general it suggests that cytosolic is significantly larger than that for pure water [16]–[19] . The hydrophobic effect is also significantly modulated by ionic strength . Increasing salt concentration increases the strength of the hydrophobic effect [20] possibly through the weakening of water hydrogen bonding [21] . Almost all theoretical treatments of these issues assume simple solutions of monatomic ions and water , sometimes at infinite dilution . There has been little examination of differences in solutions containing positive monatomic ions and larger , negatively charged solutes . Given the complexity of the cytosol environment it is very difficult to predict the true nature of structure , dynamics and thermodynamics . With a high level of electrostatic screening and heightened hydrophobicity , is it likely that metabolites and proteins engage in significant and long lasting interactions ? A recent theoretical study has attempted to make sense of non-ideal behaviour for two component solutions of some common organic molecules [22] . For some mixtures it was shown that activity can actually decrease with increasing concentration , suggesting a high level of non-ideal behavior . Another study found significantly lower thermodynamic activities between in vivo like and standard conditions for enzyme-inhibitor assays , again suggesting significant non-ideal behaviour [23] . Using a recent extensive list of metabolites and their concentrations from exponentially growing E . coli [24] , we have produced two types of atomistic molecular dynamics simulations of a simplified cytosolic model . One included metabolites only and another also included a protein component , for which we used ubiquitin . Although ubiquitin ( PDB code 1UBQ ) is a eukaryotic protein , it was chosen owing to its small size and large amount of literature dedicated to its study [25]–[27] . Molecular dynamics allowed us to compute several properties of interest , including , amount of contact between cytosolic molecules and diffusion coefficients . The simulations indicate that metabolites spend a large proportion of their time as part of ‘non-covalently interacting metabolite structures’ ( NIMS ) . Our results also indicate that the cytosolic is larger than that of water with monatomic ions . These data allow us to make suggestions about the global structure of the cytosol and the amount of time different metabolites spend free in solution .
This study involved two large cytosol simulations with cubic boxes of 100 Å dimensions , one containing metabolites with monatomic ions ( 100M ) and another with four additional ubiquitin molecules ( 100MP ) . Two smaller cytosol simulations ( 50M and 50MP ) , a pure water ( tip3p ) and water + KCl ( tip3p+KCl ) all with Å dimensions were produced for the dielectric analysis . For a complete list of the simulations of this study and their simplified labels it is instructive to refer to table 1 and the methods section . The structure of cytosol simulations quickly collapsed from almost equal spacing of metabolites to a series of non-covalently interacting metabolite structures ( NIMS ) inter-spaced with solvent , ions and fully solvated metabolites . This process was conveniently measured through solvent accessible surface area ( SASA ) of all metabolites except monatomic ions ( Figure 1 ) . Both 50 Å simulations were deemed equilibrated after 30 ns ( Figure 1 ) while the 100M and 100 MP were equilibrated after 35 and 50 ns respectively . Hence , all analyses were carried out only on this structurally equilibrated data ( see supporting information Figure S7 ) . Around 16 . 7% of SASA is lost within the 50 MP system which is similar to the 100 MP box where around 16 . 4% is lost . These percentage values were calculated using the running averages shown in red in Figure 1 . The effect of the box size on metabolite behaviour and general size of NIMS is difficult to gauge but the fact that there is little relative difference between 100 and 50 Å may suggest that smaller box sizes can be used for computationally expensive calculations . Figure 2 shows a view through the 100 M box at the beginning of the production simulation and after 200 ns . It is clear that after equilibration there is a significant difference in structure . Within the 200 ns simulation of the 100 Å boxes many NIMS were formed which were stable over relatively long time periods . The most interesting of these NIMS were those with a stacking core of nucleotide base like groups ( Figure 3 A ) . These stacks continuously gain and lose bases and persist as long as 50 ns . Some stack NIMS seem reminiscent of RNA and we speculate that these structures often show similarities with the elongation complex of RNA polymerases [28] in the way phosphates are aligned with ribose rings ( Figure S3 ) . The inclusion of four ubiquitin molecules perturbed the metabolite structures . Many large NIMSs became attached to protein surface areas containing positively charged residues ( Figure 4 ) , in many cases for time periods of 50–100 ns . The attachment or detachment of large NIMS from the protein may contribute to the large SASA fluctuations of Figure 1 . These protein-connected NIMSs can also form bridges connecting two proteins which correlates their motions ( Figure 4 ) . SASA analysis was used to investigate any propensity for metabolites to interact . For the SASA and diffusion analyses , only the 100 Å boxes are discussed , however the 50 Å boxes were found to follow similar trends . As might be expected metabolites with larger surface areas have more contact with non-water entities . A comparison of the average contact area in the 100 M and 100 MP boxes for each type of metabolite can be found in the supporting information ( Figure S4 ) . Figure 5 displays the amount of time metabolites spend in contact with other molecules and hence are unavailable for any specific interactions . The threshold for our definition of contact is two hydrogen bonds or more ( see methods section ) . Larger metabolites are contacted at least 70% of the time while smaller metabolites show much larger variability with some as low as 20% and other as high as 95% . This analysis gives an indication of metabolites availability for metabolism but of course cannot replace thermodynamic data . A comparison of time in-contact data for the 100 M and 100 MP simulations can be found in supporting information ( Figure S5 and Tables S1 and S2 ) . Also further analysis of average and maximum contact events is presented in Figure S2 . A SASA analysis was also applied to ubiquitin , in the 100 MP simulation , to find the metabolite contact area for each ubiquitin residue . Here the residue contact area is defined as the SASA without the environment minus the SASA with the environment and this was reported as a percentage of the average SASA without the environment . Those ubiquitin residues which interact with metabolites most are part of the same patch ( nine of the top ten percentage contact area , see supporting information Table S2 ) . Lys 48 , becomes covalently attached to the C-terminus of other ubiquitin molecules is part of this patch [25] . This patch was involved in a very close contact event between two ubiquitin molecules in the 100 MP simulation ( supporting information Figure S1 ) . Diffusion coefficients were calculated through the Einstein-Helfand relation . Figure 6 shows the diffusion coefficient against the number of atoms for each type of metabolite in the 100 Å boxes . Recent work has shown a periodic box size dependence for water diffusion in water [29] . Here some diffusion rates were slightly reduced in the 50 Å compared to the 100 Å boxes , however many were identical ( Figure S6 of supporting information ) . We have identified only one literature value for metabolite diffusion of for arginine-phosphate [30] , [31] , this is within the range of values for molecules with 20 atoms seen in Figure 6 . A relation between maximum D and numbers of atoms is clear . However for smaller metabolites ( atoms ) D ranges over an order of magnitude . It was not possible to find a clear relation between electrostatic charge or hydrophobicity and D . A comparison of D for the 100 M and 100 MP simulations suggesting metabolites diffuse slightly more slowly in the 100 MP simulation is in supporting information ( Figure 7 ) . The diffusion coefficient of ubiquitin in the 100 Å simulation was , and the average of lateral diffusion in the x , y and z planes was . These values can be compared to experimental values for lateral diffusion of and for green fluorescent protein ( GFP ) in E . coli [32]–[35] . The order of magnitude difference in these protein diffusion values can be rationalised by the larger size ( ) of GFP and the lack of structural proteins and membranes in our simulations . While this comparison is of limited use it is included as this is the most relevent experimental value available and it shows that our computed values are within a reasonable range . Another relevant comparison is with the large Brownian dynamics models of McGuffee et al . ; here a protein of very similar size ( CspC ) was found to have a diffusion coefficient of with the smallest observation interval used [36] . In the McGuffee et al . study the friction parameter of their Brownian dynamics was adjusted such that the diffusion of green florescent protein matched experimental values . The McGuffee model also differed in that it contained many different types of larger proteins , and so this close agreement may be fortuitous . The dielectric constant ( ) and conductivity ( ) can give insight into the electrostatic properties of a solution and other associated properties such as hydrophobicity . As suggested in the introduction and for such a complex heterogeneous solution is difficult to estimate . Owing to the necessity for long simulations with extremely frequent data collection ( every 10 fs ) , smaller simulation boxes were used for this analysis ( dimensions of 50 Å ) . and the translational dielectric constant ( ) values were found through an Einstein-Helfand analysis described in the theory section . Regression analyses were applied from 100 to 500 ps for all systems except 50 M which used 100 to 300 ps ( supporting information Figure S8 ) . Table 2 shows the results of the present analysis . is larger in the simulation without ubiquitin compared to that with ubiquitin , probably owing to the increase in ion and metabolite diffusion ( Figure 6 ) . for tip3p water is of course zero , while with the addition of 0 . 3 M KCL it is greater than the cytosol simulations , caused by higher diffusion rates of charge carriers . The tip3p + KCL value of 6 . 69 compares well with the experimental value of [37] . Unfortunately , direct experimental measurements of cytosolic are not available in the literature . However , spherical or spheroidal models ( E . coli is rod shaped ) together with various experimental data have been used to give estimates of E . coli cytosolic . Dielectrophoretic analysis gives 0 . 35 [38] , dielectric spectra analysis 0 . 22 [19] and electrorotation analysis 0 . 44 [39] . These model-based measurements also predict a cytosolic of , which does not agree with other literature values [16]–[19] . The calculated conductivity with ubiquitin ( 50 MP ) of 3 . 2 is an order of magnitude greater than these fitted measurements . Overall , contributions were small compared to total . did not relate well to values for or rates of diffusion . It may be expected that , owing to its large , the 50 M system would have the larger but the 50 MP system contributes far more to from the conductivity . Also , the tip3p+KCl system has a very small contribution . This suggests a strange difference in the dynamics of charge carriers compared with those in the ubiquitin simulation , vibrating more sharply around a similar position than those in the metabolite only simulation . The rotational component of , , ( Table 2 ) follows trends found in the literature . The pure water system has of 92 . 5 which is slightly lower than some literature calculated values of around 97 [40] . This is almost certainly related to the use of a longer simulation length in this study ( data not shown ) . The tip3p+KCl system had a reduced which agrees with another literature study of the SPC water model [6] . The metabolite only system has slightly lower than tip3p alone , as the metabolites with large dipoles compensate for the decrementing effect of the salt and those with small dipoles . Finally , the ubiquitin system displays a very large dielectric increment , however , this size of increment is not without precedence [12] . Previous values were similar but used less sampling meaning larger statistical error . Given the relatively small dipole of ubiquitin this increment may be smaller than average .
To the authors knowledge this is the first attempt to produce an atomistic simulation of the cellular cytosol solution . There is relatively little experimental data with which to compare , but comparison with available data on diffusion coefficients was satisfactory . The stacking NIMS found here ( Figure 3 ) are interesting and possibly important but are they realistic ? Studies comparing aromatic stacking interactions show a reasonable agreement between molecular mechanics free energy calculations , high level electronic structure calculations and experiment [41]–[43] . Also there is experimental evidence for self-association of ATP in solution [44] . However , for guanine-cytosine stacked dimers with and without methyl groups , OPLSAA has been shown to produce non-stacked complexes where other force fields found the correct stacked formation . This may suggest that stacked metabolite complexes could be more prevalent with other force fields [45] . The alignment of phosphate and ribose groups in NIMS , such as that in Figure S3 , has similarities to the elongation complex of RNA polymerases and may give an indication of how RNA polymers first emerged . Whilst speculative it is possible that highly reactive conditions ( high temperatures or levels of radiation ) and large amounts of time could do the job of the catalytic conditions found in RNA polymerases . The analysis presented here suggests that NIMS are mostly mediated through hydrogen bonds , charge-charge , and interactions . A recent study has found good agreement in geometries and energies of a large set of relevant intermolecular complexes with high-level ab initio calculations [45] . Two other studies have demonstrated the high accuracy of OPLSAA in reproducing association constants of relevent small molecules in chloroform and relative free energies of hydration , heats of vaporization and pure liquid densities for 40 mono- and disubstituted benzenes [43] , [46] . No parameter set is perfect but on the whole these study add weight to the idea that the metabolite interactions described here are realistic . It should be no surprise that 2+ ions are found to be important to metabolite interactions . Many metabolites such as ATP require interaction with for enzyme-mediated reactions . ions were found to have two ionic-bonds or more for more than 80% of both 100 Å simulations ( Figure 5 ) . is generally not involved in NIMSs and may populate the solvent fairly uniformly , hence its important role as an osmolyte . All larger metabolites were found to spend of their time in contact with other molecules . While smaller metabolites vary in diffusive and contact character with some diffusing quickly and maintaining contact only 20–30% of the time . The presence of ubiquitin does not effect the amount of contact time experienced by metabolites . There is a small difference in diffusion between the two 100 Å systems ( Figure 7 ) which suggests that proteins have an effect on the dynamics of metabolites . In turn this suggests that with larger protein molecules the metabolites diffusion rates would be further reduced . We can speculate that in regions without proteins metabolite diffusion rates would be increased . Recent Brownian dynamics simulations have modeled many macromolecules in cytosolic volumes [36] , [47] , [48] . These models have been used to answer questions about macromolecular diffusion and stability outside of the scope of these atomistic models . However , it is possible that effects owing to metabolites could be important in these types of model . of the cytosol of E . coli has many competing factors . Interestingly , total for the 50 M and tip3p systems are similar as the metabolite increment cancels the decrement of the monatomic ions of the tip3p+KCl 0 . 3M system . For the cytosol any increment in the rotational contribution due to proteins is an unknown and could have a large effect on , possibly only on a local level . Ubiquitin , used here , clearly has a large increment but can this be said of all proteins ? A recent study has analyzed the dipole moments of the protein database [49] and gives an average protein biological unit dipole of 639 D , with ubiquitin having a dipole of 239 D . This suggests that most proteins have a dipole at least twice as big as ubiquitin . However , excluded volume will also have an effect reducing the effect of dipoles due to larger proteins . A higher dielectric compared to pure water will decrease electrostatic screening according to Debye-Huckel theory . A recent study has explored electrostatic screening using molecular dynamics and free energy calculations [50] , and suggests that screening at high salt concentration is less than may have been expected from approximate treatments . Hence , the electrostatic screening found in cytosol solution may need further investigation . For the purposes of bio-simulations using implicit solvent it may be that a value closer to the 148 found here will give conditions closer to those found in vivo . Owing to the diffusive and electrostatic considerations discussed above , it may be possible that proteins have a specific electrostatic and diffusive spheres of influence . If some proteins attract more metabolite ions than others , then this will again affect the local screening of the solution . Hence , proteins may have a locally specific electrostatic environments and propensities for associated metabolites and NIMS . In one example the electrostatic field of a protein is suggested to attract and orient specific metabolites [51] , another study suggests that electric fields related to function are very protein specific and conserved through protein families [49] . Recently , kinetic models of cellular metabolism have started to appear in the literature [52] . These studies often attempt to approximate the thermodynamic activity of metabolites through Debye-Huckel theory [53] . Considering the high level of interaction between metabolites found in this study , the use of theory based on infinite dilution may not be sufficient to give realistic thermodynamic activities for these models . A recent experimental study has performed enzyme-inhibitor assays with an in vivo like solution ( 300 mM potassium , 50 mM phosphate , 245 mM glutamate , 20 mM sodium , 2 mM free magnesium and 0 . 5 mM calcium , at a pH of 6 . 8 ) rather than a standard concentration of the inhibitor [23] . In the in vivo like solution some enzymes have capacities ( Vmax ) which are less than half those found in optimised conditions . The solutions used are far from the complexity of the real cytosol and so further investigation of more complex solutions may be required . In the future it may be possible to calculate accurate thermodynamic activities using free energy calculations . These ideas may have implications for drug discovery . For example drug candidates predicted to spend significant amounts of time in NIMS and unavailable for binding to enzymes may not be optimal . The behaviour and effect on the cytosol environment of molecules used by the cell to protect against stresses such as high osmolarity , pressure or anhydrobiotic conditions could be explored with simulations such as those in this study . A molecule which diffuses rapidly and is generally free from NIMS will be more osmotically active , if this molecule does not affect other aspects of the environment , would be a suitable osmolyte protectant . From this study we can predict that , glyceric acid , malate , 3-phosphoglycerate , and phenolpyruvate ( metabolite codes are in supporting information Table S1 ) may be more osmotically active than other metabolites of a similar size . These models represent a specific phase in the cell cycle in optimal external conditions . The constituents of the cytosol can change in response to many factors and inevitably properties such as diffusion rates and molecular associations can be effected . Additionally , understanding the effects of different metabolites , compatible solutes , osmolytes and ions on the properties of the cytosol may allow us to better understand the reactions of the cell to extreme environments such as high salt concentration , high temperature or desiccation [54] . The simulations carried out in this study give an interesting picture of the molecular behavior in the cytosol solution . Metabolites and proteins are seen to have significant level of non-ideal behavior , with metabolites forming large non-covalently interacting metabolite structures ( NIMS ) and proteins slowing the diffusivity of metabolites . The electrostatic fields of proteins are powerful and control the local dielectric conditions possibly allowing selective filtering of metabolites . In the future these types of simulations may , as part of comparative or thermodynamic analyses , shed light on many poorly understood aspects of cellular environments .
Molecular diffusion coefficients were calculated using the Einstein relation [55] , ( 1 ) where is the displacement of the atoms of a molecule over time , is the diffusion coefficient and is the number of dimensions of the position data . The Einstein relation was chosen over the velocity correlation function owing to better convergence behavior and the lack of a need to store velocity data . Mean squared displacement ( msd ) plots were averaged over replicas of the data with 50 ps removed from the start of each successive replica and the linear regression was applied from 1000 to 3000 ps . Solvent accessible surface area ( SASA ) was employed to show the amount of time each molecule spends free in solution or as part of a larger non-covalently interacting structures . SASA was calculated , using the “Double Cube Lattice Method” of Eisenhaber et . al . [56] , for each molecule with and without the surrounding environment and the difference taken in order that the average molecular surface area in contact with other non-water molecules is found ( average contact area ) . This average contact area was then displayed as a percentage of the average SASA of the metabolite or residue without the surrounding environment , the percentage contact area . Another analysis calculates percentage of simulation which metabolites are in contact with other non-water molecules . Here only a thermodynamically significant contact was of interest . The average excluded SASA found when two hydrogen bonds were present for all metabolites was calculated from the 100 M simulation . Hence , here contact was defined by an excluded SASA threshold of 0 . 48 . The use of SASA to define this contact means that other types of interaction such as those involving clouds are also included . The calculation of using computer simulation was originally reported by Neumann and Steinhauser [57] . The dielectric constant of water models in molecular mechanics simulations has often been calculated in the literature [58] , [59] . These studies generally calculate the static dielectric constant via the fluctuations of the system dipole , ( 2 ) ( 3 ) where represents molecules and atoms in a molecule , is the Boltzmann constant , is the temperature and is the volume . is generally the origin of the coordinate system or the center of mass of the system . In the present study the use of equation 3 is difficult due to the presence of molecules with net charge . For a charged molecule the choice of reference position directly affects the molecular dipole . For an overall neutral system these differences are thought to cancel , however convergence can be extremely slow [60] . A recently developed methodology decomposes into rotational ( ) and translational ( ) contributions [61] , ( 4 ) ( 5 ) ( 6 ) where is the total charge of a molecule and is the center of mass of a molecule . describes the position of charge centers through the system and is the sum of molecular dipoles with respect to their center of mass . Combining equations 3 and 4 gives an equation for which may overcome some of the problems of equation 3 alone , ( 7 ) This can be further simplified this by assuming that we use enough data such that giving , ( 8 ) For convenience the rotational , translational and cross term contributions to are denoted , and respectively with , . is calculated through a simple ensemble average of . is directly related to the electrical current ( ) and therefore the static conductivity , ( 9 ) This means there are possible alternative routes to finding as is also easily obtainable from molecular simulation . These possibilities have recently been explored in the case of simple ionic liquids [60] , [62] , [63] . Hence , in the present study is found using the Einstein-Helfand method , as ( 10 ) where is the correlation length of current auto-correlation function . A linear regression fit of the resulting curve gives the static conductivity from the slope and from the y-axis intercept . The cross term is certain to be very small . Recent studies have evaluated for a series of ionic liquids made up of molecules which all have both translational and rotational dipoles [62] , [63] . All of these studies have found very small . In the present study , a very small minority of molecules have both a translational and rotational dipoles , hence will be very small and has not been calculated . All simulations used the GROMACS MD package [64] , the OPLS force-field [65] was used for Zwitterionic protein residues and parameters for non-standard molecules were generated using hetgrpffgen provided with the Schrödinger Suite ( Schrödinger LLC ) . This parameter generation method has recently been explored using solvation free energies of small , neutral molecules and was generally found to be of a high quality [66] . The development of the OPLSAA force field has focused on reproducing experimental measurements of thermodynamic properties for representative small molecules and was recently found to be the best at reproducing geometries and energies of inter-molecular complexes along with MMFF [45] . The recently developed Bussi et . al . thermostat was used , owing to its good reproduction of real dynamics and diffusive properties [67] , [68] . The Parrinello-Rahman barostat was used for all production calculations . Temperature was set to 37 degrees Celsius . Equation 8 must be applied to a periodic simulation using a long range electrostatic lattice summation and conducting boundary conditions , therefore periodic boundaries and particle mesh Ewald [69] was used throughout this study . Coulombic cutoffs at 1 nm have been shown to give more accurate dielectric calculations and were used throughout this study [57] . Lennard-Jones interactions were truncated with a switching function from 0 . 8 to 0 . 9 nm . System configurations were stored every 4 for the longer , 200 ns simulations . Subsequently , shorter 100 ns simulations were carried out storing configurations every 10 for the analysis . Two box sizes were used , with dimensions of 50 Å and 100 Å , to assess possible size effects and provide a more tractable simulation for the analysis . The numbers of metabolite molecules used in each box was calculated from concentrations measured by Bennett et . al . [24] . Metabolites with concentrations sufficiently low such that less than 0 . 5 metabolites would be found in a particular box size were not automatically included . However , the total observed intracellular metabolite concentration given by Bennett et . al . was . This total is a higher concentration than that found through automatically included metabolites ( 0 . 23 M ) . We chose to increase the total metabolite concentration to 0 . 28 M , by randomly selecting from a list of less abundant metabolites with a probability biased by their concentration . It is not possible to accurately estimate from published metabolomics data the concentrations of free metabolities as opposed to the total metabolite concentration . However , particularly for the most abundant species , Bennett et . al . [24] suggest that the concentrations are well in excess of the Km of enzymes that consume the metabolites , ensuring saturation of the enzymes ( which will generally have much lower concentrations ) , and suggesting that a significant portion of the high-concentration metabolites will be free in solution . Nonetheless , the concentrations we use may overestimate the free concentrations of the various metabolites to unknown and variable extents , which is a limitation of the current study . All metabolites were protonated according to pKas at pH 7 . 6 [70] found either though experimental data or calculation with Epik ( Schrödinger LLC ) . The methods used by Bennett et . al . were not able to detect putrescine ( JD Rabinowitz , personal communication , 2010 ) . Putrescine has a 2+ charge at pH 7 . 6 and thus was used to give a neutralising charge along with potassium and magnesium ions ( magnesium was used to represent all 2+ mono-atomic ions ) . Concentrations of putrescine ( 28 mM ) , magnesium ( 40 mM ) and potassium ( 290 mM ) ions in line with literature studies [71]–[78] were added such that the system was neutralised . Putrescine and magnesium are often found interacting with DNA , RNA and other large macromolecules [79]–[81] and therefore are less likely to be found free in the cytosol and in our simulation boxes . While potassium may be more likely to be found free in the cytosol and is more osmotically active [82]–[84] . Hence , the amount of potassium ions should be more related to the osmotic strength of the external medium compared to other ions or metabolites . Larger macromolecules ( proteins ) were also considered , and to this end 50 and 100 Å boxes containing ubiquitin were also constructed . Ubiquitin ( PDB code 1UBQ ) is a eukaryotic protein , it was chosen owing to its small size and large amount of literature dedicated to its study [25]–[27] . A protein concentration of was assumed along with possible protein volume of [5] , [76] , [85] . Table 1 shows the details of the four simulation boxes created for this study . The effective concentration of the single ubiquitin in the 50 Å is around which is higher than desired , however making this box larger would have prohibited running simulations long enough for the analysis . 50 Å boxes of tip3p water and tip3p with 0 . 3 M KCl ( tip3p+KCl ) were also created and equilibrated as part of the dielectric analysis . Types and numbers of metabolites used for each box are listed in supporting information , Table S1 . Model cytosol boxes were constructed through a simple Monte Carlo procedure . Each metabolite to be added to a box was treated as a buffered sphere and random positions were trialled until one was found which did not clash with the edge of the box or any other metabolite . Consequently , the initial structure of the boxes had no contact between any of the constituent metabolites . Owing to these considerations structural equilibration of the boxes was closely monitored before any analysis could be carried out . The use of a barostat throughout the structural equilibration is essential as the actual size of the simulation box reduces slightly .
The authors thanks Dr . Andrew Cossins and Dr . Olga Vasieva for useful discussions over the biological issues discussed in this work .
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The cytosol is the major cellular environment housing the majority of cellular activity . Although the cytosol is an aqueous environment , it contains high concentrations of ions , metabolites , and proteins , making it very different from dilute aqueous solution , which is frequently used for in vitro biochemistry . Recent advances in metabolomics have provided detailed concentration data for metabolites in E . coli . We used this information to construct accurate atomistic models of the cytosol solution . We find that , unlike the situation in dilute solutions , most metabolites spend the majority of their time in contact with other metabolites , or in contact with proteins . Furthermore , we find large non-covalently interacting metabolite structures are common and often associated with proteins . The presence of proteins reduced metabolite diffusion owing to long lasting correlations of motion . The dielectric constant of these simulations was found to differ from that of pure water only through a large contribution from proteins as metabolite and monatomic ion effects largely cancel . These findings suggest specific protein spheres of influence affecting metabolite diffusion and the electrostatic environment .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods",
"Acknowledgments"
] |
[
"biomacromolecule-ligand",
"interactions",
"molecular",
"mechanics",
"biochemistry",
"molecular",
"dynamics",
"biochemistry",
"simulations",
"chemical",
"physics",
"small",
"molecules",
"chemical",
"biology",
"biophysics",
"theory",
"chemistry",
"biology",
"computational",
"chemistry",
"biophysics",
"simulations",
"biophysics"
] |
2011
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A New View of the Bacterial Cytosol Environment
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Surface recognition and penetration are among the most critical plant infection processes in foliar pathogens . In Magnaporthe oryzae , the Pmk1 MAP kinase regulates appressorium formation and penetration . Its orthologs also are known to be required for various plant infection processes in other phytopathogenic fungi . Although a number of upstream components of this important pathway have been characterized , the upstream sensors for surface signals have not been well characterized . Pmk1 is orthologous to Kss1 in yeast that functions downstream from Msb2 and Sho1 for filamentous growth . Because of the conserved nature of the Pmk1 and Kss1 pathways and reduced expression of MoMSB2 in the pmk1 mutant , in this study we functionally characterized the MoMSB2 and MoSHO1 genes . Whereas the Momsb2 mutant was significantly reduced in appressorium formation and virulence , the Mosho1 mutant was only slightly reduced . The Mosho1 Momsb2 double mutant rarely formed appressoria on artificial hydrophobic surfaces , had a reduced Pmk1 phosphorylation level , and was nonresponsive to cutin monomers . However , it still formed appressoria and caused rare , restricted lesions on rice leaves . On artificial hydrophilic surfaces , leaf surface waxes and primary alcohols-but not paraffin waxes and alkanes- stimulated appressorium formation in the Mosho1 Momsb2 mutant , but more efficiently in the Momsb2 mutant . Furthermore , expression of a dominant active MST7 allele partially suppressed the defects of the Momsb2 mutant . These results indicate that , besides surface hydrophobicity and cutin monomers , primary alcohols , a major component of epicuticular leaf waxes in grasses , are recognized by M . oryzae as signals for appressorium formation . Our data also suggest that MoMsb2 and MoSho1 may have overlapping functions in recognizing various surface signals for Pmk1 activation and appressorium formation . While MoMsb2 is critical for sensing surface hydrophobicity and cutin monomers , MoSho1 may play a more important role in recognizing rice leaf waxes .
The heterothallic ascomycete Magnaporthe oryzae is an important pathogen of rice throughout the world . In the past two decades , the rice-M . oryzae pathosystem has been developed as a model to study fungal-plant interactions [1] , [2] , [3] . M . oryzae initiates infection of rice leaves by the germination of conidia and differentiation of appressoria at the tip of germ tubes . The fungus then uses turgor pressure that develops within appressoria to penetrate the plant cuticle and cell wall . After penetration , the narrow penetration peg differentiates into invasive hyphae , which are enveloped by the host cytoplasmic membrane during the biotrophic phase [4] . As a hemibiotrophic pathogen , M . oryzae does not kill plant cells initially . At late infection stages , plant cells are killed due to extensive growth of infectious hyphae and blast lesions are normally visible within 7 days post-infection . The surface of rice leaves is comprised of epicuticular waxes . Germ tubes of M . oryzae recognize the hydrophobicity of rice leaves . The fungus also forms appressoria on artificial hydrophobic surfaces . On hydrophilic surfaces , conidia produce long germ tubes without tip differentiation . Exogenous cAMP induces appressorium formation on hydrophilic surfaces . Molecular studies have confirmed the role of cAMP signaling in surface recognition and initiation of appressorium formation [5] , [6] . Besides surface hydrophobicity , other factors including surface hardness , cutin monomers , and leaf waxes also affect appressorium formation in M . oryzae [7] , [8] , [9] , [10] , [11] . Various physical and chemical signals also have been shown to affect appressorium formation in other plant pathogenic fungi , including Ustilago maydis and Colletotrichum species . While cAMP signaling controls surface recognition and tip deformation , the Pmk1 MAP kinase pathway regulates late stages of appressorium formation , penetration , and infectious growth in M . oryzae [12] . Pmk1 is orthologous to Kss1 , which is a key MAP kinase involved in the filamentous growth pathway in Saccharomyces cerevisiae [13] . A number of genes functioning upstream from Pmk1 , including the MEK ( Mst7 ) and MEK kinase ( Mst11 ) , an adaptor protein Mst50 , and Ras2 have been identified [14] , [15] . One of the downstream transcription factors regulated by Pmk1 is Mst12 , which is required for appressorial penetration and invasive growth [16] . The Pmk1 pathway is conserved in phytopathogenic fungi for regulating various infection processes [15] . Although key components of the cAMP signaling and Pmk1 pathways have been identified , the mechanisms for recognizing physical and chemical signals of plant surfaces have not been well studied in M . oryzae and other fungal pathogens . One putative receptor gene known to be involved in surface sensing is PTH11 [12] , [17] , [18] . The pth11 mutant is reduced in virulence and appressorium formation on hydrophobic surfaces , but it forms abundant appressoria in the presence of exogenous cAMP [19] . The M . oryzae genome contains about 60 putative GPCR genes , including several PTH11-like genes with the CEFM domain . The filamentation MAPK pathway is well studied in yeast [20] . The downstream target of Kss1 , Ste12 , forms a heterodimer with Tec1 to regulate the expression of genes related to filamentous growth . Various genes , including RAS1 , CDC42 , SHO1 , and MSB2 , function upstream from the Kss1-dependent filamentation pathway [21] , [22] . MSB2 encodes a surface mucin protein that interacts with Cdc42 for filamentous growth . Msb2 also interacts with Hkr1 and functions upstream from Sho1 for responses to hyperosmotic stresses [22] . SHO1 encodes a membrane sensor protein that is involved in the activation of the Ste11-Ste7-Kss1 pathway and the Hog1 osmoregulation pathway in yeast [21] , [23] . During the preparation of this manuscript , msb2 and sho1 were shown to be essential for regulating appressorium formation and tumor development in the corn smut fungus Ustilago maydis [24] . The msb2 sho1 mutant failed to form appressoria on artificial and plant surfaces and was non-pathogenic . Because of the conserved nature of the Pmk1 and Kss1 MAPK cascades and the importance of MSB2 and SHO1 in yeast filamentous growth , we examined the expression levels of their orthologs in M . oryzae ( named MoMSB2 and MoSHO1 in this study ) . Both of them were reduced over 4-fold in the pmk1 mutant . Deletion of MoSHO1 had only minor effects on appressorium formation , but the Momsb2 deletion mutants rarely formed appressoria on artificial hydrophobic surfaces and failed to respond to cutin monomers . However , they were still pathogenic and recognized leaf surface waxes for appressorium formation . Further analyses indicated that primary alcohols in rice leaf epicuticular waxes induce appressorium formation . Expression of a dominant active allele of MST7 [14] also stimulated appressorium development in the Momsb2 mutant , which had a reduced level of Pmk1 phosphorylation . Overall , these results show that primary alcohols , a major component of epicuticular leaf waxes in grasses , are recognized by M . oryzae as signals for appressorium formation . Other fungal pathogens may also recognize primary alcohols or other wax components . Our data also indicate that MoMsb2 and MoSho1 may function as upstream sensors for the activation of the Pmk1 pathway and appressorium formation . While MoMsb2 is critical for sensing surface hydrophobicity and cutin monomers , MoSho1 may plays a more important role than MoMSB2 in recognizing rice leaf waxes .
Like other filamentous fungi , M . oryzae has one distinct ortholog each for the yeast MSB2 and SHO1 genes ( Fig . S1 ) , which were named MoMSB2 and MoSHO1 , respectively . When assayed by qRT-PCR with three independent biological replicates , both MoMSB2 and MoSHO1 transcripts had reduced expression levels ( over 4-fold ) in the pmk1 mutant but only MoMSB2 expression was significantly reduced in the mst12 mutant ( Fig . 1A ) . The MoMsb2 protein has a signal peptide and one C-terminal transmembrane ( TM ) domain ( Fig . 1B ) . Therefore , only the C-terminal portion behind the TM domain is inside the cytoplasm membrane . The bulk of the mature MoMsb2 protein is predicted to be extracellular and contains numerous putative glycosylation sites . In yeast , the promoter of MSB2 has two pheromone response elements ( PRE ) recognized by Ste12 and one TEA/ATTS consensus sequence ( TCS ) recognized by Tec1 [25] . The promoter region of MoMSB2 has two PRE-like sequences and two putative TCS elements ( Fig . S2 ) . Like MoMSB2 , MoSHO1 is well conserved in filamentous fungi . It encodes a protein with four TM and one SH3 domains ( Fig . 1B ) . Although MoSho1 and Sho1 share only 34% identity in amino acid sequences , expression of MoSHO1 in yeast functionally complemented the defects of the sho1 mutant in growth on medium with 1 . 5 M sorbitol ( Fig . 1C ) . When MoMSB2 was expressed in yeast , similar to the msb2 gene from U . maydis [24] , it failed to complement filamentation defects of the msb2 mutant . To determine their functions in M . oryzae , we used the gene replacement approach to delete the MoMSB2 and MoSHO1 genes . One Mosho1 and two Momsb2 mutants ( Table 1 ) were identified and confirmed by Southern blot analysis ( Fig . S3 ) . The two Momsb2 mutants had the same phenotype although only data for mutant M6 were presented here . The Mosho1 mutant had no obvious defects in vegetative growth , but the growth rate of the Momsb2 mutant was slightly reduced ( Table 2 ) in comparison with that of Ku80 [26] , which was used to generate the mutants . On hydrophobic surfaces , both the Momsb2 and Mosho1 mutants had no defects in conidium germination . However , the Momsb2 mutant was significantly reduced in appressorium formation . Less than 2% of its germ tubes formed melanized appressoria by 24 h ( Fig . 2A ) . Under the same conditions , over 90% and 70% of the germ tubes formed appressoria in Ku80 and the Mosho1 mutant , respectively ( Table 2 ) , indicating that the Mosho1 mutant was only slightly reduced in appressorium formation . Because of the functions of Sho1 and Msb2 in yeast filamentous growth [22] and reduced appressorium formation in both Mosho1 and Momsb2 mutants , we deleted the MoMSB2 gene in the Mosho1 mutant . Transformants MS88 and MS93 ( Table 1 ) were two Mosho1 Momsb2 mutants confirmed by Southern analysis ( Fig . S3 ) . On the hydrophobic surfaces , the double mutant produced long , curved germ tubes ( Fig . 2A ) that rarely ( <1% ) differentiated into appressoria ( Table 2 ) . These results indicate that MoMSB2 plays a critical role but MoSHO1 also plays a minor role in the recognition of surface hydrophobicity . In infection assays with rice seedlings , the Mosho1 mutant was only slightly reduced in virulence ( Fig . 2B , Table 2 ) . In contrast , the Momsb2 and Mosho1 Momsb2 mutants were significantly reduced in virulence . Only rare lesions were observed on leaves inoculated with the double mutant ( Table 2 ) . The Momsb2 mutant caused a few more lesions than the double mutant MS88 but still much less than Ku80 and the Mosho1 mutant ( Table 2 ) . Lesions caused by the Momsb2 and Mosho1 Momsb2 mutants on rice leaves tended to be smaller than those caused by Ku80 ( Fig . 2B ) and have limited necrotic borders ( Fig . S4 ) . Similar results were obtained in barley infection assays . Ku80 and the Mosho1 mutant caused numerous lesions on inoculated leaves . Under the same conditions , only a few lesions were formed on barley leaves infected with the Momsb2 and Momsb2 Mosho1 mutants , indicating a significant reduction in virulence . Results from these infection assays indicate that MoMSB2 plays a more critical role than MoSHO1 in pathogenesis . However , MoSHO1 also is required for full virulence because the Mosho1 mutant had reduced virulence ( Table 2 ) and the Momsb2 mutant appeared to be more virulent than the Momsb2 Mosho1 double mutant . For complementation assays , we re-introduced the wild-type MoSHO1 and MoMSB2 alleles to the mutants . Transformants CM6 ( Momsb2/MoMSB2 ) , CS15 ( Mosho1/MoSHO1 ) , and CMS74 ( Mosho1 Momsb2/MoSHO1 MoMSB2 ) were normal in virulence ( Fig . S5 ) , vegetative growth , and appressorium formation ( Table 2 ) , indicating that reintroduction of the wild-type MoSHO1 and MoMSB2 genes complemented the defects of corresponding mutants . Because the double mutant rarely formed appressoria on artificial surfaces but still caused blast lesions , we assayed its ability to form appressoria on rice leaves . At 24 h , the Momsb2 and Momsb2 Mosho1 mutants produced abundant melanized appressoria ( Fig . 3A ) . When examined by scanning electron microscopy ( SEM ) , 68 . 7±7 . 3% and 57 . 7±8 . 4% of the Momsb2 and Mosho1 Momsb2 germ tubes , respectively , formed appressoria ( Fig . 3B ) . Similar results were obtained in appressorium formation assays with barley leaves ( Fig . S6 ) , indicating that the Momsb2 and Mosho1 Momsb2 mutants still respond to chemical cues present on rice and barley leaf surfaces for appressorium formation . Because cutin monomers , one type of plant surface molecules , are known to trigger appressorium formation in M . oryzae [27] , we assayed the effects of two cutin monomers on mutants M6 , S72 , and MS88 . In the presence of 10 µM 1 , 16-hexadecanediol , over 95% of the wild-type and Mosho1 mutant germ tubes formed appressoria ( Fig . 4A ) . Under the same conditions , appressorium formation was not induced in the Momsb2 and Mosho1 Momsb2 mutants ( Fig . 4A ) . Similar results were obtained with 10 µM cis-9-octadecen-1-ol . Therefore , MoMSB2 is required for the recognition of these two cutin monomers in M . oryzae . Appressorium formation by the Momsb2 and Momsb2 Mosho1 mutants on rice or barley leaves is likely induced by plant surface molecules other than cutin monomers that were recognized by a MoMsb2-independent mechanism . On rice leaves , germ tubes are in close contact with the epicuticular wax layer , which may play a role in surface recognition . To test this hypothesis , epicuticular waxes were removed by dipping rice leaves in hexane for 5 seconds . On de-waxed leaves , Ku80 and the Mosho1 mutant still efficiently formed melanized appressoria ( Fig . 4B ) . However , the efficiency of appressorium formation by the Momsb2 and Mosho1 Momsb2 mutants was significantly reduced ( Table S2 ) . The vast majority of mutant germ tubes failed to form appressoria on de-waxed rice leaves ( Fig . 4B ) . Similar results were obtained in appressorium formation assays with intact and de-waxed barley leaves ( Fig . S6 ) . To further prove the role of surface waxes in stimulating appressorium formation in Momsb2 mutants , crude wax extracts were prepared from rice leaves and used to coat microscope glass slides . In Ku80 and the Mosho1 or Momsb2 mutant , about 90% of the germ tubes formed appressoria by 24 h in the presence of the rice leaf wax extract ( Fig . 4C ) . Under the same conditions , less than 32% of the germ tubes formed appressoria in the Mosho1 Momsb2 double mutant ( Fig . 4C ) , indicating that both MoSHO1 and MoMSB2 are important for appressorium formation on hydrophilic surfaces coated with leaf waxes . To test whether any rice leaf-specific wax compound is responsible for inducing appressorium formation , we also used bee and paraffin waxes to coat the glass surface . Similar to the rice leaf wax extract , bee wax induced appressorium formation more efficiently in Ku80 and the Mosho1 or Momsb2 mutant than in the Mosho1 Momsb2 double mutants ( Fig . 5A ) . However , paraffin wax could only induce appressorium formation in Ku80 and the Mosho1 mutant ( Table 3; Fig . 5B ) . In repeated experiments , only bee waxes but not paraffin waxes had stimulatory effects on appressorium formation in the MoMsb2 and Mosho1 MoMsb2 mutants . Because coating with paraffin wax changed the surface hydrophobicity ( Fig . S7 ) , these results were consistent with the defects of the Momsb2 and Mosho1 Momsb2 mutants in recognizing artificial hydrophobic surface . Paraffin wax must lack the components of rice leaf or bee waxes that are recognized by M . oryzae as chemical signals for appressorium formation . Plant surface waxes contain primary and secondary alcohols , aldehydes , ketones , alkanes , esters , and long-chain fatty acids [28] . In contrast , paraffin wax mainly consists of long chain alkanes . Because primary alcohols are the major components of surface waxes in grasses , we tested the effects of primary alcohols and alkanes on appressorium formation in M . oryzae . On hydrophilic surfaces coated with 1-octacosanol ( C28 ) and 1-triacontanol ( C30 ) , the Mosho1 Momsb2 mutant formed appressoria but less efficiently than Ku80 and the Mosho1 or Momsb2 mutant ( Fig . 5C ) . In contrast , the C29 and C31 alkanes ( nonacosane and hentricacontane alkanes ) induced appressorium formation in the wild-type strain but not in the mutants ( Table 3; Fig . 5D ) . These results indicate that primary alcohols of epicuticular waxes are among the chemical cues recognized by M . oryzae . In penetration assays with onion epidermises ( Fig . 6A ) , appressoria formed by Ku80 and the Mosho1 mutant produced invasive hyphae inside plant cells by 48 hpi . Under the same conditions , most ( over 99% ) conidia from the Momsb2 and Mosho1 Momsb2 mutants produced long germ tubes without tip differentiation . Rare appressoria formed by these two mutants failed to penetrate onion epidermal cells . Similar results were obtained in penetration assays with rice leaf sheaths ( Fig . 6B ) . Because the inner surface of rice leaf sheaths lacks epicuticular waxes [29] , efficient formation of appressoria by the Momsb2 and Mosho1 Momsb2 mutants on rice leaves but not on leaf sheaths further shows that epicuticular waxes are recognized by M . oryzae as one of the surface signals . While appressorium formation was not observed in the double mutant , rare appressoria formed by the Momsb2 mutant failed to penetrate rice leaf sheath cells , indicating that MoMSB2 is important for appressorium penetration , which may explain why the Momsb2 mutants formed abundant melanized appressoria but rarely caused lesions on rice leaves . Although Pmk1 expression was not affected , the activation of Pmk1 as detected with an anti-TpEY antibody was reduced in the Momsb2 and Mosho1 Momsb2 mutants ( Fig . 7A ) . In the same western blot analysis , the expression and phosphorylation of Mps1 [30] was normal in these three mutants ( Fig . 7A ) . Osmoregulation is mediated by Osm1 , the third MAPK in M . oryzae [30] . When detected with an anti-TpGY antibody , the wild type and mutants had similar levels of Osm1 phosphorylation ( Fig . 7B ) , suggesting that MoMSB2 and MoSHO1 play only a minor role , if any , in the osmoregulation pathway . Overall , Pmk1 was the only MAPK with a reduced phosphorylation level in Momsb2 deletion mutants , indicating that MoMSB2 may function upstream from the Pmk1 MAP kinase . The MST7 gene encodes a MEK that activates Pmk1 . Expressing a dominant active allele of MST7 induces appressorium formation on hydrophilic surfaces [14] . We introduced this MST7DA allele into the Momsb2 mutant . In the resulting transformants WDA2 and WDA12 ( Table 1 ) , appressorium formation was observed on hydrophilic and hydrophobic surfaces ( Fig . 8 ) , indicating that expression of the MST7DA allele induced appressorium formation in the Momsb2 mutant . Therefore , MoMsb2 may function upstream from the Pmk1 MAPK cascade for appressorium formation . In transformant CM6 expressing the MoMSB2-eGFP fusion construct , GFP signals were detected mainly in vacuole-like structures but also on the cytoplasmic membrane in vegetative hyphae and conidia ( Fig . 9; Fig . S8 ) . During conidium germination and appressorium formation , fluorescent signals were detected on the cytoplasmic membrane and in vacuoles that were visible under DIC microscopy ( Fig . 9A ) . Interestingly , germ tubes and young appressoria had no or very weak fluorescent signals . In mature appressoria ( 24 h ) , GFP signals mainly localized to vacuole-like structures ( Fig . 9B ) . The bulk of the mature MoMsb2 protein is predicted to be extracellular . To test the importance of the signal peptide , we generated the MoMSB2ΔSP-eGFP allele and introduced it into mutant M6 . Transformant DSSM ( Table 1 ) expressing this mutant allele , similar to the original Momsb2 mutant , was defective in appressorium formation ( Fig . 9 ) and plant infection . Weak GFP signals were detected in vegetative hyphae , conidia , and germ tubes . However , the subcellular localization pattern of GFP signals in transformant DSSM differed from that of transformant CM6 . In transformant DSSM , GFP signals mainly localize to the cytoplasm ( Fig . 9B ) . Therefore , the signal peptide is essential for the localization and function of MoMsb2 . In addition to the signal peptide , MoMsb2 has a STR region , a HMH domain , and a CT domain . We generated mutant alleles of MoMSB2-GFP deleted of different regions ( Fig . 10A ) and transformed them into the Momsb2 mutant M6 . The resulting transformants ( Table 1 ) were confirmed by PCR analysis . Transformants expressing the MoMSB2ΔHMH- and MoMSB2ΔSTR-eGFP alleles were , similar to the original Momsb2 mutant , defective in appressorium formation ( Fig . 10B ) and plant infection ( Fig . 10C ) , indicating that the STR and HMH domain are essential for MoMsb2 function . In contrast , the CT domain was dispensable for appressorium formation ( Fig . 10B ) and virulence ( Fig . 10C ) . Transformants expressing the MoMSB2Δ5STR- , and MoMSB2Δ3STR-eGFP alleles were only slightly reduced in appressorium formation and plant infection ( Fig . 10B and 10C ) . Therefore , the N-terminal and C-terminal regions of the Ser- and Thr-rich mucin domain have redundant functions in MoMsb2 .
Mucin proteins are characterized by the Ser- and Thr-rich mucin domain and divided into secreted and cell surface ( signaling ) mucins [31] . Muc1 is the most extensively studied cell surface mucin that affects various cellular functions in mammalian cells , including MAPK signaling . In yeast , MSB2 was first isolated as a multiple copy suppressor gene of a temperature sensitive allele of CDC24 . Although it is dispensable for yeast growth under normal conditions , the Msb2 surface mucin interacts with Cdc42 and Sho1 for regulating filamentous growth [22] , [32] . One other mucin gene in S . cerevisiae is HKR1 that is not related to the filamentation pathway [33] . In M . oryzae , MoMsb2 has structural components conserved in cell surface mucins ( Fig . 1 ) . The other mucin-like protein in M . oryzae is the chitin-binding protein Cbp1 [34] , which shares limited homology ( 15% identity ) with Hkr1 but lacks the mucin and TM domains . Therefore , MoMSB2 likely is the only cell surface mucin gene in M . oryzae . In M . oryzae , MoMSB2 appears to play a minor role in regulating vegetative growth . The Momsb2 mutant was slightly reduced in the growth rate but it was normal in conidiation ( Table 2 ) . In contract , the Mosho1 and Mosho1 Momsb2 mutants were reduced over 5-fold in conidiation ( Table 2 ) , suggesting that MoSHO1 is involved in the regulation of conidium production in M . oryzae . However , transformants of Mosho1 mutants expressing the wild-type allele of MoSHO1 were increased in conidiation about 3-fold but they still produced fewer conidia than Ku80 ( Table 2 ) . These results indicate that ectopic integration of MoSHO1 did not fully complement the defect of Mosho1 mutant in conidiation . Nevertheless , the Mosho1/MoSHO1 complemented strain was complemented in the growth rate and appressorium formation ( Table 2 ) . Therefore , it is likely that the Mosho1 mutant was reduced in conidiation due to deletion of MoSHO1 together with an un-related event during transformation . Surface hydrophobicity and cutin monomers are two well known surface signals recognized by M . oryzae [30] . The Momsb2 mutant was significantly reduced in appressorium formation on hydrophobic surfaces ( Table 2 ) . It also failed to respond to two cutin monomers . These results indicate that MoMsb2 is involved in sensing surface hydrophobicity and cutin monomers . Interestingly , the msb2 and sho1 genes were recently shown to be essential for appressorium formation on artificial hydrophobic and plant surfaces in U . maydis [24] . However , appressoria formed by U . maydis lack the distinct morphological features of M . oryzae appressoria . It may be difficult to observe rare appressoria formed by the sho1 msb2 mutant . The Momsb2 mutant still formed appressoria efficiently on intact rice or barley leaves , suggesting that MoMSB2 is not essential for recognizing leaf surface waxes . Therefore , surface hydrophobicity , cutin monomers , and waxes are sensed by different mechanisms in M . oryzae . In comparison with the single mutants , the Mosho1 Momsb2 double mutant had more severe defects in appressorium formation and virulence . The MoMSB2 and MoSHO1 genes must have overlapping functions in appressorium development and plant infection . In yeast , the sho1 msb2 mutant displays more severe defects in filamentous growth than the msb2 mutant [22] . In M . oryzae , MoSho1 may play a role in the recognition of rice leaf waxes . On glass surfaces coated with bee or rice leaf waxes , 90% of Momsb2 germ tubes differentiated appressoria , but less than 32% of the germ tubes formed appressoria in the Mosho1 Momsb2 mutant . We also observed that primary alcohols were more efficient in inducing appressorium formation in the Momsb2 mutant than in the Mosho1 Momsb2 double mutant . These results further prove that MoMSB2 is not important for appressorium formation induced by leaf waxes . The difference between the Momsb2 and Mosho1 Momsb2 mutants in the efficiency of wax-induced appressorium formation suggests that MoSHO1 plays a more important role than MoMSB2 in recognizing surface waxes as chemical signals for appressorium formation . Coating with waxes changed the surface hydrophobicity , which may be recognized by MoMsb2 and resulted in induced appressorium formation in the Mosho1 mutant . However , the Mosho1 Momsb2 mutant still formed a few appressoria on plant leaves or wax-coated glass slides . Additional sensor genes must exist in M . oryzae for recognizing wax components . Different genes may be responsible for responding to specific physical or chemical signals in the rice blast fungus . Bee and rice leaf waxes , but not paraffin wax , induced appressorium formation in the Momsb2 deletion mutants . Because coating with paraffin wax changed the surface hydrophobicity , these results indicate that physical signals related to hydrophobicity are not sufficient to trigger appressorium development in mutants deleted of MoMSB2 . Some components of bee and rice leaf waxes must be recognized as chemical signals by the Momsb2 deletion mutants . Unlike rice leaf waxes comprised of alcohols , aldehydes , ketones , alkanes , and esters [28] , paraffin wax mainly consists of long chain alkanes . In further experiments , two C29 and C31 alkanes , nonacosane and hentricacontane , failed to induce appressorium formation in mutant M6 or MS88 . In contrast , these mutants formed melanized appressoria on hydrophilic surfaces coated with two primary alcohols 1-octacosanol ( C28 ) and 1-triacontanol ( C30 ) . Therefore , primary alcohols but not alkanes in leaf waxes may be responsible for inducing appressorium formation in the Momsb2 and Mosho1 Momsb2 mutants . One major component of leaf epicuticular waxes in grass species is primary alcohols [35] . Other plant pathogenic fungi may also recognize primary alcohols for regulating infection-related morphogenesis . Because grapes ( one of the native environments for the budding yeast ) also are covered with waxes , it is possible that waxes play a role in inducing filamentous growth in S . cerevisiae . Deletion of MoMSB2 resulted in defects in appressorium formation on artificial surfaces and plant penetration , two processes regulated by Pmk1 [36] . Although its expression was not affected , the phosphorylation of Pmk1 was reduced in the MoMsb2 and Mosho1 Momsb2 mutants . To further prove that MoMsb2 and MoSho1 function upstream from the Pmk1 cascade , the dominant active allele of MST7 [14] was transformed into the Momsb2 mutant . The resulting transformants formed appressoria on hydrophilic surfaces ( Fig . 8 ) . Overall , these results further confirm that many components of the yeast filamentation MAPK pathway are involved in the regulation of infection-related morphogenesis in M . oryzae . In U . maydis , Msb2 and Sho1 also function upstream a MAPK pathway and are important for plant infection [24] . In nature , filamentation may be important for the budding yeast to colonize the substrates , such as grapes . Domain deletion analysis with MoMSB2 indicates that the signal peptide is essential for its function . Interestingly , the cbp1 and Momsb2 mutants had similar defects in appressorium formation although the chitin-binding protein Cbp1 protein lacks the transmembrane domain . Both MoMsb2 and Cbp1 are predicted to be heavily glycosylated according to analyses with the ( http://cbs . dtu . dk/services/NetNGlyc/ ) and ( http://ogpet . utep . edu/OGPET/ ) . It will be important to generate and characterize the Momsb2 cbp1 double mutant and determine the relationship between these two genes . MoMsb2 and Cbp1 may be functionally related by forming a surface complex for recognizing different extracellular signals to activate the Pmk1 pathway . Based on GFP signals observed in transformant CM6 , MoMSB2 is constitutively expressed . The MoMsb2-eGFP protein may localize to the cytoplasmic membrane in its inactive form . Localization to the vacuoles or vacuole-like structures may be related to the internalization of the fusion proteins . In yeast , the cleavage of Msb2 at the cleavage domain ( located upstream from the TM domain ) is essential for its activity [37] . MoMsb2 has the sequence element adjacent to the TM domain that is similar to the Msb2 cleavage domain . The activation of MoMsb2 may involve protein cleavage at this site and result in its dissociation from the cytoplasmic membrane and diffusion into cell wall and extracellular space , which may explain the absence of GFP signals in germ tubes and young appressoria of transformant CM6 . It is possible that only the cleaved MoMsb2 ( the active form ) interacts with other extracellular proteins ( such as Cbp1 ) to sense various physical and chemical signals for appressorium formation . Therefore , characterizing the role of protein cleavage in the activation and localization of MoMsb2 may provide critical information about surface recognition mechanisms in M . oryzae . PTH11 encodes a putative GPCR that is involved in recognifzing surface hydrophobicity in M . oryzae [19] . In the pth11 mutant , about 10–15% of the germ tubes still form appressoria on hydrophobic surfaces [19] , which is approximately five times higher than that of the Momsb2 mutant . In addition , the pth11 mutant , unlike the Momsb2 mutant , still responds to cutin monomers for appressorium formation . PTH11 likely functions upstream from cAMP signaling [19] but its role in the activation of the PMK1 MAPK pathway has not been studied . PTH11 and MoMSB2 may be functionally related in recognizing different chemical and physical signals present on the rice leaf surface . It will be important to determine their relationship in the activation of the cAMP-PKA and PMK1 MAPK pathways for regulating appressorium formation and penetration .
All the wild-type and mutant strains of M . oryzae ( Table 1 ) were cultured on oatmeal agar plates ( OA ) at 25°C . Culture preservation , genetic crosses , transformation , and measurements of conidiation and growth rate were performed as described [38] , [39] , [40] . For nucleic acid and protein isolation , vegetative hyphae were harvested from two-day-old liquid CM cultures [41] . Lesions formed on 5-cm rice leaf tip segments were counted as described [42] , [43] . Approximately 0 . 8-kb upstream and downstream flanking sequences of MoMSB2 were amplified with primers 11F/12R and 13F/14R ( Table S1 ) and ligated with the hph cassette . The MSB2 gene replacement construct was amplified with primers 11F and 14R and transformed into Ku80 [26] . To delete the MoSHO1 gene , a DNA fragment containing the entire MoSHO1 and its 710-bp upstream and 329-bp downstream flanking sequences were amplified with primers 1F and 2R , and cloned into pGEM-T easy vector ( Promega , Madison , WI ) . The MoSHO1 gene replacement construct pLL9 ( Fig . S2 ) was generated by replacing a 355-bp BamHI/BsiWI fragment of MoSHO1 with the hph cassette amplified from pCB1003 with primers HPH5F and HPH4R . To generate the Mosho1 MoMsb2 double mutant , the MoMSB2 replacement construct was generated with a bleomycin-resistant cassette and transformed into the Mosho1 deletion mutant S72 . Transformants resistant to both hygromycin and bleomycin were screened by PCR . For complementation assays , the MoMSB2 gene was cloned into pYP1 that was generated by replacing the hph gene in pDL1 [44] with the bleomycin-resistance gene . The resulting construct pXY130 was transformed into the Momsb2 mutant M6 . The MoSHO1 complementation vector pKNSHO1 was transformed into mutant S72 or co-transformed with pXY130 into the double mutant MS88 . The MoSHO1 ORF was amplified with primer Sho1F/Sho1R and cloned into pYES2 as pMoSHO1 . The same procedure was used to generate plasmid pMoMSB2 . The resulting constructs were transformed into the sho1 mutant obtained from Open Biosystems ( Huntsville , AL ) and the msb2 mutant [45] with the alkali-cation yeast transformation kit ( MP Biomedicals , Solon , OH ) . Ura3+ transformants were assayed for invasive growth and sensitivity on YPD and YPGal plates with 1 . 5 M sorbitol as described [32] , [46] . Conidia were harvested from 10-day-old OA cultures , resuspended to 5×104 conidia/ml in sterile water , and used for appressorium formation assays with glass cover slips ( Fisher Scientific , Pittsburgh , PA ) or Gelbond membranes ( FMC , Philadelphia , PA ) as described [40] , [47] . Penetration assays were conducted with onion epidermal cells and rice leaf sheaths [4] , [48] . Conidia were harvested from 10-day-old OA cultures and resuspended to 5×105 conidia/ml in 0 . 25% gelatin . Two-week-old seedlings of rice cultivars Nipponbare and CO-39 were used for spray or injection infection assays as described [49] . Eight-day-old seedlings of barley cultivar Golden Promise were used for spray or drop inoculation assays as described [50] . Lesion formation was examined 5–7 days post-inoculation ( dpi ) . For assaying changes in virulence , lesions formed on 5-cm leaf segments were counted as described [38] , [51] . Rice leaves of two-week-old seedlings were dipped in hexane for 20 s . Epicuticular waxes dissolved in hexane were recovered by evaporation under a nitrogen stream [52] . For coating glass surface , 10 mg of the rice leaf surface wax was dissolved in 3 ml chloroform . Aliquot of 50 µL were dropped onto microscope glass slides ( Gold Seal , Portsmouth , NH ) . Bee wax ( Stakich Inc . , Bloomfield Hills , MI ) and paraffin wax ( Oak Sales , San Diego , CA ) were directly applied on to glass surface . Drops of 25 µl conidium suspensions were placed on wax-coated areas and assayed for appressorium formation . The C28 ( 1-octacosanol , C28H58O ) and C30 ( 1-triacontanol , C30H62O ) primary alcohols and C29 ( nonacosane , C29H60 ) and C31 ( hentricacontane , C31H64 ) alkanes ( Sigma ) were dissolved to 4 mg/ml in chloroform . Barley and rice leaves inoculated with the wild-type and mutant conidia were sampled at 24 hpi and immediately frozen in liquid nitrogen . After being sputter-coated with gold in the presence of argon in a Hexland CT-1000 cryo-system ( Gatan , Pleasanton , CA ) , leaf samples were examined at −140°C in a JEOL JSM-840 scanning electron microscope for germ tube growth and appressorium formation [49] . A 4 . 2-kb fragment of the MoMSB2 gene was amplified with primers MGFPF and MGFPR ( Table S1 ) and co-transformed with XhoI-digested pYP1 into XK1-25 [42] . For MoSHO1 , a 2 . 8-kb fragment was amplified with primers SGFPF and SGFPR and co-transformed into XK1-25 with XhoI-digested pDL2 [44] . Plasmids pXY130 and pXY122 containing the MoMSB2-eGFP and MoSHO1-eGFP fusion constructs , respectively , were recovered from Trp+ yeast transformants and transformed into protoplasts of 70-15 . For complementation assays , the MoMSB2-eGFP and MoSHO1-eGFP constructs were transformed into the Momsb2 mutant M6 and the Mosho1 mutant S72 , respectively . Zeocin-resistant transformants were examined for GFP signals in conidia , appressoria , and infectious hyphae as described [42] , [53] . To delete the signal peptide , PCR products amplified with primers MGFPF/ΔSSR1 and ΔSSF2/MGFPR were cotransformed with XhoI-digested pYP1 into S . cerevisiae strain XK1-25 . Plasmid pXY148 was recovered from yeast Trp+ transformants and confirmed by sequencing analysis to contain the MoMSB2ΔSP construct . The same yeast gap repair approach [44] was used to generate the MoMSB2ΔHMH , MoMSB2ΔCT , MoMSB2Δ5STR , MoMSB2Δ3STR , and MoMSB2ΔSTR alleles . PCR primers for generating these mutant alleles were listed in supplemental table 1 . All domain deletion constructs were transformed into the Momsb2 mutant M6 . RNA samples were isolated from mycelia from two-day-old liquid CM cultures and 24 h appressoria with the Trizol Reagent ( Invitrogen , CA ) . First-strand cDNA was synthesized with the AccuScript 1st strand cDNA synthesis kit ( Stratagene , La Jolla , CA ) . RT-PCR was performed with the Stratagene Gene MX 3000 PM using the RT2 Real-TimeTM SYBR Green/ROX PCR master mix ( SABiosciences , MD ) . Primer pairs Msb2QF/Msb2QR and AQF/AQR were used to amplify the MoMSB2 and actin ( MGG_03982 ) genes , respectively . The relative quantification of each transcript was calculated by the 2-ΔΔCT method [54] with the actin gene as the internal control . Vegetative hyphae were harvested from 2-day-old CM cultures and used for protein extraction as described [42] , [55] . Total proteins ( approximately 20 mg ) were separated on a 12 . 5% SDS-PAGE gel and transferred to nitrocellulose membranes for western blot analysis [56] . TEY- and TGY-specific phosphorylations of MAP kinases were detected with the PhophoPlus p44/42 and p38 MAP kinase antibody kits ( Cell Signaling Technology , Danvers , MA ) following the manufacturer's instructions . Sequence data for genes described in this article can be found in the GenBank under the following accession numbers: MSB2 ( NP_011528 ) , SHO1 ( NP_011043 ) , MoMSB2 ( MG06033 ) , MoSHO1 ( MG09125 ) , Aspergillus nidulans AnMSB2 ( ANID_07041 ) , A . nidulans AnSHO1 ( ANID_07698 ) , Neurospora crassa NcMSB2 ( NCU04373 ) , N . crassa NcSHO1 ( NCU08067 ) , Candida albicans CaMSB2 ( XP_722538 ) , C . albicans CaSHO1 ( CAC81238 ) .
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The rice blast fungus is a major pathogen of rice and a model for studying fungal-plant interactions . Like many other fungal pathogens , it can recognize physical and chemical signals present on the rice leaf surface and form a highly specialized infection structure known as appressorium . A well conserved signal transduction pathway involving the protein kinase gene PMK1 is known to regulate appressorium formation and plant penetration in this pathogen . However , it is not clear about the sensor genes that are involved in recognizing various plant surface signals . In this study we functionally characterize two putative sensor genes called MoMSB2 and MoSHO1 . Genetic and biochemical analyses indicated that these two genes have overlapping functions in recognizing different physical and chemical signals present on the rice leaf surface for the activation of the Pmk1 pathway and appressorium formation . We found that primary alcohols , a major component of leaf waxes in grasses , can be recognized by the rice blast fungus as chemical cues . While MoMSB2 is critical for sensing hydrophobicity and precursors of cutin molecules of rice leaves , MoSHO1 appears to be more important than MoMSB2 for recognizing wax components .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbiology/microbial",
"growth",
"and",
"development",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis",
"cell",
"biology/cell",
"signaling"
] |
2011
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Multiple Plant Surface Signals are Sensed by Different Mechanisms in the Rice Blast Fungus for Appressorium Formation
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Metarhizium spp . are being used as environmentally friendly alternatives to chemical insecticides , as model systems for studying insect-fungus interactions , and as a resource of genes for biotechnology . We present a comparative analysis of the genome sequences of the broad-spectrum insect pathogen Metarhizium anisopliae and the acridid-specific M . acridum . Whole-genome analyses indicate that the genome structures of these two species are highly syntenic and suggest that the genus Metarhizium evolved from plant endophytes or pathogens . Both M . anisopliae and M . acridum have a strikingly larger proportion of genes encoding secreted proteins than other fungi , while ∼30% of these have no functionally characterized homologs , suggesting hitherto unsuspected interactions between fungal pathogens and insects . The analysis of transposase genes provided evidence of repeat-induced point mutations occurring in M . acridum but not in M . anisopliae . With the help of pathogen-host interaction gene database , ∼16% of Metarhizium genes were identified that are similar to experimentally verified genes involved in pathogenicity in other fungi , particularly plant pathogens . However , relative to M . acridum , M . anisopliae has evolved with many expanded gene families of proteases , chitinases , cytochrome P450s , polyketide synthases , and nonribosomal peptide synthetases for cuticle-degradation , detoxification , and toxin biosynthesis that may facilitate its ability to adapt to heterogenous environments . Transcriptional analysis of both fungi during early infection processes provided further insights into the genes and pathways involved in infectivity and specificity . Of particular note , M . acridum transcribed distinct G-protein coupled receptors on cuticles from locusts ( the natural hosts ) and cockroaches , whereas M . anisopliae transcribed the same receptor on both hosts . This study will facilitate the identification of virulence genes and the development of improved biocontrol strains with customized properties .
Most fungi with sequenced genomes are plants pathogens or saprophytes . However , there are also thousands of entomopathogenic fungal species that play a crucial role in controlling insect populations . The genus Metarhizium includes the best studied entomopathogenic fungi at the molecular and biochemical level . They have a world-wide distribution from the arctic to the tropics and colonize an impressive array of environments including forests , savannahs , swamps , coastal zones and deserts [1] . Metarhizium species are amongst the most abundant fungi isolated from soils with titers reaching 106 conidia per gram in grasslands [2] . The genus contains M . anisopliae , which has a broad host range , as well as specialists , such as the locust-specific pathogen M . acridum . These two species in particular have emerged as excellent model organisms to explore a broad array of questions in ecology and evolution , host preference and host switching , and to investigate the mechanisms of speciation . In addition , both M . anisopliae and M . acridum have been at the forefront of efforts to develop biocontrol alternatives to chemical insecticides in agricultural and disease-vector control programs , and many commercial products are on the market or under development [2]–[4] . Our knowledge of the ecological impact of M . anisopliae and its potential as a biocontrol agent has recently been enhanced by the discovery that it colonizes plant roots where it may simultaneously act as a biofertilizer and biopesticide to boost plant growth [5] . Consistent with its broad lifestyle options , M . anisopliae exhibits an extremely versatile metabolism , enabling growth under various environmental conditions , with sparse nutrients and in the presence of compounds lethal to other fungi [6] . As the asexual stages ( anamorphs ) of medicinally valued Cordyceps spp . [7] , Metarhizium spp . are prolific producers of enzymes and diverse secondary metabolites with activities against insects , fungi , bacteria , viruses and cancer cells [6] , [8] , [9] . In addition , the enzymes from Metarhizium spp . are frequently exploited as industrial catalysts [10] , [11] . M . anisopliae has also been used in studies on the immune systems of invertebrate model hosts to provide insights into emerging human pathogens [12] , and it is a developing model for studies on aging [13] , [14] . In contrast to the versatile M . anisopliae , the specialist M . acridum is specific for certain locusts and grasshoppers [15] . However , like M . anisopliae , it is a producer of diverse cell types ( e . g . , conidia , hyphae , appressoria , unicellular blastospores , and multi-cellular hyphal bodies ) that facilitate the infection of target insects via adhesion and penetration of the host cuticle , proliferation within tissues and the haemolymph , and eventual eruption through the host cadaver ( Figure 1 ) . M . acridum is mass produced and used on a large scale for locust control [16] , whereas few other biological control agents have been such a commercial success because of poor efficacy compared to chemicals [17] . Although recent advances have identified the functions of several pathogenicity genes [18]–[22] and technical developments improved the virulence of M . anisopliae [23] , [24] , the need to understand these fungi and expand their biotechnological potential requires sequenced genomes of M . anisopliae and M . acridum . Sequencing two related species that have evolved very different lifestyles will increase their utility as models , and provide insights into the evolution of pathogenicity . Such sequences will also allow for more rapid identification of genes encoding biologically active molecules and genes responsible for interactions between fungi , plants and insects . These findings could be further translated into the development of improved strains with customized properties that could potentially function as comprehensive plant symbionts to improve plant establishment and sustainable agriculture , particularly on marginal lands .
The genomes were each shotgun sequenced to ∼100× coverage . The M . anisopliae genome ( strain ARSEF 23 ) was assembled into 176 scaffolds ( >1 kb; N50 , 2 . 0 Mb ) containing 1 , 271 contigs with a total size of 39 . 0 Mb ( loci tagged as MAA ) . The M . acridum genome ( strain CQMa 102 ) was assembled into 241 scaffolds ( >1 kb; N50 , 329 . 5 kb ) containing 1 , 609 contigs with a genome size of 38 . 0 Mb ( loci tagged as MAC ) ( Table 1 ) . These assemblies closely correspond to the genome sizes of other Ascomycetes ( Table S1 ) . By mapping >6 , 000 unique expressed sequenced tagged sequences to the scaffolds , each genome was estimated to be >98% complete . M . anisopliae and M . acridum were predicted to have 10 , 582 and 9 , 849 protein coding genes , respectively , which is similar to the coding capacity of other Ascomycetes ( Table S1 ) . We examined homology relationships between M . anisopliae and M . acridum , and a set of eight other ascomycete genomes ( Figure 2A ) . The results indicated that ∼90% of the genes in both Metarhizium genomes have homologs ( E≤1×10−5 ) in other Ascomycetes . In addition , M . anisopliae has 398 ( 3 . 8% ) genes with matches restricted to M . acridum ( Metarhizium-restricted genes ) and 263 ( 2 . 5% ) orphan sequences . M . acridum has 219 ( 2 . 2% ) orphan sequences ( Figure 2A ) . Further analysis of the M . anisopliae orphans showed that 21 . 3% had matches in bacteria , 3 . 4% in animals and 3 . 8% in viruses . Similarly , 13 . 3% , 5 . 5% and 2 . 7% of the M . acridum orphans had matches in bacteria , animals and viruses , respectively , consistent with possible horizontal gene transfer events . The proportion of genes encoding secreted proteins is remarkably large , being 17 . 6% ( 1 , 865 proteins ) in M . anisopliae and 15 . 1% ( 1 , 490 proteins ) in M . acridum as compared to 7–10% in plant pathogens [25] and ∼5% in N . crassa [26] or A . nidulans [27] . As expected , many of the secreted proteins are in families which could have roles in colonization of insect tissues , such as proteases ( Table S2 ) . However , 32 . 2% of M . anisopliae and 28 . 7% of M . acridum secreted proteins had no conserved domains or functionally characterized homologs . Of these , ∼22% were Metarhizium-restricted genes and ∼4% were orphan genes in either genome . Pairwise comparison indicated that the two Metarhizium genome structures have large areas of synteny ( Figure 2B , Figure S1A ) . The lineage specific regions of M . anisopliae and M . acridum contain high densities of transposases , species-specific genes , genes encoding proteins with unknown functions and pseudogenes ( Figure S1B ) . Similar lineage-specific regions were found in Fusarium spp . [28] . Ninety nine percent of the M . anisopliae genome comprises non-repetitive sequences , and the orthologs shared with the M . acridum genome display an average 89 . 8% amino acid identity . The two Metarhizium species are therefore more closely related than the three Aspergillus species A . nidulans , A . fumigatus and A . oryzae which share only 68% average sequence identity [29] . A phylogenomic analysis revealed that M . anisopliae and M . acridum lineages diverged about 33–43 million years ( MY ) ago and are most closely related to the mutualistic plant endophyte Epichloe festucae ( divergence time 88–114 MY ) and to the wheat head blight fungus Fusarium graminearum ( divergence time 144–187 MY ) ( Figure 2C ) . The specialist M . acridum harbors more repetitive elements than M . anisopliae but the latter has many more transposases ( Table S2 ) . Most of these are DNA transposases ( 97/148 in MAA; 12/20 in MAC ) , with subclasses hAT ( 45/97 ) and Helitron ( 26/97 ) being particularly abundant in M . anisopliae . The Copia ( 17 ) and LINE ( 26 ) retrotransposons are also abundant in the genome of M . anisopliae , while M . acridum has only three LINE elements and does not contain Copia ( Figure 3A ) . Transcriptome analysis ( see below ) showed that most ( >65% ) of the transposase genes were transcribed by the Metarhizium hyphae during the infection process ( Table S3 ) . The number of putative transposases in the M . acridum genome is lower by at least a factor of five than in most Ascomycetes , including M . anisopliae ( Table S2 ) . This could be explained by repeat induced point mutations ( RIP ) introducing CpG to TpA transitions in duplicated sequences during the sexual cycle [30] . This mutational bias is observed in M . acridum ( RIP index , 2 . 17 ) but not in M . anisopliae ( RIP index , 1 . 09 ) ( Figure 3B ) . Consistent with Neurospora crassa which has efficient RIP [31] , the genome of M . acridum contained twice as many duplicated pseudogenes ( 254 versus129 ) as did that of M . anisopliae . The M . anisopliae genome contains more processed and fragmented pseudogenes caused by mobile elements ( 234 versus 186 ) , consistent with transposons making a greater contribution to genetic instability in M . anisopliae ( Table S4 ) . The production of stable biocontrol agents for commercialization might therefore benefit from disabling transposable elements . An InterproScan analysis identified 2 , 710 protein families ( containing 7 , 178 proteins ) in M . anisopliae and 2 , 658 families ( containing 6 , 615 proteins ) in M . acridum . A stochastic birth and death model [32] showed that relative to M . acridum , 42 families including transporters , transcription factors , cytochrome P450s , proteases and lipases were expanded and three families ( protein kinase , aminotransferase and transpeptidase ) were contracted in M . anisopliae ( Table S5 ) . This resulted in M . anisopliae having more genes in most functional categories except for those involved in signal transduction ( Figure 4 , Table 2 ) . To find potential virulence-associated genes , a whole genome blast analysis was conducted against the pathogen-host interaction ( PHI ) gene database , a collection of experimentally verified pathogenicity , virulence and effector genes from fungi , oomycetes and bacteria [33] . We identified 1 , 828 putative PHI genes in M . anisopliae ( 17 . 3% of its genes , belonging to 383 protein families ) and 1 , 629 putative PHI genes in M . acridum ( 16 . 5% , 371 families ) , of which 1 , 331 genes were orthologous . Although there are no entries from entomopathogenic fungi in the PHI-base , we proceeded on the assumption that the proof of pathogenicity/virulence of a gene in one fungus also suggests a pathogenicity/virulence function in other fungi [34] . In accordance with this assumption , the search of the PHI database yielded several already characterized M . anisopliae pathogenicity determinants , including subtilisins ( see below ) and hydrophobins ( small cell wall proteins ) that have pleiotropic functions in M . anisopliae including attachment of spores to hydrophobic surfaces [35] . The class 2 ( MAA_01182 and MAC_09507 ) and class 1 ( MAA_10298 and MAC_04376 ) hydrophobins had significant similarity with PHI sequences from plant pathogenic fungi . The previously characterized adhesin , MAD1 ( MAA_03775 ) required for specific binding to insect host surfaces [20] , resembled EAP1 ( PHI acc: 517 ) from the human pathogen Candida albicans . However , the adhesin MAD2 ( MAA_03807 ) required for binding to plant surfaces [20] , had no significantly similar sequence in the PHI database . Orthologs to both MAD1 ( MAC_00987 ) and MAD2 ( MAC_00953 ) were found in the M . acridum genome . Using the PHI-base content with a focus on ascomycetes , Sexton and Howlett found many parallels in the infection mechanisms used by plant and animal pathogens [36] . To determine how many plant pathogen PHI genes are also found in Metarhizium , we screened the F . graminearum and M . oryzae genomes against the PHI-base and identified 2 , 053 genes ( in 398 families ) and 1 , 713 genes ( in 427 families ) , respectively , representing about 16% of gene contents in these two fungi ( Table S6 ) . Approximately , 70% of these genes are orthologous to PHI sequences in M . anisopliae and M . acridum . Fewer Metarhizium orthologs were found in animal pathogenic fungi such as C . albicans , which could be explained by Metarhizium being more closely related to plant pathogens ( Figure 2C ) as well as the animal pathogens lacking appressoria ( infection structures ) during host penetration [4] . Insect pathogens such as Metarhizium spp . need to penetrate the protein-chitin rich insect cuticle and solubilize host tissues for nutrition . Therefore , they would be expected to secrete large numbers of degradative enzymes . Indeed , M . anisopliae has more genes encoding secreted proteases ( 132 ) than other sequenced fungi ( Table S2 ) . The trypsin family has the highest relative expansion among the proteases with 32 genes in M . anisopliae , almost twice as many as M . acridum and 6 to 10 times as many as the other taxa evaluated ( Figure 5A , Table S2 ) . A chymotrypsin ( MAA_07484 ) that might have been imported from bacteria through horizontal gene transfer [37] and two trypsins that were recently duplicated in M . anisopliae ( MAA_05135 and MAA_05136 ) are missing from the M . acridum genome ( Table S7 ) . Subtilisins ( 55 in MAA and 43 in MAC; 7 to 31 in other fungi ) ( Figure 5B , Table S8 ) and aspartyl proteases ( 33 in MAA and 25 in MAC; 9 to 21 in other fungi ) ( Table S9 ) are also expanded in M . anisopliae due to lineage-specific duplications ( Figure S1C ) . Most of the Metarhizium subtilisins ( 48 in MAA and 37 in MAC ) and aspartyl proteases ( 27 in MAA and 23 in MAC ) had significant matches in the PHI-base . Subtilisins assist in the infection processes of M . anisopliae by degrading host cuticles , providing nutrition and disabling antimicrobial peptides [38] . The importance of Metarhizium aspartyl proteinases has not been demonstrated but they resemble the aspartyl proteases that assist the human pathogen C . albicans by degrading cell surface molecules [39] . Many plant pathogens need glycoside hydrolases , pectate lyases and cutinases to degrade the plant cuticle ( waxy layer ) and cell wall . The number of glycoside hydrolases ( GH ) possessed by M . anisopliae ( 156 ) and M . acridum ( 140 ) is close to the average for plant pathogenic fungi ( 150 ) ( Table S10 ) . However , only ∼20% of the Metarhizium GH genes ( 36 in MAA and 29 in MAC ) were similar to PHI catalogued sequences as compared to 44% ( 70 genes ) in F . graminearum and 29% ( 57 genes ) in M . oryzae ( Table S6 ) . The plant pathogens in particular have additional GH3 cellulases while Metarhizium spp . lack the GH11 family of xylanases . GH3 and GH11 family genes are catalogued in the PHI-base . Overall , fewer genes were associated with plant utilization in Metarhizium than in plant pathogens . This included fewer putative cutinases ( 2 in Metarhizium spp . 8 to 18 in plant pathogens ) and pectate lyases ( 7 in Metarhizium spp . ; 9 to 25 in plant pathogens ) . However , the GH16 family of xyloglucan/xyloglucosyl transferases involved in decomposition of plant cell walls is well represented in the Metarhizium genomes ( 18 in MAA and 16 in MAC; 6 to 16 in plant pathogens ) ( Table S10 ) . More predictably , GH18 chitinases involved in the digestion of insect cuticle chitin [40] , are over represented in Metarhizium ( 30 in MAA and 21 in MAC; 5 to 14 in plant pathogens ) ( Figure 5C , Table S6 ) . The few chitinases included in the PHI database are involved in fungal developmental processes , as chitin is not a substrate found in animal and plant hosts . The number of genes for secreted lipases ( 12 in MAA , 5 in MAC ) is well above the average found in other fungi , and 9 M . anisopliae and 5 M . acridum lipases showed significant similarity to genes in the PHI-base , as compared to 3 lipases each in F . graminearum and M . oryzae ( Table S6 ) . The role of individual Metarhizium lipases in pathogenicity has not been demonstrated , although a lipase activity inhibitor blocks infection processes in M . anisopliae [41] . Lipases MAA_03127 and MAC_09232 showed best-hit relationships with an extracellular lipase FGL1 ( PHI acc: 432 ) that is a virulence factor in F . graminearum [42] . Metarhizium genomes encode a large number of transporters ( 484 in MAA and 441 in MAC ) ( Table S11 ) . Most transporters belong to the major facilitator superfamily ( MFS ) ( 269 in MAA; 236 in MAC ) but the ATP-binding cassette ( ABC ) is also well represented ( 56 in MAA; 51 in MAC ) ( Table S11 ) . Most of the ABC transporters ( 52/56 in MAA and 46/51 in MAC ) and many of the MFS transporters ( 124/269 in MAA and 96/236 in MAC ) were similar to genes catalogued in the PHI database ( Table S6 ) . The ABC transporters are usually implicated in defending the pathogen from host-produced secondary metabolites , whereas MFS proteins are typically involved in the transport of a wide range of substrates and may function as nutrient sensors [43] . Interestingly , both Metarhizium species have more amino acid and peptide transporters than do other fungi ( 60 in MAA and 57 in MAC; 29 to 38 in other fungi ) , consistent with their being able to access a range of protein degradation products from insect sources . Homologs of these genes are absent from the PHI database . The only Metarhizium transporter with an experimentally determined function is the sucrose and galactoside transporter MRT ( belonging to the MFS superfamily ) , which is required by M . anisopliae for rhizosphere competence but not for virulence [44] . There are 6 MRT homologs in M . anisopliae and 5 in M . acridum but 12 in F . graminearum and 26 in M . oryzae , suggesting these genes could be generally important for establishing plant-fungus relationships . Additional evidence about lifestyle could be found in the relatively large number of genes involved in detoxification in both Metarhizium genomes ( Table 2 , Table S2 ) as these potentially contribute to interactions with insect hosts ( Table S6 ) . However , families of dehydrogenases , acyl-CoA N-acetyltransferases , monooxygenases and cytochrome P450s ( CYP ) were preferentially expanded in M . anisopliae relative to M . acridum ( Table 2 , Table S5 ) . One third of the dehydrogenases ( 92/271 in MAA and 80/236 in MAC ) were putative PHI genes ( Table S6 ) . M . anisopliae was particularly enriched in zinc-containing alcohol dehydrogenases ( 17 in MAA; 7 in MAC ) required for the biosynthesis of mannitol , a crucial factor for stress tolerance and virulence in the animal pathogen Cryptococcus neoformans [45] . The monooxygenases in particular might be involved in rapid elimination of insect polyphenolics by ortho-hydroxylation of phenols to catechols [46] . The genome of M . anisopliae encodes 123 highly divergent CYP genes verses 100 CYPs in M . acridum ( Figure 5D , Table S12 ) . Ninety of the M . anisopliae CYPs and 69 of the M . acridum CYPs are similar to sequences in the PHI-base ( Table S6 ) . CYP genes are involved in oxygenation steps during alkane assimilation and the biosynthesis of secondary metabolites as well as with detoxification [47] . M . anisopliae efficiently metabolizes the alkanes that are a major component of the surface layer of the insect cuticle ( epicuticle ) [48] . Although the CYP52 subfamily is particularly important for alkane oxidation [49] , M . anisopliae has only a single CYP52 ( MAA_06634 ) compared to four in M . acridum ( Table S12 ) . However , MAA_06634 and its ortholog in M . acridum ( MAC_09267 ) were highly expressed ( see below ) by M . anisopliae and M . acridum when infecting either cockroach or locust cuticles ( Figure S5A ) . The other CYP genes up-regulated on cuticles were mostly involved in detoxification . M . anisopliae and M . acridum are predicted to contain four and two CYP504s , respectively . CYP504s are used by fungi to degrade phenylacetate [50] , an antimicrobial compound found in plants and insects [51] . The subfamily CYP53 is also represented in the PHI database as it is responsible for detoxification of benzoate and its derivatives [52] . M . anisopliae and M . acridum have two and one CYP53 genes , respectively . The subfamily CYP5081 involved in biosynthesis of helvolic acid , an antibiotic toxin [53] , consists of four closely localized CYP loci ( PHI genes ) in M . anisopliae ( MAA_06585 , MAA_06586 , MAA_06589 and MAA_06593 ) that are absent in M . acridum . All four CYP5081 genes were expressed by M . anisopliae infecting cuticles ( Figure 5D ) . Both M . anisopliae and M . acridum have three CYP genes putatively encoding lipid dioxygenases ( CYP6001: MAA_04954 and MAC_00208; CYP6002: MAC_05834; CYP6003: MAA_03481 and MAC_00918; CYP6004: MAA_0003 ) and two lipoxygenases ( MAA_06278 and MAA_01260; MAC_01254 and MAC_9416 ) . Oxylipins , the end products of these genes , allow Aspergillus nidulans to colonize plant seeds [54] , and seeds are also a habitat for M . anisopliae [55] , implying that a similar strategy is employed by Metarhizium to establish plant-fungus relationships . M . anisopliae is a prolific producer of secondary metabolites including insecticidal destruxins [56] , but with the exception of the serinocyclins [57] and NG-391 [58] , the genes involved in their biosynthesis are unknown . However , diagnostic genes for secondary metabolite production include those encoding polyketides and non-ribosomal peptides ( the most prominent classes of fungal secondary metabolites ) , as well as those responsible for modifications of the core moiety ( a peptide or polyketide ) such as genes encoding dehydrogenases , methyltransferases , acetyl transferases , prenyltransferases , oxireductases and CYPs [36] . Consistent with expressed sequence tag studies [59] , M . anisopliae appears to possess a much greater potential for the production of secondary metabolites than M . acridum or most other fungi ( Tables S2 and S13 ) . The M . anisopliae genome encodes 14 putative non-ribosomal peptide synthases ( NRPS ) , 24 polyketide synthases ( PKS ) and 5 NRPS-PKS hybrid genes , which is more than M . acridum ( 13 NRPS genes , 13 PKS genes and 1 NRPS-PKS hybrid ) and the average in other Ascomycetes ( 7 NRPS , 12 PKS genes and 1 NRPS-PKS ) ( Table S13 ) . NRPSs and PKSs are strongly associated with pathogenicity in many plant pathogenic fungi and are well represented in the PHI database . As in other fungi , Metarhizium NRPS and PKS genes were often clustered together with genes that modify their products . One cluster suggests that Metarhizium might produce prenylated alkaloids ( Figure S2 ) . M . anisopliae possesses putative NRPS-like antibiotic synthetases ( MAA_08272 ) consistent with defending the cadaver against microbial competitors . It also possesses a putative bassianolide synthetase ( MAA_07513 ) , a virulence factor of the insect pathogen Beauveria bassiana [60] . The NRPS-like proteins MAA_07148 and MAC_06316 are most similar to ACE1 , a PKS/NRPS hybrid that confers avirulence to M . grisea during rice infection [61] . M . anisopliae NRPS MAA_00969 is similar ( 43% identity ) to HTS1 , the key enzyme responsible for the biosynthesis of the host-selective HC-toxin that confers the specificity of Cochliobolus carbonum to maize [62] . Sixteen out of 24 PKS and 5/14 NRPS genes in M . anisopliae are species specific versus 4/13 PKS and 5/13 NRPS in M . acridum , suggesting lineage specific expansion of these families in both Metarhizium species . However , it is reassuring for present and future commercialization of these fungi that we found no orthologs of genes for the biosynthesis of the human mycotoxins gliotoxin and aflatoxin . To recognize and adapt to invertebrate environments such as the insect cuticle , hemolymph and cadaver , Metarhizium spp . need to rapidly respond to changes in nutrient availability , osmolarity and the host immune system [18] , [63] . In Magnaporthe , the Pth11-like G-protein coupled receptor ( GPCR ) is a PHI gene ( PHI-base acc: 404 ) because it mediates cell responses to inductive cues [64] . M . anisopliae and M . acridum have 54 and 40 putative PTH11-like GPCRs , respectively compared to an average of 32 in other fungi ( Table S2 , Table S14 ) . The Metarhizium sequences could be grouped into six subfamilies ( Figure S3 ) . G protein alpha subunits have been extensively studied in fungi and many are required for pathogenicity because they transduce extracellular signals leading to infection-specific development [65] . Distinct roles for three G protein alpha subunit genes have been revealed in M . grisea , A . nidulans and N . crassa . A fourth G-alpha protein has been identified in the plant pathogens Stagonospora nodorum ( SNOG_06158 ) [66] , Ustilago maydis ( UM05385 ) [67] , and the saprophyte A . oryzae ( BAE63877 ) [68] . Each of the Metarhizium genomes also contain four G-alpha genes . The genes MAA_03488 and MAC_04984 show best hits ( >30% similarity ) with SNOG_06158 , UM05383 and BAE63877 , suggesting they may be orthologous . SNOG_06158 is the most highly up-regulated S . nodorum G-alpha gene in planta [66] . Likewise , MAA_03488 and MAC_04984 are the most highly expressed G-alpha genes during infection of either cockroach or locust cuticles ( see below , Table S20 ) . The chief mechanism used by bacteria for sensing their environment is based on two conserved proteins: a sensor histidine kinase ( HK ) and an effector response regulator ( RR ) that functions as a molecular switch controlling diverse activities . In fungi , two component pathways mediate environmental stress responses and hyphal development [69] . M . anisopliae and M . acridum have 10 and 9 histidine kinases , respectively compared to 3 to 20 in other fungi ( Table S2 ) . To regulate cell function , M . acridum has 192 protein kinases as compared to 161 in M . anisopliae which is still above the average ( 143 ) found in other fungi ( Tables S5 and S15 ) . Much of the M . acridum expansion involves cyclin dependent and cell division control kinases , suggesting that M . acridum has a particularly complex signal transduction cascade controlling cell division . As signal transduction is a critical part of fungal development and infection processes , and accordingly most of the kinases had orthologs in the PHI database ( 124/161 in MAA and 137/192 in MAC ) . The high frequency of pseudogenes among kinases ( M . acridum , 1:6; M . anisopliae , 1:8 ) , compared to transporters ( M . anisopliae , 1:82; M . acridum , 1:33 ) and other gene families suggests that protein kinases have a particularly high rate of turnover ( Table S16 ) . Differentially lost genes tend to function in accessory roles so these kinases might have had redundant functions in signal transduction that changed rapidly under strong selective constraints . Following signaling transduction , physiological responses are regulated by activation of different transcription factors . M . anisopliae has 510 putative transcription factors compared to 439 in M . acridum , the difference being largely due to M . anisopliae having more C2H2 zinc finger and Zn2/Cys6 transcription factors ( Tables S5 and S17 ) . These families are also expanded in some Aspergilli , where the characterized examples are involved in regulating diverse aspects of primary and secondary metabolism , including protein and polysaccharide degradation [70] . The cAMP response element-binding ( CREB ) protein , a basic leucine zipper transcription factor ( bZIP ) , is a major downstream transcription factor for cAMP/PKA pathways in mammals [71] . CREB has not been characterized in fungi; however , our transcriptome data shows that a putative bZIP transcription factor ( MAA_02048 or MAC_02758 ) is highly expressed by each Metarhizium species coincident with up-regulation of protein kinase A ( see below ) . The physiological role ( s ) of MAA_02048 are currently under investigation . Insect bioassays confirmed that M . acridum killed locusts but not cockroaches , while M . anisopliae killed both insects ( Figure S4 ) . In order to identify the putative signal transduction and metabolic pathways involved in formation of infection structures , we used RNA-Seq to compare transcriptional responses of M . anisopliae and M . acridum to infection of the optically clear hind wings of adult locusts and cockroaches , respectively . A time period of 24 hours was chosen to focus on the crucial processes involved in prepenetration growth e . g . , adhesion to epicuticle , germination and production of appressoria [72] . After sequencing >2 . 5 million tags for each treatment , it was calculated that >82% of predicted M . anisopliae genes and >88% of predicted M . acridum genes were expressed during pre-penetration growth . This included more than 80% of the M . anisopliae and M . acridum genes with sequences similar to those in the PHI database ( Table S18 ) . Germination and growth by M . anisopliae and M . acridum on either insect triggered high level expression of genes associated with translation ( e . g . , ribosomal proteins ) and post-translational modifications ( e . g . , heat shock proteins ) ( Figure S5 , Table S19 ) . However , otherwise , the two fungi differed greatly from each other in their transcriptional responses to each cuticle , and to a lesser extent the two cuticles elicited different responses from each fungus ( Figure 6 , Figure S6 ) . The orthologs of many differentially expressed genes are involved in appressorial formation and function in plant pathogens ( Table S19 ) , including Cas1 from Glomerella cingulata and Mas1 from M . oryzae [73] . Three of these genes were among the five most highly expressed M . acridum genes on locust cuticle . Their expression levels were ∼2-fold lower on cockroach cuticle , similar to a previously characterized cuticle binding adhesin , Mad1 [20] . This is also consistent with a previous study which showed that M . acridum up-regulated ( ∼3-fold ) a single Mas1-like gene ( MAC_03649 ) in the extracts of locust cuticular lipids but this gene was down-regulated in extracts from beetles ( ∼4-fold ) or cicadas ( ∼2-fold ) [72] . Formation of appressoria would be expected to involve significant modifications of the germ tube cell wall . Between 6 to 10% of the genes highly expressed by M . anisopliae and M . acridum on host cuticles encoded cell wall proteins . However , cell wall remodeling may be a greater feature of post penetration development because a microarray study showed that ∼20% of insect hemolymph-induced genes were involved in cell wall formation [74] . Evidently , different subsets of genes are required before and after penetration of the cuticle . Suppression-subtractive hybridization identified 200 genes expressed by M . acridum in the hemolymph of locusts [75] , and only eight of these genes involved in translation were among the 100 genes that were most highly expressed by pre-penetration germlings . About 60% of the transcripts expressed by M . anisopliae in liquid cultures containing insect cuticles encoded secreted products , including many proteases [76] , as compared to ∼20% of the transcripts in pre-penetration germlings ( Table S19 ) , indicating that growth in culture does not mimic the environment experienced on the insect surfaces . Despite the lineage-specific expansion of protease gene families in Metarhizium spp . , only a few proteases were abundantly expressed by either species on insect epicuticles . Two trypsins were highly expressed by M . anisopliae on cuticle surfaces , but similar to most subtilisins , they were not expressed by M . acridum . Early expression of proteases triggered by nitrogen starvation may allow M . anisopliae to sample the cuticle , resulting in further induction of proteases that could digest the proteinaceous procuticular layer [76] . Consistent with this hypothesis , both Metarhizium species expressed several genes involved in recognition of nitrogen starvation signals , including MAA_03429 and MAC_02501 , which resemble the STM1-like GPCR responsible for triggering adaptation to nitrogen starvation in fission yeast Schizosaccharomyces pombe [77] ( Table S20 ) . The profile of dehydrogenases produced on insect cuticles was used to highlight metabolic pathways that participate in pre-penetration growth . The expression profile of dehydrogenases produced on locust and cockroach cuticles was highly correlated ( r = 0 . 96 ) in M . anisopliae , but much less so in M . acridum ( r = 0 . 69 ) . The most abundant dehydrogenase transcripts expressed by M . anisopliae on both cuticles included enzymes involved in glycolysis , the citric acid cycle and the oxidative branch of the pentose phosphate pathway . Genes involved in metabolizing intracellular lipids , proteins and amino acids were also highly expressed , showing that lipids are an important nutrient reserve , and that there is a high turnover of proteins during the formation of appressoria as previously suggested for M . oryzae [78] . Similar to previous observations [21] , M . acridum germlings only produce appressoria on locust cuticle , and these visually resemble the appressoria produced by M . anisopliae on both insect cuticles ( Figure 7 ) . Consistent with early host recognition events being key to establishing specificity , M . acridum but not M . anisopliae transcribed different Pth11-like GPCR genes on locust and cockroach cuticles ( Figure S3A ) . The up-regulation of G-protein alpha subunit , phosphatidyl inositol-specific phospholipase C , protein kinase C , Ca/calmodulin-dependent kinase and extracellular signal-regulated protein kinases indicate that the mitogen-activated protein kinase pathway was strongly activated by M . anisopliae during infection of both insects and by M . acridum infecting locust cuticle . Unexpectedly , M . acridum expressed adenylate cyclase and protein kinase A at higher levels on cockroach cuticle even though appressoria formation was not induced ( Figure 7 , Table S20 ) . Most of the up-regulated signal-tranduction genes were similar to known PHI genes that regulate infection processes in other fungi ( Table S6 ) . Overall , our results suggest that M . anisopliae and M . acridum are able to differentiate diverse host-related stimuli on locust and cockroach cuticles using distinct or shared signaling pathways involving PTH11-like GPCRs , calcium-dependant pathways and MAP kinases that are probably under subtle and sophisticated cross-pathway controls .
Recent improvements in next generation sequencing technology and bioinformatics now allows the de novo assembly of high quality eukaryotic genomes [79] , [80] . We used such an approach to provide the first draft sequences of insect fungal pathogens M . anisopliae and M . acridum . Metarhizium species are the best studied insect pathogenic fungi and thus serve as an excellent starting point for gaining a broad perspective of issues in insect pathology . Sequencing two related species that evolved very different life styles provides a powerful method to derive lists of candidate genes controlling pathogenicity , host specificity and alternative saprophytic life styles . By using the experimentally verified pathogen-host interaction ( PHI ) gene reference database [33] , we found that >16% of the genes encoded by each genome have significant similarities with genes involved in pathogenicity in other fungi , oomycetes or bacteria . Our study also highlighted secreted proteins which are markedly more numerous in Metarhizium spp . than in plant pathogens and non-pathogens , pointing to a greater complexity and subtlety in the interactions between insect pathogens and their environments . High resolution RNA-Seq transcriptomic analyses found that the two Metarhizium spp . have highly complicated finely-tuned molecular mechanisms for regulating cell differentiations in response to different insect hosts . These were the first large scale transcriptome studies done with insect pathogenic fungi grown under simulated insect parasitism rather than in liquid cultures . Whole genome analyses indicated that Metarhizium spp . are closer to plant endophytes and plant pathogens than they are to animal pathogens like A . fumigatus and C . albicans . The finding suggested that Metarhizum may have evolved from fungi adapted to grow on plants even though they now infect insects . This inference is supported by the consistent existence of genes for plant degrading enzymes within Metarhizium genomes ( Table S2 ) . In contrast , fungal pathogens of humans that are seldom recovered from soil , such as Coccidioides , exhibit few of these enzymes or none [81] . Even necrophytes such as Trichoderma reesei lack many families of plant cell wall degrading enzymes [82] , and the existence of such families in Metarhizium spp . implies that these species are able to utilize living plant tissues . Potentially , these enzymes could also facilitate colonization of root surfaces but this must remain speculative because the genetic basis for rhizosphere competence is largely obscure in fungi [5] , [83] . Our identification of the full repertoire of Metarhizium genes will help to identify genes responsible for life on the plant root . M . acridum contains fewer transposase genes than M . anisopliae which might be due to differences in repeat-induced point mutation ( RIP ) . Both M . anisopliae and M . acridum have orthologs ( MAA_03836 and MAC_00922 ) of the N . crassa RIP defective gene ( E≤10−80 ) , the only gene known to be required for RIP [84] . The retention of this gene suggests that M . anisopliae might have undergone RIP at some stage in its evolution , even though its genome currently shows no bias towards C:G to T:A mutations . Creating new genes through duplication is almost impossible when RIP is very efficient [31] , so the apparent loss of RIP in M . anisopliae may have been a compromise for the massive expansion of some gene families , though at the cost to M . anisopliae of increased transposition . M . acridum has a strong RIP bias , but RIP is only functional when meiosis is frequent . Cordyceps taii has been described as the sexual type ( teleomorph ) of Metarhizium taii [7] , [85] but the sexual stages of M . acridum ( and M . anisopliae ) are unknown . However , both Metarhizium species have a complement of apparently functional genes whose orthologs in N . crassa and A . nidulans are known to be required for meiosis and sexual development ( Table S21 ) . These include putative α-mating type genes and genes with similarity to a high mobility group ( HMG ) mating type gene , suggesting that they may have the potential to be either self ( homothallic ) or non-self ( heterothallic ) fertile under favorable conditions [26] . More studies are required to understand the importance of the RIP mechanisms in the evolution of Metarhizium genomes and to determine the frequency of meiosis . Discovering whether M . anisopliae and M . acridum undergo sexual reproduction also has important implications for understanding the evolution of new strains of these pathogens . Alternatively to an undiscovered sexual stage , the conservation of sex genes in an asexual species could be due to a recent loss of sexuality , pleiotropy or parasexual recombination following heterokaryon formation [86] . The well known parasexual cycle that occurs in some fungi including Metarhizium provides another mechanism for hybridization [87] . As with sexual hybridization there are numerous barriers between vegetative fusions of different fungal species with the major one being vegetative incompatability , which results from heterokaryon incompatability proteins that block exchange of DNA [88] . M . acridum has fewer ( 25 genes ) heterokaryon incompatibility proteins than M . anisopliae ( 35 genes ) , which suggests that M . acridum may be less reproductively isolated than M . anisopliae . However , it is likely that M . acridum with its more specialized lifestyle and narrow environmental range encounters fewer genetically distinct individuals than the more opportunistic M . anisopliae ( Table S2 ) . The evolutionary transition of Metarhizium spp . to insect pathogenicity must have involved adaptations to insect-based nutrition , as indicated by the large number of proteases , lipases and chitinases that can digest insect cuticles and the host body ( Table S6 ) . Except for the lipid outer epicuticle , most of the barriers and nutritional resources in insects are proteinaceous , and Metarhizium has a full set of proteases including many different subtilisins , trypsins , chymotrypsins , metalloproteases , aspartyl proteases , cysteine proteases and exo-acting peptidases . The chymotrypsins are M . anisopliae specific , and may have been acquired by a horizontal gene transfer event [37] . Otherwise , the ∼2–10-fold expanded repertoire of various families of secreted proteases has been the result of preservation by natural selection of duplicated genes . These may have facilitated the adaptation to heterogenous environments . Thus , the abundance of aspartyl proteases and carboxypeptidases ( active at low pH ) and subtilisins and trypsins ( active at high pH ) , reflects the ability of M . anisopliae to grow in media with a wide range of pH values [89] . The ability to produce large quantities of secreted proteases will obviously assist in the rapid degradation of insect host barriers , but the diversity of different proteases might also have been selected because insects frequently exploit anti-fungal protease inhibitors [38] . With the exception of the trypsins , most of the proteases with orthologs in the PHI-base ( Table S6 ) are reported to have a major function in degrading host barriers . Fungal trypsins are regarded as markers of pathogenicity as they are almost exclusively found in pathogens of plants , animals or fungi [90] . M . anisopliae has more trypsins than any other sequenced fungus , including M . acridum , which indicates a recent evolution of this gene family by gene duplication in M . anisopliae ( Figure 1C ) . We also infer that differential gene loss has occurred due to the existence of six trypsin pseudogenes in M . anisopliae ( Table S16 ) . At least two active trypsins are expressed during insect infection [91] , but the role of these trypsins in disease has not been demonstrated . The only sequences similar ( E<1×10−10 ) to Metarhizium trypsins in the PHI database are from the oomycete plant pathogen Phytophthora sojae ( PHI acc . : 652 and 653 ) . Plants produce diverse glucanases to degrade pathogen cell walls , and the P . sojae trypsins quench this by degrading the glucanases [92] . It is feasible that a similar strategy could occur in insect-fungus interactions since the β-glucan recognition proteins , β-1 , 3-glucanases and β-1 , 4-glucanases involved in insect immune responses are similar to the anti-fungal glucanases produced by plants [93] . To date ∼20 Metarhizium genes that contribute to infectious capacity have been described [4] . These have provided important new insights into the novel mechanisms by which pathogens evade host immunity by masking cell wall components with a collagen [18] , differentially attach to insects or plants with different adhesins [20] and regulate intracellular lipid stores with a perilipin [21] . Some of these genes , like the collagen MCL1 ( MAA_01665 ) , seem to be specifically associated with pathogenicity in M . anisopliae , showing that analysis of orphan ( species-specific ) genes will be crucial for a full understanding of pathogenicity . Other genes and gene families are generally associated with pathogenicity and can be predicted with the help of the PHI database . The >370 families of genes categorized as containing PHI genes in Metarhizium therefore represent good leads for dissecting the molecular genetics of pathogenicity . Many families like the crotonases involved in fatty acid metabolism [94] , the PacC transcription factor that mediates the environmental pH signal , and the suppressers of defense responses such as catalases and superoxide dismutases have been well documented as virulence factors in diverse pathogens of plants and animals [36] . It would be surprising if they were not involved in Metarhizium infection processes . Other sequences identified from comparisons with the PHI database may be less generic in their impact on pathogenesis . As well as secreted proteins , the interaction between a pathogen and its host is to a large extent orchestrated by the proteins that are localized to the cell wall or cell membrane , and these categories are well represented in the PHI database . Plant and animal pathogens frequently have a subset of extracellular membrane proteins containing an eight-cysteine domain referred to as CFEM . In plant pathogens , CFEM-containing proteins function as cell-surface receptors or signal transducers , or as adhesion molecules in host-pathogen interactions [64] . Deletion of CFEM-containing proteins produces a cascade of pleiotropic effects in C . albicans , most effecting cell-surface-related properties including the ability to form biofilms [95] . The genomic sequences reveal that Metarhizium species also have CFEM-containing proteins ( MAA_03310 , MAA_04981 and MAA_07591 in M . anisopliae; MAC_09015 and MAC_09359 in M . acridum ) , and functional analysis is underway to investigate the role they play in M . anisopliae development and pathogenesis . There are many other putative PHI protein families that need to be verified as virulence or pathogenicity determinants in Metarhizium . For example , CheY-like domain proteins are response regulators in some bacterial two-component signaling systems [96] , but their roles in fungi remain to be determined . Metarhizium spp . have an average number of histidine kinases compared to other filamentous fungi , and yet M . anisopliae , M . acridum , F . graminearum and M . oryzae have 4 , 3 , 2 and 0 CheY-like proteins , respectively ( Table S6 ) , indicating that M . anisopliae is comparatively well supplied with putative effector proteins that promote responses to stimuli . Much more unexpectedly , M . anisopliae has 6 ( M . acridum has 1 ) homologs of heat-labile enterotoxins that play important roles in bacterial pathogenesis [97] . The HMG proteins involved in fungal sexuality are also required for fungal pathogenicity [98] . Both Metarhizium species have four HMG proteins that are predicted to be PHI genes ( Table S6 ) . M . anisopliae produces large numbers of proteins and secondary metabolites that might be dedicated to host interaction and countering insect defenses [6] . The identity and molecular functions of most secondary metabolite encoding genes remain to be determined in Metarhizium , and it will be intriguing to investigate which of their products are required for pathogenicity and or host specificity . However , with respect to the number of PKS and NRPS genes , M . anisopliae appears to possess a greater potential for the production of secondary metabolites than M . acridum and other sequenced Ascomycetes . M . anisopliae kills hosts quickly via toxins and grows saprophytically in the cadaver . In contrast , M . acridum causes a systemic infection of host tissues before the host dies which suggests limited production of toxins , or none [99] . The presence of NRPS MAA_00969 in M . anisopliae is remarkable as almost all similar genes encoding host selective toxins were found in the Dothideomycetes [100] . It is unlikely that MAA_00969 and HTST1 ( encoding the HC-toxin ) evolved independently , and one possibility is that MAA_00969 was acquired by an interspecific horizontal gene transfer event . There is no evidence to date that M . anisopliae has a relationship with any plant species that would require a specific toxin , and there are no reports of host-specific toxins in fungal pathogenesis of animals or insects . Specialization in host range in various Metarhizium lineages is associated with a reduction in the range of molecules the fungi can utilize for nutrition or are able to detoxify [101] . Consistent with this is the deficit of dehydrogenases ( DHGs ) in M . acridum relative to M . anisopliae or saprophytic fungi ( Table 2 ) . M . anisopliae also has more cytochrome P450s ( CYPs ) , which are used by fungi to detoxify diverse substrates [102] . Thus , the additional CYPs and DHGs encoded by M . anisopliae may enable it to detoxify the toxic repertoires of multiple insect hosts , as compared to M . acridum that infects only locusts . CYPS and DHGs also contribute to production of different secondary metabolites by oxidation ( CYPs ) and dehydroxylation ( DHGs ) of the backbone structures produced by the PKSs and NRPSs [103] . M . anisopliae's PKS and NRPS clusters contain 18 CYPs and 21 DHGs , while M . acridum's PKS and NRPS clusters contain 3 CYPs and 12 DHGs . The insecticidal destruxin A–F subclasses produced by M . anisopliae have the same backbone structure , but more than 30 different analogues [104] . These analogs presumably derive principally from the action of CYPs or DHGs , but the molecular mechanisms have not been determined . Comparative global transcriptional studies of M . anisopliae and M . acridum provided a broad-based analysis of gene expression during early colonization processes , particularly in terms of the genes involved in host recognition , metabolic pathways and pathogen differentiation ( Figure 7 , Figure S6 ) . About 20% of the genes most highly expressed by both Metarhizium species are putative PHI genes ( Table S19 ) . In spite of the abundance of protease genes in the Metarhizium genomes only a few proteases , mostly the trypins were expressed in the early stages of infection . As mentioned above , the trypsins could possibly serve as suppressors of host defenses . Studies in a range of plant pathogens suggest that early infection is characterized by the catabolism of internal lipid stores and that polymerized substrates are used after the readily available substrates are exhausted [65] , [66] . The transcriptome of M . anisopliae shows that it also uses internal lipid stores early in infection , which is consistent with a previous study [21] . Proteases and chitinases are secreted later at very high levels to digest the protein-chitin procuticles [23] . The occurrence of a stress condition during the early phase of the interaction with the insect host was indicated by the massive up-regulation of heat shock proteins ( HSPs ) . MAA_04726 and MAC_01954 are similar ( E<1×10−160 ) to an HSP90 from C . albicans that is a crucial virulence factor governing cell drug resistance and morphogenetic transition [105] . The other highly expressed Metarhizium HSPs ( e . g . , HSP30s and HSP70s ) are considered to be part of a general defense response and did not resemble sequences in the PHI database . In spite of differences in infection procedures , we were able to identify some concordance between up-regulated Metarhizium genes and metabolic networks up-regulated by M . oryzae [78] and the mycoparasite Trichoderma harzianum [106] . In particular , during early host colonization , they all up-regulated pathways associated with translation , post-translational modification , and amino acid and lipid metabolism . Metarhizium spp . also resemble M . oryzae and T . harzianum in that pathogenicity is associated with nitrogen deprivation and related stresses , indicating that at least some of the physiological conditions on insects , plants and fungal hosts might be similar . For example , the S . pombe STM1 gene links environmental nitrogen with cell differentiation [77] . The up-regulation of similar STM1-like receptors by the three pathogenic fungi could be a common mechanism for linking low levels of nitrogen on the host surfaces with differentiation of infection structures . In spite of their different host ranges , developmental processes within M . anisopliae and M . acridum are very similar , e . g . formation of appressoria and blastospores . However , comparatively analyzing their host-invading transcriptomes suggested that recognition might be determined in part by regulatory controls that exclusively limit expression of genes for pathogenicity-related developmental processes to individual hosts . Functional characterization should elucidate whether the expansion in M . anisopliae of several families of signal receptors and response elements is indicative of functional redundancy and/or reflective of a need for fine-tuned sensing of the host environments . The differentially regulated Pth11 GPCR genes are clear early candidates for further functional analysis to confirm their role as regulators of pathogenicity , and to investigate how their function varies between strains with different host ranges . Such studies could define the core set of host-specific transcripts and identify targets for effectively altering host range . In conclusion , we have identified significant differences in gene contents and transcriptional regulations between M . acridum and M . anisopliae , that have led to the latter having a wider biochemical repertoire available for infecting multiple hosts . The genomic sequences will facilitate identifying candidate genes for manipulation to increase the benefits of applying Metarhizium not just as an insecticide but also potentially as a biofertilizer . The range of exploitable fungal virulence genes is enormous as besides the putative PHI genes , other virulence factors such as the systems for evading host immunity are of particular interests .
M . anisopliae strain ARSEF 23 has been studied in the laboratory for more than 40 years [107] . It is a generalist insect pathogen that successfully infects locusts , caterpillars , flies , crickets and beetles , amongst others , and is classified as a Group A strain ( good germination in many media and production of appressoria against a hard hydrophobic surface in yeast extract medium ) [101] . M . acridum CQMa 102 can only infect acridids and is being mass produced for large-scale locust control in China [16] . It is classified as a Group D strain ( little or no germination in yeast extract or glucose media ) . A recent taxonomic revision assigns M . anisopliae ARSEF 23 to a new species , viz . , M . robertsii [108] . The genomes of M . anisopliae and M . acridum were sequenced with the next generation sequencing technology Illumina . DNA libraries with 200 bp , 2 kb and 6 kb inserts were constructed and sequenced with the Illumina Genome Analyzer sequencing technique at the Beijing Genomics Institute at Shenzhen with protocols described previously for the giant panda genome [80] . The genome sequences were assembled using SOAPdenovo [109] . For syntenic relationship analysis , the scaffolds of both genomes were oriented by MEGABLAST for dot plotting and a pair-wise comparison with an Argo Genome Browser [110] . Annotations of the genomic sequences of M . anisopliae and M . acridum were performed with Augustus [111] , specifically trained with >6000 unique sequenced Metarhizium ESTs , and the annotated information of F . graminearum was incorporated as a reference . An ab initio predictor , GeneMark [112] was additionally used for ORF prediction with both Metarhizium genomes . Thorough manual checks were conducted on parallel comparisons of the results from two prediction methods . All questionable ORFs were individually subjected to Blast searches against the NCBI curated refseq_protein database and the individual prediction with the best hit was selected for each ORF . Pseudogene identification was conducted with the pipeline of PseudoPipe [113] . Transfer RNAs ( tRNAs ) were predicted with tRNAscan-SE [114] and ARAGORN [115] . Secreted proteins were identified by SignaIP 3 . 0 analysis ( http://www . cbs . dtu . dk/services/SignalP/ ) . Ortholog conservation in fungi was characterized with Inparanoid 7 . 0 [116] . In total , 946 orthologous proteins were acquired and aligned with Clustal X 2 . 0 [117] . A maximum likelihood phylogenomic tree was created using the concatenated amino acid sequences with the program TREE-PUZZLE using the Dayhoff model [118] . The divergence time between species was estimated with the Langley-Fitch method with r8s [119] by calibrating against the reassessed origin of the Ascomycota at 500–650 million years ago [120] . Whole genome protein families were classified by InterproScan analysis ( http://www . ebi . ac . uk/interpro/ ) in combination with the Treefam methodology that defines a protein family as a group of genes descended from a common ancestor [121] . To identify potential pathogenicity and virulence genes , whole genome blast searches were conducted against protein sequences in the pathogen-host interaction database ( version 3 . 2 , http://www . phi-base . org/ ) ( E<1×10−5 ) . The families of proteases were additionally classified by Blastp against the MEROPS peptidase database ( http://merops . sanger . ac . uk/ ) . Transporters were classified based on the Transport Classification Database ( http://www . tcdb . org/tcdb/ ) . The cytochrome P450s were named according to Dr . Nelson's P450 database ( http://drnelson . utmem . edu/CytochromeP450 . html ) . G-protein coupled receptors , protein kinases , transcription factors and GH families were classified by Blastp against GPCRDB ( http://www . gpcr . org/7tm/ ) , KinBase ( http://kinase . com/ ) , Fungal Transcription Factor Database ( http://ftfd . snu . ac . kr/ ) and CAZy database ( http://www . cazy . org/ ) , respectively . All Metarhizium genes with significant hits ( E value ≤ 10−5 ) to GPCRDB sequences and that contained 7 transmemebrane helices ( analyzed with http://www . cbs . dtu . dk/services/TMHMM/ ) were included as putative GPCRs . To analyze fungal secondary metabolite pathways , the genome annotation data from both species were coordinated and analyzed with the program SMURF ( http://www . jcvi . org/smurf/index . php ) . The evolution of protein family size variation ( expansion or contraction ) was analyzed using CAFE [32] . Genome repetitive elements were analyzed by Blast against the RepeatMasker library ( open 3 . 2 . 8 ) ( http://www . repeatmasker . org/ ) and with the Tandem Repeat Finder [122] . RIP index was determined with the software RIPCAL by reference against the non-repetitive control families [30] . The transposons/retrotransposons encoding transposases/retrotransposases were classified by Blastp analysis against the Repbase ( http://girinst . org/ ) . The hind wings from locusts ( Locusta migratoria ) and cockroaches ( Periplaneta americana ) were collected and surface sterilized in 37% H2O2 ( 5 min ) , washed in sterile water twice and dipped in conidial suspensions ( 2×107 spores per ml ) of M . anisopliae ARSEF 23 or M . acridum CQMa 102 for 20 seconds . The inoculated wings were placed on 1% water agar and incubated at 25°C for 24 hrs . The wings with fungal cells were homogenized in liquid nitrogen and the total RNA was extracted with a Qiagen RNeasy mini kit plus on-column treatment with DNase I . Messenger RNA was purified from 6 µg total RNA . After reverse transcription into double strand cDNA for tag preparation according to the massively parallel signature sequencing protocol [123] , it was sequenced with an Illumina technique . We omitted tags from further analysis if only one copy was detected or it could be mapped to different transcripts [124] . Other tags were mapped to the genome or annotated genes by allowing if they possessed no more than one nucleotide mismatch . The abundance of each tag was converted to transcripts per million for quantitative comparison between samples . We used the test of false discovery rate ( FDR≤0 . 001 ) to estimate the level of differential gene expression by each species under different induction conditions [125] . Metarhizium anisoliae and M . acridum were tested for their ability to kill adult locusts Locusta migratoria and cockroaches Periplaneta americana . For these experiments , conidia from each species were applied topically by immersion of cold-immobilized insects into aqueous suspensions of 5×108 spores per ml . Each treatment was replicated three times with 15 insects per replicate and the experiments were repeated twice . Mortality was recorded every 12 hours and the lethal time values for 50% mortality ( LT50 ) were estimated [18] . The Whole Genome Shotgun projects have been deposited at DDBJ/EMBL/GenBank under the accession ANDI00000000 for Metarhizium acridum and ANDJ00000000 for Metarhizium anisopliae , respectively . The RNA-seq data have been deposited at NCBI GEO repository with accession numbers GSM612996 , GSM612997 , GSM612998 and GSM612999 for the samples of M . anisopliae infection of locust , M . anisopliae infection of cockroach , M . acridum infection of locust and M . acridum infection of cockroach , respectively .
|
Aside from playing a crucial role in natural ecosystems , entomopathogenic fungi are being developed as environmentally friendly alternatives for the control of insect pests . We conducted the first genomic study of two of the best characterized entomopathogens , Metarhizium anisopliae and M . acridum . M . anisopliae is a ubiquitous pathogen of >200 insect species and a plant growth promoting colonizer of rhizospheres . M . acridum is a specific pathogen of locusts . Important findings of this study included: 1 ) Both M . anisopliae and M . acridum have a very large number of genes encoding secreted proteins , and many of these play roles in fungus-insect interactions . 2 ) M . anisopliae has more genes than M . acridum , which may be associated with adaptation to multiple insect hosts . 3 ) Unlike M . acridum , the M . anisopliae genome contains many more transposase genes and shows no evidence of repeat-induced point mutations . The lack of repeat-induced mutations may have allowed the lineage-specific gene duplications that have contributed to its adaptability . 4 ) High-throughput transcriptomics identified the strategies by which these fungi overcome their insect hosts and achieve specificity . These genome sequences will provide the basis for a comprehensive understanding of fungal–plant–insect interactions and will contribute to our understanding of fungal evolution and ecology .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/microbial",
"evolution",
"and",
"genomics",
"genetics",
"and",
"genomics/functional",
"genomics",
"biotechnology/applied",
"microbiology",
"cell",
"biology/cell",
"growth",
"and",
"division",
"microbiology/microbial",
"evolution",
"and",
"genomics",
"microbiology/applied",
"microbiology"
] |
2011
|
Genome Sequencing and Comparative Transcriptomics of the Model Entomopathogenic Fungi Metarhizium anisopliae and M. acridum
|
Insulin/insulin-like growth factor signaling ( IIS ) plays a pivotal role in the regulation of growth at the cellular and the organismal level during animal development . Flies with impaired IIS are developmentally delayed and small due to fewer and smaller cells . In the search for new growth-promoting genes , we identified mutations in the gene encoding Lnk , the single fly member of the SH2B family of adaptor molecules . Flies lacking lnk function are viable but severely reduced in size . Furthermore , lnk mutants display phenotypes reminiscent of reduced IIS , such as developmental delay , female sterility , and accumulation of lipids . Genetic epistasis analysis places lnk downstream of the insulin receptor ( InR ) and upstream of phosphoinositide 3-kinase ( PI3K ) in the IIS cascade , at the same level as chico ( encoding the single fly insulin receptor substrate [IRS] homolog ) . Both chico and lnk mutant larvae display a similar reduction in IIS activity as judged by the localization of a PIP3 reporter and the phosphorylation of protein kinase B ( PKB ) . Furthermore , chico; lnk double mutants are synthetically lethal , suggesting that Chico and Lnk fulfill independent but partially redundant functions in the activation of PI3K upon InR stimulation .
The control of cell , organ and body size is tightly regulated to ensure proper development of multicellular organisms . A key pathway controlling growth , metabolism , reproduction and longevity is the insulin/insulin-like growth factor signaling ( IIS ) pathway [1] . The insulin receptor ( InR ) and the corresponding downstream core components are conserved in Drosophila [2]–[4] , mediating cell growth and cell division in response to environmental factors such as nutrient availability through a series of protein-protein interactions and phosphorylation events [5] . The core components of the Drosophila IIS pathway include Chico , the homolog of the insulin receptor substrates ( IRS ) , the lipid kinase phosphoinositide 3-kinase ( PI3K ) , the lipid phosphatase PTEN , and the serine-threonine kinase PKB [6] . Chico gets phosphorylated upon IIS pathway activation , providing binding sites for the Src Homology 2 ( SH2 ) domain of p60 , the regulatory subunit of PI3K . Increased PI3K activity leads to the accumulation of phosphatidylinositol- ( 3 , 4 , 5 ) -trisphosphate ( PIP3 ) at the plasma membrane , which recruits PKB to the membrane via its pleckstrin homology ( PH ) domain . PKB takes a central position in the regulation of multiple cellular processes such as cellular growth , proliferation , apoptosis , transcription and cell motility [7] . In Drosophila , mutations in IIS components result in reduced cell , organ and body size with little effect on cell fate and differentiation . For example , hypomorphic mutants of essential IIS components and , in particular , homozygous null mutants of chico are viable but only approximately half the size of wild-type flies , due to smaller and fewer cells . Furthermore , characteristic defects caused by reduced IIS activity include female sterility , an increase in total lipid levels of adults , and a severe developmental delay [8] , [9] . chico encodes an adaptor protein , a group of proteins without catalytic activity usually carrying domains mediating specific interactions with other proteins such as an SH2 domain , a PH domain , or a phosphotyrosine-binding ( PTB ) domain . Adaptor proteins play an important role in the formation of protein-protein interactions and thus in the formation of protein networks . The various interaction domains within adaptor proteins and the specificity of those domains provide adaptor molecules with the ability to elicit characteristic responses to a particular signal . Recently , a novel family of adaptor proteins , the SH2B family , has been identified in mammals . It consists of three members – SH2B1 ( SH2B/PSM ) , SH2B2 ( APS ) and SH2B3 ( Lnk ) – that share a common protein structure with an N-terminal proline-rich stretch , a PH domain , an SH2 domain and a highly conserved C-terminal Cbl recognition motif [10]–[12] . They have been shown to regulate signal transduction by receptor tyrosine kinases such as the InR , IGF-I receptor and receptors for nerve growth factor , hepatocyte growth factor , platelet-derived growth factor and fibroblast growth factor , as well as by the JAK family of tyrosine kinases [11] , [13]–[17] . Whereas SH2B3 ( Lnk ) has been described to function exclusively by negatively regulating receptor kinases that are specialized in the development of a subset of immune and hematopoietic cells , the picture for the other two family members is not as clear yet [18] . Although both SH2B1 and SH2B2 have been shown to be directly involved in the regulation of JAK tyrosine kinases and of IIS , their specificities and physiological functions are complex and remain largely elusive . For example , depletion of SH2B1 in mice leads to severe obesity , leptin and insulin resistance as well as female infertility [19] , [20] . However , a number of studies suggest that SH2B1 exerts its function predominantly in the association with JAK2 and regulation of related signaling cascades [21] . For example , binding of SH2B1 to JAK2 results in an enhancement of JAK2 activation and JAK2-mediated growth hormone signaling [22] , and depletion of SH2B1 leads to decreased leptin-stimulated JAK2 activation and reduced phosphorylation of its substrates [19] . SH2B2 is also able to bind to JAK2 and to the InR [13] , [23] but recent research has mainly focused on the mechanisms related to the connection of SH2B2 and c-Cbl [13] , [24] , [25] . Phosphorylation of Tyr618 in SH2B2 stimulates binding of c-Cbl and thus mediates GLUT4 translocation and inhibition of erythropoietin-dependent activation of Stat5 [13] , [24] . However , the general impact of SH2B2 on receptor tyrosine kinase signaling remains controversial . Whereas Ahmed and colleagues showed that SH2B2 overexpression delayed InR and IRS dephosphorylation and enhanced PKB activation [25] , several other studies ( e . g . on SH2B2 knockout mice ) have suggested a negative regulatory role for SH2B2 in IIS , which might also be mediated via c-Cbl dependent ubiquitination and subsequent degradation of target kinases [26] , [27] . Although interactions with the IIS pathway and the InR have been described for SH2B1 and SH2B2 , the physiological significance of these connections in mammals appears to be the regulation of metabolism and energy homeostasis rather than the control of cell growth and proliferation [19] , [28] . In contrast to the mammalian situation , the Drosophila genome encodes a single adaptor protein that shares a common domain structure with the SH2B family , termed Lnk . Here , we show that Drosophila lnk predominantly regulates cellular and organismal growth in a cell-autonomous way . We observed that loss of lnk function leads to a reduction in cell size and cell number , reminiscent of decreased IIS activity . A thorough genetic analysis placed Lnk as a positive regulator of IIS at the level of IRS/Chico .
We identified lnk in an unbiased screen for growth-regulating genes based on the eyFLP/FRT technique in Drosophila . In principle , mutations in growth-promoting genes led to flies with smaller heads ( the so-called pinheads ) , whereas negative regulators of tissue growth resulted in larger heads ( referred to as bighead mutants ) . Among others , we identified four mutations causing a pinhead phenotype that fell into a single complementation group on the right arm of the third chromosome ( Figure 1B ) . We mapped the complementation group close to the lnk locus ( CG 17367 ) at the cytological position 96F . Subsequent sequencing revealed EMS-induced mutations in the lnk coding region for each allele . Flies homozygous mutant for lnk are small but do not show any obvious patterning defects ( Figure 1C ) . Homozygous mutant pupae are also small , indicating that lnk is essential for proper organismal growth throughout development ( Figure 1D ) . lnk mutant flies are severely reduced in dry weight , as shown for male and female flies ( Figure 1E ) . This defect is fully rescued by introducing a genomic rescue construct comprising the entire lnk locus , proving that the mutations in lnk are responsible for the growth phenotype ( Figure 1E ) . The most closely related group of proteins to Drosophila Lnk in vertebrates is the SH2B family of adaptor proteins sharing a common protein structure . Alignment of Drosophila Lnk with its human homologs ( SH2B1 , SH2B2 and SH2B3 ) shows high sequence identity in particular in the conserved PH and SH2 domains ( Figure 1F , Figure S2 ) . The four lnk alleles recovered in the screen ( 7K1 , 4Q3 , 6S2 , 4H2 ) contain a single point mutation in either of these two highly conserved protein domains resulting in a premature stop ( 4Q3 , 6S2 ) or an amino acid exchange in conserved residues ( 7K1 , 4H2 ) ( Figure 1F and 1G ) . Since hemizygous and heteroallelic lnk mutant animals display identical phenotypes , all lnk alleles are genetically null , suggesting an essential role of both the PH and the SH2 domain for Lnk function . SH2B1 and SH2B2 , two members of the mammalian family of Lnk-related adaptor proteins , have been shown to associate with several signaling molecules including JAK2 and the InR [11] , [21] , [29] . However , the different proteins seem to have distinct impacts on the respective pathways , regulating them either in a positive or negative manner [29] , [30] . Using the new mutations in the single member of the SH2B family in Drosophila allowed us to determine whether lnk plays an essential role in either of these pathways . Although the tyrosines in JAK2 and JAK3 mediating their interaction with the SH2B family proteins in mammals are not conserved in the Drosophila homolog , we wondered whether Lnk has a function in the regulation of Drosophila JAK . Misregulation of JAK/Stat signaling in Drosophila results in formation of melanotic tumors and proliferative defects in larval blood cells , held out wings and rough or disrupted eye phenotypes as well as male sterility and fused egg chambers in the vitellarium due to the absence of stalk cells [31]–[34] . In our characterization of homozygous lnk mutant animals we did not observe any of the phenotypes that are characteristic for impaired JAK/Stat signaling ( data not shown ) . Moreover , genetic interaction experiments of lnk with any of the core JAK/Stat pathway components did not reveal a connection of Lnk to JAK/Stat signaling . These results suggest that in Drosophila , Lnk is not involved in the regulation of signaling activity downstream of JAK . The initial observation that lnk mutations reduced organ and body size pointed at a role of Lnk in the IIS pathway . We characterized the growth phenotype of lnk mutants further by quantifying ommatidia number and generating tangential sections of mosaic eyes to study the impact of lnk on cell number and cell size ( Figure 2A–2E ) . SEM pictures of heads of lnk mutant adults compared to wild type and quantification of ommatidia number revealed that mutations in lnk caused a reduction in cell number by about 30% ( Figure 2A–2C ) . Induction of lnk mutant clones in the eye resulted in a cell-autonomous reduction of cell size in photoreceptor cells and rhabdomeres , as shown by tangential eye sections ( Figure 2D ) and subsequent quantification of photoreceptor cell and rhabdomere area in lnk mutant tissue compared to wild type ( Figure 2E ) . Therefore , lnk function is important to ensure proper regulation of cell number and cell size , similar to IIS components . It has previously been shown that IIS is required in oogenesis beyond the last previtellogenic stage; a reduction in IIS activity leads to an arrest in oogenesis and female sterility [35] . Female flies lacking lnk function are also sterile and have small ovaries . These ovaries only contain oocytes that developed until the last previtellogenic stage and resemble ovaries of females mutant for chico ( Figure 2F and 2G ) . A further characteristic phenotype of impaired IIS is the accumulation of lipids in adult flies . The lipid levels in three-day old male chico flies are more than twice the level than in the control despite their smaller body size [8] . Homozygous lnk mutant flies reach the same lipid levels as chico mutants ( Figure 2H ) . Taken together , these results strongly indicate a role of Lnk in the IIS pathway . The phenotypes of homozygous lnk mutants suggest that Lnk regulates cellular growth exclusively via IIS . However , the protein sequence of Lnk contains two putative Drk/Grb2 YXN binding sites ( Figure 1F ) . In addition , all SH2B family members , except for the beta , gamma and delta isoform of SH2B1 , carry a highly conserved consensus site for binding of Cbl [36] . The functionality of this Cbl binding site has only been demonstrated in SH2B2 so far [24] , [25] . In order to test the functional significance of the individual binding motifs , we generated rescue constructs consisting of the genomic lnk locus but carrying specific mutations that result in amino acid exchanges in the core tyrosine of the respective motifs . These constructs fully rescued the reduction in dry weight in lnk mutants , suggesting that neither binding of Drk to the YXN site nor an interaction of Lnk with Cbl through the C-terminal binding motif is important in the regulation of growth ( Figure S1A , S1B , S1C ) . In contrast , both the PH and the SH2 domains of Lnk are essential for its function because the lnk alleles disrupting either domain behave genetically as null mutations . In order to study the consequences of the loss of lnk function on cell growth , we performed a clonal analysis in larval wing discs using the 4Q3 allele . We used the hsFLP/FRT system to induce mitotic recombination , thus to generate homozygous lnk mutant cell clones ( marked by the absence of GFP ) adjacent to clones that consist of wild-type cells ( marked by two copies of GFP ) ( Figure 3A ) . All mutant clones were smaller than their wild-type sister clones ( Figure 3C ) , and they contained fewer cells ( Figure 3B ) . Although a clear tendency to a cell size reduction of lnk mutant cells , as determined by the ratio of clone area to cell number , was apparent , the relative reduction was not significant in larval wing discs . We thus speculate that the influence of lnk on cell size is rather subtle in early stages of development . We further used molecular readouts of IIS activity to investigate the consequences of the loss of lnk function . Stimulation of the InR activates PI3K , which increases the levels of phosphatidylinositol- ( 3 , 4 , 5 ) -trisphosphate ( PIP3 ) at the plasma membrane [6] . Previously , a reporter containing a PH domain fused to GFP ( tGPH ) that localizes to the plasma membrane as a result of PI3K activity had been described [37] . Using this reporter , we monitored PIP3 levels in wild-type and lnk mutant fat body cells as well as in clones of lnk mutant cells in the fat body . Whereas the tGPH reporter localized to the membrane in wild-type cells ( Figure 4A ) , the GFP signal was predominantly observed in the cytoplasm in lnk mutant cells ( Figure 4B and 4C ) , indicating that the loss of lnk function causes a reduction of PI3K signaling activity . The impact of lnk on tGPH localization is comparable to the effects observed in chico mutant cells ( Figure 4D ) . As another molecular readout of IIS activity , we measured the phosphorylation levels of PKB , a downstream kinase of IIS . Lysates of homozygous lnk and chico mutant larvae were subjected to Western analysis and compared to wild-type controls . Whereas the PKB protein levels were comparable in all genotypes , the amount of phosphorylated PKB was reduced in both lnk and chico mutant larvae ( Figure 4F ) . Thus , Lnk and Chico contribute similarly to the activity of PI3K . In order to establish where lnk acts in the IIS cascade , we performed genetic epistasis experiments . We tested the ability of lnk to suppress the overgrowth phenotype caused by overexpression of InR during eye development ( Figure 5B ) . In this sensitized background loss of lnk function reduced the eye size almost to wild-type size , suggesting that Lnk modulates the IIS pathway downstream of the receptor ( Figure 5E ) . In contrast , homozygosity for lnk was not sufficient to suppress the overgrowth caused by a membrane-tethered form of PI3K ( Figure 5C and 5F ) . Thus , Lnk acts between the InR and the lipid kinase PI3K in the IIS pathway . The phenotypic similarities between lnk and chico mutants are striking . Both genes encode adaptor proteins with a PH domain and a phosphotyrosine-binding motif ( an SH2 domain in the case of Lnk and a PTB domain in the case of Chico , respectively ) , and both act between the InR and PI3K . Thus , it is conceivable that Lnk is required for proper Chico function , for example by stabilizing the phosphorylated InR and thereby allowing a stable InR-Chico interaction . We attempted to genetically test whether Lnk acts via Chico . If this were the case , chico; lnk double mutants would be expected to display similar phenotypes as the single mutants . However , chico; lnk double mutants were lethal ( Figure 5H ) . Removing one copy of PTEN ( encoding the lipid phosphatase that antagonizes PI3K ) restored viability of the chico; lnk double mutants ( Figure 5G and 5H ) , suggesting that the chico; lnk double mutants suffer from reduced IIS activity and thus insufficient levels of the second messenger PIP3 . Reducing the amount of PTEN , the negative regulator of PIP3 production , allows for PIP3 levels above a critical threshold for survival but still insufficient to ensure normal growth . These results imply that Chico and Lnk independently act downstream of the InR , and that both adaptors are required for the full activation of PI3K upon InR stimulation . Consistently , we found that the levels of phospho-PKB were further reduced in chico; lnk double mutant larvae as compared to single mutants ( Figure 4F ) . Our data clearly indicate that both Lnk and Chico are required for the full activity of PI3K , with each adaptor being sufficient for a partial stimulation of PI3K activity . This might explain why chico and lnk are among the few non-essential genes in the IIS cascade . How does Lnk contribute to the activation of PI3K ? Probably , Lnk does not exert its function in the same way as Chico . In contrast to Chico , Lnk lacks an YXXM consensus binding site for the SH2 domain of the regulatory subunit of PI3K . Upon activation of the InR , Lnk might connect the signal from the InR with Chico in order to enhance PI3K activation . Interestingly , such a mechanism has been proposed in vertebrates , where SH2B1 promotes IRS1 and IRS2-mediated activation of the PI3K pathway in response to Leptin [38] . However , we favor a model in which Lnk promotes the membrane localization of PI3K by recruiting another binding partner of PI3K or by counteracting a negative regulator of PI3K localization . It will thus be important to identify physical interactors of Lnk .
Four EMS induced lnk alleles on FRT82B chromosomes were recovered in a mosaic screen based on the eyFLP/FRT cell lethal technique [39] . The complementation group was mapped close to an y+ marked transgene in 96E , and the map position was refined to 96F by non-complementation with Df ( 3R ) Espl3 ( 96F1; 97B1 , Bloomington stock number 5601 ) and complementation with Df ( 3R ) ME61 ( 96F12-14; 97C4-5 , Bloomington stock number 5440 ) . The identity of the gene was determined by non-complementation with the P-element allele lnkd07478 ( Bloomington stock number 19274 ) and subsequent sequencing of the lnk locus . Unless otherwise stated , a heteroallelic combination of lnk alleles ( lnk4Q3/lnk6S2 ) was used to characterize the lnk phenotypes . A 6 kb fragment spanning from the 3′ end of CG17370 to the beginning of the first exon of CG5913 was used as genomic rescue . The construct was inserted by means of ΦC31 mediated integration into a landing site on the second chromosome at 51D [40] . Constitutive active forms of InR ( Bloomington stock number 8248 ) and of Dp110 ( CAAX [41] ) driven by GMR-Gal4 were used for the epistasis analyses . For the generation of chico; lnk double mutant flies lacking one copy of PTEN , a deletion uncovering the chico and PTEN loci was used ( Df ( 2L ) Exel6026 ) . To prove that the observed effect on the chico; lnk double mutants was caused by the loss of PTEN , PTEN was re-introduced by means of a genomic rescue construct . The chico alleles ( chico1 and chico2 ) have been described [8] . The heteroallelic combination chico1/chico2 was used to compare lnk and chico mutants . Flies of the respective genotypes were reared under identical conditions and collected 3 days after eclosion . They were dried at 95°C for 5 minutes and kept at room temperature for 3 days before weighing on a precision scale ( Mettler Toledo MX5 ) . Three day-old flies were collected and weighted individually . Subsequent analysis of lipid content was performed as described [42] . Clones of lnk mutant cells were induced at 24–36 hours after egg deposition ( AED ) by heat shocking larvae of the genotype y , w , hs-flp/y , w; FRT82 , w+/FRT82 , lnk4Q3 for 1 hour at 37°C . Fixation and tangential sections of the adult eyes were performed as described [43] . For the generation of mutant clones in the wing disc , animals of the genotype y , w , hs-flp/y , w; FRT82 , Ubi-GFP/FRT82 , lnk4Q3 were exposed to a 5 minute heat shock at 37°C at 54–56 hours AED . Larvae were dissected 48 hours later , fixed in 4% paraformaldehyde on ice for 1 hour , and incubated in PBS containing DAPI ( 1∶2000 ) for 10 minutes . Discs were dissected and mounted in Vectashield Mounting Medium . Pictures were taken using a Leica SP2 confocal laser scanning microscope . The quantification of the mutant clones was performed by comparing the size of the area occupied by mutant versus wild-type ( pigmented ) photoreceptor cells R6 using Photoshop CS2 . In the wing discs , the numbers of nuclei within mutant and wild-type clones were counted and the areas were measured using Photoshop CS2 . Larvae of the genotype y , w; tGPH/+; FRT 82 , w+/FRT82 , lnk4Q3 were heat shocked 6–8 hours AED for 1 hour at 37°C , collected at wandering stage , fixed for 1 hour at room temperature in 8% paraformaldehyde and stained with DAPI ( 1∶10000 in PBS ) for 20 minutes . Fat bodies were dissected and mounted in Vectashield Mounting Medium . Pictures were taken using a Leica SP2 confocal laser scanning microscope ( Figure 4A–4D ) and a Zeiss ApoTome microscope ( Figure 4E–4E″ ) , respectively . Ovaries were dissected from 3 day-old wild-type , lnk4Q3/lnk6S2 and chico1/chico2 flies , respectively , and subsequently incubated in PBS containing DAPI ( 1∶2000 ) for 10 minutes . Thereafter , ovarioles were mounted in Vectashield Mounting Medium and pictures were taken using a Leica SP2 confocal laser scanning microscope . Third instar larvae ( 10 mg of each genotype ) were collected , briefly washed in PBS , transferred to 1 . 5 ml Eppendorf tubes and flash-frozen in liquid nitrogen . Larvae were homogenized in 75 µl of extraction buffer [44] . After 15 minutes incubation at 4°C and centrifugation at 12000 g for 15 minutes , protein concentrations were determined using the RC DC Protein Assay ( Bio-Rad ) . For the Western blots , 30 µg of protein samples were loaded , blotted and detected with the following antibodies: rabbit anti-Akt ( Cell Signaling #9272 , diluted at 1∶1000 ) , rabbit anti-phospho-Drosophila Akt ( Ser 505 ) ( Cell Signaling #4054 , diluted at 1∶500 ) , and mouse anti-Actin ( Sigma A5316 , diluted at 1∶10000 ) . HRP-conjugated secondary antibodies ( Jackson ImmunoResearch ) were diluted at 1∶10000 . Signals were detected using ECL Western blotting detection reagents ( Amersham Biosciences ) .
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The regulation of growth is among the most fundamental processes during development of multicellular organisms . Research over the past years has established a key function of the insulin/insulin-like growth factor signaling ( IIS ) pathway in ensuring proper growth at the cellular and the organismal level . Impaired IIS has been associated with diseases such as type 2 diabetes , leprechaunism , and heart disease; and deregulated IIS often contributes to the development of cancer . Here , we describe the characterization of the Drosophila SH2B family adaptor protein Lnk . Mutants in lnk are viable but unable to reach the normal size due to a reduction in cell size and cell number . Our characterization of lnk mutant flies has revealed phenotypes associated with impaired IIS , such as developmental delay , female sterility , and increased lipid levels in adults . Using a combination of genetic interaction experiments and molecular readouts for IIS activity , we demonstrate that Lnk acts in parallel to the IRS homolog Chico downstream of the insulin receptor to regulate cellular growth .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/animal",
"genetics",
"genetics",
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"signaling",
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"genomics/gene",
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"developmental",
"biology/molecular",
"development"
] |
2009
|
The Drosophila SH2B Family Adaptor Lnk Acts in Parallel to Chico in the Insulin Signaling Pathway
|
ELISA-based methods of detecting Fasciola cathepsins in feces are powerful techniques for diagnosing infections by F . hepatica and F . gigantica . In the last decade , the in-house MM3-COPRO ELISA and its commercial version BIO K 201 ( BIO X Diagnostics , Belgium ) have been recognized as useful tools for detecting early infections by such trematodes and for monitoring the efficacy of anthelmintic treatments in human and animal species , as they provide some advantages over classic fecal egg counts . However , the sensitivity of MM3-COPRO ELISA can sometimes be compromised by the high variability in the concentration of cathepsins in fecal samples throughout the biological cycle of Fasciola ( mainly in cattle ) and by differences in the between-batch performance of peroxidase-labeled anti-mouse IgG polyclonal antibodies . To prevent such problems , we investigated whether the incorporation of a commercial streptavidin-polymerized horseradish peroxidase conjugate , in order to reveal bound biotinylated monoclonal antibody MM3 , can improve the sensitivity of the MM3-COPRO ELISA . We observed that inclusion of this reagent shifted the previous detection limit of the assay from 0 . 6 ng/mL to 150 pg/mL and that the modified test is able to identify infection in cows harboring only one fluke . Moreover , we demonstrated that maximal OD values can be achieved with short incubations ( 30 min each step ) at RT with shaking , rather than standard incubations , which significantly accelerates the diagnostic procedure . Finally , we did not find a significant correlation between coproantigen concentration and parasite burden in cattle , which may be due to the low parasite burden ( 1–10 adult flukes ) of the animals used in the present study . As the usefulness of the classic MM3-COPRO test for detecting animal and human infections has already been demonstrated , it is expected that the improvements reported in this study will add new insights into the diagnosis and control of fasciolosis .
Fascioliasis ( = fasciolosis ) is a worldwide emergent zoonotic disease produced by infection with trematodes of the genus Fasciola . The two main species of this genus , F . hepatica and F . gigantica , are pathogenic to humans and livestock [1] . Considering the worldwide distribution , F . gigantica is the only species present in Western Africa , while F . hepatica is the only species present in Europe , the Americas , Australia and the African Magreb [2] . However , both species have been reported to coexist in Eastern and Southern Africa as well as in several regions of Asia [3] . The existence of two species with overlapping regions has implications for developing sensitive diagnostic tests of general application . Typically , diagnosis of human and animal infections caused by Fasciola species is carried out by coproscopy or immunological techniques , including determination of circulating antigens in serum , measurement of coproantigens and detection of serum antibodies [4 , 5] . Although coprological techniques are advantageous in terms of the cheapness of laboratory material and detection of active infections , they are time-consuming , require expert personnel and have poor sensitivity . Serological methods have the advantage of permitting easy automation , which is of great interest for handling large volume of samples . These methods are also very sensitive and can be used for early monitoring of Fasciola infections in herds by using either serum or milk samples [6] . However , these techniques do not differentiate between antibodies induced by current infections/reinfections and those still present in animals or humans successfully treated with anthelmintics during the course of a past Fasciola infection . Methods for detecting circulating Fasciola antigens and/or coproantigens solve the above mentioned problems associated with coprological and serological techniques . However , detection of coproantigens is preferred as sampling is not invasive and the presence of antigens in feces is not limited by time , as may occur with circulating antigens . Moreover , these methods are of widespread application , as the same techniques can be used to detect Fasciola coproantigens in fecal samples from humans and animal species . In the past decades , several capture ELISA methods that use monoclonal and/or polyclonal antibodies were reported to be able to detect small amounts of specific Fasciola coproantigens in fecal samples [7–10] . However , since then , only the BIO K 201 kit ( BIO X Diagnostics , Belgium ) , i . e . the commercial version of MM3-COPRO ELISA [9] , has been globally used . Since being commercialized ( in 2007 ) , both versions of the test have been recognized as valuable diagnostic tools for detecting Fasciola infections or for monitoring the efficacy of treatment with anthelmintics in several studies [9–16] . Nevertheless , during years of use of the MM3-ELISA tests , two drawbacks have also been highlighted: i ) the difficulty with the commercial test [17–20] , which uses avidin-horseradish peroxidase ( HRP ) as a secondary conjugate , in maintaining the sensitivity of the original in-house MM3-COPRO test , and ii ) the dependence of the in-house MM3-COPRO test on particular batches of HRP-labeled anti-mouse IgG antibodies , for yielding good sensitivity and specificity . To prevent these disadvantages and to guarantee a homogeneous and highly sensitive product , we investigated the possibility of substituting the above secondary reagents with a streptavidin ( SA ) -polymerized HRP ( PolyHRP ) detection system and evaluated the conditions of use of this reagent . We found that this ELISA enhancement system is an improvement on classic secondary reagents as it increases the sensitivity of the above ELISA tests while enabling a reduction in the incubation time required .
The biological samples used in the present study were of animal origin ( sheep and cattle ) and were obtained from a collection of frozen fecal samples stored at INGACAL ( Mabegondo , A Coruña , Spain ) . The samples were obtained during routine diagnostic procedures and from experimental infections with F . hepatica reported in previous studies [9 , 21 , 22] . Fasciola excretory-secretory antigens ( ESAs ) were obtained as previously described [23 , 24] . Briefly , live adult flukes collected from bile ducts of naturally infected cows were washed , first in sterile saline and glucose ( 2 g/L ) at 38°C and then in RPMI 1640 cell culture medium supplemented with 20 mM HEPES , 0 . 3 g/L L-glutamine , 2 g/L sodium bicarbonate and antibiotics ( penicillin and streptomycin ) at 38°C under 5% CO2 in air . The flukes were then transferred to 75-cm2 tissue culture flasks and maintained in culture medium ( 3 mL/fluke ) at 38°C under 5% CO2 in air . After incubation for 24 h , the medium containing ESAs was removed and centrifuged at 10 , 000 g for 20 min at 4°C in the presence of protease inhibitors ( SigmaFast Protease Inhibitor Tablets; Sigma-Aldrich , Madrid , Spain ) . The supernatant was passed through a 0 . 45 μm pore filter disk and concentrated in an Amicon 8050 ultrafiltration cell ( Amicon , Inc . , Beverly , MA ) equipped with a YM10 membrane ( 10 kDa cut-off ) , dialyzed against PBS , sterilized by filtration , and stored at -80°C until required . Protein concentration was measured using the Pierce BCA Protein Assay Kit ( Thermo Fisher Scientific , Barcelona , Spain ) . Fresh or stored ( -20°C ) individual ovine and bovine stools were mixed with distilled water at a ratio of 1:4 ( 1 g + 4 mL ) or 1:1 ( 3 g + 3 mL ) , respectively . The samples were resuspended by mixing on a vortex mixer and centrifuged for 15 min at 1 , 000 g . The supernatants were collected and subsequently analyzed for the presence of F . hepatica coproantigens by ELISA . To evaluate the detectability of the MM3-COPRO test with three secondary reagents , a pool of 6 negative cattle fecal samples were mixed with distilled water , and the supernatant was collected as described previously . The supernatants were used to prepare two-fold dilutions of F . hepatica ESAs starting at 20 ng/mL prior to ELISA analysis . The limit of detection for each MM3-COPRO ELISA performed with different secondary reagents and incubation conditions was calculated by testing serial dilutions of known amounts of F . hepatica ESAs in CoproGuard or in fecal supernatants from pooled negative samples prepared in distilled water . The concentration obtained at the intersection point of the standard curve with the respective cut-off value of each MM3-COPRO ELISA was considered the limit of detection of the assay . The concentrations of the ESAs assayed ranged from 0 . 15 to 20 ng/mL . The cut-offs for cattle and sheep were determined for each ELISA model on the basis of values obtained for fecal supernatants from animals not infected with F . hepatica . Specifically , the cut-offs were determined as being 1 standard deviation ( SD ) above the highest OD value observed on testing the negative samples from fluke-free cattle and sheep populations . Values higher than this cut-off were considered positive for F . hepatica infection . This particular method of calculating the cut-off [26] was preferred over other commonly used arbitrary methods based on a statistical parameter such as 2–4 SD above the mean value of negative samples [9 , 27] . This ensured maximal specificity while preventing the penalizing effect of the SD parameter when combining a high number of OD values that are close to zero with few data with higher values among negative samples . The calculated cut-off values were 0 . 059 ( cattle ) for ELISA model A ( HRP-labeled goat anti-mouse IgG antibodies , former batch ) , 0 . 055 ( cattle ) and 0 . 064 ( sheep ) for ELISA model B ( SA-PolyHRP and OPD ) , 0 . 084 ( cattle ) and 0 . 065 ( sheep ) for ELISA model C ( SA-PolyHRP and TMB ) and 0 . 224 ( cattle ) for ELISA model D ( HRP-labeled goat anti-mouse IgG antibodies , current batch ) . As the secondary NA-HRP reagent was only used for comparison in preliminary ELISA experiments , a specific cut-off was not calculated . Instead , the same cut-off value of SA-PolyHRP was considered as a reference . Linear correlation coefficients were used to test the relationship between parasite burden and OD values measured by the MM3-COPRO test and between different MM3-COPRO models . The analysis was implemented using the GradPad Instat statistical package ( GraphPad Software Inc , CA , USA ) .
In order to evaluate the usefulness of SA-PolyHRP as a secondary reagent for quantitation of Fasciola cathepsins in fecal samples , we first determined the optimal dilution of this reagent and incubation time for samples . The optimization procedure was carried out at RT under shaking and considering several concentrations of antigen diluted in CoproGuard . The data presented in Fig 1A show that the commercial SA-PolyHRP conjugate can be optimally used in the range of 1/5 , 000-1/10 , 000 dilution for either medium ( 5 ng/mL ) or low-range ( 0 . 62 ng/mL ) concentrations of antigen . The data in Fig 1B also indicate that a reduction in the incubation time from 2 h to 30 min under orbital shaking does not decrease the sensitivity of the assay for the overall range of antigen concentrations tested ( 5 to 0 . 15 ng/mL ) , even when incubations were carried out at RT . According to the data obtained , we selected a 1/8 , 000 dilution and incubations of 30 min at RT with shaking for subsequent comparative experiments . To investigate whether the sensitivity of previous versions of the MM3-COPRO ELISA test can be improved using the SA-PolyHRP system , we compared the OD values obtained with three secondary detection reagents ( NA-HRP , HRP-labeled anti-mouse IgG antibodies and SA-PolyHRP ) , and three incubation conditions: i ) 37°C ( 2 h for samples; 90 min and 1 h for secondary reagents ) for all incubation steps , ii ) 4°C ( ON ) for incubation of samples , and 37°C ( 90 min and 1 h ) for subsequent steps , and iii ) RT ( 30 min ) with shaking at 750 rpm for all incubation steps . For these experiments , we tested several concentrations of Fasciola ESAs diluted in CoproGuard . Comparing the three detection methods , the data in Fig 2A–2D show that SA-PolyHRP provides an advantage over classic HRP-labeled anti-mouse IgG antibodies and NA-HRP detection systems and that incubation for a short period at RT under shaking yields better OD values than longer incubations at 37°C or at 4°C . In particular , for short incubations with agitation ( Fig 2D ) , we observed that the use of SA-PolyHRP in the MM3-COPRO test provided a limit of detection ( 150 pg/mL ) that is about 8 and 16 times lower than that obtained using NA-HRP ( 1 . 25 ng/mL ) and HRP labeled anti-mouse IgG antibodies ( 2 . 5 ng/mL ) , respectively . When pooled supernatants from negative fecal samples ( cattle ) extracted with distilled water were used to dilute Fasciola ESAs , the OD values for the different antigen concentrations were slightly lower ( Fig 3A ) than those obtained for the antigen diluted in CoproGuard ( Fig 2D ) ; however , the detection limits remained the same for the three secondary reagents tested . Moreover , we observed that the current batch of HRP-labeled anti-mouse antibodies used to perform these experiments yielded a higher background with negative samples in wells containing anti-Fasciola antibodies than in control wells with irrelevant antibodies , which was not previously observed with former batches . This effect , which occurred with CoproGuard and with fecal supernatants diluted in distilled water , explains why the OD values of the standard curve obtained with this secondary reagent did not fall below ∽0 . 200 at concentrations lower than the limit of detection ( 2 . 5 ng/ml ) ( Figs 2C , 2D and 3A ) . In addition to the use of enhanced detection systems , ELISA OD values can also be increased by using certain substrates for peroxidase , among which OPD and TMB are frequently used . Comparative results for these two peroxidase substrates are presented in Fig 3B , which shows that the OD values were higher , as expected , with TMB; however , the detectability of the assay ( i . e . , the smallest amount of analyte detected ) [28] did not change as signals in control wells were also higher with TMB . In order to determine the sensitivity and specificity of the MM3-COPRO test by using the SA-PolyHRP secondary reagent and to obtain a cut-off value , we evaluated its ability to correctly classify positive and negative bovine and ovine fecal samples . Two peroxidase substrates , OPD and TMB , were also evaluated . The data in Fig 4 show the results obtained with positive and negative samples with the classic MM3-COPRO ELISA ( model A , using a former batch of HRP-labeled goat anti-mouse IgG polyclonal antibodies and performed at the time of sample collection ) , the enhanced MM3-COPRO revealed with SA-PolyHRP and OPD ( model B ) or TMB ( model C ) , and the MM3-COPRO performed using a current batch of goat anti-mouse IgG-HRP conjugate ( model D ) . As indicated in the previous section , negative samples ( open circles ) from cattle ( Fig 4A ) and sheep ( Fig 4B ) were used to calculate the cut-off value for each model . Regarding the positive bovine samples ( Fig 4A , closed circles ) , models B and C were able to correctly classify all samples and the signal-to-noise ratios were optimal for both . Nevertheless , as expected , higher OD values and cut-off values were obtained when SA-PolyHRP secondary reagent and the TMB substrate were combined ( model C , cut-off value = 0 . 084 ) than with the OPD substrate ( model B , cut-off value = 0 . 055 ) . Unlike the enhanced MM3-COPRO methods ( models B and C ) , models A and D yielded poorer results with multiple mismatches . In particular , the accuracy of these models was highly dependent on the performance of each batch of polyclonal antibodies . Overall , the results obtained with model A were acceptable , as of the three false negative samples , two corresponded to animals harboring a single fluke and the other corresponded to an animal harboring 4 flukes but with an OD signal ( OD = 0 . 056 ) close to the cut-off value ( OD = 0 . 059 ) . However , with model D , the results were diagnostically unacceptable , as the cut-off value was very high ( OD = 0 . 224 ) and consequently 10/18 positive samples ( 55 . 6% ) were incorrectly classified . Positive fecal samples from experimentally infected sheep were also tested with enhanced models B and C ( Fig 4B , closed circles ) . All samples produced a high response and no significant differences were observed on comparing the OD values from animals harboring low fluke burdens ( 2–5 flukes , 6 animals ) and those harboring medium to high fluke burdens ( 12–28 flukes , 4 animals ) . Model A was also used to analyze sheep samples at the time of sample collection and also correctly classified the samples . Considering samples from cattle naturally infected with F . hepatica ( n = 18 ) , a close correlation ( Fig 5A and 5B ) was observed on comparing the classic MM3-COPRO ELISA ( model A ) with both enhanced MM3-COPRO ELISAs ( model B and C; r = 0 . 846 and r = 0 . 850 , respectively; p<0 . 0001 ) . As expected , the correlation coefficient was even higher when models B and C were compared ( Fig 5C; r = 0 . 951; p<0 . 0001 ) . However , we did not observe a correlation between fluke burden in liver and the OD values obtained with the MM3-COPRO ELISA , as shown for the ELISA model B in Fig 5D ( r = 0 . 2998; p = 0 . 2267 ) . As additional data , the results in Fig 6 show the number of parasites collected from the liver , e . p . g . counts in feces , and the ELISA OD values ( model C ) obtained for each animal . As expected , most samples ( 11/18 ) were negative for the presence of eggs by microscopic examination . Among positive samples , the presence of Fasciola eggs was low ( ranging from 1 to 4 e . p . g . , the latter corresponding to the only animal harboring 10 flukes; sample #14 ) . Regarding the sensitivity of the enhanced MM3-COPRO ELISAs , it is also interesting to determine whether the tests can correctly classify positive fecal samples collected at regular intervals during the biliary phase of the parasite from animals infected with a small number of metacercariae , considering the usual fluctuations in antigen concentration in feces . For this purpose , we investigated the kinetics of coproantigen release in fecal samples obtained sequentially ( weekly ) from lambs and cattle experimentally infected with different doses of F . hepatica metacercariae during the patent period of infection . As the enhanced MM3-COPRO ELISA performs similarly with OPD and TMB , only the former substrate was considered . The data in Fig 7A show that the release of coproantigen in sheep remained high and stable throughout the period of infection investigated ( weeks 9–18 ) . No differences were observed for fecal samples obtained from animals infected with either 5 ( fluke recovery of 53%; fluke burdens: 2 , 3 and 3 ) or 10 ( fluke recovery of 30%; fluke burdens: 1 , 3 and 5 ) metacercariae . On the contrary , regarding fecal samples from positive cattle , very variable OD values were obtained , although the test was able to correctly classify as positive all samples obtained from each animal ( Fig 7B ) . Again , no differences were observed on comparing samples from animals infected with either 50 or 25 metacercariae . Finally , in order to obtain information about the number of flukes that reached adult stages in the infected cows , one animal infected with 25 metacercariae was sacrificed in the slaughterhouse at the end of the experiment . Only 3 adult flukes were found in liver . Although only one animal was slaughtered , this indicates that the fluke burden in the animals used in this experiment was probably low as in the group of 18 animals selected at slaughterhouse to test the sensitivity of the different ELISA methods .
The limitations of the study lay in the small sample size , which was conditioned by the availability of eligible samples and the complexity of performing multiple simultaneous test comparisons . Nevertheless , we estimate that the number of samples used was adequate to draw robust conclusions about the better performance of the MM3-COPRO test using the SA-PolyHRP conjugate with respect to the other secondary reagents evaluated , which was the main purpose of this study . This does not preclude that future studies conducted with a large number of samples from areas of different disease prevalence , feeding and farming conditions are required to obtain definitive conclusions about the performance of the proposed methodology .
|
We have previously reported how the combined use of mAb MM3 with polyclonal antibodies obtained from rabbit immunized with Fasciola hepatica excretory-secretory antigens led to the development of the in-house MM3-COPRO ELISA and its commercial version BIO K 201 ( BIO X Diagnostics , Belgium ) , which are widely used to detect human and animal infections caused by F . hepatica . After more than a decade in use , both tests have proven to be useful tools for specifically detecting Fasciola infections , although it has also been found that: i ) the conditions of use of the commercial test in some field studies did not enable the sensitivity obtained with the in-house test to be reached , and ii ) the batches of the secondary reagent ( peroxidase-labeled anti-mouse antibodies ) currently available for use in the in-house test do not perform the same as previous batches . To solve these problems , we provide data showing that the incorporation of an enhancement system consisting of streptavidin-polymerized horseradish peroxidase conjugate greatly improved the sensitivity of the MM3-COPRO ELISA and enabled reduction of the incubation time . These modifications enabled the detectability of the assay to be 150 pg/mL , thus enabling detection of infection in animals harboring only one fluke .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"livestock",
"medicine",
"and",
"health",
"sciences",
"enzyme-linked",
"immunoassays",
"immune",
"physiology",
"ruminants",
"distillation",
"immunology",
"vertebrates",
"parasitic",
"diseases",
"animals",
"mammals",
"nematode",
"infections",
"trematodes",
"antibodies",
"immunologic",
"techniques",
"research",
"and",
"analysis",
"methods",
"separation",
"processes",
"immune",
"system",
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"fasciola",
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"bovines",
"organisms"
] |
2016
|
Rapid Enhanced MM3-COPRO ELISA for Detection of Fasciola Coproantigens
|
Bronchodilator response ( BDR ) is an important asthma phenotype that measures reversibility of airway obstruction by comparing lung function ( i . e . FEV1 ) before and after the administration of a short-acting β2-agonist , the most common rescue medications used for the treatment of asthma . BDR also serves as a test of β2-agonist efficacy . BDR is a complex trait that is partly under genetic control . A genome-wide association study ( GWAS ) of BDR , quantified as percent change in baseline FEV1 after administration of a β2-agonist , was performed with 1 , 644 non-Hispanic white asthmatic subjects from six drug clinical trials: CAMP , LOCCS , LODO , a medication trial conducted by Sepracor , CARE , and ACRN . Data for 469 , 884 single-nucleotide polymorphisms ( SNPs ) were used to measure the association of SNPs with BDR using a linear regression model , while adjusting for age , sex , and height . Replication of primary P-values was attempted in 501 white subjects from SARP and 550 white subjects from DAG . Experimental evidence supporting the top gene was obtained via siRNA knockdown and Western blotting analyses . The lowest overall combined P-value was 9 . 7E-07 for SNP rs295137 , near the SPATS2L gene . Among subjects in the primary analysis , those with rs295137 TT genotype had a median BDR of 16 . 0 ( IQR = [6 . 2 , 32 . 4] ) , while those with CC or TC genotypes had a median BDR of 10 . 9 ( IQR = [5 . 0 , 22 . 2] ) . SPATS2L mRNA knockdown resulted in increased β2-adrenergic receptor levels . Our results suggest that SPATS2L may be an important regulator of β2-adrenergic receptor down-regulation and that there is promise in gaining a better understanding of the biological mechanisms of differential response to β2-agonists through GWAS .
Asthma is a chronic respiratory disease that affects over 20 million Americans and 300 million people worldwide [1] , [2] . A hallmark characteristic of asthma is reversible airway obstruction , which is commonly measured via a bronchodilator response ( BDR ) test , in which the reduction of bronchoconstriction after administration of a short-acting reliever drug is quantified [3] . β2-agonists , the most common short-acting reliever drugs used during BDR tests and for asthma therapy , act in part by stimulating β2-adrenergic receptors ( β2ARs ) on airway smooth muscle cells to reduce bronchoconstriction via subsequent increases in cyclic adenosine monophosphate ( cAMP ) and protein kinase A ( PKA ) [3] . Although a comprehensive pathophysiologic understanding of BDR has not been obtained , it is a complex trait involving interactions among various tissues and cells , including inflammatory [4] , airway epithelium [5] , smooth muscle [6] , and the autonomic nervous system [7] . In addition to being used for the diagnosis of asthma , BDR tests can be used to measure whether inhaled β2-agonists are effective in patients . Although short-acting β2-agonists are widely used clinically as asthma rescue medications , they are variably efficacious among patients [8] . Studying BDR may thus provide information regarding both the pathophysiology and pharmacogenetics of asthma . The search for genetic variants that modify asthma susceptibility has resulted in the most recent multi-center asthma genome-wide association studies ( GWAS ) providing strong statistical evidence for the association of many genes , including the IKZF3-ZPBP2-GSDMB-ORMDL3 locus , HLA-DQ , IL1RL1 , IL18RL1 , IL33 , TSLP , SLC22A5 , SMAD3 , and RORA , with asthma [9] , [10] . Functional experiments to identify the role that these genes play in asthma pathophysiology are hindered by the complexity of the asthma phenotype . Familial aggregation [11] and genetic association studies [12] have provided suggestive evidence for a genetic contribution to interindividual differences in BDR . Candidate genes reported to be associated with BDR include β2-adrenergic receptor ( ADRB2 ) [13] , [14] , adenylyl cyclase type 9 ( ADCY9 ) [15] , corticotrophin-releasing hormone receptor 2 ( CRHR2 ) [16] , and arginase 1 ( ARG1 ) [17] , [18] . While BDR is a complex phenotype , functional studies of BDR candidate genes are simpler than those for a general asthma phenotype because this pharmacogenetic phenotype can be readily simulated in vitro via stimulation of cells with β2-agonists . In this study , we performed a GWAS of BDR in 1 , 644 non-Hispanic white asthmatics and found that the strongest evidence of association with BDR was at variants near the Homo sapiens spermatogenesis associated , serine-rich 2-like ( SPATS2L ) gene . We attempted to replicate the primary findings in two independent populations and investigated the function of SPATS2L via mRNA knockdown experiments and found evidence to support its involvement in BDR .
Figure 1 is an overview of our study design . Characteristics of the subjects used in the primary GWAS are provided in Table 1 . We utilized 1 , 644 non-Hispanic white subjects from six clinical trials to measure the association of SNPs to BDR . After QC filters , 469 , 884 SNPs genotyped in CAMP/LOCCS/LODO/Sepracor and either genotyped or imputed in CARE and ACRN were used to test for the association of SNPs to BDR . We utilized genotyped SNPs for CAMP/LOCCS/LODO/Sepracor because these cohorts , who were all genotyped using Illumina platforms , had the largest sample size . Due to the poor overlap of Illumina and Affymetrix platform SNPs , we utilized HapMap Phase 2 imputed SNPs for CARE and ACRN , so that the maximal number of SNPs in all cohorts could be analyzed . The quantile-quantile ( QQ ) and Manhattan plots revealed that the distribution of association P-values was similar to that expected for a null distribution and that no P-values met genome-wide statistically significant levels ( Figures S1 and S2 ) . To expand the primary association results further , all SNPs available in the June 2010 release of the 1000 Genome Project ( 1000GP ) data were imputed using MaCH in each of the three primary groups of genotype data and overall BDR GWAS results were re-computed . Among SNPs contained in the primary GWAS , imputed and genotyped P-values were similar , particularly for those with low P-values ( Figure S3 ) . Some imputed regions had P-values lower than those of the primary GWAS , but the results in most of these regions were not supported by primary GWAS data ( Figure S2 ) . Thus , we proceeded to attempt to validate the primary GWAS findings based on the combined genotyped CAMP/LOCCS/LODO/Sepracor SNP results and HapMap Phase 2 imputed CARE and ACRN SNP results . The top five primary GWAS SNPs with P-value<1E-05 are in Table 2 . Further details on these regions and all primary GWAS SNPs with P-value<1E-04 are in Tables S1 and S2 . Further details on all 1000GP imputed GWAS SNPs with P-value<1E-05 are in Tables S3 We attempted to replicate in SARP all of the SNPs with primary GWAS P-values<1E-04 ( Table S5 ) . Three had nominally significant P-values ( i . e . <0 . 05 ) , and two of these associations supported the top 5 primary GWAS associations ( Table 3 ) . The lowest combined P-value for all primary GWAS plus SARP data was 7 . 7E-07 for rs295137 . The region of BDR association spanning this SNP was in the 5′UTR region of SPATS2L , a gene of unknown in vivo function and paralog of SPATS2 ( Figure 2 ) . The effect of the rs295137 genotype on BDR is shown in Figure 3 , and a plot of the residuals of the linear regression fit of BDR while adjusting for age , sex , and height is shown in Figure S4 . We sought further evidence of association for the two SPATS2L SNPs with lowest P-values in our primary GWAS in a second independent population: DAG . There was no evidence for association in this cohort ( rs295137 P-value = 0 . 21; rs295114 P-value = 0 . 21 ) , and combined P-values for these two SNPs across all cohorts were 9 . 7E-07 and 1 . 6E-06 ( Table 3 ) . To investigate whether our top combined association represented a biologically significant finding , we sought experimental evidence that SPATS2L was involved in bronchodilator response . We found one public gene expression array experiment ( GSE13168 ) that would help to address the question of whether SPATS2L is differentially expressed in response to changes in the BDR pathway . We compared the levels of expression of two SPATS2L and one SPATS2 probes in human airway smooth muscle ( HASM ) cells that stably expressed a PKA inhibitor vs . a GFP control at baseline and when stimulated with the pro-asthmatic cytokines interleukin-1β ( IL1β ) , epidermal growth factor ( EGF ) , or both . A trend of differential expression was observed for the SPATS2 and one SPATS2L probes , but not a second SPATS2L probe ( Figure S5 ) . None of the comparisons met a Benjamini-Hochberg adjusted significance threshold , but nominally significant P-values were obtained for the SPATS2 probe under all conditions and for one of the SPATS2L probes under the condition of EGF and IL1β stimulation ( Table S6 ) . According to the Gene Enrichment Profiler , the two SPATS2L probes are highly expressed in lung , and all three probes are highly expressed in smooth muscle , especially the SPATS2 one . Overall , there are strikingly different tissue-specific expression patterns for each probe ( Figure S6 ) . We further investigated the involvement of SPATS2L in the β2-adrenergic response pathway by knocking down SPATS2L mRNA using two different small interfering RNAs ( siRNA ) and measuring subsequent changes in β2AR protein levels . The knockdown efficiency of the siRNAs was >80% reduction of SPATS2L mRNA as measured by qRT-PCR , and the corresponding increases in β2AR ( normalized against the control β-actin protein ) levels were 1 . 88- ( SD 0 . 41 ) and 1 . 86- ( SD 0 . 30 ) fold for the two SPATS2L siRNAs ( Figure 4 ) . The association of SNPs with BDR at SNPs in/near genes ( i . e . ADRB2 , ADCY9 , CRHR2 , ARG1 ) previously reported as being associated with BDR was measured in our primary GWAS imputed data ( Figure S7 ) . Nominally significant ( P-value<0 . 05 ) SNPs were found in ADCY9 , CRHR2 , and ARG1 , but not in ADRB2 ( Tables S7 and S8 ) . The SNPs with lowest P-values within 50 kb of these genes were: rs2531988 for ADCY9 ( 3 . 2E-03 ) , rs12533248 for CRHR2 ( 0 . 029 ) , and rs6929820 for ARG1 ( 0 . 012 ) .
In recent years , many GWAS that have successfully identified risk-modifying loci for a wide range of complex diseases have been published , but progress toward understanding how the loci and genes identified are functionally related to diseases has been slow [19] . The relationship of genes and gene variants to pharmacogenetic traits is often easier to test functionally than that for complex diseases because pharmacogenetic traits are more amenable to in vitro testing . However , compared to GWAS of complex diseases , GWAS of pharmacogenetic traits have been challenged by the relatively small size of drug clinical trials , which has caused many studies to be underpowered for obtaining genome-wide significant associations [20] . Nonetheless , successful pharmacogenetic GWAS have led to the identification of loci involved in modulating response to inhaled corticosteroids among asthma patients [21] , warfarin dose [22] , and lipid-lowering response to statins [23] . One of the difficulties specific to BDR GWAS is the complexity of the BDR phenotype . Regardless of how it is quantified , BDR is highly variable among asthma patients due to the time-dependent variation in baseline FEV1 and the influence of external environmental factors [24] . BDR can be quantified in various ways , with slightly varying resulting classification of patients as responders or non-responders . For our study , we selected the definition most widely utilized in clinical and human asthma research settings: percent change in baseline FEV1 following administration of a standard dose of short-acting inhaled β2-agonist [25] . We have attempted to control for the known relationship between baseline lung function and BDR [24] , [26] by ( 1 ) selecting a definition of BDR that standardizes the change in FEV1 by dividing by baseline FEV1 and ( 2 ) by using age , sex , and height , which together account for a large portion of the variability in baseline lung function , as covariates in our statistical models . Because BDR tests are routinely performed during asthma clinical trials to use as inclusion criteria and to monitor outcomes among patients , we were able to utilize subjects from several diverse asthma clinical trials that were not specifically designed to study the pharmacogenetics of BDR . Most of these trials included a wash-out period that reduces modification of BDR due to concomitant medication administration , but LOCCS and some CARE and ACRN subjects were administered BDR tests at a time when they were not necessarily off of medications ( Table 1 ) . Subjects without a placebo washout , and especially those who were on ICS ( e . g . LOCCS subjects ) , may be expected to have less BDR than those on placebo . The relationship of the magnitude of BDR to the various gene loci could therefore be blunted and show a less significant relationship than would be expected if all studies had incorporated a placebo washout . In addition to variable washout periods , the cohorts had other significant differences in their design . Two trials consisted of children with asthma ( i . e . CAMP , CARE ) , while the others consisted mostly of adults . The gender composition varied from 25% to 62% male . We attempted to control for age , gender and height , all of which are known to influence BDR , by including them as covariates in the association analysis . The mean and range of BDR also varied among trials . Of most significance , because the Sepracor trial used BDR greater than 15% as a criterion for inclusion , its subjects had markedly greater BDR than those of other trials . We attempted to control for this difference and any other trial specific differences among the cohorts that were pooled in the primary analysis by including trial as a covariate in the association model . There were additional differences among trials that were not taken into account . For example , SARP and Sepracor were composed of subjects with more severe asthma than those of other cohorts . Some ACRN , CARE , and SARP subjects were administered a different amount of albuterol during their BDR tests than those of CAMP , Sepracor , LOCCS , and LODO . DAG subjects were administered a different beta-agonist ( i . e . Salbutamol ) at a different concentration than that used with subjects of all other trials . DAG and SARP subjects were not participants of clinical trials , so there was greater heterogeneity of subjects within those cohorts . Despite the heterogeneity among trials , we utilized as many subjects as possible in an attempt to increase our statistical power to detect associations of SNPs with BDR . We reasoned that any associations detected despite the heterogeneity of the trial populations would be those most likely to generalize to all asthma patients . Another expected consequence of the trial heterogeneity is that our association results do not replicate in all cohorts . While having the largest number of subjects provides the greatest statistical power to detect statistically significant associations that are most generalizable across the clinical trials , we may be missing associations that are specific to the individual trials . For example , the subjects within clinical trials representing different ranges of asthma severity , age , and baseline characteristics may have genetic associations that are unique to subjects with their specific trial characteristics . The small sample size of each individual clinical trial makes detection of trial-specific associations more challenging . Despite the cohort heterogeneity , our meta-analysis identified a strong association that suggests a novel gene is involved in BDR . Our top association was at SNP rs295137 , with a combined P-value across all cohorts of 9 . 7E-07 . This P-value does not meet conventional genome-wide significance thresholds ( e . g . Bonferroni corrected minimally significant P-value would be 0 . 05/469 , 884 = 1 . 1E-07 ) , but performing searches through public data sources and the fact that other pharmacogenetic GWAS have discovered biologically important results without genome-wide significant associations led us to pursue our top association further . The region of association surrounding rs295137 is in the 5′UTR of SPATS2L ( Figure 2 ) . This gene maps to chromosome 2 at 2q33 . 1 , covering 176 . 78 kb from 201170592 to 201347368 ( NCBI 37 , August 2010 ) . According to data gathered via the AceView [27] tool , SPATS2L is a complex locus that may have at least 30 spliced variants , its in vivo function is unknown , and it is a highly expressed gene in many tissues , with the greatest number of GenBank accessions belonging to lung . In gene-trap experiments in myoblasts , SPATS2L ( a . k . a . SGNP ) was found to be involved in ribosomal biogenesis and translational control in response to oxidative stress [28] . The availability of one public expression array experiment that utilized HASM cells expressing a PKA inhibitor ( PKI ) to modify the β2-adrenergic pathway allowed us to perform a preliminary search for evidence that SPATS2L may be involved in BDR . We found that a probe for SPATS2 , the paralog of SPATS2L , was significantly differentially expressed in PKI vs . control cells at baseline and when stimulated with pro-inflammatory cytokines ( EGF , IL1β , or both ) . One SPAST2L probe followed this trend but had a nominally significant P-value only under the condition of stimulation with both EGF and IL1β , while the other SPATS2L probe did not exhibit any changes . As illustrated in Figure S6 , the tissue-specific expression patterns of the three probes varied widely . While all were expressed in smooth muscle , the SPATS2 probe's relative expression in this tissue was markedly greater than that of the SPATS2L probes . Taken together , the expression patterns are consistent with tissue and isoform dependent changes in SPATS2L gene products . While the public dataset SPATS2L results were inconclusive based on the differences among probes , they suggested that SPATS2L expression may change when PKA is inhibited in HASM cells . Knockdown of SPATS2L in HASM cells resulted in significantly increased β2AR protein levels , suggesting that SPATS2L may affect BDR by directly modulating β2AR protein expression . In HASM , β2-agonists exert their effects exclusively via the β2AR [6] . The relaxation of HASM occurs after the binding of β2-agonists to β2ARs via increased levels of cAMP followed by PKA activation . PKA activation leads to changes in gene transcription via activation of cAMP response element binding protein ( CREB ) . Because β2ARs are the gateway to the effects of β2-agonists in HASM cells , modulations , such as SPATS2L inactivation , that increase the levels of β2ARs in HASM cells may lead to both greater relaxation in response to β2-agonists in the short term and greater differences in gene transcription in the longer term . Further study is needed to elucidate the precise mechanism by which SPATS2L regulates β2AR and consequently modifies BDR . Among our primary GWAS subjects , those whose SPATS2L SNP rs295137 has the TT genotype have greater BDR than those with CT or TT genotypes ( median BDR 16 . 0 vs . 10 . 9 ) . In one of the simplest scenarios , it is possible that the increased BDR among subjects with the TT genotype results from this genotype playing a direct role in decreasing transcription of SPATS2L , which in turn results in increased β2AR levels . Further work is required to understand how specific SNP associations in/near SPATS2L affect SPATS2L function and/or expression and how such effects impact β2AR signaling and BDR . Because the observed influence of our most strongly associated SNP genotype on BDR is relatively small ( Figure 3 ) , our current data do not support the development of any personalized therapeutics based solely on variants in/near SPATS2L . In addition to studying top primary GWAS SNPs , we attempted to replicate findings from previous BDR candidate gene association studies . Specifically , we measured association between BDR and ADRB2 [13] , [14] , ADCY9 [15] , CRHR2 [16] , and ARG1 [17] variants . Notably , these previous findings are not entirely independent of those from the current GWAS: CAMP was a primary population utilized to identify associations in ADRB2 , ADCY9 , CRHR2 , and ARG1 in previous reports; LODO and Sepracor were replication populations in the CRHR2 , and ARG1 reports; and LOCCS was a replication population in the ARG1 report . At a nominal significance level , we replicated gene-level associations for all of the candidate genes other than ADRB2 . This gene , which encodes the β2AR , is the most studied gene related to BDR and SNPs and haplotypes in this gene have been related to decreased pulmonary function [29] , response to β2-agonist treatment [30] , an increased frequency of asthma exacerbations [31] , and BDR [13] , [14] . Initial reports of ADRB2 associations were very promising and suggested that variants of this candidate gene would be reliable markers of BDR pharmacogenetics . However , a meta-analysis of 21 studies of ADRB2 polymorphisms found that most of the positive associations identified in individual studies cannot be compared to findings in other studies due to different study designs , phenotypes considered and selective reporting , making the number of variants with true replications very small and questioning the utility of ADRB2 polymorphisms for generalizable pharmacogenetic tests [32] . Our inability to find associations with ADRB2 variants is consistent with the view that BDR genetics are complex: no individual SNPs or genes are responsible for a large proportion of BDR variability observed among all asthmatics . Our results suggest that genes other than the previously identified candidate genes are more strongly associated with BDR and that functional studies of these regions may yield important insights into BDR biology despite not having strong effects or generalizing to all populations . In summary , a BDR GWAS among asthma patients from eight cohorts found that the most strongly associated SNP , rs295137 , had a combined P-value of 9 . 7E-07 . This association led us to SPATS2L , a gene of unknown in vivo function that we showed may be involved in BDR via the down-regulation of β2AR levels . Our results support the notion that there is promise in pursuing GWAS results that do not necessarily reach genome-wide significance and are an example of the way in which results from pharmacogenetic GWAS can be studied functionally .
