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Phenotypic switching allows for rapid transitions between alternative cell states and is important in pathogenic fungi for colonization and infection of different host niches . In Candida albicans , the white-opaque phenotypic switch plays a central role in regulating the program of sexual mating as well as interactions with the mammalian host . White-opaque switching is controlled by genes encoded at the MTL ( mating-type-like ) locus that ensures that only a or α cells can switch from the white state to the mating-competent opaque state , while a/α cells are refractory to switching . Here , we show that the related pathogen C . tropicalis undergoes white-opaque switching in all three cell types ( a , α , and a/α ) , and thus switching is independent of MTL control . We also demonstrate that C . tropicalis white cells are themselves mating-competent , albeit at a lower efficiency than opaque cells . Transcriptional profiling of C . tropicalis white and opaque cells reveals significant overlap between switch-regulated genes in MTL homozygous and MTL heterozygous cells , although twice as many genes are white-opaque regulated in a/α cells as in a cells . In C . albicans , the transcription factor Wor1 is the master regulator of the white-opaque switch , and we show that Wor1 also regulates switching in C . tropicalis; deletion of WOR1 locks a , α , and a/α cells in the white state , while WOR1 overexpression induces these cells to adopt the opaque state . Furthermore , we show that WOR1 overexpression promotes both filamentous growth and biofilm formation in C . tropicalis , independent of the white-opaque switch . These results demonstrate an expanded role for C . tropicalis Wor1 , including the regulation of processes necessary for infection of the mammalian host . We discuss these findings in light of the ancestral role of Wor1 as a transcriptional regulator of the transition between yeast form and filamentous growth .
The incidence of opportunistic fungal infections has increased in recent years as a result of immunosuppressive diseases such as AIDS , as well as the use of immunosuppressive drugs in modern medical practices [1] . Candida species are typically harmless commensals of humans but are also important fungal pathogens , responsible for both systemic and mucosal opportunistic infections [2] . Most clinically relevant species belong to the Candida clade of hemiascomycete yeasts , which diverged from the model yeast Saccharomyces cerevisiae 300–600 million years ago [3] , [4] . Three Candida clade pathogens , Candida albicans , Candida dubliniensis , and Candida tropicalis , have been shown to undergo an epigenetic switch between distinct ‘white’ and ‘opaque’ states [5]–[8] , and this phenotypic switch plays a crucial role in modulating behavior . The white-opaque switch has been extensively studied in C . albicans , where the two states differ in metabolic preferences [9] , environmental responses [10]–[14] , interactions with host immune cells [15] , [16] , and the ability to undergo sexual reproduction [17] , [18] . C . albicans and C . dubliniensis are closely related species that can undergo productive mating with one another [7] . While C . albicans represents the most commonly isolated Candida species in the clinic , C . dubliniensis is rarely found in infections , which may reflect the more limited ability of this species to undergo filamentation [2] , [19] . C . tropicalis is also a prevalent human pathogen , particularly in individuals with neutropenia or hematologic malignancies [2] , and shows a similar overall genome structure to that of C . albicans , including synteny at the mating-type-like ( MTL ) locus [20] . C . albicans and C . tropicalis both contain more than 6 , 000 protein-coding genes , and analysis of the 5 , 254 orthologs shared between the two species indicates an average protein sequence identity of ∼70% [20] . However , relatively little is known about the biology of C . tropicalis compared to that of the model species C . albicans , including the factors that promote pathogenesis in the mammalian host . In all three Candida species , white and opaque forms are distinguished by differences in cell shape , colony morphology , and gene expression profiles . White cells are generally round and give rise to smooth , shiny colonies , while opaque cells are elongated and produce duller , darker colonies [5] , [6] . The transcriptional profiles of white and opaque forms are also significantly different , with one-sixth of the transcriptome regulated by the switch in C . albicans [9] , [21] , [22] . Furthermore , white and opaque forms exhibit differences in the propensity to undergo filamentous growth; C . albicans white cells are induced to filament in response to multiple environmental stimuli while opaque cells do not generally undergo filamentation [23] , [24] . The white-opaque switch plays a particularly prominent role in regulating mating , as only cells in the opaque state undergo efficient conjugation [5] , [18] . Switching is regulated by WOR1 such that loss of this gene prevents formation of the opaque state while , in C . albicans , WOR1 overexpression drives cells into the opaque state [5] , [25]–[27] . Genes encoded at the MTL locus control white-opaque switching in C . albicans; only a or α cells switch to the opaque state as a complex between MTLa1 and MTLα2 proteins blocks a/α cell switching due to repression of WOR1 [18] , [21] , [26] . An analysis of 220 clinical isolates of C . albicans further demonstrated that only MTL homozygous ( a/a or α/α ) strains could form stable opaque cells at a detectable frequency [17] . MTL regulation ensures that opaque formation only occurs in cells that have the potential to undergo mating , which can take place between MTLa and MTLα cells or via same-sex mating of these cell types [18] , [28] . In addition to Wor1 , three other transcription factors , Czf1 , Wor2 , and Efg1 , regulate the C . albicans white-opaque switch via a network of positive and negative feedback loops [29] . While Wor1 is the master regulator of the opaque state , Czf1 and Wor2 also play positive roles in promoting formation of opaque cells , while Efg1 antagonizes opaque formation and promotes switching to the white state [29] . Surprisingly , the mechanism regulating white-opaque switching in C . tropicalis appears distinct from that in C . albicans; while switching is dependent on Wor1 in both species , the three associated network transcription factors are not white-opaque regulated in C . tropicalis [5] . It is therefore likely that significant differences exist between the mechanisms regulating phenotypic switching in C . albicans and C . tropicalis [5] . The white-opaque switch is thought to have evolved relatively recently in the Candida clade , probably just prior to the divergence of C . albicans/C . dubliniensis and C . tropicalis [5] , [8] . Although this phenotypic switch is limited to within the Candida clade , the master transcriptional regulator Wor1 is conserved across the ascomycete lineage [30] . In S . cerevisiae , the Wor1 homolog Mit1 acts as part of a transcriptional network to regulate pseudohyphal formation [31] , while in the more distantly related ascomycete Histoplasma capsulatum , the Wor1 homolog Ryp1 is a master regulator of the transition between yeast and mycelial forms [32] . It has therefore been proposed that the ancestral role of Wor1 was the transcriptional regulation of morphological changes , including the control of filamentous growth [31] . In this study , we investigated the white-opaque transition in C . tropicalis and uncovered several features that further distinguish it from the analogous switch in C . albicans . We first demonstrate that a stable white-opaque switch can occur in C . tropicalis a/α cells , indicating that the a1/α2 complex does not repress switching in this species . Transcriptional profiling reveals that genes regulated by the white-opaque switch in a/α cells show a significant overlap with those regulated by the switch in a or α cell types . However , many white-opaque regulated genes are unique to MTL heterozygous or MTL homozygous cells , indicating that the MTL configuration significantly impacts gene expression in the two phenotypic states . Despite these transcriptional differences , the phenotypic switch in all three cell types ( a , α , and a/α ) is dependent on the master regulator , Wor1; deletion of Wor1 blocked white-to-opaque switching while overexpression of Wor1 forced cells into the opaque state . We further show that Wor1 promotes filamentous growth in C . tropicalis , as elevated WOR1 expression led to a significant increase in both filamentous growth and biofilm formation . These studies provide new insights into the evolution of Wor1 homologs as conserved regulators of filamentation and epigenetic switching .
A key feature of the C . albicans white-opaque switch is that a/α cells are locked in the white state due to repression of WOR1 transcription by the a1/α2 heterodimer . A direct consequence of this control is that a/α mating products formed between opaque a and α cells typically form white colonies [18] . Rare opaque colonies ( <5% ) were observed in C . albicans mating products , but in each case these colonies had undergone loss of a1 or α2 , thereby relieving repression of WOR1 and allowing propagation of the opaque state [18] . In C . tropicalis , the white-opaque switch also regulates mating , although the difference in mating efficiency between white and opaque forms is less striking than that in C . albicans . Whereas C . albicans opaque cells mate a million times more efficiently than white cells [18] , mating of C . tropicalis opaque cells is only about a hundred times greater than that of white cells [5] . These observations led us to hypothesize that C . tropicalis white cells may be capable of appreciable mating frequencies , even without switching to the opaque state . Close inspection of C . tropicalis a/α mating products revealed that colonies generated by mating white a and α cells were distinct from those formed by mating opaque cells . This is illustrated in Figure 1A , where colonies formed from mating between white a and α cells exhibited a shiny , smooth appearance , while colonies formed by products of mating between opaque cells were consistently duller and darker . Examination of the cells from these colonies revealed that the products of white×white mating were round , resembling classical white cells , while the products of opaque × opaque mating were elongated , reminiscent of opaque cells ( Figure 1B ) . PCR analysis of the mating products confirmed that a1 and α2 were still present in the products formed from mating white or opaque cells ( Figure 1C ) . The a1 and α2 loci were also sequenced and shown to exactly match the published gene sequences [20] , indicating that these genes were not mutated in the C . tropicalis strains used for these experiments . Together , these results indicate that the C . tropicalis white-opaque switch is not under strict regulation by the MTL locus . Thus , unlike C . albicans , mating of C . tropicalis opaque a and α cells ( and formation of the a1/α2 complex ) does not force cells back to the white form . Instead , C . tropicalis a/α mating products inherit the phenotype of their parental cells and can even stably propagate in the opaque form . These experiments reveal another important aspect of C . tropicalis biology , as they establish that white cells can mate without switching to the opaque form . Our observation that the products of white and opaque cell mating are distinguishable , and that white×white mating generates white mating products , indicates that C . tropicalis white cells can directly undergo conjugation with one another . In support of the fact that both white and opaque cells can mate , we also observed structural differences in zygotes formed between white cells and those formed between opaque cells . Zygotes formed from white cells were generally smaller and contained rounder cells than zygotes formed from opaque cells ( Figure 1D ) . We conclude that C . tropicalis white cells are competent for mating , albeit at a lower efficiency than that of opaque cells . To complete this analysis , we also examined the products of mating in crosses between white and opaque cells . Interestingly , the products of these mixed crosses were predominantly opaque ( data not shown ) . Since Wor1 expression is essential for induction of the opaque form , we surmise that Wor1 protein from the parental opaque cell is present at sufficient levels in the zygote to result in stable propagation of the opaque state in these mating products . A previous study by Xie et al . indicated that a/α cells of C . tropicalis were able to form opaque cells based on similar cell morphologies to a/a and α/α opaque cell types [8] . To address whether a/α cells could directly switch to the opaque state , cells were grown on medium containing N-acetylglucosamine , as this was previously shown to induce C . tropicalis switching [8] . Rare colony switching was observed with darker sectors forming at the edge of a/α colonies , similar to that in conventional white-to-opaque sectoring of a and α colonies ( Figure 2A ) . Cells from the a/α sectors were elongated rather than spherical , a characteristic feature of opaque cells [5] . Additionally , white a/α cells had a smooth surface while opaque a/α cells had an uneven or pimpled surface , and thus resembled conventional white and opaque cells ( Figure S1A and [33] ) . Once formed , diploid opaque a/α cells were stably maintained in the opaque state , similar to tetraploid opaque a/α mating products ( Figure 1A and data not shown ) . The frequency of white-to-opaque switching in a/a and a/α cells varied widely from experiment to experiment ( 0–7% switching ) , but the difference in switching between strains was not statistically significant . As the a1/α2 complex acts to repress white-to-opaque switching in C . albicans , we also examined whether the a1 and α2 genes were expressed in C . tropicalis opaque a/α cells . Reverse transcription ( RT ) -PCR was performed on each cell type and revealed that a1 and α2 genes were actively expressed in a/α cells regardless of phenotype ( Figure S1B ) . This result indicates that the white-to-opaque switch in C . tropicalis appears independent of a1 and α2 function . We further examined 8 additional C . tropicalis clinical a/α strains for their ability to undergo the white-opaque switch . Opaque formation was observed in 2 of these strains , indicating that other a/α strains are able to undergo the white-opaque switch ( Table S1 ) . The presence of the a1 and α2 genes in switching strains was confirmed by PCR . These genes were sequenced from opaque cells derived from one of the three clinical a/α strains and also found to match the published sequence ( data not shown ) . Our results therefore establish that C . tropicalis white-opaque switching occurs independent of MTL control and that multiple a/α strains can switch to a stable opaque state . To ascertain the relationship between genes regulated by the white-opaque switch in MTL heterozygous a/α cells with those in MTL homozygous a/a or α/α cells , transcriptional profiling was performed on cells from both morphological states in each cell type ( Figure 2B ) . SAM ( Statistical Analysis of Microarrays ) was used to determine gene expression changes that were significant for white-opaque regulated genes in a and a/α cells ( see Materials and Methods and Tables S5 and S6 ) . Expression profiles of a/α white and opaque forms showed significant overlap with the corresponding white- and opaque-specific genes in a cells ( Figure 2B ) . In particular , of the 120 genes significantly upregulated in white a cells , 73 of these genes were also upregulated in white a/α cells , while of the 129 genes upregulated in opaque a cells , 22 of these genes were also upregulated in opaque a/α cells ( Figure S2 ) . These results establish that C . tropicalis a/α cells undergo a white-opaque switch related to that in MTL homozygous cells [5] , and that there is significant overlap between white-opaque genes regulated in MTL homozygous and heterozygous cell types ( p<1e-150 and p<1e-20 for white- and opaque-specific genes , respectively ) . Despite significant overlap between white-opaque regulated genes in the different cell types , a large number of switch-regulated genes were unique to either MTL heterozygous or MTL homozygous cells . This was particularly striking in a/α cells , where the expression of 549 genes was significantly different between white and opaque forms , while only 249 genes were white-opaque regulated in a cells ( Figure 2B , 2C and Figure S2 ) . Many of the additional white-opaque regulated genes in a/α cells were involved in translation , biosynthetic processes , or gene expression based on GO term analysis ( Text S1 and Figure S3 ) . This indicates that the switch in a/α cells regulates additional cellular processes to those in a or α cells , and these differences could have important implications for the role of the switch in MTL heterozygous strains . Conversely , white and opaque a and α cells also differentially expressed genes that were not white-opaque regulated in a/α cells . For example , the mating-related genes STE4 and BAR1 were expressed at higher levels in opaque MTL homozygous cells than in white cells , but were not differentially expressed between white and opaque states in MTL heterozygous cells ( Figure 2C ) . Taken together , these results indicate that MTL homozygous and MTL heterozygous cells share related white-opaque expression profiles , but that the genes regulated by this switch are also influenced by the configuration of the MTL locus . A key role of the white-opaque switch in Candida species is the regulation of sexual reproduction [5] , [18] . Given that C . tropicalis a/α cells undergo the white-to-opaque switch , we tested whether these cell types undergo productive mating . We found that a/α cells could not mate with other a/α cells , and that same-sex mating of a or α cells was also not observed ( data not shown ) , in agreement with previous studies [5] . However , low levels of mating were obtained between a/α cells and either a or α cells as partners . The frequency of this a/α cell mating was 103 to 105-fold lower than conventional mating between a and α cells ( Figure 3 and Figure S4 ) . Mating could be detected using both white and opaque a/α cells , and although a/α cell mating was higher in the opaque state , this difference was not significant . C . albicans a/α cells from the SC5314 background do not undergo mating with other cell types ( [18] and data not shown ) . However , overexpression of WOR1 in these cell types can override a/α control , forcing them to adopt the opaque state and promoting low frequency mating with a or α cells [25] . These studies indicated that productive mating was likely due to loss of MTL genes , allowing C . albicans a/α cells to mate as a or α cells [25] . We similarly suggest that low level mating of C . tropicalis a/α cells is due to MTL instability , as loss of MTL genes was frequently detected in the mating products from a/α crosses ( Text S1 , Figure S5 , Table S11 ) . In addition , genetic recombination at the MTL was observed in a subset of mating products , indicating that homozygosis of the MTL could promote a/α mating ( Figure S5 ) . However , regardless of the mechanism involved , mating of a/α cells occurred at a very low frequency compared to conventional mating between a and α cells . WOR1 is the master transcriptional regulator of the white-opaque switch in both C . tropicalis [5] and C . albicans [25]–[27] , and its expression is therefore critical for opaque cell formation . To further investigate the role of WOR1 , we constructed Δwor1/Δwor1 and WOR1 overexpression strains in C . tropicalis a , α , and a/α backgrounds . Initially , a pACT1-WOR1 construct was used to overexpress WOR1 . However , unlike in C . albicans , this construct failed to induce switching to opaque in C . tropicalis ( data not shown and [8] ) . Instead , an alternative promoter , TDH3 , was utilized for WOR1 expression and found to be sufficient to induce opaque formation in C . tropicalis a , α , and a/α cells ( Figure 4A ) . Thus , WOR1 expression is both necessary and sufficient for formation of the opaque state in C . tropicalis . Previously , we showed that wor1 mutant a or α cells do not undergo detectable mating in C . tropicalis [5] . Mating assays were also performed on WOR1 overexpression strains and these strains exhibited increased mating between a and α cells ( ∼70% mating ) relative to wild-type opaque strains ( ∼15% mating , Figure S4 ) . This result further establishes the key role of Wor1 in directing expression of genes necessary for efficient conjugation . Transcriptional profiling was also performed on wor1 mutant and WOR1 overexpression a/α strains , and profiles compared to wild-type white and opaque cells ( Figure 4B ) . Consistent with the key role of WOR1 in directing the white-opaque switch , the expression profile of Δwor1 strains was similar to that of wild-type white cells , while strains overexpressing WOR1 had an expression profile similar to that of wild-type opaque cells ( Figure 4B ) . However , although the global expression patterns were similar , many genes were differentially expressed between Δwor1 strains and white cells ( Tables S7 and S8 ) and between WOR1 overexpression strains and opaque cells ( Tables S9 and S10 ) . Expression of WOR1 was also compared by RT-PCR and was found to have the following relative transcription levels: WOR1 overexpresser > wild-type opaque > wild-type white > wor1 mutant ( Figure 4C ) . WOR1 expression was ∼2–8-fold higher in WOR1 overexpression strains relative to opaque strains , ∼4–10-fold higher in opaque cells relative to white cells , and ∼1000-fold higher in white cells relative to wor1 mutants . The difference in expression levels between white cells and wor1 mutants is consistent with low-level expression of this gene in the white state . We also note that WOR1 expression levels were in line with the relative mating efficiencies of these strains ( Figure S4 ) . In C . albicans , the white-opaque switch is regulated by Wor1 together with three other factors , Wor2 , Czf1 , and Efg1 , as part of an interacting transcriptional circuit [29] . The EFG1 gene is missing from the C . tropicalis genome although a gene encoding a related APSES transcription factor , EFH1 , is present [20] . Previous studies failed to observe differential expression of WOR2 , CZF1 , or EFH1 between C . tropicalis white and opaque a cells [5] . We examined the expression of these genes in the larger white-opaque data set present in a/α cells , and between wor1 mutant and WOR1 overexpression a/α strains . CZF1 and EFH1 did not show significant expression differences between these profiles , while WOR2 expression was decreased in opaque cells , the opposite of that expected based on its expression in C . albicans . These results are therefore consistent with transcriptional control of the white-opaque switch being divergent between C . tropicalis and C . albicans ( outside of the conserved role of Wor1 ) . We also identified several other transcription factors regulated by the C . tropicalis white-opaque switch . Among these factors , UME6 , a gene known to have a role in regulation of filamentous growth in C . albicans , had consistently higher expression in C . tropicalis opaque cells . Conversely , upregulated genes in white cells included RME1 and NDT802 , genes with homologs involved in regulating meiosis in S . cerevisiae , as well as ADAEC , a gene that is also white-specific in C . albicans . Further studies are now required to determine if any of these transcription factors play an active role in regulating the white-opaque switch in C . tropicalis . In several diverse fungal species , Wor1 homologs do not mediate white-opaque phenotypic switching but still act as transcriptional regulators of cellular morphogenesis . This is evident in S . cerevisiae and H . capsulatum , where Wor1 homologs regulate the transition from budding yeast forms to filamentous forms [31] , [32] . Colony morphologies of C . tropicalis strains were compared on several media , and it was found that WOR1 overexpression strains were markedly more wrinkled than wild-type white or opaque cells when grown on Spider medium ( Figure 5A ) . Spider medium is a low nutrient medium that also induces filamentation in C . albicans [34] . Examination of cells from the wrinkled colonies confirmed that Wor1 overexpression strains were significantly more filamentous than other cell types due to a higher percentage of both hyphal and pseudohyphal cells ( Figure 5B ) . In addition , filamentation generally increased as WOR1 gene expression levels increased . Thus , for a/α cells , filamentation increased from wor1 mutants ( 4 . 4% ) to white cells ( 10 . 7% ) to opaque cells ( 23 . 9% ) to WOR1 overexpressing cells ( 32 . 9% , Figure 5B ) . These results indicate that filamentation correlates with WOR1 expression in C . tropicalis , and that Wor1 regulates filamentous growth independent of its regulation of the white-opaque switch . Filamentous growth is directly associated with biofilm formation in C . albicans . Biofilms are surface-associated communities of cells and often involve both yeast and filamentous cells in stable , complex structures that form on biotic and abiotic surfaces [35] , [36] . Furthermore , C . albicans biofilms are responsible for the seeding of serious bloodstream infections and are associated with antifungal drug resistance [35] , [36] . We therefore examined whether C . tropicalis wor1 mutant cells , white cells , opaque cells , or cells overexpressing WOR1 displayed differences in their ability to form biofilms using an adherence to plastic assay . White cells , opaque cells , and wor1 mutants generally performed poorly in these adherence assays , regardless of MTL configuration ( Figure 6A ) . In contrast , cells overexpressing WOR1 generated robust biofilms on the polystyrene plates , as noted by visual inspection as well as quantification of the biofilm by optical density ( Figure 6A ) . Increased biofilm formation was also demonstrated by an elevated level of staining with crystal violet , which indicates extracellular matrix production ( Figure 6B ) , and by increased XTT reduction , indicating that there were significantly more adherent , viable cells in the WOR1 overexpressing biofilms ( Figure 6C ) . Many cells within the WOR1-overexpressing biofilms were filamentous ( 22–59% ) , whereas comparatively few cells were filamentous in white , opaque , or Δwor1 cells ( 0–12% , Figure 6D ) . Transcriptional profiling was performed under biofilm culture conditions on WOR1 overexpression strains and compared to gene expression in wild-type opaque strains . This revealed that 58 genes were differentially expressed ( >4-fold ) between these strains ( Figure 6E ) . Gene expression changes included CHT3 , which encodes a chitinase whose expression is repressed in C . albicans hyphal cells [37] , and which was also repressed in the C . tropicalis WOR1 overexpression strain . Conversely , JEN2 was upregulated in C . tropicalis WOR1-mediated biofilms , and encodes a dicarboxylic acid transporter whose expression is also upregulated in C . albicans biofilms [38] . In addition , several C . tropicalis white-specific genes ( e . g . , ADAEC and WH11 ) were further downregulated in the WOR1-overexpressing cells relative to opaque cells ( Figure 6E ) . However , no genes with defined roles in filament regulation were obtained from these profiling studies , although many of the differentially regulated genes currently have no known function . Together , these results demonstrate that Wor1 promotes filamentous growth and biofilm formation in C . tropicalis and these functions are independent of the white-opaque switch . The role of Wor1 in this species is therefore analogous to that of several distantly related Wor1 homologs in the fungal lineage . The increase in filamentous growth is presumably a major reason for the increased adhesion and biofilm formation in the C . tropicalis Wor1-overexpression strains . We next determined if the configuration of the MTL locus contributes to overall fitness in C . tropicalis . In particular , we investigated the possibility that a/α cells exhibit increased fitness over MTL homozygous cells . We first analyzed 150 natural C . tropicalis isolates ( Table S1 ) to determine their MTL configuration and found that 119 isolates ( ∼80% ) were a/α strains , while 23 ( ∼15% ) were a strains and 8 ( ∼5% ) were α strains . A similar analysis performed by Xie et al . showed an even higher prevalence of a/α strains ( 145/150 isolates ) in the natural population [8] . Thus , similar to the case in C . albicans [17] , [39] , a/α genotypes are the predominant cell types in natural C . tropicalis isolates . We speculated that the predominance of a/α cells could result from increased fitness of these cell types relative to a or αcells . Thus , the ability to switch as an a/α cell could allow cells to retain their optimal fitness yet also be competent to undergo the white-opaque switch . The fitness of C . tropicalis a , α , and a/α strains was addressed using an in vivo model of murine candidiasis . Competition experiments were performed between a/α and a cells , or between a/α and α cells , using strains carrying different auxotrophic markers . Neutropenia was induced prior to infection with C . tropicalis cells as this increases the fungal load associated with systemic disease [40] . Mixtures of strains were injected into the tail veins of neutropenic mice and cells recovered from the brain and kidneys 72 hours following infection and characterized for cell type . Regardless of the combination of auxotrophic markers used , a/α cells consistently colonized host organs at a higher fungal burden than a or α cells in competition assays ( Figure 7 ) . This difference was statistically significant for fungal colonization of the brain , where approximately 70% of the recovered cells were a/α cell types . These experiments indicate that a/α cells are fitter than a or α cells in this in vivo model , as they outcompeted MTL homozygous cell types during systemic infection . We propose that phenotypic switching in a/α cells allows these cells to have the potential to adopt both white and opaque forms while still maintaining their fitness advantage over a or α strains .
Despite its apparent independence from MTL control , the C . tropicalis white-opaque switch still regulates sexual mating in this species . Mating between C . tropicalis a and α opaque cells occurs approximately 100 times more efficiently than that between white cells . It was not known , however , if mating of white cells required that cells first switch to the opaque state prior to mating , or if white cells were themselves mating competent . Analysis of C . tropicalis white×white and opaque×opaque mating products addressed this question , as they revealed that mating products inherited the phenotypic state of the parental cells ( i . e . mating between white cells generated white mating products while those between opaque cells generated opaque mating products ) . This simple observation established two important facts about phenotypic switching and mating in C . tropicalis . First , it demonstrated that the C . tropicalis white-opaque switch is independent of MTL status , as a/α cells formed by mating would be expected to be in the white state , regardless of the phenotype of the parental cells . Indeed , this is what has been observed in C . albicans , where the products of mating are white or , if opaque , have undergone concomitant loss of MTL genes [18] . Second , it showed that C . tropicalis white cells are themselves mating competent; if they had switched to opaque prior to mating then the products of mating would also have been opaque . Thus , C . tropicalis white and opaque cells are both mating competent , although the efficiency of mating is higher in the opaque state than in the white state . The discovery that C . tropicalis white cells are mating competent is interesting given that mating is completely abolished in mutant cells lacking WOR1 . Thus , although C . tropicalis white cells mate inefficiently , their mating frequency is still more than 1000-fold higher than that of wor1 mutants [5] . This indicates that significant WOR1 expression occurs in C . tropicalis white cells ( supported by array and qPCR data ) , and that this expression is sufficient for basal induction of genes necessary for conjugation . In opaque cells , Wor1 levels are further increased over white cells ( 4–10 fold ) and higher mating efficiencies are observed in cells with the opaque phenotype . Candidate mating genes include STE4 and BAR1 , both of which are elevated in the opaque state and are known to regulate mating in diverse fungal species [41]–[43] . Mating efficiency was further increased in Wor1 overexpression strains compared to wild-type opaque cells . These results demonstrate that mating efficiency can be separated from the white-opaque switch per se and , at least to a first approximation , C . tropicalis mating correlates directly with Wor1 expression levels . Mating was also observed between C . tropicalis a/α cells and either a or α partners . The efficiency of a/α cell mating was very low ( ∼10−6 ) regardless of whether white or opaque cells were used , and was approximately 5 orders of magnitude lower than conventional mating between opaque a and α cells . In fact , mating of a/α cells was 1000-fold lower than that between white a and α cells . Furthermore , the majority of a/α mating products had lost MTL genes or had undergone recombination at the MTL , and therefore had presumably mated as a or α cell types . Aberrant mating of a/α cells has also been described in C . albicans cells overexpressing WOR1 and was also attributed to rare loss of MTL genes [25] . Moreover , it is known that C . albicans a/α cells undergo occasional mating upon loss of a1 or α2 genes , as a1/α2 represses the expression of haploid-specific genes necessary for mating [18] , [21] . Transcriptional profiling of the three different genotypes ( a , α , and a/α ) in C . tropicalis revealed that there was significant overlap between white-opaque regulated genes in each cell type . This is to be expected given that Wor1 regulates the switch irrespective of MTL configuration . The key role of this transcription factor in determining the phenotypic state was further illustrated by comparing the profiles of white cells , opaque cells , wor1 mutants , and WOR1 overexpression strains . The profiles of white cells and wor1 mutant cells were similar , as were those of opaque cells and WOR1 overexpressing cells . In addition , wor1 mutant strains did not undergo switching to opaque , while WOR1 overexpression locked cells in the opaque state . Together , these results establish Wor1 as the master regulator of the C . tropicalis white-opaque switch in a/α cells , similar to its role in a and α cells . Surprisingly , significant differences in white-opaque regulated genes were noted between MTL homozygous and MTL heterozygous strains . For example , STE4 and BAR1 were expressed at elevated levels in opaque a or α cells , but not in opaque a/α cells . This may reflect the fact that the white-opaque switch regulates mating between a and α cells , but does not significantly promote mating in a/α cells . In addition , ∼400 switch-regulated genes were unique to a/α cells and were not observed in a or α cell types . The gene set unique to opaque a/α cells included a significant association with translation , biosynthetic processes , and gene expression control . A direct implication of these transcriptional differences is that behavioral differences ( including effects on host interactions and virulence ) may occur between white and opaque cells from MTL homozygous and MTL heterozygous cell types , and additional experiments will now test this possibility . The white-opaque switch affects virtually every aspect of Candida biology , from mating to interactions with host immune cells to pathogenesis [44]–[47] . MTL regulation of the switch in C . albicans ensures that only those cells that are capable of mating undergo the switch to the mating-competent form [18] . The discovery that C . tropicalis can form opaque cells regardless of MTL status suggests that the switch may have originally evolved to regulate processes other than sexual mating . Furthermore , the majority of natural C . tropicalis and C . albicans isolates are a/α strains ( 80–95% ) ; thus , while most C . albicans strains are unable to switch , it appears that the majority of C . tropicalis isolates are competent for switching to the opaque form . Many of the genes controlled by the white-opaque switch in C . albicans are metabolism genes , perhaps reflective of the fact that white and opaque cells colonize different host niches [9] , [21] . C . albicans white cells exhibit greater virulence in models of systemic infection , while opaque cells are more efficient at colonization of the skin and are unstable at 37°C , rapidly switching back to the white form [48] , [49] . In contrast to C . albicans , C . tropicalis opaque cells are stable at 37°C [5] and may colonize host niches that are refractory to C . albicans opaque cells . In addition , we found that C . tropicalis a/α cells exhibited increased fitness relative to a or α strains in a neutropenic model of candidiasis . Thus , the ability of a/α cells to switch phenotypes may be beneficial to C . tropicalis as it allows cells to propagate with optimal fitness , but still have the potential to form opaque cells and thereby adapt to different host niches . Alternatively , the white-opaque switch could have evolved to regulate mating in the ancestor to C . tropicalis and C . albicans , and MTL control of the switch subsequently evolved in C . albicans as a means of restricting switching to MTL homozygous strains . If the major role of the white-opaque switch is to regulate mating , then fine-tuning of the switching mechanism could have been beneficial to help prevent futile switching to the opaque state in a/α cells that are sterile . Certainly , the regulation of mating by the white-opaque switch is stricter in C . albicans than in C . tropicalis; white cells of C . tropicalis undergo appreciable mating frequencies while C . albicans white cells do not . Finally , it is formally possible that MTL control of the white-opaque switch was present in the ancestor to C . albicans and C . tropicalis , but has since been lost in C . tropicalis . Experiments will now be required to characterize C . tropicalis white and opaque states in vivo , and to determine if they exhibit different preferences for host colonization and pathogenesis as has been documented for C . albicans . An unexpected outcome of our analysis of white and opaque cells was the discovery of a link between C . tropicalis Wor1 and filamentous growth . Overexpression of WOR1 in C . tropicalis a , α , or a/α cells resulted in increased filamentation relative to wild-type white or opaque cells . C . tropicalis Wor1 is therefore a key regulator of filamentation , and this control can be separated from regulation of the white-opaque switch . The role of Wor1 was most clearly manifested in biofilm assays , where increased expression of WOR1 led to enhanced biofilm formation . This is presumably a direct result of the increase in filamentous growth , as filamentation and biofilm formation are inter-related processes in multiple Candida species [35] , [36] , [50] . Regulation of filamentation by the white-opaque switch has also been noted in C . albicans , although here white cells have been shown to be more conducive to undergoing filamentation than opaque cells [23] , [24] . The observation that C . tropicalis Wor1 induces filamentation shows interesting parallels with the role of Wor1 homologs in more distantly related ascomycete species . In S . cerevisiae and H . capsulatum , the Wor1 homologs Mit1 and Ryp1 , respectively , are both master regulators of filamentation [31] , [32] . It has therefore been proposed that the ancestral function of Wor1/Mit1/Ryp1 was to control morphological transitions such as that between yeast and filamentous forms [31] . The discovery that C . tropicalis Wor1 promotes filamentation suggests that it has retained the ancestral function of Wor1 in this species . In contrast to filamentous growth , the white-opaque switch evolved only recently in the ascomycete lineage , probably immediately prior to the divergence of C . tropicalis and C . albicans [5] , [8] , [51] . We therefore propose that C . tropicalis Wor1 has retained both the ancestral role of the Wor1 family of transcription factors ( regulation of filamentous growth ) , as well as adopting control over the more recently evolved white-opaque switch . In contrast , overexpression of C . albicans Wor1 did not promote biofilm formation under the conditions tested ( data not shown ) and can even inhibit filamentous growth [8] . Further studies on C . tropicalis Wor1 will therefore shed light on its role as a key regulator of both morphological switches: the recently evolved white-opaque switch , as well as the ancestral program of filamentous growth .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals as defined by the National Institutes of Health ( PHS Assurance #A3284-01 ) . Animal protocols were reviewed and approved by the Institutional Animal Care and Use Committee ( IACUC ) of Brown University . All animals were housed in a centralized and AAALAC-accredited research animal facility that is fully staffed with trained husbandry , technical , and veterinary personnel . Yeast extract peptone dextrose medium ( YPD ) , synthetic complete dextrose medium ( SCD ) , and Spider medium were made as described previously [34] , [52] . YPD plates containing 200 µg/ml nourseothricin ( NAT ) were used for selection of strains that were resistant to nourseothricin ( Werner Bioagents , Jena , Germany ) as previously described [53] . Lee's media containing 12 . 5 g/L N-acetylglucosamine ( Alpha Aesar ) was used for switching assays , and Lee's media containing 1 . 25% glucose was used for biofilm assays . [12] , [54] . Lee's media was supplemented with 0 . 004% histidine when used in experiments with -His strains . C . tropicalis strains used in this study are listed in Tables S1 and S2 . Gene deletions were constructed by using the SAT1 flipper strategy as described [55] . Strains were transformed with 1–4 µg of DNA by using a modified electroporation protocol [5] . To delete WOR1 , HIS1 , ARG4 , MTLa2 , and MTLα1 genes using the SAT1-flipper method , oligonucleotides were used to amplify ∼900 bp of the 5′ and 3′ homologous flanks of each gene . The resulting PCR products were digested with restriction enzymes noted in Table S3 and cloned into the plasmid pSFS2A [55] . The resulting plasmids were digested with restriction enzymes as noted in Table S4 to liberate cassettes containing the 5′ and 3′ gene flanks as well as the SAT1 selectable marker and used for transformation . Correct genomic integration of transformant colonies was confirmed by PCR of the 5′ and 3′ junctions ( for oligonucleotides , see Table S3 ) . The SAT1 marker was recycled by growing transformants on maltose media at room temperature or 30°C and subsequent replica patching to both YPD and YPD+NAT plates . Alternatively , the SAT1 marker was recycled by growing cells in liquid YEP + 2% maltose at room temperature for ∼2 days and selected by plating to YPD+ low NAT ( 10 µg/mL ) , as previously described [55] . The transformation process was repeated to delete the remaining copy of the gene , and loss of the ORF was confirmed by PCR . To overexpress WOR1 , fusion PCRs were performed to create a pTDH3-WOR1 construct , which was cloned into pSFS2A . The plasmid was linearized in the TDH3 promoter with SmaI and transformed . Correct genomic integration was confirmed by PCR . To insert a SAT1 marker next to the MTL , PCR was performed to amplify a sequence immediately upstream of the MTL , which was cloned into pSFS2A . The plasmid was linearized within this sequence with EcoRI and transformed into C . tropicalis . Correct genomic integration next to the MTLa or MTLα locus was confirmed by PCR . Quantitative mating assays between C . tropicalis strains were performed as described previously [5] , [18] . In brief , C . tropicalis cells were taken from Spider plates that had been grown at room temperature for 1–2 days and resuspended in water . Approximately 1×107 cells of each strain were mixed and pipetted onto 0 . 8-µm pore-size nitrocellulose filters and grown on the surface of Spider medium for 1–3 days at room temperature . Cells were collected from the filters and plated at different dilutions onto His− Arg− media to select for mating products and onto His− and Arg− plates to monitor each parent population . The limiting parent was used to calculate mating frequencies as follows: mating efficiency = conjugants/ ( limiting parent + conjugants ) = the greater of ( Arg− His− ) /Arg− or ( Arg− His− ) /His− . Statistical significance was determined using a Student's T-test . White phase cells were inoculated into liquid SCD medium and incubated at room temperature overnight . Cells from this culture were diluted to 0 . 1 OD600 and incubated at 37°C for 5 days . Cultures were diluted in water and plated onto Spider medium or Lee's medium containing N-acetylglucosamine at a concentration of ∼100 colonies per plate . Colonies were examined for opaque sectors after growth at room temperature for 7–10 days . RNA was isolated from cells grown in Spider liquid at 0 . 8–1 . 0 OD600 using the Ribopure-Yeast Kit ( Ambion ) . RNA was treated with Turbo DNaseI ( Ambion ) , and 2 µg of RNA used for subsequent cDNA generation using the GoScript enzyme ( Promega ) . qRT-PCR was then performed by using the gene specific primers listed in Table S3 with the SYBR Green Kit ( Applied Biosystems ) and ran on an Applied Biosystems 7300 Real-Time PCR System . Cells were harvested after being grown to OD600 1 . 0–1 . 2 in Spider medium at room temperature . Cells were collected , flash frozen , and stored at −80°C . For microarrays performed on biofilms , cells were collected after the two-day incubation detailed in the adherence assay . Total RNA was extracted from cell pellets using the RiboPure-Yeast Kit protocol ( Ambion ) . RNA was treated with Turbo DNaseI ( Ambion ) to eliminate DNA contamination and re-extracted with phenol/chloroform . Aminoallyl-labeled cDNA synthesis and hybridization to custom Agilent C . tropicalis microarrays was previously described by Porman et al . [5] . Arrays were scanned on a GenePix 4000 scanner ( Axon Instruments ) , data quantified using GENEPIX PRO version 3 . 0 and normalized using Goulphar ( http://transcriptome . ens . fr/goulphar ) . Data analysis was performed as previously described [5] . GO term analysis was facilitated by CGD ( http://candidagenome . org ) and Princeton University's Generic GO Term Mapper ( http://go . princeton . edu/cgi-bin/GOTermMapper ) . Array data is available from GEO ( accession numbers GSE40179 , GSE42517 and GSE43267 ) . Digital images of colonies were collected using a Zeiss Stemi 2000-C microscope equipped with an Infinity 2 digital camera and Infinity Analyzer software ( Lumenera Corperation , Ottawa , Canada ) . Differential interference contrast ( DIC ) of cells were captured using a Zeiss Inverted Microscope ( Axio Observer . Z1 ) fitted with an AxioCam HR . Images were processed with AxioVision Rel . 4 . 8 ( Zeiss , Germany ) . For electron micrographs , cells were resuspended in water and attached to poly-L-lysine coated-coverslips . Samples were fixed with 2 . 5% ( w/v ) glutaraldehyde in 0 . 1 M Na-cacodylate buffer , pH 7 . 4 at 4°C , and washed with 0 . 1 M Na-cacodylate buffer , pH 7 . 4 . Samples were then treated with 1% aqueous osmium tetroxide in 0 . 1 M Na-Cacodylate buffer , pH 7 . 4 , at 25°C for 90 minutes , and washed with 0 . 1 M Na-Cacodylate buffer , pH 7 . 4 . Cells were gradually dehydrated using a gradient ethanol series , dried in a critical point dryer , and coated with 20 nm gold palladium ( 60∶40 ) in an Emitech K550 sputter coater . Images were captured with a Hitachi S-2700 scanning electronic microscope with Quartz PCI software . To quantify filamentation from colonies grown on Spider medium , cells were removed from the center of patches and counted for the fraction of filamentous cells . At least 4 fields of view and 400 cells were counted for each data point . Statistical significance was determined using a Student's T-test . Cultures were inoculated in 3 ml of Spider medium then incubated at 25°C overnight . 2 ODs of cells were spun down and resuspended in 1 ml of Lee's + Glucose medium and transferred to a well of a 12-well polystyrene plate . Plates were incubated at 25°C for 1–2 days without shaking , then decanted . Each well was washed 3 times with 1 ml of water to remove non-adherent cells . Plates were imaged using a Chemidoc XRS+ with Image Lab software ( Bio-Rad ) . Adherent cells were scraped off the plastic surface and resuspended in 1 ml of water and optical density was determined . For significant differences between data sets , each was tested for normal distribution . A one-way ANOVA was performed on OD600 results . Nine representative microscope fields were counted for each condition to determine the fraction of filamentous cells . Statistical significance was determined using a Mann-Whitney pair-wise test due to non-parametric datasets . Samples were prepared similarly to the adherence assays . Following the 3 washes , 12-well plates were decanted and left to dry for 45 minutes , and subsequently stained with 385 µL of 0 . 4% aqueous crystal violet per well for 45 minutes . Each well was washed 3 times with 1 mL of water , then destained with 700 µL of 95% ethanol . Finally , 100 µL of each destain solution was transferred to a 96-well plate and diluted 10-fold . Optical density was read at 595 nm using a BioTek Synergy HT plate reader and statistics were performed similarly to the adherence assays . Samples were prepared similarly to the adherence assays . Following the 3 washes , the plates were decanted , then 315 µL of 1 mg/mL XTT ( 2 , 3-bis- ( 2-methoxy-4-nitro-5-sulfophenyl ) -2H-tetrazolium-5-carboxanilide ) and 35 µL of 360 µg/mL phenazine methosulfate were added to each well . The plates were incubated at room temperature for 20 minutes , then 100 µL of solution from each well was transferred to a 96-well plate and diluted . Optical density was read at 450 nm using a BioTek Synergy HT plate reader and statistics were performed similarly to the adherence assays . Female BALB/c mice ( 18–20 g , Charles River Laboratories ) were made neutropenic by intraperitoneal injection of 200 µg anti-Gr-1 mAb ( clone: RB6-8C5 , BioXCell , West Lebanon , NH ) one day prior to infection with C . tropicalis . C . tropicalis strains were grown overnight in Spider medium at 30°C , diluted to 0 . 2 OD600 in fresh Spider medium and grown at 30°C to log phase . Cells were collected and washed three times in sterile phosphate-buffered saline ( PBS ) . Mice were infected with a mixture of two C . tropicalis strains in a 50∶50 ratio with a total inoculum of ∼1 . 0×106 colony forming units ( CFUs ) by injection into the tail vein . Each C . tropicalis mixture included one isolate auxotrophic for histidine ( His ) and one isolate auxotrophic for arginine ( Arg ) biosynthesis . Dilutions of the inoculum were plated onto SCD medium lacking either histidine or arginine to confirm initial cell concentrations . Mice were euthanized 72 hours after infection and kidney and brains isolated . Organs were homogenized through a 70 µm filter and dilutions of the organ suspensions plated onto SCD medium lacking His or Arg . CFUs were counted on each plate to quantify the relative abundance of each strain . Mice where no cells were recovered after infection were excluded from analyses . Statistical significance was determined using a Student's T-test . | The white-opaque phenotypic switch has been extensively characterized in the human fungal pathogen Candida albicans , where it plays a central role in regulating entry into sexual reproduction . This epigenetic switch is strictly regulated by the MTL locus so that only a or α cell types can switch to the opaque state , whereas a/α cells are locked in the white state . In contrast , we show that in the related pathogen C . tropicalis white cells are capable of sexual mating and that the white-opaque switch is independent of MTL control . Thus , MTLa , α , and a/α cells all undergo reversible switching between white and opaque states . Despite these differences , switching in both C . tropicalis and C . albicans is dependent on the expression of the Wor1 transcription factor . This factor is conserved amongst fungal ascomycetes and , in several species , acts as a master regulator of the yeast-to-filament transition . We show that , in addition to regulating the white-opaque switch in C . tropicalis , Wor1 expression also promotes filamentation and biofilm formation in this species . We therefore propose that C . tropicalis Wor1 has retained the ancestral role of this family of transcription factors while also gaining control over the more recently evolved white-opaque phenotypic switch . | [
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"pathogenes... | 2013 | MTL–Independent Phenotypic Switching in Candida tropicalis and a Dual Role for Wor1 in Regulating Switching and Filamentation |
Several studies have shown that cytotoxic T lymphocytes ( CTLs ) play an important role in controlling HIV/SIV infection . Notably , the observation of escape mutants suggests a selective pressure induced by the CTL response . However , it remains difficult to assess the definite role of the cellular immune response . We devise a computational model of HIV/SIV infection having a broad cellular immune response targeting different viral epitopes . The CTL clones are stimulated by viral antigen and interact with the virus population through cytotoxic killing of infected cells . Consequently , the virus population reacts through the acquisition of CTL escape mutations . Our model provides realistic virus dynamics and describes several experimental observations . We postulate that inter-clonal competition and immunodominance may be critical factors determining the sequential emergence of escapes . We show that even though the total killing induced by the CTL response can be high , escape rates against a single CTL clone are often slow and difficult to estimate from infrequent sequence measurements . Finally , our simulations show that a higher degree of immunodominance leads to more frequent escape with a reduced control of viral replication but a substantially impaired replicative capacity of the virus . This result suggests two strategies for vaccine design: Vaccines inducing a broad CTL response should decrease the viral load , whereas vaccines stimulating a narrow but dominant CTL response are likely to induce escape but may dramatically reduce the replicative capacity of the virus .
We develop a computational model of HIV/SIV virus dynamics including a cellular immune response consisting of several CTL clones ( Fig . 1 ) . We have n different CTL clones recognizing n different epitopes derived from viral proteins . On the viral genome we allow two mutations to occur per epitope . One mutation confers escape from recognition by the specific CTL clone . Since escape mutations may be associated with a fitness cost in viral replication or infectivity , a second mutation can at least partially compensate for the fitness loss . We translate those interactions into a set of ordinary differential equations ( ODEs ) and add stochastic events for viral mutation . Classically , the processes of infecting target cells or the killing of infected cells by CTLs have been described with simple mass-action terms [25] , [26] . For instance , a previous study already described antigenic escape from CTL clones during HIV-1 infection with a simple mathematical model [27] . Recently , we developed terms describing a density-dependent infection that results in a better description of the dynamics of acute infection ( i . e . less oscillatory ) of the viral load and the immune response [28] . Moreover , the interaction of infected cells with effector CTLs , both for proliferation of effector cells and the killing of infected cells , were assumed to saturate according to Michaelis-Menten kinetics . Here , we integrate those interaction terms to devise a new virus dynamics model consisting of several CTL clones that is described by the following differential equations: ( 1 ) ( 2 ) ( 3 ) ( 4 ) Non-infected CD4+ target cells T are produced at a rate of λ cells per day , die at a rate δT and can become infected by virus particles of type Vi with fitness fi at a maximal rate of β per day . Target cell availability for virus particles is density dependent , as the infection rate per virus particle is saturating over the total number of CD4+ cells ( non-infected and infected ) . After infection of a target cell , reverse transcription occurs during which the virus can mutate with a probability of μ per position ( for further details see Methods ) . Having two positions to mutate per n epitopes , the number of different viral variants , m , is maximally 22n . Infected cells Ii die at a rate of δI per day , and are cleared by those CTL clones that can recognize an epitope on its surface . The matrix αij defines the topology with which the CTL clone Ej recognizes epitopes presented on the surface of the infected cell of type i , and contains either 1 ( recognition ) or 0 ( no recognition ) . Following Michaelis-Menten kinetics , the CTL clones compete with each other for clearance of infected cells ( Fig . 2 ) . When , an infected cell is killed at a maximal rate of k per day . With increasing hk , we approach mass-action kinetics for the killing , i . e . when cells are killed at a per capita rate of per day . Since the dynamics of virus particles Vi is much faster than that of the cell populations [29] , we assume a quasi-steady-state for the virus particles and set Vi = pIi/δV [30] . ‘Naive’ CTLs Ei are produced at a rate of σ cells per day . If they recognize antigen produced by Ij ( i . e . αji = 1 ) they proliferate at a maximal rate g per day and die at a rate δE . Upon infection , virus replicates rapidly and typically . Therefore , CTL effector cells are produced at a half-maximal rate when . To account for different avidities for the different CTL clones Ei we draw from a uniform distribution . Once the total number of CTL effector cells is high , becomes important and we get inter-clonal competition between the CTL effector cells . An overview of the parameters is given in Table 1 . For a more detailed description of the model see Methods .
The most surprising phenomenon of CTL escape in HIV/SIV is the time scale at which it occurs . Selection of escape variants has been found to happen very early after acute infection , but also late after years [17]–[19] . Additionally , it has been shown that escape can occur sequentially [31] . Although it has been suggested that compensatory mutations delay the appearance of escape variants , it is still unclear why escape variants would occur so late when the CTL clones recognizing the epitope have been present since acute infection . Our model describes the virus dynamics of an HIV/SIV infection and the subsequent immune escape in a very realistic manner ( Fig . 3 ) . Many escapes occur widely spaced out in time and we observe that their appearance is determined by the dynamics of the different CTL clones . Starting with the same CTL clone repertoire , Fig . 3A represents a simulation where killing of infected cells approaches mass-action dynamics ( hk = 1012 ) . Fig . 3B shows a simulation where killing follows Michaelis-Menten kinetics and CTL clones compete for killing of infected cells ( hk = 109 ) . In both simulations , the total number of infected cells peaks a few weeks after infection and reaches a set-point level of around 107 to 108 cells ( black line in top panels ) [32] . The colored lines represent the amount of virus-infected cells containing an escape mutation at a specific epitope . Due to the large actual population size of infected cells and the high mutation rate , all single escape and compensatory mutants are produced rapidly during the acute phase of the infection and are maintained in a mutation-selection balance . Some escape variants are being selected early , and replace the wild-type variant at this epitope ( e . g . , blue line ) . However , many escapes occur late , despite their early presence in the virus population . The dynamics of the CTL clones Ei are depicted in the bottom panels . Upon infection , the clones become stimulated depending on the parameter defining their avidity , which generates a single or a few dominant CTL clones and many sub-dominant clones . Escape preferentially occurs from dominant clones that more efficiently kill infected cells . However , due to a severe fitness cost for the escape mutation , there is no escape from the most dominant CTL clone in this particular simulation run . When an escape variant replaces the wild-type variant , the CTL clone looses antigenic stimulation and declines . Because of inter-specific competition between CTL clones , previously sub-dominant clones can increase in size , increasing the selection pressure for the epitopes they recognize . It has been suggested that many escapes occur early , i . e . during the decline phase in viral load after the peak of infection and that they potentially prevent clearance of HIV/SIV . However , in our simulations immune escape does not occur before the set-point level is reached around two to three months after infection ( Fig . 4A ) . This is because the CTL clones only become effective during the decline phase of virus after the peak of infection [28] , [33] . Due to a transient CD4+ target cell depletion [34]–[36] , there is not enough viral replication for escape variants to increase in frequency during this phase . Therefore , escape variants become selected only after the set-point is approached . Nevertheless , many escapes occur during the first months after set-point levels have been attained . After about two years , the virus population stabilizes as the ‘easy’ escapes have been done , the replicative capacity is partially restored and only few escapes are expected to appear later during infection . However , it is important to note that Fig . 4A shows only the first appearance of escape variants . Some of those variants will start to fluctuate in frequency or revert back to wild-type and possibly re-emerge at a later time point . This has implications for the analysis of longitudinal data . If an escape is found to happen late it does not necessarily mean that it had not been selected earlier during infection Infected-cell death rates from different patients have been found to be close to a normal distribution with a mean of 0 . 45 d−1 [37] . We show that variation of the average infected-cell death rate is also expected to occur within a patient due to the transient decrease of the death rate during an immune escape from CTL mediated killing ( Fig . 4B and 4C ) . However , the death rate does not necessarily decrease over time , i . e . the half-life of infected cells is not expected to increase with disease progression . Escape variants not only appear at different times during infection but also with different rates . The rate at which an escape variant replaces the wild-type , the so-called escape rate , is determined by the balance between the evaded rate of killing and the fitness cost of the escape mutation [12] , [38] . Furthermore , the heterogeneity of the wild-type and the escape variant population at all other epitopes can lead to a different selection induced by other CTL clones . In general , we can define the escape rate as the ‘escape variant growth rate - wild-type growth rate’ . A recent study quantified this process and concluded on the basis of slow escape rates and minimal fitness costs that killing of HIV-1-infected cells is inefficient in humans [12] . Unfortunately , the available data sets do not allow us to follow the process of escape in detail . In contrast , our model provides a unique tool to follow the dynamics and analyze rates of escape and link those to rates of killing . Fig . 5A shows the distribution of escape and the corresponding rates that occur in 1000 simulation runs over five years of infection . The histograms of killing and escape rates are shown in Fig . 5B and 5C , respectively . Although the total killing by all CTL clones is high ( 0 . 9 d−1 , see Table 1 ) , most individual killing rates are below 0 . 4 d−1 , and most escape rates are below 0 . 2 d−1 . The lower rates of escape are due to the fitness cost of escape mutations that cannot totally be restored by compensatory mutations . Interestingly , the rates of escape are not distributed equally over the time of infection ( Fig . 5A ) . We therefore calculate the mean of killing and escape rates per year after infection ( Fig . 5D ) . It can be seen that escape rates decrease late during infection and reach relatively low values . The corresponding killing rates decrease after the first year of infection and dramatically increase late during infection . This result shows a ) that the selection strength of CTL clones can increase after there has been escape from other clones and b ) that escape against efficient CTL clones can be associated with a dramatic cost in viral fitness that slows down the selection of the escape variant . In Fig . 5A–D we assumed mass-action kinetics for the interaction of CTLs to kill infected cells , i . e . , we set hk = 1012 . However , when CTL effector cells form a complex with infected cells before delivering their lethal hit , the killing should follow Michaelis-Menten kinetics [28] , [39] . We analyzed the influence of a saturating killing term using several lower values of hk ( Fig . 5E ) . Although the maximal total killing is constant ( see Table 1 ) , we can see that killing and escape rates decrease if the model approaches Michaelis-Menten kinetics . Moreover , escape occurs relatively frequent for high values of hk ( mass-action ) but rarely for low values of hk ( saturation ) ( black dots ) . To estimate rates of escape from in vivo data one has to obtain ratios of escape variants to the wild-type . However , a problem arises when escapes are too rapid to be followed by the relatively long sampling intervals [13] . This shortcoming can lead to an underestimation of the rate of escape . To analyze this effect we measure the frequency of an escape variant at four time points ( 100 , 150 , 200 and 250 days after infection ) and estimate the escape rate as described in reference [12] ( see also Methods ) . Fig . 5F shows the distribution of the estimated escape rates ( gray bars ) and the true escape rates ( black line ) . Even though the sampling intervals were relatively short ( 50 days ) it can be clearly seen that the estimated rates are generally lower , with a mean of about 50% of the true rates . Our analysis shows several properties of the process of escape . Even though the total killing induced by all CTL clones is high , escape rates are expected to be slow . First , escape rates slow down due to the acquired fitness cost in viral replication , especially during later phases of infection . Secondly , if killing of infected cells follows Michaelis-Menten kinetics , the rates of escape are decreased . In addition , estimating rates of escape from ratios of escape variants and wild-type virus is likely to lead to an underestimation of the true rate . These findings highlight that slow escape rates do not necessarily infer low killing rates . CTL responses during HIV infection generally consist of several CTL clones recognizing many different epitopes . The size of those CTL clones can differ substantially resulting in dominant and sub-dominant responses [21] , [40] . In our model , the relative size of a CTL clone depends on how well it can recognize viral antigen , and is defined by . By changing we can change the sizes of the CTL clones relative to each other . For high we get strong immunodominance with a single or few dominant clones and many sub-dominant clones , whereas low values of result in CTL clones that are very similar in size ( Fig . 6A ) . Note , that the total killing induced by the sum of all CTL clones remains constant ( 0 . 9 d−1 ) and is independent of . To investigate the effect of immunodominance we run simulations for different values of . Strong immunodominance , where single CTL clones can induce a strong selection pressure on the viral population , leads to more frequent escape ( Fig . 6B ) . Concurrently , viral replication is controlled less efficiently and the number of infected cells increases with increasing immunodominance ( Fig . 6C ) . However , viral escape can be associated with a fitness cost . Even though the number of infected cells increases , the replicative capacity of the viral population is reduced substantially ( Fig . 6D ) .
The interaction of the HIV quasispecies with the CTL response appears to be complex which makes the analysis of experimental data difficult . Mathematical and computational models have been helpful to investigate how different processes influence each other . For example , a previous model described how shifting immunodominance and antigenic oscillations can occur during HIV infection [27] . Now , with a model incorporating multiple CTL responses together with escape and compensatory mutations , we show that the dynamics of the CTL clones are sufficient to explain the sequential and late occurrence of escape variants . Upon loss of antigenic stimulation , CTL clones disappear slowly [41] , [42] . Concomitantly , other CTL clones can increase in size and induce more efficient killing that leads to further escape that is sequentially distributed over many years after infection . Interestingly , the outgrowth of escape variants does not occur earlier even though they are always present in the viral population . It has been suggested that a small effective population size of HIV-infected cells is responsible for the late production and selection of escape variants [43] . However , a recent study argues that stochastic effects play a minor role for the appearance of deleterious and beneficial mutations in HIV [23] . Also , our simulations show that viral evolution is fairly deterministic . The model allows us to simulate two identical ‘patients’ that are infected with the same virus and have the same CTL repertoire . In that case , the stochastic generation of the viral variants results in a slow increase in variation over years ( results not shown ) . For example , the emergence of a certain escape variant is predictable during the first years after infection but becomes more variable later on . This is in line with a recent study where concordant evolution of HIV has been observed in mono-zygotic twins early during infection but more variation has been shown at later stages of the infection [21] . Another explanation for the late appearance of escape variants that has been put forward is the waiting time for compensatory mutations [24] . Our simulations show that single compensatory mutations are expected to be present in the viral quasispecies and therefore should not slow down the emergence of escape . However , if more compensatory mutations are needed to restore the fitness loss of an escape variant they can delay their occurrence ( results not shown ) . Due to epistasis however , the fitness interactions of several mutations are highly complex and therefore simulation results depend mainly on a presumed fitness landscape . Therefore , we propose a way to test the two hypotheses for the late appearance of escape variants . First , if the delay was due to the waiting time for compensatory mutations , late escapes would be associated with more compensatory mutations than early escapes . If on the other hand , the dynamics of CTL clones mainly determines the late appearance , CTL clones where escape has been detected late should have increased in size relative to the other CTL clones in the period before . Both of these hypotheses can be tested by analyzing longitudinal data of HIV/SIV infections . Our simulations proved to be useful to analyze the process of escape . We conclude that , although killing can be very efficient , escape rates are expected to be low . The association with a fitness cost and the way how CTL clones interact with infected cells critically influence the rates of escape . Hence , it appears to be important to study those interactions to derive realistic killing terms [39] . Furthermore , we have shown that estimating rates of escape is difficult from infrequent sequence measurements . We propose that sequence intervals should be shortened to follow the outgrowth of the variants . Additionally , there might be other reasons that slow down the selection of escape variants . For example , our model does not take into account multiple infected cells . As the fraction of multiple infected cells is large [44] , escape variants are likely to appear in cells that are also infected with wild-type virus . As long as wild-type epitopes are presented on the cells surface , the escape variant does not gain any growth advantage compared to the wild-type . As a consequence , the selection of escape variants is expected to slow down . Immunodominance affects the viral evolution within a host . A higher degree of immunodominance leads to more frequent escape with a reduced control of viral replication but a substantially impaired replicative capacity of the virus . This is interesting as vaccines generally aim to induce a broad CTL response where escape is unlikely to occur . However , a dramatic reduction of the replicative capacity of the virus due to escape could indeed slow down disease progression and/or reduce transmission [45] . Based on these results we identify two strategies for vaccine design: Vaccines inducing a broad CTL response should decrease the viral load whereas vaccines stimulating a narrow but dominant CTL response are likely to induce escape and consequently reduce the replicative capacity of the virus . The balance between the CTL response and the viral population acquiring escape mutations appears to be a dynamical process over a long period of infection . We have shown that it is important to analyze the kinetics of this process and also the time scale ( acute and chronic phase ) at which it occurs . More longitudinal data of HIV infections will help to further investigate this process and future research is likely to go into this direction .
The set of ordinary differential equations ( ODEs ) is extended with stochastic events for viral mutation ( similar as in [46] ) . Initially at day 0 , infection occurs with the wild-type . Therefore the number of viral variants , m ( 0 ) , is 1 . In the following , the ODEs are integrated using the routine odeint from Numerical Recipes [47] . Every time step Δt = 1 day , we approximate the number of cells that have been infected with virus of type i during the last time step according to ( 5 ) After infection , reverse transcription of the viral RNA occurs during which every position can mutate with the probability μ . For the cells being newly infected with virus of type i , we calculate the integer number xij of cells where the virus mutated into another type j . This is done by drawing from the binomial distribution xij = B ( ΔIi , μ ) for each of the two positions ( escape and compensatory ) over a total of n epitopes . Then , the infected cell populations are updated accordingly ( i . e . Ii = Ii−xij and Ij = Ij+xij ) . Whenever a previously not existing viral variant j is generated , m ( t ) = m ( t−Δt ) +1 , and the set of ODEs is expanded . We also take into account the actual population size of virus infected cells [23] . If the number of infected cells of a certain variant falls below 1 , the variant is deleted . Note , that in order for the simulations to be computationally efficient , we only generate the viral variants stochastically but do not include possible fluctuations at small population sizes . However , this hybrid stochastic-deterministic approach is sufficient to keep the variants in a mutation-selection balance . Running the model using the Gillespie algorithm [48] , we observed that whenever the different viral variants become selected , their growth approximates the deterministic description from the ODEs . A program of the model was written in C and simulations were run under Linux . The source code can be obtained freely on request from the authors . Escape mutations in HIV and SIV are likely to confer a fitness cost in viral replication or infectivity [3]–[9] . A recent study by Parera et al . [49] showed that fitness effects caused by random single mutations are approximately uniformly distributed . Therefore , we draw the fitness of a virus containing a single mutation relative to the wild-type virus from a uniform distribution between 0 . 0 and 1 . 0 . However , the combined effect of an escape mutation together with a compensatory mutation in a single epitope confers a higher fitness that is drawn uniformly between wild-type fitness ( fwt = 1 . 0 ) and the higher fitness of the two single variants . The outgrowth of an escape variant and the subsequent replacement of the wild-type can be considered as a competitive growth between two populations: ( 6 ) ( 7 ) where w is the wild-type and e is the escape variant . Cells infected with wild-type virus replicate at a net rate a and are killed by all CTL clones recognizing epitopes other than the escape epitope at rate b . The CTL clone recognizing the escape epitope kills wild-type cells at rate c . Cells infected with the escape variant replicate at a net rate a′ and are only killed by the CTL clones recognizing epitopes other than the escape epitope . For c>0 , the escape variant gains a growth advantage as long as the fitness cost of the escape , d = a−a′ , is not higher ( i . e . d<c ) . As shown in Asquith et al . [12] this model can be fitted to longitudinal data of escape . This is done using the routine lmfit ( http://sourceforge . net/projects/lmfit ) based on the Levenberg-Marquardt algorithm to solve nonlinear least-squares problems . However , the fitting is likely to underestimate the true rates of escape as it is illustrated in Fig . 7 . We assume Michaelis-Menten kinetics for the killing of infected cells by CTL effector cells: ( 8 ) This term takes into account the effector/target cell ratio of the cellular interaction: the total killing is proportional to infected cells at high effector cell densities and proportional to effector cells at high infected cell densities [28] . However , we also analyzed the outcome of other killing terms following different kinetics: ( 9 ) Here , infected cells are killed by effector cells following simple mass-action kinetics . We mentioned that by increasing hk in Equation 8 we approach mass-action kinetics and the results are discussed . ( 10 ) Single saturation over CTL effector cells E is similar to the double-saturating term used in Equation 8: once the set-point is reached , , and Equation 8 approaches Equation 10 . ( 11 ) Taking into account that CTL effector cells need some time to interact with infected cells before delivering their lethal hit , a saturation effect occurs as given in Equation 11 ( see also reference [28] ) . We define , to have infected cells being killed at a maximal rate of 0 . 9 d−1 during the chronic phase of infection . I* and E* are the numbers of infected cells and CTL effector cells , respectively , in the steady-state in absence of escape . During the acute phase , we observe three types of dynamics: 1 ) For hk<5×107 , the infection is always cleared since an infected cell can be killed at a rate of per day that can be enormous . 2 ) For 5×107<hk<7×107 , a very rapid escape during acute infection can prevent clearance of the infection . 3 ) For hk>7×107 , we approach mass-action kinetics since hk>>I and the term becomes . | As a result of their high mutation rate , HIV and its counterpart SIV in non-human primates can evade recognition by the host immune response through the generation of viral variants , the so-called escape mutants . This avoidance of cytotoxic T lymphocyte ( CTL ) mediated killing seems to be one of the major reasons why virus replication is not controlled effectively . However , it remains difficult to investigate the critical properties of the dynamics of immune escape . To this end , we developed a new computational model of HIV/SIV infection consisting of several CTL clones that can recognize specific parts of viral proteins , i . e . , epitopes . The simulations allow us to follow the dynamics of immune escape in detail and help to interpret longitudinal data of HIV/SIV infections . Interestingly , changing the relative sizes of the CTL clones leads to a different evolution of the virus . Instead of reducing the number of infected cells , an alternative strategy of vaccine design could be to reduce the replicative capacity of the virus that might have implications for disease progression . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"infectious",
"diseases/hiv",
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"aids",
"immunology/immune",
"response",
"computational",
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] | 2008 | Dynamics of Immune Escape during HIV/SIV Infection |
Myxoma virus ( MYXV ) -encoded protein M029 is a member of the poxvirus E3 family of dsRNA-binding proteins that antagonize the cellular interferon signaling pathways . In order to investigate additional functions of M029 , we have constructed a series of targeted M029-minus ( vMyx-M029KO and vMyx-M029ID ) and V5-tagged M029 MYXV . We found that M029 plays a pivotal role in determining the cellular tropism of MYXV in all mammalian cells tested . The M029-minus viruses were able to replicate only in engineered cell lines that stably express a complementing protein , such as vaccinia E3 , but underwent abortive or abated infection in all other tested mammalian cell lines . The M029-minus viruses were dramatically attenuated in susceptible host European rabbits and caused no observable signs of myxomatosis . Using V5-tagged M029 virus , we observed that M029 expressed as an early viral protein is localized in both the nuclear and cytosolic compartments in virus-infected cells , and is also incorporated into virions . Using proteomic approaches , we have identified Protein Kinase R ( PKR ) and RNA helicase A ( RHA ) /DHX9 as two cellular binding partners of M029 protein . In virus-infected cells , M029 interacts with PKR in a dsRNA-dependent manner , while binding with DHX9 was not dependent on dsRNA . Significantly , PKR knockdown in human cells rescued the replication defect of the M029-knockout viruses . Unexpectedly , this rescue of M029-minus virus replication by PKR depletion could then be reversed by RHA/DHX9 knockdown in human monocytic THP1 cells . This indicates that M029 not only inhibits generic PKR anti-viral pathways , but also binds and conscripts RHA/DHX9 as a pro-viral effector to promote virus replication in THP1 cells . Thus , M029 is a critical host range and virulence factor for MYXV that is required for replication in all mammalian cells by antagonizing PKR-mediated anti-viral functions , and also conscripts pro-viral RHA/DHX9 to promote viral replication specifically in myeloid cells .
Poxviruses encode dozens of modulatory proteins that are involved in evasion of host immune responses and are critical virulence factors needed for viral pathogenesis [1] , [2] , [3] . The poxvirus E3 family of immune evasion proteins antagonizes multiple anti-viral cellular signaling pathways that are primarily induced by interferon ( IFN ) . The two best-characterized cellular targets of the E3 protein , encoded by the vaccinia virus ( VACV ) E3L gene , are PKR and 2′-5′-oligoadenylate synthetase ( 2′-5′OAS ) , both of which are activated by dsRNA and trigger a global inhibition of protein synthesis to combat progression of the viral replication cycle [4] , [5] , [6] , [7] . VACV E3 also blocks the activation of IFN regulatory transcription factors 3 ( IRF-3 ) and 7 ( IRF-7 ) [8] , nuclear factor κB ( NF-κB ) [9] , [10] , and IFN-stimulated gene 15 ( ISG15 ) [11] , all of which contribute to the anti-viral state of IFN-treated cells . This subversion of host anti-viral functions by VACV E3 is primarily mediated by at least two protein domains: a carboxy ( C ) -terminal dsRNA-binding domain and an amino ( N ) -terminal Z-DNA binding domain [12] , [13] , [14] , [15] , both of which are required for full virus virulence in mice . Targeted deletion of the E3L gene of VACV results in reduced cellular tropism in certain cultured cell lines and increased sensitivity to the inhibitory effects of IFNs [16] . However , the in vitro and in vivo roles of the two N- and C- terminal domains of E3 differ significantly , for reasons not yet clearly defined . For example , the N-terminal domain of E3 is not required for VACV replication in at least some cell types , but is required for in vivo pathogenicity [13] , [14] , [17] . In addition , the N–terminal domain is required for inhibition of the type I IFN response in mice and in mouse embryo fibroblasts ( MEFs ) [15] . More recent studies suggest that E3 also plays a role in antagonizing the cellular sensing pathways activated by dsDNA and dsRNAs as mediated by various cellular PRRs [18] , [19] , [20] . Several studies indicate that E3 function ( s ) in VACV can be complemented in vitro , but not in vivo , by various related viral and bacterial proteins with dsRNA-binding capacity . For example , influenza virus NS1 , reovirus S4 , Escherichia coli RNase III or the Orf Virus E3 homologue could complement some E3 host range functions in cultured cells but could not restore pathogenicity of E3L-minus VACV [21] , [22] , [23] , [24] . This suggests that the dsRNA binding properties of these proteins alone may not be sufficient for rescuing VACV in vivo replication and virulence in the absence of E3 . Moreover , E3 orthologs derived from poxviruses of other genera were also not able to restore full VACV pathogenicity in the absence of E3 [25] . These various tested E3 orthologs were also significantly diverged in terms of their capacity to complement the host range functions of E3 in cultured mammalian cells . These results all suggest that the various poxvirus E3 orthologs might have acquired unique host immune modulatory functions and have different cellular target ( s ) , depending on the evolutionary histories of the specific virus . In other words , the dsRNA-binding property alone may not be sufficient to explain all of the many known E3 functions . Myxoma virus ( MYXV ) is a rabbit-specific Leporipoxvirus that encodes a portfolio of immunomodulatory proteins , many of which are very distinct from those encoded by the Orthopoxvirus VACV [26] . Of the host-interactive modulatory proteins that are relatively more similar between the two viruses , the MYXV M029 is a truncated ortholog of VACV E3 that lacks a significant portion of N-terminal Z-DNA binding domain of VACV E3 [26] . Replacement of E3 with M029 in the VACV background can restore some of the host range functions of E3 ( for example , in HeLa cells ) presumably by suppression of common cellular anti-viral activities [25] . However , M029 replacement was not able to restore pathogenicity of E3-deficient VACV in vivo . This suggests that , M029 likely also has distinct roles in terms of MYXV biology , such as virus pathogenicity in rabbits , anti-viral activity and/or host range functions . Here we report several important and novel roles for M029 in MYXV tropism and pathogenicity , in terms of both extending viral host range in cultured mammalian cells , as well as acting as a virulence factor for myxomatosis in European rabbits . When M029 expression was abrogated , the M029-minus MYXV constructs were defective for replication in essentially all mammalian cell types tested and failed to cause any aspects of myxomatosis disease in rabbits . We exploited a proteomic approach to reveal that M029 is a major host range determinant for MYXV , with at least two distinct biological functions . One is to bind PKR protein in a dsRNA-dependent fashion in order to antagonize PKR anti-viral responses in a wide variety of mammalian cell lineages from multiple species . M029 also interacts with a cellular member of the DEXD/H box family of proteins , RHA/DHX9 , in a dsRNA-independent fashion . However , in contrast to PKR , instead of inhibiting this second target , M029 binds and conscripts RHA/DHX9 to promote MYXV replication in a cell type dependent manner . Thus , these two distinct cellular protein interactions of M029 represent the first example of a single viral immunomodulatory protein interacting with two distinct host binding partners , one of which ( PKR ) is inhibited to alleviate anti-viral responses and the other ( RHA/DHX9 ) is redeployed as a novel pro-viral effector to expand viral tropism in a select subset of target mammalian cells .