Each study was approved by the Institutional Review Board of the corresponding institution , which ensured that all procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation . Informed consent was obtained for all study participants . The primary group of subjects consisted of 1 , 644 non-Hispanic white asthmatics from the following drug clinical trials: Childhood Asthma Management Program ( CAMP ) [33] , Leukotriene Modifier or Corticosteroid Salmeterol study ( LOCCS ) [34] , Effectiveness of Low Dose Theophylline as an Add-on Treatment in Asthma trial ( LODO ) [35] , a medication trial conducted by Sepracor , Inc . [36] , [37] , and subsets of clinical trials within the Childhood Asthma Research and Education ( CARE ) network [38] , and the Asthma Clinical Research Network ( ACRN ) [39] participating in the NHLBI SNP Health Association Resource ( SHARe ) Asthma Resource project ( SHARP ) . Some basic characteristics of these cohorts are in Table 1 and further details are provided in Text S1 . BDR tests were performed according to American Thoracic Society criteria with Albuterol as the β2-agonist [25] , unless otherwise noted . Baseline BDR measures were utilized , and BDR was quantified as the percent change in FEV1 in response to administration of a β2-agonist [i . e . ( post-BD FEV1 – pre-BD FEV1 ) /pre-BD FEV1] . Genome-wide genotyping for CAMP subjects ( n = 546 ) was performed on the HumanHap550 Genotyping BeadChip or Infinium HD Human610-Quad BeadChip by Illumina , Inc ( San Diego , CA ) at the Channing Laboratory . LOCCS ( n = 135 ) , LODO ( n = 114 ) , and Sepracor ( n = 401 ) subjects were genotyped at the Riken Center for Genomic Medicine using the Infinium HD Human610-Quad BeadChip . CARE ( n = 207 ) and ACRN ( n = 241 ) subjects were genotyped on Affymetrix 6 . 0 genotyping chip by Affymetrix , Inc . ( Santa Clara , CA ) . Data from those subjects genotyped using Illumina technologies was combined into a primary dataset with 469 , 884 overlapping SNPs having missingness <1% , passing HWE ( P-value threshold of 1E-03 ) , and having minor allele frequency ( MAF ) >0 . 05 . EIGENSTRAT was used to identify 23 outliers ( not included in counts above ) based on being outside of six standard deviations of the top four principal components during five iterations [40] . The genomic inflation factor ( λGC ) of the remaining 1 , 196 subjects was 1 . 002 , demonstrating minimal population stratification . CARE and ACRN dataset quality control ( QC ) also included the removal of four related subjects ( i . e . CARE siblings ) , SNPs with MAF<0 . 05 , missingness >5% , or not passing HWE based on a threshold of 1E-03 . The λGC for CARE and ACRN genotype data were 1 . 02 and 0 . 98 , demonstrating minimal population stratification among subjects within each group . Comprehensive genotyping and QC measures are provided in Text S1 . Due to the poor overlap among SNPs genotyped on the Illumina and Affymetrix platforms , imputation of all SNPs available in HapMap Phase 2 Release 22 CEU data using the Markov Chain Haplotyping software ( MaCH ) [41] was performed for ACRN and CARE genotyped data . The primary GWAS consisted in the set of 469 , 884 SNPs that were successfully genotyped in those cohorts using Illumina arrays ( i . e . , CAMP/Sepracor/LOCCS/LODO ) and imputed with HapMap Phase 2 data in those cohorts genotyped with Affymetrix arrays ( i . e . , ACRN and CARE ) with a ratio of empirically observed dosage variance to the expected ( binomial ) dosage variance greater than 0 . 3 , indicating good quality of imputation . To expand the association results , imputation of all SNPs available in the June 2010 release of the 1000 Genome Project ( 1000GP ) data using MaCH was performed for each of the three primary groups of genotype data . An overlapping set of 4 , 571 , 615 imputed SNPs had a MAF>0 . 05 and ratio of empirically observed dosage variance to the expected ( binomial ) dosage variance greater than 0 . 5 , indicating good quality of imputation . The association of SNPs with BDR was measured with a linear regression model as implemented in PLINK [42] in the three sets of data: 1 ) CAMP/Sepracor/LOCCS/LODO , 2 ) ACRN , 3 ) CARE . Association of imputed SNPs was carried out using dosage data . Covariates for the CAMP/Sepracor/LOCCS/LODO group included age , gender , height , and study . Covariates for the CARE and ACRN groups included age , gender , and height . To get the primary GWAS results , CARE and ACRN P-values were combined with those of the CAMP/Sepracor/LOCCS/LODO group by using Liptak's combined probability method [43] where hypothesis tests in CARE and ACRN had one-sided alternatives , based on the direction of the association in CAMP/Sepracor/LOCCS/LODO , so that SNPs with association tests in opposite directions would not produce inappropriately small P-values . The overall λGC was 1 . 002 in the primary set of GWAS results and 1 . 000 in the 1000GP imputed data GWAS . Plots of association results near specific genes were created using LocusZoom with the hg18/1000 Genomes June 2010 CEU GenomeBuild/LD Population [44] . The publicly available Gene Expression Omnibus ( GEO ) dataset , GSE13168 , corresponding to an experiment in which human airway smooth muscle ( HASM ) cell cultures were generated from four donor trachea to test for the effects of glucocorticoids and PKA inhibition on the HASM transcriptome using the Affymetrix Human Genome U133A platform was used [48] . We tested for the involvement of our top primary GWAS gene in the β2-adrenergic pathway by comparing the differential expression of genes in cells stably expressing a PKA inhibitor ( PKI ) vs . control at baseline and in the presence of pro-inflammatory cytokines interleukin-1β ( IL1β ) , epidermal growth factor ( EGF ) , or both . The expression array contained two SPATS2L probes ( i . e . , 215617_at , 222154_s_at ) and one SPATS2 probe ( i . e . , 218324_s_at ) . The probe for the paralog of SPATS2L was included to account for the possibility of non-specific binding of SPATS2L mRNA to the SPATS2 probe . Analyses were conducted in R [46] . Pre-processing of raw signal intensities was performed with RMA [49] and differential expression was quantified using the limma package [50] . Tissue-specific expression of these probes was assessed using 557 microarrays from 126 human normal primary tissues in the Gene Enrichment Profiler [51] . Primary HASM cells were isolated from aborted lung transplant donors with no chronic illness . The tissue was obtained from the National Disease Resource Interchange ( NDRI ) and their use approved by the University of Pennsylvania IRB . HASM cell cultivation and characterization were described previously [52] , [53] . Passages 4 to 7 HASM cells maintained in Ham's F12 medium supplemented with 10% FBS were used in all experiments . 2×105 HASM cells were grown overnight and then transfected with 50 nM siRNA by using DharmaFECT 1 reagent ( Thermo Scientific , Lafayette , CO , USA ) . About 72 h post transfection , cells were washed with PBS and lysed with NP-40 lysing buffer ( 50 mM Tris-HCl pH7 . 5 , 150 mM NaCl , 0 . 5% Nonidet P-40 ) containing protease inhibitor cocktail ( Roche Diagnostics Corporation , Indianapolis , IN , USA ) . Protein samples were denatured 10 min at 50°C , separated on NuPAGE 4–12% Bis-Tris gels ( Invitrogen , Grand Island , NY , USA ) and transferred to PVDF membranes ( Bio-Rad Laboratories , Hercules , CA , USA ) . Immunoblot signals were developed using SuperSignal West Pico ( Pierce Protein Research Products , Thermo Fisher Scientific , Rockford , IL , USA ) and quantified by ImageJ software . Non-targeting control siRNA , SPATS2L-specific siRNA 1 ( sense 5′- guc agu cca uug auu guc u ( dT ) ( dT ) -3′ , antisense 5′- aga caa uca aug gac uga c ( dT ) ( dT ) -3′ ) and SPATS2L-specific siRNA 2 ( sense 5′-caa ccu gug uug uag cag u ( dT ) ( dT ) -3′ , antisense 5′- acu gcu aca aca cag guu g ( dT ) ( dT ) -3′ ) were obtained from Sigma-Aldrich ( Mission siRNA; St . Louis , MO , USA ) . Antibodies for β2AR ( H20 ) and β-actin were from Santa Cruz Biotechnology , Inc . ( Santa Cruz , CA , USA ) . Experiments were performed in triplicate .
|
Bronchodilator response ( BDR ) is an important asthma phenotype that measures reversibility of airway obstruction by comparing lung function before and after the administration of short-acting β2-agonists , common medications used for asthma treatment . We performed a genome-wide association study of BDR with 1 , 644 white asthmatic subjects from six drug clinical trials and attempted to replicate these findings in 1 , 051 white subjects from two independent cohorts . The most significant associated variant was near the SPATS2L gene . We knocked down SPATS2L mRNA in human airway smooth muscle cells and found that β2-adrenergic receptor levels increased , suggesting that SPATS2L may be a regulator of BDR . Our results highlight the promise of pursuing GWAS results that do not necessarily reach genome-wide significance and are an example of how results from pharmacogenetic GWAS can be studied functionally .
|
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2012
|
Genome-Wide Association Analysis in Asthma Subjects Identifies SPATS2L as a Novel Bronchodilator Response Gene
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Normal embryonic development and tissue homeostasis require precise levels of retinoic acid ( RA ) signaling . Despite the importance of appropriate embryonic RA signaling levels , the mechanisms underlying congenital defects due to perturbations of RA signaling are not completely understood . Here , we report that zebrafish embryos deficient for RA receptor αb1 ( RARαb1 ) , a conserved RAR splice variant , have enlarged hearts with increased cardiomyocyte ( CM ) specification , which are surprisingly the consequence of increased RA signaling . Importantly , depletion of RARαb2 or concurrent depletion of RARαb1 and RARαb2 also results in increased RA signaling , suggesting this effect is a broader consequence of RAR depletion . Concurrent depletion of RARαb1 and Cyp26a1 , an enzyme that facilitates degradation of RA , and employment of a novel transgenic RA sensor line support the hypothesis that the increases in RA signaling in RAR deficient embryos are the result of increased embryonic RA coupled with compensatory RAR expression . Our results support an intriguing novel mechanism by which depletion of RARs elicits a previously unrecognized positive feedback loop that can result in developmental defects due to teratogenic increases in embryonic RA .
Improper retinoic acid ( RA ) signaling during development can cause congenital malformations that affect the forelimbs , ocular , cardiovascular , respiratory , urogenital and nervous systems [1]–[4] . Despite almost a century of investigation , the mechanisms underlying many congenital defects due to fluctuations in RA signaling are still not understood . RA acts as a ligand for RA receptors ( RARs ) , members of the nuclear hormone family of transcription factors [5] . Work using disparate embryonic models has provided critical insight into the molecular mechanisms and developmental requirements of RAR function in vertebrate embryos [6]–[12] . In addition , RAR deficiency and inappropriate RA signaling are associated with numerous types of cancers [13] . In the majority of cases , the mechanism by which loss of RARs promote tumorigenesis is not understood . Therefore , understanding the roles of RARs during development will help elucidate the mechanisms underlying congenital defects , and possibly cancers , caused by inappropriate RA signaling [3] , [4] . RA signaling employs a number of feedback mechanisms in order to maintain appropriate levels in the embryo and tissues . The best characterized feedback mechanism is through regulation of the RA producing [retinol dehydrogenases ( RDHs ) and retinaldehyde dehydrogenases ( Aldh1a ) ] and degrading ( Cyp26 ) enzymes . Specifically , increased RA signaling inhibits the expression of the RA producing enzymes , while promoting Cyp26a1 expression . Conversely , decreased RA signaling promotes expression of the RA producing enzymes , while inhibiting Cyp26a1 expression [14]–[18] . While modulation of RA signaling also affects the expression of other factors that control RA signaling [5] , [19] , less well understood are feedback mechanisms that may influence RAR expression . RA response elements ( RAREs ) have been found in murine RARα2 and RARβ2 promoters and RARβ2 has been shown to be RA responsive 20–22 . However , if decreases in RA signaling , in particular due to loss of RAR expression , lead to compensatory expression of other RARs is less clear . While initial studies of mouse RAR KO mice suggested that there was not compensatory RAR expression in RAR deficient mice [11] , [12] , more recent studies using siRNA to deplete RARα have challenged this model and suggested that there may be compensatory RAR expression in RARα deficient embryos [23] . Therefore , if there are RA feedback mechanisms that influence RAR expression and how the employment of these feedback mechanisms impact embryonic development are not well understood . Here , we find that depletion of RARαb1 , a previously unrecognized yet conserved zebrafish RARα splice variant , causes an increase in CM specification and heart size , which is due to the triggering of a feedback mechanism that surprisingly promotes increased RA signaling from surplus embryonic RA and compensatory RAR expression . Our results provide insight into a newly recognized positive feedback mechanism that we posit resists fluctuations in RA signaling due to perturbation in RAR expression . However , if improperly maintained , the positive feedback can result in RA induced congenital defects . Altogether , the results from this study significantly enhance our understanding of the feedback mechanisms that are used to maintain appropriate RA signaling levels and previously unexplored mechanisms that potentially underlie congenital defects .
In contrast to the studies of RARs in mice [9]–[12] , depletion of RARs has not been able to recapitulate all of the consequences of loss of RA signaling in zebrafish [8] , which prompted us to determine if additional conserved RAR variants exist in zebrafish beyond what has already been reported [24] . We cloned a previously unrecognized RARα splice variant that is orthologous to human , mouse and Xenopus RARα1 termed RARαb1 ( Figure 1A–1C ) . The previously cloned zebrafish RARα homologs RARαa and RARαb are teleost specific paralogs and both are orthologous to the splice variant 2 found in tetrapods ( Figure 1B , 1D ) [24] . Both rarαb1 and rarαb2 are expressed maternally and zygotically ( Figure 1E ) , with ubiquitous expression until the tailbud stage ( Figure S1A–S1I ) . After the tailbud stage , their expression patterns deviate ( Figure 1F–1H and Figure S1J–S1O ) . We used a translation blocking morpholino ( MO ) to examine the function of RARαb1 ( Figure 1B ) . By 48 hours post-fertilization ( hpf ) , RARαb1 deficient embryos had enlarged hearts with increased CM number and expression of CM marker genes myl7 , vmhc and amhc ( Figure 2A , 2B , 2M , 2N and Figure S2A–S2D ) . Similar increases in CM number were also found at 55 hpf ( Figure S3A–S3C ) , suggesting the major addition of surplus CMs occurs during earlier stages of development . Consistent with this idea , we observed an expansion of CM differentiation ( myl7 , vmhc , and amhc ) and progenitor ( nkx2 . 5 and hand2 ) marker expression in RARαb1 deficient embryos at earlier stages via in situ hybridization ( ISH ) and quantitative real-time PCR ( qPCR; Figure 2C–2L , 2O–2Q ) . Injecting the RARαb1 MO along with rarαb1 mRNA that lacks the 5′UTR MO binding sequence is able to rescue the increased heart size , supporting the specificity of the phenotype ( Figure S4A–S4D ) . Together , these results suggest that RARαb1 deficient embryos have increased CM specification , number and heart size . The increased atrial and ventricular CM number in RARαb1 deficient embryos are reminiscent of RA signaling deficient embryos [25] , [26] . Therefore , we examined hoxb5b expression , which functions downstream of RA signaling to restrict atrial CM number [26] and is likely a direct target of RARs ( Figure S5A–S5D ) . Unexpectedly , we found that hoxb5b expression was increased in RARαb1 deficient embryos ( Figure 3A–3C ) . While this was initially perplexing , our recent studies showed that Hoxb5b overexpression is able to mimic many of the teratogenic effects of RA treatment [27] . Therefore , we asked if the increases in hoxb5b expression in RARαb1 deficient embryos could be a cause of the enlarged hearts . While depletion of hoxb5b alone using a low concentration of hoxb5b MO does not affect CM number ( Figure S6A–S6C ) , we found that concurrent depletion of RARαb1 and Hoxb5b largely restored heart morphology , CM differentiation marker expression , and CM number relative to the RARαb1 deficient embryos ( Figure 3F–3N ) , suggesting that the increased CM number in RARαb1 deficient embryos is in part a consequence of the increased hoxb5b expression . We next examined the expression of additional RA signaling responsive genes . Similar to hoxb5b , we found that the expression of additional RA signaling responsive genes , including cyp26a1 , dhrs3a , hoxb6b and hoxb5a , was increased in RARαb1 deficient embryos ( Figure 3A ) . Comparing RA responsive gene expression in RA treated and RARαb1 deficient embryos , we found that the trends were similar , but that RA treatment typically induced a greater increase in expression ( Figure 3A ) . Conversely , treatment with DEAB , an antagonist of the RA producing enzyme Aldh1a , inhibited RA responsive gene expression ( Figure 3A ) . These findings indicate that RARαb1 depletion paradoxically results in increased expression of RA signaling responsive genes . We next wanted to determine if increases in RA signaling responsive genes were specific to RARαb1 depletion , so we examined RA responsive gene expression in RARαb2 deficient embryos . Previous studies found that RARαb2 deficient embryos lack forelimbs ( pectoral fins ) and tbx5a expression [8] , [28] , which we confirmed ( Figure S7A , S7C , S7D , S7F , S7H , S7I ) . However , similar to RARαb1 depletion ( Figure 3A and Figure 4A ) , RARαb2 deficient embryos had increased expression of RA signaling responsive genes ( Figure 4A ) . While the previous studies found a loss of forelimbs , defects in heart development were not reported . Despite the loss of forelimbs and increase in RA signaling responsive genes , we did not observe an increase in heart size , CM number or CM gene expression ( Figure S8A–S8D ) . Therefore , although eliciting similar increases in RA signaling responsive gene expression , individual depletion of RARαb1 and RARαb2 results in distinct defects . To determine the functional consequences of concurrent RARαb1 and RARαb2 depletion , we co-injected a suboptimal dose of each MO . Unfortunately , co-injection of an optimal dose of each MO resulted in significant non-specific toxicity even when injected along with p53 MO . However , concurrent depletion of the RARαbs using suboptimal MO doses resulted in a dramatic increase in RA signaling responsive genes , above what was seen with depletion of RARαb1 and RARαb2 alone using the optimal MO doses ( Figure 4A ) . Additionally , there was an anterior shift of hoxb5a expression in the spinal cord of RARαb1+2 deficient embryos , suggesting the spinal cords are posteriorized ( Figure S9A–S9E ) . Increased RA signaling inhibits aldh1a2 expression through a negative feedback mechanism 16–18 . Although aldh1a2 expression in individual RARαb1 and RARαb2 deficient embryos was not suppressed ( Figure 4B ) , aldh1a2 expression was decreased in embryos depleted for both RARαb variants ( Figure 4B ) . To corroborate the increases in endogenous RA signaling responsive genes , we used the RA signaling reporter line Tg ( 12XRARE-ef1a:EGFP ) sk72 29 . Again , co-depletion of both RARαbs resulted in a greater expansion of egfp expression , compared to the individual depletion of each RARαb ( Figure 4D–4H ) . Therefore , these experiments support the hypothesis that the RARαb1+2 deficient embryos are sensing more significant increases in RA signaling than embryos deficient for either RARαb variant alone . We next examined the consequences of this functional interaction on heart development . We found that the hearts of RARαb1+2 deficient embryos had increased atrial size , CM number , and a dramatic increase in amhc expression ( Figure 4I , 4L–4N and Figure S10A–S10D ) . Significant effects on CM number or heart size were not found when using a suboptimal dose of either RARαb1 or RARαb2 MO alone ( Figure 4I–4K , 4M ) , though we did find a modest increase in CM marker gene expression in the RARαb1 deficient embryos ( Figure 4N ) . Interestingly , in RARαb1+2 deficient embryos we found more significant increases in atrial CM number and amhc expression ( Figure 4M , 4N ) , which were remarkably similar to the consequences of modest increases in RA signaling due to RA treatment 27 . Increased RA signaling can also inhibit forelimb development 17 and RARαb1 deficient embryos also have smaller forelimbs and a modest reduction of tbx5a expression ( Figure S7A , S7B , S7D , S7F , S7G , S7I ) . A functional interaction with the RARαb variants that resulted in loss of forelimbs was also observed ( Figure S7D , S7E ) . Therefore , concurrent depletion of RARαb variants elicits increases in RA signaling with heart and forelimb phenotypes that are strikingly similar to increases in RA signaling caused from RA treatment . We sought to understand the mechanism underlying the increase in RA signaling in RARαb deficient embryos . In the absence of RA , RARs are thought to interact with transcriptional co-repressors , while binding of RA converts the RARs to transcriptional activators 1 , 5 . A previous study in Xenopus suggested that RARs are required as transcriptional repressors in some developmental contexts 6 . However , our gain-of-function analysis did not support that these zebrafish RARs function as transcriptional repressors ( Figure S11A–S11L ) , consistent with what we have reported previously 29 . However , Manshouri et al . 23 found a compensatory increase in the expression of other RARs when using siRNA to deplete RARα in mice . Similarly , we found that the expression of other zebrafish RARs 24 was increased in RARαb deficient embryos ( Figure 4C and Figure S12A–S12L ) , suggesting that compensatory RAR expression is a conserved response to depletion of RARα homologs in vertebrates . Although Manshouri et al . 23 proposed the compensatory RAR expression was RA signaling dependent , our results suggest that the expression of most RARs is potentially regulated independent of RA signaling ( Figure 4C ) , because the effects on RAR expression did not parallel modulation of RA signaling using RA and DEAB . While we observed compensatory expression of other RARs in RARαb deficient embryos , it is difficult to conclude that increased RAR expression is the sole cause of the increase in RA signaling since overexpression of RARs in zebrafish embryos does not produce significant positive or negative effects on RA responsive gene expression ( Figure S11A–S11J ) 29 . Nevertheless , our results suggest that when depleting RARαbs in zebrafish embryos compensatory RARs are present that can mediate RA signaling . Because we did not have evidence that RARs act as transcriptional repressors or that the increased expression of RARs alone contributes to the increases in RA signaling in RARαb deficient embryos , we hypothesized that the depletion of RARs may trigger an increase in embryonic RA . Although aldh1a2 expression was suppressed in RARαb1+2 deficient embryos similar to when embryos sense increases in RA signaling ( Figure 4B ) 16–18 , the expression of rdh10a and rdh10b , which control a limiting step in RA production in vertebrates by generating retinal from retinol 14 , 15 , was increased in RARαb1 and RARαb1+2 depleted embryos ( Figure 4B and Fig . S13A–S13C ) . Interestingly , rdh10b expression , which was not sensitive to modulation of RA signaling , was increased in RARαb deficient embryos ( Figure 4B ) . Therefore , our results suggest that depletion of RARαbs triggers an increase in RA through promoting rdh10 expression . In addition to inhibiting aldh1a2 expression , increased RA signaling promotes a negative feedback mechanism that limits RA levels by positively regulating Cyp26a1 expression 16–18 . Since we observe an increase in cyp26a1 expression in RARαb1 deficient embryos ( Figure 3A , 3D , 3E and Figure 4A ) , which was also consistent with the hypothesis that there is increased embryonic RA , we postulated that the increased Cyp26a1 may be protecting the RARαb1 deficient embryos from teratogenic increases in embryonic RA . Therefore , we concurrently depleted RARαb1 and Cyp26a1 to determine if there was a functional interaction indicative of increased embryonic RA . For these experiments , a suboptimal dose of cyp26a1 MOs ( Figure S14A–S14E ) was used to more easily discern a functional interaction . In either the RARαb1 or Cyp26a1 deficient embryos alone , we never observed absence of the MHB or defects in tail elongation ( Figure 5A–5C , 5E–5G ) . However , co-depletion of RARαb1 and Cyp26a1 resulted in a loss of the MHB and truncated tails ( Figure 5D , 5H ) , similar to increases in RA signaling 17 , 19 , 29 , 30 . Furthermore , we found that RARαb1+Cyp26a1 deficient embryos had dismorphic hearts with a specific reduction in ventricular CM number compared to controls embryos hearts ( Figure 5I–5L , 5Q ) , which interestingly resembles the trend we previously found in embryos with intermediate increases in RA signaling 27 . Although one interpretation of the functional interaction of RARαb1 and Cyp26a1 depletion is that there is increased embryonic RA levels in these embryos , we wanted to further test this hypothesis using additional assays . First , we sought to use a distinct readout of embryonic RA , so we made a novel stable transgenic RA sensor line which incorporated the RARαb ligand binding domain ( RLBD ) fused to the Gal4 DNA binding domain ( GDBD ) expressed under the β-actin promoter ( Figure S15A–S15G ) 31 . Previous studies have found that similar GDBD fusions with nuclear hormone receptor LBDs create an effective reporter of nuclear hormone activity 6 , 32 , 33 . We observed a dramatic increase in reporter expression when RARαb1 and Cyp26a1 were depleted together in Tg ( β-actin:GDBD-RLBD ) ; Tg ( UAS:EGFP ) embryos ( Figure 5M–5P , 5R ) 34 . Second , our hypothesis predicted that reducing embryonic RA levels should be able to rescue teratogenic phenotypes found in RARαb1+Cyp26a1 and RARαb1 deficient embryos . Consistent with this hypothesis , DEAB treatment of RARαb1+Cyp26a1 deficient embryos was able to rescue the loss of MHB ( Figure 6A–6J ) . Additionally , treatment of RARαb1 deficient embryos with DEAB partially rescue the enlarged heart phenotype and restored atrial CM number ( Figure 6K–6O ) . Lastly , our hypothesis predicts that exogenous treatment with a concentration of RA that causes a minor increase in RA signaling should result in aberrant heart phenotypes that are similar to RARαb1 deficient embryos . Indeed , embryos treated with low concentrations of exogenous RA ( lower than we had reported using previously 27 ) had enlarged hearts with an increase in both atrial and ventricular CM number at 48 hpf ( Figure 6P–6R ) . Altogether , our results suggest that increases in embryonic RA , coupled with compensatory RAR expression , contribute to the developmental defects found in RARαb1 deficient embryos .
Together , our study supports a novel paradigm whereby RARαb depletion elicits a positive feedback mechanism that can result in teratogenic increases in RA signaling . Importantly , our work highlights that loss and gain of RA signaling can cause similar developmental defects . RA signaling is required to restrict CM specification 25 , 26 , while high increases in RA signaling can eliminate CM specification ( Figure 7A ) 27 . However , our present findings suggest that low increases in RA signaling , achieved when treating embryos with µM concentrations of RA or through RARαb depletion , can also promote increases in both atrial and ventricular CM specification ( Figure 7A ) . As we found previously , modest , but slightly higher increases of RA signaling can promote atrial CM specification without significantly affecting ventricular CM specification 27 , which is strikingly similar to what we found with concurrent depletion of the RARαb variants here ( Figure 7A ) . Moreover , intermediate increases in RA signaling can inhibit ventricular CM specification , which is similar what we observed when concurrently depleting RARαb1 and Cyp26a1 ( Figure 7A ) . It also appears that modulation of Hox activity downstream of both gain and loss RA signaling is at least partially responsible for the increases in CM specification , suggesting the hypothesis that the similar effects on CM number are actually due to opposite perturbations of anterior-posterior patterning within the ALPM . Therefore , our analysis corroborates and extends previous observations that there are differential effects on atrial and ventricular CM populations as there is a progressive increase from low to intermediate levels of RA signaling in the early embryo . It is interesting that depletion of RARα homologs using MOs in zebrafish , presented in this study , and Xenopus 6 elicit similar phenotypic responses . In Xenopus embryos , RARα depletion alone results in loss of the MHB 6 . While depletion of RARαb1 alone does not result in MHB defects in zebrafish embryos , we have found that RARαb1+Cyp26a1 deficient embryos completely lack the MHB . Taken together , these results suggest that the underlying consequences of increased RA signaling due to depletion of RARα homologs are likely conserved at least in Xenopus and zebrafish embryos , but that in Xenopus perhaps the role of Cyp26 enzymes in protecting the brain has been lost . Despite similarities in the phenotypes that both point to an increase in RA signaling in RARα and RARαb deficient Xenopus and zebrafish embryos , our results contrast with the model proposed by Koide et al . 6 , which concluded that RARs are required to function as transcriptional repressors . Importantly , the tools used in the previous study , including dominant-negative RARs , transcriptional co-repressors , and inverse agonists , would not have allowed them to distinguish between a transcriptional de-repressive model and the positive feedback mechanism involving the production of excess RA supported here . In addition to the phenotypic similarities when depleting RARα homologs in Xenopus and zebrafish , depletion of zebrafish RARαbs results in compensatory RAR expression similar to RARα depletion in mice 23 , supporting the hypothesis that this feedback response to RARα deficiency is conserved in vertebrates . Importantly , the response to RAR depletion is likely different than complete ablation of RARs . RAR KO mice have not been reported to have compensatory increases in other RARs 11 , 12 , suggesting that a complete loss of RAR expression may cause a breakdown of this feedback loop . However , when considering the probability that RAR expression would be completely lost vs . depleted , we postulate that insults resulting in depletion of RAR expression would be much more likely . Consistent with this idea , variable levels of RAR expression deficiency , which in the case of RARβ can be due to epigenetic silencing , is commonly observed in a variety of cancers 13 . Given the conserved feedback mechanisms already recognized that limit fluctuations in RA signaling in vertebrates 16 , 17 , 19 , 23 , it seems logical that a conserved mechanism that senses RAR deficiency would also exist to prevent loss of RA signaling . We propose that this newly recognized positive feedback mechanism would be more suitable to prevent transient deficiency in RARs . As demonstrated here , persistent RARαb depletion can result in a hypervigilant response of RA signaling and RA-induced teratogenic defects . Overall , these data provide insight into a previously unappreciated RAR-dependent positive feedback mechanism ( Figure 7B ) , which is active during development . Further elucidation of this RA signaling feedback mechanism may illuminate the etiology of poorly understood RA-insensitive cancers 13 , 23 and congenital defects 1 , 3 .
All zebrafish husbandry and experiments were performed in accordance with protocols approved by the Institutional Animal Care and Use Committee ( IACUC ) of Cincinnati Children's Hospital Medical Center . Zebrafish ( Danio rerio ) were raised and maintained as previously described 35 . The following transgenic lines were used: Tg ( -5 . 1myl7:DsRed-NLS ) 36 , Tg ( -5 . 1myl7:EGFP ) twu26 37 , Tg ( 12XRARE-ef1a:EGFP ) sk72 29 , Tg ( β-actin:GDBD-RLBD ) cch1 ( was created using the Gateway/Tol2 system 38 and additional characterization is reported in 31 ) , Tg ( UAS:EGFP ) 34 , and Tg ( UAS:nfsB-mcherry ) 39 . Whole-mount ISH was carried out using standard procedures 40 . All probes except rarαb1 ( accession number: KF030797 ) and rarαb2 were reported previously . myl7 ( formerly called cmlc2; ZDB-GENE-991019-3 ) , amhc ( ZDB-GENE-031112-1 ) , vmhc ( ZDB-GENE-991123-5 ) , nkx2 . 5 ( ZDB-GENE-980526-321 ) , hand2 ( ZDB-GENE-000511-1 ) , hoxb5a ( ZDB-GENE-980526-70 ) , hoxb5b ( ZDB-GENE-000823-6 ) , dhrs3a ( ZDB-GENE-040801-217 ) , cyp26a1 ( ZDB-GENE-990415-44 ) , rarαb1/2 ( which recognizes both isoforms and was formerly called rarαb 24; ZDB-GENE-980526-72 ) , rarαa ( ZDB-GENE-980526-284 ) , rarγa ( ZDB-GENE-980526-531 ) , rarγb ( ZDB-GENE-070314-1 ) , rdh10a ( ZDB-GENE-070112-2242 ) , tbx5a ( ZDB-GENE-030909-7 ) , eng2a ( ZDB-GENE-980526-167 ) , egr2b ( formerly called krox20; ZDB-GENE-980526-283 ) , egfp ( accession number: JQ064510 . 1 ) , and mcherry ( accession number: JN795134 . 1 ) . The rarαb1 MO ( 5′-TGCAGGTCATCCGTAATGCCCGATC ) was designed to the 5′ UTR of rarαb1 . Additional MOs targeting another region of the 5′ UTR and the donor splice junction , which saturated the available MO target sites , were also tried . However , injection of these MOs resulted in significant toxicity and were not able to be used for analysis . Sequences to the rarαb2 and hoxb5b MOs were reported previously 8 , 26 . The total amount of rarαb1 MO injected was 16 ng . The total amount of rarαb2 MO injected was 7 ng . The suboptimal doses used to test genetic interactions were half these concentrations . The amount of hoxb5b MO used was 0 . 25 ng . A cocktail of 4 ng cyp26a1 MO1 ( 5′-TCTTATCATCCTTACCTTTTTCTTG ) and 2 ng cyp26a1 MO2 ( 5′-TAAAAATAATACACTACCTGCAAAC ) produced a phenotype similar to gir mutants 17 . Suboptimal doses used in experiments were 0 . 9 ng ( cyp26a1 MO1 ) and 0 . 45 ng of ( cyp26a1 MO2 ) . For all injection experiments , 3 ng of p53 MO were used to help suppress non-specific MO-induced cell death 41 . For experiments , the total amount of MO injected was always kept constant by equilibrating the concentrations with Standard Control MO ( Gene Tools ) . Capped mRNA was made using a Message Machine Kit ( Ambion ) . 150 pg of mRNA was used for over-expression of all mRNAs in all experiments . Luciferase reporter assays were performed in HEK 293 cells as previously described 29 . Western blots were performed as previously described 29 . Mouse monoclonal anti-myc antibody ( Covance ) was used for both Western blot analysis and ChIP experiments . The dynabeads ( Invitrogen ) ChIP protocol was adapted from the Dorsky Lab ( University of Utah ) ZFIN Protocol . qPCR was used to quantify the enrichment of the fragment containing the RARE ( DR5 ) in embryos injected with the myc-rarαb1 mRNA with respect to control uninjected embryos . The genomic sequence flanking zebrafish hoxb5b ( −8 to +8 kb ) was compared with the corresponding region for Hoxb5 in mouse using mVista . NHR SCAN was used to identify binding sites for nuclear receptor . Rarαb1 was identified by using BLAST against the zebrafish genome ( Ensemble_V7 ) with the human and mouse RARα1 A domains . MacVector was used for sequences alignments . For RT-PCR , primer pairs were designed so that they specifically recognized rarαb1 and rarαb2 ( Figure 1B ) . Primer sequences are available upon request . The full-length coding sequence for rarαb1 was cloned into pCS2p+ . The rarαb2-pCS2p+ construct used for overexpression was reported previously 29 . The myc tagged RARαb1 was made using the pCS2+MT vector . For rarαb1 and rarαb2 probes , 536 base pairs ( bps ) of rarαb1 and 443 bps of rarαb2 , which include the 5′ untranslated region ( UTR ) and the specific A domains with no overlap , were cloned ( Figure 1B ) . These fragments were cloned into pGEM-T easy ( Promega ) . Total RNA was isolated from 25 embryos , homogenized in TRIzol ( Ambion ) and collected using Pure link RNA Micro Kit ( In Vitrogen ) . 1 µg or 0 . 5 µg RNA was used for cDNA synthesis using the ThermoScript Reverse Transcriptase kit ( Invitrogen ) . Quantitative real time PCR ( qPCR ) for myl7 , amhc , vmhc , nkx2 . 5 , hand2 , hoxb5b , hoxb5a , hoxb6b , dhrs3a , cyp26a1 , aldhh1a2 , rdh10a , rdh10b , rarαa , rarαb1 , rarαb2 , rarγa and rarγb , egfp and mcherry was performed using standard PCR conditions in a Bio-Rad CFX PCR machine with Power SYBR Green PCR Master Mix ( Applied Biosystems ) . Expression levels were standardized to ef1α expression and all the data were analyzed using the 2−ΔΔCT Livak Method . All experiments were performed in a biological triplicate . Primer sequences are available upon request . Areas of myl7 , vmhc and amhc expressing cells were measured using ImageJ and statistical analysis was performed as previously described 26 . Length of egfp expression and distance between hoxb5b and egr2b were measured also using ImageJ and statistical analysis was performed as previously described . Immunohistochemistry , cell counting and statistical analysis were done as previously described 26 . RA and DEAB , treatment of embryos was done as previously described 26 , 27 . Embryos that have been used for gene expression analysis at 8 somites were treated with 1 µM DEAB , an Aldh1a2 inhibitor , beginning at 40% epiboly or with 1 µM RA for 1 hr beginning at 40% epiboly . For analysis of the effects of low concentrations of RA on heart development , embryos were treated with 0 . 05 µM RA for 1 hr beginning at 40% epiboly and harvested at 48 hpf . For rescue experiments related to the heart phenotype of RARαb1 deficient embryos , embryos were treated with 0 . 025 µM DEAB beginning at 40% epiboly until 24 hpf . For rescue experiments related to the MHB in RARαb1+Cyp26a1 deficient embryos , embryos were treated with 0 . 25 µM DEAB . To assess whether the means of two groups are statistically different from each other , we applied the Student's t-test . A p value of <0 . 05 was considered statistically significant .
|
Retinoic acid ( RA ) is the most active metabolic product of Vitamin A . Appropriate levels of RA are required for proper embryonic development and tissue maintenance in all vertebrates . Inappropriate levels of RA in human embryos can cause congenital defects that affect many organs , including the heart and limbs , and lead to numerous types of cancers . Understanding how animals maintain appropriate RA levels and the consequences of inappropriate RA signaling will therefore provide insight into human congenital defects and diseases . RA signaling is mediated by RA receptors ( RARs ) , which are transcription factors that are activated when binding RA . We have found that depletion of RARs in zebrafish results in defects that are surprisingly due to increases in embryonic RA and not a deficiency of RA signaling . Our results are the first to demonstrate that RAR depletion elicits a positive feedback mechanism that promotes RA signaling through complementary increases in both embryonic RA and RAR expression . Therefore , our analysis provides novel insight into the molecular mechanisms that are required to maintain appropriate RA signaling and will positively impact our understanding of the mechanisms underlying congenital defects .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"And",
"Methods"
] |
[
"teratology",
"cell",
"differentiation",
"animal",
"models",
"developmental",
"biology",
"model",
"organisms",
"organism",
"development",
"molecular",
"development",
"molecular",
"genetics",
"pattern",
"formation",
"embryology",
"gene",
"expression",
"nuclear",
"receptor",
"signaling",
"biology",
"morphogens",
"zebrafish",
"signal",
"transduction",
"signaling",
"genetics",
"molecular",
"cell",
"biology",
"cell",
"fate",
"determination",
"evolutionary",
"developmental",
"biology"
] |
2013
|
Depletion of Retinoic Acid Receptors Initiates a Novel Positive Feedback Mechanism that Promotes Teratogenic Increases in Retinoic Acid
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The role of long non-coding RNA ( lncRNA ) in the progression of Nasopharyngeal carcinoma ( NPC ) has not been fully elucidated . The study was designed to explore the functional role of NKILA , a newly identified lncRNA , in the progression of NPC . We performed a lncRNA expression profile microarray using four NPC and paired para-cancerous tissues . NKILA was identified as a potential functional lncRNA by this lncRNA expression profile . We used 107 paraffin-embedded NPC tissues with different TNM stages to detect the expression of NKILA and analyzed the survival data by Log-rank test and Cox regression . The role of NKILA and its underlying mechanisms in the progression of NPC were evaluated by a series of experiments in vitro and vivo by silencing or expressing NKILA . Compared with control tissues , NKILA expression was identified to be decreased in NPC tissues . Low NKILA expression was correlated with unfavorable clinicopathological features and predicted poor survival outcome in NPC patients . After adjusting for potential confounders , low expression of NKILA was confirmed to be an independent prognostic factor correlated with poor survival outcomes . Furthermore , we found that NKILA overexpression in high-metastatic-potential NPC cells repressed motile behavior and impaired the metastatic capacity in vitro and in vivo . In contrast , RNAi-mediated NKILA depletion increased the invasive motility of cells with lower metastatic potential . Further experiments demonstrated that NKILA regulated the metastasis of NPC through the NF-κB pathway . Taken together , NKILA plays vital roles in the pathogenesis of NPC . The unique histological characteristics of NPC indicate that local inflammation plays a vital role in carcinogenesis of nasopharyngeal carcinoma .
Nasopharyngeal carcinoma ( NPC ) , a metastasis-prone cancer , which is particularly common in southeast Asia and southern China [1–4] . Due to the high radiosensitivity , radiotherapy has become the main treatment for locoregional NPC . Radiation oncology has improved the locoregional control ( the tumor control of nasopharynx and neck lymph nodes ) , the development of distant metastasis becomes the major reason for treatment failure and occurs in 30–40% of patients with locoregional advanced NPC [5] . Thus , the assessment of the metastatic potential of NPC is vital for determining prognosis and treatment . Long non-coding RNAs play pivotal regulatory roles in the physiological and pathological processes . Most lncRNAs regulate gene expression by RNA decay control , chromatin remodeling , and enhancer transcription in cis and epigenetic regulation [6–10] . Several lncRNAs are aberrantly expressed or play important roles in NPC , such as HOTAIR , ENST00000438550 , and AFAP1-AS1 [11–15] . Inflammatory cytokines have been observed in NPC tissues and can promote the susceptibility to metastasis of NPC cells via constant NF-κB activation [16–18] , therefore NF-κB is a pivotal link between NPC and inflammation . Interestingly , NF-κB is found to be overexpressed in nearly all NPC tissues [16 , 19 , 20] . NKILA is an NF-κB-interacting lncRNA [21] , our previously study found it can be upregulated by inflammatory cytokines in breast cancer . By interacting with NF-κB/IκB , NKILA forms a stable complex , subsequently it masks the IκB phosphorylation motifs to repress the phosphorylation of IκB induced by IKK , then repress NF-κB pathway activation . But the role of NKILA in nasopharyngeal carcinoma remains unknown . In our study , we examined NKILA expression in normal nasopharyngeal tissue , NPC tissue and cell lines . We proved that low NKILA expression predicts poor patient prognosis and that NKILA regulates the metastasis of NPC by the NF-κB pathway . In addition , we explored the role of NKILA in NPC carcinogenesis and metastasis .
To evaluate the role of lncRNA in the progression of NPC , we performed lncRNA expression profiles using four paired NPC and para carcinoma tissues by microarray . We observed that 2107 lncRNAs were upregulated while 2090 lncRNAs were downregulated by more than 2-fold , NKILA among these downregulated lncRNAs ( Fig 1A , GSE95166 ) . Quantitative RT-PCR verified the significant reduction in the expression of NKILA in NPC ( Fig 1B ) . To confirm the results , NKILA expression levels were detected in fresh frozen tissues ( 26 NPC and 10 control tissues ) by qRT-PCR . Compared with control tissues , NKILA was significantly downregulated in NPC tissues ( Fig 1C ) . Furthermore , patients with developed distant metastasis have a lower NKILA expression than patients with non-metastatic NPC ( P < 0 . 05 , Fig 1C , Table 1 ) . The results imply that low expression level of NKILA is correlated with the progression of NPC . We further detected the expression of NKILA in 107 paraffin-embedded NPC tissues to evaluate the clinical significance of NKILA in patients with NPC . Scattered NKILA staining was observed in NPC cells cytoplasm , and the mean optical density ( MOD ) was used to quantify the NKILA staining . NKILA expression levels were compared in normal nasopharyngeal epithelia and samples from different stages of NPC . As shown in Fig 2A , NKILA was found abundantly expressed in normal nasopharyngeal epithelia and nasopharyngeal cells of metaplasia with atypical hyperplasia ( Fig 2A ) ( P < 0 . 01 ) , with a significantly higher MOD of NKILA staining compared with NPC tissues . Furthermore , NKILA staining decreased significantly with advanced disease staging in TNM stage I to III NPC , and staining was almost absence in stage IV tumors ( Fig 2B ) . NKILA expression was associated with the clinicopathological features of NPC patients ( Table 1 ) . Low expression of NKILA was correlated with metastasis ( P < 0 . 05 ) , larger tumor size ( P <0 . 05 ) , and late clinical stage of NPC patients ( P < 0 . 005 , Table 1 ) . Other parameters ( i . e . age , gender and lymph node status ) were not found direct association with NKILA expression ( P > 0 . 05 ) . This study suggested that NPC patients with high expression of NKILA had a significantly longer survival , the median follow-up time is 83 months ( Fig 3A , P < 0 . 001 ) . Furthermore , high expression of NKILA predicts a longer disease-free survival ( DFS ) ( Fig 3B , P < 0 . 001 ) , distant metastasis-free survival ( DMFS ) ( Fig 3C , P <0 . 01 ) , and local recurrence-free survival ( LRFS ) ( Fig 3D , P <0 . 01 ) . After adjusting for potential confounders , the multivariate analysis showed that high expression of NKILA was significantly correlated with improved OS ( HR , 0 . 327; 95% CI , 0 . 171–0 . 623; P < 0 . 001 ) , DFS ( HR , 0 . 290; 95% CI , 0 . 153–0 . 549; P < 0 . 001 ) , DMFS ( HR , 0 . 353; 95% CI , 0 . 159–0 . 781; P = 0 . 010 ) , and LRFS ( HR , 0 . 227; 95% CI , 0 . 077–0 . 670; P < 0 . 01 , Table 2 ) . NKILA suppressed the activation of NF-κB , which in turn inhibited tumorigenesis induced by aberrant NF-κB signaling by modulating apoptosis and invasion [21–23] . We observed that enforcing NKILA expression increased apoptosis in S18 cells ( S18 vec vs S18 NKILA: 10 . 1% vs 19 . 3% , p<0 . 01 ) . Conversely , silencing NKILA in S26 cells reduced apoptosis ( Fig 4A , P < 0 . 05 ) , suggesting that NKILA modulates apoptosis in NPC cells . We next verified the association of the NKILA expression level with apoptosis . We examined the apoptosis level in clinical NPC tissues using a TUNEL assay and the expression level of NKILA using in situ hybridization . The expression level of NKILA was positively associated with the tumor apoptosis level ( Fig 4B , P < 0 . 001 ) . Next , the regulation of NKILA in the tumorigenic activity during anchorage-independent growth in NPC cells was evaluated . As shown in Fig 4C and 4D , overexpression NKILA induced significant inhibition of anchorage-independent growth in S18 cells , as revealed by a decrease in the number and size of colonies ( P < 0 . 001 ) . Conversely , the depletion of endogenous NKILA in S26 cells induced a significant increase in the number and size of colonies ( P < 0 . 001 ) . In addition , to explore the role of NKILA in tumorigenesis in vivo , xenograft tumor experiments were performed . S18 cells overexpressing NKILA or carrying a control vector were injected subcutaneously into the flank of nude mice , and then we measured the tumor size every 2 days to calculate the tumor volume . As shown in Fig 4E , NKILA overexpression in S18 cells greatly inhibited the tumor growth ( P < 0 . 001 ) , demonstrating that downregulation of NKILA is required for the malignant transformation of nasopharyngeal epithelial cells . Next , we detected NKILA expression in NPC cell line to further explore the regulatory function of NKILA in NPC . Two paired NPC cell lines with high metastatic potential ( S18 and 5-8F ) and low metastatic potential ( S26 and 6-10B ) were used in the experiment . We found that NKILA expression levels increased by 2 . 6-fold in S26 ( P < 0 . 001 , versus S18 ) and by 4 . 1-fold in 6–10B ( P < 0 . 001 , versus 5-8F ) ( Fig 5A ) . It was shown that NPC cell lines with low metastatic potential cells had a higher NKILA expression level . To explore the effect of NKILA on metastatic potential of NPC cells , we overexpressed NKILA in S18 NPC cells and examined the resulting metastatic potential . As shown in Fig 5B , S18 NPC cells overexpressing NKILA exhibited reduced migration and invasiveness ( Fig 5B ) . Conversely , silencing NKILA significantly promoted the mobility of S26 cells , the metastatic potential of cells was enhanced . Experimental metastasis assay was performed to evaluate the effect of NKILA on metastasis in vivo . We injected S18 cells with enforced overexpression of control vector or NKILA into the lateral tail vein of nude mice ( 4 weeks old ) , metastatic nodules in lungs were evaluated , numbers of metastatic nodules in lungs were markedly decreased in mice injected with S18 cells overexpressing NKILA , as shown in Fig 5C–5E . The number and volume of micrometastases in lungs of mice injected with S18 cells overexpressing NKILA were proven significantly reduced by H&E staining ( Fig 5C ) . The results suggest that NKILA is extremely important for the metastatic development of S18 cells . NKILA is up-regulated by inflammatory cytokines , which resulted in the inhibition of IKK -induced IκB phosphorylation and then repressed NF-κB pathway activation [21] . Nuclear translocation of P65 is emerging as a central feature of NF-κB pathway activation . Moreover , NKILA binding to the NF-κB: IκBα complex is essential for inhibition of NF-κB activation [21] . Thus , we evaluated the NF-κB activation by detecting NF-κB transcription activity and P65 translocation in NPC cells stimulated by inflammatory cytokines . We found that NKILA suppressed the enhancement of NF-κB transcriptional activity by TNFα . Conversely , silencing NKILA increased NF-κB transcriptional activity by more than 3-fold ( Fig 6A ) . The enhancement of NF-κB transcriptional activity in S26 was suppressed by 40% , but further increased by 3 . 5-fold after NKILA was silenced . In addition , upregulation of the expression of NKILA resulted in retention of most of the P65 in the cytoplasm upon TNF-α stimulation , whereas nearly all of the P65 translocated to the nucleus in cells carrying empty vector ( Fig 6B and S2 Fig ) . Conversely , silencing of endogenous NKILA expression led to prolonged P65 translocation to the nucleus upon TNF-α stimulation in S26 cells . Our results suggest that NKILA inhibits the activation of NF-κB pathway in S26 cells . Subsequently , we evaluated the role of NKILA on IκBα and IKK phosphorylation and explored the mechanisms by which NKILA inhibits NF-κB activation in NPC . It revealed that both basal phosphorylation and TNF-α-induced phosphorylation of IκBα were repressed by enforced expression of NKILA in S18 cells; additionally , silencing NKILA increased the phosphorylation of IκBα with or without the stimulation of TNF-α in S26 cells ( Fig 6C and S1 Fig ) . However , the phosphorylation of IKK was not influenced by NKILA expression ( Fig 6C ) . Our study indicates that NKILA inhibits the activation of NF-κB primarily by inhibiting the phosphorylation of IκBα but not IKK in S18 and S26 cells . To confirm that NKILA works by inhibiting NF-κB , we used sc-3060 and JSH-23 to abrogate the nuclear translocation of NF-κB . As shown in Fig 6D and 6E , NKILA did not further increase the apoptosis of S18 cells , nor did it reduce the migration and invasion of S18 cells , suggesting that role of NKILA in NPC cell lines is dependent on IκBα . Collectively , our data show that NKILA represses the progression of NPC by inhibition of NF-κB pathway ( Fig 7 ) .
NPC , a malignant tumor with high tendency for metastasis , is very common in southern China . Patients of NPC presented with distant metastasis at diagnosis accounts for 5–8% of all cases; furthermore , after standard treatment , the proportion of distant metastasis in stage III–IV NPC is still as high as 30% [5 , 24] . This tendency for metastasis emphasizes the urgency of elucidating the molecular mechanism underlying tumorigenesis and metastasis and possibly to develop new treatment for NPC . In our study , we first discovered that a long non-coding RNA named NKILA which was down-regulated in nasopharyngeal carcinoma , and we have demonstrated that overexpression of NKILA repressed the motile behavior and metastatic capacity of NPC cells . As previously reported , NKILA represses NF-κB activation by directly or indirectly inhibiting phosphorylation of IκBα in breast and hepatocellular carcinoma[21 , 25] . We further demonstrated that NKILA could repress the metastasis of NPC by inhibiting NF-κB pathway . Furthermore , we identified that decreased NKILA expression was correlated with unfavorable clinicopathological features and poor survival outcomes in NPC patients . The roles of lncRNAs ( eg: HOTAIR , MALAT1 , HOTTIP ) have been confirmed by functional studies [26–30] . Dysregulated lncRNAs have been observed in NPC , and clusters of lncRNAs are dysregulated during the metastasis cascade [11 , 12] . Nevertheless , the expression profiles of lncRNA in paired NPC and para carcinoma tissues have never been reported , and most of the dysregulated lncRNAs in NPC are largely unknown . Human lncRNA microarray was firstly performed in our study to detect the expression profiles in paired NPC and para carcinoma tissues and found that 4197 lncRNAs were dysregulated by more than 2-fold . Among these dysregulated RNAs , NKILA was found significantly downregulated in NPC , the decline of NKILA was more pronounced in patients with distant metastases , consistent with previously reported in breast cancer , Hepatocellular carcinoma and laryngeal cancer [21 , 25 , 31–33] . Second , we showed that NKILA expression decreased significantly with advanced disease staging in NPC clinical tissue samples , further research showed low expression of NKILA was correlated with metastasis ( P< 0 . 05 ) , larger tumor size ( T stage , P <0 . 05 ) , and late clinical stage ( TNM stage , P < 0 . 01 ) . No association was observed between NKILA expression and lymph node states ( N stage ) in NPC , but NKILA expression was significantly correlated with the lymph node in breast cancer ( P < 0 . 001 ) [21] . As previously reported in other type of cancers , our study indicated that NPC patients with high NKILA expression survived significantly longer ( OS , P < 0 . 001 ) or longer DFS ( P < 0 . 001 ) [21 , 31 , 32] . We further provided evidence that patients with high expression of NKILA had longer DMFS and LRFS ( P = 0 . 01 , P <0 . 01 respectively ) , which was clinically significant for patients with NPC . Our study is the first to demonstrate that low NKILA expression predicts poor prognosis of NPC patients , with reinforcement data from multivariate analysis ( Table 2 ) . Additionally , aberrant activation of NF-κB may promote chronic inflammation even tumorigenesis in certain conditions which is correlated with NPC progression [18 , 34–36] . Here , we demonstrate that overexpression of NKILA promotes apoptosis and represses the invasion of NPC cell lines as reported in breast cancer cells . NKILA can also enhance the effect of baicalein on cell apoptosis and metastasis in HCC [21 , 25] . Furthermore , NKILA is firstly proved to be positively associated with apoptosis in human NPC tissues . The present study further confirms that NKILA represses tumorigenic and metastatic ability of NPC cells . NKILA is firstly identified as upregulated by inflammatory cytokines through NF-κB pathway in breast cancer , in return NKILA regulate the metastasis of breast cancer via NF-κB pathway [21] . Whether NKILA regulates tumorigenesis and metastasis in NPC via NF-κB and its mechanism remains unclear . In the present study , we verified that NKILA suppressed the enhancement of NF-κB transcriptional activity by TNFα . In addition , upregulation of the expression of NKILA resulted in retention of most of the P65 in the cytoplasm upon TNF-α stimulation , In contrast , the depletion of NKILA expression significantly prolonged the sustained activation time of NF-κB pathway , our study firstly demonstrated that NKILA can regulate the NF-κB activation in NPC . The results are consistent with our previous study in breast cancer [21] . P65 was also found upregulated in laryngeal cancer tissues . P65 positively regulates the NKILA expression , however , NKILA inhibits the translocation of P65 to reduce the resistance of cells to X-ray radiation in laryngeal cancer [33] . Subsequently , we showed that NKILA mainly inhibits the phosphorylation of IκBα , rather than activating IKK to repress NF-κB activation in NPC cells . In addition , we used sc-3060 and JSH-23 to abrogate P65 nuclear translocation , and no further increased apoptosis or reduced migration and invasion was observed in NKILA overexpressing NPC cells . Our previous study shows that NKILA has a high affinity for P65 [21] . By binding to P65 , NKILA forms a complex with the IKB/NF-κB complex , and then masks the IKK phosphorylation site to inhibit IκB phosphorylation . In the present study , it is confirmed that NKILA exerts its effect on NPC by inhibiting NF-κB activation . The important effect of NF-κB pathway in NPC progression is well known , but most studies focus on the activation of P50/P65 in NPC cells[37–40] . It has been reported that FN1 regulates apoptosis of NPC cells though P65 in the NF-κB pathway [41] . Epstein-Barr virus ( EBV ) expresses high levels of BamHI-A rightward transcripts ( BARTs ) in NPC . LMP1 binds P50 to NF-κB sites in the promoter of BART and activates the BART promoters via NF-κB pathway , an autoregulatory loop is formed in NPC cells to maintain EBV latency [42] . The upstream component of NF‐κB or the targeted gene of NF-κB has also been reported to contribute to aberrant NF-κB pathway activation [37 , 43 , 44] , MiR-125b has been shown to regulate NF-κB pathway activity by targeting A20 in NPC cells [45] . To our knowledge , no studies have demonstrated that NF-κB activation associated with NPC progression is primarily regulated by inhibition of IκBα phosphorylation in NPC , and more importantly , in the current study , we extended the understanding of the autoregulatory loop of inflammatory factors and NF-κB activation . Consistent with the important regulatory role of NKILA in breast cancer [21 , 46] , we demonstrate that NKILA regulates the progression of NPC cells by regulating NF-κB pathway activity . Nearly all NPCs are EBV-associated tumor . EBV-encoded LMP1 , a transmembrane protein , has been identified as a viral oncogene of NPC [47] . LMP1 induces constitutive activation of NF-κB by transforming effector sites 1 and 2 , which is required for efficient B-lymphocyte transformation . NF-κB activation maintains the survival of EBV-transformed lymphoblastoid cells , and blocking NF-κB signal leads to the death of these malignant cells [48] . Interestingly , we demonstrate for the first time that NKILA , a lncRNA that is upregulated by inflammatory cytokines , inhibits NF-κB activation by repressing IκB phosphorylation induced by IKK in NPC . In addition , NKILA exerts its effect as a tumor suppressor via inhibiting tumorigenesis and metastasis of NPC , and overexpressing NKILA reverses tumorigenesis and metastasis of NPC . NKILA might be a vital gene for repressing the role of EBV and become one of the most important therapeutic targets for patients with nasopharyngeal carcinoma . In summary , NKILA plays a critical role in NPC progression . The unique histological features of NPC indicate that local inflammation is essential in NPC tumorigenesis . The present study provides new insights into the effects of inflammation on NPC biology . NKILA might be a candidate molecular marker and a novel therapy target for NPC patients .
This study was performed in accordance with the Institutional Review Board of Sun Yat-sen University Cancer Center ( GZR2016-210 ) . Written informed consent was obtained from each patient , including signed consent for tissue analysis and consent to be recorded for potential medical research at the time of sample acquisition . S18 and S26 are subclones of NPC cell lines CNE-2 , 6-10B and 5-8F are subclones of NPC cell lines SUNE-1 that were reported previously [49] . S18 and 5-8F has high metastatic potential , whereas S26 and 6-10B has low metastatic potential . All cell lines were cultured in complete RPMI 1640 medium . Tissue specimens were obtained from the Nasopharyngeal Department of Sun Yat-sen University Cancer Center . A total number of 107 paraffin-embedded NPC and 20 normal control tissues obtained between August 1999 and February 2001 were examined in the present study . Fresh frozen normal nasopharyngeal epithelial and NPC tissues were obtained by biopsy . Total RNA extraction and qRT-PCR were performed as we have reported[13] . Primer sequences of NKILA were as follows: sense , 5′-AACCAAACCTACCCACAACG-3′; antisense: 5′-ACCACTAAGTCAATCCCAGGTG-3′ . It was performed by soft agar colony formation assay and foci formation assay . Soft agar colony formation assay was performed in Six-well plates with a layer of 0 . 66% agar . Preparation of cells in 2 -fold concentration of 1640 complete medium and 0 . 33% agar , after evenly mixed and seeded at 3 different dilutions:1×103 , 2×103 , 3×103 . After culturing for 12–14 days , clone ( >50 cells ) numbers were assessed at an original magnification of ×100 , the scale bar is 100μm , which is approximately the diameter of 50 cell clusters . Spheres in ten random fields of view were counted each well . Foci formation assay was performed in six well plates , cells were seeded in triplicate at 3 different dilutions: 100 , 200 , 300 . Cells were cultured for a period of 14 days . Clone ( >50 cells ) numbers were calculated . All experiments were repeated separately at least 3 times . The expression of NKILA in paraffin-embedded samples was detected by in situ hybridization ( ISH ) . ISH was performed and analyzed as we previously reported[21] . An SI score of 2 was used as the cut-off value , SI>2 was defined as high NKILA expression , SI≤2 was defined as low NKILA expression . The probes used in the ISH assays were as follows: NKILA: TCTCCAGACAGAATCAACTTCG; NKILA antisense: CGAAGTTGATTCTGTCTGGAGA . S18 and S26 cells stably expressing lentiviral particles with NKILA or control vector were obtained from Gene Pharma ( Shanghai , China ) . For cell transduction , NPC cells ( 30–50% confluency ) were infected according to the instructions . After an incubation of 12–16 h , the medium was changed . Forty-eight hours later , the selection reagent puromycin ( Sigma-Aldrich , St . Louis , MO ) was used to select stably transfected clones . After 21days of continuous selection , cells infected with LV5-NKILA were designated S26 NKILA , cells infected with LV5-NC were designated S26 Vec . Luciferase activity was determined by the Dual-Luciferase Reporter Assay System ( E1910 , Promega , Madison , WI ) ) . Cells were transfected with the pGL3-basic or pNFκB-luc constructs together with pRL-TK at 50:1 , and then then the indicated treatments were performed . Cells were harvested and assayed 24h later , and all of the experiments were performed in triplicate . 2 × 104 NPC cells in 100μl RPMI 1640 medium with 2% FBS were plated in the upper chamber of transwell ( Corning , New York , NY , USA ) , the bottom chamber was filled with 600μl RPMI 1640 medium with 10% FBS . After 20h of incubation , fixed the cells on the lower membrane surface in 4% paraformaldehyde and stained with crystal violet , then counted . The cells were counted in ten random optical fields ( ×200 magnification ) , the average number was obtained from triplicate filters . Data are shown as the average ±SD . For the invasion assay , coated the upper chamber with Basement Membrane ( R&D , Minneapolis , MN , USA ) first . All experiments were performed at least three times . Female BALB/c ( nu/nu ) nude mice ( 4–6 weeks of age ) were used . For tumorigenesis experiments , cancer cells ( 105/mouse ) were injected subcutaneously into the flank of the mouse , and measured the tumor size every two days . Calculated the tumor volumes as follows: tumor volume ( mm3 ) = length×width2×0 . 5 . Metastasis of NKILA-overexpressing cancer cells and control cells was evaluated by tail vein intravenous injection . 4 weeks after the first inoculation , the experiment was terminated . After euthanasia , the lungs of mouse were harvested and weighed separately , then prepared for H&E staining . Metastatic nodules in mouse was counted respectively . 1 × 103 S26 cells overexpressing NKILA or NKILA shRNA were cultured on coverslips overnight prior to the experiment . After fixing with 4% paraformaldehyde , IF was done and imaged as previously reported [50] , primary antibodies against P65 was used , followed by FITC-conjugated secondary antibodies ( Invitrogen , Carlsbad , CA ) . Sc-3060 ( Santa Cruz , CA ) and JSH-23 ( Millipore , Billerica , MA ) are drugs which inhibit NF-kB nuclear translocation . 30min before specified treatment , 10uM Sc-3060 and 5uM JSH-23 were added into the culture . Statistical analyses were done by SPSS 18 . 0 . Correlation between NKILA expression and clinicopathological features was analyzed by chi-square test . The survival data were plotted and analyzed by Kaplan-Meier , log-rank test , and multivariate Cox regression analyses . All experiments for cell culture were performed in triplicate at least three times . The data were shown as means ± SD . P values were calculated by Student's t-test . A P value of no more than 0 . 05 was considered statistically significant .
|
NF-κB is a pivotal link between NPC and inflammation . Importantly , NF-κB was found to be overexpressed in nearly all NPC tissues , and inflammatory cytokines have also been observed in NPC tissues . Inflammatory cytokines promote the susceptibility of NPC cells to metastasize via constant NF-κB activation . Here , we found that NKILA , a newly identified lncRNA , is upregulated by inflammatory cytokines and is significantly downregulated in NPC . By a series of in vitro and in vivo experiments , we show that NKILA exerts its effect as a tumor suppressor via inhibiting tumorigenesis and metastasis of NPC . Further studies indicate that NKILA regulates the metastasis of NPC through NF-κB pathway . Our research demonstrates that NKILA plays a critical role in the progression of NPC . These findings are particularly important as they provide new insights into the effects of inflammation on the biology of NPC . NKILA might be a candidate molecular marker and a novel therapy target for NPC patients .
|
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"Abstract",
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"Methods"
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2019
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NKILA represses nasopharyngeal carcinoma carcinogenesis and metastasis by NF-κB pathway inhibition
|
Hybridization and gene flow between species appears to be common . Even though it is clear that hybridization is widespread across all surveyed taxonomic groups , the magnitude and consequences of introgression are still largely unknown . Thus it is crucial to develop the statistical machinery required to uncover which genomic regions have recently acquired haplotypes via introgression from a sister population . We developed a novel machine learning framework , called FILET ( Finding Introgressed Loci via Extra-Trees ) capable of revealing genomic introgression with far greater power than competing methods . FILET works by combining information from a number of population genetic summary statistics , including several new statistics that we introduce , that capture patterns of variation across two populations . We show that FILET is able to identify loci that have experienced gene flow between related species with high accuracy , and in most situations can correctly infer which population was the donor and which was the recipient . Here we describe a data set of outbred diploid Drosophila sechellia genomes , and combine them with data from D . simulans to examine recent introgression between these species using FILET . Although we find that these populations may have split more recently than previously appreciated , FILET confirms that there has indeed been appreciable recent introgression ( some of which might have been adaptive ) between these species , and reveals that this gene flow is primarily in the direction of D . simulans to D . sechellia .
Up to 10% of animal [1] and plant [2] species have the ability to hybridize with other species . Our recent ability to collect large-scale genomic data has confirmed that hybridization is common in nature . Indeed the ubiquity of hybridization upon secondary contact raises the question of how large a role hybridization plays in the emergence or collapse of new lineages [3] . Three general patterns have emerged from recent efforts to search for introgression in genomic data . First , whole-genome sequencing has shown that introgression occurs in all taxa for which its signature has been systematically sought ( primates reviewed in [4] , plants in [5 , 6] , fungi [7] and oomycetes in [8] ) . In general , genetic exchange between species through fertile hybrids might be common between closely related species [9–13] but can also occur between divergent species [14–17] . Second , introgression is heterogeneously distributed across the genome . For instance , mitochondrial genome exchange is surprisingly common ( e . g . , [18–20] among many ) between species , whereas sex chromosomes are less likely to cross species-boundaries , perhaps due to their disproportionate role in hybrid incompatibilities [17 , 21–24] . Generally it seems that functional regions of the genome might be less likely to participate in introgression . This is perhaps best known from the case of Neanderthal hybridization with non-African human populations [25 , 26] , which has left modern human genomes distinct gradients of introgression across different functional compartments of the genome . Finally , the mode and intensity of natural selection acting on introgressed DNA can vary substantially . Loci that contribute to reproductive isolation , and as such to the persistence of species in the face of hybridization , should be less likely to be introgressed [27] as a result of purifying selection in hybrids . Additionally , introgressed haplotypes containing mildly deleterious variants may be purged after migrating into a population with a larger effective size where selection is more effective [28 , 29] . On the other hand , much of the genome may be porous to introgression between closely related species if the net effect of such introgressed variation is fitness neutral . Of course genetic exchange between populations can also provide a source of adaptive alleles that may facilitate adaptation to new environments ( reviewed in ref . [30] ) . Introgressions have indeed been shown to be involved in adaptation in animals ( e . g . [31–33] ) , plants ( e . g . [34] ) and fungi [35] . For instance , adaptation to high altitude in Tibetans appears to have been caused by introgression of alleles from an archaic Denisovan-like source into modern humans [36] . Another particularly well-studied system of adaptive introgression comes from Heliconius butterflies where gene exchange has facilitated the origin and maintenance of mimetic rings [32] and even of hybrid species [37 , 38] . Clearly , hybridization and introgression play an important role in shaping the landscape of genetic variation , thus if we wish to fully understand its evolutionary role a reliable framework for the inference of introgressed alleles is needed . Approaches to detect introgression in the genome fall into a few different camps . Genome-wide approaches can identify whether admixture has occurred in a set of populations . These include clustering methods which seek to infer which individuals are admixed and to assign a proportion of admixture to each individual without previous knowledge of the parental populations [39–41] . Some genome-wide approaches instead attempt to infer the directionality of introgression by examining allele frequency differences among populations [25 , 42] . The main limitation of this class of methods is that they cannot identify which regions of the genome are likely to have crossed species boundaries . On the other hand , locus-specific ancestry approaches ( e . g . [43–47] ) seek to uncover the mosaic of ancestry for each sampled haplotype , and thus can also identify portions of haplotypes that have been introgressed between species or populations . These fine-resolution approaches are powerful but often have assumptions and requirements that cannot be fulfilled in many taxa which range from the need of phased haplotypes to recombination maps . The main limitation of these approaches is that many require a set of reference haplotypes—individuals known to be unadmixed representatives of either population—in order to properly infer the origin of each allele in each ( non-reference ) sample haplotype . The last family of approaches designed to uncover introgressed loci has focused on the use of relative and absolute levels of divergence measured in genomic windows . Largely such methods have consisted of new summary statistics that capture elements of the expected coalescent genealogy under a model of recent introgression between species . These approaches have the advantage that no donor or recipient populations must be identified a priori . Among the measurements of divergence , FST [48] is most commonly used . Another popular point of departure has been the dxy statistic of Nei and Li [49] which measures the average pairwise distance between alleles sampled from two populations . For instance , Joly et al . [50] , Geneva et al . [51] and Rosenzweig et al . [52] use the minimum rather than the mean of these pairwise divergence values , termed dmin . dmin is sensitive to abnormally short branch lengths between alleles drawn from two populations , as would be expected when introgression is recent . Each of these statistics has attractive properties and adequate power in some instances , however no one statistic has perfect sensitivity in every scenario . Here we introduce a new method for finding introgressed loci based on supervised machine learning that we call FILET ( Finding Introgressed Loci using Extra Trees Classifiers ) . FILET combines a large number of summary statistics ( Materials and Methods ) that provide complementary information about the shape of the genealogy underlying a region of the genome . These summary statistics include both previously developed statistics ( including , but not limited to , those based on dmin and dxy ) as well as 5 new summary statistics that we describe below . Our reasoning for this approach was that by combining many statistics for detecting introgression we should achieve sensitivity to introgression across a larger range of scenarios than accessible to any individual statistic . Buoyed by our recent work showing the power and flexibility of Extra Trees classifiers [53] for population genomic inference [54 , 55] , we leveraged this machine learning paradigm for identification of introgression . Using simulations we show that FILET is far more powerful and versatile than competing methods for identifying introgressed loci . Further we apply FILET to examine patterns of introgression between Drosophila simulans and its island endemic sister taxon Drosophila sechellia . The speciation event that gave rise to the island endemic Drosophila sechellia from a Drosophila simulans-like ancestor is a textbook example of a specialist species that evolved from a presumably generalist ancestor [56 , 57] . Indeed , D . sechellia has quite remarkably specialized to breed on the toxic fruit of Morinda citrifolia [58] , while D . simulans ( and D . mauritiana ) do not tolerate the organic volatile compounds in the ripe fruit [59–61] . The genetic and neurological underpinnings of this key ecological difference have been identified , at least in part [62–67] making the D . simulans/D . sechellia pair one of the most successful cases of genetic dissection of the causes of an ecologically relevant trait . While this is so , the population genetics of divergence between these species has only been examined in the context of either population samples from a handful of loci [68–71] or in the absence of population data [72] . These studies estimated population divergence time between D . simulans and D . sechellia to be as early as ~250 , 000 years ago [72] or as old as ~413 , 000 years ago [70] . All population genomic surveys demonstrate that D . sechellia harbors little genetic variation in comparison to D . simulans , perhaps as a result of a population size crash/founder event from which the population has not recovered [68 , 71] . Moreover it has been suggested that what little variation there is in D . sechellia shows little population genetic structure among separate island populations in the Seychelles archipelago [71] . Lastly there is some evidence of introgression between each pair of species within the D . simulans complex [72] , and D . simulans and D . sechellia have been found to hybridize in the field [73] . Here we characterize the population genetics of divergence between D . sechellia and D . simulans , combining existing whole-genome sequences from a mainland population of D . simulans [74] with newly generated genome sequences from D . sechellia . Applying FILET to these data confirms previous reports of introgression between these species and reveals that this gene flow is primarily in the direction of D . simulans to D . sechellia . Finally , the success of our approach underscores the potential power of supervised machine learning for evolutionary and population genetic inference .
We set out to assemble a set of statistics that could be used in concert to reliably determine whether a given genomic window had experienced recent gene flow . Several statistics that have been designed to this end ask whether there is a pair of samples exhibiting a lower than expected degree of sequence divergence within the window of interest . The most basic of these is dmin , the minimum pairwise divergence across all cross-population comparisons ( S1 Fig; [50] ) . The reasoning behind dmin is that even if only a single sampled individual contains an introgressed haplotype , dmin should be lower than expected and the introgression event may be detectable . A related statistic is Gmin , which is equal to dmin/dxy [51]; the presence of this term in the denominator is meant to control for variation in the neutral mutation rate across the genome . RNDmin accomplishes this by dividing dmin by the average divergence of all sequences from either species to an outgroup sequence [52] . The name of this statistic is derived from its constituent parts , dmin , and RND [75] . As described in the following section , we incorporated a number of previously devised statistics into our classification approach , including some of those based on dmin . We also included some novel statistics that we designed to have improved sensitivity to particularly recent introgression . The first of these is defined as: dd1=dmin/π1 where π1 is nucleotide diversity [49] in population 1 . Similarly , dd2 = dmin/π2 . dd1 and dd2 statistics are so named because they compare dmin to diversity within populations 1 and 2 , respectively . The rationale behind these statistics is that , if there has been recent introgression from population 1 into population 2 , and at least one sampled chromosome from population 2 contains the introgressed haplotype , then the cross-population pair of individuals yielding the value of dmin should both trace their ancestry to population 1 . Thus , the sequence divergence between these two individuals should on average be equal to π1 . Similarly , if there was introgression in the reverse direction dmin would be on the order of π2 . Following similar rationale , we devised two related statistics: dd-Rank1 and dd-Rank2 . dd-Rank1 is the percentile ranking of dmin among all pairwise divergences within population 1; the value of this statistic should be lower when there has been introgression from population 1 into population 2 . dd-Rank2 is the analogous statistic for introgression from population 2 into population 1 . We also included a statistic comparing average linkage disequilibrium within populations to average LD within the global population ( i . e . lumping all individuals from both species together ) , as follows: ZX= ( ZnS1+ZnS2 ) / ( 2×ZnSG ) where ZnS1 , and ZnS2 measure average LD [76] between all pairs of variants within the window in population 1 and population 2 , respectively , and ZnSG which measures LD within the global population . The reasoning behind this statistic is based on the assumption that , in the presence of gene flow , LD may be elevated within the recipient population ( s ) but not in the global population . S2 Fig shows that the distributions of these statistics do indeed differ substantially between genealogies with and without introgression ( simulation scenarios described below ) , especially when this introgression occurred recently . In addition to these and other statistics summarizing diversity across the two population samples , we also incorporated several single-population statistics into our classifier ( see below ) , as these may also contain information about recent introgression . For example , separate measures of nucleotide diversity in our two population samples would contain useful information because introgression is expected to increase diversity in the recipient population , especially if the source population was large or if the two populations split long ago . We used a supervised machine learning approach to assign a genomic window to one of three distinct classes on the basis of a “feature vector” consisting of a number of statistics summarizing patterns of variation within the window from two closely related populations . These three classes are: introgression from population 1 into population 2 , introgression from population 2 into population 1 , and the absence of introgression . Specifically , we used an Extra-Trees classifier [53] , which is an extension of random forests [77] , an ensemble learning technique that creates a large ensemble of semi-randomly generated binary decision trees [78] , before taking a vote among these decision trees in order to decide which class label should be assigned to a given data instance ( i . e . genomic window in our case ) . In an Extra-Trees classifier , the tree building process is even more randomized than in typical random forests: in addition to selecting a random subset of features when generating a tree , the separating threshold for each feature is randomly chosen , rather than selected the threshold that optimally separates the data classes . We require example regions for each class in order to train the Extra-Trees classifier , so we used coalescent simulations to generate these training examples ( described below ) . Our ultimate goal was to detect introgression within 10 kb windows in Drosophila , so to train our classifier properly we simulated chromosomal regions approximating this length ( simulation details are given below ) . The target window size could easily be altered by changing the length of the regions simulated for training ( i . e . by adjusting the recombination and mutation rates , θ and ρ ) . FILET's feature vector contains a number of single-population summaries of per-base pair genetic variation: π , the variance in pairwise distances among individuals , the density of segregating sites , the density of polymorphisms private to the population , Fay and Wu's H and θH statistics [79] , and Tajima's D [80] . The feature vector also includes two single-population summary statistics that are not normalized per base pair: ZnS ( which is averaged across all pairs of SNPs ) , and the number of distinct haplotypes observed in the window . Each feature vector included values of these 9 statistics for each population , yielding 18 single-population statistics in total . In addition , the two-population statistics included in FILET's feature vector were as follows: FST ( following ref . [81] ) , Hudson's Snn [82] , per-bp dxy , per-bp dmin , Gmin , dd1 , dd2 , dd-Rank1 , dd-Rank2 , ZX , IBSMaxB ( the length of the maximum stretch of identity by state , or IBS , among all pairwise between-population comparisons ) , and IBSMean1 and IBSMean2 which capture the average IBS tract length when comparing all pairs of sequences within populations 1 and 2 , respectively . These IBS statistics are calculated by examining all pairs of individual sequences within a population ( or across populations in the case of IBSMaxB ) , noting the positions of differences , and examining the distribution of lengths between these positions ( as well as between the first position and the beginning of the window and between the last position and the end of the window ) . Note that we did not include RNDmin or other measures such as Patterson’s D and F4 statistics [83] that require alignment to one or more additional species along with the focal pair , because we designed FILET so that it would not require outgroup information . We note however that through its use of supervised machine learning , FILET could easily be extended to incorporate such data . Instead , in order to improve robustness to mutational variation , we adopted the approach of drawing the mutation rate from a wide range of values when generating training examples to train FILET to classify data from our Drosophila samples ( see below ) . All code necessary to run the FILET classifier ( including calculating summary statistics on both simulated and real data sets , training , and classification ) along with the full results of our application to D . simulans and D . sechellia ( described below ) are available at https://github . com/kern-lab/FILET/ . Following Rosenzweig et al . [52] , we used the coalescent simulator msmove ( https://github . com/geneva/msmove ) to simulate data for testing FILET’s power to detect introgression in populations with four different values of TD ( the time since divergence ) : 0 . 25×4N , 1×4N , 4×4N , and 16×4N generations ago , where N is the population size . For each of these simulations the population size was held constant ( i . e . the ancestral population size equals that of both daughter populations ) . We developed a classifier for each of these scenarios of population divergence . Supervised machine learning techniques such as the Extra-Trees classifier require training data consisting of examples from each of the three classes , but in practice a large number of example loci known to have experienced introgression may not be available . We therefore simulated training data sets for each of the four values of TD . Again following Rosenzweig et al . [52] , the relevant parameters for each of these simulations include: TM , the time since the introgression event , which we drew from {0 . 01×TD , 0 . 05×TD , 0 . 1×TD , 0 . 15×TD , … , 0 . 9×TD} ( i . e . multiples of 0 . 05×TD up to 0 . 9 , and also including 0 . 01×TD ) ; and PM , the probability that a given lineage would migrate from the source population to the sink population during the introgression event , which we drew from {0 . 05 , 0 . 1 , 0 . 15 , … , 0 . 95} . We simulated an equal number of training examples for each combination of these two parameter values for both directions of gene flow , yielding 104 simulations in total for both of these classes , conditioning that each of these instances must have contained at least one migrant lineage . Finally , we simulated an equivalent number of samples without introgression , yielding a balanced training set ( 104 examples for each class ) . Next , we computed feature vectors as described above for each of these training examples , and proceeded with training our Extra-Trees classifiers by conducting a grid search of all training parameters precisely as described in Schrider and Kern [54] , and setting the number of trees in the resulting ensemble to 100 . All training and classification with the Extra-Trees classifier was performed using the scikit-learn Python library ( http://scikit-learn . org; [84] ) . We also calculated feature importance and rankings thereof by training an Extra-Trees classifier of 500 decision trees on the same training data ( using scikit-learn’s defaults for all other learning parameters ) , and then using this classifier’s “feature_importances_” attribute . Briefly , this feature importance score is the average reduction in Gini impurity contributed by a feature across all trees in the forest , always weighted by the probability of any given data instance reaching the feature’s node as estimated on the training data [85]; this measure thus captures both how well a feature separates data into different classes and how often the feature is given the opportunity to split ( i . e . how often it is visited in the forest ) . The values of these scores are then normalized across all features such that they sum to one . For each TD , we evaluated the appropriate classifier against a larger set of 104 simulations generated for each parameter combination along a grid of values of TM and PM . The values of PM were drawn from the same set as those in training as described above , while one additional possible value of TM was included: 0 . 001×TD . Also note that for these simulations we did not require at least one migrant lineage as we had done for training ( information that is recorded by msmove ) . For test simulations with bidirectional migration , we did not require each replicate sample to contain at least one migrant lineage , though we modified msmove to record which samples did in fact experience migration . In addition to test examples for each direction of gene flow , we simulated 104 examples where no migration occurred in order to assess false positive rates . In order to examine the performance of FILET when an unsampled ghost lineage was the source of introgression , we generated test simulations with the same values of TD , TM , and PM as above , but where the source of the introgression was a third , unsampled population that separated from the two sampled populations at time TD . In all of our simulations , both for training and testing , we set locus-wide population mutation and recombination rates θ and ρ to 50 and 250 , respectively , similar to autosomal values in 10 kb windows in D . melanogaster [86] and sampled 15 individuals from each population . We also experimented with different window sizes , simulating training and test data ( 1 , 000 replicates for each class for each set ) with window sizes corresponding to 1 kb , 10 kb , 5 kb , and 50 kb by multiplying θ and ρ by the appropriate scalar . When testing the sensitivity of our method on these data , we considered a window to be introgressed if FILET’s posterior probability of the no-introgression class was <0 . 05 , except for the scenario with TD equal to 16×4N generations ago in which case we used a posterior probability cutoff of 0 . 01 , as we found that this step mitigated the elevated false positive rate under this scenario ( reducing the rate from >10% to the estimate of 6% shown in S3 Fig ) . In windows labeled as introgressed , the direction of gene flow was determined by asking which of the two introgression classes had a higher posterior probability . Note that we used the same demographic scenario for both the training and test data for each TD , and discuss the implications of demographic model misspecification in the Results and Discussion . In order to compute receiver operator characteristic ( ROC ) curves we constructed balanced binary training sets composed of 104 examples with no introgression , and 104 examples allowing for introgression ( with equal representation to each combination of TM , PM , and direction of introgression . The score that we obtained for each test example in order to compute the ROC curve was one minus the posterior probability of no introgression as generated by the Extra-Trees classifier ( i . e . the classifier’s estimated probability of introgression , regardless of directionality ) . We compared FILET’s accuracy to that of ChromoPainter [46] , a software program that utilizes a variant of the copying model first proposed by Li and Stephens [87] . In this model each sample haplotype is a mosaic composed of chromosomal segments chosen from a set of possible ancestral haplotypes , allowing for differences caused by mutation and the potential for changes in ancestry at recombination breakpoints . Such an approach can thus be used to predict the ancestry of each individual at each polymorphism—these predictions are referred to as “paintings” by ChromoPainter . To this end we repeated our simulations above but increased the size of the chromosomal segments to 1 Mb by increasing θ and ρ to 5000 and 25000 . In these simulations only gene flow from population 2 to population 1 was allowed , and we modified msmove to record the coordinates of introgressed segments , and to restrict introgression events to those involving segments falling entirely within the central 100 kb of the chromosome . For each combination of TM and PM we generated 10 replicate simulations , including 10 replicates without introgression . We ran ChromoPainter with the following parameters: the “-a 0 0” switch to model each individual haplotype as a mosaic of each other individual rather than using a set of predefined reference haplotypes , “-i 10” and “-ip” options to estimate copying proportions over 10 Expectation-Maximization ( EM ) iterations , and the default “-s 10” switch to stochastically draw 10 chromosome paintings for each individual from the HMM following EM . We then used the output from ChromoPainter to predict introgressed chromosomal segments as follows: For each polymorphism , we examine each haplotype i among our n haplotypes , and record which of the other n-1 haplotypes serves as the best ancestor for i in each of our 10 paintings . We then examine each individual in population 2 ( the recipient population ) , and count the number of paintings for which the ancestral haplotype is from population 1 . If this number is > 5 ( i . e . a majority ) for any of our individuals in population 2 , then we consider this focal polymorphism to be introgressed . If two adjacent polymorphisms are predicted to be introgressed , all sites between them are also considered to be introgressed . If only 1 polymorphism is predicted , then just that one site is considered introgressed . We also produced a more stringent version of these predictions by only retaining introgressed segments consisting of at least 25 consecutive introgressed polymorphisms . Note that ChromoPainter requires base pair positions , and msmove uses an infinite sites model where polymorphisms are located in a continuous space between zero and one . Thus in order to perform this analysis we had to map msmove’s continuous locations to physical locations , which we accomplished by multiplying by 106 and rounding to the nearest available position . We compared ChromoPainter to a sliding-window application of FILET’s classifier for 10 kb windows ( with 1 kb step sizes ) . We also produced finer-scale FILET predictions using a 1 kb classifier ( with 100 bp step sizes ) to refine predictions made by the 10 kb classifier: only sliding windows predicted as introgressed by the 1 kb classifier and lying within introgressed segments predicted by the 10 kb classifier were retained as candidates by this version . For the refinement step , FILET’s posterior probability cutoff for introgression was relaxed to 0 . 5 ( i . e . introgression more probable than not ) ; a more lenient cutoff is appropriate here because this classifier was only applied within regions already predicted to be introgressed by the 10 kb classifier . Drosophila sechellia flies were collected in the islands of Praslin , La Digue , Marianne and Mahé with nets over fresh Morinda fruit on the ground . All flies were collected in January of 2012 . Flies were aspirated from the nets by mouth ( 1135A Aspirator–BioQuip; Rancho Domingo , CA ) and transferred to empty glass vials with wet paper balls ( to provide humidity ) where they remained for a period of up to three hours . Flies were then lightly anesthetized using FlyNap ( Carolina Biological Supply Company , Burlington , NC ) and sorted by sex . Females from the melanogaster species subgroup were individualized in plastic vials with instant potato food ( Carolina Biologicals , Burlington , NC ) supplemented with banana . Propionic acid and a pupation substrate ( Kimwipes Delicate Tasks , Irving TX ) were added to each vial . Females were allowed to produce progeny and imported using USDA permit P526P-15-02964 . The identity of the species was established by looking at the taxonomical traits of the males produced from isofemale lines ( genital arches , number of sex combs ) and the female mating choice ( i . e . , whether they chose D . simulans or D . sechellia in two-male mating trials ) . We obtained sequence data from 20 D . simulans inbred lines [74] from NCBI’s Short Read Archive ( BioProject number PRJNA215932 ) . We also sequenced wild-caught outbred D . sechellia females ( see above ) from Praslin ( n = 7 diploid genomes ) , La Digue ( n = 7 ) , Marianne ( n = 2 ) , and Mahé ( n = 7 ) . These new D . sechellia genomes are available on the Short Read Archive ( BioProject number PRJNA395473 ) . For each line we then mapped all reads with bwa 0 . 7 . 15 using the BWA-MEM algorithm [88] to the March 2012 release of the D . simulans assembly produced by Hu et al . [89] and also used the accompanying annotation based on mapped FlyBase release 5 . 33 gene models [90] . Next , we removed duplicate fragments using Picard ( https://github . com/broadinstitute/picard ) , before using GATK’s HaplotypeCaller ( version 3 . 7; [91–93] ) in discovery mode with a minimum Phred-scaled variant call quality threshold ( -stand_call_conf ) of 30 . We then used this set of high-quality variants to perform base quality recalibration ( with GATK’s BaseRecalibrator program ) , before again using the HaplotypeCaller in discovery mode on the recalibrated alignments . For this second iteration of variant calling we used the—emitRefConfidence GVCF option in order to obtain confidence scores for each site in the genome , whether polymorphic or invariant . Finally , we used GATK’s GenotypeGVCFs program to synthesize variant calls and confidences across all individuals and produce genotype calls for each site by setting the—includeNonVariantSites flag , before inferring the most probable haplotypic phase using SHAPEIT v2 . r837 [94] . The genotyping and phasing steps were performed separately for our D . simulans and D . sechellia data , and for each of step in the pipeline outlined above we used default parameters unless otherwise noted . In order to remove potentially erroneous genotypes ( at either polymorphic or invariant sites ) , we considered genotypes as missing data if they had a quality score lower than 20 , or were heterozygous in D . simulans . After throwing out low-confidence genotypes , we masked all sites in the genome missing genotypes for more than 10% of individuals in either species’ population sample , as well as those lying within repetitive elements as predicted by RepeatMasker ( http://www . repeatmasker . org ) . Only SNP calls were included in our downstream analyses ( i . e . indels of any size were ignored ) . These phased and masked data are available at https://zenodo . org/record/1166021 . Having obtained genotype data for our two population samples , we used ∂a∂i [95] to model their shared demographic history on the basis of the folded joint site frequency spectrum ( downsampled to n = 18 and n = 12 in D . simulans and D . sechellia , respectively ) ; using the folded spectrum allowed us to circumvent the step of producing whole genome alignments to outgroup species in D . simulans coordinate space in order to attempt to infer ancestral states . We used an isolation-with-migration ( IM ) model that allowed for continual exponential population size change in each daughter population following the split . This model includes parameters for the ancestral population size ( Nanc ) , the initial and final population sizes for D . simulans ( Nsim_0 and Nsim , respectively ) , the initial and final sizes for D . sechellia ( Nsech_0 and Nsech , respectively ) , the time of the population split ( TD ) , the rate of migration from D . simulans to D . sechellia ( msim→sech ) , and the rate of migration from D . sechellia to D . simulans ( msech→sim ) . We also fit our data to a pure isolation model that was identical to our IM model but with msim→sech and msech→sim fixed at zero . Finally , we fit our data to an admixture model identical to the isolation model but with the addition of two parameters: the time of a pulse admixture event from D . simulans into D . sechellia ( TAD ) and the proportion of individuals in D . sechellia migrating from D . simulans during this event ( PAD ) . Our optimization procedure consisted of an initial optimization step using the Augmented Lagrangian Particle Swarm Optimizer [96] , followed by a second step of optimization refining the initial model using the Sequential Least Squares Programming algorithm [97] , both of which are included in the pyOpt package for optimization in Python ( version 1 . 2 . 0; [98] ) as in Schrider et al . [99] . We performed ten optimization runs fitting both of these models to our data , each starting from a random initial parameterization , and assessed the fit of each optimization run using the AIC score . Code for performing these optimizations can be obtained from https://github . com/kern-lab/miscDadiScripts , wherein 2popIM . py , 2popIsolation . py , 2popIsolation_admixture . py fit the IM , isolation , and admixture models described above , respectively . For scaling times by years rather than numbers of generations , we assumed a generation time of 15 gen/year as has been estimated in D . melanogaster [100] . Having obtained a demographic model that provided an adequate fit to our data , we set out to simulate training examples under this demographic history for each of our three classes ( i . e . no migration , migration from D . simulans to D . sechellia , and from D . sechellia to D . simulans ) . For training examples including introgression , TM was drawn uniformly from the range between zero generations ago and TD/4 , while PM raged uniformly from ( 0 , 1 . 0] . In addition , in order to make our classifier robust to uncertainty in other parameters in our model , for each training example we drew values of each of the remaining parameters from [x− ( x/2 ) , x+ ( x/2 ) ] , where x is our point estimate of the parameter from ∂a∂i . In addition to the parameters from our demographic model ( TD , ρ , Nanc , Nsim , and Nsech ) , these include the population mutation rate θ = 4Nμ ( where μ was set to 3 . 5×10−9 ) , and the ratio of θ to the population recombination rate ρ ( which we set to 0 . 2 ) . Continuous migration rates were set to zero ( i . e . the only migration events that occurred were those governed by the TM and PM parameters , and the no-migration examples were truly free of migrants ) . In total , this training set comprised of 104 examples from each of our three classes . As described above , we masked genomic positions having too many low confidence genotypes or lying within repetitive elements ( described above ) before proceeding with our classification pipeline . While doing so , we recorded which sites were masked within each 10 kb window in the genome that we would later attempt to classify . In order to ensure that our masking procedure affected our simulated training data in the same manner as our real data , for each simulated 10 kb window we randomly selected a corresponding window from our real dataset and masked the same sites in the simulated window that had been masked in the real one . We then trained our classifier in the same manner as described above . In order to ensure that this classifier would indeed be able to reliably uncover loci experiencing recent gene flow between our two populations , we assessed its performance on simulated test data . First , we applied the classifier to test examples simulated under this same model ( again , 104 for each class ) . Next , to address the effect of demographic model misspecification , we applied our classifier to an isolation model with a different parameterization and no continuous size change in the daughter populations . For this model we simply set Nsim and Nsech to πsim/4μ and πsech/4μ , respectively , where π for a species is the average nucleotide diversity among all windows included in our analysis after filtering , and μ was again set to 3 . 5×10−9 . We then set Nanc to be equal to Nsim , and set T to dxy/ ( 2μ ) − 2Nanc generations where dxy is the average divergence between D . simulans and D . sechellia sequences across all windows . This latter value is simply the expected TMRCA for cross-species pairs of genomes , minus the expected waiting time until coalescence during the one-population ( i . e . ancestral ) phase of the model . This simple model thus produces samples with similar levels of nucleotide diversity for the two daughter populations as those produced under our IM model , but that would differ in other respects ( e . g . the joint site frequency spectrum and linkage disequilibrium , which would be affected by continuous population size change after the split ) . For both test sets we masked sites in the same manner as for our training data before running FILET . We examined 10 kb windows in the D . simulans and D . sechellia genomes , summarizing diversity in the joint sample with the same feature vector as used for classification ( see above ) , ignoring sites that were masked as described above . We omitted from this analysis any window for which >25% of sites were masked , and then applied our classifier to each remaining window , calculating posterior class membership probabilities for each class . We then used a simple clustering algorithm to combine adjacent windows showing evidence of introgression into contiguous introgressed elements: we obtained all stretches of consecutive windows with >90% probability of introgression as predicted by FILET ( i . e . the probability of no-introgression class <10% ) , and retained as putatively introgressed regions those that contained at least one window with >95% probability of introgression . In order to test for enrichment of these introgressed regions for genic/intergenic sequence or particular Gene Ontology ( GO ) terms from the FlyBase 5 . 33 annotation release [90] , we performed a permutation test in which we randomly assigned a new location for each cluster or introgressed windows ( ensuring the entire permuted cluster landed within accessible windows of the genome according to our data filtering criteria ) . We generated 10 , 000 of these permutations .
We sought to devise a bioinformatic approach capable of leveraging population genomic data from two related population samples to uncover introgressed loci with high sensitivity and specificity . In the Materials and Methods , we describe several previous and novel statistics designed to this end . However , rather than preoccupying ourselves with the search for the ideal statistic for this task , we took the alternative approach of assembling a classifier leveraging many statistics that would in concert have greater power to discriminate between introgressed and non-introgressed loci . Supervised machine learning methods have proved highly effective at making inferences in high-dimensional datasets and are beginning to make inroads in population genetics [101] . In this vein , we designed FILET , which uses an extension of random forests called an Extra-Trees classifier [53] . We previously succeeded in applying Extra-Trees classifiers for a separate population genetic task—finding recent positive selection and discriminating between hard and soft sweeps [54 , 55]—though other methods such as support vector machines [102] or deep learning [103] could also be applied to this task . Briefly , FILET assigns a given genomic window to one of three distinct classes—recent introgression from population 1 into population 2 , introgression from population 2 into 1 , or the absence of introgression—on the basis of a vector of summary statistics that contain information about the two-population sample’s history . This feature vector contains a variety of statistics summarizing patterns of diversity within each population sample , as well as a number of statistics examining cross-population variation ( see Materials and Methods for a full description ) . FILET must be trained to distinguish among these three classes , which we accomplish by supplying 10 , 000 simulated example genomic windows of each class , with each example represented by its feature vector . Because we expect that the majority of introgression events to be non-adaptive , these simulations did not include natural selection . Once this training is complete , FILET can then be used to infer the class membership of additional genomic windows , whether from simulated or real data . We began by assessing FILET’s power on a number of simulated datasets , examining windows roughly equivalent to 10 kb in length in Drosophila ( Materials and Methods ) . In particular , because the power to detect introgression depends on the time since their divergence , TD , we measured FILET’s performance under four different values of TD , training a separate classifier for each . In Fig 1 ( TD = 0 . 25×4N ) and S3 Fig ( TD values of 1 , 4 , and 16×4N ) , we compare FILET’s power to that of two related statistics that have been devised to detect introgressed windows , dmin and Gmin ( Materials and Methods ) . These figures show that FILET has high sensitivity to introgression across a much wider range of introgression timings ( TM ) and intensities ( PM ) than either of these statistics under each value of TD , and that this disparity is amplified dramatically for smaller values of TD . Furthermore , these figures demonstrate that FILET infers the correct directionality of recent introgression with high accuracy , whereas dmin and Gmin contain no information about the direction of gene flow . Finally , FILET does not appear especially sensitive when the source of gene flow is an unsampled ghost population rather than one of the two sequenced populations ( S4 Fig ) , though it could potentially be trained to detect such cases if desired . We also note that for dmin and Gmin we established 95% significance thresholds from our simulated training data without introgression , thereby achieving a false positive rate of 5% . In order to assess FILET’s false positive rate , we classified a set of test simulations without introgression and found that FILET’s false positive rate was considerably lower ( Fig 1 and S3 Fig ) except for our largest value of TD , where it was initially higher ( 0 . 4% for TD = 0 . 25×4N but >10% for TD = 16×4N ) , despite our selection of a posterior probability cutoff of 95% ( Methods ) . This illustrates an important issue with posterior probability estimates produced by supervised machine learning methods: they may occasionally be miscalibrated . We therefore adjusted the cutoff for the TD = 16×4N scenario ( to 99% probability of introgression ) which lowered our false positive rate to 6% as shown in S3 Fig . Thus , when an appropriate posterior probability cutoff is chosen—a task that can be performed in a straightforward manner by testing on simulated data—FILET achieves much greater sensitivity to introgression than dmin and Gmin often at a much lower false positive rate . We also demonstrate the FILET’s greater power than these statistics via ROC curves ( S5 Fig ) , where it outperforms each statistic under each scenario . Specifically , the difference in power between FILET and dmin is dramatic for smaller values of TD ( area under curve , or AUC , of 0 . 85 versus 0 . 73 when TD = 0 . 25×4N for FILET and dmin , respectively ) but comparatively miniscule for our largest TD ( AUC of 0 . 94 versus 0 . 93 when TD = 16×4N ) . It therefore appears that FILET’s performance gain relative to single statistics is highest for the more difficult task of finding introgression between very recently diverged populations , while for the easier case of detecting introgression between highly diverged populations some single statistics may perform nearly as well . We also experimented with smaller training sets , finding similar classification power ( measured by AUC ) as above when we trained FILET using only 1000 or even 100 simulated examples per class ( S6 Fig ) , though in the latter case estimated class posterior probabilities were far less accurate . In addition , we examined the effect of altering the target window size used when training and testing FILET ( S7 Fig ) . Methods designed to uncover changes in ancestry along a recombining chromosome within admixed populations can also be used to recover introgressed regions . To this end we used ChromoPainter [46] which has the advantage of not requiring a set of “reference haplotypes” known to be free of introgression from each population , and can instead predict for each haplotype , which of all the other haplotypes in the sample ( from either population ) is most closely related . We simulated two-population samples for 1 Mb chromosomes where introgression from population 2 to population 1 was allowed in the central 100 kb window , and used ChromoPainter to identify introgressed loci ( see Methods ) . We then ran FILET on these simulations , this time using a sliding-window approach to detect introgressed segments ( Methods ) . Fig 2 shows that FILET has substantially higher sensitivity than ChromoPainter—summing across the entire parameter space ( including many scenarios where introgression is quite difficult to detect ) FILET recovered 27 . 7% of introgressed base pairs compared to 19 . 4% for ChromoPainter—while having a roughly 20-fold lower false positive rate ( 0 . 42% for FILET versus 9 . 31% for ChromoPainter ) . For scenarios with more ancient and less intense introgression , we did observe somewhat higher sensitivity in ChromoPainter’s predictions . However , this seems to be driven largely by ChromoPainter’s propensity to identify a larger fraction of base pairs as introgressed regardless of their true ancestry , as evidenced by its higher false positive rate . To demonstrate this further we show for the positive predictive value ( the number of base pairs correctly predicted to be introgressed divided by the total number of base pairs predicted to be introgressed ) for each method in S8 Fig . This figure shows that FILET’s positive predictive is consistently far higher than ChromoPainter’s . We sought to improve this by adopting a more stringent threshold for ChromoPainter’s predictions , requiring at least 25 adjacent polymorphisms to be called introgressed in order to retain the candidate region . This approach did succeed at reducing the false positive rate to 1 . 15% , though this is still substantially higher than FILET’s , but this improvement came at the cost of ChromoPainter’s sensitivity , which was reduced to 8 . 6% , roughly one-third that of FILET ( Fig 2 and S8 Fig ) . We also tried an intermediate threshold ( 5 polymorphisms ) , but did not observe a substantial increase in specificity compared to our initial more lenient approach ( 8 . 49% false positive rate ) . Thus , while we cannot rule out that it may be possible to devise a method to leverage ChromoPainter’s model to predict introgression that exceeds the performance of our application of ChromoPainter here , our results suggest that it is unlikely that such an approach would eclipse the performance of FILET . We note that ChromoPainter does have the advantage of not requiring simulated training data . ChromoPainter also has the potential to identify donor and recipient haplotypes , which FILET currently does not , but the far greater power of FILET demonstrated above will make it preferable to many researchers who are interested in identifying introgressed regions . Moreover our above results imply that predictions of the span and origin of introgressed haplotypes made directly from ChromoPainter’s output may not always be particularly reliable . It is important to note that in the above simulations many introgressed regions will be considerably smaller than our 10 kb window size . This fact , combined with our use of accuracy measurements counting the number of individual base pairs correctly classified , makes the results presented above useful for evaluating FILET’s resolution and the impact of window size on our predictions . By these measures FILET outperforms ChromoPainter , which does not use windows and is only limited in scale by the density of polymorphisms . This suggests that when using sliding windows FILET is able to achieve adequate resolution regardless of its use of a predefined window size . Nonetheless we sought to improve our resolution further by using a finer-scale FILET classifier trained on 1 kb windows as described above to refine the location of putatively introgressed regions identified by the 10 kb classifier ( see Methods ) . While this did marginally reduce our false positive rates and increase our positive predictive values ( see Fig 2 and S8 Fig ) , sensitivity was also somewhat reduced ( to 25 . 7%; Fig 2 ) . The relatively minor effect of adding this refinement step reinforces the notion that a predefined window size is not a major hindrance to our methods’ effectiveness . Thus for most applications a window size that yields enough polymorphisms to reliably calculate the statistics included in our feature vectors may suffice . Overall , FILET detects introgressed regions with greater power and resolution than ChromoPainter , a method designed to detect ancestry tracks along recombining chromosomes . However we note that many methods of this class exist , and it is possible that some may achieve greater accuracy in some circumstances ( e . g . if reference haplotype panels are used ) . While FILET is designed for identifying particular genomic windows that experienced introgression as a result of a pulse migration event , genomic windows with genealogies that include introgression may of course also be produced by continuous migration , with the timing of geneflow varying from window to window . We therefore simulated genomic windows experiencing a variety of bidirectional migration rates under each of our values of TD and recorded the fraction of windows for which our sampled individuals contained at least one migrant lineage . Next , for each simulated window we applied the FILET classifier trained under the appropriate divergence time as described above , recording the fraction of windows with at least one migrant lineage that were classified as introgressed by FILET . The results of these tests ( S1 Table ) show that for each value of TD , the lowest bidirectional migration rates that we tested do not produce migrant lineages , while higher rates will produce a small to modest fraction migrants , most of which are undetected ( e . g . when m = 0 . 01 , 23% and 59% of windows contain at least one migrant when TD = 0 . 25 and TD = 1 , respectively , but <5% are detected by FILET ) . Thus , FILET , as currently trained , may not be sensitive to gene flow produced by low levels of continuous migration . However , as the migration rate increases further , more and more of these migrant windows will be detected ( e . g . when m = 1 , 70% and 100% of windows are detected as migrants by FILET when TD = 0 . 25 and TD = 1 , respectively ) . Although our goal was to use a set of statistics to perform more accurate inference than possible using individual ones , another benefit of our Extra-Trees approach is that it also allows for a natural way to evaluate the extent to which different statistics are informative under different scenarios of introgression . To this end , we used the Extra-Trees classifier to calculate feature importance , which captures the ability of each statistic to separate the data into its respective classes ( Materials and Methods ) . We find that for our lowest TD ( a split N generations ago ) the top four features , all with similar importance , are the density of private alleles in population 1 , the density of private alleles in population 2 , dd-Rank1 , and dd-Rank2 . For our next-lowest TD ( 4N generations ago ) , the top four , again with similar importance score estimates , are FST , ZX , dd1 , and dd2 . Thus our newly devised dd and ZX statistics seem to be particularly informative in the case of recent introgression between closely related populations . For the larger values of TD , dxy and dmin rise to prominence . The complete lists of feature importance for each TD are shown in S2 Table . The exceptional accuracy with which FILET uncovers introgressed loci underscores the potential for machine learning methods to yield more powerful population genetic inferences than can be achieved via more conventional tools which are often based on a single statistic . Indeed , machine learning tools have been successfully leveraged in efforts to detect recent positive selection [54 , 104–107] , to infer demographic histories [108] , or even to perform both of these tasks concurrently [109] . As described in the Materials and Methods , we used publically available D . simulans sequence data [74] , and collected and sequenced a set of D . sechellia genomes . We mapped reads from these genomes to the D . simulans assembly [89] , obtaining high coverage >28× for each sequence ( see sampling locations , mapping statistics , and Short Read Archive identifier information listed in S3 Table ) . We do not expect that our reliance on the D . simulans assembly resulted in any appreciable bias , as reads from our D . sechellia genomes were successfully mapped to the reference genome at nearly the same rate as reads from D . simulans ( S3 Table ) . After completing variant calling and phasing ( Materials and Methods ) , we performed principal components analysis on intergenic SNPs at least 5 kb away from the nearest gene in order to mitigate the bias introduced by linked selection [99 , 110 , 111] . While this is unlikely to completely eliminate the confounding effect of linked selection in Drosophila , the fraction of mutations that are deleterious is far greater in coding regions than in intergenic regions ( ~90% versus <50% according to [112] ) ; thus it is reasonable to presume that the impact of linked selection is reduced several kilobases away from genes [113] . We observed evidence of population structure within D . sechellia . In particular , the samples obtained from Praslin clustered together , while all remaining samples formed a separate cluster ( S9A Fig ) . Running fastStructure [114] on this same set of SNPs yielded similar results: when the number of subpopulations , K , was set to 2 ( the optimal value for K selected by fastStructure’s chooseK . py script ) , our data were again subdivided into Praslin and non-Praslin clusters . Indeed , across all values of K between 2 and 8 , fastStructure’s results were suggestive of marked subdivision between Praslin and non-Praslin samples , and comparatively little subdivision within the non-Praslin data ( S9B Fig ) . This surprising result differs qualitatively from previous observations from smaller numbers of loci [71 , 115] , and underscores the importance of using data from many loci—preferably intergenic and genome-wide—in order to infer the presence or absence of population structure . Next , we examined the site frequency spectra of the Praslin and non-Praslin clusters , noting that both had an excess of intermediate frequency alleles in comparison to that of the D . simulans dataset ( S10 Fig ) , in line with our expectations of D . sechellia’s demographic history . We also note that the Praslin samples contained far more variation ( 50 , 243 sites were polymorphic within Praslin ) than non-Praslin samples ( 4 , 108 SNPs within these samples ) . This difference in levels of variation may reflect a much lesser degree of population structure and/or inbreeding on the island of Praslin than across the other islands , or may result from other demographic processes . Additional samples from across the Seychelles would be required to address this question . In any case , in light of this observation we limited our downstream analyses of D . sechellia sequences to those from Praslin . Because we required a model from which to simulate training data for FILET , we next inferred a joint demographic history of our population samples using ∂a∂i [95] . In particular , we fit three demographic models to our dataset: an isolation-with-migration ( IM ) model allowing for continuous population size change and migration following the population divergence , an isolation model with the same parameters but fixing migration rates at zero , and an isolation model with one burst of pulse migration from D . simulans into D . sechellia ( Materials and Methods ) . In S4 Table we show our model optimization results , which show clear support for the IM model over the other models . The IM model that provided the best fit to our data ( Fig 3A ) includes a much larger population size in D simulans than D . sechellia ( a final size of 9 . 3×106 for D . simulans versus 2 . 6×104 for D . sechellia ) , consistent with the much greater diversity levels in D . simulans [10 , 71] . This model also exhibits a modest rate of migration , with a substantially higher rate of gene flow from D . simulans to D . sechellia ( 2×Nancm = 0 . 086 ) than vice-versa ( 2×Nancm = 0 . 013 ) . Thus , the results of our demographic modeling are consistent with the observation of hybrid males in the Seychelles [73] , and the possibility of recent introgression between these two species across a substantial fraction of the genome ( see refs . [72 , 116] ) . An interesting characteristic of the model shown in Fig 3A is that , assuming 15 generations per year , the estimated time of the D . simulans- D . sechellia population split is ~86 kya , or 1 . 3×106 generations ago . This contrasts with a recent estimate of 2 . 5×106 generations ago from Garrigan et al . [72] which was based on single genomes rather than population genomic data , but did account for ancestral polymorphism , as did estimates from Obbard et al . [117] which yielded even older split times . Supporting our inference , we note that our average intergenic cross-species divergence of 0 . 017 yields an average TMRCA of ~2 . 5×106 generations ago , assuming a mutation rate of 3 . 5×10−9 mutations per generation as observed in D . melanogaster [112 , 118] , and this estimate would include the time before coalescence in the ancestral population . Unless the mutation rate the D . simulans species complex is substantially lower than in D . melanogaster , a population split time of 2 . 5×106 generations ago therefore seems unlikely given that the ancestral population size ( and therefore the period of time between the population divergence and average TMRCA ) was probably large ( >500 , 000 by our estimate ) . Thus , we conclude that the D . simulans and D . sechellia populations may have diverged more recently than previously appreciated , perhaps within the last 100 , 000 years . Although the specific parameterization of our model should be regarded as a preliminary view of these species’ demographic history that is adequate for the purposes of training FILET , future efforts with larger sample sizes will be required to refine this model . That being said , the basic features of this model—a much larger D . simulans population size than sechellia , and a fairly large ancestral population size—are unlikely to change qualitatively . Here we present a novel machine learning approach , FILET , that leverages population genomic data from two related populations in order to determine whether a given genomic window has experienced gene flow between these populations , and if so in which direction . We applied FILET to a set of D . simulans genomes as well as a new set of whole genome sequences from the closely related island endemic D . sechellia , confirming widespread introgression and also inferring that this introgression was largely in the direction of D . simulans to D . sechellia . Future work leveraging D . simulans data sampled from the Seychelles will be required to determine whether this asymmetry is a consequence of low rate of migration of D . simulans back to mainland Africa ( where our D . simulans data were obtained ) , or whether the directionality of gene flow is biased on the islands themselves . In addition to creating FILET , we devised several new statistics , including the dd statistics and ZX which our feature rankings show to be quite useful for uncovering gene flow . Despite the success of FILET on both simulated data sets and real data from Drosophila , there are several improvements that could be made . First , by framing the problem as one of parameter estimation ( i . e . regression ) rather than classification , we may be able to precisely infer the values of relevant parameters of introgression events ( i . e . the time of the event and the number of migrant lineages ) . Deep learning methods , which naturally allow for both classification and regression , may prove particularly useful for this task [103] . Indeed , Sheehan and Song [109] used deep learning to infer demographic parameters ( regression ) while simultaneously identifying selective sweeps ( classification ) . Another step we have not taken is to explicitly handle adaptive introgression , which could potentially greatly improve our approach’s power to detect such events . While population genetic inference has traditionally relied on the design of a summary statistic sensitive to the evolutionary force of interest , a number of highly successful supervised machine learning methods have been put forth within the last few years [54 , 104–109] . These methods are often thought of as black boxes , a characterization that may not always be fair [125] . Indeed in the context of evolutionary genetics such machine learning approaches are easily interpreted as we have strong generative models that guide our intuition . Nonetheless , classical statistical estimation from parametric models may often be more interpretable . Hybrid approaches combining machine learning techniques with Bayesian approaches to estimate posterior distributions of evolutionary parameters ( e . g . [126] ) thus represent an attractive alternative to either approach in their “pure” form . As genomic data sets continue to grow , we argue that machine learning approaches—in whatever shape they eventually take—leveraging high dimensional feature spaces have the potential to revolutionize evolutionary genomic inference .
|
Understanding the extent to which species or diverged populations hybridize in nature is crucially important if we are to understand the speciation process . Accordingly numerous research groups have developed methodology for finding the genetic evidence of such introgression . In this report we develop a supervised machine learning approach for uncovering loci which have introgressed across species boundaries . We show that our method , FILET , has greater accuracy and power than competing methods in discovering introgression , and in addition can detect the directionality associated with the gene flow between species . Using whole genome sequences from Drosophila simulans and Drosophila sechellia we show that FILET discovers quite extensive introgression between these species that has occurred mostly from D . simulans to D . sechellia . Our work highlights the complex process of speciation even within a well-studied system and points to the growing importance of supervised machine learning in population genetics .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"and",
"discussion"
] |
[
"invertebrates",
"population",
"genetics",
"invertebrate",
"genomics",
"animals",
"genetic",
"mapping",
"artificial",
"intelligence",
"population",
"biology",
"drosophila",
"computer",
"and",
"information",
"sciences",
"introgression",
"animal",
"genomics",
"insects",
"arthropoda",
"machine",
"learning",
"haplotypes",
"eukaryota",
"heredity",
"evolutionary",
"processes",
"genetics",
"biology",
"and",
"life",
"sciences",
"gene",
"flow",
"genomics",
"evolutionary",
"biology",
"genomics",
"statistics",
"computational",
"biology",
"organisms"
] |
2018
|
Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia
|
Plague , caused by Yersinia pestis , was classified as a reemerging infectious disease by the World Health Organization . The five human pneumonic plague cases in Yulong County in 2005 gave rise to the discovery of a Yulong plague focus in Yunnan province , China . Thereafter , continuous wild rodent plague ( sylvatic plague ) was identified as the main plague reservoir of this focus . In this study , the epizootics in Yulong focus were described , and three molecular typing methods , including the different region ( DFR ) analysis , clustered regularly interspaced short palindromic repeats ( CRISPRs ) , and the multiple-locus variable number of tandem repeats ( VNTR ) analysis ( MLVA ) ( 14+12 ) , were used for the molecular typing and source tracing of Y . pestis isolates in the Yulong plague focus . Simultaneously , several isolates from the vicinity of Yunnan were used as controls . The results showed that during the 10-year period from 2006 to 2016 , an animal plague epidemic occurred in 6 of those years , and 5 villages underwent an animal plague epidemic within a 30-km2 area of the Yulong plague focus . Searching for dead mice was the most effective monitoring method in this plague focus . No positive sample has been found in 6937 captured live rodents thus far , suggesting that the virulence of strains in the Yulong plague focus is stronger and the survival time of mice is shorter after infection . Strains from Lijiang , Sichuan and Tibet were of the same complex based on a typing analysis of DFR and CRISPR . The genetic relationship of Y . pestis illustrated by MLVA “14+12” demonstrates that Tibet and Sichuan strains evolved from the strains 1 . IN2 ( Qinghai , 1970 and Tibet , 1976 ) , and Lijiang strains are closer to Batang strains ( Batang County in Sichuan province , 2011 , Himalaya marmot plague foci ) in terms of genetic or phylogenic relationships . In conclusion , we have a deeper understanding of this new plague focus throughout this study , which provides a basis for effective prevention and control .
Plague is an acute infectious disease caused by Yersinia pestis ( Y . pestis ) . Four Y . pestis biovars have been recognized based on their biochemical properties , i . e . , Antiqua , Mediaevalis , Orientalis and Microtus . Each Y . pestis biovar has a different geographic distribution throughout the world [1] . Three devastating plague pandemics have occurred in the last 1500 years worldwide . The third plague pandemic , caused by Y . pestis Orientalis , originated in the Yunnan province of China in the middle of 19th century and eventually affected more than 60 countries and regions in Asia , Europe , America and Africa [2] . The population structure of Y . pestis as a clonal lineage with five branches designated 0 , 1 , 2 , 3 and 4 . The Y . pestis genealogy is rooted by Y . pseudotuberculosis at the base of branch 0 , and SNPs have accumulated serially along branch 0 and subsequently along branches1 , 2 , 3 and 4 . There are nine branching lineages ( 0 . ANT1 , 0 . PE7 , 0 . ANT3 , 0 . ANT2 , 0 . PE2 , 0 . PE3 , 0 . PE4A , 0 . PE4B and 0 . PE4C ) in branch 0 , seven ( 1 . IN1 , 1 . ORI1 , 1 . ORI3 , 1 . IN3 , 1 . IN2 , 1 . ANT and 1 . ORI2 ) in branch 1 , two ( 3 . ANT1 and 3 . ANT2 ) in branch 3 and only one ( 4 . ANT1 ) in branch 4[3] . Yunnan province is located in southwestern China and borders with Burma , Laos , and Vietnam . It is adjacent to Guizhou , Guangxi , and Sichuan provinces and Tibet in China . Three plague foci exist in Yunnan ( Reference the map in Fig 1 ) : the Rattus flavipectus plague focus ( Biovar Orientalis and genealogy 1 . ORI2 , termed as focus F in studies [4–8]; termed as focus A in study [9] ) , the Jianchuan plague focus ( Biovar Antique and genealogy 1 . IN3 , focus E in studies [4–8]; termed as focus B in study [9] ) , and the Yulong plague focus ( termed as focus P in reference study [4] ) . The discovery of the Yulong focus originated from a human plague outbreak ( five pneumonia plague cases with two deaths ) in Luzi valley of Yulong county in 2005 [4 , 9 , 10] . Active animal surveillance has been conducted annually in Yulong and neighboring areas since the focus was discovered . In fact , both the Yulong plague focus and the Jianchuan plague focus are located in the middle part of the Hengduan Mountains , and the two foci are adjacent to one another , with similar landforms and ecological systems . In this ecological system , the wild rodents of Apodemus chevrieri and Eothenomys miletus are the main reservoir hosts , and the fleas of Neopsylla specialis and Ctenophthalmus quadratus are the main vectors [4 , 9] . The major rodent hosts in these two plague foci are the same wild rodents , which differ completely from the domestic rodents such as Rattus flavipectus , etc . , that are found in local residents' houses . The two plague foci were coined wild rodent ( sylvatic ) plague foci by Chinese plague researchers [9] . In this study , the epizootics in the Yulong focus were described , and three molecular subtyping methods , the different region ( DFR ) analysis , clustered regularly interspaced short palindromic repeats ( CRISPRs ) , and the multiple-locus variable number of tandem repeat ( VNTR ) analysis ( MLVA ) ( 14+12 ) , were used to genotype and source-trace the Y . pestis isolates in the Yulong plague focus . Simultaneously , several isolates from the vicinity of Yunnan were used as controls .
All procedures performed in this study were in accordance with the ethical standards of the institutional and national research committee . This study was approved by the Review Board of Ethics in the National Institute for Communicable Disease Control and Prevention , China CDC . The review board approved the collection and use of rodents in this study . During 2006-2016 , 2 surveillance periods were conducted annually , one in the spring ( April to May ) and one in autumn ( November to December ) , for approximately 15 days each time . Plague surveillance was concentrated within 30 kilometers of the center of Luzi village; this area included 25 villages in 2 townships in Yulong County and 9 villages in 1 township in Gucheng District . The collection methods included live rat capturing and dead rat searching in the surrounding villages , farmland and woodland . Captured rodents and dead rodents were sent to the laboratory and analyzed . The confirming tests of animal plagues were performed according to the World Health Organization’s criteria and the animal plague surveillance criteria issued by the China CDC ( 2008 ) . These assays included bacterium isolation , PCR tests and immunoassays ( F1 antigen test by RIHA ) . The total DNA of dead rodents was extracted according to DNeasy Blood & Tissue Kit ( QIAGEN ) instructions , and these DNAs served as templates for the PCR test ( real-time PCR and common PCR kits , Shanghai Huirui Biotechnology Co . , Ltd . ) . A total of 46 Y . pestis isolates were collected from three natural plague foci in Yunnan province and its surrounding areas in this study ( S1 Table ) . Fourteen strains of the R . flavipectus plague focus ( including 2 in Burma , 2 in Guangxi province , 2 in Guizhou province , and 9 in Yunnan province ) , 8 strains of the Jianchuan plague focus , 6 strains from Tibet , 5 strains from Sichuan province , and 7 strains from the Yulong plague focus with an additional 5 Y . pestis DNA templates were obtained from the Yulong plague focus in 2014 . The bacterial genomic DNAs were extracted by conventional SDS lysis and phenol-chloroform extraction methods [5] . DFR genotyping and CRISPR analyses were performed according to previous reports [6–8 , 11 , 12] . Twenty-three DFR primers and pMT1-specific primers were used to identify DFR loci . The spacer arrays of CRISPRs were gained in ‘‘spacers dictionary’’ [6] or analyzed online using the ‘‘CRISPR Finder Tool’’ in the CRISPRs database [13] . The nomenclature of genotypes in the DFR and CRISPR analysis were employed according to previous studies [6 , 7] . The profile data of DFR and CRISPRs were compared using Bionumerics 6 . 6 ( Applied Math ) , and the corresponding MST ( minimum spanning tree ) was drawn for the cluster analysis . If there were differences at only 1 locus between 2 neighboring types , they would be surrounded by a halo of the same color and form a complex . The strains in one complex of Lijiang strains were used for the next tracing analysis by MLVA . The MLVA analysis with 26 markers ( 14+12 ) was performed as described by Li et al [5] with the following modifications on capillary electrophoresis . The forward primers were labeled with different fluorescent dyes , FAM or Hex . The PCR amplification was diluted with water to 1:80 . After denaturing by heating , the amplicons were separated by capillary electrophoresis on an ABI 3730xl genetic analyzer with a GeneScan 1200 LIZ size standard ( Applied Biosystems ) . The lengths of the amplicons were determined according to the sizes generated by GeneMapper software V . 4 . 0 ( Applied Biosystems ) . The profile data of MLVA ( 14+12 ) were compared using Bionumerics 6 . 6 ( Applied Math ) . In addition to the VNTR data in our 23 Y . pestis isolates ( S2 Table ) , an additional 83 representative strains from previous MLVA ( 14+12 ) studies were also included for the cluster analysis[5] ( S3 Table ) . The genotyping criteria and naming refers to the paper of Cui et al [3] . The MLVA profiles were analyzed as a characteristic data using the alignment of the categorical coefficient and UPGMA ( unweighted pair group method using arithmetic averages ) . The dendrogram was constructed using the minimum spanning tree ( MST ) by parameters ( maximum and minimum neighbor distances were all selected as 1 ) .
A total of 6937 live wild rodents were captured . The rodents comprised 22 species , of which 51 . 66% were Apodemus chevrieri , 20 . 91% were Eothenomys miletus , and 7 . 88% were Eothenomys proditor . A total of 75 dead rodents were obtained . Additionally , 1323 fleas were isolated from rodents . The fleas comprised 12 species , of which 51 . 46% were Neopsylla specialis , 24 . 43% were Ctenophthalmus quadratus , and 12 . 82% were Frontopsylla spadix . For all live rodents and their fleas , the bacteria isolation , RIHA and specific PCR results were negative for Y . pestis . However , 14 of dead rodents tested positive for Y . pestis , as did 2 fleas from dead rodents ( positive dead rodents ) . After the Yulong plague focus was identified by bacteriological evidence in 2006 , continuous rodent plague epidemics were identified in the main plague reservoirs . During the 10-year period of 2006-2016 , animal plague epidemics occurred in 6 years . As a central area , cases were frequently reported in the Luzi village during these 6 years . In 2008 , the Mangzhong Village , which is located approximately 8 km northwest of the Luzi village , experienced an animal plague epidemic . A total of seven Y . pestis stains were isolated in 2006 , 2008 and 2009 , and sixteen animal samples were positive for RIHA in 2006-2009 , 2012 and 2014 ( Table 1 ) . Notably , the rodent plague occurred in 2014 . In addition to the positive results of the five dead mice based on the RIHA test ( four of Apodemus chevrieri and one of Eothenomys miletus ) , DNA templates extracted from the five dead mice were also positive according to Y . pestis specific gene PCR ( caf1 and YPO0392 ) . However , no strain was successfully isolated from these mice because of their rotted bodies . The 5 villages of Luzi , Mangshang , Mushu , Mangzhong and Runanhua have all undergone animal plague epidemics in a 30-km2 area . ( Fig 1 ) This evidence suggests that continuous epidemics of rodent plague have existed in the Yulong plague focus since 2005 . The 23 indexes of DFR , and the 16 indexes of CRISPR , were used to cluster the 47 strains of Y . pestis in this study ( S1 Table ) . Based on a complex definition of no more than 1 mutation of adjacent distance , the strains of Lijiang , Sichuan and Tibet were of the same complex ( Fig 2 , Complex 1 ) , and all 14 strains of the R . flavipectus plague focus were another complex ( Fig 2 , Complex 2 ) . All 8 strains of the Jianchuan plague focus were uniquely different from Complex 1 and Complex 2 ( Fig 2 , Single ) . In complex 1 , a further analysis is necessary to study what relationship exists among the Lijiang , Sichuan and Tibet strains . The DFR genomovars of seven isolations and five positive Y . pestis DNAs in the Yulong plague focus were identified as genomovar 05 in this study [7] , as were the strains of Sichuan and Tibet ( Fig 2 and S1 Table ) . The DFR genomovar of Y . pestis in the Jianchuan plague focus was identified as genomovar 07 , whereas the DFR of the R . flavipectus plague focus was identified as genomovar 09 [7] . The CRISPR patterns of seven isolations and five positive Y . pestis DNAs in Yulong was identified as genotype 22 in the Ca7 cluster , i . e . , Ypa ( a1-a2-a3-a4-a5-a6-a7 ) , Ypb ( b1-b2-b3-b4 ) , and Ypc ( c1-c2-c3 ) , whereas the arrays of spacers in the Jianchuan focus were genotype 35 in the Ca52 cluster [6] , and the spacer arrays of CRISPRs in the R . flavipectus plague focus were genotypes 30 or 33 in the Ca8 cluster [6] ( S1 Table ) . The CRISPR patterns of the Yulong plague focus were also found in other plague natural focuses such as Y . pestis isolates in Sichuan province in 2009 and 2011 and in Tibet in 1978 and 2011 ( S1 Table ) . The MLVA ( 14+12 ) scheme was used for the phylogenic structure analysis and for the source-tracing investigation; it was considered to produce a mostly approximated phylogenic structure and relationship with the SNP-based analysis[5] . There were 18 discrepant VNTR loci in the Yulong , Sichuan and Tibet isolates ( Table 2 ) . The genetic relationship of the VNTR profiles of the strains in this study with profiles in previous studies [5] is illustrated in the MST tree ( Fig 3 and S2 Table ) . The tree shows that the Tibet and Sichuan strains evolved from strains 1 . IN2 ( Qinghai , 1970 and Tibet , 1976 ) , and the Lijiang strains are from a clone of the Batang plague focus in Sichuan province ( Batang County , 2011 ) . The Batang plague focus in Sichuan province is located to the north approximately 350 km away from the Yulong plague focus . Within Lijiang strains , one strain ( 2014-2 ) was the earliest clone isolated in 2014 from Mangshang village , which is also the northernmost part of the Yulong plague focus . This strain then spread to Luzi Village and formed a new clone ( 2006-7 ) by adding 2U repeats in the M23 site . The clone of 2006-7 continues to spread around , producing new clones through mutations and creating new animal plague epidemics . Notably , a significant mutation ( 4 loci ) occurred during the transmission of Y . pestis clones from Luzi village to Runahua village . Topographically , the distance between the two villages is approximately 6 km , but there is a mountain barrier that forms a natural barrier , whereas there is no natural barrier among the Luzi , Mangzhong and Mushu villages .
Plague is an historical and continuous problem in many rural regions in China . The evidence of bacterium isolation and immunoassays in local reservoirs indicates that continuous rodent plague has been prevalent in the Yulong plague focus since the focus was discovered in 2005 . In our study , although no Y . pestis strain was successfully isolated in the dead rodents in 2014 , we still successfully used the total DNA samples of dead rodents as materials to perform molecular subtyping . Therefore , clinical tissue obtained from humans or specimens from rodents can also be used in PCR-based molecular genotyping . This practice can be useful in microbial forensic investigations , such as in human plague outbreaks or bioterrorism attacks . Different molecular subtyping methods are used for different purposes . With the advantages of lower cost and more feasibility , DFR , CRISPRs and the MLVA ( 14+12 ) method , together with the corresponding database [14] , could provide a feasible tool for source-tracking investigation [5–7 , 15 , 16] . CRISPR and DFR analyses were previously used to illustrate the phylogenetic relationship and microevolution of Y . pestis in China[5–8] . Y . pestis isolated from the Yulong or Jianchuan foci belonged to the Biovar Antique [12] , whereas strains in the R . flavipectus plague focus were from the Biovar Orientalis [7] . The genomovar 05 of DFR was previously identified in the Marmota himalayana plague focus of the Qinghai–Gansu–Tibet Grassland ( Focus C ) and the Marmota himalayana plague focus of the Kunlun Mountains ( Focus K2 ) [7] . The difference between the Yulong and R . flavipectus plague focus was that the Yulong plague focus lacked DFR13 , which encodes a filamentous prophage integrated into the chromosomal dif locus [7] , whereas the strains in the R . flavipectus plague focus lack DFR3 . One interesting observation was the difference of genotypes in DFR between the Yulong focus and the Jianchuan focus . Although the two foci are adjacent to one another , the landforms and their main reservoirs are similar . However , the DFR genomovar of Y . pestis in the two foci was different . The Y . pestis of the Jianchuan focus possessed the DFR4 locus , with the corresponding functions annotated as adherence proteins [11] . The CRISPR patterns of Y . pestis isolates in the Yulong focus were identified as genotype 22 in the Ca7 cluster; these results were consistent with previous reports [9] . In addition , this CRISPR pattern was also identified in the Marmota caudate plague focus of the Pamirs Plateau ( Focus A ) , the Marmota baibacina–Spermophilus undulates plague focus of the Tianshan Mountains ( Focus B ) , the Marmota himalayana plague focus of the Qinghai–Gansu–Tibet Grassland ( Focus C ) , the Marmota himalayana plague focus of the Kunlun Mountains ( Focus K ) , and the Marmota focus plague focus of the Qinghai–Tibet Plateau ( Focus M ) [6] . Some MLVA schemes , such as 25 or 42-46 VNTR markers , were used to illustrate the phylogenetic relationships of Y . pestis [5 , 14 , 15 , 17–20] . In our previous research , a scheme including 14 VNTR loci was performed to analyze a total of 213 Chinese Y . pestis strains , which included five strains isolated from the Yulong Plague focus in 2006 [4] . Common gel electrophoresis was used to identify the size of the PCR products [4] . Therefore , only VNTR loci with conservative tandem repeat sequences above 9 bps were selected as MLVA profiles from previously described VNTR loci [18] . Those strains ( n = 5 ) of the Yulong focus involved in this study [4] presented different MLVA types ( MT17 types ) with other natural plague foci in China . The cluster analysis in this study also suggested that the Yulong strains show a closer genetic relationship with the strains from the Marmota himalayana plague focus of the Qinghai-Gansu-Tibet Grassland ( Focus C ) than the Apodemus chevrieri and Eothenomys miletus plague foci of the Jianchuan plague focus ( Focus E ) [4] . It should be mentioned that , after our previous research about “14-above 9 bp -repeats” MLVA schemes , other MLVA schemes were developed by serial hierarchical assessment , and the sizes of the PCR products were resolved by capillary electrophoresis [7] , such as the MLVA “14+12” scheme . Compared to the VNTR loci selected in the scheme MLVA “14-above 9 bp -repeats” [4] mentioned above , only two VNTR foci ( M61 and M58 ) were involved in the scheme MLVA “14+12” . In this study , our research performed the MLVA “14+12” scheme to analyze the phylogenetic relationship of Y . pestis in three plague foci in Yunnan province and other plague foci in China in available previous studies [5] . We reasoned that the MLVA”14+12” scheme had the ability to obtain a phylogeny relationship mostly approximate to the SNP-based analysis[5] and possessed high discriminative ability in genotyping and could be used for source tracing . The question of where the Yulong plague focus comes from has been asked since it was confirmed in 2006 . This study shows that the Yulong strains originated from the Sichuan Batang strains of Himalaya marmot plague foci , which is consistent with the plague spreading in a route from the north to the south in China , as previously described by Morelli G [2] . The Luzi village is located in the center of the focus and was the first discovered plague epidemic; it also had the highest frequency of infection in the epidemic area . However , the tracing results of MLVA ( 14+12 ) showed that the strains from Mangshang Village were the earliest strains . The Mangshang village is located in the northernmost part of the Yulong plague focus , and the transmission line of Y . pestis in the Yulong focus also goes from the north to the south , similar to the plague spreading route in China . We observed the phenomenon that the profiles of MLVA ( 14+12 ) in the DNA from the five Y . pestis strains collected in 2014 are not completely consistent ( Fig 3 and S3 Table ) . In the Yulong plague focus , the geographic landscape consists of woods separated by cultured farm , which forms separated micro-foci . The above observation suggests that the habitat segregation of main reservoirs could cause a few phylogenetic differences in Y . pestis in the plague focus . In conclusion , the 10-year monitoring period showed that the plague epidemic continued to exist and expand among the host rodents in the Yulong plague focus . Searching for dead mice was the most effective monitoring method in this plague focus . The plague information has not been detected in the captured live rodents ( nearly 7000 ) thus far , suggesting that the virulence of strains in the Yulong plague focus is stronger and the survival time of mice is shorter after infection . In terms of genetic or phylogenic relationships , Lijiang strains are closer to Batang strains of the Himalaya marmot plague foci . In summary , we have obtained a deeper understanding of this new plague focus through this study , which provides a basis for effective prevention and control . Moreover , we also provide a set of paradigms for the systematic study of new plague foci , which is a perfect combination of traditional monitoring methods and modern research methods .
|
Plague is a type of zoonosis that is highly lethal to humans . The surveillance of animal hosts is critical for the prevention and control of plague . The Yulong plague focus is a newly discovered plague focus in China in recent years . The plague outbreak had attracted widespread attention because 5 people were infected in 2005 , 2 of whom died . We have monitored the plague focus for a decade , and isolated strains and DNAs of Yersinia pestis were studied . The structure , origin and evolutionary trend of the Yulong plague focus were clarified , which provides a scientific basis for the effective prevention and control of human plague . This article also provides a set of paradigms for the systematic study of new plague foci , which is a perfect combination of traditional monitoring methods and modern research methods .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
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"sciences",
"genomics",
"evolutionary",
"biology",
"amniotes",
"organisms"
] |
2018
|
Ten years of surveillance of the Yulong plague focus in China and the molecular typing and source tracing of the isolates
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Success rates for catheter ablation of persistent atrial fibrillation patients are currently low; however , there is a subset of patients for whom electrical isolation of the pulmonary veins alone is a successful treatment strategy . It is difficult to identify these patients because there are a multitude of factors affecting arrhythmia susceptibility and maintenance , and the individual contributions of these factors are difficult to determine clinically . We hypothesised that the combination of pulmonary vein ( PV ) electrophysiology and atrial body fibrosis determine driver location and effectiveness of pulmonary vein isolation ( PVI ) . We used bilayer biatrial computer models based on patient geometries to investigate the effects of PV properties and atrial fibrosis on arrhythmia inducibility , maintenance mechanisms , and the outcome of PVI . Short PV action potential duration ( APD ) increased arrhythmia susceptibility , while longer PV APD was found to be protective . Arrhythmia inducibility increased with slower conduction velocity ( CV ) at the LA/PV junction , but not for cases with homogeneous CV changes or slower CV at the distal PV . Phase singularity ( PS ) density in the PV region for cases with PV fibrosis was increased . Arrhythmia dynamics depend on both PV properties and fibrosis distribution , varying from meandering rotors to PV reentry ( in cases with baseline or long APD ) , to stable rotors at regions of high fibrosis density . Measurement of fibrosis and PV properties may indicate patient specific susceptibility to AF initiation and maintenance . PV PS density before PVI was higher for cases in which AF terminated or converted to a macroreentry; thus , high PV PS density may indicate likelihood of PVI success .
Success rates for catheter ablation of persistent atrial fibrillation ( AF ) patients are currently low; however , there is a subset of patients for whom pulmonary vein isolation ( PVI ) alone is a successful treatment strategy [1] . PVI ablation may work by preventing triggered beats from entering the left atrial body , or by converting rotors or functional reentry around the left atrial/pulmonary vein ( LA/PV ) junction to anatomical reentry around a larger circuit , potentially converting AF to a simpler tachycardia [2] . It is difficult to predict whether PVI represents a sufficient treatment strategy for a given patient with persistent AF [1] , and it is unclear what to do for the majority of patients for whom it is not effective . Patients with AF exhibit distinct properties in effective refractory period ( ERP ) and conduction velocity ( CV ) in the PVs . For example , paroxysmal AF patients have shorter ERP and longer conduction delays compared to control patients [3] . AF patients show a number of other differences to control patients: PVs are larger [4]; PV fibrosis is increased; and fiber direction may be more disorganised , particularly at the PV ostium [5] . There are also differences within patient groups; for example , patients for whom persistent AF is likely to terminate after PVI have a larger ERP gradient compared to those who require further ablation [1 , 3] . Electrical driver location changes as AF progresses; drivers ( rotors or focal sources ) are typically located close to the PVs in early AF , but are also located elsewhere in the atria with longer AF duration [6] . Atrial fibrosis is a major factor associated with AF and modifies conduction . However , there is conflicting evidence on the relationship between fibrosis distribution and driver location [7 , 8] . It is difficult to clinically separate the individual effects of these factors on arrhythmia susceptibility and maintenance . We hypothesise that the combination of PV properties and atrial body fibrosis determines driver location and , thus , the likely effectiveness of PVI . In this study , we tested this hypothesis by using computational modelling to gain mechanistic insight into the individual contribution of PV ERP , CV , fiber direction , fibrosis and anatomy on arrhythmia susceptibility and dynamics . We incorporated data on APD ( action potential duration , as a surrogate for ERP ) and CV for the PVs to determine mechanisms underlying arrhythmia susceptibility , by testing inducibility from PV ectopic beats . We also predicted driver location , and PVI outcome .
All simulations were performed using the CARPentry simulator ( available at https://carp . medunigraz . at/carputils/ ) . We used a previously published bi-atrial bilayer model [9] , which consists of resistively coupled endocardial and epicardial surfaces . This model incorporates detailed atrial structure and includes transmural heterogeneity at a similar computational cost to surface models . We chose to use a bilayer model rather than a volumetric model incorporating thickness for this study because of the large numbers of parameters investigated , which was feasible with the reduced computational cost of the bilayer model . As previously described , the bilayer model was constructed from computed tomography scans of a patient with paroxysmal AF , which were segmented and meshed to create a finite element mesh suitable for electrophysiology simulations . Fiber information was included in the model using a semi-automatic rule based method that matches histological descriptions of atrial fiber orientation [10] . The left atrium of the bilayer model consists of linearly coupled endocardial and epicardial layers , while the right atrium is an epicardial layer , with endocardial atrial structures including the pectinate muscles and crista terminalis . The left and right atrium of the model are electrically connected through three pathways: Bachmann’s bundle , the coronary sinus and the fossa ovalis . Tissue conductivities were tuned to human activation mapping data from Lemery et al . [9 , 11] . The Courtemanche-Ramirez-Nattel human atrial ionic model was used with changes representing electrical remodelling during persistent AF [12] , together with a doubling of sodium conductance to produce realistic action potential upstroke velocities [9] , and a decrease in IK1 by 20% to match clinical restitution data [13] . Regional heterogeneity in repolarisation was included by modifying ionic conductances of the cellular model , as described in Bayer et al . [14] , which follows Aslanidi et al . and Seemann et al . [15 , 16] . Parameters for the baseline PV model were taken from Krueger et al . [17] . The following PV properties were varied as shown in schematic Fig 1: APD , CV , fiber direction , the inclusion of fibrosis in the PVs and the atrial geometry . These are described in the following sections . To investigate the effects of PV length and diameter on arrhythmia inducibility and arrhythmia dynamics , bi-atrial bilayer meshes were constructed from MRI data for twelve patients . All patients gave written informed consent; this study is in accordance with the Declaration of Helsinki , and approved by the Institutional Ethics Committee at the University of Bordeaux . Patient-specific models with electrophysiological heterogeneity and fiber direction were constructed using our modelling pipeline , which uses a universal atrial coordinate system to map scalar and vector data from the original bilayer model to a new patient specific mesh . Late gadolinium enhancement MRI ( average resolution 0 . 625mm x 0 . 625mm x 2 . 5mm ) was performed using a 1 . 5T system ( Avanto , Siemens Medical Solutions , Erlangen , Germany ) . These LGE-MRI data were manually segmented using the software MUSIC ( Electrophysiology and Heart Modeling Institute , University of Bordeaux , Bordeaux France , and Inria , Sophia Antipolis , France , http://med . inria . fr ) . The resulting endocardial surfaces were meshed ( using the Medical Imaging Registration Toolkit mcubes algorithm [18] ) and cut to create open surfaces at the mitral valve , the four pulmonary veins , the tricuspid valve , and each of the superior vena cava , the inferior vena cava and the coronary sinus using ParaView software ( Kitware , Clifton Park , NY , USA ) . The meshes were then remeshed using mmgtools meshing software ( http://www . mmgtools . org/ ) , with parameters chosen to produce meshes with an average edge length of 0 . 34mm to match the resolution of the previously published bilayer model [9] . Two atrial coordinates were defined for each of the LA and RA , which allow automatic transfer of atrial structures to the model , such as the pectinate muscles and Bachmann’s bundle . These coordinates were also used to map fiber directions to the bilayer model . To investigate the effects of PV electrophysiology on arrhythmia inducibility and dynamics , we varied PV APD and CV by modifying the value of the inward rectifier current ( IK1 ) conductance and tissue level conductivity respectively . IK1 conductance was chosen in this case to investigate macroscopic differences in APD [19] , although several ionic conductances are known to change with AF [20] . Modifications were either applied homogeneously or following a ostial-distal gradient . This gradient was implemented by calculating geodesic distances from the rim of mesh nodes at the distal PV boundary to all nodes in the PV and from the rim of nodes at the LA/PV junction to all nodes in the PV . The ratio of these two distances was then used as a distance parameter from the LA/PV junction to the distal end of the PV ( see Fig 1 ) . IK1 conductance was multiplied by a value in the range 0 . 5–2 . 5 , resulting in PV APDs in the clinical range of 100–190ms [3 , 21 , 22] . This rescaling was either a homogeneous change or followed a gradient along the PV length . Gradients of IK1 conductance varied from the baseline value at the LA/PV junction , to a maximum scaling factor at the distal boundary . PV APDs are reported at 90% repolarisation for a pacing cycle length of 1000ms . LA APD is 185ms , measured at a LA pacing cycle length of 200ms . To cover the clinically observed range of PV CVs , longitudinal and transverse tissue conductivities were divided by 1 , 2 , 3 or 5 , resulting in CVs , measured along the PV axis , in the range: 0 . 28–0 . 67m/s [3 , 21–24] . To model heterogeneous conduction slowing , conductivities were varied as a function of distance from the LA/PV junction , ranging from baseline at the junction to a maximum rescaling ( minimum conductivity ) at the distal boundary . The direction of this gradient was also reversed to model conduction slowing at the LA/PV junction [5] . Motivated by the findings of Hocini et al . [5] , interstitial fibrosis was modelled for the PVs with a density varying along the vein , increasing from the LA/PV junction to the distal boundary . This was implemented by randomly selecting edges of elements of the mesh with probability scaled by the distance parameter and the angle of the edge compared to the element fiber direction , where edges in the longitudinal fiber direction were four times more likely to be selected than those in the transverse direction , following our previous methodology [25] . To model microstructural discontinuities , no flux boundary conditions were applied along the connected edge networks , following Costa et al . [26] . An example of modelled PV interstitial fibrosis is shown in S1A Fig . For a subset of simulations , interstitial fibrosis was incorporated in the biatrial model based on late gadolinium enhancement ( LGE ) -MRI data , using our previously published methodology [25] . In brief , likelihood of interstitial fibrosis depended on both LGE intensity and the angle of the edge compared to the element fiber direction ( see S1B Fig ) . LGE intensity distributions were either averaged over a population of patients [27] , or for an individual patient . The averaged distributions were for patients with paroxysmal AF ( averaged over 34 patients ) , or persistent AF ( averaged over 26 patients ) . For patient-specific simulations , the model arrhythmia dynamics were compared to AF recordings from a commercially available non-invasive ECGi mapping technology ( CardioInsight Technologies Inc . , Cleveland , OH ) for which phase mapping analysis was performed as previously described [28] . PV fiber direction shows significant inter-patient variability . Endocardial and epicardial fiber direction in the four PVs was modified according to fiber arrangements described in the literature [5 , 29 , 30] . Six arrangements were considered , as follows: 1 . circular arrangement on both the endocardium and epicardium; 2 . spiralling arrangement on both the endocardium and epicardium; 3 . circular arrangement on the endocardium , with longitudinal epicardial fibers; 4 . fibers progress from longitudinal at the distal vein to circumferential at the ostium , with identical endocardial and epicardial fibers; 5 . epicardial layer fibers as per case 4 , with circumferential endocardial fibers; 6 . as per case 4 , but with a chaotic fiber arrangement at the LA/PV junction . These fiber distributions are shown in S2 Fig . Cases 4–6 were implemented by setting the fiber angle to be a function of the distance along the vein , measured from the LA/PV junction to the distal boundary , varying from circumferential at the junction to longitudinal at the distal end ( representing a change of 90 degrees ) . The disorder in fiber direction at the LA/PV junction for case 6 was implemented by taking the fibers of case 4 and adding independent standard Gaussian distributions scaled by the distance from the distal boundary , resulting in the largest perturbations at the ostium . Arrhythmia inducibility was tested by extrastimulus pacing from each of the four PVs individually using a clinically motivated protocol [31] , to simulate the occurrence of PV ectopics . Simulations were performed for each of the PVs , to determine the effects of ectopic beat location on inducibility . Sinus rhythm was simulated by stimulating the sinoatrial node region of the model at a cycle length of 700ms throughout the simulation . Each PV was paced individually with five beats at a cycle length of 160ms , and coupling intervals between the first PV beat and a sinus rhythm beat in the range 200–500 ms . Thirty-two pacing protocols were applied for each model set up: eight coupling intervals ( coupling interval = 200 , 240 , 280 , 320 , 360 , 400 , 440 , 480ms ) , for each of the four PVs . Inducibility is reported as the proportion of cases resulting in reentry; termed the inducibility ratio . The effects of PVI were determined for model set-ups that used the original bilayer geometry and in which the arrhythmia lasted for greater than two seconds . PVI was applied two seconds post AF initiation in each case by setting the tissue conductivity close to zero ( 0 . 001 S/m ) in the regions shown in S3 Fig . For each case , ten seconds of arrhythmia data were analysed , starting from two seconds post AF initiation , to identify re-entrant waves and wavefront break-up using phase . The phase of the transmembrane voltage was calculated for each node of the mesh using the Hilbert transform , following subtraction of the mean [32] . Phase singularities ( PSs ) for the transmembrane potential data were identified by calculating the topological charge of each element in the mesh [33] , and PS spatial density maps were calculated using previously published methods [14] . PS density maps were then partitioned into the LA body , PV regions , and the RA to assess where drivers were located in relation to the PVs ( see S3 Fig ) . The PV region was defined as the areas enclosed by , and including , the PVI lines; the LA region was then the rest of the LA and left atrial appendage . The PV PS density ratio was then defined as the total PV PS count divided by the total model PS count over both atria .
A difference in APD between the model LA and PVs was required for AF induction . Modelling the PVs using LA cellular properties resulted in non-inducibility , whereas , modelling the LA using PV cellular properties resulted in either non-inducibility or macroreentry . The effects of modifying PV APD homogeneously or following a gradient are shown in Table 1 . Simulations in which PV APD was longer than LA APD were non-inducible ( PV APD 191ms ) . As APD was decreased below the baseline value ( 181ms ) , inducibility initially increased and then fluctuated . Comparing cases with equal distal APD , arrhythmia inducibility was significantly higher for APD following a ostial-distal gradient than for homogeneous APD ( p = 0 . 03 from McNemar’s test ) . PS location was also affected by PV APD . PV PS density was low in cases of short APD , an example of which is shown in Fig 2 where reentry is no longer seen around the LA/PV junction in the case of short APD ( 120ms ) . This change was more noticeable for cases with homogeneous PV APD than for a gradient in APD; PV reentry was observed for the baseline case and a heterogeneous APD case , but not for a homogeneous decrease in APD . Arrhythmia inducibility decreased with homogeneous CV slowing ( from 0 . 38 i . e . 12/32 at 0 . 67m/s to 0 . 03 i . e . 1/32 at 0 . 28m/s ) . In the baseline model , reentry occurs close to the LA/PV junction due to conduction block when the paced PV beat encounters a change in fiber direction at the base of the PVs , together with a longer LA APD compared to the PV APD . In this case , the wavefront encounters a region of refractory tissue due to the longer APD in the LA . However , when PV CV is slowed homogeneously , the wavefront takes longer to reach the LA tissue , giving the tissue enough time to recover , such that conduction block and reentry no longer occurs . Modifying conductivity following a gradient means that , unlike the homogeneous case , the time taken for the extrastimulus wavefront to reach the LA tissue is similar to the baseline case , so the LA tissue might still be refractory and conduction block might occur . In the case that conduction was slowest at the distal vein , the inducibility was similar to the baseline case ( see Table 2 , GA , inducibility is 0 . 38 at baseline and 0 . 34 for the cases with CV slowing ) . Cases with greatest conduction slowing at the LA/PV junction ( see Table 2 , GB ) exhibit an increase in inducibility ( from 0 . 38 to 0 . 53 ) when CV is decreased because of the discontinuity in conductivity at the junction . Fig 2 shows that reentry is seen around the LA/PV junction in cases with both baseline and slow CV , indicating that the presence of reentry at the LA/PV junction is independent of PV CV . PV conduction properties are also affected by PV fiber direction . Modifications in fiber direction increased inducibility compared to the baseline fiber direction ( baseline case: 0 . 38; modified fiber direction cases 1-6: 0 . 53-0 . 63 ) . The highest inducibility occurred with circular fibers at the ostium ( cases 1 and 4 , 0 . 63 ) , independent of fiber direction at the distal PV end . This inducibility was reduced if the epicardial fibers were not circular at the ostium ( case 3 , 0 . 56 ) , or if fibers were spiralling ( case 2 , 0 . 56 ) instead of circular . Next we investigated the interplay between PV properties and atrial fibrosis . LA fibrosis properties were varied to represent interstitial fibrosis in paroxysmal and persistent AF patients , incorporating average LGE-MRI distributions [27] into the model . These control , paroxysmal and persistent AF levels of fibrosis were then combined with PV properties varied as follows: baseline CV and APD ( 0 . 67m/s , 181ms ) , slow CV ( 0 . 51m/s ) , short APD ( 120ms ) , slow CV and short APD . PS distributions in Fig 2 show that reentry occurred around the LA/PV junction in the case of baseline PV APD for control or paroxysmal levels of fibrosis , but not for shorter PV APD . Modifying PV CV did not affect whether LA/PV reentry is observed . Rotors were found to stabilise to regions of high fibrosis density in the persistent AF case . Models with PV fibrosis had a higher inducibility compared to the baseline case ( 0 . 47 vs . 0 . 38 ) and a higher PV PS density since reentry localised there . Fig 3 shows an example with moderate PV fibrosis ( A ) in which reentry changed from around the RIPV to the LIPV later in the simulation; adding a higher level of PV fibrosis resulted in a more stable reentry around the right PVs ( B ) . The relationship between LA fibrosis and PV properties on driver location was investigated on an individual patient basis for four patients . For patients for whom rotors were located away from the PVs ( Fig 4 LA1 ) , increasing model fibrosis from low to high increased the model agreement with clinical PS density 2 . 3 ± 1 . 0 fold ( comparing the sensitivity of identifying clinical regions of high PS density using model PS density between the two simulations ) . For other patients , lower levels of fibrosis were more appropriate ( 2 . 1 fold increase in agreement for lower fibrosis , Fig 4 LA2 ) , and PV isolation converted fibrillation to macroreentry in the model . Arrhythmia inducibility showed a large variation between patient geometries ( 0 . 16–0 . 47 ) . Increasing PV area increased inducibility to a different degree for each vein: right superior PV ( RSPV ) inducibility was generally high ( > 0 . 75 for all but one geometry ) independent of PV area; left superior PV ( LSPV ) inducibility increased with PV area ( Spearman’s rank correlation coefficient of 0 . 36 indicating positive correlation; line of best fit gradient 0 . 27 , R2 = 0 . 3 ) ; left inferior PV ( LIPV ) and right inferior PV ( RIPV ) inducibility exhibited a threshold effect , in which veins were only inducible above a threshold area ( Fig 5A ) . There is no clear relationship between PV length and inducibility . PV PS density ratio increased as PV area increased ( Fig 5B , Spearman’s rank correlation coefficient of 0 . 41 indicating positive correlation ) . Fig 5C shows that rotor and wavefront trajectories depend on patient geometry , exhibiting varied importance of the PVs compared to other atrial regions . PVI outcome was assessed for cases with varied PV APD ( both with a homogeneous change or following a gradient ) , with the inclusion of PV fibrosis and with varied PV fiber direction because these factors were found to affect the PV PS density ratio . PVI outcome was classified into three classes depending on the activity 1 second after PVI was applied in the model: termination , meaning there was no activity; macroreentry , meaning that there was a macroreentry around the LA/PV junctions; AF sustained by LA rotors , meaning there were drivers in the LA body . These classes accounted for different proportions of the outcomes: termination ( 27 . 3% of cases ) , macroreentry ( 39 . 4% ) , or AF sustained by LA rotors ( 33 . 3% ) . Calculating the PV PS density ratio before PVI for each of these classes shows that cases in which the arrhythmia either terminated or changed to a macroreentry are characterised by a statistically higher PV PS density ratio pre-PVI than cases sustained by LA rotors post-PVI ( see Fig 6 , t-test comparing termination and LA rotors shows they are significantly different , p<0 . 001; comparing macroreentry and LA rotors p = 0 . 01 ) . High PV PS density ratio may indicate likelihood of PVI success .
In this computational modelling study , we demonstrated that the PVs can play a large role in arrhythmia maintenance and initiation , beyond being simply sources of ectopic beats . We separated the effects of PV properties and atrial fibrosis on arrhythmia inducibility , maintenance mechanisms and the outcome of PVI , based on population or individual patient data . PV properties affect arrhythmia susceptibility from ectopic beats; short PV APD increased arrhythmia susceptibility , while longer PV APD was found to be protective . Arrhythmia inducibility increased with slower CV at the LA/PV junction , but not for cases with homogeneous CV changes or slower CV at the distal PV . The effectiveness of PVI is usually attributed to PV ectopy , but our study demonstrates that the PVs affect reentry in other ways and this may , in part , also account for success or failure of PVI . Both PV properties and fibrosis distribution affect arrhythmia dynamics , which varies from meandering rotors to PV reentry ( in cases with baseline or long APD ) , and then to stable rotors at regions of high fibrosis density . PS density in the PV region was high for cases with PV fibrosis . The measurement of fibrosis and PV properties may indicate patient specific susceptibility to AF initiation and maintenance . PV PS density before PVI was higher in cases in which AF terminated or converted to a macroreentry; thus , high PV PS density may indicate likelihood of AF termination by PVI alone . PV repolarisation is heterogeneous in the PVs [23] , and exhibits distinct properties in AF patients , with Rostock et al . reporting a greater decrease in PV ERP than LA ERP in patients with AF , termed AF begets AF in the PVs [21] . Jais et al . found that PV ERP is greater than LA ERP in AF patients , but this gradient is reversed in AF patients [3] . ERP measured at the distal PV is shorter than at the LA/PV junction during AF [5 , 22] . Motivated by these clinical and experimental studies , we modelled a decrease in PV APD , which was applied either homogeneously , or as a gradient of decreasing APD along the length of the PV , with the shortest APD at the distal PV rim . An initial decrease in APD increased inducibility ( Table 1 ) , which agrees with clinical findings of increased inducibility for AF patients . Applying this change following a gradient , as observed in previous studies , led to an increased inducibility compared to a homogeneous change in APD . Similar to Calvo et al . [34] we found that rotor location depends on PV APD ( Fig 2 ) . Thus PV APD affects PVI outcome in two ways; on the one hand , decreasing APD increases inducibility , emphasising the importance of PVI in the case of ectopic beats; on the other hand , PV PS density decreases for cases with short PV APD , and PVI was less likely to terminate AF . Multiple studies have measured conduction slowing in the PVs [3 , 5 , 21–24] . We modelled changes in tissue conductivity either homogeneously , or as a function of distance along the PV . Simply decreasing conductivity and thus decreasing CV , decreased inducibility ( Table 2 ) . Kumagai et al . reported that conduction delay was longer for the distal to ostial direction [22] . We found that modifying conductivity following a gradient , with CV decreasing towards the LA/PV junction , resulted in an increase in inducibility in the model . This agrees with the clinical observations of Pascale et al . [1] . This suggests that PVI should be performed in cases in which CV decreases towards the LA/PV junction as these cases have high inducibility . Changes in CV may also be due to other factors , including gap junction remodelling , modified sodium conductance or changes in fiber direction [5 , 29] . A variety of PV fiber patterns have been described in the literature and there is variability between patients . Interestingly , all of the PV fiber directions considered in our study showed an increased inducibility compared to the baseline model . Verheule et al . [29] documented circumferential strands that spiral around the lumen of the veins , motivating the arrangements for cases 1 and 4 in our study; Aslanidi et al . [15] reported that fibers run in a spiralling arrangement ( case 2 ) ; Ho et al . [30] measured mainly circular or spiral bundles , with longitudinal bundles ( cases 3 and 5 ) ; Hocini et al . [5] reported longitudinal fibers at the distal PV , with circumferential and a mixed chaotic fiber direction at the PV ostium ( case 6 ) . Using current imaging technologies , PV fiber direction cannot be reliably measured in vivo . In our study , fiber direction at the PV ostium was found to be more important than at the distal PV; the greatest inducibility was for cases with circular fibers at the ostium on both endocardial and epicardial surfaces , independent of fiber direction at the distal PV end . Similar to modelling studies by both Coleman [35] and Aslanidi [15] , inducibility increased due to conduction block near the PVs . PVs may be larger in AF patients compared to controls [4 , 36] , and this difference may vary between veins; Lin et al . found dilatation of the superior PVs in patients with focal AF originating from the PVs , but no difference in the dimensions of inferior PVs compared to control or to patients with focal AF from the superior vena cava or crista terminalis [37] . We found that inducibility increased with PV area for the LSPV , LIPV and RIPV , but not for the RSPV ( see Fig 5 ) . In addition , PV PS density ratio increased with total PV area , suggesting that PVI alone is more likely to be a successful treatment strategy in the case of larger veins . However , Den Uijl et al . found no relation between PV dimensions and the outcome of PVI [38] . Rotors were commonly found in areas of high surface curvature , including the LA/PV junction and left atrial appendage ostia , which agrees with findings of Tzortzis et al . [39] . However , there were differences in PS density between geometries , with varying importance of the LA/PV junction ( Fig 5 ) , demonstrating the importance of modelling the geometry of an individual patient . Myocardial tissue within the PVs is significantly fibrotic , which may lead to slow conduction and reentry [5 , 30 , 40] . More fibrosis is found in the distal PV , with increased connective tissue deposition between myocardial cells [41] . We modelled interstitial PV fibrosis with increasing density distally , and found that the inclusion of PV fibrosis increased PS density in the PV region of the model due to increased reentry around the LA/PV junction and wave break in the areas of fibrosis . This , together with the results in Fig 6 , suggests that PVI alone is more likely to be a successful in cases of high PV fibrosis . There are multiple methodologies for modelling atrial fibrosis [25 , 42 , 43] , and the choice of method may affect this localisation . Population based distributions of atrial fibrosis were modelled for paroxysmal and persistent patients , together with varied PV properties . The presence of LA/PV reentry depends on both PV properties and the presence of fibrosis; reentry is seen at the LA/PV junction for cases with baseline PV APD , but not for short PV APD , and stabilised to areas of high fibrosis in persistent AF , for which LA/PV reentry no longer occurred . This suggests that rotor location depends on both fibrosis and PV properties . This finding may explain the clinical findings of Lim et al . in which drivers are primarily located in the PV region in early AF , but AF complexity increased with increased AF duration , and drivers are also located at sites away from the PVs [6] . During early AF , PV properties may be more important , while with increasing AF duration , there is increased atrial fibrosis in the atrial body that affects driver location . This suggests that in cases with increased atrial fibrosis in the atrial body , ablation in addition to PVI is likely to be required . Simulations of models with patient-specific atrial fibrosis together with varied PV properties performed in this study offer a proof of concept for using this approach in future studies . The level of atrial fibrosis and PV properties that gave the best fit of the model PS density to the clinical PS density varied between patients . Measurement of PV ERP and conduction properties using a lasso catheter before PVI could be used to tune the model properties , together with LGE-MRI or an electro-anatomic voltage map . It is difficult to predict whether PVI alone is likely to be a successful treatment strategy for a patient with persistent AF [44] . This will depend on both the susceptibility to AF from ectopic beats , together with electrical driver location , and electrical size . Our study describes multiple factors that affect the susceptibility to AF from ectopic beats . Measurement of PV APD , PV CV and PV size will allow prediction of the susceptibility to AF from ectopic beats . Arrhythmia susceptibility increased in cases with short PV APD , slower CV at the LA/PV junction and larger veins , suggesting the importance of PVI in these cases . The likelihood that PVI terminates AF was also found to depend on driver location , assessed using PS density . Our simulation studies suggest that high PV PS density indicates likelihood of PVI success . Thus either measuring this clinically using non-invasive ECGi recordings , or running patient-specific simulations to estimate this value may suggest whether ablation in addition to PVI should be performed . In a recent clinical study , Navara et al . observed AF termination during ablation near the PVs , before complete isolation , in cases where rotational and focal activity were identified close to these ablation sites [45] . These data may support the PV PS density metric suggested in our study . Our simulations show that PV PS density depends on PV APD , the degree of PV fibrosis and to a lesser extent on PV fiber direction . To the best of the authors’ knowledge , there are no previous studies on the relationship between fibrosis in the PVs , or PV fiber direction , and the success rate of PVI . Measuring atrial electrogram properties , including AF cycle length , before and after ablation may indicate changes in local tissue refractoriness [46] . PV APD can be estimated clinically by pacing to find the PV ERP; and PV fibrosis may be estimated using LGE-MRI , although this is challenging , as the tissue is thin . PV fiber direction data is not currently available clinically , which limits the predictive ability of the model . Areas of high PV PS density on ECGi need to be carefully interpreted in terms of expected accuracy of the inverse solution on the PVs and the incidence of false phase singularity detection [47] . In addition , multiple mechanisms may underlie areas of high PS density . Importantly , not all PSs sustain and drive AF , and represent suitable targets for ablation . Limitations to this study include that PV branching structures were not considered since PVs were trimmed at the highest level that results in a single PV rim at each distal PV . Mansour et al . found that just 56% of patients had four PVs with separate ostia [48] , 29% of patients had an additional PV , and 17% a common PV trunk . Although some studies have reported differences in ERP between the endocardium and epicardium [23] , we modelled the endocardium and epicardium ERP identically . Furthermore , we modelled changes in APD by modifying IK1 only and did not consider other ionic conductances or methods for parametrisation [20 , 49 , 50] . We used a bilayer model , rather than a volumetric model incorporating thickness , which will affect rotor drift [51] . In addition , we did not model changes in connexins [29] or cell morphology [52] . Furthermore , we modelled 2 seconds of activity following PVI in the model , where these ablation lesions were applied simultaneously rather than sequentially as in the clinic , and we did not model long term AF recurrence . Finally , we did not consider the case of AF sustained by focal beats; we either considered the inducibility due to PV ectopics , or maintenance due to reentry . Our computational modelling study suggests that measurement of fibrosis and PV properties may indicate patient specific susceptibility to AF initiation and maintenance . In addition , high PV PS density pre-ablation indicates likelihood of PVI success in our simulations , motivating a retrospective clinical study into this metric .
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Atrial fibrillation is the most commonly encountered cardiac arrhythmia , affecting a significant portion of the population . Currently , ablation is the most effective treatment but success rates are less than optimal , being 70% one-year post-treatment . There is a large effort to find better ablation strategies to permanently cure the condition . Pulmonary vein isolation by ablation is more or less the standard of care , but many questions remain since pulmonary vein ectopy by itself does not explain all of the clinical successes or failures . We used computer simulations to investigate how electrophysiological properties of the pulmonary veins can affect rotor formation and maintenance in patients suffering from atrial fibrillation . We used complex , biophysical representations of cellular electrophysiology in highly detailed geometries constructed from patient scans . We heterogeneously varied electrophysiological and structural properties to see their effects on rotor initiation and maintenance . Our study suggests a metric for indicating the likelihood of success of pulmonary vein isolation . Thus either measuring this clinically , or running patient-specific simulations to estimate this metric may suggest whether ablation in addition to pulmonary vein isolation should be performed . Our study provides motivation for a retrospective clinical study or experimental study into this metric .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
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2018
|
Variability in pulmonary vein electrophysiology and fibrosis determines arrhythmia susceptibility and dynamics
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In 2014 , a major epidemic of human Ebola virus disease emerged in West Africa , where human-to-human transmission has now been sustained for greater than 12 months . In the summer of 2014 , there was great uncertainty about the answers to several key policy questions concerning the path to containment . What is the relative importance of nosocomial transmission compared with community-acquired infection ? How much must hospital capacity increase to provide care for the anticipated patient burden ? To which interventions will Ebola transmission be most responsive ? What must be done to achieve containment ? In recent years , epidemic models have been used to guide public health interventions . But , model-based policy relies on high quality causal understanding of transmission , including the availability of appropriate dynamic transmission models and reliable reporting about the sequence of case incidence for model fitting , which were lacking for this epidemic . To investigate the range of potential transmission scenarios , we developed a multi-type branching process model that incorporates key heterogeneities and time-varying parameters to reflect changing human behavior and deliberate interventions in Liberia . Ensembles of this model were evaluated at a set of parameters that were both epidemiologically plausible and capable of reproducing the observed trajectory . Results of this model suggested that epidemic outcome would depend on both hospital capacity and individual behavior . Simulations suggested that if hospital capacity was not increased , then transmission might outpace the rate of isolation and the ability to provide care for the ill , infectious , and dying . Similarly , the model suggested that containment would require individuals to adopt behaviors that increase the rates of case identification and isolation and secure burial of the deceased . As of mid-October , it was unclear that this epidemic would be contained even by 99% hospitalization at the planned hospital capacity . A new version of the model , updated to reflect information collected during October and November 2014 , predicts a significantly more constrained set of possible futures . This model suggests that epidemic outcome still depends very heavily on individual behavior . Particularly , if future patient hospitalization rates return to background levels ( estimated to be around 70% ) , then transmission is predicted to remain just below the critical point around Reff = 1 . At the higher hospitalization rate of 85% , this model predicts near complete elimination in March to June , 2015 .
The 2014 epidemic of Ebola virus in West Africa is an emerging public health and humanitarian crisis of epic dimensions [1] . This epidemic originated in an outbreak in Guéckédou , Guinea in December 2013 . The Ministry of Health of Guinea and Médecins Sans Frontières ( MSF ) were alerted to clusters of an unknown disease with fever/vomiting/diarrhea and a high fatality rate on 10 and 12 March 2014 , respectively [2] . Through human-to-human transmission , the virus subsequently spread to Liberia ( 29 March [3] ) , Sierra Leone ( 25 May [4] ) , Nigeria ( 22 July [5] ) , Senegal ( 29 August [6] ) , the United States ( 30 September [7] ) , and Mali ( 30 September [8] ) . On 8 August 2014 , WHO determined the epidemic to be a “Public Health Emergency of International Concern . ” This declaration obligated 194 signatory nations to participate in disease prevention , surveillance , control , response , and reporting [9] . On 6 October , the first transmission outside of Africa was documented in Spain [10] . As of 14 December , 18 , 603 persons were reported ( but not confirmed ) to have been infected with a fatality rate for those cases with known clinical outcome around 70% [1] . Due to widespread under-reporting , the true number of cases is widely believed to be considerably higher . Ongoing international support has included the shipment of large quantities of personal protective equipment , diagnostic laboratory apparatus , and materiel such as vehicles; provision of medical and logistical advisors from MSF , the US Centers for Disease Control & Prevention , and WHO , among others; and the construction of new treatment facilities [11] . A range of further clinical interventions , health policies , and aid are under consideration and at various stages of mobilization . Whether these are sufficient to achieve containment and/or what further actions might extend their reach remain unknown . Epidemic modeling provides a means for structured reasoning about such complex dynamical conditions , both with respect to the information contained in this epidemic’s history to date and prospective opportunities for intervention . While several models of the 2014 West Africa Ebola epidemic have been published , the majority of these are primarily aimed at estimating the basic reproduction number ( R0 ) , a summary statistic that may be tremendously informative about the potential rate of spread and the magnitude of vaccination required to achieve herd immunity [12–14] . Knowing R0 is less useful where human behaviors—including both public health interventions [15] and avoidance or denial in the community [16]—cause the epidemic to take a more irregular path [17] . Two models that incorporate more detail have been published . A paper by the WHO Response Team [1] proposes a renewal equation for the evolution of the epidemic through time , parameterized with case reports collected by MSF . But this model , which focuses on the time course of disease and conditions for transmission , does not account for the role of transmission setting . The model of Meltzer and colleagues [18] is more tactical , but provides little analytical insight . Here , we report on a model of intermediate complexity . Our goal was to produce a model that could be used to guide policy recommendations . A supporting objective was to perform analysis of a range of scenarios to identify how actions taken in the present may influence short and medium term prospects for containment . The model comprises separate probability distributions for the number of secondary cases arising among health care workers ( HCWs ) infected in hospitals , non-HCWs infected by hospitalized patients , non-HCWs infected during non-hospital nursing care , and non-HCW infected through burial practices . Infected individuals may be treated in the hospital or in the home . Hospital treatment is assumed to result in reduced transmission but is limited to a fixed number of available hospital beds . Cases in excess of hospital capacity are assumed to be treated in the home . Cases seeking hospitalization ( whether capacity allows admission or not ) are scored as a report , separating the total number of cases ( which is unknown ) from the number of cases reported . In contrast to the models in [1] and [18] , this model allows for changing human behavior and epidemic interventions through time-varying rates of hospitalization , exposure of HCWs , and secure burial [19] . We use the theory of branching processes to derive an expression for the mean number of secondary infections .