MYXV M029 has a single predicted dsRNA-binding domain , which displays approximately 45% sequence identity with the C-terminal dsRNA-binding domain of VACV E3 , but it lacks the predicted N-terminal Z DNA binding motif that is found in E3 . In order to study the biological functions of M029 , we have disrupted M029 ORF by either inserting an eGFP expression cassette driven by a poxvirus synthetic early/late promoter ( pE/L ) [27] within the ORF ( vMyx-M029ID ) or replacing the entire M029 ORF with the same eGFP cassette ( vMyx-M029KO ) ( Fig . 1 ) . However , although we were able to detect eGFP-expressing viruses in the crude infection/transfection mixtures used to construct these viruses , we were not able to isolate any pure M029-defective recombinant viruses ( i . e . expressing eGFP ) in rabbit RK13 , primate BSC40 , hamster BHK21 or any other human cell lines that are normally permissive for wildtype MYXV replication . Instead , cloned M029-minus viruses were able to form foci only in engineered RK13 cell lines that constitutively express VACV E3 ( RK13-E3 ) alone or expressed both E3 and K3 ( RK13-E3K3 ) from VACV . Using these complementing cell lines , we were able to purify both M029-defective MYXVs , vMyx-M029KO ( i . e . M029 knockout ) and vMyx-M029ID ( i . e . M029 insertional disruption ) . Both vMyx-M029KO and vMyx-M029ID viruses replicated normally in RK13-E3 ( Fig . 2A ) and RK13-E3K3 ( not shown ) and yielded progeny titers similar to control wildtype MYXV , called vMyx-GFP ( MYXV expressing an eGFP cassette located at an intergenic site between M135 and M136 , and driven by a poxvirus synthetic E/L promoter ) [27] , [28] as assessed by single step growth curve . These M029-minus and M029-expressing viruses formed similarly sized foci in RK13-E3 cells ( Fig . 2D ) . However , in the parental RK13 cell line both vMyx-M029KO and vMyx-M029ID viruses formed much smaller foci , indicating a defect in virus replication and/or spread ( Fig . 2D ) . When analyzed by single step growth curve , the virus titers for the M029-defective viruses were consistently lower , with more than one log difference at 12 hpi when compared with vMyx-GFP ( Fig . 2B ) . This difference was accompanied by delayed viral late protein synthesis ( as detected by Serp-1 levels on Western blots ) in RK13 cells from both M029 mutant viruses ( Fig . 2E , middle panels ) . However , there was no difference in the synthesis or kinetics of viral early proteins as detected by measuring M-T7 expression ( Fig . 2E , top panels ) . In contrast , in RL5 , a rabbit CD4+ T cell line , infection with either vMyx-M029KO or vMyx-M029ID viruses were abortive compared to the control vMyx-GFP replication ( Fig . 2C ) . No late protein synthesis at all was detected in RL5 cells after infection with either vMyx-M029KO or vMyx-M029ID viruses ( not shown ) . Although MYXV is a rabbit specific poxvirus in nature , it also infects and replicates in a wide variety of cell lines derived from non-rabbit species , such as human , mouse , non-human primates , or hamster . In fact , MYXV replication is especially robust in a wide variety of human cancer cells in vitro [29] , [30] , [31] , [32] or in tumor tissues within various animal models of cancer in vivo [33] , [34] , [35] , [36] . We next tested the requirement of M029 for replication of MYXV in different cell types , from a variety of non-rabbit species , in culture . Both vMyx-M029KO and vMyx-M029ID viruses were unable to replicate in all human cell lines tested ( eg A549 , HeLa , THP1 ) including all cancer cells tested ( not shown ) and even in primary fibroblast cell lines GM02504 ( Fig . 3A ) . In these human cell lines , both M029-minus virus constructs could bind , enter and initiate the synthesis of early viral protein ( eg M-T7 ) , however , the infection was then aborted and no synthesis of late protein ( Serp-1 ) could be detected ( Fig . 3B , top and middle panels ) . No defect in viral replication or protein synthesis was observed when these cell lines were infected with control vMyx-GFP ( Fig . 3 A and B ) . This suggests that abortive infection by M029-defective MYXV in human cells was due to virus abort at some point between early and late protein synthesis . A comparable abortive infection by these two M029-defective viruses was also observed in BSC40 and NIH3T3 cell lines , which are of nonhuman primate and mouse origins , respectively ( Fig . 4A , B and D ) . However , in baby hamster kidney BHK-21 cells , both vMyx-M029KO and vMyx-M029ID viruses were able to progress further to late stages of the viral life cycle but replicated far less efficiently compared to vMyx-GFP infection ( Fig . 4C and D ) . This is likely linked to the delayed late viral protein synthesis from the M029-defective viruses as observed by monitoring Serp-1 protein expression ( Fig . 4E top panels , compare lanes 4 , 5 and 6 with 11 , 12 and 13 ) . No late protein synthesis was also observed in the M029-defective virus infected NIH3T3 or BSC40 which are permissive for the control vMyx-GFP virus ( Fig . 4E and not shown ) . In order to investigate the biological role ( s ) of MYXV encoded M029 protein , we created a recombinant MYXV expressing N-terminal V5-tagged M029 protein , leaving expression under the native M029 promoter ( Fig . 5A ) . The resulting virus , vMyx-M029V5N , was used to examine M029 protein expression , cellular localization and cellular interactions in the virus-infected cells . Western blot analysis of vMyx-M029V5N infected RK13 cells demonstrated that M029 is expressed early ( Fig . 5B , top panel ) . The treatment of infected cells with cytosine β-D-arabinofuranoside ( Ara-C ) ( Fig . 5B , lane 9 ) did not affect the synthesis of M029 , confirming that M029 is expressed as an early gene product . Ara-C blocked the expression of late MYXV protein Serp-1 ( Fig . 5B , 3rd panel ) , which served as a control for the outcome of inhibition of DNA replication . Based on the detection of M029 protein within the first hour of infection , we then tested whether M029 protein is associated with the input MYXV virions . We were able to readily detect V5-tagged M029 in the purified vMyx-M029V5N virions , but not in control vMyx-GFP virions ( Fig . 4C ) . We then prepared core ( C ) and soluble membrane ( M ) fractions of purified vMyx-GFP , vMyx-M029V5N , and vMyx-M029ID viruses as previously described [37] and assessed for the presence of V5-tagged M029 protein using anti-V5 antibody . The Western blot results indicate that M029 is associated with both the core and membrane fractions of purified virions ( Fig . 5D ) , while as a fractionation control MYXV protein M071 ( an IMV surface protein ) is detected only in the membrane fractions [38] . However , we were not able to detect any comparable association of E3 with VACV virions and this result is consistent with previous observations ( not shown , and [39] ) . We next studied the localization of M029 protein in virus-infected cells using immunofluorescence microscopy . When RK13 cells were infected with vMyx-M029V5N at an MOI of 5 for 3 hrs , V5-tagged M029 protein was detected in both the nuclear and cytoplasmic compartments of the infected cells , with some cells having relatively higher levels of M029 in the nucleus ( Fig . 5E , Panels 1 and 2 ) . We also infected RK13 cells with the same virus at an MOI of 0 . 1 for 24 hrs . At this time point , virus-infected cells express GFP driven by a viral early/late promoter , however there were no significant changes in the nuclear and cytoplasmic localization of V5-tagged M029 in the infected cells at the late time points ( Fig . 5E , panels 3 and 4 ) . Infection with vMyx-GFP served as a control ( Fig . 5E , column 1 ) . We next investigated whether M029 protein interacted with any specific host protein ( s ) in virus-infected cells that might regulate MYXV replication in diverse cell types , given that in the absence of functional M029 expression , MYXV became nonpermissive in so many diverse mammalian cell types . For this proteomic study , we primarily focused on virus-infected human cells in order to identify any potential M029 interacting cellular proteins that potentially regulate MYXV tropism . Human A549 cells were infected with vMyx-GFP or vMyx-M029V5N viruses , the protein complexes bearing V5-tagged protein were isolated using anti-V5 antibody by co-IP and unique protein bands present in vMyx-M029V5N samples were identified by mass spectrometry . In order to identify all the potential M029 interacting proteins , proteomic analysis was performed with samples that were not treated with RNase V1 , a ribonuclease that cleaves double-stranded RNAs . Multiple distinct co-precipitating proteins were identified from the vMyx-M029V5N infected sample ( Table 1 ) . However , when the same samples were treated with RNase V1 , many of the protein bands were disappeared ( data not shown ) . One of the identified proteins was RNA Helicase A ( RHA ) , also known as DHX9 , which was consistently present in the immunoprecipitates with V5-tagged M029 . This interaction of M029 with DHX9 was confirmed from multiple human virus-infected cells , including HeLa , A549 and THP1 cell lines ( Fig . 6A lane 2 and data not shown ) . The presence of PKR was also detected in the same anti-V5 co-IP samples by Western blot using anti-PKR antibody ( Fig . 6A , lane 6 ) . The interactions between V5-tagged M029 and DHX9 or PKR were also confirmed by co-IP using anti-DHX9 or anti-PKR antibody , respectively and detection of tagged M029 using anti-V5 antibody ( Fig . 6A lanes 10 and 12 ) . Next we tested whether interaction of M029 with PKR or DHX9 might be dependent on linkage via dsRNA , since both the host proteins and M029 all possess dsRNA-binding domains . Treatment with ssRNase , RNaseA/T1 , did not affect the interactions of M029 with either DHX9 or PKR ( Fig . 6A lanes 4 and 8 ) . In contrast , when the lysates were pre-treated with RNase V1 , which cleaves dsRNA substrates , the amount of PKR that interacted with M029 using dsRNA was no longer observed . However , DHX9 was still detected in the samples precipitated with anti-V5 after being treated with RNase V1 ( Fig . 6B compare lanes 2 and 4 ) . These results suggest that M029 protein interacts with PKR in a dsRNA-dependant manner but interacted with DHX9 independently of dsRNA . We also confirmed protein-protein interactions between M029 and DHX9 in the absence of PKR , using the cell lysates prepared from PKR stable knockdown HeLa cells ( Fig . 6C ) . We have examined the role of PKR activation in the regulation of MYXV infection in the absence of functional M029 protein . Infection of human cells with vMyx-M029ID or vMyx-M029KO viruses activated PKR signaling soon after infection , as compared to control wildtype-MYXV infection . In the M029-minus virus-infected cells , high level of phospho-PKR was detected 8 hpi ( Fig . 7A , compare lanes 4 to 6 with lanes 11 to 13 ) and could be observed even at 4 hpi ( Fig . 7B , lanes 4 to 8 ) . No phospho-PKR was detected before 12 hpi in the samples infected with control vMyx-GFP , suggesting that repression of PKR activation by M029 is key for permissive MYXV replication . Importantly , we noticed that treatment of virus-infected cells with AraC prevented the phosphorylation of PKR induced by both M029-minus viruses . This suggests that the dsRNA ligands , which are preferentially synthesized in poxvirus-infected cells following DNA replication , are necessary for PKR phosphorylation and are sequestered by M029 ( Fig . 7A and B ) . Identification of PKR as an M029 interacting host protein , and the activation of PKR in virus-infected cells in the absence of M029 expression , both suggest that PKR can play significant role ( s ) in the innate anti-viral response initiated in PKR-competent mammalian cells in response to MYXV infection . To study the direct role of PKR in MYXV infection , we first knocked down PKR expression in various human cells by transient transfection of an siRNA pool targeting human PKR . Knocking down PKR expression caused significant increase in the replication of both vMyx-M029KO and vMyx-M029ID viruses in all tested human cell lines such as A549 , HeLa , THP1 , and primary human fibroblast cells GM02504 ( Fig . 8A , and not shown ) . In order to further analyze the role of PKR and achieve more consistent gene expression knockdown , a lentivirus containing shRNAs targeting PKR was used to construct human cell lines with constitutively reduced PKR protein levels . In PKR-knockdown HeLa cells ( HeLa shPKR ) , the replication of wildtype MYXV , vMyx-GFP was unchanged ( Fig . 9 A and B ) . In these PKR knockdown HeLa cells , the replication of both vMyx-M029KO and vMyx-M029ID viruses increased about two logs , as monitored by single step growth curves ( Fig . 9B ) . The replication of M029-defective viruses in HeLa shPKR cells also correlated with the formation of viral foci when infected with low MOI ( Figure 9C , column 2 ) and also restored synthesis of late viral protein in the infected cells ( Fig . 9D compare lanes 12 and 13 with lanes 5 and 6 ) . The members of E3 family of proteins are believed to be key players in mediating resistance against IFN [25] . Using M029-minus mutants of MYXV , we have tested whether M029 plays a functional role in resistant to type I IFN in human A549 cells , which respond robustly to type I IFN [40] . We constructed stable PKR knockdown A549 cells using lentiviruses containing shRNAs targeting PKR . In the A549 or ShControl A549 cells , treatment with IFNβ resulted in the formation of smaller foci by vMyx-GFP and slowed the replication kinetics of M029-expressing MYXV ( Fig . 10A and not shown ) . However , significant differences ( P<0 . 01 ) were observed in the PKR knockdown A549 cells . In these shPKR-A549 cells , the rescued replication of M029-defective virus , vMyx-M029ID , was almost abolished in the presence of IFNβ ( Fig . 10A bottom panels ) . When progeny virus was titered , we also observed significant reduction in the vMyx-M029ID virus production in the PKR-knockdown cells that had been interferon-treated ( Fig . 10B , 72 hpi ) . In order to investigate the role of RHA/DHX9 in MYXV replication in human cells , we used siRNA to knockdown the expression of DHX9 in various human cells . Knocking down RHA/DHX9 protein expression in HeLa , A549 or THP1 cells did not rescue the M029 defective virus infection , nor were any significant changes in the replication of wildtype-MYXV detected ( Fig . 11A and not shown ) . Since DHX9/RHA has also been reported to interact with PKR ( [41] and our results ) , we then decided to investigate whether it has any role associated with PKR functions . We therefore transiently knocked down the expression of RHA/DHX9 in the PKR stable knockdown shPKR human cell lines . In the shPKR-THP1 cell lines , where M029 defective virus replication has been rescued , we observed significant decrease ( P<0 . 05 ) in the replication of vMyx-M029ID compare to vMyx-GFP , when RHA/DHX9 was knocked down ( Fig . 11B ) . This was reflected by a decrease in the progeny virus titer when monitored during a one-step virus replication cycle ( Fig . 11C ) . Knocking down RHA/DHX9 in the stable shPKR-THP1 cells also reduced the synthesis of both early ( M-T7 ) and late ( Serp-1 ) viral proteins in the vMyx-M029ID infected cells ( Fig . 11D ) . However , the synthesis of both viral early and late mRNAs remained unaffected in these cells , suggesting that RHA/DHX9 may have role in viral protein synthesis ( Figure S4 ) . Thus , in stark contrast to PKR , whose activation was inhibitory to MYXV in all the human cells tested , RHA/DHX9 is required for optimal MYXV replication in human THP-1 myeloid cells when PKR was depleted or absent . We next examined the role of M029 in the pathogenesis of MYXV in the European rabbits , where MYXV causes lethal disease myxomatosis . Viruses ( 1000 FFU ) were delivered by intradermal route of inoculation to the flanks of susceptible European rabbits . In this study , we used mock infected ( PBS ) , wild-type parental MYXV Lausanne strain ( vMyx-Lau ) , or revertant viruses containing a rescued intact M029 gene as controls . Infection with M029-revertant viruses showed similar progression of myxomatosis and same mortality rates ( 100% ) as that caused by the parental wild-type virus ( Table 2 and Fig . 12 ) . The vMyx-M029KO and vMyx-M029ID viruses induced either no , or very mild , primary lesions at the primary inoculation site , and no observable disease indications were detected at all throughout the entire course of observation . All the animals infected with these M029-mutant viruses survived the initial infection with no noticeable symptoms ( Table 2 ) . These observations suggest an essential role for M029 in the infections and pathogenesis of MYXV in the susceptible European rabbit hosts . We also tested whether abortive infection of M029 mutant viruses might function as potential vaccines for myxomatosis , as caused by the wild-type vMyx-Lau infection . However , we observed low but variable protection results , suggesting that the M029-mutant infections aborted so quickly at the primary site of inoculation that very little in the way of acquired immune responses were generated .
We have demonstrated that M029 is both a critical host range factor for MYXV replication in a wide spectrum of diverse mammalian cell types as well as a critical virulence factor for myxomatosis in the European rabbits . Our inability to isolate pure recombinant M029-defective MYXV constructs using any of the standard mammalian cell lines where the parental MYXV can robustly replicate indicated that M029 plays essential function ( s ) in MYXV replication for most mammalian cells . The M029-minus viruses were only successfully isolated away from complementing parental MYXV and propagated as pure clones in cell lines that stably express VACV E3 , a complementing comparable poxvirus protein . The purified vMyx-M029KO and vMyx-M029ID viruses that were grown in these E3-complementing RK13 cells exhibited severe defects in replication in essentially all established mammalian cell lines originated from diverse species , where the parental wildtype-MYXV replicates permissively . The most profound tropism defect of the M029-mutant viruses was their complete inability to replicate in any of the tested cells derived from humans ( eg HeLa , A549 , THP-1 , various cancer cell lines , primary fibroblasts , etc ) , non-human primates ( eg BSC40 ) and mouse ( eg NIH3T3 ) ( Fig . 3 and 4 ) . In these cell lines , no expression of viral late proteins was detected in the absence of M029 expression . On the other hand , abundant expression of early viral proteins was detected from M029-minus viruses at a comparable level with wildtype-MYXV infection ( Fig . 3B ) , suggesting that there was no defect in virus binding , entry , or early viral gene expression . Both vMyx-M029KO and vMyx-M029ID viruses were also unable to replicate in rabbit RL5 T lymphocytes , and although they were capable of at least some detectable progeny virus synthesis in RK13 cells , replication kinetics were reduced and cell-cell transmission was compromised . Slower replication kinetics of M029-defective viruses was also observed in hamster BHK21 cell lines . In both RK13 and BHK21 cells , the synthesis of viral late protein was significantly delayed , which resulted in a reduced progeny virus production at 12 hpi when tested by single step growth curve analysis . It is possible that the level of dsRNA produced by the viruses in these semi-permissive cell lines is lower than the completely nonpermissive cell lines , as has been suggested for VACV [16] . The deletion or insertional disruption of M029 also resulted in the elimination of essentially all disease symptoms associated with myxomatosis in susceptible rabbits . This extreme attenuation was associated with the absence of obvious viral spread and extreme reductions in the replication of virus at the primary site of infection . Following full recovery of the M029-mutant infected animals , very variable levels of protection was generated against subsequent challenge by wildtype MYXV , suggesting that virus replication at the primary inoculation site was likely aborted quickly before the elements of acquired immune responses were significantly engaged . Analyzing the distribution of V5-tagged MYXV M029 protein revealed that M029 was expressed at an early time points of infection and was packaged into infectious virions ( Fig . 5 ) , which suggests a critical role of M029 in the modulation of host innate sensing/signaling pathways during early stages of infection . This observation is particularly important because VACV E3 has not been reported in virions and we were also unable to detect E3 in purified VACV particles using comparable strategy used for vMyx-M029V5N ( [39] , and data not shown ) . Localization studies using immunofluorescence microscopy indicated that M029 protein is localized to both the nuclear and cytosolic compartments of MYXV-infected cells . This same cellular localization of M029 was also observed when V5-tagged M029 was transiently expressed in uninfected cells using plasmids under CMV promoter ( data not shown ) , suggesting that nuclear-cytoplasmic dual localization is not dependent on viral infection . Like M029 , VACV E3 protein is also localized in both the nucleus and cytoplasm , however , the nuclear localization of E3 is dependent on the N-terminal 83 amino acid residues , which are missing in the M029 protein [26] , [42] . This suggests that M029 may have acquired a different mechanism for nuclear localization . The major role of VACV E3 is thought to antagonize the PKR-mediated anti-viral functions of the infected host cell , which are upregulated by IFN and activated by dsRNA [4] . eIF2α is a primary cellular substrate that becomes phosphorylated in cells infected with E3-minus VACV , which then leads to the global inhibition of host and viral protein synthesis , the induction of apoptosis and eventual inhibition of virus replication . Infection with VACVΔE3 resulted in the phosphorylation of both PKR and eIF2a , however the defect of VACVΔE3 can be rescued in PKR-deficient HeLa cells [43] . In the PKR-deficient cells , late viral protein synthesis was restored and virus-induced apoptosis was also abolished , which resulted in productive virus replication even in the absence of E3 expression . PKR is one of the host innate immune proteins that undergo rapid evolution in order to escape being targeted by viruses capable of anti-PKR strategies , such as poxviruses [44] . Based on the profound host range phenotype of M029-defective viruses in a wide spectrum of mammalian cells , we created MYXV that expressed V5-tagged M029 and performed co-IP and mass-spectrometry to identify putative host binding partner proteins that might be functionally targeted by M029 . Based on the previous report that PKR interacts with VACV E3 , we first identified cellular PKR as an M029 interacting protein in various human cells . The interaction of PKR and M029 was validated by co-IP assay in virus-infected mammalian cells with either V5 or PKR antibody ( Figure 6A ) . Significantly , this PKR-M029 protein interaction was eliminated by digestion with RNaseV1 , which cleaves dsRNA , but not with RNaseA/T1 that targets only ssRNA , suggesting that this interaction requires dsRNA . Since both PKR and M029 possess dsRNA-binding domains , it is presumed that these proteins become linked via dsRNA bridging . VACV E3 , on the other hand , has been reported to have direct protein-protein interaction with PKR; however , the samples were not treated with an RNase that specifically cleaves dsRNA [45] , [46] . Although M029 interacts with PKR indirectly via dsRNA , this interaction is still biologically relevant , since the replication of M029-minus MYXV viruses were specifically rescued in PKR knockdown cells . In stable PKR knockdown human cells , the synthesis of viral late proteins was fully restored and allowed complete replication and progeny virus formation by vMyx-M029KO and vMyx-M029ID ( Fig . 9 ) , which demonstrates that PKR is a major functional target for M029 host range functions , as it is for VACV E3 . In addition to PKR , we also identified several other cellular RNA binding proteins that were consistently present in the co-IP samples for V5-tagged M029 in virus-infected human cells . Among these , we were particularly interested in investigating the potential interaction of M029 with RHA , a cellular RNA helicase , also known as DHX9 . RHA/DHX9 is a 130 kDa protein that belongs to the DEXD/H box family of proteins , which can unwind both double-stranded RNA and DNA , and can also regulate cellular processes such as pre-mRNA splicing , ribosome biogenesis , transcription , RNA nuclear export , and translation initiation [47] , [48] , [49] . RHA/DHX9 is also associated with regulating the replication efficiencies of various RNA viruses , including HIV-1 , HCV , FMDV and influenza A [50] , [51] , [52] , [53] . These viral regulatory functions of RHA/DHX9 are mediated by interactions with unique viral proteins , for example , the Gag protein of HIV and NS1 protein of Influenza A virus [53] , [54] . DHX9 also function as a dsRNA sensor in selected cell types [55] . In one recent report , it was demonstrated that NS1 can rescue VACVΔE3 virus replication , at least in vitro [24] , and thus we postulated that RHA/DHX9 might also have potential role ( s ) in some aspect of poxvirus tropism . VACV E3 has also been reported to interact with a wide spectrum of host proteins , including RHA/DHX9 , but the functional significance of this particular protein interaction has not been reported [56] . The interaction of DHX9/RHA with V5-tagged M029 protein was validated in virus-infected cells by co-IP assay using either anti-RHA/DHX9 or V5 antibody . Interestingly , unlike the PKR-M029 interaction , the RHA-M029 protein interaction was not eliminated by digestion of dsRNA with RNaseV1 ( Fig . 6A and B ) , suggesting that interaction between RHA/DHX9 and M029 is through direct protein-protein interactions . RHA/DHX9 has also been reported to interact with PKR , and is in fact a substrate of activated PKR kinase [41] . In the virus infected cells we have not observed any alteration in the nuclear localization of DHX9 ( data not shown ) . We therefore assessed the potential biological significance of RHA/DHX9 interaction with M029 , and discovered that this interaction regulated virus host range in a very cell-specific fashion . Knockdown of RHA/DHX9 gene expression in human THP-1 myeloid cells that had been depleted of PKR reduced the replication and viral protein synthesis for both the M029-expressing vMyx-GFP and significantly for the M029-defective viruses , indicating that RHA/DHX9 can play a required pro-viral regulatory role in the virus life cycle of MYXV in monocytic cell lines when PKR is absent or repressed . It is well known that certain individual virus-encoded immunomodulators can inhibit multiple ligands or pathways . For example , the M-T7 protein of MYXV binds and inhibits both rabbit interferon-gamma and diverse chemokines , the SECRET family of orthopoxvirus proteins can bind and co-inhibit both TNF and chemokines , the M-T5 intracellular host range factor of MYXV targets both Akt and the Skp1 component of the cellular SCF complex , and the M13 protein of MYXV targets both ASC-1 of the inflammasome complex and NF-κB1 [57] , [58] , [59] , [60] . However , this is the first report of a single viral host range protein , M029 , with two different functional cellular targets , one of which ( PKR ) is bound and inhibited to alleviate anti-viral signaling , while the other ( RHA/DHX9 ) is bound and conscripted as a pro-viral effector to upregulate viral replication in a cell-specific fashion .
All animal studies were performed following the IACUC protocol number IACUC #201005008 and titled “Studies in Poxvirus Host Range Genes and Tropism” . This protocol was approved by the University of Florida Animal Care Services in accordance with the guidelines set by the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) . Rabbit polyclonal antibody for PKR was purchased from Cell Signaling Technology . Rabbit polyclonal antibody for DHX9 , mouse monoclonal antibody for DHX9 and PKR from Santa Cruz Biotechnology , rabbit polyclonal antibody for phospho PKR from Millipore , mouse monoclonal antibody for β-actin was obtained from Ambion and mouse antibody for V5 from Invitrogen . HRP-conjugated goat anti-rabbit and anti-mouse IgG antibodies were purchased from Jackson ImmunoResearch Laboratories . Generation of rabbit polyclonal and mouse monoclonal antibodies against MYXV proteins M-T7 and Serp1 was described before [61] , [62] . Rabbit cell line RK13 ( ATCC# CCL-37 ) , human cell lines HeLa ( ATCC# CCL-2 ) , and A549 ( ATCC# CCL-185 ) , primary human fibroblasts GM02504 [40] , monkey cell line BSC-40 ( ATCC# CRL-2761 ) , hamster kidney cell lines BHK21 ( ATCC# CCL-10 ) , mouse embryonic fibroblast cell lines NIH3T3 ( ATCC# CRL-1658 ) all were cultured in Dulbecco minimum essential medium ( DMEM; Invitrogen ) supplemented with 10% fetal bovine serum ( FBS; Gibco ) , 2 mM glutamine ( Invitrogen ) and 100 µg of penicillin-streptomycin ( pen/strep; Invitrogen ) /ml . Human monocytic THP1 ( ATCC# TIB-202 ) and rabbit CD4+ T cells RL5 ( Liu et al . , 2011 ) were cultured in RPMI 1640 medium ( Lonza , BioWhittaker ) supplemented with 10% FBS , and 100 µg of pen/Sterp per ml . All cultures were maintained at 37°C in a humidified 5% CO2 incubator . RK13 cells expressing VACV E3 protein ( RK13-E3 ) was generated by transfecting RK13 cells with pcDNA3 . 1 ( Geneticin resistance ) -VACV E3L plasmid . Stably transfected cells were selected by the presence of 500 µg/ml Geneticin . Several clones were isolated and analyzed for expression of E3 . The clone which showed highest level of E3 expression was used for virus propagation . RK13 cells expressing VACV E3 and K3 proteins ( RK13-E3K3 ) was generated by transfecting RK13-E3 cells with pcDNA3 . 1 ( Zeocin resistance ) -VACV K3L-2×Flag tag plamid . Stably transfected cells were selected by the presence of 300 µg/ml Zeocin and 500 µg/ml Geneticin and maintained for 20 days under selection . The RK13-E3K3 cells were polyclonal . To make M029 mutant viruses , recombinant plasmids were first constructed using MultiSite Gateway Pro ( Invitrogen ) system . In one of the construct , vMyx-M029ID , an eGFP expression cassette ( driven by a poxvirus synthetic early/late promoter [27] ) was inserted into the M029 ORF . In another construct , vMyx-M029KO , the entire M029 gene was replaced by the eGFP expression cassette . For both the constructs upstream and downstream hybridizing sequences were amplified by PCR using specific primers to create entry clones by Gateway BP recombination system with appropriate pDONR vectors . The final recombination plasmids were constructed by recombination of three entry clones with a destination vector ( pDEST40; Invitrogen ) and using Gateway LR recombination system . The vMyx-M029ID and vMyx-M029KO viruses were constructed by infecting RK13-E3 and RK13-E3K3 cell lines respectively with wild-type MYXV Lausanne strain ( vMyx-Lau ) , followed by transfection of the recombination plasmids . Multiple rounds of foci purifications were performed on the same cell lines based on eGFP expression and continued until pure foci of M029 mutant viruses were isolated . Virus was amplified from pure isolated foci and recombination was confirmed by PCR using appropriate primers ( Figure 1 ) . The recombinant virus , vMyx-M029V5N , where a N-terminal V5 tag was inserted in front of the M029 ORF was constructed using similar method . The virus also contains an eGFP expression cassette driven by a poxvirus synthetic early/late promoter in the M135-136 locus [28] . The revertant myxoma viruses for M029 mutants , vMyx-M029ID-Rev and vMyx-M029KO-Rev were constructed by infecting nonpermissive ( for M029 mutants ) BSC40 cells with M029 mutants , followed by transfection with a revertant plasmid containing the myxoma virus M028 , M029 and M030 gene sequences . The viruses were purified by multiple rounds of focus purification based on nonfluorescent foci formation , which was confirmed by PCR using the appropriate primers . In these revertant viruses an eGFP cassette was also inserted in the M135-136 locus . Cells were seeded into twelve-well dishes and semi-confluent monolayers ( 1×105 cells ) were infected with different myxoma viruses at an MOI of 5 for one hour . The virus containing media was removed , and the cell monolayer was washed with complete medium and incubated with fresh complete medium . Samples were collected at various times post-infection and stored in −80°C until processed . The samples were freeze-thawed at −80°C and 37°C for three times and sonicated for one minute to release the viruses from infected cells . The virus was titrated back onto RK13-E3L monolayers by serial dilution in triplicate . After 48 hrs of infection the numbers of fluorescent foci were counted from each dilution and calculated the virus titer . Construction of a wild-type MYXV that express GFP under the control of a synthetic VACV early-late promoter was described previously [28] . The vMyx-M029KO and vMyx-M029ID viruses were grown and amplified in RK13-E3 cells . All other myxoma viruses were grown in BSC40 cells . Viruses were purified by centrifugation through a sucrose cushion and two successive sucrose gradient sedimentations as described previously [60] . Vesicular stomatitis virus ( VSV ) expressing GFP was prepared as described before [63] . The mock or infected cells were harvested at different time points after infection with viruses , washed with PBS and stored at −80°C or processed immediately with RIPA lysis buffer ( 50 mMTris , 150 mMNaCl , 0 . 1% SDS , 0 . 5% sodium deoxycholate , 1% NP40 , 1 mM PMSF , and protease inhibitor cocktail ( Roche ) . Amount of total proteins were estimated by Bradford assay ( Bio-Rad ) and equal amount of total proteins were used for Western blot analysis as described before [64] . Briefly , protein samples were separated on 10% SDS-PAGE gels and transferred to PVDF membrane ( GE Healthcare ) using a wet transfer apparatus ( Invitrogen ) . Membranes were blocked in TBST buffer ( 20 mM Tris , 150 mM NaCl , 0 . 1% Tween-20 pH 7 . 6 ) containing 5% non-fat dry milk for 1 hr at room temperature and then incubated overnight with appropriate primary antibody at 4°C . The membranes were washed three times , 15 minutes each with TBST and incubated with HRP-conjugated goat-anti-mouse ( 1∶10 , 000 ) or goat anti-rabbit ( 1∶10 , 000 ) secondary antibody in TBST containing 5% non-fat dry milk for 1 hour at room temperature with gentle agitation and were then washed three times , 15 minutes each with TBST . The proteins were detected using the chemiluminescence substrate ( Pierce ) and exposure to X-ray film ( Kodak ) . Protein bands on X-ray films were quantified by using ImageJ software ( rsb . info . nih . gov/ij ) . For siRNA transfection , cells were seeded at 5×104 cells per well in 24-well plates in growth medium without antibiotics . PKR ( target sequences: GUAAGGGAACUUUGCGAUA; GCGAGAAACUAGACAAAGU; CGACCUAACACAUCUGAAA; CCACAUGAUAGGAGGUUUA ) and DHX9 ( target sequences: GGAUUAAACUGCAAAUAUC; GGCUUUGGUUGUUGAAGUA; CAAACAACCUGCUAUCAUC; GUAAAUGAACGUAUGCUGA ) siRNAs used were ON-TARGETplus SMART pool siRNA purchased from Thermo Scientific ( Dharmacon ) . For transfection , siRNA solution ( 50–100 nM final concentration/well ) was prepared in 50 µl Opti-MEM I Reduced serum medium ( Invitrogen ) . Lipofectamine RNAiMAX ( Invitrogen ) solution was prepared in 50 µl Opti-MEM I Reduced serum medium and incubated 5 min at room temperature . siRNA and lipofectamine solutions were mixed and incubated for 20 min at room temperature and added to the cells to make final volume of 500 µl and incubated at 37°C in a CO2 incubator . The knock down was verified after 48–72 h of transfection . For infection , the cells were infected with viruses after 48 h of post-transfection . Lentivirus particles packaged with human PKR shRNAs or control shRNAs were purchased from Snata Cruz Biotechnology Inc . They were used independently to infect cells to construct stable cell lines according to the manufacturer's protocol . After selection of puromycin ( Sigma ) antibiotic resistant cells , Western blot analysis was performed to determine the level of PKR knockdown . For co-IP , cells were lysed using RIPA lysis buffer . The cleared cell lysates after centrifugation for 15 min at 12 , 000 rpm at 4°C were incubated with Pierce protein A/G agarose ( Thermo scientific ) for pre-clearing . The agarose beads were removed by centrifugation for 15 min at 12 , 000 rpm at 4°C . The supernatants were incubated with a monoclonal anti-V5 antibody ( Invitrogen ) for 1 h at 4°C and followed by incubation with protein A/G agarose for over-night at 4°C . In some cases cell lysates were treated with RNase A/T1 ( Fermentas ) or RNase V1 ( Invitrogen ) overnight at 4°C . The agarose beads were pelleted by centrifugation at 2 , 000 rpm for 2 minutes , washed four times with lysis buffer and samples were then analyzed by Western blotting . For mass spectrometry , protein samples from co-IP were separated on a 12% SDS-PAGE gel by electrophoresis and stained with mass spectrometry-compatible Coomassie blue ( SimplyBlue SafeStain; Invitrogen ) according to the manufacturer's protocol . Unique protein bands present in the vMyxM029V5N virus infected samples compared to the controls were dissected for trypsin digestion , followed by liquid chromatography-tandem mass spectrometry analysis ( done at the ICBR , UF , proteomic core ) . The results were searched against protein database and then analyzed and displayed with matching polypeptide sequences in the identified protein sequence using Scaffold software ( Proteome Software , Portland , OR ) . RK-13 cells grown on glass coverslips , were mock-infected or infected with vMyxGFP or vMyxM029V5N for 3 h or 24 h at an MOI of 5 . 0 or 0 . 1 , respectively . After infection , cells were fixed in 4% paraformaldehyde in PBS for 10 min at room temperature and permeabilized in 0 . 1% Triton X-100 in PBS for 10 min . Cells were quenched in 0 . 1 M Glycine in PBS for 20 min and then blocked in 1% BSA in PBS for 30 min . Cells were incubated with an anti-V5 monoclonal antibody , followed by Texas Red-conjugated goat anti-mouse antibody ( Jackson ImmunoResearch Laboratories ) . DNA in the nuclei and viral factory was stained with 4′ , 6-diamidino-2-phenylindole ( DAPI ) . Images were captured on a Leica laser scanning confocal microscope . Total RNA was purified from 106 cells plated in each well of a six-well dish . The following day cells were infected with viruses . At the given time points , cells were lysed with RLT buffer ( Qiagen ) , and total RNA was extracted using RNeasy kit ( Qiagen ) . Generally , 1–2 µg of total RNA was used to make cDNA . Genomic DNA was removed from total RNA using DNA-free kit ( Ambion ) according to the manufacturer's recommendations . Following the removal of genomic DNA , cDNA was prepared using Superscript III reverse transcriptase ( Invitrogen ) [65] . PCR amplification was performed for 35 cycles using the following cycling conditions: 94° for 1 min , followed by 35 cycles of 94° for 30 seconds , 56° for 30 seconds , 72° for 1 min , and then a final extension of 72° for 10 min . Forward and reverse primer pairs are listed in supplementary Table 1 . Lack of DNA contamination in the RNA preparation was verified by PCR amplification in the absence of reverse transcription . New Zealand White rabbits were purchased from Charles River Laboratories International . The animal study was approved by the Institutional Animal Care and Usage Committee ( IACUC ) at the University of Florida and studies were performed as described before [66] . For virus injection , 1000 focus-forming units ( FFU ) of the tested virus was resuspended in 100 µl of PBS and inoculated intradermally in the left flank of each rabbit . Daily physical examinations were conducted to evaluate rabbits condition by monitoring respiration , weight , temperature , heart rate , lung sound , food and water intake , urine and feces output , hydration status , attitude , posture and indications of primary lesion and appearance of secondary lesions . Based on the evaluations , the rabbits received daily clinical score that ranged from 0 to 34 ( the maximum score ) . The animals were humanely euthanized when the clinical score reached 26 to 34 , had open mouth breathing due to respiratory stress , orthopnea , cyanosis or no food and water intake for 48 h . The animals that survived challenge with M029 mutant virus infection , were re-challenged with a lethal dose of vMyx-Lau ( 1000 FFU ) by the intradermal route in the right flank of the animal . Data were expressed as means ± SD and were analyzed by paired t-test . Significant difference was accepted at p<0 . 05 . | Poxviruses exploit diverse strategies to modulate host anti-viral responses in order to achieve broad cellular tropism and replication . Here we report the findings that Myxoma virus ( MYXV ) , a rabbit-specific poxvirus , expresses a viral protein M029 that possesses dual immunomodulatory functions . M029 binds and inhibits the anti-viral functions of protein kinase R ( PKR ) and also binds and conscripts the pro-viral activities of another cellular protein , RNA helicase A ( RHA/DHX9 ) , a member of the DEXD/H box family of proteins . Engineered M029-minus MYXVs did not cause lethal disease myxomatosis in the European rabbits . M029-minus MYXVs were also unable to replicate in diverse mammalian cell types , but can be rescued by knocking down the expression of PKR . However , this rescue of M029-minus virus replication could then be reversed by RHA/DHX9 knockdown in human myeloid cells . These findings reveal a novel strategy used by a single viral immunomodulatory protein that both inhibits a host anti-viral factor and additionally conscripting a host pro-viral factor to expand viral tropism in a wider range of target mammalian cells . | [
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"host-pathogen"... | 2013 | Myxoma Virus Protein M029 Is a Dual Function Immunomodulator that Inhibits PKR and Also Conscripts RHA/DHX9 to Promote Expanded Host Tropism and Viral Replication |
α- ( 1 , 3 ) -Glucan is a major component of the cell wall of Aspergillus fumigatus , an opportunistic human fungal pathogen . There are three genes ( AGS1 , AGS2 and AGS3 ) controlling the biosynthesis of α- ( 1 , 3 ) -glucan in this fungal species . Deletion of all the three AGS genes resulted in a triple mutant that was devoid of α- ( 1 , 3 ) -glucan in its cell wall; however , its growth and germination was identical to that of the parental strain in vitro . In the experimental murine aspergillosis model , this mutant was less pathogenic than the parental strain . The AGS deletion resulted in an extensive structural modification of the conidial cell wall , especially conidial surface where the rodlet layer was covered by an amorphous glycoprotein matrix . This surface modification was responsible for viability reduction of conidia in vivo , which explains decrease in the virulence of triple agsΔ mutant .
α- ( 1 , 3 ) -Glucan is a major cell wall component of most ascomycetous and basidiomycetous fungi , including the human pathogens that establish their disease upon inhalation of their infective morphotypes ( e . g . , Paracoccidioides brasilisensis , Histoplasma capsulatum , Blastomyces dermatitidis , Cryptococcus neoformans Aspergillus fumigatus ) . The role of this polysaccharide during infection has been demonstrated and the mechanisms of its involvement in establishing virulence have been forwarded [1] , [2] . In C . neoformans , α- ( 1 , 3 ) -glucan anchors the capsule , a well known virulence factor of this fungus , to the yeast cell wall and has been shown to be indirectly associated with virulence since a mutant devoid of α- ( 1 , 3 ) -glucan did not have any capsule and , most importantly , was unable to grow at 37°C [2] . In the yeast H . capsulatum , α- ( 1 , 3 ) -glucan was suggested to be essential for virulence because it masked immunogenic molecules: in the α- ( 1 , 3 ) -glucan synthase mutant , β- ( 1 , 3 ) -glucan that is recognized by Dectin-1 , is exposed at the surface of the cell wall , whereas in the parental strain yeast cells , β- ( 1 , 3 ) -glucan is covered by α- ( 1 , 3 ) -glucan , preventing Dectin1-dependent immune response [1] . In A . fumigatus , α- ( 1 , 3 ) -glucan accounts for 40% and 19% of the mycelial and conidial cell wall polysaccharides , respectively [3] . It is a major adhesive involved in the aggregation of germinating conidia and in biofilm formation [4] , [5] . Moreover , it has been shown in experimental murine aspergillosis models that α- ( 1 , 3 ) -glucan has a prominent immunological function conferring a long-term survival [6] . This immune protection was associated with a reduced neutrophil recruitment in the lungs and reduced inflammatory pathology [6] . α- ( 1 , 3 ) -glucan , like conidia , confers a Th1/Treg protection and concomitant Th2 inhibition . These in vivo data were confirmed by in vitro experiments where dendritic cells pulsed with α- ( 1 , 3 ) -glucan induced Il12p70 production , a classical Th1 promoting cytokine [6] . However , the physiological role of α- ( 1 , 3 ) -glucan could not be further investigated in absence of the mutants devoid of α- ( 1 , 3 ) -glucan . In A . fumigatus , this polysaccharide is synthesized by three α- ( 1 , 3 ) -glucan synthases ( Agsp ) [3] , [7] . A triple deletion of the AGS1 , AGS2 and AGS3 genes was recently generated in our lab that resulted in an A . fumigatus mutant lacking α- ( 1 , 3 ) -glucan in the cell wall . In contrast to other fungal pathogens , this triple AGS A . fumigatus deletion mutant did not show a distinct growth phenotype in vitro [8] . In the present study , three independently constructed triple ags1Δags2Δags3Δ ( agsΔ ) mutants devoid of α- ( 1 , 3 ) -glucan were used to investigate the role of α- ( 1 , 3 ) -glucan in A . fumigatus infection . As shown here , the virulence of these A . fumigatus triple agsΔ mutants was extremely attenuated in both immunocompetent and immunocompromised murine models of experimental aspergillosis tested . The defect in virulence correlated with a lack of vegetative fungal dissemination in the lungs , associated with a highly reduced inflammation following conidial inoculation . Analysis of the conidia of the triple mutants showed that the lack of virulence of the mutants in vivo was associated to major changes occurring on the cell wall , especially on the surface of the resting and swollen conidia , which resulted in an increased killing by phagocytes .