Data were obtained from situation reports issued by WHO and the Liberia Ministry of Health ( Fig . 1 ) . All situation reports were pulled from the Liberia Ministry of Health or United Nations Office for the Coordination of Humanitarian Affairs ( UN-OCHA ) websites ( http://reliefweb . int and http://humanitarianresponse . info ) . When values had to be interpolated , data from WHO outbreak reports were used . For provenance and reproducibility , we digitally entered our own data ( data deposited in the Dryad repository: http://doi . org/10 . 5061/dryad . 17m5q [20] ) . Reported cases were scored as the sum of suspected , probable , and confirmed cases . Model . We developed a discrete time , stochastic process model for Ebola transmission . The model considers the context in which transmission occurs and who is infected as a result . This framework allows a minimal set of subpopulation differences to be articulated that nonetheless reflect the major epidemiological properties of Ebola transmission , including hospital treatment versus community care , transmission at funerals , and scenario-dependent transmission risk differences during care-giving . The model comprises separate probability distributions for the number of secondary cases arising from ( i ) HCWs infected in hospitals , ( ii ) non-HCWs infected by hospitalized patients , ( iii ) non-HCWs infected during non-hospital nursing care , and ( iv ) non-HCW infected through burial practices . Infected individuals are considered to be treated either in the hospital or in the home ( Fig . 2 ) . Specifically , our model supposes that transmission is composed of five processes that result in 11 state transitions ( Fig . 2 ) . In the following description , numbers in parentheses correspond to labels in Fig . 2 . These processes constitute a multi-type branching process composed of mixtures and convolutions of the core probability distributions ( Box 1 ) . Branching process models allow for very flexible specification of the distribution of secondary cases . Our branching process does not account for the depletion of susceptibles at the population level , however , and is therefore appropriate during the exponential phase of epidemic spread and/or where spread is controlled through human intervention rather than self-limitation . We believe these assumptions are broadly consistent with the currently prevailing conditions in West Africa . Hospital capacity . In simulations , hospital treatment was assumed to result in reduced transmission , limited by the number of available hospital beds . Patients seeking hospitalization in excess of hospital capacity were assumed to be returned to the home for treatment . Only patients seeking hospitalization ( whether capacity allowed admission or not ) were scored as a report , separating the total number of cases ( which in reality is unknown ) from the number of cases reported . To parameterize this model , we were initially guided by reports on the outbreaks of Ebola virus in Kikwit ( Democratic Republic of Congo ) in 1995 [21–24] and Gulu ( Uganda ) in 2000–2001 [25–27] . The values obtained in this section were used as a starting point for a more systematic analysis , as described in the section “Plausible Parameter Sets . ” Transmission ( N , q , θ ) and the effectiveness of infection control ( α ) . The attack rate in Kikwit was 9% among hospital workers [28] and 16% among family members [22] . The ratio of exposures to index cases in households was N = 173/27 = 6 . 4 for 27 different families . Assuming exposure was only within the family ( so each secondary case had only one exposure ) , we have q = 0 . 16 ( risk of transmission per contact ) . At Kikwit General Hospital , 37 of 429 workers met the case definition for Ebola virus disease . A reported three cases occurred after the use of barrier nursing . If we assume that these three were all in Kikwit General Hospital , then 34 HCWs were infected prior to infection control . A total of 110 out of 138 other hospital workers reported direct contact with an Ebola patient . Extrapolating to the 392 HCWs who weren’t infected , we estimate the number of workers with direct contact to be 110/138×392+34≈ 346 yielding an attack rate of 9 . 8% . Of course , hospital workers experience greater exposure than persons providing care in the community . Among 48 uninfected persons with direct contact jobs at Kikwit General Hospital there were a total of 151 patient contacts ( 3 . 15 contacts per worker ) . If this were representative , then we would have the relation 1− ( 1−qα ) 3 . 15 = 0 . 098 , yielding α = 0 . 20 prior to the implementation of barrier nursing and other infection control measures . Following barrier nursing , three out of 110/138×392+3≈ 315 HCWs were infected , yielding an attack rate of 0 . 95% . Using the relation 1− ( 1−qα ) 3 . 15 = 0 . 0095 we obtain α = 0 . 019 after the implementation of barrier nursing and other infection control measures . Hospital contact multiplier ( β ) . The parameter β relates the number of contacts in a health facility to those in a household and is expressed as a multiplier of N . This value is chosen based on intuition and narrative reports . In general , we consider values in the range 2<β<5 to be reasonable . Funeral transmission ( ϕ ) . Legrand and colleagues [29] assumed that mean duration of death to burial was 2 days and estimated transmission rates of 7 . 66 per week ( Kikwit ) and 0 . 46 per week ( Gulu ) . Translating into average number of infections , the number of secondary cases through funeral are estimated to be 2 . 18 and 0 . 13 , respectively , assuming S/N≈1 , where S is the number of susceptible individuals in the population and N is the total population size . A value of 0<ϕ<3 is consistent with the routine finding that preparation of the body constitutes a substantial risk factor and that this duty is performed by a relatively small number of people . We note that this is not consistent with anecdotal reports of large numbers of persons being infected at a funeral . We consider those events most likely to be exceptional . Parameter values of this “core model” are reported in Table 1 . Treatment facilities . From a range of reports , we compiled a time series of the operational Ebola treatment units ( ETUs ) along with estimates of their capacity , recorded as the number of patient beds available ( Fig . 1 ) . Importantly , many ETUs were regularly reported to be operating above capacity , typically by around a factor of two [30–32] . Additionally , the average hospital stay is around 6 . 5 days [1] , considerably shorter than the 15-day infection generation . Therefore , throughout our analysis , we estimate the number of patients potentially served by an ETU within an infection interval using the formula s ( t ) = 2 b ( t ) τ / σ . ( 1 ) where t marks time in infection generations , b ( t ) is hospital capacity in terms of the number of beds , τ = 15 is infection generation time , and σ = 6 . 5 is the average duration of hospitalization . Secure burial rate . Non-secure burial ( including body preparation and funeral ceremonies ) is one of the key occasions for Ebola virus transmission . The Liberia Ministry of Health and international partners have therefore sought to reduce this mode of transmission through public education about the risk of exposure from deceased Ebola patients and the mobilization of body retrieval and burial teams . There has almost certainly been a substantial reduction in transmission due to increased frequency of secure burial . For example , even during the interval from 4 July to 2 September ( prior to the downturn [33] ) , the cumulative reported number of cases shows a negative curvature on a logarithmic scale ( grey line in Fig . 1 ) . We therefore modeled g ( a rate that is the sum of the recovery rate and secure burial rate ) using the time-dependent function g ( t ) = γ 1 ( 1 − 1 / ( ( t − 7 ) γ 2 ) ) + μ . ( 2 ) where t is measured in terms of infection generations and μ = 0 . 3 is one minus the case fatality rate , γ1<0 . 7 is the maximal secure burial rate , and γ2 governs the speed at which safe burials increase . This function allows for the secure burial rate to increase beginning around 4 July , starts at a positive minimum due to natural recovery , and asymptotically approaches a maximum at γ1+μ , since we suppose that secure burial and recovery cannot go to 100% . Initial conditions . According to our data , there were 27 beds in ETUs in Liberia on 4 July . Based on the reported cumulative case count , there were approximately 108–30 = 78 active reported cases at this time . Using equation ( 1 ) , we estimated that 54 of the reported cases were under hospital care . Further assuming under-reporting by a factor of 2 . 5 [18 , 34] , we estimated that there were a total of 195 cases for 195–78 = 117 unreported cases at this time . Together , these calculations imply that 54 persons were treated in hospitals and 141 persons were treated in the community . Other parameters . In general , we treat the hospitalization rate ( h ) , secure burial rate ( γ1 and γ2 ) , funeral transmission ϕ , and overdispersion ( θ ) as tuning parameters . The time scale of this model is defined with respect to infection generations . To calibrate to calendar time , we assumed a serial interval of 15 days [1] . To calculate hospital capacity , we assumed an average hospital stay of 6 . 5 days [1] . Plausible parameter sets . Guided by these crude parameter estimates , we then tuned our model to data from the 2014 Ebola outbreak in Liberia . There were two waves of transmission in 2014 in Liberia . The first wave occurred in March and April , comprised a total of eight reported cases , and may have gone extinct in mid-May . The second wave began in late May and was the origin of the vast majority of cases . However , reported cases between the end of the first wave and around 4 July were irregular , whereas after 4 July there was a dramatic and sustained increase in the number of cases for many weeks . Around 6 September , the smoothed average number of cases per case ( a model-independent estimate of Reff ) began to decline ( unpublished data ) . The WHO Situation Report of 8 October indicates that this decline was probably due to a deterioration in reporting , rather than a true decline in transmission . For these reasons , we focused our fitting on the interval from 4 July 2014 to 2 September 2014 . In keeping with the time scale of our model , and to smooth over daily variations in reporting , reported cases were aggregated to 15 day transmission generations ( Table 2 ) . The parameters h , γ1 , γ2 , θ , α , and ϕ were first tuned so that the median simulated reports of infection among HCWs in the four infection generations between 4 July and 2 September and the median simulated number of cumulative reports among non-HCWs at the same times were close to the reported values ( S1 Fig ) . We further refined these fits by minimizing squared differences on a logarithmic scale . We then used latin hypercube sampling to explore a parameter space within ±25% of the tuned values . A parameter set was deemed plausible if the reported cumulative number of cases ( data ) and reported cumulative cases among HCWs ( data ) were within the range of 500 simulations ( model ) . Further sensitivity analyses were performed and are described in S1 Text . To forecast future cases under different scenarios for aid and intervention in the fall of 2014 , we projected cases and number of persons seeking hospitalization from 3 September 2014 until 31 December 2014 ( 120 days ) under five scenarios . Details are contained in S1 Text . By mid-December 2014 , it was evident that the effective reproductive ratio at the national level had been reduced to below one—a scenario consistent with , but not guaranteed by the data up until 2 September—significantly reducing the range of epidemic trajectories projected into the future . At this time , we updated the forecasts with a known ( rather than conjectural ) trajectory for increased hospital capacity including the capacity of referral centers , and by further reducing the set of plausible parameters to contain only those parameterizations consistent with the 5 , 836 cases observed between 3 September and 1 December . We then simulated future epidemic trajectories into 2015 under this larger base of evidence . The infection generation ending on 1 December comprised 605 reported cases , from which we estimate a total of 1 , 513 cases . Assuming 72% were admitted for hospitalization ( Table 3 ) , we initialized these simulations with 1 , 089 hospital-treated patients and 424 patients remaining in the community . Updated projections were produced for two scenarios starting on 1 December . In the first scenario , we assumed future hospitalization rate to be drawn from the revised set of plausible historical rates . In the second scenario , we assumed the future hospitalization rate to be fixed at 85% ( corresponding to Scenario C in the S1 Text ) . In both scenarios , hospital capacity was represented as the sum of the projected capacity of ETUs , community care units , and holding units in operation in the country at the given time , obtained from reports by the UN Mission for Ebola Emergency Response and International Red Cross projections .
Overall , 1 , 045 of 5 , 000 ( 20 . 9% ) parameter sets were initially determined to be plausible under the data available to 2 September . This total was reduced to 46 parameter sets in the updated model using data to 1 December . Mean values and inter-quartile range from plausible parameter sets are reported in Table 3 . The parameter that showed the greatest change between the initial parameterization and updated model was the hospitalization rate , which the updated model estimated to be greater than 70% whereas earlier in the year it was estimated to be around 60% . Parameter correlations are shown in S13–S15 Figs . The fit between the ensemble of plausible parameterizations and the cumulative number of reported cases in Liberia during the interval used for model fitting is shown in Fig . 3 . The heavy blue line shows the cumulative number of reported cases . The plausible range of case reports given the model is shown in yellow ( 95% prediction intervals ) . The plausible range of total cases , including unreported cases , is shown in blue . The fit of the model to infection generations in HCWs and in the community is shown in Fig . 4 . These figures show that the initial model reliably reproduced the observed epidemic trajectory at the time the model was produced . Model-based effective reproduction numbers at infection generations between 4 July and 17 October were calculated by evaluating the effective reproduction number ( see S1 Text ) at the 1 , 045 plausible parameter sets . The change over time in the range of plausible effective reproduction numbers is shown in Fig . 5 . Simulated trajectories illustrating the possible outcomes starting on 2 September , using data only up to that point and assuming baseline conditions , are shown in Fig . 6 . The median projected total epidemic size by 31 December was 130 , 862 cases ( inter-quartile range: 44 , 560–396 , 706 ) . The top panel shows the range of trajectories for 10 , 450 simulations distributed over 1 , 045 plausible parameter sets . An interpolation to project the daily number of persons seeking hospitalization is shown in S2 Fig . Simulated trajectories for other scenarios are shown in S3 , S5 , S7 , and S9 Figs . Daily number of persons seeking hospitalization for these scenarios are shown in S4 , S6 , S8 , and S10 Figs . Scenarios are compared in S12 Fig . These results show that given the best information available in October 2014 , it was reasonable to conclude that the total number of cases might exceed 100 , 000 by the end of the 2014 calendar year . Projections from 1 December 2014 to 13 July 2015 , fit using data up to 1 December , are shown in Figs . 7 and 8 . These simulations , accounting for information available to 1 December , show that interventions and changes to personal behavior substantially reduced transmission compared with earlier in the year . However , these results suggest that transmission could remain “near critical” ( Reff ≈1 ) if rates of patient hospitalization estimated to have occurred in July–September 2014 are maintained ( Fig . 7 ) . In this scenario , active transmission would almost certainly continue into the second half of 2015 . By contrast , if patient hospitalization of 85% can be achieved , simulations suggest the epidemic will be largely contained sometime between March and June 2015 ( Fig . 8 ) . Both scenarios predict a rapid decline in the first months of 2015 , followed by a longer “tail . ”
The transmission of Ebola virus in West Africa continues to give rise to high mortality and morbidity . Part of the challenge in predicting the progression of the epidemic lies in the fundamentally different ways in which transmission occurs: infection of hospital workers , community care-givers , and those preparing bodies for funerals [1] . Additionally , the time frame and effectiveness of increased hospital capacity compounds the problem of prediction [35] , whether it is aimed at anticipating demand for hospitalization or determining the level and speed of intervention needed to bring the outbreak under control . Our approach was to represent heterogeneity in transmission and time-varying intervention in a multi-type branching process model [36] that offers analytic tractability , efficient simulation , and the flexibility to investigate a wide range of intervention scenarios . It is closely related to sources of data; for example , stratifying cases into hospital-treated versus community-treated allows for estimating under-reporting , which is thought to be large for the current epidemic [37] . Analytical insight , particularly the derivation of a reproductive ratio , is useful when parameter estimates ( such as the hospitalization rate ) are uncertain , since the sign and magnitude of their effects on transmission can be derived . Besides recovering a full expression for the basic reproductive ratio , simplifying assumptions such as assuming that funeral-associated transmission can be reduced to zero , yield further understanding . In particular , our model shows how the additional exposure to HCWs in a hospital environment ( β ) combines with both the reduced transmission in that environment ( α ) and the hospitalization rate ( h ) to determine when community ( versus hospital ) transmission will dominate ( i . e . , when 1−h>αβ ) . Such formulas may provide “rules of thumb” to help guide infection control or could improve practical decision making by regularly updating estimates of core parameters through surveillance within health facilities . The approach we have taken to model parameterization is novel . A more familiar approach is to propose a deterministic or stochastic model that is then fit by minimizing an objective function on the errors , e . g . , sum of squared errors or negative log likelihood of the data given the model [29] . Statistical interpretation of such models ( such as hypothesis tests or confidence intervals ) relies heavily on the parametric specification of both the process model and the observation model . If the proposed models are not good approximations to their respective contributions to the data-generating process ( that is , they have considerable “structural error” ) , then these quantities may be quite biased . Moreover , such models are ineffective when they are overparameterized . Our approach—the construction of plausible parameter sets that are both epidemiologically sensible and can reproduce observed properties of the epidemic—seeks instead to understand the space of models consistent with the data . The cost of this approach is that the results do not admit probabilistic interpretations , hypothesis tests , or traditional confidence intervals . A byproduct is that the identifiability of parameters ( which is compromised by overparameterization ) is no longer an obstacle to model construction and forecasting . If two parameters , say a and b , are highly correlated ( not simultaneously identifiable ) so that either the model with large a and small b or large b and small a are both consistent with the data , then the plausible set will include parameter combinations with examples of both kinds ( but not , for example , large a and large b or small a and small b ) . It may be that these differences are in fact irrelevant to the eventual behavior of the model , in which case the space of possible solutions will be small . Alternatively , it may be that these are just the parameters that most substantially influence alternative outcomes , in which case the space of possible solutions will be large . By seeking bounds on the range of outcomes rather than a unique causal story , the method of plausible parameter sets avoids technical problems with model identifiability and more accurately emphasizes the kind of uncertainty prevalent under emergent conditions while focusing attention on the property of most practical interest: the possible future trajectories of the epidemic . In conclusion , we believe that the method of plausible parameter sets is a good starting point for exploring entire families of models and for setting bounds on the range of possible outcomes . It is a first step toward the construction of models for probabilistic inference . In this study , we have focused on Liberia , which initially experienced the fastest epidemic growth . The ramping up of hospital capacity in Liberia was dramatic during late August 2014 , adding approximately 300 beds . Throughout September , that sustained effort led to an additional ~300 beds . This heterogeneous increase in capacity over time was incorporated into our model . We investigated alternative hospital capacities and demands in a set of plausible alternative scenarios . The best and worse outcomes of these scenarios vary dramatically in the forecasted epidemic size ( S12 Fig ) . Median estimates were at around 130 , 000 cases by 31 December 2014 assuming a baseline scenario without increased hospital capacity . This was reduced to around 50 , 000 when capacity was ramped up to ~1 , 700 . Further increases in hospital capacity were shown to reduce the upper bound on our predictions , but did not substantially affect the median . Our initial model suggested that if the hospitalization rate could be increased to 85% then it was probable that the epidemic would be contained . The updated model confirms this result and predicts near elimination sometime between March and June of 2015 . The updated model also highlights the continued need for vigilance , however , suggesting that if hospitalization returns to prior levels the current outbreak may exhibit an extremely prolonged right tail . In conclusion , these modeling exercises suggested that in the absence of rapid hospitalization of most cases , none of the proposed scenarios for increasing hospital capacity would have been likely to achieve containment . Continuing on the path to elimination will require sustained watchfulness and individual willingness to be treated . Although broadly consistent with our narrative understanding of the epidemiology of Ebola virus disease in West Africa , the model we developed does not account for some known features of transmission , mainly because we believe these effects to be small relative to the processes represented . For instance , the size of the at-risk population of HCWs has varied over time , which may account for some of the fluctuations in infection within this group of people ( Fig . 4 ) . Our model assumes that the number of contacts between HCWs and infected persons is proportional to the number of infected persons limited by hospital capacity . To the extent that individual care was reduced because of exhaustion or movement of the care-giving workforce early in the epidemic , our model is unrealistic . Individual case information will be required to determine the magnitude of this effect . Similarly , our model attributes the decline in transmission primarily to hospitalization and safe burial , but not improved infection control in the hospital setting , better and safer use of personal protective equipment , or social distancing . We believe that infection control and effective use of protective equipment are in fact key elements to containing Ebola and may account for some of the proportional decline in transmission to HCWs shown in Fig . 4 . Changes in transmission in the hospital environment were not included in our model for the technical reason that time-varying hospital transmission and time-varying safe burial could not be simultaneously estimated together with evidence that ( i ) by late summer , transmission to HCWs was a small fraction of transmission overall , and ( ii ) our model already attributes a high level of effectiveness to infection control ( see section “Transmission and the effectiveness of infection control” ) . Effects of social distancing are probably captured numerically by our model in the estimated decline in funerary transmission . To the extent that transmission has been reduced by diffuse social distancing—including the use of safety precautions in households of infected persons—our estimate of the safe burial rate will be biased . The upshot is that our model may be numerically accurate , although g may not reflect the true safe burial rate . To the extent that declines in transmission are due to changes other than increased hospital capacity and safe burial , the projected benefits of future increases in hospital capacity may be exaggerated . In this respect , our forecasts are conjectures based on current understanding . Branching process models use offspring distributions to simulate forward in time . Here , the offspring of an infectious individual refers to the new cases generated from that infectious individual . This is the type of data that is frequently reported , even during early stages of an outbreak . Models that require separate quantities for the probability of infection and number of contacts are complicated by the fact that there is uncertainty about whether contact is effective or not . For example , how many “contacts” of an infectious individual transported by airplane are sufficiently intimate that infection is even a causal possibility ? Ambiguities about the causal relevance of contacts of different kinds complicate models expressed in terms of attack rates . By focusing on the empirical offspring distributions in various transmission settings , one is able to build , simulate , and analyze a model with the key epidemiological features , and to investigate a wide range of mitigation scenarios . In our case , the result was a multi-type branching process that separated the location that infection was acquired from the sites generating new infections . This approach captures the behavioral aspects of transmission that are often lacking in models [38] . Awareness of Ebola in the community and public education mean that community-acquired transmission is increasingly likely to lead to demand for hospitalization . While our methods are focused on the current Ebola outbreak in West Africa , they apply to a broad class of infectious diseases .
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There is considerable uncertainty regarding the steps needed to contain the ongoing Ebola crisis in West Africa , the timeline required to achieve control , and the projected burden of mortality . To address these issues , we develop a branching process model for Ebola transmission that focuses on offspring distributions ( i . e . , the numbers of new infections caused by each case ) . We use the model to assess the likely progression of Ebola in Liberia . The model assesses the feedback between new cases and hospital demand under a range of plausible intervention scenarios , particularly ramping-up of treatment facilities over time and increasing the number of individuals seeking hospital treatment through outreach and education . Transmission scenarios—to health care workers in hospitals , to caregivers in the community , to hospital visitors , and to individuals preparing bodies for funerals—are described by distinct offspring distributions based on available data . Results suggest that the outcome of the epidemic depends on both hospital capacity and individual behavior . Additionally , the model highlights the conditions under which transmission might have outpaced hospital capacity , and projects possible epidemic trajectories into 2015 .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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Ebola Cases and Health System Demand in Liberia
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The pathogenesis of persistent infection is dictated by the balance between opposing immune activation and suppression signals . Herein , virulent Salmonella was used to explore the role and potential importance of Foxp3-expressing regulatory T cells in dictating the natural progression of persistent bacterial infection . Two distinct phases of persistent Salmonella infection are identified . In the first 3–4 weeks after infection , progressively increasing bacterial burden was associated with delayed effector T cell activation . Reciprocally , at later time points after infection , reductions in bacterial burden were associated with robust effector T cell activation . Using Foxp3GFP reporter mice for ex vivo isolation of regulatory T cells , we demonstrate that the dichotomy in infection tempo between early and late time points is directly paralleled by drastic changes in Foxp3+ Treg suppressive potency . In complementary experiments using Foxp3DTR mice , the significance of these shifts in Treg suppressive potency on infection outcome was verified by enumerating the relative impacts of regulatory T cell ablation on bacterial burden and effector T cell activation at early and late time points during persistent Salmonella infection . Moreover , Treg expression of CTLA-4 directly paralleled changes in suppressive potency , and the relative effects of Treg ablation could be largely recapitulated by CTLA-4 in vivo blockade . Together , these results demonstrate that dynamic regulation of Treg suppressive potency dictates the course of persistent bacterial infection .
Typhoid fever is a systemic , persistent infection caused by highly adapted host-specific strains of Salmonella [1] , [2] , [3] . Human typhoid is caused predominantly by S . enterica serotype Typhi [4] , while mice develop a typhoid-like disease following S . enterica serotype Typhimurium infection . Interestingly , the early stages of this infection , in both mice and humans , are usually asymptomatic or associated with only mild , non-specific “flu-like” symptoms [4] , [5] . This represents a stark contrast to other Gram-negative bacterial pathogens ( e . g . Escherichia coli , Neisseria meningitidis , Haemophilus influenza ) that primarily cause acute infection and immediately trigger robust systemic symptoms after tissue invasion . Thus , the inflammatory response is blunted early after infection with Salmonella strains that cause persistent infection , and this feature likely facilitates long-term pathogen survival [3] . On the other hand , the blunted inflammatory response to systemic Salmonella infection also minimizes immune-mediated damage to host tissues that may outweigh the immediate risk posed by the pathogen itself [6] . Thus , dampening the immune response provides potential advantages to pathogen and host during persistent Salmonella infection . Regulatory T cells ( Tregs ) were initially identified as a CD25-expressing subset of CD4+ T cells required for maintaining peripheral immune tolerance to self-antigen . However more recent studies clearly demonstrate their importance extends to controlling the immune response during infection [7] , [8] , [9] , [10] . In this regard , the functional importance of Tregs has been best characterized for pathogens that cause persistent infection . For example , depletion of CD25+CD4+ Tregs is associated with enhanced effector T cell activation and reduced pathogen burden during Leishmania major infection [11] . Similarly , reconstituting T cell-deficient mice with CD25+CD4+ Tregs abrogates enhanced pathogen clearance that occurs after reconstitution with CD25-depleted CD4+ T cells [11] , [12] . These complementary experimental approaches initially used to identify the role of CD25+ Tregs in host defense during L . major infection have since been reproduced after infection with numerous other bacterial , viral , and parasitic pathogens [8] , [13] , [14] , [15] , [16] , [17] , [18] . Interestingly , Treg-mediated immune suppression can also play “protective” roles for infections where host injury caused by the immune response outweighs the damage caused by the pathogen itself [13] , [16] , or when pathogen persistence is required for maintaining protection against secondary infection [11] , [19] . Together , these findings suggest Treg-mediated immune suppression can provide both detrimental and protective roles in host defense against infection . Despite these observations , identifying the functional importance of Tregs during in vivo infection has been limited , in part , by the lack of unique markers that allow their discrimination from other CD4+ T cell subsets . In this regard , the majority of infection studies have experimentally manipulated Tregs based on surrogate markers such as CD25 expression on CD4+ T cells . However , since CD25 expression is also a marker for activated T cells with no suppressive function , identifying Tregs based on CD25 expression does not allow discrimination between these functionally distinct T cell subsets . These limitations have been recently overcome by the identification of Foxp3 as the master regulator for Treg differentiation , and the generation of transgenic mice that allow precise identification or targeted manipulation of Tregs based on Foxp3 expression [20] , [21] , [22] . These include Foxp3GFP reporter mice that allow ex vivo Foxp3+ Treg isolation by sorting for GFP-expressing cells , and Foxp3DTR transgenic mice that co-express a high affinity diphtheria toxin receptor ( DTR ) with Foxp3 [23] , [24] . Intriguingly , the first infection study using Foxp3DTR mice for Treg ablation revealed somewhat paradoxical roles for Foxp3+ Tregs in host defense . Within the first fours days after intravaginal herpes simplex virus 2 ( HSV-2 ) infection , reduced inflammatory cell infiltrate and increased viral burden were found at the site of infection in Treg-ablated compared with Treg-sufficient mice [25] . These effects were not limited to HSV-2 , nor were they restricted to the mucosal route of infection as increased pathogen burden associated with Foxp3+ Treg ablation also occurred after parenteral infection with lymphocytic choriomeningitis virus ( LCMV ) and West Nile virus [25] , [26] . Whether these Treg-mediated reductions in pathogen burden are limited to these specific viral pathogens , or represent re-defined roles for Tregs based on their manipulation using Foxp3-specific reagents are currently undefined . Therefore , additional studies using representative mouse models of other human infections and Foxp3-specific reagents for Treg manipulation are required . In this study , the role of Foxp3+ Tregs in controlling immune cell activation and the balance between pathogen proliferation and clearance during the natural progression of persistent bacterial infection was examined after infection with virulent Salmonella .
Commonly used inbred mouse strains have discordant levels of innate resistance to virulent S . enterica serotype Typhimurium based primarily on whether a functional allele of Nramp1 is expressed [27] , [28] . For example , C57BL/6 mice express a functionally defective , naturally occurring variant of Nramp1 and thus , are inherently susceptible to infection with virulent Salmonella dying within the first few days from uncontrolled bacterial replication . By contrast , 129SvJ mice , which express wild-type Nramp1 ( Nramp1-sufficient ) , are inherently more resistant developing a persistent infection instead [29] , [30] . Since transgenic mouse tools for Treg manipulation based on Foxp3-expression are available primarily on the susceptible , Nramp1-defective C57BL/6 background , we sought to exploit the autosomal dominant resistance to Salmonella conferred by wild-type Nramp1 , and the X-linked inheritance of Foxp3 transgenic mice by examining infection in resistant F1 129SvJ X C57BL/6 mice [30] . Similar to results after infection with virulent Salmonella in 129SvJ mice , progressively increasing bacterial burdens are found throughout the first 3–4 weeks after infection in F1 129SvJ X C57BL/6 mice ( Figure 1A ) . By contrast , Nramp1-defective C57BL/6 mice died within the first week after infection from overwhelming bacterial replication despite a 100-fold reduction in Salmonella inocula ( Figure 1A ) . The progressively increasing bacterial burden within the first 3–4 weeks after Salmonella infection in F1 129SvJ X C57BL/6 mice parallels dramatic changes in both spleen size and absolute number of splenocytes ( Figure 1B and C ) . Each of these parameters increased within the first three weeks after infection and declined subsequently at later time points that directly coincide with changes in Salmonella bacterial burden ( Figure 1A–C ) . These findings demonstrate an interesting dichotomy in infection tempo between early ( first 3–4 weeks ) and later time points during persistent Salmonella infection in resistant F1 129SvJ X C57BL/6 mice . Given the importance of T cells in host defense against Salmonella [31] , [32] , [33] , the expansion and activation kinetics for CD4+ and CD8+ T cells during this persistent infection were each enumerated . Although the absolute numbers of both cell types increased in parallel with the absolute numbers of splenocytes , a progressive and steady increase in percent CD4+ T cells became readily apparent beginning week three post-infection ( Figure 1D ) . By contrast , the percent CD8+ T cells remained essentially unchanged throughout these same time points . Additional phenotypic characterization revealed that the percent activated ( CD44hiCD62Llo ) CD4+ and CD8+ T cells both increased sharply beginning week 3 , and were sustained at high levels through week 7 after infection ( Figure 2A ) . Furthermore , the kinetics of T cell activation based on CD44 and CD62L expression directly paralleled the kinetics whereby CD4+ and CD8+ T cells each became primed for IFN-γ production ( Figure 2B ) . Thus , the kinetics of CD44 and CD62L expression and IFN-γ production each reveal delayed T cell activation early after infection , not peaking until weeks 3 to 4 , that is followed by more sustained T cell activation thereafter . Given the durability whereby T cells maintain changes in CD44 and CD62L expression , and IFN-γ production after activation , the expression of more transient T cell activation markers such as CD25 and CD69 were also quantified throughout persistent Salmonella infection . CD25 and CD69 expression on CD4+ and CD8+ T cells each peaked between weeks 3 and 4 post-infection ( Figure 2C ) . However consistent with the transient nature of their expression , CD25 and CD69 expression each declined to baseline levels over the next 2 to 3 weeks . Thus , the sharp increase in T cell activation that occurs between weeks 3 and 4 after Salmonella infection is confirmed using both transient ( CD25 , CD69 ) and more stable ( CD44 , CD62L , IFN-γ ) markers of T cell activation . Interestingly , the overall kinetics for T cell activation beginning week 3 after infection directly parallels when reductions in bacterial burden begins to occur , and suggests dampened T cell activation early after infection allows progressively increasing bacterial burden , while enhanced T cell activation later facilitates bacterial clearance . To determine the overall importance and individual contribution provided by each T cell subset in bacterial clearance during the natural course of persistent Salmonella infection , the impacts of CD4+ and/or CD8+ T cell depletion were determined . Anti-mouse CD4 and anti-mouse CD8 depleting antibodies were administered beginning day 31 post-infection . In initial studies , we found that 750 µg of each could deplete the respective T cell subset with ≥99% efficiency even in Salmonella-infected mice that contain expanded T cell numbers ( Figure 3A ) . With sustained CD4+ T cell depletion , significantly increased numbers of recoverable Salmonella CFUs were found day 6 ( day 31+6 ) after the administration of anti-mouse CD4 compared with isotype control antibody ( Figure 3B ) . Moreover , the magnitude of this difference became even more pronounced by day 14 ( day 31+14 ) after antibody treatment . By contrast , CD8+ T cell depletion alone or together with CD4+ T cell depletion did not cause significant changes in Salmonella bacterial burden except in the spleen day 14 after antibody treatment where combined depletion of both CD4+ and CD8+ T cells resulted in increased numbers of recoverable Salmonella CFUs compared to CD4+ T cell depletion alone ( Figure 3B ) . Together , these results demonstrate an essential role for CD4+ T cells in the clearance of persistent Salmonella infection , and these findings are consistent with the previously reported requirement for this T cell subset in controlling the replication of attenuated Salmonella in susceptible Nramp1-defective mice [31] . Moreover , an essential role for CD4+ T cells in host defense during persistent infection in resistant mice is further supported by the sharp increase in overall percentage and activation of these cells which coincides with reductions in Salmonella bacterial burden beginning week 3 post-infection ( Figure 1 and 2 ) . The requirement for CD4+ T cells in bacterial clearance during persistent Salmonella infection may reflect contributions from either Foxp3-negative effector or Foxp3+ regulatory T cells ( Tregs ) . To characterize the relative contributions of each CD4+ T cell subset during persistent infection , our initial studies enumerated the percent Foxp3+ cells among CD4+ T cells and the expansion kinetics of Foxp3+ and Foxp3-negative CD4+ T cells during persistent infection . Interestingly despite dramatic shifts in the percent and absolute number of CD4+ T cells among splenocytes , the percent Foxp3+ Tregs among CD4+ T cells remains remarkably stable and essentially unchanged at approximately 10% throughout the infection ( Figure 4A and B ) . By extension , the absolute numbers of Foxp3+ Tregs and Foxp3-negative effector CD4+ T cells were also found to expand in parallel ( Figure 4C ) . These findings suggest variations in the ratio of Foxp3+ Tregs among non-Treg effector CD4+ T cells alone does not account for the shift in relative T cell activation and change in infection tempo at early compared to late time points during persistent Salmonella infection . Since defined inflammatory cytokines and pathogen associated molecular patterns have each been shown to control Treg suppressive potency after stimulation in vitro [34] , [35] , [36] , [37] , [38] , [39] , we explored the possibility that intact pathogens and the ensuing immune response would also dictate shifts in Treg suppressive potency after infection in vivo . By extension , these shifts in relative Treg suppressive potency may impact the activation of non-Treg effector cells and overall tempo of persistent infection . Accordingly , we compared the suppressive potency for Foxp3+ Tregs isolated at early ( day 5 ) and late ( day 37 ) time points during persistent Salmonella infection . These specific time points where chosen because they reflect highly pronounced contrasts in T cell activation and directional changes in Salmonella bacterial burden , yet have comparable bacterial burdens ( Figure 1 and 2 ) . Nramp1-sufficient F1 Foxp3GFP reporter hemizygous male mice derived by intercrossing 129SvJ males with Foxp3GFP/GFP females ( on the C57BL/6 background ) that simultaneously allow persistent Salmonella infection and for all Tregs to be isolated based on cell sorting for GFP+ ( Foxp3+ ) cells were used in these experiments [23] ( Figure 5A ) . By first enriching for CD4+ cells using negative selection , GFP+ ( Foxp3+ ) Tregs could be routinely isolated from naïve and Salmonella-infected F1 Foxp3GFP reporter mice each with ≥99% purity ( Figure 5B ) . Potential differences in suppressive potency for GFP+ ( Foxp3+ ) Tregs isolated at each time point after infection were quantified by measuring their ability to inhibit the proliferation of responder CD4+ T cells isolated from naïve CD45 . 1 congenic mice after non-specific stimulation in vitro using previously defined methods [40] , [41] , [42] . Compared with Tregs isolated from F1 Foxp3GFP reporter mice prior to infection , the suppressive potency of Tregs isolated from mice day 5 after Salmonella infection was enhanced ( Figure 5C ) . At the same Treg to responder T cell ratio , Foxp3+ Tregs from mice day 5 after infection consistently inhibited responder CD45 . 1+ T cell proliferation ( CFSE dilution ) more efficiently . These differences in suppression were eliminated when a 2-fold reduction in Treg to responder cell ratio from mice day 5 post-infection compared with undiluted Tregs from naïve mice were co-cultured with a fixed number of naïve responder cells ( Figure 5C ) . In sharp contrast to increased suppression that occurs at this early post-infection time point , the suppressive potency for Tregs isolated from mice day 37 after infection was significantly reduced . Compared with Tregs isolated from mice 5 days after infection , the efficiency whereby Tregs isolated day 37 post-infection inhibited the proliferation of responder CD45 . 1+ T cells was reduced approximately 4-fold; and compared with Tregs isolated from naïve mice , their suppressive potency was reduced approximately 2-fold ( Figure 5C ) . In other words , a 50% reduction in Treg to responder cell ratio for Tregs isolated from naïve mice , and a 75% reduction in ratio for Tregs from mice day 5 after infection each suppressed responder cell proliferation to the same extent as undiluted GFP+ ( Foxp3+ ) Tregs isolated from mice day 37 after infection . These results demonstrate that although the ratio of Foxp3+ Tregs and non-Treg effector CD4+ T cells remains unchanged , shifts in Treg suppressive potency that directly parallel the kinetics of T cell activation and infection tempo occur during the progression of persistent Salmonella infection . In complementary experiments , the relative suppressive environment dictated by Foxp3+ Tregs during Salmonella infection was further characterized . Specifically the expansion of adoptively transferred antigen-specific T cells after stimulation with cognate peptide at defined time points during persistent infection was enumerated . This approach exploits the use of F1 129SvJ X C57BL/6 mice as recipients for adoptively transferred T cells from TCR transgenic mice on the C57BL/6 background [43] . As a control to identify the overall contribution of Tregs in suppressing the expansion of adoptively transferred T cells in vivo , F1 Foxp3DTR hemizygous male mice derived from intercrossing 129SvJ males with Foxp3DTR/DTR female mice ( on the C57BL/6 background ) , which allows targeted ablation of Foxp3+ Tregs by administering low-dose diphtheria toxin ( DT ) were used initially [24] . We found 1 . 0 µg ( 50 µg/kg ) DT given on two consecutive days was sufficient for ≥99% ablation of Foxp3+ Tregs , and continued DT dosing ( 0 . 2 µg every other day ) was able to maintain this level of Treg ablation in Foxp3DTR mice on the F1 background ( Figure 6A ) . These results are consistent with the reported efficiency whereby Foxp3+ Tregs are selectively ablated in Foxp3DTR mice on the C57BL/6 background [24] . Although in vivo injection of cognate OVA257–264 peptide could stimulate only modest levels of expansion for adoptively transferred T cells from OT-1 TCR transgenic mice in Treg-sufficient mice , the expansion magnitude was increased >50-fold in Treg-ablated F1 Foxp3DTR mice ( Figure 6B ) . Importantly , the expansion of these adoptively transferred T cells was antigen-dependent because very few cells could be recovered from either Treg-ablated or Treg-sufficient recipient mice without peptide stimulation . Thus , Tregs actively suppress the expansion of peptide stimulated antigen-specific T cells in vivo , and the relative expansion of these exogenous cells is a reflection of Treg suppressive potency . Using this approach , the relative expansion of exogenous T cells from OT-1 TCR transgenic mice after adoptive transfer into Salmonella infected F1 129SvJ X C57BL/6 and stimulation with cognate OVA257–264 peptide was enumerated . The percent and total numbers of OT-1 T cells was increased 4-fold and 5-fold , respectively , after adoptive transfer into mice at late ( day 37 ) compared with early ( day 5 ) time points during persistent infection ( Figure 6C ) . Thus , the in vivo environment at later compared with early time points during persistent Salmonella infection is significantly more permissive for peptide-stimulated T cell expansion . These results , together with the reductions in suppressive potency for GFP+ ( Foxp3+ ) cells isolated ex vivo from mice at early compared with late time points ( Figure 5C ) , and the critical role for Foxp3+ Tregs in controlling exogenous T cell expansion in response to cognate peptide ( Figure 6A and B ) clearly illustrate reductions in Treg suppressive potency occur from early to late points during persistent Salmonella infection . Furthermore , given the sharp dichotomy in infection tempo at these specific time points , these results suggest enhanced Treg suppression early after infection restrains effector T cell activation that allows progressively increasing Salmonella bacterial burden , while diminished Treg suppression at later time points allows enhanced T cell activation that more efficiently controls the infection . Multiple Treg-associated cell surface and secreted molecules have been implicated to mediate immune suppression by these cells . For example , increased expression of CTLA-4 , IL-10 , Tgf-β , Granzyme B , ICOS , PD-1 , and CD39 each have been shown independently to coincide with enhanced Treg suppressive potency [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , while expression of other Treg cell-intrinsic molecules ( e . g . GITR , OX40 ) each parallel reductions in suppressive potency [54] , [55] , [56] . Although the relative importance of each defined molecule varies significantly depending upon the experimental model used , the relative expression of Treg cell-intrinsic signals that either stimulate or inhibit suppression likely dictates the overall suppressive potency of Tregs . Therefore , we quantified the relative expression of each molecule on Foxp3+ Tregs to explore how the observed shifts in suppression potency from early to late time points during persistent Salmonella infection correlate with changes in their expression ( Figure 7A and Figure S1 ) . Consistent with the drastic reduction in suppressive potency , significant shifts in expression for some Treg-associated molecules between day 5 and day 37 post-infection were identified . For example , molecules that have independently been associated with diminished Treg suppression potency such as reduced CTLA-4 and increased GITR expression were found for Foxp3+ Tregs from mice day 5 compared with day 37 after infection [46] , [54] , [55] ( Figure 7A ) . By contrast , more modest or minimal changes were found for other Treg-associated molecules implicated to mediate suppression ( e . g . CD39 , IL-10 , Granzyme B , PD-1 , and Tgf-β ) [47] , [48] , [49] , [50] , [51] , [52] , [53] , [56] ( Figure S1 ) . Thus , reduction in Treg suppressive potency during the progression of persistent Salmonella infection directly parallels reduced CTLA-4 and increased GITR expression that each independently correlates with this shift in suppression . Given the importance of pathogen-specific Tregs in controlling pathogen-specific effector cells in other models of persistent infection [57] , [58] , [59] , the expansion kinetics and relative expression of Treg-associated effector molecules were also characterized for Salmonella-specific Tregs . The best characterized Salmonella-specific , I-Ab-restricted MHC class II antigen is the flagellin FliC431–439 peptide [60] . Using tetramers with specificity for this antigen and magnetic bead enrichment , naïve C57BL/6 mice have been estimated to contain ∼20 FliC431–439-specific CD4+ T cells [61] . Using these same techniques , we find similar numbers of FliC431–439-specific CD4+ T cells in naïve F1 mice prior to Salmonella infection ( Figure 7B ) . As predicted after Salmonella infection , the numbers of these FliC431–439-specific CD4+ T cells expand reaching ∼10-fold and 20-fold increased cell numbers day 5 and 37 post-infection , respectively ( Figure 7B ) . Interestingly , for FliC431–439-specific CD4+ cells identified in this manner , ∼10% were Foxp3+ in F1 mice prior to and at each time point after infection ( Figure 7B ) . Thus , FliC431–439-specific Tregs and effector T cells expand in parallel during this persistent infection , and these results are consistent with the stable percentage of Foxp3+ Tregs among bulk CD4+ T cells ( Figure 4 ) . Although the relatively small number ( ∼1–2 cells per mouse ) of FliC431–439-specific Foxp3+ Tregs in naïve mice precluded further analysis beyond these absolute cell numbers , the expansion of FliC431–439-specific Tregs and non-Treg effector CD4+ T cells at early and late time points after infection allowed the relative expression of likely determinants of Treg suppression to be characterized . FliC431–439-specific Tregs were found to down-regulate CTLA-4 and up-regulate GITR expression , as infection progressed from early to late time points to a similar extent in FliC431–439-specific compared with bulk Tregs at these same time points after infection ( Figure 7A and C ) . Thus , the relative expression of Treg-intrinsic molecules known to stimulate or impede immune suppression occurs for both pathogen-specific and bulk Foxp3+ Treg cells , and these changes directly coincide with reductions in their suppressive potency that occurs from early to late time points during persistent infection . To more definitively identify the relative importance of Treg-mediated immune suppression on the progression of persistent Salmonella infection , the impacts of Treg ablation on infection tempo and T cell activation were enumerated at early and late time points after infection . Given the striking contrasts in suppressive potency for Foxp3+ Tregs , directional changes in bacterial burden , and effector T cell activation between mice day 5 versus day 37 post-infection , the relative impact caused by Treg ablation using F1 Foxp3DTR mice ( Figure 6A ) on infection tempo beginning at these time points were enumerated . In agreement with their essential role in maintaining and sustaining peripheral tolerance [24] , Treg-ablated mice began to appear lethargic and dehydrated beginning day 8 after the initiation of DT treatment in Salmonella-infected mice . Thus , the impacts of Treg ablation on infection tempo and T cell activation were limited to discrete 7-day windows during persistent Salmonella infection . For mice that received DT beginning day 5 post-infection , significantly reduced numbers of recoverable Salmonella CFUs were found for Treg-ablated F1 Foxp3DTR compared with Treg-sufficient F1 Foxp3WT control mice 6 days after the initiation of DT treatment ( day 5+6 ) ( Figure 8A ) . These reductions in bacterial burden with Treg ablation early after infection were paralleled by significantly increased T cell activation ( percent CD44hiCD62lo T cells ) ( Figure 8B ) . Importantly , the reductions in Salmonella bacterial burden in Treg-ablated mice cannot be attributed to non-specific effects related to DT treatment because both Treg-ablated F1 Foxp3DTR and Treg-sufficient F1 Foxp3WT control mice each received identical doses of this reagent , nor could they be attributed to cell death-induced inflammation triggered by dying Tregs because no significant reductions in recoverable CFUs were found for F1 Foxp3DTR/WT heterozygous female mice where ∼50% Tregs express the high affinity DT receptor and are eliminated following DT treatment ( Figure S2 ) . By contrast , Treg ablation beginning later after infection ( day 37 ) when T cells are already highly activated caused no significant change in Salmonella bacterial burden and only a modest incremental increase in T cell activation between Treg-ablated F1 Foxp3DTR compared with Treg-sufficient F1 Foxp3WT control mice ( Figure 8A and B ) . Thus , the relative impact of Treg ablation at early and late time points on infection outcome directly parallel the differences in their suppressive potency . Together , these results demonstrate enhanced Treg suppressive potency at early infection time points restrains effector T cell activation and allows progressively increasing bacterial burden . By extension , Treg ablation at these early time points markedly increases T cell activation and significantly reduces the bacterial burden ( Figure 8A and B ) . Reciprocally , at later time points after infection when Treg suppressive potency is diminished , the relative contribution of Foxp3+ Tregs on T cell activation and bacterial clearance is reduced ( Figure 8A and B ) . Thus , dynamic regulation of Treg suppression dictates the balance between pathogen proliferation and clearance during the course of persistent Salmonella infection . Given the drastic shifts in Treg-associated expression of CTLA-4 and GITR that each correlates with the reduced suppressive potency of these cells from early to late time points during persistent Salmonella infection , additional experiments sought to identify the relative importance of these molecules in dictating infection tempo using well characterized CTLA-4 blocking ( clone UC10-4F10 ) or GITR-stimulating ( clone DTA-1 ) monoclonal antibodies [54] , [62] , [63] , [64] . Consistent with the essential role for CTLA-4 in Treg suppression during non-infection conditions in vivo [46] , significant reductions in Salmonella recoverable CFUs and accelerated T cell activation were found with CTLA-4 blockade initiated beginning day 5 after Salmonella infection , and the magnitude of these changes paralleled those following DT-induced Treg ablation in F1 Foxp3DTR mice ( Figure 8C and D ) . Since Foxp3-negative cells also express CTLA-4 , albeit at significantly reduced levels compared with Foxp3+ Tregs ( Figure 7 ) , we further explored the relative contribution of CTLA-4 blockade in the absence of Foxp3+ Tregs . Consistent with the reduced levels of CTLA-4 expression on Foxp3-negative CD4+ T cells , the effects of CTLA-4 blockade were eliminated with Foxp3+ Treg ablation ( Figure S3 ) . By extension , at later time points after infection ( day 37 ) when CTLA-4 expression is down-regulated on Foxp3+ Tregs , no significant change in Salmonella bacterial burden or T cell activation occurred with CTLA-4 blockade ( Figure 8C and D ) . By contrast to these results with CTLA-4 blockade that directly recapitulates the effects of Treg ablation at early and late time points during persistent infection , treatment with a monoclonal antibody that stimulates cells through GITR caused no significant changes in Salmonella bacterial burden or T cell activation when initiated at either early or late time points during persistent infection ( Figure 8E and F ) . Together , these results suggest the dynamic regulation of Treg suppressive potency during Salmonella infection is predominantly mediated by shifts in CTLA-4 expression , and reduced CTLA-4 expression by Tregs during the progression of this persistent infection dictates reduced suppression with enhanced effector T cell activation and bacterial clearance .
The balance between immune activation required for host defense , and immune suppression that limits immune-mediated host injury is stringently regulated during persistent infection [6] , [7] . Although Tregs have been widely implicated to control the activation of immune host defense components during infection , their role in dictating the natural progression of persistent infection remains undefined . In this study , we report two distinct phases of effector T cell activation with opposing directional changes in pathogen burden in a mouse model of persistent Salmonella infection . Delayed T cell activation associated with increasing bacterial burden occurs early , while enhanced T cell activation that parallels reductions in pathogen burden occurs later during infection . Remarkably , significant reductions in Treg suppressive potency between early and late infection time points directly coincide with these differences in infection tempo . In complementary experiments , the significance of these shifts in Treg suppressive potency were verified by directly enumerating the relative impact of Treg ablation on infection tempo at early and late infection time points . Together , these results demonstrate dynamic changes in Foxp3+ Treg suppressive potency dictate the natural course and progression of this persistent infection . Along with two recent studies characterizing infection outcome with Foxp3+ Treg ablation after mucosal HSV-2 , systemic LCMV , and footpad West Nile virus infections [25] , [26] , these are the first studies to characterize the importance of Tregs during infection using Foxp3DTR transgenic mice . These results comparing infection outcome after Treg manipulation based on their lineage-defining marker , Foxp3 , allow the importance of Tregs to be more precisely characterized compared with other methods that identify and manipulate Tregs using surrogate markers ( e . g . CD25 expression ) that are not expressed exclusively by these cells . Interestingly , while Treg ablation caused increased pathogen burden , delayed arrival of acute inflammatory cells , and accelerated mortality after HSV-2 , LCMV , or West Nile virus infections [25] , [26] , we find contrasting reductions in pathogen burden and increased T cell activation with Treg ablation at early , but not late time points during persistent Salmonella infection . However , the reductions in Salmonella pathogen burden with early Treg ablation are consistent with reduced Mycobacterium tuberculosis pathogen burden after partial Treg depletion using bone marrow chimera mice reconstituted with mixed cells containing congenically-marked Foxp3+ Tregs and Foxp3-deficient cells [65] . Together , these studies comparing infection outcome after Treg ablation using Foxp3-specific reagents highlight interesting and divergent functional roles for Foxp3+ Tregs during specific infections . The reasons that account for these differences – whether they are related to differences between bacterial versus viral pathogens or between pathogens that primarily cause acute versus persistent infection , are important areas for additional investigation , and require the characterization of infection outcomes after Treg manipulation using Foxp3-specific reagents with other pathogens . The dynamic regulation of Treg suppressive potency during Salmonella infection we demonstrate here is consistent with the ability of inflammatory cytokines and purified Toll-like receptor ( TLR ) ligands to each control Treg suppression after stimulation in vitro [34] , [35] , [36] , [37] , [38] , [39] . However , since these stimulation signals in isolation trigger opposing directional changes in suppressive potency , the specific contribution for each on changes in Treg suppression during infection is unclear . Therefore , the cumulative impact of multiple TLR ligands expressed by intact pathogens and the ensuing immune response on changes in Treg suppression is best characterized for Tregs isolated directly ex vivo after infection . The increased suppressive potency for Foxp3+ Treg at early time points after Salmonella infection we demonstrate here is consistent with the increased suppressive potency for CD25+CD4+ cells isolated day 5 after Plasmodium yoelii and day 10 after HSV-1 infection , as well as CD25+CD4+ cells isolated in the acute ( day 12 ) and chronic phase ( day 28 ) after Heligmosomoides polygyrus infection [14] , [66] , [67] . However , our results build upon and extend the significance of these findings in three important respects . First , by isolating Tregs based on Foxp3 rather than CD25 expression , the limitations imposed by contaminating non-Treg CD25+ effector T cells in subsequent ex vivo functional analysis is bypassed . Secondly , although an increase in CD25+CD4+ T cell suppression early after infection when pathogen proliferation occurs , potential shifts in Treg suppression at later time points during the natural progression of persistent infections has not been previously demonstrated . In this regard , the relatively short time interval that separates pathogen proliferation and clearance during persistent Salmonella infection is ideally suited for comparing differences in relative importance and suppressive potency for Tregs during these contrasting stages of infection . Using this model , we demonstrate significant reductions in Treg suppressive potency between early and late time points after infection that enables robust immune cell activation required for pathogen clearance . Despite these changes in suppressive potency , the percentage of Tregs among bulk and Salmonella FliC-specific CD4+ T cells each remained relatively constant throughout infection . These findings are consistent with the stable ratio of Tregs to effector CD4+ T cells during other models of persistent infection , and represent a striking contrast to the selective priming and expansion of pathogen-specific Foxp3-negative CD4+ T cells that occurs after acute Listeria monocytogenes infection [58] , [65] , [68] , [69] . Thus , the priming and expansion of pathogen-specific Tregs may be an important distinguishing feature between pathogens that cause acute rather than persistent infection . The development and refinement of methods for MHC class II tetramer staining and magnetic bead enrichment has allowed the precise identification of very small numbers of T cells with defined specificity from naïve mice [61] . Using these techniques , we find in this and a recent study [69] that Foxp3+ Tregs comprise approximately 10% of CD4+ T cells with specificity to both the FliC431–439 and 2W1S52–68 peptide antigens , respectively . Together , these results suggest previously under-appreciated overlap in the repertoire of antigens recognized by Foxp3+ Tregs compared with non-Treg CD4+ effectors in naïve mice [70] , [71] , [72] . However , more considerable overlap in the specificity of these two cell types is consistent with the TCR repertoires of human peripheral Tregs and non-Tregs based on genomic analysis of TCR sequences [73] , [74] . Thus , additional studies that examine the percent Tregs among CD4+ T cells with other defined antigen-specificities using recently developed tetramer-based enrichment techniques are warranted . Lastly , by enumerating the relative expression of defined Treg-associated molecules that have been implicated to directly mediate or inhibit suppression , the complexity whereby Tregs maintain the balance between immune activation and suppression becomes more clearly defined . For example , shifts in suppressive potency for Tregs isolated from early compared to late time points during persistent infection are paralleled by significant changes in the expression of numerous Treg cell-intrinsic molecules that have been demonstrated in other experimental models to control and/or mediate suppression [44] , [45] ( Figure 7 and Figure S1 ) . In particular , the drastic reductions in suppressive potency that occurs for Tregs isolated from mice day 5 compared with day 37 after infection is associated with significant reductions in CTLA-4 expression and increased expression of GITR on both bulk Foxp3+ Tregs and Salmonella FliC431–439-specific Foxp3+ Tregs ( Figure 7 ) . Based on these results , the relative contributions of CTLA-4 and GITR in controlling suppression by Foxp3+ Tregs during persistent infection were investigated using antibody reagents that block CTLA-4 or stimulate cells through GITR . We find that CTLA-4 blockade alone is sufficient to recapitulate the effects of Treg ablation on Salmonella infection tempo , while GITR stimulation had no significant effect ( Figure 8 ) . These results are consistent with the recent demonstration that CTLA-4 expression on Foxp3+ Tregs is essential for maintaining peripheral tolerance [46] , [75] . Our results expand upon these findings by demonstrating the importance of dynamic CTLA-4 expression on Tregs during persistent infection that controls the kinetics of effector T cell activation and overall infection tempo . The increase in Treg suppressive potency at early time points after Salmonella infection is consistent and may provide the mechanistic basis that explains the relative immune suppression previously observed during this infection [76] , [77] , [78] , [79] , [80] . Interestingly , increased Treg suppressive potency early after infection has also been described after viral and parasitic pathogens [14] , [66] , [67] . Is enhanced Treg suppression early after infection advantageous for the host , the pathogen , or both ? Our ongoing studies are aimed at identifying the signals activated during Salmonella infection that trigger these changes , and Treg-intrinsic molecules that sense and dictate this augmentation in suppressive potency . Perhaps more intriguing are the molecular signals during natural infection that trigger reductions in Treg suppression that transform blunted immune effectors early after infection into more potent mediators of pathogen clearance . Given the multiple known pathogen-associated molecular patterns expressed by Salmonella ( e . g . LPS , flagellin , porins , and CpG DNA ) that each stimulate immune cells through defined Toll-like and other pattern recognition receptors [81] , [82] , [83] , [84] , [85] , [86] , together with the enormous potential for cell intrinsic TLR-stimulation on Tregs to alter their suppressive potency [34] , [35] , [36] , [38] , [87] , it is tempting to hypothesize that shifts in the expression of individual , multiple , or cumulative TLR ligands during persistent infection controls the relative expression of Treg-associated molecules that mediate suppression . In this regard , our ongoing studies are also aimed at identifying the Salmonella-specific ligands and their corresponding host receptors that dictate these reductions in Treg suppression during the progression of this persistent infection . We believe these represent important prerequisites for developing new therapeutic intervention strategies aimed at accelerating the transition to pathogen clearance and further unraveling the pathogenesis of typhoid fever caused by Salmonella infection .
These experiments were conducted under University of Minnesota IACUC approved protocols ( 0705A08702 and 1004A80134 ) entitled “Regulatory T cells dictate immunity during persistent Salmonella infection” . The guidelines followed for use of vertebrate animals were also created by the University of Minnesota IACUC . 129SvJ males and C57BL/6 females were purchased from the National Cancer Institute . F1 mice were generated by intercrossing 129SvJ males with C57BL/6 females . F1 Foxp3GFP/− and F1 Foxp3DTR/− hemizygous males and F1 Foxp3DTR/WT heterozygous females were derived by intercrossing 129SvJ males with Foxp3GFP/GFP or Foxp3DTR/DTR females , respectively [23] , [24] . Both Foxp3GFP/GFP and Foxp3DTR/DTR females have been backcrossed to C57BL/6 mice for over 15 generations . OT-1 TCR transgenic mice that contain T cells specific for the OVA257–264 peptide were maintained on a RAG-deficient CD90 . 1+ background . All mice were used between 6–8 weeks of age and maintained within specific pathogen-free facilities . The virulent Salmonella enterica serotype Typhimurium strain SL1344 has been described [29] , [88] . For infections , SL1344 was grown to log phase in brain heart infusion media at 37°C , washed and diluted with saline to a final concentration of 1×104 CFUs ( for infection in F1 mice ) or 1×102 CFUs ( for infection in C57BL/6 mice ) per 200 µL , and injected intravenously through the lateral tail vein . At the indicated time points after infection , mice were euthanized and the number of recoverable Salmonella CFUs enumerated by plating serial dilutions of the spleen and liver organ homogenate onto agar plates . Antibodies and other reagents for cell surface , intracellular , or intranuclear staining were purchased from BD Biosciences ( San Jose , CA ) or eBioscience ( San Diego , CA ) , and used according to the manufacturers' recommendations . For measuring cytokine production by T cells , splenocytes were stimulated ex vivo with anti-mouse CD3 and anti-mouse CD28 ( each at 5 µg/mL ) in the presence of brefeldin A for 5 hours prior to intracellular cytokine staining . Antibodies used for depletion , blocking or stimulation experiments were purchased from BioXcell ( West Lebanon , NH ) . For T cell depletions , purified anti-mouse CD4 ( clone GK1 . 5 ) and anti-mouse CD8 ( clone 2 . 43 ) antibodies were diluted to a final concentration of 750 µg per 1 mL in sterile saline and injected intraperitoneally on days 31 and 34 post-infection . Additional injections were given on days 38 and 41 post-infection in experiments where depletion was maintained up to 14 days . For CTLA-4 blockade and GITR stimulation , anti-mouse CTLA4 ( clone UC10-4F10 ) , anti-mouse GITR ( clone DTA-1 ) , or isotype control antibodies ( hamster IgG or rat IgG , respectively ) were diluted to a final concentration of 500 µg per 1 mL in sterile saline and injected intraperitoneally beginning either day 5 or day 37 post-infection followed by an additional injection of 250 µg of the same antibody three days later [54] , [62] , [63] , [64] . For Foxp3+ Treg ablation , purified diphtheria toxin ( DT; Sigma-Aldrich , St . Louis , MO ) was dissolved in saline and administered intraperitoneally to F1 Foxp3WT control , F1 Foxp3DTR/− , or F1 Foxp3DTR/WT mice at 50 µg/kg body weight for two consecutive days beginning at indicated time point after infection , and then maintained on a reduced dose of DT thereafter ( 10 µg/kg body weight every other day ) . For enumerating relative Treg suppression in vitro , Foxp3+GFP+ Tregs were isolated from F1 Foxp3GFP/− mice by enriching CD4+ splenocytes first with negative selection using magnetic bead cell isolation kits ( Miltenyi Biotec , Auburn , CA ) . GFP+ ( Foxp3+ ) Tregs were further purified by staining and sorting for CD4+GFP+ cells using a FACSAria cell sorter . The purity of CD4+Foxp3+ cells post-sort was verified to be >99% . Responder CD4+ T cells were isolated from naïve CD45 . 1+ mice , CFSE-labeled under standard conditions ( 5 µM for 10 min ) , and co-cultured in 96-well round bottom plates ( 2×104 cells/100 µL ) at the indicated ratio of purified GFP+ ( Foxp3+ ) Tregs and responder CD45 . 1+CD4+ T cells . The relative suppressive potency for Tregs was enumerated by comparing the proliferation ( CFSE dilution ) in responder cells after co-culture and stimulation with anti-mouse CD3 and anti-mouse CD28 antibodies ( 1 µg/mL each ) for 4 days . For enumerating relative Treg suppression in vivo , 2×104 T cells from OT-1 TCR transgenic mice on a RAG CD90 . 1+ background were diluted in 200 µL sterile saline and injected intravenously at the indicated time points relative to Treg ablation or Salmonella infection followed by intravenous injection of purified OVA257–264 peptide ( 400 µg ) the following day . For each experiment , the degree of OT-1 T cell expansion was enumerated five days later . MHC class II tetramer staining and enrichment were performed as described [61] , [69] . Briefly , splenocytes were harvested at the indicated time points after infection and incubated with 5–25 nM PE or APC-conjugated FliC431–439-specific tetramer in Fc block for 1 hour at room temperature . These cells were then incubated with anti-PE or anti-APC magnetic beads ( Miltenyi Biotec , Auburn , CA ) for 30 minutes on ice and column purified according to the manufacturer's instructions . The bound and unbound fractions were stained with fluorochrome-labeled antibodies for cell surface and intracellular staining . The absence of I-Ab FliC431–439 tetramer staining on CD8+ T cells , and among CD4+ T cells in the unbound fraction of cells after bead enrichment were used as independent markers to verify the specificity of tetramer staining using methods described ( data not shown ) [61] . The differences in number of recoverable bacterial CFUs , and the number and percent T cells among from different groups of mice were evaluated using the Student's t test ( GraphPad , Prism Software ) with p<0 . 05 taken as statistical significance .
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The balance between immune activation and suppression is intricately controlled allowing optimal host defense against infection , while simultaneously minimizing collateral immune-mediated damage to host tissues . Although regulatory T cells have been implicated to play critical roles in sustaining this balance , their role in controlling the dynamic changes in immune cell activation during the natural progression of persistent infection are undefined . Herein , we explored the relative importance of regulatory T cells in controlling infection tempo using a model of persistent Salmonella infection representative of human typhoid . Early after infection when the bacterial burden is progressively increasing , the activation of protective immune components is delayed , and this coincides with increased regulatory T cell suppressive potency . Conversely , later during infection when reductions in bacterial burden occur , protective immune components are highly activated and regulatory T cell suppressive potency is markedly diminished . Moreover , the tempo of persistent Salmonella infection is controlled by regulatory T cells because ablation of these cells early after infection when their suppressive potency is increased accelerates bacterial eradication , while their ablation later when their suppressive potency is reduced causes no significant effects . Thus , regulatory T cell suppression controls the tempo of persistent Salmonella infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"immunology/immunity",
"to",
"infections",
"infectious",
"diseases/bacterial",
"infections"
] |
2010
|
Regulatory T Cell Suppressive Potency Dictates the Balance between Bacterial Proliferation and Clearance during Persistent Salmonella Infection
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Single amino acid repeats are prevalent in eukaryote organisms , although the role of many such sequences is still poorly understood . We have performed a comprehensive analysis of the proteins containing homopolymeric histidine tracts in the human genome and identified 86 human proteins that contain stretches of five or more histidines . Most of them are endowed with DNA- and RNA-related functions , and , in addition , there is an overrepresentation of proteins expressed in the brain and/or nervous system development . An analysis of their subcellular localization shows that 15 of the 22 nuclear proteins identified accumulate in the nuclear subcompartment known as nuclear speckles . This localization is lost when the histidine repeat is deleted , and significantly , closely related paralogous proteins without histidine repeats also fail to localize to nuclear speckles . Hence , the histidine tract appears to be directly involved in targeting proteins to this compartment . The removal of DNA-binding domains or treatment with RNA polymerase II inhibitors induces the re-localization of several polyhistidine-containing proteins from the nucleoplasm to nuclear speckles . These findings highlight the dynamic relationship between sites of transcription and nuclear speckles . Therefore , we define the histidine repeats as a novel targeting signal for nuclear speckles , and we suggest that these repeats are a way of generating evolutionary diversification in gene duplicates . These data contribute to our better understanding of the physiological role of single amino acid repeats in proteins .
Single amino acid repeats ( SARs ) , also known as homopolymeric tracts , are very common in eukaryotes [1] and between 18–20% of proteins in the human genome contain such repetitive sequences [2] . Although most of them are thought to be functionally neutral , recent evidence suggests they may play important functional or structural roles . Indeed , there is an overrepresentation of SARs-containing proteins ( SARPs ) among transcription factors , kinases and proteins required for development [2]–[5] . The intrinsic disorder of such repeats converts them into flexible spacer elements between individual folded domains , allowing SARPs to associate in large , multiprotein complexes [5] , [6] . In addition , it is thought that disordered regions can bind to multiple targets with weak affinity , an ideal property for elements involved in transcriptional and signal transduction processes [7] . Homopolymeric tracts are often encoded by trinucleotide repeats , a class of microsatellites . Their repetitive nature facilitates DNA replication slippage , and the expansion or contraction of the repeats ( for review , see [8] ) . Although genetic variability of these repeats provides a substrate for adaptive evolution [9] , [10] , uncontrolled expansion of such unstable regions within coding sequences has been associated with a number of developmental and inherited neurodegenerative disorders [2] , [11] , as well as with several types of cancer [12] . For example , polyglutamine expansions have been associated with Huntington's disease and certain types of spinocerebellar ataxia ( for review , see [11] ) . In addition , alanine repeats are related to several developmental disorders ( for review , see [13] ) , and aspartate hyperexpansions with two types of dysplasia and osteoarthritis [14] , [15] . Some of the mechanisms thought to underlie the pathogenic effects of expanded tracts involve the deregulation of transcriptional activity and the formation of toxic protein aggregates ( for review , see [11] , [16] ) . Nevertheless , the functions of many homopeptidic segments found in proteins have not yet been elucidated . Among homopolymeric tracts , histidine ( His ) repeats are relatively rare [5] . However , their frequency increases from about 1 . 4% to 4 . 3% when we consider repeats of at least 8 instead of 5 residues , indicating that they are generally longer than other types of SARs [4] . The physicochemical properties of His make it a versatile amino acid that can fulfill different roles , influencing protein conformation and enzymatic activity . For instance , His-repeats are found in Zn-finger domains that are implicated in interactions between nucleic acids and proteins ( for review , see [17] ) , and a His-stretch has been described as a protein interacting surface of the transcriptional regulator cyclin T1 [18] , [19] . Nevertheless , there is still no clear function associated to His homopeptides . We previously described the His-repeat in the DYRK1A protein kinase as both necessary and sufficient to target this protein to nuclear speckles [20] . A protein segment containing a His-tract is also involved in the accumulation of cyclin T1 in these nuclear structures [20] , [21] . These results provided the first evidence that His-repeats may act as nuclear speckle-targeting signals , although the extent to which this was true in other proteins remained to be determined . Nuclear speckles ( also known as the splicing factor compartment -SFC- or as interchromatin granule clusters -IGCs- ) are subnuclear structures defined as compartments in which components of the RNA splicing machinery are stored and assembled ( for review , see [22] ) . They mainly contain splicing factors ( snRNPs and serine/arginine-rich ( SR ) proteins ) , as well as transcription factors , 3′-RNA processing factors , translation factors , ribosomal proteins , a subpopulation of the RNA polymerase II and some kinases and phosphatases [23] , [24] . Like other nuclear bodies , nuclear speckles are highly dynamic structures that change in number , shape and size depending on the transcriptional state and the phase of the cell cycle [22] . Here , we have performed an in-depth analysis of polyHis-containing proteins in the human genome . A significant fraction of the proteins identified are transcription factors and developmental proteins with a nuclear phase . The subcellular localization of several of these proteins shows that most of them accumulate in nuclear speckles through their His-repeat . The presence of DNA-binding or protein-protein interaction domains , and the transcriptional state of the cell , are factors that affect the retention of transcription factors with His-repeats in nuclear speckles , illustrating the dynamic behavior of these proteins . Together , these results define the His-repeat as a novel and general targeting signal for nuclear speckles .
For a typical protein of 400 amino acids and of average composition , a run of any individual amino acid is significant if there are 5 or more consecutive residues [25] . Following this premise , we established a threshold of 5 His residues to determine the minimum number of His necessary for a His-containing protein to accumulate in nuclear speckles . We generated plasmids to express green fluorescent protein ( GFP ) fusion proteins with 5 , 6 , 7 , 8 or 9 His , and we analyzed the subcellular localization of these fusion proteins by direct fluorescence in transfected HeLa cells . Nuclear speckles were identified by indirect immunofluorescence with an antibody against the splicing factor SC35 , an endogenous marker of the nuclear speckles compartment [26] . No significant differences in the staining pattern were observed when GFP and GFP-5xHis were compared ( Figure S1 ) . However , from the 6xHis constructs onwards , a positive relationship was detected between the accumulation in nuclear speckles and the length of the His-tract . While GFP-6xHis only weakly concentrated in SC35-positive speckles , this association became stronger as the number of His residues increased , and it was clearly evident with a fusion protein containing 9 His ( Figure 1A and S1 ) . To confirm that the GFP-His fusions almost completely co-localized with SC35 positive speckles , we carried out an immunofluorescence analysis with protein markers of other subnuclear compartments that are compatible with such staining , including promyelocytic leukemia ( PML ) bodies ( for review , see [27] ) , Sumo-bodies ( for review , see [28] ) or paraspeckles [29] . No co-localization between the GFP-9xHis fusion protein and any of the protein markers ( PML , Sumo1 , PSP1 ) was detected ( Figure S2 ) . Finally , the subnuclear localization of GFP fusion proteins with polyproline or polyglutamine tracts , which are particularly enriched in transcription factors [4] and that have been shown to be functional as transcriptional activators [30] , was also analyzed . These fusion proteins showed nucleoplasmic staining and no colocalization with SC35 ( Figure 1B ) , in agreement with previous results with longer amino acid tracts [31] . Therefore , His homopolymeric tracts seem to specifically accumulate in the nuclear speckles compartment . To extrapolate these results to real proteins , we performed a bioinformatics screen of the Ensembl database [32] to identify all the human proteins containing at least one His-repeat of 5 or more residues . The lower-limit of 5 His residues was set to cover all possible functionally significant repeats [25] . Our search identified 86 Ensembl genes ( Table S1 ) . As some of the proteins encoded by these genes contained more than one repeat , there was a total of 99 repeats with 5 residues or more . The average size of the His-repeats was 7 . 5 , with the longest repeat containing 15 residues ( LOC730417 ) . The majority of the repeats were well conserved in the corresponding mouse orthologous proteins; 54% showed exactly the same length and 30% differed in only one or two repeat units . When more than one His-repeat was present in a protein , they were generally very close to each other such that they could be considered as “extended” His-repeat tracts ( for instance , H4GNSSH13 in DYRK1A ) . Thus , we defined “extended” tracts as regions that contained at least one pure His-repeat of 5 residues or more , that had His residues at the start and/or end of the tract , and that contained other “interrupting” residues ( often P , Q , G , S , A ) which covered <50% of the tract . Such extended tracts were present in half of the proteins containing pure His-repeats ( 43 out of 86 ) . Significantly , none of the His-repeats were situated within characterized protein domains and unlike other repeats [4] , we did not find them preferentially located at the amino- , carboxy- , or central part of the proteins . We compared the length distribution of His-repeats in coding sequences to that of equivalent sequences in non-coding regions , the latter defined as sequences containing at least five tandem CAY ( CAC or CAT: His encoding triplets ) . Accordingly , we identified 7815 such repeats in non-coding genomic regions . Interestingly , although much longer repeats existed in the non-coding regions ( the longest was 154 trinucleotides ) , their average size ( 7 . 24 ) was smaller than in coding regions . Indeed , the distribution of the repeat size was significantly different between coding and non-coding sequences ( p-value = 0 . 003 , non-parametric Kolmogorov-Smirnov test ) . In coding sequences , there was an under-representation of short repeats ( size 5 ) with respect to longer repeats ( around 7 ) when compared to non-coding sequences ( Figure 2A and 2B , respectively ) . As the length distribution of non-coding repeats is likely to reflect neutral mutational processes , this difference points to selective retention of relatively long His-repeats in protein sequences . The population of proteins containing other types of amino acid repeats , such as polyglutamine , polyalanine , polyglycine , polyserine and polyproline , is enriched in transcription factors [4] . We examined whether any such bias in Gene Ontology terms ( GO; [33] ) existed in the gene dataset encoding His-repeats . Among proteins containing His-repeats there was a strong over-representation of nuclear proteins ( 72% with respect to 26% in the complete protein dataset , p-value<10−5 , Figure 3A ) . In addition , 75% of the His repeat-containing nuclear proteins were also annotated with the GO term ‘regulation of transcription’ , in comparison with 49% of those in the complete nuclear protein dataset . Even more striking was the strong over-representation of developmental factors among nuclear proteins with His-repeats , especially those involved in the development of the nervous system ( 22% with respect to 3% in the complete gene dataset , p-value<10−5 , Figure 3B ) . This finding is in agreement with previous work [34] and it might be linked to the fact that increased formation of homopolymeric runs in human proteins may be a recent evolutionary event , concomitant with complex brain development [2] . The GO terms analysis indicated that most of the polyHis-containing proteins are nuclear proteins , and therefore they might be targeted to nuclear speckles . Thus , we analyzed the distribution of a group of the nuclear-annotated proteins with pure His-repeats of different lengths ( longer than 5 residues ) and several proteins with extended repeats . The subcellular localization of the His-containing proteins was analyzed by generating GFP fusion proteins with the open reading frames of candidate proteins in a mammalian expression vector . The subcellular distribution of the fusion proteins was analyzed by direct fluorescence in transient transfected cells and nuclear speckles were identified by anti-SC35 staining . As previously described for cyclin T1 and DYRK1A [20] , other polyHis-containing proteins also showed punctate nuclear staining that co-localized with SC35 , such as the transcription factors POU4F2 or YY1 , or the protein kinase NLK ( Figure 4A ) . Fluorescence images revealed differences in the staining patterns for the His-repeats-containing proteins , with some of them showing more nucleoplasmic staining than others ( Figure 4A; see other examples in Figures 5–8 ) . The His-repeat seemed to be necessary for this subnuclear localization since deletion of the polyHis segment alone from POU4F2 or DYRK1A ( the extended His-repeat ) completely abrogated the accumulation of these proteins in SC35-labelled nuclear speckles ( Figure 4B ) . These results indicate that the His-repeat can act as a nuclear speckle localization signal . Moreover , deletion of the His-repeat did not interfere with the biochemical function of the protein , that is “kinase” for DYRK1A or “transcriptional activator” for POU4F2 ( Figure 4C and 4D , respectively ) . Similar results were obtained when the His-repeat was deleted in NLK ( Figure S3 ) . These data indicate that the deletion has not induced a general alteration of protein structure , and further suggest that the His-tract conveys a novel behavior to the host protein without affecting its basic activity . Interestingly , a significant fraction ( 64% ) of the genes encoding proteins with His-repeats had closely-related paralogues in the human genome . According to Ensembl annotations , 74% of them had been presumably formed by gene duplication at the dawn of vertebrate evolution ( Table S2 ) . However , in most cases none of the paralogues contained a similar His-repeat in their primary sequence . This indicates that the repeat had only later appeared in one of the duplicate copies , probably by duplication slippage . To approximately date their appearance , we inspected all the orthologous and paralogous vertebrate proteins in Ensembl for the presence of similar His-repeats . In 11 out of 39 cases , the repeat was found in all vertebrate orthologues but in none of the paralogues , indicating that they arose soon after the duplication event . However , the dominant class was repeats formed at the base of the placental mammals ( 14 cases ) . Notably , a large number of alanine and glycine repeats are also proposed to be specific to mammals [35]–[37] . Indeed , the increased repeat expansion in this clade may be related to the increased GC content [38] , [39] . Finally , the His-repeats in the BMP2K and PBXIP1 genes were restricted to primates , suggesting they arose relatively recently . Given the significant number of polyHis-containing proteins with paralogous proteins without His-repeats , we reasoned that if the His-repeat were responsible for their accumulation in speckles then the paralogous copy without the repeat should not be found in this subnuclear compartment . To confirm this hypothesis , we examined the FAM76A and FAM76B pair of paralogues . A sequence alignment of these two proteins highlighted their high degree of conservation , except in the region containing the His-repeat ( Figure 5A ) . As hypothesized , the paralogue without the His-tract , FAM76A , presented a diffuse nucleoplasmic staining , while the protein with the polyHis segment , FAM76B , accumulated in nuclear speckles ( Figure 5B ) . Similar results were obtained for other pairs of paralogous proteins such as DYRK1A/DYRK1B or POU4F2/POU4F3 ( Figure S4 ) . Thus , these findings further indicate that the His-repeats in these proteins are necessary for their localization to nuclear speckles . The initial analysis of the nuclear localization of polyHis-containing candidates revealed that some proteins did not apparently localize to nuclear speckles . These proteins contained other protein domains such as DNA binding domains or protein-protein interacting regions . For instance , the transcription factors MEOX2 and OTX1 harbor a homeobox DNA-binding domain in their C- and N-terminal regions , respectively ( Figure 6A and Figure S5 ) . In the case of the Sumo E3 ligase CBX4 , its C-terminal region includes domains that interact with the polycomb protein CtBP2 and the transcriptional repressor RING1 ( Figure 6B ) . These domains mediate the localization of CBX4 to subnuclear foci , that are compatible with polycomb bodies [40] . Therefore , we hypothesized that the accumulation of proteins to nuclear speckles may be influenced by other interactions . To confirm this hypothesis , we deleted the DNA binding domain in MEOX2 and assessed its nuclear distribution . Accordingly , while the wild type protein presented the dispersed distribution typical of most transcription factors ( Figure 6A ) , compatible with active transcription sites [41] , the mutant protein in which the homeobox was eliminated ( MEOX2ΔHB ) fully co-localized with SC35 ( Figure 6A ) . Similar results were obtained with the OTX1 transcription factor ( Figure S5 ) . In the case of CBX4 , we assessed whether deleting the C-terminal fragment spanning the CtBP2 and RING1-interacting domains ( CBX4ΔPB ) similarly affected its distribution . While the wild type CBX4 protein was present in nuclear foci that were not positive for SC35 , the mutant CBX4ΔPB co-localized with SC35 in the nucleus ( Figure 6B ) . These results confirmed that the accumulation of some of the polyHis-containing proteins in nuclear speckles was influenced by their binding to other nuclear components , such as DNA or diverse subnuclear structures . Moreover , they suggest that competition between distinct protein regions dictates the steady state subnuclear localization of the protein . In mammalian cells , the structure and function of nuclear speckles is sensitive to the transcriptional state of the cell ( for review , see [22] ) . When cells are treated with RNA polymerase II transcription inhibitors , there is a decrease in the splicing activity and a redistribution of the components of speckles , which are recruited to larger and rounder nuclear speckles [42] . Most of the His-containing proteins were transcription factors and since our results showed that DNA binding activity influenced their accumulation in speckles , we wondered whether their failure to localize to this subnuclear compartment might be reverted by inhibiting RNA polymerase II activity . Two proteins , FOXG1B and HOXA1 , that did not produce speckled staining at the steady state , co-localized with SC35 in fewer but larger speckles after α-amanitin treatment ( Figure 7A and 7B ) . Interestingly , the diffuse nucleoplasmic distribution of several other transcription factors became punctate in cells treated with α-amanitin , and it overlapped with SC35 staining ( Table 1 and Figure S6A ) . These dynamic changes in distribution could be observed by in vivo imaging ( Videos S1 and S2 ) . For HOXA1 , we noticed that the staining not only overlapped with SC35 foci but it also adopted a “capped structure” , as described for the recently reported S1-1 nuclear domains [43] . We therefore analyzed co-localization with an anti-S1-1 antibody as a marker of this nuclear domain , and we found that the HOXA1 signal co-localized with both the SC35 and the S1-1 staining ( Figure S6B ) . Since nuclear speckles and S1-1 domains have been suggested to be functionally connected [43] , it is possible that HOXA1 could traffic between these two subnuclear domains . The dependence on the polyHis segment for this dynamic behavior was analyzed using a HOXA1 mutant protein in which the His-repeat was eliminated . Accordingly , there was no change in the subcellular distribution of this mutant protein when cells were exposed to α-amanitin ( Figure 7C ) . HOXA1-dependent reporter assays confirmed that deletion of the His-repeat did not abolish the transcriptional activity of this transcription factor ( Figure 7D ) , suggesting that the mutation affected specifically the subnuclear localization of the protein . We also analyzed the effect of RNA polymerase II inhibition on three polyHis-containing proteins considered to be cytosolic: the negative regulator of the Wnt-canonical pathway NKD2; the mitotic kinase PLK2; and the PRICKLE family member PRICKLE3 ( also known as LMO6 ) . Both NKD2 and PLK2 remained in the cytoplasm under basal conditions and upon exposure to α-amanitin ( not shown ) . However , exposure to this inhibitor produced the translocation of a proportion of PRICKLE3 to the nucleus , where it co-localized with SC35 ( Figure 8A ) . Incubation with leptomycin B , an inhibitor of CRM1-dependent nuclear export , caused the relocalization of PRICKLE3 to the nucleus ( Figure 8B ) , indicating that it is a shuttling protein and further suggesting that its targeting to nuclear speckles may be linked to the yet unknown role of PRICKLE3 within the nucleus . Leptomycin B treatment induced accumulation of PRICKLE 3 in PML bodies ( Figure S7 ) . The results of the analysis of the subcellular localization of several polyHis-containing nuclear proteins are summarized in Table 1 and notably , 15 out of 22 of these proteins displayed nuclear staining compatible with their accumulation in nuclear speckles . Thus , proteins with His-repeats seem to localize dynamically in the splicing factor compartment .
The mammalian nucleus is a highly complex organelle that is both physically and functionally compartmentalized ( for review , see [22] , [47] ) . The subnuclear structures are associated with specific biological activities related to the synthesis , processing and modification of RNA , and they can be distinguished by morphological criteria and the presence of specific protein markers . One such compartment is that of the nuclear speckles . The mechanisms responsible for the formation and regulation of these structures are not yet known and as for many other nuclear bodies , it has been proposed that they are highly dynamic self-organizing entities [48] . A rapid exchange of protein components between subnuclear compartments has been reported , which can be explained by a reaction-diffusion model [49] . However , the kinetics associated to a particular protein can be affected by its binding to other molecules , either proteins or nucleic acids , which in turn can aid its recruitment to a specific compartment . Accordingly , a few protein domains have been described that direct proteins to nuclear speckles , such as the arginine/serine-rich ( RS ) -domain in SR proteins [50] or the RNA recognition motif [51] . Other regions in specific proteins have also been reported to act as speckle-localizing sequences , like the threonine-proline repeats in SF3B1/SF3b155 [52] and the “Forkhead Associated” domain in PPP1R8/NIPP1 [53] . We previously showed that the His-tract in the DYRK1A protein kinase and the regulator of transcription cyclin T1 [20] , [21] is responsible for the accumulation of these proteins in nuclear speckles . Given that the functions of many of the polyHis-containing proteins were related to DNA and RNA metabolism , it was plausible that this role as a subnuclear targeting signal could be more general in other proteins . Indeed , a significant proportion of the polyHis-containing proteins analyzed have the ability to accumulate in nuclear speckles either at the steady-state or upon transcription inhibition . This targeting may respond to the nature of nuclear speckles as sites of storage , recycling and degradation of factors involved in DNA and RNA metabolism [22] , [54] . The uneven distribution found among different speckle-positive His-repeats-containing proteins is also observed among splicing factors that accumulate in speckles for instance [22] , [26] , and could reflect differential binding affinities for distinct targets within the nucleus . Importantly , accumulation in nuclear speckles is dependent on the presence of the His-tract , as confirmed by both deletion analysis in some candidate proteins and by the behavior of paralogous proteins lacking the His-repeat . Apart from the previously mentioned DYRK1A and cyclin T1 [20] , [21] , only HOXA9 had already been reported to accumulate in nuclear speckles of unknown nature [55] . Given that our analysis was performed by transient transfection of plasmids expressing the candidate proteins fused to GFP , we tried to rule out non-physiological effects due to overexpression . This is particularly relevant since expanded homopolymeric tracts , including polyHis expansions , have been associated with protein aggregation [16] , [31] , [56] . As a cellular defense mechanism against protein misfolding and aggregation , protein aggregates are thought to be sequestered in inclusions that also contain molecular chaperones and components of the ubiquitin proteasome system [57] . We did not detect any co-localization of candidate proteins with an anti-ubiquitin antibody ( Figure S8 ) , suggesting that the speckled staining was not produced by the formation of intranuclear protein aggregates . In addition , no cytosolic granules were detected ( Figure S1 and S2 ) , in contrast with results published with longer His tracts ( 26 His residues; [31] ) . We also analyzed the behavior of a stably expressed polyHis-containing protein ( DYRK1A ) fused to GFP during the cell cycle . Nuclear speckles disassemble when cells enter mitosis and the proteins associated with them become diffusely distributed throughout the cytoplasm [58] . As shown in Figure S9 , the fusion protein totally recapitulated these changes during the cell cycle indicating that poly-His expression does not interfere with the intrinsic dynamics of the compartment . As additional support for the specificity of the subcellular localization , we did not detect an accumulation of the GFP-9xHis chimera in other subnuclear compartments and there was no colocalization with different marker proteins or any specific accumulation in the cytoplasm of the transfected cells , suggesting that the fusion protein is not recruited to a specific cytosolic organelle . It seems most likely that the His-repeat acts as a nuclear speckle-targeting signal by serving as an interaction surface for resident molecules in the speckle . The features of His make it a versatile amino acid , strongly represented in enzymatic and binding activities . Histidine's imidazole side-chain allows it to shift from a neutral to positive charge in a pH-dependent fashion , a property that may have an impact on the binding capabilities of a His-stretch . Moreover , the presence of His in a β-strand provides a charge gradient that could mediate protein-protein or protein-DNA via electrostatic interactions . His is also known as an excellent ligand to coordinate metal ions [17] , which can also participate in organizing interacting domains . All these mechanisms may contribute to finely regulate the binding properties of His-repeats . Examples of His-stretches as protein-protein interacting domains can be found in cyclin T1 when interacting with RNA polymerase II and granulin [18] , [19] , and DYRK1A interacting with Sprouty2 [59] . The ability of His-tracts to target proteins to the nuclear speckles compartment seems to be specific to His since other homopolymeric amino acid tracts do not display such activity according to our results ( 9xGln and 9xPro as GFP fusions; 13xAla in NLKΔHis , 16xGly and 7xSer in POU4F2ΔHis ) and those published for longer amino acid tracts [31] . Speckle-positive His-repeats vary from simple amino acid runs ( for instance , H10 in HOXA1 ) to complex repeats ( HPSNH5NH2SHKHSH in cyclin T1 ) , suggesting that the number of His residues is not decisive for its functional role but rather , the spacing between residues may be important . We failed to find a specific code underlying targeting to nuclear speckles , except that a minimum of 6 His residues is required for this effect , which indicates a high degree of flexibility in this functional signal . Considering that His-repeats are widely used as tags for affinity-purification and immunodetection of expressed proteins , we would like to stress the fact that more than 6 His residues may alter the original localization of a tagged protein . Only 22% of SARPs have paralogous proteins [60] , whereas a large fraction of the genes encoding proteins with His-repeats have closely-related paralogues . We found that many of them were derived from gene duplications at the base of vertebrate evolution , when two rounds of whole-genome duplication took place [61] . Interestingly , most of the paralogues lacked the His-repeat , suggesting that this repeat had been gained after the duplication of the gene . Further analysis of the distribution of these repeats revealed that they were gained during two periods of vertebrate evolution: soon after gene duplication or before placental mammal radiation . The comparison of the subcellular distribution of three pairs of paralogous proteins , FAM76B/FAM76A , DYRK1A/DYRK1B and POU4F2/POU4F3 , confirmed that only those members containing His-repeats localized to nuclear speckles . Notably , in approximately 30% of the duplicate gene pairs derived from the S . cerevisiae whole-genome duplication event , the two protein members of the pair localize to distinct subcellular compartments [62] . This and other evidence led to the proposal that protein subcellular relocalization might be an important evolutionary mechanism for the functional diversification of duplicate genes [63] . Therefore , the appearance of a new repeat , or variations in the length and composition of an existing one , may have been an important mechanism for functional diversification . The acquisition of a new His-repeat might have contributed to the reorganization of protein-protein interaction networks and more specifically , to nuclear speckle targeting as a novel cell property associated to the paralogous protein . This might be relatively rapid on an evolutionary time scale because of the high mutation rates associated with microsatellites [64] . In fact , the expansion and contraction of repeats within transcription factors has been linked to major morphological changes in vertebrates [65] , [66] . Given that a high proportion of the polyHis-containing proteins have roles in developmental processes , mutations involving His-repeats may have played a significant part in diversification and adaptation . Several of the His-containing proteins that did not accumulate in nuclear speckles were transcription factors . The fact that these proteins contain domains that may control their specific localization within the nucleus , such as DNA binding regions or protein-protein interaction domains , led us to think that competition between His-repeats and other protein regions might regulate their intranuclear distribution . Our results show a direct correlation between loss of DNA binding activity and accumulation in nuclear speckles . Similar behavior was recently described for the transcription factor GATA-4 , although the subnuclear compartment to which it localized was not identified [67] . Although we cannot ignore that the elimination of the DNA binding domains may result in a conformational change that exposes the His-repeat , we favor a loss of retention in the chromosomal compartment as being responsible for the enrichment in nuclear speckles . This assumption is supported by the results with inhibitors of RNA polymerase II-dependent transcription , since treatment with α-amanitin caused re-localization to nuclear speckles of many of the proteins with a dispersed nuclear distribution under basal conditions . In this regard , we noted that the subgroup of proteins unable to accumulate in nuclear speckles was enriched in proteins with more than one DNA binding domain , a feature that may confer a more immobile character to these proteins . Thus , we propose that the intranuclear localization of some transcription factors with His-repeats is the net result of competition for binding to different recruiting sites within the nucleus , such as DNA , nuclear speckles or other nuclear bodies . Moreover , this dynamic behavior might also explain why among the proteins listed in Table 1 , only OTX1 appeared in a proteomic analysis of enriched preparations of interchromatin granule clusters [24] . Such a proteomic analysis would not consider proteins present in low amounts and/or proteins that are transiently found in such structures . It is widely accepted that RNA processing occurs co-transcriptionally and thus , there is a co-localization of factors related to RNA biogenesis , such as transcription and splicing factors [68] . When needed , transcription factors are recruited to specific promoters in active transcription sites whereas splicing factors are assembled into the spliceosome . During transcriptionally inactive periods , the splicing factors re-locate to the speckle domains , and some transcription factors might also behave similarly . Transit through the speckles may provide the opportunity for transcription factors to encounter RNA processing factors and/or other transcription factors , and to assemble into complexes acting on the same gene . This re-localization may also involve the targeting of transcription factors no longer able to bind DNA to other compartments for degradation or other processing activities 54 , 69 . In addition , compartmentalization of transcription-related proteins within distinct nuclear bodies may be an important mechanism to regulate gene expression . For instance , the inactivation of the transcription factor HAND1 by nucleoli retention has been implicated in trophoblast stem cell proliferation and renewal [70] , and estrogen receptor-enhanced transcription requires interchromosomal interactions at nuclear speckles [71] . The presence of a common sequence to direct a subset of proteins to nuclear speckles , such as the His-repeats , may confer functional advantages . First , it may represent a way to concentrate functionally related proteins , perhaps facilitating their physical interaction . Second , it may reflect a common mechanism to regulate these proteins . Indeed , given that most of the polyHis-containing proteins are involved in developmental processes , His-repeats may be a means of keeping transcription factors away from promoters when they are not required . Uncontrolled expansion of SARs is associated with developmental and neurodegenerative human diseases ( for review , see [2] , [11] , [13] ) , although no pathological His expansions/deletions have yet been reported . However , variants in the length of the His-repeats in the HOXA1 protein have been described in the Japanese population [56] , and the expression of these variants compromised HOXA1 function in neuronal differentiation [72] . Furthermore , a polyHis polymorphism in ZIC2 is apparently associated with neural tube defects [73] . Intriguingly , no homozygous cases of expansions have been found in either of these genes . On the basis of these data , and considering that some polyHis-containing proteins are fundamental for essential developmental processes , variation in His-repeats would be expected to contribute to human disease .
An in-house Perl computer program was used to identify all human proteins containing a tandem His-repeat of 5 residues or more from Ensembl ( version 48 , http://www . ensembl . org/ , [32] ) . When more than one protein per gene existed , we selected the longest of these . One to one orthologous proteins from mouse , as well as human paralogous genes , were identified using Ensembl Biomart annotations . The paralogous gene analysis was restricted to those genes derived from duplication events at the Euteleostome or more recent levels , since these homologues were sufficiently similar to produce reliable alignments . The procedure used to map equivalent repeats in two homologous sequences has already been described [82] . Briefly , for each repeat found in a sequence , we determined whether an equivalent repeat existed in the orthologous sequence by looking for His-repeats that overlapped with the reference repeat in the pairwise protein sequence alignment available from Ensembl . Non-coding tandem CAY ( CAC/CAT ) repeats were recovered from the non-protein coding parts of the genome ( goldenpath 200603 ) . We obtained all available Gene Ontology annotations ( GO , http://www . geneontology . org/ , [33] ) for human genes from Ensembl ( 18 , 086 genes ) . The number of genes annotated with the terms ‘nucleus’ , ‘cytoplasm’ ( excluding those also annotated as ‘nucleus’ ) and ‘membrane’ ( excluding those also annotated as ‘nucleus’ and/or ‘cytoplasm’ ) in the cellular compartment classification were counted . In the complete dataset , 4634 genes were annotated as ‘nucleus’ , 191 as ‘cytoplasm’ and 4257 as ‘membrane’ . Out of 82 polyHis-containing proteins with GO annotations , 59 were annotated as ‘nucleus’ , 2 as ‘cytoplasm’ and 7 as ‘membrane’ . Several terms related to transcriptional regulation and to developmental processes were particularly abundant among the proteins with His-repeats . To avoid redundancy in the functional analysis , three groups of nuclear proteins were selected: 1 ) genes with GO annotations related to nervous system development ( ‘nervous system development’ , ‘central nervous system development’ , ‘brain development’ , ‘hindbrain development’ , ‘forebrain development’ , ‘midbrain development’ and ‘dendrite development’ ) ; 2 ) genes with GO annotations related to other developmental processes ( terms containing ‘development’ not included in the previous class ) ; and 3 ) genes with the GO annotation ‘regulation of transcription’ ( and not included in the two previous classes ) . In the complete dataset , 142 genes were included in the first class , 585 in the second class and 1829 in the third . Among polyHis-containing genes , 13 genes were included in the first class , 12 in the second class and 19 in the third class . To detect any statistical differences in the distribution of the repeat sizes we used the non-parametric Kolmogorov-Smirnov test . To detect over-represented GO terms we used the binomial probability . Statistical analyses were performed with the R statistical package ( http://www . r-project . org/ ) . The expression plasmids encoding GFP-tagged human DYRK1A ( 754 amino acid isoform; pGFP-DYRK1A ) has been described [20] . The plasmid expressing GFP fused to the DYRK1A fragment 378–616 ( H+ ) was obtained by in-frame subcloning of the appropriate PCR fragment into pEGFP-C1 ( Clontech ) . Expression plasmids for DYRK1B ( pGFP-DYRK1B , [74] , SC35 ( pYFP-SC35 , [75] , POU4F1 ( pTS-Brn3a , [76] , cyclin T1 ( pMyc-Cyclin T1 , [19] , NKD2 ( pGFP-NKD2 , [77] , and CBX4 ( pGFP-CBX4 ) were kindly provided by W . Becker ( Aachen University , Germany ) , D . Spector ( Cold Spring Harbor Laboratory , Cold Spring Harbor , USA ) , E . Turner ( Department of Psychiatry , University of California , USA ) , M . Peterlin ( Howard Hughes Medical Institute , University of California , USA ) , C . Li ( Department of Medicine , Vanderbilt University Medical Center , USA ) , and S . Aznar-Benitah ( Centre for Genomic Regulation-CRG , Spain ) , respectively . To generate the plasmids expressing the different GFP fusion proteins , the corresponding open reading frames were PCR amplified with specific primers using IMAGE Consortium cDNA clones as templates ( http://image . llnl . gov/ , [78] ) . The identification number of the IMAGE clones and the sequence of the primers used are listed in Table S3 . All the IMAGE clones were purchased from the RZPD German Resource Center for Genome Research . Details of the generation of all constructs used in this study are provided in the Supporting Materials and Methods ( Text S1 ) . Plasmid pG4-HOXA1 was constructed by fusing the nucleotide sequence corresponding to the HOXA1 open reading frame in-frame with the yeast Gal4 DNA binding domain ( DBD ) in pG4-DBD [79] . To obtain plasmids expressing 5xHis , 6xHis , 7xHis , 8xHis and 9xHis or 9xPro and 9xGln protein segments fused to GFP , double stranded oligonucleotides ( Table S4 ) were annealed and ligated into the BglII and EcoRI sites of the pEGFP-C1 expression vector . Deletion of His-repeats was performed by site-directed mutagenesis ( Stratagene ) on pHA-DYRK1A , pFlag-POU4F2 , pGFP-NLK , pGFP-HOXA1 and pG4-HOXA1 . All plasmids generated by PCR , as well as all the in-frame fusions , were verified by DNA sequencing . The U2-OS , HeLa , CV-1 and HEK-293 cell lines were maintained at 37°C in Dulbecco's Modified Eagle's Medium supplemented with 10% fetal calf serum ( FCS ) and antibiotics . Transient transfections were performed using the calcium phosphate precipitation method and the cells were processed 24–48 h after transfection . For the generation of stable cell lines , transfected U2-OS cells were selected by incubation with G418 ( 500 µg/ml; Gibco-Invitrogen ) for 10 days and the clones derived from a single cell were isolated . Cell lines were maintained in G418 ( 250 µg/ml ) . Treatment of HeLa cells with RNA polymerase II inhibitor , α-amanitin ( 50 µg/ml; Sigma ) and with the CRM1-dependent export inhibitor leptomycin B ( 10 ng/ml; Sigma ) was carried out for 5 h at 37°C . HeLa cells ( 7×105 ) growing on coverslips in six-well dishes were transfected with the different expression constructs and 48 h after transfection , the coverslips were washed in cold phosphate buffered saline ( PBS ) , fixed in 4% paraformaldehyde in PBS for 15 min , and permeabilized in 0 . 1% Triton X-100 in PBS for 10 min . For ubiquitin detection , the cells were fixed in methanol for 2 min at −20°C , and they were then blocked with PBS-10% FCS for 30 min and incubated with primary antibodies for 1 h at room temperature . After washing extensively with PBS-1% FCS , the coverslips were incubated with the secondary antibodies for 45 min at room temperature , washed repeatedly with PBS-1% FCS , and mounted onto slides using Vectashield Mounting Medium ( Vector Laboratories ) plus 0 . 2 µg/ml 4′ , 6-diamidino-2-phenylindole ( DAPI ) or TO-PRO-3 ( Molecular Probes ) . Images were acquired with an inverted Leica SP2 Confocal Microscope and GFP was excited with the 488 nm line of the Argon laser while IgG Alexa 647 was excited with a 633 nm HeNe laser . The following antibodies were used as primary antibodies: monoclonal anti-SC35 antibody ( BD Pharmigen , 1∶100 ) , monoclonal anti-ubiquitin antibody ( P4D1 , Santa Cruz Biotechnology , 1∶50 ) , rabbit polyclonal anti-DYRK1A antiserum ( [80] 1∶250 ) , rabbit polyclonal anti-PML antiserum ( Santa Cruz Biotechnology , 1∶100 ) , mouse monoclonal anti-SUMO1 antibody ( Santa Cruz Biotechnology , 1∶100 ) , rabbit anti-PSP1 antiserum ( Dundee Cell Products , 1∶500 ) , rabbit polyclonal anti-S1-1 antiserum ( a kind gift of Dr . A . Inoue , [Osaka City University Graduate School of Medicine , Osaka , Japan]; [43] ) and goat polyclonal anti-POU4F2 antiserum ( Santa Cruz Biotechnology , 1∶1000 ) . The secondary antibodies used were an Alexa 647-conjugated goat anti-mouse ( Molecular Probes , 1∶400 ) , an Alexa 555-conjugated donkey anti-mouse ( Invitrogene , 1∶400 ) , an Alexa 488-conjugated donkey anti-goat ( Molecular Probes , 1∶400 ) , an Alexa 555-conjugated goat anti-rabbit ( Molecular Probes , 1∶400 ) and fluorescein isothiocyanate conjugated goat anti-rabbit ( Southern Biotechnology , 1∶400 ) . Transfected HEK-293 cells ( 2×106 ) were lysed in Hepes lysis buffer ( 50 mM Hepes pH 7 . 4 , 150 mM NaCl , 1% NP-40 , 2 mM EDTA , 2 mM NaVO4 , 30 mM NaPPi , 25 mM NaF ) supplemented with a cocktail of protease inhibitors ( Roche ) . Soluble extracts were immunoprecipitated either with anti-HA ( Abnova ) or anti-GFP ( Molecular Probes ) antibodies . Immunocomplexes were washed twice with kinase buffer ( 50 mM Hepes pH 7 . 4 , 5 mM MgCl2 , 5 mM MnCl2 , 0 . 5 mM DTT ) and incubated in 30 µl of kinase buffer with 10 µM ATP and [g32P]-ATP ( 6 . 5×10−3 µCi/pmol ) for 20 min at 30°C . For DYRK1A , kinase activity was followed by phosphate incorporation on the synthetic peptide DYRKtide ( 200 µM ) in a liquid scintillation B-counter ( Beckman Coulter ) as described previously [80] . For NLK , the reaction was stopped by adding 2× loading sample buffer and the samples were resolved by SDS-PAGE . 32P incorporation was detected by autoradiography of the dried gels . For the POU4F2-dependent reporter assay , CV-1 cells ( 1×105 ) were seeded in 35-mm dishes . The cells were transfected with a luciferase reporter plasmid driven by the minimal prolactin promoter plus 3 repeats of the POU4 family recognition site ( pGL2-3xBrn3a , kindly provided by E . Turner; [81] ) together with pFlag-POU4F2 or pFlag-POU4F2ΔHis and a β-galactosidase expressing plasmid as an internal control . For HOXA1-dependent reporter assays , cells were transfected with the pG5E1B-luc reporter ( luciferase is driven by five repeats of the synthetic Gal4-binding sites introduced upstream of the minimal adenovirus E1b promoter; [79] ) together with pG4-HOXA1 or pG4DBD-HOXA1ΔHis encoding chimeras of HOXA1 proteins fused at their N termini to the Gal4 DBD . A Renilla luciferase plasmid ( pCMV-RNL , Promega ) was used as an internal control . Cells were lysed 48 h post-transfection and the activity of both luciferase enzymes was measured with the Dual-Luciferase Reporter Assay kit ( Promega ) . Each transfection was carried out in triplicate .
|
Single amino acid repeats are common in eukaryotic proteins . Some of them are associated with developmental and neurodegenerative disorders in humans , suggesting that they play important functions . However , the role of many of these repeats is unknown . Here , we have studied histidine repeats from a bioinformatics as well as a functional point of view . We found that only 86 proteins in the human genome contain stretches of five or more histidines , and that most of these proteins have functions related with RNA synthesis . When studying where these proteins localize in the cell , we found that a significant proportion accumulate in a subnuclear organelle known as nuclear speckles , via the histidine repeat . This is a structure where proteins related to the synthesis and processing of RNA accumulate . In some cases , the localization is transient and depends on the transcriptional requirements of the cell . Our findings are important because they identify a common cellular function for stretches of histidine residues , and they support the notion that histidine repeats contribute to generate evolutionary diversification . Finally , and considering that some of the proteins with histidine stretches are key elements in essential developmental processes , variation in these repeats would be expected to contribute to human disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"computational",
"biology/sequence",
"motif",
"analysis",
"cell",
"biology/nuclear",
"structure",
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"function",
"genetics",
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"genomics/functional",
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2009
|
Genome-Wide Analysis of Histidine Repeats Reveals Their Role in the Localization of Human Proteins to the Nuclear Speckles Compartment
|
Dengue is an increasing public health concern in Brazil . There is a need for an updated evaluation of the economic impact of dengue within the country . We undertook this multicenter study to evaluate the economic burden of dengue in Brazil . We estimated the economic burden of dengue in Brazil for the years 2009 to 2013 and for the epidemic season of August 2012- September 2013 . We conducted a multicenter cohort study across four endemic regions: Midwest , Goiania; Southeast , Belo Horizonte and Rio de Janeiro; Northeast: Teresina and Recife; and the North , Belem . Ambulatory or hospitalized cases with suspected or laboratory-confirmed dengue treated in both the private and public sectors were recruited . Interviews were scheduled for the convalescent period to ascertain characteristics of the dengue episode , date of first symptoms/signs and recovery , use of medical services , work/school absence , household spending ( out-of-pocket expense ) and income lost using a questionnaire developed for a previous cost study . We also extracted data from the patients’ medical records for hospitalized cases . Overall costs per case and cumulative costs were calculated from the public payer and societal perspectives . National cost estimations took into account cases reported in the official notification system ( SINAN ) with adjustment for underreporting of cases . We applied a probabilistic sensitivity analysis using Monte Carlo simulations with 90% certainty levels ( CL ) . We screened 2 , 223 cases , of which 2 , 035 ( 91 . 5% ) symptomatic dengue cases were included in our study . The estimated cost for dengue for the epidemic season ( 2012–2013 ) in the societal perspective was US$ 468 million ( 90% CL: 349–590 ) or US$ 1 , 212 million ( 90% CL: 904–1 , 526 ) after adjusting for under-reporting . Considering the time series of dengue ( 2009–2013 ) the estimated cost of dengue varied from US$ 371 million ( 2009 ) to US$ 1 , 228 million ( 2013 ) . The economic burden associated with dengue in Brazil is substantial with large variations in reported cases and consequently costs reflecting the dynamic of dengue transmission .
Dengue is a viral infection transmitted by Aedes mosquitoes , with global distribution , mainly in the tropical regions [1] . Infection with one of the four antigenically distinct dengue serotypes ( serotypes 1 , 2 , 3 , and 4 ) is often asymptomatic/inapparent or mildly symptomatic , but has the potential to escalate to dengue fever and subsequently , to life-threatening dengue hemorrhagic fever or dengue shock syndrome , and death . Although life-long immunity to the infecting serotype may develop , the more severe or life-threatening cases of dengue are more often associated with subsequent secondary infection by heterologous dengue serotypes [1] . Contemporary global estimates from the World Health Organization suggest that 50–100 million dengue infections occur annually [2] . A more recent estimate , based on cartographic modeling approaches and data from various published sources between 1960 and 2012 , suggests that there are about 390 million dengue infections per year with 96 million apparent/symptomatic cases of the disease [3] . Although the majority of dengue infections occur in Asia [3] , there has been a dramatic increase in the number of reported dengue cases in the Americas over the last decade [4–6] . Over 50 million dengue infections were estimated ( using cartographic modeling approaches ) in the Americas in 2010 , and of these , about 40% ( 21 . 8 million infections ) occurred in Brazil [3] . Recent surveillance data from the Brazilian national notifiable diseases information system ( SINAN; Sistema de Informacao de Agravos de Notificacao ) showed that there were more than 2 million dengue cases reported in 2013 , the highest annual incidence registered in Brazil since dengue surveillance was implemented in the 1980´s [7] . The increase in the incidence of dengue was probably due to the introduction of dengue serotype-4 along with the rapid spread and co-circulation of the other serotypes ( serotypes 1 , 2 , and 3 ) . In addition , the reporting rates may have increased due to higher dengue awareness among the population and the health workers . Dengue can impose a significant economic and humanistic burden in countries where the disease is endemic and , as such , estimating the associated economic and disease burden can help inform policy makers and assist them in setting priorities for disease-management strategies and for the introduction of new technologies [8] . To date , there has been one previous cost evaluation of the economic burden of dengue episodes in Brazil ( undertaken only in the Midwest region , city of Goiania in 2005 ) , which was part of an international study that included five countries in the Americas ( Brazil , El Salvador , Guatemala , Panama , and Venezuela ) [9] , and was subsequently updated as part of a later study [10] . These previous evaluations , however , estimated the cost of dengue cases without taking into account seasonal fluctuation or costs associated with dengue outbreaks . Nonetheless , the estimated cost of dengue illness across the Americas between 2000 and 2007 was substantial , at US$2 . 1 billion per year ( 2010 US$ ) , with the majority of costs associated with ambulatory cases rather than hospitalized cases and with substantial year to year variation [10] . Brazil accounted for about 40% ( US$878 . 2 million ) of the total costs in the Americas . Since these publications , recommendations and guidelines have been developed for estimating the burden and socio-economic costs of dengue [11–13] . There is marked variation in dengue transmission across time and by serotype circulation , as previously published [14–18] . As such , there is a need for an updated evaluation of the economic impact of dengue in Brazil that also includes more regions within the country and adheres to the new guidelines and recommendations . The Brazilian health system is a complex mix of both public and private sectors [19 , 20] . A cost evaluation needs to consider the perspective of both sectors [12] . The aim of this study was to evaluate the economic burden of dengue from the public payer and societal perspectives across four endemic regions in Brazil , and to extrapolate these costs to the whole country .
This was a prospective , multicenter , observational study aimed at measuring the direct and indirect costs associated with dengue illness across six sites in four endemic regions of Brazil . The time horizon of analysis was one year , from September 2012 to August 2013 , which takes account of dengue seasonality . The protocol was approved by each centers’ Ethical Committee and registered at the Brazilian Ethical Office ( PlataformaBrasil: http://aplicacao . saude . gov . br/plataformabrasil ) under number: 94 . 121/2012 . All participants , or their parents/guardians ( for minors aged <18 years ) , provided written informed consent before study entry . We selected six state capitals located in four endemic regions in Brazil: ( 1 ) Midwest: Goiania/Goias State , ( 2 ) Southeast: Belo Horizonte/Minas Gerais State and Rio de Janeiro/Rio de Janeiro State , ( 3 ) Northeast: Teresina/Piaui State , Recife/Pernambuco State and ( 4 ) North: Belem/Para State ( Fig 1 ) . We selected capital cities located in regions of high transmission of dengue infection in the last decade [6] . Capital cities were chosen because they are densely populated areas with the capacity for research development and health staff to perform the fieldwork in a timely manner . We did not include cities in the Southern region as dengue transmission was only concentrated in one area of Parana State . The study population consisted of patients with clinically suspected dengue . Clinically suspected dengue was defined in accordance with the Brazilian dengue guidelines as fever plus two or more of the following manifestations: headache , retro-orbital pain , myalgia , arthralgia , prostration or rash ( with or without the presence of hemorrhage ) [21] . All sites included children ( aged <15 years ) and adults . Residents from other municipalities , individuals unable to respond to an interview request , those who refused to give informed consent and those whose residential address could not be located/accessed were excluded from the study . In five of the six sites , the fieldwork was conducted in 2013 . The other site , Teresina ( Northeast ) , the fieldwork was conducted during 2012 and 2013 . We estimated a sample size of 300 ambulatory patients and 100 hospitalized patients would be required for each site in the original protocol . This estimate was based on the assumption that these numbers were sufficient to sample patients of all age groups and disease severity , and to have sufficient statistical robustness . A minimum of approximately 30 hospitalized patients were to be recruited at the sites with low virus circulation . We contacted all patients with clinically suspected dengue or their care-givers so as to interview them with regard to their illness . Interviews with the patients or their care-givers were planned for two phases . We adopted a questionnaire for use during interviews with the patients or their care-givers identified , which was developed in a previous multi-country cost study [9] , to collect resource use data in public and private ambulatory clinics and hospitals ( see S1 Text ) . This questionnaire helped document: the characteristics of the dengue episode; history of previous dengue illness; date of initial appearance of symptoms/signs and their resolution; use of medical services ( medical visits; medications , laboratory exams ) ; work and school absences; household spending ( out-of-pocket expenses ) ; and income lost ( loss of productivity ) . We augmented the questionnaire used in the previous study by adding questions about medication use , whether dengue was laboratory confirmed , and about laboratory tests and x-ray examinations undertaken . A pilot study was conducted to evaluate the feasibility of the questionnaire and the field strategy before the full investigation . In addition to the interviews , we extracted data from the patients’ medical records for those who were hospitalized to gather resource use information ( hospital stay , laboratory/exam and medication ) . The information collected regarding resource is subject for an accompanying paper . We used the ABEP classification ( Criterio de Classificacao Economica Brasil , CCEB 2012 , available at www . abep . org ) to determine the economic level of the patients or their care-givers . This classification estimates the purchasing power of individuals and families . This index is equivalent to the mean family income per month with 8 economic classes as follows: top level of income [A1 ( US$ 5 , 698 ) ; A2 ( US$ 3 , 711 ) ]; mid-level income [B1 ( US$ 1 , 947 ) ; B2 ( US$ 1 , 131 ) ]; low-level income [C1 ( US$ 679 ) ; C2 ( US$ 451 ) ]; very low-level income [D ( US$ 315 ) to E ( US$210 ) ] . According to the SINAN database , there were 2 , 013 , 274 suspected dengue cases reported between September 2012 and August 2013 ( Table 1 ) . The majority of these reported dengue cases were aged 15 years or older ( approximately 1 . 7 million cases ) , of which , more than 50% of cases were in females . The majority ( 99 . 5% ) of the reported cases were classified as “classical” dengue , 0 . 5% were classified as severe dengue cases ( dengue with complications , dengue hemorrhagic fever and dengue shock syndrome ) . All four dengue serotypes co-circulated in Brazil , with serotype 1 and serotype 4 being the main serotypes detected during the study period . There were 569 deaths due to dengue disease reported in SINAN . Dengue epidemics were reported in two of the study regions during our investigations: the Southeast and Midwest neighboring regions with the cities of Belo Horizonte and Goiania registering dengue incidence rates ≥50 per 1 , 000 inhabitants ( Table 1 ) . The official definition of dengue epidemic by the national surveillance system is when the number of cases exceeds the number of cases of the upper limit relative to the incidence represented by diagram of control in a specific region [22] . It should be noted that SINAN is a passive surveillance system and it is likely that the number of cases would have been under-reported , particularly for non-hospitalized cases , as is the tendency with such surveillance systems [23] . The Brazilian health system consists of a complex public-private mix healthcare delivery/utilization and financing system [20] . The public sector comprises the Unified Health System ( Sistema Unico de Saude—SUS ) and is considered to be a universal system ( open to all ) funded by the government . Citizens have the right to receive preventive measures and treatment , free of charge under this system . The private sector offers health service by direct payment and/or covered by health insurance plans ( Unimed; Plamta , MedPlan , Cassi and others ) . The participation of the private sector in the Brazilian health system is classified as “setor suplementar”/supplementary care [19] . We used a micro-costing , bottom-up approach . The costs of ambulatory or hospitalized cases were calculated as the average cost of each component of direct and indirect cost as shown in S1 and S2 Tables . All costs were initially calculated in the local currency ( Real ) and converted to US dollars ( US$ ) using an average exchange rate ( R$ 1 = US$ 0 . 44079 on November 20 , 2013 ) by OANDA ( www . oanda . com ) . The cost for a medical visit for an ambulatory case in the public sector included the SUS value for one medical visit to the emergency department ( US$ 4 . 85 ) [26] . The inclusion of a medical visit to the emergency department , which is the highest price paid by SUS , was done because the current cost of an ambulatory visit was considered to be undervalued [27] . In the private sector , the unit cost for each medical visit funded from a health insurance plan was taken from the Unimed plan ( US$ 26 . 45 ) since this was the insurer used by the majority of the study population [28] . The direct cost of hospitalized patients included the cost of hospitalization ( hospital stay ) and the cost of ambulatory care for those who sought ambulatory treatment before inpatient hospitalization . We calculated the percentage of hospitalized patients who received previous ambulatory care in each city and added these costs to the hospital costs . For both medication ( http://portal . anvisa . gov . br ) and laboratory tests ( http://sigtap . datasus . gov . br ) the public sector costs were taken from Brazilian Government data . Private sector medication costs were taken from Guia Farmaceutico Brasindice ( January , 2013 ) and laboratory tests from the Associacao Medica Brasileira ( http://www . amb . org . br/_arquivos/_downloads/cbhpm_2012 . pdf ) . We estimated the loss in productivity due to illness and death . The value of lost productivity due to illness took into account the number of work days lost by patients or caregivers during the course of illness . We also included the number of days of school missed by patients ( see S1 and S2 Tables ) . The number of work days lost or school days missed were derived from the household interviews . Loss of income by patient or care-giver was based on the perceived the monetary value of the previous month’s income . The value of lost school or college days took into account the level of education in each state and was based on the annual cost per student of US$ 6 , 612 . 00 ( http://www . jusbrasil . com . br/diarios/DOU/2012/11/19 and http://ultimosegundo . ig . com . br/educacao/educacao+basica+custa+mais+na+particular+superior+na+publica/n1597000724462 . html ) . Data analyses were undertaken using SPSS 17 . 0 , Microsoft Excel 2010 and Package R ( accessed at: http://www . r-project . org/ ) . The unit of analysis was a dengue episode , defined as a symptomatic dengue case with or without laboratory confirmation . We analyzed the patients with dengue by sites , stratified by whether they were ambulatory and hospitalized , in both public and private facilities . We carried out exploratory analysis for continuous variables to evaluate the distribution patterns and outliers . For skewed distributions , we used a non-parametric test ( Kruskal Wallis test ) to compare the number of days in hospital between sites . Descriptive statistics , frequencies , means and standard deviation ( SD ) were calculated to compare costs across sites . We used the chi-square test to compare the distribution of the characteristics of dengue cases between sites and we used residual analysis to identify the significant heterogeneities among sites . The Tamhane post-hoc test was applied for multiple comparative analyses of means with non-homogenous variances . We opted to perform this first analytic approach using means and standard deviations ( SD ) as recommended for cost analyses [29] . To calculate the overall national cost we used the number of cases reported in Brazil and the number of cases reported in the six cities studied during the period ( SINAN data ) . We adopted the ratio of ambulatory:hospital ( 9:1 ) as a parameter to estimate the number of ambulatory and hospitalized cases at the national level ( Siqueira personal communication , 2013 ) . We assumed that 65% of patients attended the public sector in the Southeast and Midwest regions and 75% in the North and Northeast regions [25] . The estimated cost per case was calculated as: the total amount of direct and indirect costs from the dengue episodes calculated in each of the six cities ( primary data ) divided by the number of estimated cases in the six cities of the study . We estimated the overall national cost by multiplying the estimated cost per case by the estimated number of ambulatory and hospitalized cases notified in Brazil between September 2012 and August 2013 ( see S2 Text ) . In our study , five deaths occurred during the study period , all at one site ( Belo Horizonte ) . We estimated the cost of fatal cases at national level using the human capital approach to determine the cost associated with the loss of productivity from these cases [30] . We estimated the cost of fatal dengue as the lost income from premature death [31] . Based on the number of deaths in SINAN database ( 2011 , 2012 and 2013 ) , we estimated the average age at death due to dengue to be 45 years for males and 42 years for females . We calculated the years of life lost and used a standard discount rate of 3% per year . We estimated the remaining life labor expectancy at their age of death considering the minimum age of retirement for males of 65 years and 60 years for males and females , respectively . We multiplied the average discounted years by Brazil gross domestic product ( GDP ) per capita in 2012 ( US$ 10 , 420 . 70 ) . We applied a probabilistic sensitivity analysis to our data using RiskAMP software ( Structured Data LLC 2005 ) . We investigated how variations of ± 50% ( min; max ) affected the best estimate derived from our data by computing 10 , 000 Monte Carlo simulations . We used a beta-PERT distribution and reported the results in terms of certainty level ( CL ) bounds using 10 and 90 percentiles . We varied the mean values obtained by the field study ( direct cost per case , total cost per case ) and then estimated the annual costs of dengue at national level by ambulatory and hospital in both public payer and societal perspectives . Simulation of expansion factors took into account higher variability: EF used for ambulatory cases were 1 . 5; 3; and 6 . We assumed the variation ( 1; 1 . 6; 2 ) for the EF for hospitalized cases . In the latter the simulation includes the EF = 1 . 6 cited in the literature [32] . The estimated cost of dengue in Brazil was considered from two perspectives: public payer and societal . The public payer perspective included only direct costs in the public healthcare setting ( medical visits , medications and laboratory/examinations ) . The societal perspective included direct and indirect costs , weighted by public and private sectors . Two scenarios were evaluated for each perspective ( public payer and societal ) as a sensitivity analysis to capture some of the uncertainty in the number of cases reported in SINAN . In the first scenario ( base case ) , we used official registered dengue cases by SINAN to estimate the cost of dengue in Brazil ( as described above ) . In the second scenario , we applied EFs yielded from sensitivity analysis: for ambulatory cases ( EF = 3 . 2 ) and for hospitalized cases ( EF = 1 . 6 ) in order to adjust for underreporting [32] . We have also extrapolated the dengue burden using temporal series from 2009 to 2013 ( SINAN online database ) . We assumed the same parameters as described above .
Of the 2 , 223 patients that were screened for dengue , 2 , 035 ( 91 . 5% ) symptomatic dengue patients were included in our study ( Fig 2 ) . Patients were excluded if they did not provide signed informed consent or if their residential address could not be located/accessed . The number of dengue cases recruited ranged from 279 in the Southeast and North ( Rio de Janeiro and Belem ) to 415 in the Northeast ( Teresina ) ( Table 2 ) . Approximately 80% of the cases were ambulatory . The difference in the percentage of hospitalized cases between sites was statistically significant ( X2 = 94 . 3; df = 5 , p<0 . 001 ) . The residual analysis showed that the proportions of hospitalized patients were similar in the Northeast and North regions ( Teresina: 14 . 5%; Recife: 12 . 8%; Belem: 18 . 3% ) ; the highest proportions were found in Goiania ( 29 . 5% ) and Belo Horizonte ( 23 . 3% ) ( Table 2 ) . Overall , the adult population ( ≥ 15 years ) constituted around 80% of recruited cases . The proportion of dengue cases in the adult population by site ranged from 64 . 6% in Recife to 87 . 1% in Rio de Janeiro . There was a significant difference in the proportion of dengue cases in the adult population reported between all sites ( X2 = 65 . 4; df = 5 , p<0 . 001 ) . All sites recruited a similar proportion of dengue cases in the age group 0–14 years , except Recife where higher percentage of children and adolescents ( 35 . 4% ) were recruited ( X2 = 3 . 5; df = 5 , p = 0 . 6 ) . The female:male ratio per site was about 3:2 ( Table 2 ) . The cost per ambulatory and hospital dengue case across the four regions , from the societal perspective , stratified by public and private sector and by site are summarized in Table 3 . Direct costs for ambulatory cases varied from US$ 31 ( Rio de Janeiro ) to US$89 ( Belo Horizonte ) in the public sector and constituted the lower share of the total costs for each site except Belo Horizonte . In the private sector , the direct costs varied from US$ 77 ( Recife ) to US$168 ( Goiania ) . Results from the Anova and Kruskall-Wallis tests ( <0 . 05 ) showed significant differences between cities in terms of costs per ambulatory dengue case . For ambulatory patients in the public sector two similar subgroups were identified , one group with lower costs ( Belem , Teresina and Rio de Janeiro ) and the other the higher cost ( Belo Horizonte and Goiania ) , based on the Tamhane post-hoc test . The city of Recife was associated with both subgroups with P value <10% . The total cost for hospitalized patients in the public sector varied from US$238 ( SD 70 ) in Belem to US$479 ( SD 336 ) in Belo Horizonte . In the private sector , the total cost varied from US$ 318 ( SD 164 ) in Belem to US$ 1 , 577 ( SD 1 , 572 ) in Recife . Direct costs constituted the higher share of the total costs for hospitalized cases in both public and private sectors , except for Recife . Two main subgroups of cities were identified according to direct costs for hospitalized dengue patients in the public sector: the lowest direct cost were calculated for three sites ( Belem , Teresina and Recife ) , and highest direct cost for two sites ( Belo Horizonte and Goiania ) , according to the Tamhane post-hoc test . Fig 3 shows the highly-skewed cost data reported for the ambulatory and hospital cohorts stratified by public or private sector . The majority ( approximately 80% ) of the ambulatory cases treated in the public sector incurred costs of less than US$ 220 per dengue episode . Costs associated with ambulatory cases treated in the private sector were also skewed: few patients had costs higher than US$ 440 . For the hospitalized cohort , more than 80% of the dengue episodes cost US$440 or less in the public sector . The higher incidence of dengue during the study period was observed in the Southeast and Midwest region of the country , with more than 50 cases for every 1 , 000 inhabitants ( Table 1 ) . The estimated costs of dengue in Brazil presented in Table 4 took into account the uneven distribution of cases between regions as well as differences in dengue management costs . The adjusted direct costs ( observed costs weighted by the number of reported cases for each site ( see S2 Text ) of a dengue episode for ambulatory and hospitalized cases was US$ 64 ( 90% CL: 48–80 ) and US$ 237 ( 90% CL: 177–297 ) , respectively . Extrapolating our estimates to national surveillance system data , we estimated the annual total cost for dengue at US$ 164 million ( 90% CL: 123–205 ) from the public payer perspective . This figure increases to an estimated US$ 447 million ( 90% CL: 335–559 ) when we adjusted for underreporting ( EF = 3 . 2 for ambulatory cases and EF = 1 . 6 for hospitalized cases ) . From the societal perspective , we estimated the annual total cost for dengue at US$ 404 million ( 90% CL: 301–508 ) or US$ 1 , 147 million ( 90% CL: 885–1 , 445 ) , the latter figure also estimated with EFs for possible under-reporting ( Table 4 ) . These annual total estimated cost for dengue correspond to per capita costs of US$ 6 . 7or US$ 19 . 0 ( with EFs ) from the societal perspective based on the labor force population ( 60 . 2 million , IBGE 2011 ) . In Brazil , there were an average 529 deaths due to dengue and an average of 944 , 733 notified dengue cases ( SINAN , 2011–2013 ) , with a fatality rate of 0 . 056% ( 90% CL: 0 . 051–0 . 061 ) . At the national level , the total cost for fatal a dengue episode was approximately US$ 65 million ( 90% CL: 48–81 ) ; US$ 34 million for males and US$ 31 million for females . We estimated the cost of a fatal dengue case as US$ 122 , 477 . 41 ( Table 4 ) . Fig 4 shows the reported dengue cases and the burden of dengue from 2009 to 2013 . In this five years period , the reported dengue cases was the lowest ( 409 thousand cases ) in the year 2009 , increased to more than 1 million cases in the epidemic year of 2010 and reached 1 . 4 million in the year 2013 . Therefore , the total costs depends upon the number of reported dengue cases and deaths , varied from US$ 371 million ( 2009 ) up to US$ 1 , 228 million ( 2013 ) .
Our study is the first multi-center study to assess the cost of dengue illness across four dengue-endemic Brazilian regions , taking into account ambulatory and hospitalized cases in both the public and private sectors . The total economic burden of dengue in Brazil from a societal perspective was estimated at US$ 468 million ( 90% CL: 349–590 ) ( with no adjustment for underreporting ) , of which , the majority ( 67% ) was associated with ambulatory cases . Indirect costs ( i . e . lost productivity ) constituted the higher share of the total costs for ambulatory cases in both public and private sectors , consistent with a previous cost evaluation of the economic burden of dengue episodes in Brazil [10] . Moreover , higher total costs for ambulatory cases were observed in wealthier regions in our study which might have been due to , for example , more laboratory tests and lost productivity . From the public payer perspective , the total economic burden was estimated at US$ 164 million ( 90% CL: 123–205 million ) ( with no adjustment for underreporting ) . We adopted a similar protocol to a previous cost evaluation of the economic burden of dengue episodes in Brazil , which included the city of Goiania , as part of an international cost of dengue study that included five countries in the Americas [9 , 10] . However , the previous study applied unit cost for health services using only private sector values [9] . Therefore , the costs per case for ambulatory and hospitalized cases were higher in the earlier study compared to our results . Our data clearly show higher costs in the private sector compared to the public sector for both ambulatory and hospitalized cases . However , the estimates for the public sector ambulatory visit may be conservative even when applying the highest value of the medical visit reimbursed by the Unified Health System ( SUS ) . Our study is at least , in part , consistent with a recent retrospective cross-sectional census study by Vieira Machado et al ( 2014 ) on hospitalized dengue patients in the public and private Brazilian health sectors in Dourados City , Mato Grosso do Sul State [33] . The latter study reported direct mean medical costs per hospitalized dengue case in the public sector of US$ 510 ( SD , 1 , 135 ) ( values adjusted for inflation for the year 2013 ) , compared with US$ 198–376 in our study . Vieira Machado et al also reported higher mean direct medical cost of US$ 1 , 193 ( SD , 2 , 701 ) ( values adjusted for inflation for the year 2013 ) per hospitalized dengue case in the private sector relative to the public sector , compared with US$ 318–906 in our study . Of note , their study was based on a sample of 288 laboratory-confirmed dengue cases , whereas our study was based on a much broader case definition that included 2 , 035 suspected dengue cases . In addition , we recruited cases from different geographic areas in Brazil from the study conducted by Vieira Machado et al . The current economic evaluation was conducted concurrently with major dengue outbreaks in the Southeast and Midwest regions ( Belo Horizonte and Goiania ) , in the period August 2012 September 2013 . Approximately 100 , 000 probable dengue cases were registered in the city of Belo Horizonte in the Southeast region and about 56 , 000 cases in Goiania in the Midwest region . However , there was a decrease in the incidence of dengue cases and hospitalizations reported in the city of Rio de Janeiro compared to previous years . In addition , the cities of Belem ( North ) , Recife and Teresina ( Northeast ) had a reduction in both incidence and hospitalizations for dengue . As such , we evaluated the economic burden in areas that included both dengue outbreaks and low virus circulation . This approach , including settings with high and low virus transmission , is recommended by the dengue guidelines for cost evaluation [12] . Our study has both strengths and weaknesses that need to be considered when making generalizations for the whole of Brazil . A strength of our economic burden study is that we included several types of health facilities in both private and public sectors across four dengue-endemic regions in Brazil in order to provide more representative cost estimates of the population assessed . Moreover , the cases recruited to our study had a similar gender and age distribution across the regions , and were in line with data obtained in the official notification system for Brazil , suggesting that they were representative of the population in those regions . They also had similar income level distributions to the general population in their region according to census data . Another strength was that we designed our study in line with current guideline recommendations [12] . The main limitation of our study is that the total cost depends largely on number of dengue cases registered in the passive surveillance system in Brazil ( SINAN ) . Other drawbacks of using passive surveillance systems in economic burden studies include: selection bias; data may not be well suited for economic study; data gathered for other purposes; and inherent underlying confounding factors . Nonetheless , passive surveillance systems are a readily available source of data that is representative of the national sample of suspected dengue cases and as such , maximizes external validity . It is also well known , that the number of symptomatic dengue cases and its geographical location , like most vector-borne disease , varies each year . As such , the ambulatory:hospitalized ratio would vary according to the burden of the disease during dengue outbreaks or periods of low virus transmission rates . The ambulatory:hospitalized case ratio could also be affected by the implementation of temporary health structures ( tents or stabilization wards ) during outbreaks , as observed in Belo Horizonte in 2013 [34] . In addition , our study focused on the economic burden of dengue in urbanized areas of Brazil , and as such , may not be extrapolated to less-urbanized areas . The cost estimates obtained in our study pertain to a one year study period and outbreaks were reported in some specific capital cities . There was no concerted effort to estimate the costs associated with dengue outbreaks in this study . To fully cost dengue outbreaks , we would have needed to account for the various stages of the outbreaks—before , during and after an outbreak . For indirect costs we estimated only the loss of income of patients who perceived had a monetary income in the previous month . For example , if the dengue patient was a housekeeper and did not perceive a monetary income , their illness would still present an economic loss to the economy but would likely not have been captured in our analysis and thus would have led to an underestimation of indirect costs . Finally , it was beyond the scope of the present study to include cost components related to preventive strategies such as vector control , routine activities , recurrent costs , and operational costs , as well as the cost components related to outbreak control , community mobilization and tourism . To place the cost estimates of the epidemic year ( in the current study ) in temporal perspective , we also estimated the global costs of dengue for cases registered in the five years up to 2013 . The historical series of dengue cases reported provides an example of the large annual variation in the number of registered cases , and whether they can be considered epidemic or endemic years . Our results underscore the need for regular national cost evaluation , preferably alongside the surveillance system . However , the under-reporting or the over-reporting of dengue cases by the surveillance system are constant issues raised in the literature and by policy makers . We opted to estimate the cost of dengue for suspected cases instead of the laboratory-confirmed cases in accordance with current guidelines for cost estimates of costs [12] . In Brazil , there has been a decrease in age-standardized incidence and deaths from malaria in the last decade . Around 130 , 000 malaria cases and 71 associated deaths were registered in the 2013 [35] . In contrast , there has been a striking increase in dengue in the last decade with over 1 million cases and over 500 deaths registered in the 2013 . Of note , dengue transmission has now reached most of the regions in the country but malaria is currently restricted to the Amazon basin region [35] . The comparison of the cost of dengue with other infectious disease in Brazil is hampered since there are few cost studies of epidemic diseases nationwide . In summary , our study shows that the economic burden associated with dengue in Brazil is substantial . We hope our study will help policy makers to inform their decisions when setting goals and priorities , and assessing estimates of the impact of any proposed interventions in the management of dengue . We believe that our results are a timely estimate of the cost of a dengue episode in Brazil , considering both the public payers and the societal perspective .
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The World Health Organization estimates that 50–100 million dengue infections occur annually . However , estimates based on cartographic modeling approaches suggest that up to 390 million dengue infections annually . Dengue has become an increasing public health concern in the Americas . A substantial number of cases in the Americas are reported in Brazil . There is a need for an updated evaluation of the economic impact of dengue in Brazil so as to allow decision/policy makers to make informed decisions on resource allocation and strategic planning . We estimated the economic burden of dengue in four regions . National cost estimates were based on the number of cases reported in Sistema de Informacao de Agravos de Notificacao ( SINAN ) . Overall , 2 , 035 ( 91 . 5% ) dengue cases were recruited to our study . More than 2 million suspected dengue cases and 569 deaths were reported in SINAN from September 2012 to August 2013 . The annual national economic burden was US$ 164 million from the public payer perspective , but may be as high as US$ 447 million ( adjusting for underreporting ) . From the societal perspective , the economic burden was US$ 468 million , but may be as high as US$ 1 , 212 million . The economic burden associated with dengue in Brazil is substantial .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Economic Impact of Dengue: Multicenter Study across Four Brazilian Regions
|
Pairwise maximum-entropy models have been used in neuroscience to predict the activity of neuronal populations , given only the time-averaged correlations of the neuron activities . This paper provides evidence that the pairwise model , applied to experimental recordings , would produce a bimodal distribution for the population-averaged activity , and for some population sizes the second mode would peak at high activities , that experimentally would be equivalent to 90% of the neuron population active within time-windows of few milliseconds . Several problems are connected with this bimodality: 1 . The presence of the high-activity mode is unrealistic in view of observed neuronal activity and on neurobiological grounds . 2 . Boltzmann learning becomes non-ergodic , hence the pairwise maximum-entropy distribution cannot be found: in fact , Boltzmann learning would produce an incorrect distribution; similarly , common variants of mean-field approximations also produce an incorrect distribution . 3 . The Glauber dynamics associated with the model is unrealistically bistable and cannot be used to generate realistic surrogate data . This bimodality problem is first demonstrated for an experimental dataset from 159 neurons in the motor cortex of macaque monkey . Evidence is then provided that this problem affects typical neural recordings of population sizes of a couple of hundreds or more neurons . The cause of the bimodality problem is identified as the inability of standard maximum-entropy distributions with a uniform reference measure to model neuronal inhibition . To eliminate this problem a modified maximum-entropy model is presented , which reflects a basic effect of inhibition in the form of a simple but non-uniform reference measure . This model does not lead to unrealistic bimodalities , can be found with Boltzmann learning , and has an associated Glauber dynamics which incorporates a minimal asymmetric inhibition .
Correlated activity between pairs of cells was observed early on in the history of neuroscience [1 , 2] . Immediately the question arose whether there is a functional interpretation of this observation [3] , and this question is still with us . Hypotheses range from synchronous activation of neurons to bind representations of features into more complex percepts [4–7] , to the involvement of correlations in efficiently gating information [8] . Direct experimental evidence for a functional role of correlated activity is the observation that the synchronous pairwise activation of neurons significantly deviates from the uncorrelated case in tight correspondence with behaviour . Such synchronous events have been observed in motor cortex [9 , 10] at time points of expected , task-relevant information . In primary visual cortex they appear in relation to saccades ( eye movements ) [11 , 12] . Another argument for the functional relevance of correlations is the robustness of signals represented by synchronous activity against noise [13] . Non-Gaussian distributions of membrane potentials of neurons indeed point towards the synchronized arrival of synaptic events [14 , 15] . An opposite view regards correlated activity merely as an unavoidable epiphenomenon of neurons being connected and influencing one another [16] . In the worst case , both these views are partly true , prompting us to find ways to distinguish functionally relevant correlated events from the uninformative background . In the context of experimental paradigms that perform repeated trials , the co-variability of neurons across trials has been termed “noise correlation” . Recurrent network models are able to reproduce and explain the weak magnitude and wide spread across pairs of second-order [17–23] and higher-order correlations [24 , 25] . These simple dynamical models effectively map the statistics of the connectivity to the statistics of the activity . Even though they explain the uninformative part of correlated activity , it is unclear how to use them to distinguish this background from departures thereof . The separation of the noise- or background correlation from functionally meaningful correlation is in addition hampered by the diverse dimensions of information processing’s not being completely orthogonal . Indeed , correlation transmission may be modulated by changes of firing rate [9] . Theory [26 , 27] confirmed this entanglement in the regime of Gaussian fluctuating membrane potentials . The dynamical-model approaches just outlined pivot on a more or less realistic physical description of the network , with some stochastic features . A complementary approach is also possible , fully pivoting on statistical models . The latter try to predict and characterize neuronal activity without relying on a definite physical network model . Statistical models have two convenient features . First , intuitive statistical working hypotheses usually translate into a unique statistical model [28 , 29]; this fact streamlines the construction and selection of a such a model . For example , the assumption that first- and second-order correlations recorded in an experiments are sufficient to predict the activity recorded in a new experiment , uniquely selects a truncated Gaussian model [29 , 30] . Second , a successful statistical model implicitly restricts the set of possible dynamical physical models of the network: only those reflecting the well-modelled statistical properties are acceptable . Statistical models thus help in modelling the actual physical network structure . A limit case of this kind of statistical models is obtained by choosing probability distributions having maximum entropy under the constraints of experimentally observed quantities [31 , 32; in neuroscience see e . g . 33] . The suitability of such maximum-entropy distributions for neuronal activities has been tested in various experimental and simulated set-ups . For example , to explore the sufficiency of pairwise correlations or higher-order moments , or their predictive power for distribution tails [e . g . 34–48] , and to characterize dynamical regimes [36 , 49–51] . The probability distribution thus obtained , which includes the single-unit and pairwise statistics of the observation by construction , could help us to solve the background-correlation problem described above . In assigning to every observed activity pattern a probability , we obtain a measure of “surprise” for each such pattern; this surprise measure [e . g . 52 , 53] is related to the logarithm of the probability and thus to Shannon’s entropy . Periods of activity with low probability correspond to large surprise: these patterns cannot be explained by the statistical properties that entered the construction of the probability distribution . In this way , we are able to effectively differentiate expected , less surprising events from those that are unexpected , surprising , and functionally meaningful . Computing the maximum-entropy distribution from moment constraints—usually called the inverse problem–is simple in principle: it amounts to finding the maximum of a convex function . Hence optimization is straightforward [54 , 55] . The maximum can be searched for with a variety of methods ( downhill simplex , direction set , conjugate gradient , etc . [56 , ch . 10] ) . The convex function , however , involves a sum over exp ( N ) terms , where N is the number of neurons . For 60 neurons , that is roughly twice the universe’s age in seconds , and modern technologies enable us to record hundreds of neurons simultaneously [57–60] . Owing to the combinatorial explosion for such large numbers of neurons , the convex function cannot be calculated , not even numerically . It is therefore “sampled” , usually via Markov-chain Monte Carlo techniques [61 , 62] . In neuroscience the Glauber dynamics , also known as Gibbs sampling [61 , 63 , chap . 29] , is usually chosen as the Markov chain whose stationary probability distribution is the maximum-entropy one . Boltzmann learning [64] is the iterative combination of sampling and search for the maximum , and is still considered the most precise method of computing a maximum-entropy distribution . Alternatively one may try to approximate the convex function by an analytic expression , as done with the mean-field [65 , 66] , Thouless-Anderson-Palmer [66 , 67] , and Sessak-Monasson [68 , 69] approximations . The goodness of these approximations is usually checked against a Boltzmann-learning calculation [cf . 45] . Moment-constrained maximum-entropy models have also been used [70 , 71] as generators of surrogate data , again via a Glauber dynamics . Such surrogates are used to implement a null hypothesis to estimate the statistical significance level of correlations between spike trains [70 , 72–77] . The pairwise maximum-entropy model is applicable to experimentally recorded activities of populations of a couple hundreds neurons at most , so far; but its success , or lack thereof , cannot be automatically extrapolated to larger population sizes . Roudi et al . [78] gave evidence that the maximized Shannon entropy and other comparative entropies of such a model may present qualitatively different features above a particular population size . In the present paper we discuss a feature of the pairwise maximum-entropy model that may be problematic or undesirable: the marginal distribution for the population-averaged activity becomes bimodal , and one of the modes may peak at high activities . In other words , maximum-entropy claims that the population should fluctuate between a regime with a small fraction of simultaneously active neurons , and another regime with a higher fraction of simultaneously active neurons; the fraction of the second regime can be as high as 90% . This feature of the maximum-entropy model has been observed before in several theoretical studies that assumed a homogeneous neuronal population [see e . g . 34 , 41 , 79 , 80] . Our analysis has several points in common with Bohte & al . ’s [34] . Bohte et al . wanted to see whether a maximum-entropy distribution can correctly predict the distribution of total activity , given only firing rates and pairwise correlations from a simulated network model as constraints . They found that both the simulation and the maximum-entropy model yield a bimodal distribution of total activity within particular ranges of firing rates and correlations . The fundamental difference from our work is that our experimental data do not show a bimodal distribution , but the maximum entropy model wrongly predicts such bimodality from the measured rates and correlations . More quantitatively , the pairwise correlation found in our data is much lower than that reported in Bohte et al . ; in particular , it seems to belong to the range in which their simulation yielded a unimodal distribution [34 , p . 169] . Their simulations therefore seems to corroborate that a second mode is biologically implausible in our correlation regime . Amari & al . [79] notice the appearance of bimodal distributions for the averaged activity and analyse some of their features in the N → ∞ limit . Their focus is on the correlations needed to obtain a “widespread” distribution in that limit . Our focus is on the bimodality appearing for large but finite N , and we find some mathematical results that might be at variance with Amari & al . ’s . They seem to find [79 , p . 135] that the Dirac-delta modes are at values 0 and 1; we find that they can appear also strictly within this range . They say [79 , p . 138] that the “bigger peak” dominates as N → ∞; we find that the height ratio between the peaks is finite and depends on the single and pairwise average activity , and for our data is about 2000 as N → ∞—an observable value for recording lengths achievable in present-day experiments . We provide evidence that the bimodality of the pairwise model is bound to appear in applications to populations of more than a hundred neurons . It renders the pairwise maximum-entropy model problematic for several reasons . First , in neurobiological data the coexistence of two regimes appears unrealistic—especially if the second regime corresponds to 90% of all units being simultaneously active within few milliseconds . Second , two complementary problems appear with the Glauber dynamics and the Boltzmann-learning used to find the model’s parameters . In the Glauber dynamics the activity alternately hovers about either regime for sustained periods , which is again unrealistic and rules out this method to generate meaningful surrogate data . In addition , the Glauber dynamics becomes practically non-ergodic , and the pairwise model cannot be calculated at all via Boltzmann learning or via the approximations previously mentioned [cf . 62 , S 2 . 1 . 3; 61 , chap . 29] . This case is particularly subtle because it can go undetected: the non-ergodic Boltzmann learning yields a distribution that is not the maximum-entropy distribution one was looking for . Bohte & al . [34] remark that their neuronal-network simulation had to incorporate one inhibitory neuron , with the effect of “curtailing population bursts” [34 , p . 175] , because “the absence of inhibitory neurons makes a network very quickly prone to saturation” [34 , p . 162] . This is something that a standard maximum-entropy distribution cannot do , hence a limitation in its predictive power . It is intuitively clear that lack of inhibition and bimodality are related problems: we show this in section “Intuitive understanding of the bimodality: Mean-field picture” using a simple mean-field analysis . In the present work we propose a modified maximum-entropy model; more precisely , we propose a reference probability measure to be used with the method of maximum relative entropy [e . g . 31 , 81] ( also called minimum discrimination information [82]; see [83] for a comparison of the two entropies ) . The principle and reference measure can be used with pairwise or higher-order constraints; standard maximum-entropy corresponds to a uniform measure . The proposed reference measure , presented in section “Inhibited maximum-entropy model” , solves three problems at once: ( 1 ) it leads to distributions without unrealistic modes and eliminates the bistability in the Glauber dynamics; ( 2 ) it leads to a maximum-entropy model that can be calculated via Boltzmann learning; ( 3 ) it can also “rescue” interesting distributions that otherwise would have to be discarded because incorrect . The reference measure we propose is neurobiologically motivated . It is a minimal representation of the statistical effects of inhibition naturally appearing in brain activity , and directly translates Bohte & al’s device of including one inhibitory neuron in the simulated network . Moreover , the reference measure has a simple analytic expression and the resulting maximum-entropy model is still the stationary distribution of a particular Glauber dynamics , so that it can also be used to generate surrogate data . In the final “Discussion” we argue that the use of such a measure is not just an ad hoc solution , but a choice required by the underlying biology of neuronal networks: the necessity of non-uniform reference measures is similarly well-known in other statistical scientific fields , like radioastronomy and quantum mechanics . The plan of this paper is the following: after some mathematical and methodological preliminaries , we show the appearance of the bimodality problem in the maximum-entropy model applied to an experimental dataset of the activity of 159 neurons recorded from macaque motor cortex . Then we use an analytically tractable homogeneous pairwise maximum-entropy model to give evidence that the bimodality problem will affect larger and larger ranges of datasets as the population size increases . We show that typical experimental datasets of neural activity are prone to this problem . We then investigate the underlying biological causes of the bimodality problem and propose a way to eliminate it: using a minimal amount of inhibition in the network , represented in a modified Glauber dynamics that includes a minimal asymmetric inhibition . We show that this correction corresponds to using the method of maximum entropy with a different reference measure , as discussed above , and that the resulting maximum entropy distribution is the stationary distribution of a modified Glauber dynamics . We finally bring to a close with a summary , a justification and discussion of the maximum-entropy model with the modified reference measure , and a comparison with other statistical models used in the literature .
Our study uses three main mathematical objects: the pairwise maximum-entropy distribution , a “reduced” pairwise maximum-entropy distribution , and the Glauber dynamics associated with them . We review them here; some remarks about their range of applicability are given in . Towards the end of the paper we will introduce an additional maximum-entropy distribution . We first show how the bimodality problem subtly appears with a set of experimental data , then explore its significance for larger population sizes and other samples of experimental data of brain activity . Let us briefly summarize our results so far and the reason why a maximum-entropy model yielding a bimodal distribution in the population-averaged activity is problematic: We will propose a solution that addresses all three issues at once . This solution pivots on the idea of inhibition and can be grasped with an intuitive explanation of how the bimodality arises .
In this work we have shown that pairwise maximum-entropy models , widely used as references distributions in the statistical description of the joint activity of hundreds of neurons , are poised to suffer from three interrelated problems when constrained with mean activities and pairwise correlations typically found in cortex: We have given an intuitive explanation of the common cause of these issues: positive pairwise correlations imply positive Lagrange multipliers between pairs of neuron , corresponding to a symmetric network that is excitatory on average . For typical values of correlations observed in neuroscientific experiments , this network can therefore possess two metastable dynamic regimes , given sufficiently many units . The mechanism is identical to the ferromagnetic transition in the Ising model , as explained in “Bimodality of the inhomogeneous model for large N” . An analogous bimodality appears in the statistical mechanics of finite-size systems [e . g . 108 , 115 , and refs therein]—but it is experimentally expected and verified there , unlike our neurobiological case . Although we did not study maximum-entropy models typically used in other fields , like structural biology and genetic networks [116–118] , social behavior in mammals [119 , 120] , natural image statistics [121 , 122] , and economics [123] , the problems we have addressed are generic and emerge as soon as we study a large network with positive pairwise correlations on average; hence they might be of relevance to these fields . In this work we have also suggested a remedy , based on the explanation above: the intuitive idea is to add a minimal asymmetric inhibition to the network , in the guise of an additional , asymmetrically coupled inhibitory neuron ( Fig 8A ) [cf . 34 , p . 175] . This leads to an “inhibited” Glauber dynamics that is free from bistable regimes and has a unimodal stationary distribution Pi ( s ) , Eq ( 22 ) . This dynamics depends on an inhibition-coupling parameter JI and a threshold parameter θ . Most important , we have shown that this new stationary distribution Pi ( s ) belongs to the maximum-entropy family: it can be obtained with the maximum-relative-entropy method with respect to a reference measure , Eq ( 25 ) ( Fig 9 ) , that represents the neurobiologically natural presence of inhibition in the network . We call this model an “inhibited” pairwise maximum-entropy model . The inhibited pairwise model solves all three problems above: We wish to stress that the presence of bimodality and non-ergodicity can easily go unnoticed . Sampling from a bimodal distribution , the probability to switch to the second mode may be so small that it occurs over more sampling steps larger than those typically used in the literature , and the high mode is not visited during Boltzmann learning or surrogate generation . We then face a subtle situation: The obtained distribution is not a pairwise maximum-entropy distribution Eq ( 3 ) —the Lagrange multipliers are incorrect—yet a consistency check ( also affected by undersampling ) may wrongly seem to validate it , and also analytic approximations ( outside of their convergence domain ) may wrongly validate it . The distribution found in this circumstance is not a standard pairwise distribution , but our inhibited maximum entropy distribution Eq ( 22 ) , for appropriately chosen JI and θ . In this regard we urge researchers who have calculated pairwise ( and even higher-order ) maximum-entropy distributions for more than 50 neurons using short Boltzmann-learning procedures , to check for the possible presence of higher metastable regimes . The presence of bimodality and non-ergodicity can be checked , for example , by starting the sampling from different initial conditions , at low and high activities , looking out for bistable regimes [cf . 62 , S 2 . 1 . 3] . Another way out of this problem is to use other sampling techniques or Markov chains different from the Glauber one [61 , 62 , 97 , 98] . Alternatively , one may use the inhibited model Eq ( 22 ) with the standard approaches . In the presence of inhomogeneous and randomly chosen parameters and large network sizes , the standard pairwise maximum-entropy distribution is mathematically identical with the Boltzmann distribution of the Sherrington & Kirkpatrick infinite-range spin glass [124 , 125] . A more systematic analysis of the effect of inhomogeneity on the appearance of the second mode could therefore employ methods developed for spin glasses [126] , which could produce approximate expressions for the inverse problem: the determination of Lagrange multipliers from the data . One may think of modifying the Thouless-Anderson-Palmer ( TAP ) mean-field approach [67 , 127] , generalizations of which exist for the asymmetric non-equilibrium case [93] appearing here due to the inhibitory unit . An appropriate modification of the ideas of Sessak and Monasson [68 , 69] could also be an alternative . Another possibility is the use of cumulant expansions [17 , 128] , which unlike TAP-based approaches have the advantage of being valid also in regimes of strong coupling; recent extensions allow us to obtain the statistics at the level of individual units [129] . In this work we have not investigated other models , like general linear models or kinetic Ising models for example . Considering the fundamental mechanism by which the bimodality arises , we expect similar problems in other models . The reasoning backing this hypothesis is this: Pairwise correlations in cortical areas are on average positive but very weak . In this limit we expect that these correlations require slightly positive “excitatory” couplings between units in most other models; an independent-pair approximation also suggests this [127] . As a result of this rough approximation determined at the level of individual pairs , we expect the couplings to be independent of the number of units of a dynamic or statistical model . With increasing number of units in the model the overall “excitatory feedback” ∑ j N J i j will increase , and a simple mean-field analysis makes us expect the appearance of a second mode at a certain critical number , what in statistical mechanics is called a ferromagnetic transition; cf . Fig 7B . We expect similar ferromagnetic transitions to happen in a wide class of statistical models that only represent the observed , on average positively correlated units . Similar transitions are also reported in Bohte et al . [34] for a biological—as opposed to statistical—neuron model composed of excitatory neurons only . In fact , they had to introduce one inhibitory neuron in their model to avoid such transitions , which is also the idea behind our inhibitory term . The bimodality problem could be cured by allowing for asymmetric connections , enabling the implementation of possibly hidden inhibitory units that stabilize the activity . For example , kinetic Ising models [130–132] , which are maximum-entropy models over the possible histories of network activity [133–135] , can have positive correlations among excitatory units in the asynchronous irregular regime , while their dynamics is stabilized by inhibitory feedback [see e . g . 136 , Fig 3A] . Scaling of network properties with the number of units N is often studied in this context . In the asynchronous regime , mean pairwise correlations decrease as N−1 [18 , 22 , 110 , 136] . This scaling is the result of a fictive experiment , typically used to derive a theoretical results in the N → ∞ limit—any biological neuronal network has of course a certain fixed size N . The mean correlation measured in a sample of size M , with 1 ≪ M ≤ N , is by sampling theory expected to be roughly equal to the mean correlation of the full network , and does not vary much with M; only the variance around this expectation declines to 0 as M approaches N . The inhibited maximum-entropy model Pi , Eq ( 22 ) , solves the problems discussed above; but we may ask if this is enough to motivate its use . We consider it an interesting model for at least two reasons . First , it actually is a class of models rather than a single specific model . In the present work we have focused on its use with pairwise constraints because these are still widely discussed in the literature . But the inhibition reference measure Eq ( 25 ) can be used with higher-order constraints or other kinds of constraints as well . We leave to future works the analysis of this possibility . Second , there are neurobiological reasons why the reference measure Eq ( 25 ) can be methodologically more appropriate than the uniform measure of the standard maximum-entropy method . Let us argue this point in more depth . Standard ( i . e . uniform reference measure ) maximum-entropy distributions are often recommended as “maximally noncommittal” [137] . But this adjective needs qualification . Jaynes precised: ‘“maximally noncommittal” by a certain criterion’—that the possible events or states be deemed to have a priori equal probabilities before any constraints are enforced [31] . When the initial probabilities are not deemed equal , for physical or biological reasons for example , reference measures appear . An important example of reference measure is the “density of states” that multiplies the Boltzmann factor e − E/ ( kT ) in statistical mechanics [e . g . 138 , ch . 16]: we cannot judge energy levels to be a priori equally probable because each one comprises a different amount of degrees of freedom . The proper choice of this reference measure is so essential as to be the first manifest difference between classical and quantum statistical mechanics , from “classical counting” to “quantum counting” of phase-space cells [138 , ch . 16] . Owing to quantized energy exchanges , a quantum density of states is necessary in statistical mechanics; likewise we could say that owing to inhibitory feedback an inhibitory reference measure is necessary in the statistical mechanics of neuronal networks . The uniform reference measure of standard maximum-entropy expresses that network units have a priori equally probable {0 , 1} states . But these units are neurons , whose states are not a priori equally likely . The measure of the inhibited model Pi reflects this a priori asymmetry in a simplified way . There are surely other reference measures that reflect this asymmetry in a more elaborated way , but the one we have found is likely one of the simplest; cf . Bohte et al . ’s [34] inhibitory solution . The choice of an appropriate reference measure is critically important in neuroscientific inferences also for another reason . When maximum-entropy is used to generate an initial distribution to be updated by Bayes’s theorem , the choice of reference measure is not critical , because a poor choice gets anyway updated and corrected as new data accumulate . Not so when maximum-entropy is used to generate a sort of reference distribution that will not be updated , as is often done in neuroscience: an unnaturally chosen reference measure will then bias and taint all conclusions derived from comparisons with the maximum-entropy distribution . The inhibited pairwise model can therefore be quite useful in all applications of the maximum-entropy model mentioned in “Introduction” . For example , it can serve as a realistic hypothesis against which to check or measure the prominence of correlations in simulated or recorded neural activities , to separate the low baseline level of correlation from the potentially behaviourally relevant departures thereof . The surprise measure to effect such separation would , according to the inhibited model , take into account the presence of inhibition and the overall low level of activity that are natural in the cortex . The inhibited model can also be used for the generation of surrogate data which include the natural effect of inhibition besides the observed level of pairwise activity . It can also be useful in the study of the predictive sufficiency of pairwise correlations as opposed to higher-order moments , for example for distribution tails [e . g . 34–36 , 38–44]; and in the characterization of dynamical regimes of neuronal activity [36 , 49–51] . The inhibition reference measure Eq ( 25 ) contains the threshold θ and the inhibitory coupling JI as parameters . The choice of their values depends on the point of view adopted about the measure . Three venues seem possible: ( 1 ) One might think of choosing ( θ , JI ) to better fit the specific dataset under study , but this would counter the maximum-entropy spirit: the threshold cannot be a constraint , and the inhibitory coupling would acquire infinite values , as explained in section “Inhibited maximum-entropy model” . Moreover for our dataset this strategy would only give a worse fit ( cf . Fig 2B ) because the inhibition term flattens the distribution tails . ( 2 ) One might only want to get rid of the bistability of the Glauber dynamics and the bimodality of the distribution . In this case the precise choice of ( θ , JI ) is not critical within certain bounds . The inhibition coupling JI < 0 must be negative and sufficiently large to suppress activity once the population-averaged activity reaches θ . The self-consistency condition Eq ( 21 ) then gives [ 1 + exp ( ∑ j j ≠ i J i j m j + h i + J I ) ] - 1 ⪡ θ for all i . The threshold θ can be safely set to any value between the highest observed population activity s ¯ and the second fixed point of the self-consistency equation Eq ( 21 ) , which is indicative of the second mode and is beyond s ¯ > 1 / 2 ( see Fig 7B ) for the typically low mean activities observed in the cortex . ( 3 ) A methodologically sounder possibility , in view of the remarks about maximum-entropy measures given above , is to choose ( θ , JI ) from general neurobiological arguments and observations . This was implicitly done in Bohte & al . ’s neuron model [34] for example , but unfortunately they did not publish the values they chose . We leave the discussion of the neurobiological choice of these parameters to future investigations . Our inibition term J I N G ( s ¯ - θ ) , Eq ( 22 ) , formally includes Shimazaki et al . ’s “simultaneous silence” constraint [44] as the limit JI → −∞ , θ = 1/N . Because of this limit their model has a sharp jump in probability at s ¯ = 1 / N: their constraint uniformly removes probability for s ¯ > 1 / N and assigns it to the single point s ¯ = 0 . In contrast , our inhibited model Pi presents a kink but no jump for s ¯ = θ , with a discontinuity in the derivative proportional to JI . But besides this mathematical relationship , our inhibition term and the “simultaneous silence” constraint have different motivations and uses . As discussed at length above and in section “Inhibited maximum-entropy model” , our term is best interpreted as a reference measure expressing the effects of inhibition , providing a biologically more suitable starting point [cf . 34] for maximum-entropy , rather than a constraint . Its goal is not to improve the goodness-of-fit for activities well below threshold , in contrast to earlier works [e . g . 35 , 40 , 50 , 78 , 80] and to the “simultaneous silence” constraint [44] . The goodness-of-fit is determined by the constraints alone . In this regard we do not present any improvement of the fit compared to a pure pairwise model . Future work could explore combinations of the here proposed reference measure and additional constraints that improve the fitness of the model .
Maximum-entropy models are an approximate limit case of probability models by exchangeability [139–141] , or sufficiency [141 , 142 , §§4 . 2–5] . This approximation holds if the constraints are empirical averages ( e . g . time averages in our case ) over enough many data compared with the number of points in the sample space . How much is “enough” depends on where the empirical averages lie within their physically allowed ranges: If they are well within their ranges , then a number of data values large but still smaller than the number of sample-space points may be enough . If the empirical averages are close or equal to their physically allowed extreme values , then the number of data values should be much larger than the number of sample-space points . If these conditions are not met the maximum-entropy method gives unreasonable or plainly wrong results , as can be ascertained by comparison with the non-approximated Bayesian model . Simple examples of these limitations are illustrated in [140 , 141] together with the more reasonable predictions of the non-approximated Bayesian models [see also 61 , p . 308] . A very large positive or negative Lagrange multiplier usually signals that the maximum-entropy method is inadequate , because the constraint corresponding to the multiplier is approaching its minimal or maximal allowed values . Consider our case , discussed in section “The problem: Bimodality , bistability , non-ergodicity” . The constraints are time-averages over roughly 300000 data points , and the sample space—the possible network states—has 2N = 2159 ≈ 7 × 1047 points . Suppose that we want to use as constraints the N + 1 observed frequencies of the total activity N s ¯ [cf . 50 , 94 , 113] . Each frequency is bounded between 0 and 1 . In our data the values N s ¯ = 24 and N s ¯ = 28 have non-zero frequencies , but the intermediate values N s ¯ ∈ { 25 , 26 , 27 } have zero frequencies—the minimum possible value . The Lagrange multipliers for the latter three frequencies would be −∞ . The maximum-entropy model would therefore predict that it is possible for the network to have 24 or 28 simultaneuosly active neurons , but impossible for it to have 25 , 26 , or 27 active neurons–not even in future recordings , if we interpret the model that way . Such a prediction is unreasonable , not to say a little silly . Under the assumption that each neuron is as likely as not to be active in each time bin , the probability that in 300000 time bins we observe all possible values of the total activity N s ¯ ∈ { 0 , … , 159 }—each at least once—is of the order 10−1463 . This means that it is practically certain that some values of N s ¯ will not appear in our recording; not because of physical impossibility , but because of the exceedingly small number of observations compared with that of possible events . It is unreasonable to think that the three values 25 , 26 , 27 could not appear in a longer recording , yet the values 24 and 28 could . As signalled by the large value of the Lagrange multipliers , the conditions for the validity of the maximum-entropy limit are not satisfied in this case , and the method breaks down . The validity of the inhomogeneous pairwise model is similarly questionable if there are neuron pairs with zero coupled activity , gij = 0; some corrections to the method are necessary in that case . The limitations of the maximum-entropy method are well-known [143] in the field of image reconstruction of astronomical sources , where this method has probably most successfully been applied for the longest time . In this field the maximum-entropy principle is today used differently: to generate a distribution on the space of prior distributions , rather than a prior itself [144 , 145] . Here we show that there is a temporal process that is able to sample from the the distribution Pp ( s|h , J ) Eq ( 3 ) . This temporal dynamics is called Glauber dynamics . It is an example of a Markov chain on the space of binary neurons {0 , 1}N [63] . At each time step a neuron si is chosen randomly and updated with the update rule s i ← 1 with probability F i ( s ) = g ( ∑ k k ≠ i J i k s k + h i ) and 0 else ( 30 ) g ( x ) = 1 1 + exp ( - x ) , ( 31 ) where the coupling is assumed to be symmetric , Jij = Jji , and self-coupling is absent , Jii = 0 . The transition operator of the Markov chain , κ , only connects states that differ by at most one neuron , so for the transition of neuron i we can write , if si+= ( s1 , … , 1︸i−th , … , sN ) and si−= ( s1 , … , 0︸i−th , … , sN ) , κ ( s i + | s i - ) = F i ( s i - ) κ ( s i - | s i + ) = 1 - F i ( s i + ) . ( 32 ) The pairwise maximum-entropy distribution Pp ( s|h , J ) is stationary under the Markov dynamics above . The proof can be obtained as the JI = 0 case of the proof , given below , for the inhibited pairwise maximum-entropy model . The neuron model ginzburg_neuron in NEST , a simulator for neural network models [96] , implements the Glauber dynamics , if the parameters of the gain function are chosen appropriately . The gain function has the form g ginzburg ( h ) = c 1 h + c 2 2 ( 1 + tanh ( c 3 ( h - θ ) ) . ( 44 ) With tanh ( x ) = e x - e - x e x + e - x , setting x = c3 ( h− θ ) , c1 = 0 , c2 = 1 , c 3 = 1 2 it takes the form g ginzburg ( h ) = 1 2 e x + e - x + e x - e - x e x + e - x , = 1 1 + e - 2 x = 1 1 + e - ( h - θ ) , ( 45 ) which is identical to Eq ( 31 ) . The large N limit for the inhomogeneous pairwise model can be studied employing results from spin glass theory [125] . The first point to realize is that for weak correlations the Lagrange multipliers Jij are to dominant order determined only by the covariances between units i and j and by their respective mean activities . This follows from eq . ( 7 ) of Roudi et al . 2009 [127] , which we expand in the limit of weak correlations ( and hence only to linear order in Jij ) as - J i j + O ( J i j 2 ) =↓Roudietal . eq . 7[ C - 1 ] i ≠ j = [ { m k ( 1 - m k ) δ k l + c k ≠ l } ] i j - 1 = [ { m k ( 1 - m k ) δ k l } - 1 2 ( { δ k l + c k ≠ l m k ( 1 - m k ) m l ( 1 - m l ) } ) i j - 1 { m l ( 1 - m l ) δ k l } - 1 2 ] i ≠ j ≃ - c i j m k ( 1 - m k ) m l ( 1 - m l ) + O ( c i j 2 ) , where we used the geometric series from the second to the third line . Since considering larger networks will not change the statistics of the cij ( as long as we are within the local network of N ≃ 103–104 neurons ) , the Lagrange multipliers Jij will , to leading order , follow the same distribution . In particular their population mean J ¯ i j = 1 N ( N - 1 ) ∑ i j J i j → N ≫ 1 μ and their variance δ J i j 2 ¯ = 1 N ( N - 1 ) ∑ i j ( J i j - J ¯ i j ) 2 → N ≫ 1 σ 2 converge to values μ and σ2 that are , to leading order , independent of N . We now consider the “energy” associated with the maximum-entropy model E ( s ) = - 1 2 ∑ i j J i j s i s j - ∑ i h i s i . For this expression to possess a well-defined N → ∞ limit , we need ( see [125] , eqs . 1 . 3a and 1 . 3b ) that μ = J ˜ 0 / N and σ 2 = J ˜ 2 / N , with N-independent quantities denoted by a tilde . We may therefore determine at which point we are in the phase diagram , shown in Fig 1 of [125] . So we obtain the scaling relations J ˜ 0 = N μ . J ˜ = N σ . We may now study what happens if we increase N . We therefore investigate how , for given and N-independent values of μ and σ , we move through the phase diagram of the model ( see Fig 1 in [125] ) . The axes of this diagram are spanned by J ˜ 0 J ˜ = N μ σ , k T J ˜ = 1 N σ . So increasing N we will move to the lower right in the phase diagram , ultimately crossing the transition to ferromagnetic behaviour . This is the point at which the model becomes bistable . One may note that the position of this cross-over is not entirely correctly predicted by the replica-symmetric theory of [125] . The true solution , found by Parisi [149] is slightly displaced compared to the transition line in the diagram in Fig 1 of [125] . Still , as we are only interested in the limit N → ∞ , the result is the same and the model becomes bistable . Higer-order correlations are represented by products of K distinct activities , like e . g . s1 s3 s4 s9 , with K ∈ {0 … , N} , whose expectations are the raw K-th moments of the distribution . There are ( N K ) such products for each given K . For a network activity ( s1 , ⋯ , sN ) ∈ {0 , 1}N , each of those products amounts to either 0 or 1 . More precisely , if the total activity is S , then ( S K ) of these products will equal 1 and the others will vanish; the binomial vanishes by definition if K > S , so it covers this case as well . In the reduced , homogeneous case we can meaningfully sum together all products with K factors , because they have the same probability . Then , from what we said above , such sum equals ( S K ) when the total activity is S: ∑ i 1 < i 2 < ⋯ < i K s i 1 s i 2 ⋯ s i K = ( S K ) if ∑ i s i = S . ( 46 ) We want to rewrite the logarithm of the inhibition term N G ( s ¯ - θ ) ≔N ( s ¯ - θ ) H ( s ¯ - θ ) as a sum of such sums of K products , in order to interpret it as a combination of higher-order correlations: N G ( s ¯ - θ ) = ∑ K = 0 N f K ( ∑ i 1 < i 2 < ⋯ < i K s i 1 s i 2 ⋯ s i K ) = ∑ K = 0 N f K ( N s ¯ K ) , ( 47 ) with θ-dependent coefficients fK . Let us find them . Rewrite N G ( s ¯ - θ ) as G ( S − Θ ) , with S ≔N s ¯ ∈ { 0 , … , N } and Θ ≔ Nθ . The ( N + 1 ) -tuple v of numbers v · ≔ ( G ( 0 - Θ ) , G ( 1 - Θ ) , … , G ( N - Θ ) ) = ( 0 , … , 0 ︸ Θth , 1 , … , N - Θ ) is a θ-dependent row-vector . Expression Eq ( 47 ) can be interpreted as the matrix product fP of the row vector f–which we want to find—by the ( N + 1 ) -dimensional matrix P having element ( S K ) in its ( K + 1 ) th row and ( S + 1 ) th column . Such matrix is called a Pascal matrix [150 , 151]; for example , for N = 4 , ( P ) K S = ( 1 1 1 1 1 0 1 2 3 4 0 0 1 3 6 0 0 0 1 4 0 0 0 0 1 ) . Hence we have v = fP , and therefore f = vP−1 . The inverse P−1 of a Pascal matrix has elements ( P - 1 ) S K = ( - 1 ) K - S ( K S ) [150 , 151]; for example , for N = 4 , ( P - 1 ) S K = ( 1 - 1 1 - 1 1 0 1 - 2 3 - 4 0 0 1 - 3 6 0 0 0 1 - 4 0 0 0 0 1 ) . By multipling the expressions of v and P−1 above we find that the row-vector f = vP−1 is , explicitating its dependence on Θ , f K ( Θ ) = { ( 0 1 0 0 0 0 0 ⋯ Θ= 0 ( 0 0 1 - 1 1 - 1 1 ⋯ Θ= 1 ( 0 0 0 1 - 2 3 - 4 ⋯ Θ= 2 ( 0 0 0 0 1 - 3 6 ⋯ ⋯ ( 0 0 0 0 0 1 - 4 ⋯ ⋯ K = 0 K = 1 K = 2 ⋯ . This solution has a convenient feature: if we increase N by 1 , the matrix ( fK ( Θ ) ) of the N-dimensional solution acquires one new row and column , but the already existing entries remain unchanged . We can thus write G ( S − Θ ) as ∑ K = 0 N f K ( Θ ) ( S K ) , with fK ( Θ ) given above . But fK ( Θ ) = 0 if K ⩽ Θ , so we can also restrict the sum to K > Θ: G ( S - Θ ) = ∑ K = Θ+ 1 N f K ( Θ ) ( S K ) . ( 48 ) Compare the matrix of values for fK ( Θ ) above with that for the generalized binomial coefficient [100 , 112]: ( −ΘK−Θ−1 ) = ( - 1 ) K - Θ- 1 ( K - 2 K - Θ- 1 ) = { 1 0 0 0 0 0 ⋯ Θ= 0 1 - 1 1 - 1 1 ⋯ Θ= 1 1 - 2 3 - 4 ⋯ Θ= 2 1 - 3 6 ⋯ ⋯ 1 - 4 ⋯ ⋯ K = 1 K = 2 ⋯ ; if K > Θ we have f K ( Θ ) = ( −ΘK−Θ−1 ) . We can therefore write Eq ( 48 ) more explicitly , recalling that S ≡ N s ¯ , Θ ≡ Nθ , G ( S - Θ ) ≡ N G ( s ¯ - θ ) , and Eq ( 46 ) , as: N G ( s ¯ - θ ) = ∑ K = N θ + 1 N ( - N θ K - N θ - 1 ) ( N s ¯ K ) , ≡ ∑ K = N θ + 1 N ( - N θ K - N θ - 1 ) ( ∑ i 1 < i 2 < ⋯ < i K s i 1 s i 2 ⋯ s i K ) , which is formula Eq ( 23 ) .
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Networks of interacting units are ubiquitous in various fields of biology; e . g . gene regulatory networks , neuronal networks , social structures . If a limited set of observables is accessible , maximum-entropy models provide a way to construct a statistical model for such networks , under particular assumptions . The pairwise maximum-entropy model only uses the first two moments among those observables , and can be interpreted as a network with only pairwise interactions . If correlations are on average positive , we here show that the maximum entropy distribution tends to become bimodal . In the application to neuronal activity this is a problem , because the bimodality is an artefact of the statistical model and not observed in real data . This problem could also affect other fields in biology . We here explain under which conditions bimodality arises and present a solution to the problem by introducing a collective negative feedback , corresponding to a modified maximum-entropy model . This result may point to the existence of a homeostatic mechanism active in the system that is not part of our set of observable units .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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2017
|
Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models
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Mali is endemic for all five targeted major neglected tropical diseases ( NTDs ) . As one of the five ‘fast-track’ countries supported with the United States Agency for International Development ( USAID ) funds , Mali started to integrate the activities of existing disease-specific national control programs on these diseases in 2007 . The ultimate objectives are to eliminate lymphatic filariasis , onchocerciasis and trachoma as public health problems and to reduce morbidity caused by schistosomiasis and soil-transmitted helminthiasis through regular treatment to eligible populations , and the specific objectives were to achieve 80% program coverage and 100% geographical coverage yearly . The paper reports on the implementation of the integrated mass drug administration and the lessons learned . The integrated control program was led by the Ministry of Health and coordinated by the national NTD Control Program . The drug packages were designed according to the disease endemicity in each district and delivered through various platforms to eligible populations involving the primary health care system . Treatment data were recorded and reported by the community drug distributors . After a pilot implementation of integrated drug delivery in three regions in 2007 , the treatment for all five targeted NTDs was steadily scaled up to 100% geographical coverage by 2009 , and program coverage has since been maintained at a high level: over 85% for lymphatic filariasis , over 90% for onchocerciasis and soil-transmitted helminthiasis , around 90% in school-age children for schistosomiasis , and 76–97% for trachoma . Around 10 million people have received one or more drug packages each year since 2009 . No severe cases of adverse effects were reported . Mali has scaled up the drug treatment to national coverage through integrated drug delivery involving the primary health care system . The successes and lessons learned in Mali can be valuable assets to other countries starting up their own integrated national NTD control programs .
Neglected tropical diseases ( NTDs ) are a group of diseases that affect the most vulnerable and the poorest group of the populations in the world [1] , [2] . The World Health Organization ( WHO ) recommends five public health strategies for the prevention and control of the NTDs: preventive chemotherapy ( PCT ) ; intensified case management; vector control; provision of safe water , sanitation and hygiene; and veterinary public health [1] . The major NTDs currently being targeted through PCT include lymphatic filariasis ( LF ) , onchocerciasis , schistosomiasis , soil-transmitted helminthiasis ( STH , including ascariasis , hookworm infection and trichuriasis ) and trachoma . These five major NTDs cause high disease burden with severe disfigurement , disability and blindness , blighting the lives of more than one billion people worldwide and threatening the health of millions more [1] . The drugs needed for these five NTDs are robust , safe , low-cost and available by donation from the pharmaceutical companies or by purchasing at relatively low costs [3] . They can be delivered to the target populations either alone or in combination to prevent morbidity caused by these NTDs , or in some cases , to eliminate the diseases [4] , [5] . Mali is landlocked in West Africa with a population of 15 . 5 million . It is divided into eight administrative regions ( Kayes , Koulikoro , Sikasso , Segou , Mopti , Tombouctou , Gao and Kidal ) and Bamako . The northern part of the country extends deep into the Sahara desert and the southern region features the Niger and Senegal rivers , where the majority of the country population inhabits . The country's economy centers on agriculture and fishing . Mali is one of the poorest countries in the world and ranked 160 out of 169 countries according to the Human Development Report 2010 [6] . It is endemic with all five major NTDs [7]–[11] . Control of the NTDs before 2007 was through four independent vertical national programs under the Ministry of Health ( MoH ) : the National Onchocerciasis Control Program ( PNLO ) , the National Lymphatic Filariasis Elimination Program ( PNEFL ) , the National Schistosomiasis and Soil-Transmitted Helminths Control Program ( PNLS ) and the National Blindness Prevention Program ( PNLC ) . Onchocerciasis was originally prevalent in five regions in the country , including Kayes , Koulikoro , Sikasso , Segou and Mopti , and the PNLO was established in 1986 to address the public health implications of the disease . The eastern part of the endemic regions ( Koulikoro rive droite , Sikasso , Ségou and Mopti ) was included in the original program area of the Onchocerciasis Control Program ( OCP ) . In 2002 onchocerciasis was declared eliminated as a public health program in large parts of these areas with only epidemiological and entomological surveillance continuing to monitor the prevalence and microfilarial load in the population and to also monitor the infectivity of the vector Simulium damnosum . The western part of the endemic regions ( Kayes and Koulikoro rive gauche ) was included in the western extension of OCP in 1987 with ivermectin ( IVM , donated by Merck & Co . ) administration and later with Community Directed Treatment with Ivermectin ( CDTI ) with support from the African Program for Onchocerciasis Control ( APOC ) and using the community drug distributors ( CDDs ) . The disease is currently endemic in 17 districts ( Sikasso , one of the original 16 districts , was split into two separate districts in 2010 ) in three regions in Kayes , Koulikoro and Sikasso . LF , caused by Wuchereria bancrofti , is endemic throughout Mali [10] , [12] with the entire population being at risk of disease . The PNEFL was established in 2004 and subsequently a national mapping survey was carried out using Immunochromatographic Test cards confirming LF endemicity across Mali ( Dembélé , unpublished data ) . The MDA for LF started in 2005 in four of the five onchocerciasis districts in Sikasso using CDTI plus albendazole ( ALB , donated by GlaxoSmithKline ) , with support from the Government of Mali . Both urogenital ( caused by Schistosoma haematobium ) and intestinal ( caused by S . mansoni ) forms of schistosomiasis are present in the country [13] . Two national surveys were conducted with the first in 1984–1989 and the second in 2004–2006 [7] , [14] , [15] . The results confirmed presence of schistosomiasis throughout the country with geographically varying degrees of prevalence . The later survey in 2004–2006 showed a prevalence of 38 . 3% ( ranging 0 . 0–99 . 0% ) for S . haematobium and 6 . 7% ( ranging 0 . 0–94 . 9% ) for S . mansoni [15] . Schistosomiasis control in Mali was initiated in the Bandiagara district , Mopti as a component of a dam-building project in 1978 and became a national program ( PNLS ) in 1982 [13] , [15] . The initial control program with praziquantel ( PZQ ) distribution was implemented by the MoH in collaboration with WHO and with support from the German Technical Co-operation [13] , but the MDA ceased later due to lack of further funding . In 2005 , the MDA resumed with support from the Schistosomiasis Control Initiative ( SCI ) with PZQ procured from certified generic manufacturers , targeting school-age children and at-risk adults with PCT through school-based and community-based drug delivery in all endemic regions and Bamako ( school-age children only ) [16] , [17] . STH is a public health problem throughout Mali . The national survey in 2004–2006 ( together with schistosomiasis ) in school children from 7–14 years of age showed that the whole country is endemic for STH , with prevalence of up to 34 . 3% with hookworm infection ( in Yorosso , Sikasso ) ( R Dembélé , unpublished data ) . STH control consists of several drug delivery platforms in Mali . The National Intensified Nutrition Weeks ( SIAN , French acronym ) deliver vitamin A and ALB twice a year to children of 12–59 months and to women immediately post-partum . In 2004 , the PNLS was expanded to include STH , and ALB was delivered at the same time through school-based and community-based drug delivery to those receiving PZQ treatments for schistosomiasis during 2005–2007 with the support from the Schistosomiasis Control Initiative . The population above 5 years also benefits from annual treatment with ALB and IVM from the LF elimination program . Trachoma as a blinding disease is found in all districts of the eight regions of the country ( except Bamako ) . A national survey in all regions except Bamako was conducted in 1996–1997 [11] . The prevalence of active trachoma , follicular ( TF ) or intense ( TI ) , was estimated to be 34 . 9% among children under 10 years of age , and the prevalence of trichiasis among women over 14 years of age was 2 . 5% , and 1% had central corneal opacity [11] . The PNLC initiated a trachoma control program in 1998 following the national survey adopting the WHO recommended SAFE ( Surgery , Antibiotics , Facial washing and Environmental improvement ) strategy [18] , [19] , benefiting from the Zithromax ( ZTM ) donation program by Pfizer Inc . Significant progress had been achieved in trachoma control since the start of the national program [19]–[21] . As one of the five ‘fast-track’ countries supported by the United States Agency for International Development ( USAID ) NTD Control Program managed by RTI International [22] , Mali launched the integrated national NTD Control Program ( NTDCP ) in 2007 with technical assistance initially from International Trachoma Initiative ( ITI , 2007 ) and then from Helen Keller International ( HKI ) from 2008 onward . The overall objectives of the Mali's NTD control program are to eliminate LF , onchocerciasis and trachoma as public health problems and to reduce morbidity caused by schistosomiasis and STH through regular mass drug administration ( MDA ) , with specific objectives of achieving 80% program coverage and 100% geographical coverage yearly within the five-year program plan . This current paper serves as a report on the progress made by the integrated national NTDCP in Mali , drawing from objectives achieved , documented experiences and pertinent lessons learned of the program from 2007 to 2011 , and focusing on only aspects of integrated MDA activities .
The existing disease-specific vertical national programs achieved various degrees of coverage throughout the country and mapping of distribution of each NTD was almost complete before integration . These disease-specific national control programs already achieved significant success before 2007 as described in the introduction . Integration of control activities on certain diseases already occurred , e . g . onchocerciasis and LF , and schistosomiasis and STH , on co-delivery of drugs . Building on these successes , in 2007 Mali began to further integrate the existing disease-specific control programs to increase efficiency and program coverage for each target disease . The USAID funds support all the integrated PCT-related activities and procurement of PZQ . Although the integrated NTD control program include other non-MDA components , this paper focuses on the implementation of MDA component only . The NTDCP is led by the MoH through the National Directorate of Health . The National Steering Committee of the program was established and is chaired by the National Director of Health and its members include members of the Technical Coordinating Committee ( TCC , described below ) , the Head of Planning , Training and Health Information Unit , the Head of Public Health and Safety Division , the Head of the Nutrition Division , the Dean of the Faculty of Medicine , Pharmacy and Odonto-Stomatology ( FMPOS ) , and the representatives of non-governmental developmental organization ( NGDO ) partners . The Steering Committee meets twice a year to discuss the progress of the program and issues arising . A National Strategic Plan for integrated control of NTDs ( 2007–2011 ) was developed in 2007 as the blueprint to direct the control activities . A new five-year national strategic plan ( 2012–2016 ) is being updated and finalized . Under the National Directorate of Health , Division of Disease Control and Prevention ( DPLM ) is responsible for coordinating the activities of control and elimination of priority diseases in Mali . The existing four disease-specific national control programs are under the remit of DPLM , which provides an ideal framework for coordination of integrated NTD control activities . The dedicated NTD coordinator at HKI works closely with the four National Coordinators of the disease-specific control programs to facilitate the integrated activities . Under the DPLM , the TCC was established and is chaired by the Chief of DPLM , comprising four National Coordinators of the disease-specific control programs , the Head of Nutrition Division , the representative from the National Public Health Research Institute , the representative from the National Center of Information , Education and Communication for Health ( CNIECS ) , and the representative of the grantee NGDO ( initially ITI and currently HKI ) . This committee meets every quarter . The program review and planning workshop was conducted annually to review the progress and to plan for the coming year , attended by the TCC members , the Regional Health Directors , and the regional NTD focal persons . The Regional Health Teams in turn planned the MDA activities for each district with the District Health Teams . In Mali , community health centers play a very important role in providing primary health care at local level . Within each district , there are a number of community health centers , each responsible for a number of villages . For long-term sustainability and local capacity building , the NTD control activities were integrated into the primary health care system at local level . Community health center workers ( CHCWs ) play an important role in the program as their routine health care activities . These CHCWs provided training and supervision of CDDs , and were responsible for drug allocation , treatment data collation in their catchment area , and data reporting to the district health officers . Figure 1 shows the structure of the program . To integrate the PCT activities of each existing control program , a situation analysis was conducted to map out the overlaps of the disease distribution in each district using the existing disease distribution data . Figure 2 shows the overlapping distributions of the five targeted NTDs in each districts of the country . The PCT strategy for each disease in each district was decided according to the known prevalence of the disease in the district and the WHO PCT guidelines [4] . Drug packages for each district were determined as shown in Figure 3 according to the disease distribution shown in Figure 2 above and the WHO PCT guidelines . There was insufficient evidence and hence lack of clear guidance for combinations of available drugs , therefore , to avoid possible side effects due to combination , different drug packages were delivered in sequential fashion with one week between deliveries , where two or more drug packages were required . For example , where all three packages were required , MDA was organized as ZTM for week 1 , ALB/IVM for week 3 and PZQ for week 5 . This was also to avoid confusion among the CDDs with managing different dose poles at the same time , considering the relatively low education level in Mali villagers . Several successful strategies for drug delivery were used by existing disease-specific national control programs , e . g . CDTI for onchocerciasis and LF , school-based and community-based drug delivery for schistosomiasis and STH , and community-based drug delivery for trachoma . Each of these was operating in disease-specific program areas . To scale up each program to a national coverage in the integrated control program , the four existing national programs worked together to plan and coordinate the MDA activities . A number of drug delivery strategies were used in combination in districts to deliver the drug packages by the trained CDDs: 1 ) School-based distribution by trained school teachers , taking place in schools targeting school-going children; 2 ) Community-based distribution by CDDs , including door-to-door/household distribution , focal distribution in the market , mosque , or other busy places , and mobile distribution through CDDs travelling on motorbike to households in remote areas , particularly in nomadic zones; and 3 ) Health center distribution by CHCWs , taking place at the health centers . Before MDA , in villages the trained CDDs work together with village chiefs to register the target population including name , age and sex . They receive drug allocations from community health centers according to the estimated population in each village and take the drugs to the village . CDDs , village chiefs and CHCWs discuss to decide the best drug delivery strategies for each village , mainly using community-based door-to-door distribution and if MDA happens during school terms , school-based distribution as well . In cities/towns , all the above mentioned three strategies are normally used . During MDA , CDDs distribute drugs according to the registered list , and they first confirm that the person has not been treated before treating him/her . The drugs administered are recorded in the register . MDA normally takes 2–3 days in each village . Extensive advocacy was conducted before each round of MDA and sufficient information was given to the general public about the national program . The drugs were voluntarily taken by the persons targeted in the endemic districts . CDDs in each village were selected by the village and the management team of the community health center , and were used in the program to conduct the MDA activities in communities . The criteria for CDDs include: they were respected by the community; they had ability to read and write; and they were available during the MDA campaigns . Cascade training for integrated drug administration was carried out throughout the implementation areas . The training sessions started at the regional level and cascaded down to the community level . Training of trainers was organized in the regions and these trainers subsequently trained the CHCWs ( as supervisors ) at the district level . The supervisors then trained the CDDs at the community health centers . Refresher training was also provided for supervisors and CDDs each year before the MDA campaign started . Table 1 shows the number of people trained or retrained from 2007 to 2011 . In view of the usually low educational level of Mali villagers , the NTDCP decided to train the CDDs in the drug administration before each treatment round with different drug packages in order to avoid confusion in CDDs to calculate and administer the drugs using different dose poles . As the national program has matured and in efforts to reduce costs and streamline the program , integrated training is now being introduced . In total , 86 , 248 persons have so far been trained and retrained across the country . Advocacy activities undertaken aimed to promote country ownership of the control program through increasing government funding and support to the NTDCP activities and to mobilize resources from existing and potential partners . At sub-country level , advocacy activities were focused on mobilizing support from local authorities at the regional , district and community levels . Before each campaign , an official notice was sent by the National Director of Health to all Regional Directors of Health to inform them the mass treatment campaign and to request them to achieve the objectives of the program . Several meetings between the various stakeholders ( Regional Directorates of Health , the Regional Offices of Education , Social Development , local councils ) involved in the control of NTDs were conducted to galvanize interest , support and participation in the campaign . Posters were produced and sent to all health districts and radio and television messages were broadcast to announce the mass treatment campaign . Meetings with local officials were held to mobilize communities during mass treatment campaign . Images of severe cases of each of the five NTDs were shown , including the short-and long-term signs and symptoms and the treatment available . These meetings also served as means of motivating communities to participate in the mass treatment campaign . These meetings also proved to be effective in mobilizing funds to support CDDs in some districts . Behavior change communication has been a very important part in the Mali integrated NTD control program . A workshop to develop and harmonize health messages was organized each year . It was followed by the development of audiovisual materials . These messages were broadcast on the various channels during the month immediately preceding the campaigns , and throughout the duration of the campaigns . Posters and banners were also posted strategically during the course of the weeks preceding the campaign . Short documentaries on NTDs and mass treatment campaigns were broadcast on television at least three times during the 20 days preceding the campaign as well as during the campaign . The same schedule was used for broadcasting the radio messages . Counseling cards on the five NTDs were designed and these cards are used by the CDDs during mobilization and drug distribution to educate people about the disease and the importance of treatment . The cards also contained information for communities to understand the behaviors that could cause or complicate these diseases and the behaviors that could help prevent them from getting the diseases , such as hand washing and face washing . To date , 3 , 000 counseling cards and 500 posters have been produced . Data on treatment and serious adverse events ( SAEs ) in this paper were the CDD-reported data from the NTDCP . During the mass treatment campaign , the CDDs recorded data on drug usage , treatment numbers and SAEs using specific reporting forms . The data were reported to national NTDCP through health reporting system . In 2009 , the reported coverage data were verified through a post-PCT coverage survey ( details not shown here ) . In the current paper , to standardize the calculation for all targeted NTDs , national census population was used and population at risk for each NTD was estimated according to the annual projected population figures from the National Directorate of Population , Mali . Eligible population was estimated as the total population at risk for trachoma and 80% of the total population at risk for LF , onchocerciasis , schistosomiasis and STH . The coverage rates were calculated according to the WHO guidelines for drug coverage monitoring , including geographical coverage , program coverage and national coverage [23] . The geographical coverage is the percentage proportion of the targeted districts among the total number of endemic districts for each disease . The program coverage is the percentage proportion of the population treated among the eligible population in the targeted program areas . The national coverage is the percentage proportion of the population treated among the total population at risk in the country . The cost data were from the HKI program accounts specific for direct expenditure in Mali on the NTD program activities . HKI receives expense receipts after completion of each activity from the NTDCP . The original receipts for all expenses are maintained by HKI , and are spot checked during internal financial reviews as well as during HKI's federally-mandated annual A-133 audit . Expenses , such as vehicle fuel , per diems , and supplies etc . directly incurred during the implementation of program activities , are uniquely coded in HKI's financial system based on the type of activity supported ( e . g . training of CDDs , drug transport and distribution , etc . ) . On a monthly basis , all program expenses are categorized by activities based on these unique codes , and a running cost total is maintained for each activity over the life of the project .
The integrated MDA activities started in 2007 . To gain experience of the integrated delivery of different drug packages by the CDDs , the integrated drug delivery started in three regions ( Kayes , Koulikoro and Sikasso ) which included 24 districts . It was then gradually scaled up to include more regions in the following years to achieve national coverage in 2009 . The number of districts covered by MDA each year since 2005 and the cumulative coverage are summarized in Table 2 . Onchocerciasis MDA achieved 100% geographical coverage before 2007 and this has been maintained since . Trachoma MDA started in two regions ( Kayes and Koulikoro ) and already met the program target after three rounds of treatment before 2007 . It was gradually expanded to include all other endemic regions in 2009 . The significant gain of the integrated NTD program was the scale-up for LF MDA which achieved full national geographic coverage in 2009 , and this has since been maintained . The national coverage of LF MDA also benefited STH control throughout the country . MDA for schistosomiasis achieved national coverage for school-age children in 2007 , and each endemic district had been targeted two to five times by the end of 2010 according to the endemicity level . In the scarcely populated Kidal region , the mapping of schistosomiasis in this region was not conducted due to the insecurity and will be done later . MDA for schistosomiasis targeting school-age children in this region was delivered based on the historical and clinical knowledge . With gradual scale-up of geographical coverage , the number of people targeted and treated/retreated each year increased noticeably . The annual treatment numbers for each targeted NTD and the percentage coverage ( program coverage and national epidemiological coverage ) rates are shown in Table 3 ( including data from 2005–06 before integration ) . Overall , satisfactory program coverage rates had been achieved each year in the targeted areas since 2007 and maintained at high level since 2009 , with those for LF , onchocerciasis , STH and trachoma ranging from 76% to over 100% . Although overall program coverage rate for schistosomiasis was relatively lower each year , program coverage rates had been high among school-age children , the main targeted group according to the WHO recommendations . Most notably , the national epidemiological coverage for LF steadily increased over the years to reach over 65% , treating around 10 million people each year since 2009 , and this also benefited STH control throughout the country with national epidemiological coverage of 66–75% . Some minor side effects from taking the drugs had been recorded such as diarrhea and headaches and these were usually dealt with at the community health centers . However , no cases with severe adverse effects have been recorded so far . The total direct cost of the program in Mali is $3 . 575 million from the start of the program in 2007 to March 2011 , which covers four rounds of drug delivery . The cost shown here does not include the significant contribution from the MoH on housing , logistics , staff salaries etc . , and the cost of drugs , which were either donated free of charge or directly procured by RTI . It also does not include any opportunity costs and monetary contributions from local governments , for example , in 2010 the Kayes mayor's office donated an amount of five million francs ( CFA ) to help motivate the CDDs during the campaign . As expected , the major expenditures were for MDA activities which included training of CDDs , drug transport , storage and administration , and M&E , supervision and annual program reviews ( Figure 4 ) .
Built on the existing success of individual national control programs , the Government of Mali has shown commitment in the control of NTDs in the country . The coordination of NTD control has been integrated at the central level and implementation activities are integrated with the primary health care system at the local communities . With the financial support from the USAID and other donors , Mali has scaled up the drug treatment to a national coverage through integrated drug delivery , with around 10 million people receiving one or more drug packages each year since 2009 . With the progress of the program , the focus is now on consolidating the achievements to achieve the goals of eliminating LF and blinding trachoma , perhaps also onchocerciasis , and reducing the morbidity caused by schistosomiasis and STH , in the country by the preset timelines , and on mobilizing resources for the next phase of the NTD control according to the new national strategic plan . Mali's successes and lessons learned can be valuable assets to other countries starting up their integrated national NTD control programs .
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Neglected tropical diseases ( NTDs ) are a group of chronic infections that affect the poorest group of the populations in the world . There are currently five major NTDs targeted through mass drug treatment in the affected communities . The drug delivery can be integrated to deliver different drug packages as these NTDs often overlap in distribution . Mali is endemic with all five major NTDs . The integrated national NTD control program was implemented through the primary health care system using the community health center workers and the community drug distributors aiming at long-term sustainability . After a pilot start in three regions in 2007 without prior examples to follow on integrated mass drug administration , treatment for the five targeted NTDs was gradually scaled up and reached all endemic districts by 2009 , and annual drug coverage in the targeted population has since been maintained at a high level for each of the five NTDs . Around 10 million people received one or more drug treatments each year since 2009 . The country is on the way to meet the national objectives of elimination or control of these diseases . The successes and lessons learned in Mali are valuable assets to other countries looking to start similar programs .
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2012
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Implementing Preventive Chemotherapy through an Integrated National Neglected Tropical Disease Control Program in Mali
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As the Global Programme to Eliminate Lymphatic Filariasis ( LF ) approaches its 2020 goal , an increasing number of districts will enter the endgame phase where drug coverage rates from mass drug administration ( MDA ) are used to assess whether MDA can be stopped . As reported , the gap between reported and actual drug coverage in some contexts has overestimated the true rates , thus causing premature administration of transmission assessment surveys ( TAS ) that detect ongoing LF transmission . In these cases , districts must continue with additional rounds of MDA . Two districts in Indonesia ( Agam District , Depok City ) fit this criteria—one had not met the pre-TAS criteria and the other , had not passed the TAS criteria . In both cases , the district health teams needed insight into their drug delivery programs in order to improve drug coverage in the subsequent MDA rounds . To inform the subsequent MDA round , a micronarrative survey tool was developed to capture community members’ experience with MDA and the social realm where drug delivery and compliance occur . A baseline survey was implemented after the 2013 MDA in endemic communities in both districts using the EPI sampling criteria ( n = 806 ) . Compliance in the last MDA was associated with perceived importance of the LF drugs for health ( p<0 . 001 ) ; perceived safety of the LF drugs ( p<0 . 001 ) and knowing someone in the household has complied ( p<0 . 001 ) . Results indicated that specialized messages were needed to reach women and younger men . Both districts used these recommendations to implement changes to their MDA without additional financial support . An endline survey was performed after the 2014 MDA using the same sampling criteria ( n = 811 ) . Reported compliance in the last MDA improved in both districts from 57% to 77% ( p<0 . 05 ) . Those who reported having ever taken the LF drug rose from 79% to 90% ( p<0 . 001 ) in both sites . Micronarrative surveys were shown to be a valid and effective tool to detect operational issues within MDA programs . District health staff felt ownership of the results , implementing feasible changes to their programs that resulted in significant improvements to coverage and compliance in the subsequent MDA . This kind of implementation research using a micronarrative survey tool could benefit underperforming MDA programs as well as other disease control programs where a deeper understanding is needed to improve healthcare delivery .
More than fifteen years ago , the Global Programme to Eliminate Lympatic Filariasis ( GPELF ) was launched with the goal to interrupt transmission of the disease in endemic countries by 2020 [1] . Considerable progress in reducing transmission and burden of disease has been made since World Health Assembly Resolution 50 . 29 prioritized the elimination of lymphatic filariasis ( LF ) in 1997 . Since the start of LF elimination , there has been an estimated 46% reduction of the population living at risk for LF infection [2] , over 96 million LF cases cured or prevented [3 , 4] as well as billions of dollars of direct economic benefits in endemic countries [5] . At the end of 2014 , of the 73 countries known to be endemic for lymphatic filariasis ( LF ) , 55 required ongoing mass drug administration ( MDA ) as the recommended preventive chemotherapy ( PC ) to eliminate LF [4] . Eleven endemic countries still need to begin MDA and 23 countries have less than 100% geographical coverage [4] . As 2020 approaches , there is an increased urgency to scale up activities in these remaining countries . On the other side of the spectrum , implementation units ( IUs ) that have completed at least five effective MDA rounds qualify for Transmission Assessment Surveys ( TAS ) to evaluate the level of LF transmission in the population and to determine if MDA can be stopped [6] . For those IUs who do not qualify for TAS due to persistent low MDA coverage or who must repeat MDA rounds because the critical threshold has been surpassed , a new set of implementation challenges appears . The peer-reviewed literature has not sufficiently addressed these issues . As such , there is a gap in our understanding as to how to guide and assist those IUs when additional MDA rounds must be implemented past the expected 4–6 rounds suggested by the programme [7] . This research aims to respond to that gap in understanding through the development of a tool and process to assist ‘endgame’ IUs in understanding why drug coverage may be persistently low , what specific actions may be undertaken to improve delivery and uptake and how those responsible for delivering MDA may be better supported . Although the issue of low coverage is not a new one , it has become increasingly recognized as the 2020 deadline approaches for LF elimination . Recent reviews on factors associated with coverage and compliance with antihelmintic treatment highlight some of the pertinent issues that need addressing [8–10] . These reviews report similar findings across global and Indian-specific contexts . Notable issues that negatively impact compliance with treatment include fear of side effects , not feeling LF drugs are needed , lack of trust , distributor not coming and taking too many tablets . These reviews focus primarily on findings from the quantitative research portfolio while in this paper we describe the use of a novel survey methodology that combines the use of both qualitative and quantitative data . Indonesia was chosen as the location for this research . It is the only country in the world with three forms of LF present: Wuchereria bancrofti , Brugia timori and Brugia malayi . Indonesia has participated in the GPELF since 2002 , using Albendazole and Diethylcarbamazine in yearly MDA to endemic districts . Across the archipelago , a variety of stages of the LF elimination programme exist–those completing mapping , some just beginning MDA , others moving onto post-TAS surveillance and increasingly , more IUs are applying for TAS . The process presented in this paper can be described as implementation research ( IR ) . By its design implementation research follows a systematic process that begins with close collaboration between the research team , stakeholders and program implementers to identify a problem related to healthcare delivery and through research finds feasible solutions to improve delivery and access [11] . This paper describes the development of a tool using micronarratives to identify the bottlenecks related to LF drug delivery and drug uptake and the use of that survey to identify feasible recommendations for use in LF endemic communities in two endgame districts in Indonesia . The research also describes how the district health offices used these recommendations in the implementation of an additional MDA round and how that impacted reported drug coverage rates . Finally the implications of this research for LF elimination programmes with IUs in the endgame stages will be discussed .
Following recommendations from the National LF Programme in Jakarta , two districts were selected as research sites: Depok City and Agam District . Both sites had completed multiple MDA rounds and were entering the endgame stage of their elimination programmes . Depok City is part of the greater metropolitan areas known as Jabodetabek ( Jakarta , Bogor , Depok , Tangerang and Bekasi ) , which has a population greater than 28 million people , making it one of largest metropolis areas in the world . Depok City is located in West Java province , with a population of 1 . 75 million in 2010 . LF species in this area is W . bancrofti and the mf rate was recorded as 1 . 83% ( Ministry of Health Indonesia ) . In 2013 , Depok City had completed five rounds of MDA to the whole IU , with coverage rates varying between 46% and 84% , per district calculations . In 2013 , mf rates in the spot check and sentinel sites were 0% and the city health department applied to the National LF Programme to implement the TAS . They were denied due to persistent low coverage below 65% in all five previous rounds , using standardized census estimates as the source of total population data , and they were instructed to conduct a further three MDA rounds . Agam District is located on the western coast of Sumatra , roughly 1200 km from Banda Aceh , site of the Boxing Day tsunami in 2006 . In 2010 , Agam had a population of just over 450 , 000 living in both urban and rural areas . LF species in this area is B . malayi and the mf rate was 8 . 06% at the beginning of the elimination programme ( Ministry of Health Indonesia ) . Agam District conducted five MDA rounds by 2011 with an average epidemiological coverage rate of 78 . 2% for the entire IU . The reported drug coverage for these five rounds ranged from 89 . 6% to 96 . 7% based on District Health Authority data . Therefore , based on the achieved coverage rates for MDA in Agam and sentinel and spot-check site data assessed as <1% microfilaremia rates , the district qualified for a TAS in 2012 . In total , 1315 students from 35 primary schools in all 16 subdistricts were included in the sample . From these , 102 Brugia Rapid tests were positive ( from 28 primary schools ) ( Ministry of Health Indonesia ) . As a result , Agam District did not qualify to stop MDA and was required to continue MDA for an additional two years ( 2013 , 2014 ) . The survey tool developed during the course of this research was rooted in the use of a micronarrative or a brief story reflecting personal experiences with the most recent MDA . Unlike Knowledge , Attitudes and Practice ( KAP ) surveys , the majority of the survey questions related to this specific experience or story . In order to solicit a story , the respondent was asked a specific ‘prompting’ question , like “Tell me what happened after you received the drugs for LF ? ” Following the respondent’s story , a series of closed questions related to that specific experience were asked , including details about the story participants , the location , the outcome ( swallowed the LF pills or not ) as well as related emotions . The micronarrative survey is based on the recognition that participation with MDA is a social process , rather than a strictly individual one . As such , an individual’s direct and indirect experiences with the MDA and with the people associated with MDA will be most revealing about how the implementation of MDA can be improved . One of the important advantages of working with micronarrative is that it does not constrain the respondent to provide information within a tightly prescribed framework of questions and answer options . Storytelling provides a mechanism to explore both expected and unexpected themes , using the respondent’s personal experience as the reference point for subsequent closed questions . Because the use of micronarrative combines the range and depth commonly seen in qualitative research methodologies with the accuracy and precision of cross sectional surveys , it offers a range of analytical possibilities that will be explored in a subsequent publication . Development of the survey tool was done together with stakeholders and health staff from both districts . Through a series of workshops relevant themes known to be associated with MDA outcomes were identified . The conceptual model used to guide this research used the outcome of taking LF drugs ( e . g . compliance ) as a function of the interactions between the deliverer , the endemic community member and the MDA setting itself . In actuality , two survey tools were created–one to address the experiences of those involved in the drug delivery and one for the endemic community member receiving the LF drug . This paper presents the survey tool and results for the endemic community survey . Prior to the baseline survey , the questionnaire was tested in Depok City with 40 community members using enumerators from the Center for Health Research at the Universitas Indonesia in an area outside of the selected research sample . Changes to the questionnaires were made based on this test . After the implementation of the baseline survey and prior to the start of the endline survey , enumerators , the research team and the district health team provided inputs for further refinement of the survey instrument . Some basic changes were made to the overall format , however none of the outcome variables of interest were altered . The final survey tool included the following components: socio-demographic information , a prompt to elicit a specific story related to the last MDA respondents participated in ( e . g . “tell me what happened the last time you were offered the LF drugs” ) , questions related to that experience ( side effects , person distributing the drug , reported drug taking behavior ) , and attitudes towards the MDA , the LF drug , and the perceived drug taking behavior of the household and community . The EPI cluster survey design was used to calculate the number of clusters in each district ( proportionate to population size ) for the endemic community surveys ( n = 406 in each research site ) . The sample size was calculated on the following criteria: an anticipated population proportion of 90% with a confidence level of 95% and absolute precision of 5% . The required sample size for these parameters was 138 persons . From four previous similar LF surveys carried out in Indonesia , the intra class correlation coefficient was calculated as 0 . 235 . Using a cluster size of 7 , the design effect for this survey was set at 2 . 41 . As a result , the necessary sample size was 333 persons ( 138 x 2 . 41 ) . A buffer of 20% was added in the event of refusals and/or incorrectly administered questionnaires . The total sample size required for the survey in each location was 406 persons , or 58 clusters of 7 respondents . Henderson and Sundaresan ( 1982 ) recommend a minimum of 30 clusters to ensure that the sample has a normal distribution [12] . The basic sampling unit is the household , rather than the individual . Households were randomly selected at the village level ( throwing a pen and walking in the direction of the first house ) . At the household level , one person was identified through a random selection of all household members present at the time the enumerator visited . One person per household was interviewed . Only those above the age of 15 years were included in the sample . In both sites , locally based enumerators were selected and trained by Universitas Indonesia researchers on the survey methodology . All questionnaires were administered to respondents by these trained enumerators . This sampling frame and methodology was used for both the baseline and endline surveys . For both the baseline and endline surveys , data was double entered using Epi-Info and then transferred for analysis to STATA 14 . Data was checked for response bias , and range and consistency checks were completed . Data was adjusted for the cluster effect and was weighted for sex using district population statistics as a reference . Univariate and bivariate analysis informed the construction of multivariable models for outcomes of interest that included: receipt of LF drug , reported drug taking behavior ( e . g . compliance ) in the last MDA and previous drug taking behavior ( e . g . history of having taken LF drugs during any MDA round ) . In the baseline survey , a multivariable model testing the outcome of “compliance in the story” was done; this model was not constructed in the endline survey . Backward elimination was applied to remove factors from the model that were not significant at the level of 5% . Only the significant predictors for each outcome were retained in the models . However age was selected a priori and retained in each model regardless of its significance level . The adjusted Wald test was used for all multivariable models . This paper presents results from the closed questions in the survey . The analysis of the micronarratives will be discussed separately . Analysis of the survey results for both the baseline and endline surveys was done in close collaboration with the district health authority in both sites . This process facilitated ownership of the data and its results by those responsible for implementation of the MDA at the district level . A wider range of stakeholders was consulted to discuss research findings and resulting recommendations in a series of workshops in both locations . Prior to the start of the research , the last MDA round in Agam District occurred in November 2013 and the baseline survey was conducted there in December 2013 . In Depok City , the last MDA round had been conducted in 2013 , after which time the District health authority applied for TAS and while waiting for the response ceased MDA activities . The baseline survey was conducted in Depok City in January 2014; roughly one year after the last MDA was conducted . Analysis of survey results was performed in March and April 2014 . Presentation and discussion of results at the district health offices was carried out in September 2014 , followed by one technical visit to each site by one member of the research team to assist with the incorporation of the research recommendations in the upcoming MDA . Flowcharts were developed for use by drug distributors in both research sites and were finalized during the technical meetings . MDA rounds in both locations were carried out in November 2014 and the endline surveys were performed in both locations within two months of the end of that MDA . Results were discussed at a workshop with the district health authorities in June 2015 and presented to the National LF Elimination programme in Jakarta . The Faculty of Public Health , Universitas Indonesia gave ethical clearance for both the baseline and endline surveys . All questionnaires were anonymous and no personally identifying information was collected . Informed consent from the respondent was obtained prior to the start of the data collection . Eligible respondents were 15 years and above and each respondent gave their own consent in writing to participate in the survey after being informed about the questionnaire , the time required for participation in the survey and understanding whom to consult if there were any additional questions . At the end of the interview , survey respondents were offered a small pencil case for their participation in the research and a leaflet on LF with Universitas Indonesia details , according to Indonesian ethical requirements . Definitions related to persons receiving LF drugs and persons taking LF drugs vary significantly in the peer reviewed literature and in the field [8–10] . In the reality of MDA , directly observed treatment is not always implemented on the ground during MDA , and as a result , the WHO definition of drug coverage may not always reflect the true rate of those who took the treatment . Because of the heterogeneity of the definitions used , this research uses the following definitions . Coverage is defined as the percentage of targeted persons who receive MDA medications and compliance refers to the WHO definition for drug coverage , specifically , the percentage of a targeted population who ingest the medication [8 , 13] .
A total of 401 questionnaires were accepted for analysis from Agam District and 405 questionnaires from Depok City in the baseline survey ( n = 806 ) . In the endline surveys , 405 questionnaires were accepted for analysis from Agam District , and 406 from Depok City ( n = 811 ) . Both rounds had similar demographic distributions with the exception of occupation . There were more private workers in the endline survey as compared to the baseline survey ( Table 1 ) . Housewives represented the highest proportion of professions recorded across all surveys ( 35% overall ) . Age distribution was similar between baseline and endline survey rounds ( p = 0 . 879 ) with 13% of respondents under the age of 25 years , 24% between 26–35 years , 25% between 36–45 years , 19% between 46–55 years and 19% above the age of 56 years . Education was also similar across the two survey rounds ( p = 0 . 445 ) with most respondents having completed secondary school ( 37% ) . Ten percent had not completed primary school or had never attended school across all surveys . There were some variations in demographics between Depok City and Agam District in terms of education level and occupations , but this was expected due to inherent urban and rural characteristics . Both survey rounds had proportionately ( relative to the population ) more females in the sample , likely due to the interview scheduled during the daylight hours in consideration of security and logistical constraints . As a result , the sample was adjusted for gender for analysis purposes . In addition the data was also adjusted for the effect of the cluster design . All data presented here use the adjusted results . Respondents were asked in their narrative prompt to respond to the following question , “Earlier you mentioned that you had received the LF drug during MDA . Could you tell me about it , what happened ? ” Most of the recorded stories were related to receiving and taking the LF drugs ( 53% ) , receiving the drugs ( 28% ) or taking the drugs ( 16% ) . A sample micronarrative from a woman in her thirties in Agam District: Half of the survey respondents reported that they had received LF drugs from a community health worker ( 50% ) whilst over a quarter received LF drugs from a family member , friend or neighbor ( 27% ) . Sixty-three percent reported that they took all of the pills they were given while 8% reported that they took only some of the pills . Most respondents indicated “myself” as the greatest influence on their decision to take the pills ( 77% ) , followed by the health worker and community health worker ( 10% ) . Nearly half ( 49% ) reported no side effects after taking the treatment . Women were less likely than men ( AOR = 0 . 53 ) to have complied with treatment in the last MDA ( p = 0 . 011 ) . Predominant reasons for noncompliance in the last MDA included being pregnant ( 4% of total noncompliers ) , too old ( 4% ) , sick at the time of distribution ( 17% ) , taking other drugs ( 12% ) and lack of information ( 19% ) . In the Indonesian eligibility guidelines for MDA at the time of the baseline survey , breastfeeding women and people above the age of 65 years were excluded from treatment . Specific questions related to the last MDA included: where the LF drugs were received , awareness about MDA , knowledge of other family members’ compliance with MDA and one question related to knowledge of the cause of LF . In Agam District , 71% of respondents were aware of the MDA before it occurred , compared to 67% in Depok City . Most people in Agam District received the LF drugs inside their homes ( 79% ) confirming the house-to-house distribution method preferred in this area . In Depok City , 56% of respondents received their LF drugs inside their house reflecting the higher use of distribution posts here due to the high population density , presence of apartment buildings and the mobile nature of an urban population . Respondents were asked if they knew of anyone else in their household who had complied with the LF drugs: in Agam District 75% knew someone in their household , compared with 69% in Depok City . In both locations , around a quarter of respondents identified worms ( 22% in Agam District; 25% in Depok City ) , and mosquitoes ( 31% in Agam District; 48% in Depok City ) as the cause of LF . Respondents were asked some attitudinal questions about MDA and LF drugs . In Agam District , more respondents cited that LF drugs were safe ( 73% ) , compared to Depok City ( 62% ) . However in both research locations , a majority of respondents believed that MDA was very important for their health ( 85% in Agam District and 77% in Depok City ) . Multivariable logistic regression models were created for four key outcomes of interest ( Table 2 ) : ever complied with LF drugs , ever received LF drugs , reported compliance in the last MDA offered and reported compliance described in the story . Data from the baseline surveys showed that 19% of respondents in Agam District and 24% of respondents in Depok City had never received the LF drugs during any MDA . In the multivariable model ( after adjusting for district , education , income and occupation ) age and sex were determined to have had an effect on whether or not respondents had ever received the LF drugs . Overall , women were three times more likely to receive the LF drugs as compared to men ( AOR = 3 . 02; p = 0 . 001 ) . This may reflect the distribution strategies used in both sites where MDA was conducted primarily during the day . Respondents aged 15–25 years were the least likely to receive the LF drugs as compared to respondents aged above 26 years . Those aged between 46–55 years were 7 times more likely to have ever received the LF drugs as compared to respondents aged 14–25 years ( AOR = 7 . 38; p = 0 . 001 ) . In the questionnaire , respondents were asked if they had ever taken the LF drug during any MDA offered in the past . Nearly 62% of those who had ever received the drugs had a history of compliance in both research sites , meaning that 38% of those who had been offered the LF drugs had never taken them . These individuals , called systematic noncompliers , can be defined as people who persistently refuse or do not ingest the antifilarial medications over the course of an MDA program [8] . Systematic noncompliers may harbor LF infection and have the potential to contribute to LF resurgence [14 , 15] . Factors related to systematic noncompliance in our study included the perception that the LF drugs were unsafe ( AOR = 0 . 6; p<0 . 001 ) and not knowing anyone in the household who had taken the LF drugs ( AOR = 0 . 18; p<0 . 001 ) . Positive associations with a history of having taken the LF drugs included being given the LF drugs outside of the house ( AOR = 2 . 74; p = 0 . 004 ) and perceiving media stories to be informative and helpful ( AOR = 2 . 10; p = 0 . 002 ) . For compliance in the last MDA , the multivariable model was stratified by location to elucidate if there were differences between the urban ( Depok City ) and rural ( Agam District ) datasets . As discussed earlier , the survey was conducted within one month of the last MDA in Agam District , and more than one year after the last MDA in Depok , so we anticipated some differences due to recall of events . The following variables were associated in these analyses . In Depok City , ( 1 ) age was not a factor associated with compliance; ( 2 ) Working in the private sector had a lower odds for compliance than those who were unemployed ( p = 0 . 006 ) ; ( 3 ) The perceived importance of LF drugs for health positively influenced drug taking behavior ( p = 0 . 02 ) ; ( 4 ) Past history of compliance was seen as an important influence , e . g . if respondents had never taken the LF drug , then they were less likely to comply in the last MDA ( p = 0 . 002 ) . In Agam District specifically: ( 1 ) Those who were 26–35 years were less likely to comply in the last MDA than the 15–25 year old group ( p = 0 . 013 ) ; ( 2 ) Working in the private sector had a higher odds for compliance than the unemployed ( p = 0 . 02 ) ; ( 3 ) Perceived good drug safety positively influenced the decision to comply in the last MDA ( p = 0 . 004 ) . In the multivariable model for reported compliance in questions related to the story ( adjusted for district , age , income , education and occupation ) several factors were associated with taking the LF drug . In the stories , the value of “being healthy” had a strong positive influence on compliance . Those reporting that being healthy was an important influence on their decision to take LF drugs were nearly 11 times more likely to report compliance in their stories than those who did not cite “being healthy” as an influence ( AOR 10 . 74; p<0 . 001 ) . Perceived common good ( AOR 1 . 5; p = 0 . 019 ) had a positive influence on compliance , suggesting respondents understood the norm that taking LF drugs benefits the community . This social norm of compliance was also seen in the positive influence of knowing others had taken the treatment in the stories . Those who reported that others taking the LF drugs influenced their own behavior were 2 . 27 times more likely to report their own compliance in their stories ( AOR 2 . 27; p = 0 . 030 ) . Women were less likely than men to have taken the LF drugs in the last MDA ( AOR = 0 . 53; p = 0 . 011 ) as well as in the MDA experiences they described ( AOR = 0 . 48; p = 0 . 015 ) . Pregnancy and breastfeeding ( considered as contraindicated in some Indonesian district MDAs ) may explain why women were less likely to comply . After the results were compiled , a series of workshops were held to discuss the results with the District Health teams and to present the findings to relevant stakeholders in both districts and at the national level . Feasible actions to address issues related to coverage and compliance were identified with program personnel from each location . In addition to the workshops , prior to the start of MDA awareness activities in each site , one member of the research team gave a brief technical visit to further discuss the survey results with stakeholders and other district health staff . In order to improve distribution of LF drugs during MDA in both areas , the primary groups that needed more targeted attention were men and youths between the ages of 15–24 years . At the time of the MDA , women were successfully receiving the drugs in both areas with the distribution strategies in place . In order to widen the reach and to increase men’s participation , it was recommended to consider an approach to MDA that would occur simultaneously at schools , government and private offices , households as well as factories . In order to reach younger persons , use of social media and text messaging were suggested . For those who had never complied with taking the LF drugs in the past ( considered as systematic noncompliers ) , the findings suggest that they were also unlikely to comply in future MDA rounds , thus continuing their pattern of behavior . As such , it was recommended to develop a method to identify these persons at the start of the drug delivery encounter so that the drug distributors could target them with specific messaging . A flow chart of questions was created for the drug distributors to use . It began with the question , “When was the last time you took the LF drugs during MDA ? ” Subsequently , the distributor was prompted through a series of questions and responses to aid them in persuading this person to accept LF drugs . As media stories considered as positive and helpful were seen to be associated with compliance , it was recommended to have an intentional media campaign , if possible emphasizing the social norm of compliance , e . g . “I took it with the other people in my family , neighborhood , city . ” Another issue that was identified through the baseline survey was the ineligibility criteria used by drug distributors during MDA . Individuals taking other medications at the time of MDA ( namely for hypertension and diabetes ) , those over the age of 65 years and breastfeeding women were frequently excluded from treatment . It was recommended to the national LF program to increase the upper age limit for MDA eligibility and reinforce the international guidelines regarding exclusion . In addition , messages about drug safety should be used to help promote trust and reduce fear of side effects for communities . Baseline data revealed that perceived drug safety , number of pills and packaging of LF drugs had an important influence on the decision to swallow the pills . As a result , it was suggested to package the pills with specific messages addressing the following: drug safety ( “# million people in Indonesia safely took LF drugs last year” ) ; drug-taking procedure ( “take all the drugs at once , preferably with a meal” ) ; ineligibility information ( children under 2 years , pregnant women and severely ill persons ) ; benefits of compliance for yourself , your family and community and finally where to go if you need assistance . Finally in terms of messaging surrounding the next round of MDA , it was recommended that the district health authorities focus their messages on the social norm of compliance ( e . g . “everybody is doing it” ) and on the safety of LF drugs globally and in Indonesia . It was suggested that messaging regarding side effects continue to be used , with a focus on promoting the message that side effects indicate that the medicine is working . Ancillary benefits to treatment regarding the elimination of intestinal helminthes should also be promoted , particularly in Agam District . Finally MDA should be promoted as a preventive activity , rather than a treatment ( “taking it will keep you healthy” ) . This would counteract the argument some community members made that they were not sick , so therefore did not need to take LF drugs . Because the remit of this project at the outset was primarily to design and test an effective research tool , there was no budget available to assist the districts with their MDA operations . Furthermore , the research team did not monitor the planning or execution of MDA in either site . Both research sites followed through on many of the discussed recommendations , as described here . In Agam District , the district health office was able to secure additional funding from the local government to implement the two MDA rounds they were requested to complete . Note that with decentralized health financing in Indonesia , many districts are required to fund the operational costs for LF elimination themselves . Based on the recommendations of this study , the district health office in Agam retrained the 4000 community health workers responsible for drug distribution . Promotional media was used , including stickers on government vehicles , additional production of leaflets as well as banners . Prior to 2014 , schools had never been approached to aid in the promotion of MDA . After interpreting the baseline survey results with the district health team , teachers were provided with the flowcharts produced by the project . These flowcharts aided teachers in promoting the drug distribution by guiding them to ask their students if they had taken their LF drugs after the recent MDA . In an attempt to better reach men , the district team worked with local factories , distributing the drugs during working hours after securing consent from the factory management . Depok City was unable to secure additional local financing for its repeated MDA rounds . As a result the head of the program used every opportunity to integrate LF promotional and educational messages into existing activities . In most district health meetings , the LF program promotes the MDA to those stakeholders present . Using existing primary health care center funds , community health workers participated in “refreshing” activities prior to MDA , where previous training was reviewed . New stakeholder groups were approached , specifically police and army barracks located in the city , private and public hospitals , the Indonesian Association of Midwives , the Indonesian Doctors Association as well as local NGOs to promote and facilitate MDA . In terms of additional promotional activities , a running text billboard ran messages one month prior to the start of MDA and a radio show integrated LF messages into their regular programming . A number to call or text questions was posted and promoted so that the community members could present concerns to the health team . Finally , a small leaflet was produced for inclusion with the drug packages . This provided point of contact information for the drug recipients on how to take the pills . Health staff and community health workers were provided with a Frequently Asked Question ( FAQ ) sheet to aid with drug distribution . The three primary outcome variables related to compliance showed a marked improvement in the endline surveys relative to the baseline surveys ( Table 3 ) . Of those who had ever received LF drugs , Agam in particular showed a marked increase from 81% to 100% of those surveyed . This indicates that Agam District was able to reach significant numbers of new individuals who had never received LF drugs in previous MDA rounds . Specifically this represented an increase in drug receipt across all age ranges , with the highest being a 40% increase for those under the age of 25 years , one of the key target groups identified in the baseline survey . For those who reported a history of taking LF drugs in the past , the most prominent change was seen in Depok , from 72% in the baseline to 88% in the endline survey . This represents an increase in the uptake by first time compliers . For the total sample , the change in compliers went from 79% in the baseline to 90% in the endline survey , of those who had ever received LF drugs . Table 3 provides the summary of results of key outcome variables for both baseline and endline surveys . Respondents were asked about their participation in the last round of MDA . Agam District increased the proportion of those receiving LF drugs in the last round by 32% while Depok City’s improvement was 6% . The change in those reported to have taken the drugs in the last MDA was more marked in Depok City from 48 . 2% in the baseline round to 67 . 3% in the endline round , representing a 40% improvement between the two survey rounds . In Agam the change was from 66 . 6% to 84 . 1% , representing an improvement of 27% for compliance in the last MDA . Other changes between baseline and endline datasets included key indicators related to the recommendations that were given prior to the 2014 MDA . Awareness of MDA prior to drug distribution increased in both sites , as did the awareness of someone taking the treatment in the household . The message to take all of the pills at the same time appears to have been well communicated in both research locations with a marked improvement from 63% to 75% ( p<0 . 001 ) overall . In the multivariable analysis ( Table 4 ) , some factors remained strongly associated with compliance as seen in the first survey round . In the multivariable model for compliance in the last MDA , perceived importance of LF drugs for health continued to be strongly associated with compliance ( AOR 42 . 76; p = 0 . 001 ) . Similarly those who believed that LF drugs were safe were 3 . 7 times ( AOR ) more likely to comply in the last MDA round than those who perceived the drugs as dangerous ( p = 0 . 027 ) . As in the baseline models , those who did not know anyone else in their household who took the drugs were less likely ( AOR 0 . 16 ) to comply in the last MDA round than those who did ( p<0 . 001 ) . In addition , some new factors emerged that were associated with compliance . In the model for compliance in the last MDA , length of stay in the region less than two years was associated with higher odds of compliance than those who had lived in the area for more than two years ( p = 0 . 001 ) . Additionally those who did not know a cause of LF were less likely to have complied in the last round of MDA ( AOR 0 . 25; p = 0 . 005 ) than those who knew that worms caused the disease in the body . In the model for “having ever taken LF drugs” some new factors emerged as well . In terms of the identity of the drug distributor , respondents who knew the drug deliverer were more likely to comply compared with those who did not know the deliverer ( p = 0 . 001 ) . External influence on the decision to comply remained strong . Respondents who cited an external influence on their decision to comply were 2 . 6 times ( AOR ) more likely to have ever taken LF drugs compared to those who reported no outside influence ( p = 0 . 034 ) . Table 5 summarizes the key factors that were positively associated with complying with the LF treatment from both the baseline and endline surveys .
There are some the limitations in our research that are worth noting . The EPI methodology when applied to the context and combined with the logistical constraints in Agam District and Depok City resulted in an overrepresentation of females in the survey total . Research enumerators limited their household visits to daylight and early evening hours , thereby missing some males who work outside the home . To control for this , we weighted our sample according to the demographics in those two areas . Another limitation related to the data collection related to the start of the rainy season in January . This resulted in some delays in our data collection which meant that the results were not available to the district health teams before March / April , after the time when the district budgets were allocated . Based on the results of our endline survey , it appears that this delay did not affect the uptake of recommendations that showed an impact on coverage and compliance . District health staff may not be aware of the actual compliance ( drug ingestion ) in their area due to use of different definitions in reporting or metrics used in calculation [10] therefore they may be surprised when they do not fulfill pre TAS and TAS requirements . During the 4–6 year course of MDA , it is therefore recommended to carry out at least one coverage survey to assess the presence of a coverage-compliance gap in IUs especially where directly observed treatment is not enacted . Furthermore in those areas where coverage has known to be problematic or a considerable coverage-compliance gap is known , district health staff may not always have the tools or information to understand how to improve their MDA . Therefore it is recommended to consider this tool as a substitute coverage survey before reaching the pre-TAS stage so that results can be interpreted and applied towards the next MDA . Using a tool such as the one presented here could alert district health staff as to where to re-direct their efforts to ensure effective drug distribution and that distributed drugs are actually ingested . District health teams who must implement additional MDA rounds can benefit from specialized technical support based on reliable social research findings . It is recommended that national programs consider on a case-by-case basis which IUs would be helped most by a process as described here . This would ensure that valuable resources are not invested into MDA systems that continue to underperform without first having a deeper understanding of barriers to uptake and where programmatic adjustments can be made . Moreover , when additional MDA rounds are needed , staff report feeling demoralized and uncertain as to how they would secure additional funds to support further MDA rounds . Assistance with advocacy and understanding of the TAS requirements should be available to IUs that must continue MDAs beyond their planning . This is especially recommended in decentralized health systems where the local government provides some of the funding for MDA activities . The tool and process used in this implementation research reveal that districts have the potential to implement their own feasible and affordable improvements to MDA without additional funds and with minimal technical support . It is recommended to further promote this tool and the implementation research process so that national programs can assist and guide IUs that appear to be problematic with their MDA interventions . Furthermore a tailored approach that aims to reach specific groups in the population is an effective way to improve both drug distribution and uptake . By recognizing that all population groups will not respond to MDA in the same way , the district programs in this research reached out more efficiently and effectively to their populations and demonstrated better MDA outcomes as a result . Simple promotional materials like flowcharts , frequently asked question sheets ( FAQ ) and drug packaging inserts aid those individuals at the frontline of drug distribution and do not require a significant financial commitment to develop and reproduce . These tools can be tailored to the context and those factors that have been shown to influence both coverage and compliance . The cost of improving a program’s drug coverage rates reduces the necessity for future MDA rounds . It is recommended to evaluate the cost effectiveness of this technique so that it can be balanced against the cost of additional MDA rounds where improvement in reported coverage rates does not occur . Finally a research tool based on people’s experiences with the MDA program provided reliable and valid results that could be interpreted into feasible and applicable recommendations for the LF program . It is recommended that the use of this micronarrative methodology be more widely explored in other areas where health behavior is studied .
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This research describes the process used to assist two districts endemic for lymphatic filariasis ( LF ) in Indonesia to better understand the reasons why their LF elimination programs have had suboptimal results . A novel survey design was used to collect stories about people’s direct experiences with mass drug administration ( MDA ) for LF . These questionnaires also explored the reasons community members took or did not take the LF drugs . Following MDA in 2013 , two baseline surveys in endemic communities provided insight into the district MDA programs . Together with district health officials , feasible recommendations were provided before the next MDA round in 2014 . Uptake of these recommendations by the districts was high , although no additional funding was made available for programmatic changes . As a result , both districts reported significant improvements in their MDA coverage and compliance rates after the endline surveys were completed in 2015 . This demonstrated the utility of the survey tool and process to impact change and improvement in MDA programs .
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2016
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Improving Coverage and Compliance in Mass Drug Administration for the Elimination of LF in Two ‘Endgame’ Districts in Indonesia Using Micronarrative Surveys
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The evolution of degenerate characteristics remains a poorly understood phenomenon . Only recently has the identification of mutations underlying regressive phenotypes become accessible through the use of genetic analyses . Focusing on the Mexican cave tetra Astyanax mexicanus , we describe , here , an analysis of the brown mutation , which was first described in the literature nearly 40 years ago . This phenotype causes reduced melanin content , decreased melanophore number , and brownish eyes in convergent cave forms of A . mexicanus . Crosses demonstrate non-complementation of the brown phenotype in F2 individuals derived from two independent cave populations: Pachón and the linked Yerbaniz and Japonés caves , indicating the same locus is responsible for reduced pigmentation in these fish . While the brown mutant phenotype arose prior to the fixation of albinism in Pachón cave individuals , it is unclear whether the brown mutation arose before or after the fixation of albinism in the linked Yerbaniz/Japonés caves . Using a QTL approach combined with sequence and functional analyses , we have discovered that two distinct genetic alterations in the coding sequence of the gene Mc1r cause reduced pigmentation associated with the brown mutant phenotype in these caves . Our analysis identifies a novel role for Mc1r in the evolution of degenerative phenotypes in blind Mexican cavefish . Further , the brown phenotype has arisen independently in geographically separate caves , mediated through different mutations of the same gene . This example of parallelism indicates that certain genes are frequent targets of mutation in the repeated evolution of regressive phenotypes in cave-adapted species .
The blind Mexican cave tetra , Astyanax mexicanus , is a troglobitic characin fish exhibiting a variety of cave-specialized traits . In general , the cave ecosystem supports the evolution of some traits that are enhanced or increased over time ( i . e . , “constructive” traits ) , as well as some traits that decrease or degenerate over time ( i . e . , “regressive” traits ) [1] , [2] . It is important to note that the term “regressive” does not connote anything about whether the trait in question is more adaptive or whether its loss is selected , only that it is lost . Examples of constructive traits include enhanced chemosensory reception , e . g . , increased number of taste buds and organs of the lateral line system [3] . Alternatively , examples of regressive traits include reduction of eye size and depigmentation [4]–[6] . At least 29 different cave populations from northeastern Mexico have been described , with Pachón cavefish being geographically isolated and cave-specialized ( Figure 1 ) [7]–[9] . Members of each cave can be crossed with the Surface , sighted ancestral form to create viable F1 hybrids [10] . Crosses and trait distribution analyses in F2 individuals have demonstrated that several regressive traits , e . g . eye loss and depigmentation are polygenic [4] , [11]–[15] . Among the most notable traits characterizing these fish is the marked reduction in skin pigmentation [4] , [16] , occurring independently in multiple cave forms [17] . While broadly defined , pigmentation in Astyanax is polygenic; some particular aspects of pigmentation are inherited in a monogenic , recessive fashion [4] , [18] . As an example , albinism was recently discovered to be a monogenic trait caused by loss-of-function alleles of Oca2 , this gene having been independently mutated in three different cave forms [1] . An additional simple trait affecting body pigmentation , termed the brown mutation , was described in the late 1960's as being present in several caves ( Figure 1 ) . The brown phenotype , affecting eye color as well as the number and size of melanophores on the body [18] , was observed in the wild in fish from the Chica , Pachón and Sabinos caves . In addition , complementation test crosses carried out between F1 individuals derived from surface and various cave populations showed that the same locus was responsible for the brown phenotype in the Curva , Pachón , Piedras and Yerbaniz caves [16] . Three cave populations have been reported to harbor albinism mutations , including individuals in the Molino , Pachón and the inter-connected Yerbaniz and Japonés caves [1] , [4] , [16] , [19] . As noted above , the brown phenotype also is found in two of these cave systems . Further , in contrast to the Pachón cave , where the brown phenotype has been observed in individuals that do not carry the albino mutation , there is no published evidence that fish from the linked Yerbaniz/Japonés populations ever display the brown phenotype in nature . Therefore , it is not clear whether the brown mutation arose prior to the evolution of albinism in this population or , alternatively , if the brown mutation became fixed following the presence of epistatic albino mutations . Therefore , the cave populations that exhibit the brown mutation in nature , based on published data and/or inference through lack of albinism in these caves , include the Chica , Curva , Pachón , Piedras and Sabinos populations ( Figure 1; green ) . Laboratory crosses have been used to examine the inheritance of the brown phenotype . Segregation was analyzed in fish descended from a Surface×Pachón cave cross by scoring eye color of seven-day-old F2 larvae ( derived from a cross of F1 hybrids of Surface and Pachón cavefish ) as black , brown or pink ( i . e . , albino ) . When controlling for albinism , the frequency of individuals demonstrating the brown phenotype strongly predicted the participation of a single , recessive allele ( black-eyed frequency = 0 . 73 , brown-eyed frequency = 0 . 27 , N = 5094 ) [18] . In this report , we investigate the genetic basis for the brown mutation by screening F2 individuals derived from an equivalent cross ( Surface×Pachón cave hybrids ) to that used in the original descriptions of this mutant [18] . We screened a pedigree of 488 individuals with 262 microsatellite markers , expanding upon pedigrees previously described [1] , [2] . Consistent with other studies , our linkage analysis revealed a single , strong QTL influencing melanophore number in the post-optic region of the head and the dorsal flank in individuals derived from the Surface×Pachón cross ( Figure 2 ) . When we used the same criteria for mapping melanophore number in a Molino cave×Surface cross no statistically significant QTL were obtained . This is consistent with the reported absence of the brown mutation in this particular cave population ( Figure 1 ) [16] . Using a candidate gene approach , we cloned and characterized the Astyanax form of the gene , melanocortin type 1 receptor ( Mc1r ) , as the likely locus controlling this trait . Sequence analyses of the open reading frame ( ORF ) of Mc1r in Pachón individuals revealed a 2-base-pair deletion in the extreme 5′ end of the coding sequence , corresponding to the N-terminal domain of Mc1r ( Figure 3A ) . The Mc1r protein is a member of the GPCR superfamily of genes , comprised of an N-terminal domain , seven hydrophobic transmembrane domains , and a carboxy terminal domain [20] . One of the primary functions of Mc1r is to activate adenylyl cyclase in response to ligand binding , resulting in an intracellular increase in cAMP levels [21] . Mc1r binding leads to activation of downstream effectors in the pigmentation pathway , including the target gene mitf , which is transcriptionally upregulated by cAMP signaling in melanocytes [22] . Coding mutations in this gene have been described in model systems , including the classical ‘extension’ locus mouse mutant , which lacks normal functioning of Mc1r [23] . Coding sequence alterations are also known from natural populations , associating strongly with distinct coat and plumage color morphs in a variety of mammals and birds , respectively [24] . Depigmentation has arisen multiple times in different caves; therefore we extended our search for variant alleles to twelve other caves . We found an additional , independent mutation in the Yerbaniz cave ( known to harbor the brown mutation ) as well as Japonés cave individuals ( Figure 3B , C ) . The point mutation present in these caves , C490T , alters an arginine residue homologous to that identified in certain human individuals with the red hair color ( RHC ) phenotype [25]–[28] . This analysis identifies a novel role for Mc1r in the evolution of degenerative phenotypes in blind Mexican cavefish . Further , we demonstrate that the brown phenotype has arisen independently in multiple forms of cavefish , mediated through different mutations of the same gene . This example of parallelism is consistent with other recent studies suggesting that certain genes may be frequent targets of mutation in the repeated evolution of similar phenotypes .
We used a previously described Pachón F2 pedigree obtained from a cross between the Surface×Pachón cave morphs of Astyanax mexicanus [1] , [2] . Briefly , a Pachón individual was crossed to a Surface individual , and two sibling F1 individuals were crossed to produce 539 F2 individuals . 488 of these individuals were genotyped for Mc1r . All F2 progeny were raised with two individuals per tank ( to control for size variation ) and euthanized at 7 months of age . We performed an additional analysis of the melanophore number trait in a Molino backcross comprised of 111 individuals . This cross was performed by crossing a Molino cavefish to a Surface fish , and then mating an F1 individual to a second Molino fish . This backcross progeny set was reared in group tanks , euthanized at 14 months and fixed in 4% paraformaldehyde . Fin clips were collected from all individuals to isolate genomic DNA for subsequent genotyping . All Astyanax animal care protocols were approved by the NYU/University Animal Welfare Committee . Each individual was genotyped with 262 microsatellites using PCR reactions carried out in a 10 µl volume containing: 0 . 1 mM MgCl2 , 6 mM Tris-HCl , pH 8 . 3 , 30 mM KCl , 0 . 006% glycerol , 0 . 25 mM dNTP mix ( Roche ) , 0 . 06% Tween , 0 . 06% Nonidet P-40 , 0 . 25 units of Taq DNA polymerase ( Roche ) , 5 nM forward primer , 200 nM reverse primer and 200 nM of the fluorescent tag primer: 5′-CACGACGTTGTAAAACGAC-3′ labeled with one of two phosphoramidite conjugates ( Hex and Fam ) and amplified using the PCR program previously described [1] . QTL mapping was carried out using the interval mapping function of MapQTL ( version 4 . 0 ) to determine the LOD scores and percent variance explained ( PVE ) at the melanophore number locus using a permutation test , as previously reported [1] . Mc1r was placed on the linkage map using primers designed around an informative size-length polymorphism in the coding sequence ( forward primer: 5′-TTCCTAAAGAGACCCCAGACC-3′; reverse primer: 5′-GCATTCATATCCCCCAGAGA-3′ ) . Similarly , the gene trpm7 was placed on the linkage map using the following primers designed around an informative size-length polymorphism in an intron ( forward primer: 5′-TGCAGGCACTAAATATGCTACAA-3′; reverse primer: 5′-GATGGATAAAAAGGAGGTGAGG-3′ ) . Several additional cave and surface individuals collected from the wild were genotyped at the Mc1r locus ( Table 1 ) . Individuals were collected in the wild from the following cave localities: Arroyo , Caballo Moro , Chica , Curva , Japonés , Molino , Pachón , Río Subterraneo , Tinaja , Toro , Yerbaniz; and the following surface localities: Carolina , Honduras , Jutiapa , Mosquito Coast , Pantepec . Note that the Mosquito Coast and Jutiapa populations were drawn from the Atlantic drainage in Honduras; Carolina is in east central Oaxaca in Mexico in the Río Coatzacoalcos Atlantic drainage; and Río Pantepec is a tributary of the Río Tuxpan in Veracruz . In addition , individuals from an inbred strain of Piedras cavefish were analyzed . The extent to which cavefish and Surface fish at various localities are threatened is unknown . Therefore , genotyping was carried out in the most individuals we could sample from each locality given collection limits . Each individual was genotyped for the 2-bp deletion using size-length polymorphic primers ( as above ) and the C490T SNP ( forward primer: 5′- ATGATCTGCAGTTCCGTGGT -3′; reverse primer: 5′-TCCGTGTGGTAGACGATGAA -3′; SNP primer: 5′-ACAGCATCATGACCACGAGG-3′ ) using the SNaPshot kit ( Applied Biosystems ) . Melanophore number was assayed in two distinct regions of non-albino Pachón F2 cross individuals as previously described [2] , [15] . Both phenotypic measurements yielded the identical QTL , indicating that both measurements reflect the same genetic basis . We performed an analysis of melanophore number in three distinct regions of adult Molino backcross individuals . No QTL was detected in this pedigree , consistent with the lack of the brown mutant phenotype in fish derived from this cave [16] . Pigmentation differences were compared between individuals demonstrating the brown phenotype ( i . e . , those carrying two copies of the Pachón Δ23 , 24 allele ) and surface ( wild type ) individuals . First , individual scales were carefully removed from the dorsal region of the fish , in the same region assayed in the original phenotypic analysis [2] , [15] . Each scale was assessed for the number of melanophores and the amount of melanin per scale . In sum , 60 scales were collected from surface ( n = 4 ) and brown mutant ( n = 4 ) individuals of approximately the same age , washed briefly in physiological saline solution ( 130 mM NaCl , 2 . 7 mM KCl , 5 . 6 mM D-glucose , 1 mM EDTA , 5 mM Tris-HCl , pH 7 . 2 ) and fixed for several minutes at room temperature in 4% Paraformaldehyde ( pH 7 . 4 ) as previously described [29] . Scales were whole-mounted on slides and imaged at various magnifications using either a Leica MZ FLIII stereoscope or Zeiss Axiophot compound microscope . All images were collected with the ACT-1 software program using identical lighting and software settings . Mean melanin content for wild type versus brown phenotype melanophores was compared between 40× images of surface ( wild type ) and brown mutant individual melanophores . Photographs were light/dark-inverted using Adobe Photoshop CS3 and traced using the “freehand selection” tool in ImageJ 1 . 40 g ( NIH ) . The mean pixel intensity value ( ranging from 0 to 256 ) was collected for each inverted image ( n = 16 , each phenotype ) . All measures were tested for significance at the p = 0 . 01 level using a Student's t-test . All statistical analyses were carried out using Microsoft Excel ( version X ) . Two additional qualitative comparisons of pigmentation were performed . Cryosections of the dorsal flank from the identical regions of the surface and brown mutant individual were collected to compare sectioned melanophore morphology and melanin content . 20 µm thick sections were collected on Superfrost slides ( VWR ) , incubated at room temperature for 1 hour , rinsed in PBS and mounted using gelvatol mounting medium . All images were collected ( as above ) using identical lighting and imaging software settings . Ultrastructural analysis was performed using representative surface ( wild type ) and brown mutant tail tissue . Tissues were fixed and embedded in resin prior to ultrathin ( 95 nm ) sectioning for electron microscopy analysis . Samples were analyzed with a Tecnai G2 Spirit BioTWIN scope at the Electron Microscopy Core Facility at Harvard Medical School using an AMT 2 k CCD camera . We modified the method of Stemshorn et al . , 2005 [30] to determine in the physical genome of Danio rerio the presumptive location of sequences homologous to the microsatellites we have identified in Astyanax mexicanus . Accordingly , we performed BLAST searches of the complete genomic DNA clone sequence from each of our microsatellites . All searches were carried out in Ensembl , assembly Zv7 , release 47 , using the following search parameters: E value cutoff: 10; Search sensitivity: No optimization; Search engine: BlastN; number of returned hits: 10 . For each returned hit , we recorded the number of alignments , number of hits , chromosomal position , Stats score , E value , length of the identified sequence , percent of the sequence identified , additional hits , and whether the top hit was part of a coding or non-coding region . We assembled an ‘anchored’ version of the linkage group ( Figure 2 ) containing the melanophore number QTL only using the strongest hits , as determined by the Stats score and E values . Total RNA was isolated and pooled from either 4 adult Surface individuals or 4 adult Pachón cavefish individuals using Trizol reagent ( Invitrogen; Carlsbad , CA ) , according to manufacturer protocols . cDNA was generated from these pools using Transcriptor RT according to manufacturer protocols ( Roche ) , and subjected to gradient PCR amplification . Using a degenerate cloning strategy , we designed primers against the following conserved amino acid residues: GLISLVENI ( forward: 5′-GGGCCTGATCTCCCTGGTNGARAAYAT-3′ ) and IICNSLIDPL ( reverse: 5′-GGGGGTCGATCAGGGAGTTRCADATDAT-3′ ) using the online primer design software CODEHOP [31] ( blocks . fhcrc . org/codehop . html ) . We amplified a 736-bp fragment in both Surface and Pachón cave form cDNA template using a gradient PCR program ( 95°C for 2:00; 95°C for 0:30; 48°C–58°C for 0:30; 72°C for 1:30; cycle to Step 2 , 34 times; 72°C for 10:00; 4°C ) . PCR fragments were subcloned into pGEM-Teasy vector ( Promega; Madison , WI ) , sequenced , and identified using the NCBI BlastX search algorithm . We extended our transcript sequence in the 3′direction using primers designed from known sequence in combination with the Smart RACE kit ( Clontech ) prepared using fresh total RNA from several representative Surface and Pachón cave individuals . Attempts to clone the 5′UTR using the same kit were unsuccessful , therefore we used a GenomeWalker kit ( Clontech ) to clone the 5′ end of Mc1r , using gene-specific primers designed using the online software program Primer3 ( frodo . wi . mit . edu ) . The open reading frame of Mc1r was determined using the “search ORF” function in EditSeq 6 . 1 ( DNASTAR Lasergene , version 6 ) . The predicted amino acid structure was then compared to the known amino acid structure of closely related teleost fish and other vertebrates to infer correct Mc1r protein size . Using primers designed to amplify full-length Mc1r , we cloned a 972-bp fragment of the ORF from Surface and genomic DNA derived from individuals from the following caves: Arroyo , Chica , Curva , Japonés , Micos , Molino , Piedras , Tinaja and Yerbaniz . We cloned a 970-bp fragment in genomic DNA derived from Pachón cave form individuals . Sequences were aligned and analyzed using the Clustal V method in MegAlign 6 . 1 . 2 ( DNASTAR Lasergene , version 6 ) . The morpholino , 5′-AGTGATGGCGCGAAGAGTCGTTCAT-3′ , was designed to the first 25-bp of Danio Mc1r ORF sequence based on the accessioned sequence NM_180970 ( NCBI ) . Morpholino injections were carried out using a variety of concentrations ranging from 0 . 2 µM to 1 µM in a 1 nl volume . All concentrations of morpholino injection resulted in decreased melanin , with correspondingly stronger phenotypes with increased concentration of morpholino injection . Several different concentrations were tested alone and in combination to determine the optimal concentrations and volumes for survival . We injected single-celled embryos with 1 nl of 0 . 2 µM concentration of morpholinos either alone , or in combination with in vitro transcribed RNA derived from Surface , Pachón or Yerbaniz alleles of Mc1r using the mMessage mMachine kit ( Ambion ) . All RNA injections were carried out in 1 nl injection volumes of 5 picomolar concentrations . Embryos were injected at day 0 and reared at ∼27°C . Phenotypes were assayed and scored every 24 hours . By the fifth day of development , we noted a decrease in the phenotypic penetrance , likely as a consequence of compensatory rescue from endogenous Mc1r activity . Therefore , we scored phenotypes of double-injected individuals at the fourth day post-fertilization , around hatching ( prim-25 , Danio rerio ) [32] . Embryos were scored as having “normal” pigmentation corresponding to wild-type , uninjected individuals; or , “reduced” pigmentation that corresponded to the same phenotype as morpholino-alone injected individuals .
Studies initially describing the brown mutation focused on its presence in the Pachón , Chica and Sabinos caves ( Figure 1 ) , near Tamaulipas , Mexico [8] , [18] . More recently , the brown mutation was characterized in additional cave forms of Astyanax through a series of complementation test crosses carried out among several independently derived cave forms , including: Curva , Piedras , Molino and Yerbaniz caves ( Figure 1 ) [16] . These studies demonstrate the presence of the brown phenotype in every cave studied with the exception of the Molino cave , in which skin pigmentation in F2 offspring did not differ from Surface form individuals ( Figure 1 ) [16] . We presume each of the classical reports refer to the same trait , given that all describe identical manifestations of the brown mutant: diminished melanin content and decreased numbers of melanophores on the head and flank ( Figure 4G ) [4] , [16] , [18] . It should be noted , however , that Wilkens , 1988 [p . 307 [4]] describes the number of melanophores as an “additively polygenic” trait , indicating that additional genes may be relevant for the number of melanophores developing on the entire body , or different regions thereof . Nevertheless , consistent with our work , these authors report the existence of a recessive allele ( the brown gene mutation ) that “reduces the melanin content of the melanophores” in multiple cave forms through convergent evolution [p . 550 [16]] . Our linkage studies were designed to allow us to address as many traits as possible in a single cross , and the stage at which the fish were assessed ( see Methods ) was chosen accordingly . Thus , we could not replicate the initial assay that identified the brown gene [18] , i . e . , fixing 7-day-old larvae and screening eye color . As an alternative , we counted the number of melanophores in a circumscribed region immediately posterior to the optic region ( see Figure 4A; red box ) , and on the dorsal flank of fish ( not shown ) from the Pachón F2 cross . Both phenotypic analyses yielded the identical result , a single QTL on Astyanax linkage group P09 ( Figures 2 , 5 ) , based on the linkage map published by Protas et al . , 2008 [2] . This QTL had a LOD score of 6 . 6 for the dorsal melanophore count and 9 . 8 for the eye region melanophore count , each explaining 16 . 5% and 23 . 4% , respectively , of the phenotypic variance of this trait . To accelerate our search for candidate genes that could mediate the effect of the brown phenotype QTL , we decided to “anchor” the linkage group encompassing our QTL for melanophore number to the Danio rerio physical genome . We adapted the method of Stemshorn et al . , 2005 [30] to analyze the genomic DNA sequences flanking our polymorphic microsatellites via BLAST searches against the latest version of the Danio rerio physical genome ( Ensembl Assembly Zv7 , Sanger Institute; www . ensembl . org ) . For approximately 50% of the Astyanax linkage group P09 ( LG P09 ) genomic markers analyzed , the Ensembl BLAST search algorithm identified unique Danio rerio sequences as plausible homologs ( Figure 2 ) . As described in Methods , these were obtained by only accepting hits with the lowest E values and highest Stats scores ( indicating the highest support for sequence similarity between Astyanax and Danio ) . Using these criteria , the sequences homologous to Astyanax LG P09 are localized within a roughly 15 Mb stretch of chromosome 18 , including markers flanking the brown QTL ( Figure 2 ) . We also performed BLAST searches in all of the other available sequenced teleost genomes ( data not shown ) , but found the highest number of significant hits were from Danio rerio . This likely reflects a more recent phylogenetic relationship between Danio and Astyanax , compared with other teleost model systems with a sequenced physical genome [33] . We could not exclude the possibility that the brown QTL itself might reside in a small genomic interval translocated from chromosome 18 to an entirely different genomic region in Astyanax . In spite of this formal possibility , the large ∼70 cM block of synteny between the genomes was suggestive enough that we developed a list of candidate genes within or near the syntenic critical region ( Figure 2; orange ) that could potentially affect the trait of interest ( i . e . , could decrease melanin and/or reduce numbers of melanophores ) based on published activities . Two genes residing on chromosome 18 in Danio rerio stood out , transient receptor potential melastatin 7 ( trpm7 ) and melanocortin 1 receptor ( Mc1r ) . Size-length polymorphisms between the two morphotypes ( intron of trpm7 , coding region of Mc1r ) were used as markers to place these genes on the Astyanax linkage map . Trpm7 was indeed placed on Astyanax LG P09 , however its position is over 25 cM from the melanophore number QTL ( Figure 2 ) . In contrast , Mc1r was positioned directly at the QTL for melanophore number with a peak LOD score , making Mc1r our strongest candidate gene for the melanophore number QTL in Astyanax . We also cloned several other Astyanax genes involved in pigmentation in model organisms that did not lie on Danio chromosome 18 , including melanin concentrating hormone receptor 1a ( mch1ra ) , kit receptor ( kita ) and shroom2 . As predicted , these genes mapped outside LG P09 in Astyanax . To try to identify coding changes that alter Mc1r activity in Pachón cavefish , and hence could be responsible for the brown phenotype , we cloned the entire open reading frame and portions of the 5′ and 3′ UTR from both Surface and Pachón cavefish DNA . Based on predicted protein homology to Danio rerio , we identified the predicted start codon , N-terminus , seven transmembrane domains , C-terminus and stop codon in the Surface fish sequence of Mc1r ( Figure 6 ) . There is no evidence of a non-functional , derived allele in Surface fish when the sequence is compared across multiple teleost and other vertebrate species . We next compared these sequences to the open reading frame in Pachón cavefish and discovered a 2-base-pair deletion in the extreme 5′ region of the ORF ( Figure 3A; positions 23 , 24 ) . Our sequence analysis predicts this deletion to result in a non-functional transcript as it produces a frame-shift , as well as the introduction of a premature stop codon at nucleotide position 315 ( Figure 3A ) . The resulting protein carries no sequence similarity to the wild type form of the protein , lacking transmembrane domains and C-terminus ( Figure 6 ) . Aside from the 2-base-pair deletion , there were no other SNPs or sequence changes evident in the remainder of the ORF sequence of Pachón compared to Surface Mc1r sequence . This result , taken together with the genetic mapping data , strongly suggests that the two base pair deletion in the Pachón Mc1r allele , could represent the brown mutation . Complementation tests had indicated that the same gene is responsible for the brown phenotype in several different caves [16] . To determine whether Mc1r coding mutations could be identified in other independently derived populations of cavefish , we extended our complete sequence analyses to representative individuals from nine other caves , specifically the Arroyo , Chica , Curva , Japonés , Micos , Molino , Piedras , Tinaja and Yerbaniz populations . Interestingly , the 2-base-pair deletion discovered in members of the Pachón cave was also found , in heterozygous form , in a single member from each of the Yerbaniz and Japonés cave localities ( Table 1 ) . Further , with the exception of these two localities , we found no differences in Mc1r coding sequence relative to the Surface ( wild type ) populations . In the Yerbaniz and Japonés caves , however , we discovered the identical point mutation ( C490T ) causing an arginine to cysteine mutation ( R164C ) in the second intracellular loop of Mc1r protein ( Figures 3B , 6 ) . In addition , we identified a silent mutation ( G666A ) in Yerbaniz fish that was not present in the Japonés individuals we sampled ( Figure 3C ) . The Yerbaniz and Japonés caves are located within ∼5 km of one another and likely represent the same cave system ( perhaps connected through a contiguous underground network ) . Therefore , functional analyses ( see below ) were carried out using the R164C mutant allele cloned from a representative member of the Yerbaniz population . Interestingly , mutations have been described in other species in the Mc1r arginine residue homologous to the one mutated in Yerbaniz and Japonés individuals , including rock pocket mice ( Chaetodipus intermedius ) and humans . In particular , in humans the R160W Mc1r variant ( homologous to position 164 in Astyanax ) is one of two alleles strongly associated with the inheritance of red hair and pale skin [26] , [27] , [34] , [35] . Mutations of this amino acid have been demonstrated to convey diminished receptor function [21] , [36]–[38] . Therefore , we reasoned that the identical charge-changing amino acid mutation at the homologous position would be extremely likely to cause diminished function of Mc1r protein in the Yerbaniz and Japonés populations of cavefish , explaining the presence of the brown mutation in these fish . Prior reports of the brown mutation did not provide detailed descriptions of the depigmentation phenotype exhibited by mutant individuals . The first paper on the subject reported brown mutants “to have melanophores smaller in size and fewer in number” than wild type fish [p . 10 [18]] . Wilkens , 1988 [4] similarly reported a decrease in number and melanin content , depicting scale melanophores derived from Sabinos cave individuals as having much less melanin than surface individuals [p . 305 [4]] . To have a clearer understanding of the phenotypic consequences of the Mc1r mutation we identified , we quantified the number of melanophores present on scales collected from surface and brown mutant individuals ( Figure 7A , B ) , in the region employed in our phenotypic analysis . We found that wild type melanophores derived from surface individuals were more numerous and darker , compared to those of brown mutants ( Figure 7A–N ) . Further , melanophores in wild type scales were most often localized to the distal periphery of the scales ( Figure 7C ) , however were rarely found in the equivalent position in brown mutant scales ( Figure 7D ) . Representative melanophores from wild type and brown mutants were comparable in size , however the amount of melanin present appeared significantly decreased in brown mutant scales ( Figure 7E–L ) . Brown mutant scales demonstrated significantly lower numbers of melanophores compared to wild type ( Figure 7M ) . Further , brown mutants had a significantly lower amount of melanin per melanophore compared to wild type melanophores ( Figure 7N ) . We also compared melanophores in cryosectioned tissues through the epidermis of the identical region of a representative surface ( wild type ) and brown mutant individual . These sections were also collected at the same region assayed in our phenotypic analyses ( see Methods ) . We routinely found less ( or absent ) melanophores and or/melanin in the same regions of brown mutant versus wild type fish . This is likely due to the fact that brown mutants have lower numbers of melanophores than wild type ( Figure 7B , M ) . In equivalent regions where we found melanophores , however , there was far less melanin per cell in brown mutants compared to wild type ( Figure 7O–T ) . To determine the basis for decreased melanin in brown mutants , we processed identical regions of tail tissue for electron microscopy . Surface tissue demonstrated rich clusters of granules within melanophores ( Figure 8A , C , E ) , while brown mutant tissue demonstrated infrequent and few clusters of melanin granules ( Figure 8B , D , F ) . Overall , the amount of melanin per granule and the size of melanin granules were comparable between wild type and brown mutant tissues ( Figure 8E , F ) . To demonstrate that decreased functioning of Mc1r results in reduced pigmentation , we took advantage of the zebrafish system where techniques of gene knockdown are well established . We first injected zebrafish embryos with a morpholino ( MO ) targeted to the first 25 base pairs of the Danio Mc1r mRNA ( Figure 9 ) . Consistent with the well-described role of Mc1r in other vertebrate systems , we found a qualitatively decreased melanin content within melanophores of MO-injected individuals ( Figure 9 ) . Further , we observed a corresponding increase in the severity of reduction with increased dosage of MO injection ( data not shown ) . We also noted a decrease in eye pigmentation compared to same-aged wild type zebrafish recapitulating one of the hallmarks of the brown phenotype ( Figure 9 ) . In some cases , we could clearly detect a qualitative decrease in the number of melanophores on the developing yolk sac . However , this trait was highly variable in uninjected controls , suggesting a variation in the number or migration rate of early melanophores on to the developing yolk sac even in control embryos . Next , we co-injected individuals with Mc1r -MO and in vitro transcribed RNA derived from the Surface , Pachón or Yerbaniz allele of Mc1r ( Figure 9 ) . Individuals who were injected with both the MO and Surface RNA had a rescued melanin phenotype in 86 . 1% of cases ( Figure 9 ) . This demonstrates that the Surface form of Mc1r is properly translated and expressed in Danio , and further , can function normally in place of the endogenous protein . In contrast , individuals who were injected with either the in vitro transcribed Pachón or Yerbaniz RNA did not recover normal levels of melanin ( compare wild type with 20 . 7% in Pachón and 7 . 4% in Yerbaniz , Figure 9 ) . This data provides in vivo evidence that the two Astyanax cave alleles of Mc1r , in contrast to the surface allele , fail to rescue the depigmented phenotype in the zebrafish system . To estimate the frequency of the variant Mc1r alleles identified in this study , we sampled and genotyped a total of 140 individuals from 12 different cave localities , and a total of 231 individuals from 13 different surface localities ( Table 1 , Figure 1 ) . Every individual collected from the Pachón cave population were homozygous for the Δ23 , 24 allele . The majority of individuals sampled from the Yerbaniz ( n = 9 ) and Japonés ( n = 8 ) cave populations were homozygous for the C490T allele . One individual sampled from each cave , however , were heterozygous for the C490T allele and the Δ23 , 24 allele . No individual from any of the surface populations carried the Δ23 , 24 or C490T allele . Our sampling does indicate that both variant alleles of Mc1r are extremely rare outside of the Pachón , Yerbaniz and Japonés caves and are not polymorphisms in the current river population . However , an important caveat is that the frequency of alleles currently found in the surface fish population may not reflect the allelic frequencies from the time when the surface fish colonized the caves . Indeed , it is extremely likely that at least the Δ23 , 24 allele was present in the ancestral surface population and did not arise as a de novo mutation in the caves , since the same two-base pair mutation was identified in the Yerbaniz and Japonés caves . The Pachón and linked Yerbaniz/Japonés caves , which are approximately 35 km apart , do not appear to be connected geologically and moreover molecular analyses indicate that different microsatellite alleles were fixed in the two caves at all loci examined . If , indeed , the Δ23 , 24 allele was maintained in the two caves independently and assuming that this allele was relatively rare in the ancestral population as it is in the current surface population , it would at least raise the possibility that the brown mutation was positively selected in the two caves , even though the adaptive significance of the loss of Mc1r is less than obvious .
Numerous studies have characterized Mc1r in an extensive variety of model systems [29] , [39]–[41] and domesticated species [42]–[45] . More recently , the role of Mc1r variants in the adaptive coloration in several natural populations has been demonstrated [24] , [46]–[50] . In human beings , the role of various Mc1r variants has similarly been previously explored , particularly those showing strong associations to skin cancer development [51]–[53] . In this study , we identify two coding alterations to the gene Mc1r that presumably destroy normal functioning of the receptor protein . These are the first examples of coding mutations in Mc1r reported in a wild species of fish . Furthermore , this study implicates Mc1r in the novel role of depigmentation in wild animals populating a cavernous environment . The 2-bp deletion discovered in Pachón cavefish is predicted to cause a frame-shift leading to the introduction of a premature stop codon at position 315 . This allele is likely to be amorphic , and the truncated protein encoded by this locus to be non-functional . The mutation discovered in populations of Yerbaniz and Japonés individuals , C490T , causes a cysteine substitution at position R164 , which is homologous to the R160W mutant in human individuals ( Figure 6 ) . This point mutation , in two copies , has been reported to cause loss of Mc1r receptor function in humans [26] , [54] . In addition , the Mc1r null phenotype in humans is red hair and pale skin [55] , a phenotype similar to individuals harboring the R160W mutation . Thus , we believe the allele identified in members of the Yerbaniz and Japonés caves is also amorphic . Consistent with this interpretation , the brown phenotype is similar in the Pachón and Yerbaniz caves [16] . Morpholino knockdown studies in zebrafish further imply these to be non-functional alleles since neither Pachón nor Yerbaniz forms of Mc1r are capable of rescuing diminished pigmentation in MO-injected individuals ( Figure 9 ) . The phenotype we observe in these studies does produce a phenotype very similar to what one would predict from loss-of-function alleles of Mc1r in teleost fish . This claim of complete abrogation of Mc1r function remains speculative until a time when these receptors can be tested in vivo ( e . g . , gene deletion studies ) for the possible presence of residual function . The decrease in eye pigmentation , previously described as part of the brown phenotype and recapitulated in our zebrafish MO injections differs , however from what is seen in mammals , where altered Mc1r functioning does not appear to affect eye color [26] . Further , Astyanax carrying the brown mutation and Mc1r zebrafish morphants demonstrate a decrease in the total pigmentation of melanophores as well as pigment distribution within cells ( Figure 9 ) . This phenotype differs from the established role for α-MSH ( and the inferred role for Mc1r ) in mediating the distribution of melanin within melanocytes [reviewed in [21]] . These results suggest that there are differences in the function of the Mc1r receptor between higher and lower vertebrates . Regressive changes are defined as those traits that decrease or degenerate within a lineage over the course of evolution [56] , [57] . Specifically , the loss of pigmentation in Astyanax cave morphs is frequently cited as an example of regressive evolution [5] , [16] , [58] , [59] . For the brown trait to be considered regressive , it must be expressed in the wild ( i . e . , not be a molecular alteration that only manifests itself as a visible phenotype under the artificial conditions of crosses set up in the laboratory ) . In this study , albino individuals drawn from the wild were used in all crossing experiments . Therefore , in our pedigrees , albinism is epistatic to the brown mutation . However , it is clear from the literature that , in at least some caves , the brown phenotype is a true regressive trait . Sadoglu and McKee , 1969 [18] performed a series of intercrosses of wild-caught Pachón cave individuals . A pedigree of 340 offspring derived exclusively from wild-caught Pachón×Pachón crosses included 233 ( 68 . 5% ) individuals with the brown phenotype , 107 ( 31 . 5% ) albino individuals and no individuals with black ( wild-type ) coloration [p . 11 [18]] . While this may be a limited sample size and hence the absolute frequencies may be open to question , this result clearly demonstrates that not all of the fish in the Pachón cave were at that time albino . Moreover , additional expeditions during the 1960s confirmed that albinism was not present ( i . e . , not fixed ) in all individuals derived from the Pachón cave [13] , [60] . Even if the brown mutation was not completely fixed in the population , as in the data set reported by Sadoglu and McKee , 1969 [18] , at some frequency the brown mutation would be found in non-albino fish and hence the regressive trait would be expressed . In contrast , it is more difficult to determine whether the C490T mutation present in Yerbaniz and Japonés individuals arose as a regressive trait . Fish in the Yerbaniz [4] , [16] , [19] and Japonés [1] caves are reported as albino . Since it is impossible to determine whether the mutations in Oca2 or Mc1r arose first and/or were fixed first , the brown mutation in the Yerbaniz and Japonés caves may have arisen amidst the background of epistatic mutations causing albinism . In this scenario , the brown phenotype would not have been expressed and hence the trait would not be properly termed as regressive . Most likely , in this case , the brown coding alteration would have arisen by neutral mutation and drift . There are other caves , however , where the brown phenotype is clearly regressive . There are only three independent cave populations in this system that are reported to be albino ( Molino , Pachón and Yerbaniz/Japonés ) . Therefore , the brown phenotype would not be masked in caves that failed to complement the brown mutation such as in fish from the Piedras or Chica caves , where albinism has not been reported ( Figure 1 ) . There has , thus far , been no description of a loss-of-function mutation in a teleost fish . The phenotype we describe following morpholino knockdown of Mc1r closely resembles that seen in Pachón , Yerbaniz and Japonés individuals carrying the coding mutations described here . Moreover , co-injection of the Δ23 , 24 or C490T alleles did not rescue the morphant phenotype . We therefore strongly suspect this is the null phenotype in teleosts . However , since the knockdown could be incomplete , in principle , it would be worth considering what might be learned about the role of the Mc1r gene from comparison to other species . One might expect some level of functional conservation between higher and lower vertebrates . For example , just as in amniotes , Mc1r is expressed in the skin and its associated structures ( i . e . , scales ) of fish [61] , [62] . Additionally , numerous analyses have identified a single orthologous copy of the gene Mc1r in birds , mammals and fish species [63]–[66] . Further , the high degree of amino acid conservation of the Mc1r receptor across vertebrates ( ∼400 million years of evolution ) suggests an essential role for Mc1r in vertebrate physiological function [63] , [66] . Currently , data on the null phenotype for Mc1r is only available for two species , mice and humans . The classical mouse mutant , extension , carries two non-functional copies of Mc1r [23] . The phenotype of this mouse is a yellow coat , however , some residual eumelanin synthesis in the fur of this mouse has been reported [67] . In contrast , the phenotype of a homozygous Mc1r null human individual is red hair and fair skin [55] . Therefore , the expression of Mc1r loss-of-function alleles in different animals results in slightly different phenotypes . Moreover , greater caution must be maintained in extrapolating from the mammalian mutants to teleost fish since there are important differences in the role of the Mc1r pathway in these taxa . In mammals , the Mc1r ligand , α-MSH , stimulates de novo melanin synthesis in melanocytes [40] , [68] . The function of α-MSH in fish has most frequently been attributed to inducing rapid dispersal of melanin granules from clustering around the nucleus to dispersal throughout the cell body and dendritic extensions of the melanophores [61] , [63] , [64] , [69] . This role correlates extremely well with the phenotype we observe in the brown individuals at the ultrastructural level . In the wild , this rapid redistribution of melanin grants fish the ability to adapt their color to the surrounding substrate [63] . There are also significant differences in the physiology of pigmentation between amniotes and fish . For example , mammals have a single pigment cell type , the melanocyte , capable of producing black , brown , red or yellow pigment [70] . Normal signaling of the hormone , α-MSH , acting through Mc1r on melanocytes induces cells to produce black or brown eumelanin; while attenuated signaling results in production of red or yellow phaeomelanin [reviewed in [71]] . In contrast , teleost fish have three types of pigment-producing cells ( chromatophores ) : melanophores , xanthophores and iridophores [70] . Further , fish melanophores contain only eumelanin and do not produce phaeomelanin , the primary pigment produced in mouse and human Mc1r-null individuals [69] . Thus , a cross-species comparison likely sheds more light on the range of effects Mc1r can have on pigmentation than on the probable null phenotype in teleosts . Although the mutants we observe in the Pachón and Yerbaniz/Japonés caves are likely to be null , this remains to be confirmed . The question of how regressive phenotypes arise in nature has long interested biologists . The fascinating examples of parallelism provided by cave animals have stimulated a rich history of hypotheses on the evolution of cave phenotypes stretching back to Charles Darwin , who observed: “it is difficult to imagine that eyes , although useless , could be in any way injurious to animals living in darkness” , and therefore attributed loss of cave form phenotypes to “disuse” [72] . Two predominant theories seek to explain the regression of characters in cave animals: neutral mutation and selection [56] , [73] , [74] . According to the neutral mutation hypothesis , a particular trait that is no longer under selection is free to drift and accumulate mutations [4] . This hypothesis is tied closely to whether the trait plays an important ecological role . In fish , the primary function of Mc1r is to mediate distribution of melanin granules in fish melanophores , allowing the organism to adapt to its background substrate [63] . The cave environment includes the complete absence of light , and therefore this trait is rendered useless . Therefore , in the absence of phenotypic consequences , the function of Mc1r is likely no longer under selection . Thus , the most probable scenario in this case may be that , following colonization into the cave , the pigmentation function of Mc1r became free to accumulate mutations via drift . Nonetheless , as noted above , the presence of Δ23 , 24 allele in both cave populations could indicate that a preexisting mutation was independently selected in the two caves . In this case Mc1r may have a currently unappreciated pleiotropic effect that , although perhaps relatively small , is sufficient to provide a selection in the cave environment . If so , the C490T mutation is likely to confer the same phenotypic effect , as all fish in the Yerbaniz/Japonés cave we sampled carry two mutant Mc1r alleles , but neither of the two mutant alleles has become fixed under selection . In amniotes living above ground , the selection on the Mc1r locus is clearer . In many cases , alterations of Mc1r functioning lead to phenotypic consequences for coloration ( i . e . , coat color in mammals , plumage color in birds ) . These phenotypic differences are presumably selected for their adaptive functions for crypsis [46] or sexual ornamentation [75] . In humans , Mc1r variation presents a confusing picture . The phenotypic effects caused by variation at the Mc1r locus in non-African populations have been explained as a consequence of sexual selection [76] , as well as drift [77] , [reviewed in [78]] . Genome-wide analyses , however , failed to find strong evidence for selection at Mc1r locus in humans [79] . Regressive phenotypes have long been of interest to evolutionary biologists , given the many examples of regressive ( degenerate ) phenotypic variation when comparing humans to their primitive ape-like ancestors , e . g . , hair loss [80] . While many theories seek to explain the process of phenotypic degeneration , genetic insights into the underlying bases of these traits has remained elusive . Here , we describe the genetic basis of one of the earliest described genetic traits in the blind Mexican cave tetra , the brown mutation . We have found coding sequence modifications in two independent cave forms , the Pachón and Yerbaniz/Japonés caves , that explain the reduced function of the 7-transmembrane domain receptor protein , Mc1r . Complementation tests indicate that other cave populations of Astyanax , including Piedras and Curva , have brown phenotypes caused by mutations at the same locus as in Pachón and Yerbaniz . We failed to find any coding alterations in Mc1r sequence from fish collected from these other caves , suggesting that these populations likely carry regulatory mutations leading to a decrease or loss of Mc1r activity ( Figure 1 ) . The genetics of pigmentation have been explored in several model teleost fish including zebrafish [81] , [82] , medaka [83] and fugu [84] . More recently , the evolution of pigment variation in natural populations of sticklebacks has been explored using linkage analysis [85] . These authors discovered the relevant locus controlling lighter pigmentation to be due to cis-regulatory changes affecting expression of the gene , kit ligand ( kitl ) . Further , it was discovered that evolution in the cis-regulatory regions of the kitl locus similarly appears to control the evolution of lighter skin in recent humans [85] , [86] . Interestingly , another genetic locus examined in the zebrafish golden mutant ( SLC24A5 ) appears to also play a role in pigmentation evolution in recent humans [86] , [87] . Mutations in Mc1r likewise are found in humans in addition to Astyanax and other species . Thus , there seems to be a common tool-kit of pigmentation genes used to modify coloration during evolution of widely divergent taxa . The participation of Mc1r in the evolution of Astyanax cavefish depigmentation clarifies the essential role of this gene in vertebrates . Its role in the regressive evolution of pigmentation in independently derived cavefish indicates that certain genes may be important loci for convergent evolution of specialized traits of cave-adapted animals .
|
As we approach the 150th year since publication of On the Origin of Species , understanding the genetic architecture underlying evolutionary change remains an important challenge . When an organism enters a completely new environment or ecological niche , certain traits are no longer necessary for survival , while other new traits become critical for maintaining fitness . An example of such a transition is provided by cave animals . Many disparate taxa ( e . g . , crustaceans , salamanders , fish ) have colonized caves , presumably to escape predation or expand populations into an unexploited niche . Strikingly , similar traits evolve convergently despite significant phylogenetic distance between these organisms . Caves provide a unique environment including the absence of light , few predators , few sources of food , etc . Under these conditions , one observes striking changes in morphology including reduction in eyes , expansion of non-visual sensory systems , and a suite of metabolic and behavioral changes . To understand the genetic underpinnings of these shifts , we have established the blind Mexican cave tetra , A . mexicanus , as a genetic system . In this paper , we use this system to investigate a classic morphological feature in these animals , depigmentation . We identify the gene Mc1r as being responsible for reduction in melanin content in multiple caves .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"genetics",
"and",
"genomics",
"evolutionary",
"biology/animal",
"genetics",
"developmental",
"biology/developmental",
"evolution",
"ecology/evolutionary",
"ecology"
] |
2009
|
A Novel Role for Mc1r in the Parallel Evolution of Depigmentation in Independent Populations of the Cavefish Astyanax mexicanus
|
The intracellular parasite Theileria is the only eukaryote known to transform its mammalian host cells . We investigated the host mechanisms involved in parasite-induced transformation phenotypes . Tumour progression is a multistep process , yet ‘oncogene addiction’ implies that cancer cell growth and survival can be impaired by inactivating a single gene , offering a rationale for targeted molecular therapies . Furthermore , feedback loops often act as key regulatory hubs in tumorigenesis . We searched for microRNAs involved in addiction to regulatory loops in leukocytes infected with Theileria parasites . We show that Theileria transformation involves induction of the host bovine oncomiR miR-155 , via the c-Jun transcription factor and AP-1 activity . We identified a novel miR-155 target , DET1 , an evolutionarily-conserved factor involved in c-Jun ubiquitination . We show that miR-155 expression led to repression of DET1 protein , causing stabilization of c-Jun and driving the promoter activity of the BIC transcript containing miR-155 . This positive feedback loop is critical to maintain the growth and survival of Theileria-infected leukocytes; transformation is reversed by inhibiting AP-1 activity or miR-155 expression . This is the first demonstration that Theileria parasites induce the expression of host non-coding RNAs and highlights the importance of a novel feedback loop in maintaining the proliferative phenotypes induced upon parasite infection . Hence , parasite infection drives epigenetic rewiring of the regulatory circuitry of host leukocytes , placing miR-155 at the crossroads between infection , regulatory circuits and transformation .
Both infection and cancer have been extensively linked to the induction of microRNAs ( miRs ) which can exert diverse effects on cellular phenotypes by targeting many genes [1] , [2] . microRNAs ( miRNAs ) are a class of small non-coding RNAs , 22 nt in length , that modulate post-transcriptional gene expression [1] . It is likely that miRNAs play critical roles in fine-tuning the host response to infection and inflammation [1] , [3] . OncomiRs are miRNAs that are upregulated in tumours and which have oncogenic functions depending on the genes they target [4] , [5] . However , It has been relatively difficult to identify essential miR pathways in infection and critical OncomiR target genes in tumorigenesis [6] , [7] . ‘Oncogene addiction’ is an emerging concept which suggests that underlying the multistep nature of tumour progression , cancer cell growth and survival can often be impaired by targeting a single oncogene pathway , thereby offering a promise for the development of targeted molecular therapies [8] , [9] , [10] . To investigate whether microRNAs could link infection to tumorigenesis , we studied a unique model of reversible transformation induced following infection by an intracellular parasite . The lymphoproliferative disease induced by the intracellular protozoan parasite Theileria constitutes a powerful model system to explore the signaling and epigenetic mechanisms underlying transformed phenotypes [11] , [12] , [13] . The tick-transmitted parasites T . annulata and T . parva infect bovine leukocytes leading to proliferative and invasive phenotypes which partially mirror lymphoma pathologies when injected into immunocompromised mice [12] , [14] , [15] . Theileria-infection causes hyperproliferation , invasiveness and escape from apoptosis , presumably through the manipulation of host cellular pathways [16] , [17] . Several host signaling mechanisms have been implicated , including c-Jun N-terminal Kinase ( JNK ) and host nuclear factors c-Myc , NFκB and AP-1 [16] , [18] , [19] , [20] , [21] , but the transcriptional networks regulated by these factors are not fully defined . Furthermore , the transformed phenotypes of Theileria-infected cells are curable by treatment with the theilericidal drug Buparvaquone ( BW720c ) , which kills the parasite without any apparent toxicity towards host cells [13] , [18] . This led us to investigate whether oncogene addiction pathways and epigenetic switches contribute to transformation in these cells . We studied TBL3 cells which were derived by in vitro infection with T . annulata of BL3 cells , an immortalized , bovine B lymphocyte cell line . Specifically , we investigated whether the transformed phenotype of the Theilieria-infected cells is associated with deregulation of miRNA pathways . miRNA networks are affected by several parasites of the apicomplexa phylum ( e . g . Toxoplasma [22] , Cryptosporidium [23] or Eimeria [24] ) . However , Theileria offers a particularly interesting study model because of its unique ability to transform host leukocytes . The oncomir miR-155 is one of the best studied oncogenic miRNAs and it has been extensively linked to inflammation , induced by a range of bacterial pathogens and viruses [25] , [26] , [27] . miR-155 resides in a non-coding transcript , called BIC , first identified in chickens as a site of retroviral insertion in avian leukosis virus-induced lymphomas [28] , [29] . Homologues of BIC ( B-cell integration cluster ) have been identified in humans and mice and contain the precursor hairpin encoding miR-155 . BIC and miR-155 are overexpressed in lymphomas , including Hodgkin's lymphoma , and acute myeloid leukemia patients , as well as several solid tumours [26] , [27] . The promoter of the BIC gene contains a highly conserved recognition motif for the transcription factor AP-1 formed by heterodimers of Jun and Fos proteins [30] . Transgenic mice overexpressing miR-155 in B cells developed lymphoproliferative disorders , whereas knockout mice have also demonstrated that miR-155 plays a critical role in the development of the immune system and the adaptive immune response [31] , [32] . The mechanisms by which the oncomiR-155 drives and maintains tumorigenesis remain relatively unclear and few molecular targets have been identified that explain miR-155 contribution to inflammation or the cancer cell phenotypes . Here we show that miR-155 and BIC upregulation are features of cells infected by the parasite Theileria . We identified AP-1/Jun as a transcriptional regulator of BIC in these cells . We also identified a new miR-155 target , transcripts encoding the DET1 protein which is involved in targeting c-Jun for degradation by ubiquitination . Thus , miR-155 expression leads to DET1 down-regulation , accumulation of the c-Jun protein and activation of the BIC promoter . This feedback loop is essential for miR-155 oncogenic function and , thus , Theileria infection of the host leukocytes creates a transformed state involving addiction to both parasite and oncomiR .
To investigate the molecular mechanisms underlying the phenotypes of Theileria-infected cells , we studied TBL3 cells which were derived by in vitro infection with T . annulata of BL3 cells , an immortalized , bovine B lymphocyte cell line . Treatment with the theilericidal drug Buparvaquone caused reduced proliferation in TBL3 cells , while it had no effect on the growth of the parental BL3 cells ( Figure 1A ) . The TBL3 growth arrest was due to reduced cell cycle progression ( as measured by Ki67 labeling ) ( Figure S1B ) and apoptosis in these cells ( as measured by flow cytometry and caspase activation ) ( Figure 1B and 1C ) . The parasitized TBL3 cells have constitutive AP-1 activation [20] and formed colonies when grown in soft agar , which was also reversed by Buparvaquone treatment ( Figure 1D ) . We observed similar effects of Buparvaquone on Thei cells , a naturally infected macrophage cell line derived from a tumour of an infected cow ( Figure S1A ) . Thus , Theileria-infected cells are ‘addicted’ to the presence of live parasites which is necessary for maintaining growth and survival . Infection is increasingly linked to the induction of microRNAs which can exert diverse effects on host cellular phenotypes by targeting many genes [1] , [3] . We hypothesized that miRs could play a role in Theileria-induced transformation and that the presence of active parasites could induce oncomiR expression . We examined the expression of host microRNAs in parasitized TBL3 , with or without Buparvaquone treatment , by microarray analysis ( Figure 2A ) . We found that six miRs were consistently down-regulated more than 2-fold in the Theileria-infected cells following Buparvaquone treatment ( Figure 2A and Table S1 ) . Several of these microRNAs have been linked to tumorigenesis and human leukemia ( Table S1 ) . We focused on the miR-155 oncomiR for a number of reasons; miR-155 was lowly expressed in the parental BL3 cell line ( see below ) and miR-155 is overexpressed in human B cell lymphoma , leukemia , breast cancer and pancreatic cancer [27] , [33] . miR-155 was also shown to cause cancer in genetically engineered mice and has been extensively linked to infection and inflammation [31] , [34] , [35] . The miR-155-containing BIC gene , was originally identified as a common site for insertion of proviral DNA in avian virus-induced lymphomas [28] , [29] and is induced in human lymphomas transformed by Epstein-Barr Virus ( EBV ) [30] , [36] , [37] . These results suggested that miR-155 could be a common target used by viruses and parasites to manipulate host cell functions during cancer and infection . Comparative genomic analysis revealed that the BIC gene is conserved across species and that the mature miR-155 sequence is identical between human and bovine genomes [38] ( Figure 2B ) . We confirmed the miRNA microarray data by TaqMan quantitative PCR ( qPCR ) analysis . miR-155 was significantly upregulated in TBL3 cells compared to the non-parasitized parental BL3 cells ( Figure 2C ) . Furthermore , Buparvaquone treatment caused a dramatic decrease in miR-155 expression in both TBL3 lymphocytes and Thei macrophage cell lines ( Figure 2C ) . We also tested the expression of immature transcripts pri-miR-155 and pre-miR-155 by qPCR; we observed that these forms were reduced by Buparvaquone in TBL3 cells , but not in non-infected BL3 cells ( Figure 2D ) . The human BIC gene was shown to be transcriptionally regulated by the AP-1 transcription factor in EBV-transformed lymphomas [30] . Moreover , Theileria transformation is characterized by constitutive AP-1 activation [19] , [20] . We therefore investigated whether miR-155/BIC induction in Theileria-infected cells is dependent on AP-1/Jun . Sequence alignments showed that the AP-1 binding site in the proximal BIC promoter is highly conserved across species ( Figure 3A ) . To test whether BIC is transcriptionally regulated by Theileria , we transfected cells with a reporter construct containing the BIC promoter driving the luciferase gene ( BIC-Luc ) . The activity of the BIC promoter was significantly higher in TBL3 cells compared to non-infected BL3 cells ( Figure 3B ) . Moreover , the BIC promoter activity in infected cells was decreased by treatment with Buparvaquone ( Figure 3B ) . The activity of the BIC-Luc reporter was also decreased by Buparvaquone treatment in Thei cells ( Figure 3B ) . To test the involvement of AP-1 and NFκB transcription factors in BIC expression in these cells , we used promoter constructs mutated within the AP-1 or NFκB binding sites [30] . We observed that mutation of the conserved AP-1 binding site dramatically reduced BIC promoter activity in both cell lines , whereas mutation of the NFκB binding site had less effect ( Figure 3C ) . Therefore , we conclude that Theileria regulates miR-155 primarily by AP-1 driven transcription of the bovine BIC gene . To understand the contribution of upregulated miRs to cellular phenotypes , it is important to identify functionally-relevant targets whose expression is regulated by miR action . To identify putative miR-155 target genes , we performed a computational screen for genes with complementary miR-155 sites in their 3′UTR using online software ( including Microcosm targets , TargetScan and PicTar ) . We found that DET1 , JARID2 and TP53INP1 genes are putative miR-155 targets; they exhibit a strong seed match and the binding site is conserved across species ( Supplementary Figure S2A ) . We performed qPCR analysis to investigate the mRNA level of these genes in infected leukocytes , but found no significant difference between the expression of these genes in TBL3 or Thei cells upon Buparvaquone treatment ( Supplementary Figure S2C ) . Consequently , we tested whether the effect of miR-155 on these potential targets could occur via inhibition of translation . We transfected luciferase reporters fused to the miR-155-targeted 3′UTR of these genes into Theileria-infected cells and tested the effect of Buparvaquone [30] . The relative activities of 3′UTR-Luciferase constructs were significantly increased by Buparvaquone treatment in TBL3 cells and in Thei cells , but not in BL3 cells ( Figure 4A and Supplementary Figure S2B ) . We focused on one of these potential targets; DET1 , a highly conserved protein reported to promote the ubiquitination and degradation of the proto-oncogenic transcription factor c-Jun [39] . Mutation of the miR-155 target site in the DET1 Luciferase-3′UTR reporter ( mDET1 ) abolished the Buparvaquone-induced luciferase activity in TBL3 cells ( Figure 4A ) . We used TP53INP1 , a pro-apoptotic tumour suppressor protein reported to be repressed by miR-155 in pancreatic tumours [40] , as a positive control ( Figure 4A ) . The effects of Buparvaquone could include changes in many miRs , so we performed experiments in BL3 cells in which we cotransfected either the DET1 or the TP53INP1 Luciferase-3′UTR reporters with a miR-155-expressing plasmid . The DET1 and TP53INP1 3′UTR-reporters were inhibited by co-transfection with miR-155 , but not control ( Figure 4B ) . Conversely , we performed a series of experiments involving co-transfection with a miR-155 “Sponge” construct , which functions as a miR-155 inhibitor . The Sponge inhibitor increased expression of the DET1 and TP53INP1 3′UTR-reporters , but not the mutated DET1 construct ( Figure 4B ) . These results suggest that DET1 protein translation is directly targeted by miR-155 binding to the 3′UTR sequence . To confirm this at the protein level , we performed Western blot analysis; DET1 levels were reduced in TBL3 cells compared to BL3 cells ( Figure 4C ) . In TBL3 cells , treatment with Buparvaquone or transfection with the miR-155 Sponge inhibitor both resulted in elevated DET1 protein , and decreased c-Jun levels ( Figure 4C and 4D ) . Furthermore , the effect of the miR-155 Sponge is DET1-dependent , as it was reversed by siRNA specifically targeting DET1 expression ( Figure 4D ) . Conversely , transfecting miR-155 into BL3 cells reduced DET1 levels and led to elevated c-Jun protein ( Figure 4E ) . This could be mimicked by transfecting with siRNA against DET1 ( Figure 4E ) . Although DET1 regulates c-Jun degradation by the ubiquitin-dependent proteosome [39] , DET1 was recently shown to participate in transcriptional repression in plants [41] . To confirm that the miR-155 levels and DET1 targeting affected c-Jun protein stability in our cells , rather than transcription , we investigated c-Jun stability by pulse-chase labeling with cycloheximide ( Figure 5A and 5B ) . We showed that both miR-155 and siDET1 decreased c-Jun degradation in BL3 cells ( Figure 5A ) . Conversely , the miR-155 Sponge enhanced c-Jun degradation in infected TBL3 cells and this was rescued by siDET1 ( Figure 5B ) . Additional experiments using the MG132 proteosome inhibitor , confirmed that c-Jun inhibition by the miR-155 Sponge in TBL3 cells was due to proteosome-dependent degradation ( Figure 5C ) . Analysis of the c-Jun mRNA levels by qPCR also confirmed that the effects of miR-155 , siDET1 and the miR-155 Sponge are at the protein level without changes in c-Jun transcripts ( Supplementary Figure S3A and S3B ) . Finally , we looked at c-Jun ubiquitination in our cells; transfection with either miR-155 or siDET1 decreased c-Jun ubiquitination in BL3 cells , consistent elevated c-Jun stability ( Figure 5D ) . In contrast , c-Jun ubiquination levels were higher in TBL3 cells transfected with the miR-155 Sponge ( Supplementary Figure S3C ) . Together , these experiments show that miR-155 can target DET1 leading to c-Jun accumulation in transformed Theileria-infected leukocytes . As the expression of miR-155 led to reduced DET1 protein and elevated c-Jun levels , we hypothesized that this might increase AP-1 activity , thereby creating a positive feedback loop to drive expression of the BIC promoter . We tested this hypothesis in BL3 cells using the BIC-Luc reporter that we showed above was Theileria-regulated via AP-1 ( Figure 6 ) . We found that the expression of either miR-155 or siDET1 or c-Jun resulted in induction of BIC promoter activity in uninfected BL3 cells ( Figure 6A ) . This suggested that upregulation of AP-1/c-Jun is sufficient to induce BIC expression in these cells and that miR-155 may induce the expression of its own promoter via AP-1/Jun activation . Conversely , we found that the BIC-Luciferase activity in TBL3 cells was reduced by co-transfection with the miR-155 inhibitory Sponge or a Dominant-Negative c-Jun ( DN c-Jun ) ( Figure 6B ) . Finally , the inhibitory effect of the miR-155 Sponge on the BIC promoter could be overcome by suppressing DET1 using siRNA ( Figure 6B , middle ) . These experiments suggested that the miR-155/DET1/Jun/BIC loop creates a regulatory feedback circuit . To test the functional significance of this miR-155-DET1-Jun loop , we investigated the effect of blocking the hubs in the loop on the ability of transformed TBL3 cells to form colonies in the soft agar assay . Transfection of parasitized TBL3 cells with either the miR-155 Sponge or DN c-Jun caused a dramatic decrease in the number of colonies ( Figure 7A ) . Notably , DN c-Jun has also been reported to reduce tumour formation by parasitized cells in mice experiments ( 19 ) . The inhibitory effects of the Sponge were reversed by co-transfection with siDET1 , but not control siRNA ( Figure 7A ) . Thus , the regulatory loop seems to be essential for colony growth . Furthermore , we tested the effect of inhibiting the miR-155 loop on cell survival . The transfection of TBL3 cells with the miR-155 Sponge also caused significant apoptosis , as revealed by flow cytometry or Caspase-3 activation , equivalent to that induced by killing the parasite with Buparvaquone ( Figure 7B and 7C ) . Thus , the miR-155 oncomiR loop is essential for parasite-induced host cell growth and survival , thereby creating a state of oncogene addiction ( Figure 7D ) .
Theileria-induced transformation offers an attractive experimental model , as it appears that infection of host leukocytes is accompanied by a rewiring of the cellular circuitry [13] , [17] , [18] . The identification of molecular players that play key roles in maintaining proliferative phenotypes could be relevant for identifying effective therapeutic strategies to reverse transformation . Thus , oncogenic pathways in Theileria-infected cells may highlight examples of oncogene addiction for future studies . We have extended this hypothesis to investigate microRNA pathways and identified molecular targets that create an addictive regulatory loop . This is the first study to show that Theileria manipulates host gene expression via microRNAs . This observation underlines the increasing importance being given to non-coding RNAs in the regulation of gene expression , inflammatory response and tumour cell phenotypes [2] , [3] , [27] . miRNA networks are affected by several parasites of the apicomplexa phylum ( e . g . Toxoplasma [22] , Cryptosporidium [23] or Eimeria [24] ) . Some of these may be related to the infection process and initial inflammatory responses , while others may be relevant to long-term features of host-parasite interactions . C . parvum infection of epithelial cells was also shown to induce a range of host miRNAs which are regulated by NFκB-dependent transcription [23] . However , there does not seem to be any significant overlap with the miRNA network regulated by Theileria . Similarly , T . gondii was shown to induce transcriptional regulation of a distinct set of host miRNAs , whereas the related Neospora caninum parasite did not [22] . Future studies might reveal common and distinct pathways related to miRNA induction by parasites across the apicomplexa phylum . miR-155 induction does seem to be a common feature in several inflammatory and tumorigenic scenarios . For example , Helicobacter pylori infection , which is associated with gastric adenocarcinoma , also induces miR-155 expression in T cells , but via Foxp3 [42] . We show here that activated AP-1 transcription factors in parasitized transformed cells drives the transcription of the BIC gene , leading to increased miR-155 expression in both artificially infected bovine B cells and in naturally-infected bovine Thei macrophages . We provide evidence that miR-155 targets the DET1 protein , which leads to accumulation of the c-Jun protein and increased transcription of the miR-155-encoding BIC gene ( Figure 7D ) . This feedback loop is critical for maintaining the transformed phenotype , as inhibiting any node in the loop reverses the transformed phenotypes ( growth in soft agar and cell survival ) of parasitized cells . Thus , our study has provided the molecular events in a miR-155 loop that links infection and transformation . Host cell infection by Theileria parasites is accompanied by a range of signal transduction pathways including the IKK/NFκB and JNK/AP-1 pathways [16] , [19] , [20] . It is not clearly defined how these signaling pathways are integrated in the nucleus to drive gene expression programs that underlie the transformed phenotype . We found that AP-1 is critical for BIC promoter activity in both TBL3 lymphoctytes and Thei macrophages , whereas the contribution of NFκB , was relatively minor . It is possible that NFκB plays a role in BIC induction upon infection and that a epigenetic switch subsequently creates a dependence on the BIC/miR-155/c-Jun loop to maintain the transformed phenotype . Indeed , Theileria-infected cells can grow in immunocompromised mice [14] , [15] and c-Jun was previously shown to be critical for Theileria-associated B cell growth in vivo [19] . Furthermore , BIC induction in B lymphocytes cause by infection with the Epstein-Barr Virus ( EBV ) is also driven by AP-1 activity [30] . These observations offer an interesting parallel between viruses and parasites in miRNA modulation during tumorigenic progression . We report here that miR-155 represses DET1 in Theileria-infected cells . Human DET1 ( de-etiolated 1 ) is a component of the Cul4A-DDB1 ubiquitin ligase complex and was shown to promote the ubiquitination and degradation of the proto-oncogenic transcription factor c-Jun [39] . CUL4-based E3 ligases have been shown to act in tumour suppression , but the DET1/c-Jun link has not been clearly placed in a tumorigenic context or in infection models . Our results show that miR-155 can activate c-Jun and AP-1 in our cells by targeting DET1 and inhibiting its translation . DET1 has also been implicated in transcriptional repression in plants [41] . Here , we showed that the miR-155 effects in DET1 levels led to changes in c-Jun ubiquitination and stabilization without affecting c-Jun transcriptional control . These results explain the elevated c-Jun levels observed in TBL3 cells despite relatively low JNK activity [20] . Furthermore , as the miR-155 binding site is highly conserved across species , it is likely that a similar loop could function in human cancers . Indeed , previous studies of miR-155 in EBV-transformation indicated an enrichment for induced genes with AP-1 binding sites in their promoters [30] . It is worth noting that there is an emerging role of miRNAs as regulators of protein turnover by targeting ubiquitinating proteins . For example , miR-137 targets the mind bomb-1 ubiquitin ligase in neuronal maturation [43] and miR-223 targets the Fbw7 component of the SCF ubiquitin ligase complex [44] . We did not find evidence for changes in the expression of these two miRNAs upon Theileria infection . It is also possible that miRNA targeting of ubiquitination machinery may contribute to other aspects of Theileria-induced host signaling , such as effects on p53 and NFκB pathways [16] , [45] . Overexpression of miR-155 has been functionally linked to tumorigenesis and inflammation in animal models [31] , [32] , [34] . Moreover , miR-155 appears to be commonly de-regulated by a wide range of infectious agents , including bacteria and viruses [27] , [30] , [36] , [37] . Recent studies have documented the existence of feedback mechanisms between microRNAs and their transcriptional regulators and these autoregulatory loops likely play important roles to balance the state of microRNAs and their protein targets [46] . The regulatory circuit that we have uncovered is unusual in that it involves two negative regulators; one involving miR inhibition of protein translation and the other involving ubiquitin-dependent protein degradation . Each of these steps may be of therapeutic value in attempts to block the oncomiR addiction state . Furthermore , this study highlights the critical role of microRNA pathway function in the parasite-host relationship . Thus , our results place miR-155 at an exciting crossroads between parasitology , regulatory circuits and transformation .
The TBL3 cell line was derived from in vitro infection of the spontaneous bovine-B lymphosarcoma cell line , BL3 , with Hissar stock of T . annulata . The culture conditions and B cell characteristics of these two cell lines have been described previously [47] . The macrophage cell line Thei was isolated from T . annulata naturally infected cow . Cell were provided by G Langsley ( Paris , France ) . All cell lines were cultured in RPMI 1640 ( Gibco Ltd . , Paisley , UK ) , supplemented with 10% heat-inactivated Fetal calf serum , 4 mM L-Glutamine , 25 mM HEPES , 10 µM β-mercaptoethanol and 100 µg/ml penicillin/streptomycin in a humidified 5% CO2 atmosphere at 37°C . For all experiments , cells were seeded at a density of 3×105 cells/ml and exponentially growing cells harvested . The number of cells and viability , as judged by the trypan blue dye exclusion test , were determined by counting the cells in a Mallassen chamber . The anti-parasite drug Buparvaquone ( BW720c ) [48] was used at 100 ng/ml , as described previously [49] for 64 hours ( TBL3 and BL3 cell lines ) or 72 hours ( Thei cells ) . BW720c has no effect on the growth of mammalian cells [48] . For isolation of long ( >200 nt ) and small ( <200 nt ) cellular RNA , 5×106 cells , which had been cultured in the absence or presence of the indicated agents , were harvested and RNAs were prepared with the Nucleospin miRNA kit ( Machery Nagel , Hoerdt , France ) according to the manufacturer's instructions . The quality and quantity of the resulting RNAs were determined using a Nanodrop spectrophotometer . Oligonucleotides were designed ( Supplementary Table S2 ) and first-strand cDNA was reverse transcribed from 1 µg long RNA using random primers and VILO Superscript III ( Invitrogen , Carlsbad , CA , USA ) ; and 10 ng small RNA using TaqMan probes for miR-155 and U6 and TaqMan microRNA reverse transcription kit ( Applied Biosystems , Foster City , CA , USA ) . The cDNA was diluted 1∶10 for detection of all transcripts . Quantitative PCR analyses of miRNAs and mRNAs were performed using Taqman miRNA expression or SYBR green , respectively ( Applied Biosystems , Foster City , CA , USA ) assays according to the manufacturer's protocols in the ABI 7500 real-time PCR system ( Applied Biosystems , Foster City , CA , USA ) . Bovine β-actin and B2M ( long RNA ) or RNU6B ( miRNA ) were used as endogenous controls for normalization . The detection of single products was verified by dissociation curve analysis . Relative quantities of mRNA and miRNA were analyzed by using the delta Ct method . qRT-PCR was repeated for three independent biological replicates of infected cells and experimental duplicates . Cells were sonicated in 2× Laemmli buffer : 15 secs ON/30 secs OFF for 5 mins . Proteins extracts were resolved on 10 . 5% acrylamide/bis acrylamide SDS-PAGE gels and transferred to nitrocellulose membranes ( Thermo Fisher Scientific , Waltham , USA ) in transfer buffer . Protein transfer was assessed by Ponceau-red staining . Membranes were blocked in Tris-buffered saline pH 7 . 4 containing 0 . 05% Tween-20 and 5% milk for 1 hour at room temperature . Incubations with primary antibodies were carried out at 4°C overnight using antibody dilutions as recommended by the manufacturer in Tris-buffered saline pH 7 . 4 , 0 . 05% Tween-20 and 5% milk . Following 1 hour of incubation with goat-anti-rabbit peroxidase-conjugated antibody ( Sigma , St . Louis , MO , USA ) at room temperature , proteins were detected by the chemiluminescence method ( Thermo Fisher Scientific , USA ) according to the manufacturer's instructions . Antibodies used in immunoblotting were as follows: Rabbit anti-DET1 ( Abcam , Cambridge , UK . Ref: ab75918 ) , Rabbit Anti-c-Jun ( Santa Cruz Biotechnology , CA , USA . Ref: sc1694 ) , Mouse Anti-αTubulin ( Sigma , Ref: T9026 ) , Rabbit Anti-Active Caspase 3 ( Sigma , Ref: C8487 ) , Mouse Anti-Ubiquitin ( P4D1 ) ( Santa Cruz Biotechnology , CA , USA . Ref: sc-8017 ) . c-Jun or DET1/Tubulin ratios were calculated after western blot signal quantification with the Plot Lanes Analysis tool of Image J software ( NIH ) . Most Luciferase reporters were constructs previously described [30] . Briefly , the BIC promoter extends from −1556 to +166 and was cloned into pGL3basic ( Promega , Madison , WI , USA ) . AP1 and NFκB point mutations were generated using the QuikChange II site-directed mutagenesis kit ( Stratagene ) as previously described [30] . Wild-type or mutated 3′UTRs were cloned downstream from the Luciferase gene in the pMIR-REPORT plasmid ( Applied Biosystems , USA ) . DET1 reporter contains most of the 3′ UTR ( 131–425 of UTR ) . For mutant DET1 ( previously unpublished ) , the miR-155 binding sites were mutated by exchanging 4 bases within the seed sequence . Mutations were generated using QuikChange II site-directed mutagenesis kit ( Stratagene ) . TP53INP1 reporter contains 400 bases of 3′ UTR sequences from 312–712 . JARID2 reporter contains the 3′ UTR ( sequences from 9 to 1214 ) . miR155 ( Gene ID: 406947 ) sequences were cloned downstream from the GFP gene in pMSCV-puro-GFP as previously described [30] . For the miR-155 Sponge plasmid , 10 inverted copies of a bulge forming anti-sense miR-155 sequences ( 5′- ACTAGTACCCCTATCAGTCTAGCATTAAGGGTTTACCCCTATCAATGTAGCATTAACACAGAACCCCTATCAGAGTAGCATTAAGAGCAGACCCCTATCATTGTAGCATTAAGTGGAAACCCCTATCAACTTAGCATTAACCTTGAACCCCTATCAAGGTAGCATTAAGGACCAACCCCTATCATACTAGCATTAACGAGATACCCCTATCATCTTAGCATTAACCAGGTACCCCTATCAGGATAGCATTAAGGTGCTACCCCTATCAGCCTAGCATTAATCTAGA-3′ ) were cloned downstream from the GFP gene in the vector , pMSCV-puro-GFP-miRcntl between the NotI and EcoRI sites , as previously described [50] . The complete sequences and maps of this and other plasmids can be found at www . flemingtonlab . com . Unique/different short spacer regions are included between inverted miR-155 sequences to prevent the formation of exact repeats ( to prevent recombination events ) . A Flag-tagged c-Jun dominant negative ( DN ) mutant Δ169 cDNA was cloned into pCDNA1 , and c-Jun cDNA into pHT108 . These two plasmids were kindly provided by G Langsley ( Paris , France ) . The DET1 gene was targeted using siRNA oligonucleotides against the bovine DET1 sequence ( AAAACCACCTGTTTATCAAGT ) and results were compared to transfection with a non-relevant ‘scrambled’ control siRNA . Thei cells were transfected using Nucleofector kit solution L according to the manufacturer's instructions using Amaxa Nucleofector II device ( program T-20 ) ( Lonza , Basel , Switzerland ) . BL3 and TBL3 cells were transfected using Neon Transfection kit ( Invitrogen , CA , USA ) . Cells were seeded in 24-well plates for 24 h , and then transfected or co-transfected with 1 µg of the indicated constructs for 36 h . The cells are transfected with luciferase constructs with or without miR-155 , Sponge , c-Jun , DN c-Jun and with or without Buparvaquone treatment . Efficiencies of transfections were normalized to Renilla activity by co-transfection of a pRL-TK Renilla reporter plasmid ( Promega Ref: E6241 ) . Luciferase assay was performed 36 h after transfection using the Dual-Luciferase Reporter Assay System ( Promega , Ref: E1980 ) in a microplate luminometer . Relative luminescence was represented as the ratio firefly/renilla luminescence and then compared with the corresponding empty vector as a control . Cells were collected and fixed in 3 . 7% Formaldehyde for 40 min on ice and then cold Ethanol 70% for 15 min at 4°C . The cells were stained with propidium iodide ( 50 µg/ml ) and RNase A ( 1 µg/ml ) for 15 min at room temperature . Flow cytometric analysis was done using a FACScan instrument ( Becton Dickinson , Mountain View , CA , USA ) and CellQuest software . Total RNAs was prepared using QIAGEN RNeasy mini kit ( Qiagen , Germantown , MD , USA ) according to the manufacturer's protocol from two separate Buparvaquone-treated samples of TBL3 cells . The quality and quantity of the RNA samples were assessed using the Experion machine ( Bio-Rad Laboratories , USA ) . The microRNA expression profiling service from Dharmacon ( Thermo Fisher Scientific ) performed the miRNA microarray analysis . SPSS 19 . 0 program ( SPSS Inc . Chicago , IL , USA ) was used for statistics . The results presented in all the figures represent the mean ± SEM of at least three independent experiments . Statistical analysis was performed using the paired-samples t-test to analyze the significant difference between the control and treatment groups . p values of <0 . 05 were considered statistically significant and are indicated with asterisks . A two-layer soft agar culture system was used . Cell counts were performed on a Malassen chamber . A total of 20 , 000 cells were plated in a volume of 1 . 5 ml ( 0 . 7% SeaKem ME Agarose ( Lonza , Ref: 50011 ) +2× DMEM 20% Fetal calf Serum ) over 1 . 5-ml base layer ( 1% SeaKem ME Agarose +2× DMEM 20% Fetal calf Serum ) in 6-well plates . Cultures were incubated in humidified 37°C incubators with an atmosphere of 5% CO2 in air , and control plates were monitored for growth using a microscope . At the time of maximum colony formation ( 10 days in culture ) , final colony numbers were counted manually after fixation with 0 . 005% Cristal Violet ( Sigma , Ref: C3886 ) . Cells were treated for 3 h at 37°C with 20 uM MG132 and lysed 10 min on ice in the following buffer: 150 mM NaCl , 1% Nonidet P-40 , 0 , 5% Deoxycholate , 0 , 1% SDS , 50 mM Tris-HCl pH 7 , 5 , 20 mM NEM , 5 mM Iodoacetamide , 100 uM MG132 , 2 mg/mL Pefabloc SC ( Roche ) and 5 ug/mL each Aprotinin , Leupeptin , Pepstatin . Equal amounts of total cellular proteins were immunoprecipitated with Rabbit Anti-c-Jun ( E254 ) ( Abcam , Cambridge , UK . Ref: ab32137 ) coupled to protein G sepharose beads ( Sigma , Ref: P3296 ) for 90 min at 4°C . After three washes , immunoprecipitated proteins were eluted in Laemmli sample buffer at 95°C for 5 min , resolved by SDS-PAGE and analyzed by western blot using the indicated antibodies . Immunoprecipitation was repeated for three independent biological replicates . Transient transfected cells with indicated constructs were treated 30 , 60 or 120 min with 100 µg/mL Cycloheximide , 36 h post transfection . Cells were lysed in Laemmli sample buffer , resolved by SDS-PAGE and analyzed by western blot using the indicated antibodies . Relative quantification indicates the c-Jun/Tubulin ratios calculated with Image J software ( NIH ) and c-Jun levels at time 0 was set as 1 . Cycloheximide chase experiments were repeated for three independent biological replicates . Transient transfected TBL3 cells with miR-155 Sponge were treated 3 h with 20 µM MG132 . Cells were lysed in Laemmli sample buffer , resolved by SDS-PAGE and analyzed by Western blot using the indicated antibodies . Relative quantification indicates the c-Jun/Tubulin ratios calculated with Image J software ( NIH ) . MG132 treatment was repeated for three independent biological replicates . Cells were plated on Fibronectin coated slides and then fixed in PBS 3 . 7% Formaldehyde for 15 min at room temperature . Slides were rinsed in PBS and permeabilized with PBS 0 . 2% Triton X-100 for 5 min and then blocked for 30 min with PBS 1% SVF and 1% BSA to prevent non-specific staining . The slides were incubated with Mouse monoclonal anti-Ki67 ( 1∶50 , Abcam Cambridge , UK . Ref :ab10913-1 ) in PBS 1% SVF and 1% BSA at room temperature for 40 min . After washing in PBS 0 . 2% Tween , the slides were incubated with Cy2 AffinyPure anti-mouse IgG ( 1∶5000 , Jackson Immunology , USA . Ref :715-225-150 ) for 30 min . Slides were subsequently washed in PBS 0 . 2% Tween , mounted on slides and coverslipped with ProLong Gold Antifade Reagent with Dapi ( Invitrogen , USA . Ref : P-36931 ) . Images of immunofluorescence staining were photographed with a camera attached to a fluorescent microscope ( Leica Inverted 6000 ) and percentage of Ki67 positive cells was calculated . This staining was repeated for three independent biological replicates .
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Theileria is the only intracellular eukaryotic parasite known to transform its host cell into a cancer-like state . Infection by the T . annulata parasite causes tropical theileriosis , killing large numbers of cattle in North Africa and Asia , and the related T . parva parasite causes East Coast Fever . We investigated whether transformation of host bovine leukocytes was associated with deregulation of small , non-coding RNAs . We discovered that transformation by Theileria leads to upregulation of an oncogenic small RNA called miR-155 which is contained within the BIC gene . Parasite induction of the microRNA involves activation of the transcription factor c-Jun which controls the BIC gene promoter . We identified a new target for the miR-155; the DET1 protein which is responsible for degradation of the c-Jun factor . This leads to a regulatory feedback loop that is critical for the transformed phenotype of the infected cells . We show that miR-155 expression inhibits DET1 protein translation , leading to accumulation of c-Jun protein and activation of the BIC gene containing miR-155 . This is the first study to report regulation of oncogenic non-coding RNAs by Theileria and the novel feedback loop underlying the parasite-induced transformation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"rna",
"rna",
"interference",
"nucleic",
"acids",
"microbial",
"pathogens",
"biology",
"microbiology",
"molecular",
"cell",
"biology",
"parasitology"
] |
2013
|
OncomiR Addiction Is Generated by a miR-155 Feedback Loop in Theileria-Transformed Leukocytes
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The protozoan parasite , Toxoplasma , like many intracellular pathogens , suppresses interferon gamma ( IFN-γ ) -induced signal transducer and activator of transcription 1 ( STAT1 ) activity . We exploited this well-defined host–pathogen interaction as the basis for a high-throughput screen , identifying nine transcription factors that enhance STAT1 function in the nucleus , including the orphan nuclear hormone receptor TLX . Expression profiling revealed that upon IFN-γ treatment TLX enhances the output of a subset of IFN-γ target genes , which we found is dependent on TLX binding at those loci . Moreover , infection of TLX deficient mice with the intracellular parasite Toxoplasma results in impaired production of the STAT1-dependent cytokine interleukin-12 by dendritic cells and increased parasite burden in the brain during chronic infection . These results demonstrate a previously unrecognized role for this orphan nuclear hormone receptor in regulating STAT1 signaling and host defense and reveal that STAT1 activity can be modulated in a context-specific manner by such “modifiers . ”
Interferon gamma ( IFN-γ ) and STAT1 signaling play an essential role in cellular immunity , as indicated by extreme susceptibility to infection in mice and humans carrying mutant alleles for these genes and pathways [1–5] . The binding of IFN-γ to its cell surface receptor leads to phosphorylation , dimerization , and subsequent nuclear translocation of the transcription factor STAT1 [6] . Once inside the nucleus , STAT1 dimers recognize a consensus “gamma-activated sequence” ( GAS ) element ( TTCN3–5GAA ) in target genes and initiate a transcriptional program that is essential for resistance to a broad range of pathogens . While the core components of the IFN-γ signaling pathway , including Janus kinase 1 and 2 ( JAK1 , JAK2 ) , and STAT1 , have been known for nearly two decades [7 , 8] , regulatory mechanisms dictating the specificity and strength of STAT1 activity within the nucleus are poorly understood , limiting our understanding of how IFN-γ /STAT1 signaling can be tailored or harnessed to respond to different challenges . STAT1 activation is known to transcriptionally regulate several hundred genes [9] , including pathways involved in host defense , apoptosis , and differentiation [10] , but the biological output of STAT1 signaling can vary dramatically depending on the context in which activation occurs . For example , while STAT1 signaling suppresses cell proliferation during hematopoiesis [11 , 12] , IFN-γ-induced STAT1 signaling can also drive hematopoietic stem cells to enter the cell cycle and proliferate to replace leukocytes lost during infection [13] . This apparent duality of STAT1 function also extends to additional immune cells: STAT1 signaling in macrophages activates a potent antimicrobial program and promotes antigen processing and presentation to T cells , yet STAT1 is also required for the ability of tumor-associated macrophages to suppress T cell function [14] . Similarly , while STAT1 drives T helper type 1 ( Th1 ) cell development by activating the transcription factor T-bet [15] , it is also required for the suppressive function of regulatory T cells [16 , 17] . The diversity of outcomes associated with STAT1 activation highlights the need to identify the cellular factors that modify or modulate STAT1 target selection to appropriately tailor the output of this core signaling pathway . The intracellular protozoan parasite Toxoplasma gondii is a common pathogen of humans and other warm-blooded vertebrates and a valuable model for understanding IFN-γ-mediated immunity . Infection is initiated in the gastrointestinal tract and proceeds through an acute phase characterized by rapid parasite replication within hematopoietic and nonhematopoietic cells . IFN-γ signaling is critical in controlling parasite replication during this phase [18] , and mice deficient in IFN-γ or STAT1 rapidly succumb to infection [19–21] . IFN-γ responses do not completely eradicate Toxoplasma , and parasites that evade this response differentiate into a slow-growing cyst form that persists as a latent infection in the central nervous system . During this chronic phase , IFN-γ remains essential in restricting parasites and prevents reactivation to the rapidly dividing form [22–24] . As a testament to the importance of IFN-γ and STAT1 signaling in the control of infections , many pathogens have evolved mechanisms to directly inhibit pathway components [25–28] . Toxoplasma can attenuate IFN-γ receptor-dependent STAT1 signaling [29–33] . We exploited this host–pathogen interaction as the basis for a high-throughput screen in order to identify host factors that when ectopically expressed could overcome the Toxoplasma-dependent block of STAT1 activity . This approach led to the identification and validation of nine transcription factors , including the orphan nuclear hormone receptor TLX ( tailless , also known as NR2E1 ) , which acts as a potent enhancer of STAT1 target gene expression . Furthermore , we found that TLX selectively potentiates a subset of STAT1-dependent targets , providing insight into how specific STAT1 programs can be tailored to impact immunity .
In order to identify host cell modifiers of STAT1 signaling in Toxoplasma-infected cells , we utilized a STAT1-responsive luciferase reporter , consisting of two tandemly repeated GAS elements able to bind STAT1 homodimers . Treatment of cells with IFN-γ leads to robust activation of the GAS luciferase reporter , but prior infection with Toxoplasma suppresses this activation by >5-fold ( S1A Fig ) . This did not reflect a general impairment of host cell signaling , as parasites were unable to suppress tumor necrosis factor alpha ( TNF-α ) induction of an nuclear factor kappa B ( NF-κB ) reporter ( S1B Fig ) . Overexpression of STAT1 failed to rescue pathway activity , suggesting that Toxoplasma impacts a step downstream of STAT1 stability ( S1C Fig ) . Since STAT1 phosphorylation is required for dimerization , phospho-specific antibodies were used to further characterize pathogen suppression of the STAT pathway . Human osteosarcoma U2OS cells were infected with a low multiplicity of infection ( MOI ) to leave some cells uninfected , allowing us to evaluate STAT1 phosphorylation in both infected and naïve cells from the same cultures . Infection alone was not sufficient to trigger STAT1 activation ( S1D Fig ) , whereas a 15-min stimulation with IFN-γ triggered phosphorylation of STAT1 in nearly every infected and uninfected cell ( S1D Fig ) . STAT1 dimerization allows for nuclear import and subsequent DNA binding to induce transcription , and immunofluorescence revealed that STAT1 translocation to the nucleus is unaffected by infection ( S1E Fig ) . Taken together , these data are consistent with previous reports [29–33] , which indicate that Toxoplasma impairs STAT1 signaling by acting downstream in the pathway , at the level of nuclear STAT1 function ( S1F Fig ) . Conditions were optimized for high-throughput screening to identify genes that when ectopically expressed restored activity of the STAT1 pathway in infected cells , yielding Zʹ-factor scores > 0 . 5 ( S2 Fig ) , a measure of assay robustness [34] . Pathway suppression could not be overcome by increasing the IFN-γ concentration ( S2C Fig ) Moreover , this STAT1 assay is 30 times more sensitive to the STAT1 homodimers triggered by IFN-γ stimulation than the STAT1/STAT2 heterodimers formed upon activation by type I interferon ( S2D Fig ) . These data indicate that Toxoplasma suppression of the STAT1 pathway provides a robust , sensitive , and specific screen to identify enhancers of IFN-γ-induced STAT1-mediated transcription . The Mammalian Gene Collection ( MGC ) , a library of over 18 , 000 human and mouse full-length and sequence-validated cDNAs [35] , was screened to identify genes able to restore function to the STAT1 pathway upon ectopic expression in Toxoplasma-infected cells ( Fig 1A ) . The primary screen identified 32 cDNAs ( 17 mouse and 15 human; representing 28 genes ) that enhanced STAT1 activity ≥ 2 . 5-fold in replicate screens of the library and had robust Z-scores ≥ 4 ( Fig 1B , inset; S1 Table ) . Gene ontology ( GO ) analysis of these 32 STAT1 enhancers indicates that 21 cDNAs are involved in regulation of transcription , a 4 . 6-fold enrichment relative to the complete MGC library ( p-value < 0 . 001 ) . Of these 21 cDNAs , two represent orthologs of HOX5A , while two others are isoforms of mouse CRTC2 . In addition , the screen identified all three ETS2 isoforms present in the library ( one human and two mouse ) . In total , 17 unique transcriptional regulators were identified in the primary screen . Analyzing these 17 genes for Pfam domains identified motifs that defined at least six transcription factor families ( Fig 1C ) . A network analysis ( Fig 1D ) reveals no previously reported direct protein–protein interactions between STAT1 ( red ) and the screen hits ( black ) , but eight screen hits have been found to interact with network neighbors ( green ) known to directly bind STAT1 , including the well-known STAT1 regulators PIAS1 , CREBBP , and EP300 [36 , 37] . To assess whether genes identified in this screen are bona fide regulators of STAT1 activity , individual cDNA clones for the 21 putative STAT1 enhancers were sequence verified and tested for their ability to rescue STAT1 activity during parasite infection ( S2 Table ) . Activity was also assayed in uninfected cells transfected with these cDNAs . Seventeen of the 21 cDNAs were found to enhance STAT1-dependent transcription by at least 2 . 5-fold in infected cells . Fifteen of these 17 cDNAs also activated transcription in uninfected cells , suggesting that the transcription factors identified through this screen represent general enhancers of IFN-γ-dependent STAT1 activation , irrespective of Toxoplasma infection . In order to rule out nonspecific induction of the luciferase reporter , these genes were tested for induction of a control reporter lacking the GAS elements . Six of the 17 STAT1 enhancers , including all Crtc genes and several Hox genes , induced the control reporter and therefore were not pursued further , leaving 11 cDNAs ( nine genes , including two orphan nuclear hormone receptors ) that specifically enhance STAT1 activity in infected and/or uninfected cells ( S2 Table and S3 Fig ) . Finally , to rule out the possibility that these hits affected parasite fitness , we carried out additional screens of the MGC using transgenic parasites in which luciferase reports either cell invasion [38] or parasite viability [39] . These nine genes did not impact either of those assays ( S2 Table ) , indicating that our screen identified modulators of host cell signaling , rather than direct parasite effectors , consistent with their ability to potentiate STAT1 signaling in the absence of Toxoplasma infection . This expands our knowledge of genes that can regulate this important signaling pathway . Since the screen employed in this study was based on a reporter that responds to STAT1 homodimers , seven candidate transcription factor genes were tested against six additional pathway reporters to determine if they act specifically on the STAT1 pathway , or whether they might function as enhancers of additional immune-related signaling pathways . U2OS cells were co-transfected with candidate cDNAs along with luciferase reporter constructs under the control of either interferon response factor-1 ( IRF1 ) , NF-κB , interferon-stimulated gene factor-3 ( ISGF3 ) , serum response factor ( SRF ) , activator protein-1 ( AP1 ) , the STAT1 homodimer reporter used in the screen ( positive control ) , or negative control reporters ( Fig 2 ) . Cultures were then stimulated with IFN-γ to induce STAT1 and IRF1 , IFN-α to induce ISGF3 , or TNF-α to induce NF-κB . These particular pathway reporters and stimuli were selected because they represent well-known STAT1-dependent and STAT1-independent inflammatory pathways . Reporters for STAT1 , IRF1 , ISGF3 , and NF-κB all responded to the appropriate stimulus with increased luminescence , while the two control reporters and AP1 and SRF reporters were not activated by any of the stimuli tested ( Fig 2 ) . All of the genes tested robustly enhanced IFN-γ-induced STAT1 homodimer activity ( Fig 2 ) . Four of the seven genes also activated IRF1 activity , consistent with the known role of IRF1 as a STAT1 target gene [40] . Moreover , these factors were not promiscuous; they displayed little activity on the other reporters ( Fig 2 ) . Taken together , these data indicate that this high-throughput screen has identified a set of transcription factors that show clear specificity for the STAT1 pathway . We identified two orphan nuclear receptors , COUPTF2 and TLX , which exhibited strong enhancement of our STAT1 reporter ( S2 Table ) . Because nuclear hormone receptors are well-known transcriptional regulators , acting as co-activators and/or co-repressors , and are druggable targets , and because TLX is expressed in the brain [41–43] , where STAT1 activation is required to control chronic Toxoplasma infection [22–24] , we focused our studies on TLX . First , we set out to identify the spectrum of endogenous genes regulated by TLX . U2OS cells transiently expressing TLX or empty cDNA vector were untreated or stimulated with IFN-γ for 8 h and transcriptionally profiled . Hierarchical clustering of 341 differentially expressed genes ( ≥1 . 5-fold , false discovery rate ( FDR ) ≤ 5% ) delineated at least three clusters of co-regulated transcripts ( Fig 3A ) . GO enrichment analysis of these clusters showed that TLX overexpression enhanced transcription of 104 IFN-γ-independent genes involved in neuron differentiation and tissue morphology ( Fig 3B , cluster 3 ) , consistent with the fact that TLX is expressed in the brain , where it is an essential regulator of neurogenesis [41] . Amongst these genes are known regulators of brain physiology ( Fig 3C ) , including brain-specific solute carriers ( SLC17A7 and SLC30A3 ) , a neuronal calcium sensor ( HPCAL4 ) , a neuron specific vesicular protein ( CALY ) , and an essential regulator of dopamine neuron development ( CDNK1C ) [44] , suggesting that they may represent natural targets either directly or indirectly regulated by TLX . As expected , IFN-γ treatment of U2OS cells enhanced expression of 162 genes involved in immunity and inflammation ( Fig 3A and 3B , cluster 1 ) . Importantly , expression of 19 IFN-γ-inducible genes involved in host defense was potentiated by TLX expression ( Fig 3A and 3B , cluster 2 ) , including CXCL9 , CXCL10 , and CXCL11 ( Fig 3C , cluster 2 ) , all of which are well-known STAT1-dependent targets . Next , we sought to identify cell types that expressed high basal levels of endogenous TLX to determine the role of TLX in STAT1-dependent responses . A survey of nuclear receptor expression in the National Cancer Institute ( NCI ) 60 panel , a collection of cancer cells lines targeted for extensive study , including gene expression profiling [45] , suggested that astrogliomas express the highest levels of TLX mRNA , with U251 cells having the highest expression [46] . Transfection of U251 cells with small interfering RNAs ( siRNAs ) against TLX resulted in a greater than 80% reduction in TLX transcript compared to control ( Fig 4A ) . Next , cells transfected with siRNAs targeting TLX or with a control siRNA were either untreated or stimulated with IFN-γ for 8 h and were subject to expression profiling by microarray . Hierarchical clustering of 1 , 418 differentially expressed genes ( ≥1 . 5-fold , FDR ≤ 5% ) delineated three clusters of co-regulated transcripts ( Fig 4B ) . Knockdown of TLX resulted in marked repression of 352 IFN-γ-independent genes associated with cell cycle regulation ( Fig 4B and 4C , cluster 3 ) , consistent with the critical role for TLX in maintaining a proliferative state in adult neural progenitor cells [41 , 47] . Amongst these TLX-dependent transcripts were several genes involved in central nervous system function ( Fig 4D ) , including a synaptic adhesion molecule ( CADM1 ) ; the neuronal signal transducer , chimerin-1 ( CHN1 ) ; a serotonin binding glycoprotein ( GPM6B ) ; and sorting nexin family member 27 ( SNX27 ) , a gene recently shown to regulate developmental and cognitive impairment in Down syndrome [48] . Moreover , four genes previously reported to be TLX dependent in mouse brain [47] were also identified as TLX dependent in this experiment ( Fig 4D , asterisks ) . TLX depletion also resulted in enhanced expression of 411 genes that were enriched for sterol metabolism and endocytosis ( Fig 4B and 4C , cluster 2 ) . U251 astroglioma cells exhibited a robust transcriptional response to IFN-γ treatment , resulting in up-regulation of a similar profile of immune defense genes as seen in U2OS cells ( Fig 4B ) . A subset of these genes ( Fig 4A , cluster 1 ) was TLX dependent and was enriched for GO terms relating to inflammation and antigen presentation ( Fig 4C , cluster 1 ) . These IFN-γ- and TLX-dependent genes included CXCL9 and UBD , as well as guanylate-binding proteins ( GBPs ) ( Fig 4E ) . Taken together with our ectopic expression studies , these data identify TLX as a transcription factor that regulates the steady-state expression of STAT1-independent genes involved in brain function , brain development , and cell cycle while enhancing the output of a subset of IFN-γ-dependent target genes . Nuclear receptors can regulate gene expression either directly through DNA binding or indirectly by physically interacting with other transcription factors [49] . To determine whether DNA binding was required for TLX to regulate STAT1-dependent transcripts , U2OS cells were transfected with TLX constructs lacking either the DNA binding domain or the ligand binding domain . As expected , wild-type TLX markedly enhanced IFN-γ induction of our STAT1 reporter , and this induction was completely abrogated if only the DNA binding domain ( TLX ∆DBD ) or ligand binding domain ( TLX ∆LBD ) was used ( Fig 5A ) . Similarly , when CXCL9 and CXCL10 expression were used as readouts of STAT1 function , both domains of TLX were also required for efficient induction of these genes following IFN-γ stimulation ( Fig 5B ) . Taken together , these data show that TLX requires both DNA binding activity and ligand binding activity to enhance STAT1-mediated transcription . Although endogenous ligands for TLX have not been described , a recent small-molecule screen identified famprofazone , a nonsteroidal anti-inflammatory drug , as a synthetic ligand for TLX that induces transrepression [50] . We reasoned that if endogenous levels of TLX potentiate the expression of specific IFN-γ-inducible STAT1 target genes , then famprofazone should inhibit this response . To test this , U251 astroglioma cells were pretreated with famprofazone and subsequently stimulated with IFN-γ , IFN-α , or TNF-α , and the expression of CXCL10 and OAS2 was measured by RT-qPCR ( Fig 5C ) . CXCL10 and OAS2 were most potently induced by IFN-γ and IFN-α , respectively—consistent with their known role as canonical targets of these cytokines—whereas TNF-α had the weakest effect on both of these targets . Famprofazone treatment dramatically impaired the ability of IFN-γ and IFN-α to induce CXCL10 ( Fig 5C ) , while a more modest impairment was observed on TNF-α induction of CXCL10 . Interestingly , famprofazone also impaired induction of OAS2 , but only when IFN-γ was used as the stimulus . In contrast , TNF-α and IFN-α induction of OAS2 was unaffected by famprofazone . These data suggest that TLX regulates STAT1 function in a stimulus- and target-specific manner . The observation that TLX required both a DNA binding domain and a ligand binding domain for enhancement of CXCL9 and CXCL10 ( Fig 5B ) suggested that TLX might interact directly with the promoter of select STAT1 target genes . We hypothesized that this could increase the amount of phosphorylated STAT1 at the promoters of these genes , thereby leading to enhanced transcription . To test this hypothesis , we carried out chromatin immunoprecipitation ( ChIP ) in U2OS cells using antibody specific for phosphorylated STAT1 , followed by quantitative PCR ( qPCR ) for the region of the CXCL9 and CXCL10 promoters where STAT1 is known to bind ( Fig 6 ) [51] . Cells ectopically expressing a control vector showed increased ChIP signal at both promoters after a 2-h IFN-γ stimulation , relative to control antibody . Compared to control vector , cells expressing TLX showed a 3-fold and 2-fold increase in phosphorylated STAT1 ( pSTAT1 ) binding at the CXCL9 and CXCL10 , respectively ( Fig 6 ) . These data demonstrate that TLX can enhance transcription of IFN-γ-dependent genes by enhancing promoter occupancy of phosphorylated STAT1 . Although studies have explored the cell types expressing TLX in the developing and adult brain , whether TLX expression is regulated , in particular during a proinflammatory insult to the brain , is unknown . Given our finding that TLX potentiates expression of IFN-γ-inducible chemokines , we reasoned that in vivo infection with Toxoplasma—a potent inducer of IFN-γ production—might alter TLX expression in the brain . To test this , mice were infected and allowed to progress to chronic infection , at which point whole brains were removed , sectioned , and stained with a polyclonal antibody to TLX . Brain sections from naïve animals showed only modest , nuclear-localized staining in the granular cell layer of the dentate gyrus , a region previously described to contain TLX-positive neural progenitor stem cells ( S4 Fig ) [41] . In contrast , in the infected brain , numerous cell types stained positive for TLX ( Fig 7 ) , including cells with an apparent leukocyte morphology that were found near parasite cysts ( Fig 7A and 7B , arrow ) . In addition , neurons within the cerebral cortex stained intensely for TLX ( Fig 7C and 7D ) . These data demonstrate , for the first time , that although TLX is expressed selectively in granular layer of the dentate gyrus in the normal adult brain , CNS infection can induce TLX expression in a variety of cell types , many of which are proximal to the microbial insult . The critical role for IFN-γ and STAT1 in restricting Toxoplasma , taken together with our finding that TLX is induced in the brain during infection and can regulate IFN-γ-dependent expression of molecules such as CXCL9 , CXCL10 , GBP4 , and GBP5 , prompted us to examine whether TLX impacted infection or pathogenesis . Mice carrying a floxed allele of TLX were crossed to mice expressing Cre under the control of Mx1 , allowing inducible deletion of TLX [47] . Following treatment with Poly ( I:C ) to induce deletion , mice were rested for 2 wk and then infected with Toxoplasma . Animals were allowed to progress to chronic infection when parasites had established long-lived infection in the brain , at which point two key parameters of the immune response to this parasite were examined: induction of a potent Th1 response marked by IL-12 production and control of parasite replication . Dendritic cells are a critical source of IL-12 during Toxoplasma infection , and their ability to produce this cytokine is required for parasite control [52] . In addition , IL-12 is STAT1-dependent during Toxoplasma infection [20] , as well as in other protozoan infections [53] . Therefore , we compared dendritic cell IL-12 production in our wild-type ( WT ) and TLX-deficient mice . As expected , splenic dendritic cells recovered from control TLXf/f infected mice produced IL-12 after in vitro treatment with brefeldin A and monensin ( Fig 7E and 7F ) . This was associated with control of chronic infection as brain tissue stained with antisera to parasite antigens revealed intact cysts with well-formed cysts walls ( Fig 7G ) . In contrast , Mx1Cre-TLXf/f mice showed a near complete loss of IL-12 production by splenic dendritic cells ( DCs ) ( Fig 7E and 7F ) and had a marked increase in parasite burden in the brain , as demonstrated by the presence of parasites outside of cysts ( Fig 7H , arrows ) . Furthermore , these animals presented with a higher parasite burden as assessed by qPCR for parasite DNA ( Fig 7I ) . Taken together with our in vitro data , these results suggest that TLX plays a critical and previously unrecognized role as a STAT1 enhancer that is required for protection from Toxoplasma infection
This report presents the development and application of a host–pathogen interaction screen to interrogate a poorly understood aspect of STAT1 signaling: the regulation of STAT1 transcriptional activity in the nucleus . This strategy identified a novel set of genes that enhance STAT1 function and are highly enriched in transcription factors . The orphan nuclear receptor TLX was amongst the strongest STAT1 enhancers identified , and we have shown that TLX not only enhances the expression of endogenous STAT1 target genes following IFN-γ stimulation but is also required for control of Toxoplasma infection . Taken together , our data raise the possibility that TLX , as well as natural or synthetic ligands for this orphan nuclear receptor , represents an important new drug target to modulate cellular immunity and inflammation . The use of transcription factors to elicit distinct transcriptional programs driving biological function is a common theme . This is particularly evident in the mammalian immune system , in which a small number of core transcription factors ( e . g . , STATs , IRFs , and NF-κBs ) orchestrate diverse innate and adaptive responses . STAT1 exemplifies this plasticity in that it acts in different settings to promote both activated and suppressive macrophage and T cell function . The context-specific activity of transcription factors and signaling pathways that converge on them can be explained by a variety of mechanisms including epigenetic regulation [54] , signal intensity or duration [55] , and altered transcription factor complexes , since transcription factors form elaborate and dynamic multimeric complexes with co-activators/co-repressors and basal transcriptional machinery at the promoters of target genes [56] . Consistent with this notion , a recent study found that cell-type-specific responses mediated by transforming growth factor beta ( TGF-β ) -induced SMAD3 signaling is dictated by a small set of cell-type-specific transcription factors interacting with SMAD3 and directing its promoter binding activity [57] . Our data suggest that transcriptional modifiers also contribute to the context-specific activity of STAT1 and that TLX , in addition to its roles outside of STAT1 signaling , enhances the transcription of specific IFN-γ-regulated immune genes . Little is known about the natural targets of TLX outside of neuronal progenitors or the involvement of TLX in immunity or as a regulator of STAT1 signaling . Our transcriptional profiling experiments reveal the spectrum of genes and functional categories regulated by TLX in two different cellular contexts . Interestingly , another nuclear receptor , the liver X receptor ( LXR ) , in addition to its direct role in lipid metabolism , also binds to STAT1 during IFN-γ stimulation , resulting in suppression of STAT1 targets in macrophages , astrocytes , and microglial cells [58–60] . These data provide an important complement to the LXR/STAT1 interactions , by highlighting another druggable target that could drive enhanced STAT1 function in the brain , as well as in cells of the immune system . While TLX has been described as being exclusively expressed in only a select subset of cells in the brain , these data were largely derived from transgenic reporter mice [41] . Recent studies using immunohistochemistry [61] or qPCR [62] suggest that TLX may be expressed more widely than previously thought in the brain , as well as in a range of immune cell types , including CD4 and CD8 T cells , and monocytes [62] . In addition to defining TLX as a novel STAT1 enhancer , our screen revealed additional enhancers , a subset of which was previously reported to impact IFN-γ /STAT1 target genes . Upstream stimulatory factor-1 ( USF1 ) and zinc finger X-linked duplicated family member C ( ZXDC ) have previously been shown to enhance IFN-γ-dependent transcription of major histocompatibility ( MHC ) class II [63–65] . USF1 forms a complex with STAT1 on the class II transactivator ( CIITA ) promoter , a well-known STAT1 target [63] , and is proteolytically degraded by Chlamydia in order to block IFN-γ-induced transcription of STAT1 target genes [66 , 67] . In addition , ZXDC directly binds CIITA to enhance its function [64 , 65] . We also identified all three isoforms of ETS2 present in the library as STAT1 enhancers , which demonstrates the robustness of the screening assay . ETS2 physically interacts with CP300 and CBP [68]—both nuclear proteins that act as STAT1 enhancers by linking transcription factors to the basal transcriptional machinery [37] . Finally , another orphan nuclear receptor , COUPTF2 , was identified in our screen . Interestingly , both TLX and COUPTF2 have recently been shown to share high homology , particularly in the region of the ligand binding domain , suggesting a functional relatedness . While the genetic screen described in this report capitalizes on pathogen suppression of IFN-γ responses , a characteristic of a wide range of host–microbe interactions , these results highlight the potential of functional genomic screens to identify regulators of immune signaling pathways more broadly , representing a novel approach for the systematic identification of genes that modulate transcription factor activity . In addition , the data presented here specifically highlight the previously unrecognized role of the orphan nuclear receptor TLX in regulating STAT1 activity . Just as the glucocorticoid receptor and estrogen receptor are drug targets in inflammation and cancer , our data suggest that ligands for TLX may constitute new therapeutic targets to modulate inflammation and host defense in the brain .
RH strain Toxoplasma were maintained by serial passage in human foreskin fibroblast monolayers as described previously [69] . Me49 cysts were obtained from either Swiss Webster or CBA donor mice , enumerated by light microscopy , and used to infect mice by intraperitoneal injection of 20 cysts . TLXf/f mice [47] were a kind gift from Dr . Ron Evans ( The Salk Institute ) and were crossed to Mx1-Cre mice to create TLXf/f Mx1-Cre mice , allowing inducible deletion of TLX . TLX was deleted using 200 μg/mouse of Poly ( I:C ) ( Imgenex ) administered intraperitoneally every 3 d ( five administrations total ) . Animals were rested for 10 d after the final dose before being infected with 20 Me49 cysts administered intraperitoneally . The human osteosarcoma ( U2OS ) and astroglioma ( U251 ) cell lines were obtained from the American Type Culture Collection ( ATCC ) and maintained as recommended . This study was carried out in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . Protocols were approved by the Institutional Animal Care and Use ( IACUC ) committee of the University of Pennsylvania ( animal welfare assurance number A3079-01 ) . The University of Pennsylvania Animal Care and Use Programs are fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) . The MGC is a publically available cDNA library of complete open reading frames from the mouse and human genome [35] . cDNAs are packaged in a cytomegalovirus ( CMV ) promoter-based overexpression vector ( sport6 vector , Invitrogen ) . Screening of MGC v2 was carried out in 384-well luminescence CulturPlates ( Perkin Elmer ) prespotted with 40 ng/well of each cDNA clone [70] . U2OS cells ( 7500/well ) were reverse transfected for 24 h with MGC cDNA ( or empty sport6 vector for control wells ) and the STAT1 luciferase reporter ( 40 ng/well; Panomics ) , using Fugene 6 transfection reagent ( 0 . 24 ul/well; Roche ) . Cells were infected with a 10:1 ratio of RH strain Toxoplasma for 2 h and subsequently stimulated with recombinant mouse IFN-γ ( Peprotech ) for 7 h . Plates were assayed by adding BriteLite luciferase reagent ( PerkinElmer ) and measuring luminescence on an Analyst HT ( Molecular Devices ) set for 0 . 1-s integration time . The entire MGC library was screened in duplicate . Cells , transfection reagent , and cytokines were dispensed to each plate using a Matrix WellMate ( Thermo Scientific ) . To minimize evaporation and edge effects during incubation , plates were covered with metal lids with a rubber gasket . For whole-genome expression microarray , RNA was isolated using the RNeasy Plus kit ( Qiagen ) . Biotin labeled complementary RNA ( cRNA ) was generated using the Illumina TotalPrep RNA amplification kit . Total RNA and cRNA quality were assessed by Bioanalyzer ( Agilent ) . Illumina HumanHT-12 version-4 expression beadchips were hybridized with cRNA from two biological replicates per condition and scanned on an Illumina BeadStation 500GX . Scanned images were converted to raw expression values using GenomeStudio v1 . 8 software ( Illumina ) . Data analysis was carried out using the statistical computing environment , R ( v3 . 0 . 2 ) , the Bioconductor suite of packages for R , and RStudio ( v0 . 97 ) . Raw data were background subtracted , variance stabilized , and normalized by robust spline normalization using the Lumi package [71] . Differentially expressed genes were identified by linear modeling and Bayesian statistics using the Limma package [72 , 73] . Probes sets that were differentially regulated ( ≥1 . 5 fold , FDR ≦ 5%; after controlling for multiple testing using the Benjamini-Hochberg method [74 , 75] ) were used for hierarchical clustering and heatmap generation in R . Clusters of co-regulated genes were identified by Pearson correlation using the hclust function of the stats package in R . Data have been deposited on the Gene Expression Omnibus ( GEO ) database for public access ( GSE55751 ) . GO enrichment analysis was carried out using the Database for Visualization and Integrative Discovery ( DAVID ) [76 , 77] . Enrichment of GO terms was defined relative to the complete MGC library or the whole human transcriptome for screen data and microarray data , respectively . Protein domains were identified using Pfam [78] . Network analysis of protein–protein interactions among human orthologs of the 17 nonredundant , putative transcription factors and STAT1 was carried out using Cytoscape v2 . 6 . 3 [79 , 80] and the Michigan Molecular Interaction ( MiMI ) plug-in [81] . MiMI was queried to find all “nearest neighbor” genes shared by at least two of the 17 query genes . Zʹ-factor analysis of STAT1 reporter assay was used to optimize conditions for screen [34] . A modified robust Z-score was calculated for each cDNA as described previously [82] . All experiments were repeated two-to-four times . Means and standard deviations were calculated from biological replicates . Significance was determined using a Student’s t test . Statistical analysis and data visualization were performed with GraphPad Prism 4 and DataGraph 2 . 3 ( Visual Data Tools ) . At 4–8 wk postinfection , infected mice were perfused with 40 ml of ice-cold PBS to remove peripheral blood . For splenocyte preparation , spleens were dissected , dissociated , and subjected to hypotonic red blood cell lysis to generate a single cell suspension that was used for ex vivo cytokine analysis , splenocytes were stimulated for 4 h with brefeldin A and monensin , and then cells were rinsed , stained for surface markers with CD3-Pacific Blue , CD19-Pacific Blue , B220-Pacific Blue , CD11b-APC , and CD11c-PE-Cy7 at 4°C , and fixed with 4% PFA in PBS for 10 min at RT . Intracellular IL-12 staining with IL-12p40-PE was detected by staining in FACS buffer containing 0 . 5% saponin ( Sigma , St . Louis , Missouri ) . DCs were identified as Dump ( CD3 , CD19 , NK1 . 1 ) - , CD11c+ . Data were collected on a BD LSRFortessa cell analyzer ( BD Bioscience ) and analyzed using FlowJo software ( TreeStar , Ashland , Oregon ) . Antibodies were purchased from BD Biosciences ( San Jose , California ) and eBioscience ( San Diego , California ) . For detection of phospho-STAT1 , U2OS cells were infected for 2 h with Toxoplasma parasites engineered to express tdTomato and subsequently stimulated with 10 ng/ml of rIFN-γ ( Peprotech ) . At various times poststimulation , cells were trypsinized ( for flow cytometry only ) , fixed with 2% formaldehyde , permeabilized 10 min with cold methanol at 4°C , and stained with Alexa-488-conjugated monoclonal antibody specific for phosphorylated tyrosine residue 701 of STAT1 ( clone 4a; BD Biosciences ) . For validation of primary hits from the high-throughput screen , cDNA clones were expanded from bacterial stocks of the library , and DNA was isolated using a HiSpeed Maxi Kit ( Qiagen ) . Each clone was sequence verified and retested in six replicate wells in 384-well format using the conditions described above for the full screen . Each clone was also tested for its ability to regulate STAT1 in uninfected cells , as well as its ability to trigger a control luciferase reporter lacking GAS elements . Additional reporters were also used for monitoring ISGF3 ( Stratagene ) , IRF1 ( ActiveMotif ) , NF-κB , SRF , and AP1 ( Clontech ) , as described in the text . U251 cells were plated at 200 , 000 cells per well in 6-well plates and allowed to adhere overnight . Cells were transfected with either 10 nM siRNA to human TLX ( Qiagen; target 5ʹ-CCGGTTGATGCTAACACTCTA-3ʹ , sense 5ʹ-GGUUGAUGCUAACACUCUATT-3ʹ , and antisense 5ʹ-UAGAGUGUUAGCAUCAACCGG-3ʹ ) or 10 nM siRNA to luciferase as a negative control in HiPerfect transfection reagent ( Qiagen ) and incubated for 72 h before stimulating with rIFN-γ ( Peprotech ) for 8 h . Total RNA was isolated and used for either qPCR or expression profiling by microarray as described below . For inhibitor experiments , U251 cells were treated with 20 μM famprofazone ( Santa Cruz Biotechnology ) , for 4 h before stimulation with 10 ng/ml rIFN-γ . RNA was isolated 8 h poststimulation and used for qPCR . Full-length human TLX ( NR2E1 ) cDNA ( GenBank accession BC028031 ) from the MGC was used as a template for PCR with primers specific for the DNA binding domain ( forward: 5ʹ- ccatctcgagATGAGCAAGCCAGCCGGA-3ʹ; reverse: 5ʹ- ccattctagaTTAGCGGATGGTGGACGTCCG-3ʹ ) and the ligand binding domain ( forward: 5ʹ-ccatctcgagATGGAATCAGCTGCCAGACTTCTCTTCATGAG-3ʹ; reverse: 5ʹ- ccattctagaTTAGATATCACTGGATTTGTACATATCTGAAAGCAGTC-3ʹ ) . Primer nucleotides in bold indicate start or stop codons; underlined are restriction sites for directional cloning , and italics indicate a 4 nt pad region permitting efficient restriction of PCR amplicon ends . PCR products were gel purified , digested overnight with XhoI and XbaI , and cloned into the pCMV-Sport6 plasmid with DNA ligase ( New England Biolabs ) . Inserts were sequence verified , clones were cultured overnight , and DNA was isolated by maxiprep . Truncation mutant constructs were then used in STAT1 luciferase reporter and qPCR assays as described above . U2OS cells were transfected in 10 cm dishes with 6 μg/dish of either Sport6-empty control plasmid or Sport6-hNR2E1 using Fugene 6 reagent . Twenty-four hours post-transfection , cells were left untreated or were stimulated with 20 ng/ml of IFN-γ and then cross-linked with 1% formaldehyde for 10 min before quenching with 125 mM Glycine for 5 min . Cells were recovered by scraping , pooled , and 20–40 million cells were pelleted and treated with cell lysis buffer ( 10 mM Tris pH 8 . 0 , 10 mM NaCl , and 0 . 2% NP-40 ) for 10 min on ice . Nuclei were lysed with 50 mM Tris pH 8 . 0 , 10 mM EDTA , and 1% SDS for 10 min at room temperature . Lysate was diluted in immunoprecipitation buffer ( 20 mM Tris pH 8 . 0 , 2 mM EDTA , 150 mM NaCl , 1% Triton X-100 , and 0 . 01% SDS ) and sonicated seven times for 30 s each time , with 1 min on ice between each sonication . Lysis and dilution buffers were supplemented with protease and phosphatase inhibitors . Sonicated samples were further diluted to 3 . 6 ml each , and 1 ml was used for immunoprecipitation overnight at 4°C with either rabbit monoclonal antibody to pSTAT1 ( Cell Signaling Technology , clone 58D6 ) or control rabbit IgG ( Cell Signaling Technology ) . Bound chromatin was pulled down with protein G Dynabeads ( Life Technologies ) for 3 hat 4°C . Beads were washed and incubated with elution buffer ( 50 mM Tris pH 8 . 0 and 10 mM EDTA ) at 65°C for 30 min . Chromatin was recovered , and cross-links were reversed by overnight incubation at 65°C . Samples were treated with RNAse A for 2 h at 37°C , followed by treatment with proteinase K for 30 min at 55°C . DNA was purified over PCR purification columns ( Qiagen ) and used for qPCR with primers designed based on known binding sites of STAT1 in the promoters of CXCL9 and CXCL10 [51] . CXCL9 primers were forward: 5ʹ-CAGATCCAAGGGAATTTCTGC-3ʹ and reverse: 5ʹ-TGTGCCAAAGGCTATCAGTG-3ʹ . CXCL10 primers were forward: 5ʹ-TGCCCTGACAAACTAATGAGC-3ʹ and reverse: 5ʹ-CAAGGCATATTCTGCACCAG-3ʹ . Whole brain recovered from Swiss Webster mice at 4–6 wk postinfection were either snap frozen in OCT embedding media or fixed in 10% neutral buffered formalin before embedding in paraffin . For frozen tissue , 5 μm sections were cut on a Leica CM 3050 S cryostat and stained with 5 μg/ml rabbit polyclonal antibody to the N-terminus of human TLX ( LifeSpan Biosciences ) , and bound antibody was detected using 5 ng/μl of alexa-488-conjugated goat anti-rabbit secondary . For paraffin sections , antigen retrieval was carried by microwaving slides in citrate buffer ( pH 6 ) . Images were captured on Nikon E600 microscope outfitted with a Nikon Digital Sight DS-FI1 camera ( bright-field ) and a Roper Scientific Photometrics CoolSnap EZ camera ( fluorescence ) . To detect Toxoplasma by immunohistochemistry , paraffin sections were deparaffinized , rehydrated , and endogenous peroxidase blocked using 0 . 3% H2O2 in PBS for 10 min at room temperature . Sections were blocked with 2% normal goat serum before being incubated overnight at 4°C with rabbit polyclonal antibody specific for the SAG1 protein of Toxoplasma . Bound antibody was detected with 1 μg/ml biotinylated goat anti-rabbit IgG using the VectaStain kit ( Vector Labs ) , followed by DAB substrate kit ( Vector Labs ) according to the manufacturer’s instructions . Sections were counterstained with hematoxylin and images collected on a Nikon E600 fluorescent microscope ( Nikon , Tokyo , Japan ) and analyzed using NIS-Element ( Nikon ) . Quantification of parasite DNA by qPCR was performed as previously described [83] . Briefly , 50 mg of tissue was disrupted by repeated passage through an 18 gauge needle , and DNA was isolated using the High Pure PCR template preparation kit ( Roche ) . Real-time PCR specific for the Toxoplasma B1 repeat region was used to quantify the amount of parasite DNA from 500 ng of DNA purified from tissue . Samples were amplified using Toxoplasma B1 primers ( forward: 5ʹ-TCCCCTCTGCTGGCGAAAAGT-3ʹ and reverse: 5ʹ-AGCGTTCGTGGTCAACTATCGATTG-3ʹ ) and Power SYBR Green PCR Master Mix and a 7500 Fast Real-Time PCR System . A standard curve prepared from known amounts of purified Toxoplasma DNA was used for quantification . Analysis was performed with system software v1 . 3 . 1 ( Applied Biosystems , Warrington , United Kingdom ) .
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Immune responses are orchestrated by a diverse array of secreted ligands , yet the downstream transcriptional responses are coordinated by a relatively small set of key transcription factors , including nuclear factor kappa B ( NF-κB ) and signal transducers and activators of transcription ( STATs ) . The molecular mechanisms that tailor the output of these immune signaling pathways to generate cell- , tissue- , or context-specific responses are poorly understood . In this study , we exploit a host–pathogen interaction , Toxoplasma gondii infection in mice , using a genetic screen to identify host factors that overcome parasite suppression of STAT1 signaling . We show that the orphan nuclear receptor TLX , a key regulator of brain development , enhances expression of a subset of STAT1-dependent genes in response to IFN-γ stimulation . Through genetic and pharmacological studies , we show that endogenous TLX function is required for triggering appropriate responses to IFN-γ in astrocytes . Moreover , we found that genetic disruption of TLX in mice impairs their ability to mount an effective immune response and control T . gondii infection in the brain . These data suggest that natural or synthetic ligands for TLX might be effective tools for modulating immune responses , particularly in the brain where TLX expression is highest .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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The Orphan Nuclear Receptor TLX Is an Enhancer of STAT1-Mediated Transcription and Immunity to Toxoplasma gondii
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Transcranial brain stimulation and evidence of ephaptic coupling have recently sparked strong interests in understanding the effects of weak electric fields on the dynamics of brain networks and of coupled populations of neurons . The collective dynamics of large neuronal populations can be efficiently studied using single-compartment ( point ) model neurons of the integrate-and-fire ( IF ) type as their elements . These models , however , lack the dendritic morphology required to biophysically describe the effect of an extracellular electric field on the neuronal membrane voltage . Here , we extend the IF point neuron models to accurately reflect morphology dependent electric field effects extracted from a canonical spatial “ball-and-stick” ( BS ) neuron model . Even in the absence of an extracellular field , neuronal morphology by itself strongly affects the cellular response properties . We , therefore , derive additional components for leaky and nonlinear IF neuron models to reproduce the subthreshold voltage and spiking dynamics of the BS model exposed to both fluctuating somatic and dendritic inputs and an extracellular electric field . We show that an oscillatory electric field causes spike rate resonance , or equivalently , pronounced spike to field coherence . Its resonance frequency depends on the location of the synaptic background inputs . For somatic inputs the resonance appears in the beta and gamma frequency range , whereas for distal dendritic inputs it is shifted to even higher frequencies . Irrespective of an external electric field , the presence of a dendritic cable attenuates the subthreshold response at the soma to slowly-varying somatic inputs while implementing a low-pass filter for distal dendritic inputs . Our point neuron model extension is straightforward to implement and is computationally much more efficient compared to the original BS model . It is well suited for studying the dynamics of large populations of neurons with heterogeneous dendritic morphology with ( and without ) the influence of weak external electric fields .
Extracellular electric fields in the brain and their impact on neural activity have gained a considerable amount of attention in neuroscience over the past decade . These electric fields can be generated endogenously [1–3] or through transcranial ( alternating ) current stimulation [4–6] , and can modify the activity of neuronal populations in various ways [1 , 7–9] . Although the fields generated by this type of noninvasive brain stimulation are rather weak ( ≤1 V/m [4 , 5] ) and do not directly elicit spikes , they can modulate spiking activity and lead to changes in cognitive processing , offering a range of possible clinical interventions [10–12] . How external fields lead to changes of the membrane voltage in single cells has been studied in detail [13–15] . However , their effects on population spike rate and the underlying mechanisms are largely unexplored . Computational models of neurons exposed to electric fields offer a useful tool to gain a better understanding of these mechanisms . Multi-compartment models of neurons are well suited for corresponding investigations at the level of single cells and small circuits [16] but are too complex for a purposeful application in large populations . Single-compartment ( point ) neuron models of the integrate-and-fire ( IF ) type are well applicable to study the dynamics of large neuronal populations , due to their computational efficiency and analytical tractability [17] . However , typical IF model neurons lack the dendritic morphology required for a biophysical description of electric field effects . Furthermore , even in the absence of an extracellular field , the dendritic morphology strongly shapes neuronal response properties [18] . In this contribution , we extend the popular class of IF point neuron models to quantitatively account for morphology dependent modulations of neural activity due to: ( i ) dendritic influences on the integration of synaptic inputs and ( ii ) the effects of extracellular electric fields . Furthermore , we describe how oscillatory electric fields affect neuronal subthreshold and spiking activity and identify field-induced spike rate resonance . Specifically , we considered a canonical spatial pyramidal neuron model which consists of a somatic compartment and one ( apical main ) passive dendritic cable , and which is exposed to in-vivo like fluctuating synaptic input and an electric field . Based on that model we analytically derived an extension for the classical leaky and the refined exponential , [19] , IF point neuron models in order to exactly reproduce the subthreshold dynamics of the spatial model for arbitrary parametrizations . We then evaluated the extended IF models by quantitatively comparing their spiking activity with the spiking activity of the corresponding spatial model . Finally , we used these models to study the effects of an oscillating electric field ( due to the presence of the dendritic cable ) on the spike rate dynamics .