In the immunocompetent mice after four days of infection , the number of CFUs of the agsΔ mutants per lung was much lower than the CFUs per lung of the parental ku80 strain ( Fig . 1A; Fig . S1A ) . The reduced fungal burden of agsΔ was correlated to an absence of inflammation whereas a huge inflammatory response was observed with the parental strain ( Fig . 1B , Fig . S1B ) . This was confirmed by the broncho-alveolar lavage ( BAL ) analysis , which showed a higher PMN recruitment after infection with ku80 conidia compared with agsΔ ( Fig . 1C , Fig . S1C ) . The reduced growth and inflammation in agsΔ infections was associated with an increase in the expression of the gene coding for the anti-inflammatory IL10 and a decreased expression of the gene coding for the pro-inflammatory TNFα in the lungs ( Fig . 1D , Fig . S1D ) . In contrast , ku80 infection was characterized by higher and lower expressions of TNFα and IL10 , respectively . The increased susceptibility of the agsΔ mutants was confirmed in vitro with murine alveolar macrophages isolated from BAL . After phagocytosis by the isolated macrophages , the killing of the agsΔ conidia was much higher than the parental strain . The resting conidia of agsΔ mutants were killed twice more than the parental strain after 2 h incubation with the macrophages ( Fig . 1E ) . Further , after 6 h of incubation , the killing of the mutant reached 60–80% whereas a maximum of 30% of the parental strain conidia were killed at this time point ( data not shown ) . Similar difference in the killing ratio between the mutant and parental strains was obtained when the conidia were pre-germinated ( swollen conidia; after 6½ h incubation of the conidia in RPMI medium , at 37°C ) , suggesting that both resting and swollen conidia of the agsΔ mutants were more susceptible to conidial killing than the parental strain . This twofold increased killing susceptibility of the agsΔ mutants compared to parental strain did not change in the germinating morphotypes . In the experimental model of aspergillosis using immunocompromised mice , the virulence of the agsΔ mutants was also significantly reduced . In a cyclophosphamide model of immuno-suppression , infection with the ku80 strain resulted in the mortality of all the mice within 4 days with a high inflammatory response , large foci of pneumonia and exudative bronchiolitis with destruction of bronchi and alveoli , whereas 60 to 80% mice infected by the agsΔ mutants survived and did not develop any inflammatory response ( Fig . 2A–C , Fig . S2 ) . Similar results were obtained when mice were immunocompromised by the injection of the RB6-8C5 MAb , which depletes circulating PMNs . Inhalation of the ku80 conidia resulted in an extensive pulmonary fungal invasion with high inflammation ( Fig . 2D–E ) . In contrast , in the RB6-8C5 MAb-treated mice lungs , only resting and swollen agsΔ conidia were observed and their incapability to grow vegetatively culminated in low inflammation ( Fig . 2D–E ) . These results showed that the reduced virulence of the agsΔ mutant was due to a defect in their conidial survival or vegetative growth in the lung of the infected mice . To investigate the mechanisms responsible for the in vivo growth defect , the germination of agsΔ mutant conidia was tested in vitro under stress conditions mimicking the in vivo environment , such as , in the presence of reactive oxidants ( ROS ) , cationic peptides , hypoxia and depletion of iron . The agsΔ mutants showed similar growth rates as their parental strain in the presence of Menadione , hydrogen peroxide and Luperox®101 with minimum inhibitory concentrations ( MIC ) of 30 µM , 10 mM and 2 µM , respectively ( data not shown ) irrespective of the pH of the medium ( pH 7 or 4 ) . The killing of resting conidia after 2–6 h of incubation with macrophages purified from uninfected p47phox−/− mice ( depleted in ROS production ) were similar to the killing by purified macrophages from uninfected wild type mice ( C57BL6 H-2b ) ( Fig . 2B , data not shown for 6 h and Fig . 3 ) . These results suggested that the agsΔ mutant conidia were not more susceptible than the parental strain conidia to reactive oxidants in vitro as well as in vivo . Interestingly , these results also suggested that in our experimental models , conidia from both mutant and parental strains were efficiently killed by ROS-independent mechanisms . Moreover , the absence of iron or the presence of a hypoxic environment did not modify the survival and conidial germination of agsΔ mutants compared to their parental strain ( data not shown ) . In vitro , the agsΔ conidia germinated like parental strain conidia in culture medium without supplementation with iron as well as under hypoxic conditions ( <1% ( v/v ) O2 and 9–13% ( v/v ) CO2 ) . The agsΔ mutants were not more susceptible than the parental strain to cationic peptides . At doses of 230 , 100 , 40 and 230 µg/ml of Cathelicidin LL-37 , α HNP2 and β hBD2 defensins and lactoferrin , respectively , no germination differences were seen between parental and mutant strains ( data not shown ) . Similarly , both mutant and parental strain conidial killing was comparable with 0 . 05% SDS ( data not shown ) . In addition , no increase in the intracellular labeling of the agsΔ mutant conidia was seen after incubation with Calcofluor White or FITC ( data not shown ) . These results suggested that the agsΔ conidia were not more permeable to extracellular toxic molecules than the parental strain . Testing of these different inhibitors in combination ( such as H2O2 or SDS , with Lactoferrin or LL-37 ) did not result in a differential sensitivity between the parental and mutant strains ( data not shown ) . These results suggested that , in vitro , the triple agsΔ mutants were not more susceptible to environmental stresses and antifungal molecules compared to the parental strain . To further investigate the differences in virulence between the mutant and parental strains in vivo , we hypothesize that the killing of the agsΔ mutant conidia could be due to the induction of an early and strong host immune response towards the mutant conidial morphotypes . Resting conidia of the agsΔ mutant were more efficiently phagocytosed by mouse alveolar macrophages than that of the parental ku80 strain . After 1 h incubation , an average of 3 . 4 and 1 . 4 conidia of agsΔ mutants and ku80 were engulfed per macrophage , respectively ( Fig . 4 , Fig . S3 ) . This result suggested that the agsΔ mutant and parental strain conidial surfaces are different . To investigate such structural modifications , conidial surfaces were imaged by atomic force microscopy ( AFM ) . In contrast to the ku80 conidia that are covered with a crystalline-like array of rodlets [9] , the agsΔ mutant conidial surface was amorphous without any organized structure ( Fig . 5A ) . The presence of an amorphous material covering the surface of the agsΔ conidia was further confirmed by TEM ( Fig . 5B ) . To investigate if the rodlet layer is still present on the agsΔ mutant conidial surface but masked by this amorphous material , ku80 and agsΔ resting conidia were treated with hydrofluoric acid ( HF ) to extract the rodlet protein . Similar amount of the hydrophobic RodA protein , which constitutes the rodlet layer , could be extracted from the agsΔ and parental strain conidia ( 26 . 7±4 . 9 µg and 26 . 5±3 . 0 µg per 109 conidia , respectively ) . Figure 5C shows that the two bands , 16 kDa and 14 . 5 kDa of RodAp classically seen from HF treatment of the conidia [10] were present in the SDS-PAGE profiles of agsΔ and ku80 resting conidial HF-extracts . These data confirmed AFM and TEM observations that on the agsΔ mutant conidial surface the rodlets were present but hidden by an amorphous material . Because of the presence of this amorphous material covering the hydrophobic rodlets , we asked whether the observed surface changes correlated with differences in conidial adhesive properties . To understand this , we mapped and quantified the nanoscale adhesion properties of ku80 and agsΔ mutant conidia by AFM using bare Si3N4 tip . Figure 6 ( and Fig . S4 ) showed that the presence of this unorganized material on the agsΔ mutant conidial surface was associated with a dramatic reduction in their conidial surface adhesive properties . For the parental strain , force-distance curves recorded across the cell surface revealed large adhesion forces , with a magnitude of 0 . 6±0 . 039 nN as shown by the adhesion force histogram ( Fig . 6A–C ) . In contrast , structural changes in agsΔ conidia caused profound modifications of the cell surface physico-chemical properties ( Fig . 6D–F , Fig . S4 ) . Force-distance curves showed the absence of adhesion forces over the entire surface of the mutant conidia . This decrease in the agsΔ conidial adhesion capacities indicated a modification of the cell surface hydrophobicity that could have influenced conidial phagocytosis . Further , chemical nature of the amorphous layer present on the agsΔ mutant conidial surface was investigated . It was not composed of polysaccharides since the labeling of β- ( 1 , 3 ) -glucan with the β- ( 1 , 3 ) -glucan receptor GNBP3 , chitin with WGA , galactomannan ( GM ) with an anti-GM monoclonal antibody and galactosaminogalactan ( GAG ) with an anti-GAG monoclonal antibody were negative ( data not shown ) . In contrast , a strong labeling of the resting agsΔ conidium with ConA was observed suggesting that the surface layer was rich in glyco-conjugates ( Fig . 7 ) . To extract these amorphous surface materials , agsΔ resting conidia were incubated in 0 . 5 M NaCl for 2 h and the extracted materials were positive for protein assay . As shown in the Figure 8 ( and Fig . S5 ) , incubation with NaCl did not release any proteins from the parental ku80 strain whereas the extracts from agsΔ mutant conidia contained 160 µg proteins per 1010 conidia . It was verified that the amorphous glycoprotein layer was removed after NaCl treatment because ConA labeling on the conidia after NaCl treatment was negative ( data not shown ) . Further , extracted protein mixture was subjected to proteomic analysis . Thirty-four proteins were identified and in-silico analysis of these proteins by SigPred ( http://www . cbs . dtu . dk/services/SignalP/ ) and CADRE ( http://www . cadre-genomes . org . uk/Aspergillus_fumigatus/ ) revealed that all of them had a signal peptide except Sod1 ( AFUA_5G09240 , [11] ) ( Table 1 , Table S1 ) . Most of these proteins were hydrolases and the most abundant protein was a putative β- ( 1 , 4 ) -glucan hydrolase ( AFUA_7G06140 ) . Other glycosylhydrolases were hexosidases or N-acetylhexosaminidases ( AFUA_1G05770; AFUA_1G14560 , AFUA_1G10790 , AFUA_8G05020 , AFUA_6G10730 ) . A unique aspartic phosphatase was identified that was different from the one previously identified as a major mycelial cell wall protein [12] . Three peptidases ( AFUA_2G03510 , AFUA_4G03490 , AFUA_8G04120 ) and the two aspartic proteases , Pep1p and Pep2p ( AFUA_5G13300 , AFUA_3G11400 ) , known to be associated with the conidial cell wall were found [13] . Two well known allergens of A . fumigatus were also detected ( Aspf1 ( AFUA_5G02330 ) and Aspf13 ( AFUA_2G12630 ) [14] ) . Other protein such as oxidoreductases and enzymes of sugar metabolism ( pyruvate dehydrogenase kinase AFUA_2G11900 and isopropylmalate dehydrogenase AFUA_1G15780 ) were present in lower amount as they were identified only once or twice in the proteomic survey . Interestingly , Sod1p and RodAp ( AFUA_5G09580 ) , known to be highly expressed in resting conidia [11] , were also found in this NaCl extract . A similar SDS-PAGE profile was obtained when urea/thiourea buffer was used to extract agsΔ conidial surface material , indicating that the proteins recovered were not depending on the extraction buffer ( data not shown ) . The fact that many proteins were present above the surface rodlet layer suggested that in contrast to the parental strain , the lack of α1 , 3 glucan has led to a different cell wall retainment of these glycoproteins in the agsΔ mutant conidia . In vitro analysis of the cytokines produced during the first 5 h of incubation with alveolar macrophages showed that high amounts of pro-inflammatory TNFα cytokine were produced upon interaction with agsΔ mutant conidia whereas no TNFα was produced when the parental strain was incubated with macrophages under the same incubation conditions ( Fig . 9A , Fig . S6A ) . Stimulation of the macrophages with the agsΔ conidial NaCl extract also induced TNFα expression ( Fig . 9B; Fig . S6B ) . These results suggested that the surface glycoprotein layer on the resting agsΔ conidia was responsible for the induction of pro-inflammatory cytokine production immediately after conidial phagocytosis . Thus , the deletion of the AGS genes resulted in an unexpected modification of the mutant conidial surface with the emergence of an amorphous layer on the resting conidial surface over the rodlet layer , which altered biophysical properties , consequently affecting conidial interaction with the host immune system . Increased cytokine production seen in the macrophages over a 5 h-time period could also come from changes occurring at the surface of germinating conidia since it has been shown previously that conidia starts germinating intracellularly in the macrophage lysosome after the first 2 h of phagocytosis [15] . In addition , Figure 2 shows that agsΔ conidia undergo swelling in the infected lungs before being killed . The structural changes of the early germ tubes resulting from the AGS deletion were investigated by cytochemistry . The swollen conidia of the triple agsΔ mutants presented an increased labeling by WGA compared to the parental strain ( Fig . 10A and data not shown ) . In addition , swollen agsΔ conidia were positive with the β- ( 1 , 3 ) -glucan receptor GNBP3 , whereas both resting and swollen conidia of the parent strain were negative ( Fig . 10B and data not shown ) . In contrast , there were no differences in the immunolabeling of the swollen conidia of parental and agsΔ mutants with anti-GAG and anti-GM monoclonal antibodies ( Fig . S7 ) . These results suggest that the absence of α- ( 1 , 3 ) -glucan that normally hides β- ( 1 , 3 ) -glucan and chitin , exposes these PAMPs at the surface of the swollen agsΔ conidia . These results were also in agreement with the chemical analysis of the cell wall: the mycelium cell wall of the agsΔ contained 1 . 7 and 2 times more chitin and β- ( 1 , 3 ) -glucan , respectively , than the cell wall of the parental strain [8] . Figure 11 represents a model to explain the sequential immune events upon inhalation of the agsΔ mutant and parental strain conidia and their differential impact/in vivo fate based on our in vitro assays as well as in vivo experiments using murine aspergillosis models . The presence of glycoproteins hiding the rodlet layer increases the phagocytic rate and promotes an immediate host immunological response towards the triple agsΔ mutants during phagocytosis . Once the mutant conidium is internalized , the conidial swelling results in an increased exposure of PAMPs on the swollen agsΔ conidial surface . Such surface modifications further boosts pre-existing host defense induced by the resting agsΔ conidia . In contrast , the resting conidium of the parental strain are not recognized by the phagocytes and do not display major PAMPs on the surface of the conidium during the intracellular swelling . Since agsΔ conidia did not seem more sensitive to host antifungal molecules compared to the parental strain , we hypothesize that differences in the killing in the later growth stages resulted from an early and enhanced host response induced by the modified surface of the resting agsΔ conidia . This early stimulation will be responsible for the killing of the germinating agsΔ conidia . On the contrary , in the partially immunosuppressed experimental murine models , limited and delayed killing of the parental strain conidia enables their further vegetative growth .
In this study we showed that the agsΔ mutants displayed a reduced virulence associated with an inhibition of germination in vivo and a reduction of the inflammatory response after 24 h infection ( decreased TNFα and increased IL10 expressions and reduced recruitment of PMNs ) . The low level of TNFα seen with the triple agsΔ mutants fits with the lack of recruitment of neutrophils seen with this mutant after 24 h infection . However , during our in vitro experiments with macrophages incubated during 5 h with agsΔ or ku80 conidia , we observed the induction of pro-inflammatory cytokines . This indicated that the lack of inflammation seen at later stages of infection in mice was due to the inhibition of vegetative growth of the agsΔ mutants rather than a failure to stimulate inflammation . This was in agreement with the fact that agsΔ conidia were killed before their hyphal development . The primary phenotype of the resting conidia of the agsΔ mutants was the absence of visible rodlet layer on the conidial surface . Even though the rodlets were present in the mutant conidia , their masking by a ( glyco- ) protein layer restored the immune sensing that is usually silenced when the rodlets are present on the surface of the wild type conidia [10] , [16] . The agsΔ conidia were covered by proteins , which are usually secreted during vegetative growth . Most hydrolases found in the additional amorphous surface layer of the resting agsΔ conidia were usually identified during mycelial growth in a protein-based medium [14] , [17] . How these proteins are able to cross the conidial cell wall remains an open question . Their presence on the surface is certainly due to the modifications of the cell wall integrity resulting from the three AGS deletions . Interestingly , in three independent HF extractions , the amount of 14 . 5 kDa RodAp was slightly higher than the 16 kDa RodAp ( 20–23% 16 kDa RodA in agsΔ mutants compared to 40–50% in the parental strain; Fig . 5C ) suggesting that the rodlet structure of the mutant was less organized than the rodlet of the parental strain , which putatively modified the ionic strength of the hydrophobin layer in the agsΔ mutants [18] . Such structural modifications may affect the adherence of the hydrophilic glycoproteins to rodlets through electrostatic binding , since these proteins were easily extracted by salt . How these glycoproteins reached the surface of the cell wall is still not understood . This should not be related to changes in cell wall permeability since the agsΔ mutants were not more permeable to FITC or drugs that affect viability such as ROS , cationic peptides or Calcofluor White than the parental strain ( data not shown ) . Alternatively , the hydrolases , because of their enzymatic activity , may harm the cell wall structure itself and this would help the proteins to cross the cell wall barrier . The stimulation of the expression of TNFα after incubation with macrophages ( isolated from naive mice BAL ) with agsΔ mutant conidial NaCl extract showed that these proteins located on the conidial surface were sensed first by the immune system and were able to induce an immediate immune response towards agsΔ conidia . It was previously shown that some of these surface proteins are recognized by T cells and can induce a Th1 protective response [6] . In particular , the secreted aspartic protease Pep1 that has been found in NaCl extract from agsΔ conidia conferred protection against infection , associated with a reduced neutrophil recruitment in BAL and a reduced inflammatory pathology in the lung . Hiding of the rodlet layer by an amorphous glycoprotein layer that stimulates the host response is not exclusively specific to the agsΔ deletion , since a similar conidial phenotype was observed on chitin synthase mutants [19] , [20] . Similarly , in B . dermatitidis , the absence of α- ( 1 , 3 ) -glucan at the surface of the yeast increased the expression of W1-1 adhesin/antigen that were bound to phagocytic cells and suppressed the generation of the pro-inflammatory cytokine TNFα [21] , [22] . The exposure of polysaccharide PAMPs on the surface of germinating conidia consecutively to triple AGS deletions also plays a role in stimulating the host innate immune response and inducing the production of antifungal molecules by the innate immune cells . The exposure of β- ( 1 , 3 ) -glucan at the surface of germinating agsΔ conidia will favor a Dectin-1-mediated host response [23] . Similarly , increased β- ( 1 , 3 ) -glucan exposure due to caspofungin treatment stimulated the host defense reaction against A . fumigatus [24] , [25] . In addition , the positive binding of WGA and ConA also suggested that other receptors such as the mannose or/and chitin/N-acetylglucosamine , which are known to stimulate an antifungal response , can also be involved in this modified immune response [26] . Similar to the situation with the agsΔ mutants , it was shown that the lack of α- ( 1 , 3 ) -glucan in H . capsulatum also led to the unmasking of PAMPs [1] . The protective role of α- ( 1 , 3 ) -glucan has been also shown in B . dermatitidis and P . brasiliensis where the absence of α- ( 1 , 3 ) -glucan at the surface of the yeast and/or its replacement by β- ( 1 , 3 ) -glucan stimulated the host defense reaction [21] , [27] . Recently , the masking of chitin by α- ( 1 , 3 ) -glucan has been shown to be essential for the virulence of the plant pathogen Magnaporthe grisea [28] . The molecules responsible for the killing of the agsΔ conidia remain unknown . However , it is clear that ROS were not responsible for the differences in killing between the agsΔ mutants and the parental strain conidia since the agsΔ mutants did not display a higher sensitivity to ROS in vitro and the killing of agsΔ conidia was similar in p47phox−/− mice compared to C57BL/6 ( Fig . 3 ) . Although a link between increased oxidative response and enhanced damage to A . fumigatus has been repeatedly demonstrated in the past [29] , [30] , recent studies , especially with chronic granulomatous disease ( CGD ) patients , have shown that NADPH-independent mechanisms can contribute to Aspergillus killing as much as ROS [31] , [32] . Among possible mechanisms of NADPH-independent activity , D'Angelo et al . [33] have suggested that defensins and cathelicidins , known for their role in host defense , could be responsible for A . fumigatus killing in CGD mice . This seems however not the case for the agsΔ mutants as our in vitro studies indicated that the agsΔ mutants did not show a higher susceptibility to cathelicidin LL-37 or HNP2 and hBD2 defensins . Modification of the conidial surface may also lead to an increased binding of Surfactant Proteins A and D , Mannose Binding Lectin C or Penthraxin 3 that are known to be associated to an increased phagocytosis and an activation of the complement pathway known to play a major role in the killing of A . fumigatus [21] , [34] , [35] , [36] , [37] . Based on our data , it remains impossible to infer the killing of the agsΔ mutant conidia to currently known antifungal immune defense mechanisms . It can also be postulated that the killing may be due to an early burst of unknown toxic molecules or that the killing is the result of several antifungal molecules acting synergistically [38] . Our cell wall analysis suggested also that the cell wall architecture is perturbed in the inner as well as in the outer layer and that this perturbation may result in modifications of the cell wall permeability to specific antifungal molecules [8] . These could be responsible for an increased susceptibility of the agsΔ mutant to the host defense molecules . The story of A . fumigatus α- ( 1 , 3 ) -glucan remains a two-sided coin . In the wild type strain , α- ( 1 , 3 ) -glucan induces an anti-A . fumigatus response as the injection of this polysaccharide into mice was immunoprotective and obviously responsible for the production of a Th1 response that is directed against A . fumigatus [6] . It could be expected that their removal favors the virulence of the mutant . In reality , the opposite happens due to the reorganization of the cell wall of the resting and germinating conidia upon triple AGS deletions . The presence of glycoproteins hiding the rodlet layer and the exposure of PAMPs in the germinating conidia modified the immunological response of the host , which increased phagocytosis and killing of the agsΔ mutants , and induced pro-inflammatory cytokine production . It is the structural modification of the entire cell wall consecutive to the AGS deletions that is responsible for an early stimulation of the host defense reactions . Interestingly , these structural modifications did not modify the survival of the fungus in vitro but are essential for the in vivo survival . The difference in the surface composition of the resting and swollen conidia of the agsΔ mutants led to an immediate sensing of the immunogenic molecules resulting in an early response of the phagocyte towards the agsΔ conidia . The deleterious effect of a delayed immune response on the microbial virulence is well known . The α- ( 1 , 3 ) -glucan study tells us that the deletion of one cell wall gene does not lead only to the disappearance of the product of the encoded gene but results in a complete restructuration of the fungal cell wall . This has been shown with the deletion of the AGS genes in this study but also with other cell wall genes or consecutively to the use of antifungals acting on the cell wall in several fungal species [39] . Such structural and chemical modifications in the cell wall will have an obvious impact on the immune response of the host towards the corresponding mutant . Our study also suggests , any interpretation stating that the immune response towards a cell wall mutant is only due to the lack of the product of the deleted gene should be considered with care [40] , [41] .
All strains were grown in 2% ( w/v ) malt agar slants and 1 week-old conidia were recovered from the slants by vortexing with 0 . 05% ( v/v ) Tween 20 aqueous solution . Swollen conidia and germ tubes were produced after 5 h and 10 h , respectively , after incubation at 37°C in Brian's medium ( Brian ) [42] The A . fumigatus parental strain AkuBku80ΔpyrG ( ku80 , [43] ) and three agsΔ mutant strains independently obtained: ags1Δags2Δags3Δ_5T ( agsΔ_5T ) obtained previously [8] and two new ones , ags1Δags2Δags3Δn8and ags1Δags2Δags3Δ_n6 . 2 ( agsΔ_n8 and agsΔ_n6 . 2 ) , were used in this study . Since it had been impossible to complement agsΔ mutant for reasons explained previously [8] , two new triple agsΔ mutants were constructed independently using the strategy described previously to exclude the possibility that undesired mutations had occurred during the deletion process . The lack of α- ( 1 , 3 ) -glucan in the cell wall of mutant strains was confirmed by both chemical and immunolabeling assays ( Fig . S8 ) . Chemical analysis of the cell wall was performed as previously described [44] . For immunolabeling assays , 5–10 h germinated conidia were labeled using the MOPC 104E monoclonal antibody , which binds specifically to α- ( 1 , 3 ) -glucan [45] ( Beauvais A . Institut Pasteur , Paris , France , unpublished results ) . Paraformaldehyde ( PFA ) fixed swollen and germinating conidia were permeabilized prior to immunolabeling as previously described [46] . MOPC 104E ( Sigma ) and control mouse IgM ( Sigma ) were used at a dilution of 1∶25 and the goat antimouse IgG-TRITC ( H+L , Sigma ) was used as the secondary antibody at a dilution of 1∶50 . The three triple mutants used in this study germinated , sporulated and conidiated like the parental strain in vitro ( data not shown , [8] ) . Conidial surface was analyzed by Atomic Force Microscopy ( AFM ) . The sample immobilization is achieved by assembling the living conidia within the patterns of microstructured , functionalized poly-dimethylsiloxane ( PDMS , Sylgard 184 ) stamps using convective/capillary deposition [47] . Images and force measurements were performed in deionised water , respectively in contact mode and in Quantitative Imaging ( QI ) mode and Force Volume ( FV ) mode . For both experiments we used bare MLCT AUWH cantilever ( nominal spring constant 0 . 01 N/m ) ( Bruker ) . Single cells were first localized and imaged and then switched over to QI and FV modes to record adhesion force maps . AFM Nanowizard II and III ( JPK Instruments , Berlin , Germany ) were used to capture the images . The cantilevers spring constants were measured by the thermal noise method [48] ranging from 0 . 0160 to 0 . 0190 N/m . Force curves were analyzed in order to determine the adhesion force between the conidia and the AFM tip . These adhesions were plotted as bright pixels , brighter colors indicating larger adhesion values . For each strain , images that were obtained for at least three conidia from independent cultures and analyzed with different tips , were representative of the entire conidial population inside each mutant and parental strain . The results acquired on the spores were analyzed on JPK Data Processing software . The rodlet layer was extracted from the spore surface by incubating 109 dry conidia with 48% ( v/v ) hydrofluoric acid ( HF ) for 72 h at 4°C . The contents were centrifuged ( 10 , 000 rpm , 10 min ) and the supernatant obtained was dried under N2 . The dried material was reconstituted in H2O and an aliquot was subjected to 15% ( w/v ) SDS-PAGE analysis and visualized by silver nitrate staining . Bands were quantified using Image lab software ( BioRad ) . To analyze the components present on the surface , conidia were incubated in 0 . 5 M NaCl solution for 2 h at room temperature at a ratio of 1010 conidia per ml . The NaCl supernatant was recovered after centrifugation and directly subjected to 10% SDS-PAGE ( w/v ) . The protein concentrations in the extracts were determined by the Coomassie brilliant blue method [49] , using BioRad kit and BSA as the standard . Proteomic analysis of the NaCl extract was carried out as described previously with slight modifications [50] . A total amount of 50–100 µg protein was loaded onto IPG strips ( 11 cm , pH 3–7; GE Healthcare Life Sciences ) by in-gel rehydration . After equilibration of the IPG strips , SDS-gel electrophoresis was carried out using Criterion AnykD TGX gels ( Bio-RAD ) . Proteins were visualised by colloidal Coomassie staining [51] . After scanning , gel images were analysed with the software Delta 2D 4 . 3 . ( Decodon ) . Protein spots were excised and analysed by mass spectrometry using an ultrafleXtreme MALDI-TOF/TOF device ( Bruker Daltonics ) . Resting and swollen conidia were PFA-fixed ( 2 . 5% ( v/v ) PFA in PBS ) for one night at 4°C , washed three times with 0 . 1 M NH4Cl in PBS , once with PBS and then incubated with different antibodies or lectins as described previously [52] . Galactosaminogalactan ( GAG ) was labeled with a monoclonal mouse antibody as described previously [53] ( 20 µg/ml ) and a mock monoclonal antibody was used as a control . The secondary goat anti-mouse IgG-TRITC ( Sigma ) antibody was used at a dilution of 1∶200 . Galactomannan was labeled with a rat anti-Galactofuranose ( Galf ) monoclonal antibody ( EBA2 , diluted 1∶1000 , a kind gift of M . Tabouret from BioRad , Steenvorde [54] ) . Control Rat monoclonal antibody of the same isotype and the secondary goat anti-rat FITC ( Sigma-Aldrich ) antibody were used at a dilution of 1∶1000 and 1∶500 , respectively . β- ( 1 , 3 ) -glucan was labeled with the N-terminal β- ( 1 , 3 ) -glucan binding domain of Drosophila pattern recognition receptor , GNBP3 ( homologous to Mammalian Dectin 1 ) at a concentration of 3 µg/ml and a polyclonal mouse antiserum against GNBP3 at 1∶200 dilution ( kind gifts from A . Roussel , CNRS , Orleans and D . Ferrandon , CNRS , Strasbourg , France [55] ) . Goat anti-mouse IgG FITC 1∶200 diluted ( Sigma ) was used as secondary antibodies . The glucosamine moiety of chitin/chitosan and mannose/glucose moieties of glycoproteins and glucans were labeled respectively with WGA-FITC and ConA-FITC ( Sigma ) at 0 . 1 mg/ml concentrations upon incubating the conidia for 15 min at lab temperature . Stress conditions induced by Menadione ( 0 to 30 µM ) and 2 , 5-Bis ( tert-butylperoxy ) -2 , 5-dimethylhexane ( Luperox®101 ) ( 0 to 2 mM ) were tested on both parental and mutant A . fumigatus strains grown on agar-RPMI ( RPMI 1640 , Sigma without glutamine ) supplemented with 1% agar ( Difco ) , 0 . 3 g/1 L-glutamine and 0 . 1 M MOPS or MES ( to obtain a pH of 7 or 4 , respectively ) at 37°C for 24–48 h . Stress conditions induced by Lactoferrin 0 . 45–231 µg/ml ( Sigma ) or Cathelicidin LL-37 0 . 45–231 µg/ml ( Sigma ) , SDS ( 0 . 006–0 . 2%; Merck ) and H2O2 ( 0 . 003–0 . 1%; Fluka ) were tested on A . fumigatus strains grown on Brian medium without supplementation with iron or RPMI-glutamine-MOPS medium ( described above ) [38] . Combinations of 0 . 05% SDS or 0 . 012% H2O2 and Lactoferrin or Cathelicidin LL37 at concentrations of 231 µg/ml were tested in the same media , as described in Clavaud et al [38] . Stress condition induced by HNP2 ( 100 µg/ml; Sigma ) and hBD2 ( 25 µg/ml; Sigma ) defensins were also tested by incubating 106 conidia/ml with the defensins for 10–16 h at 37°C in RPMI-glutamine-MOPS medium . The growth of A . fumigatus strains was tested in Brian medium without supplementation with iron at 37°C and under hypoxia conditions using AnaeroGen sachet ( Oxoid ) , which reduces the oxygen level in a jar to below 1% that results to a CO2 level between 9–13% . Aliquots ( 20 µl ) of concentrated conidia were placed onto a Formvar-coated nickel or gold mesh grids , which were then placed between the flat sides of two B-type brass planchets ( Ted Pella Inc . , Redding , CA ) . The grids were used as spacer creating a thin layer of cells that allows higher yields of well-frozen cells . The samples were immediately frozen with liquid nitrogen under high pressure ( 2 , 100 bar ) using a Bal-Tec HPM 010 high pressure freezing machine ( Bal-Tec Products , Middlebury , CT , USA ) . Following cryofixation , the samples were freeze-substituted at −85°C in 1% glutaraldehyde ( Electron Microscopy Sciences , Washington , PA , USA ) and 1% tannic acid in acetone for 72 h . After , the samples were rinsed thoroughly with three changes of fresh acetone at −85°C for a total of 45 min . Cells were infiltrated with 1% OsO4 in acetone for 1 h at −85°C before being slowly warmed to room temperature over 5 h . The cells were then rinsed in acetone and slowly infiltrated with and polymerized in Spurr's resin . Embedded cells were cut into serial 70 nm thick sections with an Ultracut R Microtome ( Leica , Vienna , Austria ) and collected on Formvar-coated copper slot grids . Sections were post-stained with 2% uranyl acetate in 50% ethanol for 5 min followed by 5 min with Sato's lead citrate [56] . The grids were carbon-coated and viewed at 80 kV using a JEOL 1200EX transmission electron microscope ( JEOL USA , Inc . , Pleasanton , CA , USA ) . Female 8- to 10-week-old inbred C57BL6 ( H-2b ) mice were obtained from Charles River Breeding Laboratories ( Calco , Italy ) . Experiments were performed according to the Italian Approved Animal Welfare Assurance A-3143-01 . Breeding pairs of homozygous p47phox−/−mice , raised on C57BL6 background , were purchased from Harlan Laboratories and bred under specific-pathogen free conditions at the breeding facilities of the University of Perugia , Perugia , Italy [33] . Infections were performed on one model of immunocompetent mice and in two different models of invasive pulmonary aspergillosis as previously described [6] . In the first immunosuppressed model , mice were subjected to intra-peritoneal administration of cyclophosphamide ( 150 mg/kg body weight ) one day before infection as described previously [6] . In the second immunosuppressed model , mice were treated with anti-Ly6G monoclonal antibody ( clone RB6-8C5 MAb; eBienscience; 100 µg/mouse ) administered intra-peritoneally one day before infection . Rat anti-E . coli β-galactosidase ( clone GLL 113 ) was used as a control IgG . Treatment with the anti-Ly6G MAb is known to selectively deplete mature neutrophils , eosinophils and dendritic cells [57] and at 24 h after administration , the number of circulating neutrophils dropped to 20±12/mm3 compared to 1120±227/mm3 in controls , and the treated mice continued to be low for circulating neutrophils counts up to 5-days . Mice were monitored for survival and fungal growth ( determined as colony forming unit ( CFU ) per organ ) four days post-infection as described previously [58] . All mice underwent necropsy for histopathological observation of fungal burden in the lungs four days post-infection . For histology , sections ( 3–4 µm ) of paraffin-embedded lungs were stained following periodic acid-Schiff ( PAS ) protocol . Collection of the bronchoalveolar lavage ( BAL ) fluid and the morphometry [% monocytes ( MNC ) or polymorphonuclear ( PMN ) cells] was performed after four days infection as previously described [6] . Total and differential cell counts were performed after staining BAL smears with May-Grünwald Giemsa reagents ( Sigma ) before analysis . At least 200 cells per cytospin preparation were counted and the absolute number of each cell type was calculated . Cytospin preparations were observed using a BX51 microscope ( Olympus , Milan , Italy ) . Histology images were captured using a high-resolution DP71 camera ( Olympus ) . For phagocytosis and conidiocidal activity , alveolar macrophages from uninfected mice were isolated from BAL as described [15] . For phagocytosis , macrophages were incubated at 37°C with unopsonized FITC ( Sigma ) labeled conidia [59] at an effector to conidial ratio of 5∶1 , for 1 h in RPMI medium in micro-chambers ( Ibitreat ) . Unbound conidia were removed by washing with RPMI and cells were fixed with 3% ( v/v ) PFA for 1 h in PBS . After fixation , the cells were incubated with a rabbit polyclonal anti-FITC antibody ( Invitrogen ) diluted 1∶2000 and a secondary rabbit antibody conjugated to Alexafluor 568 ( dilution , 1∶2000 ) ( Invitrogen ) . This last procedure labels only cell surface-associated conidia and the ingested conidia remained unlabeled . The number of ingested conidia per macrophage was determined on 200 macrophages . For conidiocidal activity , macrophages isolated from uninfected C57BL6 ( H-2b ) and p47phox−/− mice were incubated at 37°C with unopsonized resting or swollen conidia ( 6½ h in RPMI at 37°C ) , at an effector to fungal cell ratio of 1∶10 , for 2–6 hours in an ELISA plate wells . After removing the supernatant , Triton X100 ( 1% ) was added to the wells and incubated at 37°C for 10 min to lyse the macrophages and to collect phagocytized conidia . The percentage of phagocytized conidia capable of further germination was determined by spotting phagocytized conidia ( at suitable dilution ) on a nutritive agar medium and counting those conidia capable of forming germ tube among spotted conidial population . We verified that the use of Triton X100 to lyse macrophage did not affect conidial germination as the percentage of germinations were similar ( 97±1% ) for the agsΔ_5T , agsΔ_n6 . 2 , agsΔ_n8 mutants and the parental strain with or without Triton-treatment . The differences in the germination of the conidia from the stock solution used for macrophage conidicidal activity study permitted the calculation of conidiocidal activity . For cytokine quantification , total RNA was extracted from lungs of immunocompetent mice four days post-infection , or from macrophages isolated from BAL fluid of uninfected mice and incubating with agsΔ NaCl extracts containing 3 . 2 µg proteins , for 5 h . The cytokines expressed and productions were quantified by Real-time PCR and ELISA , respectively as described previously [6] . Statistical significance was analyzed by one- or two-way ANOVA or paired t-test with Prism software ( GraphPad software , San Diego , CA ) and p-values≤0 . 05 were considered to be significant . Data were representative of at least two independent experiments or pooled from three to five experiments . The in vivo groups consisted of six mice/group and experiments were repeated at least three times . Macrophage experiments were done three times with three different batches of macrophages and conidia . All experiments were performed using the agsΔ_5T ( Figs . 1–10 , Table 1 , Table S1 ) . Virulence and proteomic analyses were performed also using agsΔ_n8 ( Figs . S1 , S2 , S3 , S4 , S5 , S6 , S7 , S8 , Table 1 and Table S1 ) . Major phenotypes and virulence data were verified with agsΔ_n6 . 2 ( Figs . S1 , S5 , S6 , S7 , S8 ) . Mouse experiments were performed according to the Italian Approved Animal Welfare Assurance 245/2011-B . Legislative decree 157/2008-B regarding the animal license was obtained by the Italian Ministry of Health lasting for three years ( 2008–2011 ) . Infections were performed under avertin anesthesia and all efforts were made to minimize suffering . | Aspergillus fumigatus is the predominant mold pathogen of humans , responsible for life-threatening systemic infections in patients with depressed immunity . Because of its external localization and specific composition , the fungal cell wall represents a target for recognition by and interaction with the host immune cells . In A . fumigatus , α- ( 1 , 3 ) -glucan is a key component of the extracellular matrix , which encloses the cell wall β- ( 1 , 3 ) -glucan-chitin fibrillar core . Interestingly , the deletion of the genes responsible for α- ( 1 , 3 ) -glucan synthesis resulted in a mutant that exhibited wild type phenotype in vitro; while the altered cell wall organization resulted in this fungus being avirulent in vivo . This study confirms that any modification in the cell wall components is associated with compensatory reactions developed by the fungus to counteract stress on the cell wall that may result in unexpected fungal response when challenged with the host immune system . | [
"Abstract",
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"Results",
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"Methods"
] | [] | 2013 | Deletion of the α-(1,3)-Glucan Synthase Genes Induces a Restructuring of the Conidial Cell Wall Responsible for the Avirulence of Aspergillus fumigatus |
During nervous system development , gradients of Sonic Hedgehog ( Shh ) and Netrin-1 attract growth cones of commissural axons toward the floor plate of the embryonic spinal cord . Mice defective for either Shh or Netrin-1 signaling have commissural axon guidance defects , suggesting that both Shh and Netrin-1 are required for correct axon guidance . However , how Shh and Netrin-1 collaborate to guide axons is not known . We first quantified the steepness of the Shh gradient in the spinal cord and found that it is mostly very shallow . We then developed an in vitro microfluidic guidance assay to simulate these shallow gradients . We found that axons of dissociated commissural neurons respond to steep but not shallow gradients of Shh or Netrin-1 . However , when we presented axons with combined Shh and Netrin-1 gradients , they had heightened sensitivity to the guidance cues , turning in response to shallower gradients that were unable to guide axons when only one cue was present . Furthermore , these shallow gradients polarized growth cone Src-family kinase ( SFK ) activity only when Shh and Netrin-1 were combined , indicating that SFKs can integrate the two guidance cues . Together , our results indicate that Shh and Netrin-1 synergize to enable growth cones to sense shallow gradients in regions of the spinal cord where the steepness of a single guidance cue is insufficient to guide axons , and we identify a novel type of synergy that occurs when the steepness ( and not the concentration ) of a guidance cue is limiting .
During embryogenesis , axons grow through a complex environment to make specific connections with their targets . The growth cone follows concentration gradients of guidance cues by sensing a difference in receptor occupancy across its width , and it turns to align with its interpretation of the gradient direction . Moreover , multiple guidance cues are often needed to correctly guide axons . For example , commissural axons are initially repelled by bone morphogenic proteins ( BMPs ) in the dorsal half of the spinal cord [1 , 2] . They are then attracted by gradients of Netrin-1 [3] , Sonic hedgehog ( Shh ) [4] and vascular endothelial growth factor ( VEGF ) [5] towards the floor plate . While it isn't understood why multiple guidance cues are needed to guide axons to the same targets , it is clear they are non-redundant , as interfering with each of these pathways individually results in guidance errors [4–8] . Both Netrin-1 [9 , 10] and Shh [11 , 12] diffuse from the floor plate cells which secrete them and establish gradients which guide commissural axons [4 , 10] . Shh signals through its receptor Boc [8] , while Netrin signals through its receptor DCC [7 , 13] . Shh- and Netrin-1-mediated axon guidance also both require Src-family kinase ( SFK ) activity [14 , 15] , whose asymmetric activation reflects the direction of the external gradient and is sufficient to cause the growth cone to turn [15 , 16] . While it is known that both Shh and Netrin-1 form gradients , it is not clear how steep the gradients are in vivo and how this steepness influences axon pathfinding in gradients formed by single or multiple guidance cues . Although theoretical chemotaxis modeling has suggested that two overlapping attractive concentration gradients could increase the probability of a cell making a correct decision about the gradient direction [17] , this prediction has not been tested experimentally . There are several potential mechanisms by which multiple guidance cues could collaborate to improve how well the growth cone estimates the direction of the gradient . In one model , the concentration of individual guidance cues is too low to elicit a robust turning response . When the cues are combined , the response is higher than the sum of responses from the same concentration of either cue individually . We will refer to this as concentration-limited synergy , as the concentration of either guidance cue is limiting for the pathway to be engaged . When a second cue is present , there is some crosstalk or convergence between pathways , which overcomes the activation threshold . In an alternative mechanism , which we will refer to as steepness-limited synergy , the concentration of guidance cue present at the growth cone is not limiting; instead , it is the concentration difference of an individual guidance cue across the growth cone that is too small compared to the ambient guidance cue concentration to be accurately detected by the growth cone . When two guidance cues are present , corroborating directional information is supplied and integrated by the growth cone through crosstalk or convergence between the two guidance cue pathways . We demonstrate that commissural axon guidance errors occur in vivo when the Shh concentration gradient is relatively shallow . We then use a novel microfluidic guidance assay to show the importance of gradient steepness for commissural axon guidance in vitro . We find that a combined gradient of the attractive guidance cues Shh and Netrin-1 can act in steepness-limited synergy to attract axons when the steepness of a single guidance cue is insufficient to guide axons . Mechanistically , we demonstrate that combined Shh and Netrin-1 gradients polarize SFK phosphorylation in the growth cone at the same gradient steepness when the two cues behaved synergistically to attract axons .