The BS neuron model consists of a lumped soma attached to a passive dendritic cable of length L . The dynamics of its membrane voltage , when receiving synaptic inputs at the soma , Is ( t ) , and the distal dendrite , Id ( t ) , and when exposed to a spatially homogeneous external electric field , E ( t ) , are governed by the cable equation: c m ∂ V BS ∂ t - g i ∂ 2 V BS ∂ x 2 + g m V BS = 0 , 0 < x < L , ( 1 ) subject to the boundary conditions: C s ∂ V BS ∂ t - g i ∂ V BS ∂ x + G s V BS - G s Δ T e V BS - V T Δ T = I s ( t ) - g i E ( t ) , x = 0 , ( 2 ) ∂ V BS ∂ x = I d ( t ) g i + E ( t ) , x = L , ( 3 ) at the soma ( x = 0 ) and the end of the dendrite ( x = L ) . VBS denotes the deviation of the membrane voltage from rest , Vrest , VBS ( x , t ) ≔ VBS , i ( x , t ) − VBS , e ( x , t ) − Vrest , where VBS , i and VBS , e are the intra- and extracellular potentials . The effects of a spike are described by the IF-type reset condition for the soma: if V BS ( 0 , t ) ≥ V s then V BS ( 0 , t ) : = V r ( 4 ) and by a short refractory period of length Tref during which VBS ( 0 , t ) is clamped at the reset value Vr . Spike times are defined by the times at which the somatic membrane voltage VBS ( 0 , t ) crosses the spike voltage value Vs from below . cm denotes the membrane capacitance , gm the membrane conductance , and gi the internal ( axial ) conductance of a dendritic cable segment of unit length . Cs and Gs are the somatic membrane capacitance and leak conductance . The exponential term with threshold slope factor ΔT and effective threshold voltage VT approximates the somatic sodium current at spike initiation [19] . For details see Methods . In the proposed IF point neuron extension , that is , the eP model , the deviation of the membrane voltage , VeP , from rest is governed by C eP d V eP d t + G eP V eP - α G eP Δ T e V eP - V T Δ T = [ L s * I s ] ( t ) + [ L d * I d ] ( t ) + I E ( t ) , ( 5 ) and by the reset condition: if V eP ≥ V s then V eP : = V r ′ , ( 6 ) where VeP is clamped to V r ′ for the duration of the refractory period Tref after every spike . CeP and GeP are the membrane capacitance and leak conductance . The scaling factor α ensures an equal membrane voltage response to the depolarizing current described by the exponential terms in both models ( BS and eP ) . We consider two versions of these models separately . First , we treat the LIF versions in detail , where we omit the exponential terms in Eqs 2 and 5; specifically , by taking the limit ΔT → 0 ( and setting Vs = VT ) . In the subsequent part we then consider the ( full ) EIF versions of the BS and eP models . Below we explain in detail how the components of the point model extension are derived: the linear input filters Ls ( t ) , Ld ( t ) , the additional input current equivalent to the field effect , IE ( t ) , and , in case of the ( full ) EIF type models , the scaling factor α . The analytical expressions of these model components are given in Eqs 10 , 13 , 20 and 21 ( for the LIF case ) , and in Eqs 22–26 ( for the EIF case ) . To mimic the remaining depolarization along the dendritic cable after each spike , we choose an elevated reset voltage for all point neuron models: V r ′ = ( V r + V T ) / 2 . For comparison we also use a point neuron model ( of LIF and EIF type , respectively ) without the extension , that is , Ls ( t ) = Ld ( t ) = δ ( t ) and α = 1 , and we fit the parameters of that model to best reproduce the activity of the BS model for equal synaptic inputs ( details see below ) . We refer to this model as the P model . We first consider the BS and eP model neurons of the LIF type ( i . e , ΔT → 0 , Vs = VT ) receiving subthreshold synaptic input at the soma in the absence of an electric field ( E ( t ) = 0 , IE ( t ) = 0 , Id ( t ) = 0 ) . To avoid ambiguity we use the superscript Is for the membrane voltage variables in this case . The somatic membrane voltage response of the BS model ( Eqs 1–3 ) can be calculated as ( see Methods ) V ^ BS I s ( 0 , ω ) = I ^ s ( ω ) C s i ω + G s + z ( ω ) g i tanh ( z ( ω ) L ) , ( 7 ) z ( ω ) = g m + g m 2 + ω 2 c m 2 2 g i + sgn ( ω ) i - g m + g m 2 + ω 2 c m 2 2 g i , ( 8 ) where . ^ indicates the temporal Fourier transform and ω = 2πf denotes angular frequency . The somatic membrane voltage response of the eP model ( Eq 5 ) is given by V ^ eP I s ( ω ) = L ^ s ( ω ) I ^ s ( ω ) C eP i ω + G eP . ( 9 ) The dendritic filter Ls required to exactly reproduce the somatic membrane voltage response of the BS model , i . e . , V ^ e P I s ( ω ) = V ^ B S I s ( 0 , ω ) , must then be equal to ratio of the impedances of both models: L ^ s ( ω ) = C eP i ω + G eP C s i ω + G s + z ( ω ) g i tanh ( z ( ω ) L ) , ( 10 ) where z ( ω ) is given by Eq 8 . In the following , we choose the membrane capacitance and conductance of the eP model to be equal to the corresponding somatic quantities of the BS model , CeP = Cs , GeP = Gs . To see the necessity of the filter , let us consider the P model ( no dendritic filter , L ^ s ( ω ) = 1 ) whose subthreshold response is given by V ^ P I s ( ω ) = I ^ s ( ω ) C P i ω + G P . ( 11 ) Because of the additional frequency-dependent term in the denominator of Eq 7 compared to Eq 11 , it is not possible to adjust the parameters CP and GP of the P model such that V ^ P I s ( ω ) = V ^ B S I s ( 0 , ω ) for all frequencies ω . The somatic response of the BS model can only be approximated in this case . Fig 2A shows the impedances , Z m I s ( ω ) ≔ V ^ m I s ( ω ) / I ^ s ( ω ) , m ∈ {BS|x = 0 , eP , P} , of the three neuron models for an example set of parameter values for the BS model . The two parameters of the P model ( CP and GP ) were determined by matching the steady-state somatic voltage , Z P I s ( 0 ) = Z B S I s ( 0 ) , and minimizing the mean squared distance between Z P I s and Z B S I s over the visualized range of input frequencies . The impedance of the eP model matches the impedance of the BS model exactly while the impedance of the P model deviates substantially , in particular for larger frequencies . Fig 2B–2D show the amplitudes and phases of the input filter L ^ s ( ω ) for various sets of parameters for the BS morphology . L ^ s ( ω ) is always a high-pass filter , which attenuates the somatic inputs at lower and amplifies them at higher frequencies . This effect is more pronounced for a larger dendritic and a smaller somatic compartment . It becomes stronger with increasing ratio of dendritic over somatic size . Nevertheless , the filter does not differ qualitatively for changes in neuron morphology . We next compare how well the point neuron models eP and P reproduce the spiking activity of the BS model neuron . For this purpose we consider an in vivo-like fluctuating synaptic input current Is ( t ) described by an Ornstein-Uhlenbeck process ( see Methods ) . The model outputs are compared over a range of values for the input mean I s 0 and standard deviation σs . The parameter values of the P model were adjusted to best reproduce the spike train of the BS model ( see Methods for details ) . Fig 3A displays the time series of the somatic membrane voltage of the three models in response to the same input currents—a weak ( small I s 0 , σs ) and a strong current ( large I s 0 , σs ) . For both input currents , the eP model well reproduces the somatic voltage dynamics of the BS model . Consequently , the spike times are also well reproduced . There is , however , a mismatch between the voltage traces during short periods ( of less than approximately 10 ms duration ) after spikes have occurred . This discrepancy is a result of the remaining dendritic depolarization after a spike has occurred in the BS model , which is only approximated by the elevated reset voltage V r ′ ( see section Models above ) in the point neuron models . In comparison , the P model performs worse in reproducing the BS membrane voltage dynamics , particularly the fast fluctuations are poorly recovered . This is expected from the mismatch in the impedance for high frequencies ( cf . Fig 2A ) . In Fig 3B–3E we compare spiking activity in terms of spike coincidences and spike rates for a wide range of input parameters . We used the spike coincidence measure Γ which quantifies the similarity between two spike trains for a given precision of 3 ms ( see Methods ) . The maximum value of 1 indicates an optimal match , i . e . , spike times always coincide , a value of 0 corresponds to pure chance , i . e . , the degree of coincidences for two Poisson spike trains with equal rates . The P model was fitted to the BS model for each input ( in terms of I s 0 , σs ) separately . The parameters of the eP model , on the other hand , are constant and do not depend on the input at all . The eP model very accurately reproduces the BS spike times for small spike rates ( Γ ≥ 0 . 9 for small I s 0 and σs ) , see Fig 3B and 3E . This performance decreases only slightly for increasing σs ( noise dominated input ) and somewhat stronger for increasing I s 0 ( mean dominated input ) . Generally , Γ decreases with increasing spike rates . This can be attributed to the transient periods after spikes during which the dendritic cable is still loaded and the membrane voltages of both neuron models deviate . Those periods do not depend on the spike rate and therefore have a stronger deteriorating effect when the interspike intervals are smaller . In addition , when σs is small the model neurons spike repetitively in a rather clock-like manner , with comparable rate but most likely out of phase due to mismatches caused by the membrane voltage resets . This helps understand the rather low values of Γ for mean dominated inputs . The spike rate of the BS model is also reproduced quite well by the eP model , which underestimates it only slightly ( Fig 3D ) . Spike coincidence and spiking rate reproduction of the eP model can be improved even further by additionally tuning the reset voltage V r ′ using Γ or the spike rate distance as a cost function . The P model , in comparison , is substantially worse in reproducing the spike times at small spike rates and only slightly better than the eP model for large spike rates ( Fig 3B and 3C ) . The spike rate of the BS model is slightly overestimated by the P model ( Fig 3D ) . Even though the parameters of the P model were optimized in an input-dependent manner the eP model leads to an improved reproduction of the BS spiking activity overall . In summary , the dendritic cable implements a high pass filter for inputs at the soma . Due to the derived filter for somatic inputs , the eP model—without having fitted any of its parameters—well reproduces the BS model dynamics for subthreshold and suprathreshold inputs . Notably , the computation time required for the BS model was at least 25 times that of the eP model , using measurements on a single core of a desktop computer . We next consider subthreshold synaptic input at the distal dendrite instead of somatic input , but otherwise the same setup as in the previous section . Here we use superscipt Id for the membrane voltage variables to better distinguish from the previous scenario . The somatic membrane voltage response of the BS model can be expressed as ( see Methods ) V ^ BS I d ( 0 , ω ) = I ^ d ( ω ) sech ( z ( ω ) L ) C s i ω + G s + z ( ω ) g i tanh ( z ( ω ) L ) , ( 12 ) where z ( ω ) is given by Eq 8 . In order to reproduce that voltage response using the eP model , for which V ^ e P I d ( ω ) = L ^ d ( ω ) I ^ d ( ω ) / ( C e P i ω + G e P ) ( cf . Eq 9 ) , we obtain L ^ d ( ω ) = ( C eP i ω + G eP ) sech ( z ( ω ) L ) C s i ω + G s + z ( ω ) g i tanh ( z ( ω ) L ) . ( 13 ) As in the previous section , we choose CeP = Cs , GeP = Gs . In contrast to the somatic input filter Ls the filter Ld for distal inputs exhibits low pass properties for various BS morphologies , see Fig 4A . The shape of this filter is largely independent of the soma size . Compared to the attenuation of low frequency in case of somatic input , the filter gain for high frequency dendritic input is much lower . This results in a stronger filtering effect for dendritic inputs than for somatic inputs . An evaluation of the distal input filter in terms of reproduction of BS spiking activity ( Γ and rates ) is shown in Fig 4B–4E for a range of input mean I d 0 and standard deviation σd values . For comparison we used the P model ( without filter ) whose parameters were tuned to best reproduce the spike train of the BS model for each input ( i . e . , ( I d 0 , σd ) -pair ) separately . The eP model very accurately reproduces the BS spike times for small spike rates ( Γ ≥ 0 . 9 for small I d 0 and σd ) . The accuracy drops somewhat as I d 0 increases , which can be explained as in the previous section . Interestingly , the performance does not deteriorate with increasing spike rate in general; it remains high if the noise intensity σd is sufficiently strong ( Γ ≥ 0 . 8 for σd ≥ 80 pA , independent of I d 0 in the considered range ) . The spike rate of the BS model is somewhat underestimated by the eP model ( Fig 4D ) . It should be noted that the spike rate reproduction could be substantially improved by an increased reset voltage value V r ′ , as the remaining dendritic depolarization after spikes is more pronounced in case of distal input compared to somatic input . The computational speed-up of the eP model here is the same as in the previous section . The P model , in comparison , is less accurate across all inputs ( Fig 4B–4D ) , even though its parameters depend on the input . In summary , the dendritic cable implements a low pass filter for inputs at the distal dendrite , and due to the corresponding derived filter the eP model reproduces the BS model dynamics for subthreshold and suprathreshold inputs much better than the P model . We now consider an extracellular electric field—in addition to the synaptic inputs—to which the neuron is exposed to . We characterize the effects of that field on the subthreshold somatic membrane voltage and spiking dynamics of the BS neuron and we determine an explicit expression for the additional input current of the extended point neuron model to reproduce these effects . The electric fields we are interested in are oscillatory , spatially uniform on the neuronal scale and weak such as induced by transcranial brain stimulation [6] . In the following , we consider a field with amplitude E1 and angular frequency φ , E ( t ) = - ∂ V BS , e ∂ x ( t ) = E 1 sin ( φ t ) . ( 14 ) Recall that VBS , e ( x , t ) is the extracellular potential . The BS subthreshold somatic membrane voltage response to this field , V B S E ( 0 , t ) , is determined by Eqs 1–3 . Using the temporal Fourier transform the solution can be expressed analytically as V ^ BS E ( 0 , ω ) = E ^ ( ω ) g i [ sech ( z ( ω ) L ) - 1 ] C s i ω + G s + z ( ω ) g i tanh ( z ( ω ) L ) , ( 15 ) where z ( ω ) is given by Eq 8 ( see Methods ) . Note , that we again neglect the exponential current in this section ( LIF case , but see next section for the EIF case ) . In the time domain this yields V BS E ( 0 , t ) = | A ( φ ) | sin φ t + arg ( A ( φ ) ) , ( 16 ) A ( φ ) = E 1 g i [ sech ( z ( φ ) L ) - 1 ] C s i φ + G s + z ( φ ) g i tanh ( z ( φ ) L ) , ( 17 ) where arg ( x ) denotes the argument of the complex number x . The overall subthreshold response in presence of the electric field and synaptic input can be decomposed as V ^ BS ( 0 , ω ) = V ^ BS I s ( 0 , ω ) + V ^ BS I d ( 0 , ω ) + V ^ BS E ( 0 , ω ) , ( 18 ) with V ^ B S I s ( 0 , ω ) , V ^ B S I d ( 0 , ω ) and V ^ B S E ( 0 , ω ) given by Eqs 7 , 12 and 15 . For the eP model , on the other hand , we have V ^ eP ( ω ) = L ^ s ( ω ) I ^ s ( ω ) + L ^ d ( ω ) I ^ d ( ω ) + I ^ E ( ω ) C eP i ω + G eP . ( 19 ) To guarantee an equal subthreshold response in both models , i . e . , V ^ e P ( ω ) = V ^ B S ( 0 , ω ) , we obtain the following expression for the additional input current , I E ( t ) = | B ( φ ) | sin φ t + arg ( B ( φ ) ) , ( 20 ) B ( φ ) = E 1 g i ( C eP i φ + G eP ) [ sech ( z ( φ ) L ) - 1 ] C s i φ + G s + z ( φ ) g i tanh ( z ( φ ) L ) , ( 21 ) where we set CeP = Cs and GeP = Gs ( as in the previous sections ) . It should be noted that these results are not restricted to sinusoidal field variations , as considered here , and can be easily adjusted for any time-varying or constant description of the electric field using its Fourier transform . The equivalent input current IE ( t ) as well as the somatic subthreshold sensitivity to the field , |A ( φ ) |/E1 and the phase shift between oscillating membrane voltage and field , arg ( A ( φ ) ) , with A ( φ ) from Eq 17 , are shown in Fig 5 . Interestingly , the amplitude of IE ( t ) increases with increasing field frequency ( Fig 5A ) , while the sensitivity decreases ( Fig 5B ) . The sensitivity curve changes quantitatively , but not qualitatively , with varying neuronal morphology ( Fig 5B ) . Specifically , its dependence on the field frequency becomes more pronounced with increasing ratio of dendritic size over somatic one . The cable length has the strongest impact in this respect . Notably , the morphology parameters can be adjusted such that the sensitivity curve well matches with empirical results obtained from rat hippocampal pyramidal cells in vitro . The phase shift between the somatic membrane voltage and field oscillations also depends on the field frequency . It exhibits an anti-phase relation for slow oscillations , and decreases with increasing frequency ( Fig 5B ) . We next assess how the electric field affects spiking activity for a range of field frequencies using the BS and eP models . For that purpose , we simulated both model neurons subject to the field and noisy synaptic input at the soma or at the distal dendrite . The synaptic drive alone is strong enough to cause stochastic spiking with rate r0 . The oscillatory field leads to an oscillatory spike rate modulation quantified as r1 ( φ ) sin ( φt + ψ ( φ ) ) around the constant baseline spike rate r0 ( see Methods for details ) . Note that this spike rate modulation measure is related to the frequently used spike field coherence measure . The amplitude r1 and phase shift ψ of the spike rate modulation for various somatic inputs ( in terms of I s 0 and σs ) , a range of field oscillation frequencies and two field strengths are shown in Fig 6 . The eP model well reproduces the spike rate dynamics of the BS model exposed to the field for all considered field and input parameter values . The amplitude r1 increases linearly with increasing field magnitude E1 . In contrast to the subthreshold sensitivity to the field ( cf . Fig 5B ) , the spike rate modulation exhibits a clear resonance in the beta and gamma frequency bands across the different inputs . In other words , the spike rate oscillations are strongest for field oscillations of that frequency range . The amplitude peak is more pronounced for stronger inputs and most prominent when the input is dominated by its mean ( large I s 0 , small σs ) . This resonance amplitude rapidly increases with increasing baseline spike rate—by increasing both , mean and standard deviation of the background input from small values—and saturates at about r0 = 30 Hz ( Fig 6 , center ) . The resonance frequency shifts rather gradually from the beta to the gamma range as the baseline spike rate increases from a few spikes per second to about 60 Hz . The phase shift ψ varies around π , depending on the input and field frequency . Note that ψ = π implies that the probability of spiking is largest at the trough of the field oscillation . This results from the orientation of the field , which , in case of E ( t ) = E0 > 0 , induces a ( hyper- ) polarized somatic membrane voltage . To examine the importance of the specific shape of IE ( t ) , we also considered an alternative sinusoidal input current IE ( t ) = I1 sin ( φt + ϕ ) for the eP model . Note that the amplitude and phase shift of that current are constant across different field frequencies . Using that current , the typical resonance of the spike rate modulation due to the field cannot even roughly be reproduced ( Fig 6 ) . Let us now inspect spike rate modulation due to the field in presence of distal dendritic inputs instead of somatic ones . In Fig 7 the results are shown for various distal inputs ( in terms of I d 0 and σd ) . Interestingly , for all considered distal dendritic inputs , spike rate modulation amplitudes increase monotonically with the field frequency over the whole considered range ( up to 1 kHz , see Discussion for an explanation ) . Similarly as for somatic inputs , modulation is strongest for mean dominated ( large I d 0 , small σd ) distal inputs , and the phase shift ψ varies around π . Overall , the eP model well reproduces the modulation observed in the BS model . In the previous sections , we considered only capacitive and leak currents through the neuronal membrane; the model extension presented there applies to the LIF type model neurons . Here , we consider the BS and eP models described by Eqs 1–3 , 5 without neglecting the exponential term , that approximates the voltage dependent sodium current at spike initiation . That is , we derive and evaluate the model extension for model neurons of the EIF type . To derive the required model components Ls ( t ) , Ld ( t ) , α and IE ( t ) we linearize the exponential terms in Eqs 2 and 5 around a baseline voltage value V0 and then proceed similarly as above . Specifically , we calculate the subthreshold somatic membrane voltage response of the BS model , using the ( temporal ) Fourier transform , and obtain four response components: V ^ B S ( 0 , ω ) = V ^ B S I s ( 0 , ω ) + V ^ B S I d ( 0 , ω ) + V ^ B S Δ T ( 0 , ω ) + V ^ B S E ( 0 , ω ) , where V B S I s , V B S I d and V B S E denote the voltage response components to Is , Id and E , respectively , and the additional term V B S Δ T is due to the ( linearized ) exponential term . These four voltage response components are given by the explicit expressions Eqs 41–43 in the Methods section . For the eP model , on the other hand , we can also calculate the subthreshold membrane voltage response in the Fourier domain , V ^ e P ( ω ) , given by Eq 48 . By requiring equal subthreshold responses , V ^ e P ( ω ) = V ^ B S ( 0 , ω ) , we obtain the following explicit expressions for the components Ls , Ld , α and IE , considering the electric field defined in Eq 14: L ^ s ( ω ) = C eP i ω + G eP 1 - α e V 0 - V T Δ T C s i ω + G s 1 - e V 0 - V T Δ T + z ( ω ) g i tanh ( z ( ω ) L ) , ( 22 ) L ^ d ( ω ) = C eP i ω + G eP 1 - α e V 0 - V T Δ T sech ( z ( ω ) L ) C s i ω + G s 1 - e V 0 - V T Δ T + z ( ω ) g i tanh ( z ( ω ) L ) , ( 23 ) α = G s G s + tanh ( L / λ ) g i / λ , ( 24 ) I E ( t ) = | B ( φ ) | sin φ t + arg ( B ( φ ) ) , ( 25 ) B ( φ ) = E 1 g i C eP i φ + G eP 1 - α e V 0 - V T Δ T [ sech ( z ( φ ) L ) - 1 ] C s i φ + G s 1 - e V 0 - V T Δ T + z ( φ ) g i tanh ( z ( φ ) L ) , ( 26 ) where z ( ω ) is given by Eq 8 . The scaling factor α guarantees that the voltage response component caused by the exponential term , V B S Δ T , is reproduced . In other words , α ensures that the spike initiation current , described by the exponential term , leads to an equal steady state in both models . Note that the two filters for EIF neurons and those for LIF neurons depend on input frequency in qualitatively the same way ( by comparing Eqs 22 and 23 with Eqs 10 and 13 ) . We assessed the reproduction of BS spiking activity by the extended EIF model for somatic inputs using the spike coincidence factor Γ and estimated spike rates ( Fig 8 ) . Here again the parameter values of the P model were adjusted to maximize ΓBS , P for each input separately . The range of input parameter values was chosen to obtain similar spike rates as in Fig 3 . Despite the linearization in the derivation , the eP model achieves a correct reproduction of the BS spike trains ( Γ ≥ 0 . 7 for a wide range of input parameters ) . In particular , ΓBS , eP is large for small spike rates ( small I s 0 and σs ) and decreases for increasing I s 0 ( towards mean dominated input ) , see Fig 8A and 8D . The eP model tends to underestimate the firing rate of the BS model ( Fig 8C ) . This discrepancy in the rate could be reduced by optimizing the point model reset voltage , V r ′ , to better account for the remaining dendritic cable depolarization in the BS model . Similarly , an improved performance of the eP model in terms of spike train reproduction could be achieved by tuning this reset voltage . The P model , on the other hand , rather poorly reproduces the BS spiking dynamics for small input noise intensity ( Γ ≤ 0 . 6 for σs ≤ 30 pA , see Fig 8A ) . Overall , also in presence of the exponential term the eP model clearly outperforms the simpler P model for small spike rates ( ΓBS , eP − ΓBS , P ≥ 0 . 3 for small I s 0 and σs ) and achieves similar performance for high spiking activity ( Fig 8B ) . The reproduction of spiking activity of the BS model was also assessed for distal dendritic inputs . The range of input parameters ( I d 0 and σd ) was adjusted to obtain similar BS spike rates as for the LIF case . The eP model performs well , in particular for small spike rates or sufficiently strong noise intensity; its performance decreases in the mean driven regime ( Fig 9A ) . On the contrary the P model fails to reproduce the BS spiking activitiy ( see Fig A in S1 Text for more details ) . In summary , the somatic and distal dendritic input filters obtained for EIF neurons are qualitatively similar to the ones obtained for LIF neurons . The eP model , in contrast to the P model , well reproduces the BS model dynamics for subthreshold and suprathreshold inputs—also for the EIF case . Spike rate modulations due to an oscillatory electric field using EIF type model neurons for synaptic background input at the soma or distal dendrite are displayed in Fig 9 ( see also Fig B and Fig C in S1 Text for additional parameter values of the background input ) . Similarly to the LIF case , spike rate modulation amplitudes do not decrease monotonically with the field frequency . For somatic background input , we find spike rate resonance in the beta and gamma frequency range , similarly as shown by LIF type models . However , in case of distal dendritic input , EIF neurons exhibit resonance peaks in the high gamma frequency band , in contrast to LIF neurons , whose resonance frequency is substantially larger ( see Discussion for an explanation ) . For both input locations the spike rate modulations shown by the BS model are well reproduced by the eP model and resonance amplitudes are stronger for large spike rates ( i . e . , large I s 0 , σs and large I d 0 , σd , respectively ) .
We have demonstrated that synaptic input is integrated at the soma in distinct ways due to the presence of the dendrite , depending on the input site . Distal dendritic input is low-pass filtered ( cf . Fig 4A ) , in accordance with previous results [24] , whereas somatic input is high-pass filtered ( cf . Fig 3B ) . The latter effect is consistent with recent measurements from Purkinje cells and with theoretical results [18] which show a similar change in somatic impedance due to the presence of a dendritic tree ( Fig . 4 in [18] , in comparison with Fig 2A here ) . Consequently , the presence of a dendrite can lead to an enhanced neuronal spiking response to high-frequency somatic inputs [18] , which may be further amplified by the dendritic effect on the sharpness of spikes at the axon initial segment [25] . The derived IF model extension enables efficient analyses of the BS spike rate response to modulations of the input current—which are , however , not within the scope of this paper . There are two different strategies for taking into account complex neuron morphologies in models while keeping numerical simulation computationally efficient . One option is to reduce the number of compartments while retaining important properties of the dendritic tree [26] . Alternatively , one can extend point neuron models with temporal kernels which are calibrated to reproduce the somatic membrane voltage response to synaptic inputs as observed in complex morphological cells [27 , 28] . Our approach is of the latter type , with the advantage that the temporal kernels ( filters ) are analytically derived from the underlying morphological BS model . A similar extension for point model neurons to reproduce dendritic input integration of model cells with complex morphology has been recently proposed in [29] . Using the Green’s function formalism a synapse model was developed , whose computational complexity practically allows for only a small number of synaptic input locations . Based on the BS model we were able to derive input filters for point model neurons using only the Fourier transform ( without having to rely on the Green’s function ) and these filters are simple to implement . We have demonstrated that our extended model outperforms the simpler point neuron model in terms of spike train reproduction . Overall , it performs well for suprathreshold inputs , particularly in case of distal inputs and for somatic inputs that are not too strong . That performance could be further enhanced by optimizing the reset voltage to better reflect the remaining dendritic membrane depolarization in the BS model after each spike , as was mentioned previously . In our study we have considered passive dendrites . Nonlinear ( spike-generating ) currents along the dendrite , which cause nonlinear synaptic input integration [30–32] , could be incorporated using our approach in a “quasi-active” framework [24] . This would involve solving the cable equation with linearized nonlinear components , similarly as for the exponential terms used here ( EIF case ) . We investigated in detail the effects of a spatially homogeneous , oscillating , weak electric field , as induced by transcranial electrical stimulation , on the activity of the BS neuron . Such a one-dimensional spatial ( cable plus soma ) model provides a good approximation for neurons with elongated ( apical ) dendrites exposed to a uniform extracellular electrical field as long as the dendritic ( apical main ) cable is not substantially smaller than its electrotonic length [33 , chapter 2 . 5] . Following the somatic doctrine [6] , we focused on the effects of the field that are due to the polarization of the membrane voltage at the soma . We analytically calculated the subthreshold voltage response , whose properties are in accordance with electrophysiological observations: the response magnitude scales linearly with the field amplitude [13] , as shown by the sensitivity in Fig 5 . This sensitivity is of the same order of magnitude as that measured in pyramidal cells [15] , i . e . , around 0 . 30 mm for low frequency fields , and decreases with increasing field frequency in a morphology dependent manner [14] . For non-uniform electric fields , e . g . , as generated by point source stimulation , however , the sensitivity can be roughly constant for frequencies up to at least 100 Hz [8] . Interestingly , such a behavior can also be observed for a uniform field in case of a rather short dendritic cable ( cf . Fig 5B ) . While polarization effects due to direct current fields have been extensively studied [34–36] , the effects of time-varying fields are less well understood . The response of the subthreshold membrane voltage to time-varying fields has been calculated in [37] for a finite dendritic cable with leaky currents at one end , and in [38] for a spatially non-uniform field . Using a one-dimensional cable model [33] showed that the electrotonic length is a key quantity that determines the neuronal subthreshold response to an electric field . Specifically , elongated neurons are less sensitive to high frequency fields than compact ones . How the voltage response to an input current at a particular location along the cable depends on input frequency is largely determined by the membrane time constant . In case of an electrical field , however , which corresponds to symmetrical stimulation at both ends of the cable , the voltage response is also strongly influenced by currents flowing through the low-resistant intracellular medium . This results in an enhanced high frequency response to an extracellular field when compared to an input current [33 , chapter 5] . Nevertheless , a somatic compartment was not considered in these studies . Using the BS model we have shown that the relative size of the soma compared to the dendritic cable substantially affects the neuronal sensitivity to the field . Further , we found frequency-dependent spike rate modulation ( and hence , spike field coherence ) caused by the electric field . Unlike neuronal subthreshold sensitivity , spike rate modulation amplitude did not decrease with the field frequency and its precise relationship to field frequency depended on the synaptic input location . Spike rate modulation exhibited a clear resonance in the beta and gamma frequency bands in presence of only somatic inputs ( cf . Fig 6 and B in S1 Text ) , whereas for only distal dendritic inputs , spike rate modulation amplitudes are strongest at much larger frequencies ( cf . Fig 7 and C in S1 Text ) . This can be linked to a theoretical result showing that the response of single-compartment model neurons to high frequency inputs is stronger for larger autocorrelation times of a fluctuating synaptic input current [39] . Since fluctuating synaptic inputs arriving at the distal dendrite are low-pass filtered , the autocorrelation time of the corresponding input current felt by the soma is increased ( or rather limited from below ) . Spike rate resonance frequencies were lower for EIF neurons as compared to LIF neurons , in particular for background inputs only at the distal dendrite . This may be explained by the fact that the presence of the exponential term , describing the spike initiating sodium current , decreases the rate response to high frequency inputs [19] ( see also the analytical results in [18] ) . In all cases , the amplitude of the modulation also depended on the input strength ( input mean and noise intensity ) , but its relationship to field frequency was not strongly affected by the input parameters . Recently it has been shown that Purkinje neurons , due to their large dendritic trees , exhibit spike rate resonance at rather high frequencies in response to somatic input modulations and in the presence of noisy dendritic input [18 , Fig 5] , which is qualitatively similar to the field-induced resonance effects described here ( cf . Figs 7 and 9C ) . It should be noted , however , that an oscillatory ( spatially uniform ) external field corresponds to oscillatory input currents with opposite sign at the soma and the distal dendrite , respectively ( cf . Eqs 2 and 3 ) . The effects of the field can thus not be easily anticipated from those of an input current modulation at the soma alone . Furthermore , the dendritic membrane surface compared to the somatic one for Purkinje cells [18] is substantially larger than that of pyramidal neurons as considered here , which additionally impedes to directly relate the results . Existing experimental studies on the modulation of neuronal activity by extracellular fields have considered a small number of field frequencies ( see [40] for a review ) . Therefore , our results on spike rate resonance are currently not completely confirmed and may be regarded as predictions . In accordance with our findings weak alternating electric fields ( of 30 Hz ) have been shown to increase the spiking coherence of pyramidal cells in rat hippocampal slices [41] , where this increase was proportional to the subthreshold membrane polarization . Moreover , spatially uniform extracellular fields with high-frequency components entrained spiking activity in ferret primary visual cortex more effectively than fields that only contain low-frequency components [1 , Fig . S6] . Our predictions on spike rate modulation by an oscillating electric field are thus in agreement with current knowledge and are informative for future experimental studies . Those results may further be of potential interest for the design of transcranial electrical stimulation protocols . Regarding the point model extension , we analytically derived an expression for an input current to reproduce the effect of the field as extracted from the biophysically grounded BS model . The amplitude and phase of this input current depend on the parameters of the BS neuron and the electric field . Previously , simple phenomenologically obtained input currents have been used for point neuron network simulations , with either constant amplitudes ( across frequencies ) [1 , 9] or amplitudes fitted to electrophysiological data [7] . Interestingly , the latter study used an input current whose magnitude decreases with increasing frequency , in contrast to the equivalent current we obtained ( whose magnitude increases with frequency up to 10 kHz ) . The neuronal subthreshold sensitivity in that study and the ones shown here , however , are similar . This apparent discrepancy in the currents describing the field effect may be explained by the impedance of the applied model neurons , which naturally influences the equivalent input current . In [7] the model parameters ( and thus the impedance ) were not fitted to real cells; hence it is unlikely that the model impedance matched with the impedance of the cells from which the current amplitudes were estimated [15] . The successful reproduction of the BS spike rate modulation due to the field by the eP model presented here supports the high-pass properties of the equivalent input current . In the present study , we derived an extension for point neuron models of the LIF and EIF types . Additional model variables with slow dynamics [42] may also be included in this framework , in order to reflect , for example , effects of slowly deactivating potassium channels that mediate spike rate adaptation and associated characteristic neuronal response properties [43 , 44] . In that case , a separation of timescales argument could be used to derive the model extension . The results we extracted from a canonical spatial neuron model provide insight into the effects of cellular morphology on synaptic input integration and the impact of extracellular electric fields on neuronal activity . In particular , the presented point model extension , which is straightforward to implement and efficient to simulate , shall give rise to comprehensive computational investigations of neuronal population activity entrainment due to transcranial stimulation .
The subthreshold voltage dynamics of the eP model is specified by Eq 5 which is complemented by the reset condition 6 together with a refractory period ( see Models in the section Results ) .
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How extracellular electric fields—as generated endogenously or through transcranial brain stimulation—affect the dynamics of neuronal populations is of great interest but not well understood . To study neuronal activity at the network level single-compartment neuron models have been proven very successful , because of their computational efficiency and analytical tractability . Unfortunately , these models lack the dendritic morphology to biophysically account for the effects of electric fields , and for changes in synaptic integration due to morphology alone . Here , we consider a canonical , spatially extended model neuron and characterize its responses to fluctuating synaptic input as well as an oscillatory , weak electric field . In order to accurately reproduce these responses we analytically derive an extension for the popular integrate-and-fire point neuron models . We show that the dendritic cable acts as a filter for the synaptic input current , which depends on the input location , and that an electric field modulates the neuronal spike rate strongest at a certain ( preferred ) field frequency . These phenomena can be successfully reproduced using integrate-and-fire models , extended by a small number of components that are straightforward to implement . The extended point models are thus well suited for studying populations of coupled neurons with different morphology , exposed to extracellular electric fields .
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2016
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Extending Integrate-and-Fire Model Neurons to Account for the Effects of Weak Electric Fields and Input Filtering Mediated by the Dendrite
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Almost one third of herpesvirus proteins are expressed with late kinetics . Many of these late proteins serve crucial structural functions such as formation of virus particles , attachment to host cells and internalization . Recently , we and others identified a group of Epstein-Barr virus early proteins that form a pre-initiation complex ( vPIC ) dedicated to transcription of late genes . Currently , there is a fundamental gap in understanding the role of post-translational modifications in regulating assembly and function of the complex . Here , we used mass spectrometry to map potential phosphorylation sites in BGLF3 , a core component of the vPIC module that connects the BcRF1 viral TATA box binding protein to other components of the complex . We identified threonine 42 ( T42 ) in BGLF3 as a phosphoacceptor residue . T42 is conserved in BGLF3 orthologs encoded by other gamma herpesviruses . Abolishing phosphorylation at T42 markedly reduced expression of vPIC-dependent late genes and disrupted production of new virus particles , but had no effect on early gene expression , viral DNA replication , or expression of vPIC-independent late genes . We complemented failure of BGLF3 ( T42A ) to activate late gene expression by ectopic expression of other components of vPIC . Only BFRF2 and BVLF1 were sufficient to suppress the defect in late gene expression associated with BGLF3 ( T42A ) . These results were corroborated by the ability of wild type BGLF3 but not BGLF3 ( T42A ) to form a trimeric complex with BFRF2 and BVLF1 . Our findings suggest that phosphorylation of BGLF3 at threonine 42 serves as a new checkpoint for subsequent formation of BFRF2:BGLF3:BVLF1; a trimeric subcomplex essential for transcription of late genes . Our findings provide evidence that post-translational modifications regulate the function of the vPIC nanomachine that initiates synthesis of late transcripts in herpesviruses .
Lytic infection is intrinsic to the pathogenesis of herpesviruses . Virus particles are synthesized and assembled exclusively during the lytic phase . The lytic phase of oncogenic gamma herpesviruses contributes to tumor development by expanding the population of latently infected cells that possess the potential to become neoplastic . Lytic gene products also encode and upregulate expression of inflammatory cytokines , anti-apoptotic proteins , signaling molecules , and immunoevasins that promote cell proliferation and suppress immune recognition . Temporal control of expression of lytic viral genes , a common theme among all herpesviruses , can be categorized into pre- and post-replication events . Mechanisms that regulate expression of these two classes of viral genes are quite distinct . Pre-replication genes , referred to as early genes , are regulated in a manner similar to that of cellular genes . Early gene promoters encompass multiple binding sites for transcription factors that facilitate recruitment of the basic transcription machinery . Post-replication genes , referred to as late genes , have unique promoter structures featuring a non-canonical TATA box element ( reviewed in [1 , 2] ) . Activation of late promoters is dependent on amplification of the viral genome . The strict dependence of late gene expression on replication of the viral genome represents one of the longstanding conundrums in the biology of DNA viruses . Major progress in our current understanding of regulation of late gene expression resulted from identifying a group of lytic herpesvirus proteins that function as late gene transcription regulators [3–5] . This group of late gene regulators is conserved among beta and gamma herpesviruses [6–12] . We and others identified seven EBV proteins as essential for expression of late genes . These EBV late gene regulators are: BcRF1 ( viral TATT box binding protein , vTBP ) , BDLF3 . 5 , BDLF4 , BFRF2 , BGLF3 , BGLF4 ( viral protein kinase ) and BVLF1 [13–18] . The current model suggests that late gene regulators assemble to form a viral pre-initiation complex ( vPIC ) on late promoters [1 , 13] . Using specific siRNAs to all seven late gene regulators combined with RNA-seq of EBV gene transcripts , we demonstrated that a subgroup of late viral genes is transcribed in a manner independent of vPIC [19] . This phenomenon was confirmed by other groups using different approaches including CAGE-seq analyses [20 , 21] . Two of these vPIC-independent late genes encode viral immunoevasins , BCRF1 ( viral IL10 ) and BPLF1 ( viral deubiquitinase ) [19] . This new insight demonstrates the presence of distinct mechanisms for expression of EBV late structural proteins ( vPIC-dependent ) versus expression of late viral immunoevasins ( vPIC-independent ) . The mechanism by which vPIC-independent late genes ( viral IL10 and viral deubiquitinase ) are transcribed is yet to be characterized . While expression of all components of vPIC occurs during the early phase of the lytic cycle , transcription of late genes is nonetheless dependent on viral DNA replication . Recent reports demonstrated that late transcripts are synthesized from newly replicated viral genomes and require continuous genome amplification [17 , 22] . Relatively little is known about the exact function of the various components of vPIC in transcription of late genes . Several late gene regulators have no identifiable domains or cellular homologs ( e . g . BDLF4 , BDLF3 . 5 , BGLF3 , BFRF2 , and BVLF1 ) . BcRF1 , a viral protein predicted to have a saddle-like structure that is characteristic of the cellular TATA-box binding protein ( TBP ) [10] , selectively recognizes late promoters by binding to a non-canonical TATA box element ( TATT ) [14 , 23] . To understand the role of individual proteins in transcription of late genes , several protein interactions were identified between components of vPIC and subunits of RNAPII . BcRF1 and its orthologs of vTBPs in beta and gamma herpesviruses interact with several subunits of RNA polymerase II ( RNAP II ) ; a step considered necessary to recruit RNAPII complex to late promoters [4 , 13 , 24] . Davis et al mapped the motif interacting with RPB1 , RNAPII catalytic subunit , to three leucine residues at the N-terminal domain of ORF24 ( the ortholog of EBV vTBP BcRF1 ) [24] . HCMV UL79 , the ortholog of EBV BVLF1 , also interacts with multiple subunits of RNAPII . These interactions augment the transcriptional activity of RNAPII suggesting a role in transcript elongation [25] . A number of additional protein-protein interactions were mapped among the various components of vPIC that provide insight into the general organization of the whole complex . For example , BVLF1 orthologs in KSHV ( ORF18 ) and CMV ( UL79 ) form crucial interactions with their corresponding BDLF3 . 5 orthologs [6 , 11] . Whether these interactions are necessary for the role of BVLF1 orthologs in promoting the elongation activity of RNAPII is yet to be determined . In addition , the KSHV ortholog of BGLF3 ( ORF34 ) serves as a core component; the protein physically interacts with four other members of vPIC and is thought to serve as a bridge between vTBPs and the rest of the complex [12 , 26] . Mutations that disrupt interaction of KSHV TBP ( ORF24 ) with the KSHV ortholog of BGLF3 ( ORF34 ) abolished synthesis of late transcripts [26] . Despite the significant progress made towards comprehending the organization of vPIC , the role of post-translational modifications leading to assembly , regulation , and function of vPIC need to be addressed to gain better understanding of the dynamics of the complex . Here , we asked whether phosphorylation regulates the function of vPIC in transcription of late genes . Phosphorylation regulates many primary biological processes in eukaryotic cells , such as cell division , DNA replication , transcription , differentiation , and apoptosis . To address the role of phosphorylation in regulating late gene expression , we studied the phosphorylation state of BGLF3 . We found that BGLF3 is phosphorylated in vivo at threonine 42 . Phosphorylation of BGLF3 is essential for transcription of vPIC-dependent late genes . Our findings indicate that phosphorylation of BGLF3 regulates the capacity of the protein to form a trimeric complex with two other late gene regulators , BFRF2 and BVLF1 . Formation of this trimeric complex is crucial for expression of late genes .
BGLF3 and its herpesvirus orthologs are indispensable for transcription of late viral genes encoding structural proteins [18 , 19] . The exact role of BGLF3 in the process of late gene expression remains largely unknown . The KSHV ortholog of BGLF3 was shown to interact with individual components of vPIC [12] . These interactions led to the hypothesis that BGLF3 functions as a core protein that connects various components of vPIC . How BGLF3 accommodates the formation of vPIC and whether post-translational modifications regulate the capacity of the protein to mediate one or more of these interactions are questions that were not addressed previously . In this report , we first assessed whether BGLF3 is phosphorylated in vivo during the late phase of the EBV lytic cycle . We immunoprecipitated FLAG-tagged BGLF3 from 2089 cells co-expressing the lytic cycle activator , ZEBRA . A fraction of the immunoprecipitated BGLF3 protein was resolved on SDS-PAGE and stained with colloidal Coomassie blue stain . A distinct protein band with a molecular weight equivalent to that of BGLF3 was detected ( Fig 1A ) . The remainder of the BGLF3 eluate was subjected to trypsin digestion followed by phosphopeptide-enrichment using TiO2 resins . Bound phospho-peptides were eluted and analyzed by liquid chromatography-tandem mass spectrometry ( LC-MS/MS ) . Fig 1B shows a representative MS/MS spectrum of the single BGLF3 phospho-peptide ( amino acids 39 to 45 ) that was reproducibly shown to be phosphorylated . Phosphorylation of this peptide was evident by the detection of fragment y5 with and without a phosphate moiety ( -98 Da ) . Moreover , other peptide fragments in which the phosphate group was either present or lost were also detected , y6 and y4 , respectively . Phosphorylation of BGLF3 was reproduced seven independent times; the 39–45 peptide was consistently identified with Mascot score greater than identity score; the expectation values of the detected 39–45 BGLF3 peptide were as follows: 0 . 047 , 0 . 018 , 0 . 017 , 0 . 0028 , 0 . 0028 , 0 . 0029 , and 0 . 0029 ( Fig 1C ) . Sequence alignment of the motif encompassing threonine at position 42 revealed that this region is conserved in other gamma herpesviruses ( Fig 1D ) . Based on the fragmentation pattern and the fact that threonine 42 is the only amino acid susceptible to phosphorylation in this BGLF3 peptide , we conclude that BGLF3 is phosphorylated at threonine 42 during EBV lytic infection . We examined the functional importance of phosphorylation of BGLF3 at threonine 42 on different stages of the EBV lytic cycle , particularly late gene expression . The experiment was performed in 2089 cells induced into the lytic phase by ectopic expression of ZEBRA . In accordance with our previous data [18] , knockdown of endogenous BGLF3 using specific siRNA ( siBGLF3 ) markedly reduced expression of BFRF3 late protein , a component of the viral capsid protein ( Fig 2A ) . To demonstrate that the effect of siBGLF3 on expression of BFRF3 was specific to silencing BGLF3 rather than an off-target activity , we inserted silent mutations to generate a form of BGLF3 that is resistant to the siRNA , referred to as rBGLF3 . Ectopic expression of rBGLF3 suppressed the effect of siBGLF3 on synthesis of late products and restored expression of the late BFRF3 protein . Expressing a mutant form of rBGFL3 in which threonine 42 was mutated to alanine , rBGLF3 ( T42A ) , failed to suppress the effect of siBGLF3 on synthesis of the late BFRF3 protein . Neither knockdown of BGLF3 nor mutation of T42 had any significant effect on expression of the BMRF1 early protein , a component of the viral DNA polymerase complex ( Fig 2A ) . To determine whether phosphorylation of BGLF3 at threonine 42 is important for expression of late genes in physiologically relevant cell lines , we assessed the effect of the T42A mutation in HH415-16 and SNU-719 cells , which are derived from naturally EBV infected Burkitt lymphoma and gastric carcinoma , respectively . HH415-16 cells and SNU-719 cells were induced into the lytic cycle by expression of ZEBRA . Expression of endogenous BGLF3 was silenced using siBGLF3 . We found that synthesis of the late BFRF3 protein was markedly reduced when threonine 42 was substituted with alanine in rBGLF3 ( Fig 2B and 2C , compare lanes 4 and 5 ) . Our results demonstrate that abolishing phosphoacceptor threonine 42 in BGLF3 is deleterious for expression of the late BFRF3 viral capsid protein in three different cell lines . One approach that is commonly used to study constitutive protein phosphorylation is to mutate the phosphorylated site to a phosphomimetic residue , aspartate or glutamate . While phosphmimetic substitutions contributed to the understanding of the role of phosphorylation in many proteins , it often fails to mimic phosphorylation events that are regulated and not constitutive . To determine whether a phosphomimetic mutation would substitute for the presence of phospho-T42 , we mutated T42 in rBGLF3 to aspartate ( T42D ) and glutamate ( T42E ) residues . As shown previously , ectopic expression of wild type rBGLF3 suppressed the effect of siBGLF3 and restored expression of the late BFRF3 protein . However , neither the aspartate nor glutamine substitutions lead to restoration of BFRF3 expression ( Fig 3A ) . To determine whether the presence of a phosphorylatable residues ( e . g . serine ) is sufficient to maintain expression of late genes , we mutated T42 to serine . We found that ectopic expression of BGLF3 ( T42S ) suppressed the effect of siBGLF3 and restored expression of BFRF3 ( Fig 3B ) . These results demonstrate that a phosphorylatable residue at position 42 is essential for the capacity of BGLF3 to mediate expression of late genes . To study the effect of abolishing phosphorylation of BGLF3 at threonine 42 on synthesis of late transcripts , we used RT-qPCR to assess the level of seven EBV lytic transcripts representing three different groups of viral lytic genes: ( A ) early transcript: BMRF1 ( polymerase associated factor ) ; ( B ) vPIC-independent late transcripts: BCRF1 ( viral IL10 ) and BPLF1 ( viral deubiquitinase ) , and ( C ) vPIC-dependent late transcripts: BFRF3 ( capsid ) , BLLF1 ( glycoprotein ) , BdRF1 ( scaffold ) , and BLRF2 ( tegument ) . We found that expression of all seven lytic genes was up-regulated in samples transfected with the lytic cycle activator , ZEBRA , relative to cells transfected with empty vector ( CMV ) ( Fig 4A , 4B and 4C columns 1 and 2 ) . Co-transfection of siBGLF3 significantly reduced the level of the four vPIC-dependent late transcripts , BFRF3 , BLLF1 , BdRF1 , and BLRF2 but did not affect the level of early or vPIC-independent late transcripts ( Fig 4A , 4B and 4C column 3 ) . Expression of rBGLF3 suppressed the effect of siBGLF3 and restored expression of the four BGLF3-dependent late genes ( Fig 4A , 4B and 4C column 4 ) . However , alanine substitution of threonine 42 disrupted the capacity of rBGLF3 to support expression of the four BGLF3-dependent late genes ( Fig 4A , 4B and 4C column 5 ) . To determine whether mutating threonine 42 to alanine affects the process of viral DNA amplification , we purified DNA from aliquots of the same cells that were examined in Figs 2A , 4A , 4B and 4C for protein and RNA expression , respectively . We found that expression of ZEBRA increased the level of viral DNA replication by an average of 100-fold relative to empty vector ( CMV ) ( Fig 4D—Intracellular ) . Knockdown of BGLF3 or mutating the phospho-receptor threonine residue ( T42 ) to alanine did not compromise the extent of viral genome amplification ( Fig 4D—Intracellular ) . However , alanine substitution of phospho-T42 annihilated the capacity of the virus to produce new virus particles , as assessed by detecting the amount of the extracellular viral DNA ( Fig 4D—Extracellular ) . These findings show that phosphorylation of BGLF3 at threonine 42 is essential for the capacity of the protein to support transcription of late genes encoding EBV structural proteins and hence production of new virions but is dispensable for viral DNA replication . As a hub protein , the function of BGLF3 in transcription of late genes is likely to be influenced by the protein’s capacity to interact with other subunits of the viral pre-initiation complex . A conceivable explanation for the phenotype of BGLF3 ( T42A ) is that lack of phosphorylation at T42 might compromise the ability of the mutant BGLF3 protein to interact with one or more components of vPIC . Previous work studying assembly of various protein complexes demonstrated that point mutations that reduce the affinity of a protein to a complex could be overcome by increasing the concentration of the protein’s respective interactors in the complex [27 , 28] . To test the postulate that increasing the concentration of vPIC proteins might suppress the defect in BGLF3 ( T42A ) and restore late gene expression , we eliminated expression of endogenous BGLF3 in 2089 cells using siRNA . Absence of endogenous BGLF3 was complemented by ectopic expression of wild type rBGLF3 or rBGLF3 ( T42A ) . Similar to Fig 2 , expression of rBGLF3 ( T42A ) failed to support synthesis of the late BFRF3 protein . Co-expression of four late gene regulators ( BcRF1 , BDLF4 , BFRF2 , and BVLF1 ) partially suppressed the phenotype of rBGLF3 ( T42A ) and increased the expression level of BFRF3 to 54% relative to cells transfected with ZEBRA alone ( Fig 5 compare lanes 2 and 6 ) . This result suggests that increasing the protein concentration of four late gene regulators can partially suppress the defect in BGLF3 ( T42A ) and restore expression of late genes . To delineate the contribution of each late gene regulator in restoring expression of EBV structural proteins , we expressed BGLF3 ( T42A ) in 2089 cells together with different mixtures of vPIC components . In each mixture , one of the four late gene regulators was omitted . Cells were harvested after 48 hours and protein lysates were prepared and analyzed by Western blotting to assess the level of the late BFRF3 protein . We found that eliminating BFRF2 or BVLF1 from the mixture of late gene regulators abolished the ability of vPIC to restore expression of the late BFRF3 protein; however , omission of BcRF1 and BDLF4 had no effect ( Fig 6 ) . Since BFRF2 and BVLF1 are the only two proteins necessary for the ability of vPIC to restore synthesis of late products in cells expressing BGLF3 ( T42A ) , we asked whether provision of these two proteins was sufficient to suppress the defect in BGLF3 ( T42A ) . We transfected 2089 cells with ZEBRA to induce the lytic cycle . Expression of endogenous BGLF3 was knocked down using siBGLF3 . Lack of BGLF3 was complemented with the mutant rBGLF3 ( T42A ) . We found that co-transfection of BFRF2 and BVLF1 suppressed the phenotype of the T42 mutation and partially restored synthesis of the late BFRF3 protein ( Fig 7 , lane 2 ) . To assess the specificity of the BFRF2/BVLF1 combination , we studied the capacity of all possible combinations of the four late gene regulators to suppress the phenotype of rBGLF3 ( T42A ) . BFRF2/BVLF1 was the most competent combination to restore late gene expression in 2089 cells complemented with rBGLF3 ( T42A ) ( Fig 7 ) . Our findings indicated a novel functional interaction between the phosphoacceptor threonine 42 of BGLF3 and the two late gene regulators , BFRF2 and BVLF1 . In summary , lack of phosphorylation at T42 has detrimental effects on synthesis of EBV structural proteins; however , this defect could be partially complemented by increasing the concentration of BFRF2 and BVLF1 . Based on previous protein interaction studies , BGLF3 serves as a bridge connecting BcRF1 , the component of vPIC that binds to late promoters , to other subunits of the complex . To determine whether phosphorylation of BGLF3 at threonine 42 mediates the protein’s capacity to interact with BcRF1 , we compared interaction of wild type BGLF3 or BGLF3 ( T42A ) with BcRF1 . Co-immunoprecipitation was performed using 2089 cells expressing either form of FLAG-tagged BGLF3 in the absence and presence of BcRF1 . We found that both BGLF3 and BGLF3 ( T42A ) interacted with BcRF1 to the same extent ( S1 Fig lanes 3 and 5 ) . This result suggests that wild type and mutant BGLF3 could be equally recruited to late promoters via their interaction with BcRF1 . Furthermore , it corroborates our findings in Figs 6 and 7 demonstrating that ectopic expression of BFRF2 and BVLF1 , but not BcRF1 , was essential to partially suppress the phenotype of BGLF3 ( T42A ) and restore expression of late genes . To understand the nature of the functional interaction between phospho-threonine 42 in BGLF3 and the BFRF2 and BVLF1 proteins , we used co-immunoprecipitation to study the potential protein complexes formed by these three proteins . In Fig 8A , we assessed the capacity of BGLF3 and BGLF3 ( T42A ) to interact with BFRF2 and BVLF1 when expressed individually or together in transfected 2089 cells . Neither BFRF2 nor BVLF1 were non-specifically immunoprecipitated in the absence of FLAG-tagged BGLF3 ( Fig 8A lane 1 ) . Both wild type BGLF3 and the BGLF3 ( T42A ) mutant interacted with BFRF2 and BVLF1 when provided individually in a pairwise co-immunoprecipitation ( Fig 8A lanes 2 , 3 , 4 , and 5 ) . Co-expression of BFRF2 and BVLF1 in the presence of wild type BGLF3 resulted in a trimeric complex ( Fig 8A lane 6 ) . However , interestingly , mutation of threonine 42 abolished the interaction between BGLF3 and BVLF1 without affecting the interaction between BGLF3 and BFRF2 ( Fig 8A lane 7 ) . To further confirm formation of a trimeric complex that includes BGLF3 , BFRF2 , and BVLF1 , we performed reciprocal co-immunoprecipitation using FLAG-tagged BVLF1 to pull down BFRF2 alone or together with BGLF3 . In the absence of BGLF3 , BVLF1 had weak affinity to the BFRF2 protein ( Fig 8B ) . However , interaction of BVLF1 and BFRF2 increased substantially in the presence of wild type BGLF3 , around 6-fold relative to no BGLF3 based on two independent experiments ( Fig 8E ) . Mutation of threonine 42 to alanine compromised the ability of BGLF3 to form a stable complex with BFRF2 and BVLF1 ( Fig 8D and 8E ) . Our experiments demonstrating that either BGLF3 or BVLF1 can pull down the other two components of the subcomplex suggest that all three proteins assemble into a trimeric complex . Abolishing phosphorylation of BGLF3 at T42 , disrupts formation of this trimeric complex and functionally impairs expression of EBV transcripts encoding structural proteins ( Fig 4 ) .
Much of the current understanding of the overall organization of vPIC is derived from previous studies using pairwise co-immunoprecipitation experiments . These studies suggest that components of vPIC are involved in an intricate network of protein-protein interactions that form a functional pre-initiation complex . While these studies present a plausible model for the overall structure of the complex , several questions remain unanswered . For instance , what are the dynamics of vPIC assembly ? Do these mapped protein interactions take place simultaneously to generate one main complex , as previously proposed , or does assembly of vPIC occur in a dynamic stepwise manner that involves formation of various subcomplexes of late gene regulators ? Formation of these subcomplexes might be strictly regulated by certain post-translational modifications to synchronize the proper assembly of vPIC and the timing at which a particular late gene regulator is added or removed from the complex . It is also conceivable that a late gene regulator , such as BGLF3 , might be involved in more than one subcomplex . This interpretation might explain how BGLF3 accommodates multiple interactions previously reported using pairwise coimmunoprecipitation . Furthermore , the time at which a particular late gene regulator is added to the complex is also significant . The BVLF1 protein , for example , might be recruited to the complex at a later time point . UL79 , the CMV ortholog of EBV BVLF1 , was previously shown to promote the transcriptional elongation activity of RNAPII [25] . This result posits the question of whether BVLF1 is part of the viral pre-initiation complex during promoter recognition or the protein is recruited to the complex as RNAPII exits the promoter . Our data demonstrate that BGLF3 is phosphorylated at threonine 42 ( Fig 1 ) . Mutation of this phopshoacceptor residue abolished expression of the late BFRF3 protein and markedly reduced transcription of several late transcripts ( Figs 2 and 4 ) . The effect of mutating T42 to alanine was selective to vPIC-dependent late genes encoding structural proteins; expression of early genes or vPIC-independent late genes was not affected ( Fig 4 ) . As a core protein , BGLF3 coordinates multiple interactions within vPIC . Failure to synthesize late transcripts suggested a defect in the capacity of the BGLF3 mutant to establish a specific interaction with one or more late gene regulators that form vPIC . Previous reports demonstrated that point mutations that reduce the affinity of a protein to a particular complex could be suppressed by increasing the concentration of the protein’s respective partners in the complex [27 , 28] . Following a similar approach , we managed to partially suppress the phenotype of BGLF3 ( T42A ) and restore expression of late genes by increasing the protein concentration of two specific components of vPIC , BFRF2 , and BVLF1 ( Figs 6 and 7 ) . The combined effect of BFRF2 and BVLF1 was not reproduced when other combinations of late gene regulators were ectopically expressed ( Fig 7 ) . These findings led to the hypothesis that phospho-threonine 42 is likely to mediate or regulate interaction of BGLF3 with BFRF2 and/or BVLF1 . Indeed , co-immunoprecipitation experiments demonstrated that BFRF2 , BVLF1 , and the phosphorylated form of BGLF3 interact together . The ability of BGLF3 and BVLF1 to co-precipitate the two other proteins suggests that BFRF2 , BGLF3 , and BVLF1 form a stable trimeric subcomplex of that is essential for the assembly of a functional vPIC ( Fig 8 ) . Phosphorylation of BGLF3 at threonine 42 is likely to augment the affinity of BVLF1 to the BFRF2:BGLF3 subcomplex . In our experiments , removal of phospho-threonine 42 weakens binding , while higher levels of the BVLF1 and BFRF2 proteins partially restore interaction with BGLF3 ( T42A ) by slightly shifting the equilibrium towards complex formation ( Fig 8C ) . Our data suggest that formation of this trimeric subcomplex is essential for transcription of late genes . Alanine mutation of BGLF3 at T42 disrupted complex formation ( Fig 8 ) and markedly reduced expression of vPIC-dependent late genes ( Figs 2 and 4 ) . Furthermore , failure of BFRF2 and BVLF1 to fully restore expression of late genes ( Fig 7 ) is corroborated by the reduced ability of the BGLF3 ( T42A ) :BFRF2 subcomplex to interact with BVLF1 ( Fig 8C ) . Collectively , our approach involving mutation of BGLF3 at threonine 42 and suppression of the BGLF3 ( T42A ) phenotype strongly correlates with our protein interaction studies to demonstrate the importance of the BFRF2:BGLF3:BVLF1 trimeric subcomplex in transcription of late genes ( Fig 9 ) . In our efforts to understand the phenotype of BGLF3 ( T42A ) we compared the ability of wild type and mutant BGLF3 proteins to interact with BVLF1 . Using pairwise immunoprecipitation experiments , both wild type BGLF3 and BGLF3 ( T42A ) were equally competent to interact with BVLF1 ( Fig 8A lanes 3 and 5 ) . Addition of BFRF2 to the complex revealed a substantial defect in the ability of BGLF3 ( T42A ) to bind to BVLF1 . One possible explanation of this outcome is that association of BFRF2 with BGLF3 results in a new interface that accommodates interaction with BVLF1 in a manner dependent on phosphorylation of BGLF3 at threonine 42 . BFRF2 and BVLF1 might form a pocket that fits the motif encompassing phospho-T42 in the BGLF3 protein . Our data suggests that phosphorylation regulates formation of this trimeric complex but is not involved in recruitment of BGLF3 to late promoters; both wild type BGLF3 and BGLF3 ( T42A ) are capable of interacting with BcRF1 , the vTBP-like protein that recognizes late promoters ( S1 Fig ) . Therefore , lack of phosphorylation at T42 in BGLF3 is likely to impede subsequent binding of other subunits to form a functional pre-initiation complex . One approach that is frequently used to study a constitutively phosphorylated site is to mutate this phosphoacceptor residue into a phosphomimetic one . In Fig 3A , we mutated T42 to aspartate , and glutamate residues . None of the phosphomimetic mutations restored expression of late genes; however , mutating T42 to a different phosphorylatable residue , serine , maintained expression of late genes ( Fig 3B ) . This outcome is not unexpected considering a phosphoamino acid has unique chemical characteristics relative to other amino acids including aspartate and glutamate . A phosphate group in a phosphoamino acid has a bigger hydrated shell and more negative charge relative to a carboxyl group in a phosphomimetic residue [29] . Furthermore , a phosphate group forms stronger and more stable hydrogen bonds and salt bridges in protein-protein interactions relative to a carboxyl group [30] . Signal transducing adaptor proteins , such as 14-3-3 protein and proteins containing FHA- or SH2-domains , are phospho-binding proteins that are incapable of recognizing a phosphomimetic replacement [31–33] . Studying the structure of these interactions revealed that phosphomimetic residues do not fit in the binding pocket of adaptor proteins [31 , 34 , 35] . Our results suggest that transcription of late genes is dependent on the presence of a phosphate group at position 42 . A phosphomimetic mutation is also less likely to substitute for the presence of a phosphate group if both phosphorylated and non-phosphorylated forms play distinct roles in the function of the protein . It is conceivable that both phosphorylated and non-phosphorylated forms of BGLF3 play separate roles in transcription of late genes . BGLF3 is capable of interacting with BVLF1 and BFRF2 individually , such interaction might occur at a specific stage of transcription of late genes that differs from that requiring formation of the trimeric complex . An alternative interpretation of our data is that BGLF3 has two separate motifs for interaction with BVLF1 . One motif binds to nonmodified BVLF1 and a second BGLF3 motif that binds to modified BVLF1 . In the absence of BFRF2 , BGLF3 favors interaction with the modified form of BVLF1 . However , in the presence of BFRF2 , BGLF3 interacts with the nonmodified form of BVLF1 . Interaction of BGLF3 with the nonmodified form of BVLF1 is dependent on phosphorylation of BGLF3 at threonine 42 . Changes in modification of BLVF1 and its impact on formation of various subcomplexes might represent different stages during the process of transcription of late genes . We are currently studying the possibility that BVLF1 is modified and assessing its role in transcription of late genes . In conclusion , our results demonstrate the essential role protein phosphorylation plays in regulating the function of vPIC during transcription of late genes . Lack of a single phosphorylation site in BGLF3 abolishes expression of late structural proteins and prevents virus release . Phosphorylation of late gene regulators might serve as checkpoints to ensure the precise timing for assembly of vPIC subcomplexes during synthesis of late products ( Fig 9 ) . Identifying additional post-translational modifications that are indispensable for expression of late genes and the responsible modifying enzymes has the potential to inform the generation of a novel class of drugs against EBV and its associated diseases .
The ZEBRA protein expression vector was constructed as previously described [36] . The constructs expressing BGLF3 , BcRF1 , BFRF2 , BVLF1 , and BDLF4 were cloned into the eukaryotic pCMV6-Entry vectors using the SfgI and MluI restriction sites . The mutants BGLF3 ( T42A ) , BGLF3 ( T42D ) , BGLF3 ( T42E ) , and BGLF3 ( T42S ) were generated by introduction of the indicated point mutations in the BGLF3 sequence using the following mutagenic primers: 5´-CAGTTTAAGCTCGTGGAGGCGCCCCTGAAGTCCTTTC-3’ , 5’- CAGTTTAAGCTCGTGGAGGACCCCCTGAAGTCCTTTC-3´ , 5’-CAGTTTAAGCTCGTGGAGGAGCCCCTGAAGTCCTTTC-3’ , and 5’- AACAGTTTAAGCTCGTGGAGTCGCCCCTGAAGTCCTTTCTG-3’ and their complementary strands , respectively . siRNA-resistant BGLF3 ( rBGLF3 ) was produced by inserting silent mutations in the region of the late gene regulator mRNA that is recognized by the siRNA . These silent mutations disrupt the complementarity between the siRNA and BGLF3 mRNA without affecting the amino acid sequence of the protein . Production of rBGLF3 and experiments establishing specificity of the utilized BGLF3 siRNA were described in details in our previous studies [18 , 19] . The following commercial antibodies were used in Western blotting: monoclonal anti-FLAG M2 antibody ( Sigma ) ; Anti-Myc-Tag rabbit monoclonal antibody ( Cell signaling ) ; anti-β-Actin antibody ( Sigma ) ; anti-GAPDH antibody ( abcam ) . Antibodies to BMRF1 ( EAD ) , ZEBRA , and BFRF3 were previously described [37 , 38] . 2089 cells are human embryonic kidney ( HEK ) 293 cells stably transfected with a bacmid containing wild-type EBV B95-8 genome [39 , 40] . Cells were cultured in Dulbecco’s modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) ( Gibco ) , and penicillin-streptomycin at 50 units/ml . Hygromycin B ( Calbiochem ) 100 μg/ml was added to the medium to select for 293 cells containing the EBV bacmid . The HH514-16 Burkitt lymphoma cell line is a subclone of EBV-infected P3J-HR-1 cell line [41] . SNU-719 cells is a gastric carcinoma cell line derived from a human tumor biopsy naturally infected with EBV [42] . The eukaryotic plasmids were transfected using lipofectamine 2000 ( Invitrogen ) following the manufacture’s protocol . Transfections were carried out in OPTI-MEM medium ( Gibco ) . Cells were incubated at 37° C in 5% CO2 incubator and harvested 48 h after transfection . Harvested cells were lysed in sodium dodecyl sulfate ( SDS ) sample buffer at 106 cell/10ul . After sonication , protein lysates were denatured at 100°C for 5 min and resolved on 10% SDS-polyacrylamide gel or 4–15% Criterion TGX Precast Protein Gel ( Bio-Rad ) . Resolved proteins were transferred to a nitrocellulose membrane ( Bio-Rad ) . The membrane was blocked in TBS buffer ( 50 mM Tris-Cl , pH 7 . 5 and 150 mM NaCl ) supplemented with 5% non-fat milk and 0 . 1% Tween-20 . Nitrocellulose membranes were blotted with specific primary antibodies to cellular and viral proteins . Immunocomplexes were visualized by ECL ( GE ) or by autoradiography using 125I-protein A ( PerkinElmer ) . 2089 cells were harvested , washed in cold phosphate-buffered saline , and resuspended in lysis buffer ( 20 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 1 mM Na2EDTA , 1 mM EGTA , 1% Triton ) containing Halt Protease and Phosphatase inhibitors ( ThermoFisher ) . Lysates were passed through a 25-G needle 9 times then centrifuged at 21 , 000 x g for 10 min at 4°C . Supernatents were pre-incubated with protein A agarose beads to reduce non-specific interactions . Five percent of each supernatant was stored at -80°C as input sample . The rest of the supernatant was incubated with pre-washed anti-FLAG M2 affinity agarose beads ( Sigma ) for 2h at 4°C . The beads were washed four times with lysis buffer and once with elution buffer ( 50 mM HEPES , pH 7 . 4 , 100mM NaCl , 1 mM DTT , 5 mM βglycerophosphate , 0 . 1 mM Na3VO4 , 0 . 01% Igepal CA630 , 10% glycerol ) . Immunoprecipitated proteins were eluted in elution buffer containing 0 . 5mg/ml 3X FLAG Peptide ( Sigma ) . Input samples and immunoprecipitated proteins were detected by Western blotting using appropriate antibodies or by protein staining using colloidal Coomassie blue [43] . Sample preparation of the liquid chromatography-tandem mass spectrometry ( LC MS/MS ) analysis was performed following the previously described protocol [44] . Briefly , immunoprecipitated proteins were subjected to Dithiothreitol ( DTT ) reduction , Iodoacetamide ( IAN ) -mediated alkylation followed by trypsin digestion . The digested sample was desalted by Spin Desalting column ( Thermo ) and acidified with 0 . 5% Trifluoroacetic acid ( TFA ) , 50% acetonitrile then subjected to titanium dioxide enrichment using the Top Tips system ( Glygen Corp ) . The resulting phosphopeptide-enriched sample , dissolved in 70% formic acid and diluted with 0 . 1% TFA , was then subjected to LC-MS/MS analysis using the Orbitrap Fusion Mass Spectrometer that is equipped with a Waters nanoACQUITY UPLC system . A Waters Symmetry C18 180 μm x 20 mm trap column and a 1 . 7 μm , 75 μm x 250 mm nanoACQUITY UPLC column was utilized for online peptide separation . The acquired data was peak picked and searched using the Mascot Distiller and the Mascot search algorithm , respectively . Manual examination of the MS/MS spectra ( as shown in Fig 1B ) and the corresponding assigned fragment ions were conducted to verify the identified phosphopeptide . RNA was prepared from cells using the Qia-shredder and the RNeasy Plus products from Qiagen . The concentration of RNA in each sample was determined by measuring the optical density at 260 nm . The level of viral transcripts was assessed from 100 ng of total RNA using iScript One-Step RT-PCR with SYBR Green ( Bio-Rad ) in a total volume of 25 μl . The level of 18S RNA was measured to normalize for the total amount of RNA . Each sample was analyzed in triplicate; the fold change in expression was calculated using the ΔΔCT formula implemented in the software of the CFX real-time PCR system ( Bio-Rad ) . The efficiency of the primers used in RT-qPCR was determined against 10-fold increasing concentrations of viral DNA . The sequences of the primers are provided in Table 1 . The relative expression levels of target proteins were measured according to the density of bands from Western blot using densitometer machine ( GS-800 Calibrated Densitometer , Bio-Rad ) . Expression of the late BFRF3 protein was calculated relative to the expression level of GAPDH or β-Actin . Statistical analysis for viral transcripts ( Fig 4 ) was performed using paired t test available in GraphPad Prism software ( La Jolla , CA , USA ) . A value of p < 0 . 05 was considered statistically significant .
|
EBV is an oncogenic virus involved in the development of about 1 . 5% of human cancers worldwide . EBV infection has latent and lytic forms . Both forms of infection contribute to the oncogenic capacity of the virus . During the lytic cycle , a cascade of temporally regulated events takes place leading to release of new virus particles . A crucial event in the lytic cascade is expression of the class of EBV late genes , which occurs after viral genome amplification . Late genes mainly encode virus structural proteins that are essential for virus transmission . For many years , the mechanisms regulating expression of late genes remained unknown . Recently , a set of proteins that control expression of late genes was discovered . These proteins form a unique viral pre-initiation complex ( vPIC ) , which initiates synthesis of late gene mRNAs . To this day we have yet to fully understand the process by which assembly of vPIC is synchronized to result in a functional transcription machinery . In this report , we demonstrated that BGLF3 , a component of vPIC , is modified by phosphorylation during the lytic phase of the viral life cycle . Phosphorylation of BGLF3 is essential for the ability of the protein to interact with two other components of vPIC , BFRF2 and BVLF1 . Our results show that formation of the BGLF3 , BFRF2 and BVLF1 complex is integral for synthesis of viral structural proteins . This report establishes the importance of post-translational modifications in regulating the function of vPIC in synthesis of herpesvirus structural proteins . Our findings have the potential to promote the discovery of new anti-viral drugs that inhibit assembly and release of oncogenic herpesviruses .
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2019
|
A single phosphoacceptor residue in BGLF3 is essential for transcription of Epstein-Barr virus late genes
|
Genome-wide association studies have revealed that many low-penetrance breast cancer susceptibility loci are located in non-protein coding genomic regions; however , few have been characterized . In a comparative genetics approach to model such loci in a rat breast cancer model , we previously identified the mammary carcinoma susceptibility locus Mcs1a . We now localize Mcs1a to a critical interval ( 277 Kb ) within a gene desert . Mcs1a reduces mammary carcinoma multiplicity by 50% and acts in a mammary cell-autonomous manner . We developed a megadeletion mouse model , which lacks 535 Kb of sequence containing the Mcs1a ortholog . Global gene expression analysis by RNA-seq revealed that in the mouse mammary gland , the orphan nuclear receptor gene Nr2f1/Coup-tf1 is regulated by Mcs1a . In resistant Mcs1a congenic rats , as compared with susceptible congenic control rats , we found Nr2f1 transcript levels to be elevated in mammary gland , epithelial cells , and carcinoma samples . Chromatin looping over ∼820 Kb of sequence from the Nr2f1 promoter to a strongly conserved element within the Mcs1a critical interval was identified . This element contains a 14 bp indel polymorphism that affects a human-rat-mouse conserved COUP-TF binding motif and is a functional Mcs1a candidate . In both the rat and mouse models , higher Nr2f1 transcript levels are associated with higher abundance of luminal mammary epithelial cells . In both the mouse mammary gland and a human breast cancer global gene expression data set , we found Nr2f1 transcript levels to be strongly anti-correlated to a gene cluster enriched in cell cycle-related genes . We queried 12 large publicly available human breast cancer gene expression studies and found that the median NR2F1 transcript level is consistently lower in ‘triple-negative’ ( ER-PR-HER2- ) breast cancers as compared with ‘receptor-positive’ breast cancers . Our data suggest that the non-protein coding locus Mcs1a regulates Nr2f1 , which is a candidate modifier of differentiation , proliferation , and mammary cancer risk .
An important indicator for breast cancer risk is the family history , suggesting a strong genetic component in breast cancer susceptibility [1] . The heritable portion of a woman's risk to breast cancer consists of numerous risk-increasing and risk-decreasing alleles . Through familial linkage studies in the 1990s , deleterious mutations affecting the coding regions of well-known tumor suppressor genes , i . e . BRCA1 and BRCA2 , were found to associate with increased breast cancer risk [2] , [3] . Such mutations are rare in the population . More recently , genome-wide association studies ( GWAS ) have been employed to discover association of common variants with breast cancer susceptibility . GWAS have proven to be successful at uncovering loci harboring low-penetrance breast cancer susceptibility variants [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] . In the future , the identification of these variants will likely impact population-based risk prediction [15] , [16] . Many of the variants are located in non-protein coding areas of the genome , such as promoters , introns and intergenic areas with large genomic regions without known genes , called gene deserts . It is anticipated that these variants are involved in gene regulation as exemplified by breast cancer susceptibility-associated variant rs2981582 that is correlated with FGFR2 transcript levels in breast cancer [17] and normal breast tissue [18] . For other non-protein coding breast cancer-associated variants , their spatiotemporal regulatory function and gene targets are largely undefined . Moreover , mechanisms underlying common breast cancer-associated variants on the level of the mammary gland tissue and mammary epithelial cells ( MECs ) are unknown . MEC proliferation and differentiation are strongly interconnected processes for which ample evidence exists that these are involved in the development of breast cancer . Recently , flow cytometry-based approaches have yielded many markers that aid in the understanding of the hierarchical order of MEC differentiation [19] . For mouse mammary epithelial cells ( MMECs ) , luminal and basal/myoepithelial populations were identified based on expression of heat stable antigen ( HSA; CD24 ) and β1 integrin ( CD29 ) , or HSA and α6 integrin ( CD49f ) . Specific cells of the basal lineage expressing high levels of CD29 or CD49f and high levels of CD24 were shown to harbor repopulating ability in single cell transplantation assays in mice , suggesting the presence of bipotential mammary stem/progenitor cell activity [20] , [21] . Markers for lineage-specific progenitor cells have also been identified in the mouse , including CD61 , which enriches for mouse luminal progenitors [22] . Later , additional markers for luminal progenitors have been identified , which include c-kit and ALDH [23] , [24] . We have reported a flow cytometry-based approach to identifying the luminal and basal/myoepithelial cell lineages in the rat mammary gland [25] . In the early 1990s , clonogenic rat mammary epithelial cells ( RMECs ) were found to stain with peanut lectin [26] , [27] . There is strong interest in the biology of mammary stem/progenitor cells as these are thought to be the target cells for tumorigenic transformation events , mainly because of their immortality and ability to sire many generations of daughter cells . Interestingly , human germ line mutation of BRCA1 has been shown to stimulate luminal-to-basal tumor formation by affecting the luminal progenitor cell pool and luminal cell fate [28] , [29] , exemplifying a consequence of the involvement of breast cancer susceptibility variants in MEC differentiation . It is currently unclear if other ( e . g . low-penetrance , non-protein coding ) breast cancer-associated variants affect MEC proliferation and differentiation . In order to model breast cancer susceptibility loci in a mammalian organism , we have conducted a rat-human comparative genetics approach . Upon initiation of this approach we selected the rat mammary carcinogenesis model , as the arising mammary carcinomas well reflect specific aspects of human breast adenocarcinoma , i . e . staged progression and ovarian hormone responsiveness . The advantage of the rat-human comparative genetics approach is that the availability of mammalian genetic model organisms aids in the dissection of the mechanisms underlying the susceptibility loci [30] . The rat mammary carcinoma resistance quantitative trait locus ( QTL ) Mcs1 was identified in the backcross progeny of an intercross between the resistant Copenhagen ( Cop ) and susceptible Wistar-Furth ( WF ) parental inbred rat strains [31] , [32] . Physical confirmation of this resistance locus was presented in a study using a congenic recombinant inbred line having a large portion of the original Mcs1 QTL from the Cop strain introgressed onto the WF genetic background [33] . Congenic rats harboring a homozygous Mcs1 Cop allele had a 85% reduction of 7 , 12-dimethylbenz ( a ) anthracene ( DMBA ) -induced mammary carcinoma multiplicity as compared with congenic control animals homozygous for the susceptible WF Mcs1 allele . Testing various other congenic lines with smaller Cop Mcs1 portions on the WF genetic background revealed that the initial Mcs1 QTL harbors three modifier loci of mammary carcinoma susceptibility , namely Mcs1a , Mcs1b and Mcs1c [33] . In this study , we describe the congenic fine-mapping of the Mcs1a critical interval to a ∼277 Kb region entirely embedded within a large gene desert on rat chromosome 2 . Using a congenic rat mammary gland transplantation assay , we show that the Mcs1a locus controls DMBA-induced mammary carcinoma development in a mammary cell-autonomous manner . While the rat mammary carcinogenesis model has proven value to study certain aspects of breast cancer etiology , complex genome-engineering technology for the rat is still under development . Since Mcs1a shows good evolutionary conservation to human , mouse and other mammalian species , we describe the genetic engineering of a novel megadeletion ( MD ) model in the mouse . Homozygous MD mice lack a large piece of the gene desert including the region orthologous to the Mcs1a critical interval . Taking advantage of both rodent genetic model systems we found an effect of this non-protein coding locus on MEC proliferation/differentiation and identified the orphan nuclear factor Nr2f1/Coup-tf1 as the Mcs1a target gene . To investigate its translational potential we analyzed NR2F1 transcript levels in available global gene expression data for human breast cancers . We show the correlation of low NR2F1 transcript levels with high-grade and discuss the implication of this finding for human breast cancer . Using this rat-mouse-human comparative genetics approach we identified Nr2f1 as a novel gene target for the development of breast cancer prevention or therapeutic strategies .
We previously showed that the mammary carcinoma resistance allele Mcs1a from the Cop inbred strain when introgressed onto the susceptible WF inbred genetic background reduced DMBA-induced mammary carcinoma multiplicity by ∼50% , as compared with the susceptible congenic control line , not carrying the Cop Mcs1a allele [33] . Using multiple additional congenic lines , we now present further fine-mapping of the interval conferring the reduction in DMBA-induced mammary carcinoma multiplicity phenotype ( Figure 1A ) . The resistant congenic lines have a significantly ( P<0 . 001 ) lower mammary carcinoma multiplicity as compared with the susceptible congenic control line ( WF . Cop; Figure 1B ) . The susceptible congenic lines have a mammary carcinoma multiplicity not different from the susceptible congenic control line ( P>0 . 2 ) . The resistant congenic lines together with the susceptible congenic lines V5 define the Mcs1a critical interval as a ∼277 Kb genomic region located in a gene desert on rat chromosome 2 . The gene desert is flanked by Nr2f1 at the proximal side ( Figure 1A ) and Arrdc3 at the distal side ( outside the window in Figure 1A ) . Using resistant congenic lines W4 and W5 , susceptible congenic line R5 and the susceptible congenic control line WF . Cop , we tested if Mcs1a also confers resistance to N-methyl-N-nitrosourea ( MNU ) -induced mammary carcinogenesis . The resistant congenic line W5 showed a decreased mammary carcinoma multiplicity phenotype ( P = 0 . 008 ) as compared with WF . Cop and the resistant congenic line W4 showed a strong trend ( P = 0 . 08 ) towards a decreased MNU-induced mammary carcinoma multiplicity . The susceptible congenic line R5 was not different from the WF . Cop line ( Figure 1C ) . Subsequently , carcinoma multiplicities following mammary ductal infusion of retrovirus expressing the activated HER2/neu oncogene were determined [34] . In this assay , the resistant congenic line R3 had a significantly reduced mammary carcinoma multiplicity ( P = 0 . 04 ) as compared with the susceptible congenic line A4 ( Figure 1D ) . These data show that Cop inbred strain-derived alleles of the Mcs1a locus ( that include the smallest critical interval ) introgressed on the susceptible genetic background confer resistance to three distinctly acting mammary carcinogenic treatments . These data suggest that the resistance mechanism likely manifests beyond the stage of ( carcinogen-specific ) cancer initiation . To ask if Mcs1a acts via a mammary cell-autonomous mechanism , a mammary gland transplantation assay was carried out . Mammary gland tissue from donor animals of the susceptible inbred WF rats or the Mcs1a resistant congenic line Y4 was transplanted into the interscapular white fat pads of recipient animals with the same genotype or F1 animals of an intercross between WF and Y4 . A total of 228 transplantations were performed , of which only 2 failed to produce a mammary outgrowth . For the 4 transplant groups ( donor to recipient; susceptible to susceptible S:S , susceptible to F1 S:F1 , resistant to F1 R:F1 , and resistant to resistant R:R ) carcinoma development following DMBA exposure was monitored ( Table 1 ) . The mammary carcinoma incidence at the transplant site was 41% , 36% , 13% , and 9% for transplant groups S:S , S:F1 , R:F1 , and R:R , respectively . Logistic regression analysis revealed that donor genotype ( P = 0 . 0024 ) , but not recipient genotype ( P = 0 . 59 ) was significantly associated with transplant site carcinoma development ( Table 1 ) . The interaction between donor and recipient genotype was not significant ( P = 0 . 44 ) for the dependent variable mammary gland transplant carcinoma susceptibility ( Table 1 ) . These data demonstrate that the mammary carcinoma susceptibility phenotype mediated by Mcs1a is transferable by transplantation of the mammary gland , indicating that Mcs1a modulates susceptibility in a mammary cell-autonomous manner . Considering the evolutionary conserved nature of the locus , we sought to genetically engineer a mouse model for the rat Mcs1a locus . In mouse ES cells , we employed a MICER vector-assisted double targeting strategy to insert ( on the same chromosome ) loxP sites at either side of the gene desert region orthologous to the rat Mcs1a critical interval that was known at the time of design ( Figure 2A ) . Both targeting steps were checked for proper integration of the MICER construct by Southern blot analysis . The proximally located MICER construct harbors the 3′ half ( exons 3–9 ) of the Hprt gene and the distal construct harbors the 5′ half ( exons 1–2 ) of Hprt that upon proper Cre-lox recombination form a functional Hprt gene . Following Cre-recombinase transfection and Hprt selection , the MD mutation was created and the mouse model was generated through blastocyst injections of karyotypically normal ES cells . After germ line transmission of the mutation , homozygous mutants were obtained and tested for lack of the 535 Kb targeted region in the Mcs1a orthologous gene desert on mouse chromosome 13 ( Figure 2A ) . PCR tests using 4 different primer combinations within the deleted sequence and 2 primer combinations spanning the deletion showed consistent results that the region is indeed deleted . An example of a genotyping gel image is shown in Figure S1 . The mutation was transferred through 10 generations of breeding to 2 inbred genetic backgrounds , namely FVB/N ( FVB ) and C57Bl/6 ( B6 ) . Homozygous MD mice of both genetic backgrounds are viable and litter sizes are normal as compared with wild type ( WT ) animals , suggesting there is no embryonic or neonatal lethality associated with the mutation . Groups of homozygous MD and WT mice were monitored for obvious phenotypes . There was no difference in body weight up to 1 year of age and life span was not affected during the same period . An obvious phenotype we noticed was delayed eyelid opening of homozygous MD mice on both genetic backgrounds ( Figure 2B , shown for FVB ) . While all WT animals had both eyelids completely open by 17 days of age , this was observed for only 65% of homozygous MD mice . For some animals , the closed eyelid phenotype persisted for months ( unpublished data ) . It is anticipated that a large portion of the non-protein coding capacity of the genome may be involved in the spatiotemporal regulation of gene expression [35] . For many non-coding elements , the target genes of regulation are unknown . We performed a global gene expression study by RNA-seq on mammary gland RNA samples from MD and WT mice ( FVB ) . First , the quality-filtered reads were mapped to the mouse genome . We focused on reads mapping to the Mcs1a-associated gene desert to check for putative unknown transcripts located within the deleted region . We found only 6 reads from the WT samples aligning to the MD region , suggesting that no highly expressed unknown transcript exists within the region . As expected , no reads from the MD samples aligned to the deleted region . Next , the reads were mapped to the mouse Ensembl reference set of 82 , 508 transcripts ( annotated to 31 , 034 genes ) using Bowtie [36] . Relative transcript abundance was determined using the RSEM algorithm [37] . For the detection of differential gene expression between MD and WT samples , the edgeR package was used [38] . First , we looked at the levels of transcripts within 2 . 5 Mb of either side of the Mcs1a-associated gene desert ( Figure 2C ) . The only transcript with significantly different levels between the MD and WT samples was Nr2f1 ( P<0 . 001 ) . This gene is located adjacent to the gene desert at a genomic distance of approximately 800 Kb from the Mcs1a orthologous locus . To verify that Nr2f1 is indeed a target gene , we used TaqMan gene expression assays on additional mammary gland samples . Nr2f1 was found to be downregulated by more than 80% in the MD samples as compared with the WT samples ( Figure 2D , P<0 . 001 ) . We also checked Nr2f1 transcript levels in three other tissues . In thymus and ovary , we found that Nr2f1 transcript levels are greatly reduced ( Figure S1 , P<0 . 001 ) , to similar levels as the mammary gland . In the liver , however , Nr2f1 transcript levels were not significantly different between MD and WT samples ( Figure S1 , P = 0 . 27 ) , suggesting that there is some tissue-specificity in this regulatory mechanism . Considering Nr2f1 as the main target of the Mcs1a locus , we also looked at its transcript levels in mammary glands ( MG ) , rat mammary epithelial cells ( RMECs ) and mammary carcinomas ( carc . ; induced by DMBA and MNU ) from susceptible congenic control ( WF . Cop ) and Mcs1a resistant congenic rats . The resistance allele was provided by the W4 or W5 congenic lines . Since the W4 and W5 lines did not differ significantly , data from both lines was included . For the RMECs , only data for the W4 resistant congenic line was obtained . The Mcs1a resistance allele was associated with increased Nr2f1 levels in mammary gland ( P = 0 . 01; Figure 3A ) and RMEC ( P = 0 . 02; Figure 3B ) . Both DMBA- and MNU-induced carcinomas from Mcs1a resistant congenic animals had strongly increased Nr2f1 transcript levels , as compared with DMBA- and MNU-induced carcinomas from susceptible control congenic rats ( P<0 . 001; Figure 3C ) . As the Mcs1a locus is located at a genomic distance of over 800 Kb from Nr2f1 , we asked if a chromatin looping structure exists that would support such long distance regulation . The chromosome conformation capture ( 3C ) assay was developed to detect higher-order chromatin interactions for any locus of interest [39] , [40] . To apply this methodology and investigate higher-order chromatin interactions between Mcs1a and Nr2f1 , RMECs were fixed using formaldehyde to crosslink proteins and DNA , thus capturing interacting chromatin fragments . Crosslinked chromatin was digested using the BglII restriction enzyme and ligated in a large volume ( of 7 ml ) . The large volume ligation reaction reduces random ligations and favors ligations of crosslinking-captured DNA fragments . Ligation events were detected and quantified using PCR and agarose gel electrophoresis . The PCR detection assay was designed such that the fixed primer was located in the rat Nr2f1 promoter ( Figure 3D ) . The experimental primers were located within the Mcs1a critical interval ( Table S2 ) , overlapping with areas of the strongest evolutionary conservation ( Figure 3D ) . Each experimental primer was tested in combination with the fixed primer on the 3C templates and a positive control ( BAC-derived ) template . The relative interaction frequency of two chromatin fragments represented by a primer pair equals the PCR signal intensity of the RMEC 3C template relative to that of the positive control template . We found an area of multiple BglII restriction fragments with increased relative interaction frequencies above background levels ( P<0 . 05 ) to exist within the Mcs1a critical interval ( Figure 3D ) . The main peak coincides with genetic elements of the highest evolutionary conservation present in Mcs1a . These findings suggest that a putative regulatory element within Mcs1a forms a higher-order chromatin structure with the Nr2f1 promoter over 820 Kb of genomic sequence ( Figure 3E ) . None of the interactions is significantly different between the susceptible and resistant Mcs1a genotypes , suggesting that the DNA-binding proteins facilitating the interactions do not involve polymorphic sites . To identify genetic variants within and in the vicinity of the looped fragments that may explain Nr2f1 transcript regulation , we resequenced approximately 12 . 5 Kb of the genomic region involved in the higher-order chromatin structure in the WF and Cop parental inbred strains and found 17 genetic variants ( Figure S2; Table S3 ) . For only 1 variant , the resistance ( Cop ) allele ( a 14 bp deletion ) is predicted to disrupt a rat-mouse-human-conserved binding motif , namely COUP-TF ( V$Coup_01; Figure S2 ) . As the Mcs1a resistance allele is associated with increased Nr2f1/Coup-tf1 expression ( Figure S2 ) , it is possible that the 14 bp deletion polymorphism of the Mcs1a resistance allele omits a Nr2f1 self-repressive gene expression modulatory function that acts through the intact COUP-TF binding motif on the susceptible Mcs1a allele . Such regulatory function would have to be investigated in detail in the future . Since Mcs1a is mammary cell-autonomous , we asked if the locus has an effect on MEC biology , i . e . proliferation and differentiation . First , we tested for differential repopulating ability of RMECs from susceptible congenic control ( WF . Cop ) and Mcs1a resistant congenic animals . The resistance allele was provided by the W4 or W5 congenic lines . Differential repopulating ability could be indicative of a quantitative or functional difference in the mammary stem cell pool potentially underlying the susceptibility phenotype . Freshly isolated RMECs from Mcs1a resistant congenic animals and susceptible congenic control animals were grafted into the interscapular white fat pads of recipient animals of the same genotype . A dilution series of 250 , 500 , 1000 , 2000 , 4000 and 8000 cells was tested ( Figure 4A ) . Six weeks after transplantation , the interscapular fat pads were harvested and scored for presence of mammary ductal structures as previously described [41] . The repopulating ability ( determined by the estimated number of cells required to give 50% outgrowth occurrence ) of RMECs from the susceptible congenic control animals was found not to be different ( P>0 . 05 ) than that of the Mcs1a resistant congenic animals of either line W4 or W5 . Another statistical approach was taken to seek for a possible difference in outgrowth potential at each cell number individually between the susceptible and resistant ( W4 and W5 combined ) genotypes . Therefore , Chi-square tests for independent distributions in a 2×2 contingency matrix were conducted . At all cell numbers the outgrowth potential for the resistant genotype was not different ( P>0 . 05 ) than that of the susceptible genotype . These data suggest that the Mcs1a allele does not affect mammary stem cell activity . In the next experiment , we tested the colony-forming ability in Matrigel of a purified population of clonogenic RMECs from Mcs1a resistant congenic ( line W4 ) and susceptible congenic control ( WF . Cop ) animals . This assay tests the proliferating potential of the clonogenic RMEC pool . Freshly isolated single RMECs were antibody-stained and sorted using FACS . Gating strategies were used to exclude hematopoietic cells ( CD45 ) and endothelial cells ( CD31 ) . From the RMEC-enriched ( CD31–CD45− ) population , the luminal cells ( CD24hiCD29med ) were selected and separated based on staining with anti-CD61 and peanut lectin ( PNL; Figure 4B ) . We chose to focus on the luminal population , since CD61hi luminal cells have previously been identified in the mouse as the luminal progenitor population [22] and PNLhi clonogenic rat cells were previously found to overlap largely with the luminal population [25] . The colony-forming ability of 3 sorted cell fractions was tested , namely luminal CD61hiPNL+ , CD61+PNLhi and CD61+PNL+ ( Figure S3 ) . From each sorted fraction , 10 , 000 cells were plated in Matrigel for each well . After 10 days of culturing , the colony-forming ability was determined by counting the spherical mammary colonies ( Figure 4C , lower panel ) . For every sorted sample , we found that the CD61+PNLhi cell fraction had a ∼6-fold ( P<0 . 001 ) increased colony-forming ability and the CD61hiPNL+ cell fraction a ∼1 . 6-fold ( P = 0 . 02 ) decreased colony-forming ability , as compared with the CD61+PNL+ cell fraction ( Figure S3 ) , verifying that in rats PNLhi luminal MECs are enriched with progenitor cells . When comparing the susceptible congenic control and the Mcs1a resistant congenic samples , we found that the CD61+PNLhi cell fraction from the susceptible congenic control samples had a more than 2-fold increased colony-forming ability ( Figure 4C; P = 0 . 02 ) . These results indicate that the susceptible congenic control luminal RMEC fractions have increased proliferative capacity as compared with the Mcs1a resistant congenic RMEC fractions . We also looked for quantitative differences in RMEC differentiation between susceptible congenic control ( WF . Cop ) and the Mcs1a resistant congenic animals . The resistance allele was provided by the W4 or W5 congenic lines . Fresh RMECs were obtained and stained for FACS analysis , as described previously [25] . The Mcs1a resistant congenic animals have more luminal ( P = 0 . 02 ) and less basal ( P = 0 . 02 ) RMECs as compared with susceptible congenic animals ( Figure 5A ) , which significantly shifts the luminal-to-basal ratio by 34% ( P = 0 . 008 ) . The abundance of PNLhi or CD61hi subpopulations ( Figure 5B ) as well as the mean fluorescence intensities ( not shown ) of these stainings were not different between susceptible and resistant Mcs1a congenic animals . In addition , we looked at MMEC differentiation in WT and MD mice by FACS analysis , as described previously [42] . Like for the rat , we used antibodies against CD45 and CD31 to exclude hematopoietic and endothelial cells , respectively , and antibodies against CD24 and CD29 to quantify luminal and basal MMEC populations ( Figure 5C ) . Like for Mcs1a congenic resistant and susceptible control rats , we found a shift of 36% in luminal-to-basal ratio between WT and MD mice . MD mice had a less abundant luminal and a trend towards a more abundant basal population ( Figure 5C , P<0 . 001 and P = 0 . 10 for luminal and basal , respectively ) . Similar results were obtained for the MD mutation on both genetic backgrounds ( FVB , B6 ) . We also performed this analysis based on CD29 and CD61 expression to quantify mature luminal ( ML , CD29medCD61− ) , luminal progenitors ( LP , CD29medCD61+ ) and mammary stem/progenitor cells ( MaSc , CD29hiCD61+ ) . As compared with WT mice , the MD mice had a less abundant mature luminal population ( P<0 . 001 ) , a trend towards a less abundant luminal progenitor population ( P = 0 . 09 ) and no significantly different mammary stem/progenitor cell population ( P = 0 . 26; Figure 5D ) , suggesting that the Mcs1a-associated gene desert locus primarily affects luminal differentiation . Both MD mice and susceptible congenic control rats have a lower abundance of luminal cells and lower Nr2f1 transcript levels , as compared with WT mice and Mcs1a resistant congenic rats , respectively , suggesting that Nr2f1 modulates luminal MEC differentiation . Next , we tested if short interfering RNA ( siRNA ) -mediated knockdown of Nr2f1 transcript levels in cultured human breast cells is capable of directly influencing the cellular differentiation pattern . The human breast cancer cell line MCF7 and breast epithelial cell line MCF10A were transfected with siRNAs against NR2F1 ( siNR2F1 ) and non-targeting control siRNA ( siCONTROL ) . No morphological differences were observed between cells transfected with siNR2F1 or siCONTROL . At 40 hours after transfection , cells were harvested for Nr2f1 expression analysis and stained for FACS analysis with fluorescently labeled antibodies against commonly used markers of MEC differentiation , namely CD24 , CD29 , CD44 and CD49f . As expected , the siNR2F1-treated cells have an over 2-fold reduction of NR2F1 transcript level , as compared with siCONTROL-treated cells ( Figure S4 ) . In both MCF7 and MCF10A we found that NR2F1 knockdown upregulated CD24 , as compared with treatment of the cells with non-targeting siRNAs . None of the other markers of differentiation was affected by NR2F1 knockdown ( Figure S4 ) , suggesting that Nr2f1 transcript levels have a direct effect on cellular differentiation through upregulation of CD24 . In the global RNA-seq expression analysis , 1 , 531 genes were found to be differentially expressed ( DE ) between the mammary gland samples from MD and WT ( FVB ) mice ( Table S4 ) . Of these , 412 genes have annotated 1-1-1 mouse-rat-human orthologues . Nr2f1 is listed in the top 10 genes with the lowest P-value and is the top of the list of genes with 1-1-1 mouse-rat-human orthologues ( Table S4 ) . We applied a gene expression correlation clustering analysis using the 412 DE genes with 1-1-1 orthologues . The DE genes mainly clustered into three groups and Nr2f1 is found in the first group ( Figure 6A ) . To functionally annotate the groups of correlated genes , two online gene ontology ( GO ) category enrichment calculation tools were used , namely the Gene Ontology enRIchment anaLysis and visuaLizAtion tool ( GOrilla ) and the Database for Annotation Visualization and Integrated Discovery ( DAVID ) [43] , [44] . The Nr2f1 containing group is weakly enriched for genes related to cell migration , the extracellular matrix and innate immunity/inflammation ( Table S5 ) . The second group of strongly correlated genes was found not to correlate or anti-correlate with groups 1 and 3 . This group was enriched for genes related muscle contractile function ( Table S5 ) . Group 3 is anti-correlated to the Nr2f1-containing group and is strongly enriched in genes related to the cell cycle , proliferation and DNA-damage response ( Table S5 ) . These results implicate that reduced Nr2f1 transcript levels in the MD mammary gland is associated with an increased expression of cell cycle-related genes , which may render the mammary gland in a more proliferative state . From the publicly available breast cancer global gene expression study GSE3494 containing data for 243 breast cancers [45] , we selected 412 human probe sets from the Affy U133a array that are annotated to correspond to the 412 mouse DE genes . By performing the same correlation clustering analysis , an expression correlation pattern was identified to consist of 3 groups ( Figure 6B ) . Group 1 , again , is the NR2F1-containing group , weakly enriched with genes involved in developmental processes such as brain segmentation and morphogenesis ( Table S6 ) . Group 2 is now a large group that can be split up into subgroup 2a and 2b . Group 2 as a whole and subgroup 2b are strongly enriched with muscle contraction-related genes , whereas subgroup 2a is weakly enriched for muscle protein- and extracellular-related genes and is considered to be a mix between group1 and 2 . Similar to the mouse mammary gland correlation analysis , group 3 is very strongly enriched for cell cycle/proliferation and DNA-damage response-related genes and is anti-correlated to the NR2F1-containing cluster ( Table S6 ) , suggesting that also in human breast cancer low NR2F1 transcript levels are associated with an increase in cell cycle-related gene expression . Next , we looked if the similarities in gene expression patterns between the mouse MD mammary gland and the human breast cancer data set hold up within the genome-wide data sets . From the GSE3494 global gene expression study , we selected 9 , 828 human probe sets from the Affy U133a array that are annotated to have 1-1-1 mouse-rat-human orthologues . First , we checked for similarities between the human and mouse data sets in gene lists anti-correlated to Nr2f1 transcript levels . From the list of 126 human genes in the clustered group of genes anti-correlated to NR2F1 transcript levels ( Group 3 , Table S6 ) , 51 and 64 genes were found to be present in the top 100 and 200 anti-correlated genes to the Nr2f1 transcript level in the mouse study , respectively . The probability that 51 or 64 of the anti-correlated genes ( Group 3 ) would be in the top 100 or 200 anti-correlated from all 9 , 828 genes by chance would be <10−74 or <10−78 , respectively , suggesting high similarity in genes anti-correlated to NR2F1/Nr2f1 between human breast cancer and the mouse MD mammary gland . Similar analysis for the 37 genes in the NR2F1-containing cluster ( Group 1 , Table S6 ) yielded 3 and 5 genes in the top 100 and 200 genes correlated to the Nr2f1 transcript level , which translates to a probability of this occurring randomly of 0 . 000571 and 0 . 000106 , much higher than the probabilities for the anti-correlated genes . We also functionally explored the genes most correlated and anti-correlated to NR2F1/Nr2f1 in both the human and mouse data set , regardless of the occurrence of the genes in the mouse DE gene-based cluster analysis . From the 59 genes with strongest anti-correlation ( r<−0 . 3 ) to NR2F1 in the human breast cancer data set , 41 ( 69% ) were also found to be among the strongest anti-correlated ( r<−0 . 3 ) to Nr2f1 in the mouse MG data set , which is 2-fold enrichment in comparison to strongly anti-correlated genes ( r<−0 . 3 ) to Nr2f1 in the entire mouse data set ( 34% ) . These 41 genes are found to be strongly enriched with cell cycle/proliferation-related genes ( Table S7 ) . From the 297 genes with strongest correlation ( r>0 . 3 ) to NR2F1 in the human breast cancer data set , 64 ( 22% ) were also found to be the strongest correlated ( r>0 . 3 ) to Nr2f1 in the mouse MG data set , which is 1 . 3-fold enrichment in comparison to equally strongly correlated genes ( r>0 . 3 ) to Nr2f1 in the entire mouse data set ( 17% ) . These 64 genes are found to be enriched with genes involved in a wide variety of processes , including extracellular matrix and developmental processes , as well as signaling pathways ( Table S7 ) . These analysis suggest that the human breast cancer and the MD mouse MG gene expression data sets are particularly similar in genes anti-correlated with NR2F1/Nr2f1 , which are strongly enriched with genes involved in cell cycle/proliferation . Proliferation gene signatures have been explored for usage as prognostic markers in breast tumor expression studies [46] . Upregulation of such genes in breast cancer is generally indicative of poor prognosis [47] , [48] . In a study to identify a novel gene list for “breast cancer intrinsic” subtype classification , a 20-gene proliferation signature was found to form one of the predictive modules [49] . Of these 20 genes , we found 14 to have human-rat-mouse orthologues of which 10 were DE in the mouse MG RNA-seq study with all 10 genes upregulated in the MD samples . The probability of selecting by chance 10 of these 14 proliferation signature genes into the 412 mouse DE gene set out of a total of 9 , 828 genes is lower than 10−11 . This result suggests that the MD mammary gland gene expression profile ( with low Nr2f1 transcript levels ) shows signs of a proliferative environment . This result is in accordance with the result from the Matrigel assay that indicated an increased colony-forming ability for selected cells from the susceptible mammary gland ( with lower Nr2f1 transcript levels ) as compared to those from the resistant congenic mammary gland . Since Nr2f1 is implicated in MEC proliferation and differentiation in mice and rats , we asked if NR2F1 transcript levels correlate with clinical features of human breast cancer . The Oncomine database encompasses a comprehensive listing of breast cancer gene expression studies , including available clinical information on the samples [50] . When including 12 studies with 120+ samples for each study we found that the average of the median NR2F1 transcript levels reduces with increased histological grade ( Figure 7A ) . Histological grade 3 tumors are more proliferative and more poorly differentiated than grade 1 or 2 tumors . The therapy-resistant and most aggressive form of breast cancer , based on hormone receptor status is the ‘triple-negative’ class of tumors that are more likely to be of grade 3 when resected . Median NR2F1 transcript levels were found to be lower in triple-negative breast cancers , as compared with ‘receptor positive’ ( non-triple-negative ) breast cancers ( Figure 7A ) . In accordance with this finding , estrogen receptor ( ER ) -negative and progesterone receptor ( PR ) -negative breast cancers had lower median transcript levels of NR2F1 as compared with their positive counterparts ( Figure 7A ) . Notably , NR2F1 transcript levels were found to be lower in human epidermal growth factor receptor 2 ( HER2 ) -negative breast cancers ( that mostly are ER-positive ) , as compared with the more aggressive HER2-positive breast cancers ( Figure 7B ) . Finally , we asked if Nr2f1 transcript level anti-correlated with histological grade within both ER-positive/ER-negative and within both HER2-positive/HER2-negative breast cancer subtypes . For 2 of the 12 previously mentioned breast cancer gene expression studies we obtained the raw data from the gene expression omnibus ( GEO; GSE3494 and GSE5460 ) . The GSE3494 data set consists of microarray gene expression data for ER-positive and ER-negative breast tumors including histological grade 2 and 3 tumors in both ER classes and the GSE5460 data set consists of data for HER2-positive and HER2-negative breast tumors including histological grade 2 and 3 tumors in both HER2 classes . We found that NR2F1 transcript levels are significantly lower in grade 3 tumors as compared with grade 2 tumors in both ER classes ( Figure 7B , left panel ) , as well as in both HER2 classes ( Figure 7B , right panel ) . Additionally , in the GSE5460 data set the grade 3 HER2-positive breast cancers were found to have higher NR2F1 transcript levels as compared with the grade 3 HER2-negative breast cancers ( Figure 7B , right panel ) , suggesting a regulatory effect of HER2 amplification on NR2F1 transcript levels . In summary , these analyses indicate that low transcript levels of NR2F1 are strongly associated with high histological grade , poorly differentiated , highly proliferative breast cancers , including therapy-resistant ‘triple-negative’ breast cancer .
The inherited portion of breast cancer susceptibility is complex and likely involves numerous genetic factors [51] , [52] . With the results from genome-wide association studies ( GWAS ) it became clear that the genetic landscape of breast cancer susceptibility largely consists of low-penetrance alleles that are common in the population [53] . Many such alleles are located in non-protein coding regions of the genome , including in gene deserts , such as the one on human chromosomal band 8q24 [5] . Mechanisms underlying the genetic associations are largely unknown . It is generally hypothesized that non-protein coding variants could modulate disease processes through the regulation of gene expression . Like for human breast cancer susceptibility , many loci associated with rat mammary cancer susceptibility have been discovered over the last decade [54] . Multiple of these QTL have been identified by our laboratory through linkage analysis and fine-mapping using congenic recombinant lines [32] , [33] , [55] , [56] , [57] . For some of the rat loci , common alleles associated with breast cancer susceptibility in the human orthologous loci were found [58] , [59] . One major advantage of such rat-human comparative genetics approach is the availability of a highly relevant genetic mammalian model system for mechanistic studies [30] . Understanding how loci affect breast cancer susceptibility will be informative in the design of preventative or early intervention strategies that would be applicable to many women at risk . In this study , we identified a 277 Kb critical interval for the previously discovered Mcs1a resistance allele that is derived from the Cop inbred rat strain [33] . The allele when introgressed on the susceptible genetic background from the WF inbred rat strain modulates mammary carcinoma multiplicity by approximately 50% . The protective effect of the allele works against three distinctly acting carcinogenic treatments , indicating that the mechanism modulates mammary carcinogenesis beyond a carcinogen-specific initiation stage . Since the susceptibility or resistance phenotype of the WF or Cop Mcs1a allele , respectively , is transferrable in a mammary gland transplantation/carcinogenesis study , we concluded that the locus modifies mammary carcinoma development in a mammary cell-autonomous manner . This is in contrast to the Wistar-Kyoto ( WKy ) inbred strain-derived Mcs5a resistance locus , for which we previously published a non-mammary cell-autonomous mode of mammary carcinoma development modulation using a similar transplantation/carcinogenesis assay [60] . Markedly , the location of the 277 Kb critical interval lays within a 3 Mb gene desert , which classifies the Mcs1a allele as non-protein coding . To identify the gene targets for regulation by the Mcs1a associated non-protein coding region , we developed a mouse model lacking a 535 Kb gene desert region orthologous to Mcs1a . The mouse model organism was chosen , as a large deletion ( i . e . megadeletion; MD ) engineering resource by means of MICER vectors was readily available . A MICER vector-assisted large deletion mouse model had aided before in discovering the gene targets of regulation by a non-protein coding region associated with coronary artery disease [61] . At the time we developed our MD mouse model , targeted rat genetic manipulation technologies were still under development . With the current maturation of zinc-finger nuclease-mediated genome editing technology [62] , [63] , a similar MD approach will be applicable to the rat model organism in the near future . Using RNA-seq we characterized mammary gland gene expression of MD and WT mice . Of genes surrounding the gene desert within 2 . 5 Mb , we only found the transcript level of the orphan nuclear receptor gene Nr2f1/Coup-tf1 to be strongly reduced upon deletion of the non-protein coding region . In addition , in the global RNA-seq gene expression analysis , Nr2f1 had the lowest P-value of all differentially expressed genes with annotated 1-1-1 mouse-rat-human orthologs . This gene is located at a genomic distance of over 800 Kb from the MD mutation , suggesting the presence of a strong Nr2f1 distal enhancer in the deleted region . Nr2f1 transcript levels were found to be downregulated in whole mammary gland , RMEC and mammary carcinoma samples from susceptible congenic controls as compared with Mcs1a resistant congenic rats . These results identify Nr2f1 as a strong candidate breast cancer susceptibility gene whose increased mammary transcript levels are associated with resistance to mammary carcinoma development . It is worth mentioning that the difference in Nr2f1 transcript levels between the susceptible and resistant Mcs1a alleles are more substantial in tumors as compared with untransformed cell types of the mammary gland . A plausible explanation for this observation could be that Nr2f1 transcript levels in an unidentified progenitor ( or perhaps cancer-initiating ) RMEC population may show similar substantial differences , which could be masked by other cell types present in the whole mammary gland or RMEC samples . The presence of a higher-order chromatin structure connecting the Nr2f1 promoter with a strongly conserved element within Mcs1a supports the long-range ( ∼820 Kb ) regulatory potential of the Mcs1a locus . It should also be noted that the 3C assay was biased towards elements with the strongest evolutionary conservation ( through fishes ) . Potentially interesting interacting elements in less conserved sequences may have been overlooked . Because the intensity of the chromatin loop is not affected by the Mcs1a alleles , but Nr2f1 transcript levels are , we hypothesize that the proteins involved in the higher-order chromatin interaction may be not be the same factors regulating Nr2f1 . Resequencing of the interacting region in the Cop and WF parental inbred strains revealed the presence of 17 polymorphisms . Only one polymorphism , a 14 bp deletion in the Cop strain , affects a human-rat-mouse conserved binding motif , which is a COUP-TF binding site . Since the resistance ( Cop ) allele is associated with increased Nr2f1 ( Coup-tf1 ) transcript levels we hypothesize that the 14 bp deletion removes a repressive autoregulatory module of Nr2f1 communicating with its own promoter . Since the MD mutation of the entire locus profoundly downregulates Nr2f1 transcript levels , the entire region is acting as a strong enhancer . Thus , we propose that in the susceptible strain harboring the WF allele with the intact COUP-TF binding motif , the repressive autoregulatory mechanism may modulate Nr2f1 transcription in the context of the activity of the enhancer . A germ-line mutation in the orthologous binding motif is not known to exist in mice or humans . Other variants outside of the conserved element ( perhaps located in closer genomic proximity to the NR2F1 promoter ) may confer similar NR2F1 regulation and thus potentially associate with breast cancer risk . By taking advantage of the available congenic rat and genetic mouse models , we focused on dissecting the mechanisms underlying the non-protein coding Mcs1a locus on the organismal level . Because of the mammary cell-autonomous mechanism and change in mammary Nr2f1 transcript levels , we looked for differences in MEC biology between susceptible and resistant Mcs1a congenic rats . We found in a limiting dilution RMEC transplantation assay for repopulation ability that mammary stem cell activity is not affected by Mcs1a . Next , we tested the proliferation potential of a specific clonogenic population of RMECs . Clonogenic RMECs have previously been described to stain brightly with peanut lectin ( PNL ) and to be mostly located within the luminal population [25] , [26] . In this study , we show that the luminal clonogenic RMEC population with colony-forming ability in Matrigel is indeed marked by bright PNL staining and not by high CD61 expression , which was shown to enrich for luminal progenitor cells with colony-forming ability in the mouse mammary gland [22] . In later studies , c-kit and ALDH have been identified as more specific markers for luminal progenitor cell populations , illustrating the heterogenity of the luminal MEC population [23] , [24] . In the future , these markers can be tested on RMECs in combination with PNL staining to pinpoint the rat luminal progenitor population , provided good antibodies are available for the rat . Interestingly , the colony-forming ability of the luminal PNLhi population was found to be reduced in animals carrying the Mcs1a resistance allele , as compared with susceptible controls . As determined by multiparameter FACS analysis of freshly isolated RMECs , the abundance of PNLhi and luminal PNLhi cells among CD31–CD45− RMECs was not significantly different . Hence , we concluded that the proliferation potential of the colony-forming luminal population is affected by Mcs1a . The FACS analysis , however , did reveal a RMEC differentiation phenotype associated with Mcs1a . Resistant congenic animals have a higher abundance of luminal and lower abundance of basal RMECs , as compared with susceptible congenic control animals . Basal RMEC are mainly characterized by high β1-integrin ( CD29 ) expression and loss of β1-integrin in the mouse mammary gland impairs mammary cancer development [64] , suggesting that lower abundance of basal RMECs in the resistant Mcs1a congenics may contribute to lower mammary carcinoma susceptibility . Interestingly , in both the Mcs1a congenic rat model and genetically engineered mouse model , higher expression of Nr2f1 in the mammary gland is associated with higher abundance of luminal MMECs , identifying Nr2f1 as a candidate MEC differentiation gene modulating luminal cell fate . Since in the MD mouse model the abundance of mature luminal cells was significantly affected , and the abundance of luminal progenitors and basal cells were not , we propose that the Mcs1a-associated locus may impact luminal cell fate through activities in the luminal progenitors . Several other genes have been shown to regulate luminal cell fate mainly through activities in luminal progenitors . Downregulation of Gata-3 and upregulation of FoxM1 have been demonstrated to lead to impaired luminal cell differentiation [22] , [65] . Interestingly , we found the transcript level of FoxM1 significantly upregulated in the MD mammary gland samples , whereas the Gata3 transcript level was not affected ( Table S4 ) . FoxM1 is predicted not to have a COUP-TF binding motif in its vicinity , thus the exact relationship of FoxM1 and Nr2f1 transcript levels remains to be investigated . NR2F1 is an orphan nuclear receptor of the steroid hormone receptor superfamily [66] . Homodimers of NR2F1 bind the DR1 ( direct repeats with 1 spacer ) motif with the highest affinity [67] . NR2F1 is thought to act as a transcriptional repressor [68] , [69] , but can activate target genes as well [70] , [71] . Nr2f1 has been previously recognized as an important factor in the development of the mouse nervous system [72] , [73] , [74] and the inner ear [75] , [76] . Interestingly , ectopic Nr2f1 expression in the developing telencephalon and knockdown of Nr2f1 in primary neurospheres have been shown to result in defect neuronal cell fate determination [77] , [78] , suggesting that Nr2f1 may function as a neuronal as well as a MEC differentiation gene . The MD mice generated in this study have normal bodyweight , lifespan , and startle response to finger flicking above the cage ( to test for hearing loss ) , but do display a delayed eyelid opening phenotype . Delayed eyelid opening could be indicative of an eye development defect or an eyelid epidermal apoptotic defect [79] . Interestingly , Nr2f1 has previously been implicated in eye development and was found to be highly expressed in progenitor cells of the developing eye [80] , suggesting that the delayed eyelid opening phenotype in the MD mice is likely due to aberrant Nr2f1 expression in the differentiating eye progenitors . There is a moderate amount of evidence that NR2F1 is involved in breast cancer , mainly through its cross-talk activities with the ER- [81] , the arylhydrocarbon receptor- [82] , and/or retinoic acid-mediated signaling [83] . Again , several studies emphasize NR2F1's dual role as a transcriptional repressor and activator , depending on the promoter its acting on and the cellular context , i . e . presence of other nuclear factors such as ER [84] , [85] . We show in this paper that in all large human breast cancer gene expression studies examined , triple-negative ( aggressive , therapy-resistant ) and histological grade 3 ( poorly differentiated , highly proliferative ) breast cancers display lower NR2F1 transcript levels as compared with ‘receptor-positive’ and histological grade 1/2 breast cancers , respectively . This observation is in accordance with the mouse and rat MEC differentiation and mouse mammary gland gene expression studies that show reduced Nr2f1 transcript levels associated with less luminal differentiation and a more proliferative epithelial environment . Recently , NR2F1 was presented in a breast cancer dormancy gene signature as a gene upregulated in dormant cells [86] . Notably , in the same study , MCF7 cells with siRNA-mediated depletion of NR2F1 , when injected into the mammary fat pad of immunosuppressed mice resulted in increased growth as compared with negative control siRNA-treated MCF7 cells [86] . Again , consistent with our findings , this result provides functional evidence that lower NR2F1 transcript levels increase the proliferative potential of breast cancer cells in an in vivo model system . In addition , we found in the MCF10A and MCF7 cell lines that siRNA-mediated reduction of NR2F1 transcript levels results in increased expression of CD24 . CD24 has previously been implicated in breast cancer , for example as a marker for the breast cancer-initiating cell population [87] . Ectopic expression of CD24 in breast cancer cells has been shown to result in increased proliferation , as well as cell motility and invasion [88] and the expression of CD24 in breast carcinomas has been associated with poor prognosis [89] . It should be noted that according to available HER2 status classification in the human breast cancer gene expression studies , the more aggressive HER2-positive breast cancers ( also associated with poorer clinical outcome ) were found to express higher NR2F1 transcript levels , as compared with HER2-negative breast cancers ( that are mostly ER-positive and less aggressive ) . In addition , we show for the GSE5460 gene expression data set that within both HER2 classes , histological grade 3 tumors have lower NR2F1 transcript levels as compared with grade 2 tumors . In this data set , the grade 3 tumors from the HER2-positive class have higher NR2F1 transcript levels as compared with grade 3 tumors from the HER2-negative class . We speculate that over expression of HER2 and subsequent stimulation of downstream signaling pathways increases NR2F1 transcript levels . The cellular effects of higher NR2F1 transcript levels may be very different for the amplified and unamplified HER2 backgrounds . NR2F1 is located on human chromosomal band 5q15 . Interestingly , a hotspot for copy number alterations ( CNA ) in breast cancer maps to chromosomal arm 5q [90] , [91] , with deletions most frequently occurring at 5q11-5q34 [92] . These CNA have been associated with high histological grade , basal-like tumors , p53-mutation status , triple-negative tumors , and tumors from BRCA1 carriers [90] , [91] , [93] . A recently published study describing comprehensive molecular portraits of human breast tumors identified the 5q deletion hotspot as a large trans-eQTL , as the expression levels of hundreds of genes across the genome are associated with occurrence of 5q deletions [93] . Interestingly , this study found the associated genes to enrich in GO categories involved in cell cycle processes , FoxM1 transcriptional regulation and proliferation , many of which are also found in our MD and WT RNA-seq study to be anti-correlated to Nr2f1 transcript levels . Placement of the Mcs1a-orthologous gene desert and NR2F1 within the deletion hotspot suggests that NR2F1 may play a role in deregulation of a fraction of these cell cycle-related genes associated with the triple-negative breast cancer-specific 5q deletions . In summary , we describe the genetic dissection of the gene desert breast cancer susceptibility locus Mcs1a . We hypothesize that the resistance allele ( from the Cop strain ) carrying a truncated , potential transcriptionally suppressive COUP-TF ( autoregulatory ) binding motif , leads to increased Nr2f1 transcript levels in the mammary gland , which increases luminal RMEC differentiation and creates a less proliferative , more differentiated mammary epithelium with decreased mammary carcinoma susceptibility ( Figure 8 ) . We present NR2F1 as a strong candidate breast cancer susceptibility gene and MEC differentiation gene . In addition to a potential role in breast cancer susceptibility , we propose that reduced NR2F1 transcript levels associated with the human breast cancer 5q chromosomal deletions play a role in high-grade , poorly differentiated , proliferative breast cancer , including therapy-resistant triple-negative breast cancer . The human non-coding region orthologous to Mcs1a as well as NR2F1 are located on chromosomal band 5q in the region of frequent deletion . Raising NR2F1 transcript levels , or enhancing NR2F1's activities has great potential as a strategy to aid in breast cancer prevention or breast cancer intervention , including for triple-negative breast cancer ( and possibly excluding HER2-positive breast cancer ) . As a steroid hormone receptor family member , NR2F1 potentially is an attractive therapeutic target . To our current knowledge NR2F1 is still an orphan nuclear receptor , which means a ligand has not been identified yet . Based on strong amino-acid conservation of the NR2F1 ligand binding domain ( 96% ) with that of NR2F2 , the crystal structure of the NR2F2 ligand binding domain suggests that NR2F1 may also be activated by retinoic acid through coactivator recruitment-based release of its autorepressed conformation [94] . Identification of ligand-mediated activator mechanisms for NR2F1 is important to begin to exploit its therapeutic potential in the near future .
All animal protocols were approved by the University of Wisconsin School of Medicine and Public Health Animal Care and Use Committee . The congenic rat lines were established and maintained in an AAALAC-approved facility as previously published [57] . Congenics are defined as genetic lines developed on a Wistar-Furth ( WF; susceptible ) genetic background and carrying the selected Copenhagen ( Cop; resistant ) Mcs1a alleles in homozygous fashion [33] . Resistant congenic lines with decreased susceptibility phenotypes ( Q , R3 , V4 , W4 , Y4 , W5 ) carry Cop alleles that define the Mcs1a locus critical interval . The susceptible congenic lines ( P5 , V5 , R5 , A4 , Y3 ) are WF-homozygous at the newly defined Mcs1a locus . The susceptible congenic control line ( WF . Cop ) was derived from congenic line W5 and is WF-homozygous for all Mcs1 loci . The primer sequences for the genetic markers polymorphic between the WF and Cop inbred parental stains that are used to define the congenic lines are listed in Table S1 . Female rats , 7–8 weeks of age , were either orally gavaged with 7 , 12-dimethylbenz ( a ) anthracene ( DMBA ) at 65 mg/kg of body weight , injected intraperitoneally with N-nitroso-N-methylurea ( MNU ) at 50 mg/kg of body weight , or subjected to mammary ductal infusion of replication-defective retrovirus expressing the activated HER2/neu oncogene ( HER2/neu ) at a concentration of 1×105 Colony Forming Units ( CFU ) /ml [34] . To obtain multiplicities , mammary carcinomas >3×3 mm were counted at 15 weeks post-DMBA , 15 weeks post-MNU , and 7 weeks post-HER2/neu treatment . Multiplicity data were statistically analyzed using Mann-Whitney nonparametric tests . All mice are maintained in an AAALAC-approved facility . The megadeletion mice were produced in collaboration with the University of Wisconsin Biotechnology Center Transgenic Animal Facility ( http://www . biotech . wisc . edu/facilities/transgenicanimal ) . MICER clones ( MHPP256h04; MHPN5k06; Sanger Institute , UK ) were obtained and the vectors were purified . To ensure proper directionality of the construct upon insertion , the genomic insert from vector MHPP256h04 was flipped by AscI digestion and religation . The vectors were prepared by creating a gap in the genomic insert , as efficient targeting using MICER vectors relies on the embryonic stem ( ES ) cell's gap repair mechanism [95] . AB2 . 2 ( HPRT deficient ) ES cells from the 129/SvEv strain were transfected by electroporation in the presence of a linear gap-containing MICER vector . The first electroporation was done in the presence of the ( flipped ) gap-containing MHPP256h04 vector . Puromycin-resistant ES cell clones were expanded and checked for proper targeting by Southern blot analysis ( Figure S1 ) . Following karyotyping , karyotypically normal clones were selected and expanded for targeting with the second MICER vector . After electroporation in the presence of the gap-containing MHPN5k06 vector , neomycin-resistant clones were expanded and checked for proper targeting by Southern blot analysis . A karyotypically normal doubly targeted ES cell clone was expanded . To excise the 535 Kb region ( megadeletion , MD ) between the loxP sites present in both inserted vectors , a Cre-recombinase expressing vector ( pTurbo-Cre ) was introduced through electroporation . As proper Cre-loxP recombination restores the functional HPRT gene [95] , hypoxanthine/aminopterin/thymidine ( HAT ) resistant clones were expanded and checked for recombination by PCR using 2 primer combinations spanning the deletion ( mmMICERdel:TGTCTAGAGCTTGGGCTGCAG mmMICERdel:AGACAGAATGCTATGCAACCT and Del2F:CATGGACTAATTATGGACAGG Del2R:CTCCTTCATCACATCTCGAGC ) . Karyotypically normal ES cell clones were monodispersed and microinjected into C57Bl/6 blastocysts to produce chimeric founders . After germ line establishment , the MD mutation was introgressed onto the FVB/N and C57Bl/6 inbred genetic backgrounds for >10 generations . Deletion of the 535 Kb region was further verified by PCR using 4 primer combinations within the deleted sequence ( mmdelNeg:TGGACTTGATGTGCTCCTTG mmdelNeg:TGCCATCAATGAGTTTGAGG , 2F:AAGTGAAAGATGCTGACATTTCC 2R:AAGACTGAATTCTTGCCACTCAC , 3F:GGGAGCCATTTATCACAGTCCTA 3R:GACCCTCACAAAAGCTGGTTTA , 4F:ACACATTTGGAGATGCAAACAG 4R:CACAAAAGTCACCTAAAAGGATCA ) . Examples of genotyping images for primers mmMICERdel and mmdelNeg are shown in Figure S1 . Females from the susceptible ( S ) inbred strain WF , the resistant ( R ) congenic line Y4 , and WFxY4 or Y4×WF intercross ( F1 ) were used . Donor mammary glands with lymph nodes ( LN ) excised ( both abdominal and adjacent inguinal glands ) from 30–35 day old females were finely minced over ice and divided into four equal volumes . One volume was transplanted onto the interscapular white fat pad of each 30–35 day old recipient ( 1 donor/4 recipients ) . Three weeks after transplantation , all recipients were treated with DMBA . At 15 weeks post-DMBA , interscapular fat pads were examined for carcinoma development . In addition , each fat pad was whole mounted and stained with aluminum carmine to verify mammary gland establishment . As only 15 out of 228 rats developed multiple carcinomas in the transplant sites , the mammary carcinoma incidence data were analyzed as a binary response by logistic regression . The four transplant groups ( S:S , S:F1 , R:F1 , R:R ) form a 2×2 factorial design with donor and recipient genotypes as the main effects . Standard logistic regression was applied to the binary response data with two main effects and an interaction term . Female rats ( 11 weeks of age ) of the susceptible congenic line WF . Cop ( susc . ) and resistant congenic lines W4 and W5 ( res . ) , or female MD and WT mice ( 9 weeks of age; FVB ) were used as tissue donors . RNA was extracted from snap-frozen mammary gland and mammary carcinoma tissues , from fresh RMEC samples , or from siRNA-treated human cell line samples . To synthesize cDNA from 800 ng of TURBO-free DNaseI-treated total RNA , the reverse transcriptase Superscript II kit ( Invitrogen ) was used according to manufacturer's directions . Quantitative real-time PCR was used to quantify transcript levels . TaqMan quantitative PCR primers and probes were ordered as premade assays ( ABI/Applied Biosystems ) : rat Nr2f1 Rn01489978_m1 ( FAM ) , mouse Nr2f1 Mm01354342_m1 ( FAM ) , human NR2F1 Hs00818842_m1 ( FAM ) , rat ActB Rn00667869_m1 ( VIC , endogenous control ) , mouse ActB Mm00607939_s1 ( VIC , endogenous control ) and human GAPDH Hs03929097_g1 ( VIC , endogenous control ) . Reactions were run as described previously [96] . Quantities of transcripts were measured by comparison of Ct values with a standard curve calculated from serial dilutions made from reverse transcriptase reactions that contained 2 µg of total RNA . Sample measurements are an average of three or four replicates within 0 . 5 Ct value . Sample measurements were normalized by dividing the gene specific transcript quantity over the endogenous control quantity . For each sample , the ratio was scaled to the average ratio of the control group from the same experiment , which are the susceptible congenic control group ( rat ) , the WT group ( mouse ) or the siCONTROL-treated group ( human cell lines ) . Data were analyzed using Mann-Whitney nonparametric tests . RNA was extracted from snap-frozen mammary gland tissue from MD and WT mice ( FVB ) using the MagMax-96 Total RNA isolation kit ( Ambion ) according to manufacturer's directions . RNA samples were checked for integrity using Agilent 2100 Bioanalyzer . Two RNA samples were pooled using 5 µg of each for 10 µg of total RNA per library preparation sample . Sample preparation and next-generation sequencing was done at the University of Wisconsin Biotechnology Center Gene Expression Center . Sample preparation was done using the Rev . D mRNA sample preparation kit ( Illumina ) , according to the manufacturer's recommendations . Samples were run on the Illumina GAIIx . Reads that made the quality cut-off were aligned to the mouse Ensembl set of 82 , 508 transcripts ( http://genome . ucsc . edu ) using Bowtie ( v0 . 12 . 3; http://bowtie-bio . sourceforge . net/index . shtml ) with the following settings: -f –v 1 −3 0 –a –m 100 . Transcript levels were estimated using the RSEM algorithm [37] . Differential expression between the MD and WT samples was determined using edgeR [38] . Correlation analysis of gene expression was done on the 1 , 531 DE mouse genes and 412 DE mouse genes with 1-1-1 mouse-rat-human Affy ( U133a probe set ) orthologues . The Pearson correlation between the mean-centered RSEM tau expression values was calculated and visualized in R using the gplots library . Similar correlation analysis was done on the 412 human orthologous genes for which microarray data ( Affy U133a ) was available from a human breast cancer gene expression study . For this analysis , the GSE3494 dataset was downloaded from the Gene Expression Omnibus ( GEO; www . ncbi . nlm . nih . gov/geo ) . The raw data was normalized using the RMA approach in R [97] . The online functional annotation tools GOrilla ( http://cbl-gorilla . cs . technion . ac . il ) and DAVID ( http://david . abcc . ncifcrf . gov ) were used to find Gene Ontology ( GO ) categories and biological processes enriched with DE genes [43] , [44] . For the 412 DE mouse-rat-human orthologs genes analysis , the full mouse-rat-human orthologues gene list ( 9 , 828 genes , Table S5 , S6 ) was used as the background list . Templates were prepared from isolated RMECs from 6 animals of the susceptible congenic control line WF . Cop and 6 animals from the Mcs1a resistant congenic line , as described previously [96] . The restriction enzyme of choice was BglII . The fixed primer was chosen to be located in the predicted promoter of the Nr2f1 gene . The experimental primers were chosen to be located within the Mcs1a critical interval , biased towards regions of evolutionary sequence conservation ( Figure 3d ) . Primer sequences are listed in Table S2 . The relative interaction frequency for each experimental primer in combination with the fixed primer was determined for each sample as the average of 3 replicate measurements divided by the average of a positive control ( BAC-derived ) template run in the same PCR plate [96] . A non-parametric Mann-Whitney test was performed to test for differences between genotypes . Since no genotype differences were detected , data for the Mcs1a profile were taken from all samples . Next , non-parametric Krukal-Wallis tests were performed to test for increased ( P<0 . 05 ) interaction frequency of a primer pair above the background levels . The interaction frequencies of peak fragments were tested against interaction frequencies below two background cut-offs , namely 0 . 05 and 0 . 1 . Genomic DNA was isolated from spleens of inbred WF and Cop rats using phenol and chloroform extractions . A total of 10 ng of genomic DNA was used in each PCR reaction . Primers were designed using Primer3 software and are listed in Table S3 . Successful PCR reactions as verified by agarose gel electrophoresis were diluted 6 times . Of this dilution , 1 µl was used in a BigDye sequencing reaction in a total volume of 15 µl , according to the vendor's ( Applied Biosystems ) specifications with the exception that we used 0 . 7 µl BigDye enzyme mix . Sequencing reactions were cleaned-up using the CleanSeq kit ( Agencourt ) and submitted for sequencing through the UW Biotechnology DNA Sequencing Facility . Reads were visualized using 4Peaks software ( Meckentosj ) and scanned for mutations using BLAT ( UCSC Genome Browser ) . Rat and mouse mammary epithelial cells ( RMECs and MMECs ) were prepared from LN-excised abdominal and adjacent inguinal mammary glands , as described previously [25] , [42] . For phenotyping RMECs and MMECs , staining was done using the low-volume staining method to reduce antibody costs [42] . To stain the single RMECs , antibodies against rat CD49f ( Santa Cruz ) , CD24 , CD29 , CD31 , CD45 , and CD61 ( BD Biosciences ) or peanut lectin ( Sigma ) were used . To stain MMECs , antibodies against mouse CD24 , CD29 , CD31 , CD45 , CD49f , and CD61 ( BD Biosciences ) were used . Live cells were gated based on FSC , SSC , and Hoechst staining for 2n–4n DNA content . Single cells were gated using forward scatter ( FSC ) and side scatter ( SSC ) width . For phenotyping , the stained samples were acquired on a BD LSR II flow cytometer equipped with 4 lasers ( multi-line UV , 405 nm , 488 nm and 633 nm ) . The data were collected as fcs3 files using FACS Diva software and analyzed using FlowJo software ( Treestar Inc ) . Data files obtained from cell samples stained with single antibodies and control unstained cell samples were used for compensation . Data on percentages of cells in various gated populations or mean fluorescence intensities of entire populations were exported and statistically analyzed using Student's t-test . For RMEC sorting , 50–70 million single cells were stained with anti-rat CD24 , CD29 , CD31 , CD45 , CD61 and peanut lectin at a concentration recommended by the vendor's specifications . Sorting was done on a BD FACSAria flow cytometer equipped with 5 lasers ( multi-line UV , 405 nm , 488 nm , 540 nm and 640 nm ) . Cells were collected in 50% FBS . We have previously shown that the clonogenic cells assayed in our transplant system are capable of dividing and differentiating into morphologically and functionally normal mammary parenchyma [41] . For this RMEC transplantation assay , donor and recipients from the susceptible congenic control line WF . Cop and Mcs1a resistant congenic lines W4 and W5 were used . The final dilutions of single RMECs were mixed with an equal volume of a 50% brain homogenate , which was extracted from the donor rats . Aliquots of 40 µl of the mixture containing a known number of cells were then injected into the interscapular fat pad of recipient animals of the same genotype . The frequency of viable clonogenic stem-like cells in a cell suspension was quantitated using a limiting dilution assay as previously described [98] , [99] . In each rat , two sites were used for grafting . For each cell dilution , between 8 and 32 sites were transplanted per genotype . Recipient rats were sacrificed 6 weeks after mammary cell grafting , and the fat pads injection sites were removed , fixed , stained , and examined for the growth of mammary tissue . The percentages of transplant sites with a mammary outgrowth were then plotted against the number of cells injected per site . The data were fit to the transplantation model of Porter et al . [100] and according to the statistical methodology for this model there was no significant difference between any of the 3 groups in the estimated number of cells required to give 50% outgrowth occurrence . For display purposes , the data for transplant sites from congenic lines W4 and W5 were combined and plotted in Figure 4A as a single line for the resistant genotype . A second statistical approach was taken to detect a possible difference in outgrowths at each cell number individually between the susceptible and resistant ( W4 and W5 combined ) genotypes . Therefore , Chi-square tests for independent distributions in a 2×2 contingency matrix were conducted for comparing susceptible to resistant for each cell number . The P-values were adjusted for multiple comparisons . For the matrigel assay , single sorted RMEC suspensions containing 10 , 000 cells were spun 450× g for 5 minutes at 4°C . Supernatant was discarded , the cell pellet was resuspended in 100 uL phenol red free Matrigel ( BD Biosciences ) and immediately plated in 12- or 24-well plates while on ice . Plates were placed at 37°C ( with 5% CO2 ) for 30–60 minutes to allow gelling process . Mammary Epithelial Cell Medium ( PromoCell ) with 5% bovine calf serum and antibiotics was then added to the wells containing the sorted cells in matrigel . Fresh media was provided on days 2 and 5 . On day 10 , Matrigel containing RMEC colonies was fixed with 2% paraformaldehyde in phosphate buffer pH 7 . 4 ( PB ) for 30 minutes at 37°C followed by staining with 0 . 5% methylene blue in PB for 5 minutes at 37°C . Colonies were counted using a microscope . Count data were normalized to the average colony count of the susceptible congenic control line run in the same experiment and was statistically analyzed using Student's t-test . MCF10A and MCF7 cell lines were obtained ( ATCC ) and cultivated according to the manufacturer's recommendations . Transient transfections were done in 24-well plates using the Lipofectamin2000 reagent ( Invitrogen ) . Short interfering RNAs ( NR2F1 SMARTpool and Non-targeting pool ) were obtained ( Dharmacon ) and used at a final concentration of 125 nM in the transfection media . The transfection media was washed off the cells after 6 hours . The cells were cultured for 40 additional hours before harvesting for FACS and expression analysis . The Oncomine database ( www . oncomine . org ) was queried using the following filters: Gene: NR2F1 , Cancer Type: Breast carcinoma , Sample Type: Clinical Specimen . Only studies with 120+ samples were considered . If available , the median levels of NR2F1 for the clinical parameters , histological grade , ER-status , PR-status , HER2-status , TN-status were entered in Excel . To be able to compare clinical parameters across studies , the median levels for each clinical parameter in a study were normalized by the median level for the entire study . The average of the normalized median values was plotted . For statistical analysis , the non-parametric Kruskal-Wallis test was used . To ask if lower NR2F1 levels are associated with histological grade 3 in both ER-positive and ER-negative breast cancers or HER2-positive and HER2-negative breast cancers , the RMA normalized NR2F1 probe set levels ( probe ID 209505_at ) from the GSE3494 and GSE5460 studies were used , respectively . To match the Y-axis scale in panel A , the RMA normalized values were expressed relative to the median level for the entire study . The values were statistically compared between groups using the non-parametric Mann-Whitney U test .
|
Most non-Mendelian disease variants identified through genome-wide association studies are low-penetrance , common in the population and located in non-protein coding genomic loci . It is currently unknown how these loci modulate disease risk . Insights in their mechanisms could lead to the development of novel prevention or early intervention strategies . We used comparative genetics to model such loci in a rat model for breast cancer susceptibility . For the Mcs1a locus presented in this paper , we describe its non-protein coding localization and the mechanism through which it affects mammary carcinoma susceptibility , involving transcriptional regulation of the orphan nuclear receptor gene Nr2f1/Coup-tf1 and mammary epithelial cell proliferation/differentiation . In addition , we show that low NR2F1 transcript levels are associated with upregulation of cell cycle-related genes and high histological grade ( grade 3 , poorly differentiated , highly proliferative ) in human breast cancers , including triple-negative therapy-resistant breast cancers . Our findings highlight the orphan nuclear receptor NR2F1 as a novel target for breast cancer prevention and/or intervention strategies . Since COUP-TFs ( including NR2F1 ) are nuclear hormone receptors , whose crystal structure suggests these are ligand controlled , identification of the ligand for NR2FI could provide a potential breast cancer therapeutic .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"animal",
"models",
"medicine",
"oncology",
"developmental",
"biology",
"genetic",
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2013
|
The Gene Desert Mammary Carcinoma Susceptibility Locus Mcs1a Regulates Nr2f1 Modifying Mammary Epithelial Cell Differentiation and Proliferation
|
The detection of Trypanosoma cruzi genetic material in clinical samples is considered an important diagnostic tool for Chagas disease . We have previously demonstrated that PCR using clot samples yields greater sensitivity than either buffy coat or whole blood samples . However , phenol-chloroform DNA extraction from clot samples is difficult and toxic . The objective of the present study was to improve and develop a more sensitive method to recover parasite DNA from clot samples for the diagnosis of Chagas disease . A total of 265 match pair samples of whole blood–guanidine ( GEB ) and clot samples were analyzed; 150 were from Chagas seropositive subjects . DNA was extracted from both whole blood-guanidine samples , using a previously standardized methodology , and from clot samples , using a newly developed methodology based on a combination of the FastPrep technique and the standard method for GEB extraction . A qPCR targeting the nuclear satellite sequences was used to compare the sample source and the extraction method . Of the 150 samples from Chagas positive individuals by serology , 47 samples tested positive by qPCR with DNA extracted by both GEB and clot , but an additional 13 samples tested positive only in DNA extracted from clot . No serology-negative samples resulted positive when tested by qPCR . The new methodology for DNA extraction from clot samples improves the molecular diagnosis of Chagas disease .
Chagas disease , caused by the protozoan parasite Trypanosoma cruzi , is endemic in many parts of the Americas [1 , 2] , where 6 to 7 million people are infected [3] . In the acute phase of the infection , serology by IgG may still be negative but positive for parasite by microscopic examination or by culture in specialized medium [1 , 4] . Infected individuals then enter a chronic phase where parasites are rarely seen in the blood and diagnosis relies on the use of serological tests . About 20–30% of infected individuals will develop cardiomyopathy during the subsequent chronic phase , the most important consequence of Chagas disease [1 , 2 , 5] . The Polymerase Chain Reaction ( PCR ) has become an important and sensitive diagnostic tool . The test is highly sensitive for diagnosis of acute and congenital Chagas disease [1] . Serial monitoring by quantitative PCR provides the earliest indication of Chagas disease reactivation [6] . PCR sensitivity during the chronic phase is highly variable depending on several factors such as volume and processing of specimens , characteristics of the analyzed population , primers and PCR methods . Negative PCR results do not prove that infection is absent . Systematic monitoring by means of PCR of serial blood specimens has been suggested to improve PCR diagnosis [1 , 7 , 8] . Conventional PCR has several drawbacks , especially cross-contamination . For this reason , quantitative PCR ( qPCR ) has now been more widely used and accepted . For Chagas disease , the qPCR is now increasingly used as a research tool for monitoring the disease [4] and also as a primary outcome in clinical trials for new drug candidates [9–11] . To achieve a high sensitivity of PCR analysis , efficient nucleic acid purification is required . Whole blood samples are primarily the main source of DNA for diagnosis . It has been recommended that when blood samples are drawn they should be immediately mixed with one volume of 6M guanidine hydrochloride– 0 . 2 M EDTA , pH 8 . 00 ( GE ) to stabilize DNA for shipping [12 , 13] . However , shipping and handling of guanidine is troublesome and the logistics for specimen collection with GE are complex . Recently we have demonstrated , by conventional PCR , that clot samples are superior as a source of DNA for Chagas diagnosis [14] . However , DNA extraction was performed using the phenol–chloroform method , which has several disadvantages: the extraction process is complex , the clot needs to be washed , the reagents are toxic , and the quantification cycle ( Cq ) values of qPCR assays are variable when the assay is repeated . The objective of the present study was to improve and develop a more sensitive method to recover parasite DNA from clot samples for the diagnosis of Chagas disease . Clot was initially disrupted using the FastPrep technique and then DNA was extracted using the recommended methodology of the Roche extraction kit . DNA extracted from clot proved to have a higher sensitivity and presented a lower Cq value compared to DNA extracted from whole blood-GE ( GEB ) samples .
A total of 265 pairs of samples ( one GEB and a clot sample ) were obtained from archived specimens in our biorepository . The samples were obtained from two sample sets: Archived samples from women presenting for delivery at the Hospital Municipal Camiri in Camiri , Bolivia ( n = 100 ) [15]; and samples collected at the Hospital Municipal San Juan de Dios , Santa Cruz , Bolivia ( n = 165 ) [16 , 17] . The study protocol at the Hospital Municipal Camiri was approved by Institutional Review Board of Johns Hopkins Bloomberg School of Public Health , Asociacion Benefica PRISMA ( Lima , Peru ) , and Universidad Catolica Boliviana ( Santa Cruz , Bolivia ) . The study protocol at the Hospital Municipal San Juan de Dios was approved by the Institutional Review Board of Johns Hopkins Bloomberg School of Public Health ( Baltimore , MD , USA ) , Universidad Catolica Boliviana ( Santa Cruz , Bolivia ) , and University of California San Francisco School of Medicine ( San Francisco , CA , USA ) . Peripheral blood from individuals suspected of Chagas diseases was collected in tubes with EDTA and tubes without additives . The blood obtained without additives was allowed to sit for at least 30 min to let the clot to form ( no more than 60 min ) and then centrifuged at 1100 x g for 20 min [18] . After transferring the serum , the clot was frozen at –80°C and shipped with dry ice to the Infectious Research Laboratory at the Universidad Peruana Cayetano Heredia in Lima , Perú . Upon arrival , samples were immediately stored at -80°C until use . The blood obtained with EDTA was immediately mixed with one volume of guanidine hydrochloride 6M/EDTA 0 . 2M , pH = 8 . 0 ( GE ) , shipped and stored at 4°C until use [19] . Blood samples mixed with Guanidine/EDTA ( GEB ) were processed using the High Pure PCR Template Preparation kit ( Roche Diagnostics GmbH , Mannheim , Germany ) as previously described [12 , 20] . Extraction was performed using 300 μl of GEB and 5 μl ( 40 ρg/μl ) of an internal amplification control ( IAC ) , were added to each sample at the beginning of the purification process to ensure the quality of the DNA purification process and the absence of PCR inhibitors . Clot samples were initially homogenized using the FastPrep machine followed by extraction using the High Pure PCR Template Preparation kit . Briefly , 300 μl of clot , 300 μl of Guanidine/EDTA ( GE ) , 40 μl of Proteinase K ( 20 mg/ml ) and 5 μl of IAC ( 40 ρg/μl ) were place in a Lysing Matrix E tube ( MP Biomedicals , Santa Ana , CA ) . The mixture was FastPrep processed at 5 , 5 m/s for 30 sec and then centrifuged at 14000 x g for 2 min at room temperature . A total of 450 μl of supernatant was transferred to a new tube and 150 μl of binding buffer from the High Pure PCR template preparation kit ( Roche Diagnostics ) was added . The mixture was incubated at 70 °C for 10 min and the extraction process was performed using High Pure PCR Template Preparation kit ( Roche Diagnostics ) . Initially Lysing Matrix C , F , H and J were tested using spiked blood clot , only Lysing Matrix E and Lysing Matrix H gave comparable results as measured by the Cq values . However , Lysing Matrix E was chosen because the results were more reproducible . The standard curves for parasitic load quantification were built using an isolate ( BIO-6398 ) from Santa Cruz , Bolivia; identified as TcDTU V following the previously described typing methodology [21] . To build the curve 9 ml of whole blood extracted with EDTA from seronegative subjects was spiked with 1 ml of 106 cultured epimastigote-stage parasites/ml suspended in PBS [22] . After properly mixing the sample the DNA was extracted as described for GEB clinical samples . Similarly , to build a standard curve for clot samples , 9 ml of blood extracted without additives was spiked with 1 ml of 106 parasites/ml suspended in PBS immediately after the blood was drawn and thoroughly mixed by inverting the tube for at least 20 times . The spiked sample was then allowed to sit for at least 30 min to let the clot to form ( no more than 60 min ) . Clot was then recovered after centrifugation at 1100 x g for 20 min . DNA from clot was extracted as described for clinical clot samples . To assure adequate DNA purification , a recombinant plasmid ( pZErO-2 ) containing the aquaporin gene of Arabidopsis thaliana as a heterologous extrinsic internal amplification control ( IAC ) was used [12 , 20 , 23] . The recombinant plasmid was provided by Dr . Alejandro Schijman’s laboratory ( INGEBI-CONICET , Argentina ) . The diagnosis of Chagas disease in human samples was based on serological assays . ELISA was performed using Chagatek Wiener Recombinante v3 . 0 ELISA ( Wiener laboratories , Rosario–Argentina ) . Indirect hemagglutination assay ( IHA ) was performed using the Chagas Polychaco kit ( Lemos Laboratories , Buenos Aires–Argentina ) . Western blot analysis was performed using the TESA antigen harvested from T . cruzi Y strain growth in LLC-MK2 cells as previously described [24] . A duplex qPCR was performed targeting the satellite sequence of the nuclear genome of T . cruzi and the sequence of an internal amplification control [25] . The qPCR reaction was carried out as previously described using 5 μl of re-suspended DNA in a final volumne of 20 μl [12] . The Mastermix consisted of 1X FastStart Essential DNA Probes Master ( Roche Diagnostics GmbH Corp . , Mannheim , Germany ) , 0 . 75 μM of each primer Cruzi1 and Cruzi2 , 0 . 1 μM of each primer IACFor and IACRev , and 0 . 5 μM of each Cruzi3 probe and IACtq probe . The cycling conditions were a first step of 10 min at 95 °C followed by 40 cycles at 95 °C for 15 sec and 58 °C for 1 minute . The qPCR for a sample was considered as valid when the Cq corresponding to the amplification of its IAC was lower than the 75th percentile plus 1 . 5 the interquartile range of the median of each extraction batch ( CqIAC<75th percentile + 1 . 5*IQR ) . The Cq values for the positive clinical sample were normalized respect to the efficiency of the DNA extraction procedure measured by the amplification of the IAC . The following formula was used: Cqnor = Cq IACpos / CqIACneg , where Cqnor is the normalized Cq value for a given positive sample , Cq IACpos is the mean Cq IAC value for the positive controls , and the CqIACneg is the mean Cq IAC value for the negative controls included in the plate . Statistical analysis was performed using Stata14 ( Stata Corp , College Station , TX ) . Two-way tabulations of frequencies and means were performed . Results of the initial testing of the different lysing matrix were compared using the Kruskal Wallis test . Confidence intervals and McNemar’s test were used to compare the frequency of PCR results . Mean Cq values were compare using McNemar’s test . Agreement between qPCR results from clot and GEB samples was assessed using kappa statistic .
No differences on the Cq values were observed when qPCR for T . cruzi was performed on DNA extracted using lysing matrix C , H or J from clot samples spiked with 5 x 106 parasites ( P = 0 . 13 , S1A Fig ) , however the use of lysing matrix C and J were discarded because of destruction of the lysing matrix components during FastPrep process . The Cq values on the qPCR for T . cruzi were lower on DNA extracted using lysing matrix E than F from clot samples spiked with 1 x 106 parasites/ml ( P<0 . 001 , S1B Fig ) . Lysing matrix E was used in the present study because of the Cq values on the qPCR seemed to be less disperse compared to the Cq values obtained with matrix H . Although , no differences on the Cq values were observed when clot samples , from individuals known to be positive by qPCR in GEB ( n = 10 , P = 0 . 5340 , S1C Fig ) or when Cq values for the IAC were compared on samples extracted with lysing matrix E or H ( n = 28; P = 0 . 5310 , S1D Fig ) . Because the normalization value in all plates was close to 1 . 0 ( mean correction factor for GEB = 1 . 00136 and mean correction factor for CLOT = 0 . 99737 ) , the original Cq values were used in the final analysis . Out of the 265 samples analyzed , 150 samples were from Chagas seropositive individuals . The mean DNA concentration was higher on clot samples than in GEB samples ( S1 Table ) . Among the Chagas seropositive samples , the qPCR tested positive in 31 . 3% ( 47/150 ) [95% CI: 24 . 02–39 . 41] of GEB samples ( Table 1 ) and in 40 . 0% ( 60/150 ) [95% CI: 32 . 09–48 . 31] of clot samples ( Table 2 ) extracted using the new technique . When the proportion of qPCR positive samples using , DNA extracted from clot samples were compared to the proportion of positives using DNA extracted from GEB using the McNemar’s test ( Table 3 ) , the difference in the proportions was statistically significant ( P = 0 . 0002 ) . Similarly , when the mean Cq values obtained by the qPCR were compared , the mean Cq value was significantly lower , when qPCR was performed using DNA extracted from clot samples , than using DNA extracted from GEB samples 27 . 32 [CI: 26 . 69–27 . 95] and 29 . 02 [CI: 28 . 36–29 . 67] , respectively ( P < 0 . 0003 ) ( Fig 1 ) . The agreement between the qPCR using clot DNA and GEB DNA was 95 . 9% ( Kappa = 0 . 8483 , CI:0 . 769–0 . 928 ) .
Chagas disease is still considered an important health problem globally . The current diagnosis is based on antibody detection by several methods , however the presence of antibody may not necessarily represent current infection . Trypanosoma cruzi DNA detection has been increasingly used not only as a diagnostic technique but also as a surrogate for treatment failure [11 , 26] We have previously shown that for PCR based diagnosis of Chagas , DNA extraction of clot samples shows better sensitivity than either whole blot or buffy coat [14] . However , phenol-chloroform based DNA extraction has several drawbacks , including toxicity and carcinogenicity of phenol/chloroform . Because of the complicated DNA extraction procedure , samples are more prone to cross-contamination . Here we have developed and improved technique for DNA extraction from clot samples . The technique is based on the initial disruption of clot using the fast prep machine followed by the internationally standardized DNA extraction protocol for the molecular diagnosis of Chagas disease for GEB samples . The qPCR using DNA extracted from GEB samples has been standardized to be used as a universal diagnostic technique [12 , 14 , 20 , 27 , 28] . Unfortunately , shipping and handling of Guanidine and GEB samples has become troublesome because of the International Air Transport Association ( IATA ) regulations . Moreover , PCR performed using DNA extracted from clot shows higher sensitivity than PCR using DNA extracted from whole blood [14] . Although , two samples tested positive in qPCR from clot ( Cq 32 . 74 and 35 . 89 ) and negative in the qPCR ( Cq >40 ) using DNA from GEB , a one to one dilution of the whole blood with the GE only explains a Cq difference of 1 . 66 , considering an efficiency of 100% . It should be noted that serology in chronic cases of Chagas infection is more sensitive than qPCR . In contrast , in acute infection with congenital infection qPCR is more sensitive than serology [1] . Thus , serology might not be an adequate gold standard , comparing sensitivity of the qPCR to serology might not be the best approach . Although the use of GE for DNA preservation and sample shipping is avoided; the technique described here still depends on the use of GE for DNA extraction . Guanidine is a chaotropic agent that helps to disrupt the clot and the cell membranes , and solubilizes the DNA . Other home-made and commercial buffers to disrupt the clot have been tested by us with results that were not comparable to the ones obtained using GE . By using DNA extracted from clot , the sensitivity of the qPCR is increased as reflected by the low Cq values obtained in comparison to the Cq values obtained using DNA extracted from GEB . Several DNA extraction methods from clot samples have been reported previously , most of them oriented to the recovery of DNA from human ( host ) origin[29–32] or from intracellular microorganisms [33] . T . cruzi , if present in the blood , is not intracellularly located . Free DNA from parasite origin seems to be circulating in the bloodstream , as DNA has been successfully recovered from serum samples by PCR detection [34] , moreover injected T . cruzi kDNA in mice circulates in blood for at least 48 hrs [35] . Clot probably traps the parasites and the DNA present in the blood , thus concentrating them . In addition , washing the clot with water or with erythrocyte lysing buffers as previously reported [14 , 33] most likely lyses the T . cruzi parasites releasing the genetic material , which will also be washed out along with hemoglobin . Thus , washing the clot might lead to decrease the sensitivity of any technique targeting the T . cruzi DNA , such as the qPCR . Our protocol avoided any washing steps of the clot previous to the DNA purification to ensure that most of the DNA from the parasites could be recovered from the sample . Additionally , the use of individually packed FastPrep Lysing matrix ensures that cross contamination is almost completely eliminated during sample extraction . Improving the sensitivity of the techniques that demonstrate the presence of the parasite during Chagas disease constitutes an important advancement in the diagnosis of Chagas disease . Contrary to the detection of antibodies , which is an indicator of either active chronic infection or merely exposure to the parasite , detection of parasite DNA is generally an indication of active infection , as detection of T . cruzi genetic material is consider equivalent to parasite detection[36] . Improvement of qPCR sensitivity is especially important for identifying active infection in those individuals with very low blood parasite loads , such as those with chronic or indeterminate phase where it is used for assaying response to treatment . However , our results shown a low sensitivity of the qPCR compared to serology suggesting that the qPCR technique might still need further improvement . In summary , we describe here an improved methodology for the detection of T . cruzi DNA using clot samples which might improve early diagnosis and therefore will improve treatment and prognosis of infected individuals , especially of those with low blood loads of parasites .
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Detection of nucleic acid has become an important tool for the diagnosis of Chagas disease . Whole blood samples are usually the source of DNA and qPCR the preferred technique to demonstrate the presence of T . cruzi DNA . Although DNA extracted from clot samples has shown higher sensitivity than from whole blood , DNA extraction is performed using phenol-chloroform , which has biohazard issues . We theorize that a clot traps parasites , making it a better source of DNA for Chagas diagnosis using PCR . The present study describes a new DNA extraction methodology from clot samples which avoids the use of phenol-chloroform . The new methodology was compared to the internationally standardized diagnostic method , which is based on extraction of DNA from whole blood preserved with guanidine EDTA and a commercial kit .
|
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2019
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Improved DNA extraction technique from clot for the diagnosis of Chagas disease
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Nuclear factor of activated T cells ( NFAT ) transcription factors are required for induction of T-cell cytokine production and effector function . Although it is known that activation via the T-cell antigen receptor ( TCR ) results in 2 critical steps , calcineurin-mediated NFAT1 dephosphorylation and NFAT2 up-regulation , the molecular mechanisms underlying each are poorly understood . Here we find that T cell p38 , which is activated by an alternative pathway independent of the mitogen-activated protein ( MAP ) kinase cascade and with different substrate specificities , directly controls these events . First , alternatively ( but not classically ) activated p38 was required to induce the expression of the AP-1 component c-Fos , which was necessary for NFAT2 expression and cytokine production . Second , alternatively ( but not classically ) activated p38 phosphorylated NFAT1 on a heretofore unidentified site , S79 , and in its absence NFAT1 was unable to interact with calcineurin or migrate to the nucleus . These results demonstrate that the acquisition of unique specificities by TCR-activated p38 orchestrates NFAT-dependent T-cell functions .
Many activation events downstream of the T-cell antigen receptor ( TCR ) are up-regulated by the nuclear factor of activated T cells ( NFAT ) transcription factor family [1] . There are 5 NFAT family members: NFAT1 ( also known as NFATc2 or NFATp ) , NFAT2 ( NFATc1 or NFATc ) , NFAT3 ( NFATc4 ) , NFAT4 ( NFATc3 or NFATx ) , and NFAT5 ( tonicity-responsive enhancer binding protein [TonEBP] ) [2] . T cells express NFAT1 , NFAT2 , and NFAT4 , which are activated by TCR-mediated stimulation to induce proliferation and cytokine secretion [2] . Whereas expression of NFAT2 is inducible , NFAT1 and NFAT4 are constitutively expressed [2] . In resting cells , NFATs are highly phosphorylated by a number of serine/threonine kinases ( e . g . , casein kinase 1 , glycogen synthase kinase 3 , Jun N-terminal kinases , and p38 ) , with as many as 21 phosphorylation sites having been identified in NFAT1 [3] . The phosphorylated species are retained in the cytosol ( and thus inactive ) by binding cytoplasmic proteins such as 14-3-3 [4] . Stimuli that increase cytosolic calcium activate the Ca2+-dependent serine-threonine phosphatase calcineurin , which dephosphorylates NFATs and allows their migration to the nucleus , where they induce gene transcription [2] . In T cells , stimulation via the TCR also induces NFAT2 expression [5] . NFATs often up-regulate gene transcription in cooperation with other transcription factors , most commonly AP-1 , the combination being required for the expression of interleukin ( IL ) -2 [6] , IL-3 [7] , granulocyte-macrophage colony-stimulating factor ( GM-CSF ) [8] , IL-4 [9] , IL-5 [10] , IL-13 [6] , interferon gamma ( IFN-γ ) [11] , Fas ligand ( FasL ) [12] , CD25 [13] , Cox2 [14] , IL-8 , and migration inhibitory factor 1 alpha ( MIF-1α ) [11] . Other reported partners include Sp1 and activating transcription factor 2 ( ATF2 ) /Jun to induce expression of tumor necrosis factor alpha ( TNF-α ) [15] and myocyte enhancer factor-2 ( MEF-2 ) to induce Nurr77 [16] . p38 , a mitogen-activated protein kinase ( MAPK ) , has important positive regulatory roles in a variety of cellular functions , including proliferation , proinflammatory cytokine production , and apoptosis [17] . In the classical MAPK cascade , stimuli such as environmental stress or proinflammatory cytokines activate a kinase cascade that results in the phosphorylation of p38 on Thr-180 and Tyr-182 ( T180/Y182 ) in the activation loop [17] . In addition to this enzymatic cascade , T cells possess a unique mode of p38 activation that is downstream of the TCR . Stimulation via the TCR activates the protein tyrosine kinase zeta-associated protein ( ZAP-70 ) , which phosphorylates p38 on Tyr-323 ( p38 pY323 ) , leading to autophosphorylation on Thr-180 ( the p38 alternative pathway ) [18] . The adaptor protein hDlg1 has been implicated in coupling TCR signaling to alternative p38 activation , as its small interfering RNA ( siRNA ) -mediated knockdown reduces p38 activity [19] . The alternative p38 pathway is essential for the expression of several key T-cell effector functions , best shown in mice lacking the alternative pathway because of knock-in of p38α and p38β in which Tyr-323 was replaced with Phe ( double knock-in [DKI] mice ) [20] . TCR-activated cells from such animals produce low levels of proinflammatory cytokines such as TNF-α , IFN-γ , and IL-17A compared to wild-type ( WT ) cells . Importantly , the mice are resistant to the autoimmune disease models collagen-induced arthritis and experimental autoimmune encephalomyelitis ( EAE ) [20] , have less aggressive Kras-induced inflammatory pancreatic cancer [21] , and fail to mount an effective IL-17A-dependent immune response to Citrobacter rodentium infection [22] . In T cells , p38 activated via the MAPK cascade phosphorylates NFAT1 and NFAT2 on serine/threonine residues , resulting in cytosolic retention and inhibition [23] . It is well documented , however , that p38 is also a positive regulator of TCR-induced and NFAT-dependent cytokine production [24] . For instance , p38 is upstream of the up-regulation of NFAT2 upon TCR stimulation [5] and activates NFAT1 via phosphorylation of S54 in its transactivation domain [3 , 19] . In this study , we have addressed the molecular mechanisms by which p38 regulates NFAT1 and NFAT2 . We found that the T-cell-specific p38 alternative pathway controls NFAT activation and dependent cytokine secretion by 2 mechanisms: ( 1 ) up-regulating c-Fos , which is required for induction of NFAT2 expression , and ( 2 ) phosphorylating NFAT1 S79 to allow its interaction with calcineurin and subsequent nuclear translocation .
Activation of p38 via the alternative pathway ( but not the classical MAPK cascade ) is required for up-regulation of NFAT2 , its downstream target IRF4 , and proinflammatory cytokine production [22] . In T cells , NFAT2 expression has been reported to be regulated by NFAT1 [25] and/or NFAT2 itself in association with the AP-1 transcription factor [26] . AP-1 , a heterodimer of c-Fos and c-Jun , plays an important role along with NFATs in the induction of a number of T-cell products [26] . Because c-Fos is expressed only at low levels in resting T cells [27] , we asked if its induction upon activation is dependent on p38 . Little c-Fos was detected in primary resting T cells , but it was markedly induced after stimulation with anti-CD3/CD28 ( Fig 1A , left lanes ) . The induction was blocked by the p38 catalytic inhibitor SB203580 , suggesting a role for this kinase . Notably , activation of T cells with phorbol myristate acetate ( PMA ) plus ionomycin , which activates p38 via the classic MAPK cascade , was relatively ineffective at inducing c-Fos ( Fig 1A , right lanes ) . Unlike anti-CD3/CD28 stimulation , PMA/ionomycin poorly induced c-Fos expression , which peaked at 3 hours but was not sustained at the later time points ( Fig 1B ) . This was not due to the poor activation by PMA/ionomycin , because the combination induced robust IL-2 production ( Fig 1A , right panel ) . To directly test if alternatively activated p38 was required , c-Fos expression was measured in primary T cells from WT or p38α and β DKI mice that lack the alternative pathway . In contrast to WT T cells , there was little if any c-Fos upregulation in T cells from DKI mice ( Fig 1C ) . There was no difference in c-Jun expression between the WT and DKI T cells ( S1 Fig ) . Therefore , alternatively activated p38 is necessary for c-Fos induction in cells activated via the TCR . Given that up-regulation of both c-Fos and NFAT2 requires alternative p38 activation and that c-Fos is an immediate early gene , whereas NFAT2 induction is late [22] , we asked if c-Fos regulates NFAT2 expression . Chromatin immunoprecipitation ( ChIP ) was performed on extracts from primary T cells using anti-c-Fos . In cells stimulated with anti-CD3/CD28 , but not with PMA/ionomycin , there was clearly inducible binding of c-Fos to the NFAT2 promoter ( Fig 1D ) . To determine if this binding was functionally important , the AP-1 binding site GTCAG in the nfat2 promoter was mutated to AAAAA , inserted into a luciferase reporter plasmid , and transfected into the human Jurkat T leukemia cell line . Anti-CD3/CD28 induced luciferase production from a vector containing the WT nfat2 promoter but had no effect when the AP-1 binding site was mutated ( Fig 1E ) . To determine if p38-mediated c-Fos up-regulation was the only limiting factor in NFAT2 induction in DKI T cells , we infected primary mouse WT or DKI T cells with a retrovirus encoding c-fos . The expression vector included an internal ribosome entry site ( IRES ) upstream of a sequence encoding enhanced green fluorescent protein ( eGFP ) so that transfection efficiency could be monitored . Transfection efficiency was comparable for the empty vector ( EV ) control and the vector encoding c-fos ( S2A Fig ) . Real-time quantitative reverse transcription PCR ( qRT-PCR ) revealed that anti-TCR-mediated activation of WT T cells transduced with EV up-regulated nfat2 mRNA , whereas there was no response in EV-transduced DKI T cells , consistent with our previous report ( Fig 1F ) [22] . Importantly , DKI T cells in which c-fos was introduced up-regulated nfat2 in response to TCR signaling . Interestingly , the baseline nfat2 levels in cfos-transduced cells was elevated , probably reflecting the fact that the cells had recently been activated to facilitate retroviral transduction . Similar experiments were performed with PMA/ionomycin-stimulated WT cells , in which NFAT2 is poorly induced [22] . Enforced expression of c-fos allowed T cells to up-regulate nfat2 when stimulated with PMA/ionomycin ( Fig 1G and S2B Fig ) . Therefore , p38 activated via the TCR uniquely up-regulates c-Fos expression , which is essential for downstream induction of NFAT2 . Unlike NFAT2 , NFAT1 is constitutively expressed , and activation-induced increases in cytosolic Ca2+ cause its dephosphorylation by calcineurin and nuclear migration [2] . Because NFAT1 activity was increased in T cells in which hDlg1 was overexpressed and decreased when p38 was inhibited [19] , we directly asked if p38 is required at this early step . Purified T cells from WT or DKI mice were cultured in medium alone or stimulated with anti-CD3/CD28 for 3 hours , and the cytoplasmic versus nuclear distribution of NFAT1 was determined . As expected , in resting WT T cells , NFAT1 was entirely cytoplasmic , but after anti-TCR-mediated activation , it was found in the nucleus ( Fig 2A ) . In contrast , in T cells from DKI mice that lack the alternative p38 activation pathway NFAT1 did not translocate to the nuclear fraction after activation . This was specific to TCR-mediated signaling , because NFAT1 translocated to the nucleus in both WT and DKI T cells stimulated with PMA plus ionomycin . This was confirmed by confocal microscopy . Whereas in resting T cells of both genotypes NFAT1 was largely excluded from the nucleus , after anti-CD3/CD28 stimulation NFAT1 translocated to the nucleus only in WT T cells , remaining in the cytosol of DKI T cells ( Fig 2B and 2C ) . Therefore , the p38 alternative activation pathway is required for NFAT1 activation after TCR-mediated stimulation . NFAT1 is a target of classically activated p38 , but these phosphorylation events are thought to be responsible for cytosolic retention [23] . The finding that p38 activity was actually required for NFAT1 translocation downstream of the TCR , therefore , raised the possibility that the classic and alternative p38 pathways lead to differential NFAT1 phosphorylation . To determine if NFAT1 is a substrate of alternatively activated p38 and , if so , whether the sites of phosphorylation differ from those of MKK-activated p38 , an in vitro kinase assay was performed using recombinant p38 that had been activated by ZAP-70 ( alternative pathway ) or MKK6 ( MAPK cascade ) . As substrate we used an NFAT1 fragment containing the first 350 amino acids ( tNFAT1 ) , in which the bulk of the known phosphorylation sites have been identified [3] , or ATF2 as a positive control . Both ZAP-70- and MKK6-phosphorylated p38 phosphorylated ATF2 , the former actually having more activity ( Fig 3A , left panel ) . They also phosphorylated tNFAT1 , with MKK6-activated p38 presumably phosphorylating more sites as manifested by slower migration in SDS-PAGE ( Fig 3A , right panel ) . This was confirmed by mass spectrometry . Whereas 5 phosphorylated serine residues ( 54 , 110 , 136 , 150 , and 223 , 4 of which have been reported in a resting T-cell clone [3] ) were identified in tNFAT1 incubated with classically activated p38 , only one phosphorylation site was detected in tNFAT1 incubated with ZAP-70 activated p38 , serine 79 ( S79 ) ( S3 Fig; S1 Table ) . S79 is in the transactivation domain of NFAT1 and to our knowledge has not been previously identified as a phospho-acceptor site . We generated a rabbit antiserum against an NFAT1 peptide containing phosphorylated Ser-79 ( pNFAT1S79 ) . The affinity-purified antibodies were highly selective for the phosphorylated species , as measured by ELISA ( S4 Fig ) . In vitro kinase assays with recombinant p38 confirmed that ZAP-70-activated ( but not MKK6-activated ) p38 phosphorylated NFAT1 on Ser-79 ( Fig 3B ) . The anti-pNFAT1S79 antibody was used in confocal imaging of T cells that were stimulated with either anti-CD3/CD28 or PMA/ionomycin . NFAT1 ( green ) was non-nuclear ( blue ) in unstimulated cells , and no pNFAT1S79 ( red ) was detected . NFAT1 migrated to the nucleus upon both anti-CD3/CD28 or PMA/ionomycin stimulation , but Ser-79 phosphorylation was only detected in the nuclei of T cells stimulated with anti-CD3/CD28 ( Fig 3C ) . Analysis of cells from multiple experiments found that approximately 60% of the T cells activated via the TCR had nuclear NFAT1 , all of which were positive for pNFAT1S79 ( Fig 3D , left panel ) . In contrast , after PMA/ionomycin stimulation , NFAT1 was detected in the nucleus of approximately 80% of the T cells , but no pNFAT1S79 was found ( Fig 3D , right panel ) . Thus , TCR signaling ( but not the MAPK cascade ) results in phosphorylation of NFAT1 on Ser-79 . To determine the biological relevance of NFAT1S79 phosphorylation , NFAT1 was targeted using CRISPR-Cas9 technology in Jurkat cells ( N1KO cells ) . WT Jurkat and 2 independent N1KO clones ( #9 and #18 ) had similar levels of TCR-β and CD3 expression ( S5 Fig ) . In contrast to WT Jurkat cells , the N1KO cells stimulated with anti-CD3/CD28 or PMA/ionomycin failed to produce IL-2 or TNF-α ( Fig 4A ) . Interestingly , despite a report that NFAT1 is upstream of NFAT2 in murine primary T cells stimulated with PMA/ionomycin [25] , in the absence of NFAT1 , anti-TCR activation of the N1KO clones induced NFAT2 expression to the same level as in WT Jurkat ( Fig 4B ) . Therefore , at least in Jurkat cells , NFAT1 is not required for NFAT2 expression . As previously shown , stimulation with PMA/ionomycin induced little if any NFAT2 ( Fig 4B ) [22] . The N1KO cells ( clone #9 ) were retrovirally transduced with vectors encoding HA-NFAT1 or NFAT1S79A , and 2 independent clones of each that had similar levels of HA-NFAT1 were selected for study ( Fig 4C ) . Importantly , introduction of NFAT1 but not NFATS79A restored activation-induced IL-2 secretion ( Fig 4D ) . Therefore , T-cell p38-mediated phosphorylation of NFAT1S79 is essential for NFAT1 nuclear translocation and downstream gene transcription in response to TCR signaling . To investigate the functional significance of pNFAT1S79 , NFAT1-deficient Jurkat cells were infected with retrovirus encoding HA-NFAT1 or HA-NFAT1S79A . The cells were stimulated with anti-CD3/CD28 or PMA/ionomycin , and the cellular localization of the HA-tagged proteins was determined by confocal microscopy . Infection efficiency was similar between the HA-NFAT1- and HA-NFAT1S79A-expressing cells ( S6A Fig ) . Whereas the HA-tagged proteins were located in the cytosol of unstimulated cells , activation with anti-CD3/CD28 induced nuclear translocation of HA-NFAT1 , but not of HA-NFAT1S79A ( S6B Fig ) . This was confirmed by immunoblotting for HA in the cytoplasmic and nuclear fractions of anti-CD3/CD28-stimulated cells ( S6C Fig ) . Anti-CD3/CD28 caused the nuclear translocation of NFAT1 , but not of NFAT1S79A . In response to PMA/ionomycin , there was only a modest impairment of NFAT1S79A , but to a much less extent than NFAT1 . Similar results were obtained with primary murine T cells , which were retrovirally transduced with HA-NFAT1 or HA-NFAT1S79A ( S6D Fig ) . As observed in Jurkat cells , HA-NFAT1 but not HA-NFAT1S79A migrated to the nucleus upon stimulation with anti-CD3/CD28 ( Fig 5A and 5B ) . Given that calcineurin-mediated dephosphorylation is required for NFAT1 nuclear translocation , we characterized the interaction between these proteins . Stable Jurkat cell lines expressing HA-NFAT1 or HA-NFAT1S79A were stimulated with anti-CD3/CD28 , the lysates were immunoprecipitated with anti-HA , and the immunoprecipitated material was immunoblotted for the catalytic subunit , calcineurin A . Although total cell lysates had similar amounts of HA-NFAT1 and calcineurin ( S6E Fig ) , there was much more calcineurin coimmunoprecipitated with HA-NFAT1 than HA-NFAT1S79A ( Fig 5C ) . One way to assess molecular interactions in situ is a proximity ligation assay ( PLA ) , in which protein-protein interactions ( <40 nm ) can be visualized as individual dots by microscopy . PLA revealed a robust interaction of NFAT1 and calcineurin ( red ) in anti-TCR-activated cells expressing HA-NFAT1 , but not in HA-NFAT1S79A ( Fig 5D , left panel ) . Quantitative analysis showed that both the number and the intensity of dots were much greater in anti-CD3/CD28-stimulated HA-NFAT1-expressing cells than in HA-NFAT1S79A-expressing cells ( Fig 5D , right panel ) . These results indicated that in TCR-stimulated cells alternatively activated p38 phosphorylates NFATS79 , which is necessary for binding to calcineurin and subsequent nuclear migration .
Studies with gene-targeted mice have shown that NFAT family members have both unique and overlapping functions in many tissues . For example , germ line deletion of NFAT2 results in embryonic lethality due to a failure of cardiac valve formation [28] , and its disruption in embryonic stem cells causes a substantial defect in osteoclast differentiation [29] . NFAT1 knockout mice survive but have a substantial reduction in mast cell cytokine production and T-cell-produced TNF-α , but not IL-4 [30 , 31] . Functional redundancy between some family members was inferred from the mild phenotypes found in some single-gene disruptions [32] , and a number of studies have been carried out in animals in which multiple family members were deleted For example , deletion of NFAT3 and NFAT4 resulted in excessive and disorganized neural tubes in the embryo [33] , and deletion of NFAT1 , NFAT3 , and NFAT4 impaired axonal outgrowth in the central and peripheral nervous systems [34] . Although T cells express NFAT4 , NFAT1 and NFAT2 appear to play the predominant role in T-cell activation and function [35] . Deletion of both NFAT1 and NFAT2 prevented cytokine production by T cells stimulated with anti-CD3/CD28 [11] , whereas the introduction of constitutively active forms of both increased the expression of many cytokines in response to anti-CD3/CD28 or PMA/ionomycin stimulation [36 , 37] . Nonredundant functions of NFAT1 and NFAT2 in T cells were revealed in studies of mice deficient in one or both [38] . When immunized with myelin oligodendrocyte glycoprotein ( MOG ) to induce EAE , NFAT1-deficient mice produced less TNF-α , whereas NFAT2-deficient mice produced less IL-17A and IL-10 . Consistent with this , overexpression of NFAT1 ( but not of NFAT2 ) induced TNF-α production in human T cells stimulated via the TCR [39] . Although NFATs have been extensively investigated , much of the mechanistic information about their regulation has come from studies using Ca2+ ionophores rather than physiologic stimuli . Whereas NFAT1 expression is constitutive , NFAT2 is expressed at low levels in resting T cells and induced by stimulation via the TCR . We have previously shown that the alternative p38 pathway is required for up-regulation of NFAT2 , because it is poorly induced in either WT T cells activated with PMA/ionomycin or , notably , in DKI T cells stimulated via the TCR [22] . This was in agreement with the finding that siRNA-mediated knockdown of the p38-binding scaffold protein Dlgh1 inhibited TCR-mediated p38 activation and downstream Nfat2 mRNA induction [19] . There is relatively little known about the transcription factors that mediate TCR-signaled nfat2 up-regulation . The role of NFAT1 is controversial . Although NFAT2 expression was higher in the nucleus of nfat1-/- CD4+ T cells [38] , NFAT2 reporter activity was decreased in NFAT1-deficient T cells stimulated with PMA/ionomycin [25] . NFAT2 may itself participate in its own up-regulation via a positive feedback loop [40] , which leaves open the identity of the initiating factors . One clue suggesting the possibility that T-cell c-Fos may be involved comes from bone marrow-derived macrophages , in which receptor activator of nuclear factor κB ligand ( RANKL ) -induced osteoclast differentiation was impaired in c-fos-/- cells because of lack of NFAT2 expression [29] . This , and the fact that AP-1 is often required as an NFAT1 cofactor [10 , 41 , 42] , led us to examine c-fos expression downstream of TCR stimulation . c-Fos , like NFAT2 , was up-regulated robustly , in marked contrast to the poor and transient induction in response to PMA/ionomycin . Analysis by ChIP showed that c-Fos bound the nfat2 promoter , and enforced expression of c-Fos allowed NFAT2 up-regulation in PMA/ionomycin-stimulated WT T cells and TCR-stimulated DKI T cells , demonstrating that c-Fos is a limiting factor for NFAT2 expression . Although PMA/ionomycin induced c-Fos expression , it was not sustained , consistent with the ChIP assay performed 16 hours after stimulation . What is downstream of alternative p38 signaling that up-regulates c-Fos expression is not known . One possibility is c-Fos itself , as it has been suggested that it can participate in its own up-regulation . In one example in National Institutes of Health ( NIH ) 3T3 cells ( mouse fibroblast cell line ) , extracellular signal-regulated kinase ( ERK ) -mediated phosphorylation of c-Fos on serine/threonine residues preceding prolines recruits Pin1 , a prolyl isomerase , resulting in further induction of c-fos in a feed-forward manner [43] . It is possible that p38 ( also a proline-directed kinase ) phosphorylates c-Fos on residues involved in Pin1 recruitment and c-Fos induction . Further studies will be required to elucidate the mechanism . NFAT1 has been reported to have at least 21 serine phosphorylation sites , 13 of which are in the regulatory domain and are conserved among NFAT family members [3] . Although investigation of activation of NFATs has largely focused on their dephosphorylation , one inducible phosphorylation site in the NFAT1 transactivation domain , S54 , has been described [3] . In that report , it was found that inducible phosphorylation of S54 was critical for its transcriptional activity in PMA/ionomycin-stimulated Jurkat cells [3] . We also found that MKK-phosphorylated p38 ( classical pathway ) caused NFAT1 S54 phosphorylation , along with 4 other sites critical for cytosolic retention . In contrast to MKK6-activated p38 , ZAP-70-activated p38 phosphorylated NFAT1 at a single residue in the transactivation domain , S79 , which we found was necessary for nuclear translocation . Nuclear shuttling of NFATs is largely regulated by calcineurin-mediated dephosphorylation , which exposes nuclear localization signal ( NLS ) regions [44] , but how calcineurin targets NFATs in response to elevations in intracellular Ca2+ is not well understood . Here we have identified a new phosphorylation site , S79 , that is necessary for calcineurin-NFAT1 association in TCR-signaled T cells . Both coimmunoprecipitation assays and PLAs demonstrated that phosphorylation of this site , which is near the NFAT1 calcineurin-binding sequence PxIxIT ( residues 111–116 ) , promotes NFAT1-calcineurin interactions . Whether this is a direct effect on NFAT-1-calcineurin binding or is indirect via , for example , enhanced phosphorylation of other residues or dissociation of an inactive cytoplasmic protein complex containing NFAT1 remains to be determined . Although we observed NFAT1 nuclear translocation in response to PMA/ionomycin , we were unable to detect phosphorylation of NFAT1S79 . In this case , it may be that phosphorylation of NFAT1S54 is the functional equivalent . The identification of a mechanism for regulated access of calcineurin to its substrate NFAT1 identifies a new rate-limiting step in the propagation of signals from the TCR to the nucleus and eventual induction of effector cytokines . MAPKs are proline-directed serine/threonine ( S/T ) kinases , meaning that the targeted S/T residues are followed by a proline ( +1 ) . Whereas ERK and c-Jun N-terminal kinase ( JNK ) have been shown to be strictly proline guided , p38 is more promiscuous [45] . Unlike ERK and JNK , in in vitro kinase assays , MKK6-activated p38 phosphorylated microtubule-associated Tau protein on residues in which proline was at the +1 position or the +3 position . Interestingly , among the latter was Tau residue Ser-185 in the sequence pSGEPPKS , which is quite similar to the region following NFAT1 Ser-79 , pSGEPPGR . Note that in our case p38 was activated by ZAP-70 , not MKK6 , so this specificity is not inherent in the mode of activation but reflects a laxity in the consensus sequence not shared by other members of the MAPK family . There is a longstanding paradox in T cells that activation of p38 is upstream of both NFAT1 inhibition ( by phosphorylating residues that cause cytoplasmic retention ) and the production of NFAT1-dependent cytokines [23] . The findings in the present report resolve this paradox by demonstrating that the alternative p38 pathway , which is the physiologic pathway downstream of TCR signaling , and the classic pathway have very different effects on NFAT1 and NFAT2 activation because of different substrate specificities . The widely studied stress-induced classic p38 pathway leads to phosphorylation of NFAT1 on inhibitory residues , preventing its nuclear migration , and fails to induce c-Fos and thus NFAT2 . In contrast , alternatively activated p38 phosphorylates NFAT1 on S79 , which promotes recruitment of calcineurin , dephosphorylation of inhibitory sites , and nuclear migration . Moreover , alternatively activated p38 signals for c-Fos up-regulation , which is required for NFAT2 induction . We have previously shown that differences in the substrate specificity of classically activated versus alternatively activated p38 result in antagonism of TCR-induced cytokine production and proliferation [22] . In that case , the latter induced NFAT2 expression , but the former blocked its function by directly phosphorylating S/T residues that prevented nuclear translocation . This report demonstrates the converse , in which the alternatively activated p38 has a specificity not shared by the classically activated form . T cell p38 is therefore a master regulator of NFATs , controlling the production of effector cytokines essential in adaptive immunity .
Mice expressing p38αβY323F ( DKI mice ) [20] were crossed onto C57BL/6 ( B6 ) for at least 12 generations . Mice were maintained in the National Cancer Institute pathogen-free animal facility , and all animal experiments were performed under an NCI ACUC-approved animal study protocol ( LICB-008 ) , which follows AAALAC guidelines . Antibodies against mouse CD3 ( 145-2C11 , BD Biosciences ) , CD28 ( 37 . 51 , BD Biosciences ) , p38α ( 5F11; Cell Signaling ) , c-Fos for immunoblotting ( sc-52 FITC; Santa Cruz Biotechnology ) , and c-Fos for immunoprecipitation ( sc-52 X; Santa Cruz Biotechnology ) were used . Antibodies against human CD3 ( OKT3; eBioscience ) , CD28 ( CD28 . 2; eBioscience ) , and NFAT1 ( BD Biosciences ) were used . Anti-β-actin ( Sigma , A5441 ) and anti-HDAC1 ( Cell Signaling Technology , 10E2 ) detect both human and mouse proteins . PMA and ionomycin were purchased from Sigma-Aldrich . Recombinant active human ZAP-70 was purchased from R&D System ( NM_001079 ) . [γ-32P]ATP was purchased from Perkin Elmer . Donkey anti-rabbit IgG-HRP ( GE Healthcare , NA934V ) was used as a secondary antibody for ELISA . Jurkat cells were purchased from ATCC ( Clone E6-1 ) . Primary T cells were purified from the spleens of 6–12-week-old B6 or DKI mice using negative-selection T-cell purification columns ( CL101 ) from Cedarlane . Purified T cells and Jurkat cells were cultured in RPMI 1640 supplemented with 10% fetal bovine serum ( Invitrogen ) , 2 mM glutamine , 50 μM β-mercaptoethanol , and 100 μM gentamicin . Plat E and Plat GP cells were cultured in DMEM supplemented with 10% fetal bovine serum ( Invitrogen ) , 2 mM glutamine , 50 μM β-mercaptoethanol , and 100 μM gentamicin . To identify sites of phosphorylation on tNFAT1 , in vitro kinase reactions were separated by SDS-PAGE , and the tNFAT1 band was excised and subjected to in-gel trypsin digestion ( Shevchenko PMID: 17406544 ) ; the resultant peptides were extracted and lyophilized to dry . Phosphopeptides were enriched using TiO2 magnetic sepharose ( GE Healthcare ) following manufacturer’s protocols . The flow-through and eluted peptides were desalted by C18 ZipTip ( Millipore ) before mass spectrometry analysis on an Orbitrap Fusion mass spectrometer ( Thermo ) . The raw data were analyzed using Proteome Discoverer ( Thermo ) . C-terminal cysteine-containing peptides of NFAT1 corresponding to residues 74–85 , which are conserved between human and mouse , without or with phosphorylated Ser-79 [Ac-PLASLpSGEPPGRC] , were created by solid-phase peptide synthesis utilizing 9-fluorenylmethoxycarbonyl ( Fmoc ) /tert-butyl chemistry . The peptides were characterized by matrix-associated laser desorption ionization time-of-flight mass spectrometry ( MALDI microMX , Waters ) , and purity ( >95% ) was confirmed with RP-HPLC . To conjugate with keyhole limpet hemocyanin ( KLH ) , 6 mg of the phosphorylated peptide was dissolved in 120 μl PBS and mixed with 350 μl of IMA-KLH ( 10 mg/ml in H2O ) ( Thermo Fisher Scientific ) and stirred at room temperature for 2 hours , followed by dialysis against PBS overnight . Antisera were raised by immunizing rabbits with the KLH-coupled phosphorylated peptide in Complete Freund’s Adjuvant ( Pocono Rabbit Farm & Laboratory ) . Phospho-specific antibodies were isolated by affinity purification . Cytosolic and nuclear fractions were collected using NE-PER nuclear and cytoplasmic extraction reagents ( Thermo Scientific ) . ChIP was performed using the Magna ChIP G kit ( Millipore ) with minor modifications according to the protocol published elsewhere [46] . Briefly , primary T cells were unstimulated or activated with anti-CD3/CD28 or PMA/ionomycin , fixed with 1% formaldehyde , and sonicated , and immunoprecipitation was performed with rabbit IgG control or rabbit anti-c-Fos antibody [47] . The coimmunoprecipitated DNA served as a template for subsequent PCR with primers encompassing the NFAT2 promoter: Fwd: 5′-TGA TGT CAC TGA AGG GAG GG-3′ and Rev: 5′-GGA GCC TCT CGG TCT CAC TCT G-3′ ) . Quantitative real-time PCR consisted of an initial incubation for 1 minute at 95 °C followed by 30 seconds at 95 °C , 30 seconds at 52 °C , and 2 minutes at 72 °C . The reaction was subjected to 40 thermal cycles . A CRISPR lentiviral vector ( plentiCRISPRv2#52962 , Addgene ) carrying a Cas9 and puromycin resistance gene was used to clone guide RNA . The guide RNA sequences targeting NFAT1 were chosen from http://crispr . mit . edu . Oligonucleotides were designed for cloning into the vector as described [48 , 49] . The final guide sequences selected for NFAT1 were as follows: gRNA1: 5′-CACCGATCCGGCTCTCCGAATCGGC-3′; gRNA2: 5′-CACCGGACGGAGTGATCTCGATCCG-3′ . The gRNAs were cloned into a BsmB1 site and subsequently sequenced using U-6 primer ( 5′-GAGGGCCTATTTCCCATGATT- 3′ ) . To produce virus for infection , HEK 293T cells were transfected with 5 μg of gRNA1 , 5 μg of gRNA2 , 8 μg of packaging plasmid psPAX2 ( Plasmid#12260 , Addgene ) , and 2 μg of envelope-expressing plasmid pMD2 . G ( Plasmid#12259 , Addgene ) using Lipofectamine 2000 ( Thermo Fisher ) . After overnight incubation , the old growth medium was replaced with 10 ml of fresh warm medium . Virus was harvested 48 hours post transfection by passing the supernatant through 0 . 45 μM filters ( Millipore ) . Jurkat cells were infected with the virus , and 72 hours after infection , selection was performed using RPMI medium containing 2 μg/ml puromycin , which was replaced every 48 hours . After 2 weeks , single cells were sorted by BD FACSAria Fusion using live cell gating . Putative NFAT1 KO clones were screened for lack of NFAT1 expression by immunoblotting . Purified mouse primary T cells were stimulated with plate-bound anti-CD3 ( 2 μg/ml ) and anti-CD28 ( 2 μg/ml ) for 24 hours and infected with retrovirus containing genes encoding HA-NFAT1 , HA-NFAT1S79A , c-Fos , or EV . Cells were further stimulated with anti-CD3/CD28 for 48 hours followed by rest in medium alone for 24 hours before being stimulated in assays . WT Jurkat cells were infected with retrovirus carrying HA-NFAT or HA-NFAT1S79A and cultured in complete medium for 48 hours before analysis . NFAT1 KO Jurkat cells were infected with retrovirus carrying HA-NFAT1 or HA-NFAT1S79A followed by single-cell sorting for cells expressing GFP . Cells were grown in complete medium , and NFAT1 expression was determined by immunoblotting with anti-HA . Clones with similar levels of HA-NFAT1 or HA-NFAT1S79A were used for further analysis . Forty-eight hours after retroviral transduction of genes in mouse primary T cells or Jurkat cell lines , the cells were washed in FACS buffer ( 1% BSA plus 0 . 1% sodium azide ) and analyzed by flow cytometry ( FACSCalibur using CellQuest Pro 5 . 2 . 1 software [BD Biosciences] ) . Data were analyzed with FlowJo 9 . 2 software ( TreeStar ) . IL-2 and TNF-α in culture supernatants were quantitated with Ready-SET-Go ELISA kits ( Invitrogen ) according to the manufacturer’s instructions . To determine the purity of the pS79-NFAT1 antibody , plates were coated in PBS with 50 μl of a 1 μM concentration of the phosphorylated peptide or nonphosphorylated peptide or blank overnight at room temperature . Plates were washed with PBS-0 . 05% Tween and blocked with 2% BSA in PBS-0 . 05% Tween , and antibody was added at different dilutions . Plates were incubated for 1 hour at RT , washed with PBS-0 . 05% Tween , and incubated with mouse anti-rabbit IgG-HRP . Plates were developed with TMB substrate , the reactions were stopped with 1M H3PO4 , and OD was measured at 405 nm . Recombinant p38α , constitutively active MKK6 ( S207E/T211E ) , ATF2 , and truncated NFAT1 ( tNFAT1 , amino acids 1–350 ) were purified as described [50] . In brief , pET15b vectors containing p38α or MKK6 and pGEX-4T1 vectors containing ATF2 or tNFAT1 were expressed in BL21 ( DE3 ) cells . After cultures reached an A600 of 0 . 6–1 . 0 , protein expression was induced with 0 . 5 mM isopropyl β-D-thiogalactopyranoside ( IPTG ) , and the cultures were incubated overnight at 14 °C . For pET15b vectors containing His-tagged fusion proteins p38α or MKK6 , cells were resuspended in binding buffer containing 20 mM Tris pH 7 . 5 , 0 . 5 M NaCl , 20 mM imidazole , and 1 mM phenylmethylsulfonyl fluoride , sonicated , and centrifuged at 20 , 000 × g for 20 minutes at 4 °C . Proteins were purified with cobalt-charged chelating-Sepharose Fast Flow beads ( Amersham Biosciences ) and eluted with 0 . 35 M imidazole in binding buffer . For pGEX-4T1 vectors containing GST-tagged fusion proteins ATF2 or tNFAT1 , cells were resuspended in cold PBS and 1 mM phenylmethylsulfonyl fluoride , sonicated , and centrifuged at 20 , 000 × g for 20 minutes at 4 °C . Proteins were purified with Glutathione Sepharose 4 fast Flow beads ( GE Healthcare ) and eluted with 10 mM reduced glutathione in cold PBS . Proteins were concentrated and washed into water using Microcon YM-30 spin columns ( Millipore ) . Total RNA from retrovirally transduced mouse T cells was extracted with RNeasy mini kit ( Qiagen ) . After reverse transcription using the Omniscript RT Kit ( Qiagen ) , Power Sybr Green premix ( Applied Biosystems ) was used for quantitative PCR . All data were normalized to Hprt ( hypoxanthine-guanine phosphoribosyl transferase ) and were presented as relative expression to the background value . The primers used in this study for real-time PCR are as follows: Nfat2-Fwd: 5’-GGGTCAGTGTGACCGAAGAT-3’ , Rev: 5’-GGAAGTCAGAAGTGGGTGGA-3’; Hprt-Fwd: 5’-AGCCTAAGATGAGCGCAAGT-3’ , Rev: 5’-TTACTAGGCAGATGGCCACA-3’ . Purified recombinant mouse His-p38α was activated or not by incubation with either 300 ng of active ZAP-70 ( R&D systems ) or 1 μg of active MKK6 at 30 °C in 20 μl of kinase buffer ( 20 mM Tris , pH 7 . 5 , 20 mM MgCl2 , 1 mM dithiothreitol [DTT] , 10 mM β-glycerophosphate , and 1 mM Na3VO4 and 50 μM ATP ) . After 1 hour , 1 μg ATF2 or tNFAT1 and 10 μCi [32P]ATP were added , and incubation proceeded for 45 minutes at 30 °C . Phosphorylated products were resolved by SDS-PAGE , transferred to nitrocellulose membranes , and visualized with a Storm PhosphorImager ( GE Healthcare ) . Duolink in situ PLA enables detection , visualization , and quantification of protein interactions ( < 0 nm ) as an individual dot by microscopy . Interaction between HA-NFAT1 and calcineurin A was detected in Jurkat cells by PLA using Duolink in Situ detection reagents ( Sigma ) according to the manufacturer’s protocol with minor modifications . In brief , cells were stimulated with anti-CD3/CD28 or PMA/ionomycin on ibidi 1 μ-slides , fixed with 4% paraformaldehyde , stained with Alexa 488-conjugated wheat germ agglutinin ( WGA ) , permeabilized with methanol , and stained with mouse anti-HA and rabbit anti-calcineurin A primary antibodies . Cells were treated with anti-mouse MINUS and anti-rabbit PLUS probes , ligated , and amplified , and detection was performed by confocal microscopy ( Zeiss LSM 880 NLO Airyscan ) . Slides were analyzed at 80× magnification . After stimulation , primary T cells or Jurkat cells were fixed with 4% formaldehyde ( Affymetrix ) at room temperature for 15 minutes , air-dried on superfrost microscopic glass slides , and blocked with 5% normal goat serum before being incubated overnight with primary antibodies: mouse monoclonal Alexa 647-conjugated anti-HA ( 6E2 , Cell Signaling Technology ) , anti-NFAT1 ( MA1-025 , Thermo Scientific ) , or rabbit polyclonal pS79-NFAT1 . Species-matched secondary antibodies conjugated with fluorochrome dye Alexa 594 or Alexa 488 were added for 1 hour . The slides were washed , air-dried , and mounted directly by Prolong Diamond Antifade Mountant with DAPI ( P36962 , Molecular Probes ) . Cells stained with only secondary antibodies were used as controls . Images were taken with a Zeiss confocal microscope ( Nikon Instruments , Melville , New York , United States ) , and 10–15 fields/condition were analyzed with ImageJ . PLA data were analyzed by Image-Pro Premier program ( version 9 . 3 . 1 ) . P-values were calculated by Student t test using GraphPad Prism Software .
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The p38 MAP kinase , which is required for a large number of important biological responses , is activated by an enzymatic cascade that results in its dual phosphorylation on p38T180Y182 . T cells have evolved a unique pathway in which T-cell antigen receptor ( TCR ) ligation results in phosphorylation of p38Y323 ( the alternative pathway ) . Why T cells acquired this pathway is the subject of conjecture . In this study , we examine the activation of 2 members of the nuclear factor of activated T cells ( NFAT ) family , which , when dephosphorylated by calcineurin , migrate from the cytoplasm to the nucleus . In T cells with the alternative pathway ablated by a single amino acid substitution ( p38Y323F ) , NFAT1 remained in the cytoplasm after stimulation via the TCR . Studies identified NFAT1S79 as a target for alternatively ( but not classically ) activated p38 , and phosphorylation of this residue was required for binding calcineurin and nuclear translocation . Furthermore , although classically activated p38 induced NFAT1 translocation in the absence of NFAT1S79 phosphorylation , unlike alternatively activated p38 it did not cause NFAT2 up-regulation . This paradox was resolved by the finding that only the latter induces c-Fos , which binds to the NFAT2 promoter and participates in its up-regulation . These T-cell-specific p38 activities provide a strong rationale for the acquisition of the alternative mechanism for activating p38 .
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2018
|
Unique properties of TCR-activated p38 are necessary for NFAT-dependent T-cell activation
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Sumoylation regulates a wide range of essential cellular functions through diverse mechanisms that remain to be fully understood . Using S . cerevisiae , a model organism with a single essential SUMO gene ( SMT3 ) , we developed a library of >250 mutant strains with single or multiple amino acid substitutions of surface or core residues in the Smt3 protein . By screening this library using plate-based assays , we have generated a comprehensive structure-function based map of Smt3 , revealing essential amino acid residues and residues critical for function under a variety of genotoxic and proteotoxic stress conditions . Functionally important residues mapped to surfaces affecting Smt3 precursor processing and deconjugation from protein substrates , covalent conjugation to protein substrates , and non-covalent interactions with E3 ligases and downstream effector proteins containing SUMO-interacting motifs . Lysine residues potentially involved in formation of polymeric chains were also investigated , revealing critical roles for polymeric chains , but redundancy in specific chain linkages . Collectively , our findings provide important insights into the molecular basis of signaling through sumoylation . Moreover , the library of Smt3 mutants represents a valuable resource for further exploring the functions of sumoylation in cellular stress response and other SUMO-dependent pathways .
Small ubiquitin-related modifiers ( SUMOs ) are ~100 amino acid proteins that are covalently attached to other proteins and thereby function as reversible , posttranslational protein modifications . Hundreds of proteins are regulated through sumoylation , accounting for effects on nearly every aspect of cell function including control of gene expression , DNA replication and repair , mRNA processing and export , mitochondrial fission , cytoskeleton assembly and signaling at the plasma membrane [1–4] . Explaining how sumoylation regulates such a wide range of proteins and processes remains an actively pursued research challenge . Some of the complexities of sumoylation in vertebrates may be explained in part by the expression of multiple SUMO paralogs , the best characterized being SUMO1 , SUMO2 , and SUMO3 . Whereas SUMO2 and SUMO3 are ~96% identical ( and therefore referred to as SUMO2/3 ) , SUMO1 is only ~45% identical to SUMO2 and SUMO3 and may have unique signaling properties and functions [5] . In contrast to vertebrates , the budding yeast S . cerevisiae express a single SUMO gene originally identified as a high copy suppressor of a mutation in the centromere protein Mif2 and therefore named Suppressor of Mif Two ( SMT3 ) [6] . Because S . cerevisiae contains a single SUMO gene , it represents an ideal model organism in which to investigate the essential functions of sumoylation and the molecular mechanisms underlying the complexities of SUMO signaling . A wealth of information concerning SUMO substrates , protein-protein interactions and genetic interactions between pathway components have also been generated through high throughput studies and contribute to the utility of S . cerevisiae as a model system [7–13] . Early genetic analysis of the SUMO pathway components in S . cerevisiae revealed essential roles for sumoylation in regulating progression through mitosis . Yeast sumoylation mutants arrest as large budded cells in metaphase and have defects in the anaphase promoting complex/cyclosome ( APC/C ) mediated proteolysis of securin , Pds1 and mitotic cyclins , demonstrating an essential role for sumoylation in the metaphase to anaphase transition [14–17] . In addition , mutational analysis of SUMO conjugating and deconjugating enzymes in S . cerevisiae , as well as SUMO-targeted ubiquitin E3 ligases ( STUbLs ) , have provided critical insights into the roles for sumoylation in DNA damage repair and maintenance of genome integrity [18–21] . Recent genetic studies in yeast have also contributed important insights to an emerging view of SUMO as “molecular Velcro” , due its ability to promote protein-protein interactions [22] . This effect is mediated by the ability of SUMO to be covalently conjugated to proteins and simultaneously associate non-covalently with proteins containing SUMO-interacting motifs ( SIMs ) [23] . Studies in yeast have also contributed to understanding the functions of sumoylation in multiple other pathways , including transcription regulation [24–26] , nuclear transport [27–29] , mRNA metabolism [30 , 31] and cellular stress response pathways [32] . Despite this progress , mutational analysis of the Smt3 protein itself represents an opportunity for discovery that has not been fully explored . Lysine to arginine substitution mutants of Smt3 predicted to affect chain formation have been studied , leading to the general conclusion that polymeric chains regulate normal chromatin structure but are not required for essential SUMO functions [8 , 33] . In addition , two conditional mutations corresponding to amino acid substitutions in buried residues that likely disrupt Smt3 folding have also been reported [15 , 34] . In an effort to develop a comprehensive structure-function based map of Smt3 and identify mutant alleles that could be used to further explore critical functions , we generated a library of 252 mutant smt3 alleles that can be expressed and screened in S . cerevisiae . Each mutant in the library was uniquely barcoded to allow quantitative high-throughput analysis of complex phenotypes . Through an initial screening of this mutant library , we identified 12 lethal smt3 alleles as well 45 alleles with conditional growth phenotypes that can be used to further explore the roles of sumoylation in cellular stress response pathways . Our studies provide a comprehensive analysis of the Smt3 protein and insights into the molecular basis of signaling through sumoylation .
In order to identify residues of yeast SUMO critical for its many essential functions , we developed a library consisting of >250 smt3 mutant alleles . As a first step in the construction of the SMT3 mutant collection , a SMT3 cassette that would be used for the generation of each mutant was created . The SMT3 cassette was based on a previously described synthetic cassette used for the generation of a histone mutant library and was designed to increase the versatility of the final SMT3 mutant collection [35] . The SMT3 cassette was synthesized by Bio Basic Incorporated ( Canada ) and cloned into the pRS413 vector ( Fig 1A ) . The cassette contains ~1400 base pairs of sequence flanking the 5’ and 3’ ends of the SMT3 open reading frame that allow the mutant collection to be expressed using the natural SMT3 promoter as well as allowing integration of the mutant alleles into the endogenous SMT3 gene locus . The pRS413-SMT3 construct contains two selectable markers , HIS3 and LEU2 . While either marker allows for expression of the mutant collection from a CEN ( episomal single copy ) vector , the LEU2 marker adjacent to smt3 allows for selection of integrated mutant alleles and therefore expression from the endogenous SMT3 gene locus . The LEU2 marker is flanked by LoxP sites to facilitate its Cre-dependent removal following integration or exchange with any other marker flanked by LoxP sites . Another important feature of the SMT3 cassette is that it contains a “TAG” region that would allow complex phenotypes of the mutant collection to be analyzed by microarray . The “TAG” region consists of a unique pair of barcodes for each mutant flanked by universal primer sequences . Finally , numerous restriction enzyme sites were engineered into the SMT3 cassette in order to easily exchange sections of the cassette as needed . The SMT3 mutant library consists of 252 uniquely bar-coded mutants of the SMT3 gene . Each mutant was synthesized by as a 600 base pair section of the original SMT3 cassette that was unique in the SMT3 and “TAG” regions . In order to probe the functionality of each Smt3 residue , every residue , including surface residues as well as those predicted to be buried , was mutated as illustrated in Fig 1B . Each residue was mutated to alanine while all alanine residues were mutated to serine . To neutralize the charge of acidic residues , aspartic and glutamic acid residues were mutated to asparagine and glutamine , respectively . Likewise , lysine residues were mutated to glutamine . To reverse charges , both aspartic and glutamic acid were mutated to arginine while lysine and arginine were mutated to glutamic acid . Tyrosine residues were mutated to phenylalanine to probe for dependence on the tyrosyl moiety . To probe for effects of potential post-translational modifications , all residues that can be phosphorylated , including serine , threonine , tyrosine and histidine , were mutated to aspartic ( serine and threonine ) or glutamic ( tyrosine and histidine ) acid to mimic a constitutively phosphorylated state . Lysines were mutated to glutamine and arginine to mimic the acetylated and deacetylated states , respectively . Prolines were mutated to valine to eliminate proline isomerization and arginines were mutated to lysine to prevent arginine methylation . Finally , the two previously identified conditional mutations , L26S and F52S , were also included as controls [15 , 34] . Since a substitution mutant at a single residue may not be sufficient to induce a phenotype , several multi-site mutants were created in regions that are highly conserved between the yeast and human SUMOs . Smt3 contains nine lysine residues that localize to four surface-exposed regions ( Fig 1C ) . Smt3 lysines can serve as sites for Smt3 or ubiquitin ( Ub ) conjugation , as well as potential sites for acetylation or methylation . Individual and multi-site lysine to arginine mutations were therefore generated to determine the effect of losing SUMO chain formation or post-translational modification at particular lysine residues ( Fig 1D ) . To further probe the functionality of chain linkage or modification at particular sites , either single or multiple lysines were added back to a lysine-less mutant , KallR . In addition to substitution mutations , deletion mutants of the N- and C-termini of Smt3 were also included in the library ( Fig 1E ) . The N-terminal extension of Smt3 is twenty-one amino acids in length and is absent in other ubiquitin-related proteins . This extension contains three SUMO consensus motifs ( ΨKXE/D ) that are the major sites of chain formation [33] . To test the functionality of the entire N-terminal region as well as each of the SUMO consensus motifs , six N-terminal deletion mutants were included in the mutant collection . In addition to the N-terminal extension , Smt3 also contains a three amino acid C-terminal extension that is removed by the SUMO protease , Ulp1 , to reveal the terminal G98 that is essential for conjugation to substrate lysines [36] . Since regulating the processing of the Smt3 C-terminal extension could be a way to control the level of Smt3 available for conjugation , a C-terminal truncation mutant was included in the library to determine the functional significance of losing precursor processing . Lastly , the library also included human SUMO1 , SUMO2 and SUMO3 to determine the functionality of each of the different human paralogs in yeast . All of the mutants were integrated into the SMT3 gene locus of a smt3Δ strain harboring wild-type SMT3 on a URA3 based shuffle plasmid . Previous work has shown that mutants in the SUMO pathway can have amplified levels of the 2μm circle plasmid that lead to slow , cold-sensitive growth and cell cycle delays [37] . In order to identify mutant phenotypes that are distinct from those that arise from 2μm hyper-amplification , the smt3Δ shuffle strain used to construct the mutant collection was cured of the 2μm circle plasmid prior to mutant integration . Human SUMO1 , SUMO2 and SUMO3 each share ~50% sequence homology with yeast SMT3 ( Fig 2A ) . To address the functionality of human SUMO1 , SUMO2 and SUMO3 in S . cerevisiae , we designed three gene constructs encoding human SUMO1 , 2 and 3 based on our original SMT3 cassette . To optimize expression of the human constructs in our smt3Δ strain , each human SUMO cDNA was re-coded with yeast-optimized codons using the Codon Juggling module of the GeneDesign software [38] . In addition , each human SUMO paralog was encoded as both the precursor and mature form of the protein in order to eliminate defects due to precursor processing by the yeast SUMO isopeptidase , Ulp1 . Plasmids encoding the human SUMOs were transformed into the smt3Δ strain harboring wild-type SMT3 on a URA3-based CEN plasmid . Transformants were then plated onto synthetic complete media lacking histidine in the absence or presence of 5-FOA at 30°C to select for cells that had lost the wild-type SMT3 containing plasmid . Previous work had shown that SUMO1 could complement a smt3Δ strain [39] . Similarly , we found the human SUMO1 precursor and mature proteins were able to complement the smt3Δ strain ( Fig 2B ) . However , neither the precursor nor the mature forms of SUMO2 or SUMO3 were able to complement the smt3Δ strain ( Fig 2B ) . Although both the precursor and mature forms of human SUMO1 could complement the deletion strain at 30°C , both strains displayed increased sensitivity to a variety of stress conditions including heat , DNA damage , cadmium chloride and oxidative stress ( Figs 2C and 4D ) . In order to understand why human SUMO2 and SUMO3 could not compensate for the loss of Smt3 , the protein expression of all of the human SUMOs was analyzed by western blotting using antibodies specific for the human SUMO paralogs . The human SUMOs were expressed in a wild-type strain also expressing Smt3 . SUMO1 was expressed and also appeared to be conjugated to substrates as it was visible in high molecular mass smears in the SMT3 shuffle strain ( Fig 2D ) . Since both SUMO1 and Smt3 were expressed in the strain , competition between the two proteins could prevent robust substrate conjugation by SUMO1 . To determine if SUMO1 and Smt3 were in competition for substrate conjugation , we also profiled the expression of the SUMO1 integrated strain that lacked expression of Smt3 . Consistent with competition , the high molecular mass SUMO1 smears increased in the integrated strain that lacked Smt3 expression . Notably , the level of human SUMO1 conjugates in the integrated strain appeared modest compared to the level of free SUMO1 and may contribute to the stress sensitivity of the human SUMO1 strains ( Fig 2D ) . Like SUMO1 , SUMO2 and SUMO3 precursor and mature proteins were expressed in the SMT3 shuffle strain ( Fig 2D ) . However , both the SUMO2 and SUMO3 precursors failed to be processed to the mature forms of the proteins based on their electrophoretic mobilities ( Fig 2D ) . Furthermore , neither SUMO2 nor SUMO3 appeared to be competent for conjugation since the mature forms of both proteins were not detected in high molecular mass conjugates . Since the defects associated with the mature forms of SUMO2 and SUMO3 were not due to expression defects , we examined the first step of the SUMO conjugation pathway , activation and E1 thioester formation . For these experiments , purified recombinant Smt3 , SUMO1 or SUMO2 were incubated with the His-tagged S . cerevisiae Uba2p/Aos1p E1 heterodimer . The samples were incubated in the presence of ATP and E1 thioester formation was monitored by non-reducing and reducing SDS-PAGE and anti-His western blots . Both Smt3 and SUMO1 formed a thioester with His-Uba2p; however , SUMO2 did not ( Fig 2E ) . Our findings indicate that , whereas SUMO1 complements smt3Δ under normal growth conditions , SUMO2 and SUMO3 do not due to defects in both precursor processing and E1 activation ( Fig 2F ) . The ability of human SUMO1 to complement loss of Smt3 indicates significant plasticity in primary amino acid sequences required for functionality . To identify residues essential for viability , the ability of individual mutants in the library to survive in the absence of Smt3 was determined by a plasmid shuffle technique [40] . Mutants that failed to produce plasmid-free segregants when integrated into the endogenous SMT3 gene locus were initially considered lethal . Using this approach , 15 mutant alleles failed to support growth in the absence of wild-type SMT3 ( Fig 3; S1A Fig ) . Three of the identified alleles , F37/I39/T42A , R55/Q56A and L81/E84/D87/I89A , were multi-site mutants that overlapped with already represented lethal alleles and were excluded from the final lethal mutant list . Previously described work with a synthetic histone collection showed that 25% of the lethal mutants identified by integration were episome remedial presumably due to multiple plasmid copies [35] . To determine if the lethal smt3 alleles could also be remediated by episomal expression , each mutant was also expressed from a centromeric plasmid . Only two alleles , R71E and G98A , were episome remedial . Thus , 10 alleles failed to complement growth of the smt3Δ strain whether expressed from the SMT3 genomic locus or a plasmid and were characterized further ( S1A Fig ) . A defect in precursor processing could provide a simple explanation for the lethality of the identified Smt3 mutants . Each lethal mutant was therefore expressed as the mature form of the protein and 5-FOA resistance of each mutant in the smt3Δ shuffle strain was assessed . Like their full-length counterparts , none of the mature , lethal mutants were viable in the absence of wild-type SMT3 ( S1B Fig ) . However , two of the mutants , F37/I39A and T43D , did give rise to weak 5-FOA resistant growth after extended incubation . Since lethal mutations may also cause protein instability and reduced expression , each lethal mutant was also expressed on a 2μm plasmid in the smt3Δ shuffle strain . Overexpression of the mutants also failed to suppress lethality and in some cases , overexpression led to a dominant negative phenotype ( S1B Fig ) . To better understand the defects associated with the lethal alleles at the molecular level , each mutation was mapped on the structure of Smt3 together with residues previously identified through biochemical and crystallographic studies as important for SIM and SUMO pathway component interactions ( Fig 3A and Table 1 ) [23 , 41–45] . Since mutating a buried residue might reduce protein stability and lead to lethality , we first assessed whether any of the identified lethal mutations map to buried residues in the wild type Smt3 structure . Of the lethal mutations , the side chain of one , I89A , is predicted to be buried , while several mutants including H23/I24/V28A , F37/I39A , F65A and Y67E , correspond to partially buried residues ( Fig 3A; S2A Fig ) . To further explore possible effects of these mutations on Smt3 stability , we used the macromolecular modeling software Rosetta to estimate changes in the protein stability for single point mutants in our library [46 , 47] . Based on this analysis , each mutant was classified as destabilizing or neutral/stabilizing ( Fig 4D , S2 Table ) . Notably , the lethal mutations at each of the buried or partially buried residues were predicted to have destabilizing effects , suggesting that defects in Smt3 folding or stability may contribute to their lethal phenotypes . Although F65 and Y67 are partially buried , these residues are also part of an important interaction surface for Ulp1 binding , suggesting a possible defect in isopeptidase recognition [42] . Other lethal mutations , including F37/I39A , T43D , R47E and R55A , map to regions at or near the SIM binding surface on Smt3 ( Fig 3A ) . SIMs are critical for interactions between SUMO and E3 ligases , thus effecting substrate conjugation , as well as for interactions with downstream effector proteins [48–51] . The SIM binding surface on Smt3 consists of a hydrophobic groove capped by basic residues that bind hydrophobic and negatively charged residues , respectively , within a SIM ( S1C Fig ) . T43D and R47E introduce a negative charge predicted to affect interactions with acidic or hydrophobic SIM residues , respectively [44] . Because T43D is a phosphorylation mimic , phosphorylation at this site might be a way to regulate SIM binding . Although R55A is also on the SIM binding surface , disruption of the positive charge does not seem to be the defect associated with this mutation since the charge reversal mutant , R55E , did not lead to lethality . Additional mutations , not mapping to the SIM binding surface , were also identified . The D30R mutation mapped to a surface residue of Smt3 implicated in E2 interactions , indicating a potential defect in conjugation [45 , 52] . The G97D/G98I/ΔATY mutant was included as a non-conjugatable control and expected to be lethal; it maps to the extreme C-terminus of Smt3 . Surprisingly , the G98A lethal mutation was episome remedial , suggesting only partial defects in processing and conjugation . To further characterize the lethal mutants , the Smt3 protein expression and conjugation profiles of individual strains were analyzed by western blotting . Since the lethal mutants do not support viability , each mutant was expressed in a human SUMO1 expressing strain and Smt3 specific antibodies were used to detect the yeast protein ( it should be noted that the effect of individual mutations on antibody recognition is uncertain ) . Although SUMO1 and Smt3 could potentially form mixed chains in these strains , individual mutants nonetheless displayed unique properties . The H23/I24/V28A protein , mutant in two buried residues , I24 and V28 , appeared to be unstable since very little protein expression was detected ( Fig 3B ) . The D30R , F37/I39A , T43D and R47E mutants each had expression patterns that were similar to wild-type Smt3 in that both conjugates and a pool of free Smt3 were detected ( Fig 3B ) . However , D30R and R47E appeared to be expressed at lower levels compared to wild-type Smt3 and all lethal mutants showed some reduction in molecular mass conjugates between 34–72 kDa and in free Smt3 ( at ~17 kDa ) . The R55A , F65A , Y67E and I89A mutants were expressed at varying levels relative to wild-type Smt3 , but each of these mutants shared a unique phenotype , namely the presence of ultra-high molecular mass Smt3 smears that could be detected extending into the stacking gel ( Fig 3B and 3C ) . To understand the nature of the ultra-high molecular mass smears observed with R55A , F65A , Y67E and I89A mutants , in vivo ATP depletion experiments were performed to determine if the mutant proteins could be recognized by isopeptidases . Since Smt3 conjugation is an ATP-dependent reaction , ATP depletion prevents conjugation and allows isopeptidase mediated deconjugation of substrates to be observed; time-dependent deconjugation and recovery of conjugation can be observed in vivo by inhibiting and restoring ATP production ( S1D Fig ) . Ultra-high molecular mass smears present in the R55A mutant were rapidly reduced during ATP depletion , demonstrating efficient isopeptidase recognition , while the ultra-high molecular mass smears present in F65A , Y67E and I89A mutants persisted following ATP depletion , indicating inefficient recognition by the isopeptidases ( Fig 3C ) . In order to determine if the ultra-high molecular mass smears represented polymeric Smt3 chains , the F65A mutation was combined with another smt3 allele , KallR , in which all of the lysines had been mutated to arginine to prevent chain formation . The F65A/KallR combination mutant failed to produce ultra-high molecular mass smears , indicating that the smears represent polymeric chains resulting from a defect in isopeptidase recognition ( S1E Fig ) . Previous studies have shown that ultra-high molecular mass conjugates formed in Ulp2Δ strains contribute to toxicity [33] . To determine if introducing the KallR mutations suppressed the lethality of the F65A allele , the combination mutant was introduced into the smt3Δ shuffle strain harboring SMT3 on a URA3-based plasmid . The lethality of F65A was weakly suppressed by the introduction of the KallR mutations at 25°C but not at higher temperatures ( S1F Fig ) . ATP depletion and recovery analysis of the mutants that did not show ultra-high molecular mass smears ( D30R , F37/I39A , T43D and R47E ) revealed that all of these proteins were recognized by the conjugating and deconjugating enzymes ( Fig 3D ) . Finally , each of the lethal mutants was characterized by immunofluorescence microscopy to determine cellular localization . Wild-type Smt3 is typically enriched in the nucleus and also co-localizes with the septin ring during mitosis ( Fig 3E ) . Lethal mutants that had Smt3 protein expression profiles similar to the wild-type SMT3 strain , such as R47E , had a localization pattern indistinguishable from wild-type protein ( Fig 3E ) . Lethal mutants that formed ultra-high molecular mass smears , such as F65A , also showed Smt3 protein enriched in the nucleus and faint septin ring staining . Notably , however , these mutants also frequently contained a single prominent nuclear focus ( Fig 3E ) . Previous studies performed in Schizosaccharomyces pombe showed that the SUMO targeted ubiquitin ligase mutant , slx8-29 , formed nuclear foci containing SUMO and the Cdc48 segregase [53] . To determine if Cdc48 co-localized with the foci we observed , F65A was expressed in a GFP-tagged CDC48 strain . In cells expressing either empty vector or wild-type SMT3 , the GFP-Cdc48 signal was diffuse throughout the cell and nuclear foci were not seen; however , when F65A was expressed , GFP-Cdc48 foci formed in the nucleus and co-localized with the Smt3 foci ( Fig 3F ) . Our findings indicate that SIM binding and efficient isopeptidase recognition of Smt3 polymeric chains are two properties essential for Smt3 function . To identify SMT3 alleles with conditional defects , the integrated collection of 240 viable mutants was tested for growth in 19 different plate assays to probe for sensitivity or resistance to low and high temperature , DNA damage , hypoxia , oxidative stress , protein folding stress , osmotic stress , heavy metal , EtOH and microtubule perturbation . The growth assays were performed under conditions predetermined to slightly inhibit the wild-type SMT3 strain in order to identify sensitive and resistant mutations . From this analysis , 45 conditional smt3 mutations ( ~18% of the collection ) were identified , of which nine are altered at one or more lysine residues and 2 correspond to either the full-length or mature version of human SUMO1 . The remaining 34 alleles represented 32 different residues . Similar to the lethal mutants , the conditional mutants mapped to multiple Smt3 surfaces including those important for SIM binding , conjugation and deconjugation ( Fig 4A and Table 1 ) . In addition , 7 residues identified were predicted to be partially or completely buried ( Fig 4B; S2 Fig ) . Mutations in 6 of these 7 residues were predicted to have potential destabilizing effects based on modelling using Rosetta analysis ( Fig 4D , S2 Table ) . Notably , only 47% of the identified alleles were alanine substitutions and 41% of the residues represented by the conditional alleles would not have been identified in a simple alanine scan ( Fig 4C ) . Analysis of protein expression and conjugation patterns under normal growth conditions suggested significantly reduced expression for a number of conditional mutants ( L26S , D30A and H92E ) compared to wild-type Smt3 . However , subtler qualitative differences in the global SUMO modification patterns were detected for a majority of strains ( S3 Fig; S1 Table ) . To summarize the phenotypes observed , each mutant was given an assay score reflective of its sensitivity or resistance to a certain growth condition relative to wild-type SMT3 . A heat map was generated using these scores to visualize the observed phenotypes of each mutant ( Fig 4D ) . This heat map revealed that the majority of conditional growth mutants showed sensitivity , rather than resistance , in the growth conditions tested . In addition , ~60% of the mutants were pleiotropic in that they showed sensitivity or resistance in more than one growth condition . Mutants such as R64E and D68R showed sensitivity in numerous assays , but also displayed resistance in other assays such as microtubule inhibition . Finally , growth conditions affecting the largest number of mutant alleles were those causing DNA damage , temperature stress and heavy metal stress , while no mutant alleles were sensitive to osmotic stress caused by high salt . Many of the mutant alleles identified as either lethal or conditional mapped to the SIM binding surface on Smt3 ( Fig 5A ) . Importantly , these mutants had varying phenotypes that could be useful in future genetic studies , including high copy suppressor analysis . Lysines 38 and 40 are of particular interest , as they are within the SIM binding surface but are located outside of the hydrophobic groove where they could interact with negatively charged regions of the SIM or with phosphorylated SIMs [54–58] . Charge reversal of K38 or K40 to glutamic acid gave rise to specific defects limited to HU and MMS sensitivity ( Fig 4C and Fig 5B ) . In contrast , mutation of isoleucine 35 , which is located within the hydrophobic groove important for interacting with hydrophobic residues within a SIM , led to numerous phenotypes including HU and MMS sensitivity , as well as weak temperature sensitivity ( Fig 4C and Fig 5B ) . The heat sensitivity seen in I35A did not appear to be due to protein instability since western blot analysis revealed protein expression levels comparable to wild-type Smt3 at 30°C and 39°C ( Fig 5C ) . However , the pattern of conjugated proteins seen in I35A was different at both temperatures in comparison to the pattern seen in the wild-type strain ( Fig 5C ) . In addition , while wild-type SMT3 and I35A both displayed increased levels of conjugates after 20 hours at 39°C , I35A had an increased level of ultra-high molecular mass conjugates observed in the stacking gel ( Fig 5C ) . Since previous studies have shown that smt3 mutants such as smt3-331 and smt3-KallR display chromosome segregation defects [8 , 15] , the cell cycle profiles of wild-type SMT3 and the I35A mutant grown at high temperature were compared . When grown at 39°C for 20 hrs , the I35A mutant gave rise to large budded pre-anaphase cells that accounted for a small subset ( 10 . 7% ) of the cell population consistent with the phenotypes seen with smt3-331 and smt3-KallR ( Fig 5D ) . Another interesting mutant identified in the SIM binding site was the lethal T43D phosphomimic ( Fig 5E ) . Threonine 43 lies within the loop between the first alpha helix and second beta sheet in Smt3 adjacent to lysine residues important for binding negatively charged residues within a SIM [44]; therefore , it is possible that phosphorylation of this residue , or mutation to an acidic residue such as aspartic acid , could inhibit SIM binding ( Fig 5A ) . If phosphorylation at threonine 43 serves as a means to regulate SIM binding , then it is possible that loss of this regulation would result in reduced viability . In order to test this hypothesis , T43 was also mutated to alanine to mimic a protein that could not be phosphorylated at that site . In all of the assays tested , T43A exhibited no growth defects indicating that regulation of SIM binding through phosphorylation of this site is not critical for viability under these conditions ( Figs 4D and 5E ) . Similarly , mutation of the neighboring threonine , T42 , to either aspartic acid or alanine resulted in no notable defects ( Fig 4D ) . Like ubiquitin , Smt3 contains multiple lysine residues that could serve as sites for synthesis of topologically and functionally distinct chains ( Fig 1C ) . Three of the lysines at amino acid positions 11 , 15 and 19 are within modification consensus motifs; moreover , lysine 15 appears to be the major site of chain formation in vivo [33] . Previous work has shown that although chain formation through internal lysines is not essential for viability in S . cerevisiae , cells expressing a version of Smt3 that contains no lysines ( KallR ) , have an increased doubling time , chromosome segregation defects and sensitivity to agents that cause DNA damage and replication stress [8 , 33] . We also found that the KallR chain forming mutant displayed sensitivity to agents that cause DNA replication stress such as HU and MMS , as well as a weak sensitivity to growth at high temperature ( 39°C ) , hypoxia , CdCl2 and the protein damaging agents AZC and canavanine ( Figs 4D and 6A ) . To investigate more specifically the functional significance of linkage-specific chains , lysine residues were restored in the KallR mutant to allow chain formation at individual positions . Consistent with previous data demonstrating that lysine 15 is the major site for chain formation , restoration of lysine at this position rescued the associated phenotypes of the KallR mutant ( Fig 6A ) [33] . Furthermore , restoring lysines within either of the other two modification consensus sites at amino acid positions 11 or 19 , also fully rescued the phenotypes of the KallR mutant ( Fig 6A ) . Although lysines 11 , 15 and 19 restore the functionality of the KallR mutant , loss of chain formation at these sites in the K11/15/19R mutant or N-terminal deletion mutant , resulted in no detectable growth defects , demonstrating that one or more of the other six lysine residues may serve as sites for functional chain formation ( Fig 6A ) . Restoration of individual lysines at amino acid positions 41 , 54 or 58 suppressed the temperature sensitivity of the KallR mutant . However , restoration of lysines at amino acid positions 27 , 38 , 41 , 54 or 58 only partially suppressed HU-induced DNA damage sensitivity ( Fig 6B ) . Notably , restoration of lysine 40 , a lysine located within the SIM binding site , did not suppress the temperature or HU sensitivity of the mutant , indicating that chain synthesis through this lysine is either inefficient or that chains formed at this site are unable to function in these stress responses ( Fig 6B ) . These results indicate that while chain formation through internal lysine residues is not essential for viability , it is important for optimal response to certain stress conditions , including replication stress , high temperature , heavy metal stress , hypoxia and protein folding stress . Furthermore , chain formation at lysines 11 , 15 and 19 are the preferred sites for modification under the conditions tested .
Although only ~50% identical to Smt3 , human SUMO1 was previously shown to complement a smt3Δ strain [39] . Our findings with both the precursor and mature forms of SUMO1 validated these results . Because homology is distributed evenly across the full length of Smt3 and SUMO1 , complementation suggests a considerable degree of plasticity built into the SUMO structure and interactions with factors required for conjugation , deconjugation and downstream signaling . Nonetheless , plate growth assays used to test the response to different stress conditions showed that strains expressing either the precursor or mature form of SUMO1 were hypersensitive to various stresses . These sensitivities correlated with relatively the low levels of high molecular mass conjugates detected in these strains ( despite robust SUMO1 expression ) , indicative of an imbalance between rates of conjugation and deconjugation . Consistent with low conjugation levels affecting fitness , strains lacking the major Smt3 E3 ligase , Siz1 , exhibit a similar imbalance in conjugation and deconjugation rates and similar stress sensitivities [60] . Notably , the lack of growth defects in both SUMO1 and Siz1Δ strains under normal growth conditions also suggests that viability can be maintained with diminished levels of SUMO conjugates [60] . Unlike SUMO1 , all of the SUMO2 and SUMO3 constructs that we tested failed to complement the smt3Δ strain . SUMO2 and SUMO3 were expressed , but were not conjugated to proteins due to defects in precursor processing and E1 activation . The observed defects in precursor processing are consistent with in vitro studies demonstrating that the C-terminal tails of SUMO2 and SUMO3 precursors are incompatible with Ulp1 recognition and cleavage [61] . Although differences in E1 recognition are not as readily explained , it is noteworthy that residues of SUMO1 important for human E1 interaction are more highly conserved with Smt3 ( 7 of 11 residues ) than with SUMO2 and SUMO3 ( only 3 of 11 residues ) [41] . Although phylogenetic studies indicate that SUMO1 and SUMO2/3 gene families arose early in metazoan evolution , the question of whether SUMO1 or SUMO2/3 more closely resemble the single ancestral SUMO of lower eukaryotes remains uncertain [62] . Taken together , our observed functional differences suggest that SUMO1 may be more closely related evolutionarily to Smt3 than SUMO2 and SUMO3 . The screens that were performed on the smt3 mutant collection identified 12 lethal and 45 conditional alleles . Notably , the entire 21 amino acid N-terminal extension of Smt3 could be deleted without detectable effects on growth under the conditions assayed . The function of the N-terminal extension , present in SUMO proteins from yeast to human , therefore remains unclear . Similarly , we observed no consequences of deleting the C-terminal extension of the Smt3 precursor protein . Why SUMO , ubiquitin and Nedd8 proteins are all synthesized as precursors in organisms from yeast to human , again remains unclear . Mutant alleles that did exhibit growth defects represented mutations in residues that mapped to two distinct surfaces on the Smt3 structure . One surface comprised the “backside” and C-terminal tail that make important contacts with conjugating and deconjugating enzymes . Lethal mutations mapping to this surface included F65A , Y67E and I89A , which may affect interactions with the Ulp1 and/or Ulp2 isopeptidases either directly or through indirect effects on structural stability . Analysis of these mutants in SUMO1 expressing strains revealed that they are expressed and processed by Ulp1 , the dominant processing isopeptidase [36] , and accumulate as high molecular mass conjugates resistant to deconjugation following ATP depletion . These mutations may therefore more severely affect recognition by Ulp2 , whose loss also results in formation of toxic , high molecular mass conjugates [33 , 63] . Lethal mutations mapping to this surface , including D30R , Y67E , and I89A may also affect non-covalent interactions with the backside of Ubc9 thought to be important for E3 ligase function , polymeric chain synthesis , or recruitment of Ubc9 to sumoylated substrates and protein complexes [45 , 50 , 52] . Mutations in Ubc9 that affect this non-covalent interaction with Smt3 are inviable [45] , consistent with a role in promoting essential functions . The second critical surface on Smt3 identified in our screens mediates interactions with the major class of SIMs present in E3 ligases and interacting proteins functioning downstream of conjugation . Structural studies have shown that these SIMs , consisting of a hydrophobic core flanked on either side by negatively charged residues , interact with a common surface formed by the alpha helix and second beta strand on SUMO [43 , 44 , 48 , 55 , 64–66] . We identified multiple lethal mutations within the SIM binding surface , consistent with an expected essential role for non-covalent SUMO-SIM interactions . Of the lethal mutations , two are predicted to affect interactions with the SIM hydrophobic core ( F37/I39A and R47E ) , and two are likely to disrupt formation of stabilizing interactions with acidic or phosphorylated residues ( R55A and T43D ) [44 , 55] . Thus , these mutations reinforce the importance of both hydrophobic and electrostatic contacts in forming functionally stable SUMO-SIM interactions . It should be emphasized that defects in SIM binding may affect both substrate modification , through defects in E3 activity [50] , as well as binding to downstream effector proteins . Defects in protein stability , as suggested by Rosetta modelling analysis , and reduced expression levels of some of these mutants may also contribute to their lethal phenotypes . The conditional alleles with mutations mapping to these two surfaces of Smt3 are of particular interest due to their potential utility in further exploring the roles of sumoylation in genotoxic and proteotoxic stress response pathways . A number of these mutants , including R64E , D68R , R71A and G97A are predicted to affect Ulp1 recognition [61] , and consistent with this prediction these mutants accumulate unprocessed precursor protein ( S3 Fig ) . However , these mutants also accumulate near normal levels of free processed Smt3 and protein conjugates , suggesting that conditional growth defects may be more directly related to Ulp1- or Ulp2-dependent deconjugation of specific substrates . Notably , Ulp2-deficient strains are also hypersensitive to DNA damaging agents similar to the R64E , D68R , R71A and G97A mutants [33 , 67] . Further proteomic and genetic studies with these specific mutants could prove valuable in identifying SUMO conjugates and affected pathways critical for DNA damage and other stress responses . Multiple conditional mutant alleles predicted to affect SIM binding were also identified . Among these mutants , I35A and F37A are predicted to affect binding to the conserved hydrophobic core common to all SIMs [44] , and these mutants exhibited broad and overlapping sensitivities to multiple stress conditions . In contrast , mutant alleles predicted to affect interactions with more variable acidic or phosphorylated residues surrounding the SIM hydrophobic core ( K38E , K40Q and K40E , R55E ) exhibited growth defects more tightly restricted to DNA damaging agents . This suggests the possibility that SIMs present in vital DNA repair factors contain unique acidic features or are potentially phosphorylated in response to DNA damage . Alternatively , these mutations may inhibit the modification of specific DNA repair factors by affecting interactions with Siz1 or other SIM-containing E3 ligases . High copy suppressor screens and other genetic studies are currently being performed with a number of these alleles to identify functionally important interacting proteins . Notably , many of the identified conditional mutant alleles exhibited overlapping sensitivities to different stresses , and no individual surface on Smt3 emerged as being uniquely important for a specific stress response . However , it is important to note that many of the assay conditions tested could activate overlapping stress response pathways , thus limiting the ability to identify function-specific surfaces . Ubiquitin can form chains through conjugation to its N-terminus and 7 internal lysines residues , and multiple studies indicate that chains of varying linkages have unique functions [68] . For example , chain formation through lysine 48 is essential for protein degradation while formation through lysine 63 gives rise to conditional , DNA damage sensitivities [69 , 70] . Previous studies exploring Smt3 chain function found that a KallR mutant , in which all lysines are mutated to arginine , was slow growing and exhibited a variety of defects caused by aberrant higher order chromatin organization , transcription activation and DNA repair [8 , 33] . Similarly , chain-forming mutants of S . pombe SUMO are viable but exhibit abnormal cell morphology and sensitivity to DNA replication stress [71] . Consistent with these findings , we found that the Smt3 KallR mutant strain is viable , but hypersensitive to numerous stress conditions . Importantly , we also found that mutating any individual lysine in Smt3 resulted in no detectable phenotypes under the assay conditions tested . Thus , we obtained no evidence for linkage-specific polymeric chain functions as observed in the ubiquitin system . Consistent with this , reintroducing individual lysines in the KallR mutant at positions K11 , K15 and K19 fully restored Smt3 function , while reintroducing lysines at K41 , K54 and K58 restored all functions except minor sensitivities to cadmium and HU . Single lysines at positions K38 and K40 were less effective at suppressing KallR phenotypes , possibly due to their important roles in SIM recognition that may be disrupted through chain linkages at these positions . K27 was also largely ineffective at suppressing most KallR phenotypes . This lysine is uniquely positioned in the central core of Smt3 and may form chains inefficiently or with a geometry not recognized by downstream effector proteins such as the Slx5/Slx8 STUbL [20] . Taken together , our results indicate that while the potential to form polymeric Smt3 chains is important for optimal growth under a variety of stress conditions , chains formed through multiple different lysines appear to be functionally equivalent . It should be noted that our analysis does not formally discriminate between a requirement for Smt3-Smt3 polymeric chains in stress responses and Smt3-Ub hybrid chains ( see below ) . In addition to functioning as a posttranslational modification , SUMO itself can be modified by phosphorylation , acetylation and ubiquitylation . Phosphorylation has been detected at four sites in Smt3 ( S2 , S4 , S32 and S33 ) , and also at N-terminal serines in human SUMO1 and S . pombe SUMO [71–73] . The functional significance of these modifications , however , remains uncertain . Although our collection contained alanine and aspartic acid substitutions at all serine and threonine residues , to inhibit or mimic phosphorylation respectively , we obtained no clear evidence that phosphorylation of Smt3 affects cell growth under our specific assay conditions . Similarly , we observed no significant effects of inhibiting or mimicking lysine acetylation at any of the nine lysines in Smt3 ( by arginine or glutamine substitutions , respectively ) , despite evidence that acetylation of lysine residues located in the SIM binding surface of human SUMOs inhibits binding to specific SIM-containing proteins [74] . Finally , regarding ubiquitylation , STUbLs have the ability to conjugate ubiquitin onto SUMO to produce hybrid SUMO-ubiquitin chains that affect protein turnover and localization [20 , 75 , 76] . Although the functions of hybrid chains in S . cerevisiae have not been directly analyzed , the intimate link between Smt3 function and the Slx5/Slx8 STUbL implies important roles [77–81] . Our analysis of Smt3 lysine mutations argues against a requirement for linkage-specific SUMO-Ub hybrid chains , at least for the conditions that we assayed . However , as noted above , our analysis of lysine mutations does not formally discriminate between requirements for Smt3-Smt3 polymeric chains and Smt3-Ub hybrid chains . Determining whether ubiquitin modification of Smt3 may be a critical component of cellular stress responses is an important question that will therefore require more detailed analysis . Multiple mutagenic studies have been performed in S . cerevisiae to identify and characterize essential residues and properties of ubiquitin . A review of these studies and comparisons with our Smt3 findings reveals a number of common themes and important distinctions . An early alanine scan of Ub surface residues [69] and more recent high throughput mutagenesis studies [82 , 83] identified an essential “hydrophobic patch” on the surface of ubiquitin that mediates non-covalent interactions with the majority of ubiquitin-binding proteins functioning downstream of ubiquitylation [84] . Thus , mutagenesis of both ubiquitin and Smt3 reveals that binding to downstream effector proteins is a critical determinant of function and that this is largely mediated by a single surface on each protein . Notably , however , the hydrophobic patch of Ub is not conserved in Smt3 and the α-helix and β-strand that mediates SIM binding maps to the opposite face of the Smt3 protein . Thus , ubiquitin and Smt3 have evolved to interact with distinct effector proteins using unique surfaces . A second essential surface of Ub identified in mutagenesis studies includes residues of the tail domain which are critical for conjugation and deconjugation [69 , 82 , 83] . While conjugation and deconjugation are also essential for Smt3 function , our findings revealed that tail residues other than the di-glycine motif are tolerant of substitutions , suggesting greater flexibility in recognition of Smt3 by conjugating and deconjugating enzymes . Alternatively , the greater sensitivity of tail domain substitutions in ubiquitin may be reflective of constraints imposed by the need to interact with a much broader range of E2 and E3 conjugating enzymes and isopeptidases relative to Smt3 . In addition to surface residues , mutagenesis studies have also revealed important roles for core residues in determining ubiquitin structure and function [82 , 83 , 85 , 86] . Alanine substitutions in ubiquitin at I30 and L43 are lethal , and it has been proposed that L43A mutant proteins are degraded by the proteasome together with modified substrates due to reduced structural stability [86] . We also identified lethal core residue substitutions , including F65A ( equivalent to ubiquitin L43 ) , F37/I39A , H23/I24/V28A and I89A that were predicted to be destabilizing by Rosetta modeling analysis . Although the Smt3 F65A mutant accumulated in high molecular mass conjugates resistant to deconjugation , making it distinct from ubiquitin L43A , the H23/I24/V28A mutant displayed some similar properties , including reduce levels of conjugates and free protein . It is currently not known whether SUMO is degraded by the proteasome together with substrates or recycled similar to ubiquitin . Further analysis of the H23/I24/V28 mutant could provide valuable insights . Core ubiquitin residues at or near important binding surfaces were also found to be more sensitive to substitutions , suggesting that subtle structural defects may affect binding [83] . Several potentially destabilizing Smt3 core residue mutations also corresponded to residues near binding surfaces , including F37/I39A near the SIM binding surface , and F65A and I89A near Ulp1 and Ubc9 binding surfaces , suggesting a common theme . More detailed analysis of Smt3 core residue mutations and their effects on protein structure and dynamic stability will be required to more fully understand their effects on function . In summary , we have developed a versatile library of smt3 mutants that can be used to interrogate the functions of sumoylation in the budding yeast , S . cerevisiae . Based on structural analysis , we have provided plausible interpretations of defects associated with individual mutants that are intended to serve as starting points for more detailed molecular and cellular studies . This preliminary analysis revealed essential functions for non-covalent interactions with SIM-containing proteins and the importance and redundancy of polymeric chains linked through internal lysines . We have demonstrated the utility of this library by identifying >40 conditional mutant alleles sensitive to a variety of cellular stress conditions . It can be anticipated that future characterization of these mutants , and alternative screens of the library , will reveal new and fundamentally important insights into SUMO functions .
Prior to library construction , the 2μm plasmid was evicted from the SMT3 shuffle strain ( a derivative of S288C ) , Mata his3Δ1 leu2Δ0 met15Δ0 lys2Δ0 ura3Δ0 smt3::kanMX [pRS316 , SMT3 CEN-URA3] , using a dominant negative allele of Flp recombinase . In short , the SMT3 shuffle strain was transformed with pRS413-GAL10-flp-H305L ( CEN HIS ) ( gift from Dr . Erica Johnson , Thomas Jefferson University ) . Transformants were passaged on synthetic media containing 2% galactose four times then were grown under non-selective conditions to isolate cells that had segregated the pRS413-GAL10-flp-H305L plasmid . In order to confirm that the 2μm plasmid had been evicted , PCR assays targeting two genes found on the 2μm plasmid , FLP1 ( Flp1 F: 5’ GGTGCTTGTTCGTCAGTTTGTG and Flp1 R: 5’ GACAATATCGAAACTCAGCGAATTGC ) and REP1 ( Rep1 F: 5’ GCCAGAGGATGGCGAAC and Rep1 R: 5’ GCTCGCGTTGCATTTTCG ) , were performed . Mutant constructs were synthesized and cloned into pRS413 by BioBasic Incorporated ( Canada ) and supplied as bacterial stocks . To generate the integrated mutant library , plasmids were isolated and digested with EarI to release the mutant construct from the pRS413 backbone . Linearized constructs were integrated into the 2 μm-less SMT3 shuffle strain with leucine selection . Colonies were replica printed and transformants that were LEU+ , ura- , G418 sensitive and 5-FOA resistant were isolated . Two independent colonies were selected for each construct and frozen in 96-well plate formats identical to the original collection . Lethal mutants that could not segregate pRS316 [SMT3 URA] were frozen separately in 96-well plates . For plasmid remediation studies , the 2 μm-less SMT3 shuffle strain was transformed with mutant constructs within the intact pRS413 backbone with leucine selection . As a first step in library validation , each mutant construct that was synthesized by BioBasic Inc . was sequenced verified and analyzed by restriction digests ( BamHI/NotI/XhoI and BamHI/HindIII/XhoI ) to ensure the mutant fragment had been cloned properly into pRS413 . 100% sequence identity and proper cloning were required to pass quality control . Upon receipt of the mutant library , ~20% of the mutant plasmids were isolated and analyzed by restriction digests to ensure the identity of each mutant within the well . Finally , the integrated constructs within every strain in the Smt3 library were PCR amplified ( SMT3–874 F: 5’ GCACCTATAACTCTCAACTTTGAAG 3’ and LEU2 R: 5’ CGAATTTGATTCTGTGCGATAGC 3’ ) to ensure proper targeting of the construct and all PCR products were sequence verified . Synthetic complete ( SC ) growth media for culturing yeast was prepared with 0 . 67% ( w/v ) yeast nitrogen base without amino acids ( US Biological ) , 2% ( w/v ) glucose , 2% ( w/v ) bacto-agar supplemented with 2% ( w/v ) amino acids . Strains were cured of URA3-based plasmids by culturing on synthetic media containing 1 mg/ml 5-fluoroorotic acid ( 5-FOA ) [87] . YPD was prepared with 2% ( w/v ) bacto-peptone , 1% ( w/v ) yeast extract , 2% ( w/v ) glucose and 2% ( w/v ) bacto-agar . Luria broth for culturing bacteria was prepared with 1% ( w/v ) bactopeptone , 0 . 5% ( w/v ) yeast extract and 0 . 5% ( w/v ) NaCl supplemented with either 50 μg/ml carbenicillin ( CARB ) , 34 μg/ml chloramphenicol ( CAM ) and/or 50 μg/ml kanamycin ( KAN ) . The S . cerevisiae E1 activating enzyme expression constructs were provided by Dr . Chris Lima ( Memorial Sloan Kettering Cancer Center ) as pET15b-AOS1 ( CARB ) and pET28b-HIS-UBA2 ( KAN ) . The E1 constructs were co-expressed in E . coli BL21 Rosetta cells . SUMO1 , SUMO2 and Smt3 were all expressed as N-terminally tagged GST proteins in BL21 Codon Plus cells . The N-terminal GST tag was removed by digestion with Precission protease after purification . E1 thioester formation was assayed in reactions containing 0 . 0225 μg/μl E1 , 0 . 0375 μg/μl SUMO or Smt3 , 5 mM MgCl2 , 20 mM Hepes pH 7 . 5 and 50 mM NaCl . Thioester formation was initiated with 5 mM ATP . Samples were collected at various time points then were combined with an equal volume of 2X sample buffer ( 0 . 125 M Tris-HCl pH 6 . 8 , 4% SDS , 20% glycerol , bromophenol blue ) with or without 1 . 43 M beta-mercaptoethanol ( β-ME ) . The samples were separated by SDS-PAGE and proteins were transferred to 0 . 2 μm PVDF membrane ( Immobilion , BioRad ) overnight at 4°C ( 100 volts , 350 mAmps ) . Membranes were analyzed by incubation with a monoclonal antibody to detect the His epitope ( GE Healthcare Life Sciences ) followed by chemiluminescent detection ( SuperSignal West Pico substrate—Thermo Scientific ) . Cells were grown overnight in 100 μl of synthetic complete media in 96-well plates at 25°C . Overnight cultures were serial diluted 25-fold . For SC plates , 8 μl of two dilutions ( 1:625 , 1:15 , 625 ) was spotted onto the test plates . For YPD plates , 2 . 5 μl of two dilutions ( 1:625 , 1:15625 ) was spotted onto the test plates . Spots were allowed to dry on the bench at 25°C for 30 minutes before the plates were incubated . For heat sensitivity experiments , plates were incubated at 25°C , 30°C , 34°C , 37°C and 39°C for 3 days . For cold sensitivity experiments , plates were incubated at 16°C for 2 weeks . For drug studies , SC plates were supplemented with 150 mM hydroxyurea ( HU ) , 0 . 05% methylmethanesulfonate ( MMS ) , 1 . 5 mM hydrogen peroxide ( H2O2 ) , 1 mM paraquat ( PQ ) , 100 μM CdCl2 , 10% EtOH , 0 . 8 M NaCl , 8 mM dithiothreitol ( DTT ) and 0 . 5 mM Azetidine-2-carboxylic acid ( AZC ) . To test camptothecin ( Cpt ) sensitivity , SC plates were buffered with 25 mM HEPES ( pH 7 . 2 ) and supplemented with 15 μg/ml Cpt . To test canavanine ( CAN ) sensitivity , SC–Arg plates were supplemented with 0 . 95 μg/ml canavanine . For β-ME experiments , YPD media was supplemented with 25 mM β-ME . For tunicamycin ( Tn ) experiments , YPD media was supplemented with 0 . 5 μg/ml Tn . Benomyl ( Ben ) sensitivity was tested on YPD media containing 30 μg/ml benomyl . For UV experiments , cells were spotted onto YPD plates , irradiated using a UV Stratalinker ( Invitrogen ) and incubated in the dark . For all of the aforementioned drug and UV studies , plates were incubated at 30°C for up to 7 days . Plates were photographed at 2 and 3 days as well as at later time points between 5 and 7 days , as appropriate . For hypoxia and hyperoxia experiments , SC plates were supplemented with 15 mg/L ergosterol and 0 . 5% Tween-80 . For hypoxia experiments , the plates were incubated in an anaerobic workstation ( Coy Laboratory ) equilibrated to 30°C for 3 days . For hyperoxia experiments , plates were placed in a chamber that was flushed with 100% oxygen for 30 minutes . After the chamber was sealed , the chamber containing the plates was placed in an incubator equilibrated to 30°C for 3 days . Plates for the hypoxia and hyperoxia experiments were photographed at 3 days . All assays were scored at the 3 day time point using a 5-point scale ranging from -3 to +1 with -3 being the most sensitive , 0 representing growth equivalent to wild-type SMT3 and +1 representing resistance . Heat maps were generated by comparing the scores of each strain under the control and test conditions . Mutant residues identified in this study as well as other mutagenic studies with ubiquitin and Nedd8 were mapped onto the surface of their corresponding protein , Smt3 ( 1EUV: ChainB ) , Ubiquitin ( 1UBQ ) or Nedd8 ( 1NDD: Chain B ) , using Pymol [42 , 69 , 88–90] . Cells were grown overnight to saturation in synthetic media at 30°C . The cells were diluted to an OD600 of 0 . 25 in 10 mls of synthetic media the following day and were allowed to grow to an OD600 of 0 . 8 . Given that the SUMO profile does change during different growth phases , all OD600 measurements were monitored closely . The cells were harvested at 1 , 500 x g for 3 minutes then washed with 1 ml of fresh medium . The wash step was performed with fresh medium since SUMO conjugation is rapidly lost in PBS . The supernatant was removed and cell pellets were immediately frozen on dry ice and stored at -80°C . For temperature shift experiments , cells were grown to an OD600 of 0 . 8 then shifted to 37°C or 39°C for varying times between 3 and 20 hours . For the 20 hour shift experiments , the cultures were diluted 1:5 in pre-warmed media before the temperature shift to prevent saturation of the culture . Whole cells lysates were prepared by resuspending the cell pellets in 200 μl of 20% trichloroacetic acid ( TCA ) . The pellets were lysed by bead beating . After the bead beating , 1 ml of 5% TCA was added to the pellets and the samples were left on ice for 10 minutes . The TCA precipitated samples were centrifuged at 15 , 000 x g for 5 minutes and the supernatant was removed . The TCA pellets were resuspended in 2X sample buffer ( 0 . 125 M Tris-HCl pH 6 . 8 , 4% SDS , 20% glycerol , 1 . 43 M β-ME , 0 . 04 M Tris base , 0 . 02% bromophenol blue ) . The samples were boiled for 5 minutes , centrifuged at 15 , 000 x g for 5 minutes to pellet insoluble debris and the supernatants were used as the whole cell lysate . The cell lysates were then separated by SDS-PAGE on either a 12 . 5% or 15% acrylamide gel . Proteins were transferred from the gel to 0 . 2 μm PVDF membrane ( Immobilion , BioRad ) overnight at 4°C . Membranes were blocked in 5% milk and washed with 1X TS ( 0 . 05 M Tris base , 0 . 135 M NaCl , 0 . 32% v/v HCl ) supplemented with 0 . 05% Tween20 . Primary antibodies used included mouse anti-SUMO1 ( 21C7 , 1:1000 ) , mouse anti-SUMO2/3 ( 8A2 , 1:800 ) , rabbit anti-Smt3 ( affinity purified against recombinant Smt3 , 1:200 ) and rabbit anti-tubulin 2 ( 1:20 , 000 ) . All antibodies were diluted in 1X PBS supplemented with 2% bovine serum albumin ( BSA ) and were incubated with PVDF membranes for 1 hour at 25°C . Anti-mouse and Anti-rabbit HRP-linked secondary antibodies ( Amersham , 1:3 , 000 ) were diluted in 5% milk and incubated with membranes for 1 hour at 25°C . Membranes were analyzed by chemiluminescent detection ( ECL plus substrate , Amersham ) . Cells were grown overnight to saturation in synthetic media at 30°C . The cells were diluted to an OD600 of 0 . 25 in synthetic media the following day and were allowed to grow to an OD600 of 0 . 8 in a final volume of 10 mls . The cells were harvested at 1 , 500 x g for 3 minutes , washed with 1 ml of fresh medium and then resuspended in 1 ml of fresh medium . At this point 333 μl of the sample was removed , the cells were harvested and the pellet was frozen on dry ice . This was considered the “Start” sample . The remaining 667 μl of cells were washed with 1X PBS , harvested , then resuspended in 1 ml of ATP depletion solution ( 10 mM sodium azide , 10 mM 2-deoxyglucose in 1X PBS ) . Cells were incubated in the ATP depletion solution at 30°C for anywhere between 30 seconds and 30 minutes . After the depletion , 500 μl of the sample was removed , cells were harvested and the pellet was frozen on dry ice . This was considered the “Deplete” sample . The remaining 500 μl of cells were washed with 1ml of 1X PBS , harvested and resuspended in 1 ml of pre-warmed synthetic medium . Cells were allowed to recover at 30°C for anywhere between 30 seconds and 10 minutes . Following recovery , the cells were harvested and the pellet was frozen on dry ice . This was considered the “Recover” sample . Cells were grown overnight to saturation in synthetic media at 30°C . The cells were diluted to an OD600 of 0 . 25 in synthetic media the following day and were allowed to grow to an OD600 of 0 . 8 in a final volume of 10 mls . The cells were fixed by incubation with 0 . 1 volumes of 37% formaldehyde for 1 hour at 25°C with gentle rocking . After fixation the cells were washed with 1 ml KSorb ( 0 . 1 M KPO4 , 1 . 2 M Sorbitol ) three times . All centrifugation steps were performed at 500 x g for 3 minutes . The cells were spheroplasted by resuspending the washed cell pellet in 1 mL of KSorb supplemented with 0 . 14 M β-ME and 7 . 5 mg/ml zymolyase 20T ( US Biological ) . The cells were rocked gently on a nutator at 25°C for 20 minutes ( ~50% phase dark ) . The spheroplasts were washed gently two times in 1 ml KSorb then stored on ice . The spheroplasts were placed on a poly-lysine coated slide and allowed to settle for 1 hour in a humidity chamber at 25°C . The supernatant was aspirated from the slide then the slide was transferred to -20°C methanol for 6 minute and -20°C acetone for 30 seconds . The slide was allowed to air dry for at least 2 minutes then the slide was blocked with 2% BSA in 1X PBS for 1 hour at 25°C in a humidity chamber . The block was aspirated off of the slide and primary antibody was added to the slide and incubated overnight at 4°C in a humidity chamber . Primary antibodies used included rabbit anti-Smt3 ( affinity purified against recombinant Smt3 , 1:200 ) and mouse anti-GFP ( 1:200 , N86/6 Neuro MAb UC Davis ) . All antibodies were diluted in 1X PBS supplemented with 2% BSA . The slide was washed four times in 2% BSA in 1X PBS . Secondary antibody conjugated to Alexa Fluor 488 or Alexa Fluor 594 ( Life Technologies , Grand Island , NY ) was diluted ( 1:1 , 000 in 2% BSA in 1X PBS ) and added to the slide for 2 hours at 25°C in a humidity chamber . The slide was kept in the dark for all future steps . The slide was washed four times in 2% BSA in 1X PBS and two times with 1X PBS . The slide was allowed to dry , mounting solution ( 100 mM Tris-HCl pH 8 . 8 , 50% glycerol , 2 . 5% DABCO and 0 . 2 μg/ml DAPI ) was added and then the slide was sealed with a coverslip . The CDC48 strain tagged C-terminally with GFP was obtained from the Yeast GFP Clone Collection ( Invitrogen ) . Cells were grown overnight to saturation in synthetic media at 30°C . The cells were diluted to an OD600 of 0 . 25 in synthetic media the following day and were allowed to grow to an OD600 of 0 . 8 in a final volume of 10 mls . The cells were fixed by incubation with 0 . 1 volume of 37% formaldehyde for 1 hour at 25°C with gentle rocking . The cells were harvested by centrifugation at 1 , 500 x g for 3 minutes then were washed twice with 1 ml of 1X PBS . The cells were permeabilized with 70% EtOH on ice for 40 minutes then harvested and washed twice with 1X PBS . The cells were sonicated ( 7 pulses at 1 . 5 output / 20% duty cycle ) , placed on a microscope slide , mounting solution ( 100 mM Tris-HCl pH 8 . 8 , 50% glycerol , 2 . 5% DABCO and 0 . 2 μg/ml DAPI ) was added and the slide was sealed with a coverslip . To predict the effect of each mutation on thermodynamic stability ( ΔΔG ) , differences in energy score ( Rosetta Energy Units ) were calculated for each single mutant in residues 20–98 of the Smt3 crystal structure ( PDB: 1EUV ) using PyRosetta v3 . 4 . 0 [46 , 47] . To create a baseline , all side chains in the wild type structure were repacked , minimized , and scored using the 2010 Dunbrack rotamer library and the talaris2013 score function [91] . For each mutation , 50 structures were produced and scored using the following procedure: ( 1 ) the mutation was made and any residue within 10 Å of the affected residue was repacked , ( 2 ) the backbone was then minimized , along with all side chains , and ( 3 ) the reported ΔΔG was derived from the average of these structures .
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The Small ubiquitin-related modifier ( SUMO ) is a 100 amino acid protein that is attached to other proteins and thereby regulates nearly all essential cell functions . To explore how this small protein modifier functions to regulate so many different processes , we generated a library of >250 mutant alleles of the SUMO gene in the budding yeast , S . cerevisiae . Our analysis of these mutants provides the first comprehensive structure-function based map of the SUMO protein and identifies conditional mutant alleles that can be used to explore the role of sumoylation in protecting cells from proteotoxic and genotoxic stress .
|
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"Abstract",
"Introduction",
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"Discussion",
"Materials",
"and",
"methods"
] |
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"phosphorylation",
"chemical",
"compounds",
"molecular",
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"organic",
"compounds",
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2017
|
A high throughput mutagenic analysis of yeast sumo structure and function
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Infection with human cytomegalovirus ( HCMV ) is a threat for pregnant women and immunocompromised hosts . Although limited drugs are available , development of new agents against HCMV is desired . Through screening of the LOPAC library , we identified emetine as HCMV inhibitor . Additional studies confirmed its anti-HCMV activities in human foreskin fibroblasts: EC50−40±1 . 72 nM , CC50−8±0 . 56 μM , and selectivity index of 200 . HCMV inhibition occurred after virus entry , but before DNA replication , and resulted in decreased expression of viral proteins . Synergistic virus inhibition was achieved when emetine was combined with ganciclovir . In a mouse CMV ( MCMV ) model , emetine was well-tolerated , displayed long half-life , preferential distribution to tissues over plasma , and effectively suppressed MCMV . Since the in vitro anti-HCMV activity of emetine decreased significantly in low-density cells , a mechanism involving cell cycle regulation was suspected . HCMV inhibition by emetine depended on ribosomal processing S14 ( RPS14 ) binding to MDM2 , leading to disruption of HCMV-induced MDM2-p53 and MDM2-IE2 interactions . Irrespective of cell density , emetine induced RPS14 translocation into the nucleus during infection . In infected high-density cells , MDM2 was available for interaction with RPS14 , resulting in disruption of MDM2-p53 interaction . However , in low-density cells the pre-existing interaction of MDM2-p53 could not be disrupted , and RPS14 could not interact with MDM2 . In high-density cells the interaction of MDM2-RPS14 resulted in ubiquitination and degradation of RPS14 , which was not observed in low-density cells . In infected-only or in non-infected emetine-treated cells , RPS14 failed to translocate into the nucleus , hence could not interact with MDM2 , and was not ubiquitinated . HCMV replicated similarly in RPS14 knockdown or control cells , but emetine did not inhibit virus replication in the former cell line . The interaction of MDM2-p53 was maintained in infected RPS14 knockdown cells despite emetine treatment , confirming a unique mechanism by which emetine exploits RPS14 to disrupt MDM2-p53 interaction . Summarized , emetine may represent a promising candidate for HCMV therapy alone or in combination with ganciclovir through a novel host-dependent mechanism .
Infection with Human Cytomegalovirus ( HCMV ) continues to be a major threat for transplant recipients and patients with AIDS [1–3] . It is also the most common congenital infection worldwide [4] . The systemic anti-HCMV drugs all target the viral DNA polymerase and effectively suppress HCMV replication [5] . However , their use is associated with toxicities to the bone marrow ( ganciclovir-GCV ) and kidneys ( foscarnet and cidofovir ) [6 , 7] . GCV and its oral formulation val-GCV are the only drugs used for congenital HCMV infection , based on improved hearing and neurodevelopmental outcomes achieved in infected children [8 , 9] . Because of the limited drugs approved for HCMV , the side effects associated with them , and the emergence of resistant viral mutants during therapy [7 , 10 , 11] , there is a pressing need to develop anti-HCMV compounds with novel mechanisms of action . HCMV inhibitors that target viral proteins other than the DNA polymerase have been identified; UL97 kinase inhibitor , maribavir [12 , 13] and the terminase inhibitor , letermovir [14 , 15] . These agents are in different stages of development [14–16] , and because of the direct virus targets they also select for virus mutants . As part of developing new therapeutics for HCMV , deciphering its complex and evolving interaction with the cellular machinery is necessary for identification of new targets required for HCMV replication . Towards this goal , we screened a library of pharmacologically active compounds ( LOPAC1280 ) and identified emetine as potential HCMV inhibitor . We report on the in vitro anti-HCMV activities of emetine , in vivo activities in a mouse CMV ( MCMV ) model , and a novel host-dependent anti-viral mechanism of HCMV inhibition .
Screening of the LOPAC library using a pp28-luciferase HCMV Towne identified emetine as a potential HCMV inhibitor . A dose response curve was generated to confirm the anti-HCMV activity of emetine . The EC50 of emetine against pp28-luciferase Towne was 40±1 . 72 nM , and the CC50 in non-infected human foreskin fibroblasts ( HFFs ) —8±0 . 56 μM , yielding a selectivity index of 200 . The Hill slope of the concentration-response curve was 3 . 1 , indicating a robust virus inhibition at higher concentrations [17] ( Fig 1A and 1B ) . A ganciclovir ( GCV ) -resistant pp28-luciferase Towne was also inhibited by emetine . Inhibition of HCMV and mouse CMV ( MCMV ) by emetine was confirmed by plaque reduction assay ( S1 Table ) . The activity of emetine against herpesvirus 1 ( HSV-1 ) and HSV-2 was determined by luciferase and plaque assay in HFFs , respectively , revealing virus inhibition at nM concentrations ( S1 Table ) . The expression of HCMV proteins IE1/2 , UL44 and pp65 was significantly reduced by emetine at 72 hours post infection ( hpi ) ( Fig 1C ) . Combination of emetine and GCV was synergistic in HCMV inhibition , as determined by the Bliss model ( Fig 1D ) . These results indicate robust in vitro inhibition of HCMV , GCV-resistant HCMV , MCMV and HSVs at nM concentrations of emetine . At these concentrations emetine did not inhibit protein synthesis in non-infected or HCMV-infected cells ( S1 Fig ) , in agreement with previous studies [18 , 19] . Using immunofluorescence assay for pp65 , neither emetine nor GCV inhibited viral entry , but CPG 2006 ( a TLR9 ligand ) , used as positive control , did ( Fig 2A ) . In add-on and removal assays emetine or GCV were added or removed at 0 , 6 , 12 , 24 , 36 , 48 and 60 hpi , and supernatants were collected at 72 hpi for titration of infectious virus by plaque assay . Addition of emetine after 12 h resulted in its loss of activity against HCMV ( Fig 2B , p< 0 . 0001 ) . The removal assay revealed that emetine was required for at least 24 h to fully inhibit HCMV ( Fig 2C , p< 0 . 05 ) ) . Thus , HCMV inhibition occurred during the immediate-early to early stages of HCMV replication . Since PK data with low dose emetine have not been reported before , we conducted a PK study in BALB/c mice after single oral administration of 0 . 1 mg/kg emetine . In vivo exposures of emetine in plasma , liver , lung and spleen were monitored ( S2 Table ) . Emetine achieved levels that exceeded its in vitro EC50 against HCMV ( Fig 3A ) and its calculated half-life was 35 h . The effect of emetine on MCMV replication was tested . BALB/c mice ( 3–4 week old ) were infected intraperitoneally with tissue-culture derived MCMV [106 plaque forming units ( PFU ) /mice] and treated with 0 . 1 or 1 mg/kg of emetine orally every three days beginning at 24 h or 72 h after infection until day 11 post infection . On day 14 post infection , mice were euthanized , intracardiac blood was collected and tissues were harvested and assayed for MCMV replication by plaque assay . Emetine treatment resulted in 2 to 4 log10 decrease in MCMV DNA copy number in blood , compared to infected control ( Fig 3B , p<0 . 001 ) . All treatment regimens resulted in 4–6 fold reduction of viral PFUs in salivary gland ( Fig 3C ) and 3–6 fold reduction in liver ( Fig 3D , p<0 . 0001 ) . Both doses of emetine were highly efficacious in MCMV inhibition , when administered at 24 or at 72 hpi . The following in vitro studies were undertaken to elucidate the mechanism of anti-HCMV activity of emetine and should not point to in vivo efficacy , since MCMV was inhibited with emetine , and most HCMV-infected cells in vivo are high-density . We observed that cell density at the time of infection determined HCMV inhibition by emetine . Lack of HCMV inhibition by emetine was not because of selection of resistant viruses . Increasing emetine concentration did not select for resistant mutants , while GCV selected for a C607Y mutation in UL97 , confirmed by sequencing . To investigate the contribution of cell density to the anti-HCMV activities of emetine HFFs were seeded at 0 . 5–2 million cells/plate followed by infection and drug treatment . HCMV inhibition by emetine improved as cell confluence at the time of infection increased . There was no reduction in normalized pp28-luciferase activity by emetine at low cell density , whereas with increased cell density significant reduction was observed ( Fig 4A ) . Western blots revealed decreased expression of viral UL44 and pp65 with emetine at high but not at a lower cell density ( Fig 4B ) . Second cycle infection similarly showed dependence of HCMV inhibition by emetine on cell density; there was 1 . 5-fold increase in pp28-luciferase activity at 0 . 5 million cells/plate ( p>0 . 5 ) , and near complete inhibition at 1 and 2 million cells/plate ( p<0 . 001 ) , compared to infected control ( Fig 4C ) . Plaque reduction assays showed 30-fold reduction in the number and size of plaques with emetine at 2 million cells/plate ( p<0 . 001 ) as compared to 0 . 5 million cells/plate ( Fig 4D , p>0 . 05 ) . A plaque reduction assay of MCMV similarly showed a cell-density dependent response to emetine ( Fig 4D , p<0 . 001 ) . GCV inhibited HCMV and MCMV irrespective of cell density ( p<0 . 001 ) . These results suggest that cell cycle related activities may determine HCMV inhibition by emetine . Resistance to emetine in Chinese hamster ovary cells was associated with mutations in the ribosomal protein S14 ( RPS14 ) [20] . This protein was reported to interact with MDM2-p53 , major regulators of cell cycle progression [21] . We theorized that the regulation of RPS14 by emetine determines its anti-HCMV activities in high-density cells . RPS14 expression was measured in high-density and low-density cells , infected or non-infected . In high-density cells , RPS14 expression was induced at 72 hpi , and reduced with emetine ( Fig 5A , left ) . However , in infected low-density cells RPS14 expression was unchanged ( Fig 5A , right ) . At 24 hpi the expression level of RPS14 was unchanged in high-density or in low-density cells ( Fig 5A , upper panel , left and right ) . Irrespective of cell density , there was no change in RPS14 expression in non-infected cells treated with emetine ( Fig 5B ) . Thus , induction of RPS14 expression at 72 hpi and its reduction by emetine was specific to infected high-density cells . The anti-HCMV activity of emetine occurred at the immediate early-early stage of HCMV replication ( Fig 2A and 2B ) , thus , we suspected that the changes in RPS14 expression at 72 hpi represent an outcome of an earlier event . Since RPS14 was reported to interact with MDM2 during ribosomal stress [21] , the outcome of the MDM2-RPS14 interaction during HCMV infection and emetine treatment was investigated . As an E3 ubiquitin ligase , MDM2 degrades proteins to which it binds [22] , therefore experiments were performed in the presence of the proteasomal inhibitor , MG132 . At 24 hpi emetine strongly induced the interaction between RPS14 and MDM2 in high-density cells , but not in low-density cells ( Fig 5C ) . Similarly , a reverse immunoprecipitation ( IP ) showed enhanced interaction between RPS14-MDM2 in high-density cells ( Fig 5D ) . Anti-RPS19 and isotype control antibodies further confirmed the specificity of the RPS14-MDM2 interaction ( S2A Fig ) . Emetine did not induce an interaction between RPS19 and MDM2 . Since the inhibition of MCMV replication by emetine was also cell-density dependent in vitro ( Fig 4D ) , the RPS14-MDM2 interaction was tested in MCMV-infected mouse embryonic fibroblasts ( MEFs , S3A Fig ) , again revealing RPS14-MDM2 interaction only in high-density MEFs . In non-infected HFFs emetine did not induce an interaction between RPS14 and MDM2 ( S4A Fig ) . A summary model shows the differences in RPS14 and MDM2 interaction between infected high-density and low-density cells with emetine treatment ( Fig 5E ) . Since RPS14 expression was reduced at 72 hpi with emetine treatment ( Fig 5A , left ) , the enhanced interaction between MDM2 and RPS14 at 24 hpi suggested that MDM2 may be targeting RPS14 for degradation in infected cells . To interact with MDM2 , we predicted that RPS14 would translocate into the nucleus . Using confocal microscopy , RPS14 was located in the cytoplasm of non-infected or HCMV-infected cells ( Fig 6 , upper two panels ) . In high-density cells emetine treatment induced RPS14 translocation into the nucleus at 24 hpi ( Fig 6A , 66% localization , p<0 . 001 ) , but at 72 hpi nuclear localization of RPS14 decreased to a similar level as in non-treated cells ( Fig 6B , 11% localization , p>0 . 5 ) . However , in infected low-density cells nuclear localization of RPS14 was observed at both 24 and 72 hpi ( Fig 7A & 7B , p<0 . 001 ) , indicating that although emetine could initially induce nuclear translocation of RPS14 in infected low-density cells , subsequent localization changes did not occur , likely because RPS14 did not interact with MDM2 . Since in emetine treated high-density cells , RPS14 expression and its nuclear localization were decreased at 72 hpi ( Figs 5A and 6B ) , while a strong interaction with MDM2 was observed at 24 hpi ( Fig 5C and 5D ) we conjectured that RPS14 may be targeted for degradation . MG132-treated samples were pulled-down with anti-Ubiquitin antibody and detected with anti-RPS14 antibody . In both cell densities , RPS14 ubiquitination mildly increased with infection . However , only in high-density cells , emetine increased both the mono and poly-ubiquitinated forms of RPS14 ( Fig 8A , left ) . A reverse IP similarly showed increased RPS14 ubiquitination in high-density cells . In agreement with RPS14 ubiquitination , at 72 hpi there was almost no nuclear RPS14 in high-density infected emetine treated cells , indicating that emetine targets RPS14 for ubiquitination and degradation ( Fig 6B ) . In infected low-density cells RPS14 ubiquitination was not increased with emetine , in agreement with its persistent nuclear localization at 72 hpi ( Fig 8A , right ) . The rate of degradation of RPS14 in high- and low-density cells was tested with the protein synthesis inhibitor , cycloheximide . As expected , RPS14 showed significant degradation in infected high-density cells , compared to infected only or infected GCV-treated cells ( Fig 8B , left ) . However , there was no substantial degradation of RPS14 in the low-density cells under any of the conditions used ( Fig 8B , right ) . Taken together , emetine treatment of infected high-density cells results in early RPS14 translocation into the nucleus of infected cells followed by its relocalization into the cytoplasm for ubiquitination and degradation , ultimately resulting in decreased RPS14 expression at 72 hpi ( Fig 5A , left ) . Since emetine inhibited HCMV in high-density , but not in low-density cells , and induced an interaction between MDM2 and RPS14 in the former , the expression level of p53 and MDM2 was measured in the different cell densities at 24 and 72 hpi . MDM2 expression was reduced after infection and increased with emetine treatment at 72 hpi in high-density cells ( Fig 9A ) . The expression of p53 was unchanged with infection and increased with emetine treatment at 72 hpi ( Fig 9A ) . There was no difference in the expression of either protein among the different conditions at 24 hpi or in infected low-density cells at 72 hpi ( Fig 9A ) . The downstream activity of p53 was measured by quantitative reverse transcriptase ( qRT ) -PCR for p21 ( 22 ) . Emetine treatment resulted in 5 . 4- and 6 . 7- fold increase in p21 mRNA at 24 and 72 hpi , respectively in high-density cells ( Fig 9B , p<0 . 01 ) . In non-infected cells , the expression of p53 and MDM2 was increased with emetine , indicating both proteins were stabilized with emetine , irrespective of infection ( Fig 9C ) . RPS14 was reported to bind to the acidic domain of MDM2 , which is also the binding site for p53 [23] . Therefore , the effect of infection and emetine treatment on MDM2-p53 interaction was studied . Since p53 is degraded by the E3 ubiquitin ligase activity of MDM2 [24 , 25] , MG132 treated samples were used for IP . The interaction between MDM2 and p53 was favored upon infection , disrupted with emetine in high-density cells , but not in low-density cells ( Fig 9D ) . A reverse IP confirmed this interaction in infected cells and loss thereof with emetine ( Fig 9E ) , and isotype control antibodies did not pull down either MDM2 or p53 ( S2B Fig ) , indicating that emetine disrupts HCMV-induced interaction of MDM2-p53 ( model , Fig 9F ) . Similar interactions were observed in MCMV-infected MEFs ( S3B Fig ) . Since the expression of p53 and MDM2 was increased with emetine in non-infected cells , MDM2-RPS14 interaction was investigated in non-infected cells . Emetine could not induce MDM2-RPS14 interaction in non-infected cells ( S4A Fig ) . Therefore , emetine could stabilize MDM2 and p53 irrespective of infection but its ability to associate RPS14 with MDM2 resulting in p53 activation was achieved only in infected high-density cells , probably because in non-infected cells it could not trigger RPS14 localization into the nucleus ( S4B Fig ) . Finally , the enhanced interaction between RPS14-MDM2 could have effects on HCMV proteins that bind to MDM2 . The immediate early 2 ( IE2 ) was reported to interact with MDM2 [26] , therefore the effect of emetine on IE2-MDM2 interaction was tested in HEK293 cells . An IP was performed after IE1/2 transfection and emetine treatment . In emetine-treated cells , the interaction between MDM2 and IE2 was significantly decreased , while GCV had no effect on this interaction ( S5 Fig ) . These results suggest that emetine-induced occupancy of MDM2 by RPS14 may prevent it from binding IE2 . As emetine induced nuclear translocation of RPS14 and its interaction with MDM2 , we investigated the anti-HCMV activity of emetine in RPS14 knockdown cells ( sh-RPS14 ) . The expression of RPS14 was reduced in sh-RPS14 cells as compared to its expression in TRC-control shRNA cells ( Fig 10A ) . Cell viability was similar between control transduced and sh-RPS14 cells during infection and drug treatment ( Fig 10B ) . In high-density sh-RPS14 cells , emetine was unable to inhibit HCMV , evident from luciferase assay at 72 hpi ( Fig 10C ) , a plaque reduction assay , ( Fig 10D ) and expression of pp65 , UL44 and IE1/2 at 72 hpi ( Fig 10E ) , indicating the requirement of RPS14 for emetine activities . In shRPS14 cells a stable interaction between MDM2 and p53 was observed despite emetine treatment , while as expected the interaction was disrupted in TRC control cells ( Fig 10F ) . These results indicate that a certain level of RPS14 is required for its interaction with MDM2 , in emetine treated HCMV-infected cells , resulting in displacement of IE2 and p53 from MDM2 .
The existing antiviral drugs effectively suppress HCMV replication , but their considerable side effects , and selection of resistant viral strains , call for the identification of new HCMV inhibitors [27] . HCMV perturbs a myriad of cellular signaling pathways for its own benefit of replication and survival [28] , some of which could serve as novel targets for virus inhibition . We report here on the anti-HCMV activities of emetine at low nM concentration ( Fig 1 ) , and its mode of action through modifying the interaction of MDM2-RPS14/-p53 , thus providing a novel host-dependent antiviral approach . Emetine inhibited GCV-resistant HCMV , MCMV and HSV1&2 at nM concentrations as well ( S1 Table ) . Inhibition of HCMV replication by emetine occurred after virus entry and before GCV activities ( Fig 2 ) . MCMV was inhibited at low 0 . 1 mg/kg . There was no significant difference in MCMV inhibition between 0 . 1 and 1 mg/kg . Plaque number was lower in the liver compared to the salivary gland ( as expected ) , but virus was inhibited in both organs ( Fig 3 ) [29] . The observed lack of activity of emetine against HCMV and MCMV in low-density as compared to high-density cells ( Fig 4 ) , and a previous study that correlated emetine resistance in Chinese hamster ovary cells with mutations in RPS14 [30] , prompted us to investigate the functional role of RPS14 in HCMV infection and emetine treatment ( summary model , Fig 11 ) . HCMV induces multiple ribosomal proteins , but whether any of these ribosomal proteins may be utilized by drugs to inhibit virus replication has not been studied [31] . Our results show that HCMV induces RPS14 , likely as a strategy for viral protein synthesis ( Fig 5A ) . In low-density non-infected cells , higher expression of RPS14 was observed compared to high-density cells , suggesting the former are more active in synthesizing proteins for ongoing growth and division . Reduced RPS14 expression at 72 h in HCMV-infected emetine-treated cells was a result of its degradation , which was consequential to its interaction with MDM2 . The reported interaction of RPS14 with MDM2 [21] directed us to investigate the role of RPS14 in the setting of MDM2-p53 interaction in HCMV replication ( model , Fig 11 ) . Emetine treatment improved the interaction between RPS14 and MDM2 in infected high-density cells . In non-infected cells ( S4A Fig ) the interaction of RPS14-MDM2 was not modified by emetine , likely because RPS14 did not translocate into the nucleus ( S4B Fig ) . Therefore , infection facilitates nuclear translocation of RPS14 by emetine . In infected cells emetine induced RPS14 translocation into the nucleus ( Fig 6A ) , where it could interact with MDM2 and compete with viral proteins , such as IE2 ( S5 Fig ) and cellular proteins such as p53 on the acidic domain of MDM2 ( Fig 9C and 9D ) . Although the binding site of MDM2-IE2 has not been characterized , it is possible that RPS14 bound MDM2 is incapable of binding to IE2 . Later during infection , RPS14 relocalized to the cytoplasm , was ubiquitinated and degraded ( Figs 6B , 8A & 8B ) . RPS14 interacted with MDM2 at 24 hpi ( Fig 5C ) , but at this time point its expression level was similar in both low and high cell density ( Fig 5A ) , suggesting that a balance between virus-induced RPS14 and its emetine-triggered degradation was still maintained at this time point . However , at 72 hpi , when virus replication was sufficiently inhibited , a significant reduction in the expression of RPS14 was observed in emetine-treated condition , indicating emetine-mediated RPS14 degradation ( Fig 5A ) . The dependence of emetine activity against HCMV on cell density ( Fig 4 ) suggested that interaction of RPS14 with cell cycle regulators MDM2 and p53 may contribute to its activities . Reduced MDM2 expression during HCMV infection has been reported [26] , and also shown here at 72 hpi ( Fig 9A ) . Although infection at MOI 2–5 was reported to induce p53 expression , albeit reduction in p53 activity [32–35] , at MOI 1 we did not observe changes in p53 expression . Emetine treatment resulted in increased p53 activity in infected cells , evidenced by p21 mRNA expression ( Fig 9B ) . Levels of MDM2 and p53 were stabilized with emetine ( Fig 9A and 9C ) , suggesting their interaction might be modified by the drug . Our data reveal that while the MDM2-p53 interaction is induced by HCMV , emetine disrupts it by 24 h , resulting in stabilization of each interacting partner ( Fig 9D ) . In infected low-density cells in which HCMV escaped from emetine suppression ( Fig 4 ) , MDM2-p53 interaction was not disrupted by the drug ( Fig 9D and 9E ) . Notably , in non-infected low-density cells the interaction between MDM2 and p53 was more significant than in high-density cells , suggesting that emetine fails to disrupt pre-existing interaction between MDM2-p53 in the low density cells ( Fig 9D and 9E ) [36 , 37] . The p53 protein binds to MDM2 at the acidic domain ( amino acid residues 235–300 ) [23] . RPS14 also binds to the same acidic domain of MDM2 at an overlapping region with the p53 binding site ( residues 215–300 ) [21] . Therefore , RPS14 may be competing with p53 for the same binding domain on MDM2 . The reported mutations in the C-terminus domain of RPS14 that lead to emetine resistance; Arg-149-Cys and Arg-150-His [38] , may imply altered binding to MDM2 . Alternatively , haploinsufficiency of RPS14 may affect the interaction of MDM2-p53 [39] . Similarly , our RPS14 knockdown data indicate its necessity for the anti-viral activity of emetine , and that this activity affects the interaction of MDM2-p53 ( Fig 10 ) . In HCMV-infected cells , binding of RPS14 to MDM2 was required for emetine activities . In low-density cells , MDM2 and p53 already interacted , suggesting the acidic domain of MDM2 was already occupied by p53 . Therefore , RPS14 could not bind to MDM2 , resulting in loss of emetine activity against HCMV . In contrast , in high-density cells , emetine induced RPS14 binding to the free MDM2-acidic domain and prevented virus-mediated interaction between MDM2-p53 , resulting in stabilization of p53 and MDM2 . These findings were also supported by the early activity of emetine ( Fig 2B ) . If added after 12 hpi , emetine failed to inhibit HCMV , since by that time HCMV may engage p53 with MDM2 , resulting in blocking of the RPS14 binding site on MDM2 . Although in non-infected cells , emetine stabilized MDM2 and p53 in both cell densities ( Fig 9C ) , it could not induce the nuclear translocation of RPS14 or its binding with MDM2 ( S4 Fig ) . Future studies will address the consequences of disruption of MDM2-p53 binding and stabilization of p53 and MDM2 as a cellular strategy for HCMV inhibition . Our results show efficient inhibition of HCMV replication in vitro and MCMV replication in vivo , suggesting that repurposing of emetine at a much lower dose may provide therapeutic/prophylactic strategy for HCMV through a host-directed antiviral mechanism . Although the route of administration and potential cumulative toxicities must be tested , the doses required for virus inhibition are substantially lower than the traditional emetine doses that have been used in the past . For amebiasis , emetine has been administered daily at 1 mg/kg for up to 10 days ( maximal dose of 600 mg ) . Severe side effects occurred rarely and were only observed at high doses . Emetine was well-tolerated when delivered intravenously at 1 . 5 mg/kg doses twice a week in clinical trials as an anti-tumor agent [40] . Patients treated with 1 mg/kg emetine daily for 10 days did not experience any toxicity [41] . We show that MCMV replication was inhibited using an oral dose of 0 . 1 mg/kg . Using allometric scaling this translates to a human dose of 0 . 008 mg/kg [42] . Based on our PK data , emetine can be administered every 3 days . For a 60 kg individual 0 . 5 mg/dose would be considered for HCMV therapy . In one month , 10 doses will result in a cumulative dose of 5 mg . Therefore , to reach the 600 mg amebiasis dose 120 months of HCMV therapy would need to be provided . The expected cumulative dose with regimens used in general for HCMV therapy would be substantially lower than the doses that have been used in the past . The therapeutic plasma concentration of emetine is 0 . 005–0 . 075 μg/mL [43] . Our in vitro data suggest that at therapeutic plasma concentration HCMV replication can be fully inhibited . In addition , our PK studies support wide and prolonged tissue distribution , which may be an important factor for HCMV inhibition . Although emetine displays activities against other intracellular pathogens [44–49] , its mode of action has been largely unknown . The killing of Entamoeba histolytica was attributed to inhibition of protein synthesis , an activity that is distinct from HCMV inhibition . Our study provides evidence for use of an old agent with distinct cellular activities resulting in HCMV inhibition .
Animal work 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 animal protocol ( protocol number MO13M296 ) was approved by the Institutional Animal Care and Use Committee ( IACUC ) of Johns Hopkins University . Ganciclovir ( GCV ) , MG132 , CPG 2006 , cycloheximide and emetine dihydrochloride hydrate were purchased from Sigma-Aldrich ( St . Louis , MO ) . The pp28-luciferase HCMV Towne and a GCV-resistant pp28-luciferase were used [50 , 51] . The recombinant viruses express luciferase under the control of the late CMV gene promoter , pp28 . Luciferase expression is activated 48–72 hours post infection ( hpi ) . The recombinant viruses provide a highly-sensitive reporter system which correlates with plaque reduction assay [50] . The Towne HCMV strain ( ATCC VR-977 ) was used for plaque reduction , quantitative reverse transcriptase PCR ( qRT-PCR ) , and immunoprecipitation assays . Human herpes virus strains were: luciferase HSV1- KOS/Dlux/oriS [52] and clinical isolates of HSV2 . The clinical isolates were provided by the clinical microbiology laboratory with no identifiers that can link to a specific subject . Murid Herpesvirus ( MCMV: ATCC VR-1399 ) was used for infection of mouse embryonic fibroblasts ( MEFs ) and mice . Human Foreskin Fibroblasts ( HFFs ) passage 12–16 ( ATCC , CRL-2088 ) and mouse embryonic fibroblasts ( MEFs ) passage 9–14 ( ATCC , CRL-1658 ) were grown in Dulbecco's Modified Eagle Medium ( DMEM ) containing 10% fetal bovine serum ( FBS ) ( Gibco , Carlsbad , CA ) in a 5% CO2 incubator at 37°C . Infection with HCMV or HSV was performed in HFFs , and infection with mouse CMV was performed in MEFs . For HCMV , cells were seeded in either a 96-well plate or 100 mm culture plates ( Corning Costar , Sigma Aldrich ) at 0 . 5 or 2 million cells /plate . Following 90 minute adsorption , media was removed and cells were washed with PBS . Media containing 4% FBS and compounds were added to each well . Infected treated HFFs were collected at 72 hpi and lysates were assayed for luciferase activity using a luciferase assay kit ( Promega , Madison , WI ) on GloMax-Multi+ Detection System ( Promega ) . In second cycle assays , supernatants were collected from all conditions of the first cycle at 96 hpi and used for infection of fresh HFFs in 96-well plates . Luciferase activity was measured 72 h following second cycle infection . For HSV1-KOS/Dlux/oriS , a luciferase assay was performed at 24 hpi . Plaque assays were performed with clinical isolates of HSV2 and HCMV Towne ( ATCC VR-977 ) . HFFs were seeded at 3 million cells/plate in 12-well plates and infected 24 h later with HSV2 at 200 PFU/well . For HCMV or MCMV , HFFs or MEFs were seeded at 0 . 5 or 2 million cells /plate in a 12-well plate and infected 24 hours later at 100 PFU/well . Following virus adsorption ( 60 min and 90 min for HSV and HCMV , respectively ) , virus was aspirated , and DMEM containing 4% fetal bovine serum ( FBS ) with ( for HSV ) or without ( for HCMV/MCMV ) 0 . 5% carboxymethyl-cellulose , were added with the compounds at indicated concentrations into triplicate wells . After incubation at 37°C for 10 days ( for HCMV ) , 3 days for MCMV or 2 days ( for HSV ) , the overlay was removed and plaques were counted after crystal violet staining . Screening of drug resistant virus was performed as reported [53] . Two million cells were plated on a 6-well plate and infected with HCMV ( MOI 0 . 05 ) . After 90 min cells were washed with PBS and 10 nM of emetine or 0 . 5 μM of GCV were added . The cells were maintained in DMEM with 4% FBS until plaques were observed . The supernatants from these plates were used to infect fresh HFFs in a 6-well plate and each time drug concentration was increased by two fold . The cells were passaged 5 times until a final drug concentration of emetine ( 2 . 4 μM ) and GCV ( 10 . 5 μM ) . DNA extracted from supernatants collected at the last stage was used for UL97 sequencing . Cells were seeded in 96-well plates , treated with various concentrations of emetine and incubated at 37°C for 3 days . Cell viability was determined by an MTT-based colorimetric assay ( Sigma-Aldrich ) , and performed at the same time points as the antiviral assay . The combined inhibitory effect of compounds on HCMV replication was determined in infected HFFs as previously reported [54] . The Bliss model , in which drug combination represents the product of two probabilistically independent events , was used for analysis [55 , 56] . One million HFFs were seeded in a 96-well black , clear bottom tissue culture plate . After 24 h , cells were infected with HCMV Towne and treated with 50 , 100 , 1000 , 2000 and 5000 nM emetine for 24 h or 100 mM cycloheximide for 30 minutes . A protein synthesis assay kit ( Cayman Chemical , Ann Arbor , Michigan ) was used according to manufacturer's protocol to quantify emetine-mediated translation inhibition using a fluorescence based assay [57] . HFFs were infected with HCMV Towne , and at 0 , 6 , 12 , 24 , 36 and 48 hpi , emetine or GCV were added . For time-of-removal studies , medium containing the compounds was removed at 0 , 6 , 12 , 24 , 36 and 48 hpi , cells were washed three times with PBS , and drug-free medium was added . Culture supernatants were collected at 72 hpi and titration of infectious virus was performed after 14 days by plaque assay . Emetine , GCV and a human-specific Toll-like receptor 9 ( TLR9 ) ligand , CpG 2006 [58] , were used to determine inhibition of virus entry . Compounds were diluted in serum-free medium and added to HFFs seeded on chamber slides 24 h prior to infection . After infection and treatment , cells were fixed , permeabilized , and air-dried . Cells were incubated with mouse monoclonal anti-pp65 antibody at 37°C in humidified chambers for 1 h , washed three times with 0 . 1% Tween 20 in PBS ( PBST ) , incubated with rhodamine conjugated anti-mouse IgG ( Sigma Chemical Co ) at 37°C in humidified chambers for 1 h , and washed with PBST ( 0 . 1% Tween 20 ) . A drop of mount oil containing DAPI ( 4 , 6-diamidino-2-phenylindole ) ( Santa Cruz ) was added to the slides before visualization with a Nikon Eclipse E-800 fluorescence microscope . Male BALB/c mice ( n = 3 per time point ) were treated with a single oral administration of 0 . 1 mg/kg emetine . The dosing solution was prepared in saline with a dosing volume of 10 mL/kg . Blood and tissue samples including liver , lung and spleen were collected at 0 , 0 . 083 , 0 . 25 , 0 . 5 , 0 . 75 , 1 , 2 , 3 , 4 , 7 , 24 , 30 , 48 , 72 and 96 hr . Emetine concentration in plasma and tissue homogenates was determined by LC-MS/MS . Pharmacokinetic parameters ( Cmax , Tmax , AUC and t1/2 ) were calculated with a non-compartmental approach using the Pharsight WinNonLin software ( Ver . 6 . 4 ) . The experimental procedures were approved by the Animal Care and Use Committee of Division of Veterinary Resources , NIH . For infection experiments BALB/c mice , 4–6 weeks old , were purchased from Harlan Laboratories ( Indianapolis , Indiana ) . The Animal Care and Use Committee of Johns Hopkins University approved the experimental procedures . After 2–3 days of adaptation to the housing environment , mice were randomly divided into seven groups as follows: control ( 5 mice ) , infected ( 12 mice ) , infected + emetine , 0 . 1 mg/kg treated at 72 hpi ( 10 mice ) , infected + emetine 1 . 0 mg/kg ( 10 mice ) treated at 72 hpi , infected + emetine , 0 . 1 mg/kg treated at 24 hpi ( 8 mice ) , infected + emetine 1 . 0 mg/kg ( 8 mice ) treated at 24 hpi and infected + GCV 10 mg/kg ( 10 mice ) . Mice were infected intraperitoneally with 106 PFU/mice ( 0 . 1 mL in 0 . 8% saline ) . Control mice received 0 . 1 ml of saline intraperitoneally . Emetine was administered orally every three days . GCV was administered intraperitoneally twice daily . A total of three doses of emetine and ten doses of GCV was administered . Control and infected mice received equivalent volumes of saline . Mice were sacrificed at day 14 after infection . Blood samples were collected by cardiac puncture . Salivary glands and liver were harvested and stored at -80°C . Organs were homogenized in DMEM with 4% FBS at a final concentration of 100 mg/mL . Two million MEFs were seeded into 24-well plates . From each sample , 5% of the salivary gland homogenate or 10% of the liver homogenate was used for infection of MEFs in triplicates . Plaques were counted after three days . Whole blood viral load was measured at day 14 by real-time PCR of the glycoprotein B ( gB ) gene [59] . DNA was extracted using the DNA blood mini kit ( Qiagen , Georgetown , MD ) . Total RNA was isolated from cultured cells using RNeasy Mini kit ( Qiagen ) . RevertAid first strand cDNA synthesis kit ( Fermentas life sciences , Cromwell Park , MD ) was used to synthesize first strand cDNA from total RNA using oligo-dT primers . Negative RT reactions were included to ensure the specificity of qRT-PCR reactions . Synthesis of first strand cDNA from mRNA template was carried out at 42°C for 1 h . Quantitative RT-PCR was performed in triplicates using specific primers for p21 ( F: 5’ TGG AGA CTC TCA GGG TCG AAA 3’; R: 5’ CGG CGT TTG GAG TGG TAG AA 3’ ) and SYBR green ( Fermentas life science ) with two-step cycling protocol ( 95°C for 15 s , 60°C for 1 min ) . GAPDH ( F: 5’ CGG AGT CAA CGG ATT TGG TCG TAT 3’; R: 5’ AGC CTT CTC CAT GGT GGT GAA GAC 3’ ) was used as the internal control . Cell lysates were quantified for protein content using bicinchoninic acid ( BCA ) protein assay kit ( Pierce Chemical , Rockford , IL ) . Equivalent amount of proteins were used for Western blot analysis as described previously ( 11 ) . Protein bands were visualized by chemiluminescence using Western Blotting Luminol Reagent ( Santa Cruz Biotechnology , Santa Cruz , CA ) . The following antibodies were used for detection of HCMV proteins: mouse anti-IE1 and IE2 , ( MAb810 ) ; mouse anti-UL44 ( Santa Cruz biotechnology ) ; mouse monoclonal anti-pp65 ( Vector laboratories , Burlingame , CA ) ; mouse monoclonal anti-β-actin antibody ( Millipore , Billerica , MA ) ; rabbit polyclonal anti-RPS14 and mouse monoclonal anti-MDM2 ( AbCam , Cambridge , UK ) , mouse monoclonal anti-p53 ( Santa Cruz ) ; horseradish peroxidase ( HRP ) -conjugated goat anti-rabbit IgG ( Cell Signaling ) ; and HRP-conjugated sheep anti-mouse IgG , ( GE Healthcare , Waukesha , WI ) . For detection of co-immunoprecipitated proteins , protein A-HRP conjugate ( Cell Signaling Technologies , Beverly , MA ) was used as secondary antibody to eliminate the interfering IgG bands [60] . HEK-293 cells seeded into 10 cm petri dishes were transfected with HCMV plasmid encoding IE1 and IE2 ( pRL45 ) [61] , using Lipofectamine 2000 in serum free medium . After 6 h , 10% FBS containing media was added along with 10 μM of MG132 . Following overnight incubation emetine ( 75 nM ) or GCV ( 5 μM ) were added for 4 h . Cells were then harvested to prepare lysates for IP . Non-infected , infected or emetine-treated HFFs were treated with MG132 ( 10 μM ) for 12h . Cells were harvested after 24 hpi and lysed with IP buffer containing 150 mM NaCl , 50mM Tris pH 7 . 5 , 2 mM EDTA , 0 . 5% TritonX-100 and 0 . 5% NP-40-40 . 1 mg of lysate was precleared with bead slurry for 30 min . The precleared lysates were incubated with anti-MDM2 ( 2 μg ) antibody overnight . The antibody complexes were isolated using protein A/G beads ( Santa Cruz ) , washed three times with 50% IP buffer . The immunoprecipitate-protein A/G beads were boiled in SDS sample buffer , and the supernatant was analyzed on SDS-PAGE gels after immunoblotting as described previously . 1% of the cell lysate used for IP was loaded on gels as ‘Input’ . Confirmatory reverse IPs were performed as described in each experiment . Isotype control antibodies included: rabbit IgG polyclonal , mouse IgG2a kappa and mouse IgG2b kappa monoclonal ( Abcam ) . Mouse monoclonal RPS19 antibody ( Abcam ) served as an additional negative control for the IPs . Densitometry analysis was performed to determine relative ratio of immunoprecipitated protein to its input level . Two million cells were plated on a chamber slide followed by infection with HCMV and treatment with emetine or GCV for 24 or 72 h . Cells were fixed with 3 . 7% paraformaldehyde for 20 min at room temperature , permeabilized with ice cold methanol for 10 min at -20°C and blocked with 5% bovine serum albumin in 0 . 5% Tween-20 for 20 min at room temperature . Cells were incubated with primary antibodies at 4°C overnight , washed and incubated with fluorescently-labeled secondary antibodies for 2 h at 37°C . Fluorescence microscopy was performed using a confocal laser scanning microscope ( Nikon EZ C1 ) . All images were captured at 60X magnification and processed under identical conditions with constant parameters ( including scan speed and excitation and emission wavelengths ) using Nikon EZ C1 software . Data analysis ( percent nuclear localization ) was performed by NIS-Elements software ( Nikon ) for a minimum of 40 cells in the high-density and 25 cells in the low-density samples from two fields per condition . HFFs were plated at 0 . 5 or 2 million/plate in 6-well plates , Cells were infected and either untreated or treated with emetine ( 75 nM ) or GCV ( 5 μM ) . At 24 hpi cells were treated with cycloheximide ( 100 μg/mL ) . Cell lysates were collected for RPS14 Western blot analysis at 0 , 15 min , 1 h , 2 h , 4 h and 8 h post cycloheximide . Human TRC lentiviral shRNA constructs ( Sigma-Aldrich ) were used for RPS14 knockdown ( KD ) in HFFs . Multiple validated shRNAs ( Clone ID: TRCN0000008641-4 ) targeting different regions of RPS14 mRNA were used to generate stable cell lines [62 , 63] . TRC non-targeting control plasmid was used to rule out non-specific effects of shRNA constructs . Individual shRNA constructs were packaged using lentivirus as described [64 , 65] . Lentivirus particles containing shRNA were transduced into HFFs . Puromycin ( 2 μg/ml ) was added to select for stably transduced cells . Control HFFs and RPS14 KD HFFs were counted and equal number of cells was plated prior to infection or treatment . Statistical significance was assessed with GraphPad Prism 5 . 0 software . Data are presented as mean ± standard deviation , SD ( n ≥ 3 ) . Student’s t test was used to determine whether the mean of two groups are significantly different . In all analyses , two sided p values were used , and p<0 . 05 was considered statistically significant . For animal studies , one way Anova was used to determine significance . Densitometry analysis was performed with ImageJ .
|
Infection with human Cytomegalovirus ( HCMV ) is a growing and pressing problem , creating ongoing management and therapeutic challenges . Despite the availability of DNA polymerase inhibitors , development of new strategies for HCMV therapy is needed . We report for the first time on the efficacy of an old drug ( emetine ) against HCMV in vitro and mouse CMV in vivo , using exceedingly low drug doses . We also provide evidence for a specific host-dependent anti-CMV mechanism of emetine in vitro , thus uncovering a cellular function that can be further studied for drug development . Our work provides a novel direction for HCMV therapeutics through repurposing of an old agent , at substantially lower doses , and inhibiting HCMV indirectly through host activities critical for virus replication .
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2016
|
Efficacy and Mechanism of Action of Low Dose Emetine against Human Cytomegalovirus
|
Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health . Following the detection of a disease outbreak , a response in proportion to the severity of the outbreak is required . It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory . However , there is often a lack of situational awareness that may lead to over- or under-reaction . There is a widening range of tests available for detecting pathogens , with typically different temporal characteristics , e . g . in terms of when peak test response occurs relative to time of exposure . We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ ( infer the historical trend of ) an infectious disease epidemic . Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak , we use a Bayesian Markov Chain Monte Carlo ( MCMC ) approach to estimate time of exposure , and the overall epidemic trend in the population prior to the time of sampling . We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends . We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle , and a whooping cough outbreak in humans . Together , these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends , and can be used to distinguish whether an outbreak is increasing or past its peak . We conclude that if temporal characteristics of diagnostics are known , it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time .
Infectious disease surveillance is the first line of detection and defence against infectious pathogens and therefore crucial to maintaining animal and public health . However , the current state of disease surveillance has been characterised as deficient in terms of both coverage and reporting speed for both humans [1] and animals [2 , 3] . The challenge is to use the data generated by this often sparse and biased surveillance to decide on an appropriate response to disease outbreaks . This is dependent on the extent of situational awareness , which can be defined as “Knowledge and understanding of the current situation which promotes timely , relevant , and accurate assessment … in order to facilitate decision making . ” ( taken from [4] , p 171 ) . Such situational awareness is necessary in order to balance the social and economic consequences of the adopted control strategy with the social and economic risks posed by the outbreak [5] . Limited situational awareness can have substantial negative impact . In the case of the pandemic H1N1 flu in 2009 , early analyses mistakenly assumed that the epidemic had been only recently introduced , causing substantial overestimates of the basic reproduction ratio [6] and case fatality rates [7] that suggested a far greater risk to human life than was actually the case , leading to a more resource-intensive response than was necessary [8] . The more complex settings typical of livestock and particularly wildlife systems tend to result in the available surveillance data being sparser still for animal diseases [9] . Adding missing information on the time of exposure of detected cases would allow for a better awareness of the early development of an epidemic and would help inform evaluations of the potential risks posed by an outbreak , leading to a more proportionate response than would be the case when waiting for the epidemic trends to be revealed by subsequent real-time monitoring . In the current study , we introduce a novel statistical approach to infer the timing of exposure events for individuals by combining knowledge of the dynamic characteristics of multiple diagnostic tests . This approach could be integrated into any model of a disease epidemic to replace missing information on case exposure times . In this paper , we demonstrate its usefulness by recovering population-level trends of exposure from cross-sectional data collected from a single point in time . Here we refer to the process of recovering such trends as “hindcasting” , following terminology established in other papers [10–12] for reconstructing historical trends from currently available data . Disease surveillance has been described [13] as improving the situational awareness in relation to a disease outbreak on three levels: Perception , Comprehension , and Projection . Perception refers to the collection of data that allows us to monitor disease; Comprehension to extracting information from this raw data that places the current disease situation in a context that allows us to understand its characteristics; and Projection to statistical models as well as more holistic approaches that aim to describe what is likely to happen in the future . Research focused on improving the collection of surveillance data [14–16] , on risk-based surveillance [17 , 18] , or the extensive literature focusing on the early detection of statistical deviations in surveillance data to outbreaks [19–21] , can be seen as improving the Perception stage . Approaches such as phylodynamics contribute to the Comprehension stage by modelling the genetic change of the pathogen , e . g . using this to estimate the epidemiological parameters governing an outbreak such as the recent Ebola outbreak [22–24] . Models that use current information to predict the future [25–27] instead focus on improving the situational awareness at the Projection stage . From this perspective , hindcasting contributes to the Comprehension stage by leveraging quantitative diagnostic test results ( using the statistical methods described in this paper ) to add a temporal dimension to data for which the times of exposure of cases are missing , thus improving the understanding of unfolding epidemics . Several papers have recovered limited historical characteristics of epidemics from cross-sectional data using a single diagnostic test , e . g . an antibody test . For example , Giorgi et al . estimated the time of the start of an HIV outbreak under assumptions of exponential growth of viral load [28] . Others have exploited information on diagnostic test kinetics , i . e . , the pattern of diagnostic test values during the course of infection , to estimate average incidence rates . Examples include the use of antibody test kinetics to estimate sero-incidence rates for influenza [29] , Salmonella in cattle [30] and Salmonella in humans [31] . One challenge in these kinds of studies is that the relationship between the magnitude of signals from diagnostic tests and time since exposure is usually not monotonic; they tend to increase and then decrease . This means that the inverse problem of estimating time since exposure given a test value is non-unique , and although this can be framed as a statistical problem the resulting inference is highly uncertain [28 , 32] , limiting what can be estimated from test data . However , there are often several diagnostic tests available that target different aspects of the multi-faceted dynamic interaction between host and pathogen , and thus exhibit different test kinetics [33] . That is , the profile of test responses , as a function of time since exposure , will differ depending on the underlying diagnostic used and the immune-pathogenesis of the disease . Thus , in principle we can generate a unique signal for a given time since exposure by combining results of diagnostic tests that respond on different time scales . Here , we exploit this fact to develop a more robust statistical approach for analysing cross-sectional field data from multiple diagnostic tests . To do so we make use of empirical infection models that characterise test kinetics to infer the time since exposure for each individual . While there is considerable uncertainty in the estimated exposure time for each individual , the combined estimates from multiple individuals can be used to describe the overall population-level distribution of infection times and estimate the shape of the overall epidemic trend with a high level of confidence . A detailed description of the hindcasting framework and implementation of the evaluation scenarios can be found in the methods section . We demonstrate the hindcasting of epidemic trends by applying the framework developed here to case studies of real outbreaks of two contrasting diseases , whooping cough in humans and bluetongue in cattle ( see Fig 1 ) . For each disease , we investigate two scenarios representing detection during either the increasing or the decreasing phase of the epidemic . We conclude that when combined with knowledge of the temporal characteristics of two ( or more ) appropriate diagnostic tests , our methods allow historical epidemic trends to be recovered from cross-sectional sample data . Moreover , for the example diseases considered suitable diagnostic tests and data describing their temporal characteristics already exist .
We generate exposure times from four different lognormal distributions , each representing a different epidemic scenario as follows: Epi1∼logN ( log ( μ ) =log ( 2 ) , log ( σ ) =log ( 5 ) ) Epi2∼logN ( log ( μ ) =log ( 4 ) , log ( σ ) =log ( 5 ) ) Epi3∼logN ( log ( μ ) =log ( 20 ) , log ( σ ) =log ( 2 ) ) Epi4∼logN ( log ( μ ) =log ( 50 ) , log ( σ ) =log ( 2 ) ) where the above notation means that the exposure times in each epidemic are drawn from the corresponding log-normal distribution . These represents epidemics peaking 2 , 4 , 20 , and 50 days before the time of sampling , with the relative standard deviation chosen to provide more and more gradual increasing trends . We found that we could reliably recover the epidemic trends when using sample sizes of 30 or more , and with levels of test variability less than 1 . 5 , and that the estimated trend showed better fit when the peak was less recent than if it had just occurred ( likely due to a difficulty in resolving very rapid dynamics relative to diagnostic test characteristics ) . Fig 2 shows the hindcasted trends for 320 simulations that were conducted with a sample size of between 30 and 100 , with a test variability of 1 . 3 , evenly split across the four parameterizations . As can be seen , these trends all manage to adequately capture the timing and duration of the true epidemic , with a clear separation between the estimates for different sets of true parameter values . Turning to summary statistics of the epidemic fit across these sets of posterior mean trends , the median R2 ( and root mean squared error of prediction—RMSEP ) for the Epi1 parameterisation was 0 . 71 , with a 95% inter-quantile range ( IQR ) of 0 . 19–0 . 97 ( RMSEP of 0 . 054[0 . 021–0 . 109] ) , a median of 0 . 85 with a 95% IQR of 0 . 48–0 . 99 ( RMSEP of 0 . 019[0 . 005–0 . 041] ) for the Epi2 parameterisation , a median of 0 . 96 with a 95%IQR of 0 . 64–0 . 998 ( RMSEP of 0 . 005[0 . 01–0 . 020] ) for the Epi3 parameterisation , and a median R2 of 0 . 97 with a 95% IQR of 0 . 69–0 . 999 ( RMSEP 0 . 002[0–0 . 007] ) for the Epi4 parameterisation . Fig 3 shows the relationship between sample size and estimation performance . As can be seen , increasing the sample size improved the performance as measured with R2 for all of the parameterizations except Epi1 ( fitting Epi1 was limited by the time resolution of the diagnostic test kinetics used ) . The posterior credible intervals for the parameters of the epidemic also shrunk in width , as would be expected . The performance when hindcasting using sample sizes of 10 was not very reliable; however , for sample sizes of 30 or more , the recovered trends reliably represented the true trend , with R2 values of 0 . 75 or more for all parameterizations except Epi1 . In order to evaluate the robustness of the hindcasting framework , we explored a range of testing errors from 1 . 1 up to 2 . 0 ( multiplicative standard deviation ) . We found that that the width of the credible intervals increased moderately with increasing variability , but that the recovered parameters exhibited similar levels of bias regardless of the level of test variability . This was true even for testing errors of as high as 2 . 0 , far beyond the reported variability of the examined diagnostic tests for bluetongue and whooping cough . ( See S1 Text for full plots regarding the relationship between test variability and performance . ) Finally , we investigated the effect of violating the assumption of conditional independence of antibody and nucleic acid tests . Changing the amount of correlation from 0 up to 1 in 0 . 25-unit intervals showed no detectable difference in the results , whether measured with R2 , RMSEP , or parameter estimates . ( See S1 Text for related figures . ) We also applied the hindcasting framework to two case studies based on a recorded outbreak of whooping cough in humans , and a bluetongue outbreak in cattle ( see Fig 1 ) . See the methods section for details . For each outbreak we simulated two scenarios , firstly where a random subset of all individuals exposed thus far was sampled and tested at a single time , midway through the outbreak ( increasing epidemic trend/early detection ) , and in a second scenario where a random subset of all exposed cases were sampled and tested at a time point at the end of the outbreak ( decreasing epidemic trend/late detection ) . We assumed that no information about the time since exposure was available , nor any other information about the epidemic trend . Based on published temporal characteristics of real diagnostics , test results were then simulated for these samples ( see Methods and Fig 4 ) . For each disease ( whooping cough and bluetongue ) and each scenario ( increasing and decreasing outbreaks ) the hindcasting framework was applied to the corresponding test results to assess performance in recovering early increasing phases and late decreasing phases of outbreaks . The results show that the recovered epidemic trends provided a representative picture for both increasing and decreasing scenarios , in both whooping cough and bluetongue outbreaks ( Fig 1 ) . For the increasing whooping cough epidemic , when assuming a sample of all 122 cases that had occurred between the start of the epidemic up to week 25 , the R2 between underlying case counts ( smoothed by a 7-day moving average ) and the estimated epidemic trends was 0 . 74 , with a 95% confidence interval of [0 . 69–0 . 78] . The corresponding RMSEP was 0 . 0017[0 . 0015–0 . 0018] When sampling 230 cases from the full whooping cough epidemic up until week 36 , after it had declined , the curve fit was somewhat better , with R2 of 0 . 82[0 . 68–0 . 94] ( RMSEP 0 . 0013[0 . 0008–0 . 0017] ) . The results from hindcasting the bluetongue outbreak indicated that when assuming that a sample of the 26 animals had occurred during the increasing phase , the fitted curve was nearly perfect , with an R2 of 0 . 9[0 . 86–0 . 92] ( RMSEP 0 . 0019[0 . 0018–0 . 002] ) . However , for the corresponding decreasing scenario , assuming a sample of the 61 animal cases that had occurred up to week seven , the hindcast trend could not fully capture the erratic nature of the underlying case count data , as indicated by R2 values of 0 . 21[0 . 15–0 . 27] ) . The trend did indicate an elevated incidence over the stretch of time when the majority of cases occurred , thus capturing the approximate time that had elapsed between the start of the epidemic and the time of sampling . This aspect is also captured by the RMSEP , which is more sensitive to shifts in locations , and less sensitive to upward- and downward trends , and which slightly improved to 0 . 0016[0 . 0015–0 . 0016] . When reducing the sample size , the hindcasting technique was still able to recover both increasing and decreasing phase for the whooping cough scenarios . The good fit was maintained with sample sizes as low as 20 individuals , with R2 values of 0 . 77[0 . 27–0 . 83] ( RMSEP 0 . 0064[0 . 0055–0 . 0124] ) , for the increasing and 0 . 67[0 . 09–0 . 86] ( RMSEP 0 . 0038[0 . 0022–0 . 0069] ) for the decreasing scenario . The performance was also maintained for the increasing bluetongue scenario , also assuming 20 samples , with an R2 of 0 . 91[0 . 87–0 . 93] ) ( RMSEP 0 . 042[0 . 034–0 . 051] ) . However , the full bluetongue scenario performed substantially worse with the reduced sample size with an R2 of 0 . 12[0 . 07–0 . 36] ( RMSEP 0 . 014[0 . 013–0 . 015] ) . We further investigated how the hindcasting framework would be affected by different combinations of test kinetics . Fig 5 shows the mean likelihood surface of true vs . posterior times of exposure when using antibody-based tests , nucleic-based tests , or a combination , for the whooping cough and bluetongue exemplar diseases . For the hindcasting framework the ideal combination of diagnostic tests would have a likelihood surface with a single narrow diagonal representing a maximum likelihood coinciding with the true exposure time given observed data . A likelihood surface with a more diffuse diagonal implies a wider posterior distribution . A likelihood surface where there is an “X” of high-likelihood regions implies that the true exposure times are not uniquely identifiable . Each pixel represents the average likelihood of 10 observations from the distribution of test measurements at a time since exposure given by the X axis , calculated at a time of exposure given by the Y axis . Areas in dark red indicate regions of higher likelihood . The times shown are times since exposure , with low numbers indicating more recent exposure , where the test response is changing rapidly . Looking at Fig 5a , during the first 20 days the probable exposure times ( red pixels ) , given the data , are centred on the diagonal ( i . e . the true exposure times ) with a narrow band of high-probability red pixels . For times since exposure of greater than 20 days , when the kinetics of the antibody test are developing at a slower pace , the diagonal of red pixels becomes more diffuse , indicating a greater variation around the true times since exposure . Furthermore , we can see that there are two different diagonals crossing at 25 days . This corresponds to the peak of the diagnostic response curve , with the two diagonals indicating the possibility that a given test result could have been the result of testing an individual during either the increasing or the decreasing phase of the response curve . Estimation of the time since exposure is more precise when the true time since exposure corresponds to phases where the response is changing rapidly , and is more difficult to infer when the test response levels out ( Fig 5b and 5d ) . For diagnostic tests with a peaking response , estimating the time of exposure can be precise but not unique , with two different regions of probable exposure times for a given test response ( Fig 5e ) . To further evaluate the gain from utilizing two different tests , we ran simulations against the four parameterizations of a lognormal epidemic mentioned above , using two diagnostic tests and starting with respectively whooping cough and bluetongue test kinetics . The kinetics were then modified to be increasingly similar to each other ( full details and results in S1 Text ) . We found that for the bluetongue scenarios , the level of similarity of the tests did not seem to noticeably affect the accuracy ( as measured with R2/RMSEP ) of the estimated trends , while for the whooping cough scenarios when recovering Epi3 and Epi4 scenarios , performance degraded gradually , with a very low R2 when using two identical tests . bluetongue test kinetics , the MCMC sampler converged well for the unmodified test configuration ( as measured using Gelmans R statistic ) , but the convergence behaviour then degraded as the tests became increasingly similar . It completely failed to converge in the limit when the two tests were identical ( either identical NA tests , or identical antibody based tests ) . For the whooping cough scenarios the sampler converged for all combinations of diagnostic tests , no matter how similar .
We have shown how to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time . We were able to recover this temporal information using a novel statistical framework which combines paired diagnostic test measurements made on collected samples with known temporal kinetics of diagnostics test measurements over the course of infection . The inferential framework introduced here allow us to extract rich temporal information from collected diagnostic samples . Here we focused on purely cross-sectional samples , but the methods are applicable to longitudinal data and data sets combing both longitudinal and cross-sectional samples . We were able to estimate the trends of both increasing epidemics and decreasing epidemics , as well as estimate the approximate pace of increase or decrease . Such information would improve situational awareness during outbreaks , enabling appropriate management decisions to be implemented immediately when an outbreak has been detected , without the need to observe its subsequent spread to estimate the trend . The implementation of the framework used in this paper combines surveillance data with information on the test kinetics using a simplified model . For example , individual variation in the test response is modelled as variation around a common mean test curve , rather than as variation in the shape of the curve itself . Variations in the two tests are considered independent , and the error distribution is assumed to be log normal . This limits the pattern and range of variation our model can capture , but facilitates model specification and estimation . We also assume that the test variability is known . While this is partly owing to technical limitations ( models with unknown variance parameters tended to converge to degenerate solutions by maximising the variance ) , it is a realistic assumption since the reliance on test kinetics require that the diagnostic test has been studied in depth . More detailed modelling of the individual and population level processes ( including the effect of various covariates such as age or gender ) in order to tailor the model to a particular disease is entirely consistent with the statistical framework introduced and would increase the real-world validity and predictive power beyond what has been demonstrated here . The current implementation of the framework does not include a sampling process component , and so the generated posterior distribution does not currently take sampling uncertainty into account . If the samples are randomly drawn from a larger population of infected individuals , the estimated trend will be an unbiased estimator of the wider population trend . A potential avenue for future research would be to integrate hindcasting into a wider framework describing the sampling process in detail; such an approach might also allow for simultaneously estimating potential sampling bias . We make use of the lognormal distribution as a parsimonious parameterization of the epidemic trend . This is suitable for epidemics where a single peak is expected , allowing fast model fitting whilst capturing the time span and general direction of the trend . The trade-off is that more complex aspects of trends in the epidemic are omitted . A second limitation is that the lognormal distribution requires the trend to decline to zero after any peak . Should either of these limitations pose a problem , more complex parameterizations of the epidemic trend—with multiple peaks and stages , or even compartmental SEIR-type models—could be used , though such models are likely to come at substantially higher computational cost . The hindcasting framework introduced here estimates epidemic trends by combining observed data with information on how test responses develop after exposure . Woolhouse and Matthews [37] give an extensive overview of studies that incorporate different data sources to recover the underlying dynamics of disease spread [38 , 39] , and argue that the future of disease analysis lies in models taking account of a wider range of inputs , such as diagnostic test performance , disease pathogenesis , or transmission mechanics , in addition to regular surveillance data . Our methodology improves on earlier studies incorporating test kinetics [29–31] in three ways: by incorporating information from more than one diagnostic test; by considering their joint kinetic pattern; and by modelling non-constant incidence . It could be further extended to model other aspects of the disease system such as population demography , contact networks , or the spatial distribution of cases . The hindcasting approach make use of knowledge of the within-host development of test markers . Phylodynamics , on the other hand , leverages information about how the genetics of the pathogen change as it spreads through the population to estimate between-host transmission events , and use this to e . g . reconstruct the transmission network of outbreaks [40] and to inform future control measures and forecasts of outbreak trajectories such as the 2015 Ebola outbreak [41] , and the 2009 H1N1 influenza outbreak [42] . However , phylodynamics requires sequenced samples of genetic material , and that the pathogen of interest is mutating quickly enough that the dynamics of the epidemic can be resolved . In contrast , the hindcasting approach relies on test kinetics and measures within-host times since infection . Recent papers [43 , 44] discuss ways to integrate epidemiological and genetic information when modelling disease epidemics; given the complementary nature of phylodynamics and hindcasting , a natural future step would be to combine the two sources of information into a single framework . Since hindcasting exploits knowledge of the host-pathogen interaction , it relies on previously conducted longitudinal studies of such interactions , and requires that the test response after initial pathogen exposure has been described . Our results demonstrate one of the many ways in which experimental infection studies can provide substantial additional benefits to disease control and research . Currently , only a fraction of pathogen tests have published information on how time since exposure affects test response; this has limited which pathogens we could usefully simulate . Similarly , data sets of infectious diseases often only record whether a test has been positive or not . Presentation of the underlying continuous test response is rare—and it is rarer still to find such results for paired diagnostic tests . It is hoped that the method introduced here can give some further motivation to record continuous test responses from more than one diagnostic test , and that it can also serve as an argument for conducting further studies on test kinetics . The results regarding the impact of combining diagnostic tests indicate that combining diagnostic tests increases the robustness of the hindcasting procedure . The bluetongue scenarios exhibited severely degraded convergence behaviour with two identical tests , while the whooping cough scenarios converged , but produced posterior estimates that did not match the true trend . Further research is required to characterise how combinations of tests interact to affect hindcasting . In general , however , the diagnostic tests used in disease surveillance should be chosen to complement each other . By combining early responding tests with later responders , it becomes possible to create a joint test signature that combines the best features of both tests . In terms of the method presented here , the best combination of tests is likely that which provides a unique and precise signature along the timeline of infection for an individual . The hindcasting framework described here makes use of diagnostic test information , that has up until now been under-utilized , to improve situational awareness during an outbreak . This approach could also be used to improve the detection of new outbreaks by extracting more information from existing surveillance data and thus make outbreak detection algorithms [20 , 21] more sensitive . It could similarly be used to provide additional sources of information when estimating epidemiological parameters and trends , and thus improve the accuracy of forecasting models . We have described a new framework for hindcasting the temporal patterns of epidemics , using two example host-pathogen systems and the pairing of antibody tests with pathogen load . The framework demonstrates the potential to utilise the information inherent in the increasing variety of diagnostic tests . We were able to estimate both increasing and declining epidemic trends under the assumption that all individuals were being tested at a single point in time , implying its usefulness for cross-sectional surveillance data as well as in less restrictive settings . Recovering temporal incidence trends using multiple tests on cross-sectional field data has the potential to be of considerable value , and is a key determinant of introducing proportionate responses to ongoing disease outbreaks .
Our method assumes test data yik from multiple disease diagnostics indexed by k = 1 , … , K on individuals i = 1 , … , N . We assume that each individual is tested at some time ti , after having been exposed to the pathogen at some earlier time ei . We further assume that these individuals are chosen in an unbiased , random manner from a larger population . Each diagnostic test is assumed to return a value in the form of a continuous ‘level’ , which might , for example , be the highest dilution at which antibodies are detected in a serological test . Without loss of generality we assume that these levels are scaled to the unit interval [0 , 1] . Initial exposure to a pathogen is the start of a complex dynamical process within the host . We conceptualize such internal host-pathogen interactions as a multivariate process that depends on the time since initial exposure . Each diagnostic test is assumed to target the state of a different component of this process so that each test k carried out at time ti on individual i can be modelled as a latent variable lik ( ti , ei ) = lik ( di ) , with each test having differing but correlated response patterns over the time since initial exposure di = ti−ei . We model these latent variables using results from experimental infection studies for a given host-pathogen system , where the length of time since initial exposure di is known . The known data , across all individuals in the sample , comprises a set of test results denoted by Y = {yik} with sampling times T = {ti} . Our aim is to infer the unknown set of exposure times E = {ei} , using information on the behaviour of the latent processes L = L ( T , E ) = {lik ( ti , ei ) } generating the test results . Note that when describing these sets the limits of each index k = 1 , … , K and i = 1 , … , N are implicit . Under our statistical model we assume that the sampling times T are precisely known whereas the quantities Y , L and E are assumed to be subject to uncertainty and variation . There are thus three components to the statistical model: a latent process model P ( L|T , E , θL ) describing uncertainty and variation in the host-pathogen interaction process within the host in terms of the time since initial exposure; a testing or observation model P ( Y|L , θY ) describing the distribution of results from tests carried out on the hosts conditional on the internal latent process; and an epidemic trend model P ( E|T , θe ) , describing the historical development of the epidemic in terms of the distribution of exposure times in the sampled host population , at the time of sampling . We discuss specific implementations of each of these components in the examples described below . Combining the three parts of the model , we write the full data likelihood given an observed data set {Y , T}as P ( Y , E , L|T , θ ) =P ( Y|L , θY ) P ( L|T , E , θL ) P ( E|T , θE ) , where θ = {θY , θL , θE} . Thus the likelihood combines models for testing with those for within and between host pathogen interactions . According to Bayes’ theorem , the so-called posterior distribution for the unknown parameters is proportional to the data likelihood and prior P ( θ ) . We can express this relationship for the parameters of interest , the latent process L , the exposure times E and the parameters θ , given the observed test data Y and sampling times T , by the equation P ( L , E , θ|Y , T ) =P ( Y , E , L|T , θ ) P ( θ ) P ( Y , T ) Within the Bayesian framework all inference is based on the posterior . The prior P ( θ ) can result from previous measurements or expert opinion , and represents knowledge about the values of parameters before we see the data used in the likelihood . In what follows , we will make the simplifying assumption that the latent process L is modelled by a known deterministic function of T and E , and represents the expected value of the test results given the times since exposure . This means that the term P ( L|T , E , θL ) drops out of the likelihood which then simplifies to P ( Y , E|T , θ ) = P ( Y|L ( T , E ) , θY ) P ( E|θE ) , and the posterior becomes P ( E , θ|Y , T ) = P ( Y , E|T , θ ) P ( θ ) P ( Y , T ) Note that under this notation any parameters defining the deterministic latent process L ( T , E ) = {lnk ( tn , en ) } are suppressed since they are not inferred i . e . θ = {θY , θE} . In both cases above the normalisation factor P ( Y , T ) is typically unknown and computationally expensive to calculate . However , standard Markov Chain Monte Carlo ( MCMC ) methods circumvent this problem and are able to generate samples from the posterior even though the normalisation is unknown . The results presented in this paper are generated from an MCMC sampler implemented with a Metropolis-Hastings algorithm in JAGS [45] using Gibbs sampling [46] . Whooping cough is a human disease caused by the bacteria Bordetella pertussis , causing prolonged spasmodic coughing . Despite widespread vaccination coverage there has been a resurgence of cases in several countries . In the Netherlands there has been a steady increase in the incidence since 1996; and in California , USA , in 2011 , there was a widespread outbreak with 9000 cases and ten deaths [47] . The reasons for such resurgence is currently a matter of scientific debate; some hypotheses include antigenic drift of the bacterium [48 , 49] , asymptomatic transmission of B . pertussis by vaccinated individuals [50] , or the resurgence being the consequence of changing vaccines and vaccination schedules [51] . Here we make use of data describing a county-wide outbreak of whooping cough primarily among adolescents and adults in Fond du Lac County , Wisconsin , USA in 2003–2004 , [52] . After an early cluster of cases in a high school in early May 2003 , there was a large outbreak of whooping cough throughout the county starting from October . After some time , this outbreak was contained , and the final cases occurred in February 2004 . The upper part of Fig 1 shows interpolated case counts per 48-hour period over the duration of the outbreak . Bluetongue virus ( BTV ) is a midge-borne virus that can infect ruminants such as sheep , cattle , deer , and camelids , causing bluetongue disease with symptoms such as internal haemorrhages , swelling of the tongue , lesions in the mouth , and in some species death ( most notably in naïve sheep and white-tailed deer ) . Bluetongue infections can have severe economic consequences for livestock farming , both due to loss of productivity , and because of the severe control measures needed to prevent spread [53] . In 2006 , bluetongue emerged throughout northern Europe , with recorded outbreaks in the Netherlands , Belgium , Germany , and Luxembourg . In 2007 , the UK had its first recorded outbreak [54] . The first infections occurred sometime in early August 2007 [54] when midges introduced the pathogen to the British Isles , but the first case was not detected until September . The lower part of Fig 1 shows the case count per day , with numbers interpolated from the published weekly data [54] . In order to assess our methodology , we consider two scenarios for each pathogen outbreak . In the “increasing” scenarios we assume the epidemic is recognised early and explore test results from samples taken at a time early on in the outbreak ( when the outbreak is increasing , see e . g . Fig 1 ) . In contrast in the “decreasing” scenarios we use test results assumed to be obtained from individuals exposed during the entire outbreak , with samples collected at a relatively late stage in the outbreak ( i . e . when it is in decline ) . The goal was to see how well hindcasting could distinguish between increasing scenarios and scenarios where the epidemic is in decline . We were also interested to see if it was possible to estimate the approximate time span of the epidemics . The results of diagnostic testing are characterised in terms of an underlying mean trend and a model which accounts for variation around this reflected measurement error , and within and between individual variability in test response . Given simulated times of exposure , we then simulated test results , based on the elapsed time between the time of exposure in the outbreak and the assumed time of sampling , using published kinetics of real-time PCR analysis and quantitative ELISA for B . pertussis [34 , 56] , to inform a latent process P ( L|T , E , θL ) . Specifically these were the kinetics of ELISA IgG B . pertussis antitoxin [35] for antibody test response ab ( d ) as a function of time since exposure d , and real-time PCR measurement of persistence over time of B . pertussis DNA in nasopharyngeal secretions [34] ( see Fig 2 ) for the pathogen load DNA ( d ) . As noted earlier formally , we defined the deterministic function L ( di ) = ( DNA ( di ) , ab ( di ) ) by fitting interpolated curves to the published data on DNA and antibody levels using LOESS [55] . The distribution P ( Yi|L ( di ) ) of test measurements was modelled as a lognormal distribution conditional on the state of the latent process: let yi = ( yNA , yab ) i represent a bivariate measurement of nucleic acid and antibody levels on individual i , and define the distribution P ( Yi|L ( di ) ) = lN ( L ( di ) , Σ2 ) ) , where Σ2 is a diagonal covariance matrix , reflecting the assumption of no correlation between test results when conditioned on the time since exposure . The variance for each test ( i . e . the diagonal elements of Σ2 ) was assumed to be known . Antibodies as well as levels of pathogens in a host often follow log-normal distributions , as has been rigorously argued [57]; the suitability of using the lognormal distribution for modelling a wide range of biological phenomena has also been described more recently [58] . We modelled the test behaviour based on published data [36] , and assumed lognormal distributions for the epidemic trend , as well as for the variance of the diagnostic tests ( test kinetics shown in Fig 4 ) . Specifically , we based the behaviour of the latent process P ( L|T , E , θL ) on a study of experimental infection of European red deer with BTV serotype 1 and 8 that described the dynamics of BTV serotype 1 viral load ( vl ) as measured with RT-PCR , and antibody levels ( ab ) as measured with ELISA . As above , we define the latent process describing antibody concentration and viral load as a deterministic bivariate function of the duration d elapsed since exposure as L = {l ( di ) } ≡ ( vl ( di ) , ab ( di ) ) , which does not vary between individuals . We estimate L by fitting smooth and interpolated curves to the experimental study data on viral load and antibody levels independently and take the values of these curves at each exposure time d to define the values of the deterministic functions , vl ( d ) , ab ( d ) . A smoothing spline algorithm [55] was used as a nonparametric fitting method . Conditional on the time since exposure , the observed test values yi = ( yvl , yab ) i were modelled as a bivariate log-normal distribution with mean equal to the deterministic latent process = {l ( di ) } = ( vl ( di ) , ab ( di ) ) . For individual i , this can be formally written as P ( yi|l ( di ) ) =lN ( l ( di ) , Σ2 ) , where lN indicates a bivariate lognormal probability function , and Σ2 is the covariance matrix . We assumed that the variation in observed antibody levels and viral loads to be independent so that the covariance matrix Σ2 is diagonal , with variance components σ12 , σ22 . The variance for each test ( i . e . the diagonal elements of Σ2 ) was assumed known . The third and final part of the model , the distribution of times since exposure P ( E|T , θe ) , was modelled as a lognormal distribution P ( E|T , θe={μ , σ} ) =lN ( μ , σ ) . In this case , we exploit the ability of the lognormal to model extreme skewness to capture both increasing and decreasing epidemics using only two parameters . Note that the implementation of the lognormal distribution as an epidemic trend must be conducted in such a way as to allow the sampler to jump between tails of the distribution for the individual times since infection . Details for how to do this can also be found in the supplementary information . We followed the recommendations of Gelman et al . [59] and used vague priors for the parameters . Such priors incorporate information about what parameter values are nonsensical in a given problem setting , but without using any previously collected data . The means for the lognormal distribution describing the epidemic trends were themselves given lognormal priors . These priors were set to indicate the timescale of relevance for the epidemics in question . This translated to setting the prior means for the for the increasing whooping cough to 100 days , and prior means for the decreasing whooping cough scenario to 200 days . The corresponding values for bluetongue were 10 days and 100 days , respectively . The standard deviations for the prior distributions were chosen as log ( 10 ) corresponding to 99% confidence intervals of ( mean/100 , mean*100 ) . This standard deviation was chosen to model that any peak time more than a factor 100 different from the time scale of interest was nonsensical . The standard deviation of the lognormal distributions for the epidemic trend was given a vague prior parametrized as a folded , non-standardized t-distribution with five degrees of freedom , a standard deviation of log ( 100 ) , and a mean of 0 , indicating in the spirit of vague priors that a spread of on the order of more than 100 days in the past was not sensible . In the process of developing this work , we also explored the use of uninformative priors with nearly flat distributions , such as the standard gamma distribution , and uniform priors with very wide support; however , these were found to lead to very slow mixing and a high rate of convergence failure of the MCMC algorithm . Changing the specific values of the priors did not influence the posterior estimates noticeably . See the supplementary information for MCMC traceplots , details of convergence evaluation and sensitivity analysis of the priors .
|
We have developed a Bayesian approach that can estimate the historic trend of incidence from cross-sectional samples , without relying on ongoing surveillance . This could be used to evaluate changing disease trends , or to inform outbreak responses . We combine two or more diagnostic tests to estimate the time since infection for the individual , and the historic incidence trend in the population as a whole . We evaluate this procedure by applying it to simulated data from synthetic epidemics . Further , we evaluate its real-world applicability by applying it to two scenarios modelled after the UK 2007 bluetongue epidemic , and a small outbreak of whooping cough in Wisconsin , USA . We were able to recover the epidemic trends under a range of conditions using sample sizes of 30–100 individuals . In the scenarios modelled after real-world epidemics , the hindcasted epidemic curves would have provided valuable information about the distribution of infections . The described approach is generic , and applicable to a wide range of human , livestock and wildlife diseases . It can estimate trends in settings for which this is not possible using current methods , including for diseases or regions lacking in surveillance; recover the pattern of spread during the initial “silent” phase once an outbreak is detected; and can be used track emerging infections . Being able to estimate the past trends of diseases from single cross-sectional studies has far-reaching consequences for the design and practice of disease surveillance in all contexts .
|
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"Abstract",
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"Methods"
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2016
|
Using Combined Diagnostic Test Results to Hindcast Trends of Infection from Cross-Sectional Data
|
Parent-of-origin effects comprise a range of genetic and epigenetic mechanisms of inheritance . Recently , detection of such effects implicated epigenetic mechanisms in the etiology of multiple sclerosis ( MS ) , a chronic inflammatory disease of the central nervous system . We here sought to dissect the magnitude and the type of parent-of-origin effects in the pathogenesis of experimental neuroinflammation under controlled environmental conditions . We investigated inheritance of an MS-like disease in rat , experimental autoimmune encephalomyelitis ( EAE ) , using a backcross strategy designed to identify the parental origin of disease-predisposing alleles . A striking 37–54% of all detected disease-predisposing loci depended on parental transmission . Additionally , the Y chromosome from the susceptible strain contributed to disease susceptibility . Accounting for parent-of-origin enabled more powerful and precise identification of novel risk factors and increased the disease variance explained by the identified factors by 2-4-fold . The majority of loci displayed an imprinting–like pattern whereby a gene expressed only from the maternal or paternal copy exerts an effect . In particular , a locus on chromosome 6 comprises a well-known cluster of imprinted genes including the paternally expressed Dlk1 , an atypical Notch ligand . Disease-predisposing alleles at the locus conferred lower Dlk1 expression in rats and , together with data from transgenic overexpressing Dlk1 mice , demonstrate that reduced Dlk1 drives more severe disease and modulates adaptive immune reactions in EAE . Our findings suggest a significant epigenetic contribution to the etiology of EAE . Incorporating these effects enables more powerful and precise identification of novel risk factors with diagnostic and prognostic implications for complex disease .
Complex diseases , like common chronic inflammatory conditions , arise from an interplay between multiple risk genes and environmental factors . Etiology is often largely unknown with variable penetrance and expressivity making it difficult to identify contributing factors . Epigenetic mechanisms might act at the interface between the genome and environmental signals and determine stable and heritable changes in gene expression that do not require changes in the DNA sequence . Such states are mediated by DNA methylation and post-translational modifications to core histones that have an impact on gene expression [1] . Thus , it is not surprising that deregulated epigenetic mechanisms can lead to pathological conditions extensively studied in tumor biology . Therefore , while the DNA sequence confers the primary information for expression and protein structure , epigenetic mechanisms are dynamic and can mediate information about the cellular environment to regulate the specific establishment and maintenance of gene expression . Studies in mice have shown that maternal diet is associated with changes in DNA methylation in offspring [2] , [3] . Additionally , monozygotic twins acquire differences in chromatin structure during their life span [4] , [5] . Such altered epigenetic states might confer differences in disease susceptibility between monozygotic twins , as shown in systemic lupus erythematosus [6] . Moreover , some environmentally-associated epigenetic changes might even be transmitted through generations , as suggested in humans [7] , [8] and demonstrated in mice and rats [9]–[11] . A number of recent studies implicate epigenetic mechanisms in the inheritance of multiple sclerosis ( MS ) , a chronic inflammatory disease of the central nervous system ( CNS ) . For example , there is a significantly higher risk for maternal half-siblings to develop MS compared to paternal half-siblings [12] . Similar parent-of-origin effects have recently been demonstrated for the major MS risk factor , the HLA haplotype [13] , [14] . HLA is also under direct and indirect epigenetic regulation as DNA methylation has been demonstrated to alter the expression of HLA and its transcriptional activator [15] . The increasing prevalence of MS among women during the last several decades is speculated to result from changes in the environment [16] and the risk for MS is increased in children of affected mothers [17] . Thus , there is emerging evidence for complex interactions between genetic , environmental and epigenetic mechanisms underlying the pathogenesis of MS . We here sought to dissect the extent of parent-of-origin effects in the etiology of an experimental MS-like disease , myelin oligodendrocyte glycoprotein ( MOG ) -induced experimental autoimmune encephalomyelitis ( EAE ) in rodents . We used a backcross strategy between susceptible DA and resistant PVG . AV1 rat strains designed to identify the parental origin of disease-predisposing alleles . Typically , DA rats display a relapsing-remitting disease course with an average onset around two weeks after immunization with MOG antigen , which is used to trigger the immune response in this model . Conversely , PVG . AV1 rats are nearly completely resistant to the same induction protocol [18] . Our study establishes the magnitude and the type of parent-of-origin effects in inheritance of rat EAE , a robust MS model that mimics many features of the human disease [19] providing valuable insight into the mechanism of susceptibility to complex inflammatory diseases and identifying new risk factors .
Genes on the sex chromosomes are inherited in a parent-of-origin dependent manner and the influence of the Y chromosome has been well documented in mouse EAE [20] , [21] . To assess the impact of the sex chromosomes in EAE in rats , we bred rats that have the X and the Y chromosome inherited either from the EAE-susceptible DA or from the EAE-resistant PVG rat strain ( Figure 1 ) . The F1xDA male offspring ( N = 104 ) that carry the susceptible DA Y chromosome had an overall higher EAE incidence with earlier disease onset and more severe disease than DAxF1 offspring males ( N = 104 ) that carry resistant PVG Y chromosome ( Table 1 ) . Thus , the Y chromosome from the susceptible DA strain conferred higher incidence and more severe EAE . This is further supported by absence of a difference in disease susceptibility between PVGxF1 ( N = 106 ) and F1xPVG ( N = 126 ) offspring males that inherited the Y chromosome from the resistant PVG strain ( Table 1 , Figure 1 ) . Potential interactions between Y and nuclear genes could not be explored due to the backcross design where the Y effect in the DABC population segregates between reciprocal crosses together with the parent-of-origin effects . Furthermore , we did not observe any global influence of the X chromosome in this study ( Table 1 ) . Exclusively maternally inherited are genes encoded by the mitochondrial genome . To date , the influence of mitochondria has not been unequivocally established in EAE [22] . To assess the impact of mitochondria on EAE in rats , we bred rats that inherited mitochondria either from the DA or from the PVG strain ( Figure 1 ) . We did not detect robust differences between rats with varying mitochondrial genomes ( Table 1 , Text S1 ) . Therefore considering parental effects of sex chromosomes and mitochondria , the Y chromosome is primarily responsible for MOG-induced EAE in these rat strains . In the next stage , we investigated the impact of epigenetic mechanisms such as imprinting on inheritance of EAE . To that end , we identified genome-wide QTLs , which are genomic loci that encode EAE risk genes with the disease status dependent on the genotype at these loci ( presence of either DA = D or PVG = P alleles ) . To identify parent-of-origin dependent QTLs , we used a reciprocal backcross breeding strategy that allowed the risk alleles inherited from mothers to be unequivocally discriminated from those of fathers in the heterozygous animals [23] . QTLs detected in DABC were considered as parent-of-origin dependent QTLs if they showed an effect in only one of the reciprocal crosses , either DAxF1 ( PVG alleles are paternally inherited ) or F1xDA ( PVG alleles are maternally inherited ) . We also detected overlapping or additional parent-of-origin QTLs in PVGBC that showed an effect in only one of the reciprocal crosses , either PVGxF1 ( DA alleles are paternally inherited ) or F1xPVG ( DA alleles are maternally inherited ) . This pattern of inheritance , whereby a gene variant affects the phenotype only when expressed from the maternal or the paternal copy is typical for imprinted genes . The reciprocal backcross strategy was first tested on the weight of naïve rats , which is a physiological phenotype not related to EAE . PVG rats are typically heavier than age-matched DA rats , and several QTLs regulate this trait in DABC ( Figure S1A ) . We detected a QTL on chromosome 1 in females with the peak around 185 Mb ( Figure S1B ) where the PVG allele predisposed for higher weight preferentially when paternally inherited ( Figure S1C ) . The same locus predisposed for higher weight when paternally inherited in the validation population of PVGBC females ( p<0 . 001 ) . The IGF2 gene , known to regulate growth and weight and to be expressed only from the paternal copy [24] , is encoded in this locus . Concordantly , the weight of newborn F1 hybrid pups was higher when the PVG allele was paternally inherited ( p<0 . 05 ) ( Figure S1D ) . To confirm IGF2 imprinting in our rat strains , we measured allele-specific IGF2 expression utilizing a single nucleotide polymorphism ( SNP ) in the 5′ UTR of IGF2 that segregates between DA and PVG ( Figure S1E ) . IGF2 was exclusively expressed from the paternal copy in livers of newborn rats . Together , these data suggest that the gene responsible for weight regulation in a parent-of-origin manner in this QTL is likely IGF2 . It also shows that the reciprocal backcross strategy can be used to identify parent-of-origin dependent QTLs . We then used the reciprocal backcross strategy to carry out identification of QTLs that control EAE susceptibility ( reflected by incidence and onset ) , EAE severity ( reflected by maximum score and duration ) and subclinical disease ( reflected by weight loss ) . We performed genome-wide linkage analysis in DABC and PVGBC populations using forward selection followed by backward elimination model in R/qtl [25] ) . In total , 16 and 11 loci showed evidence for linkage with different EAE phenotypes in DABC and PVGBC , respectively ( Table S1 ) . We next performed genome-wide linkage analysis in the separate reciprocal crosses to identify parent-of-origin dependent QTLs . All reciprocal crosses had similar and sufficient power to detect QTLs of effects typical for EAE ( Table S2 , Table S3 ) . Of all EAE QTLs , 44% ( 7/16 ) and 73% ( 8/11 ) showed parent-of-origin dependent transmission of risk alleles in DABC and PVGBC , respectively ( Table 2 , Table 3 ) . The experimental design , comprising two independent populations ( DABC and PVGBC ) , allows validation of identified parent-of-origin dependent QTLs ( Figure 1 ) . Six out of 8 ( 75% ) QTLs that showed linkage in only one of the reciprocal crosses in the DABC population ( Table 2 ) also showed evidence for linkage in one of the reciprocal crosses within the independent PVGBC population ( Table 3 ) , with the same type of parental transmission of risk , i . e . maternal or paternal . Likewise , six out of 8 ( 75% ) QTLs , identified in only one reciprocal cross in the PVGBC population ( Table 3 ) were confirmed in one of the reciprocal crosses within the independent DABC population ( Table 2 ) , with the same type of parental transmission . To additionally confirm that these are true parent-of-origin QTLs and not a failure to detect a significant QTL in one of the crosses , we performed analysis of cross-by-QTL interactions , which is a statistical way of showing that the parental origin of the allele ( inferred by the “cross” ) affects the expressivity of the QTL . The majority of identified parent-of-origin QTLs ( 80% ) showed significant dependence on the cross/origin ( Table 4 ) . Only a QTL on chromosome 10 ( peak at 23 Mb ) failed to show interaction and a QTL on chromosome 7 ( peak at 50 Mb ) showed significance only for one phenotype . Nonetheless , we wanted to confirm the parent-of-origin dependent QTLs in an entirely different experimental population . For that purpose we used 794 rats originating from the same DA and PVG . AV1 parental strains that were randomly bred for 10 generations ( G10 ) and induced with EAE . We repeated the QTL interaction analysis described above in this large population . We found a significant origin-by-QTL interaction in G10 for the same QTLs that showed evidence of cross-by-QTL interaction in the backcrosses ( Table 5 ) . Only two out of 10 QTLs , on chromosomes 6 and 18 , could not be tested because there was no significant evidence for a QTL in G10 . To confirm that parent-of-origin QTLs were not just randomly detected effects , we tested loci ( N = 9 ) that represent main-effect EAE QTLs that did not show evidence of parent-of-origin in the backcross ( Table S4 ) and randomly selected loci ( N = 10 ) ( Table S5 ) for origin-by-QTL interaction . None of the main-effect QTLs in G10 or randomly selected loci showed a level of evidence that was considered to be substantial enough for parent-of-origin QTLs . These analyses demonstrate that the majority of detected parent-of-origin QTLs is genuine . Considering only QTLs that demonstrated significant interaction with the cross/origin , 37% ( 6/16 ) and 54% ( 6/11 ) of loci displayed significant parent-of-origin dependent transmission of risk alleles in DABC and PVGBC populations , respectively ( summarized in Table 6 ) . Moreover , accounting for the parent-of-origin defines risk factors that explain a 2–4 fold higher percentage of disease variance compared to the factors identified in populations where parental origin is not considered ( Figure 2 , Table S1 , Table S6 ) . Contribution from more QTLs can be established if analyses are done in separate reciprocal crosses ( Table S1 ) . We identified parent-of-origin QTLs with maternal transmission of EAE-predisposing alleles only in the F1xDA and F1xPVG reciprocal crosses , i . e . QTLs on chromosomes 1 , 3 , 4 , 5 , 14 and 18 ( Table 2 , Table 3 ) . A QTL on chromosome 4 ( peak at 144 Mb ) was identified in two independent experimental populations ( DABC and PVGBC ) with the DA allele predisposing for disease only when maternally inherited . In PVGBC , the DA allele predisposed for EAE only in F1xPVG offspring , which inherited the DA allele maternally ( Figure 3A ) . Interestingly , in DABC females , analysis in reciprocal backcrosses separated what originally appeared to be one wide QTL , with a peak at 185 Mb , into two QTLs at 144 Mb and 185 Mb ( Table 2 , Figure 3b ) . The first of them overlapped the QTL identified in PVGBC ( peak at 144 Mb ) and also displayed linkage in only one of the reciprocal crosses , F1xDA . Additionally , this QTL displayed significant cross-by-QTL interactions in all three populations , DABC , PVGBC and G10 for multiple phenotypes ( Table 4 , Table 5 ) . The genetic variants ( EAE-promoting DA vs . EAE-protective PVG ) at this QTL could exert the effect on EAE only when present on the maternally inherited chromosome , resembling genomic imprinting . This pattern of transmission of risk alleles implicates that the underlying gene is preferentially expressed from the maternal copy while it is fully or partially repressed on the paternal copy . We then investigated parent-of-origin QTLs that depend on paternal transmission , identified only in the DAxF1 and PVGxF1 reciprocal crosses , i . e . QTLs on chromosomes 6 , 7 and 10 ( Table 2 , Table 3 ) . A QTL on chromosome 6 ( peak at 131 Mb ) was identified in PVGBC and confirmed in DABC females ( Table 3 , Table 2 ) . In PVGBC , the paternal PVG allele predisposed for EAE only in PVGxF1 offspring ( Figure 4A ) . Accordingly , in DABC , the PVG allele predisposed for EAE only in DAxF1 offspring , which inherited the PVG allele paternally ( Figure 4B ) . The QTL displayed significant cross-by-QTL interaction in the backcross population ( Table 4 ) . The genetic variants ( DA vs . PVG ) at this QTL could exert the effect on EAE only when present on the paternally inherited chromosome , resembling genomic imprinting . This pattern of transmission of risk alleles implicates that the underlying gene is preferentially expressed from the paternal copy while it is fully or partially repressed on the maternally inherited copy . QTL confidence intervals in backcross populations usually comprise large genomic intervals . To narrow the QTL on chromosome 6 , we used probability mapping . The most likely interval to harbor the EAE-predisposing gene was between 130–134 Mb and 131–134 Mb in PVGBC and DABC , respectively . This region overlaps with a well-known cluster of imprinted genes , Dlk1-Dio3 , on rat chromosome 6 and syntenic mouse and human chromosomes 12 and 14 , respectively [26]–[28] . The predicted intergenic differentially methylated region ( IG-DMR ) , known to control the imprinting status of the locus [29] , showed around 50% methylation in spleens of backcross rats , which is typical for imprinted genes ( data not shown ) . Thus , paternally expressed genes in the cluster , Dlk1 , Rtl1 and Dio3 [28] , [30] , could explain paternal transmission of EAE risk allele at chromosome 6 . We did not find any coding SNPs between DA and PVG in the Dlk1 , Rtl1 and Dio3 genes ( whole-genome SOLiD sequencing , Bäckdahl et al , manuscript ) . Therefore , we investigated if their expression levels are under parent-of-origin dependent regulation . Indeed , the PVG risk allele predisposed for lower levels of Dlk1 in spleen compared to DA alleles only when paternally transmitted ( Figure 5 ) . Rats that inherited the PVG allele from their fathers had lower expression of Dlk1 compared to rats with paternally inherited DA allele , in the two independent DABC and PVGBC populations ( Figure 5A , B ) . This was further confirmed in reciprocal F1 hybrids with offspring rats that inherited PVG allele from their father displaying lower Dlk1 expression in spleen compared to rats with paternally inherited DA allele ( Figure 5C ) . We found no evidence for parent-of-origin dependent expression differences of Rtl1 and Dio3 ( Figure 5 ) . Additionally , Dlk1 has previously been shown to be involved in regulation of immune responses [31]–[33] . Taken together , these findings suggest that Dlk1 may at least in part , be responsible for the effect of the parent-of-origin dependent QTL on chromosome 6 , and that PVG alleles can promote EAE by means of lower Dlk1 expression when paternally inherited . We next investigated the effect of differential Dlk1 expression on EAE using transgenic C57BL/6 mice that express a double dosage of Dlk1 in multiple tissues [34] . The Dlk1 transgenic mice were created by pronuclear injection of a bacterial artificial chromosome ( BAC ) transgene that encompasses the entire Dlk1 gene and endogenous flanking sequences but without the imprinting control region and the other genes in the cluster [34] . We first confirmed that levels of Dlk1 were elevated in three different immune tissues of transgenic mice compared to wild type littermate controls ( Figure 6A ) . As expected from the backcross data , lower expression of Dlk1 in wild type mice predisposed to more severe EAE while higher expression of Dlk1 in transgenic mice was protective against EAE ( Figure 6B ) . The observed differences in clinical disease were accompanied with differences in the immune response with lower frequency of activated CD4+ T cells and B cells in protected transgenic mice compared to their wild type littermate controls ( Figure 6C ) . Furthermore , during the in vitro differentiation of naïve T cells into IFNγ-producing Th1 cells , known to have a pathogenic role in EAE [35]–[37] , we observed that transgenic mice produced lower numbers of Th1 cells compared to wild type controls ( Figure 6D ) . Taken together , our data demonstrate parent-of-origin effects in EAE , including imprinting-like patterns of transmission of disease-predisposing alleles . Furthermore , we show that imprinted Dlk1 specifically modulates the adaptive immune responses and regulates susceptibility to EAE in vivo .
Our data demonstrate that a striking 37–54% of loci predisposed for EAE in a parent-of-origin dependent manner . One of the very few studies in EAE that used a reciprocal backcross design in mice similarly demonstrated that 50% of EAE loci depend on parental transmission , although this result was based on a total of two out of four identified QTLs [38] . Parent-of-origin dependent loci on chromosomes 6 , 10 and 18 identified in this study have been previously reported [39]–[42] as well as the majority of identified loci that did not depend on parental transmission [22] , [39]–[42] . Replication of EAE loci in independent populations and strain combinations is important as it justifies investments in further candidate gene identification , which can also be significantly facilitated by exploiting information about locus segregation between multiple inbred strains . Additionally , replication of the majority of the loci that did not depend on parental origin confirms that our study was adequately powered to investigate parent-of-origin effects . Indeed , taking into account parent-of-origin enabled identification of multiple new risk loci on chromosomes 3 , 4 , 5 , 7 , 10 and 14 , which have not been previously identified in rat EAE . Also , the loci on chromosomes 4 and 5 previously displayed linkage to immunological sub-phenotypes: IgG levels and the number of MHC class II positive cells in rat CNS , respectively [40] , [43] , but did not link to the clinical disease phenotypes . This likely reflects the lack of power in previous studies to identify disease QTLs at these loci in populations that are confounded by the parent-of-origin . Parent-of-origin dependent EAE loci identified in this study overlap with experimentally confirmed or clusters of highly predicted imprinted genes ( Figure S2 ) . One example is the well-studied GNAS complex locus located at the peak of the maternally transmitted EAE QTL on chromosome 3 . This locus comprises multiple gene products including maternally expressed G-protein a-subunit transcripts [44] that couple many receptors to cAMP signaling that is important in the immune and the nervous systems . Other known imprinted gene clusters are contained within the maternally transmitted EAE QTL on chromosome 1 , including maternally inherited insulin growth factor 2 receptor ( Igf2r ) [45] . IGF2R has been shown to have an important role in T cell activation [46] . The maternally transmitted EAE locus on chromosome 14 comprises growth factor independence 1 ( Gfi1 ) predicted though not shown to be maternally expressed in mice [47] . GFI1 has recently emerged as an important transcriptional repressor involved in lymphocyte development and activation ( reviewed by [48] ) . Further functional studies in cell types relevant for EAE pathogenesis will demonstrate if known or novel imprinted genes are EAE loci . In this study , we addressed the locus on chromosome 6 that overlaps well known imprinted Dlk1-Dio3 cluster . Taking parent-of-origin of inherited alleles into consideration enabled us to identify Dlk1 as a novel candidate risk gene for EAE . Indeed , predisposition to develop more severe EAE when the risk allele was exclusively paternally transmitted strongly implicated paternally expressed genes , Dlk1 , Rtl1 or Dio3 , encoded in the EAE QTL on chromosome 6 [30] . Furthermore , the paternally inherited risk allele at chromosome 6 predisposed for lower expression of Dlk1 in spleen in both DABC and PVGBC populations and in reciprocal hybrids between DA and PVG strains . The Dlk1 protein is shown to be involved in signaling pathways like the ERK/MAPK pathway [49] , [50] and the FGF signaling pathway [51] . Moreover , Dlk1 protein , which is very similar to the signaling molecules of the Notch delta family [52] , is an atypical Notch ligand suggested to inhibit Notch signaling [52]–[54] . The fact that Notch signaling has been strongly implicated in EAE and MS pathogenesis [55] thus suggests that lower levels of Dlk1 might fail to appropriately control Notch signaling thereby predisposing for a more severe disease in rats . To directly establish a role of the Dlk1 gene in EAE pathogenesis , we used Dlk1 transgenic mice [34] and we demonstrated that the mice express elevated levels of Dlk1 in several immune tissues and , importantly , develop less severe EAE . This observation may , at least in part , be attributed to a role of Dlk1 in blocking Notch signaling . Accordingly , inhibiting Notch signaling has been shown to prevent and ameliorate EAE and decrease production of the proinflammatory cytokine IFN-γ [56] , [57] . Nevertheless , besides its role in the immune system , Dlk1 might affect cells in the target organ in EAE/MS as Notch has been shown to suppress oligodendrocyte differentiation [55] and Dlk1 has been shown to affect neurogenesis [58] . Thus , the exact molecular mechanisms underlying a role for Dlk1 in EAE pathogenesis remain to be investigated . In the present study , higher Dlk1 expression ameliorated EAE and was associated with reduced frequency of activated CD4+ T cells in peripheral lymphoid tissues . This is of particular interest since CD4+ T cells have been ascribed a driving role in EAE , which can be induced with the transfer of CD4+ T cells reactive against CNS antigens [59] . In this regard , it has been previously shown that enhanced Notch signaling increases T cell proliferation [60] and protects activated T cells from going into apoptosis [61] . Accordingly , we observed that Dlk1 transgenic mice fail to differentiate the same number of IFN-γ secreting Th1 cells compared to the wild type controls . This can explain lower severity of EAE in Dlk1 mice considering the well documented pathogenic role of Th1 cells [35]–[37] . Because of the known association of Notch protein with nuclear-factor-kB ( NF-kB ) , we hypothesize that less Notch protein would be able to bind to NF-kB in the Dlk1 transgenic mice and thereby lead to less T cell activation , in general , and less IFN-γ producing Th1 cells , in particular . Moreover , Dlk1 transgenic mice displayed lower frequency of B cells in peripheral lymphoid tissues , which is in line with a previously demonstrated effect of Dlk1 deletion on B cell differentiation and function [31] . In addition , Notch signaling has been shown to regulate B cell activation and differentiation into antibody secreting cells [62] , [63] . This is important given that B cells exert important roles not only as antigen presenting cells that activate T cells , but also as cells that produce anti-MOG antibodies , which can cause demyelination in MOG-EAE . Our study highlights for the first time Dlk1 as a regulator of the adaptive immune responses and an autoimmune disease that models MS . Our findings also have important implications for genetic studies of complex human diseases , currently dominated by genome-wide association studies ( GWAS ) that do not take into account parental origin of alleles . It is generally accepted that GWAS have a ‘missing heritability’ component , some of which may reside in parent-of-origin effects . Our study supports that parent-of-origin should be accounted for and could be one of many explanations for why all the identified risk variants together explain typically less than 30% of heritability [64] . For example , 180 loci identified in GWAS explain around 12% of height heritability [65] . In Crohn's disease , 71 risk genes explain less than 25% of heritability [66] . Similarly , in MS we found that 61 genetic variants explain ∼20% of genetic risk for disease [67] . Kong et al used the detailed genealogical information and long-range phasing of haplotypes to determine the parent-of-origin of alleles in Icelanders to identify five additional SNPs in imprinted genes that associate with disease [68] . Thus , information on the parental transmission of risk alleles is likely to add to the ‘missing heritability’ of complex diseases . Our data advocate family studies that can address the impact of parent-of-origin combined with the development of more powerful statistical methods to detect parent-of-origin effects in human populations . Indeed , Wallace et al identified a SNP neighboring the Dlk1 locus that strongly associates with type 1 diabetes depending on the parental origin [69] supporting our findings of a role of Dlk1 in autoimmunity . Taken together , these results reinforce the importance of parent-of-origin effects and demonstrate that incorporating these effects into models of inheritance not only enables more powerful and precise identification of risk factors but also can provide a better understanding of the pathogenesis of complex diseases .