To determine the Shh gradient steepness that growth cones of commissural axons are exposed to in vivo , we examined spinal cord cross sections of embryonic day 9 . 5 ( e9 . 5 ) and e10 . 5 mouse embryos , stages when axons are actively being guided towards the floor plate . We visualized the distribution of Shh protein in paraformaldehyde-fixed spinal cords using immunofluorescence with an anti-Shh antibody [18] . The Shh staining present in the floor plate and the spinal cord were not present in Shh-/- embryos ( S1A Fig ) , demonstrating that the antibody specifically recognized Shh . We then measured the fluorescence intensity profiles of the Shh protein gradient along the dorso-ventral axis at several angles for each image ( Fig . 1A ) and pooled these measurements from multiple embryos to obtain a prototypical gradient profile ( Fig . 1B ) . Shh fluorescence signal was highest at the floor plate and rapidly decreased for approximately 50 μm from the floor plate , followed by a slower decrease for the remainder of the spinal cord . We observed that the gradient profiles were remarkably consistent between embryos ( S1B Fig ) and that they did not depend on the concentration of the primary antibody ( S1C Fig ) . We then demonstrated that there is a linear relationship between the fluorescence intensity and the concentration of Shh protein ( S1D Fig ) . Furthermore , the gradient profiles were similar whether the measurements were made medially ( as in Fig . 1A ) or more laterally , overlapping with Tag-1 positive axons ( S2 Fig ) . Both the concentration ( C ) and steepness of the gradient can influence axon guidance responses . Because growth cones must be able to determine the direction of a gradient , it is essential that they can sense a difference in concentration across their width . This can be expressed as the absolute change in concentration across a growth cone ( ΔC ) . The fractional change in concentration ( δ = ΔC/C ) is a measure of the steepness of the gradient across the growth cone , typically estimated at 10 μm [19] . The fractional change is usually expressed as a percentage and reflects the change in concentration across a growth cone relative to the ambient concentration at the growth cone . Although it is not possible to accurately quantify absolute protein levels in vivo using immunohistological methods , measuring the fractional change in concentration does not require knowledge of the actual concentration of the cues , only the relative concentration of the cue . Thus we estimated the fractional change in concentration using the Shh fluorescence intensity . Within 50 μm of the floor plate ( relative distance of 0–0 . 1 from the floor plate to the roof plate ) , there is a rapid decrease in Shh , with a fractional change ( δ ) of 46%–72% ( Fig . 1B ) . In the region beyond 50 μm of the floor plate , the Shh gradient was shallower . We then determined where along the spinal cord guidance defects occur for commissural axons from mice genetically deficient for Shh or Netrin-1 signaling . We analyzed images from previously reported guidance cue or guidance receptor mutants [4 , 6–8] and measured the relative distance from the floor plate at which misguided axons begin to deviate from their normal trajectory ( S1 Table ) . For Shh and Netrin-1 signaling dependent defects , guidance errors occurred at a relative distance of 0 . 35–0 . 6 , which corresponds to 158–270 μm from the floor plate for a spinal cord ~450 μm in height . In the region where Shh dependent errors occur ( relative distance of 0 . 41–0 . 56 ) , the Shh gradient at e9 . 5 was very shallow , with a fractional change of 0 . 6 < δ < 0 . 7% . At e10 . 5 , when the majority of the commissural axon growth cones are en route from the roof plate to the floor plate , the fractional change in this region was 1 . 9 < δ < 2 . 1% , slightly higher than that measured at e9 . 5 ( Fig . 1B ) . The Netrin-1 gradient has been previously visualized at mouse e10 . 5 using alkaline phosphatase immunohistochemistry [10] . Similarly to what we observed for Shh , Netrin-1 signal is highest at the floor plate and decreases rapidly in the first ~50 μm from the floor plate , with a shallow gradient present in the remainder of the spinal cord , which includes the region from the floor plate where Netrin-1 dependent errors occur ( relative distance of 0 . 35–0 . 6 ) . This gradient shape is reminiscent of the gradient shape for Shh and suggests that the Netrin-1 gradient is also steep close to the floor plate and shallow in the remainder of the spinal cord . However , we were unable to confirm this by more precise quantification using immunofluorescence because the Netrin-1 antibodies that work for immunohistochemistry are no longer available . Intriguingly , the Shh- and Netrin-1-dependent guidance errors occur in the region of the spinal cord where Shh and most likely Netrin-1 gradients are shallow , not steep ( Fig . 1C ) , indicating that loss of one guidance cue is sufficient to cause guidance defects in shallow gradients . Since guidance defects occur in this shallow gradient region , we hypothesized that having multiple guidance cues may be most important when the fractional change is low , when it is more difficult for a growth cone to obtain an accurate sense of direction from a single gradient . The guidance of commissural neuron axons towards the floor plate in mice occurs between e9 . 5 and e11 . 5 [20 , 21] . Considering that commissural axons grow at 13–20 μm/h in vivo [21 , 22] and that the distance from the roof plate to the floor plate is about 500 μm , an individual axon will therefore take ~25–38 h to reach the floor plate . Since neurons vary in when they differentiate and begin their axon outgrowth , we approximate that commissural neurons are exposed to guidance cues en route to the floor plate over 1–2 d . We thus developed a guidance assay capable of simulating , over 1–2 d , the shallow Shh gradients that we observed in the spinal cord in vivo . Microfluidic mixing networks allow gradients to be controlled in space and time , allowing for long-term gradients to be established , in contrast to passive source-sink diffusion gradients ( e . g . , pipette assay and Dunn chamber ) . We used a linear gradient generator because it allowed us to test a range of fractional change ( δ ) values . We modified a pre-mixer microfluidic gradient generator [23] by increasing both the length and width of the gradient region , thus maximizing the surface area on which neurons could be exposed to the gradient and thus the sample size . By increasing the width of the gradient , we also decreased the range of gradient steepness to physiologically relevant levels , as determined in vivo ( Fig . 1B ) . Our wider gradient chamber required an increase in the number of sequential mixing channels ( Fig . 2A ) , which offered the added benefit of increasing the overall resistance , thus decreasing the flow velocity and resulting shear stress , which can be harmful to axons [24] . With these device improvements , we were thus able to generate stable , long-term gradients . In our microfludic device ( Fig . 2A ) , gravity-driven flow ( Fig . 2B ) directs fluid into the mixing network ( Fig . 2C ) , resulting in a linear gradient throughout the chamber ( Fig . 2C–E ) . We used fluorescent dextran to measure the concentration and fractional change of the gradient , and found that as predicted , the gradient is linear and maintained throughout the chamber ( Fig . 2F , H ) , and stable over a 24 h period ( Fig . 2J–K ) . Furthermore , the measured fractional change ( δ ) values match the predicted values , ranging between 0 . 3% and 2 . 2% ( Fig . 2G , I ) . Since the gradient is linear ( Fig . 2F , H ) , the fractional change increases as the concentration decreases across the device ( Fig . 2G , I ) . The device was biocompatible , as dissociated commissural neurons could be cultured in the device and were observed to extend axons ( Fig . 2L , M ) . To test whether the slow flow rate present in the chamber would bias the direction of axon growth , we measured the angle at which the axon emerged from the cell body and the angle at which the tip of the axon was oriented . We found that the presence of fluid flow did not change the random distribution of these angles ( S3 Fig ) , and therefore the shear stress in our device is negligible and does not bias the direction of axon initiation from the cell body nor the direction of axon growth . Therefore , we have developed an assay , which we named le Massif , to challenge commissural neurons with physiologically relevant gradients . We established gradients of Shh or Netrin-1 after commissural neurons had been cultured for 24 h , when the majority of neurons had already initiated an axon . We calculated the turned angle of an axon as the difference between the base and tip angles ( Fig . 3A ) and scored the angle as positive if the axon turned towards the gradient and negative if it turned away . By varying the maximal concentration of ligand in a particular chamber , we could test a wide range of concentrations . In a control gradient ( Phosphate buffered saline/BSA ) , we observed a wide range of turned angles towards and away from the gradient , resulting in a net turned angle of 0° ( Fig . 3B , E ) . Neurons exposed to a gradient of Shh ( Fig . 3C , E ) or Netrin-1 ( Fig . 3D , F ) , however , turned towards the higher concentration of chemoattractant . For either cue , we observed axon turning in response to a wide range of concentrations at the growth cone ( Fig . 3E , F ) . The distribution of turned angles of individual axons confirmed that wide concentrations of Shh and Netrin-1 induced biases towards attraction ( Fig . 3G , H ) . To eliminate the possibility that Shh or Netrin-1 influences the orientation at which the axon exits the cell body ( axon base angle ) , thus confounding our measurement of the angle turned , we performed experiments where gradients were established 4–6 h after the neurons were plated , before the majority of neurons had initiated an axon . We found that Shh and Netrin-1 gradients induced no significant bias in the distribution of axon base angles facing up-gradient ( higher concentration ) compared to those facing down-gradient ( lower concentration ) ( Fig . 3I , J ) . Therefore , le Massif generates gradients that can induce axon turning without any effect on axonal initiation . Since we observed similar turning over a wide range of concentrations ( Fig . 3E , F ) , we then analyzed axon turning as a function of the fractional change in concentration , δ , across a growth cone . The fractional change is a function of the chamber geometry , and not of the maximum concentration used ( Fig . 4A ) . Therefore , the fractional change is independent of the maximum concentration in the gradient chamber , so long as the minimum concentration is 0 . We found that axon turning increased as a function of fractional change for both gradients of Shh and Netrin-1 ( Fig . 4B , C ) . This corresponded with an increase in the ratio of axons that turned towards the gradient compared with those turning away ( Fig . 4D , E ) . Thus , there seem to be fewer guidance errors as the fractional change across the growth cone increases . This was also illustrated with the distribution of turned angles of individual axons ( Fig . 4F ) . At low fractional change , the population of axons have variable turned angles , with a slight bias toward attraction . As the fractional change increased , a bias towards attraction became more pronounced , as there were fewer axons that were erroneously repelled . We then compared the turned angles of axons experiencing the same fractional change ( δ > 1% ) with different concentrations at the growth cone . When δ > 1% , we observed no trend toward increased turning as the concentration at the growth cone increased ( Fig . 4G , H ) . Therefore , for commissural neurons in gradients of Shh or Netrin-1 , the turning response is more sensitive to changes in the fractional change than the local concentration at the growth cone . Since guidance errors occur in the region of the spinal cord where Shh and Netrin-1 gradients are shallow , not steep ( Fig . 1 ) , we hypothesized that multiple guidance cues might be most important for guiding axons in shallow gradients . Therefore , we next tested whether combining gradients of two guidance cues might modulate the axon turning response in relation to fractional change . We performed guidance assays with 20 nM Shh and 0 . 69 nM Netrin in the inlet , generating local concentrations ranging from 2 . 5 to 18 . 55 nM for Shh and 0 . 08 to 0 . 65 nM for Netrin-1 , encompassing the range for which we see axon turning ( Fig . 3E–H , Fig . 4G , H ) . Gradients of Shh or Netrin-1 alone and in combination were established 6 h after neurons were plated and maintained for 45 h . In either Shh or Netrin-1 gradients , the turned angle peaked at the highest fractional change , δ = 2 . 2% ( Fig . 5A ) . Upon applying both Shh and Netrin-1 simultaneously , the average turned angle increased more quickly as a function of fractional change , such that axons were turning robustly in a region of the double gradient where a single cue was not eliciting much turning ( 1 . 4 < δ < 1 . 8% ) . Interestingly , at the maximal zone of fractional change ( 1 . 8 < δ < 2% ) , there was no observable difference in the angle turned induced by the individual or combined cues ( Fig . 5A ) . To assess the relationship between the combined cues compared to the individual cues , we calculated the synergy quotient as the turned angle in the combined gradient divided by the sum of the turned angles to both cues individually ( described in the Materials and Methods section ) . With this measurement , a value below 1 indicates sub-additive effects , 1 is defined as additive , while a value above 1 is synergistic . We observed additive and sub-additive effects for the majority of the fractional change range , apart from fractional change range of 1 . 44 < δ < 1 . 82% , in which the effect of the combined cues is much greater than the sum of the individual cues , demonstrating synergy ( Fig . 5B ) . Hence the synergistic effect of the combined gradient is greatest when the fractional change is below the maximum . At this fractional change ( 1 . 44 < δ < 1 . 82% ) in which the combined gradients lead to synergy , axons responded much more robustly to the combined cues than for either cue individually , resulting in a higher average turned angle ( Fig . 5C ) . This effect was remarkably consistent , with every independent gradient chamber with combined cues having strong positive turning in this range of fractional change , whereas the gradient chambers with the single cues had variable turned angles with no consistent bias ( Fig . 5D ) . The synergy occurring when the two cues are present was also demonstrated by the larger proportion of axons which turn up the combined gradient than for either cue individually ( Fig . 5E ) . The influence of combining cues on the proportion of correct versus incorrect guidance decisions was also apparent when we observed the distribution of the turned angles in the different conditions ( Fig . 5F ) . For the control gradient ( vehicle ) and gradients of Shh and Netrin-1 , there was no bias towards either attraction or repulsion . Remarkably , when both cues were presented as a combined gradient , there was a clear bias towards attraction , wherein very few axons failed to reorient their direction . Together , these results indicate that a combination of guidance cues can act in synergy to guide axons when the gradient steepness is sub-optimal for the growth cone to sense the direction of a single cue gradient . Since SFKs act downstream of Shh [15] and Netrin-1 [14] to guide commissural axons , it has been proposed in a recent review by Dudanova and Klein [25] that Shh and Netrin-1 signaling may converge on SFKs . Furthermore , the active form of SFKs , phosphorylated at Y418 ( pSFK ) , accumulates on the side of the growth cone proximal to the higher concentration of Shh and is sufficient to relay the direction of the gradient [15] . Using le Massif , we challenged commissural growth cones with gradients of either Shh , Netrin-1 , or a combination of both for 2 h , and then assessed the distribution of growth cone pSFK along the direction of the gradient ( Fig . 6A ) . Growth cone pSFK distribution was measured by the fractional change in signal intensity across the width of a growth cone ( δGC ) , which represents the difference in the amount of pSFK at the proximal versus the distal side of the growth cone , relative to the overall levels . Growth cones with more pSFK on the proximal side closer to the higher guidance cue concentration had positive δGC values , and growth cones with more pSFK on the distal side closer to the lower guidance cue concentration had negative δGC values . For axons exposed to a fractional change of 1 . 44 < δ < 1 . 82% in single cue gradients of Shh ( Fig . 6B ) or Netrin ( Fig . 6C ) , there was no consistent bias in the direction of pSFK distribution ( mean and median δGC ~0% , Fig . 6E , F ) . In the combined gradient , however , more growth cones had a proximally biased pSFK distribution ( Fig . 6D–F ) . This shift towards proximally distributed pSFK was also apparent when we calculated the ratio of the number of proximal to distally polarized growth cones in each independent gradient chamber for 1 . 44 < δ < 1 . 82% . Independent chambers with single cue gradients of Shh or Netrin-1 vary between having a net proximal or distal pSFK growth cone distribution . In contrast , for the combined Shh and Netrin-1 gradient , there are consistently more chambers with a net proximal pSFK growth cone distribution and not a single chamber in which there is a net distal pSFK growth cone distribution ( Fig . 6G ) . Therefore , single cue gradients of Shh and Netrin-1 that do not elicit axon turning ( Fig . 5 ) , also do not elicit a polarized pSFK distribution ( Fig . 6 ) . Remarkably , when the Shh and Netrin-1 gradients synergize to elicit turning , this also corresponds to a higher pSFK distribution on the side of the growth cone facing the high concentration of guidance cues . Taken together with our quantification of gradients and guidance defects in vivo , these results indicate that a combination of guidance cues can act in synergy to polarize growth cones in regions where the gradient steepness is sub-optimal for the growth cone to be polarized by a single cue gradient .
An essential component of the current study is the use of microfluidic mixing networks to generate spatially and temporally stable concentration gradients . le Massif guidance assay allows us to assess axon turning over the course of days . Since an image only has to be taken at the final time point , le Massif is compatible with high-content screening microscopes , allowing assays to be performed in a high-throughput manner , such that a large number of axons can be imaged and analyzed ( over 200 per chamber ) . An additional advantage of le Massif over other axon guidance assays is that it generates gradients with low-to-moderate fractional change , 0 . 3 < δ < 2 . 2% , which sits near the lowest fractional change eliciting detectable guidance responses ( Fig . 4B , C ) . This is critical for studying the influence of fractional change on axon turning . This contrasts with techniques such as the pipette assay , which generates gradients with a steep fractional change ( 5 < δ < 35% ) [26] . While printed gradient assays allow precise control over the gradient parameters [19 , 27–29] , the gradient is printed prior to the addition of the neurons , making it difficult to distinguish the effect of the gradient on direct axon turning , rather than differential axon outgrowth or growth rate modulation ( a notable exception to this is Mortimer et al . [30] , which tested the effect of printing the guidance cue before and after addition of explants ) . Furthermore , in these assays , axons are either growing along pre-defined corridors or are growing from an explant , making individual axon trajectories often difficult to identify . In contrast , individual axon trajectories can be easily visualized in le Massif because the dissociated neurons are grown at low density , so we can clearly measure directed turning of individual axons . Also , by imposing the gradient after axon outgrowth has commenced , we avoid the gradient influencing the orientation of axon protrusion from the cell body [29] . Thus , owing to the versatile process of microfluidic design , we were able to create a customized gradient generator and generate gradients with physiologically relevant steepness that are stable over days . In addition to axon turning , axon growth [28] and growth rate modulation [19 , 31] are also processes important in guiding axons to their correct targets . Compared to direct axon turning , growth-rate modulation occurs when axons growing up-gradient grow faster than those growing down-gradient [31] . Previous studies have found that gradient steepness affects axon growth [28] and growth rate modulation [19 , 31] . We found that gradient steepness also influences axon turning , with robust turning observed for steepness δ~1%–2% . This contrasts with what has been reported for growth rate modulation by NGF gradients , where steepness as low as 0 . 1% is sufficient to bias DRG axon trajectories [19 , 31] . Consistent with our results , these 0 . 1% NGF gradients had no effect on direct axon turning [31] . Similarly , growth of axons is also modulated by gradients with steepness of ≥0 . 4% ( 1% over 25 μm ) , possibly also by influencing the growth rate [28] . Therefore , our results suggest that steeper gradients of 1%–2% are required to induce direct axon turning rather than growth-rate modulation , as hypothesized by Mortimer and colleagues [31] . The gradient steepness at which robust turning occurs is ~1%–2% , within a similar range to our estimate of Shh gradient steepness in the spinal cord ( Fig . 1B ) . For Netrin-1 , the lack of effective antibodies for Netrin-1 for use in immunofluorescent staining hampers our ability to directly measure the Netrin-1 gradient steepness in the spinal cord . Previously published images of Netrin-1 in the spinal cord are not amenable to precise quantification because they use alkaline phosphatase immunohistochemistry combined with darkfield imaging [10] . However , examination of the pattern of Netrin-1 staining in the spinal cord [10] does show that the Netrin-1 gradient is steeper closer to the floor plate and shallower further away from the floor plate , consistent with what we observe with Shh and consistent with our hypothesis that Netrin-1–dependent guidance errors occur in shallow , not steep , regions of the gradient . While significant evidence indicates that multiple guidance cues act on the same axons , precisely how these cues converge to regulate the behavior of the growth cone is poorly understood . The response to two combined cues may be additive or synergistic , depending on whether the output is equal to or above the combined response of either cue individually . Dudanova and Klein [25] define additivity as resulting from cues that act in parallel pathways , and synergy as resulting from cues that have crosstalk between pathways . Additive effects of guidance cues have been observed with ephrin-A and glial cell line-derived neurotrophic factor ( GDNF ) on lateral motor column ( LMCL ) axons [32] , whereas a synergistic attractive response was seen between EphA and GDNF for the same axons [33] . The former demonstrates that ephrin-A and GDNF act in parallel pathways , while the latter demonstrates crosstalk between EphA and GDNF . EphA and GDNF signal through their respective GPI-anchored receptors ephrin-A and GFRα1 , and they crosstalk by sharing a common co-receptor , Ret . The co-activation of ephrin-A and GFRα1 through sharing Ret acts as a coincidence detector and generates synergy [33] . The interaction between EphA and GDNF is an example of concentration-limited synergy , as the combination of low concentrations of guidance cues induced turning when neither cue alone was sufficient . One of our major findings is that synergy occurs between Netrin-1 and Shh to guide commissural axons . In contrast to Bonanomi and colleagues [33] , we find that this synergy is steepness-limited rather than concentration-limited . Steepness-limited synergy occurs when the gradient of individual cues is too shallow to guide axons , but a combined gradient of two cues elicits axon turning . We know that in our case the concentration of the individual cues is not limiting because we observe axon turning when the fractional change is high , despite this corresponding to a lower absolute concentration ( Fig . 4A ) . Furthermore , the range of concentrations used in our experiments all elicit axon turning when the steepness is not limiting ( Fig . 4G , H ) . Thus , we demonstrate for the first time that synergy can also be steepness-limited , when the amount of ligand is not limiting but instead the steepness of the gradient is insufficient for the growth cone to estimate the direction of a single cue gradient . We also identify SFK as a downstream signaling molecule that integrates Shh and Netrin-1 signaling when the two cues synergize . Both Shh and Netrin-1 can activate SFKs , and SFKs are required for Shh and Netrin-1–mediated axon guidance [14 , 15] . Furthermore , pSFK polarization at the growth cone reflects the direction of the external gradient [15] . Gradients of Shh and Netrin-1 too shallow to guide axons were also insufficient to correctly polarize pSFKs at the growth cone . Only in the presence of Shh and Netrin-1 together was the direction of the gradient correctly reflected by the growth cone pSFK polarization . Hence , activated SFKs appear to be a node where information from the Shh and Netrin-1 gradients are integrated . In addition to synergy resulting from sharing a common co-receptor as for EphA and GDNF , we find that for Shh and Netrin-1 , synergy can arise from shared intracellular signaling molecules . Therefore , diverse mechanisms exist through which synergy between two guidance cues can occur , and more mechanisms likely remain to be discovered . In the developing nervous system , it is likely that many types of synergistic interactions play a role in the correct guidance of axons to their targets . In the developing limb , where guidance cues act at a choice point for motor axons , concentration-limited synergy may be more important than steepness because the gradient is very abrupt . For commissural axons , which climb a shallow gradient of guidance cues over a long distance , steepness-limited synergy may initially be more critical . Later in their journey , when they reach the steep part of the gradient , it appears that one cue alone may be sufficient to guide axons—for example , the axons in Boc mutant mice that cannot respond to Shh but by chance make it close to the floor plate do eventually reach the floor plate [8] , possibly because of the effect of the steep Netrin-1 gradient in the ventral spinal cord . Thus , it appears that single steep gradients can guide axons over short distances and allow for more precise guidance near the floor plate , whereas midway along the commissural axon trajectory , synergy between shallow gradients of Shh and Netrin-1 allows these gradients to guide axons that are far from the floor plate , thus extending the distance that guidance cues can act in the spinal cord .
All animal work was performed in accordance with the Canadian Council on Animal Care Guidelines . Wild type C57Bl6 or Shh-/- mouse embryos were sacrificed at e9 . 5 or e10 . 5 and fixed in 4% paraformaldehyde ( PFA ) in phosphate buffered saline for 1–1 . 5 h at 4°C and cryoprotected in 30% sucrose . 12–20 μm thick serial sections were cut with a cryostat . Sections were rinsed several times in buffered saline , and then treated for 1 h with a blocking solution containing 0 . 1% Triton X-100 and 10% heat-inactivated goat serum ( HiGS ) . Spinal cord sections were stained with anti-Shh antibody ( kindly provided by S . Scales , Genentech ) to detect Shh protein [18] . This antibody is specific , as no signal is detected in Shh mutant embryos ( S1A Fig ) . The primary antibody was then replaced with a buffered solution containing 1% HiGS and Alexa Fluor 546-coupled secondary antibody ( Molecular Probes; 1:1 , 000 ) or Cy3 conjugated secondary antibody ( Jackson Immunoresearch , 1:1 , 000 ) for 1 h . After staining , slides were mounted with Mowiol ( Sigma ) and allowed to dry for at least 24 h before imaging . Dot blot immunochemistry was performed by pipetting serial dilutions of recombinant human NShh C24II ( R&D ) onto a glass microscopy slide , followed by the same procedure and reagent concentrations as above . For pSFK asymmetry assays , guidance cues were added , then the microfluidic devices were returned to the incubator for 2 h , after which they were fixed with 4% PFA at room temperature for 15 min . Phosphorylated Src-family kinase was detected using a phosphospecific ( pY418 ) primary antibody ( Invitrogen , 1:1 , 000 ) , followed by Alexa Fluor 488-coupled secondary antibody ( Molecular Probes; 1:1 , 000 ) . Chambers were imaged with an IXM high-content screening microscope ( Molecular Devices ) using a 40X Nikon objective . Spinal cord cross-sections were imaged on a Leica upright microscope with 10X and 20X objectives at multiple exposure times to ensure that the images contained the entire dynamic range of the gradients that had been revealed by immunohistochemistry . Images were then analyzed with a custom ImageJ macro , which measured the intensity profile along the dorso-ventral axis at five discrete angles ranging from 95° to 105° , emanating from a region just outside the floor plate . This was performed for both sides of the spinal cord for each image ( Fig . 1A ) . The data was then pooled and visualized using a custom MATLAB script to calculate the mean intensity of the Shh gradient . The background fluorescent signal contribution from both the primary and secondary antibodies was determined by measuring the staining intensity in the neural tube of Shh-/- littermates , which were processed simultaneously and imaged identically . The background signal was then subtracted from each quantified Shh gradient profile before the fractional change was calculated . To calculate the fractional change of the measured gradient , the mean intensity profile of the regions of interest were fit to a straight line using Open Office Calc ( Maryland ) , and the fractional change calculated from the fit line . A microfluidic gradient generator [23] was modified to increase the surface area over which the gradient can be applied . Positive relief master molds were fabricated from a 17 . 78 cm ( 7 in ) chrome photomask ( FineLine Imaging , Colorado ) by the McGill Nanotools Microfabrication Facility by spin coating SU-8 2050 ( Microchem ) to a height of 50 μm onto a 15 . 24 cm ( 6 in ) silicon wafer . The silicon master wafer with positive relief features was exposed to CHF3 plasma for 1 min , then treated with 3 , 3 , 3 trifluoroperfluoro-octylsilane in a vacuum desiccator for 30 min to ensure that the polydimethylsiloxane ( Silgard 184—PDMS ) would not stick to the SU-8 features . PDMS was then mixed thoroughly as per manufacturer's recommendations ( 10:1 base polymer: curing agent ) before being degassed for >15 min in a vacuum and poured onto the silicon master wafer . The PDMS was cured for >3 d at 60°C . The PDMS was then peeled off from the master and cut to individual chips . Through-holes at the two inlets and outlet were made using a biopsy punch . Glass slides ( Schott Glass D ) were soaked in concentrated nitric acid for 24–36 h , before being rinsed in milliQ water 12 times over 2 h and sterilized by baking at 225°C for 4–6 h . On the day prior to beginning the experiment , both glass slides and PDMS chips were exposed to an oxygen plasma ( Plasmaline 415 Plasma Asher , Tegal Corporation , 0 . 2 mbar for 30 s at 75 W ) before bringing the surfaces into contact to form an irreversible bond . Within 20 min following bonding , devices were filled with 0 . 1 μg/ml poly-D-lysine ( PDL; Sigma ) to generate an adhesive substrate onto which neurons could attach . After coating for 1 h , the PDL was removed and the microfluidic chamber rinsed twice by adding sterile milliQ water to the outlet . Fluid reservoirs were crafted by cutting the bottoms from 200 μl PCR tubes and positioning the tubes into the punched holes such that both tubes were an equal height . The tubes were both filled with 200 μl of Neurobasal media containing serum , generating a forward gravity-driven flow , which was left to further rinse the PDL coated channels overnight . The range of ligand concentration imposed on the axons in the gradient chamber depended on the guidance cue concentration added to the reservoir at inlet 1 ( Fig . 2A ) . The reservoir at inlet 2 was filled with culture media without guidance cue . To visualize and quantify the gradient , we used 40 kDa tetramethylrhodamine-dextran . Hydrostatic pressure was created by filling the inlet reservoirs higher than the outlets ( Fig . 2B ) , which drove fluid flow uni-directionally from left to right throughout the device . When fluid from the two inlets converge , the concentrations at inlet 1 and inlet 2 are mixed and subsequently divided to three discrete concentrations ( Fig . 2C ) . This mixing and splitting occurs a total of 18 times , generating 20 discrete concentrations that are spaced at linear gradations between the concentration at inlet 1 and inlet 2 ( no cue ) . The 20 discrete concentrations then flow from the premixer channels into the gradient chamber , where they meet and diffuse to establish a linear concentration gradient ( Fig . 2C ) . Because the fluid volume is on the microliter scale and the Reynold's number is low ( Re < 1 ) , the flow is laminar and there is no convective mixing [34] . Because diffusion is slow over long distances , the diffusion of the guidance cue is slow relative to the flow velocity and the gradient remains linear for the entire 9 mm length of the gradient chamber ( Fig . 2E ) as long as there is a continuous flow driving the mixing . Consequently , long-term gradients can be maintained without actively controlling the flow rate , so long as the reservoir at the outlet is emptied periodically ( approximately every 24 h ) . The upstream and downstream regions of the gradient chamber were imaged using a 2 . 5X objective on an upright fluorescence microscope ( Leica ) . Commissural neurons were prepared from the dorsal fifth of E13 rat neural tubes as described previously [15 , 35] . Cells were re-suspended in plating media composed of Neurobasal ( Gibco ) supplemented with 10% heat-inactivated FBS and 2 mM GlutaMAX ( Life Tech ) . 50 μl of plating media was added to both inlet reservoirs and 50 μl of cell suspension ( 3 , 160 , 000–5 , 630 , 000 cells/ml ) was added to the outlet . One of the inlet reservoirs was removed and a reverse flow induced by connecting a syringe to the inlet hole via a short rubber hose and pulling on the plunger . While observing with an inverted microscope , neurons were drawn into the gradient chamber , after which the flow was stopped by releasing the plunger , disconnecting the syringe , and then returning the reservoir to the hole . After 4–6 h , inlet reservoirs were filled with 200 μl of plating media , again inducing a forward flow . Approximately 15 h later , the plating media was replaced with serum-free growth media composed of Neurobasal ( Gibco ) supplemented with 2% B27 ( Gibco ) , 2mM GlutaMAX ( Gibco ) and penicillin/streptomycin ( Gibco ) . Shh guidance experiments were performed using the recombinant human NShh C24II ( R&D ) . Netrin-1 guidance experiments were performed using the VI , V peptide [36] , which was a kind gift from Dr . Tim Kennedy . The guidance assay was started within 24 h of plating , when most of the neurons had initiated a neurite . Culture media ( 200 μl ) was added to one of the inlet reservoirs and guidance cue or vehicle control ( 0 . 1% BSA; Sigma ) diluted in culture media ( 200 μl ) to the other . Gradient devices were then returned to the incubator until the following morning ( ~20 h following gradient application ) , at which point a Pasteur pipette was used to remove any fluid which had accumulated in the outlet . The devices were then returned to the incubator for a further 4 h , for the remainder of the 24 h assay . Guidance assays over 45 h were performed as described above , except the gradient was established 4–6 h after the neurons were loaded into the device . The assay was ended by quickly removing all culture media from the inlets and outlets by aspiration and adding 4% PFA ( 100 μl ) to the outlet reservoir . After 15 min , the PFA was removed and replaced with a staining buffer consisting of DAPI ( Sigma , 1:10 , 000 ) to stain cell nuclei , TRITC-phalloidin ( Molecular Probes , 1:250 ) to stain F-actin , and Triton ( Sigma , 1:400 ) to permeabilize the cells . Neurons were left to stain overnight ( ~12 h ) and the staining buffer was replaced with buffered saline for 1–2 h before imaging . Fixed specimens were imaged using an IXM high content screening automated microscope ( Molecular Devices ) with a laser-based auto-focus and a 20X objective ( Nikon ) . To include the entire area of the gradient chamber , 275–300 images were obtained for each device using MetaExpress imaging software ( Molecular Devices ) . All analyses were performed by an observer naive to the gradient conditions for each device . For each image , we traced all isolated axons in each field of view using a custom ImageJ macro . So that the observer would be blind to the direction of the gradient , every image had a 50% chance of being flipped vertically when opened . Image files were analyzed by Flatworld Solutions ( Bangalore ) . All calculations of neuron position , concentration , fractional change , axon length , and turned angle were performed using a custom MATLAB script . We defined the axon base angle as the angle between the proximal 20 μm of the axon and the direction of flow , and the axon tip angle as the angle between the distal 20 μm of the axon and the direction of flow ( parallel to the arrow in Fig . 2C ) . We defined the turned angle as the difference between the base and tip angles of the axon , where the sign of the difference was positive if the axon turned toward the gradient and negative if the axon turned away from the gradient ( Fig . 3A ) . We considered only axons that faced against the direction of the flow , and we excluded those facing directly towards or against the gradient ( within 20° of the gradient direction ) . We excluded from further analysis axons that were shorter than 20 μm . Because of the local flattening of the gradient near the boundaries caused by the no-slip condition , we excluded any neurons positioned within 450 μm of either boundary ( red boxes Fig . 2K ) . To estimate the concentration of guidance cue at each growth cone position , we calculated each neuron’s position relative to the gradient chamber , and thus relative to the gradient itself . We assumed a growth cone width of 10 μm for fractional change calculations , which we calculated using the difference in concentration between a point 5 μm above and 5 μm below the neuron , divided by the concentration at the neuron's position . All included growth cones experience fractional change within the range 0 . 3 ≤ δ < 2 . 2% . To calculate the synergy quotient , we first calculated a central moving average ( CMA ) of the turned angle of all axons within a window of 0 . 5% fractional change for each Shh , Netrin , and the combined gradient Shh+Netrin ( Fig . 5A ) . We then calculated the synergy quotient ( SQ ) as: SQ = CMAShh+Netrin/ ( CMAShh+ CMANetrin ) . After being exposed to the gradient ( s ) for 2 h , neurons were fixed and stained for pSFK . Chambers were imaged with an IXM high-content screening microscope ( Molecular Devices ) using a 40X Nikon objective . Each growth cone was outlined . Then a line was placed spanning the width of the growth cone outline , parallel to the direction of the concentration gradient . The intensity profile was measured across five parallel lines spaced 1 pixel apart . The average intensity profile of the five lines was then processed using a custom MATLAB script . The fractional change in staining intensity across each growth cone ( δGC ) was then calculated as the difference in mean intensity between the proximal and distal thirds , divided by the mean intensity of the entire area of the growth cone ( Fig . 6A ) . This value was scored as positive if the higher staining intensity was on the side of the growth cone proximal to the gradient and negative if the higher staining intensity was on the side of the growth cone distal to the gradient . All analysis of variance , Chi-square and Wilcoxon signed-rank tests were performed using Graphpad Prism 5 ( La Jolla , CA ) . All Rayleigh tests for unimodal deviation from uniformity were performed using the circStat toolbox for MATLAB . The majority of graphs were generated using GraphPad Prism or Open Office Calc ( The Apache Software Foundation ) , unless otherwise mentioned . Tricolor radial scatter plots ( Figs . 3G , H , 4F , 5F ) and radial frequency histograms ( Figs . 3I , J , S3 ) were scripted manually with Processing , an open-source sketchpad software ( www . processing . org ) . Random samples of axons were generated with a Processing script using a uniform probability distribution , wherein each axon was equally likely to be selected as the next data point was plotted , and the same data point could not be plotted twice . | During development of the nervous system , axons are propelled by the growth cone , a motile structure that is specialized to detect the direction of concentration gradients of guidance cues . Although it is known that commissural axons—those that cross the midline from one side of the nervous system to the other—of the spinal cord are guided by multiple cues simultaneously , it is unclear whether they integrate multiple guidance cues and , if that is the case , the advantage of doing so . In the developing spinal cord , the gradients of the guidance cues are shallow and , thus , their direction is difficult to determine . We hypothesize that under these circumstances , a combination of cues could be used synergistically by the growth cone . To test this hypothesis , we built a microfluidic gradient generator capable of simulating the shallow gradients that growth cones encounter in the developing spinal cord . Using this guidance assay , we demonstrated that commissural axons are best at reorienting themselves in the steepest part of a gradient of Shh or Netrin , two guidance cues that these axons encounter in the spinal cord . We then challenged axons with combined gradients of both cues . At low gradient steepness , we observed synergy in their turning response and in the asymmetry of the shared downstream signaling molecules Src-family kinases ( SFKs ) . We therefore propose a model in which SFKs integrate distinct signaling pathways , and we define this as steepness-limited synergy . | [
"Abstract",
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"Methods"
] | [] | 2015 | Integration of Shallow Gradients of Shh and Netrin-1 Guides Commissural Axons |
Bacterial type III protein secretion systems inject effector proteins into eukaryotic host cells in order to promote survival and colonization of Gram-negative pathogens and symbionts . Secretion across the bacterial cell envelope and injection into host cells is facilitated by a so-called injectisome . Its small hydrophobic export apparatus components SpaP and SpaR were shown to nucleate assembly of the needle complex and to form the central “cup” substructure of a Salmonella Typhimurium secretion system . However , the in vivo placement of these components in the needle complex and their function during the secretion process remained poorly defined . Here we present evidence that a SpaP pentamer forms a 15 Å wide pore and provide a detailed map of SpaP interactions with the export apparatus components SpaQ , SpaR , and SpaS . We further refine the current view of export apparatus assembly , consolidate transmembrane topology models for SpaP and SpaR , and present intimate interactions of the periplasmic domains of SpaP and SpaR with the inner rod protein PrgJ , indicating how export apparatus and needle filament are connected to create a continuous conduit for substrate translocation .