All experiments in this study were approved and performed in accordance with the guidelines from the Swedish National Board for Laboratory Animals and the European Community Council Directive ( 86/609/EEC ) under the ethical permits N332/06 , N338/09 and N298/11 entitled ‘Genetic regulation , pathogenesis and therapy of EAE , an animal model for multiple sclerosis’ , which were approved by the North Stockholm Animal Ethics Committee ( Stockholms Norra djurförsöksetiska nämnd ) . Rats were tested according to a health-monitoring program at the National Veterinary Institute ( Statens Veterinärmedicinska Anstalt , SVA ) in Uppsala , Sweden . Inbred DA and PVG . AV1 rats were originally obtained from the Zentralinstitut für Versuchstierzucht ( Hannover , Germany ) from which colonies have been established at Karolinska Hospital ( DA/Kini and PVG . 1AV1/Kini ) . The Dlk1 transgenic mice were generated by and originally obtained from the Ferguson-Smith laboratory ( Cambridge , UK ) . All animals were bred and kept in 12 h light/dark- and temperature-regulated rooms . Animals were housed in polystyrene cages containing aspen wood shavings and had free access to standard rodent chow and water . Animals were tested according to a health-monitoring program at the National Veterinary Institute . Reciprocal backcrosses were established between EAE-susceptible DA and MHC-identical EAE-resistant PVG . 1AV1 strains ( Figure 1 ) . To create the F1 generation , four breeding pairs with DA female founders were established . The reciprocal N2 generation was created from DA ( N = 4 ) and PVG . 1AV1 ( N = 4 ) females bred to F1 males and F1 females bred to DA ( N = 4 ) or PVG . 1AV1 ( N = 4 ) males . Four N2 litters were produced for MOG-EAE experiments . The population with the susceptible DA strain ( DABC ) consisted of 421 rats ( 213 females and 208 males ) and the population with the resistant PVG strain ( PVGBC ) consisted of 471 rats ( 239 females and 232 males ) ( Table S2 ) . Advanced intercross line was established from two DA and two PVG females that were bred with PVG and DA males , respectively , to produce the F1 generation . Seven F1 couples from DA female founders and seven from PVG . AV1 female founders produced the F2 generation . The G3 generation originated from 50 breeding couples and random breeding of 50 males and 50 females , avoiding brother-sister mating , produced all subsequent generations , according to Darvasi and Soller [70] . In the G10 generation , three litters similar in size were produced for MOG-EAE experiments , comprising 428 females and 366 males . Generation and characterization of the Dlk1 transgenic mice has previously been described in detail by da Rocha et al . [34] . In short , the Dlk1 transgenic mice were created by pronuclear injection of a bacterial artificial chromosome ( BAC ) transgene that encompasses the entire Dlk1 gene and endogenous flanking sequences but without other genes in the cluster . The foreign DNA is stably and randomly integrated into the genome with an estimated copy number of 4–5 . Three different lines with Dlk1 BAC transgene ( 70A , 70B , 70C ) demonstrated no difference in phenotype indicating no impact of integration site . Transgenic Dlk1 lines were then bred to C57BL/6 mice , known to be susceptible to EAE and extensively used as a background strain for different genetic models in EAE [71] , for more than 10 generations . All experimental animals and littermate controls were derived from heterozygous Dlk1 transgenic breeding . Recombinant rat and mouse MOG ( amino acids 1-125 from the N terminus ) was expressed in E . Coli and purified to homogeneity by chelate chromatography [72] . Animals were anesthetized with isoflurane ( Abbott Laboratories ) and immunized subcutaneously ( s . c . ) in the dorsal tail base . Each rat received a 200 μl inoculum containing MOG in phosphate buffered-saline ( PBS ) ( Life Technologies ) emulsified 1∶1 with incomplete Freunds adjuvant ( IFA ) ( Sigma-Aldrich ) . With the aim of 50% disease incidence in each population , to achieve the highest power to detect EAE QTLs , different induction doses were used for the two backcrosses and for females and males within each backcross ( Table S2 ) . EAE was induced in 8–12 weeks old Dlk1 transgenic and wild type littermate C57BL/6 females with 50 μg rMOG that was emulsified in Complete Freud's Adjuvant ( Sigma-Aldrich ) and injected s . c . in the dorsal tail base . On day zero and day two post immunization ( p . i . ) each mouse was injected intraperitoneally ( i . p . ) with Bordetella pertussis toxin ( Sigma-Aldrich ) . Animals were weighed and clinical signs of EAE were evaluated daily from day 7 p . i . until end of experiment . The scale for EAE scoring was: 0 , healthy; 1 , tail weakness or tail paralysis; 2 , hind leg paresis or hemiparesis; 3 , hind leg paralysis or hemiparalysis; 4 , tetraplegy and 5 , death . The following clinical parameters were assessed and used in analysis: incidence of EAE ( INC ) i . e . , scored as 1 if signs of EAE were present for more than one day; onset of EAE ( ONS ) i . e . , day of first clinical sign; duration of EAE ( DUR ) i . e . , number of days animals showed clinical signs; maximum EAE score ( MAX ) and weight loss ( WL ) , calculated by subtracting the lowest weight during the experiment from the weight at the time of immunization and expressing the difference as a percentage of the weight at the time of immunization . Genomic DNA was extracted from tail tips . Genotypes were determined by PCR amplification of microsatellite markers . Fluorophore-conjugated primers were used ( Applied Biosystems , Eurofins MWG Operon ) and PCR products were size fractionated on an electrophoresis capillary sequencer ( ABI3730 , Applied Biosystems ) . Genotypes were analyzed using the GeneMapper software ( v . 3 . 7 , Applied Biosystems ) and all genotypes were manually confirmed by two independent observers . Dlk1 transgenic mice were genotyped using the following primers: Dlk1_wt/Dlk1_tg_fwd_ CCA AAC TGC ACA ACG TGC TG; Dlk1_wt_rev_GAT CTT GAA CTA CCA AGG GC; Dlk1_tg_ rev_ACT TTA TGC TTC CCG CTC GT . Two experimental populations were created by backcrossing F1 hybrids with either the susceptible DA strain ( DABC ) or the resistant PVG strain ( PVGBC ) ( Figure 1 ) . Within each population , two reciprocal crosses were established . The DAxF1breeding and the F1xDA breeding within DABC refer to the two reciprocal backcrosses . Likewise , the PVGxF1 breeding and the F1xPVG breeding within PVGBC refer to the two reciprocal backcrosses . The term “cross” always refers to one of these four reciprocal breedings , in which the first and the second strain refer to mother and father , respectively . To identify parent-of-origin QTLs , QTL mapping was performed in the two reciprocal crosses ( within the DABC or the PVGBC ) separately . For example , the DAxF1 offspring inherited the PVG allele exclusively from fathers . Therefore , a QTL identified in the DAxF1 offspring and not in the F1xDA offspring would be dependent on the PVG allele predisposing for EAE only when paternally inherited . Moreover , DABC and PVGBC were used to validate parent-of-origin dependent QTLs found in each population respectively . To control for genetic parent-of-origin effects , sex chromosomes and mitochondria varied only in DABC or PVGBC . For example , all DABC rats ( offspring of the DAxF1 and the F1xDA breeding ) had the DA mitochondria , while mitochondria varied between the two reciprocal crossed in the PVGBC , with PVG mitochondria in offspring from the PVGxF1 breeding and the DA mitochondria in offspring from the F1xPVG breeding . Similarly , while the Y chromosome varied between the two reciprocal crosses in DABC , all PVGBC ( offspring of both the PVGxF1 and the F1xPVG breeding ) were bred to have the same PVG Y chromosome . The genetic map was defined using publicly available genome sequence ( http://www . ensembl . org/v . 55 ) . The physical map was used to enable comparison of linkage analyses between crosses and sub-populations . All animals were genotyped with 140 evenly-spaced microsatellite markers providing 97% and 91% genome coverage with 25 cM and 20 cM spacing , respectively . Linkage analysis was performed using R/qtl software [25] . A single-QTL model analysis was performed using Haley-Knott regression on phenotypes transformed to account for experimental sets [73] ( data not shown ) . Similar results were obtained using non-transformed phenotypes as well as using non-transformed phenotypes corrected for sex , experimental set and litter size that were used as additive and interactive covariates ( data not shown ) . Permutation tests ( N = 1000 ) were performed to determine the threshold levels for significant linkage and genome-wide p<0 . 05 thresholds were reported [74] . All analyzed sub-populations had similar size ( Table S2 ) and displayed no difference in phenotypic variation between the compared sub-populations ( p>0 . 4 for the majority of phenotypes ) , apart from the weight loss in DABC males ( p<0 . 01 ) . Differences between phenotypic variance in the compared sub-populations were tested with two variance - F test and Levene's test in Rcmdr . In addition , all analyzed crosses had similar and sufficient power to detect QTLs ( Table S3 ) . Due to the polygenic nature of EAE [75] we used a multiple-QTL model mapping , i . e . forward selection to a model of 10 additive QTLs followed by backward elimination to the null model to identify a multiple-QTL model . A threshold LOD for a model of choice was set to allow detection of QTLs with modest effects , which we previously identified and confirmed in the same disease and the same strain combination . The fit to a multiple-QTL model was used to statistically validate the independent effect of each identified QTL and percentage of phenotypic variance explained by identified multiple-QTL models . Similar results were obtained in populations combining both reciprocal crosses and using cross as an interactive covariate with Haley-Knott regression ( data not shown ) . Allelic effects of QTLs identified in the multiple-QTL model were calculated in Rcmdr using Student's t-test ( Table 2 , Table 3 ) and confirmed with the non-parametric Mann-Whitney test ( data not shown ) for all phenotypes except for incidence that was tested with the Fisher's exact test . To confirm the parent-of-origin dependent QTLs in DABC or PVGBC , a cross-by-QTL interaction analyses were performed . For each detected parent-of-origin QTL the fit-multiple QTL modeling was performed that allows the statistical validation of the independent effect of each identified QTL and its interactions . It does so by subtracting the effect of each QTL or QTL interaction and comparing that model to the initial model of phenotypic variance where all QTLs have a full effect . Here , we built a full model that comprised all identified QTLs ( from the forward selection - backward elimination analysis , as in Table S1 ) and CROSS x QTL interaction terms for the QTLs that displayed parent-of-origin effect ( i . e . QTLs that could be mapped only in one of the reciprocal crosses ) . The full model: Phenotype ∼ pQTL1 + QTL2 + pQTL3 + … + QTLn + pQTL1*CROSS + pQTL3*CROSS + CROSS ( pQTL indicates QTLs that could be identified only in one of the crosses , see table and figure legends for models used ) . In the next stage the effect of each QTL or QTL*CROSS interaction was subtracted from the full model and the contribution of the subtracted term to the full model was evaluated and expressed in p-values . All independent QTLs showed significant contribution and were not included in Table 4 . The table contains p-value of the full model and the p-value of the contribution of the each tested parent-of-origin dependent CROSS x QTL interaction . Genotypes at the estimated QTL locations were simulated by the imputation method ( N = 128 simulations on the step = 1Mb ) implemented in the R/qtl statistical software [25] . Similar results were obtained using linear regression ( data not shown ) . To confirm the parent-of-origin dependent QTLs in the G10 , fit-multiple QTL modeling tested all parent-of-origin dependent QTLs from the backcross analysis that displayed linkage in the G10 and their parent-of-origin interactions . The full model: Phenotype ∼ 1:25 + 3:161 + 4:144 + 5:157 + 7:50 + 10:23 + 10:82 + 14:5 + 1:25*Origin + 3:161*Origin + 4:144 *Origin + 5:157*Origin + 7:50*Origin + 10:23*Origin + 10:82*Origin + 14:5*Origin , where first and second number refer to chromosome and peak location in Mb of parent-of-origin dependent QTLs from the backcross analysis , respectively , and “Origin” refers to G9 parental/family origin of G10 rats . Table 5 contains the p-value of the contribution of the each tested parent-of-origin dependent Origin x QTL interaction . Genotypes at the estimated QTL locations were simulated by the imputation method ( N = 128 simulations on the step = 1Mb ) implemented in the R/qtl statistical software [25] . To identify the most likely location of the gene of interest in the chromosome 6 QTL , we calculated the probability of the gene being located at each position using a bootstrap approach in R/qtl [25] . The imputation method was chosen as it could be used with multivariate and non-normally distributed phenotypes , covariates , missing genotype data and genotyping errors in inbred line crosses . Simulated pedigrees were sampled with replacement from the observed DABC and PVGBC individuals to create a new data set with the same number of samples ( Table 1 ) , which was mapped using a single-QTL model in R/qtl [25] . The maximum LOD and the location of that maximum were recorded and the resampling was repeated 1000 times to obtain an estimate of the probability of the QTL effect being present at each position within the confidence interval . This procedure was repeated for each phenotype . Exons of IGF2 gene were sequenced from the genomic DNA . Primers were designed using the Oligo 6 . 0 software ( National Biosciences ) . PCR was performed using Platinum Taq protocol ( Invitrogen ) , amplified DNA was purified ( Qiagen Gmbh ) and sent for sequencing ( Eurofins MWG Operon ) . Sequence alignment was performed with Vector NTI software ( InforMax ) . The identified SNP in the 5′ UTR of IGF2 [GeneBank:184956655] was confirmed by re-sequencing . A common reverse and two allele-specific forward primers , one that ends with C , complementary to the DA IGF2 allele and one that ends with T , complementary to the PVG IGF2 allele , were designed using Primer Express software ( Applied Biosystems ) . The primer sequences for IGF2 are: forward primer , 5′ TCC TCT TGA GCA GGG ACA GC 3′ ( DA allele ) ; 5′ TCC TCT TGA GCA GGG ACA GT 3′ ( PVG allele ) ; reverse primer , 5′ AAA CCT GGG AAG GGA AGT GG 3′ . The primer sequences for HPRT are: forward primer , 5′ CTC ATG GAC TGA TTA TGG ACA 3′; reverse primer , 5′ GCA GGT CAG CAA AGA ACT TAT 3′ . Snap frozen liver tissue from new born rats was disrupted using Lysing Matrix D tubes ( MP Biomedicals ) on a FastPrep homogenizer ( MP Biomedicals ) and mRNA was extracted using RNeasy mini columns ( Qiagen Gmbh ) , including on column DNA-digestion . Reverse transcription was performed with random hexamer primers ( Gibco BRL ) and Superscript Reverse Transcriptase ( Invitrogen ) . Real-time PCR was performed on a BioRad iQ5 iCycler Detection System ( BioRad ) with a three-step PCR protocol ( 95°C for 3 min . followed by 40 cycles of 95°C for 10 sec . , 67°C for 30 sec . and 72°C for 30 sec . ) and with SYBR green fluorophore . PCR conditions ( in specific , the annealing temperature ) were optimized using DA and PVG samples to assure allele-specific amplification . At the annealing temperature of 67 degrees and using specific forward primer there was no amplification of the non-complementary allele ( Ct >36 ) . Relative quantification of mRNA levels was performed using the standard curve method , with amplification of target mRNA and HPRT mRNA . The standard curves were created using five serial 10-fold dilutions . The relative amount of mRNA in each sample was calculated as the ratio between the target mRNA and the corresponding endogenous control HPRT mRNA . RNA was extracted from rat and mouse tissues using Qiagen RNeasy Mini Kit and cDNA created with BioRad iScript Kit . Quantitative real-time PCR of rat Dlk1 , Rtl1 and Dio3 in the BC material was performed using a BioRad CFX384 Touch real-time PCR system with a two-step PCR protocol ( 95°C for 3 min . followed by 40 cycles of 95°C for 10 sec . , 60°C for 30 sec . followed by melt curve analysis ) , using SYBR Green as the fluorophore ( Bio-Rad ) . Cycle of threshold ( Ct ) , efficiencies and melt curves were analyzed in CFX Manager software ( Bio-Rad ) and relative expression was calculated in relation to the mean of housekeeping genes , hypoxanthine phosphoribosyltransferase ( Hprt ) using 2-ΔΔCt . The following primers were used: Hprt_fwd CTC ATG GAC TGA TTA TGG ACA , Hprt_rev GCA GGT CAG CAA AGA ACT TAT; Dlk1_fwd CGG GAA ATT CTG CGA AAT AGA T , Dlk1_rev TCT CGA GGT CCA CGC AAG TC; Rtl1_fwd GCA TCG CAC TCG AGA ACT ACA G , Rtl1_rev CGT CGG CCA GGT CTG AGT AT; Dio3_fwd CAT CTG CGT ATC CGA CGA CA , Dio3_rev CTC ATG GGC CTG CTT GAA GA . We also used TaqMan quantitative PCR in mice and rat F1 reciprocals to measure Dlk1 expression and expression was normalized to Hprt . All qPCR reactions were carried out in 10 μl final volume using Standard Tagman qPCR conditions ( Applied Biosystems protocol ) and all samples were run in triplicates . Dlk1 expression levels were measured using TaqMan gene expression assay ID Rn00587011_m1 and Mm00494477_m1 for rat and mouse Dlk1 , respectively . Single cell suspension was prepared from spleen and lymph node tissue dissected 25 days p . i . and 10∧6 cells/well were plated in a 96-well V bottom plate ( Corning ) for FACS staining . For MOG recall 10∧6 cells/well were plated in a 96-well flat bottom plate ( Corning ) in RPMI medium ( Life Technologies ) supplemented with 10% FCS ( Life technologies ) and challenged with 20 μg of rMOG . After 48 h cells were transferred to a V bottom plate and prepared for FACS staining . For Th1 differentiation naïve CD4+ T cells were purified from whole lymph node cells using the CD4+ T cell isolation kit ( Milteny Biotec ) . After isolation naïve CD4+ T cells were cultured with 1 μg/ml anti-CD3 ( BD ) , 1 μg/ml anti-CD28 ( BD ) and 10 ng/ml of interleukin 12 ( R&D systems ) in RPMI medium supplemented with 10% FCS with and without 10 ng/ml of interleukin 2 for three days . To characterize different immune cell subsets cells were stained with the following markers: FITC and A700 labeled CD3 , FITC and APC labeled CD4 , PE and PECy7 labeled CD8 , A700 labeled CD44 Texas Red labeled CD45R and V450 labeled Ki67 and FoxP3 ( from BD and eBioscience ) . For detection of IFNγ producing Th1 cells , naïve CD4+ T cells were incubated for 4 h with PMA and Ionomycin and then stained with IFNγ APC ( BD ) . Surface stainings were done in PBS containing LIVE/DEAD fixable far-red dead cells exclusion dye ( Life Technologies ) and intracellular/cytokine stainings were done with the FoxP3 staining Kit ( eBioscience ) . Cells were acquired in a Gallios flow cytometer and analyzed with the Kaluza software ( both from Beckman Coulter ) .
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Even with recent progress in determining the genetic basis of complex diseases , the issue of ‘missing heritability’ remains and its potential sources are frequently speculated about but rarely explained . Parent-of-origin effects might contribute to the ‘missing heritability’ and involve genetic and epigenetic mechanisms of inheritance . Our study is the first that establishes ( i ) the magnitude and ( ii ) the type of parent-of-origin effects in the pathogenesis of a multiple sclerosis-like disease , experimental autoimmune encephalomyelitis ( EAE ) in rat , using a strategy designed to identify genes that confer risk only when inherited from either mother or father . A striking 37-54% of all risk loci depended on parental origin . Accounting for parent-of-origin enabled more powerful and precise identification of novel risk factors for EAE , such as the imprinted Dlk1gene . Disease-predisposing alleles conferred lower Dlk1 expression in rats and transgenic Dlk1 mice demonstrated that lower Dlk1 drives more severe EAE and modulates adaptive immune responses . Because parental-origin effects are epigenetically regulated , our data implicate a contributory role for epigenetic mechanisms in complex diseases . Considering parent-of-origin effects in complex disease has enabled more powerful and precise identification of novel risk factors .
|
[
"Abstract",
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"Results",
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"Methods"
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"genetics",
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"disease",
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2014
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Parent-of-Origin Effects Implicate Epigenetic Regulation of Experimental Autoimmune Encephalomyelitis and Identify Imprinted Dlk1 as a Novel Risk Gene
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While most of the world is thought to be on long-term economic growth paths , more than one-sixth of the world is roughly as poor today as their ancestors were hundreds of years ago . The majority of the extremely poor live in the tropics . The latitudinal gradient in income is highly suggestive of underlying biophysical drivers , of which disease conditions are an especially salient example . However , conclusions have been confounded by the simultaneous causality between income and disease , in addition to potentially spurious relationships . We use a simultaneous equations model to estimate the relative effects of vector-borne and parasitic diseases ( VBPDs ) and income on each other , controlling for other factors . Our statistical model indicates that VBPDs have systematically affected economic development , evident in contemporary levels of per capita income . The burden of VBDPs is , in turn , determined by underlying ecological conditions . In particular , the model predicts it to rise as biodiversity falls . Through these positive effects on human health , the model thus identifies measurable economic benefits of biodiversity .
The primary challenge for understanding relationships between the ecology of human health and global patterns of economic development through statistical analysis of country-level indicators is the problem of endogeneity [38]: economic activity is hypothesized to be both a cause and a consequence of health . Simple ordinary least squares regression analysis would therefore produce biased estimates . Endogeneity problems are addressed in econometrics through structural equation methods that rely on instrumental variables ( IVs ) in multi-stage regressions ( for details on IVs see Methods ) [39] . IVs must be “relevant” and “excludable”—i . e . , correlated with an endogenous explanatory variable of interest but not independently correlated with the dependent variable . There have been a number of studies that have attempted to measure the economic impacts of disease through IV methods [16] , [23] , [24] , [26] , [40] , [41] . All such studies are limited by a general tradeoff between using broad-based health indicators ( such as life expectancy or disability-adjusted life years [DALYs] ) , which are likely to have the most significant economic impacts , and identifying plausible instruments that are not independently correlated with income . While narrower health indicators , such as specific infectious diseases , are easier to instrument for , their effects on aggregate outcomes are more difficult to measure . As a result , conclusions from this literature have been challenged based on questions of the legitimacy of the instruments [42] , [43] . In light of these issues , we focus on the per capita burden of VBPDs as our health indicator; this has several advantages . First , VBPDs have been especially implicated in impacting economic growth . While many directly transmitted diseases , such as measles and influenza , are known to have had significant impacts on global mortality rates , their systematic relationship to economic growth over long time scales is less direct . Their high rates of transmission and short infectious periods are associated with rapid acquisition of host immunity , which often lasts a lifetime . Many directly transmitted diseases are also known as “crowd diseases” and tend to be associated with modern economically driven urbanization , and are less dependent on external environmental conditions . In contrast , VBPDs , such as malaria , leishmaniasis , schistosomiasis , ascariasis , and hookworm , are more often associated with longer infectious periods , diminished immunity , and serial reinfection . They spend much of their life cycle outside of the human host in other animal hosts or free-living stages , and are thus especially dependent on external environmental conditions [44] , [45] . While etiologically varied , their common ecological properties provide a basis for instrumentation . We accordingly use a structural equation modeling approach that estimates two simultaneous equations for income and the disease burden , using relevant geographic and ecological variables as IVs [46] . A schematic of the analysis is presented in Figure 2 , which corresponds to the following structural equations: ( 1 ) ( 2 ) where M represents the natural log of per capita income , and the subscript i corresponds to the country; D represents the natural log of per capita DALYs lost to the following VBPDs: malaria , trypanosomiasis , Chagas disease , schistosomiasis , leishmaniasis , lymphatic filariasis , onchocerciasis , dengue , Japanese encephalitis , ascariasis , trichuriasis , and hookworm [1]; and I is a composite index of six World Bank Governance Indicators ( WGI ) : voice and accountability , political stability and absence of violence , government effectiveness , regulatory quality , rule of law , and corruption [47] . The variable , L , represents distance in latitude from the equator; T is a dummy variable for whether the country is located in the tropics; K is a dummy variable for whether the country is landlocked; E is the natural log of the per capita value of oil , natural gas , and coal production; B is a biodiversity index based on the species richness of plants , birds , and mammals; S is a dummy variable for whether the country is an island; and and are error terms . All variables are for the year 2002 unless otherwise noted . The model structure is discussed in detail in the Methods section , which also presents analysis of a wide range of alternative model specifications . More details on the variables can be found in Table S1 ( Text S1 ) .
Table 1 presents the results of our analysis , which tells a coherent story of the relationship between the geography of VBPDs and income ( R2 = 0 . 84 ) . The coefficient estimate of the impact of VBPDs on income , γ1 , is −0 . 40 , and is significant at the 1% level . This suggests that the average tropical country , with a logged per capita burden of VBPDs of 1 . 99 , would more than double their per capita income if their disease burden were reduced to that of an average temperate country of 0 . 19 . The effect of VBPD burden on income is also found to be statistically significant in all other supplementary analyses ( Methods ) . Other statistically significant explanatory variables for income are the quality of institutions ( γ2 = 0 . 38 ) , the value of primary energy production ( γ5 = 0 . 12 ) , and landlocked status ( γ4 = −0 . 54 ) . These results broadly echo general conclusions from the literature [13] , [48] . The fitted values of the model are presented along with the observed values in Figure 3 ( left panel ) . The model for the VBPD burden also appears to be well-specified , with an R2 of 0 . 75 and statistical significance at the 1% level for most of the explanatory variables . Consistent with the literature , the VBPD burden falls with income ( β1 = −0 . 16 ) , absolute latitude ( β2 = −2 . 99 ) , island status ( β5 = −0 . 63 ) , and rises discretely in the tropics ( β3 = 0 . 96 ) . The coefficient estimate for biodiversity ( β4 = −0 . 29 ) is significant at the 1% level and suggests that if a country with a relatively high biodiversity index of 663 ( such as Indonesia ) , were to lose 15% of its biodiversity , the burden of VBPDs would be expected to rise by about 30% . Figure 3 ( right panel ) presents the VBPD burdens along with the fitted values . Figure 4 ( left panel ) presents the biodiversity index along the latitudinal gradient , and Figure 4 ( right panel ) depicts the partial correlation of biodiversity and the burden of VBPDs .
As far back as Darwin and Wallace's theory of evolution , which was inspired by Malthus' An Essay on the Principle of Population , natural scientists have systematically borrowed theoretical approaches from economics . In the modern era , economic tools such as game theory , optimization theory , and time series analysis , have significantly contributed to our understanding of a range of biological systems , from the evolution of pathogen virulence and animal behavior , to the analysis of population dynamics and ecosystem structure [49]–[55] . However , with a few exceptions [56] , [57] , integration in the reverse direction ( from biology to economics ) has lagged behind , leaving many open questions on broad-based biological determinants of economic growth . The economic conditions of the extremely poor are , indeed , largely due to biological processes , which are manifest in health status [58] , [59] . Infectious and parasitic diseases effectively “steal” host resources for their own survival and transmission [60] , [61] . These within-host processes at the individual level scale up to global patterns of poverty and disease , and are evident along a latitudinal gradient . What drives these patterns ? There are significant differences between the respective approaches of economics and the natural sciences to understanding the importance of geographic and latitudinal variation . Correlated with latitude is a seemingly endless list of biophysical and socioeconomic phenomena , from soil quality and biodiversity to per capita income and religious diversity . Understanding the latitudinal gradient in biodiversity , for example , is one of many unifying questions in the search for underlying principles of biological organization . Scientists have thus addressed the problem with a correspondingly wide range of approaches and scales of analysis , from population genetics and kinetic theory to population , community , and ecosystem ecology [6]–[10] , [62] . The result has been a number of competing paradigms as well as some important consensuses . The latitudinal gradient in income , in contrast , has not been widely used to explore underlying principles in economics , and does not generally serve as a basis for integration with the natural and physical sciences . One of the most influential explanations in the economics literature is that it is merely an historical artifact , due to the process of colonial expansion from Europe [24]–[27] . Methodologically , one challenge to understanding the relationship between geography , health , and economic development is a lack of scientifically based IVs . For example , [24] used settler mortality rates as an IV for institutions , relying on the assumption that they influenced the formation of institutions but are independent of indigenous health conditions . This finding contradicts basic knowledge in microbiology and epidemiology . Vector-borne diseases , such as malaria , continue to be among the dominant causes of morbidity and mortality of tropical populations , just as they were of colonial settlers; partial immunity is acquired among those ( foreign or indigenous ) who are able to survive repeated infections [63] , [64] . The analysis presented here is based on an opposing hypothesis: VBPDs , while influenced by socioeconomic factors , are also determined by independent ecological processes , thus explaining their geographic signature . Disease conditions have , in turn , persistently influenced economic productivity . Our statistical model is derived from these conceptual differences and accordingly estimates income and the burden of VBPDs simultaneously . We find that the burden of VBPDs has had a substantial and statistically significant impact on per capita income after controlling for other factors . This result stands for a wide range of model specifications . Among the ecological variables that are found to influence the burden of VBPDs , biodiversity is notable . There is an emerging literature on the relationship between biodiversity and human health , which emphasizes that VBPDs are part of broader ecosystems , and their prevalences are dependent on densities of natural predators , competitors , and other host species [32] , [33] . However , understanding broader aggregate relationships have been confounded by three important considerations: ( 1 ) general biodiversity indices and disease burdens are positively correlated along a latitudinal gradient [30] , [37]; ( 2 ) biodiversity and poverty are highly correlated [65]; and ( 3 ) the relationship between ecosystem structure and the disease burden may be highly variable over time and space , depending on the specific diseases and specific ecological assemblages [32] . Because of these different factors , a general theory of the effect of biodiversity on VBPDs does not exist . After accounting for the effects of income , geography , and other relevant confounders , we find that biodiversity is predicted to lower burdens of VBPDs . Given the inherent underlying complexity , a fuller understanding requires more detailed studies of these relationships across disease types and ecozones . The policy implications of these results are straightforward: ( 1 ) health conditions have influenced the ability of economies to grow over the long-term , as indicated in differences in contemporary levels of per capita income , and ( 2 ) well-functioning , diverse , ecosystems can serve public health interests . The health benefits of biodiversity therefore constitute an ecosystem service that can be quantified in terms of income generated . The theoretical implications may be equally important: economic development is coupled to ecological processes . Such integrated approaches between economics and the natural sciences are therefore necessary for explaining economic heterogeneity around the world and for identifying policies that can lead to sustainable global health and economic development .
Table 1 presents the results of two simultaneous equations estimated from a two-step IV method . For a better understanding of the data and methods , here we first heuristically present a simple example of our statistical model , which is used as a foundation from which we systematically build in control variables . The primary goal of this study is to measure the simultaneous effects of the burden of VBPDs and the distribution of income on each other . In the process of controlling for confounders we address a secondary objective , which is to measure the effect of biodiversity on disease . For heuristic purposes , we begin with a regression model of per capita income as the dependent variable and the burden of VBPDs as an explanatory variable . This approach is guided by a couple of basic statistical considerations , such as avoiding omitted variable bias and simultaneity bias . Omitted variable bias occurs if the burden of VBPDs is correlated with other variables that are not included in the regression model but are themselves correlated with per capita income . It can be addressed by including the appropriate independent variables into the analysis , the choice of which is guided by theory and previous empirical work . In our preliminary analysis , we control for latitude , which is the most conspicuous variable that is correlated with VBPDs and also may be related to economic activity through other indirect mechanisms . Simultaneity bias occurs when the explanatory variable is itself a function of the dependent variable . This is a serious issue in our study because poverty is known to be an underlying cause of disease . The standard approach to overcoming simultaneity bias in the econometrics literature is through the use of IVs in a structural equation model [66] . The basic requirements for the IVs are ( 1 ) they are correlated with the endogenous explanatory variable ( “relevance” ) and; ( 2 ) they are uncorrelated with the error term ( “excludability” ) ( see Assumptions and Limitations in Text S1 for more discussion of IV methods ) . Identifying IVs for the burden of VBPD presents an opportunity for disease ecology to inform our understanding of the role of health on economic development . Two IVs for VBPDs that we test in this preliminary analysis are island status and biodiversity . Island status is a natural choice for an IV because: ( 1 ) ecological theory tells us that islands should generally have lower disease burdens due to lower rates of immigration/transmission and higher rates of extinction/eradication [35] , [67]; and ( 2 ) island status is not independently important for economic growth in ways unaccounted for in the model . The characteristics of islands that could have economic relevance is their size and access to ports . Because we do not have complete data for many small islands , the island countries that we include cover a wide range of sizes , locations , and histories ( discussed in more detail in Assumptions and Limitations in Text S1 ) . We account for port access with a dummy variable for landlocked countries in subsequent models . These properties of the IVs are discussed in more detail in the section , Assumptions and Limitations of Instrumental Variables in Text S1 . Biodiversity , however , is a potentially more controversial choice for an IV because the literature on the relationship between biodiversity and health is ambiguous . On the one hand , biologically diverse ecosystems are thought to regulate populations of parasites and vectors through predation , competition , and dilution , putting downward pressure on human disease [32] , [33] , [35] . On the other hand , species richness has been shown to be correlated with diversity of human pathogens , potentially increasing the burden of disease [37] . The first-stage regression is used to generate fitted values of VBPDs based on the IVs and all other exogenous variables . The first stage regression in this example is: ( 3 ) where represents the natural log of the per capita burden of VBPDs for country i; B is an index of the species richness of plants , mammals , and birds ( see Table S1 for details ) ; L is the absolute value of the latitude; and is an error term . Column a in Table 2 presents the parameter estimates of equation ( 3 ) . Column b presents results where islands are also included as IVs . Both island status ( p = 0 . 00 ) and biodiversity ( p = 0 . 00 ) are negative and highly statistically significant correlates of the burden of VBPDs . This is further confirmed by a simple F-test ( in the case of both IVs , we test their joint significance ) ( p = 0 . 00 ) , such that they easily satisfy the “relevance” criterion [68] . Note that the parameter estimates for biodiversity ( −0 . 34 ) and islands ( −0 . 71 ) in these simple first-stage regressions are very similar to the parameter estimates for the full model presented in Table 1 ( −0 . 29 and −0 . 63 , respectively ) . Figure 5 ( left panel ) presents the partial correlation of biodiversity and income that corresponds to the results presented in Column b of Table 2 . The second-stage regression is an estimation of the income equation . To overcome simultaneity bias , we substitute the disease independent variable with fitted values of disease from the first-stage regression: ( 4 ) where Mi represents the natural log of per capita income of country i , and is the fitted value of disease . Note that the IVs for disease ( biodiversity and islands ) must be excluded from this second-stage regression ( otherwise the model is not “identified” ) . The results of the second-stage regression are presented in Table 3 , and the regression line between disease and income that corresponds to Table 3 ( column b ) is presented in Figure 5 ( right panel ) . Testing the excludability criterion is not possible in models with only one IV . However , because the second specification has more IVs than endogenous explanatory variables ( it is “over-identified” ) , we test the over-identifying restriction ( Hansen's J ) . We find no indication that the IVs are correlated with the error term ( p = 0 . 23 ) [69] ( for more details see the Assumptions and Limitations of Instrumental Variables in Text S1 ) . Despite the simplicity of equation ( 4 ) , the regression has a relatively high goodness of fit ( R2 = 0 . 52 ) , and is highly consistent with the results from the complete analysis presented in Table 1 . Specifically , VBPDs are correlated with lower income , and biodiversity is correlated with lower burdens of VBPDs . Our goal now is to test the robustness of these results through a more rigorous analysis that includes a fuller range of statistical considerations . While equation ( 3 ) is an appropriate first-stage estimation of disease for the purposes of estimating a second-stage regression of income , it is not complete for our purposes . Because we hypothesize that income and disease influence each other , the most appropriate statistical approach is to simultaneously estimate equations for both variables . Consider the following second-stage equations of interest: ( 5 ) ( 6 ) Equations ( 5 and 6 ) represent the simplest possible set of simultaneous equations of income and disease that account for latitude , are “just-identified” ( i . e . , one IV per endogenous explanatory variable ) , and can therefore be estimated empirically . They each consist of one IV , which is , by definition , an exogenous explanatory variable in one equation that is excluded from the other equation ( for details , see Assumptions and Limitations in Text S1 ) . Landlocked status , K , is a common control variable in economics because a lack of ports is a major barrier to trade . However , being landlocked is an irrelevant factor for disease transmission and it is thus qualified as an IV for income; biodiversity , B , is the IV for disease . The fitted values , and , are generated from first-stage regressions: and . Equations ( 5 and 6 ) are estimated via two-step generalized method of moments [66] , [69] with Stata 12 . The results are presented in columns 1a and 1b of Tables 4 and 5 , respectively . A first-stage F-test indicates that landlocked status is a relevant instrument in this simple specification ( p = 0 . 00 ) . Equations ( 5 and 6 ) represent a system of equations that are sufficient to estimate the effects of the disease burden and income on each other . As in the simpler regression results presented in Tables 2 and 3 , the burden of disease predicts lower income , and biodiversity predicts lower burden of disease . In order to test the robustness of these results , we introduce a fuller range of control variables in a stepwise fashion . There are two criteria that we used in selecting these variables: ( 1 ) they have been found in the literature to be determinants of the dependent variable; and ( 2 ) they are expected to be exogenous to this system ( in particular , they are not determined by income or disease; for more details , see Assumptions and Limitations in Text S1 ) . As mentioned above , one of the primary hypotheses of interest is that the latitudinal gradient in income is partly due to disease ecology . The most prominent competing hypothesis is that it is instead due only to economic institutions . We therefore control for the quality of institutions via a composite index of World Bank Governance Indicators ( WGI ) , similar to other studies ( Table S1; Text S1 ) . Because institutions , like disease , are thought to be influenced by income , we also instrument for institutions . Previous studies have used settler mortality rates as IVs for institutions , based on the premise that these mortality rates directly influenced colonial expansion , but are not independently correlated with income today [24] , [26] , [70] . However , we do not use settler mortality for two reasons: ( 1 ) we consider it a direct indicator of disease conditions , which we hypothesize to influence income today ( these studies did not separately control for general disease burdens ) ; and ( 2 ) there is no data on settler mortality rates for most of the countries in our dataset ( only for countries that were colonized ) . Instead , invoking the same premise as these earlier studies , we allow the IVs for disease to also serve as IVs for institutions . First-stage regression results indicate that the IVs for disease are also statistically significant predictors of institutions ( p = 0 . 05; Table S3 ) . Though under-identification tests indicate that the instruments are relatively weak , our inferences are unaffected whether or not institutions is included as a control variable , and whether or not it is instrumented for ( these different variations are not presented here ) . For income , we consider two more potential IVs: ethnolinguistic fractionalization , F , and primary energy production , E ( for details , see Table S1 ) . Ethnolinguistic fractionalization is a natural consideration because it is considered to be a barrier to trade , a potential cause of civil strife , and is accordingly a common IV in global economic studies [70] . However , over-identification restriction tests indicate that ethnolinguistic fractionalization is strongly correlated with the error term and therefore does not meet the criteria for an IV ( Table 4 , column 6b ) ; this is highly consistent with recent studies by [71] , [72] that the disease burden may itself influence human “assortative sociality” and thereby drive patterns of human diversity . On the other hand , the value of primary energy production ( oil , natural gas , and coal ) is a useful control variable because it is an exogenous source of revenue for economies . For the disease equation , we add a dummy variable for tropical countries , T , because there is overwhelming evidence that many VBPDs thrive in tropical conditions due to metabolic and ecologic reasons [73] . We do not , however , include tropics as a control variable in the income equation because preliminary analyses indicated that tropics are not statistically significant predictors of income , after controlling for other variables ( i . e . , latitude , disease , and institutions ) ( p = 0 . 90 ) , but is collinear with institutions . Thus tropical conditions also serves as an IV for disease . Tables 4 and 5 present the results of eight different specifications of the simultaneous equations estimated by two-step generalized method of moments in Stata 12 ( details of the variables are in Table S1 ) . Each of these specifications has been tested for identification ( i . e . , the strength of the IVs ) , spatial autocorrelation , and over-identifying restrictions wherever possible . The IV Moran's I test measures spatial-autocorrelation in the residuals . Statistically significant spatial-autocorrelation was not found in any of the estimates of the income equation ( p-values ranged from 0 . 24 to 0 . 80 ) , but were found in four of the eight estimates of the disease equation ( p-values ranged from 0 . 07 to 0 . 54 ) . Such spatial autocorrelation in the residuals tends to vanish when additional variables ( i . e . , that are geographically determined ) are controlled for [74] . However , the addition of more IVs increases the possibility of violating the excludability criterion , indicated by the over-identifying restriction test . The last three model specifications suffer from this problem ( p-values for over-identifying restriction test are less than 0 . 1 in columns 6b , 7b , and 8b , indicating that the IVs are correlated with the error term ) . Despite these considerations , the parameters are very consistent across all models . The best overall specification is presented in columns 5a and 5b , which has R2s of 0 . 84 and 0 . 76 , is well-identified with strong instruments and no statistically significant spatial autocorrelation . This system is represented by the following reduced-form equations that correspond to structural equations ( 1 and 2 ) : ( 7 ) ( 8 ) The first stage regressions for the estimation of the income equation ( 7 ) are: ( 9 ) ( 10 ) Table S3 presents the outcomes of these first stage regressions . The identification criteria are easily satisfied . Island status and biodiversity are both significant negative predictors of the disease burden in both simple and more complex models . The first stage regression for the estimation of the disease equation ( 8 ) is: ( 11 ) which is presented in Table S4 . The identification criteria are easily satisfied here as well . The landlocked and energy variables are especially effective predictors of income . The estimated effect of biodiversity on disease , and of disease on income , were statistically significant for all model specifications .
|
While most of the world is thought to be growing economically , more than one-sixth of the world is roughly as poor today as their ancestors were hundreds of years ago . The extremely poor live largely in the tropics . This latitudinal gradient in income suggests that there are biophysical factors , such as the burden of disease , driving the effect . However , measuring the effects of disease on broad economic indicators is confounded by the fact that economic indicators simultaneously influence health . We get around this by using simultaneous equation modeling to estimate the relative effects of disease and income on each other while controlling for other factors . Our model indicates that vector-borne and parasitic diseases ( VBPDs ) have systematically affected economic development . Importantly , we show that the burden of VBPDs is , in turn , determined by underlying ecological conditions . In particular , the model predicts that the burden of disease will rise as biodiversity falls . The health benefits of biodiversity , therefore , potentially constitute an ecosystem service that can be quantified in terms of income generated .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"social",
"and",
"behavioral",
"sciences",
"biology"
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2012
|
Disease Ecology, Biodiversity, and the Latitudinal Gradient in Income
|
There remains a lack of epidemiological data on the geographical distribution of trachoma to support global mapping and scale up of interventions for the elimination of trachoma . The Global Atlas of Trachoma ( GAT ) was launched in 2011 to address these needs and provide standardised , updated and accessible maps . This paper uses data included in the GAT to describe the geographical distribution and burden of trachoma in Africa . Data assembly used structured searches of published and unpublished literature to identify cross-sectional epidemiological data on the burden of trachoma since 1980 . Survey data were abstracted into a standardised database and mapped using geographical information systems ( GIS ) software . The characteristics of all surveys were summarized by country according to data source , time period , and survey methodology . Estimates of the current population at risk were calculated for each country and stratified by endemicity class . At the time of writing , 1342 records are included in the database representing surveys conducted between 1985 and 2012 . These data were provided by direct contact with national control programmes and academic researchers ( 67% ) , peer-reviewed publications ( 17% ) and unpublished reports or theses ( 16% ) . Prevalence data on active trachoma are available in 29 of the 33 countries in Africa classified as endemic for trachoma , and 1095 ( 20 . 6% ) districts have representative data collected through population-based prevalence surveys . The highest prevalence of active trachoma and trichiasis remains in the Sahel area of West Africa and Savannah areas of East and Central Africa and an estimated 129 . 4 million people live in areas of Africa confirmed to be trachoma endemic . The Global Atlas of Trachoma provides the most contemporary and comprehensive summary of the burden of trachoma within Africa . The GAT highlights where future mapping is required and provides an important planning tool for scale-up and surveillance of trachoma control .
The last decade has witnessed tremendous progress towards the global elimination of trachoma , the leading infectious cause of blindness worldwide . Since the establishment in 1998 of the Global Elimination of Trachoma by 2020 ( GET2020 ) initiative , an increasing number of endemic countries have implemented national programmes incorporating the SAFE strategy of Surgery to correct trichiasis , Antibiotic to clear Chlamydia trachomatis infection , Facial cleanliness and Environmental improvement to reduce transmission . Morocco was one of the first countries in Africa to implement SAFE at the national level and achieved its Ultimate Intervention Goals ( UIG ) in 2006 [1] . Several other African countries , including The Gambia and Ghana , are in the post-endemic surveillance stage [2] . Supporting these country efforts there has in the last 24 months been increased political commitment by the global health community to the elimination of trachoma , as part of coordinated neglected tropical disease ( NTD ) control programmes , with a commensurate increase in funding . To fully realise the goals of GET2020 , it will be necessary to scale up to a full SAFE programme in all endemic districts in every country by 2016–2018 in order to allow sufficient time for programme impact . Yet for some countries , especially those in Africa , there still remains a lack of epidemiological data on the geographical distribution of trachoma and efforts are required to first complete the global trachoma map , then to keep it updated as interventions begin to take effect . This will help inform where and when to start and stop trachoma control efforts . To respond to this need , the Global Atlas of Trachoma ( www . trachomaatlas . org ) was launched in early 2011 as a collaborative venture between the London School of Hygiene and Tropical Medicine ( LSHTM ) , The Carter Center , and the International Trachoma Initiative ( ITI ) . It provides regularly updated , open-access district-level prevalence maps of the current distribution of trachoma [3] . These maps and the underlying database provide a planning tool that can help to define the known geographic distribution of trachoma at sub-national levels and identify gaps in survey data where further mapping is required . The aim of this paper is to describe the geographical distribution of trachoma in Africa using existing data from the Global Atlas of Trachoma and estimate the burden of disease in Africa . Specifically , we will describe the methods of data assembly and mapping and use these data to define the current geographical distribution , calculate the population at risk of TF and TT , and estimate numbers requiring treatment . We also assess the remaining effort required to finalise the mapping of trachoma on the continent .
The Global Atlas of Trachoma has adopted an identification and data assembly strategy similar to other mapping initiatives , including those for malaria [4] , helminth infections [5]–[7] and human African trypanosomiasis [8] . In brief , epidemiological data on the burden of trachoma are identified through structured searches of published and unpublished literature , with a number of inclusion rules applied to identified information . Data are then abstracted into a standardised database and mapped using geographical information systems ( GIS ) software . The burden of trachoma in a given community is typically measured by the prevalence of clinical signs of disease . This diagnosis is based on ocular examination , usually using the 1987 WHO simplified grading system , to identify the presence of key clinical signs [9]: trachomatous inflammation–follicular ( TF ) in children aged 1–9 years and trachomatous trichiasis ( TT ) in adults aged over 14 years . Although the presence of TF does not always correspond to infection with C . trachomatis infection [10] , these measures are easily collected in the field and used to guide the planning and implementation of the SAFE strategy at the district ( second administrative ) level [11] . These indicators are also used to define GET2020 UIGs , which are less than one case of TT unknown to the health system per 1000 total population and <5% TF in children aged 1–9 years , at the district , sub-district or community level [12] . Surveys assessing the burden of trachoma use one of four methodologies: population based prevalence surveys ( PBPS ) ; acceptance sampling trachoma rapid assessment ( ASTRA ) ; “Integrated Threshold Mapping” ( ITM ) ; or trachoma rapid assessment ( TRA ) [13] . PBPS are the preferred method since they provide a representative measure of the prevalence of trachoma within a population . The most common PBPS strategy is cluster randomized sampling ( CRS ) which uses a representative , two-stage sampling methodology to provide a “gold” standard prevalence estimate at the district level , used for targeting SAFE interventions including mass drug administration ( MDA ) according to treatment thresholds . General guidelines recommend sampling 20 clusters per district , although this varies in practice , and the precision of prevalence estimates is rarely reported [14] . TRA was developed as a rapid and inexpensive method using convenience sampling to rank communities in terms of priority for control programmes [15] . TRAs are “optimally biased” to find trachoma where it is endemic , and do not provide a reliable estimate of trachoma prevalence; a negative TRA probably reliably identifies the absence of trachoma . ASTRA is a form of lot quality assurance sampling and can reliably classify communities in relation to a threshold value [16] , but has in practice been rarely used as it requires modification to derive overall population estimates of trachoma prevalence . More recently , ITM has been developed; it employs convenience sampling of school children , pre-school children and women of child-bearing age to determine whether the prevalence of trachoma , as well as prevalences of other NTDs , exceed some designated threshold , with initial piloting having been conducted in Mali and Senegal [17] and further use in Togo and Zambia . To assemble a global database of trachoma risk , survey data were identified through a combination of ( i ) searches of electronic bibliographic databases; ( ii ) review of programmatic data submitted to the International Trachoma Initiative ( ITI ) ; ( iii ) manual searches of local archives and WHO GET2020 documents; and ( iv ) direct contact with programme managers and researchers . These searches , conducted in 2010 and annually thereafter , build on an earlier effort in 2003 as part of a collaboration between the International Centre for Eye Health at the London School of Hygiene and Tropical Medicine ( LSHTM ) and the Programme for the Prevention of Blindness and Deafness at the WHO , to develop a first global atlas of trachoma [18] . The online bibliographic databases PubMed and Embase were searched to identify relevant studies , using the Medical Subject Headings trachoma , trichiasis , and Chlamydia trachomatis . These searches were restricted to surveys conducted after 1980 for trichiasis and 1988 for active trachoma . The latter restriction was applied because 1987 is when the new simplified grading system for trachoma was introduced [9] . Authors were contacted if additional information was required on survey design or indicators collected . Countries for which no up-to-date information was available from the literature , GET 2020 country data forms , or submitted to ITI , were contacted on an individual basis for local knowledge and clarification . As a whole , these data are unpublished and use the standardised survey methodologies recommended by WHO . Work initially focused on the 53 countries classed in 2004 as trachoma endemic by The World Health Organization , 36 of which are in Africa [19] . There is currently no reliable data indicating the status of trachoma in Libya , Namibia or Zimbabwe . These countries were therefore not included in the analysis . The aim was also to collect the most contemporary data possible in order to inform current control efforts . Literature searches are conducted annually ( most recently in April , 2012 ) , and additional data submitted directly to ITI by national trachoma program managers are routinely used to update the database and resulting prevalence maps . Data available as of September , 2012 , were used in the preparation of this manuscript . The title and abstract of each source of information were reviewed and evaluated against a number of pre-defined inclusion and exclusion criteria: only cross-sectional population based prevalence surveys were included as measures of trachoma prevalence; TRAs were only used to indicate the presence or absence of trachoma where no prevalence data were available . Data were excluded if based on hospital or clinic surveys , or surveys among sub-populations such as among refugee populations . Where multiple surveys were available from the same district but surveyed at different times , they were included as separate entries and coded as “current” or “historical” in order to ensure that only the most recent data are used to estimate the current burden of disease . Estimates of disease prevalence were typically available at the district level as this is the administrative unit at which control is implemented . Where estimates were representative of point locations or the result of a non-random selection of communities within a district , data were only used to provide information on the presence of trachoma . Abstracted data included details on the source of the data , location of survey ( including geographical co-ordinates for cluster data when available ) , survey year , characteristics of the surveyed population , survey methodology , the numbers of children aged 1–9 years and adults aged over 14 years examined , the number of children graded positive for TF and the number of adults graded positive for TT . Any variation in clinical indicator or age group was also recorded in the database . A unique identifier linked each record in a bibliographic database to the survey data and to an electronic copy of the source when this could be obtained . All data were entered into a standardized Microsoft Access 2007 geodatabase ( Microsoft Corporation , Redmond , WA , USA ) , which is linked to a geographic information system ( GIS ) . Data can be queried to produce custom tables , thus allowing simple and rapid generation of country and regional maps using Arc GIS 10 . 1 ( ESRI , Redlands , CA , USA ) . Data were assigned wherever possible to the second administrative level ( e . g . , district level ) , which has direct relevance to implementation of trachoma control . However , data from some older surveys and hyperendemic areas are available at the first administrative level ( e . g . , province , region ) , and data were assigned accordingly . The most recent data are displayed on the main maps . Where historical data are also available they are displayed on separate maps online . Prevalence data were banded into categories corresponding to current intervention guidelines for TF and TT ( Table 1 ) . TRA data were categorized into three bands for active trachoma ( No active trachoma found , <10% and ≥10% of children aged 1–9 years examined found positive ) and two bands for trichiasis corresponding to UIG targets ( <0 . 1% and ≥0 . 1% of the total population examined found positive ) . Geographical boundaries used for mapping were derived from: ( i ) the United Nations Second Administrative Level Boundaries data set project ( http://www . unsalb . org/ ) , ( ii ) Global Administrative Areas ( http://www . gadm . org/ ) , and ( iii ) shapefiles created specifically for this project from maps provided by programme managers . Updated district-level maps were launched in 2011 on an open-access website ( www . trachomaatlas . org ) . The characteristics of all surveys that met the inclusion criteria were summarized by country according to data source , time period , and survey methodology . Districts and regions were categorized as suspected endemic or assigned to a prevalence category using the most current PBPS data representative at this level . Districts were classified as ‘suspected endemic’ or ‘suspected non-endemic’ based on information from TRA surveys , point locations , reported cases or anecdotal information from national programs . Surveys which only collected data on one clinical sign were also used to inform this classification ( i . e . a district known to be endemic for TF was classified as ‘suspected endemic’ for TT where no other data were available ) . Note that , in some cases , identified districts may not include all endemic or non-endemic areas within a country , but their classification does reflect available evidence supporting the presence of trachoma . A total of 24 district-level surveys of TT were conducted in populations aged 40 or 50 years and over . Based on a review of age-stratified TT prevalence ratios from published and unpublished data , a conversion factor of 0 . 54 was applied to estimate the corresponding prevalence in adults aged ≥15 years . Survey data at the region and district level were presented separately in this analysis , with district defined here as the unit of implementation typically used for SAFE control activities . While this is usually the second administrative unit within a country , in some cases these are distinct health districts ( Cameroon and Burkina Faso ) , third administrative areas ( Ethiopia ) or first administrative areas ( Chad , Guinea-Bissau and CAR ) . This was based on the aim to present data most relevant to current guidelines relating to the implementation of SAFE control strategies . Estimates of the current population at risk were calculated for each country using district-level population estimates and summarised by endemicity class ( Table 1 ) . Population figures were derived from the Afripop project , which provided a continental 1 km gridded population map produced using projected population census data and settlement extents ( www . afripop . org ) [20] . This map was overlaid with district classification to allow summation and mapping of the population in each category of risk .
A total of 167 unique surveys with data on either active trachoma or trichiasis met GAT inclusion criteria . These included data from CRS ( 152 ) , ASTRA ( 1 ) , TRA ( 9 ) , ITM ( 2 ) and surveys at single sites ( 3 ) from 31 of the 33 countries in Africa classified as endemic . Prevalence data on active trachoma were available in 29 countries and data on TT in 25 . In total , there are 1342 records included in the database representing surveys conducted between 1985 and 2012 , 1253 of which provide implementation unit-level estimates of prevalence ( usually district-level ) and an additional 79 records that provide region–level estimates . The remaining 10 records were site-specific surveys or those of unclear methodology , which were used to provide information on the presence or absence of trachoma at the district level . The primary source of included survey data was direct contact with national control programmes and academic researchers ( 67% ) , followed by peer-reviewed publications ( 17% ) and unpublished reports or theses ( 16% ) . These sources of data were found to vary considerably by country with a good deal of overlap between sources in countries with established control programmes ( Table 2 ) . The number of surveys available has consistently increased over the last two decades , as highlighted in Figure 1 which presents the total number of PBPS surveys conducted by year for each region of Africa . Surveys in north Africa and the Middle East were conducted earlier than other regions , mainly reflecting active control programmes in Morocco and some earlier surveys in Egypt . While west Africa has some historical surveys , recent survey activities are increasingly focused in this region and in east Africa . The 33 African countries endemic for trachoma consist of 5308 districts . Of these , 1095 ( 20 . 6% ) districts had representative TF data collected through PBPSs , 1024 ( 19 . 3$ ) with PBPS prevalence estimates for TT ( Tables 3 & 4 ) , and data from TRA surveys for an additional 101 districts . While the majority of data collected at the first administrative level are outdated and have been replaced by more recent second administrative level surveys , Tables 5 and 6 present current data on TF and TT available at this level . Only 5 first administrative level units have trachoma prevalence data that are being used programmatically . At the time of writing , 38% of the trachoma endemic countries in Africa have more than 50% of their districts mapped by PBPS and this number is even higher when excluding districts presumed to be non-endemic from the denominator , as illustrated in Figure 2 . These data reflect a rise in the number of large-scale national or regional surveys taking place in recent years ( e . g . in Republic of Sudan and South Sudan ) as well as conduct of pre-and post-implementation surveys in the context of large-scale control programmes in several countries . Since 2007 , surveys have been conducted in a number of countries that previously had no data , including Burundi , Cameroon , Central African Republic , Cote d'Ivoire , Eritrea , Rwanda , Uganda and Zambia . While a number of other countries have seen a rise in survey activities during this period ( e . g . Ethiopia , Guinea Bissau , Nigeria , Republic of Sudan , South Sudan , Tanzania , Togo and Zambia ) , prevalence estimates are still lacking in Algeria , Chad and Djibouti and no data are currently available for Benin , Botswana , or Somalia ( Tables 3 & 4 ) . The geographical distribution of trachoma in Africa varies between regions . Trachoma is believed to be endemic in 33 of the 56 countries in Africa , which are mainly located in east and west sub-Saharan Africa , north Africa and a few endemic coastal countries in central Africa ( Figure 3 ) . Based on available data , the highest prevalence of active trachoma and trichiasis remains in the Sahel area of west Africa and Savannah areas of east and central Africa ( Tables 3–6 ) . A high proportion of surveyed districts are hyperendemic ( defined as TF prevalence in 1–9 year-olds of ≥30% ) in South Sudan ( 83% ) , Ethiopia ( 64% ) , Guinea ( 50% ) , Uganda ( 37% ) , Chad ( 38% ) , CAR ( 38% ) and Tanzania ( 32% ) , but large areas suspected to be endemic remain unmapped in each of these countries . West African countries have been the focus of a number of national surveys in the last decade ( Figure 1 ) providing both pre- and post-intervention data for a high proportion of districts in Burkina Faso , The Gambia , Ghana , Mali and Mauritania . Many countries in Central Africa continue to lack data , making estimation of the burden in this region difficult . Based on survey data currently included in the atlas and population estimates , an estimated 129 . 4 million people live in areas that are confirmed empirically to be trachoma endemic ( based on district-level prevalence of TF in 1–9 year-olds greater than 5% ) and a further 155 million in areas suspected to be endemic ( Table 3 ) . The latter is likely to be a conservative estimate , as it only includes areas classed as suspected endemic based on available TRA or anecdotal information about cases presenting to the health care system . A substantial burden of disease is likely in Chad , Ethiopia and Nigeria due to their large populations in areas of high endemicity ( Table 3 ) . As a direct consequence of repeated infections , the burden of TT follows similar geographical trends to TF within Africa . However , there is a significant backlog of TT surgeries remaining in countries with historically high endemicity levels ( Tables 4 & 6 ) . These countries include Ghana and Morocco which , despite success in reducing the burden of active disease , continue to have a high burden of TT arising from both prevalent and incident cases . Nearly a sixth ( 13 . 7% ) of surveyed districts fall in the 5–10% TF prevalence category which indicates that they may require higher resolution mapping at the subdistrict level to target MDA to disease foci . In many countries , particularly Central African Republic , Ethiopia , South Sudan , Tanzania and Zambia , there remain a large proportion of unmapped districts that are suspected to be endemic based on higher level prevalence surveys , health systems data or rapid assessments . Based on median TF prevalence in 1–9 year-olds of >20% in surveyed districts ( where more time may be needed for control activities to reduce disease prevalence to below elimination thresholds ) , countries which should be prioritized to finish mapping include Chad , Egypt ( based on limited and outdated data at the regional level ) , Ethiopia , Guinea , Mozambique , Nigeria , South Sudan , Tanzania , Uganda and Zambia . Several other countries with ongoing control programmes , including Cameroon , Kenya , Malawi and Uganda , have few remaining unmapped districts that are suspected to be endemic for trachoma and mapping could be completed within a shorter time frame ( Table 3 ) . The distribution of trichiasis in Africa ( Table 4 ) reflects both the known distribution of trachoma as well as areas where trachoma was historically a public health problem and a backlog of cases remain .
With prevalence estimates for at least parts of 29 of the 33 endemic countries in Africa and for 20 . 6% of all districts in these countries , the Global Atlas of Trachoma ( GAT ) represents the most comprehensive resource on the geographical distribution of trachoma and an important planning tool for efforts to finalise the global trachoma map . Based on the current data and population estimates , an estimated 129 . 4 million people live in areas of Africa that are confirmed to be trachoma endemic ( TF prevalence greater than 5% in children ) and a further 155 million in areas of Africa suspected to be endemic . This corresponds to 98 million people who live in areas of Africa where the prevalence of active trachoma is known to be greater than 10% and currently require access to the SAFE strategy including annual MDA with azithromycin , and a further 31 million people where treatment may need to be targeted at the subdistrict level ( Table 3 ) . Summary data collated for this project are a useful advocacy and planning tool , but change as new data become available and estimates can be refined . Iterations of these data have been used at the global level by the International Task Force for Disease Eradication [21] and the International Coalition for Trachoma Control [22] . The online GAT provides an open access platform for all partners to assess what survey data are already available within Africa and the population at risk of trachoma , but also a means to identify gaps in data where further surveys are required and rapidly assess progress in mapping as these activities are scaled up . This resource assists national programmes in planning interventions and provides visualisation of areas where cross-border transmission could be a concern , as well as providing an effective tool to advocate within a country for additional mapping . Trachoma endemic countries are concentrated in east and west sub-Saharan Africa , north Africa and a few endemic countries in central sub-Saharan Africa ( Figure 3 ) . Variation in risk of trachoma both within and between countries has been linked to socioeconomic factors that are associated with transmission through hygienic behaviours and sanitation , as well as varying climatic conditions [23]–[26] . Current data from the GAT confirm that countries with the highest burden of active trachoma and trichiasis remain in the Sahel and Savannah areas of Africa . Well established control programmes in several west and north African countries are likely to have had an impact on the burden of trachoma in the last decade , with successes in control activities documented in Burkina Faso , The Gambia [27] , Ghana [28] , [29] , Mali [30]–[32] , Mauritania [33] and Morocco and highlighted by comparison of current and historical maps available on the GAT website ( www . trachomaatlas . org ) . The Gambia , Ghana and Morocco have now reported achievement of trachoma elimination targets and trachoma is believed to be no longer a public health concern in these countries . Information from this analysis highlights a number of important next steps for defining the burden of trachoma to inform programmatic action . First , a number of countries have both a high prevalence of active trachoma in mapped areas and a large proportion of unmapped districts that are suspected to be endemic . These countries include Central African Republic , Ethiopia , Nigeria , South Sudan and Tanzania . Second , Chad , Guinea , Mozambique , and Uganda are likely to have sizeable areas of high endemicity contributing to the current magnitude of the burden of trachoma in Africa . Generation of baseline data where required , and commencement of interventions in these countries should be accelerated . Third , prioritising countries that have large populations in highly endemic areas , such as Chad , Ethiopia , and Nigeria , will have a greater impact on the overall burden of disease within the programmatic timeframe . Egypt also may be prioritised , based on this rationale , due to the high endemicity of trachoma found in populous areas by earlier regional surveys and a lack of data excluding other geographical areas . Targeting future survey activities to areas which are likely to be highly endemic will allow the initiation of control activities in those areas in which control of trachoma is likely to take the longest . In addition , 13 . 7% of surveyed districts lie in the 5–10% prevalence category and may require higher resolution mapping at the subdistrict level to target MDA to disease foci ( Table 3 ) . Finally , scaling up surgical interventions for TT alongside MDA poises an important challenge in reducing the burden of disease and is increasingly perceived as a limiting factor in meeting UIG targets . There is a substantial backlog of surgeries in countries with historically high endemicity rates . While the incidence of TT will decrease over time along with the number of active infections , the reduction of TT cases is a main goal of control programmes and necessitates scaling up of surgical services in order to meet UIG targets . This presents a number of logistical challenges and demands on human resources; requiring considerable investment in health infrastructure and training in order to identify TT cases and optimise surgical outcomes in order to achieve a sustainable impact . It should be recognised that data included in GAT vary in quality and methodology , which limit the comparability of the data . The methods used to collect data ( sample size , age groups and sampling method ) vary and data are collected over a range of years , in which potential socioeconomic changes could introduce further variation . While differences in the age groups surveyed for TT have been adjusted for , older data may not represent current levels of endemicity where mass antibiotic treatments , TT surgery campaigns and secular trends have had an impact on the prevalence of trachoma . Information on treatment and maps of antibiotic and surgical interventions are available on a partner website developed by the International Coalition for Trachoma Control ( http://www . trachomacoalition . org/ ) . In practice , these detailed data are assessed contextually and used alongside treatment data to make mapping decisions within a country . Prevalence estimates are rarely reported with confidence intervals , limiting our ability to assess their precision . Generally , precision for TT prevalence estimates is likely to be low as surveys are usually powered only to provide estimates for active trachoma . In addition , the sampling frame of population-based prevalence surveys often excludes urban areas , which are commonly perceived to be at lower risk . These urban populations are typically defined locally and thus vary between countries and districts . Urban populations were included in estimates of the population at risk , due to ( i ) a lack of reliable evidence that there is no risk of trachoma in urban areas and ( ii ) the absence of a comparable definition of urban with which to identify these populations . However , in contexts where non-surveyed urban populations have a different risk of trachoma , this decision will result in an under- or over-estimation of the population at risk . The wide prevalence bands used to display these data minimizes the effect of this imprecision and of variation in survey methodologies . Future work could include methods , such as small area estimation , to estimate uncertainty and provide realistic confidence intervals for population estimates [34] . And finally , the estimate of population at risk in areas suspected to be endemic do not include populations of countries currently classified as endemic , but for which no data are currently available ( ie Botswana , Somalia and Djibouti ) . While much of the available survey data in Africa have helped to inform trachoma control activities , some survey data have not been used to inform control due to limited resources , outdated prevalence data or use of unreliable sampling methodologies . Where prevalence data are felt to be unusable because of their age or the methods used for their collection , the corresponding areas will need to be resurveyed . Variation in the geographical scale at which surveys are conducted introduces a further level of complexity . While the unit of implementation is defined by WHO as the district ( which generally corresponds to the second administrative level ) , in some cases the region ( first administrative level ) is used instead . Recent recommendations allow data from larger geographic areas ( e . g . regions ) to justify programme launch in areas where local knowledge or higher level data demonstrate that trachoma is widespread and highly endemic , as was the case in Unity state in South Sudan and Amhara region in Ethiopia [35] , [36] . Much historical data in west Africa are representative at regional level and thus not directly comparable to district level data . In addition , it is well established that trachoma is a focal disease and varies with individual and community level risk factors [24] , [37]–[39] . Displaying data aggregated at higher administrative levels belies the small scale spatial heterogeneity of clinical disease and inclusion of more densely populated urban areas , which are likely to have lower risk than rural areas , may overestimate the population at risk . Finally , some areas of countries currently regarded as non-endemic may have small pockets of transmission occurring , such as areas in DRC bordering CAR , South Sudan and Zambia . This work collates all sub-national data available in trachoma endemic countries into a single GIS database in order to summarise current availability of data and the distribution of trachoma . We hope that the maps generated using the GAT and the results presented in this paper will serve as a planning tool and aid efforts to complete the global trachoma map . Prioritising early survey activities to areas where trachoma is suspected to be highly endemic will allow for additional rounds of MDA , implementation of F&E activities and greater time for programmatic impact before 2020 . In addition , a number of key research priorities remain to meet GET2020 objectives . First , a standardized mapping methodology should be agreed upon and rolled out in unmapped districts . Ideally this methodology would balance cost and precision in order to appropriately define treatment requirements and provide prevalence estimates for later assessment of programmatic impact . Second , risk mapping using environmental and socioeconomic factors to predict the distribution of trachoma in areas where TRA and clinical data are not available may help identify areas likely to be endemic . Third , this work can be extended to generate estimates of the actual numbers of individuals with TF and TT , by incorporating detailed information on population structure and age-prevalence curves into the analysis , and to other trachoma endemic regions in Asia and South America . Finally , assessing different approaches to trachoma surveillance will be increasingly important as more districts reach this stage of control .
|
In order to target resources and drugs to reach trachoma elimination targets by the year 2020 , data on the burden of disease are required . Using prevalence data in African countries derived from the Global Atlas of Trachoma ( GAT ) , the distribution of trachoma continues to be focused in East and West Sub-Saharan Africa , North Africa and a few endemic countries in Central Sub-Saharan Africa . Currently , 129 . 4 million people are estimated to live in areas that are confirmed to be trachoma endemic and 98 million are known to require access to the SAFE strategy . The maps and information presented in this work highlight the GAT as important open-access planning and advocacy tool for efforts to finalize trachoma mapping and assist national programmes in planning interventions .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"disease",
"mapping",
"global",
"health",
"epidemiology",
"neglected",
"tropical",
"diseases",
"trachoma",
"public",
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] |
2013
|
The Geographical Distribution and Burden of Trachoma in Africa
|
p53 can serve as a paradigm in studies aiming to figure out how allosteric perturbations in transcription factors ( TFs ) triggered by small changes in DNA response element ( RE ) sequences , can spell selectivity in co-factor recruitment . p53-REs are 20-base pair ( bp ) DNA segments specifying diverse functions . They may be located near the transcription start sites or thousands of bps away in the genome . Their number has been estimated to be in the thousands , and they all share a common motif . A key question is then how does the p53 protein recognize a particular p53-RE sequence among all the similar ones ? Here , representative p53-REs regulating diverse functions including cell cycle arrest , DNA repair , and apoptosis were simulated in explicit solvent . Among the major interactions between p53 and its REs involving Lys120 , Arg280 and Arg248 , the bps interacting with Lys120 vary while the interacting partners of other residues are less so . We observe that each p53-RE quarter site sequence has a unique pattern of interactions with p53 Lys120 . The allosteric , DNA sequence-induced conformational and dynamic changes of the altered Lys120 interactions are amplified by the perturbation of other p53-DNA interactions . The combined subtle RE sequence-specific allosteric effects propagate in the p53 and in the DNA . The resulting amplified allosteric effects far away are reflected in changes in the overall p53 organization and in the p53 surface topology and residue fluctuations which play key roles in selective co-factor recruitment . As such , these observations suggest how similar p53-RE sequences can spell the preferred co-factor binding , which is the key to the selective gene transactivation and consequently different functional effects .
p53-response elements ( p53-REs ) are two 10-bp palindromic DNA segments with the consensus sequence of 5′-Pu1Pu2Pu3C4 ( A/T ) 5 ( A/T ) 5′G4′Py3′Py2′Py1′-3′ for each of the two half sites , where Pu and Py stand for purine and pyrimidine bases , respectively [1] , [2] . The two half sites can be separated by as many as 20 bps [1]–[6] . Hundreds of p53-REs have been identified [2] , [5] , and the numbers continue to grow [7] . Many of these are known to be related to regulation of genes involved in cellular pathways such as apoptosis , cell cycle arrest and senescence [8] , [9] . However , upon stimulation only a small subset are selectively activated for transcriptional activation or repression through sequence-specific binding to tumor suppressor p53 . Understanding the factors that determine the selective activation is crucial for deciphering the complex gene regulation by p53 [7] , [10]–[14] . Binding affinities of functionally-diverse p53-REs showed that apoptosis-related p53-REs have higher affinities than cell cycle arrest-related p53-REs; however , at the same time , the affinities do not always correlate with functional effects [7] , [12] , [15] , [16] . Spacer sizes also affect affinities: in spacers consisting of three or more bps , the two 10-bp half-sites are on opposite faces of the DNA [17] , suggesting specific p53-RE interactions only with a single half-site , which results in lower affinity [7] , [17] . Although several structures are available [9] , [18]–[23] , they involve a few engineered p53-REs and do not explain the in vivo selectivity . In vivo , p53-RE binding is affected by chromatin packaging epigenetic events known to be a key factor in RE occupancy [24] , [25] . Nonetheless , even assuming genomic p53-REs availability , the question of the selective recognition by p53 still remains [12] , [13] . Allostery is key to cellular signal transduction [26]–[30] . Mechanistically [12] , [13] , allostery can play a role either via protein co-factors binding to p53 prior to RE binding as could be in HIF-1 regulation of p53 and p300 [31] , or ASPP family binding [32]; or via allostery-induced by RE sequences [33]–[37] , or spacer sizes as in the pituitary-specific POU domain factor Pit-1 [38] , in both cases through preferential interactions with certain side chain conformations [34] . In p53 , RE bp changes were observed to relate to transactivation [39] . In the glucocorticoid receptor ( GR ) [40] , [41] , single bp changes were shown to allosterically affect GR conformational changes . These were amplified by ligand binding and propagated to the co-regulator binding site . Allosteric effects can shift the population toward co-factor binding-favored states . DNA methylation can lead to packing of the genome , making the REs unavailable; but it was also proposed to change the affinities of the REs [42] , [43] either via direct interactions , or through allosteric effects on the DNA or the protein . In proteins , covalent modifications such as phosphorylation , glycosylation , and acetylation are well established to be allosteric effectors . The tetrameric p53 DNA-binding domains ( DBD ) are responsible for specific RE binding . However , the impact of the DNA sequence on the binding patterns , specificities and complex conformation has been studied only for the central 4 bps [44] , [45] . Computational studies revealed that variation of the central four bps in the half site which contained the C ( A/T ) ( T/A ) G , conserved in most REs , resulted in conformational changes in the DNA and the DBD [45] . However , the impact of RE sequence variation in other bps on the complex organization and its dynamic properties is largely unknown due to the sparseness of available crystal structures . Here , using molecular dynamics ( MD ) simulations we study the conformational and dynamic consequences of p53 binding to six diverse p53-REs . We focus on the impact of specific interactions of Lys120 , Arg280 and Arg248 with DNA as these are the most crucial for binding . We find that p53 Lys120-DNA interactions can change dramatically depending on the bp at positions 1-3 of the quarter site , which in turn affects the Arg280 binding . We find that such binding pattern changes at the DNA-protein interface have allosteric effects in terms of the p53 tetrameric organization and the fluctuations of residues on the p53 surface away from the DNA binding site . We propose that this combined allosteric effect could hold the key to selective transcriptional activation by the degenerate p53-REs and can serve as a paradigm for selective activation of transcription factors [13] .
Lys120 can interact with bps at three positions ( positions 1–3 in a quarter site ) ( Fig 1a ) . However , the interaction patterns can vary , depending on the base identity . With a G base , Lys120 can make three center HBs ( Fig 1c ) . For C , Lys120 can make the same interactions with the G on the other chain , but the protein has to adjust its relative position . For an A or T , Lys120 can only make one HB with either base but not both because the two HB acceptors are 6–7 Å apart in a Watson-Crick bp ( Fig 1d ) . The methyl group next to the T O4 atom can also influence the interactions . All six potential HB distances for the three bps were monitored ( Fig . S1 ) and the percentage of distances less than 3 . 5 Å are summarized in Table 1 . Fig 2 highlights the average local conformation of Lys120 and Arg280 for selected binding sites . The results show that ( a ) with a quarter site whose sequence conforms to the consensus , Lys120 interacted mainly with the central G or A base , as in the crystal structures ( Table 1: 14-3-3σ Q1 and Q4 , Gadd45 Q2 , Noxa Q1 and Q2 , p21-5 Q1 and Q2 , p53R2 Q2 , Q3 and Q4 , puma Q2 and Q4 ) ; the representative structure in Fig 2A shows that all four hydrogen bonds are well maintained . The simulations showed that Lys120 also interacted with G or A at positions 1 or 3 in these cases; the only exception is Gadd45 Q1 where Lys120 mainly interacted with G1 ( Table 1 and Fig 2B ) , suggesting that G is preferred for HB; this was not observed in Gadd45 Q3 and p21-5 Q1 , suggesting that geometrically the central position is more favorable for Lys120 interactions . ( b ) When there is a single base mutation , the mutation is at position 1 and the mutated base is C , Lys120 interacted with the central A or G ( Noxa Q4 , p21-5 Q3 and Q4 , Puma Q3 ) or with both bases at the 2nd and 3rd positions ( Gadd45 Q4 , Noxa Q3 ) ; this is expected since Lys120 is unlikely to interact with G on the other chain at the 1st position . A typical structure is shown in Fig 2C . The interaction with the central base is usually weak if the base is A ( Gadd45 Q4 , Noxa Q4 , p21-5 Q4 ) ; however , if T , the interaction is either abolished ( p53R2 Q1 ) or weakened even when G is at the 2nd position ( Puma Q3 in Fig 2D ) ; the extra methyl group of T hampered the favorable Lys120 interaction with the 2nd G . ( c ) If the mutation is at the 2nd position ( 14-3-3σ Q2 ) , Lys120 interacted with G at the 1st position ( Fig 2E ) ; although in this case Lys120 could interact with the A at the 3rd position , the fact that it did not suggests that Lys120 preferred G over A . Reaching the base at the 3rd position is also more difficult due to steric hindrance , requiring the movement of the whole protein . ( d ) When there were two mutations in a quarter site , Lys120 interacted weakly with the unmutated base ( 14-3-3σ Q3 and Puma Q1 ) ; in the case of 14-3-3σ Q3 the result is expected since both mutated bases were C which does not have HB acceptors; in the case of Puma Q1 , the 2nd mutated base was T which was able to form HB; however , there was very little interaction with this base due to the presence of the protruding methyl functional group on T . The only option is the G at the 3rd position , which was also weak for reasons discussed earlier . More dramatic conformational adjustment is needed for better interactions between Lys120 and bases at the 2nd or 3rd positions . These results indicate that both base position and identity are important for specific binding . Lys120 is able to interact with bases at all three positions , depending on the environment; however , unless more significant conformational adjustment is involved , the binding of Lys120 to bases on the opposite DNA strand is not likely as it was only observed in a quarter site with a small population . The outcome is a unique binding pattern which can lead to a shift of the p53 organization and DNA conformation . The C at the 4th position is absolutely conserved in all the REs studied here and in most other known p53-REs . The importance of this bp for specificity and affinity has been shown ( 39 , 44 ) . In addition , Arg280 formed a salt bridge with Glu281 as part of the HB network in Fig 1b . Arg280 distance fluctuation details are shown in Fig S2 and the HB percentages are summarized in Table 2 . Unexpectedly , in many cases the Arg280-C HBs were disrupted for at least two of the four quarter sites for each of the six REs and the salt bridges were also very dynamic ( Table 2 and Fig S2 ) , suggesting HB sensitivity to environmental changes , possibly influenced by Lys120-DNA interactions . For example , in the complex of RE 14-3-3σ , Arg280 HB with DNA was intact for Q1 ( Fig 2A ) and 4 , where Lys120 maintained its HB with the 2nd bp ( Tables 2 and 3 ) . This was also the case for Noxa Q1 where Lys120-DNA had good interactions at the 2nd and 3rd positions and Arg280 specific interactions were reasonably maintained as well , showing a good correlation between Lys120 and Arg280 interactions . In Q2 of the 14-3-3σ complex , Lys120 interacted with the base at the 1st position , which loosened the p53 from its original position and reduced the tightness of the Arg280 interaction with the G ( Fig 2E , Tables 2 and 3 ) . When Lys120 flipped out of the binding site , as in Q1 of the p53r2 complex , Arg280 also lost both HBs ( Fig 2G ) . Similarly in Noxa Q3 , Lys120 interacted with G3 , which pushed Arg280 away from its original position , resulting in a conformation in which Arg280 interacted with the DNA backbone ( Fig 2C ) . These results indicate cooperativity between the Arg280 and Lys120 interactions . Interestingly , in the case of Noxa Q4 , Lys120 also flipped out of the major groove , yet the Arg280 interactions were still present ( Fig 2H ) . However , such interactions without the concurrent HB of Lys120 nearby are expected to be vulnerable to environmental perturbations . There are also cases where Lys120 interacted with the 2nd base ( G or A ) but the Arg280 interactions were disrupted . Such changes were observed in the RE p21 , Q1 and Q2 complexes . In both cases , Arg280 only partially maintained HBs with the bases ( Fig 2I ) . These results indicate that specific HBs of Lys120 and Arg280 not only affect each other , but are also influenced by other interactions , such as the dynamic Arg248 interactions ( Fig S3 ) and the Arg280 , Glu281 and Arg273 salt bridge network ( Table 2 , Fig S4 ) . However , the major factor in determining the conformational changes of the p53-DNA complex is the RE sequence at the Lys120 interaction site , which forces p53 to adjust its conformation locally and consequently the overall organization with respect to the DNA . Interactions at other sites such as those involving Arg280 and Arg248 also adjust their interactions even if the DNA sequences are unchanged . Thus , even very similar REs , which vary only by a single or a few bps , elicit different patterns of p53-RE interactions perturbing the p53 , the DNA and their organization in different ways . The conformation with Arg248 inserted into the DNA minor groove was captured only in one crystal structure [46] . In others , Arg248 docked only at the edge/surface of the DNA backbone [20] , [21] , [47] . Arg248 was inside the minor groove at the beginning of our simulations . Once the simulations started , the residue was “ejected” in several complexes and then interacted with the backbone from the outside ( Fig S3 ) . As a result , Arg248 shifted away and adopted a conformation similar to those observed in some of the crystal structures . The change in Arg248 interaction patterns would affect the p53 conformation and cause conformational differences among the complexes . In order to further confirm the relationship between the sequence and the resulting complex conformations , the simulations of 14-3-3σ 1st half site , Gadd45 1st half site , and the Puma 2nd half site were repeated . In 14-3-3σ Q1 ( Fig S5A ) where Lys120 was expected to interact with the 2nd G base , these HBs were well maintained . In the Gadd45 Q1 ( Fig S5B ) , the respective DNA sequence G1A2A3C4A5 suggests that Lys120 may prefer to interact with the G1 base as observed previously . These interactions were retained reasonably well , with Lys120 positioned within distance capable of HB formation . Because the DNA sequence in Puma Q3 is T1G2A3C4T5 , it is expected that the presence of the methyl group on the T base at the 1st position would disrupt the Lys120 HB with the 2nd G base , which was indeed observed ( Fig S5C ) . Comparison of these HB patterns for Lys120 and Arg280 with the corresponding panels in Fig 2A , B and D illustrates consistent and reproducible conformational preferences for a given DNA sequence . The other quarter sites for each of the three complexes were also analyzed and the results were consistent as well . Above , depending on bp identity in each RE the interactions were different . These subtle differences can allosterically propagate in both DNA and p53 . To characterize these features , conformational changes for both the p53 and DNA were calculated . For p53 , the RMS deviation ( RMSD ) of selected residues and RMS fluctuations ( RMSF ) of all residues were calculated ( Figs 3 and 4 ) . We focused on residues near Lys120 and Arg280 . For 14-3-3σ , large RMSDs were observed for Lys120 in Q3 ( Fig 3A ) ; correspondingly , larger RMSF were observed for residues 96–100 and 125–135 next to Lys120 ( Fig 4A ) . For Gadd45 , Lys120 shifted significantly away in Q3 ( Fig 3B ) , resulting in its large fluctuations and in nearby residues 115–140; although Lys120 in Q1 also had large RMSD , its interactions with the DNA backbone stabilized ( Fig 3b ) . Noxa has a large RMSD for Lys120 in Q4 ( Fig 3c ) . However , the RMSF was small , similar to Q1 in Gadd45 . In p21 , Q2 and Q4 had large Lys120 deviations ( Fig 3d ) , slight increase in RMSF nearby in Q2 , and large RMSF increase in nearby residues ( 100–110 ) in Q4 ( Fig 4d ) . The RMSD for Arg248 were large in Q3 and Q4 . Although the RMSF increase for Arg248 was not significant , it was higher for nearby residues 225 and 244 . In the case of p53r2 , large RMSDs of Lys120 in Q1 and of Arg248 in Q3 were observed ( Fig 3e ) ; the RMSF of residues 114–136 in the 1st and of residues 230–250 in Q3 also increased correspondingly ( Fig 4e ) . For Puma , the RMSD of Lys120 in Q1 and Q3 were relatively large ( Fig 3f ) , resulting in neighboring residues 111 and 125–132 in the 1st and 115–125 in Q3 fluctuating more ( Fig 4f ) . While the RMSD for Arg248 in Q3 was also large , the RMSF of nearby residues changed little , although the pattern of the fluctuation magnitude was somewhat different from the other quarter sites . For the DNA , Table 3 summarizes the bending extent from the last 5 ns of each trajectory , illustrating the allosteric impact on the interactions . Thus , adjustments of specific interactions lead to larger fluctuations of nearby residues . In some cases these residues extended to the other side of the protein , suggesting amplified allosteric effect of the DNA on p53 , which is likely to be important for selective co-regulator recruitment . To characterize the conformational changes of the complex elicited by the specific interactions , an angle and a dihedral angle were defined with two atoms from the protein ( Cα of S269 and G112 ) and two from the DNA ( C3′ at positions 0 and 4′ ) ( see Fig 1B ) . These two geometrical parameters were expected to reflect the organizational change of the p53 core domain with respect to the DNA because the two protein atoms are located at the centers of the β-sheet secondary structures and the two DNA atoms belong to the base pairs that are in close contact with the corresponding p53 . The calculated results ( Table 4 ) show that the organizations of the p53 monomer-DNA varied to a large extent , ranging from 96 to 112 and from 14 to 44 degrees for the angle and dihedral angle , respectively ( Table 4 ) . In the context of the tetrameric p53-DNA complex , such orientation changes for each p53 core domain with respect to the DNA will propagate to the p53 surface away from the DNA binding site . The two examples shown in Figs 5 and 6 illustrate the conformational adjustments between p53 and the DNA . In the 14-3-3σ complex , the RMSDs of both p53 core domains were small ( 2 . 5 Å for all atoms ) ( Figs 5a and 5b ) . However , when the systems were superimposed with the DNA as the pivot , the p53 orientation changes significantly ( Figs 5c and 5d ) . A major reason for such a change is the interaction pattern . Fig 5e shows that when Lys120 interacts with the G at the 1st position , Lys120 , Arg280 and the whole molecule shifted significantly . The significant change of the helix orientation highlights this organizational difference ( Fig 5d ) which is also reflected in the small dihedral angle ( 17° ) ( Table 4 ) . Although no large conformational changes were observed in the p53 itself in this case , allostery can be at play even with minor conformational changes [28] . In the p53 core domain , allosteric fluctuations were observed at locations distant from the allosteric perturbation site [48] . In the case of the p53r2 complex , the flip-out of the Lys120 in one core domain resulted in large protein backbone change ( Fig 6a ) relative to the other p53 ( Fig 6b ) , leading to a conformational change on the surface of p53 away from the DNA binding site . Both p53 core domains shifted significantly in their orientation with respect to their corresponding DNA quarter sites ( Figs 6c , 6d ) , an outcome of the amplified allosteric effect between the protein and DNA . Lys120 and Arg280 are the two major factors that determine the binding specificity to the p53-REs . While Arg280 mostly interacts with the G base at the 4th position within a quarter site , the adjustment of Lys120 interaction may affect the Arg280 interaction since these two residues are next to each other . To see if the two interactions are correlated , covariance map ( Fig S6 ) , interaction energy between the two residues ( Fig S7 ) , and the correlation between the HB distances of the two residues with DNA bases ( Fig 7 ) were calculated . The covariance map revealed that the movements of residues 115–125 were negatively correlated with different portions of the p53 core domain , depending on the DNA sequence . One common negatively correlated portion was residues from 175–185 , suggesting that the movement of the residues near Lys120 will affect the residues at the dimerization interface . Since these correlations were quarter-site specific , it is difficult to draw a general rule regarding the correlation between the conformational change and the RE type . The interaction energies between the two residues showed near zero net interaction energy ( e . g . 14-3-3σ Q1 , Q2 , Q4 ) when Lys120 and Arg280 assumed near crystal structure conformation . When Lys120 popped out of the binding pocket , the interaction energies became either more favorable ( 14-3-3 σ Q3 , Noxa Q4 , Puma Q1 ) ( Fig S7A , C , F ) , or less favorable ( Gadd45 Q1 , p21-5 Q2 , Q4 ) , or mostly changed little when Lys120 did not flip out . These results suggest that the altered packing of Lys120 triggers the readjustment of the Arg280 interactions with the new environment . Such a relationship is also reflected in the HB distances . Fig S8 shows that when the Lys120 HB broke , those of Arg280 also quickly disrupted ( 14-3-3σ Q2 , Q3; Gadd45 Q3 , Q4; p53R2 Q1; Puma Q3 ) . Although in some cases the Lys120 HB disruption did not necessarily result in the disappearance of Arg280 HBs within the limited simulation time ( Noxa Q4; p21-5 Q4; Puma Q1 ) , their stability in the long run is likely to be compromised due to the lack of tight packing . To further demonstrate the correlation between the movement of Lys120 and Arg280 , we present snapshots from two trajectories . Fig 7 shows that the conformational changes happened very early in the trajectories . For 14-3-3σ Q2 ( Fig 7A ) , the distance between Lys120 and the C base at the 2nd position of the quarter site was too close ( 1 . 63 Å ) and too far ( 3 . 66 Å ) to interact with the G base at the same position on the complementary chain in the initial structure . After 0 . 01 ns , Lys120 shifted away from the 2nd bp moving toward the 1st bp , causing the weakening of the neighboring Arg280 HB ( Fig 7A ) with subsequent adjustment of the interactions of both residues with the DNA . While Lys120 was settling with the G1 base from 0 . 01 to 1 ns , Arg280 continued to lose contact with G4 base , shown by the longer interaction distances . In the p53R2 Q1 trajectory , both Lys120 and Arg280 HBs were nicely organized in the starting structure ( 0 ns ) ( Fig 7B ) . Because of the protruding methyl group of the T base at the 1st position of the quarter site , Lys pulled away from the G base at the 2nd position to avoid steric clash ( 0 . 1 ns ) and drifted further away from the starting point ( 0 . 5 ns ) . While Lys120 was searching for favorable positions after pulling away from the major groove , Arg280 started to fray and the HB distance from the G base became longer and out of range from 1 to 1 . 5 ns . The final settled conformation is similar to that at 2 ns ( Fig 7B ) . When compared with structures where both Lys120 and Arg280 maintained their HBs with the 2nd and 4th bases , these two examples clearly demonstrate that the movement of Arg280 or the loss of Arg280 HBs was the outcome of the Lys120 movement .
In each quarter site , the p53-REs largely conform to the consensus sequence and are highly similar to each other . This raises a key question that has been largely overlooked [12] , [13]: how does the small , often minor sequence variation of a single or few bps , translate into vastly different functional consequences , spelling transcription activation or repression ? The in vitro , or cell-based affinity experiments do not necessarily correlate with the functional consequences [8] , [9] and the sparseness of available experimental structures makes such an investigation highly challenging [49] . Our computational results provide insight into this crucial question , illustrating how minor DNA sequence changes can impact subsequent recognition events which in turn determine the functional outcome . We show that subtle conformational changes elicited by DNA sequences which can differ by as little as a single bp can result in altered p53 core domain organization and protein surface dynamics . The DNA is an allosteric effector; slightly different RE sequences lead to minor alterations in the core domain-DNA interactions . The core domain conformational changes may propagate and thus allosterically impact the full protein including the N- and C-terminal domains , providing preferred surfaces for recruitment of specific co-regulators such as STAGA [50] , [51] , CBP/p300 and HDM2 [52] . The amplified allosteric changes at the p53 surface can select different co-regulators [13] . Conformational selection and population shift have been proposed to play a key role in biomolecular recognition [26]–[28] , [53] , [54] . Cofactor binding can also affect RE selectivity by transcription factors through an alternative allosteric mechanism [12] , [13] . In this case , the prior binding of the co-regulator will shift the population of the transcription factor leading to altered DNA-binding site conformation . ASPPs ( apoptosis-stimulating proteins of p53 ) for example , when bound to p53 core domain , can shift the p53 ensemble enhancing a conformation that favors binding to specific p53-REs [12] , [13] , [55] . In light of the findings from this work , it is likely that the ASPP binding changes the loop L1 conformation of the p53 core domain , which has been demonstrated to be of crucial importance to the specificity of RE binding . The structured L1 loop could govern the allosteric pathway mediating these binding sites . The features captured here are only part of the story . DNA sequence variation can also code for the differential binding of p53 family proteins . For example , RE2 of the target gene GDF15 contains sequence variations that allow only p53 but not p63 and p73 binding [56] . This may explain why DNA sequences GGG , GGA or AGG all have similar binding patterns and affinities with p53 [20] but in combination can exclude the binding of other proteins . We further note that although our results clearly show that the p53-DNA interaction patterns and conformational and residue fluctuations vary with DNA sequence , allostery may not be saliently evident in some cases . The allosteric structural perturbations observed in experiments or simulations are the sum of multiple , major and minor pathways [57] and these may not be detected in the current analysis . The transmission of the signal over long distances may be difficult to observe in short MD simulations , and conformations that are relevant for cofactor binding may have high barriers to go through or higher energy , i . e . be less populated [58] and difficult to observe in simulations [59] and in experiment [58] , [60] . However , recently a series of crystal structures coupled with biochemical and cell-based assays have shown how the glucocorticoid ( GR ) REs that vary by even a single bp can lead to different GR conformations at a cofactor binding site , thus affecting GR regulatory activity [13] , [14] , [40] . The cellular network , which reflects the environment , contributes critically to transactivation selectivity [12] , [13] and p53 acetylation was shown to be related to the differential activation of apoptosis or cell cycle arrest [61] , [62] . Methylation of cofactors such as the heterogeneous nuclear ribonucleoproteins hnRNP K can hamper the recruitment of p53 to the REs [63] . Similarly , arginine methylation in p53 may also control target gene selectivity [64] . Post-translational modifications of p53 , including phosphorylation and acetylation [65] , allosterically alter its activity . Covalent modifications provide an added level of cellular network regulation , in addition to protein co-regulator availability which is also regulated by the network in response to changes in the cellular environment . Although not addressed here , sequences flanking the REs are important for the overall organization of the complex , likely also via allosteric effects , combinatorial assembly of other transcription factors binding in these regions [13] and chromatin remodeling . Flanking segments assist in co-regulator transcription recruitment , as shown for the human BAX promoter [66] which can allosterically trigger conformational changes in p53 and neighboring DNA sequences , rendering the binding surface that is specific for cofactor binding . Further , the p53 core domain dimers interactions with DNA and with each other are primary factors responsible for specific cooperative DNA binding , with the interactions enhanced in the full-length protein [16] . The C-terminal domain is also involved in the interactions . While not included here , allosteric effects observed in this work further implicate the conformations of other p53 domains . p53-REs can have spacers with sizes ranging between 1–20 bps . p53-REs with 5- or 6-bp insertions have the weakest binding even with full fledged p53 [67] . p53 dimer-dimer cooperative interactions are important for function [17] , and such cooperative interactions are unlikely for systems with 3–6 ( and probably more ) bp spacers [17] . In some cases , there is only one RE half site and there can still be significant transcriptional activity [68] . In these cases , the allosterically amplified p53 conformational changes induced by half-site DNA could still be large enough for specific recruitment of transcription co-regulators , while the second p53 dimer may bind DNA non-specifically . The notion that even when there is one bp change allosteric effects can still specify biomolecular recognition and hence determine function supports the likelihood that specificity of the 10-bp half site p53-REs is sufficient . Selective p53-related gene expression requires p53 binding to DNA and pre- and post-DNA binding regulatory events such as modifications of both p53 protein and DNA [69] , the recruitment of transcriptional cofactors and RE availability . In a recent example [70] , there exists an identical transcriptional target in apoptosis promoters such as BAX and Puma that was selectively blocked by SMAR1 expressed under mild DNA damage conditions . Under severe DNA damage , other factors displace the SMAR1 protein to allow the initiation of apoptotic processes . The actual repression of the relevant genes might involve direct p53 binding onto the target sites [71] . While selective transcription mechanisms are still unclear [12]–[14] , our findings here on the p53-RE binding-induced selectivity and future developments are expected to provide further insight into the mechanisms of RE selectivity and the regulation of the first step in transcription initiation . To conclude , here we describe a molecular dynamics study of the p53-DNA interaction , particularly focusing on amino acids that make direct contact with DNA bases . We found that the side chain of Lys120 was able to make a number of alternative contacts with DNA bases at positions 1–3 . This observation is consistent with low experimentally observed sequence specificity for p53 binding . We further observed that the conserved interaction of Arg280 with its cognate base pair may be broken in some cases , and that Arg248 is more likely to interact with the DNA backbone than make specific contact with DNA . We show that variant Lys120 interactions with bases at different positions can shift the overall p53-DNA interaction patterns , and how the conformation adopted by Lys120 influences the conformation adopted by other DNA-interacting residues . Most interestingly , the relative orientation of the p53 core domain and DNA changes depending on the sequence of the response element . This leads us to conclude that different response elements will result in different organization of p53-DNA complexes , potentially exposing different surfaces . This , in turn , could result in recruitment of different co-factors and explain the different functionality of response elements whose sequence differs by only a few nucleotides .
MD simulations were performed on 12 p53 dimer-DNA half site complexes constructed based on the p53-DNA crystal structure with the PDB code 1tsr [46] . The detail construction methods of the models were described in the next section . Each system was solvated with a rectangular TIP3P water box [72] with a margin of at least 10 Å from any edge of the box to any protein or DNA atom . Solvent molecules within 1 . 6 Å of the DNA or within 2 . 5 Å of the protein were removed . The systems were then neutralized by adding sodium ions . The resulting systems were energy minimized for 1000 steps before the dynamic run using the CHARMm program [73] and the CHARMm 22 and 27 force field for the protein and nucleic acid , respectively [74] . The production MD simulations were performed at temperatures of 300 degrees Kelvin using the NAMD program [75] and the CHARMm force field . Periodic boundary conditions were applied and the non-bonded lists were updated every 20 steps . The NPT ensemble was applied and the pressure kept at 1 atom using Langevin-Nose-Hoover coupling . SHAKE constraints on all hydrogen atoms and a time step of 2 fs and a nonbonded cutoff of 12 Å were used in the trajectory production . The sizes of the systems were about 110 , 000 atoms and the duration for each simulation was 30 ns . The p53 core domain dimer-half site DNA complex was generated based on the crystal structure template ( PDB code: 1tsr ) [46] , as described earlier [44] , [45] . Briefly , we used two copies of the p53 monomer-DNA complex crystal structure and then superimposed the 10 consensus base pairs from the two copies of the extracted p53-DNA complex in reverse order so that the two copies of p53 were bound to two consecutive quarter sites of the DNA . The resulting p53 dimer-DNA complex structure ensures specific DNA-p53 binding and that the two copies of p53 have a C2 symmetry , with formation of the two salt bridges between Arg180 and Glu181 from the H1 helices of the p53 core domains . The DNA sequences that capped the 5′ and 3′ ends were 5′-ATAATT-3′ and 5′-ATTAA-3′ , respectively . Each base pair that was different from the target sequence was mutated by removing the atoms in the base motif and these atoms were regenerated with GENERATE module in the CHARMm program . The systems were then minimized for 2000 steps with SD algorithm , the mutated base pairs were allowed to move with the NOE restrictions that all the distances between hydrogen bond partners ( heavy atoms ) were within 2 . 6 and 3 . 0 Å . The rest of the system was not allowed to move by applying a force constant of 2 kcal/mol/å during the minimization . The obtained structures were then further minimized for 1000 steps with the ABNR algorithm without any restriction . The models obtained in such a manner yielded reasonable local and overall conformations and served as the starting structure for the MD simulations . For the three duplicate simulations for the purpose to ensure the reliability of the results , additional 1000 steps with the ABNR algorithm was applied before the start of MD trajectories .
|
p53-response elements ( p53-REs ) are 20 base pairs ( bps ) DNA segments recognized by the p53 transcription factor ( TF ) . They are found in promoters and enhancers across the genome and are associated with genes that have diverse functions . Because the DNA sequences of p53-REs can be very similar to each other , differing by as little as one or two bps , it is challenging to understand how p53 distinguishes between these to activate a specific function . Here we show that even a slight RE sequence change can be sufficient to elicit allosteric structural and dynamic perturbations in the p53 which propagate to other binding sites , and as such are expected to affect co-regulator recruitment . Among the major interactions between p53 and its REs involving Lys120 , Arg280 , and Arg248 , the Lys120 interaction partners vary less than interactions between other residues . The outcome of our simulations of six p53-RE complexes shows that the variance of the interaction patterns triggers changes in the organization of tetrameric p53 and of residues away from the interaction sites . Subsequent events can depend on the level and post-translational states of co-regulators that are able to bind the unique p53 surface caused by the specific p53-RE binding .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"computational",
"biology/transcriptional",
"regulation",
"cell",
"biology",
"biochemistry/bioinformatics",
"computational",
"biology",
"biophysics",
"computational",
"biology/molecular",
"dynamics"
] |
2010
|
Lysine120 Interactions with p53 Response Elements can Allosterically Direct p53 Organization
|
Commissural axons must cross the midline to establish reciprocal connections between the two sides of the body . This process is highly conserved between invertebrates and vertebrates and depends on guidance cues and their receptors to instruct axon trajectories . The DCC family receptor Frazzled ( Fra ) signals chemoattraction and promotes midline crossing in response to its ligand Netrin . However , in Netrin or fra mutants , the loss of crossing is incomplete , suggesting the existence of additional pathways . Here , we identify Brain Tumor ( Brat ) , a tripartite motif protein , as a new regulator of midline crossing in the Drosophila CNS . Genetic analysis indicates that Brat acts independently of the Netrin/Fra pathway . In addition , we show that through its B-Box domains , Brat acts cell autonomously to regulate the expression and localization of Adenomatous polyposis coli-2 ( Apc2 ) , a key component of the Wnt canonical signaling pathway , to promote axon growth across the midline . Genetic evidence indicates that the role of Brat and Apc2 to promote axon growth across the midline is independent of Wnt and Beta-catenin-mediated transcriptional regulation . Instead , we propose that Brat promotes midline crossing through directing the localization or stability of Apc2 at the plus ends of microtubules in navigating commissural axons . These findings define a new mechanism in the coordination of axon growth and guidance at the midline .
Organisms with bilateral symmetry coordinate the left and right sides of their body by establishing reciprocal connections in the central nervous system . During development , commissural axons navigate across the midline to form contralateral connections by responding to attractant and repellant cues expressed at the midline and in other cells [1] . To alter growth cone motility , guidance receptors must signal to the underlying growth cone cytoskeleton [2] . Midline and ventricular zone-derived Netrin and its receptor DCC ( Deleted in Colorectal Carcinoma ) or Fra ( Frazzled ) in Drosophila , are a highly conserved chemoattractive guidance pathway [3–5] . Loss of DCC leads to profound commissural axon guidance defects , as well as developmental and movement disorders [6–10] . Despite the clear importance of Netrin signaling in promoting axon growth across the midline , many axons still cross the midline in netrinAB double mutants or fra mutants in Drosophila . This indicates that additional pathways must promote midline crossing [11 , 12] . Indeed , several additional factors implicated in promoting midline axon attraction have been identified in invertebrate and vertebrate systems , such as Shh/Boc [13] , Sema2a/Sema1a [14] , and VEGF/Flk1 [15] . In addition , many other mechanisms have been described that promote axon growth across the midline by preventing premature responses to repulsive molecules secreted from the midline , such as Slits and Semaphorins [16–20] . Despite this progress , it is clear from genetic analysis that additional pathways are likely to be required to ensure the precise regulation of midline circuit formation . To identify additional pathways implicated in the midline crossing process , we performed a genetic screen using a sensitized genetic background . In this background a dominant negative Fra receptor ( FraΔC- missing its entire cytoplasmic domain ) is expressed in a subset of commissural neurons in the Drosophila embryo resulting in an easily quantifiable defect in midline crossing [21] . From this screen , we identified Brain Tumor ( Brat ) , as a new regulator of midline crossing . Brat belongs to the tripartite motif ( TRIM ) -NHL family of proteins and is conserved throughout evolution from C . elegans to humans . Brat contains two B-box domains ( BB ) , a Coiled-coil domain ( CC ) in the N-terminus , and a NHL domain in the C-terminus [22] . Identified first as a translational repressor [23] , Brat has been shown to play important roles in various biological processes , such as in the regulation of microRNA activity during development [24 , 25] , and in the control of the proliferation and differentiation of specific neural precursor lineages during early neural development [26] . Moreover , previous studies showed that each domain executes distinct and specific functions . The NHL domain is essential to suppress the translation of hunchback mRNA in the posterior part of the embryo during early development [27] , and for the maintenance of mushroom body axon connections [28] . In addition , it has recently been shown that during neurogenesis Brat regulates asymmetric protein segregation through the CC domain and specifies intermediate neural progenitor ( INP ) identity via its B-box domains [29] . This process involves Apc2 , a key component of the destruction complex in the canonical Wnt signaling pathway [30] . The destruction complex attenuates the transcriptional activity of armadillo/βcatenin to prevent the activation of Wnt target genes , and thereby promotes the self-renewal of intermediate progenitors . Interestingly , in addition to its role in the destruction complex , Apc2 is a microtubule plus-end binding protein ( +TIP ) [31 , 32] . In Drosophila sensory neuron dendrites , Apc2 interacts with EB1 ( for End Binding ) to control microtubule polarity [31] . In growth cones , APC , the vertebrate homologue of Drosophila Apc2 [33] , regulates axonal projections and changes in axon behavior by regulating microtubule stability and growth directionality [34 , 35] . In this context , tethered to the microtubule plus-ends , APC allows active axon elongation by linking microtubules to the leading edge of the growth cone . In this study , we report that Brain Tumor maintains Apc2 at the plus-ends of microtubules to promote axon elongation and midline crossing . Brat acts independently of the Fra/Netrin pathway and independently of its common partners Pumilio , Nanos and d4EHP , which are required for the inhibition of mRNA translation . In addition , we show that this process requires the B-Box domains of Brain Tumor . Reducing the function of Apc2 in brat mutants , results in enhanced commissural guidance defects in the FraΔC sensitized background . Moreover , Apc2 expression and localization are altered in brat mutant embryos suggesting that Brat function in this context is critically dependent on Apc2 . These data suggest a model where Brat promotes the elongation of the axon before crossing by maintaining Apc2 at the microtubule plus-ends .
In order to identify new molecules and factors implicated in midline crossing , we performed a genetic modifier screen using a truncated Fra receptor ( FraΔC ) missing its cytoplasmic domain that functions as a dominant negative [11] . By targeting the expression of the FraΔC transgene to a small subset of commissural neurons , the eagle neurons , we are able to generate a highly sensitized background . The eagle neuron population is comprised of two pools of neurons , the EWs and the EGs , which are found in each hemisegment . Around ten EG neurons project their axons through the anterior commissure , while only three EW neuron axons project through the posterior commissure [36] ( Fig 1A and 1G ) . In fra mutants , EW axons fail to cross the midline in 36% of embryonic segments , while the axons of EG neurons are unaffected [11] ( Fig 1B and 1G ) . A similar phenotype is observed in stage 16 wild type embryos expressing FraΔC specifically in eagle neurons ( Fig 1C and 1G ) . We started by screening large deficiencies covering a majority of the second chromosome and identified dominant enhancers of the FraΔC crossing defects ( S1 Fig ) . One deficiency , Df ( 2L ) Exel8040 , significantly enhances the FraΔC phenotype resulting in 44% crossing defects ( Fig 1G ) . After testing the different candidate genes present in this interval , we identified the enhancer as Brain Tumor ( Brat ) . A null allele , brat11 , fully recapitulates the enhanced EW defects observed in the deficiency . ( Fig 1D and 1G ) . Moreover , when both copies of brat are removed in the FraΔC screening background , EW crossing defects are strongly enhanced to 69% ( Fig 1E and 1G ) . Importantly , this mutant phenotype can be rescued when full-length Brat ( UAS-Brat ) is expressed selectively in eagle neurons ( Fig 1F and 1G ) , suggesting that Brat functions in commissural axons to promote midline crossing . To determine whether the crossing defects are a consequence of a failure of axon growth or a failure to turn toward the midline , we carefully examined the trajectory of EW axons in these embryos and observed several qualitatively distinct phenotypes ( Fig 1H ) . In addition to crossing the midline normally , EW axons can either continue to grow ipsilaterally and fail to turn , or they can stall before or during midline crossing . For example , when UAS-FraΔC is expressed , EW axons cross in 72% of embryonic segments ( Fig 1O and 1I ) , continue to grow ipsilaterally in 22% of segments ( Fig 1O and 1K ) and stall in only 6% of segments ( Fig 1O and 1M ) . In the FraΔC background , heterozygosity for brat enhances the EW crossing defects , decreasing the proportion EW crossing axons to 47% ( Fig 1O and 1J ) and increasing the proportion of stalled axons to 30% ( Fig 1O and 1N ) . However , the proportion of the ipsilaterally growing axons remains the same ( 22% ) ( Fig 1O and 1L ) , suggesting that the increase of the crossing defects is due to more stalled axons when one copy of brat is removed . The distribution of the EW axon trajectories can be rescued when full-length Brat ( UAS-Brat ) is expressed selectively in eagle neurons ( Fig 1O ) , restoring the proportion observed in the FraΔC background . These results strongly suggest a cell autonomous role for brat in promoting midline crossing and axon growth . Previously , we showed that Brat enhances the EW axon crossing defects induced by FraΔC . Since brat mRNA is expressed and functions in commissural neurons during axon growth and midline crossing , it is a good candidate to interact with Fra in this process ( Fig 1 and S1 Fig ) . To test if Brat functions together with or independently of the Netrin-Fra pathway , we examined genetic interactions between brat and fra mutants . We scored EW axon crossing defects , as well as the pattern of the entire axon scaffold stained with anti-horse radish peroxidase ( HRP ) antibody , in fra mutants , brat mutants and brat , fra double mutants . In wild type embryos , HRP staining reveals that thick anterior and posterior commissures form in each segment and GFP staining reveals that EW and EG axons cross the midline ( Fig 2A and 2E ) , ( Fig 2A’ ) . In fra mutants , the EW neurons fail to extend axons across the midline in 45% of segments ( Fig 2B and 2E ) and a significant crossing defect is also observed when all CNS axons are visualized ( Fig 2B’ ) . In contrast , brat zygotic null mutants show no significant crossing defects in either eagle neurons or in the axon scaffold ( Fig 2C , 2C’ and 2E ) , suggesting that brat is likely to act redundantly to promote crossing . If brat and fra are functioning in the same pathway , we would expect to find the same extent of crossing defects in fra mutants and brat , fra double mutants . In contrast , enhancement of the defects observed in fra mutants would be expected in the brat , fra double mutants if brat and fra function in independent pathways . While trans-heterozygous embryos for brat and fra display no defects ( Fig 2E ) , the double mutants enhance the EW crossing defect to 62% and thinner commissures are observed in the axon scaffold ( Fig 2D , 2D’ and 2E ) . When we carefully analyze the trajectory of the EW axons in these genotypes , we observe the same categories of phenotypes described above: axons can cross the midline , continue to grow ipsilaterally and fail to turn , or stall before or during midline crossing ( Fig 2F ) . In fra mutants , 55% of the EW axons cross the midline ( Fig 2G and 2M ) while in brat , fra double mutants , this proportion is reduced to 38% ( Fig 2H and 2M ) . The proportion of axons that grow ipsilaterally remains the same in the both genotypes with 28% in the fra mutants and 29% in the brat , fra double mutants ( Fig 2I , 2J and 2M ) . However , the proportion of stalled axons observed increases from 16% to 34% in the double mutants ( Fig 2K , 2L and 2M ) , suggesting that the increase of crossing defects is due to more stalled axons in the absence of Brat . Moreover , while the overexpression of brat in all neurons does not induce ectopic midline crossing ( S2A–S2B’ Fig ) , brat expression in the eagle neurons can significantly suppress the non-crossing phenotype observed in the FraΔC background ( S2C–S2E Fig ) . These phenotypes strongly support a role for Brat in axon guidance and indicate that Brat must function independently of the Netrin-Fra pathway to promote midline crossing and axon elongation . In early embryonic development and in the larval peripheral nervous system , Brat cooperates with its cofactors Nanos ( Nos ) and Pumilio ( Pum ) to repress the translation of target mRNAs [23 , 37 , 38] . Thus , we next sought to determine whether Brat function during commissural axon guidance depends on the ability of Brat to interact with Nos and Pum . To address this question , we took advantage of previous studies that identified specific amino acid residues within the Brat NHL domain that are required for the association of Brat with Nos and Pum ( Fig 3E ) [23 , 39] . Interestingly , expression of UASBratG774D is just as efficient as wild-type UASBrat in restoring midline crossing in the FraΔC , brat/+ sensitized background , suggesting that Brat functions independently of these factors during midline guidance ( Fig 3A–3C and 3F ) . In addition , it has been shown that Brat can repress the translation of hunchback by interacting with d4EHP an EIF4e-related cap binding protein [37 , 40] . Expression of UASBratR837D , which is unable to associate with d4EHP , also rescues the midline guidance defects ( Fig 3D and 3F ) . Importantly , we verified that the relative levels and localization of the HA-tagged Brat transgenes used in these rescue experiments are equivalent by visualizing transgene expression in Eg neurons using anti-HA immunostaining ( S3A–S3C Fig ) . These results would seem to preclude a role for Brat acting as a component of a translational repressor complex in the context of midline guidance; however , there is previously published evidence that Brat can regulate translation independently of the Nos/Pum complex [28] . Specifically , Brat has been shown to regulate the maintenance of mushroom body axons in the Drosophila brain , and this function appears to depend on the ability of Brat to attenuate the translation of the Src64B protein [28] . Similarly to what we have shown here for commissural axon guidance ( Fig 3 ) , the UASBratG774D or UASBratR837D variants can also fully rescue the mushroom body axon maintenance defects observed in brat mutants [28] . In addition , previous work from our lab indicates that Src64B acts to negatively regulate midline crossing , and that it does so independently of the Netrin-Fra pathway [41] , raising the possibility that Brat may promote midline crossing by attenuating Src expression . Therefore , to directly investigate a link between Brat and Src64B during commissural axon guidance , we took advantage of a GFP reporter line that indicates the level of Src64B expression . In contrast to the elevated reporter expression observed in mushroom body axons in brat mutants , we observe no difference in Src64B levels in CNS axons in brat mutant embryos ( S4 Fig ) . Finally , and in direct contrast to its role in mushroom body axon maintenance , we find that the NHL domain of Brat is dispensable for its midline axon guidance function , since UASBratΔNHL can rescue the EW crossing defects in the FraΔC , brat/+ sensitized background just as well as wild-type ( Fig 4B and 4H ) . Taken together these observations indicate that the mechanism underlying Brat activity in embryonic commissural axons is independent of translational regulation and distinct from its function in mushroom body axon maintenance . In order to gain insight into the mechanism underlying Brat activity in commissural axon guidance , we carried out a series of structure function experiments to define the sequence requirements for Brat activity during commissural axon guidance . The results described above eliminated a possible role for the C-terminal NHL domain in this process , so we turned our attention to the N-terminal coiled-coil domain ( CC ) , which is known to play a role in regulating asymmetric protein segregation , and the pair of B-box domains ( BB1 and BB2 ) , which have been implicated in the control of intermediate neural progenitor ( INP ) cell identity [29] . We used a series of previously described Myc-tagged UAS Brat transgenes bearing deletions in these domains and tested them in our midline crossing rescue assay ( Fig 4G ) . In contrast to wild-type UAS Brat , UAS BratΔNHL and UAS BratΔCC , all of which recued the EW crossing defects in the FraΔC , brat/+ sensitized background ( Fig 4A–4C and 4H ) , deletion of the B-box domains , either singly or in combination show no significant rescuing activity ( Fig 4D–4F and 4H ) , although we did detect a trend indicating that the combined deletion of both B-boxes may cause a greater impairment in Brat function than single BB deletions . Importantly , we verified that the relative levels and localization of the Myc-tagged Brat transgenes used in these rescue experiments are equivalent by visualizing transgene expression in Eg neurons using anti-Myc immunostaining ( S3D–S3I Fig ) . Given the observation that the role of Brat in regulation of INP identity depends on its B-box domains and that in this context Brat interacts with components of the Wnt signaling pathway , we next investigated whether its role in commissural axon guidance could share common mechanistic features . Consistent with findings in the study of the specification of INP identity , reducing the function of Apc2 , a component of the destruction complex , either with specific point mutations or with a deletion of the Apc2 locus , significantly enhances the EW crossing defects in the FraΔC sensitized background ( Fig 5A and 5E ) , while reducing the function of the Drosophila B-catenin Armadillo ( Arm ) leads to a reciprocal effect and significantly suppresses EW crossing defects caused by the FraΔC transgene ( Fig 5F ) . Furthermore , reducing the function of Apc2 in eagle neurons in brat mutants expressing FraΔC further enhances the commissural guidance defects to 72% ( Fig 5B and 5E ) . We also tested whether expressing UAS-Apc2 selectively in eagle neurons in the FraΔC background could suppress the effect of reducing brat , but this manipulation had no effect on the midline crossing phenotype ( Fig 5E ) . Together , these observations are consistent with the hypothesis that Brat promotes midline crossing by cooperating with Apc2 to limit the activity of Arm , potentially by attenuating Arm-activated gene transcription . This hypothesis makes a number of explicit predictions about the consequences of manipulating components of the Wnt pathway on EW axon midline crossing and we therefore conducted a series of genetic interaction experiments to test this model . First , if Apc2 is acting to attenuate Arm activity , we predicted that over-expressing Apc2 should lead to a similar outcome to decreasing Arm function , resulting in a suppression of the FraΔC phenotype . However , we find that over-expressing Apc2 does not have any effect on the EW crossing phenotype in the FraΔC background ( Fig 5E ) . Similarly , we expected that over-expressing UAS-Arm or a mutant variant of Arm that leads to elevated protein stability , UAS-ArmS10 [42] , should lead to a significant enhancement of the EW midline crossing defects; however , these manipulations have no effect on the midline crossing phenotypes ( Fig 5F ) . We also targeted downstream components of the Arm pathway that are specifically required for Arm-dependent gene transcription , reasoning that if the strong suppression of the midline crossing defects observed in arm loss of function results from a failure of Arm-dependent gene expression , then blocking transcriptional activity with dominant negative variants of TCF should also suppress the midline crossing defects . However , as we found for Arm and Apc2 over-expression , expressing multiple different TCF transgenes or TCF dominant negative transgenes had no impact on the EW crossing defects ( Fig 5F ) . The same set of genetic manipulations using many of the same transgenic lines gave very different outcomes in the context of Brat-mediated regulation of INP identity , where results are consistent with the model that the primary role of Brat in INPs is to antagonize Armadillo activity [29] . In the context of axon elongation and guidance , it is clear that the experiments described here do not support the idea that Brat and Apc2 regulate midline guidance through antagonizing Arm-dependent gene expression . Despite the fact that the results described above argue strongly against the idea that Brat and Apc2 regulate axon growth and guidance through antagonizing Arm-dependent gene expression , the fact that reducing arm function suppresses the midline crossing defects in the FraΔC background point to a possible contribution of Arm to midline axon guidance . Therefore , to further explore a relationship between Arm and Brat , we analyzed the effect of modulating Arm levels in embryos that are heterozygous for mutations in brat . Interestingly , while brat , arm compound heterozygotes have no crossing defects , there is a significant reduction in midline crossing when UAS-Arm is expressed in brat heterozygotes ( Fig 5F ) . These observations could suggest that Arm may act to inhibit Brat function; however , given that the complete removal of zygotic brat does not result in any guidance defects , this explanation does not seem likely . We favor the alternative possibility that Arm may impinge on a parallel pathway to prevent crossing . Here , it is intriguing to note that the similar over-expression of Arm in the FraΔC genetic background does not enhance the crossing defects , suggesting a possible role for Arm in modulating Fra-dependent axon attraction . It will be interesting in the future to explore potential genetic and biochemical links between Arm and Fra . The observation that simultaneous heterozygosity for both Apc2 and brat leads to significantly stronger midline crossing defects in the FraΔC background than heterozygosity for either brat or Apc2 alone is consistent with the idea that they work together to promote midline crossing . We also examined the consequences of removing both copies of Apc2 in an otherwise wild-type background . Similar to our findings with brat zygotic null mutants , Apc2 zygotic null mutants show no significant crossing defects in either eagle neurons or in the axon scaffold ( Figs 2C , 2C’ , 2E and 5E ) . Since both Apc2 and Brat are maternally deposited , we reasoned that the simultaneous removal of the zygotic copies of both of these genes might sufficiently limit Brat and Apc2 function to reveal defects in midline crossing in an otherwise wild-type background . Indeed , the double mutants for brat and Apc2 show significant crossing defects in EW axons ( Fig 5C , 5D and 5E ) . In addition , brat , Apc2 double mutants also lead to additional disruptions to the axon scaffold that could reflect roles in processes other than midline crossing . Together with the dose-dependent genetic interactions , these observations further support an important role for Brat and Apc2 in promoting axon growth across the midline . Taken together our findings suggest that the role of Brat in commissural axon guidance is mechanistically distinct from previous described functions of Brat in either the control of mushroom body axon maintenance or in the regulation of INP progenitor identity , although there are some shared features with the latter process . How then does Brat contribute to the growth of axons across the midline ? One possibility is that rather than affecting Arm-dependent gene expression that Brat and Apc2 could influence axon growth through regulation of the neuronal cytoskeleton . Indeed , Apc2 has been shown to interact with the plus ends of microtubules and there is in vitro evidence that it can regulate axon growth . To test this idea , we first examined the distribution of Apc2 in wild-type Eg neurons using an Apc2-GFP fusion protein . In order to simultaneously visualize plus-ends of microtubules , we co-expressed an EB1-RFP fusion protein . Interestingly , in wild-type embryos Apc2-GFP and EB1-RFP are more clearly co-localized in stages where axons are actively growing toward the midline , relative to stages when midline crossing is complete , suggesting that Apc2 may contribute to promoting axonal growth toward the midline ( Fig 6A–6F’ ) . We next tested whether the localization or expression of Apc2 is dependent on Brat by monitoring the levels and distribution of a GFP-tagged Apc2 protein in the eagle neurons of wild-type and brat mutant embryos . Strikingly , we find that in the absence of Brat , there is a significant reduction of Apc2-GFP expression in both the cell bodies and axons of EW commissural neurons relative to heterozygous sibling controls ( Fig 6G–6I ) . In addition , in contrast to wild-type neurons where Apc2-GFP expression appears to localize to discrete puncta , the expression of Apc2-GFP is more uniform in brat mutant neurons ( Fig 6G and 6H ) . We also examined the relative levels of Brat transgene expression in apc2 mutants but did not observe any significant difference ( S5 Fig ) . These results support the model that Brat may promote axon guidance by maintaining the expression and localization of Apc2 to the plus-ended tips of growing microtubules during growth toward the midline ( Fig 7 ) .
We identified Brat in a genetic modifier screen based on a sensitized background where Netrin-dependent axon attraction is selectively reduced in a small subset of commissural axons . Genes identified in this screen could either act in the Netrin-Fra pathway or in independent pathways to regulate midline crossing . Our genetic data indicates that Brat function is independent of Netrin-Fra , since double mutants result in significantly stronger phenotypes than fra or brat single mutants . Indeed , brat single mutants have no zygotic loss of function phenotype in the absence of additional perturbations to pathways that promote midline crossing , suggesting that Brat may function redundantly . There are many examples of redundant pathways that promote midline crossing in both invertebrates and in the mammalian spinal cord , whose functions are only revealed when other pathways are limited . For example , Nell2 , a recently identified ligand for the Robo3 receptor , has no significant spinal commissural axon guidance phenotype unless other pro-crossing pathways are also limited [43] . Similarly , requirements for Drosophila Robo2 and Semaphorin1a to promote midline crossing are only revealed when the Netrin-Fra pathway is disrupted [14 , 16] . Most recently two studies made the surprising finding that floor-plate specific removal of Netrin does not result in significant midline crossing defects [44 , 45] , suggesting that floor-plate derived Netrin may act redundantly with other pathways to promote crossing . One common interpretation of these findings is that these redundant pathways exist and are conserved to ensure robustness in the essential process of forming correct midline circuitry . An alternative possibility that could account for the absence of a phenotype in brat mutants is that maternal contribution of brat mRNA and/or protein may be sufficient to fulfill the Brat requirement to promote crossing . There is ample precedent for maternal contribution of gene products masking requirements for embryonic axon guidance in Drosophila . For example , signaling components in the Robo pathway , such as Dock , Son of Sevenless , and Kuzbanian are all contributed maternally and expression is maintained throughout the entire duration of embryogenesis [46–48] . In the case of Dock , it has been shown that loss of both maternal and zygotic gene products reveals a strong phenotype , not observed in zygotic mutants [47] . The observation that simultaneous removal of zygotic expression of both Brat and Apc2 reveals a significant defect in midline crossing would lend support to the possibility that maternal gene products may be the explanation for the absence of single mutant phenotypes . Whether redundancy or maternal compensation explain the absence of brat and apc2 single mutant phenotypes , our data support the interpretation that Brat and Apc2 constitute part of an important mechanism to promote axon growth across the midline . Brat is a multi-domain protein with diverse functions in developing tissues . Interestingly , discrete and separable structural features of the protein control many of Brat’s distinct activities . For example , Brat’s NHL domain is required for its role as a translational repressor , but is dispensable for the control of intermediate neural progenitor identity and commissural axon guidance . Instead , these functions both appear to depend on the N-terminal B box domains of Brat , and in both axon guidance and INP specification genetic evidence points to important interactions with the Apc2 protein , a critical component of the B-catenin/Armadillo destruction complex [29] , and this study . Intriguingly , in both of these cases , the loss of brat function results in decreased expression and altered localization of the Apc2 protein , but the role of Apc2 in these two processes appears to be distinct . Specifically , during the specification of INP identity , Apc2 acts in its classical role as a component of the destruction complex to attenuate B-catenin-dependent transcriptional activity [29] . In contrast , during commissural axon guidance , Apc2 does not regulate B-catenin-dependent transcription , but instead more likely acts locally to stabilize microtubules in the advancing growth cone . This idea is supported by our observations that the genetic manipulation of B-catenin-dependent transcription does not affect commissural axon guidance and that the enrichment of Apc2 at microtubule plus ends is diminished in brat mutants . In addition , previous studies in cultured vertebrate neurons support a role for Apc2 in controlling axon growth through the regulation of microtubule stability [34 , 35] . It will be interesting to determine whether vertebrate orthologs of Brat are also involved in these processes . How might the activity of Brat control the specific localization of Apc2 in commissural axons to promote axon growth across the midline ? One possibility is that Brat could directly associate with Apc2 and somehow deliver it to or stabilize it at microtubule plus-ends; however , no physical interactions between Brat and Apc2 have yet been detected . Alternatively , Brat may indirectly promote Apc2 localization and function through interactions with unidentified upstream signals . Based on the observation that Wnt signaling can induce the loss of Apc2 microtubule localization in vertebrate neurons [34] , and that this leads to the formation of looped microtubules ( a characteristic feature of paused growth cones ) [49 , 50] , it is interesting to speculate that Brat may promote continuous axon growth across the midline by stabilizing Apc2 localization at the plus-ends of microtubules . Future identification of additional components that contribute to Brat and Apc2 mediated commissural axon guidance will allow for continued dissection of the underlying cell biological mechanisms .
The following Drosophila mutant alleles were used: fra3 , egMZ360 ( eg-GAL4 ) . The following stocks were from Bloominton: Df ( 3R ) Exel6198 , Df ( 2L ) Exel8040 , arm8 , UAS-arm-GFP , UAS-Apc2-GFP , UAS-arm . S10 , UAS-TCF and UAS-ΔTCF . The stock Src64B-GFP was from the Kyoto Stock Center . The following stocks were a gift from C-Y Lee: brat11 , UAS-brat-Myc , UAS-bratΔNHL-Myc , UAS-bratΔCC-Myc , UAS-bratΔBB-Myc , UAS-bratΔBB1-Myc , and UAS-bratΔBB2-Myc . The following stocks were a gift from F Besse: UAS-brat-HA , UAS-bratGD-HA , and UAS-bratRD-HA . The following stocks were a gift from M . Peifer: Apc2g10 . The following transgenes were used UAS-FraΔC-HA , UAS-A5CD8-GFP . The UAS-EB1-RFP stock was a gift from Yuanquan Song . GAL4 drivers used were elav-GAL4 and eg-GAL4 . All crosses were carried out at 25°C . Embryos were genotyped using balancer chromosomes carrying lacZ markers or by the presence of epitope-tagged transgenes . See S1 Table for a complete list of genotypes for all the figures . Dechorionated , formaldehyde-fixed , methanol devitellinized embryos were fluorescently stained as previously described [51] . The following primary antibodies were used: mouse anti-1D4/FasII [Developmental Studies Hybridoma Bank ( DSHB ) ; 1:100] , mouse anti-Beta gal [DSHB; 1:150] , mouse anti-Myc [DSHB ( 9E10 ) ; 1:500] rabbit anti-GFP [Invitrogen ( #A11122 ) ; 1:500] , Mouse anti-HA [Covance ( 16B12 ) 1:250] , Chicken anti-GFP [Aves Labs ( GFP-1020 ) 1:1000] . The following secondary antibodies were used: Alexa647- conjugated goat anti-HRP [Jackson Immunoresearch ( #123-605-021 ) ; 1:500] . Cyanine 3-conjugated goat anti-rabbit [Jackson; 1:1000] , Alexa488-conjugated goat anti-mouse [Molecular Probes; 1:500] and Alexa488-conjugated donkey anti-chicken [Jackson Immunoresearch; 1:500] . Embryos were mounted in 70% glycerol/PBS . Fluorescent mRNA in situ hybridization was performed as described , with digoxigenin labeled probes [52] . Briefly , hybridized probe was detected with anti-digoxigenin-HRP ( Roche ) , using fluorescein-labeled tyramide as a substrate ( TSA Fluorescence System , Perkin Elmer ) . Embryos were mounted in 70% glycerol/PBS . Phenotypes were analyzed and images were acquired using a spinning disk confocal system ( Perkin Elmer ) built on a Nikon Ti-U inverted microscope using a Nikon OFN25 60x 40x or 10x objective with a Hamamatsu C10600-10B CCD camera and Yokogawa CSU-10 scanner head with Volocity imaging software . Images were processed using ImageJ and Adobe Illustrator software . For fluorescence quantification of GFP antibody staining in embryos , ten embryos per genotype ( +/+; UAS-Apc2GFP or brat ( - ) ; UAS-Apc2GFP , +/+; src64bGFP or brat ( - ) ; scr64bGFP ) were imaged using identical settings . Max projections were generated using ImageJ . After subtracting the staining background , the average pixel intensity was measured on twelve to sixteen clusters of EW neurons or across five regions within longitudinal axons for each embryo . The values from the five to ten embryos for each phenotype were averaged . For EW commissural neuron axon crossing phenotypes , whole-mount or filleted embryos were analyzed at Stages 15 and 16 . Eight abdominal segments were analyzed per embryo when possible , and for each embryo , the percentage of non-crossing segments was calculated . A segment was considered non-crossing when both clusters of EW axons ( six axons per segment ) failed to reach the midline . Embryos were scored blind to genotype when possible . For statistical analysis , comparisons were made between genotypes using the Student’s t-test , ANOVA or Chi-squared test . For multiple comparisons , significance was assessed by using a Bonferroni correction .
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The establishment of neuronal connections that cross the midline of the animal is essential to generate neural circuits that coordinate the left and right sides of the body . Axons that cross the midline to form these connections are called commissural axons and the molecules and mechanisms that control midline axon crossing are remarkably conserved across animal evolution . In this study we have used a genetic screen in the fruit fly in an attempt to uncover additional players in this key developmental process , and have identified a novel role for the Brain Tumor ( Brat ) protein in promoting commissural axon growth across the midline . Unlike its previous described functions , in the context of midline axon guidance Brat cooperates with the microtubule stabilizing protein Apc2 to coordinate axon growth and guidance . Molecular and genetic analyses point to the conserved B box motifs of the Brat protein as key in promoting the association of Apc2 with the plus ends of microtubules . Brat is highly conserved and future studies will determine whether homologous genes play analogous roles in mammalian neural development .
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2018
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Brain Tumor promotes axon growth across the midline through interactions with the microtubule stabilizing protein Apc2
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We report the results of an investigation of a small outbreak of hantavirus pulmonary syndrome in 2002 in the Department of Santa Cruz , Bolivia , where the disease had not previously been reported . Two cases were initially reported . The first case was a physician infected with Laguna Negra virus during a weekend visit to his ranch . Four other persons living on the ranch were IgM antibody-positive , two of whom were symptomatic for mild hantavirus pulmonary syndrome . The second case was a migrant sugarcane worker . Although no sample remained to determine the specific infecting hantavirus , a virus 90% homologous with Río Mamoré virus was previously found in small-eared pygmy rice rats ( Oligoryzomys microtis ) trapped in the area . An antibody prevalence study conducted in the region as part of the outbreak investigation showed 45 ( 9 . 1% ) of 494 persons to be IgG positive , illustrating that hantavirus infection is common in Santa Cruz Department . Precipitation in the months preceding the outbreak was particularly heavy in comparison to other years , suggesting a possible climatic or ecological influence on rodent populations and risk of hantavirus transmission to humans . Hantavirus infection appears to be common in the Santa Cruz Department , but more comprehensive surveillance and field studies are needed to fully understand the epidemiology and risk to humans .
Hantaviruses ( family Bunyaviridae , genus Hantavirus ) are enveloped , tripartite , single-stranded , negative-sense RNA viruses . On the American continents , these viruses can evoke a severe , acute disease in humans known as hantavirus pulmonary syndrome ( HPS ) [1] . Hantavirus pulmonary syndrome is characterized by fever , headache , myalgia , and , in severe cases , rapid cardiopulmonary dysfunction , with case fatalities up to 70% depending on the particular virus . Hantaviruses are maintained in rodents and insectivores , usually with a tight pairing between the specific virus and host species . All hantaviruses known to cause human disease are associated with rodent hosts . Since the first recognized case of HPS in 1993 , an estimated 200 cases per year associated with more than 25 different hantaviruses have been recognized in the Americas , the majority in South America [1] . Sin Nombre virus in North America and Andes , Araraquara , and Laguna Negra ( LANV ) viruses in South America are among the most frequent etiologic agents . Antibody-prevalence studies in some area of South America suggest hantavirus exposure in up to 40% of the population [2] . Furthermore , hantavirus-host reservoir pairs continue to be discovered and details of the epidemiology and risk of hantaviruses to humans continue to emerge . Between May and June , 2002 , the Bolivian National Center for Tropical Diseases ( CENETROP ) reported HPS in two residents of geographically disparate areas of the Department of Santa Cruz , Bolivia ( Figure 1 ) . Because HPS had not been previously recognized in Santa Cruz , a multinational effort was undertaken in August 2002 to 1 ) assess the circumstances surrounding these cases , 2 ) clarify the public health risk posed by hantaviruses in the region , and 3 ) characterize the virus-reservoir pairing ( s ) . Results of the investigation of the rodents implicated in the outbreak have been previously reported [3] . Here we report the results on the human cases and ancillary epidemiological studies conducted as part of the outbreak investigation .
The activities in which the human samples were taken were approved by the U . S . Naval Medical Research Center in compliance with all applicable Federal regulations governing the protection of human subjects . All subjects provided informed oral consent . Oral rather than written consent was chosen because the literacy level of the population was estimated to be low ( less than 30% ) . Oral consent was documented by two witnesses , one from the study team and one from family members or friends present at the time of the interview and blood draw . Parents or guardians provided consent on behalf of all minors , with the assent of the minor . The consent process and all other activities reported in this manuscript were approved by CENETROP; the U . S . Centers for Disease Control and Prevention , Atlanta , USA; U . S . Naval Medical Research Unit 6 , Lima , Peru; and Argentine National Institute for Viral Diseases “Dr . Julio I . Maiztegui” , Pergamino , Argentina as components of the emergency response to a hantavirus outbreak occurring in the Department of Santa Cruz , Bolivia , in 2002 . Samples tested from already-existing collections at CENETROP were numerically coded , with the identity of the person available only to clinicians interacting with the patient . Cross-sectional antibody-prevalence studies , approved by the Bolivian Ministry of Health , were conducted in Mineros and Concepción to elucidate the transmission dynamics in this outbreak as well as the overall risk of hantavirus infection in the region . After obtaining informed consent , 5 ml of blood were collected from a convenience sample of persons aged ≥5 years . In addition , a one-page questionnaire addressing potential risk factors for hantavirus infection and types of exposure was administered in Spanish to each study participant . In Mineros , three communities ( Dinamarca , La Patria , and Oriental ) were selected based on the hypothesis that exposure to rodents and hantaviruses would be higher in communities actively involved in the sugarcane harvest . Dinamarca consisted of approximately 50 dwellings ( 4–5 persons/house ) constructed of plastic tarps on the periphery of the sugarcane fields . This community migrates frequently to follow the sugarcane harvest . The male inhabitants work in the fields while women and children typically attend to domestic chores . La Patria is a permanent town of approximately 60–80 inhabitants , mostly subsistence farmers of rice and vegetables , on the outskirts of the sugarcane fields . Oriental is an urban neighborhood on the edge of Mineros , outside of the sugarcane growing area , whose inhabitants work in diverse occupations in and around Mineros , with only a few persons working in the sugarcane fields . In Concepción , blood was collected from persons living on the physician's ranch and in surrounding communities . Testing was performed at CENETROP with assistance from U . S . Naval Medical Research Unit 6 from Lima , Peru , following standard operating procedures supplied by the Centers for Disease Control and Prevention . All samples were tested by ELISA for IgG antibody to hantavirus using a previously described methodology [5] . Because there were unconfirmed reports of recent cases of HPS in Concepción , samples collected from this area were also tested for IgM antibody . The antigens used for the IgG and IgM assays were from Sin Nombre virus and LANV , respectively , which have been shown to be broadly cross-reactive with New World hantaviruses . The starting dilution was 1∶100 with subsequent 4-fold dilutions . Conservative cut-offs for a positive result were used—optical densities of >3 . 0 for the IgG assay and >2 . 0 for IgM .
A total of 494 persons were enrolled in the serosurvey , 415 from Mineros and 79 from Concepcion ( Table 1 ) . The mean age was 21 years ( range 5–81 ) and 62% were male . The overall IgG antibody prevalence in all regions was 9 . 1% and did not differ significantly between any of the towns or communities studied . IgG-positive cases were noted in 35 households , with seven households having two cases ( five in Dinamarca and one each in La Patria and Oriental ) and one household in Concepcion having four cases . There were no significant differences in the frequency of IgG antibody in persons with respect to sex , occupation , house construction materials , hunting or fishing , home department in Bolivia , time spent living or working in Santa Cruz Department , rural or urban house location , or noting the presence of rodents at home or at work . Antibody-positive persons were significantly older than antibody negative ones ( 33 versus 24 years , respectively , p = . 006 ) . The IgG antibody prevalence stratified by age group is shown in Figure 2 . Although not statistically significant , the highest IgG prevalence was noted in persons with professions that would likely put them at increased risk of exposure to rural rodents , such as farmers ( 15 . 2% ) and sugarcane workers ( 9 . 4% ) , as opposed to housewives ( 7 . 2% ) and students/children ( 7 . 1% ) . The study population reported living in or coming from eight of Bolivia's nine departments , as well as from out of the country . However , IgG antibody-positive persons were found only from Beni ( 5/22 , 22 . 7% ) , Cochabamba ( 3/15 , 20 . 0% ) , Chuquisaca ( 14/134 , 10 . 4% ) , Santa Cruz ( 20/245 , 8 . 2% ) , and Potosí ( 3/57 , 5 . 3% ) . No positive persons were noted from La Paz , Oruro , or Tarija , but there were only 16 people collectively from these departments , as well as six antibody-negative persons from outside the country . Interestingly , 29 ( 64% ) of the 45 IgG antibody-positive persons had lived at their present residence for 1 year or less , suggesting that many hantavirus exposures may have occurred elsewhere , although the previous residence of this highly mobile population may often have been in the same region . Four ( 5 . 1% ) persons in Concepcion were IgM antibody-positive , all of whom lived and worked on the physician's ranch for a year or more . Three of the four cases , a ranch hand and his wife and 9-year-old daughter , clustered in a single household . These three persons , as well as a fourth member of the household , were also IgG-positive . The fourth IgM-positive person was a farmer . All IgM-positive persons were asymptomatic at the time of testing , but two reported febrile diseases accompanied by headache and myalgia in May and June , 2002 , for which they did not seek medical care .
The cases described here are the first reported laboratory-confirmed cases of HPS in the Department of Santa Cruz , Bolivia . The sequence of the LANV from the physician was 99% identical to viruses obtained from a large vesper mouse ( Calomys callosus ) caught on his ranch , clearly marking this area as endemic for LANV [3] . This is further evidenced by the finding of other IgM-positive persons on the ranch , some of whom recently had syndromes consistent with mild HPS . Although the small vesper mouse ( Calomys laucha ) is considered the primary reservoir of LANV , the virus has been found in large vesper mice in Argentina , suggesting that LANV is also adapted to this rodent species [7] . Laguna Negra virus was also isolated from a Chilean traveler who developed HPS after traveling throughout Bolivia , including Santa Cruz Department [8] . The precise location of his infection is unknown . Hantavirus pulmonary syndrome due to LANV or LANV-like viruses has also been confirmed in Paraguay [6] , Brazil [9] , and Argentina [10] . Unfortunately , no sample was available to identify the specific virus that infected the sugarcane worker . However , a virus with 90% nucleotide homology with the hantavirus Río Mamoré ( RIOMV ) was obtained from a small-eared pygmy rice rat ( Oligoryzomys microtis ) trapped in the region where the man worked and was presumably infected [3] . Although RIOMV has not been definitively linked to human disease , fatal HPS has been reported in eastern Brazil putatively linked to viruses ( coined Anajatuba and Río Mearim ) phylogenetically very similar ( 94–96% nucleotide homology ) to RIOMV [11] , [12] . Although RIOMV-infected small-eared pygmy rice rats have been found to date only in the Bolivian departments of Santa Cruz , La Paz , and Beni , and in neighboring Peru , the range of this species , and thus the potential area at risk for RIOMV transmission , is vast , including the Amazon Basin of Brazil and contiguous lowlands of Peru , Bolivia , and Paraguay [13]–[17] . Assuming that the episode of rodent urine falling on the physician's face was the infecting event , we can calculate a precise incubation period of 33–35 days for this case ( still with a small range , since the physician did not recall which day during his weekend stay the exposure occurred ) . This is in keeping with other reported incubation periods for HPS [18] . Four different hantaviruses have now been confirmed in Bolivia: LANV [3] , [6] , RIOMV [15] , Bermejo [19] , and Andes [7] , [20] . Furthermore , two distinct hantaviruses circulate in the Department of Santa Cruz ( LANV and RIOMV ) and three in Tarija ( LANV , Andes , and Bermejo ) ( Figure 1 ) . Cases of HPS have also been reported from Cochabamba Department , although no information is available on the specific hantavirus involved [21] . The risk of infection should probably be considered particularly high in the Departments of Santa Cruz , Tarija , and Cochabamba , especially to agricultural workers and others with frequent exposure to rodents . The prevalence of antibody to hantavirus in Santa Cruz Department was relatively high and consistent throughout the areas studied . Age stratification showed a steady increase in IgG antibody-prevalence with increasing age group , suggesting continuous exposure to hantaviruses over the course of the lives of the population ( Figure 2 ) . It is notable that , despite the apparent consistently high rate of exposure to hantaviruses , confirmed cases of HPS have never before been reported from Santa Cruz Department . Possible explanations for this finding include frequent mild or asymptomatic infection not necessitating medical care ( as we have documented here in the four IgM antibody-cases ) , inadequate surveillance or reporting , and misdiagnosis of HPS by clinicians , perhaps due to unfamiliarity with the condition compounded by lack of readily available diagnostic testing for HPS in most hospitals . Similar high antibody prevalence with infrequent reporting of HPS has been reported from Cochabamba Department in Bolivia , as well as from neighboring areas of Paraguay , Argentina , and Chile [2] , [7] , [20] , [22]–[25] . In contrast , data do not suggest frequent mild or asymptomatic transmission of Sin Nombre or other hantaviruses in North America [26]–[28] . The occurrence of six human hantavirus infections ( including both PCR- and IgM-positive cases ) in two ecologically disparate areas of Santa Cruz Department within a few months may relate to region-wide climatic or ecological influences resulting in increased rodent populations [29]–[31] . Deviations in precipitation or temperature , sometimes associated with effects on flowering and fruiting or seed-set of particular plants known to be significant sources of food for rodents , have been implicated in other hantavirus outbreaks , although the precise relationship remains ill-defined [32] , [33] . Interestingly , precipitation in the city of Santa Cruz in the months prior to the outbreak appeared to be particularly heavy in comparison to other years ( Figure 3 ) . The cases occurred at the end of the rainy season at a time of cooler temperatures . The cases reported here occurred in areas of significant anthropogenic perturbation of the landscape ( i . e . , clearing forest for ranching and sugarcane farming ) , which is thought to increase the risk of rodent-borne virus infection to humans through the intrusion of opportunistic rodents as well as increased exposure to animal excreta [34] . Interestingly , hantavirus antibody prevalence did not vary among the regions and communities we studied , despite the presumed lower risk in the groups not primarily involved in agriculture . Possible explanations for this finding include the non-random sample and that many of the non-agricultural workers still had significant rodent exposure in and around their homes . Furthermore , it is possible that the rate of exposure varies significantly between the populations , with the antibody prevalence in the more stable urban population representing life-time exposure while the largely immigrant agricultural population may have achieved a similar prevalence in the much shorter time since coming from other , perhaps non-endemic regions . The work reported here was part of an outbreak investigation . Larger and more comprehensive antibody prevalence surveys , including longitudinal studies , systematic rodent surveys with hantavirus testing , and aggressive case surveillance would help elucidate the epidemiology of HPS and the potential risk to humans in Santa Cruz Department .
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Hantaviruses can evoke a severe , acute disease in humans known as hantavirus pulmonary syndrome with case fatalities up to 70% . Pathogenic hantaviruses are carried by rodents , with each virus species usually carried by a specific species of rodent . Hantavirus-host reservoir pairs continue to be discovered and details of the epidemiology and risk of hantaviruses to humans continue to emerge . We report the results of an investigation of a small outbreak of hantavirus pulmonary syndrome in 2002 in the Department of Santa Cruz , Bolivia , where the disease had not previously been reported . Two cases were initially noted , with four additional persons shown to be recently infected with hantaviruses through thorough field investigation and antibody evidence . An antibody prevalence study conducted as part of the outbreak investigation showed over 9% of the population studied to have previous exposure to hantaviruses . Precipitation in the months preceding the outbreak was particularly heavy in comparison to other years , suggesting a possible climatic influence on rodent populations and risk of hantavirus transmission to humans . Hantavirus infection appears to be common in the Santa Cruz Department , but more comprehensive surveillance and field studies are needed to fully understand the epidemiology and risk to humans .
|
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"medicine",
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2012
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Hantavirus Pulmonary Syndrome in Santa Cruz, Bolivia: Outbreak Investigation and Antibody Prevalence Study
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Helical cell shape of the gastric pathogen Helicobacter pylori has been suggested to promote virulence through viscosity-dependent enhancement of swimming velocity . However , H . pylori csd1 mutants , which are curved but lack helical twist , show normal velocity in viscous polymer solutions and the reason for their deficiency in stomach colonization has remained unclear . Characterization of new rod shaped mutants identified Csd4 , a DL-carboxypeptidase of peptidoglycan ( PG ) tripeptide monomers and Csd5 , a putative scaffolding protein . Morphological and biochemical studies indicated Csd4 tripeptide cleavage and Csd1 crosslinking relaxation modify the PG sacculus through independent networks that coordinately generate helical shape . csd4 mutants show attenuation of stomach colonization , but no change in proinflammatory cytokine induction , despite four-fold higher levels of Nod1-agonist tripeptides in the PG sacculus . Motility analysis of similarly shaped mutants bearing distinct alterations in PG modifications revealed deficits associated with shape , but only in gel-like media and not viscous solutions . As gastric mucus displays viscoelastic gel-like properties , our results suggest enhanced penetration of the mucus barrier underlies the fitness advantage conferred by H . pylori's characteristic shape .
Helicobacter pylori is a helical rod shaped Gram ( - ) Proteobacterium with only one known niche , the viscous epithelial mucus layer of the human stomach [1] . Infection with H . pylori generally occurs during infancy or childhood , persists through adulthood unless treated , and leads to serious clinical pathologies including peptic ulcer and gastric cancer in 10–20% of those infected [2] . Pathologic examination of gastric biopsy specimens reveals H . pylori dispersed within the gastric mucus layer and in direct contact with the gastric epithelial cells [3] . It is believed the bacteria localize to these areas to escape the low pH of the stomach lumen , which they can survive only for a matter of minutes [4] , and to avoid elimination by peristalsis . H . pylori requires flagella-mediated and chemosensory-directed motility to access and maintain itself in the mucus layer [5]–[8] . H . pylori's helical cell shape may contribute to this process by enabling the bacteria to bore into the mucus layer via a cork-screwing mechanism [9] . More specifically , the turning helical cell body is thought to interact with large polymers to generate torque that enhances translational movement and reduces circumferential slip [10] . Mathematical modeling has predicted helical shape improves propulsion efficiency in the form of speed in viscous polymer solutions [11] . H . pylori and Campylobacter jejuni have been shown to swim faster at higher viscosities than certain rod-shaped species ( e . g . Escherichia coli ) in solutions of methylcellulose [3] , [12] . The cell envelope-embedded peptidoglycan ( PG ) layer is essential to maintain osmotic stability and cell shape in most bacteria including H . pylori [13] , [14] . Gram ( - ) bacteria have a thin layer of PG meshwork in their periplasm [15] . This PG sacculus consists of glycan chains of repeating N-acetylglucosamine-N-acetylmuramic acid ( GlcNAc-MurNAc ) units that are crosslinked by short peptides attached to MurNAc . During enlargement of the PG sacculus , the disaccharide-pentapeptide precursor lipid II is polymerized and inserted into the sacculus by the coordinated action of PG synthases and hydrolases [16] . Penicillin binding protein 1 ( PBP1 ) is the only PG synthase in H . pylori and is predicted to serve as both glycan-polymerizing glycosyltransferase and peptide-crosslinking DD-transpeptidase [17] . The other two high molecular weight PBPs encoded by H . pylori , PBP2 and PBP3 , are both predicted to act as monofunctional DD-transpeptidases . Pentapeptides that do not participate in crosslinking can be trimmed by DD- , LD- , and DL-carboxypeptidases ( CPases ) that successively trim pentapeptides to tetra- , tri- , and dipeptides , respectively . No low molecular weight PBP homologues have been identified in the H . pylori genome , but the existence of trimmed peptides in the PG sacculus suggests the existence of these peptidase activities [18] . PG derived tripeptide is an agonist for the intracellular pathogen-associated molecular pattern recognition molecule Nod1 which becomes activated during engagement of the H . pylori Cag type IV secretion system . Thus increased tripeptide content of the sacculus could potentially increase proinflammatory activity during H . pylori infection [19]–[21] . Bacteria also possess DD-endopeptidases ( EPases ) for cleavage of peptide crosslinks . We previously identified three genes , csd1-3 , that encode putative DD-EPases and contribute to H . pylori's helical cell shape through alterations in PG crosslinking [13] . Upon deletion of each of these genes individually or in tandem , H . pylori assumes various curved rod morphologies . Overexpression of csd3 ( hdpA ) also alters normal helical shape [22] . Here we identify two additional genes , csd4 and csd5 , that promote helical cell shape . One , csd4 , encodes a zinc metallopeptidase that functions as a CPase on PG tripeptide . H . pylori loses nearly all curvature in its absence yielding a straight rod . Csd4 proteins are found throughout the Epsilonproteobacteria and the Campylobacter jejuni homologue Pgp1 also promotes helical cell shape [23] . Genetic analyses of cell shape and cell wall composition suggest distinct peptidoglycan modifications cooperatively produce helical morphology . We demonstrate straight rod mutants of H . pylori are attenuated in stomach colonization without apparent changes in proinflammatory activity . Finally , in our motility analyses of straight , curved and helical rod shaped H . pylori strains , we genetically uncouple specific cell wall modifications from shape phenotypes to identify a role for normal helical shape in directional motility through gel-like media .
As previously reported , we discovered the cell shape determinant Csd1 , a LytM EPase homologue , in a visual screen of an H . pylori transposon mutant library [13] . While the csd1 mutant has curved rod morphology , two additional mutants with straight rod morphology were also identified in this screen of 2000 random clones . Both transposon insertion sites mapped to HPG27_353 ( Figure 1A ) , a gene encoding a hypothetical protein conserved in Helicobacter and other select species in the Delta/Epsilonproteobacteria , all of which are curved or helical ( Figure S1A in Text S1 ) . Targeted deletion of HPG27_353 reproduced the rod shape of the transposon mutants ( Figure 1D–E ) and was complemented by re-expression from the rdxA locus ( Figure S1C–D in Text S1 ) . Having confirmed HPG27_353 is required for helical curvature and twist in H . pylori , we designated this gene csd4 . We identified five other transposon mutant clones that display only slightly helical morphology easily distinguishable from wild-type . Each of these mutants contained an insertion in one of two neighboring genes , HPG27_1197 or HPG27_1196 , encoding the OppA/OppB members of the oligopeptide ABC transporter that transports small peptides , including PG recycling products , into the cell ( Figure 1F ) [24] . However , as deletion of each of these genes resulted in cells with normal helical morphology ( data not shown ) , we suspected the transposons affect another gene in the operon . Upon deleting the gene immediately downstream , HPG27_1195 , we obtained cells with largely straight rod morphology , though unlike csd4 mutants , some cells have slight irregular bends and curves ( Figure 1G–H ) . Helical cell shape was restored with complementation ( Figure S1C–D in Text S1 ) . HPG27_1195 encodes a hypothetical protein well-conserved in H . pylori and the closely related species H . acinonychis , but not other Epsilonproteobacteria ( Figure S1B in Text S1 ) . We named this gene csd5 . Despite their dramatically altered morphology , csd4 and csd5 mutants grew as well as wild-type through log and into stationary phase in broth culture ( Figure S2A in Text S1 ) . Neither mutant showed growth deficiency in 72 hrs of log-phase co-culture with wild-type ( Figure S2B–C in Text S1 ) . Aside from loss of helical rod shape , neither mutant had any other deformity; formation of cell poles and division septa appeared normal for both mutants , as did polar flagellation ( Figure 1E , H , and data not shown ) . Each mutant is slightly longer than wild-type and the csd5 mutant is also slightly wider than wild-type , but these differences represent changes of less than 10% ( mean length/width in microns: wild-type 2 . 39/0 . 58; csd4: 2 . 62/0 . 58; csd5: 2 . 62/0 . 62 ) . Both mutant strains underwent coccoid transformation in late stationary phase with similar kinetics to wild-type , showing 100% transformation at 72 hrs ( data not shown ) . Csd4 contains a putative N-terminal signal sequence and an M14 peptidase domain , the latter placing it in the zinc-dependent carboxypeptidase superfamily [25] . One of the few well-characterized bacterial M14 peptidases is Bacillus sphaericus endopeptidase I , which cleaves the D-glutamic acid-meso-diaminopimelic acid ( D-Glu-mDap ) peptide linkage of PG tetrapeptides ( EPase activity ) and tripeptides ( CPase activity ) [26] , [27] . Due to its involvement in cell shape determination , we hypothesized Csd4 may also exhibit endo- or carboxypeptidase activity on PG substrates . We over expressed His-tagged Csd4 protein in E . coli ( Figure 2A ) and tested enzymatic activity of the purified protein in vitro using sacculi from a csd4 mutant strain as substrate . In the presence of Zn2+ , Csd4 removed virtually all monomeric ( uncrosslinked ) tripeptides , yielding dipeptides ( Figure 2B ) . No reaction was observed in the buffer control or when the enzyme sample contained EDTA , confirming its dependency on divalent cations such as Zn2+ . No other muropeptide species showed significant change ( Table S1 in Text S1 ) , indicating Csd4 is a DL-CPase that trims monomeric tripeptides to dipeptides . Further confirmation of Csd4's substrate specificity was obtained using purified disaccharide tripeptide and disaccharide tetrapeptide monomers; Csd4 was enzymatically active against the tripeptide , but not the tetrapeptide species ( Figure 2C–D ) . Structural threading of the Csd4 protein sequence revealed several strong matches to M14 peptidases with solved crystal structures , including human carboxypeptidase M ( E-value 1 . 8E−6 ) [28] , [29] . Positional mapping of residues involved in zinc binding and catalysis on the carboxypeptidase M crystal structure enabled the deduction of corresponding residues on the threaded Csd4 structure ( Figure S3 in Text S1 ) . We identified Csd4 E222 as a candidate for the catalytic glutamate and targeted this residue using site-directed mutagenesis . A single nucleotide substitution in the csd4 gene , A665C , resulted in an E222A substitution in the protein . Allelic exchange was used to introduce the mutant allele at the endogenous locus [30] . The csd4E222A mutant strain had straight rod morphology ( Figure 2E–F ) , suggesting Csd4 CPase activity is vital for generating helical cell shape . We sought further evidence of Csd4 DL-CPase activity in vivo by comparing the PG sacculus muropeptide composition of the csd4 and csd4E222A mutants to wild-type and a complemented strain ( Table 1 and Table S2 in Text S1 ) . The point mutant and null mutant strains showed identical muropeptide profiles . The most striking differences in the PG of both mutants was a >400% increase in monomeric tripeptide and the absence of virtually all monomeric dipeptide ( Table 1 ) , suggesting Csd4 catalyzes the trimming of tripeptides to dipeptides via its DL-CPase activity , as we observed in vitro . Both csd4 mutants also showed changes in other muropeptides , most notably a >400% decrease in tetrapeptide and increases and decreases in various crosslinked species . csd4 mutants showed an approximately 300% increase in tetra–tripeptide crosslinking while tetra–tetrapeptide and tetra–pentapeptide crosslinked dimers were both reduced ( by 51% and 12% , respectively ) . Since tetra–tripeptide crosslinks are not very abundant in the wild-type cells and the other more abundant crosslinked species were decreased , the overall degree of crosslinking was unchanged in the mutants . We found a markedly different PG profile for the csd5 mutant compared to the morphologically similar csd4 mutants ( Table 1 ) . The csd5 mutant exhibited very modest increases in tetra–tripeptide crosslinks and monomeric tripeptides compared to wild-type ( increased by 4% and 13% , respectively ) . The csd5 mutant strain also showed modest decreases in tetra–tetrapeptide crosslinks ( 9% ) and monomeric tetrapeptides ( 11% ) . Similar decreases in tetrapeptide-containing species occurred in the LytM homologue mutants ( csd1 , csd3 ) , as well as csd4 mutants ( Table 1 ) . Csd5 does not contain any known enzymatic domains , but does contain a bacterial SH3 motif , which could play a role in protein-protein interactions or PG binding [31]–[33] . A csd4csd5 double mutant strain has a straight rod shape and displays a more severe loss of curvature than the csd5 mutant and overlaps the csd4 mutant profile ( Figure S4A–B in Text S1 ) . Furthermore , the global PG profile of the csd4csd5 strain mirrors that of csd4 mutants ( Table 1 ) . Altogether these findings indicate that perturbation of monomeric and/or crosslinked PG species influences H . pylori cell shape , but global cell wall perturbations are not necessary for loss of helical cell shape , as the csd5 mutant PG profile remains similar to wild-type . Our earlier work revealed that mutation of csd1 , csd2 , or ccmA individually or in combination results in curved rod morphology and increased tetra–pentapeptide crosslinking [13] . We employed genetic interaction studies to determine the relationship between the csd1 network and csd4 and csd5 . We found that csd1csd4 and csd1csd5 double mutants are both curved like the csd1 mutant ( Figure 3A , B , E , S4C in Text S1 ) . Both double mutants also accumulated excess tetra–pentapeptide crosslinks in the PG sacculus similar to csd1 ( Table 1 ) . Csd3 is a predicted homologue of Csd1 and Csd2 , but csd3 mutants show a distinct cell shape profile comprised of specific ratios of straight rods , curved rods , and highly curved “c” shapes [13] . Combined mutation of csd3 along with csd1csd2ccmA gave rise to a population morphologically indistinguishable from the csd3 mutant , indicating csd3 is epistatic to and perhaps upstream of these other shape-generating genes [13] . However , the csd3 cell shape phenotype was not epistatic to csd4 or csd5 . The csd3csd4 mutant displayed a curved rod shape distinct from csd3 and the straight rod phenotype of csd4 ( Figure 3C–D ) , while the csd3csd5 mutant retained a side curvature profile very similar to that of csd5 ( Figure 3F , S4D in Text S1 ) . PG analysis of these double mutants again showed increases in tetra–pentapeptide crosslinked dimers ( Table 1 ) . In summary , the shape phenotypes of double mutants of csd1 and csd3 with either csd4 or csd5 showed evidence of epistasis ( Figure 3G ) , whereas their PG profiles were largely additive ( Table 1 ) . For example , both Csd1-dependent increases in tetra–pentapeptide crosslinked species and Csd4-dependent increases in tripeptide monomer were present in the csd1csd4 mutant . As the exception , tetra–tripeptide crosslinking was increased in the csd4 mutant , decreased in the csd3 mutant , and at an intermediate level in the double mutant . Together these findings suggest that Csd4 DL-CPase activity does not depend on LytM EPase activity and vice versa . However , Csd3 and Csd4 have opposing influences on the abundance of tetra–tripeptide crosslinks in the sacculus . Previous work revealed csd1 curved rod mutants and csd3 variably curved rod mutants are attenuated in stomach colonization [13] , [22] . As csd4 mutants are the straightest of the two rod-shaped mutants , we focused further characterization on this mutant to understand the impact of its dramatic cell shape change on stomach colonization . The csd4 mutant was strongly outcompeted by wild-type and the csd4 complemented strain in the C57BL/6 mouse model ( Figure 4A ) . In contrast , during co-culture in broth no competitive defect was observed ( Figure S2B in Text S1 ) , suggesting the cell shape and/or cell wall changes present in this mutant are uniquely required during stomach colonization . PG is both a stress-bearing structure responsible for withstanding turgor pressure and a dynamic part of the assembly and function of many cell wall protein complexes . We thus tested whether alterations in its chemical content might alter the function of the wall so as to render the cells less able to survive environmental stresses H . pylori encounters in the stomach: acid , antimicrobial peptides , and osmotic stress . The csd4 mutant survived exposure to low pH , an antimicrobial peptide similar to those found in the stomach ( polymyxin ) , and high salt as well as wild-type ( Figure 4B–D ) . These results show that the cell wall changes produced by the loss of csd4 do not appreciably alter cell wall integrity and further support a direct role for normal shape in efficient stomach colonization . Successful stomach colonization by H . pylori requires penetration of the gastric mucus and intimate association with the epithelium . Once contact with the host epithelium is established , the Cag type IV secretion system ( T4SS ) engages host cells and exposes them to toxins that are associated with more serious disease outcomes [2] . The Cag T4SS induces pro-inflammatory cytokine secretion by introducing PG fragments into the host cell , which activates the mammalian intracytoplasmic pathogen recognition molecule Nod1 and ultimately NFκB [20] , [34] , [35] . All our cell shape mutants show changes in global PG composition and several have increased overall crosslinking of the cell wall ( Table 1 ) . Of particular interest , the csd4 mutants showed profound accumulation of mDap-containing tripeptide monomers , which are Nod1 agonists [19] , [21] , [36] . The wild-type strains used in our studies contain the cag pathogenicity island ( PAI ) that encodes the Cag T4SS and thus induce robust secretion of IL-8 upon co-culture with the AGS gastric epithelial cell line [37] , [38] . We wondered whether the increased crosslinking of the csd1 mutant sacculus interferes with periplasmic assembly of the Cag T4SS or if the csd4 mutant would elicit higher IL-8 induction due to the higher tripeptide content of the sacculus . As shown in Figure 4E , neither mutant showed increased or decreased IL-8 secretion relative to wild-type . Thus altered PG crosslinking in several cell shape mutants likely does not impair Cag T4SS assembly and the extra tripeptide in the csd4 mutant PG sacculus may not be available for host cell delivery by the Cag T4SS . As neither cell wall integrity nor innate immune detection appear to explain the colonization defects of the csd4 mutant we investigated motility . The csd4 and csd5 mutants were highly motile in broth culture , but were deficient in a soft agar motility assay , generating halos that were ∼20% smaller than wild-type on day four ( Figure 5A , D ) . The mutant phenotype of csd4 was reversed by reintroduction of the gene at a distal locus . Motility in soft agar depends on many aspects of swimming behavior including velocity , switching of flagellar rotation in response to chemosensory cues , and ability to bore through the pores of the gel . The cork-screw premise predicts helical-shaped cells will swim more rapidly than rod-shaped cells at high viscosities [9] , so we compared the swimming velocity of the csd4 mutant to wild-type . csd4 mutants swam at the same velocity as wild-type in broth and in three different viscous polymer solutions: crude porcine mucin , methylcellulose , and Ficoll ( Figures 5B–C , Videos S1 , S2 , and data not shown ) . However , even the highest polymer concentrations used in this experiment do not mimic the viscoelastic gel-like properties of gastric mucus [39] . We thus returned to our gel-like soft agar assay to further explore the relationship between motility and shape under gel-like conditions . We previously reported that mutants lacking helical twist but retaining curvature ( csd1 , csd2 , ccmA ) formed halos in soft agar gel similar to wild-type bacteria while the variably shaped csd3 mutant was significantly deficient in soft agar halo formation [13] . As shown in Figure 5D , csd1 mutants made halos 11% smaller than wild-type ( p = 0 . 02 ) , whereas both csd3 and csd4 mutants show more significant reductions in halo size ( 25% and 17% , p<0 . 001 ) . We then examined the motility phenotype of the csd3csd4 double mutant , which is morphologically similar to the csd1 mutant ( Figure 3D ) , but has a significantly different PG profile ( Table 1 ) . The csd3csd4 double mutant's motility in soft agar is similar to the csd1 mutant ( 9% reduction compared to wild-type , p = 0 . 05 , Figure 5D ) . The csd1csd4 mutant ( another strain morphologically similar to csd1 , but with a different PG profile ) also showed enhanced motility relative to the csd4 mutant with a halo formation phenotype indistinguishable from the csd3csd4 mutant ( p = 1 . 0 , Figure 5D ) . Partial suppression of the soft agar motility phenotypes of the csd3 and csd4 mutants suggests a relationship between shape and motility whereby more severe perturbations of shape , including large increases ( “c” shape ) or decreases ( straight rod ) of curvature lead to more severe attenuation of directional motility in gel-like media compared to strains that have curvatures profiles closer to those of wild-type ( csd1 , csd3csd4 , csd1csd4 , Figures 3 , 5D ) .
Our collection of genetically defined and morphologically diverse cell shape mutants enabled us to establish a connection between cell shape and motility in H . pylori , but exclusively in gel-like media . H . pylori motility in gel-like media decreases with increasing perturbation of cell shape such that the straight rod csd4 mutant shows greater defects than the curved rod csd1 mutant . The motility defect of the csd4 mutant is partially suppressed by csd3 or csd1 mutation and since the PG peptide changes in the sacculus were largely preserved in the double mutants , the partial suppression of the motility phenotype correlates most strongly with the reintroduction of cell curvature . We were unable to detect shape-dependent velocity changes in viscous polymer solutions , but future experiments in purified gastric mucin that retains gel-like properties may reveal velocity defects of the straight and/or curved rod mutants . In addition to velocity , altered cell shape may affect chemotaxis , particularly since H . pylori does not tumble but relies on Brownian forces for redirection . Shape may also alter the cells' ability to swim straight , as is the case for some of the “c”-shaped cells in the csd3 mutant population , which swim in circles [13] . Altogether our findings provide evidence that H . pylori's tight control of cell shape is critical for optimal motility in the stomach environment . While our in vitro experiments showed only subtle perturbations of motility , particularly for curved rod shaped mutants , all H . pylori mutants with non-helical morphology tested to date ( curved rod csd1 , variably shaped csd3 , and straight rod csd4 ) are deficient in a mouse colonization assay [13] , [22] . We explored whether loss of cell wall integrity might underlie the observed colonization defects , but our mutants do not show increased sensitivity to pH , high osmolarity or the antimicrobial peptide polymyxin . We also investigated the possibility that altered colonization is secondary to changes in innate immune detection of H . pylori-derived PG , but found no evidence for alteration of proinflammatory cytokine induction by mutants with increased PG crosslinking or monomeric tripeptides . The cag PAI-encoding strain of H . pylori used in our infection experiments induces a Nod1-mediated proinflammatory response capable of affecting Helicobacter loads in the mouse [40] , [41] , but the source of PG fragments delivered to host cells by the Cag T4SS is not clear . In contrast to previous work showing enhanced Nod1 activation when cultured HEK293 cells were treated with digested purified H . pylori sacculi containing elevated tripeptide [20] , our results suggest that the tripeptide content of the sacculus does not correlate with Nod1 activation in gastric epithelial cells during infection with live bacteria . Efficient directional motility is required for robust stomach colonization [5] , [7] , [8] , [42] , suggesting the colonization defect of csd4 ( and csd1 and csd3 ) mutants relates to altered motility . As we could only measure motility defects in gel-like media and gastric mucin attains gel-like properties only at low pH [39] , helical shape may be particularly required for penetration of the more luminal ( and acidic ) mucus layer of the stomach to gain access to its extracellular niche within the more neutral , cell proximal gastric mucus . In addition to helical morphology , another defining characteristic of Helicobacter pylori is its highly plastic genome . As described here and in a previous study [13] , microscopic analysis of 2000 randomly mutagenized clones yielded nine mutants with altered cell shape . This rather limited screen led to the discovery of six genes required for helical cell shape but not cell growth , cell polarity ( as measured by normal polar flagellar assembly ) , or the coccoid cell shape transformation that occurs in late stationary phase . Each of these genes is conserved in all H . pylori genomes that have been sequenced to date , suggesting H . pylori maintains a complex molecular program dedicated to promoting helical rod shape during log phase growth . In contrast , the recently described H . pylori coiled coil rich proteins ( Ccrp ) , which form cytosolic filaments and may influence cell shape , are variably present across strains [43] . Unlike E . coli , H . pylori contains high levels of uncrosslinked pentapeptide in the PG sacculus [18] and does not encode low molecular weight penicillin binding protein homologues . However , three cell shape-determining genes encode DD-EPases/CPases ( Csd1-3 ) [13] , [22] , and here we show csd4 encodes a DL-CPase ( Csd4 ) . Thus remodeling of PG peptides does occur in this organism ( Figure 6 ) . Our PG analysis and in vitro assay of protein activity show that Csd4 has DL-CPase activity on tripeptide monomers , cleaving the terminal mDap residue to produce dipeptide monomers . Additional enzymes must convert uncrosslinked pentapeptides into tetrapeptides and tetrapeptides into the tripeptide substrate of Csd4 . Csd3 was shown to have in vitro DD-CPase activity on a monomeric pentapeptide substrate in addition to DD-EPase activity on tetra–pentapeptide dimers [22] and thus may initiate a trimming cascade on uncrosslinked muropeptides in H . pylori . However , csd3 PG changes are not epistatic to csd4 , which suggests Csd3 is not required to generate Csd4 tripeptide substrate and insinuates the existence of another peptidase with redundant DD-CPase activity . How trimming of uncrosslinked muropeptides by Csd3 , Csd4 , and likely other proteins contributes to cell shape remains to be determined . H . pylori may control the availability of specific monomeric species to limit or localize the formation of crosslinks . We and others have proposed models of cell curvature and twist based on the overall and/or localized extent of PG crosslinking [13] , [44] . Since the transpeptidation reaction requires both donor pentapeptides and mDap-containing acceptors ( penta- , tetra- , or tripeptides ) , the dipeptide-generating function of Csd4 may prevent crosslinking in certain regions of the sacculus . As such , the increase in tetra–tripeptide crosslinking observed in csd4 mutants could simply result from the overabundance of crosslinking-active tripeptide in the sacculus . This scenario seems likely since purified Csd4 only shows activity on monomeric species . Alternatively , the occurrence of shorter monomeric species , namely dipeptides , in the sacculus is thought to signify “old” PG and may serve as a signal for the synthesis machinery to assemble and insert new PG [16] , [45] . Localized differences in the rate of PG synthesis have been shown to drive cell curvature in Caulobacter crescentus [46] . Surprisingly , the csd5 mutant shows negligible perturbations of global PG composition indicating Csd5 is not required for Csd4 enzymatic activity . The observations that csd1 is epistatic to both csd4 and csd5 , and that csd3 is epistatic to neither csd4 nor csd5 , suggest that csd4 and csd5 act at a similar stage of helical cell shape specification . The csd4csd5 double mutant resembled the csd4 mutant both in global PG changes and by having a straighter shape than the csd5 mutant . Csd5 bears a probable transmembrane domain or signal sequence allowing localization to the inner membrane and/or periplasm , as well as a bacterial SH3 domain in the C-terminus , which could allow for interactions with other PG peptidases and/or PG . The epistasis of csd4 on csd5 could suggest Csd5 acts downstream of Csd4 . Csd5 might recognize dipeptides generated by Csd4 enzymatic activity and provide an activating signal for cell shape modulation , perhaps through recruitment of PG synthesis enzymes . Alternatively , Csd5 may localize Csd4 activity in a particular pattern conducive for helical cell shape generation . In this model , absence of Csd5 would lead to randomly located Csd4 activity which could alter cell shape without altering the global PG composition . Recently two lipoprotein activators of the major PG synthases in E . coli have been identified , providing a paradigm for localized activation of PG modifying enzymes [47] , [48] . The PG modifications caused by the straight and curved rod classes of shape genes appear largely independent . There is no epistasis in the PG phenotypes of the double mutants we tested; each double mutant shows changes that are additive or intermediate compared to the single mutant phenotypes . Moreover , we do not observe a genetic hierarchy of shape phenotypes; straight rod shape is not epistatic to the seemingly more complex curved and helical rod shapes . Instead , the curved rod shape of csd1 is epistatic to the straight rod shape of csd4 and csd5 . Our genetic interaction studies suggest a minimum of two distinct networks that alter PG and cell shape in H . pylori: a network containing Csd1 , Csd2 , and CcmA that generates helical twist through relaxation of tetra–pentapeptide crosslinking and a network containing Csd4 and Csd5 that generates curvature through some consequence of monomeric muropeptide trimming . While straight rod csd4 and csd5 mutants appear to lack curvature and twist , we cannot be certain whether their protein activities contribute to twist or whether the activities of Csd1 , Csd2 , and CcmA are generating twist in the absence of Csd4/5 , but in a manner that is not apparent in the absence of curvature . Csd3 appears to play a role in both networks , as it has activity on both crosslinked and uncrosslinked muropeptides . Further refinement of a model incorporating these complex modifications of crosslinked and uncrosslinked muropeptide species in the generation of helical cell shape will require further characterization of Csd and CcmA protein activities and spatial organization , as well as identification of missing peptidases and other co-factors . Some components of the H . pylori helical shape-generating program are found throughout the Proteobacteria while others appear subdivision- or even species-specific . Homologues of the LytM peptidase Csd1 are the most widely conserved and found in all subdivisions of the Proteobacteria , but not exclusively in organisms with curved to helical shape [13] . Several species have more than one Csd1 homologue ( up to 9 ) , including H . pylori ( Csd1 and Csd2 ) . The LytM peptidase Csd3 and the M14 peptidase Csd4 are both conserved within the Delta/Epsilonproteobacteria and Csd4 homologues showing >50% similarity to Csd4 are found only in curved and helical rod shaped organisms . The Campylobacter jejuni Csd4 homologue , Pgp1 , also has LD-CPase activity and promotes the helical rod shape of that organism [23] . Additionally , Csd1/3-encoding Epsilonproteobacterial species with other morphologies , such as rod-shaped Campylobacter hominis ( ATCC BAA-381 ) and oval-shaped Sulfurovum ( NBC37-1 ) , do not encode a Csd4 homologue ( BLASTP E-values>0 . 1 ) . Of the two shape-generating proteins that do not encode putative enzymes , CcmA-like bactofilins are found throughout the Proteobacteria as well as other bacterial phyla . These proteins form cytoplasmic filaments that in one case can bind a PG synthesis enzyme leading to localized activity and have been shown to cause diverse cell shape phenotypes when over or under expressed [49]–[51] . In contrast , Csd5 appears restricted to H . pylori and the closely related species H . acinonychis . Differences in the genomic carriage of these proteins may contribute to the diversity of species- and strain-specific bacterial cell shapes . In summary , we have discovered additional components of the helical cell shape program in H . pylori , including a new PG modification enzyme ( Csd4 ) and a protein ( Csd5 ) that may localize or participate in sensing the activity of PG modification machinery . We also provided evidence that the six shape-determining proteins identified in our screen form two or more networks that cooperatively shape the cell wall through two types of cell wall modifications . For the first time we were able to establish a pattern of association between H . pylori's cell shape and motility in gel-like media , bolstering the conclusion that the stomach colonization defects of H . pylori cell shape mutants are rooted in shape-dependent alterations of motility .
Mouse infection studies were done under practices and procedures of Animal Biosafety Level 2 and 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 facility is fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care and complies with all United States Department of Agriculture , Public Health Service , Washington State and local area animal welfare regulations . All activities were approved by the FHCRC Institutional Animal Care and Use Committee . Strains used in this work , as well as primers and plasmids used in strain construction are described in Tables S3 , S4 and S5 in Text S1 . H . pylori were grown on horse blood ( HB ) agar plates or in Brucella broth ( BD Biosciences ) containing 10% fetal bovine serum ( Hyclone ) but no antimicrobials ( BB10 ) under microaerobic conditions as previously described [13] . For resistance marker selection , HB plates were supplemented with 15 µg/mL chloramphenicol , 25 µg/mL kanamycin , 36 µg/mL metronidazole , or 60 mg/mL sucrose . For culturing bacteria from mouse stomachs , 200 µg/mL bacitracin was added to eliminate contaminating species of the normal mouse microbiota . For plasmid selection and maintenance in E . coli , LB agar or broth was supplemented with 30 µg/mL kanamycin or 100 µg/mL ampicillin . Phase contrast microscopy and TEM were performed as described [13] . Quantitative analysis of phase contrast images of bacteria were performed with the CellTool software package as described [13] . A detailed description of Kolmogorov–Smirnov statistical comparisons is provided in Text S1 . Cell length was estimated using the central axis length calculated by CellTool for 300–350 cells/strain . Cell width was measured manually using ImageJ from TEM images ( http://rsbweb . nih . gov/ij/ ) of 25–50 cells/strain . Signal peptide predictions were obtained from the Comprehensive Microbial Resource web database ( http://cmr . jcvi . org/tigr-scripts/CMR/CmrHomePage . cgi ) , structural threading was performed with Phyre [52] , and 3D molecular structures were visualized using PyMOL [53] . Further detail is provided in Figure S3 in Text S1 . PG was prepared from H . pylori cells ( 100–500 ODs ) grown on HB plates as described [13] . Purified PG ( 0 . 5 mg/mL ) was incubated with His-tagged Csd4 ( 5 µM ) purified from E . coli ( as described in Text S1 ) in 20 mM sodium phosphate , 5 mM ZnCl2 , 100 mM NaCl , pH 4 . 8 for 4 hrs at 37°C on a Thermomixer at 750 rpm . A control sample received no enzyme , and another enzyme sample contained 10 mM EDTA and no ZnCl2 . The samples were incubated with 10 µg of cellosyl ( Hoechst , Frankfurt am Main , Germany ) for 1 hr , boiled for 10 min and centrifuged at room temperature for 15 min at 16 , 000×g . The muropeptides present in the supernatant were reduced with sodium borohydride as described [54] . HPLC analysis was performed as described [13] , [55] . Eluted muropeptides were detected by their absorbance at 205 nm . The muropeptide profile of the wild-type was similar to the published profile of Helicobacter muropeptides [18] allowing the unambiguous assignment of known muropeptide structures to the peaks detected [13] . To study the specificity of Csd4 , the above assay was conducted with pure , unreduced muropeptides , the disaccharide tripeptide ( 0 . 02 mg/mL ) and disaccharide tetrapeptide ( 0 . 07 mg/mL ) , obtained from the laboratory of J . -V . Höltje ( Max-Planck-Institute , Tübingen , Germany ) in lieu of PG . Soft agar motility experiments were performed as described [56] . Growth and stress testing was accomplished using 100–200 µL BB10 mini-cultures grown in a 96-well plate as described [13] . For analysis of live motile cells , fresh liquid cultures were grown to an optical density of 0 . 5–0 . 7 at 600 nm ( OD600 ) , concentrated 10× , and kept warm at 37°C in a CO2 incubator . Just prior to imaging , 5–10 µL of cell concentrate was added to 100 µL of pre-warmed test solution: Brucella broth ( BD Biosciences ) supplemented with 5% fetal bovine serum ( Hyclone , BB5 ) , or BB5 containing 0 . 25–1 . 0% methylcellulose , 2 . 5–10% Ficoll PM 400 , or 0 . 1–2 . 5% crude porcine mucin ( all Sigma ) . Each cell suspension was mixed by gentle pipetting and immediately applied to a depression slide . Movies were captured using a 60× ELWD Plan Fluor ( NA 0 . 7 ) objective mounted on a Nikon TE 200 microscope at a frame rate of 100 millisecond intervals with a Nikon CoolSNAP HQ CCD camera controlled by MetaMorph software ( MDS Analytical Technologies ) . Cells were tracked using the ImageJ Manual Tracker ( http://rsbweb . nih . gov/ij/ ) and velocity calculations performed with Intercooled Stata 8 . 0 ( StataCorp ) . Female C57BL/6 mice 24–28 days old were obtained from Charles River Laboratories and certified free of endogenous Helicobacter infection by the vendor . Mice were housed and infected as described [57] using 5×107 cells/strain in the inocula for competition experiments . After 1 week the mice were euthanized by inhalation of CO2 and the glandular stomach removed and opened to remove any food . The whole stomach was homogenized in 1 mL BB10 . Dilutions of homogenate were plated to non-selective and selective HB plates to enumerate bacteria of each genotype . If no bacteria were recovered we set the number of colonies on the lowest dilution plated to 1 to calculate the competitive index . The human gastric adenocarcinoma cell line AGS ( ATCC CRL-1739 ) was co-cultured with H . pylori strains at a multiplicity of infection of 10 for analysis of IL-8 release at 6 and 24 hrs as described previously [58] .
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The only habitat of Helicobacter pylori is the human stomach , where it can promote stomach ulcers and cancer . Cells lining the stomach are protected from luminal acid by a thick layer of gastric mucus composed of polymerized gastric mucins . Gastric mucin undergoes a physical transition between a viscoelastic solution at neutral pH to a viscoelastic gel-like state at low pH . Helical rod shape in bacteria has been suggested to enhance swimming velocity in viscous solutions by a cork-screw mechanism , but H . pylori mutants lacking helical twist show normal swimming velocity in viscous polymer solutions used in prior studies comparing motility across bacterial species . These same mutants , however , show diminished colonization suggesting helical shape promotes stomach infection by another mechanism . Here we identified Csd4 , a protease of cell wall tripeptides , which induces curvature in the cell body independently from the changes in cell wall crosslinking previously shown to promote helical twist . Cells lacking Csd4 form straight rods that also show colonization defects but normal velocity in several viscous polymer solutions . Upon examination of motility in gel-like media , however , we discovered that elimination or exaggeration of cell curvature perturbs motility . Thus H . pylori's helical shape may aid penetration of gel-like stomach mucus .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"gastroenterology",
"and",
"hepatology",
"biology",
"microbiology"
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2012
|
Multiple Peptidoglycan Modification Networks Modulate Helicobacter pylori's Cell Shape, Motility, and Colonization Potential
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Plasmodium falciparum–infected erythrocytes bind endothelial receptors to sequester in vascular beds , and binding to ICAM1 has been implicated in cerebral malaria . Binding to ICAM1 may be mediated by the variant surface antigen family PfEMP1: for example , 6 of 21 DBLβC2 domains from the IT4 strain PfEMP1 repertoire were shown to bind ICAM1 , and the PfEMP1 containing these 6 domains are all classified as Group B or C type . In this study , we surveyed binding of ICAM1 to 16 DBLβC2 domains of the 3D7 strain PfEMP1 repertoire , using a high throughput Bioplex assay format . Only one DBL2βC2 domain from the Group A PfEMP1 PF11_0521 showed strong specific binding . Among these 16 domains , DBL2βC2PF11_0521 best preserved the residues previously identified as conserved in ICAM1-binding versus non-binding domains . Our analyses further highlighted the potential role of conserved residues within predominantly non-conserved flexible loops in adhesion , and , therefore , as targets for intervention . Our studies also suggest that the structural/functional DBLβC2 domain involved in ICAM1 binding includes about 80 amino acid residues upstream of the previously suggested DBLβC2 domain . DBL2βC2PF11_0521 binding to ICAM1 was inhibited by immune sera from east Africa but not by control US sera . Neutralizing antibodies were uncommon in children but common in immune adults from east Africa . Inhibition of binding was much more efficient than reversal of binding , indicating a strong interaction between DBL2βC2PF11_0521 and ICAM1 . Our high throughput approach will significantly accelerate studies of PfEMP1 binding domains and protective antibody responses .
The variant surface antigen Plasmodium falciparum erythrocyte membrane protein 1 ( PfEMP1 ) is a virulence factor of the human malaria parasite P . falciparum . PfEMP1 variants are encoded by about 60 var genes per parasite , and have been implicated in the cytoadhesion of P . falciparum-infected erythrocytes ( PE ) to vascular endothelium [1] . PE bind numerous receptors ( reviewed in [2] and [3] ) , including thrombospondin [4] , CD36 [5] , ICAM1 [6] , E-selectin and VCAM-1 [7] , chondroitin sulfate A ( CSA ) [8] , [9] , complement receptor 1 [10] , PECAM-1 [11] , heparan sulfate [12] , [13] , bloodgroup sugars A and B [14] , and the serum proteins IgG/IgM and fibrinogen [15] . Cytoadhesion allows sequestration of PE in deep vascular beds , prevents clearance of PE in spleen , causes vascular occlusion and inflammation of different organs , and is related to cerebral malaria [16] and placental malaria [17] . PE sequestration may lead to occlusion of the microvasculature and thereby contributes to the acute pathology of severe forms of malaria [18]–[22] . Distinct domains of different var genes have been shown to bind specific ligands in vitro . For example , the CIDR1-α domain was implicated in binding CD36 [23] , [24] . Different DBL1-α domains from various PfEMP1 were shown to bind the CR1 receptor on RBC [10] , glycosaminoglycans on RBC , and heparan sulfate on the endothelial surface [23] , [25] . The DBLβC2 domain combination binds ICAM1 [26] , and DBLβ alone binds PECAM1 [23] . Specific host receptors have been implicated in specific malaria syndromes ( reviewed in [27] ) . PE sequestration in cerebral capillaries and venules is a hallmark of cerebral malaria . ICAM1 is expressed at high levels in brains of patients with cerebral malaria , and has been implicated in this syndrome [28] . Some DBLβC2 domains of different PfEMP1 proteins bind ICAM1 [26] . In a survey of the entire repertoire of DBLβ-C2 domains ( n = 25 ) from the IT4 line genome [29] , 6 domains bound ICAM1 . These studies employed a complex assay based on adhesion of ICAM1-coated beads to COS-7 cells that express PfEMP1 domains , and manual counting by microscopy . This approach is semiquantitative , time-consuming , and low throughput . We have now developed a high throughput DBL domain-receptor binding assay and used it to study ICAM1 binding to the DBLβC2 domain repertoire of 3D7 clone parasite . Of the 16 DBLβC2 domains tested , we find that a single domain from a Group A PfEMP1 protein ( PF11_0521 ) binds ICAM1 strongly . Our structural analyses suggest that DBL2βC2PF11_0521 binding to ICAM1 may be due to key conserved residues previously identified in IT4 line domains . Also , our truncation and binding analyses suggest that DBLβC2 domain extends about 80 amino acid residues upstream of its previously suggested boundary [26] . Binding-inhibition studies using our high throughput platform suggest that neutralizing antibodies may be infrequent in African children , but are common in immune African adults .
In our expression system , recombinant protein levels can be monitored during immobilization/purification on immobilized anti-GFP antibodies by fluorescence , as we previously described using the multi-well plate format [30] . To immobilize DBLβC2 constructs on BioRad BioPlex beads , we used a similar scheme here ( Figure S1A ) : anti-GFP antibody was cross-linked to beads of different fluorescence intensity ( i . e . , different bead regions ) , then bead regions of distinct intensity were incubated with lysates of COS cells expressing individual domains as GFP-fusion proteins , and washed extensively for fast immobilization/purification [30] . Domain immobilization was confirmed by reactivity of beads with biotinylated anti-GFP . Signal intensity was similar for all constructs ( other than mock-transfected cells , data not shown ) indicating saturation of beads with recombinant domains . In binding assays that used this bead array ( Figure S1B ) , only the DBL2βC2PF11_0521 domain among the 16 domains tested bound to ICAM1 at high levels ( Figure 1 ) . This result was reproducible in numerous assays using different preparations of recombinant domains . We also tested the DBL2βC2PF11_0521 domain and several other non-binding domains using the traditional multi-well plate format [30] , and obtained identical results ( data not shown ) . In addition , ICAM1-Fc binding to DBLβC2 domains was detected by anti-human IgG at a nearly identical level to detection by monoclonal antibody ( mAb ) RR1 ( data not shown ) , independently confirming the above results . To confirm the specificity of ICAM1 binding to DBL2βC2PF11_0521 , we tested the well-characterized mAb My13 for its ability to inhibit the interaction . According to earlier studies , My13 strongly inhibits binding of infected erythrocytes to ICAM1 , but does not block binding of non-inhibitory mAb RR1 to the ICAM1 molecule [31] . Our results ( Figure 2 ) demonstrate complete inhibition of ICAM1 binding to DBL2βC2PF11_0521 by an excess of My13 , confirming the specificity of DBL2βC2PF11_0521-ICAM1 binding in our assay . Using CLUSTALW 2 . 0 . 5 and subsequent manual curation , we aligned and analyzed sequences of DBLβC2 domains that bind and do not bind ICAM1 . Figure S2 shows the alignment of DBLβC2 domains from both FCR3/IT and 3D7 strains , with four loops predicted to participate in ICAM1 contacts [32] indicated in boxes . We find that residues previously shown to be conserved in the ICAM1-binding DBL2βC2 domains of FCR3/IT are conserved in ICAM1-binding domain of 3D7 as well . The level of conservation among the residues in or directly adjacent to the ICAM1-binding structural loops is much higher in the binding versus non-binding DBLβC2 domains . This may indicate that these residues have an important role in structure or a direct interaction with the ligand . Detailed analysis of the four ICAM1-binding loops revealed additional conserved residues . In loop 1 , a Thr residue in the middle of the loop is conserved in every ICAM1-binding domain ( and absent in 50% of non-binding domains ) . In loop 3 , a 3-amino acid motif containing a hydrophilic residue-hydrophobic residue-hydrophilic residue , is completely conserved in binding domains . The hydrophobic residue in this motif is Ile with a single conservative exception ( Val in var16 ) , and appears to be in close contact with the ICAM1 molecule in the model [32] . This residue is absent in 37% of non-binding sequences . We speculated that preference in usage of Ile over Val may be explained by slightly larger surface of Ile in contact with the residues of ICAM1 ( Figure S3A and S3B ) . In previous analyses , an Ala or Leu residue was observed in position 3 of loop 4 , in all but one ICAM1 binding domain ( the exception being the FCR3/IT var1 DBL2βC2 domain where His is present ) ( Figure S2 ) . In the 3D7 repertoire , 3 other non-binding domains carry Ala or Leu at this position , in addition to the ICAM1 binding DBL2βC2PF11_0521 domain . Similarly , in FCR3 strain parasites , 3 non-binding domains carry Ala or Leu at this position . However , the non-binding domains with the Ala or Leu residue in loop 4 contain multiple substitutions in other conserved regions and positions , which may explain their non-binding status . Generally , ligand interactions involving substantial surfaces of amino acid residues are not significantly altered by substituting a single residue that participates in binding , so long as the substitution fits into the structure without clashes and does not affect structural integrity ( e . g . , substitutions in flexible loops ) [33] . This was elegantly demonstrated by the Smith group [29] , which examined amino acid substitutions and their combinations on ICAM1 binding by DBL2βC2 domains from two genes , var16 and var31 from FCR3/IT parasite . Using the model of DBL2βC2 complexed with ICAM1 [32] and the Deep View/Swiss-pdb viewer program ( v . 3 . 7 ) , we examined the effect of replacing conserved Ala286 with Tyr in loop 4 . The substitution does not introduce any amino acid residue clashes , and may provide an additional intra-domain hydrogen bond to R113 ( data not shown ) . We infer that the loss of binding to ICAM1 is not due solely to substitutions of Ala or Leu in loop 4 , but results from combinations of substitutions that involve this and other residues in the protein . We are currently testing this hypothesis using our quantitative BioPlex approach and site-specific mutagenesis . Other residues that we predict may have an effect on the domain structure or ligand binding are indicated in red in Figure S2 . Our sequence and binding analyses indicate that structural DBLβC2 domain is larger than previously suggested [26] . We propose that the domain starts about 80 aa residues upstream of the first Cys residue of A4tres DBL2βC2-ICAM1 domain . With a single exception ( var6 from FCR3/IT parasite that preserves only Cys ) this N-terminal region starts with a conserved Asn-Pro-Cys sequence and contains multiple conserved residues ( Figure S2 ) , independent of the type of domain located upstream of DBLβC2 . With regard to downstream C2 region , our analysis demonstrates that previously described isolated DBLβ domains ( e . g . DBL6β in 3D7 PFE1640 , DBL5β in FCR3/IT var14 , DBL3β in MC var1 ) are , in fact , DBLβC2 domains with degenerate C2 as well as upstream N-terminal sequences . These domains contain easily recognizable C2 features including the Y-motif and other conserved residues , as well as the upstream N-terminal fragment described above ( Figure S4 ) . A degenerate Y-motif was previously recognized in DBL6β of FCR3 VAR1CSA protein [34] . N-terminal and C2 sequences appear to diverge from the consensus sequence to similar degrees , suggesting a possible interaction between these fragments in 3-dimensional structure . We inferred that the additional N-terminal sequence contributes to the complete DBLβC2 domains , and tested its effect on ICAM1-binding activity . We re-cloned DBL2βC2PF11_0521 domain ( amino acid residues 1–522 in Figure S2 ) into pHisAdEx vector as well as two truncated constructs: one ( named N-term ) lacked 32 amino acid residues at the C-terminus ( construct ends with conserved ACNC sequence plus two residues at the C-terminus ) , and another one ( named C-term ) lacked 68 amino acid residues at the N-terminus ( construct starts with conserved Asn-69 at the N-terminus and includes complete ICAM1 minimal binding domain [34] ) ( Figure 3 ) . The amount of all proteins immobilized on beads was similar by reactivity with anti-GFP antibody , and all proteins had His-tag at their N-termini confirmed by reactivity with anti-His antibody ( data not shown ) . Binding of ICAM1 ( Figure 3 ) clearly indicate that removal of the N-terminal fragment profoundly reduces ICAM1 binding activity . Since full-length and truncated variants all demonstrated similar and strong GFP fluorescence , which is a good indicator of correct folding of the entire membrane protein [35] , our results suggest an important role for the N-terminal sequence in ICAM1 binding . A similar effect on adhesion was previously observed earlier in a semi-quantitative assay of the A4tres DBL2βC2 domain with an N-terminal truncation down to the first conserved Trp ( var31 Trp-106 in Figure S2 ) . This N-terminal truncation combined with C-terminal truncation up to the end of Y-motif ( var31 His-449 in Figure S2 ) completely abolished ICAM1 binding [34] . We tested inhibition of ICAM1 binding to DBL2βC2PF11_0521 domain using pooled human plasma from immune adult males living in East Africa and from non-immune US adults ( Figure 4 ) . Pooled immune plasma from Africa blocked binding of ICAM1 to the DBL2βC2PF11_0521 domain by 78% , compared to binding in NI plasma that did not reduce binding compared to media alone . However , immune plasma was not efficient ( ∼15% reduced binding ) in assays that measured reversal of adhesion ( Figure 4 ) , indicating a strong association between ICAM1 and the DBL2βC2PF11_0521 domain with low OFF rate . This result complements previous data with ICAM1-binding parasites that demonstrated less than 30% reversal of parasitized erythrocyte ( PE ) adhesion with immune sera [36] . To study the acquisition of neutralizing antibodies against the ICAM1 binding interaction , we assayed plasma samples collected from infants and toddlers participating in longitudinal birth cohort studies in Tanzania . Plasma from 7 children that were collected at several time points between 24 and 148 weeks of age , were tested for inhibition of ICAM1 binding to DBL2βC2PF11_0521 domain ( Figure 5A ) . In parallel , we tested reactivity of IgG from the same plasma to DBL2βC2PF11_0521 domain ( Figure 5B ) . Inhibition of ICAM1 binding activity was uncommon , and appeared to be short-lived in at least one child . The IgG reactivity curves appear almost as mirror images of the ICAM1 binding-inhibition curves ( with the exception of the 148 week time point for the brown line child , discussed below ) , suggesting that naturally acquired anti-DBL2βC2PF11_0521 domain antibodies include at least a fraction of functional antibodies as they develop in individual children . We tested levels of neutralizing antibody in additional plasma samples collected from children during the first 2 years of life , compared to plasma from adult males , all living in malaria endemic areas in East Africa ( Figure 6 ) . Plasma from most adults but only a few children contained neutralizing antibodies against the ICAM1—DBL2βC2PF11_0521 domain interaction , and neutralizing activity was significantly higher in plasma from adults versus children ( P<0 . 01 when adults were compared to children 24 to 76 weeks old , and P<0 . 05 when compared to children 100 weeks or older , Kruskal-Wallis test ) . Neutralizing activity did not increase significantly in the first 2 years of life , although a trend to increasing activity was observed after 100 weeks of life . The same trend appeared in our longitudinal cohort study of 7 patients described above ( Figure 5A ) , with a statistically significant difference in neutralizing activity between 48 and 148 weeks ( P<0 . 05 by Kruskal Wallis test with Dunn's multiple comparison post-test ) .
PE bind endothelial receptors to sequester in vascular beds , and PE binding to ICAM1 has been implicated in cerebral malaria . In this study , we developed a functional BioPlex micro-bead protein array , and applied it to study ICAM1-binding P . falciparum ligands and the acquisition of neutralizing antibodies in naturally exposed individuals . Our results indicate that a single DBL2βC2PF11_0521 domain in the 3D7 genome binds at high levels to ICAM1 , and the corresponding PfEMP1 protein is classified as Group A . Binding involves an N-terminal region that has not previously been recognized as an integral part of the DBLβC2 domain . While immune adults in East Africa commonly display neutralizing antibodies against this interaction , such antibodies are uncommon in infants and toddlers in the same region . Immunological profiling of sera for reactivity against different antigens is a common method for assessing acquired immunity and identifying potential vaccine candidates [37] . However , relating immune responses to malaria resistance is not straightforward since exposed individuals are typically infected repeatedly throughout life , and develop diversified immune responses against multiple antigens , in many cases without comprehensible relevance to disease severity . Multiple studies have sought to relate seroreactivity with disease susceptibility in young African children [38]–[44] , but no candidate antigens for a severe malaria vaccine have been identified . While seroreactivity studies are useful for defining the immunoepidemiology of existing vaccine candidate antigens [30] , [45] , functional assays may be essential for the discovery of novel vaccine candidates . Functional antibody responses are likely to be less diverse , to target fewer antigens , and to have a stronger association to protection from severe forms of malaria . In this paper , we describe a high throughput approach to measure the presence and relative amount of functional antibodies in patient sera . An earlier approach , though elegant , is semi-quantitative and does not allow for high throughput studies [29] . The earlier approach was based on expression of recombinant PfEMP1 domains on the surface of mammalian cells; incubation of these mammalian cells with small resin beads chemically cross-linked to the host cell receptors; removal of unbound beads from mammalian cells attached to microscope glass by inversion and gravity sedimentation of unbound beads; and manual counting of the beads bound to the surface of mammalian cells . The approach exploited in our work is based on expression of functional antigens in mammalian cells , and rapid antigen immobilization in a directed manner on the surface of BioPlex fluorescence-coded beads . This approach allows multiplexed analyses of protein features including receptor binding activity ( Figure S1 ) as well as seroreactivity studies in a high throughput manner . Our studies focused on the construction of a 3D7 genome-wide array of the DBLβC2 domain , which was previously shown to bind ICAM1 in studies of other parasite lines [26] , [28] . Analysis of ICAM1-binding activity in this array revealed that only the DBL2βC2PF11_0521 variant out of 17 domain variants , binds the receptor at high levels . Alignment of 3D7 ICAM1 binding and non-binding domains with previously identified ICAM1-binding domains from other parasite strains revealed new structural features related to the ICAM1 interaction ( Figure S2 ) , in particular , a conserved Thr residue in loop 1 and a conserved 3-amino acid motif in loop 3 . This analysis further highlighted the potential role of conserved residues within predominantly non-conserved flexible loops in adhesion , and , therefore , as targets for intervention . All DBLβC2 domains that we tested share highly conserved structural features , like helices and loops , and therefore , their general architecture should be similar . Constructs of these domains used the same boundaries and yielded recombinant protein at similar levels ( according to GFP fluorescence ) in a system that is well-suited for folding of transmembrane disulfide-rich proteins . Therefore , all recombinant domains have a high probability of folding similarly well . Because one of DBLβC2 domain variants clearly demonstrated binding activity , we assume that other variants that did not bind were properly folded but do not function as ICAM1 ligands . Nevertheless , since every protein is unique , false negatives can not be excluded completely without direct proof of correct folding by methods like X-ray or NMR , which are outside the scope of this study . Our findings also suggest that the structural/functional DBLβC2 domain involved in ICAM1 binding includes about 80 amino acid residues upstream of the previously suggested DBLβC2 domain [26] . This N-terminal sequence contains an alpha-helix ( shown in Figure S4 ) predicted by several algorithms [46] in each DBLβC2 domain described here . Two other short segments associated with conserved and semi-conserved residues downstream of the alpha-helix were variously predicted to be alpha-helical or extended strands ( not shown ) . This high throughput assay platform can be used to profile functional antibody levels among naturally exposed children and adults . We find that antibodies that inhibit ICAM1 binding to DBL2βC2PF11_0521 appear sporadically in the first 2 years of life ( Figure 5 and 6 ) . Conversely , many immune adults have these antibodies in their sera . Neutralizing activity in adult plasma did not correlate with age in this group of adults ( 18 to 54 years old ) , consistent with the solid and stable protective immunity enjoyed by all adults in these communities . The slow acquisition of functional antibody may reflect that this domain variant is rare in the community , that the immature immune system of young children responds poorly to some PfEMP1 , or that some other host-parasite interaction thwarts the development of functional immunity . In another study in a malaria endemic area [47] , serum anti-rosetting activity against a particular lab strain ( FCR3 ) appeared in only about 10% of children 2–5 years old , but in up to 60% of 15–16 year old adolescents , demonstrating a similar slow accumulation of functional responses . We do not know at present whether the functional response ( inhibition of ICAM1 binding ) is variant-specific . Future studies will clarify this question . With regard to longevity of the immune response , an earlier study [48] found that anti-PfEMP1-like responses are short lived and variant-specific , at least in a low malaria endemicity area . We observed functional antibody to ICAM1 binding was short-lived in one child ( green line in Figure 5 ) . We are preparing to test whether this phenomenon is common using a larger set of children's plasma collected in longitudinal cohort studies . We will also examine other features of the natural immune response to malaria , such as the apparent discordance of seroreactivity and functional activity observed in some children ( brown line in Figure 5 ) . This may indicate that the amount of non-functional antibodies may increase without increasing the amount of functional antibodies , or that non-functional antibodies that increase with time may successfully compete with functional antibodies and block their activity . However , this dataset is limited and it would be premature to make definitive conclusions at present . Our high throughput approach will now allow us to test numerous additional ICAM1 binding domains , and to determine which of these is targeted by neutralizing antibodies that also block parasite binding . With an expanded dataset , we can correlate functional antibody responses with clinical outcomes in these vulnerable populations . These future studies will also examine the concordance between this assay and the traditional binding-inhibition studies using parasitized red blood cells , including the ability to detect variant-specific versus broadly reactive functional antibodies .
Human plasma samples used in these studies were collected from East African donors under protocols approved by relevant ethical review committees . Study participants provided written informed consent before donating samples . Ethical clearance was obtained from Institutional Review Boards of SBRI and the National Medical Research Coordinating Committee in Tanzania . All constructs were cloned into the pAdEx vector described earlier [30] . Expression of constructs in COS-7 cells and lysate preparation were also described in [30] . All expressed constructs are GFP-fusion proteins that contain an extracellular DBLβC2 domain , a short trans-membrane region , and a cytoplasmic domain fused to green fluorescent protein ( GFP ) . In addition , full-length and truncated forms of DBL2βC2PF11_0521 domain were cloned into modified vector pHisAdEx . This vector was constructed as follows: pAdEx plasmid was digested with SfiI and BamHI restriction enzymes , then the large fragment was isolated by agarose gel electrophoresis and ligated with double-stranded oligonucleotide adaptor prepared by annealing of two oligonucleotides 5′-GAT CCC TGC GTG GTG GTG GTG GTG GTG CT-3′ and 5′-ACC ACC ACC ACC ACC ACG CAG G-3′ . The resulting construct was verified by sequencing . Proteins expressed from this vector are similar to proteins expressed from pAdEx vector but contain His6-tag at their N-termini . Various PfEMP1 domains that supported binding of ICAM1 ( see above ) and CD36 ( data not shown ) in the pAdEx expression system also supported binding in the pHisAdEx expression system . Primers used for cDNA amplification using 3D7 genomic DNA are shown in supplementary Table S1 . Alignment of all DBLβC2 domains is shown in supplementary Figure S2 . Five DBLβC2 domains , that were shorter at their N-termini than other 11 domains after amplification with primers indicated in Table S1 , were also obtained within the same boundaries as for other domains and cloned into pAdEx and pHisAdEx for expression . New forward primers for their PCR amplifications are shown in Table S2 . 25 µg of anti-GFP antibody ( Rockland , Gilbertsville , PA ) was coupled to 200 µl of each different BioPlex bead region ( Bio-Rad ) ( 19 regions total ) as described by the manufacturer , then resuspended in PBS containing 1 mg/ml BSA , 0 . 05% Tween-20 , and 0 . 02% sodium azide ( PBS-TBN buffer ) . Anti-GFP-coupled beads were incubated for 2 hours at 4°C with COS-cell lysates containing expressed domains , washed in PBS-TBN , and used in ligand binding experiments . These beads are designated as DBLβC2-coupled beads . All DBLβC2-coupled beads were mixed together in quantities of ∼40–60 beads of each bead region ( beads with distinct fluorescence intensity ) per µl . 50 µl of bead mixtures were transferred into individual wells of HTS 96-well plates ( Whatmann ) that were pre-incubated with PBS-TBN for 30 minutes . Beads were washed in wells 3 times with PBS-TBN and incubated with different concentrations ( 20 – 0 . 1 µM ) of ICAM1-human Fc receptor ( R&D Systems , Minneapolis , MN ) . After 1 hour incubation at room temperature ( RT ) at constant rotation at 600 rpm , beads were washed in PBS-TBN and incubated in similar fashion with 1∶10 diluted biotinylated anti-ICAM1 monoclonal antibody ( mAb ) RR-1 ( Axxora , San Diego , CA ) followed by 1 hour incubation with 1∶250 diluted streptavidin-phycoerythrin ( SA-PE ) fluorescent molecules ( Jackson ImmunoResearch , West Grove , PA ) . Also , in some experiments ( binding only , not binding-inhibition by human serum ) we used anti-human IgG coupled to phycoerythrin ( 1∶250 dilution , Jackson ImmunoResearch ) to detect bound ICAM1-human Fc and obtained almost identical results . After a final wash , 96-well plates were transferred into the BioPlex apparatus ( Bio-Rad ) to quantify ICAM1 binding ( measured in phycoerythrin channel ) to the individual DBLβC2 domains . For negative controls , lysates prepared from mock-transfected cells , and from pAdEx vector transfected cells , were also coupled to beads . These control beads were mixed with the DBLβC2-coupled beads and assayed simultaneously . The pAdEx vector produces GFP-fusion proteins that contain an irrelevant peptide of 37 amino acids in the extracellular domain . To confirm the specificity of ICAM1 binding to DBL2C2PF11_0521 , ICAM1-Fc ( 1 µg/ml ) was incubated with various concentrations of mAb My13 ( Axxora ) for 1 hour at room temperature and then used to bind to the mixture of His-DBL2C2PF11_0521 and HisAdEx ( negative control ) coupled beads as described above using RR1 mAb for detection . For binding inhibition assays DBLβC2-coupled beads were pre-incubated for 1 hour at RT with various plasma samples diluted ( 1∶5 ) in PBS-TBN . The beads were then assayed in the same fashion as for the binding assay described above using RR1 mAb for detection . For binding-reversal assays , the binding assay was performed as described above , except that the beads were incubated with various plasma samples diluted ( 1∶5 ) in PBS-TBN for 1 hour at RT after the reaction with SA-PE , and then washed just prior to quantification in the BioPlex apparatus . Human plasma samples used in these studies were collected from East African donors under protocols approved by relevant ethical review committees . Study participants provided written informed consent before donating samples , and included adult males from Kenya [49] , [50] and children of different ages from Tanzania [51] . Malaria is endemic in both these regions . Plasma from 5 randomly selected non-immune donors in the US were separated from whole blood obtained from commercial sources ( Valley Biomedical ) and used in a pool as a negative control . DBL2βC2PF11_0521 domain-coupled beads were washed in wells 3 times with PBS-TBN and incubated with children plasma samples at 1∶100 dilution in PBS-TBN . After 1 hour incubation at room temperature at constant rotation at 600 rpm , beads were washed in PBS-TBN and incubated in similar fashion with 1∶250 diluted anti-human IgG coupled to phycoerythrin ( Jackson ImmunoResearch ) for 1 hour . Signal was measured in the BioPlex apparatus to quantify bound IgG . Signals obtained for beads coated with protein expressed by pAdEx vector were used as negative controls and were subtracted from signal obtained with DBL2βC2PF11_0521 domain-coupled beads . Also , pooled samples of non-immune US plasma ( n = 5 ) and immune plasma from adults living in malaria endemic region ( n = 5 ) were used as additional negative and positive controls , respectively .
|
Plasmodium falciparum exports the protein PfEMP1 to the surface of parasitized erythrocytes for roles in immunoevasion and adhesion . The size and structural complexity of this diverse protein family have limited earlier studies of PfEMP1 biology to low throughput and semi-quantitative approaches . We developed a high throughput quantitative assay of PfEMP1 adhesion and used it to analyze structural features of domains that bind the putative cerebral receptor ICAM1 , as well as to survey the acquisition of functional antibodies in malaria-exposed children and adults . In studies of the PfEMP1 repertoire of clone 3D7 parasites , a single specific domain bound ICAM1 strongly . PfEMP1 domains that bind ICAM1 strongly have conserved features , including conserved amino acids within otherwise highly variant flexible loops of the protein . While neutralizing antibodies against the PfEMP1–ICAM1 interaction were uncommon in Tanzanian children , such antibodies were common in east African adults , possibly explaining why immune adults rarely carry ICAM1-binding parasites . This high throughput platform will significantly accelerate studies of PfEMP1 binding domains and the corresponding antibody responses involved in protective immunity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/tropical",
"and",
"travel-associated",
"diseases",
"biophysics/protein",
"folding",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"immunology/immune",
"response",
"biochemistry/biomacromolecule-ligand",
"interactions",
"microbiology/parasitology",
"biotechnology/protein",
"chemistry",
"and",
"proteomics",
"cell",
"biology/cell",
"adhesion",
"immunology/immunity",
"to",
"infections"
] |
2009
|
High Throughput Functional Assays of the Variant Antigen PfEMP1 Reveal a Single Domain in the 3D7 Plasmodium falciparum Genome that Binds ICAM1 with High Affinity and Is Targeted by Naturally Acquired Neutralizing Antibodies
|
Object perception is inherently multidimensional: information about color , material , texture and shape all guide how we interact with objects . We developed a paradigm that quantifies how two object properties ( color and material ) combine in object selection . On each experimental trial , observers viewed three blob-shaped objects—the target and two tests—and selected the test that was more similar to the target . Across trials , the target object was fixed , while the tests varied in color ( across 7 levels ) and material ( also 7 levels , yielding 49 possible stimuli ) . We used an adaptive trial selection procedure ( Quest+ ) to present , on each trial , the stimulus test pair that is most informative of underlying processes that drive selection . We present a novel computational model that allows us to describe observers’ selection data in terms of ( 1 ) the underlying perceptual stimulus representation and ( 2 ) a color-material weight , which quantifies the relative importance of color vs . material in selection . We document large individual differences in the color-material weight across the 12 observers we tested . Furthermore , our analyses reveal limits on how precisely selection data simultaneously constrain perceptual representations and the color-material weight . These limits should guide future efforts towards understanding the multidimensional nature of object perception .
In daily life , we rely on vision to select objects for a variety of goal-directed actions . For example , when we crave tomatoes , we use color to decide which tomato on the vine is the ripest ( Fig 1A ) ; when we sip coffee , we use glossiness to judge whether a cup is made of porcelain or paper , which in turn affects how we handle it ( Fig 1B ) . Indeed , we continually use visual information to effortlessly and confidently judge object characteristics . Instances in which vision misleads us are sufficiently rare to be memorable , as in the case of a deflated basketball sculpture made of glass ( Fig 1C ) . Extracting information about object properties from the image formed on the retina by light reflected from objects is a challenging computational problem . This is because the process of image formation entangles information about the intrinsic properties of objects ( such as color or material ) with information about the conditions under which they are viewed . For example , the retinal image is affected by changes in the illumination , the objects’ position and pose , and the viewpoint of the observer . Understanding the perceptual computations that transform the retinal image into stable representations of objects and their properties is a longstanding goal of vision science . A large literature has employed a "divide and conquer" strategy to investigate the perception of object properties: different object attributes ( color , texture , material , shape , etc . ) have each been studied within their own subfields . This approach has leveraged well-controlled laboratory stimuli and relatively simple psychophysical tasks to build a quantitative understanding of how information is transduced and represented early in the visual pathways [1 , 2] . In addition , careful case studies have provided insight into how stable perception of object properties may be achieved when the experimental conditions are relatively simple and well-specified [3–7] . Our work builds on the foundations provided by this approach and aims to extend the study of object perception in two critical ways . First , we want to move beyond highly-simplified laboratory stimuli and tasks and devise paradigms in which object perception is probed using both naturalistic stimuli and naturalistic tasks . Second , to explain real-life object representations , we need to describe how perceptual judgments along multiple dimensions ( color , material , shape , size , texture , etc ) combine and interact . For example , even though color provides an important cue for selecting the ripest tomato ( Fig 1A ) , other characteristics , such as tomato size , shape , gloss , and surface texture also provide useful information that can guide selection . Our experimental paradigm employs naturalistic stimuli in combination with a two-alternative forced-choice object selection task . This task captures a core aspect of how vision is used in real life , where it guides object selection in service of specific goals [e . g . , selecting nutritious and avoiding spoiled food , 8] . We have previously shown how a version of the elemental selection task can be embedded within more complex and naturalistic tasks to probe color perception [9 , 10] . Here we elaborate the selection task to measure the underlying perceptual representations of both object color and material ( specifically , glossiness ) and quantify how these two perceptual dimensions combine in object selection . Note that we use the term material to refer to the physical glossiness , which is a function of the geometric reflectance properties of object surfaces . Similarly , we use the term color to refer to the diffuse surface spectral reflectance of objects . When it is not clear from context , we will explicitly distinguish the perceptual correlates of these physical properties ( e . g . , perceived material and perceived color ) . The object selection task is illustrated by Fig 2 . On each trial , observers viewed three blob-shaped objects—the target and two tests—and selected the test that was more similar to the target . Across trials , the color and glossiness of the target object was fixed . The tests were identical to the target in their shape , size and pose , but their color and material varied from trial to trial . Test color could vary from blue to green , across 7 different levels; test material could vary from matte to shiny , also across 7 levels ( Fig 3 ) . For each pair of test objects , we measured the probability that each member of the pair was selected . We report three primary results . The first is theoretical . To understand the selection data , we need an observer model that translates the raw data into an interpretable form . An important advance of the work we present here is the development of such a model . The model describes the data in terms of how the stimuli are positioned along underlying perceptual dimensions and how distances along these dimensions are combined to guide object selection . Our second result is experimental . We show that there are large individual differences in the degree to which observers rely on object color relative to object material in selection . Some observers base their selections almost entirely on color , some weight color and material nearly equally , and others rely almost entirely on material . Third , a fine-grained analysis of our data , in parallel with model comparisons , clarifies limits on how precisely selection data may be leveraged to simultaneously reason about perceptual representations and color-material trade-off . These limits , which we make explicit , are important to recognize as we and others move towards understanding the multidimensional nature of object perception . Below , we present each of these results in detail .
Our observer model builds directly on our recent work on color selection [8–10] and incorporates concepts from multidimensional scaling [11 , 12] , the theory of signal detection [13] , and maximum likelihood difference scaling [14 , 15] . As in multidimensional scaling , our model assumes that each stimulus is represented in a subjective perceptual space where , in our case , the dimensions are color ( C ) and material ( M ) . Rather than using a fixed location to represent each stimulus , we incorporate the idea that perception is noisy [e . g . , 13 , 16] and model the representation of each stimulus as a bivariate Gaussian distribution . The mean of each Gaussian locates the corresponding stimulus in the perceptual space , while the covariance specifies the precision of the representation . The model assumes that on each trial of the experiment , the actual representation of each stimulus ( target and two tests ) is a draw from the corresponding distribution and that the observer chooses the test stimulus whose representation on that trial is closest to that of the target . The probability that one test is chosen over another depends on the mean positions of their underlying representations , the magnitude of the perceptual noise , and a color-material weight . This weight describes how differences along the two perceptual dimensions ( color and material ) are integrated when the observers select objects based on similarity . In the model , we define the origin of the perceptual space by setting the position of the target to zero on each dimension . Note that the target remains constant across all trials ( and therefore the origin is fixed ) . Similarly , we define the scale of the perceptual dimensions by setting the variance of the perceptual noise to one for each dimension . These conventions do not affect the model's ability to account for the data . The observer’s performance is then described in terms of two key sets of parameters: ( 1 ) parameters that describe physical-to-perceptual mapping i . e . , the mean position of each stimulus in the color-material perceptual space and ( 2 ) the color-material weight , which we denote as w . The computation of perceptual distances between the target and each test occurs only after distances on the color dimension have been scaled by w and distances along the material dimension have been scaled by 1-w . The color-material weight thus characterizes the relative importance of object color relative to object material in selection . The model does include some substantive assumptions . First , we assume that the perceptual representation of our stimuli is two-dimensional . Second , we assume that positions along the color and material dimension are independent . That is , varying the position of a stimulus on the material dimension does not affect its position on the color dimension and varying the position of a stimulus on the color dimension does not affect its position on the material dimension . Third , we assume that the perceptual noise along the two underlying dimensions is independent . Fourth , we assume that the noise is additive and independent of the stimulus level . We return to consider the implications of these assumptions in Discussion . We considered multiple variants of the perceptual model . These differed in two ways . First , we considered two different distance metrics ( Euclidean and City-Block ) for computing overall test-to-target distances , based on the weighted distances along each underlying dimension . There is large literature in perception and cognition that discusses whether Euclidean or City-Block metric best describes perceived similarity [17–19] . We did not have an a prori reason to favor one type of metric vs . another and we chose to compare them empirically . Second , we considered four different ways of mapping nominal stimulus positions ( labeled as -3 to +3 for each dimension , Fig 3 ) to the corresponding mean perceptual positions . In the Full model variant , each non-zero nominal label was mapped onto its own mean perceptual position , so that 12 parameters were needed to describe to the mapping ( 6 for each dimension ) . In the Cubic , Quadratic , and Linear model variants , the mean perceptual positions were obtained from cubic , quadratic and linear functions of the nominal labels ( 6 , 4 and 2 parameters respectively ) that pass through the origin ( target coordinate of [0 , 0] ) . Thus 8 model variants were considered ( 2 metrics crossed with four positional-mapping variants ) . Additional details about the model implementation and how it was fit to the data are provided in Methods together with a formal expression of the model . Our experimental design used Quest+ , an adaptive trial selection procedure [20] , together with the Euclidean/Cubic variant of our model . Given the parametric model , Quest+ selects for each trial the pair of test stimuli ( 7 levels per dimension , 49 possible stimuli , 1176 possible test stimulus pairs ) that is predicted to yield the most information about the model parameters , given the selection data collected up to that point . The use of an adaptive method was critical for making the experiment feasible , as we estimate it would have taken ~40 hours per observer to measure the selection probabilities for all possible test pairs ( ~20 trials each for 1176 possible test pairs ) . Development of the model and experimental procedures were guided by our findings in a preliminary experiment that used a subset of possible trial types . This experiment is described in a conference proceedings paper [21 , also reviewed in 10] . For each observer , we used a preregistered model selection procedure based on cross-validated fit error to find which of the 8 model variants best accounted for each observer’s selection data . A detailed description of this procedure is available in Methods . We then used the best-fitting model variant to infer the positions of the stimuli in the perceptual color-material space and the color-material weight for each observer . Fig 4 shows the model solution for three of our observers . These observers differ in their color-material weight ( dca w = 0 . 12; sel , w = 0 . 45; nkh , w = 0 . 85 ) . Each row shows data for one observer . The model solution is represented across three panels , which illustrate the recovered parameters , the quality of model fit , and what we refer to as color-material trade-off functions . Table 1 indicates which model variant was best for each observer . The left column of Fig 4 shows the inferred stimulus positions . The target is located at the origin . The x-axis shows the color dimension: points to the left of the origin indicate stimuli that are greener than the target and points to the right indicate stimuli that are bluer . The y-axis shows the material dimension: points below the origin indicate stimuli that are glossier than the target , while points above indicate stimuli that are more matte . Within each dimension , the mapping between nominal stimulus positions and perceptual positions is ordered ( from C-3 on the left to C+3 on the right and from M-3 on the bottom to M+3 on the top ) . The inferred stimulus positions differ across observers . In addition , the inferred stimulus spacing along each dimension is not uniform . This should not be surprising: without extensive preliminary experimentation there is no reliable way to choose stimuli that have uniform perceptual spacing for each observer . The center column illustrates the quality of the model fit to the data . For each stimulus pair shown more than once , the measured proportion of trials one test was chosen relative to another is plotted against the corresponding proportion predicted by the best-fitting model . The diagonal represents the identity line: the closer the points are to the diagonal , the better the agreement between model and data . The area of each plotted point is proportional to the number of trials run for a given stimulus pair: the larger the data point the more trials were shown . The model provides a reasonable account of the data , with the large plotted points lying near the diagonal . The right column shows color-material trade-off functions . These are the model predictions for trials in which one of the tests is a color match and the other test is a material match . We use the term color match to refer to tests that have the same color as the target but differ in material , and the term material match to refer to tests that have the same material as the target but differ in color . The color-material trade-off functions show the proportion of time a color match is chosen ( y-axis ) , when paired with the material matches . The color difference of the material match from the target is indicated on the x-axis . The black line shows the trade-off for a color match that is identical to the target ( zero material difference: M0 ) . When paired with the material match that is also identical to the target ( zero color difference: C0 ) , predicted selection proportion is at chance . As the color difference of the material match increases , the predicted probability that the observer chooses the color match increases and approaches 1 . The red lines show the trade-off for a color match for which the material difference from the target is large ( dashed line: M-3; solid line: M+3 ) . When paired with the material match that is identical to the target ( C0 ) , the observer is predicted to select the material match ( color match selection proportion near 0 ) . As the color difference of the material match increases , however , the observer switches to selecting the color match , tolerating the difference in material . The green and blue lines indicate trade-off functions for intermediate values of color match material difference ( small difference step in blue: M-1 is dashed and M+1 is solid line; medium difference step in green: M-2 is dashed and M+2 , is solid line ) . These fall between the black and red lines . The relative steepness of the color-material trade-off functions reflects how readily the observer transitions to preferring the color matches over material matches . The steepness of the functions also varies across the three observers and is qualitatively consistent with the differences in the inferred color-material weight . For example , the trade-off functions for the observer nkh , who has a high color-material weight ( tends to make selection based on color ) , indicate very low tolerance for color differences of the material matches before the predicted selections switch to the color matches . The trade-off functions for observer dca , who has a low color-material weight are flattened , in comparison , indicating a large degree of tolerance for color differences of the material match . Note , however , that the trade-off functions depend both on the perceived spacing between the stimuli along the color and material dimensions , as well as on the color-material weight . The relative symmetry in the model solution for most observers ( Fig 4 left and right ) reflects the degree to which test-to-target differences in the ( nominally ) positive and negative directions are perceptually equated . For a given observer , right-left symmetry of the predicted color-material trade-off functions indicates the degree to which the same-size steps in the positive and negative direction are perceptually equated in the color dimension ( Fig 4 right; also left-right symmetry of positions in Fig 4 left ) . Similarly , the degree of overlap between the predicted color-material trade-off functions shown in dashed and solid line of the same color indicates the extent to which the same-size steps in the positive and negative direction are perceptually equated in the material dimension ( Fig 4 right; also top-bottom symmetry of positions in Fig 4 left ) . As we note in Methods , we tried to choose stimulus levels on each dimension that were spaced in a perceptually uniform manner . This was only approximate , however , and there was no guarantee that the steps would also appear uniform to our observers . We evaluated the quality of the fit of the color-material trade-off functions to the data by plotting the measured selection proportions for the trials in which a color match test is paired with the material match . Because the trial selection was determined by the Quest+ procedure , only a subset of such trials was presented and the number of trials per pair varied across observers . Points are plotted only for pairs shown at least 10 times . Comparison of plotted points with corresponding prediction lines shows good agreement in most cases . One of the key goals of our model was to independently describe the underlying stimulus representation in a perceptual color-material space and the color-material trade off . In other words , we aimed to uniquely determine ( 1 ) the parameters describing the stimulus positions and ( 2 ) the color-material weight . Two aspects of the results indicate that there are limits on how well this can be accomplished . First , some of the bootstrapped confidence intervals for the color-material weight are large ( Fig 5 ) . Second , examination of Fig 4 indicates the possibility of a systematic relationship between stimulus positions and the color-material weight . For the observer ( dca ) for whom the color-material weight is small , the inferred stimulus positions on the color dimension are expanded relative to those on the material dimension . For the observer ( nkh ) for whom the color-material weight is large , the opposite relation is seen: positions on the material dimension are expanded relative to those on the color dimension . To investigate this further , we summarized the relation between the positions on the color and material dimension ( inferred from the best-fitting model ) by first finding for each dimension the slope of the linear function mapping nominal stimulus position labels ( integers between -3 and +3 ) to perceptual positions . We then computed the ratio of the slope for color to the slope for material . This color-material slope ratio is large when the positions on the material dimension are compressed relative to the positions on the color dimension ( e . g . , dca ) and small when material is expanded relative to color ( e . g , nkh ) . Thus , the color-material slope ratio provides an index for relative positional expansion on the two perceptual dimensions , and we can examine how it varies with the color-material weight . Fig 6 ( top panel ) shows the set of bootstrapped color-material slope ratios against the corresponding color-material weights , with results for each observer shown in a different color ( color-to-observer mapping is shown in the bottom panel ) . The black open circles show the slope ratio and weight inferred from the best-fitting model applied to the complete data set for each observer . The figure illustrates that there is a systematic trade-off between the two aspects of the model solution . Within each observer , the higher the color-material weight , the lower the color-material slope ratio . The correlation between the two numbers is highly statistically significant for every observer ( Pearson correlation coefficients ranged from -0 . 99 to -0 . 91 , p < 0 . 0001 for all observers ) . The distribution shown for each observer reflects the measurement uncertainty in determination of the two aspects of the solution . The confidence intervals shown in Fig 5 represent the central 68% of the x-axis variation for each observer shown in Fig 6 . Fig 6 demonstrates that stimulus positions and the color-material weight are entangled in the model solution: changes in color-material weight can be compensated for , by adjusting the stimulus positions without a large effect on the quality of the model fit . In other words , the observer selection pattern can be explained either in terms of a higher weight being placed on color , accompanied by inferred stimulus positions that yield a lower color-material slope ratio , or a lower color-material weight , accompanied by a higher color-material slope ratio . There are two important features of this parameter trade-off . First , the degree to which it occurs varies across different observers . For some observers , the color-material weight is well-determined , despite the parameter trade-off , while for other observers the trade-off limits how well we can determine the color-material weight given our data . This is summarized by the bootstrapped confidence intervals in Fig 5 , which are obtained from the distribution of the bootstrapped solutions along the x-axis shown in Fig 6 . Although confidence intervals on the color-material weight are reasonably small for most observers , there are cases for which they are large ( e . g . , observers hmn , cjz and nzf ) , suggesting that for these observers the data do not have sufficient power to determine the color-material weight . Second , despite the parameter trade-off , Fig 6 also illustrates clear individual differences across observers . In particular , the distributions of the bootstrapped color-material weight overlap minimally for some observers ( e . g . , green and yellow points vs . pink and lime points ) . Furthermore , even when the range of color-material weights overlaps , the data for different observers can fall along distinct lines ( e . g . red versus yellow points; pink versus lime points ) : there are individual differences in performance even for observers whose color-material weights are not differentiated by the data . These differences cannot be ascribed either to differences in perceptual representation or to color-material weight , but rather to some undetermined combination of the two ( differences in stimulus positions if the color-material weight is equated and differences in color-material weight if the color-material slope ratio is equated ) . A goal for future work is to reduce this ambiguity , as we consider in more detail in Discussion . Our current modelling clarifies what individual differences can and cannot be forcefully characterized within our experimental framework .
Color , material , texture and shape inform us about objects and guide our interactions with them . How vision extracts information about individual object properties has been extensively studied . Little is known , however , about how percepts of different properties combine to form a multidimensional object representation . Here we describe a paradigm we developed to study the joint perception of two different object properties , color and material . Our work builds on the literature on cue-combination , which also considers the multidimensional nature of object perception [23–27] . What distinguishes our approach is that we move beyond threshold measurements to study supra-threshold differences [see also 28 , 29] . On each trial of our object selection task observers viewed objects that vary in color and material ( glossiness ) and made selections based on overall similarity . We interpret the selection data using a novel observer model . The model allows us to describe the data in terms of the underlying perceptual stimulus representation and a color-material weight , which quantifies the trade-off between object color and object material in selection . We find large individual differences in color-material weight across twelve observers: some observers rely predominantly on color when they select objects , others rely predominantly on material , and yet others weight color and material approximately equally . Although our results show salient individual differences across observers , they also show that for some observers the confidence intervals on the color-material weight are large , encompassing most of the possible 0 to 1 range . In other words , for three of our observers ( hmn , cjz and nzf ) the color-material weight is underdetermined , given the data . Currently , we can only speculate about why this occurs . One possibility is that large confidence intervals emerge because these observers change their selection criteria over the course of the experiment , which may amplify the ambiguity between color-material weight and color-material slope ratio we discuss above ( Fig 6 ) . Development of our observer model required us to overcome two fundamental challenges . The first arises because both the underlying perceptual representation of the stimuli and the way information is combined across perceptual dimensions are unknown and thus need to be recovered simultaneously . Although these two factors are conceptually different , their variation can have a qualitatively similar influence on the observers’ selection behavior . An important advance of our model is that it allows us to separate the contribution of the two factors . This separation works sufficiently well to allow us to establish that individual observers employ different color-material weights . At the same time , our work reveals limits on how precisely the contribution of the two factors can be separated . Improving the precision this separation represents an important direction for future work . We return to this point later in the Discussion . The second challenge arises because as the number of dimensions studied increases and the stimulus range extends to include supra-threshold differences , the set of stimuli that could be presented grows far too rapidly for exhaustive measurement . This highlights the need for a theoretically-driven stimulus selection method , which would enable estimation of model parameters from a feasible number of psychophysical trials . To address this challenge , we implemented an adaptive stimulus selection procedure , which incorporated a seven-parameter variant of our model . The procedure is based on the Quest+ method [20] and selects on each trial the test stimulus pair that is most informative about the underlying model parameters . The strength of this approach is that it allows us to exploit appropriately complex models of how observers perform our task . One side-effect of using this efficient procedure , however , is that the power of the data to test the how well the model accounts for performance is reduced . We handled this by conducting a preliminary experiment as a part of model development [21] . In this experiment we studied only a subset of stimulus pairs ( color matches paired with material matches ) , using the method of constant stimuli , and showed that our model ( Full positional variant with Euclidean distance metric ) accounts well for the selection data . As we noted above , our analyses show that in the model solution the recovered stimulus positions and color-material weight are not entirely independent: variation in the color-material weight can be compensated by variation in stimulus positions with minimal effect on the quality of the model's fit . This trade-off in the recovered model parameters emerges because our model explicitly includes perceptual noise along each perceptual dimension . Counter-intuitively , this means that even when stimuli vary only along a single perceptual dimension ( e . g . color ) , changing the stimulus spacing along that dimension need not have a large effect on observers’ predicted selection probabilities . Instead , such change in spacing can be compensated by changing the relative weight , which in turn affects contribution of noise in the other ( non-varied ) dimension on the predicted selection probabilities . Although this compensatory effect is not complete , developing ways to more forcefully identify model parameters remains an important future research direction , as we and others move towards understanding the multidimensional nature of object representations . Below we outline three possible approaches to address this challenge . The first approach is to obtain direct measurements of the underlying stimulus representations along each perceptual dimension . This could be achieved by conducting separate experiments in which observers are explicitly instructed to select objects based on only one aspect of the stimulus at a time ( either color or material ) . This approach was taken in recent work that focuses on the independence of the perceptual representations of multiple object properties [28–32] . It relies on the assumption that when instructed to attend to just one stimulus dimension , observers are able to ignore variations along the other dimensions . If the assumption holds , this approach combined with ours would provide additional power to recover stimulus positions along the attended dimension because it enables fitting the data without introducing the effects of noise along the non-attended dimension . There is no guarantee , however , that observers can or do strictly follow experimental instructions that direct them to attend only to a single perceptual dimension . The second approach is to simplify our observer model and assume that the underlying stimulus representation is common across observers , so that any variation in performance is entirely due to the variation in the color-material weight . While the assumption that all observers perceive the stimuli in the same way may be questioned , it has a long history in the study of perception . Indeed , this approach is implicit in ( 1 ) efforts to develop standardized perceptual distance metrics and stimulus order systems [e . g . , 33] , ( 2 ) studies in which the conclusions about perceptual representations are based on data averaged across multiple observers [e . g . , 34 , Fig 2] and ( 3 ) work that employs multidimensional scaling to recover the common perceptual representation across observers together with the parameters that describe different weighting of the underlying dimensions by each observer [e . g . , 35] . Finally , the third approach is to employ a multiplicative rather than an additive noise model . The precision of perceptual representations is often the highest at the current adaptation point [36–38] . This observation can be incorporated in the model by assuming that along each perceptual dimension noise scales as a function of test distance from the target . Reducing the noise near the perceptual axes might reduce the model's ability to trade-off stimulus positions parameters and the weight . Along similar lines , one could consider modeling trial-by-trial variability as noisiness of the perceptual differences between stimuli , after the information has been combined across dimensions . Adding noise to the decision variable has been used in related work [see also 28 , 39 , 40] . Switching to a different noise model , however , would require careful consideration of whether the alternative model provides a better account of the data . An assumption of our model is that the underlying perceptual space is two dimensional ( color and material ) , and that color and material are independent perceptual dimensions , so that variation in stimulus color has no effect on perceived glossiness and variation in stimulus glossiness has no effect on perceived color . The model embodies this assumption because the stimulus position along a perceptual dimension is a function of its nominal stimulus position along that dimension alone and is unaffected by the nominal stimulus position along the other dimension . For example , the stimuli that vary only in material relative to the target all have a color coordinate of 0 and vice versa . While this simplification provided a good starting point , it may be too restrictive . Ho et al . [28] have shown that perceived glossiness varies with changes in the physical roughness of an object surface , and that perceived roughness varies with changes in physical glossiness , while Hansmann-Roth and Mamassian [29] found a similar relationship between glossiness and perceived lightness . These findings suggest that such interactions might be present in our experiment [see also 30 , 31 , 32] . Future work could investigate this issue explicitly using a number of approaches . One would be to test whether adding parameters that described color-material interactions improved the cross-validated fit of our model , similar to the approach taken by Ho et al . [28] . Another would be to conduct ancillary experiments that directly assess the independence [see 19] . The results we present here are based on experimental work that used a static object with single shape , one type of material change , and a limited range of variation in both color and material . Future research should expand the set of dimensions studied and the stimulus range along each dimension . In addition , the use of dynamic stimuli where observers compare objects as they rotate , each in a different phase , would minimize observers' ability to make selections based on local image information ( e . g . , reflections in the specular highlights ) and enforce similarity judgments based on global object appearance . In many natural tasks , we select objects based on a combination of their characteristics . For example , when picking fruit , we rely on fruit color but also on size , shape , gloss , and surface texture . An important goal of vision science is to describe how different object characteristics are integrated in natural tasks that require object selection and identification . The task and model we develop here represent a step towards this goal . They allow us to describe quantitatively the extent to which observers' selections are driven by multiple object characteristics ( here color and material ) . When observers perform objective psychophysical tasks ( e . g . , when thresholds are measured using forced-choice methods ) , insight about performance and underlying mechanisms may be obtained by comparing observers' performance to that of an ideal observer [41 , 42] . In our object selection task , observers made subjective judgments ( "select the test object that is most similar to the target" ) for which there is not a well-defined correct answer . Therefore , ideal observer analysis is not applicable to our current data . It would be possible , however , to develop a selection task with an explicitly defined reward function . For example , one could imagine a task in which object color is highly-correlated with some underlying property that matters to the observers , while material is uncorrelated with that property . In that case , observers with high color-material weights could be considered to be performing better , and this could be formalized via an ideal observer analysis . A key issue in the perception of object properties is how the visual system stabilizes its perceptual representations against variation in the conditions under which objects are seen , particularly against changes in the spectral and geometric structure of the illumination [43 , 44] . It is of considerable interest to understand whether the way vision combines information about different classes of object properties is sensitive to viewing conditions . For example , perceived object color might be weighted less when objects are compared across changes in illumination spectrum . Similarly , perceived material might be weighted less when the comparisons are made across changes in the geometric properties of the illumination . By handling perceptual representations along multiple perceptual dimensions and by quantifying the relative importance of each dimension in performance , our methods and model extend naturally to the study of these questions .
Computation of perceptual distances . We compute perceptual distances using either the Euclidean or City-block metric . With the Euclidean metric , the distance between the target T and the test T1 ( dT-T1 ) on trial k is computed as: dkT−T1= ( w ( ΔCkT−T1 ) ) 2+ ( ( 1−w ) ( ΔMkT−T1 ) ) 2 ( 1 ) while the distance between the target T and the test T2 ( dT-T2 ) is computed as: dkT−T2= ( w ( ΔCkT−T2 ) ) 2+ ( ( 1−w ) ( ΔMkT−T2 ) ) 2 ( 2 ) With the City-block metric version of the model , the corresponding formulas are: dkT−T1=w|ΔCkT−T1|+ ( 1−w ) |ΔMkT−T1| , ( 3 ) dkT−T2=w|ΔCkT−T2|+ ( 1−w ) |ΔMkT−T2| . ( 4 ) In the equations above , w denotes the color-material weight , ΔC denotes the distance between the target and a given test along the perceptual color dimension and ΔM denotes the distance between the target and a given test along the perceptual material dimension . On a given trial , the observer selects test T1 if dkT−T1<dkT−T2 and T2 otherwise . Model origin and scale . Our model employs two conventions that define the units for the underlying perceptual dimensions . First , we define the target position as the origin of the perceptual space . Second , we model the zero-mean Gaussian perceptual noise for each stimulus as having a standard deviation of one along each perceptual dimension . This assumption defines the scale of the perceptual dimensions , with the mapping between physical and perceptual positions then fit with respect to this scale . Note that we are agnostic as to whether what we refer to as perceptual noise arises when the stimuli are encoded , when they are read out to make the comparisons , or a combination of both . In the model implementation , for each dimension we restricted the range over which stimulus positions can vary to -20 to +20 and we enforced the minimum spacing between adjacent stimuli to be at 0 . 25 ( ¼ of the standard deviation of the representational noise ) . Model variants . The full variant of our model ( for either choice of distance metric ) has 13 free parameters: the color-material weight w , 6 positions on the color dimensions that correspond to the 6 non-target color levels and 6 positions on the material dimension that corresponds to 6 non-target material levels . We use numerical search to find the weight and the positions that best fit each observers’ selection data in a maximum likelihood sense . Conducting the numerical search required us to be able to compute the likelihood of an observer’s responses for any pair of tests , given the color-material weight and the positions of the two tests in the underlying perceptual color-material space . We do not have an analytic formula for computing these likelihoods . We therefore pre-computed them using forward Monte Carlo simulation and stored them in a gridded multidimensional lookup table . More specifically , we constructed a 5-dimensional lookup table with dimensions: ( 1 ) color-material weight , ( 2 ) perceptual color coordinate of the first test , ( 3 ) perceptual material coordinate of the first test , ( 4 ) perceptual color coordinate of the second test , ( 5 ) perceptual material coordinate of the second test . For the color-material weight , we linearly sampled 10 grid weight values between 0 and 1 . For color and material dimensions we linearly sampled 20 grid values between -20 and +20 corresponding to perceptual positions on each dimension . We simulated 3000 trials of test comparisons for each combination of parameters in the grid , and averaged over these to obtain the likelihood of each test being chosen when paired with each other test ( for any sampled weight value ) . This table was used to estimate the likelihood of each response for any pair of stimulus positions within a predefined range ( -20 to 20 ) and any weight value , using cubic interpolation ( via Matlab’s griddedInterpolant function ) . We constructed two separate lookup tables , one based on the Euclidean distance metric and one based on the City-block distance metric . For each metric , we compared four variants of our model , which differed in the complexity of the mapping between physical and perceptual stimulus positions . The full variant , described above , had 13 free parameters . In the simpler variants , we constrained the mapping between physical and perceptual stimulus positions along each dimension to have a parametric form , with perceptual positions being described as a linear , quadratic , or cubic function of nominal stimulus positions ( thus requiring 2 , 4 or 6 free positional parameters , respectively , in addition to the color-material weight ) . Because the nominal position of target object color and material were 0 and mapped onto the [0 , 0] coordinate in the perceptual color-material space we did not include an affine term in our linear , quadratic or cubic mappings . For each observer and choice of distance metric we used 8-fold cross-validation to compare the full model variant to three simpler ( more constrained ) variants . We evaluated the models by comparing the average cross-validated log-likelihoods of the fits across 8 cross-validation partitions using a paired-sample t-test . For each partition , model parameters were determined from 7/8 of the trials from the full data set and the cross-validated log-likelihood was evaluated on the remaining 1/8 of the trials . We started the comparisons from the full model , which is the most complex , and we asked if the cross-validation log-likelihood was significantly higher for the full than for the cubic model ( using the α-level of 0 . 05 for a one-tailed test ) . If it was , we concluded that full model best accounted for the data . Otherwise , we eliminated the full model from consideration and proceeded to compare log-likelihoods for the cubic and quadratic model , using the same method and criterion . If cubic model was significantly better than the quadratic , we concluded that cubic model best accounted for the data . Otherwise , we eliminated cubic model and continued to compare the quadratic and linear models using the same procedure . We followed this procedure separately for each observer to establish the model that best accounted for this observer’s selection data , given the choice of metric . We conducted this procedure for both Euclidean and City-block metric separately . In the comparison for a given observer we used the same cross-validation data partition both across model variants and across different metrics . A different partition was used for each observer . To select the best overall model , we compared the mean cross-validation log-likelihood of the best-fitting Euclidean-based and the best-fitting City-block-based model and selected the model that had the highest average cross-validated log-likelihood . The model comparison procedures described above are conducted following the pre-registered analysis plan . To examine whether the log-likelihoods of the two best-fitting models based on City-block-distance and Euclidean-distance differed significantly from each other , we also compared their average cross-validated log-likelihoods using a paired t-test ( two-tailed , with α criterion level adjusted for multiple comparisons , one test for each observer: p = 0 . 05/12 = 0 . 0042 ) . We consider this to be a post-hoc analysis ( as it was not described in the pre-registration document; see S1 Appendix ) , but never-the-less useful for understanding how model goodness-of-fit depends on the underlying metric . Thus we report the result of this comparison in addition to specifying which model had the lowest average cross-validated log-likelihood . Formal expression of model . We describe the model with respect to stimuli that vary along two physical dimensions , indicated here by C and M , which serve as mnemonics for color and material . The formal model , however , does not depend on what stimulus properties are represented by the dimensions , and can easily be generalized to the case where there are more than two stimulus dimensions . Each stimulus is thus described by its position in a two-dimensional stimulus space s = ( sC , sM ) , where sC and sM are the stimulus coordinates on the two dimensions . We assume that perceptual processing maps each stimulus into a perceptual space , so that each stimulus s has a corresponding mean perceptual coordinates which are a function p = f ( s ) . In the general case , f ( ) is an arbitrary vector-valued function and the dimensionality of p need not be the same as that of s . In our work we assume that p is two-dimensional and we write p = ( pC , pM ) . We also restrict the form of f ( ) as described in more detail below . The perceptual representations of each stimulus on a particular trial are assumed be obtained from the mean perceptual coordinates for the stimulus perturbed by additive zero-mean Gaussian noise with standard deviation of 1 . The noise is assumed to be independent for each perceptual dimension . The choice of 1 as noise standard deviation specifies the units of the perceptual dimensions . On each trial of the experiment , the observer makes a judgment as to which of the two test stimuli , sT1 and sT2 , is most similar to the target stimulus sT . The model assumes that this judgment is based on computing a perceptual distance between the representation of each test on that trial to the perceptual representation of the target on that trial . The computation of perceptual distance incorporates a color-material weight , w . More specifically , let the perceptual coordinates for the target on trial k be ( pC , kT , pM , kT ) and similarly for the two tests . Then on trial k we can define ΔCkT−T1=pC , kT−pC , kT1 and ΔMkT−T1=pM , kT−pM , kT1 and similarly for T2 . From these we can compute dT−T1=‖ ( wΔCkT−T1 , ( 1−w ) ΔMkT−T1 ) ‖ and dT−T2=‖ ( wΔCkT−T2 , ( 1−w ) ΔMkT−T2 ) ‖ , where ‖‖ denotes the vector norm according to a chosen metric . In this paper we consider the Euclidian and City-block metrics and the expressions here are those provided in Eqs 1–4 above . If we knew the mean perceptual positions pT , pT1 and pT2 , corresponding to stimuli sT , sT1 and sT2 , the development above determines the probability p ( sT , sT1 , sT2 ) that the observer will choose the first test stimulus sT1 on a trial over the second test stimulus sT2 on that trial , relative to the target stimulus sT , given a choice of distance metric and a value for w . That is , the model allows computation of the likelihood of observer choices from the stimulus specification once the mapping from stimuli to mean perceptual positions is determined . We do not have an analytical expression for this probability , but can estimate it with arbitrary precision by simulating many trials with independent noise added on each trial . When we fit the model , we perform such simulations to fill out the entries of a discrete lookup table that relates perceptual positions to choice likelihoods , given the choice of metric and a value for w . We then interpolate the lookup table as necessary to make model predictions ( see Methods ) . Our goal is to use the experimental data to determine the parameters of the model . This requires finding a mapping f ( ) that gives the mean perceptual positions for each stimulus . We consider several possible forms for this mapping . In the current development , each of these embodies the assumption that pC = fC ( sC ) and pM = fM ( sM ) . We refer to this as an assumption of independence between the perceptual representations of color and material . We then consider four possible forms for fC ( ) and fM ( ) , which we refer to as the linear , quadratic , cubic and full models . In the model variants we tested , the same functional form was used for both color and material . Thus we have: We always assigned the target stimulus a nominal stimulus value sT = ( 0 , 0 ) . For the linear , quadratic and cubic models the form of the mapping functions means that pT = ( 0 , 0 ) . For the full model , we also constrained the mean perceptual position of the target to be pT = ( 0 , 0 ) . This choice simply sets the origin of the perceptual space , without loss of generality since the model's predictions depend only on the relative perceptual positions of the target and tests . For all of the mappings , we constrained the perceptual positions to be monotonically increasing functions of the stimulus positions . This is reasonable because we chose our stimuli for each dimension to have a clear subjective order . Once we have chosen a parametric form for f ( ) and a distance metric , the predictions of the model are a function of a small number of parameters . To fit the model , we use numerical optimization to find the parameter values that maximize the likelihood of the experimental data . As noted in methods , we enforced upper and lower bounds on the mean perceptual positions of 20 and -20 . This was done so that we could pre-compute the lookup tables described above . Simulated observer analysis . To evaluate how reliably our model can recover underlying observer parameters , we used the model to simulate experimental data for three sets of known observer parameters . We then used our analysis procedures to recover the parameters from the data , and compared these with the known simulated parameters . The three sets of observer parameters were chosen to match those we estimated for three of the actual observers who participated in the study . We chose these three observers as ones whose recovered color-material weights collectively spanned a wide range . In addition , they each had narrow bootstrapped confidence intervals around their recovered weight and different inferred positions in the color and material dimensions ( observers gfn , lza and nkh; see Fig 5 ) . The simulated experimental and modeling procedures were identical to those of the main experiment . For each selected real observer , we created a simulated observer with the same underlying parameters ( the weight and stimulus positions in the color and material dimension , as inferred using the best-fitting model for that observer ) . We then simulated 8 experimental blocks for each simulated observer , using the actual experimental code . Responses on each experimental trial were determined stochastically , using the model together with the simulated parameters for each observer . We analyzed the data by selecting the best-fitting model for each simulated observer via cross-validation and we used the best-fitting model to recover the underlying parameters for each observer . For two observers , the identified best-fitting models were the same as those for the real observer counterpart ( gfn and nkh ) both in terms of underlying distance metric and positional mapping . The third observer differed in distance metric ( Euclidean metric was preferred to City-block metric for simulated version of lza ) . Fig 7 shows the recovered color-material weights for the three simulated observers ( in black ) , each obtained with the identified best fitting model for that observer's simulated data . The simulated weights are shown in green and are identical to those shown for the corresponding observers in Fig 5 . For each set of weights , the error-bars show 68% bootstrapped confidence intervals . The simulated and real weights vary in the same order across real and simulated observers , although the quantitative agreement is not perfect . Fig 8 shows the inferred perceptual positions for the three simulated observers plotted against the simulated positions ( gfn-equivalent is shown in yellow , lza-equivalent in violet , nkf-equivalent in green ) . The plots indicate that simulated and recovered positions are in good agreement . We take these simulation results to indicate that our experimental and modeling procedures have sufficient power to recover individual differences in color-material weights and the perceptual positions expressed within our model . The magnitude of these individual differences is comparable to what we observed in our experiment . The results of the simulations also indicate , as we discuss when we consider the precision of model parameter recovery , that there are limits to how precisely we can recover the underlying parameters from the experimental data .
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Much is known about how the visual system extracts information about individual object properties , such as color or material . Considerably less is known about how percepts of these properties interact to form a multidimensional object representation . We report the first quantitative analysis of how perceived color and material combine in object selection , using a task designed to reflect key aspects of how we use vision in real life . We introduce a computational model that describes observers’ selection behavior in terms of ( 1 ) how objects are represented in an underlying subjective perceptual color-material space and ( 2 ) how differences in perceived object color and material combine to guide selection . We find large individual differences in the degree to which observers select objects based on color relative to material: some base their selections almost entirely on color , some weight color and material nearly equally , and others rely almost entirely on material . A fine-grained analysis clarifies the limits on how precisely selection data may be leveraged to simultaneously understand the underlying perceptual representations on one hand and how the information about perceived color and material combine on the other . Our work provides a foundation for improving our understanding of visual computations in natural viewing .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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"infographics",
"physical",
"mapping",
"statistics",
"social",
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"neuroscience",
"charts",
"perception",
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] |
2019
|
The relative contribution of color and material in object selection
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Understanding the molecular pathways by which oncogenes drive cancerous cell growth , and how dependence on such pathways varies between tumors could be highly valuable for the design of anti-cancer treatment strategies . In this work we study how dependence upon the canonical PI3K and MAPK cascades varies across HER2+ cancers , and define biomarkers predictive of pathway dependencies . A panel of 18 HER2+ ( ERBB2-amplified ) cell lines representing a variety of indications was used to characterize the functional and molecular diversity within this oncogene-defined cancer . PI3K and MAPK-pathway dependencies were quantified by measuring in vitro cell growth responses to combinations of AKT ( MK2206 ) and MEK ( GSK1120212; trametinib ) inhibitors , in the presence and absence of the ERBB3 ligand heregulin ( NRG1 ) . A combination of three protein measurements comprising the receptors EGFR , ERBB3 ( HER3 ) , and the cyclin-dependent kinase inhibitor p27 ( CDKN1B ) was found to accurately predict dependence on PI3K/AKT vs . MAPK/ERK signaling axes . Notably , this multivariate classifier outperformed the more intuitive and clinically employed metrics , such as expression of phospho-AKT and phospho-ERK , and PI3K pathway mutations ( PIK3CA , PTEN , and PIK3R1 ) . In both cell lines and primary patient samples , we observed consistent expression patterns of these biomarkers varies by cancer indication , such that ERBB3 and CDKN1B expression are relatively high in breast tumors while EGFR expression is relatively high in other indications . The predictability of the three protein biomarkers for differentiating PI3K/AKT vs . MAPK dependence in HER2+ cancers was confirmed using external datasets ( Project Achilles and GDSC ) , again out-performing clinically used genetic markers . Measurement of this minimal set of three protein biomarkers could thus inform treatment , and predict mechanisms of drug resistance in HER2+ cancers . More generally , our results show a single oncogenic transformation can have differing effects on cell signaling and growth , contingent upon the molecular and cellular context .
The elevated rate of proliferation and apoptotic-resistance characteristic of cancer cells depends on the activation of oncogenic signaling pathways . Such oncogenic pathway dependence creates molecular vulnerabilities , which can be exploited by targeted therapies . The effectiveness of such drugs however requires prospectively identifying which specific pathway ( s ) among many possibilities a given tumor is dependent on . This is a non-trivial task given the molecular and genetic heterogeneity of the disease , and the complexity of cell signaling networks . As a result , the majority of patients treated with targeted anti-cancer drugs fail to respond , and those that do often develop resistance over time [1] . The receptor tyrosine kinase HER2 is prototypic of oncogene addiction and a target for personalized anti-cancer therapy [2] . Overexpression of the receptor via amplification of the gene ERBB2 results in ligand-independent homo-dimerization and constitutive signaling [3] primarily through the phosphoinositide 3-kinase ( PI3K ) cascade [4 , 5] . The monoclonal antibody trastuzumab ( Herceptin; Genentech ) is standard of care therapy for HER2+ disease . While its use has significantly reduced mortality from HER2+ breast cancer since approval in 1998 [6] , many patients do not respond to treatment , particularly those with metastatic disease [7] . While subsequent HER2-targeted agents lapatinib , pertuzumab , and ado-trastuzumab-emtansine ( T-DM1 ) have improved survival as components of combination regimens , patients still progress on these therapies [8] . Mutational activation of the PI3K pathway ( via PIK3CA point mutations or PTEN deletions ) is known to mediate resistance to HER2-targeted therapies in both pre-clinical models and through retrospective analysis of clinical data [9] . Consequently , many small molecules targeting components of the PI3K cascades , including PI3K , AKT , and mTOR inhibitors , are currently undergoing clinical trials in combination with HER2 therapy [10] . The mitogen activated protein kinase ( MAPK ) signaling cascade is another pathway hyper-activated in a large number of cancers , and many small molecule inhibitors targeting its pathway components such as BRAF [11] and MEK [12] are approved or in clinical development . While critical for transducing signals emanating from oncogenes such as KRAS [13] and other receptor tyrosine kinases including ErbB-family receptors [14] , the pathway is not known to play a critical role in HER2-amplified cancers . On the other hand , the dual inhibition of PI3K and MAPK cascades can result in synergistic effects on cell proliferation and apoptosis in multiple cancer models [15] , including HER2+ breast cancer [16 , 17] , suggesting a potential role of MAPK signaling in the growth and survival of HER2+ cancers . Many combinations of targeted therapies are currently undergoing clinical evaluation for treating trastuzumab-refractory HER2+ disease , including small molecule inhibitors of HER2 , histone deacetylases ( HDAC ) , heat shock proteins ( HSP ) , insulin-line growth factor-1 receptor ( IGF-1R ) , and the HER2 binding partner ERBB3 [8] . However , the molecular and genetic determinants of sensitivity to these agents , let alone their combinations , remain largely obscure . Rational strategies to functionally classify tumors by dependence on oncogenic signaling pathways using minimal sets of biomarkers would thus be highly valuable in designing improved treatment strategies . The goal of this study was to characterize the dependence of HER2+ cancers on two such pathways , the canonical PI3K and MAPK cascades . Further , we explored whether such dependence can be predicted from phenotypic , proteomic , or genomic biomarkers that could ultimately be used to stratify patients and inform treatment strategies .
Amplified HER2 is known to signal predominantly through the PI3K/AKT pathway in breast cancers [4] . However , it is unclear whether different indications with this genomic alteration are wired similarly downstream of the receptor . Also , it is well established that the HER3 ligand heregulin ( HRG ) stimulates PI3K signaling through induction of HER2/HER3 hetero-dimerization [14] . Yet the degree to which this ligand affects MAPK signaling downstream of the ErbB receptors in different cellular contexts is unclear . To answer these questions , we examined whether dependence on the PI3K and MAPK signaling cascades varies across HER2+ cancers , both in the presence and absence of heregulin . Specifically , a panel of 18 HER2+ , but otherwise diverse cell lines was assembled , including breast , lung , gastric/esophageal , and ovarian cancer models . To characterize pathway dependence , each cell line was treated with a full 5x6 dose combination matrix of AKT and MEK inhibitors MK-2206 and GSK-1120212 ( trametinib ) . In vitro cell proliferation was then quantified via video microscopy over 96 hours . All cell lines tested displayed some sensitivity to at least one of the inhibitors used . To characterize the shapes of these response surfaces , quantitative logic-based models of cell growth kinetics were parameterized for each cell line . These phenomenological models characterize the balance of cell proliferation vs . cell death as functions of drug concentration ( and by extension , pathway dependence ) using combinations of quantitative logic gates . While nine alternate model variations were assessed ( S1 Table ) , a logical OR-Gate regulating cell survival as a function of active ( phosphorylated ) AKT and ERK was found to perform optimally across the panel ( S1–S3 Figs , S4 Fig ) . With only six parameters , it has the additional benefit of easy interpretation for comparison between cells . The six model parameters characterizing each cell consist of the maximal proliferation rate and cell death rates ( µMAX , δMAX ) , EC50 and Hill coefficients characterizing inhibitor dose-responses ( τ , k ) , and empirical weights toward PI3K and MAPK dependence ( wAKT , wERK ) ( see Materials and Methods and S2 Table ) . To develop a single metric of relative PI3K vs . MAPK pathway dependence , we define Pathway Bias as the normalized difference of the weighting parameters , where a value of 1 signifies complete PI3K-dependence ( wAKT >> wERK ) , 0 dual-dependence ( wAKT ~ wERK ) , and -1 complete MAPK-dependence ( wAKT << wERK ) . As shown in Fig 1A , 5/18 cell lines are classified as PI3K-dependent , 9/18 as MAPK-dependent , and unexpectedly 4/18 switch from PI3K to MAPK-dependence upon HRG stimulation . Error bars shown represent 95% confidence intervals ( 2 standard deviations ) from 100 parameter estimation runs . The Bias estimates are very well constrained , with a median coefficient of variation of 3 . 7% ( S5 Fig , part B ) . Only the SKOV3 cells would be considered undetermined ( 95% CI cross the axis ) , a result of the uniquely profound synergistic response of these cells to dual pathway inhibition ( S2 Fig ) . Our models implicitly assume the PI3K/AKT and MAPK pathways function independently ( i . e . no “cross-talk” ) , a shortcoming revealed by this specific case . Our primary motivation with the models were for data compression; reducing a 30-point response surface to three intuitive parameters ( rates of cell proliferation , cell death , and pathway bias ) . While complexities could be added to the current models to better capture this phenomenon with SKOV3 cells , this is beyond our primary motivation . Representative surface responses for each class are shown below in Fig 1B . Heregulin stimulation reduced the sensitivity of all cells to AKT inhibition , and correspondingly increased relative sensitivity to MEK inhibition , though the effect was much more pronounced in the switching class ( S5 Fig ) . Heregulin is known to desensitize cells to PI3K inhibitors [18] , however the converse increased relative sensitivity to MEK inhibition was unexpected . Overlaying information on basal proliferation , mutational status of three PI3K pathway key genes , and tissue source reveals some interesting patterns . First , consistent with the canonical classification of the MAPK pathway as mitogenic [14] , we observed that proliferation rate ( population doubling; PD ) correlates with MAPK-dependence . Mutational status of the PI3K pathway , while correlated with PI3K-dependence , is not a predictive classifier . That is , while PI3K-biased cells are enriched for PIK3CA , PIK3R1 , and PTEN mutations , some MAPK-dependent cells harbor PIK3CA mutations . These genetic metrics alone ( HER2 amplification and PI3K pathway mutations ) are thus insufficient for determining dependence of the tumor cells on PI3K vs . MAPK signaling . Most interestingly , our results show that , whereas breast cancers cover all three functional classes , all of the non-breast indications are MAPK-dependent . These are clinically significant findings , suggesting that current use of PI3K/AKT inhibitors in either unselected HER2+ cancer patients , or based on PIK3CA and PTEN mutations may be sub-optimal [8] , and some HER2+ patients may benefit from treatment MEK inhibitors . To better define the HER2+ patient sub-populations that could respond to PI3K/AKT or MEK inhibitors , we sought to identify molecular features of the cells that are predictive of dependency on the PI3K/AKT and MAPK signaling . Applying a targeted proteomics approach , the same panel of cell lines was profiled for ErbB receptor expression , total and phosphorylated forms of ERK and AKT , and the cell cycle regulator CDKN1B ( P27 ) using quantitative Luminex assays ( S6 Fig; raw data provided in S1 Data ) . The relationships between protein expression and cellular functional properties were then analyzed by computing Spearman’s rank correlation coefficients between protein measurements and the characteristic model parameters across the panel of cell lines ( Fig 2A ) . Some of the protein species were quantified with more than one detection antibody ( annotated a , b , c ) as a quality control check . The effect of heregulin stimulation was accounted for solely by its effect on cell signaling , as each cell line +/- heregulin was treated as two independent samples . For our analysis of the proteomic and cellular response data , we treated the same cell line +/- heregulin treatment as independent samples , thus producing 36 ( 18x2 ) samples . Functional relationships are revealed from these correlations; highly proliferative cells express increased levels of EGFR and are MAPK-signaling dependent , while slowly proliferating cells have higher levels of ERBB3 , CDKN1B ( p27 ) , and are PI3K-dependent . This is consistent with the canonical association of PI3K and MAPK pathways with regulating cell survival and proliferation , respectively [14] , but to our knowledge the first instance of this functional partition revealed in a purely data-driven manner . To further explore this relationship , we created a tenth model variant ( M10 ) which explicitly encodes proliferation and cell survival as separately regulated by MAPK and PI3K/AKT signaling , respectively ( see Methods ) . The model parameters were estimated from the surface response data as with the previous nine , and parameter estimates , simulations , and goodness of fit metrics ( MSE and AIC values ) are shown in S1 Data for all . In comparison to the chosen model ( M4 ) , this variant produced a better fit to the data in the majority ( 21/36 ) of samples , with average MSE slightly reduced by 0 . 5% . This further supports the observed relationship between cell proliferation and MAPK signaling , and cell survival with PI3K/AKT signaling . It is notable that neither the phosphorylated or total amounts of AKT or ERK proteins correlated with pathway dependence , confounding our naïve expectations . This non-intuitive finding is nevertheless consistent with previous studies examining biomarkers of PI3K/AKT and MEK inhibitor sensitivity in different cancer models [19–21] . To assess whether these molecular correlations were predictive , logistic regression models were parameterized to classify cells as PI3K vs . MAPK-dependent using different sets of input features ( protein expression , PI3K pathway mutational status , the phenotypic properties of proliferation rate and tissue origin [breast vs . non-breast] , or all features combined ) . Models were evaluated for predictive accuracy via leave-one-out cross validation ( LOOCV ) , and compared against 10 , 000 random permutations to assess statistical significance ( Fig 2B ) . Consistent with the correlation analyses , the most intuitive biomarkers , pAKT and pERK , were in fact no better predictors of Pathway Bias than random chance ( Accuracy = 50% , P = 0 . 75 ) . Accuracy of the “full” model ( containing all molecular , genetic , and phenotypic features ) , the protein-based model , and the genetic model were quite poor ( 67% , 72% , and 72% corresponding to P-values of 0 . 45 , 0 . 18 , and 0 . 055 ) . In contrast , model predictions based solely on “phenotype” ( tissue and proliferation rate ) were quite accurate ( 81% , P = 0 . 006 ) . To assess whether a subset of the protein measurements could provide a molecular explanation for this result , a model was built using the 3 proteins best correlated with pathway bias: EGFR , ERBB3 , and CDKN1B . The accuracy of this 3-biomarker model matched the phenotype-based predictions ( 81% , P = 0 . 0002 ) , and was statistically superior to all alternatives assessed ( S7 Fig ) . Combining the 3 protein biomarkers and phenotypic features did not improve accuracy , demonstrating redundancy between these measurements . That is , the observed association between tissue , proliferation rate , and pathway dependence can be explained solely by differential expression of these three proteins . Relative importance of the three protein features can be inferred from the normalized regression coefficients ( Fig 2C; raw data provided in S3 Table and S4 Table ) , in order of descending importance EGFR , ERBB3 , and CDKN1B . The ability of heregulin to shift pathway dependence from PI3K to MAPK is an unexpected observation , given that this growth factor is commonly associated with PI3K signaling . Consistent with this established role , pAKT ( pS473 and pT308 ) was induced in the majority of cells treated with the ligand ( S8 Fig ) . And while pAKT induction was greater in the PI3K-depdendent cells , this nor any other single protein change consistently correlated with pathway dependence switching . Including heregulin treatment as an additional discrete feature ( 1/0 ) in addition to the three protein biomarkers did not improve model accuracy ( 78% , P = 0 . 001 ) . The context-dependent Bias of the four cell lines which switch dependence was poorly predicted ( Fig 2D ) . In fact , the majority of the error in model ( 4 of 7 misclassifications ) is attributable to its inability to predict the switching behavior as the AU565 , HCC419 , and ZR751 cell lines are classified as PI3K-depdendent , and SKBR3 as MAPK-dependent , regardless of heregulin . The 3 protein biomarkers are thus able to accurately predict intrinsic dependence on PI3K/AKT vs . MAPK signaling . However , the shift in dependence induced by heregulin stimulation may occur through alternative mechanisms not accounted for in our panel of protein measurements . We observed that all non-breast cell lines are MAPK-dependent , and this is explained by expression of the three protein biomarkers ( Fig 3A ) . Thus , if cell lines are representative of the derivative disease , one would expect to see higher levels of EGFR and lower levels of ERBB3 and CDKN1B in non-breast indications vs . breast tumors . To test this hypothesis , we next queried available clinical gene expression data from The Cancer Genome Atlas ( TCGA ) for expression of EGFR , ERBB3 , and CDKN1B by indication . RNAseq profiling data ( V2 RSEM ) was extracted for all indications available , and classified as HER2+ vs . HER2- sub-classes for analyses based on ERBB2 gene expression ( S9 Fig ) . Consistent with molecular profiles of the cell lines , in all 10 indications with significant numbers of HER2+ samples , EGFR expression was relatively higher and/or ERBB3 and CDKN1B lower in in comparison to breast cancers ( Fig 3B ) , and these patterns hold for both the HER2+ and HER2- subsets . The expression patterns observed in our panel of immortalized cell lines are thus consistent with their derivative indications , suggesting that non-breast HER2+ cancers are likely to be dependent on MAPK rather than PI3K signaling . We speculate this may arise though differential hetero-dimerization , with ERBB2-EGFR complexes preferentially activating MAPK signaling , and ERBB2-ERBB3 PI3K/AKT signaling . To validate the functional utility of the three protein biomarkers ( EGFR , ERBB3 , and CDKN1B ) , we assessed whether they could predict pathway dependence using data from independent experiments . First , we sought to determine whether protein expression of EGFR , ERBB3 and CDKN1B , could be used to predict differential sensitivity to anti-cancer drugs in HER2+ cancers . An ELISA-based protein profiling dataset across 90 cancer cell lines [22] was intersected with the Genomics of Drug Sensitivity in Cancer database ( GDSC; 714 cell lines screened for sensitivity to 138 cancer drugs [23] ) . While CDKN1B measurements were not available , the relevance of EGFR , ERBB3 , pAKT , and pERK as predictive biomarkers were evaluated by focusing on the eight PI3K/AKT/MTOR inhibitors and four MEK inhibitors in the GDSC database ( S6 Table ) . Within the HER2hi population ( the 67th percentile , corresponding to 22 intersecting cell lines ) , differential sensitivity ( IC50s ) to each of the agents was evaluated between the biomarker-high vs . low group , defined by median cuts and statistical threshold of P < 0 . 1 ( rank-sum test ) . Consistent with the logistic model , EGFR was the best single marker , identifying 1 of 8 PI3K/AKT/MTOR and 2 of 4 MEK inhibitors . While neither pAKT nor pERK expression predicted differential sensitivity to any of the drugs ( S6 Table ) , examining combinations of the biomarkers , comparing EGFRloERBB3hi ( PI3K-bias ) vs . EGFRhi ( MAPK-bias ) yielded 2 of 8 PI3K/AKT/MTOR and 2 of 4 MEK inhibitors as differentially sensitive between the two groups ( Fig 4A ) . Based on this EGFRloERBB3hi ( PI3K-bias ) vs . EGFRhi ( MAPK-bias ) classification scheme , thirteen drugs were found to display differential sensitivities between the groups ( Fig 4A ) . This includes the AKT inhibitor used in our studies ( MK2206 ) , and the MEK inhibitors CI1040 and RDEA119 . The PI3K-predicted subset was also increasingly sensitive to AZD8055 , an agent targeting MTOR , a canonical downstream effector of this pathway . Examining properties of the cell lines in each cohort , it is notable that the PI3K-predicted subset was relatively enriched in breast cancers ( 55% vs . 0% ) , and PI3K pathway mutations ( PIK3CA , PTEN , or PIK3R1; 64% vs . 44% ) as compared to the MAPK-predicted subset ( Fig 4B ) , consistent with the characteristics of our internal 18 cell line panel . It was however unclear as to why only a subset of the AKT and MEK inhibitors came up in this analysis . To explore the reason for this discrepancy , we examined patterns of sensitivity to the drugs across all 714 cell lines in the database by computing pair-wise Spearman correlation coefficients between their IC50 values . As depicted in the correlation matrix in S11 Fig , sensitivity to inhibitors of the same pathway , and even the same target across cell lines are often poorly correlated . For example , correlation coefficients between the AKT inhibitor we employed ( MK-2206 ) and the two other AKT inhibitors in the dataset ( AKT Inhibitor VIII and A-443654 ) are 0 . 22 and 0 . 08 . Correlations between sensitivity to MK-2206 and the four PI3K inhibitors vary between 0 . 23 ( AZD6482 ) and 0 . 55 ( GDC0941 ) , and the four MTOR inhibitors from 0 . 16 ( Rapamycin ) to 0 . 42 ( AZD8055 ) . Correlations between the four MEK inhibitors are significantly better ( 0 . 61 to 0 . 75 ) but still not nearly as tight as would be expected for inhibitors of the same target . These discrepancies may be attributable to different mechanisms of action , off-target specificities between the alternate inhibitors , or other technical issues . Regardless of the underlying reason , this could explain why our biomarker stratification scheme only identified a subset of the MEK and PI3K/AKT/MTOR inhibitors . We next sought to validate our predictions using functional genomics data . Here , we utilized two cancer cell line data repositories; mRNA expression profiles from the Cell Line Encyclopedia ( CCLE; [24] ) , and functional genomics data from Project Achilles , which catalogues vulnerabilities of cancer cell lines to shRNA or Cas9/sgRNA-mediated gene silencing [25] . We first assessed whether mRNA expression of the biomarkers could substitute for protein . EGFR , ERBB2 , and CDKN1B gene expression correlated well with protein levels across the 18 HER2+ cell line panel ( Spearman ρ = 0 . 84 , 0 . 67 , 0 . 77 ) but ERBB3 less so ( Spearman ρ = 0 . 50; S10 Fig , part A ) . The poorer correlation between ERBB3 mRNA transcript and protein may be attributable to the multiple feedback circuits regulating expression of this receptor [26] . Also consistent with protein expression patterns , CDKN1B transcript expression is positively correlated with ERBB3 and anti-correlated with EGFR ( Spearman ρ = 0 . 27 and -0 . 23 respectively; S10 Fig , part A ) . Using the CCLE mRNA expression data , we classified cell lines in the dataset as HER2+ and HER2- based on the 80th percentile of ERBB2 expression , and examined differences in sensitivities to gene knockdowns between cell lines based on expression of EGFR , ERBB3 , and CDKN1B mRNAs . Among the 43 HER2+ cell lines found in the Achilles portal , 11 were predicted to be PI3K-dependent ( EGFRloERBB3hiCDKN1Bhi ) and 9 to be MAPK-dependent ( EGFRhiERBB3loCDKN1Blo ) based on median cuts of the 3 biomarkers . Of the 5711 genes tested for growth dependence , 781 showed differential sensitivity between the two sets of cell lines ( P < 0 . 05 , rank-sum test ) . This is almost 3-fold more than expected by chance , suggesting real biological differences between the biomarker-defined subsets . The PI3K-predicted cells were significantly more sensitive towards silencing of three canonical PI3K/AKT signaling nodes , PIK3CA , AKT1 and MTOR . Within the MAPK-predicted set , while MEK1 ( MAP2K1 ) did not come up as a differentially sensitive target , the main MAPK effector ERK2 ( MAPK1 ) did . A full list of the genes and associated statistics is provided in S1 Data , both for the combined three biomarker results , and each biomarker in isolation . To evaluate the relative utility of this three-gene classifier , we performed the same analysis using PIK3CA mutation status as a predictor of PI3K pathway dependence . This is the most widely used clinical biomarker associated with the use of PI3K/AKT inhibitors , and thus could be considered the gold standard comparator , despite being a poor predictor of clinical activity in actuality [27] . PIK3CA-mutant cells are indeed significantly more sensitive to knock-down of PIK3CA itself , as well as MTOR within the HER2+ population . However , AKT1 dependence was not associated with PIK3CA mutants , nor was either of the two MAPK targets ( MAP2K1 and MAPK1 ) associated with the PIK3CA-wildtype cells ( Table 1 ) . Applying the same analyses to HER2- cells reveals that the relationship between expression of the three genes and pathway dependence is specific to HER2+ cancers; only AKT1 comes up as differentially sensitive target using the three biomarker combination , while PIK3CA , MTOR and MAPK1 do not , as in the HER2+ population . The predictive utility of PIK3CA mutations however appears independent of HER2 status , as the same target ( PIK3CA ) comes up in both HER2+ and HER2- groups . The identification of 4/5 canonical gene targets ( PIK3CA , AKT1 , MTOR , MAP2K1 , and MAPK1 ) as differentially sensitive using the three biomarker enrichment strategy in HER2+ cells is highly unlikely to be due to chance alone ( P = 1 . 5 × 10−3 , Hyper-Geometric test ) , much more so than the 2/5 targets uncovered using PIK3CA mutational status . The three-biomarker set thus appears a better differentiator of PI3K/AKT vs . MAPK/ERK pathway dependence in HER2+ cells as compared to commonly used genetic marker PIK3CA . To interrogate the molecular mechanisms underlying this relationship , we mapped the differentially sensitive genes on to the NCI pathway interaction database ( NCI-PID ) , a curated resource of cancer-associated signaling pathways [28] . To limit the network size and enrich for biologically meaningful components , we further filtered for genes with median differential effects ( ATARiS scores [29] ) > 0 . 75 and with at least one interaction annotated in the NCI database , and included EGFR , ERBB3 , CDKN1B , and MAP2K1 . The resulting network , consisting of 41 nodes and 56 edges , is represented in Fig 4C . Besides direct protein-protein interactions , an edge in the network could represent transcriptional and translational regulation , as well as a macroprocess whose internal composition is not included [28] . Many core components of PI3K/AKT signaling and downstream effectors are connected ( i . e . E1F4E , RHEB , FOXA1 , MAX , CCND1 , AKT2 , TSC1 ) in a giant component associated with PI3K-pathway dependence , while the MAPK-predicted genes are more diffuse , and cover diverse signaling pathways and mechanisms ( such as components of the WNT signaling pathway ) . The three biomarkers ( EGFR , ERBB3 , and CDKN1B ) are connected with each other , both directly and through the intermediary network hub AKT1 . These connections suggest that the three biomarkers are functionally linked to AKT and MAPK signaling , and their relative expression levels thus could induce differential dependence on the two signaling cascades . Together , these results support our initial finding that that EGFR , ERBB3 and CDKN1B expression predict differential dependence on PI3K/AKT and MAPK signaling in HER2+ cancer cells .
Our finding that many HER2+ cancer cell lines are dependent on MAPK signaling contrasts with canonical view of HER2 signaling predominantly through the PI3K pathway [4 , 5] . We believe this novelty is due to our profiling of a larger and more diverse panel of HER2+ cell lines than any previous study to our knowledge , and the fact that MEK inhibitors are typically not examined in these cells as a result of this established dogma . The finding is however not completely unprecedented; a recent study describing the construction of Boolean network models using proteomic data from HER2+ cell lines revealed that the cell lines varied in their intrinsic bias toward PI3K vs . MAPK signaling [30] . If results translate beyond in vitro cell culture , this finding has implications for the design of treatment strategies in HER2+ cancers , as multiple PI3K/AKT/MTOR inhibitors are being tested in HER2+ cancers , and MEK inhibitors are being tested in a variety of other tumor types [12] . Combined measurement of these three proteins in tumor biopsies could thus inform the use of PI3K/AKT or MEK inhibitor treatments . It is worth noting that mutations in any of these three genes may affect their predictive utility in this context , however this rarely occurs in HER2+ cancers ( less than 10% harbor mutations in EGFR , ERBB3 , or CDKN1B [31] ) . Our results predict that MAPK pathway-activating mutations ( such as KRASG12V ) may be genetic mechanisms of resistance to HER2-direted therapy in indications outside of breast cancer , with higher EGFR and lower ERBB3 and CDKN1B expression [32] . While clinical data supporting this prediction are lacking , mechanistic model simulations are consistent with the role of KRAS mutations as dominant mechanisms of resistance in MAPK-dependent HER2+ cancers [33] . As with all molecularly targeted agents , predictive biomarkers are needed to realize the utility of PI3K/AKT and MEK inhibitors . Our results highlight the difficulty in identifying such predictive markers , as the most intuitive protein ( pAKT and pERK ) , and genetic ( PIK3CA ) candidates turned out to be largely uninformative and surpassed by a fairly non-intuitive multivariate classifier . These findings are consistent with clinical experience to date with PI3K/AKT/MTOR inhibitors [34] and MEK inhibitors [12] , in that mechanistically intuitive genetic markers have proven poor predictors of activity . Results from large cell line-based functional genomics projects [35 , 36] more broadly support this finding . Despite substantial efforts to find robust genetic predictors of drug sensitivity , these have proven largely disappointing [37] . We believe the root of this challenge lies in two related sources . First , cellular dependence on a given signaling pathway may arise through multiple mechanisms , such as expression patterns of regulatory ligands , receptors and downstream effectors , in addition to mutations in core signaling nodes . Predictors of sensitivity to pathway targeted inhibitors are thus expected to be necessarily multivariate , and often non-genetic , which would favor the use of proteomic technologies for predictive biomarker discovery [38] . Second , the very properties of oncogenic signaling networks that confer robustness to therapeutic intervention ( adaptive feedback circuits and redundancies ) also obscure predictors of responsiveness to such interventions . In addition to our data , recent functional proteomic studies support these conclusions . Phospho-kinase expression has been proven a poor predictor of cellular sensitivity to inhibitors targeting those kinases and the cascades in which they are embedded , including pAKT and pERK in relation to PI3K/AKT and MEK inhibitors [20 , 39] . Similarly in line with our results , PI3K inhibitor sensitivity across a panel of breast cancer cell lines was predicted by responsiveness to the ERBB3 ligand heregulin much better than by pAKT expression level or PIK3CA mutations [19] . We speculate that differential HER2-heterodoimerization accounts for the association between ERBB3 vs . EGFR receptor expression and PI3K vs . MAPK pathway dependence . The EGFR cytoplasmic domain contains multiple binding sites for the adaptor proteins Growth-factor-Receptor-Bound 2 ( GRB2 ) and Src-homology-2-containing ( SHC ) which activate the MAPK cascade , while ERBB3 has six binding sites for PI3K and only one SHC site [40] . Competition for binding to HER2 between the receptors could thus shift receptor complex formation to favor one pathway over the other . CDKN1B ( p27 ) is likely a functional surrogate of pathway activity , rather than a causal regulator . As a negative regulator of cell cycle progression , its expression level may be indicative of the intensity of pro-proliferative MAPK signal flux . These results also demonstrate that the functional effect of an oncogene can be context-dependent . In this case , ERBB2-amplification can result in either PI3K or MAPK-signaling-dependent cell growth , depending on molecular context . Consistent with the “ERBB network theory” [14 , 41] , signal output depends upon the composition of surface receptors and presence of extracellular ligands . Whether such context-dependent signaling effects are confined to the ErbB-family , or shared by other oncogenes is an open question . It is however clear that cancers harboring the same oncogenic driver can respond very differently to targeted inhibitors based on their tissue of origin , the most notable example being vemurafenib responses in BRAFV600E-mutant melanoma vs . colorectal cancers [42] . Context-dependent signaling differences may play a role in such cases . The finding that PI3K vs . MAPK pathway dependence is not genetically hardwired into cells , and can be affected by exposure to the ligand heregulin was somewhat unexpected . However , there is precedent for observations of cellular plasticity with respect to reliance on oncogenic signals . Growth factor stimulation is known to mediate resistance to many kinase inhibitors through the activation of alternate but functionally redundant pathways [43 , 44] . PI3K/AKT inhibitors and MEK inhibitors themselves can also induce compensatory signaling though alternative pathways via the relief of negative feedback regulatory controls on cell surface receptors [16 , 45–47] . Many cancer cells are thus endowed with the capacity for using alternate pathways in response to environmental changes . While we have focused solely on the role of the ERBB3 ligand heregulin , other growth factors and cytokines may have similar effects . This is important to consider when interpreting biomarker-response relationships . If both molecular profiles and drug response patterns are fluid , such relationships would be amenable to shift under different experimental [48] , and possibly pathophysiological conditions . In light of all the aforementioned challenges , it is perhaps unsurprising that despite all the resources and efforts committed date , a very limited number of predictive biomarkers have proven clinical utility [49] . Though counter-intuitive , strong relationships between inhibitor sensitivity and target expression appear to be the exception rather than the norm . We were able to support our initial findings from the 18-cell line panel using both chemical genomic and functional genomic data from independent sources . Besides expected hits in the PI3K/AKT and MAPK/ERK signaling pathways , our three biomarker stratification scheme revealed differential sensitivity towards additional small molecules and shRNAs . It is likely that some of these are false positives . However , there appear to be mechanistic connections between the targets of these compounds and shRNAs , the three biomarkers , and PI3K/AKT and MAPK signaling . For example as shown in Fig 4A , cells predicted to be PI3K or MAPK dependent are also differentially sensitive towards an AMPK inhibitor ( AICAR ) , consistent with known biological functions of PI3K/AKT signaling in metabolism [50] . In addition , histone deacetylase ( HDAC ) inhibitors are known to mediate at least part of their effects through suppression of PI3K/AKT signaling [51] . Elesclomol induces apoptosis through disruption of mitochondria metabolism and in cancer cells upregulates AKT signaling to promote survival [52] . BMS708163 targets presenillin1 as a NOTCH-sparing gamma-secretase inhibitor [53] and regulates 4ICD release upon NRG1 binding to ERBB4 [54] . While the exact mechanistic connections between these small molecules and EGFR/ERBB3 expression are unclear , these results are likely to be biological meaningful and not merely random . In addition , we performed a GO enrichment analysis [55 , 56]on biological processes for the genes from the Achilles’ analysis using genes tested in this dataset as background . We found that the shRNA targets are enriched for regulators of mRNA transcription , mRNA transport , translation , and mitotic progression . Consistently , the small molecules that we found in addition to direct inhibitors of PI3K/AKT/MTOR and MEK target components of the transcriptional , translational and cell cycle machinery . For example , vorinostat is known to cause abnormal mitosis through inhibition of HDAC [57] . Vinblastine has been shown to block mitosis through inhibiting microtubule dynamics indirectly or directly [58] . We also observed differential proliferation rates between AKT and MAPK-dependent cell lines experimentally ( Fig 1A ) , consistent with differential sensitivity to knockdowns of cell cycle regulators . These results suggest that the hits from both analyses are likely to be mechanistically connected and biologically meaningful . In summary , we have demonstrated that PI3K vs . MAPK pathway dependence varies across HER2+ cancer cells . This dependence varies by indication , and can be predicted using a set of three non-intuitive protein measurements . These results might help stratify HER2+ patients for treatment with targeted therapeutics . More generally , our findings reveal that oncogenic signaling can be context dependent . A single genetic transformation , in this case ERBB2 amplification , can have differing effects on cell signaling and growth , contingent upon on the molecular and cellular background . Together , we believe that our results and approach will enable the design of more effective cancer treatment strategies for HER2+ cancer patients .
AU565 , HCC1419 , NCI-H2170 , HCC202 , HCC1954 , NCI-N87 , ZR75-1 , SKOV3 , ZR75-30 , MDAMB175VII , CALU3 , MDAMB453 , MDAMB361 , JIMT1 , SKBR3 and HCC2218 cells were obtained from ATCC . OE19 and OE33 were obtained from ECCC; COLO-678 was obtained from DSMZ , and KYSE-410 from Sigma-Aldrich . BT-474-M3 cells ( hereafter simply referred to as BT-474 ) were obtained from Hermes biosciences . All cell lines were maintained in RPMI supplemented with 10% FBS , penicillin , and streptomycin . GSK-1120212 and MK-2206 were purchased from Selleckchem . Recombinant human HRG-β1 ( EGF domain ) was from R&D Systems . Cells were seeded at 600 cells per 384-well plate in 4% FBS cell growth medium , stimulated ( or not ) with 2 nM HRG-b1 for 4 hours , and then treated with individual or combinations of the AKT and MEK inhibitors . Treatments consisted of 5x6 dose combination matrices covering a 3-fold dilution series from 1 μM ( MK-2206 ) and 10μM ( GSK-1120212 ) . Cell confluency was then monitored over 5 days in culture by video microscopy ( IncuCyte , Essen BioScience ) , and data normalized to density measured at initiation of treatment ( S1 Data ) . Cell lines were seeded at 7 , 500 cells per well in 384-well culture plates in RPMI containing 4% FBS . 48-hour post plating , cells were stimulated ( or not ) with 2 nM HRG-β1 for four hours . At harvest , cells were placed on ice , and 70 μl RIPA lysis buffer ( Sigma-Aldrich ) supplemented protease inhibitor and phosphatase inhibitor tablets ( Roche ) was added to each well . The plates were stored at -80°C until analysis . On the first day of protein profiling , the lysates were thawed at 4°C and centrifuged at 4000 rpm for 10 minutes . The supernatant was used for further analysis with multiplex Luminex protein assays as described below . Twenty micrograms of antibodies was conjugated to 100 μl ( ~1 . 25×106 beads ) of MagPlex beads ( Luminex Corp . ) according to the manufacturer’s instructions . Conjugated beads were then mixed and diluted 1000-fold in phosphate buffered saline ( PBS ) –1% bovine serum albumin ( BSA ) ( Sigma ) . Diluted beads were transferred into 384-well assay plates ( Corning ) at 30 μl per well and then washed three times with PBS–1% BSA . Washed beads were incubated with 20 μl of total protein lysates overnight with shaking at 4°C . The beads were then washed with PBS–1% BSA . Detection antibodies ( see S3 Table ) were added and incubated at 4°C overnight with shaking . After washing with PBS–1% BSA , streptavidin-conjugated phycoerythrin ( Invitrogen ) was added at 2 μg/ml and incubated at room temperature for 30 min . Finally , the beads were washed with PBS–1% BSA , and data were acquired with a FlexMap3D instrument ( Luminex Corp . ) according to the manufacturer’s instructions . Raw signals were normalized by background subtraction to signals from control lysates prepared from non-human cells . Antibodies are listed in S3 Table , and background-subtracted data is provided in S1 Data . Observed changes in cell density over time are determined by the balance of cell proliferation vs . death within the culture . Both cell proliferation and survival are regulated by PI3K/AKT and MAPK/ERK signaling cascades , which assuming an exponential growth can be expressed as: dXdt=μMAX⋅f1 ( pAKT , pERK ) −δMAX⋅f2 ( pAKT , pERK ) Where X = number of cells ( assumed proportional to surface area ) , μMAX = maximum rate of proliferation , δMAX = maximal rate of cell death , and f1 and f2 are functions integrating pAKT and pERK signaling . We implemented a quantitative logic-based formalism [59] to describe changes in cell density as function of PI3K/AKT and MAPK/ERK pathway activation . AKT and MEK inhibitor concentrations ( μM ) were used as surrogates for pathway activities , assuming monotonic dose-response relationships . As the logic by which cells integrate and interpret these signals remains obscure , we initially assessed 9 alternate growth regulatory functions combining null ( K ) , OR , and AND-type logic gates as proliferation and survival functions ( f1 and f2 ) : K=1 OR= ( wakt⋅AKT+werk⋅ERK ) kτ+ ( wakt⋅AKT+werk⋅ERK ) k AND= ( AKTk_aktτakt+AKTk_akt ) ⋅ ( ERKk_erkτerk+ERKk_erk ) Parameters for each of the 9 models ( S1 Table ) were estimated for each cell line using a Particle Swarm Optimization algorithm [60] minimizing the mean squared error between experimental measurements ( fold cell expansion over 96 hours ) and model simulations . Relative model performance was assessed using the Akakie Information Criterion ( AIC ) : AIC=2⋅P+N⋅log10 ( MSE ) Where P = number of parameters ( 2–10 ) , N = number of experimental measurements ( 30 ) , and MSE = mean squared error . The fourth model structure assessed ( M4 ) , consisting of an OR-Gate regulating cell survival , was found to be optimal ( lowest AIC ) for the largest number of cell lines tested . The final formulation of the cell growth regulatory model used in subsequent analyses was thus: dXdt=μMAX−δMAX ( ( wakt⋅AKTi+werk⋅MEKi ) kτ+ ( wakt⋅AKTi+werk⋅MEKi ) k ) Pathway Bias was then defined as the normalize differential between the parameters wakt and werk: Bias= ( wakt−werk ) ( wakt+werk ) Based on our finding that PI3K/AKT dependence correlated with the cell death rate ( δMAX ) , and MAPK-dependence with proliferation ( μMAX ) , we created a tenth model ( M10 ) which separates the regulatory terms accordingly: dXdt=μMAX ( 1-MEKik_erkτerk+MEKik_erk ) −δMAX ( AKTik_aktτakt+AKTik_akt ) The raw cell growth data , model parameters associated with each of the ten models ( M1-M10 ) , goodness-of-fit metrics ( MSE and AIC ) and simulations are provided in S1 Data . Parameter estimates across alternate models are quite consistent , indicating our results are robust regardless of the model chosen . The Pathway Bias measurement for each cell was first discretized into MAPK vs . PI3K-dependence ( Bias = -1 vs . +1 ) , a reasonable simplification given the observed bimodal distribution of this metric . The probability of MAPK-dependence ( PMAPK ) vs . PI3K-dependence ( PPI3K = 1 –PMAPK ) was then modelled as a function of input features using a logistic regression equation: ln ( PMAPKPPI3K ) =β0+∑i=1Nβi⋅Xi Where N = number of features ( Xi ) and βi = regression coefficients . The βi parameters were estimated by maximum likelihood estimation , and predictive power of the model assessed using leave-one-out cross validation ( LOOCV ) procedure . Model-predicted Bias was then back-calculated using the probabilities as: Predicted Bias=−1⋅PMAPK+1⋅PPI3K Statistical significance of model predictions was assessed by computing LOOCV accuracy , Pearson correlations , and mean squared error ( MSE ) from 10 , 000 randomized permutations of the cellular properties: Bias mapping . RNAseq was downloaded from the GDAC Firehose portal in June , 2014 ( http://gdac . broadinstitute . org/ ) . HER2+/- classifications were based on ERBB2 expression . Using BRCA , LUAD , and OV samples as controls , setting ERBB2 RNAseq count thresholds at 14 , 000 resulted in HER2+ frequencies consistent with known ERBB2-amplification frequencies of 13% , 2 . 5% , and 1 . 5% . This threshold was then applied across all indications , though results were insensitive to the specific value chosen . mRNA expression data was downloaded from CCLE ( www . broadinstitute . org/ccle/home ) and gene knockdown sensitivity from Project Achilles ( www . broadinstitute . org/achilles ) . Signaling networks were defined in NCI-PID ( http://pid . nci . nih . gov/index . shtml ) and accessed via the Pathway Commons portal ( www . pathwaycommons . org ) , and visualized using Cytoscape ( www . cytoscape . org ) . All analysis and simulations were carried out in MATLAB R2013b .
|
Biomarkers capable of accurately predicting patient responses to alternate therapies are critical to realizing the vision of precision medicine . Identifying such biomarkers is , however , challenging due to the inherent complexity of biological networks . Here we sought to identify molecular features that predict how a genetically defined subset of cancers ( HER2+ ) differentially depend on two oncogenic signaling pathways , the PI3K/AKT and MAPK/ERK cascades . We find that combined measurement of three non-intuitive proteins ( EGFR , ERBB3 , and CDKN1B ) accurately predicts cellular dependence on these signaling pathways , and responsiveness to drugs targeting their constituents . Notably , this three-biomarker model outperformed both biological intuition ( phosho-AKT and phospho-ERK ) and current clinical practice ( PIK3CA mutations ) . More broadly , this exemplifies how the functional consequences of a single oncogenic driver ( HER2 ) can depend upon molecular and cellular context . Expression of these markers also varies by indication , such that breast cancers are biased toward PI3K-dependnece , while non-breast indications ( lung , ovarian , and gastric ) are particularly MAPK-dependent , and thus may respond differently to therapeutic strategies developed for breast cancer . Together , we believe that our results will aid the design of novel , stratified treatment strategies for HER2+ disease .
|
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2016
|
HER2+ Cancer Cell Dependence on PI3K vs. MAPK Signaling Axes Is Determined by Expression of EGFR, ERBB3 and CDKN1B
|
The transition from vegetative to reproductive growth is a critical process in the life cycle of higher plants . Previously , we cloned Rice Indeterminate 1 ( RID1 ) , which acts as the master switch for the transition from the vegetative to reproductive phase in rice . Although the photoperiod pathway of RID1 inducing expression of the florigen genes Hd3a and RFT1 via Ehd1 has been established , the alternative pathways for the essential flowering transition need to be further examined . Here , we identified a Suppressor of rid1 ( SID1 ) , which rescues the never-flowering phenotype of rid1 . SID1 encodes an INDETERMINATE DOMAIN ( IDD ) transcription factor . Mutation in SID1 showed the delayed flowering phenotype . Gain-of-function of SID1 , OsIDD1 , or OsIDD6 could restore the rid1 to flowering . Further analyses showed SID1 and RID1 directly target the promoter regions of Hd3a and RFT1 , two florigen genes in rice . Taken together , our results reveal an autonomous flowering pathway might be mediated by RID1 , thereby controlling the phase transition from vegetative to reproductive development in rice .
The post-embryonic development of flowering plants can be divided into two major phases: the vegetative and reproductive growth stages . During vegetative development , shoot apical meristems continue to produce leaves for the generation of organic materials through photosynthesis . After a given number of leaves are generated , endogenous genetic factors and environmental signals control the time of flowering [1] . Molecular regulatory networks that monitor the changes in the environment and complex endogenous signals determine the timing of the developmental transition [2–4] . Great progress has been made in elucidating the molecular basis for the flowering transition in Arabidopsis , which represents a long-day ( LD ) plant [5–10] . Numerous genes were identified and integrated into six major pathways: the photoperiod , vernalization , age , autonomous , gibberellin , and ambient temperature pathways [11] . Rice is not only a leading cereal crop in the world , but also a representative short-day ( SD ) plant for flowering time ( heading date ) studies . As an important agronomic trait , heading date is crucial for determining the regional adaptability and grain yields [12–14] . The molecular mechanisms for flowering time control have been well studied in Arabidopsis . However , studies of heading date control in rice have almost exclusively focused on the photoperiodic pathway [15] . Although rice is regarded as a SD plant , it also has evolved its flowering pathway to induce flowering under LD conditions during artificial domestication at high latitudes [16–20] . Thus , photoperiodic flowering in rice can be artificially considered as two distinct pathways: the evolutionarily conserved OsGI-Hd1-Hd3a pathway for adaption under SD conditions , which is parallel to the GI-CO-FT module in Arabidopsis [21 , 22] , and the uniquely evolved Ghd7-Ehd1-Hd3a/RFT1 pathway for adaptation under LD conditions [12 , 13 , 15 , 23] . To understand the photoperiodic control of flowering in rice more comprehensively , recent investigations have identified some flowering mutants that are insensitive to photoperiod variations . Mutants with RID1/OsID1/Ehd2 , a rice ortholog of the maize INDETERMINATE1 ( ID1 ) gene , showed a late- or never-flowering phenotype under SD or LD conditions [24–26] , indicating that RID1 might function as an autonomous factor to induce the floral transition in rice [26] . Ehd3 encodes a plant homeodomain finger-containing protein [27] . Mutation in Ehd3 results in no flowering under LD conditions , suggesting that Ehd3 acts as a flowering inducer in the unique genetic pathway Ehd3-Ghd7-Ehd1 in rice [27] . In addition , Ehd4 , encoding a novel CCCH-type zinc finger protein , was identified as a critical regulator promoting flowering under both SD and LD conditions [16] . ehd4 also showed a never-flowering phenotype under LD conditions [16] . All these flowering switches ( RID1 , Ehd3 , Ehd4 ) thus far identified in rice and have no direct homologs in Arabidopsis [16 , 26 , 27] . Thus , it appears that RID1 , Ehd3 , and Ehd4 may participate in a rice-specific flowering transition pathway , the underlying molecular mechanisms of which are still not well understood . RID1/OsID1/Ehd2 encodes a highly conserved zinc finger protein in plants [24–26] . The zinc fingers and its surrounding sequence compose a so-called INDETERMINATE DOMAIN ( IDD ) , which was identified in all higher plant genomes [28] . Maize ID1 is the founding member of the IDD family and controls the transition to flowering in maize [29] . In vitro DNA binding experiments showed that ID1 binds selectively to an 11-bp DNA sequence with the consensus motif TTTGTCG/CT/CT/aT/aT via the IDD [30] . Sixteen and fifteen IDD members were identified in the genomes of Arabidopsis and rice , respectively [28] . Previous studies of IDD members in Arabidopsis revealed that IDD genes participate in multiple developmental processes . AtIDD8 is involved in photoperiodic flowering by modulating sugar transport and metabolism [31] . AtIDD8 , AtIDD3 , and AtIDD10 , either physically or genetically interact with the GRAS domain proteins SHR and SCR to regulate root development or patterning [32–34] . AtIDD1 is required for seed maturation and germination [35] . AtIDD14 , AtIDD15 , and AtIDD16 play a critical role in lateral organ morphogenesis and gravitropism by regulating spatial auxin accumulation [36] . Recent investigations showed that some IDD members ( AtIDD2 , AtIDD3 , AtIDD4 , AtIDD5 , AtIDD9 , and AtIDD10 ) interact with DELLAs to control gibberellin homeostasis and signaling and modulate flowering time in Arabidopsis [37 , 38] . RID1 is the only IDD member being functionally analyzed in rice . RID1 and its putative orthologs , ID1 in maize and SbID in Sorghum , are preferentially expressed in immature leaves and may exhibit conserved function for flowering transition [26 , 28 , 29] . In maize , ID1 is a key regulator of the transition from vegetative to reproductive growth [29 , 39] . The id1 mutant has prolonged vegetative growth and retains vegetative features in the inflorescence [29] . The ID1 gene was proposed to regulate the production or transmission of a mobile florigenic signal [29 , 40] . Transcript and metabolite profiles indicated that expression levels of major sucrose and starch metabolism genes were altered in the id1 mutant , suggesting that ID1 might be involved in the starch to sucrose transition and sucrose utilization within the leaf [39] . However , similar changes in transitory starch and sucrose are not observed in the photoperiodic flowering plants [39] . Thus , it appears that ID1 is likely engaged in a novel autonomous flowering pathway that is distinct from the photoperiod induction pathway [39 , 41] . Our previous study showed that RID1 acts as a master switch of the flowering transition in rice [26] . Loss of function of RID1 seriously suppressed the expression of Ehd1 and florigen genes Hd3a and RFT1 , suggesting that RID1 plays important roles in photoperiodic flowering promotion in rice [26] . At present , the direct target of RID1 and whether RID1 controls an autonomous flowering pathway in rice remain unclear . In this study , a gain-of-function mutant suppressor of rid1-D ( sid1-D ) was identified . sid1-D restored the rid1 mutant to flowering successfully . SID1 belongs to the IDD family in rice . Loss-of-function mutants of SID1 exhibit late flowering under LD conditions . Moreover , our results show that RID1 and SID1 directly regulate the expression of Hd3a and RFT1 , two florigen genes in rice . Our results indicate that the original function of RID1 might trigger the expression of florigen genes , thus controlling the flowering transition in rice .
Our previous study identified a RID1 knockout mutant ( rid1 ) , which shows a never-flowering phenotype under LD or SD conditions [26] . Further examination showed that T-DNA insertion at the second intron of the RID1 gene caused the never-flowering phenotype , and a 5 . 7-kb genomic fragment containing the entire RID1 coding region and its promoter could successfully rescue the mutant phenotype [26] . To examine whether the full-length cDNA of RID1 could rescue the mutant phenotype , we generated genetic complementary plants transformed with construct ( pUBQ::RID1 ) harboring the RID1 cDNA fragment driven by the Ubiquitin promoter . Among 80 independent transgenic plants , we analyzed 7 positive transgenic plants with the restored normal flowering phenotype . Surprisingly , we discovered one line ( #4 ) in which the transcript of RID1 was undetectable but exhibited a restored flowering ( Fig 1A ) . Its progeny of 200 plants exhibited a phenotypic segregation of flowering to never-flowering of 3:1 ( 143:57 , χ2 = 1 . 13 , P < 0 . 05 ) , in which all flowering plants contained the selection marker Kanamycin gene . This observation suggests that the restored flowering plant results from a dominant mutation of a single gene that is likely to co-segregate with a T-DNA insertion event . Thus , we designated this mutant as suppressor of rid1-D ( sid1-D ) ( Fig 1B ) . Next , we investigated the heading date of sid1-D compared to wild-type plants . Under natural-long-day ( NLD ) conditions during the growing season at Wuhan , China , the heading date of sid1-D was delayed 10 days compared with the wild type . In the growth control room , the heading date of sid1-D ( 72 . 4 ± 1 . 1 days for SD; 101 . 1 ± 1 . 4 days for LD ) was delayed about 2 weeks compared with the wild type under SD or LD conditions ( Fig 1C ) . Furthermore , sid1-D exhibited a similar leaf emergence rate as that of rid1 under both SD and LD conditions ( S1A Fig ) . The heterozygotes and homozygous sid1-D exhibited an indistinguishable heading date under distinct day length conditions ( S1B Fig ) . These results indicate that sid1-D is a dominant mutant that partially rescued the never-flowering phenotype of rid1 . Because sid1-D is generated by a single gene mutation and co-segregates with a T-DNA insertion , the genomic sequence flanking the left border of the T-DNA insertion site was isolated by thermal asymmetric interlaced PCR [42] . BLAST analysis of the flanking sequence indicated that a T-DNA was inserted into the intergenic region between the annotated genes LOC_Os02g45054 ( OsIDD4 ) and LOC_Os02g45040 ( Fig 2A ) . PCR analysis using the primers P1 , P2 , and P3 [26] indicated that the genomic background is homozygous for rid1 ( S2A Fig ) . We determined the genotypes of the re-introduced T-DNA insertion site by PCR amplification using the primers P4 , P5 , and P6 ( Fig 2A and S2A Fig ) . All the plants homozygous or heterozygous for the T-DNA insertion showed the restored heading phenotype , whereas the plants without T-DNA insertion showed the never-flowering phenotype , like that of rid1 . Our further analysis of the T-DNA sequence integrated into the genome indicated that a truncated T-DNA insertion event occurred . The truncated T-DNA only remained in the left region containing the selection marker Kanamycin driven by the CaMV 35S promoter and a sequence of the 3' untranslated region of RID1 ( 162 bp ) ( Fig 2A ) . Because sid1-D is a dominant mutant with re-introduced T-DNA inserted in the intergenic region , we examined the expression levels of genes flanking the T-DNA insertion . Quantitative reverse transcription PCR ( QRT-PCR ) analysis indicated that the transcript of LOC_Os02g45054 ( OsIDD4 ) was significantly increased , while the other genes showed identical expression patterns in sid1-D and rid1 ( Fig 2B ) . To determine whether the elevated transcript level of OsIDD4 is responsible for the rescued flowering in sid1-D , we introduced a pUBQ::OsIDD4 construct ( S2B Fig ) into rid1 callus . All the transgenic plants overexpressing OsIDD4 ( Fig 2C ) recovered the flowering of sid1-D ( Fig 2D ) , whereas plants transformed with empty vector ( negative control ) retained a never-flowering phenotype similar to that of rid1 ( S2C Fig ) . In the progenies of the rescued flowering plants , segregation of the OsIDD4 transgene coincided very well with successful flowering , whereas the negative transgenic plants did not head , similar to rid1 ( S2C and S2D Fig ) . These results suggest that increased expression of OsIDD4 is responsible for reversing the never-flowering phenotype of rid1 . Thus , OsIDD4 is the Suppressor of rid1 ( SID1 ) . In rice , SID1 encodes a typical Cys-2/His-2 ( C2H2 ) zinc finger protein belonging to the plant-specific IDD protein family , comprising 15 members in rice [28] . Phylogenetic analysis showed that SID1 belongs to a different clade than that of RID1 ( S3A Fig ) . However , SID1 shares 43% identity with RID1; in particular , they have a highly conserved IDD at the N-terminal region ( S3B Fig ) . The conserved IDD in SID1 contains four putative zinc finger domains ( S3B Fig ) . To investigate whether the zinc fingers of SID1 are essential to complement rid1 , we generated four constructs via ectopic expression of SID1 with mutation in each zinc finger in rid1 ( S4A Fig ) . A normal SID1 CDs overexpression construct was used as a positive control . For each transformation , at least 100 independent transgenic plants were generated . The transgenic results showed that mutating each zinc finger of SID1 abolished rescuing the never-flowering phenotype of rid1 , whereas plants transformed with normal SID1 CDs overexpression construct recapitulated the phenotype of sid1-D ( S4B Fig ) . Our results showed that the four zinc fingers of SID1 are required to restore the flowering transition in rid1 . Phylogenetic analysis showed there are 15 identifiable IDD genes in rice ( S3A Fig ) . To investigate the possible redundancy of the OsIDD genes with SID1 , we generated transgenic plants overexpressing the OsIDD genes OsIDD1 , OsIDD3 , OsIDD6 , OsIDD10 , OsIDD12 , and OsIDD14 , respectively . At least 100 independent transgenic plants of each transformation were generated . The overexpression of OsIDD1 or OsIDD6 in rid1 recapitulated the phenotype of sid1-D plants , showing restored flowering of rid1 ( Fig 3 ) . However , plants transformed with other OsIDD genes retained a never-flowering phenotype similar to rid1 . These results suggested that OsIDD1 and OsIDD6 might have redundant function in floral transition with SID1 when they were overexpressed . To determine the spatial expression profile of SID1 , we examined the expression level of SID1 in various tissues by qRT-PCR at seedling stage ( S5A Fig ) . The analysis showed that SID1 was preferentially expressed in vegetative tissues ( Fig 4A ) . We also made a construct pSID1::GUS and generated transgenic plants to precisely examine SID1 expression patterns . GUS staining was detected in mature leaves , young leaves , sheath , and root tips and was most abundant in mature leaves and young leaves ( Fig 4B to 4H ) . To examine SID1 expression during the vegetative stage , we harvested young and expanding leaves from wild-type plants every 5 days from day 15 until floral transition . SID1 showed an expression pattern similar to that of RID1 , with continual expression in all examined points of young leaves ( S5B Fig ) . In expanding leaves , the transcription level of SID1 was higher than that of RID1 in less than 30-day-old seedling , and then they decreased gradually during the remaining vegetative stage ( S5C Fig ) . Like RID1 , the expression of SID1 did not show a diurnal expression pattern under either SD or LD conditions ( S5D Fig ) . This expression of SID1 and RID1 in leaf blades indicated that their roles in flowering control for reproductive transition in rice . Considering that SID1 encodes a C2H2-type zinc finger transcription factor , we also assayed the subcellular localization of SID1 . The construct 35S::SID1::GFP was transiently transformed into rice protoplasts . The SID1-GFP exclusively co-localized with the Ghd7-CFP fusion protein ( an established nuclear marker; [43] ) ( Fig 4I ) , indicating that SID1 is localized in the nucleus . We further examined the transcriptional activity of SID1 in rice protoplasts using a dual luciferase reporter ( DLR ) assay system . All fragments of SID1 , especially its N terminus ( amino acids 1-251aa ) enhanced the relative luciferase activity compared with the GAL4 binding domain negative control ( Fig 4J ) . These results suggest that SID1 is a nuclear protein showing transcription activation activity . To examine the function of SID1 in rice , we generated sid1 mutants using the CRISPR-Cas9 system [44] . The construct containing the Cas9 and sgRNA targeting the first exon of SID1 was designed , and 97 transgenic plants were generated ( Fig 5A ) . PCR amplification products containing the target region were digested by CELI enzyme to detect potential mutations ( Fig 5B ) . Confirmation of the mutations by sequencing showed that the target region had small deletions of 1–7 bp and three mutant lines were used for further analysis ( Fig 5C ) . T1 family of 40 homozygotes for each sid1 plants ( D1 , D5 , D7 ) presented a small but statistically significant ( P < 0 . 05 ) delayed flowering in NLD ( 82 . 1±1 . 4 days for D1; 81 . 6±1 . 4 days for D5; 81 . 5±1 . 2 days for D7 ) compared to the wild type ( 79 . 2±1 . 3 days ) ( Fig 5D and 5E ) . These results suggest that mutation of SID1 results in late heading . A previous study reported that the expression of Ehd1 , Hd3a , and RFT1 were suppressed in rid1 under both SD and LD conditions [26] . Therefore , we performed qRT-PCR analysis to detect the expression levels of Ehd1 , Hd3a , RFT1 , and Hd1 in sid1 under SD and LD conditions ( Fig 5F ) . Hd1 showed an almost identical expression level in sid1 and the wild type under both conditions , suggesting that SID1 had no effect on the expression of Hd1 . In sid1 , the transcript levels of Ehd1 were partially reduced under LD conditions . Transcript levels of Hd3a and RFT1 were largely reduced in the sid1 mutants under both conditions . These results suggest that SID1 might be involved in flowering regulation through modulation of the expression of Ehd1 , Hd3a , and RFT1 . To investigate the possible regulation of Hd3a and RFT1 by SID1 and RID1 , we generated transgenic plants overexpressing SID1 or RID1 , respectively . The independent transgenic plants overexpressing SID1 showed a similar heading date as that in wild type ( S6A to S6C Fig ) . Similarly , no significant changes in heading date were detected between plants with enhanced RID1 expression and wild-type plants ( S6D to S6F Fig ) , although the transcript levels of Hd3a and RFT1 were slightly increased in some of overexpressing plants with SID1 or RID1 , respectively ( S6G to S6J Fig ) . These findings indicate that overexpression of SID1 or RID1 would not result in early flowering in rice . Because overexpression of SID1 rescued the failed flowering transition in rid1 , we wondered whether overexpression of SID1 would recover flowering pathways activated by RID1 . As shown in Fig 6 , the transcript levels of Hd3a , RFT1 , and Ehd1 were completely suppressed in rid1 under either LD or SD conditions , which were the same results as in our previous investigation [26] . However , in sid1-D background , with the overexpression of SID1 , the expression of Hd3a , RFT1 , and Ehd1 were elevated and diurnal under both SDs and LDs at all-time points examined during the 24 h period ( Fig 6 ) . Because RID1 had only a slight effect on the expression of Hd1 [26] , Hd1 showed identical expression patterns in rid1 and sid1-D under both conditions ( Fig 6 ) . Thus , overexpression of SID1 might take over the role of initiating the flowering transition in RID1-dependent photoperiodic flowering pathways in sid1-D . Because SID1 has a highly conserved IDD belonging to members of the plant-specific zinc finger protein family , we speculated that they might exhibit the same DNA binding characteristic . Previous experiments demonstrated maize ID1 selectively binds to the consensus motifs TTTGTCG/CT/CT/aT/aT and TTTTGTCG/C by IDD in vitro , but the consensus motifs without T in the 5' position did not affect the binding affinity of ID1 [30] . To identify possible targets of the SID1 , we surveyed the consensus motifs in the promoter regions of genes controlling flowering time in rice . As shown in Fig 7A , a core sequence containing TTTGTC was found at –2191 ( I region ) and –1923 ( II region ) in the promoter regions of Hd3a and RFT1 , respectively . To examine whether SID1 could directly bind to these fragments , we performed electrophoresis mobility shift assays ( EMSA ) to assess the potential binding ability in vitro . The recombinant SID1 protein was able to bind to the fragments containing the consensus motif TTTGTC in the promoter regions of Hd3a or RFT1 , respectively ( Fig 7B ) . However , the negative controls with mutation in TTTGTC ( bio-Hd3a-M or bio-RFT1-M ) abolished these binding evens ( Fig 7B ) , indicating that SID1 could specially bind to the TTTGTC motif . The results suggest that SID1 might have the ability to drive the expression of Hd3a and RFT1 . We further examined the transcriptional activity of SID1 using a DLR assay system in rid1 protoplasts . Hd3a or RFT1 promoter driving the firefly luciferase gene was used as reporter and transfected into protoplasts of rid1 , respectively . The construct harboring the SID1 gene driven by CaMV 35S promoter was used as the effector . With the increasing effector construct containing the SID1 gene , the luciferase activity was gradually enhanced ( Fig 7C and 7D ) . This result further confirms that a considerable amount of SID1 has the transcriptional activation ability to drive the expression of Hd3a and RFT1 when RID1 was abolished . RID1 is also a member of the IDD family in rice , and QRT-PCR results demonstrated that transcription of Hd3a and RFT1 were seriously reduced in the rid1 mutant [26] . Likewise , we performed EMSA to test the potential interactions between RID1 and the promoters of Hd3a and RFT1 . EMSA competition experiments demonstrated that the recombinant RID1 protein could bind to the fragments containing the consensus motif TTTGTC ( Fig 8A ) . The binding activities of RID1 were abolished when the consensus motif was mutated to TTAATC ( bio-Hd3a-M or bio-RFT1-M ) , indicating RID1 could specially bind to the fragments containing the consensus motif TTTGTC in the promoter regions of Hd3a or RFT1 , respectively ( Fig 8A ) . Next , we generated the construct ProRID1::RID1:FLAG:HA and introduced it into the rid1 mutant background by Agrobacterium-mediated transformation . The transgenic plants successfully rescued the flowering of rid1 ( S7 Fig ) . Using the ProRID1::RID1:FLAG:HA transgenic plants , a chromatin immunoprecipitation ( ChIP ) -QPCR assays was carried out in the young leaves using HA antibody . As expected , the selected regions I and II of the Hd3a and RFT1 promoters were significantly enriched in young leaves ( Fig 8B ) . These results indicate that RID1 may initiate the flowering transition through its direct targets Hd3a and RFT1 . Because the two florigen genes Hd3a and RFT1 were shown to be the direct targets of RID1 , we generated transgenic plants overexpressing Hd3a to investigate whether they can rescue the flowering transition in rid1 . We introduced the p35S::Hd3a construct into rid1 and obtained more than 40 transgenic plants overexpressing Hd3a ( Fig 9A ) . Interestingly , all the positive transgenic plants reached flowering at the seedling stage ( Fig 9B ) . Thus , overexpression of Hd3a caused early flowering in rid1 . Our previous investigation demonstrated that the expression of Ehd1 and Hd3a were completely repressed in the rid1 mutants [26] . Subsequently , we generated transgenic plants with overexpression of Ehd1 in rid1 ( Fig 9C ) . All of the transgenic plants exhibited the never-flowering phenotype , similar to rid1 ( Fig 9D ) . This observation shows that overexpression of Ehd1 is not sufficient to restore flowering transition in rid1 . Our results further confirm that RID1-Ehd1-Hd3a/RFT1 is not the sole pathway for floral induction mediated by RID1 in rice [26] .
Although the identification of SID1 could be due to mere chance in the transgenic events , our genetic and molecular analyses clearly suggested that SID1 is required for promoting flowering in rice . SID1 encodes an IDD-type zinc finger transcription factor , preferentially expressed in mature leaves ( Fig 4A ) , where floral inductive cues are perceived or initiated [22 , 29] . Mutation in SID1 caused delayed flowering time compared to that of the wild type ( Fig 5D and 5E ) . Overexpression of SID1 could successfully restore the flowering transition in rid1 ( Fig 2C and 2D ) . In addition , the expression levels of Hd3a and RFT1 were greatly suppressed in sid1 ( Fig 5F ) . However , the expression of Ehd1 was slightly reduced in sid1 plants ( Fig 5F ) , but was almost completely repressed in the rid1 mutants [26] , suggesting that the Ehd1-mediated flowering pathways may differ between rid1 and sid1 mutants . This observation coincides with evidence that rid1 shows the strongest phenotype , never flowering , whereas sid1 shows only slightly delayed flowering ( Fig 5D and 5E ) . RID1 acts as a master switch for floral transition . SID1 and RID1 might exert their function in the flowering transition with RID1 having priority for driving the expression of Hd3a and RFT1 . When the function of RID1 is abolished , only the normal expressing level of SID1 may not enough to trigger the expression of Hd3a and RFT1 , or due to non-overlapping expression patterns between SID1 and RID1 . Thus , rid1 plants remain in the vegetative growth stage . However , increasing or ectopic expressing SID1 transcripts is responsible for reverting rid1 to the phase of flowering . Subsequently , we demonstrated that florigen genes , Hd3a and RFT1 , are up-regulated in sid1-D ( Fig 6 ) . Furthermore , SID1 binds Hd3a and RFT1 promoter region in vitro ( Fig 7B ) , and the LUC activity enhanced with increasing levels of SID1 in rid1 protoplasts ( Fig 7C and 7D ) . These evidences support the function of SID1 recovering , at least in part , the SID1-Hd3a/RFT1 pathway to elicit flowering when RID1 is abolished . Expression of Ehd1 was also elevated in sid1-D plants ( Fig 6 ) , suggests that other pathways regulated by RID1 may also be activated by overexpression of SID1 . Both SID1 and RID1 are IDD zinc finger proteins . Proteins containing an IDD comprise a family of zinc finger transcription factors that are unique to plants [28] . The recognition of the DNA consensus sequence is likely to be mediated by the zinc finger modules located in the IDD [30] . The highly conserved IDD is composed of four putative zinc finger domains with spacer sequences between them [28 , 30] . In vitro DNA binding experiments showed that the second and third zinc fingers in the IDD are required for interaction with the DNA consensus motif [30] . Moreover , a different spacer between these zinc fingers modules in the IDD does not alter DNA binding specificity [30] . In this study , genetic evidences suggest that overexpression of SID1 , OsIDD1 or OsIDD6 could restore the rid1 mutant to flowering successfully . We propose the function of SID1 , OsIDD1 , and OsIDD6 are redundant and that overexpression any of them could take the place of RID1 to initiate the flowering transition when RID1 is absent . However , because mutation of any of zinc fingers of SID1 abolished rescuing the never-flowering phenotype of rid1 ( S4A and S4B Fig ) , suggesting that the first and fourth zinc fingers in the IDD also have critical roles in mediating DNA-protein interactions . Given that SID1 , OsIDD1 , and OsIDD6 are some co-expression ( S8A and S8B Fig ) and sid1 null mutants displayed moderate late flowering phenotypes ( Fig 5D and 5E ) , future research is needed to develop the double and triple mutants to understand whether these OsIDDs coordinately modulate flowering time or not . Our previous investigation indicated that RID1 activates the expression of florigen genes ( Hd3a , RFT1 ) mainly by regulating the expression of Ehd1 and Hd1 [26] . These findings suggest that RID1 is involved in two independent photoperiod pathways , mediated by Ehd1 and Hd1 , respectively . However , plants harboring nonfunctional alleles of Hd1 and Ehd1 still flower under either SD or LD conditions [48] , demonstrating that RID1 may play a role in alternative flowering pathway ( s ) for the flowering transition . In this study , overexpression of Ehd1 could not reverse the never-flowering phenotype of rid1 ( Fig 9C and 9D ) , further verifying this speculation . A striking finding of our study is that Hd3a and RFT1 are the direct targets of RID1 ( Fig 8 ) . Transcription of Hd3a and RFT1 was completely repressed in rid1 [26] , and genetic analysis showed that ectopic expression of Hd3a could reverse the never-flowering phenotype of rid1 ( Fig 9A and 9B ) , indicating that florigen genes indeed act downstream of RID1 . Furthermore , ChIP investigations indicated that RID1 could bind the promoter region of Hd3a and RFT1 in young leaves ( Fig 8B ) , suggested that RID1 initiates the expression of Hd3a and RFT1 most likely occurred in the early vegetative stages . Hd3a and RFT1 , two florigens in rice , be synthesized in leaves and transported to the shoot apex , and induced flowering [49–51] . As previously reported , photoperiod variation , temperature , gibberellin , age , and nutrition have been implicated in floral induction [11 , 52]; thus , the transition to flowering is complex and involves the convergence of multiple signals onto the florigen genes . RID1 acts as the master switch of phase transition and may function in initiating the expression of florigen genes ( Hd3a and RFT1 ) at the early stage , and then several floral induction cues converge to accumulate florigens to promote floral transition . Indeed , RID1 expression was detected most abundantly in young leaves at early seedling stages and is unaffected by photoperiod , indicating that RID1 may regulate an autonomous signal for flowering transition . Recent transcription and metabolism analyses showed that maize ID1 actually affects primary carbohydrate metabolism–related genes’ function and is closely associated with florigen production in maize mature leaves [39] . Although no clear ortholog of RID1 exists in Arabidopsis , the AtIDD8 was found to regulate sugar transport and metabolism contributing to photoperiodic flowering time [31] . Therefore , mediation of autonomous floral induction by RID1 may involve coordinating the state of carbohydrate metabolism in rice . Characterizing the transcript and metabolite signature changes in rid1 would further provide clues to help us further understand the mechanism ( s ) underlying the vegetative–reproductive phase transition in rice .
The rice variety used in this study was Oryza sativa subsp . japonica ‘Zhonghua11’ ( ZH11 ) . Plants were grown under NLD conditions in the experimental field during the rice growing season of Huazhong Agriculture University in Wuhan , China , and in a greenhouse during the winter . All transgenic plants were grown under similar growth conditions . Plants were grown in controlled-growth chambers ( Conviron ) under SD ( 10 h light at 26°C/14 h dark at 24°C ) or LD ( 14 h light at 26°C/10 h dark at 24°C ) conditions with a relative humidity of 70% . The light intensity was 800 μmol m-2 s-1 . To generate pUBQ::OsIDDs transgenic plants , the OsIDD genomic DNA sequence was amplified and then cloned into pU2301 , which was modified from pC2301 vector with the maize Ubiquitin promoter , and then verified by sequencing . An empty pU2301 vector was used as a negative control . For overexpression of Ehd1 , the Ehd1 genomic DNA sequence was amplified with primer pair Ehd1-OX-F/Ehd1-OX-R and then cloned into pU2301 by KpnI-BamHI digestion . For overexpression of Hd3a , the Hd3a genomic DNA sequence was amplified with primer pair Hd3a-OX-F/Hd3a-OX-R and then cloned into pS2300 by XbaI-KpnI digestion . To obtain ProRID1::RID1:FLAG:HA transgenic plants , genomic fragments containing the RID1 promoter and coding region lacking a stop codon were amplified with primer pair PFA2300-RID1-F/PFA2300-RID1-R and then cloned in frame into pFA2300 ( kindly provided by Saifeng Cheng , Huazhong Agricultural University ) by KpnI digestion . The constructs were introduced into Agrobacterium tumefaciens EHA105 and homozygous callus from rid1 was used as the transformation recipient . To generate pUBQ::SID1 ( cDNA ) and pUBQ::RID1 ( cDNA ) transgenic plants , the coding sequences were amplified by RT-PCR and ligated into the pEASY-T3 vector ( TransGen Biotech ) , and then verified by sequencing . The resulting plasmids were used as templates . Full-length cDNA of SID1 were amplified with primer pair SID1 ( CDs ) -OX-F/SID1 ( CDs ) -OX-R and then cloned into pU2301 by KpnI-BamHI digestion; full-length cDNA of RID1 was amplified with primer pair RID1 ( cDNA ) -OX-F/RID1 ( cDNA ) -OX-R and then cloned into pU2301 by KpnI-BamHI digestion . To investigate the tissue-specific expression of SID1 , approximately 3-kb promoter fragments of SID1 were amplified from genomic DNA and then cloned into pC2300-EX-GUS [53] to create pSID1::GUS . To introduce targeted mutations in SID1 protein , sgRNA:Cas9 expression vector of the SID1 gene was constructed as described previously [44] . The constructs were introduced into A . tumefaciens EHA105 and transformed into the callus derived from ZH11 . All primers used for genotyping and vector construction are listed in S1 Table . Site-directed mutagenesis was introduced by three-step PCR . Full-length SID1 CDs in pEASY-T3 vector were used as templates in the first and second PCR amplifications . In the first PCR , the forward primer SID1 ( CDs ) -OX-F and reverse primers containing the desired mutation were used . In the second PCR , the forward primers containing the desired mutation , which was the complement sequence of the first PCR reverse primer , and reverse primer SID1 ( CDs ) -OX-R were used . The first and second PCR products were purified , and this mixture was used as a template for the final PCR amplification with primers SID1 ( CDs ) -OX-F/SID1 ( CDs ) -OX-R . The final products were inserted into the pU2301 vector and confirmed by sequence analyses . The resulting plasmids were introduced into A . tumefaciens EHA105 and homozygous callus from rid1 was used as the transformation recipient . All primers for site-directed mutagenesis are listed in S1 Table . Genomic DNA from individual transgenic plants was extracted for PCR analysis . The CELI assay was used to identify the potential mutations . The PCR products amplified with SID1-specific primers SID1-CE-F/SID1-CE-R from individual mutant plants were cloned into pEASY-T3 vector ( TransGen Biotech ) for sequencing . GUS staining and imaging were carried out as described previously [26] . Total RNA was extracted using TRIzol reagent ( Invitrogen ) . RNA ( 2 μg ) was treated with RNase-free DNaseI ( Invitrogen ) , and first-strand cDNA was synthesized by M-MLV reverse transcriptase ( Invitrogen ) in a volume of 150 μl . For RT-PCR analysis , 3 μl of the first-strand cDNA described above was used as a template for PCR in a reaction volume of 20 μl . GAPDH served as a control for mRNA levels . qRT-PCR was run in a total volume of 10 μl containing 4 . 4 μl of the reverse-transcribed product described above , 0 . 3 μM gene-specific primers , and 5 μl FastStart Universal SYBR Green Master ( Rox ) superMIX ( Roche ) on an Applied Biosystems ViiA 7 Real-Time PCR system or the ABI PRISM 7500 sequence detection system according to the manufacturer’s instructions . Rice Ubiquitin was set as an internal control . The measurements were obtained using the relative quantification method . All primers are listed in S1 Table . To construct the subcellular localization plasmids , the full-length CDs of SID1 were amplified with primers SID1-pM999-F and SID1-pM999-R with EcoRI-KpnI digestion sites and inserted into pM999-GFP for fusion with the reporter gene . Rice protoplasts were isolated from 13-day-old etiolated seedlings and transformed with the tested pairs of constructs . Fluorescence in the transformed protoplasts was imaged using a confocal laser scanning microscope ( TCS SP2; Leica ) after incubation at 23°C for 12–16 h . The transcriptional activity of SID1 was analyzed using the DLR assay system in rice protoplasts prepared from etiolated seedlings [54] . The firefly luciferase gene driven by the minimal TATA box of the CaMV 35S promoter following five copies of the GAL4 binding element was used as a reporter . The Renilla luciferase gene driven by CaMV 35S was used as an internal control . The different deletion fragments of SID1 were amplified and then fused with the yeast GAL4 DNA-binding domain as effectors , driven by CaMV 35S followed by the translational enhancer Ω from tobacco mosaic virus . For each assay , 2 . 5 μg reporter plasmid DNA , 2 . 5 μg effector plasmid DNA , and 0 . 5 μg internal control plasmid DNA were co-transfected . After incubating for 12–16 h at 23°C , the relative luciferase activity was measured using the DLR assay system and the TECAN Infinite M200 microplate reader . To assess the specific binding and activity of Hd3a and RFT1 promoters , protoplasts were prepared from 2-week-old fully green tissue of rid1 [55] . The coding sequence of SID1 was amplified and then fused into the NONE vector , an effector plasmid driven by the CaMV 35S promoter followed with the translational enhancer Ω sequence . To generate the Hd3a::LUC and RFT1::LUC reporter genes , the Hd3a and RFT1 promoters were amplified ( specific primers are listed in S1 Table ) and inserted into 190-LUC vector , respectively . The Renilla luciferase gene driven by CaMV 35S was used as a normalization control . The recovery assays were performed with Hd3a::LUC or RFT1::LUC plus 35S::SID1 at various dosages , respectively . To express the RID1 protein in Escherichia coli , the CDs of RID1 were amplified with primers 32a-RID1-F and 32a-RID1-R cloned into the BamHI-EcoRI sites of the pET-32a expression vector ( Novagen ) and then introduced into Transetta ( DE3 ) cells ( TransGen Biotech ) . The target protein was purified with Ni-NTA agarose ( Qiagen ) . To express the SID1 protein in E . coli , the CDs of SID1 were amplified with primers pGEX-4T-SID1-F and pGEX-4T-SID1-R cloned into the BamHI-EcoRI sites of the pGEX-4T-1 expression vector ( GE Healthcare ) and then introduced into Transetta ( DE3 ) cells ( TransGen Biotech ) . The target protein was purified with GST Fast Flow ( GE Healthcare ) . The Hd3a promoter ( including the consensus motif TTTGTC ) , Hd3a-M promoter ( with nucleotide TTAATC replacement in the consensus motifs ) , RFT1 promoter ( including the consensus motif TTTGTC ) , and RFT1-M promoter ( with nucleotide TTAATC replacement in the consensus motifs ) were produced by annealing of oligonucleotides with biotin 5'-end labeled Hd3a-EMSA-F/R , Hd3a-EMSA-MF/MR , RFT1-EMSA-F/R , and RFT1-EMSA-MF/MR , respectively . For each reaction , 50 fmol biotin-labeled probes were incubated with the His-RID1 or GST-SID1 protein in the binding buffer ( 10 mM Tris , 50 mM KCl , 10 μM ZnCl2 , 1 mM DTT , 1 μg/μl poly ( dI-dC ) , 0 . 1% BSA , 2 . 5% glycerol , and 0 . 05% NP-40 ) for 30 min on ice using the LightShift Chemiluminescent EMSA kit . After incubation , the DNA–protein complex was separated by 6% native polyacrylamide gel electrophoresis . After separation , the signal of biotin was developed using the Chemiluminescent Nucleic Acid Detection Module ( Thermo , USA ) according to the manufacturer’s protocol . Images were visualized on Tanon-5200 Chemiluminescent Imaging System ( Tanon Science and Technology ) . Chromatin co-immunoprecipitations ( ChIPs ) were performed as described previously [56] . In brief , the young leaves of ProRID1::RID1:FLAG:HA transgenic plants were fixed in formaldehyde in a vacuum . The chromatin solution was sonicated and the soluble chromatin fragments were obtained from isolated nuclei . Pre-adsorption with Dynabeads protein G ( Invitrogen ) was performed to remove nonspecific binding DNA . Immunoprecipitation with anti-HA specific antibody ( Pierce HA Tag IP/Co-IP; #26180 ) and IgG were performed as described previously [56] . Immunoprecipitated DNA was analyzed by qRT-PCR , and the primers are listed in S1 Table online .
|
Transition from vegetative to reproductive phase is a critical developmental switch in the life cycle of higher plants . In rice , our previous work suggested Rice Indeterminate 1 ( RID1 ) acts as the master switch for the transition to flowering . Mutation in RID1 results in a never-flowering phenotype . In order to uncover the molecular network regulated by RID1 , a Suppressor of rid1 ( SID1 ) was identified in this study . Both SID1 and RID1 encode a plant-specific INDETERMINATE DOMAIN ( IDD ) transcription factor . Overexpression of SID1 , OsIDD1 , or OsIDD6 could rescue the never-flowering phenotype of rid1 . Molecular data indicate both SID1 and RID1 physically bind the promoters of the florigen genes Hd3a and RFT1 in rice . Thus , we propose that the transition to flowering could be regulated by RID1 through the autonomous pathway , in addition to the photoperiod pathway .
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2017
|
Suppressor of rid1 (SID1) shares common targets with RID1 on florigen genes to initiate floral transition in rice
|
With the goal to identify novel trypanothione reductase ( TR ) inhibitors , we performed a combination of in vitro and in silico screening approaches . Starting from a highly diverse compound set of 2 , 816 compounds , 21 novel TR inhibiting compounds could be identified in the initial in vitro screening campaign against T . cruzi TR . All 21 in vitro hits were used in a subsequent similarity search-based in silico screening on a database containing 200 , 000 physically available compounds . The similarity search resulted in a data set containing 1 , 204 potential TR inhibitors , which was subjected to a second in vitro screening campaign leading to 61 additional active compounds . This corresponds to an approximately 10-fold enrichment compared to the initial pure in vitro screening . In total , 82 novel TR inhibitors with activities down to the nM range could be identified proving the validity of our combined in vitro/in silico approach . Moreover , the four most active compounds , showing IC50 values of <1 μM , were selected for determining the inhibitor constant . In first on parasites assays , three compounds inhibited the proliferation of bloodstream T . brucei cell line 449 with EC50 values down to 2 μM .
Trypanosomatidae are responsible for approximately half a million of human fatalities per annum in subtropical and tropical regions around the world [1] . Trypanosoma brucei rhodesiense and T . b . gambiense are the causative agents of African sleeping sickness [2] . T . cruzi is responsible for Chagas’ disease . The disease complex Leishmaniasis including Kala Azar ( Leishmania donovani ) is caused by different species of Leishmania . Above all , these parasitic protozoa cause substantial economic losses by affecting life stock ( T . congolese , T . b . brucei , T . evansi ) [3] , [4] . For the treatment of the diseases only a handful of chemotherapeutics are available and their efficacy suffers from widespread drug resistance and serious side effects . Thus , there is an urgent need to discover new compounds as starting point for the development of potent drugs with less side effects , preferably interfering with unique essential pathways of these parasites [5] , [6] . Trypanothione reductase ( TR ) is an essential enzyme of the unique trypanothione-based thiol metabolism of Trypanosomatidae [7] , [8] . The flavoenzyme catalyzes the NADPH-dependent reduction of trypanothione disulfide [TS2] to the dithiol trypanothione [bis ( glutathionyl ) spermidine , T ( SH ) 2] ( Fig 1 ) [1] , [9] , [10] . Trypanosomatids lack both glutathione reductase ( GR ) and thioredoxin reductase and therefore TR is the only connection between the NADPH- and thiol-based redox systems [11] , [12] . T ( SH ) 2 is the substitute for many pathways and antioxidant functions [13] , [14] , [15] which in other organisms including the mammalian host are fulfilled by the glutathione and/or thioredoxin systems . Parasites with reduced TR levels are highly sensitive towards oxidative stress [7] . The nearest homologue of TR in human cells is GR with about 40% sequence identity . However , both enzymes display significant differences with respect to their active sites which results in a mutually exclusive specificity towards their disulfide substrate . TR was validated by different genetic approaches to be essential for the proliferation of Leishmania and Trypanosoma [7] , [8] , [16] . Taken together , these facts render TR a promising target for the development of selective inhibitors . A typical target-based approach starts either with high throughput screening of large libraries of small molecules [17] or with in silico experiments like a virtual screening to create a focused data set containing in silico hits which are subsequently tested by in vitro assays [18] , [19] , reducing screening costs significantly . Although several crystal structures of TR are available , their applicability for common structure-based virtual screening campaigns is inappropriate compared to other druggable protein targets like proteases [20] , [21] , [22] , [23] or kinases [24] , [25] , [26] . TR has a very wide and featureless active site with approximate dimensions of 15 x 15 x 20 Å ( Fig 2 ) [27] , [28] . In addition , the mainly hydrophobic TS2 binding site does not provide many directed interactions like hydrogen bonds . Therefore , ligands can bind with many different binding modes all over the active site . As a consequence , in silico approaches like molecular docking or pharmacophore screening are not capable to identify a reasonable and correct binding conformation [19] , [21] , [27] . Here we describe the approach that started with an in vitro screening of a highly diverse compound library to come up with a set of hits . These compounds were then used as a starting point for a ligand-based in silico screening that resulted in a focused library of similar structures with potential TR activity . Finally , the activity-enriched data set was subjected to a second in vitro screening campaign . This iterative combination of in vitro and in silico screening methods led to a higher number of TR inhibitors compared to pure in vitro or in silico approaches .
The kinetic analysis of TR was performed in 384-well plates ( Greiner Bio-One GmbH , Frickenhausen , Germany ) . The reaction mixture ( 50 μl ) contained 5 mU/ml TR , 40 mM Hepes , pH 7 . 5 , 1 mM EDTA , 300 μM NADPH ( Sigma-Aldrich ) , 0 . 1 mg/ml BSA , and 0 . 01% Pluronic ( BASF ) . The detergent was used to prevent the formation of droplets during pipetting and had no negative effects on the assay kinetics . In a volume of 30 μl , the assay components and the inhibitor were pre-incubated for 30 minutes and the reaction started by adding 20 μl of 375 μM TS2 resulting in final concentrations of 150 μM TS2 and 2% DMSO . The absorption decrease at 340 nm was measured by start and end point determination resulting in a delta of the optical density ( delta A ) for 30 min . All experiments were performed at room temperature . The TR concentration was kept constant at 5 mU/ml for all types of experiments . Therefore , after an initial calculation of the turnover number ( kcat ) , only delta A values under identical conditions were compared . Initial screening was performed twice in two independent experiments . Eleven compound concentrations were used ranging from 100–0 . 01 μM or 200–0 . 004 μM . The compounds were freshly dissolved in DMSO . All measurements were performed twice in three independent series . Enzyme activities were plotted versus increasing inhibitor concentrations . IC50 values were calculated using the four-parameter equation model 205 and the option “unlock” from the XLfit add-in ( IDBS , Guildford , United Kingdom ) in Excel ( Microsoft Corporation , Redmond , WA ) . The similarity search workflow was implemented by using the workflow application Pipeline Pilot [32] . TGT , TGD and MACCS fingerprints [33] were calculated by the MOE program [34] while ECFP6 and FCFP4 fingerprints were provided by built-in Pipeline Pilot components [32] . TGD and TGT fingerprints are pharmacophore-based descriptors . While TGD represents the existence of topological binding property pairs for seven pre-defined pharmacophore features , TGT encodes triplets of pharmacophore features for four pre-defined pharmacophore features . MACCS fingerprints are substructure descriptors encoding the presence of up to 960 molecular patterns . Extended Connectivity Fingerprints ( ECFP ) and Functional Connectivity Fingerprints ( FCFP ) are topological descriptors encoding information on atom-centered fragments . Within the workflow all six fingerprints for each of the 21 active query structures were calculated resulting in 126 ( 21 x 6 ) single similarity searches . Each similarity search was limited to a result set containing the 13 most similar hits . Afterwards the resulting structures were combined and duplicates retrieved by different similarity searches were removed . All in silico hits passing the in vitro hit confirmation step were filtered towards the existence of Pan Assay Interference compounds ( PAINS ) [35] . The filter was implemented by using the 480 defined PAINS substructures and the ‘Substructure Map’ component of Pipeline Pilot [32] . Before determining kinetic constants reversibility of compound binding has to be tested . Therefore , Amicon Ultra 0 . 5 ml centrifugal filters with a cut-off of 10 , 000 MW ( Millipore Corporation , Billerica , MA , USA ) were washed with 400 μl TR assay buffer and centrifuged for 5 min at 12 , 500 rpm at room temperature . Remaining buffer was removed by placing the filter upside down into a microcentrifuge tube and centrifugation at 3 , 500 rpm for 2 min . The filter was then loaded with 50 μl assay mixture containing 5 mU/ml TR ( 50 kDa ) , 100 μM inhibitor and 300 μM NADPH or the control mixtures which had been incubated for 30 min . The filter was centrifuged for 15 min at 12 , 500 rpm , washed three times with 400 μl buffer and centrifuged again . The retentate was collected by placing the filter upside down on a new tube and centrifugation at 3 , 500 rpm for 2 min . The recovered protein solutions were subjected to a standard TR assay . Measurements were performed at 60 , 80 , 120 , and 200 μM TS2 and five inhibitor concentrations resulting in 20 different data points per inhibitor . The Ki values were derived from two independent experiments each done in duplicate . SigmaPlot 10 ( Systat Software , Inc . , San Jose , USA ) with the Enzyme Kinetics Module was used to evaluate the inhibition type and Ki values . This software automatically estimates the initial parameters for different fit models , and uses the Marquardt-Levenberg algorithm to determine the parameter values . A detailed statistical report as well as a data report is generated to compare multiple models and graphs . Based on these statistics , the appropriate binding model can be selected . Graphs are created based on the models and the calculated parameter values instead of fitting each individual inhibitor concentration curve . ADME parameters and physicochemical properties were calculated and predicted for the four nanomolar inhibitors and chlorhexidine by using the software packages Pipeline Pilot by Accelrys [32] and Volsurf+ by Molecular Discovery [36] . The measured properties cover standard descriptors like number of H-bond donors and acceptors , number of rotatable bonds , molecular weight , logP and PSA but also ADME features like permeability ( logBB [36] , Blood Brain Barrier Leve l [32] , SKIN [36] , CACO2 [36] ) , solubility ( Molecular Solubility [32] , logS7 . 5 [36] , ADME Solubility Level [32] ) , metabolic stability [36] , protein binding [32] and intestinal absorption [32] . Compounds were dissolved in DMSO to 10 mM stock solutions and added to bloodstream T . brucei ( strain 449 ) in concentrations of 100 , 50 , 5 , 0 . 5 , and 0 . 05 μM in 24-well plates . The final DMSO content in the cultures was 1 , 0 . 5 , 0 . 05 , 0 . 005 and 0 . 0005% , respectively . The initial cell density was 2500 cells/ml . After 48 h and 72 h incubation at 37°C in HMI-9 medium , viable cells were counted in a Neubauer chamber . The assay was performed in triplicate . The parasite strain and the culture conditions are described in the literature [31] . The ATPlite 1step assay system ( PerkinElmer , Waltham , MA , USA ) was used as described in Füller et al . [37] to quantify T . brucei survival in medium-throughput dose-response series . Experiments were conducted in 96-well microplates ( PerkinElmer , Waltham , MA , USA ) , each well containing 90 μl cell-culture and 10 μl compound . Initial cell densities were 2500 cells/ml . Compounds 1 and 2 were diluted stepwise with HMI-9 medium from the 10 mM DMSO stock solutions . Unfortunately , compound 3 was no longer available for testing . The highest concentration of DMSO added with the compounds was 0 . 5% which did not affect viability of the parasites . Treatment with 10% DMSO served as positive control ( 100% inhibition ) . For each compound , three identical plates were prepared , incubated at 37°C and analyzed after 24 h , 48 h , and 72 h , respectively . Samples from each cell-line left untreated were added to the plates prior to each measurement as additional controls . 50 μl of the ATPlite 1step solution ( PerkinElmer , Waltham , MA , USA ) were added to each well and the relative luminescence was measured immediately using a VICTOR Multilabel Plate Reader ( PerkinElmer , Waltham , MA , USA ) at room temperature . The values obtained were plotted against the logarithmic compound concentrations . A dose-response curve was generated from which EC50-values were calculated using the program PRISM 5 . 0 ( GraphPad Software , La Jolla , CA , USA ) .
For the initial in vitro screening a robust assay was developed based on recombinant T . cruzi TR , which is much more stable than the recombinant T . brucei enzyme . This is reasonable since both proteins display a sequence identity of more than 80% and show comparable inhibition [38] . A robust NADPH-linked photometric assay [39] was chosen that firstly had to be adapted to the high throughput screening format . The assay volume was reduced from the 1 ml cuvette format to 50 μl total volume in 384 well plates . A high concentration of TS2 was chosen to mimic severe oxidative stress conditions and to discriminate against weak competitive inhibitors . In order to stabilize the enzyme and to prevent adhesion and reduce surface tension as well as the formation of droplets in the small wells , 0 . 1 mg/ml BSA and 0 . 01% detergent ( Pluronic ) were added to the reaction mixture . The assay was validated by a ) comparing the published and measured Km value for TS2 ( measured Km 20 μM , published Km 18 μM [39] and 29 . 6 μM [40] ) and b ) comparing the measured inhibitor constants ( Ki ) of three known inhibitors—chlorhexidine , mepacrine , BG237—with published data [41] , [42] , [43] ( Table 1 , Fig 3 ) . The kinetic values obtained in the high throughput screening ( HTS ) assays were in the same order of magnitude as the published data . Subsequently , the IC50 values of the three inhibitors were determined in the screening format to evaluate the robustness of the assay . In the presence of 150 μM TS2 , mepacrine , BG237 , and chlorhexidine yielded IC50 values of >200 μM , 98 μM , and 26 μM , respectively ( Table 1 ) . The accordance of the measured and published kinetic values as well as the capability of the assay to discriminate against weak inhibitors confirmed the successful transfer of the original TR assay to the HTS format . The initial compound library contained 2 , 816 chemicals that represented a highly diverse subset of the MSD screening library of over 200 , 000 substances . In addition , the three known inhibitors ( mepacrine , BG237 , chlorhexidine ) used for the assay validation were included as controls . All compounds were studied at a concentration of 20 μM in the presence of 150 μM TS2 . The screening was performed twice in two independent experiments and the data obtained were analyzed using the software ActivityBase ( ID Business Solutions Ltd . , Guildford , UK ) . The dimensionless statistical value Z-prime which assesses the measurement quality of each plate was ≥ 0 . 87 reflecting the robustness of the screening system [44] . The analysis showed a typical percentage inhibition distribution and 64 compounds displayed a mean inhibition of > 30% resulting in a hit rate of 1 . 8% ( Figs 4 and 5 ) . All 64 substances were subjected to further hit confirmation and structure verification experiments . The selected 64 compounds were freshly dissolved from the solid material instead of using the stock solutions used in the primary activity screen . This procedure ensured that the observed effect was caused by the authentic compound and not by putative decomposition products that might have been formed in the stored stock solution . The determination of the IC50 values revealed a correlation coefficient ( R2 ) of 0 . 968 proving that the activity of all validated compounds could be reliably determined . Finally , 29 compounds were identified that showed IC50 values down to 1 . 15 μM with 21 compounds being more potent than chlorhexidine ( IC50 < 26 μM ) . After structural validation by LC/MS and NMR analyses , 21 highly active compounds could be confirmed by the initial campaign ( Fig 4 ) . The 21 actives retrieved by the in vitro screening were used as new structural starting positions for a ligand-based in silico screening . Each in vitro hit was used to search for analogues in an in-house database of 200 , 000 compounds . Because the molecular properties responsible for the inhibitory potency were not known , six fingerprints describing and representing each structure in different ways were used instead of only one molecular descriptor . The fingerprints were selected to cover various structural aspects that might be responsible for the on-target activity like similar topology ( ECFP6 , FCFP_4; Accelrys [32] ) , similar structural fragments ( MACCS Keys ( public and private keys ) ; Symyx [33] ) and pharmacophoric features ( TGT , TGD; CCG [34] ) . In summary , 126 single similarity searches were performed in parallel using the workflow program Pipeline Pilot [32] . Each search was limited to the best 13 resulting structures to ensure a high degree of similarity . Finally , a focused compound set containing 1 , 204 duplicate-free , novel in silico hits with potential TR activity was obtained . 976 out of the 1 , 204 compounds identified by the in silico screening campaign were available in sufficient amounts as stock solutions or solid material and could be tested in a second in vitro study . The compound set was studied under the same conditions as in the initial in vitro screening described above . Finally , 179 further hits displaying > 30% inhibition were obtained resulting in a hit rate of about 17% ( Fig 5 ) . This hit rate is nearly ten times higher compared to that in the initial screening , although the compound data set was 3-times smaller . This clearly demonstrates that the introduced in silico approach was able to generate a highly enriched focused library of TR inhibitors . The 160 most active hits were selected for IC50 measurements using 11 concentrations ranging from 200 to 0 . 003 μM . The activity of 121 substances could be confirmed by IC50 values of which most were more potent than the well-known standard chlorhexidine . Finally , a second set of additional 61 novel TR inhibitors remained after structural confirmation ( NMR and LC/MS ) and substructure-based filtering against Pan Assay interference compounds [35] ( Fig 4 ) . Combining all actives from the first and second in vitro screening campaign , 82 compounds active on TR with confirmed in vitro activity could be identified . Before determining the kinetic constants for the most promising compounds , we investigated whether the compounds bind reversibly or irreversibly to the enzyme . A reaction mixture containing TR , inhibitor and NADPH was incubated for 30 minutes . NADPH reduces the disulfide bridge in the active site and thus allows the putative covalent binding of the inhibitor to an active site cysteine . Afterwards , the protein was separated from the low molecular mass components by centrifugation in an Amicon tube . Inhibitors with a reversible binding mode would be washed out , whereas irreversible binders remain bound to the protein . Finally , the recovered protein solution was subjected to a standard assay to determine the remaining activity . Two controls were run to determine the maximum activity that could be recovered . One contained the protein assay mixture without inhibitor , while the second control was a mixture of TR and the reversible inhibitor chlorhexidine . In both controls , 70–80% activity could be recovered compared to the initial kinetics . The four most promising compounds showing IC50 values of < 1 μM ( Fig 6 ) were evaluated . They all revealed a reversible binding mode . Subsequently the inhibitor type and constants were determined . The R2 values for the fits in the Lineweaver-Burk plots ( Fig 7 ) for uncompetitive and noncompetitive action were comparable whereas those for competitive inhibition were lower ( Table 2 ) . Thus , a competitive inhibition is very unlikely for these compounds . Since the screening assay used high substrate concentrations well above the Km value , it was expected to identify inhibitors which don’t compete with TS2 for binding . The four most active compounds out of the 82 in vitro hits ( Fig 6 ) were tested towards wild-type bloodstream T . brucei . The parasites were cultured in the presence of different concentrations of the compounds and after 48 h and 72 h living cells were counted . Compounds 1 , 2 , and 3 inhibited parasite proliferation with EC50 values between 50 and 5 μM ( Fig 8 ) , whereas compound 4 was not active . Chlorhexidine , which was used as a positive control , displayed an EC50 value between 200 and 100 nM . To determine accurate EC50 values , compounds 1 and 2 were re-evaluated using the ATPlite 1step assay system ( PerkinElmer , Waltham , MA , USA ) as described in Füller et al . [37] . The assay is based on the emission of light caused by the reaction of ATP with D-luciferin in the presence of luciferase , which is proportional to the amount of ATP serving as a marker for cell viability . The compounds were tested on wild-type cells as well as on parasites transfected with pHD1700-TbTR treated with 1 μg/ml tetracyclin for 1–2 weeks to induce the expression of an ectopic copy of TR . Unfortunately , the T . brucei strain allowing the down-regulation of TR which was generated more than a decade ago in the laboratory of Christine Clayton [7] is not available anymore . Therefore , this tet-inducible overexpressing system leading to up to three fold higher TR levels had to be used instead . Wild-type cells treated with 1 μg/ml tetracyclin were included to exclude any effect of the antibiotic on parasite proliferation . For compound 1 , the ATPlite assay confirmed the time-dependent decrease of inhibition observed in the cell counting experiments , resulting in an EC50 value of 2 μM at 24 h and of 15 μM after 72 h ( Table 3 ) . Compound 2 displayed also a slightly lower degree of inhibition with time , the EC50 value after 72 h being 58 μM compared to 42 μM at 24 h and 48 h . With both inhibitors , no significant difference was observed between wild-type parasites and cells that expressed also the ectopic TR-copy at an approximately 3-fold level compared to wild-type cells . Thus , it could not be proven that the efficacy of these compounds against parasites is really due to inhibition of TR . In summary , three compounds out of four in vitro hits showed activity down to 2 μM in cell culture , which is comparable to the activity of nifurtimox , a nitro-heterocyclic drug against T . cruzi which is now used in a combination therapy towards African trypanosomes and inhibits proliferation of T . brucei with an EC50 value of 2 . 5 μM [45] . Compound 4 with the second lowest IC50 and Ki on the recombinant TR was inactive in bloodstream T . brucei cell cultures . Inspecting the physicochemical properties of all four compounds revealed that compound 4 is the least lipophilic and has the largest Polar Surface Area ( PSA and fractional PSA ) indicating poor cell membrane permeability . These initial predictions were confirmed by the ADME permeability parameters CACO2 , logBB and SKIN which all predicted a poor permeability for this structure ( Table 4 ) . Compound 4 also revealed a very high level for intestinal absorption compared to the other three candidates . These observations support the hypothesis that compound 4 may not enter T . brucei sufficiently to inactivate TR , assuming that all four compounds are probably incorporated by passive diffusion . The findings are supported by the circumstance that a genome-scale RNA interference target sequencing screen did not identify a transporter for the nitro-aromatic nifurtimox [45] and another nitro-aromatic antitrypanosomal drug megazol enters the parasite by passive diffusion [46] . Surprisingly , chlorhexidine was much more active in the cell culture assays than the best in vitro active ( compound 1 ) , although the on target activity ( Ki and IC50 ) of compound 1 was 10–20 times better compared to chlorhexidine . In addition , the predicted membrane permeability of chlorhexidine is even lower compared to compound 4 . The poor permeability of chlorhexidine has also been described in many pharmacokinetic studies [47] . All these observations do not explain the observed high activity on bloodstream form trypanosomes of chlorhexidine . It might be that TR is not the only target of chlorhexidine in trypanosomes or that chlorhexidine can enter the parasite e . g . by active transport or interacts with the cell membrane . It is known that the antibacterial activity of chlorhexidine is caused by its incorporation and destabilization of the bacterial cell wall [47] . The time-dependent activity loss of compound 1 and 2 might be caused by stability or solubility issues in the culture medium . Both compounds show the least solubility and metabolic stability values of all four compounds . Nevertheless , such hurdles are rather common in the drug discovery process and can be addressed by further optimization steps . Trypanothione reductase ( TR ) , an essential enzyme and validated target in the redox metabolism of pathogenic trypanosomes , has a large , wide and featureless active site making the protein inappropriate for target-based in silico screenings like high-throughput docking . In this paper we presented a new approach to identify novel TR inhibitors as starting points for drug discovery by combining target-based in vitro screening and 2D in silico screening methods that are capable to exceed the hit identification rates of common pure high-throughput assays or in silico approaches . In our screening campaign only ~4 . 000 compounds had to be tested in the adapted assay to identify 82 novel compounds being more active than chlorhexidine , a well-known TR inhibitor . Four compounds showed TR inhibition in the nanomolar range and three of them also reveal activity against intact bloodstream form T . brucei . These compounds provide promising starting points for a hit-to-lead process within a drug discovery project , where solubility , stability , toxicity , and activity of derivatives are used to identify the best compounds for further optimization . The screening compound library and also the diverse subset used in the first target-based in vitro screening campaign contained structurally diverse compounds . It was applied as general library in many other target-based screening approaches . Nitro compounds and especially nitroheterocyclic compounds were only represented in a minor extend . Interestingly , with TR as target , this kind of compound class was found with an over-averaged frequency compared to other target-based approaches [48] , [49] . Moreover , all best hits including the four compounds studied in detail belong to this specific compound class . Compounds containing nitro groups are usually underrepresented in known drugs . This is related to at least two reasons: a ) the nitro group is often not involved in the interactions with the target protein and therefore not essential , b ) nitro groups are undesired functional groups that are usually replaced in the lead/drug optimization phase . In contrast , nitroheterocyclic compounds show an over-averaged chemotherapeutical potential in pathogens causing neglected diseases . The interest in this compound class has grown since the success of the nifurtimox/eflornithine combination against African trypanosomiasis and has prompted in many subsequent screening and research activities until today [48] , [49] . In addition , the nitro-heterocyclic pro-drug fexinidazole is the first drug candidate in 30 years that entered clinical phase II/III against African trypanosomiasis for both stages of the disease [50] , [51] . The compound also shows activity against T . cruzi and L . donovani and therefore is evaluated against Chagas disease [50] , [52] and Leishmaniasis [53] in clinical proof-of-concept studies . Despite these research efforts the role of the nitro group concerning the activity of such compounds could not be explained yet . In silico modeling and analysis of binding modes of compound 1 to TR suggest a potential interaction of the nitro group with Asn339 and/or Arg354 of TR [27] . Although these analyses are artificial to a certain extent , they might give a hint that the nitro group plays an important role in the binding of the compounds to TR . Nevertheless , other explanations like an effect of the nitro group on the specific redox systems of such pathogens are also discussed [48] , [48] . Further experiments leading to better understanding of the prominent role of nitro compounds are needed and may push the importance of heterocyclic compounds as therapeutic agents towards neglected diseases .
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Trypanosomatidae are responsible for approximately half a million human fatalities per annum and the situation is compounded by substantial economic losses due to affecting live stock as well . Trypanothione reductase ( TR ) is an essential key enzyme of the unique trypanothione-based thiol metabolism of the trypanosomatidae and TR is a promising target for the development of selective inhibitors . However , TR is a very hard to attack target in standard drug discovery approaches . Therefore , we developed a combined and iterative in vitro and in silico screening approach , which led to a high number of novel TR inhibitors . 82 of those showed activities down to the nM range against T . cruzi TR . Moreover , the four most active compounds were selected for determining the inhibitor constant . In first on parasite efficacy studies , three of those compounds inhibited the proliferation of bloodstream T . brucei cell line 449 with EC50 values down to 2 μM .
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[
"Abstract",
"Introduction",
"Material",
"and",
"Methods",
"Results",
"and",
"Discussion"
] |
[] |
2015
|
Trypanothione Reductase: A Target Protein for a Combined In Vitro and In Silico Screening Approach
|
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