Type III secretion systems ( T3SSs ) are used by many Gram-negative bacterial pathogens and symbionts to translocate effector proteins in one step across the bacterial envelope and into eukaryotic host cells [1] where they modulate host cell physiology to promote bacterial survival and colonization [2] . The core of T3SSs is formed by the so-called injectisome , a macromolecular machine composed of up to 20 different proteins [1] . The base of the injectisome , consisting of an outer membrane secretin ring and two inner membrane ring components , anchors the system to the bacterial cell envelope [3] . A filamentous needle projects away from the base towards the host cell and serves as conduit for translocated effectors [4 , 5] . Five cytoplasmic proteins select and unfold the substrates , which are then handed over to the actual export apparatus [6 , 7] housed in a membrane patch at the center of the inner ring [8 , 9] . The five export apparatus components are thought to facilitate the actual secretion function of T3SSs , including energy coupling , membrane translocation , and substrate specificity switching [1] . Base , needle filament , and export apparatus are together also termed needle complex . While analyses by X-ray crystallography and cryo electron microscopy have revealed the structure of most soluble components of injectisomes or of the related flagellar system [10 , 11] , the structure and in particular the function of the hydrophobic transmembrane ( TM ) domains of the export apparatus components remain poorly defined . In the T3SS encoded within the pathogenicity island 1 ( SPI-1 ) of Salmonella enterica serovar Typhimurium ( S . Typhimurium ) , the export apparatus is composed of the proteins SpaP , SpaQ , SpaR , SpaS , and InvA in a 5:1:1:1:9 stoichiometry [12] . Of these components , InvA and SpaS are structurally and functionally best characterized: the atomic structures of their soluble cytoplasmic domains have been solved [13 , 14] . The large cytoplasmic domain of InvA ( or its homologs ) forms a nonameric ring with a central pore of about 50 Å in diameter [15] and has been proposed to play a role in substrate switching and translocation [16 , 17] while its 8 predicted TM helices have been proposed to serve in utilization of the proton motive force for secretion [18] . SpaS and its homologs play a role in switching of specificity from secretion of early to intermediate and late substrates [19] . Autocleavage of a highly conserved NPTH motif in the cytoplasmic domain of SpaS is required for this function , possibly to facilitate a high conformational flexibility of this domain for secretion of later substrates [20] . The substantially hydrophobic export apparatus components SpaP , SpaQ , and SpaR and their homologs were shown to be critical for assembly of the needle complex [9 , 21–23] and essential for secretion function [9 , 24] but their precise role in secretion is still unknown . It was suggested that SpaP and SpaR form the cup substructure of the needle complex [9] . Given the presumed central location of SpaP and SpaR at the center of the membrane patch of the needle complex and their substantial hydrophobicity , we hypothesized that these two proteins may constitute the actual substrate translocation pore of T3SSs in the bacterial inner membrane , a function that as yet has not been assigned to any T3SS component . In this study , we have biochemically characterized a stable subcomplex formed by SpaP and SpaR , and mapped its place within the needle complex using in vivo photocrosslinking and complementary techniques . We show that an isolated complex of five SpaP and one SpaR forms a donut-shaped structure with an approximately 15Å wide recession at its center . Sole expression of the SpaP pentamer in the bacterial membrane allowed the permeation of compounds of 500 Da into the cytoplasm , suggesting that these proteins form a channel large enough for translocation of secondary structures . We further show that a complex of SpaP , SpaQ , SpaR , and SpaS assembles in vivo before incorporation into the needle complex base , and that these four export apparatus components form a compact assembly with multiple reciprocal interactions at TM helices three and four of the SpaP pentamer . We also present evidence that SpaP and SpaR interact on their periplasmic side with the inner rod protein PrgJ , which provides a basis to explain how the substrate translocation conduit is continuous from the export apparatus through the inner rod into the needle filament and suggests that the hitherto unaccounted electron density of the socket substructure is made of the periplasmic domains of SpaP and SpaR , together with PrgJ . In summary , we describe physical interactions among export apparatus components of bacterial T3SSs and identify the components that form its substrate translocation pore . This work will facilitate further structural and functional work on these machines and may help to develop novel antiinfective therapies targeting these virulence-associated molecular devices .
We previously showed that a stable complex of SpaP and SpaR can be isolated from S . Typhimurium lacking the inner ring components PrgH and PrgK [9] . For further characterization , we expressed the spaPQRS operon in Escherichia coli and purified the SpaPR complex by immunoprecipitation of epitope-tagged SpaR . The isolated complex eluted as a sharp peak from a size exclusion chromatography column at an apparent size of 400 kDa ( Fig 1A ) . Separation of the protein complex by SDS PAGE followed by Coomassie staining or Western blotting and immunodetection of SpaP and SpaRFLAG , respectively , showed that the complex contained more SpaP than SpaR ( 1B ) . Since the masses of membrane protein complexes deduced from analysis by size exclusion chromatography are skewed by the presence of bound detergent , we analyzed the fraction of protein and detergent contained in the isolated SpaPR complexes by size exclusion chromatography-multi angle laser light scattering . This analysis determined that the SpaPR peak was monodisperse , corresponding to a size of 311 kDa with a calculated protein content of 160 kDa ( Fig 1C , S1 Table , S1 File ) , suggesting a total of 6 molecules of SpaP ( 25 . 2 kDa ) and SpaR ( 31 . 7 kDa including C-terminal 3xFLAG tag ) . Given a mean error of 7% ( S1 Fig ) , these data did not allow to distinguish whether the complex composition was 4 SpaP + 2 SpaRFLAG ( calc . 164 kDa ) or 5 SpaP + 1 SpaRFLAG ( calc . 158 kDa ) . Native mass spectrometry was then performed to assess the exact stoichiometry of a purified SpaPRSTREP complex . A major species of complex produced peaks of 157 . 882 kDa and a minor species of 158 . 595 kDa . These masses are consistent with a stoichiometry of 5 SpaP and 1 SpaRSTREP ( calculated molecular mass of 157 . 280 kDa ) with bound phospholipids . In summary , these results show that the isolated SpaPR complex obtained from overexpression in the absence of other needle complex components has the same stoichiometry as SpaP and SpaR assembled into complete needle complexes [12] and indicates that the isolated SpaPR complex is a relevant functional module of the needle complex . To further validate the stoichiometry of SpaP and SpaR and to characterize the placing of this module within the assembled needle complex , we employed an in vivo photocrosslinking approach based on the genetically encoded UV-reactive amino acid para-benzophenylalanine ( pBpa ) [25] . pBpa was built into the predicted TM helices of SpaP and SpaR , respectively , so that possible interactions at every face of the predicted TM helices were sampled ( Fig 2A and 2B ) . spaP or spaPQRS deletion mutants of S . Typhimurium were complemented with SpaPFLAG or SpaPQRFLAGS containing pBpa at selected positions and expressed from a low copy number plasmid . Complementation of T3SS function of these mutants was assessed by analyzing type III-dependent secretion of substrate proteins into the culture supernatant ( S2 Fig ) . Crosslinking of pBpa to nearby interactors was induced by UV irradiation of intact bacterial cells immediately after harvesting . Subsequently , crude membranes were isolated and crosslinking patterns were analyzed by SDS PAGE and immunodetection of the FLAG-tagged bait protein . Crosslinked adducts of different sizes were identified at various positions of SpaP and SpaR ( Fig 2C and 2D ) . To exclude crosslinking artifacts resulting from plasmid-based complementation , pBpa positions that produced representative crosslinking patterns were also introduced into the chromosome-encoded genes , and crosslinking was performed accordingly . Notably , for all tested chromosomal positions the quality of previously identified crosslinks could be confirmed while the efficiency of crosslinking improved in some cases , possibly due to a more efficient complex assembly achieved by expression of pBpa-containing proteins from its native context ( Fig 2E and 2F ) . To identify the nature of crosslinked adducts , needle complexes with pBpa-containing SpaPFLAG or SpaRFLAG were purified , UV-irradiated , resolved by SDS PAGE , and gel slices of the positions of the crosslinks were analyzed by mass spectrometry ( S3 Fig ) . This analysis identified crosslinks between SpaP and the export apparatus components SpaS and SpaQ , and between SpaP and the inner rod protein PrgJ . Furthermore , crosslinks between SpaR and SpaP , SpaQ , and PrgJ were also identified ( S2 Table , Fig 2C and 2D ) . The detailed validation and interpretation of the crosslinking analysis is presented in the following three sections . UV-irradiation of SpaPFLAG-containing pBpa at positions L7 , L10 , A12 , F13 , S14 , T15 , M187 , S189 , I193 , and T195 showed a ladder of crosslinks at 40 kDa , 70 kDa , 120 kDa , and 200 kDa ( Fig 2C and 2E ) . We reasoned that this crosslink ladder might correspond to a homo-oligomeric crosslinking of the SpaP pentamer . Two further experimental results supported this hypothesis: First , crosslinking of SpaPT15XFLAG expressed in E . coli in the absence of other T3SS components showed the same crosslink ladder ( Fig 3A ) ; and second , crosslinking plasmid-complemented SpaPT15X in an S . Typhimurium strain with chromosome-encoded SpaPFLAG also produced the 40 kDa FLAG-containing crosslink , which proved at least a bipartite SpaPT15X-SpaPFLAG interaction ( Fig 3B ) . Several of the SpaP pBpa mutants that produced a ladder upon crosslinking ( A12X , T15X , M187X , S189X , I193X ) were non-functional ( S2 Fig ) . Analysis of two of these pBpa mutants ( T15X and M187X ) by 2-dimensional blue native/SDS PAGE indicated that the observed SpaP-SpaP interaction occurred between SpaP assembled into the complete needle complex as well as between SpaP molecules that had not yet been yet incorporated into this structure ( Fig 3C ) . These results suggest that the loss of function of these mutants is unlikely due to improper folding or assembly but rather due to subtle conformational changes that alter their function . Overall , these results indicate that TM helix one and to a smaller extent the cytoplasmic face of TM helix three and four are involved in protomer contacts in the SpaP homopentamer while only few homotypic interactions were observed at positions of TM helices two and three . To cross-validate the experimental findings , we performed an independent prediction of SpaP-SpaP interactions based on analysis of sequence co-variation using the software EV couplings [26–28] . 27 of the experimentally tested SpaP positions were predicted to be involved in SpaP-SpaP interactions with a normalized coupling score >0 . 80 ( S3 Table ) . 18 of the 27 experimentally tested positions yielded indications of SpaP-SpaP interactions , 2 positions were experimentally ambiguous because of very low expression levels of the mutated proteins , and 7 positions showed no signs of SpaP-SpaP interactions . As used , EV couplings does not distinguish between intra and intermolecular interactions . 6 of the predicted but experimentally negative positions are likely to be involved in intramolecular interactions , which are not detectable by the in vivo photocrosslinking approach used ( Fig 3D ) . Many intermolecular interactions at experimentally tested SpaP positions were predicted to connect two TM helices 1 or TM helix 1 and 3 in a parallel fashion , and TM helices 1 and 2 or TM helices 3 and 4 in an antiparallel fashion ( Fig 3D ) , supporting a SpaP topology as depicted in Fig 2A , while only the coupling prediction of SpaPS189 ( to L11 ) opposed this model . Overall , the bioinformatic analysis supports our experimental results , strengthens the topology model of SpaP , and provides a first picture of the buildup of the SpaP pentamer . Mass spectrometry analysis of crosslinked SpaP and SpaR adducts produced evidence for multiple interactions among the export apparatus components SpaP , SpaQ , SpaR , and SpaS ( Fig 2 , S3 Fig , S2 Table ) . To validate these results by immunoblotting , we assayed the SpaP-SpaR as well as the SpaP-SpaS interactions by FLAG-tagging the target instead of the pBpa-containing bait protein . We found that SpaP interacts with SpaRFLAG through its residues V170 and L210 but not through V203 and A204 ( Fig 4A ) and that SpaR contacts SpaPFLAG via its residue N151 ( Fig 4B ) . Using an autocleavage-deficient FLAG-tagged variant of the switch protein SpaS , we could further validate interactions between SpaS and SpaPV200X/SpaPV203X ( Fig 4C ) . In summary , these crosslinking data indicate that , consistent with our previous report [12] , 1 SpaQ , 1 SpaR , and 1 SpaS form a closely interconnected assembly that contacts SpaP at TM helix three ( V170: SpaQ , SpaR ) and TM helix four ( V200/203: SpaQ , SpaS ) . The interaction of these four proteins seems to be integrated by SpaQ as this small protein makes contacts to all other three proteins ( in vivo photocrosslinking-identified SpaS-SpaQ contacts communicated results of J . Monjarás Feria ) . Previous results showed that SpaQ is critical for efficient formation of the needle complex base but due to technical limitations of the blue native PAGE approach used at the time , it was not clear whether assembly proceeds through a pre-assembled complex of all four minor export apparatus components before integration into the base or whether these components only interact upon base integration [9] . To examine the early events of the assembly of the T3SS export apparatus components , we probed the SpaP-SpaQ , SpaP-SpaS , and SpaR-SpaQ interactions identified by the crosslinking studies in strains deficient in the inner ring protein PrgK . These mutants are deffective for base assembly thus allowing to prove the requirement of a fully assembled base for the assembly of the export apparatus . Indeed , we detected SpaP-SpaQ and SpaP-SpaS interactions at SpaPX203 in the absence of PrgK ( Fig 4D and 4E ) , and SpaR-SpaQ interactions at SpaRX209 ( Fig 4F ) . SpaP-SpaP and SpaPV170X-SpaR crosslinks were also identified when plasmid-encoded SpaPQRS were expressed in E . coli BL21 , lacking all other T3SS components ( Fig 4G and 4H ) . Altogether , these results indicate that assembly of the export apparatus precedes and is independent of base assembly . UV-irradiation of SpaPFLAG with pBpa at position L7 or SpaRFLAG with pBpa at positions F20 , N44 , and A45 resulted in an 8 kDa mobility shift of these proteins in SDS-PAGE ( Fig 2C , 2D and 2E ) . Mass spectrometry analysis of the shifted bands identified PrgJ in both cases ( S3 Fig , S2 Table ) . In an effort to characterize the extent of the SpaP-PrgJ interaction in more detail , we also noted the same mobility shift of SpaP after UV-irradiation of SpaPFLAG with pBpa at positions G2 , N3 , D4 , I5 , and S6 , where crosslinked PrgJ was confirmed by immunodetection ( Fig 5A ) . To rule out potential artifacts due to overexpression of the plasmid-borne constructs , we confirmed the crosslinks of SpaPG2XFLAG and SpaPS6XFLAG after expression from their native chromosomal context ( Fig 5B ) . 2-dimensional blue native/SDS PAGE analysis of the crosslinks resulting from UV-irradiation of SpaRA45XFLAG showed that the observed SpaR-PrgJ interaction is only observed when SpaR is incorporated into the needle complex ( Fig 5C ) . Furthermore , SpaP-PrgJ as well as SpaR-PrgJ interactions were not observed in an ATPase activity-deficient InvCK165E mutant , demonstrating that the detected interactions dependent on active type III secretion , which is consistent with the observation that incorporation of PrgJ into the needle complex and inner rod assembly require a functional type III secretion system ( Fig 5D ) . Taken together , these results indicate that the periplasmic domains of SpaP and SpaR serve to anchor the inner rod protein PrgJ to the export apparatus , thus creating a continuous conduit for substrate translocation from the export apparatus to the needle filament . The location of the SpaP5R1 complex at the center of the needle complex base , right underneath and connected to the filamentous conduit formed by the inner rod and needle proteins , suggests that this complex forms the T3SS’s substrate translocation pore in the bacterial inner membrane . To obtain structural evidence for its putative pore-forming function , we analyzed the purified , negative-stained SpaPRFLAG complex by electron microscopy . 11202 individual particles were classified and aligned into 91 class averages ( S4 Fig ) . A number of class averages showed a symmetric , donut-shaped complex with an iconic recession at its center ( Fig 6A ) . The diameter of these particles was about 80 Å and the diameter of the recession was about 15 Å . Other class averages showed a more asymmetric shape with an extra density outside of the ring-structure or a mushroom-like shape ( Fig 6A ) . Even though the sample analyzed consisted of a homogeneous population of SpaPRFLAG complexes , it cannot be ruled out that SpaP and SpaRFLAG partly dissociated during sample preparation so that a mixture of SpaP5 and SpaP5R1 complexes was imaged , explaining the diversity of observed classes . It is therefore possible that the donut-shaped particles represent SpaP5 complexes and the asymmetric extension the SpaP-bound SpaRFLAG . Overall , the particles’ shape and dimensions conformed well with the structure of the cup region of assembled bases reported previously ( 3 ) . We reasoned that the recession at the center of the observed particles might represent the protein translocation pore of the T3SS . To probe the conducting properties of the SpaPR complex , we assessed its ability to allow the access of biotin maleimide ( BM , molecular mass = 500 Da ) into the bacterial cytoplasm , an approach that has been used previously to test the gating of the Sec-translocon [29] . The maleimide moiety of BM can only react with and biotinylate free thiol groups of cysteine residues of cytoplasmic proteins if BM can penetrate the inner bacterial membrane through a sufficiently large pore . The extent of biotinylation can then be detected on a Western blot by utilizing streptavidin . Strong BM labeling of proteins was observed in whole cell lysates when SpaPR or SpaP alone were overexpressed from a medium copy plasmid ( Fig 6B ) . Cell fractionation of the expression host showed that only cytoplasmic proteins were differentially labeled by BM upon expression of SpaPR and SpaP , labeling of periplasmic proteins , however , was almost indistinguishable in control and expressing bacteria ( Fig 6B ) . General lysis of the expression host could be ruled out to cause the observed phenotype as neither the cytoplasmic protein RNA polymerase nor the periplasmic maltose binding protein were observed in the culture supernatant of SpaPR or SpaP expressing bacteria ( S5A and S5B Fig ) . Formation of a sizable , ungated pore by these complexes was also indicated by the strong impact even modest overexpression of SpaP and SpaPR had on the viability of the expression host ( S5C Fig ) . Altogether , these results suggest that BM accessed the cytoplasm of the expression host through a pore formed by the expressed proteins . Since SpaP expression alone led to BM labeling of cytoplasmic proteins , it is conceivable that SpaP alone is sufficient to form the actual substrate translocation pore . In line with this idea , overexpressed SpaPEPEA was observed to assemble into high molecular weight complexes when analyzed by blue native PAGE ( Fig 6C ) , however , we were not able to isolate and investigate stable SpaP-only complexes . The access of 500 Da BM to the cytoplasm through the pore of the SpaP pentamer suggests a pore diameter of about 15 Å , which is consistent with the diameter of the recession observed by electron microscopy of the isolated SpaP5R1 complexes .
The export apparatus of bacterial T3SSs is its central unit that facilitates translocation of substrates across the bacterial inner membrane and likely the only gated barrier of these one-step secretion devices . While functions have been proposed for some export apparatus components , the components forming the actual substrate translocation pore in the bacterial inner membrane have not been defined . In this study we present evidence that a homopentamer of the minor hydrophobic export apparatus component SpaP is a central component of the translocation pore in the inner membrane of the injectisome T3SS encoded by Salmonella pathogenicity island 1 . We purified a stable complex of 5 SpaP and 1 SpaR that under electron microscopy exhibited a donut-like shape of about 80 Å in diameter and a 15 Å wide central recession . Expression of the components of this complex in E . coli rendered the bacterial cells permeable to 500 Da compounds , supporting the notion that it may work as translocation channel . Extensive mapping of protein-protein interactions of the TM domains of SpaP and SpaR by in vivo photocrosslinking revealed that SpaQ , SpaR , and SpaS form a compact assembly connected to the central pentamer formed by SpaP . We further demonstrated that assembly of this complex does not require its incorporation into the needle complex . We also detected crosslinks between SpaP and SpaR and the inner rod protein PrgJ showing that the inner rod makes direct contact with the export apparatus . Previous analysis by blue native PAGE showed that SpaP and SpaR form stable complexes in an S . Typhimurium mutant unable to assemble the needle complex [9] . We now present evidence based on size-exclusion chromatography-multi angle laser light scattering and native mass spectrometry that this complex is composed of 5 SpaP and 1 SpaR . The stoichiometry of the isolated SpaP5R1 complex is consistent with the stoichiometry of SpaP and SpaR in the context of a fully assembled needle complex [12] , which indicates that the isolated complex represents a relevant intermediate of needle complex assembly . This notion is further supported by the good match of the dimensions of the observed SpaPR complex with the dimensions of the cup substructure of the needle complex [30] , which we previously showed to be composed of SpaP and SpaR [9] . Electron micrographs of the isolated SpaP5R1 complex and BM permeation experiments suggested a pore size of the substrate translocation channel of about 15 Å . Within the range of uncertainty , this diameter conforms with the 10 Å that were reported for the dimensions of the channel of an assembled S . Typhimurium SPI-1 needle complex containing a trapped translocation intermediate [5] . A tight seal during substrate translocation is expected to be important for T3SS to avoid leakage of ions through the open pore , so it is conceivable that the pore diameter closely resembles the dimensions of extended polypeptides or alpha helices . However , a larger pore diameter in its fully open state cannot be excluded given that the herein investigated isolated SpaP5R1 complex most certainly lacks the necessary elements for gating of the pore . We detected extensive crosslinks of up to five consecutive SpaP at TM helix one and at the cytoplasmic face of TM helices three and four , suggesting that these regions form the major contact area between protomers of the SpaP pentamer . This notion was supported by results of a sequence co-variation-based prediction of residue-residue interactions of SpaP . The formation of these crosslinks was independent of the presence of other needle complex components , supporting the notion that the SpaP pentamer nucleates assembly of the needle complex . Interestingly , the presence of SpaP pentamer crosslinks at TM helices three and four correlated with secretion defects of the respective pBpa mutants , a phenomenon also seen for SpaPA12X and SpaPT15X . The secretion defect was not due to defects in their incorporation into assembled needle complexes , suggesting that these residues may play a critical role in protein translocation . The recently reported stoichiometry of SpaP , SpaQ , SpaR , and SpaS of 5:1:1:1 [12] suggests that these export apparatus components form an asymmetric assembly within the needle complex . We show here that SpaQ , SpaR , and SpaS contact the SpaP pentamer at its TM helices three and four . We further demonstrate that SpaQ interacts with SpaP and SpaR . These observations , together with the observation that a fusion of SpaR and SpaS homologs retains function [31] , suggest that SpaQ , SpaR , and SpaS are not wrapped around the SpaP pentamer but form a compact assembly at one side of SpaP , with SpaQ as the central component that makes contacts to all other components ( Fig 7A and 7B ) . Besides SpaR’s contribution in anchoring the inner rod protein PrgJ , the assembly formed by SpaQ , SpaR , and SpaS might also facilitate gating of the SpaP pore , a critical aspect to prevent detrimental effects of nutrient and ion leakage across the bacterial inner membrane . The assessment of the dependence of crosslinks between SpaP , SpaQ , SpaR , and SpaS on the presence of the inner ring protein PrgK allowed us to refine a model for the early steps of export apparatus assembly ( Fig 7C ) . We propose that assembly starts with the formation of the SpaP pentamer . This initially unstable complex is stabilized upon binding SpaR . The high stability of the resulting SpaP5R1 intermediate suggests that this complex is the major nucleus of further needle complex assembly . Next , SpaQ and SpaS associate with the SpaP5R1 complex but presumably with weaker affinity since this complex could only be captured after in vivo crosslinking . InvA would then be recruited to the SpaPRQS complex although it is not clear whether its recruitment occurs prior or after this complex initiates the assembly of the needle complex rings . Subsequently , association of the outer membrane secretin InvG and the inner ring protein PrgH would lead to formation of the completed base-export apparatus holo-complex [21 , 23] . Beyond interactions among the export apparatus components , we also identified crosslinks between the periplasmic domains of SpaP and SpaR and the inner rod protein PrgJ . The close interaction of SpaP , SpaR , and PrgJ is likely to create a continuous conduit for substrate translocation , where PrgJ might serve as an adapter to connect the flat translocation pore of the inner membrane with the helical needle filament . Analysis of the needle complex by cryo-electron microscopy revealed a central juxtamembrane structure at the periplasmic interior of the base , which was termed socket [30] , however , no protein could be assigned to contribute to this density . Our results suggest that the socket is composed of the periplasmic parts of SpaP and SpaR , together with the inner rod protein PrgJ . The mass of six PrgJ [12] and the periplasmic domains of five SpaP and one SpaR could well account for the observed density of the socket structure . Our observation now opens the door for further investigations of the relevance of the export apparatus-PrgJ interaction for needle length control , substrate specificity switching , and host cells sensing , functional roles that were suggested for PrgJ [32 , 33] . The positions of SpaP and SpaR that interact with PrgJ also help to consolidate the TM topology models of these two export apparatus proteins . SpaP is predicted to contain four TM helices ( Fig 2A ) and the presence of a cleavable signal sequence in flagellar homologs suggests an N-out/C-out TM orientation [34] . This model is supported by the interaction between the N-terminus of SpaP and the periplasmic inner rod detected in this study . Further support for this topology model comes from the presented sequence co-variation-based analysis of SpaP residue-residue interactions , which strongly predicted antiparallel interactions between TM 1 and 2 , and between TM 3 and 4 ( Fig 3D ) . The TM topology predictions of SpaR and its homologs are very uncertain , ranging from five to eight TM helices with mostly N-out orientation ( Fig 2B , S6 Fig ) [34 , 35] . A C-in orientation , on the other hand , was suggested based on the report of a functional protein fusion of the flagellar SpaR and SpaS homologs of Clostridium , given that the N-terminus of SpaS and its homologs is strongly predicted to reside in the cytoplasm [31 , 35 , 36] . Here we presented interactions of SpaR F20 , N44 , and A45 with the periplasmic protein PrgJ . These residues are predicted to be located within SpaR’s first two TM helices , however , our results rather suggest a periplasmic localization of this part of SpaR . This notion is supported by rather high ΔG values for membrane partitioning of the predicted TM helices one , two , and four ( S6B Fig ) , so that a SpaR model comprising an N-out/C-in topology with only three TM helices is conceivable ( S6C Fig ) . In summary , we have presented evidence that a pentamer of SpaP forms the substrate translocation pore of T3SSs in the bacterial inner membrane . We show that this pentamer closely interacts with the export apparatus components SpaQ , SpaR , and SpaS in the plane of the membrane , an accessory assembly that may facilitate gating of the export pore . We further show that SpaP and SpaR intimately contact the periplasmic inner rod protein PrgJ and propose that the inner rod serves as an adapter to connect the flat export pore and the helical needle filament , thus creating a continuous conduit for substrate translocation from the bacterial cytoplasm into the host cell .
Chemicals were from Sigma-Aldrich unless otherwise specified . Detergent n-dodecyl-maltoside ( DDM ) was from Affimetrix-Anatrace . para-benzophenylalanine was from Bachem . SERVA Blue G and SERVAGel TG PRiME 8–16% precast gels were from Serva . NativePAGE Novex Bis-Tris 3–12% gels were from Life Technologies . Primers are listed in S5 Table and were synthetized by Eurofins and Integrated DNA Technologies . Polyclonal rabbit anti-MBP antibody were from New England Biolabs . Monoclonal mouse anti-RNApol antibody was from BioLegend . Monoclonal M2 anti-FLAG antibody , M2 anti-FLAG agarose beads , and 3xFLAG peptide were from Sigma-Aldrich . CaptureSelect-biotin , Streptavidin DyLight 800 , and secondary antibodies goat anti-mouse IgG DyLight 800 conjugate and goat anti-rabbit IgG DyLight 680 conjugate were from Thermo-Fisher . Bacterial strains and plasmids used in this study are listed in S4 Table . Primers for construction of strains and plasmids ere listed in S5 Table . The position and sequence of epitope tags introduced into SpaP , SpaR , and SpaS is shown in S7 Fig . All Salmonella strains were derived from S . Typhimurium strain SL1344 [37] . Bacterial cultures were supplemented as required with streptomycin ( 50 μg/mL ) , tetracycline ( 12 . 5 μg/mL ) , ampicillin ( 100μg/mL ) , kanamycin ( 25 μg/mL ) , or chloramphenicol ( 10 μg/mL ) . The SpaP , and SpaPR complexes were expressed in E . coli BL21 ( DE3 ) from rhamnose-inducible medium copy number plasmids encoding SpaPEPEA , SpaPQRFLAGS , or SpaPQRSTREP , respectively . Expression was autoinduced by over night growth at 37°C in TB medium . Bacterial cells were harvested , crude membranes purified as described previously [9] , and membrane proteins were extracted with 1% DDM in PBS . After removal of unsolubilized material by ultracentrifugation for 30 min at 100 . 000 x g , complexes were immunoprecipitated according to the manufacturers instructions using CaptureSelect affinity gel for SpaPEPEA , M2 anti-FLAG agarose beads for SpaPRFLAG , and Strep-Tactin sepharose ( IBA ) for SpaPRSTREP . Complexes were natively eluted with 150 ng/ml SEPEA or 3xFLAG peptides , respectively , or with 2 . 5 mM desthiobiotin , each in PBS/0 . 04% DDM . The SpaPEPEA and the SpaPQRFLAGS , complexes were subsequently purified by anion exchange ( Mono Q 5/50 GL , GE ) , while this step was omitted for the SpaPQRSTREP complex . Samples were further purified by size exclusion ( Superdex 200 10/300 GL , GE ) chromatography , and concentrated to 1 mg/ml using Amicon Ultra 100 k cutoff spin concentrators ( Merck Millipore ) . Purified SpaP and SpaPR complexes were stored in liquid nitrogen until further use . The detergent and polypeptide content of the purified SpaPRFLAG complex in PBS/0 . 04% DDM was determined by size exclusion chromatography—multi angle laser light scattering and analysis by the ASTRA software ( Wyatt , Santa Barbara , CA ) as previously described [38] . Purified SpaPRSTREP complex was concentrated to 20 μM in PBS/0 . 04% DDM , and buffer exchanged to 250 mM ammonium acetate , pH 7 . 5 , complemented with 0 . 01% polyoxyethylene ( 9 ) dodecyl ether ( C12E9 ) prior to native mass spectrometry analysis . Buffer exchange was carried out using Amicon Ultra 0 . 5 ml centrifugal filters with a 100-kDa cut-off ( Millipore UK Ltd , Watford UK ) . Mass measurements were carried out on a Synapt G1 HDMS ( Waters Corp . , Manchester , UK ) Q-ToF mass spectrometer [39] . The instrument was mass calibrated using a solution of 10 mg/ml cesium iodide in 250 mM ammonium acetate . 2 . 5 μL aliquots of samples were delivered to the mass spectrometer by means of nano-electrospray ionization via gold-coated capillaries , prepared in house [40] . Instrumental parameters were as follows: source pressure 6 . 0 mbar , capillary voltage 1 . 40 kV , cone voltage 150 V , trap energy 200 V , transfer energy 10 V , bias voltage 5 V , and trap pressure 1 . 63x10-2 mbar . SpaP and SpaR TM topology was predicted using TOPCONS ( http://topcons . cbr . su . se ) [41] . The extent of the hydrophobic regions constituting TM helices was predicted using dGpred full portein scan ( http://dgpred . cbr . su . se ) [42] setting the minimal helix length to 18 and the maximal helix length to 31 aa . For visualization , the online tool PROTTER ( http://wlab . ethz . ch/protter/start/ ) was used [43] . Analysis of type III-dependend secretion of proteins into the culture medium was carried out as described previously [20] . For protein detection , samples were subjected to SDS PAGE using SERVAGel TG PRiME 8–16% precast gels , transferred onto a PVDF membrane ( Bio-Rad ) , and probed with primary antibodies anti-SipB , anti-InvJ , anti-PrgJ , anti-SpaP , anti-MBP , anti-RNApol , and M2 anti-FLAG . Secondary antibodies were goat anti-mouse IgG DyLight 800 conjugate and goat anti-rabbit IgG DyLight 680 . EPEA-tagged SpaP was visualized using CaptureSelect-biotin anti C-Tag conjugate and Streptavidin DyLight 800 . Scanning of the PVDF membrane and image analysis was performed with a Li-Cor Odyssey system and image Studio 2 . 1 . 10 ( Li-Cor ) . S . Typhimurium strains were grown at 37°C in LB broth supplemented with 0 . 3 M NaCl with low aeration to enhance expression of genes of SPI-1 . For in vivo photocrosslinking of SpaPFLAG in Escherichia coli BL21 ( DE3 ) , bacteria were cultured at 37°C in LB broth . Cultures were supplemented with 500 μM rhamnose to induce expression of SpaPFLAG , SpaPFLAGQRS or SpaPQRFLAGS from low copy number pTACO10 plasmids [9] . To boost general SPI-1 expression , S . Typhimurium strains were transformed with pBAD24-hilA . Expression of the SPI-1 master regulator HilA was induced by addition of 0 , 05% arabinose to the cultures . Additionally the cultures were supplemented with the artificial amino acid para-benzoyl phenyl alanine ( pBpa ) to a final concentration of 1 mM and afterwards incubated for 5 . 5 h . 2 ODU of bacterial cells were harvested and washed once with 1 mL cold PBS . Cells were resuspended in 1 mL PBS and transferred into 6-well cell culture dishes . UV irradiation with λ = 365 nm was done on a UV transilluminator table ( UVP ) for 30 min . 10 OD units of bacterial lysates of S . Typhimurium or E . coli , respectively , were resuspended in 750 μl buffer K ( 50 mM triethanolamine , pH 7 . 5 , 250 mM sucrose , 1 mM EDTA , 1 mM MgCl2 , 10 μg/ml DNAse , 2 mg/mL lysozyme , 1:100 protease inhibitor cocktail ) , and incubated for 30 min on ice . Samples were bead milled and beads , unbroken cells and debris were removed by centrifugation for 10 min at 10 . 000 x g and 4°C . Crude membranes contained in the supernatant were precipitated by centrifugation for 45 min at 55 , 000 rpm and 4°C in a Beckman TLA 55 rotor . Pellets containing crude membranes were frozen until use . 1-dimensional blue native PAGE and 2-dimensional blue native/SDS PAGE of crude membranes was carried out as previously described [9] . S . Typhimurium ΔspaP or ΔspaPQRS mutants , respectively , transformed with pSUP , pSB3292 , and pSB3398-based rhamnose-inducible low copy number plasmids containing SpaPFLAG amber mutants or SpaPQRS with SpaRFLAG amber mutants , respectively , were grown in 200 ml LB broth supplemented with 0 . 3M NaCl , 1 mM pBpa , 500 μM rhamnose , 0 . 02% arabinose , and appropriate antibiotics for 5 h at low aeration to express SPI-1 and assemble needle complexes . Purification of needle complexes was carried out as published previously [4 , 20 , 12] but LDAO was replaced by DDM ( 0 . 7% for lysis/extraction , 0 . 1% for maintenance ) for lysis of cells and extraction of needle complexes throughout the protocol . Furthermore , an initial concentration of 35% ( wt/vol ) of CsCl was used to prepare the gradient . Purified needle complexes containing SpaPFLAG or SpaRFLAG with pBpa at desired positions were irradiated with UV light ( 365 nm ) for 30 min to induce photocrosslinking to nearby proteins . Samples were subsequently analyzed by SDS PAGE , Western blotting , and immunodetection with M2 anti-FLAG antibodies . For MS analysis of crosslinked adducts , gel pieces at positions of observed crosslinks of pBpa-containing and control samples were cut out of Coomassie-stained SDS PAGE gels and subjected to in gel digestion . For identification of crosslinked proteins , the area of a Coomassie-stained gel corresponding the position of the crosslinked band detected by Western blotting were excised and in-gel digested with trypsin [44] . For a better recovery , remaining proteins in the gel were again subjected to another tryptic digestion step . After each step extracted peptides were desalted using C18 StageTips [45] . Corresponding eluates were combined and subjected to LC-MS/MS analysis . LC-MS/MS analyses were performed on an EasyLC II nano-HPLC ( Proxeon Biosystems ) coupled to an LTQ Orbitrap Elite mass spectrometer ( Thermo Scientific ) as decribed elsewhere [46] with slight modifications: The peptide mixtures were injected onto the column in HPLC solvent A ( 0 . 5% acetic acid ) at a flow rate of 500 nl/min and subsequently eluted with a 106 min gradient of 5–33% HPLC solvent B ( 80% ACN in 0 . 5% acetic acid ) . During peptide elution the flow rate was kept constant at 200 nl/min . For proteome analysis , the 20 ( Orbitrap Elite ) most intense precursor ions were sequentially fragmented in each scan cycle using collision-induced dissociation ( CID ) . In all measurements , sequenced precursor masses were excluded from further selection for 90 s . The target values for MS/MS fragmentation were 5000 charges and 106 charges for the MS scan . The MS data were processed with MaxQuant software suite v . 1 . 2 . 2 . 9 as described previously [47–49] with slight modifications . Database search was performed using the Andromeda search engine [48] , which is part of MaxQuant . MS/MS spectra were searched against a target database consisting of 10 , 152 protein entries from S . Typhimurium and 248 commonly observed contaminants . In database search , full tryptic specificity was required and up to two missed cleavages were allowed . Carbamidomethylation of cysteine was set as fixed modification , protein N-terminal acetylation , and oxidation of methionine were set as variable modifications . Initial precursor mass tolerance was set to 6 parts per million ( ppm ) and at the fragment ion level 0 . 5 dalton ( Da ) was set for CID fragmentation . The MS data have been deposited to the ProteomeXchange Consortium ( http://proteomecentral . proteomexchange . org ) via the PRIDE partner repository with the data set identifier PXD005028 . Sequence co-variation analysis was performed using EVcouplings [26–28] with pseudo-maximum likelihood approximation [50–52] . The multiple sequence alignment used as input for the model inference was created by jackhmmer 3 . 1 [53] ( 5 iterations ) using the full sequence of Salmonella SpaP ( UniProt: SPAP_SALTY , residues 1–224 ) as query against the November 2015 release of the UniProt Reference Cluster database ( UniRef100 ) [54] . Sequences with more than 30% gaps are subsequently removed from the alignment . We then excluded alignment columns that contained 50% or more gaps from model inference and subsequent couplings predictions . Lastly , sequences were clustered at 80% sequence identity and then downweighted according to the cluster size to reduce redundancy . This resulted in an alignment of 5663 unique sequences with an effective number of 1080 . 4 non-redundant sequences ( sequences/alignment length = 4 . 8 ) included in model inference and coupling prediction . The coupling scores of residue pairs were further normalized by estimating the background noise analogously to the procedure described in Hopf et al . , 2014 [28] . Evaluation of the co-evolution prediction was done in the light of topology predictions obtained from deltaG , resulting in four predicted TM segments: ( 7 , 38 ) , ( 50 , 75 ) , ( 163 , 193 ) , ( 194 , 211 ) . Python ( Python Software Foundation , http://www . python . org ) and Ipython/Jupyter notebooks [55] were used for data analysis . The multiple sequence alignment , EC scores file , a contact map of the strongest couplings and an Ipython notebook of the analysis are available as supplement ( S3 Table , S8 Fig , S2 and S3 Files ) . Isolated SpaPRFLAG complexes were deposited on glow-discharged carbon coated copper-palladium grids and stained with 0 . 75% uranyl formate . Micrograph acquisition was performed on a FEI Tecnai F30 Polara at 300 kV , equipped with a Gatan Ultrascan 4000 UHS CCD ( 4k x 4k pixels , physical pixel size of 15 μm ) , using the LEGINON automated image acquisition system [56] . The corrected magnification was 71950x , resulting in a pixel size of 2 . 08 Å/pixel . 11202 particles were picked from the micrographs with EMAN2 boxer [57] . Particle images were first subjected to a maximum-likelihood classification and alignment ( ML2D ) in XMIPP [58] and then further processed in IMAGIC-5 ( Image Science Software GmbH ) through multi-reference alignment and classification by multi-variate statistical analysis . SpaP or SpaPQRFLAG were moderately overexpressed in S . Typhimurium strain SB1770 ( ΔprgHIJK , flhD::tet ) from a rhamnose-inducible medium copy number plasmid by induction with 20 μM rhamnose . BM labeling was performed essentially as previously described [29] , with minor modifications: After 3 h of induction , 0 . 2 ODU of bacterial cells were transferred to a fresh reaction tube and brought to the same volume by addition of fresh LB broth . Cells were labeled by addition of BM ( EZ-link maleimide-PEG2-biotin , Thermo Pierce , final concentration 0 . 4 mM ) for 30 min at room temperature with gentle agitation . The reaction was quenched by addition 2M β-mercaptoethanol to a final concentration of 10 mM . Cells were pelleted , re-suspended in SB buffer and incubated at 70°C for 10 min . BM labeling of proteins was analyzed by SDS PAGE , Western blotting , and detection of BM with streptavidin DyLight 800 dye ( Thermo pierce ) . Scanning of the PVDF membrane and image analysis was performed with a Li-Cor Odyssey system and image Studio 2 . 1 . 10 ( Li-Cor ) . For subcellular fractionation , BM-labeled bacterial cells were pelleted by centrifugation . The culture supernatant was harvested and TCA precipitated . The bacterial cell pellet was resuspended and used to prepare the periplasmic and cytoplasmic fractions as described elsewhere . Briefly , pellets were resuspended by pipetting gently in ice-cold spheroplast buffer ( 40% sucrose , 33 mM Tris-HCl , pH 8 . 0 ) with freshly prepared lysozyme to a final concentration of 200 μg/ml , 50 μg/ml DNAse and 1 . 5 mM EDTA . The mixture was left on ice for 30 min with gentle stirring . Spheroplasts were stabilized by adding 20 mM MgCl2 and centrifuged at 3000 x g for 10 min at 4°C . The supernatant was transferred to ultracentrifugation tubes and centrifuged at 30 krpm for 30 min at 4°C in a Beckman TLA55 rotor to remove insoluble material . The supernatant ( periplasmic fraction ) was collected into fresh tube . The cytoplasmic fraction was prepared by resuspending the pellet of spheroplasts in 1 ml of 20 mM Tris-HCl , pH 8 . 0 and subsequent lysis by bead milling as described above . Lysates were transferred to ultracentrifugation tubes and centrifuged at 55 krpm for 45 min at 4°C in a Beckman TLA55 rotor . The supernatant ( cytoplasmic fraction ) was collected into fresh tubes . | Many Gram-negative bacteria use type III secretion systems to inject bacterial proteins into eukaryotic host cells in order to promote their own survival and colonization . These systems are large molecular machines with the ability to transport proteins across three cell membranes in one step . It is believed that the only gated barrier of these systems lies in the bacterial cytoplasmic membrane but it was unclear so far how this gate looks like and of which components it is composed . Here we present evidence based on in depth biochemical and genetic characterization that an assembly of five SpaP proteins forms this gate in the cytoplasmic membrane of the type III secretion system of Salmonella pathogenicity island 1 . We further show that one subunit each of the proteins SpaQ , SpaR , and SpaS are closely associated to the SpaP gate and may function in the gating mechanism , and that the protein PrgJ is attached to this gate on the outside to connect it to the hollow needle filament projecting towards the host cell . Our findings elucidate a hitherto ill-defined aspect of type III secretion systems and may help to develop novel antiinfective therapies targeting these virulence-associated molecular devices . | [
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"e... | 2016 | Structural and Functional Characterization of the Bacterial Type III Secretion Export Apparatus |
Long-term potentiation ( LTP ) of synaptic transmission represents the cellular basis of learning and memory . Astrocytes have been shown to regulate synaptic transmission and plasticity . However , their involvement in specific physiological processes that induce LTP in vivo remains unknown . Here we show that in vivo cholinergic activity evoked by sensory stimulation or electrical stimulation of the septal nucleus increases Ca2+ in hippocampal astrocytes and induces LTP of CA3-CA1 synapses , which requires cholinergic muscarinic ( mAChR ) and metabotropic glutamate receptor ( mGluR ) activation . Stimulation of cholinergic pathways in hippocampal slices evokes astrocyte Ca2+ elevations , postsynaptic depolarizations of CA1 pyramidal neurons , and LTP of transmitter release at single CA3-CA1 synapses . Like in vivo , these effects are mediated by mAChRs , and this cholinergic-induced LTP ( c-LTP ) also involves mGluR activation . Astrocyte Ca2+ elevations and LTP are absent in IP3R2 knock-out mice . Downregulating astrocyte Ca2+ signal by loading astrocytes with BAPTA or GDPβS also prevents LTP , which is restored by simultaneous astrocyte Ca2+ uncaging and postsynaptic depolarization . Therefore , cholinergic-induced LTP requires astrocyte Ca2+ elevations , which stimulate astrocyte glutamate release that activates mGluRs . The cholinergic-induced LTP results from the temporal coincidence of the postsynaptic activity and the astrocyte Ca2+ signal simultaneously evoked by cholinergic activity . Therefore , the astrocyte Ca2+ signal is necessary for cholinergic-induced synaptic plasticity , indicating that astrocytes are directly involved in brain storage information .
Compelling evidence obtained by different groups during the last years indicate that astrocytes play important roles in synaptic function [1]–[4] . In addition to their well-known passive homeostatic control of synaptic function , astrocytes sense synaptic activity responding with Ca2+ elevations to synaptically released neurotransmitters and , in turn , release gliotransmitters that regulate synaptic transmission and plasticity [5]–[14] . This evidence has led to the establishment of the Tripartite Synapse concept , in which astrocytes actively exchange information with the neuronal synaptic elements , suggesting that astrocytes may be considered as integral elements of the synapses being directly involved in synaptic physiology [1]–[4] . While this evidence has been largely obtained in brain slices , recent in vivo studies that used transgenic mice in which the gliotransmitter release of ATP was impaired have shown the participation of astrocytes in certain cortical network activity and in animal behaviour [2] , [3] , [15] , [16] . However , the exact underlying cellular mechanisms are largely undefined . Furthermore , while the involvement of astrocytes in some forms of long-term potentiation ( LTP ) has been shown in hippocampal slices ( e . g . , [6] , [11] ) , the active participation of astrocytes in specific forms of synaptic plasticity in vivo remains unknown . Cholinergic system is involved in many different processes of brain function [17] . In the hippocampus , cholinergic activity modulates neuronal excitability [18] , network activity [19] , as well as synaptic transmission and plasticity [20] , [21] . In the CA1 region , acetylcholine ( ACh ) induces CA1 pyramidal neuron depolarization [18] , theta rhythm generation [19] , and LTP of glutamatergic CA3-CA1 synaptic transmission [20] , [21] , as well as astrocyte Ca2+ elevations [22] , [23] . However , the physiological meaning of the cholinergic evoked astrocyte Ca2+ signal remains unknown . In the present work we have investigated two fundamental questions regarding the direct involvement of astrocytes in synaptic physiology , i . e , whether astrocytes actively participate in physiological processes underlying synaptic plasticity , and whether astrocyte synaptic modulation occurs in vivo . We have recently shown that the coincidence of astrocyte Ca2+ elevations evoked by Ca2+ uncaging and mild postsynaptic depolarization induces LTP in hippocampal synapses [11] . Therefore , we have investigated whether the astrocyte Ca2+ signal evoked by cholinergic activity [22] is involved in the generation of cholinergic-induced LTP of glutamatergic CA3-CA1 synapses . Using in vivo experimental approaches , we have found that cholinergic activity evoked by sensory stimulation or electrical stimulation of the septal nucleus , the main cholinergic input to the hippocampus , elevated Ca2+ in hippocampal astrocytes and induced LTP in CA3-CA1 synapses . Using hippocampal slices to investigate the underlying cellular mechanisms , we have found that stimulation of cholinergic axons evoked astrocyte Ca2+ elevations , depolarization of CA1 pyramidal neurons , and LTP in CA3-CA1 synapses . Like in vivo , astrocyte Ca2+ elevations and LTP required mAChR activation , and LTP also required mGluR activation . Cholinergic-induced astrocyte Ca2+ elevations and LTP were absent both in IP3R2 knock-out mice and in wildtype mice after loading astrocytes with BAPTA or GDPβS ( which prevented astrocyte Ca2+ signalling ) . Notably , LTP was rescued by simultaneous astrocyte Ca2+ uncaging and postsynaptic depolarization . Taken together , these results indicate that astrocyte Ca2+ signal is necessary for cholinergic-induced hippocampal synaptic plasticity . In summary , present results show that cholinergic LTP requires the astrocyte Ca2+ signal , which stimulates the release of glutamate from astrocytes that activates mGluRs on neurons . Then , cholinergic-induced hippocampal LTP results from the coincidence of astrocyte and postsynaptic activities simultaneously evoked by cholinergic signalling .
We first assessed in vivo whether cholinergic activity regulates astrocyte Ca2+ signal and synaptic transmission ( see Materials and Methods ) . In anesthetized rats , somatosensory stimulation by tail pinch , which stimulates cholinergic activity and hippocampal theta rhythm [24] , [25] , evoked Ca2+ elevations in hippocampal astrocytes ( 34 out of 66 astrocytes from n = 8 rats ) that were abolished by the cholinergic muscarinic receptor ( mAChRs ) antagonist atropine ( 5 mg/kg ) ( n = 4 rats; Figure 1A–C ) . We analyzed hippocampal synaptic transmission in CA3-CA1 synapses , recording field EPSPs ( fEPSPs ) evoked by Schaffer collaterals ( SC ) stimulation in the CA1 pyramidal layer . Sensory stimulation also induced the LTP of fEPSPs ( n = 7; Figure 1D and 1E ) . Similar LTP was also found after electrical stimulation of the medial septal nucleus ( the main cholinergic input to the hippocampus ) with a theta-like burst stimulation paradigm ( TBS ) ( n = 9; see Materials and Methods; Figure 1F and 1G ) [26] , [27] . This LTP evoked by sensory or electrical stimulation was prevented in the presence of antagonists of either muscarinic receptors ( mAChRs; 5 mg/Kg atropine ) or metabotropic glutamate receptors ( mGluRs; 1 mM MCPG ) ( n = 6 in each case ) ( Figure 1D–G ) , indicating that septohippocampal cholinergic activity induced the long-term potentiation ( c-LTP ) of CA3-CA1 synapses , which also required mGluR activation . Furthermore , because astrocyte responsiveness to sensory stimulation was similar in control and in the presence of MCPG ( n = 3; Figure 1C ) , mGluR activation was downstream the astrocyte Ca2+ signal . We then investigated the cellular mechanisms underlying c-LTP . Using rat hippocampal slices , we simultaneously monitored EPSCs evoked by SC stimulation in CA1 pyramidal neurons and intracellular Ca2+ levels in stratum radiatum astrocytes . After basal control recordings , we stimulated afferent pathways in the alveus , which contains cholinergic axons from the medial septal nucleus [22] , [28] . To prevent possible NMDAR-mediated synaptic plasticity , experiments were performed in the presence of the NMDAR antagonist AP5 ( 50 µM ) . Alveus stimulation with TBS evoked transient postsynaptic depolarizations of CA1 pyramidal neurons ( 12 . 6±1 . 7 mV; n = 13 , Figure 2A ) , astrocyte Ca2+ elevations ( n = 132 astrocytes from 13 slices ) , and c-LTP of CA3-CA1 synaptic transmission ( n = 13; Figure 2A–F ) . The c-LTP was accompanied by a reduction of the paired-pulse facilitation index ( 0 . 43±0 . 05 in basal and 0 . 35±0 . 04 60 min after TBS; n = 10; p = 0 . 017; Figure S1 ) , which is consistent with a presynaptic mechanism of action . Both astrocyte Ca2+ elevations and c-LTP were reduced in the presence of 50 µM atropine ( n = 94 astrocytes from 10 slices; n = 10 neurons; Figure 2D and 2G ) , indicating the involvement of cholinergic muscarinic receptors and confirming that c-LTP was also present in hippocampal slices . Furthermore , MCPG ( 1 mM ) prevented c-LTP ( n = 12; Figure 2G ) , without affecting TBS-induced postsynaptic depolarization ( in control: 12 . 6±1 . 7 mV; n = 10; in MCPG: 10 . 4±2 . 1 mV , n = 7 , p = 0 . 41 ) and astrocyte Ca2+ elevations ( n = 71 astrocytes from 7 slices; Figure 2D ) ( cf . , [22] , [23] ) , indicating that c-LTP also requires mGluR activation . We have recently shown that the coincidence of Ca2+ uncaging-evoked glutamate release from astrocytes and a mild postsynaptic depolarization of CA1 pyramidal neurons induced LTP at CA3-CA1 synapses through presynaptic mGluR activation [11] . Therefore , we then investigated whether c-LTP required astrocyte Ca2+ elevations by analyzing the consequences of the dialysis of either BAPTA ( which chelates intracellular Ca2+ ) or GDPβS ( which prevents G protein-mediated intracellular signaling ) into the astrocytic network ( Figure 3A and 3B ) . We recorded single astrocytes including either BAPTA ( 40 mM ) or GDPβS ( 20 mM ) in the whole-cell recording pipette . These substances are known to spread to a large number of gap-junction connected hippocampal astrocytes [7] , [14] , [29]–[31] . In either BAPTA- and GDPβS-loaded astrocytes , astrocyte Ca2+ elevations evoked by alveus TBS were prevented in an area at least 150 µm around the recorded astrocyte ( Figure 3C–E ) , without significantly affecting the postsynaptic depolarization ( 11 . 9±1 . 4 mV and 11 . 4±2 . 3 mV , n = 7 and 5 , respectively; compared with 12 . 6±1 . 7 mV; n = 10 , in control; p = 0 . 74 and p = 0 . 67 ) . Simultaneous recordings of CA3-CA1 EPSCs showed that c-LTP was also prevented ( Figure 3F–H ) , indicating that G-protein-mediated astrocyte Ca2+ signaling is necessary for c-LTP induction , and supporting the idea that c-LTP results from mGluR activation induced by Ca2+-dependent glutamate release from astrocytes . To further test this idea , we analyzed the phenomena in slices from wildtype and inositol-1 , 4 , 5-trisphosphate ( IP3 ) -receptor type 2-deficient mice ( IP3R2−/− ) [32] , which is the primary functional IP3R expressed by astrocytes that mediate intracellular Ca2+ mobilization [33] . In agreement with this report , local application of ACh increased intracellular Ca2+ in all CA1 pyramidal neurons tested in both wildtype and IP3R2−/− mice ( 6 and 10 neurons from n = 6 and 10 slices , respectively; Figure 4C ) and induced Ca2+ elevations in most astrocytes from wildtype animals but not from knockout mice ( 78 out of 111 and 13 out of 157 astrocytes from n = 6 and 15 slices , respectively; p<0 . 001; Figure 4C ) . Alveus TBS stimulation also elevated Ca2+ in neurons from both wildtype and IP3R2−/− mice ( 6 and 10 neurons from n = 6 and 10 slices , respectively; Figure 4A and 4C ) . In wildtype mice , alveus TBS induced astrocyte Ca2+ elevations ( 62 out of 81 astrocytes from n = 9 slices ) , which were abolished by atropine ( 14 out of 25 astrocytes from n = 4 slices responded to TBS in control but not in atropine ) ( Figure 4C–E ) , and evoked c-LTP ( n = 8; Figure 4F and 4G ) , which was prevented by atropine ( n = 4 ) or MCPG ( n = 5; Figure 4G ) . In contrast , both astrocyte Ca2+ elevations ( 64 astrocytes from n = 10 slices ) and c-LTP induced by alveus TBS were largely prevented in IP3R2−/− mice ( n = 8; Figure 4B–G ) confirming the requirement of astrocyte Ca2+ signaling for c-LTP . Consistent with these results , the LTP observed in vivo after sensory stimulation in wildtype mice ( n = 6 ) was strongly diminished in IP3R2−/− mice ( n = 6 ) ( Figure 4H and 4I ) . The presence of the initial potentiation and the residual LTP observed in transgenic mice suggest that additional synaptic plasticity mechanisms may also be present in vivo , where the phenomenon could not be pharmacologically isolated as it was in vitro ( see Discussion ) . Taken together , these results indicate that astrocyte Ca2+ elevations play a significant role in the cholinergic-induced LTP . We next investigated whether the cholinergic-induced postsynaptic activity is required for c-LTP generation . We performed simultaneous paired-recordings from two pyramidal neurons that were loaded through the recording pipette with 5 mM QX314 that intracellularly blocks Na+-mediated action potentials . In QX314-loaded neurons , TBS-induced action potential firing was absent , but low-amplitude mild depolarizations ( 10 . 4±2 . 1 mV; n = 7 ) and c-LTP were still present ( n = 7 ) ( Figure 5A and 5B ) , indicating that c-LTP does not require postsynaptic action potentials . However , preventing the neuronal depolarization by holding the postsynaptic cell at −70 mV in voltage-clamp conditions abolished c-LTP ( n = 5 ) ( Figure 5C ) , while it was preserved in adjacent neurons recorded under current clamp conditions ( n = 5 ) , which displayed TBS-induced postsynaptic depolarizations . Furthermore , in voltage-clamped neurons , c-LTP was rescued by pairing alveus TBS with a postsynaptic mild depolarization to −30 mV ( n = 5; Figure 5D ) . The TBS-induced astrocyte Ca2+ signal was unaffected by these postsynaptic manipulations ( not shown ) . To further confirm the involvement of cholinergic receptors in the postsynaptic depolarization required for c-LTP , we applied ACh to directly activate astrocytic and postsynaptic receptors while recording pyramidal neurons in either current- or voltage-clamp conditions . While ACh evoked postsynaptic depolarizations and induced LTP in current-clamped neurons , it failed to induce LTP in voltage-clamped neurons ( Figure 5E–G ) . Although a partial contribution of glutamatergic signaling cannot be discarded , these results are consistent with the idea that cholinergic activity is responsible for the postsynaptic depolarization required for c-LTP . Taken together , these results indicate that c-LTP requires both the postsynaptic depolarization and the astrocyte Ca2+ elevations . To confirm that astrocyte Ca2+ signal is necessary for the induction of c-LTP , we monitored SC synaptic transmission at single synapses using the minimal stimulation technique that activates single or very few presynaptic axons [11] , and selectively elevated Ca2+ in astrocytes while preventing G protein-mediated signaling cascades in the astrocyte network ( Figure 6A ) . For this purpose , astrocytes were whole-cell recorded and loaded with both 20 mM GDPβS , which prevented cholinergic-induced Ca2+ signal in astrocytes ( Figure 3C–E ) , and the Ca2+-cage NP-EGTA ( 5 mM ) , which selectively and reliably elevates astrocyte Ca2+ after UV stimulation ( Figure 6B ) [11] . In agreement with previous results [7] , [11] , astrocyte UV Ca2+ uncaging evoked a transient potentiation of the probability of release ( Pr ) ( n = 4 from 9 synapses; Figure 6D ) that was abolished by MCPG ( n = 4; not shown ) , which agrees with a presynaptic mGluR activation by Ca2+-dependent glutamate release form astrocytes . However , in these conditions , either Ca2+ uncaging or alveus TBS alone did not evoke long-term changes of SC synaptic transmission properties ( measured 20 min after the stimuli; n = 9 ) ( Figure 6C , 6E , and 6F ) . In contrast , pairing UV Ca2+ uncaging in astrocytes and alveus TBS induced LTP of Pr and the synaptic efficacy , without significantly affecting the synaptic potency ( n = 9; see Materials and Methods ) ( Figure 6E and 6F ) , which is consistent with a presynaptic mechanism . Furthermore , LTP induced by simultaneous astrocyte Ca2+ uncaging and alveus TBS was prevented by MCPG ( n = 6; Figure 6E and 6F ) , indicating that astrocyte Ca2+ elevations stimulate the release of glutamate that activates presynaptic mGluRs . Similar results were obtained when the different stimulus were delivered independently in different cells ( Figure S2 ) . Taken together , these results indicate that cholinergic-induced LTP results from the temporal coincidence of postsynaptic activity and astrocyte Ca2+ elevations , which stimulate Ca2+-dependent glutamate release that activating mGluRs potentiate synaptic transmitter release ( Figure 6G ) .
Present results obtained in vivo and in vitro show that hippocampal LTP evoked by cholinergic activity was associated with Ca2+ elevations in astrocytes , and that both phenomena were mediated by mAChRs . Unexpectedly , the cholinergic-induced LTP also required mGluR activation , which prompted us to evaluate the involvement of astrocytes because glutamate is a Ca2+-dependent released gliotransmitter [2]–[4] , [7] , [11] , [23] , [29]–[31] . Analysis of the underlying cellular mechanisms in hippocampal slices indicate that astrocyte Ca2+ elevations were necessary for the generation of this LTP , which could be elicited by pairing postsynaptic depolarizations and astrocyte Ca2+ uncaging ( Figure 6; cf . , [11] ) . Taken together , the present results indicate that cholinergic-induced LTP results from the temporal coincidence of postsynaptic and astrocyte Ca2+ activities simultaneously triggered by cholinergic axons , which stimulating Ca2+-dependent gliotransmission persistently potentiate synaptic transmitter release through activation of mGluRs . Present data provide the first demonstration in vivo that astrocytes are responsible for a specific physiological phenomenon of synaptic plasticity triggered by sensory stimuli , in which the astrocyte calcium signal and the release of the gliotransmitter glutamate are key elements . Our results indicate that cholinergic activation of astrocytes stimulates the release of glutamate , which leads activating mGluRs to the long-lasting potentiation of synaptic transmission when coincident with a postsynaptic depolarization . Consistent with our previous work ( see [7] , [11] ) , we propose that these receptors are located presynaptically because this c-LTP results from the enhancement of transmitter release probability without changes in synaptic potency , which indicates a presynaptic rather than a postsynaptic underlying mechanism , and it is associated with changes in the paired-pulse facilitation ( Figure S1 ) , which is consistent with a presynaptic mechanism . Therefore , although anatomical evidence for presynaptic mGluRs at Schaffer collaterals needs to be confirmed , electrophysiological data strongly suggest that c-LTP is mediated by activation of presynaptic mGluRs . The cholinergic-induced LTP requires the temporal coincidence of the astrocyte signaling and a mild postsynaptic depolarization , which suggests the existence of a retrograde signaling from the postsynaptic neuron to induce the presynaptic expression of LTP . Although further detailed studies are required to identify the possible postsynaptic signal and to elucidate their molecular targets , perhaps the presynaptic molecular events responsible for the astrocyte-induced mGluR-mediated transient potentiation of transmitter release ( see Figure 6G; cf . [7] , [11] ) could become persistently altered by the signaling pathway stimulated by the postsynaptic signal . The consequences of AChR activation on synaptic transmission and plasticity have been extensively studied [17]–[21] , [25]–[28] , and the ability of cholinergic signaling to induce LTP in hippocampal CA3-CA1 synapses is well known [20] , [21] , [26] , [27] . Yet the requirement of mGluR activation was previously untested , probably because its involvement was logically unexpected . However , this participation is not surprising when considering the ability of astrocytes to respond to synaptic transmitters and to release gliotransmitters such as glutamate that can regulate synaptic transmission and plasticity , according to the Tripartite Synapse concept [2]–[4] . Thus , a well-known process newly examined considering the possible involvement of astrocytes is revealed to be mediated by novel unexpected mechanisms . Other physiological processes , whose underlying mechanisms are currently interpreted as overlooking the possible participation of astrocytes , might also provide novel unexpected results if revisited , as recently shown for hippocampal heterosynaptic depression [14] or endocannabinoid-mediated synaptic potentiation [7] . Nevertheless , possible novel mechanisms mediated by astrocytes might be rather additional than exclusive . Indeed , our results propose a novel mechanism underlying the cholinergic-induced LTP that may co-exist with the classical NMDAR-mediated LTP , which was pharmacologically blocked in our experimental conditions to isolate the studied phenomenon . Compelling evidence provided by many laboratories has shown the relevance of the astrocyte Ca2+ signal and the gliotransmission in neurophysiology [2]–[8] , [11] , [14] . However , recent reports [33]–[35] have questioned their physiological importance based on negative results that failed to detect changes in synaptic transmission and plasticity using particular tests in transgenic animals , such as IP3R2−/− mice . In contrast , here we provide evidence showing that both cholinergic-induced astrocyte Ca2+ signal and LTP are impaired in this type of transgenic mice . Therefore , astrocyte-synapse interactions are based on complex signaling processes that are not unselectively triggered by any type of stimulus and do not result in unspecific neuromodulation of any type of process; rather they probably depend on specific types of activity of particular circuits and synapses and cause neuromodulation of precise phenomena . Hence , these results further support the concept of the Tripartite Synapse that highlights the relevance of the astrocyte Ca2+ signal and the gliotransmission , and proposes a key role of astrocytes in synaptic physiology . Astrocytes may regulate synaptic function through the release of different gliotransmitters , such as glutamate , ATP , or D-serine , with different neuromodulatory mechanisms and physiological consequences [3] , [36] . The present study was designed to investigate the involvement of astrocytes in a particular phenomenon , the cholinergic-induced LTP . To isolate this particular phenomenon , in vitro experiments were conducted in the presence of NMDA and GABAergic receptor antagonists to prevent interferences from other astrocyte-neuron signaling and other possible synaptic plasticity mechanisms , such as the classical neuronal NMDAR-mediated LTP , the postsynaptic NMDA receptor activation by astrocytic glutamate [23] , the D-serine-mediated NMDAR modulation involved in LTP [6] , or the presynaptic NMDAR-mediated synaptic efficacy increase induced by astrocytic glutamate [13] . Although the present results indicate that NMDAR-mediated mechanisms are not required for the cholinergic-induced hippocampal LTP , additional mechanisms responsible for the involvement of astrocytes in synaptic plasticity may be present . These different mechanisms may be complementary rather than alternative , suggesting that different neuronal and astrocytic signaling processes may coexist , which would result in multiple mechanisms of synaptic plasticity that may be triggered under different network activities , thus providing a higher richness to the synaptic communication ( see [36] ) . The cholinergic system is involved in multiple brain functions , including learning and memory , as well as behavioral states [37]–[40] . The present results show a key role of astrocytes in cholinergic signaling , suggesting that astrocytes may directly participate in those brain functions . Furthermore , because dysfunctions of cholinergic transmission contribute to memory loss in some brain disorders such as Alzheimer's disease [41] , present data suggest that astrocytes may be directly involved in these pathological states of the nervous system . Present data extend the classical Hebbian model for LTP based on the coincident pre- and postsynaptic activity , by including astrocytes as key cellular elements involved in the intercellular signaling occurring during synaptic function , and where the coincidence of astrocyte and postsynaptic activities evoked by a physiological process ( i . e . , cholinergic activity ) induces long-term changes in synaptic efficacy . In conclusion , the present findings show that the astrocyte Ca2+ signal is required for the generation of LTP in hippocampal synapses induced by cholinergic activity , indicating that astrocytes are necessary elements in some forms of synaptic plasticity and , hence , they are directly involved in memory processes and brain information storage .
Adult Wistar rats ( 3 and 4 mo old; weigh: 180–300 g ) and C57BL/6 and IP3R2−/− mice ( weigh: 45–60 g ) were anesthetized with urethane ( 1 . 5 g/Kg and 1 . 8 g/Kg , respectively ) and placed in a stereotaxic device . The body temperature was maintained at 37°C , and the end-tidal CO2 concentration was monitored . Hippocampal slices were obtained from Wistar rats ( 12–17 d old ) . In some cases , slices from C57BL/6 wildtype mice and IP3R2−/− mice ( 13–18 d old ) , generously donated by Dr . J Chen , were used [32] . Animals were anaesthetized and decapitated . The brain was rapidly removed and placed in ice-cold artificial cerebrospinal fluid ( ACSF ) . Slices ( 350–400 µm thick ) were incubated during >1 h at room temperature ( 21–24°C ) in ACSF that contained ( in mM ) : NaCl 124 , KCl 2 . 69 , KH2PO4 1 . 25 , MgSO4 2 , NaHCO3 26 , CaCl2 2 , and glucose 10 , and was gassed with 95% O2 / 5% CO2 ( pH = 7 . 3 ) . Slices were then transferred to an immersion recording chamber and superfused with gassed ACSF including 0 . 05 mM Picrotoxin and 5 µM CGP 55845 to block GABA receptors . To prevent possible NMDAR-mediated plasticity , experiments were performed in the presence of 50 µM AP5 . Cells were visualized under an Olympus BX50WI microscope ( Olympus Optical , Tokyo , Japan ) . For rats , electrodes were placed stereotaxically according to [42] . Field potentials were recorded through tungsten macroelectrodes ( 1 MΩ ) placed in the CA1 layer ( A , −3 . 8; L , 1; V , 2 . 5 mm from Bregma ) . For mice , electrodes were placed stereotaxically according to [43] . Recording electrodes were placed at the CA1 area ( 1 . 2 mm lateral and 2 . 2 mm posterior to Bregma; depth from brain surface , 1 . 0–1 . 5 mm ) and bipolar stainless steel stimulating electrodes aimed at the right Schaffer collateral–commissural pathway of the dorsal hippocampus ( 2 mm lateral and 1 . 5 mm posterior to Bregma; depth from brain surface , 1 . 0–1 . 5 mm ) . Extracellular excitatory postsynaptic field potentials ( fEPSPs ) were amplified ( DAM80; World Precision Instruments , Sarasota , FL ) , bandpass filtered between 0 . 1 Hz and 1 . 0 kHz , and digitized at 3 . 0 kHz ( CED 1401 with Spike 2 software; Cambridge Electronic Design , Cambridge , UK ) . SC fibers continuously stimulated with single pulses ( 100 µA , 0 . 3 ms , 0 . 5 Hz ) using a bipolar stainless steel stimulating electrode ( 0 . 1 mm diameter ) placed in the stratum radiatum ( A , −3 . 8; L , 4; V , 4 mm from Bregma ) . The medial septum ( A , −0 . 2; L , 0; V , 7 mm from Bregma ) was stimulated using a similar electrode . The initial phase of the fEPSP was used to quantify SC synaptic transmission . To mimic theta activity ( theta burst stimulation , TBS ) , the medial septum was stimulated with four trains at 5 Hz of 5 stimuli ( at 40 Hz ) delivered 10 times at 0 . 1 Hz . Electrophysiological recordings from CA1 pyramidal neurons and astrocytes located in the stratum radiatum were made using the whole-cell patch-clamp technique . Patch electrodes had resistances of 3–10 MΩ when filled with the internal solution that contained ( in mM ) for pyramidal neurons: KGluconate 135 , KCl 10 , HEPES 10 , MgCl2 1 , ATP-Na2 2 ( pH = 7 . 3 ) ; and astrocytes were patched with 4–9 MΩ electrodes filled with an intracellular solution containing ( in mM ) : MgCl2 1 , NaCl 8 , ATP-Na2 2 , GTP 0 . 4 , HEPES 10 , and either 40 mM BAPTA or 20 mM GDPβS , titrated with KOH to pH 7 . 2–7 . 3 and adjusted to 275–285 mOsm . Recordings were obtained with PC-ONE amplifiers ( Dagan Corporation , Minneapolis , MN ) . Fast and slow whole-cell capacitances were neutralized and series resistance was compensated ( ≈70% ) . Recordings were rejected when the access resistance increased >20% during the experiment . Recordings from pyramidal neurons were performed in voltage-clamp conditions and the membrane potential was held at −70 mV to record SC-evoked EPSCs . During alveus TBS , recordings were performed in current-clamp conditions , unless stated otherwise ( e . g . , Figure 5C and 5D ) . Signals were fed to a Pentium-based PC through a DigiData 1440 interface board ( Axon Instruments ) . The pCLAMP 10 software ( Axon Instruments ) was used for stimulus generation , data display , acquisition , and storage . Experiments were performed at room temperature ( 21–24°C ) . To stimulate cholinergic axons , theta capillaries ( 10–30 µm tip; WPI , Sarasota , FL ) filled with ACSF were used for bipolar stimulation . The electrodes were connected to a stimulator S-900 through an isolation unit ( S-910 , Dagan Corporation ) and placed in the stratum oriens/alveus near the subiculum area ( for simplicity herein termed alveus ) , which contains cholinergic axons from the diagonal band of Broca and septum [22] , [44] . For TBS , four trains at 5 Hz of 5 stimuli ( at 40 Hz ) were delivered 10 times at 0 . 1 Hz . To stimulate SC fibers , electrodes were placed in the stratum radiatum of the CA1 region . Single pulses ( 250 µs duration ) or paired pulses ( 50 ms interval ) were delivered at 0 . 33 Hz . Basal EPSC values were recorded 10 min before the stimulus , and the relative mean amplitudes of 10 consecutive EPSCs from basal values were plotted over time ( e . g . , Figure 2F ) . Long-term changes of synaptic transmission were assessed from the relative amplitude of 30 consecutive EPSCs recorded 54–60 min after the stimulus ( e . g . , Figure 2G ) . Paired-pulse facilitation was quantified as PPF = [ ( 2nd EPSC−1st EPSC ) /1st EPSC] . For minimal stimulation of SC , the stimulus intensity ( 10–50 mA ) was adjusted to meet the conditions that putatively stimulate a single , or very few , synapses ( cf . [7] , [11] , [45] , [46] ) and was unchanged during the experiment . The recordings that did not meet these criteria [7] , [11] , [45] , [46] and synapses that did not show amplitude stability of EPSCs were rejected . The synaptic current parameters analyzed were: synaptic efficacy ( mean peak amplitude of all responses including failures ) , synaptic potency ( mean peak amplitude of the successes ) , probability of release ( Pr , ratio between number of successes versus total number of stimuli ) , and paired-pulse facilitation . The responses and failures were identified by visual inspection . Adult animals were craniotomized and the cortical tissue above the hippocampus was removed by aspiration to expose the dorsal hippocampus ( see [47] , [48] ) , which was bathed with 4 µl of Fluo-4 AM ( 2 mM ) and sulforhodamine 101 ( SR101 , 125 µM ) , for 30–60 min and covered with 2% agar and a glass coverslip . Most of the Fluo-4-loaded cells were astrocytes as indicated by their SR101 staining ( Figure 1A ) ( cf . , [49] , [50] ) . Cells were imaged with an Olympus FV300 laser-scanning confocal microscope . Ca2+ variations recorded at the soma of 5 to 11 astrocytes in the field of view were estimated as changes of the fluorescence signal over the baseline ( ΔF/F0 ) . Astrocytes were considered to respond to the stimulation when ΔF/F0 increased two times the standard deviation of the baseline during the stimulus or with a delay ≤15 s after the end of the stimulus , and the proportion of responding astrocytes in different conditions was compared . Ca2+ levels in astrocytes located in the stratum radiatum of the CA1 region of the hippocampus were monitored by fluorescence microscopy using the Ca2+ indicator fluo-4 ( Molecular Probes , Eugene , OR ) . Slices were incubated with fluo-4-AM ( 2–5 µl of 2 mM dye were dropped over the hippocampus , attaining a final concentration of 2–10 µM and 0 . 01% of pluronic ) for 20–30 min at room temperature . In these conditions , most of the cells loaded were astrocytes [23] , as confirmed in some cases by their electrophysiological properties [22] , [51]–[53] . Astrocytes were imaged using a CCD camera ( ORCA-235 , Hamamatsu , Japan ) attached to the microscope . Cells were illuminated during 100–500 ms with a xenon lamp at 490 nm using a monochromator Polychrome V ( TILL Photonics , Gräfelfing , Germany ) , and images were acquired every 0 . 5–1 s . The monochromator and the camera were controlled and synchronized by the IP Lab software ( BD Biosciences , MD ) that was also used for quantitative epifluorescence measurements . Astrocyte Ca2+ levels were recorded from the astrocyte cell body and Ca2+ variations were estimated as changes in the fluorescence signal over the baseline . Astrocytes were considered to respond to the stimulation when ΔF/F0 increased two times the standard deviation of the baseline . In some cases , Ca2+ levels in single neurons or astrocytes were monitored by including 50 µM fluo-4 in the corresponding internal solution and recording pipette . The astrocyte Ca2+ signal was quantified from the probability of occurrence of a Ca2+ spike , which was calculated from the number of Ca2+ elevations grouped in 5-s bins recorded from 5 to 20 astrocytes in the field of view [7] , and mean values were obtained by averaging different experiments . To test the effects of alveus stimulation on Ca2+ spike probability under different conditions , the respective mean basal ( 15 s before the stimulus start ) and maximum Ca2+ spike probability ( i . e . , 5–10 s after ) from different slices were averaged and compared . Local application of ACh ( 1 mM ) was delivered by 30-s duration pressure pulses through a micropipette . In photo-stimulation experiments , single astrocytes were electrophysiologically recorded with patch pipettes filled with the internal solution containing ( in mM ) : MgCl2 1 , NaCl 8 , ATP-Na2 2 , GTP 0 . 4 , HEPES 10 , GDPβS 20 , NP-EGTA 5 , and 50 µM fluo-4 ( to monitor Ca2+ levels ) . Ca2+ uncaging was achieved by delivering 10 trains at 0 . 1 Hz of 5 pulses ( 1-ms duration , 6–15 mW ) at 5 Hz of UV light ( 340–380 nm ) to the soma and processes of the recorded astrocyte ( optical window of 15–25 µm diameter ) using a flash photolysis system ( Rapp OptoElectronic , Hamburg , Germany ) . D- ( - ) -2-Amino-5-phosphonopentanoic acid ( D-AP5 ) , ( S ) -α-Methyl-4-carboxyphenylglycine ( MCPG ) , ( 2S ) -3-[[ ( 1S ) -1- ( 3 , 4-Dichlorophenyl ) ethyl] amino-2-hydroxypropyl] ( phenylmethyl ) phosphinic acid hydrochloride ( CGP 55845 ) , and 1 , 2-bis ( 2-aminophenoxy ) ethane-N , N , N′ , N′-tetraacetate ( BAPTA ) were purchased from Tocris Cookson ( Bristol , UK ) . Fluo-4-AM , o-nitrophenyl EGTA , tetrapotassium salt ( NP-EGTA ) , and sulforhodamine B were from Molecular Probes , Eugene , Oregon . All other drugs were from Sigma . For in vivo experiments , atropine sulfate ( 5 mg/kg ) was intraperitoneally injected and its effects tested 10–15 min after the injection . For in vivo electrophysiological experiments , MCPG ( 100 nl , 1 mM ) was injected into the hippocampus with a Hamilton microliter syringe . For in vivo Ca2+ imaging , MCPG ( 0 . 8 mM ) was included in the solution bathing the dorsal hippocampus . Data are expressed as mean ± s . e . m . Results were compared using a two-tailed Student's t test ( α = 0 . 05 ) . Statistical differences were established with p<0 . 05 ( * ) , p<0 . 01 ( ** ) , and p<0 . 001 ( *** ) . | Information processing in the brain was classically thought to rely solely on neurons , whereas astrocytes , the most abundant glial cells in the brain , were considered supportive cells for neurons . However , astrocytes are known to respond to neuronal signals and regulate the function of synapses , so they may indeed serve active roles during information processing and storage in the brain . We investigated whether these phenomena occur in vivo and whether astrocytes participate in synaptic plasticity and long-term potentiation ( LTP ) , which are thought to represent the cellular basis of learning and memory . We found that sensory stimulation in rodents triggers the activity of neurons that release the neurotransmitter acetylcholine and activates astrocytes in the hippocampus , a brain region involved in learning and memory . Acetylcholine elevates intracellular calcium in astrocytes , which then stimulates the release of glutamate , another neurotransmitter . Glutamate released from astrocytes acts on synaptic receptors , increasing synaptic strength and leading to LTP of the efficacy of transmission in synapses . This is the first demonstration of the direct involvement of astrocytes in the generation of in vivo LTP . We suggest that astrocytes are cellular sources of signals underlying synaptic plasticity and are directly involved in memory processes and brain information storage . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biology",
"neuroscience"
] | 2012 | Astrocytes Mediate In Vivo Cholinergic-Induced Synaptic Plasticity |
Trypomastigotes of Trypanosoma cruzi are able to invade several types of non-phagocytic cells through a lysosomal dependent mechanism . It has been shown that , during invasion , parasites trigger host cell lysosome exocytosis , which initially occurs at the parasite-host contact site . Acid sphingomyelinase released from lysosomes then induces endocytosis and parasite internalization . Lysosomes continue to fuse with the newly formed parasitophorous vacuole until the parasite is completely enclosed by lysosomal membrane , a process indispensable for a stable infection . Previous work has shown that host membrane cholesterol is also important for the T . cruzi invasion process in both professional ( macrophages ) and non-professional ( epithelial ) phagocytic cells . However , the mechanism by which cholesterol-enriched microdomains participate in this process has remained unclear . In the present work we show that cardiomyocytes treated with MβCD , a drug able to sequester cholesterol from cell membranes , leads to a 50% reduction in invasion by T . cruzi trypomastigotes , as well as a decrease in the number of recently internalized parasites co-localizing with lysosomal markers . Cholesterol depletion from host membranes was accompanied by a decrease in the labeling of host membrane lipid rafts , as well as excessive lysosome exocytic events during the earlier stages of treatment . Precocious lysosomal exocytosis in MβCD treated cells led to a change in lysosomal distribution , with a reduction in the number of these organelles at the cell periphery , and probably compromises the intracellular pool of lysosomes necessary for T . cruzi invasion . Based on these results , we propose that cholesterol depletion leads to unregulated exocytic events , reducing lysosome availability at the cell cortex and consequently compromise T . cruzi entry into host cells . The results also suggest that two different pools of lysosomes are available in the cell and that cholesterol depletion may modulate the fusion of pre-docked lysosomes at the cell cortex .
Trypanosoma cruzi , the etiological agent of Chagas' disease , is a protozoan parasite capable of invading several types of non-professional phagocytic cells including fibroblasts , endothelial cells , and myocytes [1] , [2] . Invasion occurs when parasite attaches to and stimulates host cell , leading to intracellular calcium signaling events that culminate with lysosome recruitment and fusion with the host cell plasma membrane and formation of the parasitophorous vacuole [3] , [4] , [5] . Several factors , such as parasite membrane proteins and proteins shed or secreted by the parasite , are known to interact with host cell membrane receptors during the T . cruzi entry process into host cells [6] , [7] , [8] , [9] , [10] , [11] . Therefore , host cell plasma membrane plays an important role in T . cruzi adhesion and internalization , and modulates intracellular signaling events that are imperative for a successful infection of host cells by the parasite . The host cell plasma membrane is a complex structure formed by a fluid and dynamic lipid bilayer to which various proteins and ligands with different biological functions are associated [12] . It is well established that the plasma membrane is not a homogeneous structure . On the contrary , the plasma membrane not only presents an asymmetric lipid distribution over its exoplasmic and cytoplasmic leaflets [13] , but also shows inhomogeneities in the lateral distribution of lipids . In 1997 , these lateral asymmetries were well described by Simons and Ikonen as sphingolipids and cholesterol-enriched microdomains known as lipid rafts [14] . These microdomains are likely to be kept together due to lateral association between carbohydrate heads of glycosphingolipids and the presence of cholesterol molecules filling the empty area between those lipids . Several proteins were also identified inside lipid rafts: e . g . , GPI- anchored proteins , transmembrane proteins , and tyrosin kinases among others [15] , [16] , [17] . Due to their specific characteristics , lipid rafts play several roles in cell signaling , molecular organization and membrane trafficking [18] . Beyond these cellular functions , several works show that these microdomains are also involved in internalization of pathogens like virus , bacteria and protozoans [19] , [20] , [21] . Recently , two independent groups have shown that cholesterol-enriched regions might be involved in T . cruzi entry into host cells [22] , [23] . According to these authors , cholesterol localized in cell membranes contributes significantly to the infectivity of metacyclic trypomastigotes and extracellular amastigotes in non-professional phagocytic cells ( Vero and HeLa cells [23] ) , or to the infectivity of tissue culture trypomastigotes in professional phagocytic cells [22] . In both works , methyl-beta cyclodextrin ( MβCD ) depletion of host cell membrane cholesterol considerably reduced parasite infectivity . However , the mechanism by which cholesterol-enriched membrane microdomains contribute to infectivity of T . cruzi was not elucidated . It is well established that several proteins and receptors associated with lipid rafts are responsible for triggering intracellular signaling cascades [24] . T . cruzi interaction with host cells also evokes various host signaling events that culminate with recruitment and fusion of lysosomes with the plasma membrane and the subsequent formation of a viable parasitophorous vacuole , without which parasites are able to escape from its host cell [3] , [4] , [5] , [25] . SNARE complex proteins ( Soluble N-ethylmaleimide-sensitive factor Attachment protein Receptor ) , SNAP-23 ( Synaptosome-Associated Proteins ) and Syntaxin 4 at the plasma membrane and VAMP-7 on lysosomal membranes , have been shown to coordinate lysosome fusion with plasma membrane [26] . Interestingly , it has been demonstrated that SNARE protein complexes are preferentially localized in lipid rafts [27] , [28] , [29] . Since lysosomal fusion is essential for the successful invasion of T . cruzi into host cells and signaling proteins , as well as proteins of the SNARE complex , reside in rafts , we decided to evaluate if altering the concentration and distribution of cholesterol interferes with the T . cruzi invasion process and if these changes affected the lysosomal fusion events during this process . We tested this by sequestering the cholesterol from cell membranes of primary mouse cardiomyocytes with MβCD before exposure to T . cruzi trypomastigotes . Our results show that the diminishment of T . cruzi entry into host cells after cholesterol sequestration is a consequence of the reduction in lysosomal recruitment during the formation of the parasitophorous vacuole . We then demonstrate that this decrease in lysosome recruitment and fusion during parasite entry into host cells is a consequence of unregulated lysosomal exocytosis events , which reduce the number of lysosomal vesicles that are normally localized near the cell cortex and available for the formation of the parasitophorous vacuole .
All animals were maintained in our animal facilities in compliance with the guidelines of the UFMG ( Universidade Federal de Minas Gerais ) ethics committee for the use of laboratory animals ( protocol 45/2009 approved by CETEA-UFMG ) and are in accordance with CONCEA , the Brazilian institution that regulates animal husbandry . Primary cultures of murine neonatal cardiomyocytes were prepared from fifteen neonatal ( 1–3 day old ) Swiss mice . After euthanization by decapitation hearts were removed aseptically and kept on ice in Hanks' balanced salt solution ( HBSS ) ( Sigma-Aldrich ) ( pH 7 . 4 ) . Hearts were washed three times with fresh ice-cold HBSS , minced into small fragments and washed twice during mincing . Cardiac tissue was first dissociated overnight at 4°C in an enzymatic solution containing 0 . 05% ( vol/vol ) trypsin-EDTA 0 . 25% ( Sigma-Aldrich ) in HBSS and then trypsin was inhibited with 1 mL of soybean trypsin inhibitor ( Sigma-Aldrich ) , 1 mg/mL in HBSS . Next , samples were submitted to a second dissociation step with 5 mL collagenase type 2 ( Worthington ) , 1 mg/mL in Leibovitz medium ( Sigma-Aldrich ) . The cell suspension was filtered through a 70 µm cell strainer and then centrifuged at 300 g for 5 minutes . The pellet containing dissociated cells was resuspended in high-glucose DMEM ( Invitrogen ) , supplemented with 10% ( vol/vol ) fetal bovine serum ( FBS ) and 1% ( vol/vol ) penicillin/streptomycin ( 100 U/mL/100 µg/mL ) ( Invitrogen ) . The cell suspension was pre-plated for 2 hours at 37°C in a 5% CO2 incubator in order to remove most fibroblasts and other non-muscle cells . Supernatant enriched in cardiomyocytes was then collected and seeded at a density of 1 , 0×105 cells/well onto 24-well plates containing round coverslips pre-treated with fibronectin ( Sigma-Aldrich ) . Cells were incubated at 37°C in a humidified atmosphere containing 5% CO2 for 72 hours before experimental procedures . New cultures were prepared for each experiment . Tissue culture trypomastigotes from T . cruzi ( T . cruzi TCTs ) , Y strain , were obtained from the supernatant of infected monolayers of the LLC-MK2 cell line and purified as described previously [30] . For cholesterol depletion from host cell plasma membrane , cardiomyocytes were washed three times with phosphate buffered saline containing Ca2+ and Mg2+ ( PBS+/+ ) and incubated in high-glucose DMEM containing different concentrations of methyl-beta cyclodextrin ( MβCD ) ( Sigma-Aldrich ) for 45 minutes at 37°C . Alternatively , cells were incubated in high-glucose DMEM containing different concentrations of hydroxypropyl-gamma cyclodextrin ( HγCD ) ( Sigma-Aldrich ) , an inactive analog of MβCD which does not release cholesterol from cells in significant amounts as an internal control . After drug treatment , monolayers were washed three times with PBS+/+ and used in the different experimental procedures . Cholesterol repletion was performed by incubating cells , previously treated with the highest concentration of MβCD , in high-glucose DMEM containing 0 . 05 mM of water soluble cholesterol ( WSC ) ( Sigma-Aldrich ) , for 30 minutes at 37°C . Cardiomyocyte cultures pre-treated or not with MβCD were exposed to purified T . cruzi TCTs ressuspended in high-glucose DMEM at a multiplicity of infection ( MOI ) of 50 . The infection was performed for 40 minutes at 37°C . After infection , monolayers were washed at least four times with PBS+/+ and fixed in 4% ( wt/vol ) paraformaldehyde ( Sigma-Aldrich ) /PBS+/+ . After fixation , cells were processed for immunofluorescence or other labeling . After treatment , infection and fixation , coverslips containing attached cells were washed with PBS+/+ , incubated for 20 min with PBS+/+ containing 2% bovine serum albumin ( BSA ) ( Sigma-Aldrich ) and processed for an inside/outside immunofluorescence invasion assay as described previously [30] . Briefly , cells were fixed and extracellular parasites were immunostained using a 1∶500 dilution of rabbit anti-T . cruzi polyclonal antibodies in PBS containing 2% BSA ( PBS/BSA ) followed by labeling with Alexa Fluor-546 conjugated anti-rabbit IgG antibody ( Invitrogen ) . After extracellular parasite staining , cells were permeabilized using a solution containing 2% BSA and 0 . 5% saponin ( Sigma-Aldrich ) in PBS ( PBS/BSA/saponin ) for 20 minutes . Host cell lysosomes were then immunostained using a 1∶50 dilution of rat anti-mouse LAMP-1 hybridoma supernatant ( 1D4B; Developmental Studies Hybridoma Bank , USA ) in PBS/BSA/saponin for 45 minutes followed by labeling with Alexa Fluor-488 conjugated anti-rat IgG antibody ( Invitrogen ) , as described previously [3] . Subsequently , the DNA of both host cells and parasites were stained for 1 min with 10 µM of DAPI ( Sigma-Aldrich ) , mounted with ProLong Gold antifade reagent ( Molecular Probes ) , and examined on a Zeiss Axioplan microscope equipped with an oil immersion objective ( 100× , 1 , 3 NA ) and with an Axiocam HRC camera controlled by Axiovision Software ( Zeiss ) . Cell surface area for the different conditions ( treated or not with MβCD or HγCD ) was determined using a plasma membrane labeling agent , CellMask orange plasma membrane stain ( Invitrogen ) , according to manufacturer instructions . Briefly , control non-treated cardiomyocytes , as well as cardiomyocytes treated with either 15 mM MβCD , 15 mM HγCD or 15 mM MβCD followed by incubation with 0 . 05 mM of WSC , were washed with PBS+/+ and incubated with a solution of 5 µg/mL CellMask in DMEM without serum for 5 minutes , at 37°C . After this period , cells were fixed in a solution of 4% paraformaldehyde in fresh media for 10 minutes , at 37°C . Coverslips were then washed three times with PBS+/+ and mounted using antifade medium . Images were collected immediately afterwards using an Olympus FV300 confocal/WX61WI microscope system ( Figure S1 ) . After cholesterol depletion and repletion , cells were fixed and labeled with either Filipin III ( Sigma-Aldrich ) or subunit B of cholera toxin-Alexa Fluor 488 ( CTXb ) ( Sigma-Aldrich ) for detection of plasma membrane cholesterol and GM1 ganglioside , respectively , as described previously [23] . Briefly , after cholesterol depletion/replenishment cells were washed and fixed with paraformaldehyde as described above . After fixation , cells were permeabilized with PGN solution ( PBS+/+ , 0 . 15% gelatin and 0 . 1% sodium azide ) containing 0 . 1% saponin for 15 minutes . Following permeabilization , cells were labeled with CTXb ( 1 µg/mL ) diluted in PGN for 30 minutes . Cells labeled with CTXb were analyzed using the confocal microscope system described above . Images were collected and analyzed using Fluoview version 5 . 0 . In order to visualize the distribution of cholesterol in cell plasma membrane , fixed cells were labeled with both Filipin III ( for cholesterol detection ) ( 100 µg/mL in PGN ) and DAPI ( for nuclei staining ) and examined on a Zeiss Axioplan microscope equipped with an Axiocam HRC camera controlled by Axiovision Software ( Zeiss ) . For quantitative assays of cholesterol depletion/repletion , only Filipin III was stained . Images of 10 fields/coverslip were collected with an oil immersion objective ( 100× , 1 , 3 NA ) , using the same CCD exposure time and illumination intensity and then analyzed using the ImageJ image processing program ( http://rsb . info . nih . gov/ij/ ) for fluorescence quantification . Four equal squared areas were chosen in each image and the fluorescence intensity of each area was determined . These values were then used to calculate the average fluorescence of each image and then of each experimental group . To evaluate the level of lysosomal exocytosis after treatment with MβCD , a time dependent β-hexosaminidase secretion assay was performed according to previous work [31] . Briefly cells were exposed to 10 mM MβCD or HγCD for different incubation periods in the presence or absence of calcium . In the latter calcium was substituted by the same concentration of Mg2+ and EGTA was also added . After drug incubation , 350 µL of extracellular media was collected and adhered cells were lysed using 1% Triton x-100 ( Sigma-Aldrich ) in PBS . Extracellular media and lysates were incubated with 50 µL of β-hexosaminidase substrate , 6 mM 4-methylumbelliferyl-N-acetyl-B-D-glucosaminide ( Sigma-Aldrich ) , dissolved in Na-citrate-PO4 buffer ( pH 4 . 5 ) . Reactions were stopped by adding 100 µL of Stop Solution ( 2 M Na2CO3-H2O , 1 . 1 M glycin ) and read at excitation 365 nm and emission 450 nm in a spectrofluorimeter ( Synergy 2 , Biotek in the Center of Flow Cytometry and Fluorimetry , Department of Biochemistry and Immunology , ICB-UFMG ) . After incubation with either 10 mM MβCD or HγCD for 45 minutes , or with 10 µM Ionomycin for 10 minutes , in the presence or absence of calcium , cardiomyocytes were trypsinized , pelleted and incubated with Hypotonic Fluorochrome Solution ( HFS - 50 µg/mL Propidium Iodide ( PI ) in 0 . 1% sodium citrate ) for 4 hours at 4°C protected from light . This assay was performed in order to quantify cell death after drug treatment according to previous work [32] . The PI fluorescence of 20 , 000 individual cells was measured using a Becton Dickinson FACscan ( BD Biosciences , USA ) and data were analyzed using the Cell Quest Pro software ( BD Biosciences , USA ) . Using a set of images , obtained from treated and non-treated cells with labeled nuclei and lysosomes , we analyzed the mean lysosome distance relative to the mean center position in the respective nuclei . For each image ( associated with a specific drug treatment ) , we visually selected a number of isolated nuclei , and using its borders' positions ( i . e . , the image's [X , Y] coordinates ) , we computed the mean center position and the mean radius ( namely R ) of each nucleus . A computational code has been written using the IDL ( Interactive Data Language ) programming language in order to assist in these calculations . Subsequently , using the ( X , Y ) position of each lysosome distributed around each nucleus , the distances to the center were calculated . An average distance ( namely D ) was computed by using the distances of each lysosome relative to the center . Lysosomes farther than 2 radii from cell center were excluded from the computation of this average value . Finally , the mean lysosome distance ( D ) relative to the mean nucleus' radius ( R ) was defined as the ratio D/R . This procedure was repeated for the maximum number of isolated cells available from the image sets of each drug treatment . The results of this analysis are a distribution of D/R values associated to each drug treatment , and are represented as histograms . All experiments were performed in triplicate and repeated at least three times . For Filipin labeling , one-way ANOVA followed by post- hoc comparison Newman-Keuls was performed to evaluate statistically significant differences . For invasion assays , a minimum of 200 cells was counted per coverslip and analyzed using the Student's t-test . For histogram distributions , the cumulative frequency was calculated and analyzed using the Kolmogorov-Smirnov statistical test .
Before investigating the influence of cholesterol removal in invasion of cardiomyocytes by T . cruzi TCTs , we tested whether MβCD treatment was able to sequester cholesterol from cultured murine cardiomyocyte membranes . Cells treated with different concentrations of MβCD were stained with Filipin III , a sterol-binding fluorescent polyene , which is able to bind to cholesterol present in the plasma membrane [33] ) . Fluorescence microscopy images of cells pre-treated with 15 mM of MβCD ( Fig . 1B ) show decreased staining with Filipin when compared to control cells ( Fig . 1A ) , confirming the removal of cholesterol upon drug treatment . Addtionally , if MβCD treated cultures were incubated with 0 . 05 mM of water soluble cholesterol ( WSC ) diluted in serum free media , causing membrane cholesterol to be replenished , surface staining with Filipin was recovered ( Fig . 1C ) . Quantitative assays , using the ImageJ program to quantify fluorescence intensity , were also performed to measure cell staining with Filipin III before and after treatment with MβCD , as well as upon cholesterol depletion and repletion . As was seen for the qualitative assays , Filipin staining of cholesterol decreased upon treatment with increasing concentrations of MβCD ( Fig . 1D ) . A small reduction was observed upon treatment with 5 mM of the drug , increasing to 30% after treatment with a concentration of 15 mM . Cholesterol replenishment with WSC reverted the process , showing an increase in cell staining as a consequence of the increase in membrane bound cholesterol . Previous studies have shown that host cell cholesterol-enriched microdomains play a significant role in the adhesion and internalization of T . cruzi TCTs in professional phagocytic cells [22] , as well as T . cruzi metacyclic trypomastigotes or extracellular amastigotes forms in non-professional phagocytic cells [23] . Since T . cruzi TCT invasion into non-professional phagocytic cells , such as cardiomyocytes , is an important event during clinical infection , in the present work we first investigated whether host cell cholesterol was also relevant in cell invasion of this T . cruzi form into cultured , primary neonatal murine cardiomyocytes . Previously plated cells were incubated with 10 or 15 mM of MβCD for 45 minutes at 37°C ( i . e . , conditions known to be effective in membrane cholesterol removal [31] ) , followed by a 40 minute exposure to T . cruzi TCTs . As observed before for macrophages [22] , we determined that cholesterol depletion from murine cardiomyocytes membranes also leads to a reduction in T . cruzi TCT invasion ( Fig . 2A and C ) . Upon treatment with 10 mM or 15 mM MβCD , a reduction of 85–90% in T . cruzi cell invasion was observed ( Fig . 2A ) . As a control for MβCD treatment , cells were treated with the same concentrations of HγCD , a cyclodextrin analog of MβCD with low affinity for cholesterol . HγCD-treated cells did not show any differences in T . cruzi cell invasion as compared to control non-treated cells ( Fig . 2A and C ) . Plus , reduction in the number of invading T . cruzi TCTs after MβCD treatment did not appear to be the result of host cell death upon drug treatment since the total number of cardiomyocytes per 10 fields in all conditions tested was not statistically different from each other ( Fig . 2A , number above bars ) . To address whether the reduced T . cruzi invasion observed after host cell treatment with MβCD was really due to cholesterol removal from host cell membrane , cells pre-treated with the drug were subsequently incubated with WSC , an exogenous source of cholesterol which replenishes membrane cholesterol , washed with PBS and then exposed to T . cruzi TCTs . T . cruzi invasion into cholesterol-replenished cells was similar to that observed for non-treated control cells ( Fig . 2A and C ) . In order to understand why cholesterol depletion was leading to a reduction in T . cruzi internalization , we investigate whether association between host cell lysosomes and T . cruzi was altered by MβCD incubation since this organelle is crucial for a successful parasite invasion [3] , [4] , [5] , [25] . Untreated control cells , or cells treated with MβCD in different concentrations , were exposed to T . cruzi TCTs for 40 min . , washed with PBS , fixed and submitted to the inside/outside parasite staining method , as well as labeled with LAMP-1 antibody for lysosomal membrane detection . Cholesterol removal from host cell membrane upon treatment with MβCD not only diminished host-cell invasion by the parasite ( Fig . 2A and C ) , but also reduced the number of internalized parasites that had acquired lysosomal markers ( Fig . 2B and C ) . Pre-treatment of cardiomyocytes with 10 mM MβCD caused a 60% reduction in the number of internalized parasites co-localizing with LAMP-1 ( Fig . 2B ) . Moreover , increasing concentration of MβCD decreased both the number of internalized parasites ( Fig . 2A ) , as well as the number of parasites co-localizing with the lysosomal marker in a dose-dependent manner ( Fig . 2B ) . 15 mM of MβCD led to a reduction of 75% of lysosomal association with internalized trypomastigotes . Cells treated with HγCD , the inactive analog of MβCD , on the other hand , showed no statistically significant difference in T . cruzi invasion ( Fig . 2A ) or association of T . cruzi with host cell lysosomes when compared to control non-treated cells ( Fig . 2B and C ) . Cholesterol replenishment not only re-established the ability of T . cruzi to invade host cells ( Fig . 2A and C ) , but also elevated T . cruzi association with host cell lysosomes to values comparable with control non-treated cells ( Fig . 2B and C ) . In order to evaluate how cholesterol depletion affected membrane raft organization , cardiomyocytes , which had their membrane cholesterol removed by treatment with 10 or 15 mM of MβCD , were fixed and labeled with subunit B of cholera toxin ( CTXb ) . Subunit B of cholera toxin is a homopentamer that binds to GM1 , a ganglioside that resides in membrane rafts , on the extracellular leaflet of plasma membrane [34] , [35] , [36] . Cells treated with 10 or 15 mM of HγCD or with 15 mM of MβCD , followed by cholesterol replenishment with 0 . 05 mM of WSC , were likewise stained . Cells with intact membrane cholesterol content show a more intense GM1 labeling , especially of larger lipid rafts ( Fig . 3A ) in comparison to cholesterol-depleted cells ( Fig . 3B ) . In cardiomyocytes treated with HγCD ( Fig . 3C ) , raft labeling was similar to control cells . In cholesterol-replenished cells ( Fig . 3D ) , some cardiomyocytes retained a labeling pattern similar to that of cholesterol-depleted cells ( arrows ) , while others showed a staining pattern more similar to untreated controls ( asterisks ) . Therefore , MβCD treatment not only induced cholesterol sequestration from cell membranes , but also interfered with membrane raft organization in cardiomyocytes , which could not be totally recovered by cholesterol replenishment . Lysosomal fusion with the plasma membrane ( lysosomal exocytosis ) , which occurs during T . cruzi entry into host cells , is an event regulated by calcium and proteins from the SNARE complex , in a mechanism similar to the fusion of synaptic vesicles with the pre-synaptic membrane [26] , [37] . Depletion of cholesterol from the membrane has been shown to alter synaptic vesicle fusion with the plasma membrane leading to unregulated events of vesicle exocytosis [38] . In order to verify the behavior of lysosomal exocytosis in cholesterol-depleted cells , we performed a time-dependent assay in which the activity of beta-hexosaminidase , an enzyme resident within lysosomes , was measured in the extracellular media of cultured cells . Cardiomyocytes were incubated with 10 mM of either MβCD or HγCD and both the extracellular media and cell lysates of treated cells were incubated with a fluorescent substrate of beta-hexosaminidase . Non-treated and Ionomycin ( an ionophore , which allows calcium influx into cells and induces lysosomal exocytosis [39] ) treated cells were used as negative and positive controls , respectively . Experiments were performed in the presence or absence of calcium . Figure 4 shows that cardiomyocytes treatment with MβCD leads to lysosomal exocytosis events . As early as 10 minutes after the addition of the drug , in the presence of calcium , the rate of lysosomal exocytosis in cardiomyocytes was 3 . 5 times higher than control non-treated cells ( Fig . 4 ) . The levels were even higher the longer the incubation period with the drug . After 20 or 40 minutes of exposure with the drug the exocytosis level was about 5 . 5 times higher than control non-treated cells ( Fig . 4 ) . On the other hand , treatment with HγCD , the control drug , did not induce expressive exocytosis of lysosomal vesicles in cardiomyocytes ( Fig . 4 ) . To test whether these events were occurring spontaneously without calcium regulation , as was the case for synaptic vesicles , the same assay was performed in the absence of calcium and presence of the same concentration of magnesium . The extracellular calcium chelator , EGTA , did not inhibit lysosomal exocytosis induced by the incubation with MβCD . On the contrary , it seemed to slightly enhance exocytosis ( Fig . 4 ) . To confirm that the high levels of beta-hexosaminidase observed in the extracellular media of MβCD treated cells were the result of triggered lysosomal exocytosis and not a consequence of cell injury upon treatment , a cell viability assay was performed . Cardiomyocytes treated or not with MβCD/HγCD were trypsinized and incubated with HFS solution , containing PI ( propidium iodide ) , a nuclei dye impermeable to cell membranes . Negative controls ( untreated cardiomyocytes ) and positive controls ( ionomycin treated cells ) were also analyzed . Figure 5 shows that treatment with MβCD did not interfere with cell viability either in the presence or in the absence of calcium ( Fig . 5 ) . As expected , treatment with HγCD also did not lead to cell death . In order to understand the effect of exocytosis triggered by cholesterol removal from cell membranes on lysosomal distribution , images from cells submitted or not to treatment with MβCD , HγCD or MβCD + WSC and labeled with both DAPI ( nuclei dye ) and anti-LAMP-1 ( lysosomal marker ) were collected . Representative images of each condition are shown in figure 6 . Qualitative analyses of the images revealed a more restricted distribution of lysosomes , closer to the cell nuclei , in cells treated with 10 mM or 15 mM MβCD ( Fig . 6B and C ) , in comparison to control non-treated cells ( Fig . 6A ) or cells treated with HγCD , the MβCD inactive analog ( Fig . 6D and E ) . Cholesterol replenishment after MβCD treatment seemed to revert the distribution of lysosomes to a pattern similar to the control non-treated cells ( Fig . 6F ) . In order to precisely determine these differences , the same images were used to perform a quantitative assay of lysosomal dispersion ( Fig . 7 ) . First , for each isolated nucleus , the mean radius ( R ) . was calculated . The next step was to select each lysosome associated with its respective nucleus and to measure the mean distance between a lysosome and cell center ( D ) . Finally , the mean lysosome distance ( D ) relative to the mean nucleus' radius ( R ) was defined as the ratio D/R , where values closer to one indicate lysosomes are closer to perinuclear region whereas the opposite indicate lysosomes are more frequent at cell borders . This ratio D/R was measured for several groups of lysosomes associated with each nucleus in the different treatments . The results of this analysis are distributions of D/R values associated to each drug treatment , and are represented as histograms in Figure 7 . Gaussian fits from control cells show that the majority of lysosomes are preferentially localized at ratio 1 . 3 from cell center whereas the peak of Gaussian fits from 10 mM MβCD treated cells show a ratio of 1 . 2 ( Fig . 7A ) . The same pattern is seen upon treatment with higher concentrations of the drug ( Fig . 7B ) . Moreover , lysosomes at higher ratios , in other words more distant from the cell nuclei , are mostly found in control non-treated cells , with none or only a few found in MβCD treated cells ( Fig . 7A and B ) . On the other hand , no difference in lysosomal distribution was observed when cells were treated with 10 or 15 mM of HγCD as compared to control non-treated cells ( Fig . 7C and 7D ) . Finally , cholesterol replenishment after MβCD treatment was able to , at least in part , revert lysosomal dispersion to a pattern more similar to control cells ( Fig . 7E ) . Cumulative frequencies of lysosomes ( Fig . 7F ) from the histograms of figures 7A to E were plotted and analyzed using Kolmogorov-Smirnov statistical test . Statistically significant differences were only observed between 10 or 15 mM of MβCD treated and control non-treated cells . 10 and 15 mM HγCD or 15 mM of MβCD + WSC treated cells presented cumulative frequencies similar to control non-treated group . In order to prove that this rearrangement in lysosome distribution was not a consequence of cell surface area decrease upon cholesterol removal from plasma membrane , cells were treated or not with MβCD or its inactive analog , HγCD , as well as MβCD + WSC , and labeled with the plasma membrane stain , CellMask ( Invitrogen ) . Images were collected and cell surface area measured using the ImageJ software . No difference in cell surface area was found among the distinct groups ( Supplementary Fig . 1 ) .
Two different groups have previously demonstrated the participation of cholesterol and membrane rafts as “hot spots” for T . cruzi entry into host cells [22] , [23] . In 2007 Fernandes and co-workers showed that cholesterol and membrane rafts participate in the internalization of metacyclic trypomastigotes and extracellular amastigotes from two different strains , CL and G , into non-professional phagocytic cells . In this same year , Barrias and co-workers verified the participation of membrane rafts in the internalization of T . cruzi TCTs in phagocytic cells ( murine peritoneum macrophages ) . However , in both studies it was not clear how cholesterol and/or rafts participated in the process of parasite entry into host cells . In the present work we show the participation of cholesterol in T . cruzi TCT entry into non-professional phagocytic cells and , most importantly , the mechanism by which plasma membrane cholesterol interferes with parasite invasion into non-professional phagocytic cells . In order to investigate the participation of cholesterol in T . cruzi TCT invasion of non-professional phagocytic cells , we pre-treated cells with different concentrations of MβCD followed by invasion assays with T . cruzi . Cyclodextrins , like MβCD , are oligosaccharides constituted by glucopyranose units that are linked by α- ( 1-4 ) bonds [40] . These compounds are broadly used as liposoluble drug carriers since they are soluble in water and have a hydrophobic core in which non-soluble substances are transported [41] . β-cyclodextrins , especially MβCD , present a higher affinity for cholesterol as compared to the α and γ cyclodextrins , [42] . Labeling MβCD treated cardiomyocytes with Filipin III ( a fluorophore with high affinity for cholesterol ) confirmed the ability of the drug to remove cholesterol from cell membranes . As previously shown for the metacyclic trypomastigotes and extracellular amastigotes forms of T . cruzi , we showed that a reduction in host cell surface cholesterol decreases the rate of invasion of non-professional phagocytic cells by T . cruzi TCTs , even though these forms of the parasite present different surface molecules and consequently stimulate cells by distinct mechanisms [43] . This effect on TCT host cell entry was indeed due to cholesterol removal from host cell plasma membrane , since invasion assays with cells previously treated with HγCD , an inactive analog of MβCD which presents very low affinity for cholesterol , did not change the parasite invasion profile in comparison to control untreated cells . Also , host cell viability was not compromised after treatment as shown by a cell viability assay , confirming that the observed reduction in host cell invasion levels was not a result of cell loss due to drug treatment . Moreover , cholesterol replenishment after cell treatment with MβCD re-established invasion to control levels . Together , these results undoubtedly show that cholesterol is also important for T . cruzi TCT entry into cardiomyocytes and that this model can be used to investigate the role of cholesterol in this process . We found that the low invasion rate of T . cruzi into cholesterol-depleted cells was accompanied by a diminishment in lysosome recruitment , which is required for the formation of the parasitophorous vacuole . Lysosome recruitment and fusion has been shown to be essential not only for inducing parasite internalization , but also for holding T . cruzi inside host cells [3] , [44] . Fusion of lysosomes with host cell plasma membrane induced by T . cruzi leads to a compensatory endocytic pathway that drives parasites into cells [44] . Parasites are known to tightly interact with parasitophorous vacuolar membrane , probably through lysosomal integral membrane proteins such as LAMP [45] , [46] . Membrane fusion events , such as synaptic vesicle and lysosomal exocytosis as well as other types of vesicle secretion , are regulated by calcium and occur through a mechanism dependent on proteins from the SNARE complex [26] , [47] , [48] . It is well known from microscopy studies that these proteins concentrate in submicrometre-sized , cholesterol-dependent clusters , such as membrane rafts , at which sites vesicles fuse [27] , [28] . Since membrane rafts are cholesterol-enriched microdomains ( about 50% of total cellular cholesterol ) located in cell plasma membrane [42] and cholesterol removal from cell membranes induces changes in raft organization and function [49] , [50] , [51] , it is possible that the effect of cholesterol removal on T . cruzi entry was a consequence of the disruption of these microdomains . GM1 labeling , a known raft marker , has demonstrated that MβCD treatment of cardiomyocytes leads to changes in raft organization in these cells , suggesting a role not only for cholesterol but also for membrane raft microdomains in TCT's invasion of non-professional phagocytic cells . Raft disorganization , on the other hand , could alter membrane fusion events , by changing SNARE proteins distribution and/or function , disturbing the exocytic events regulated by these proteins . In fact , cholesterol removal led to massive non-regulated lysosomal exocytosis events , which occurred in the absence of calcium , suggesting that disruption of raft organization de-regulates lysosomal exocytosis . Corroborating this idea , it has been demonstrated for neuronal exocytosis that SNARE localization in rafts work as negative regulators of secretion and reducing SNAP 23 partitioning to raft sites enhanced vesicle exocytosis [52] . Interestingly , SNAP 23 is one of the SNARE complex proteins involved in lysosomal fusion events [26] . Similar exocytic events , triggered by treatment with MβCD , have already been demonstrated in other animal models . Zamir and Charlton ( 2006 ) [53] , analyzing neuromuscular junctions in crayfish , realized that treatment with 10 mM MβCD induced a 5-fold increase in the rate of spontaneous miniature excitatory post synaptic potentials ( mEPSPs ) , as a consequence of unregulated , calcium independent , synaptic vesicle fusion events . Other authors have also shown changes in vesicle secretion upon cholesterol removal from plasma membrane [38] , [54] , [55] , [56] . Recently , Chen and co-workers studying cells derived from a mouse model of Niemann-Pick disease ( a disorder characterized by a massive accumulation of lipids , including cholesterol , in the endosomal/lysosomal system ) have shown that treatment with hydroxypropyl-β-cyclodextrin ( HPβ-CD ) , a cyclodextrin similar to MβCD , leads to lysosomal exocytosis , as early as 15 minutes post exposure to the drug [57] . This result corroborates our data since cell incubation with MβCD also led to lysosomal exocytosis at early time points . However , contrary to what was observed by these authors , lysosomal exocytosis triggered by incubation with MβCD in cardiomyocytes is independent of extracellular calcium . It is still possible though that intracellular calcium is responsible for these exocytic events . Another possibility , since HPβ-CD and MβCD differ in their efficiency of extracting cell membrane cholesterol , is that the effect of these drugs on exocytosis might be different [42] . In fact , it has been shown that the effect of MβCD on spontaneous release of synaptic vesicles , generating mEPSPs in neuromuscular junctions , occurs in the absence of intracellular and extracellular calcium [53] . Finally , since our data shows that lysosomal exocytosis happened in the early stages of MβCD treatment , one could assume that a significant reduction in lysosomal reservoir occurred during the period of drug incubation . Quantitative analysis of lysosomal distribution in cells before and after cholesterol depletion showed that control cells have their lysosomal pool well distributed throughout the cell cytosol , with vesicles around the perinuclear and cell cortex area ( Fig . 6A , qualitative image ) . However , when cholesterol is sequestered by MβCD , only the lysosomes near the perinuclear area remain ( compare Fig . 6A and B ) , without a change in cell surface area upon treatment ( Figure S1 ) . Based on these results it is plausible to assume that cholesterol depletion evokes exocytosis of docked lysosomes localized near the cell cortex . Taken together , these data suggest the existence of two independent lysosomal pools ( one near the cell surface and another in the perinuclear area ) , which might be differentially regulated . The docked lysosomes near the cell surface would then represent the pool triggered by T . cruzi and therefore involved in the exocytic events that initiate its internalization process . Without enough lysosomes available at the cell surface for fusion and formation of the parasitophorous vacuole , T . cruzi entry is compromised . We cannot discard however that reduction in membrane cholesterol content and its consequent raft disorganization may also affect intracellular signaling pathways [58] , [59] , [60] . Therefore , receptors present in membrane rafts , which might be important for recognition and signal transduction during T . cruzi interaction and internalization into host cells , may have their functions attenuated or compromised and consequently affect parasite invasion rates . In this sense the diminishment in T . cruzi association with lysosomes observed upon treatment with MβCD could be also partly due to the compromised function of these receptors . Studies are being carried out to evaluate these possibilities . | Trypanosoma cruzi , is the etiological agent of a neglected tropical malady known as Chagas' disease , which affects about 8 million people in Latin America . 30–40% of affected individuals develop a symptomatic chronic infection , with cardiomyopathy being the most prevalent condition . T . cruzi utilizes an interesting strategy for entering cells: T . cruzi enhances intracellular calcium levels , which in turn trigger the exocytosis of lysosomal contents . Lysosomes then donate their membrane for the formation of the parasitophorous vacuole . Membrane rafts , cholesterol-enriched microdomains in the host cell plasma membrane , have also been implicated in T . cruzi invasion process . Since both plasma membrane and lysosomes collaborate in parasite invasion , we decided to study the importance of these membrane domains for lysosomal recruitment and fusion during T . cruzi invasion into host cells . Our results show that drug dependent depletion of plasma membrane cholesterol changes raft organization and induces excessive lysosome exocytosis in the earlier stages of treatment , leading to a depletion of lysosomes near the cell cortex , which in turn compromises T . cruzi invasion . Based on these results , we propose that cholesterol depletion leads to unregulated exocytic events of pre-docked lysosomes , reducing lysosome availability at the cell cortex and consequently compromising T . cruzi infection . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"biology",
"microbiology",
"molecular",
"cell",
"biology"
] | 2012 | Membrane Cholesterol Regulates Lysosome-Plasma Membrane Fusion Events and Modulates Trypanosoma cruzi Invasion of Host Cells |
Giardia duodenalis is the most common gastrointestinal protozoan parasite of humans and a significant contributor to the global burden of both diarrheal disease and post-infectious chronic disorders . Although G . duodenalis can be cultured axenically , significant gaps exist in our understanding of the molecular biology and metabolism of this pathogen . The present study employed RNA sequencing to characterize the mRNA transcriptome of G . duodenalis trophozoites in axenic culture , at log ( 48 h of growth ) , stationary ( 60 h ) , and declining ( 96 h ) growth phases . Using ~400-times coverage of the transcriptome , we identified 754 differentially transcribed genes ( DTGs ) , mainly representing two large DTG groups: 438 that were down-regulated in the declining phase relative to log and stationary phases , and 281 that were up-regulated . Differential transcription of prominent antioxidant and glycolytic enzymes implicated oxygen tension as a key factor influencing the transcriptional program of axenic trophozoites . Systematic bioinformatic characterization of numerous DTGs encoding hypothetical proteins of unknown function was achieved using structural homology searching . This powerful approach greatly informed the differential transcription analysis and revealed putative novel antioxidant-coding genes , and the presence of a near-complete two-component-like signaling system that may link cytosolic redox or metabolite sensing to the observed transcriptional changes . Motif searching applied to promoter regions of the two large DTG groups identified different putative transcription factor-binding motifs that may underpin global transcriptional regulation . This study provides new insights into the drivers and potential mediators of transcriptional variation in axenic G . duodenalis and provides context for static transcriptional studies .
Giardia duodenalis ( syn . G . lamblia or G . intestinalis ) is a gastrointestinal protozoan parasite , and a major cause of chronic infectious diarrhoea in the developed and developing world . G . duodenalis infects approximately one billion people world-wide , causing 200–300 million reported clinical cases each year [1] . G . duodenalis is proposed to account for ~15% of cases of childhood diarrhoea in developing countries [2] . High rates of chronic diarrhoea in the first two years of life is significantly associated with physical and cognitive 'stunting , ' and predisposes sufferers to a variety of adult-onset metabolic disorders [3] . In particular , infection with G . duodenalis is associated with post-infectious gastrointestinal disorders such as irritable bowel syndrome , chronic fatigue , and obesity [4 , 5] . Control of giardiasis depends primarily on chemotherapeutic treatment with one of two major drug classes: nitroheterocyclics ( e . g . , metronidazole ) and benzamidazoles ( e . g . , mebendazole ) [6 , 7] . Treatment failure rates as high as 30% are reported for these compounds [8 , 9] , and in vitro resistance to widely used chemotypes is documented in isolates from treatment-refractory patients ( reviewed in [8 , 10] ) . The recently reported increasing incidence of metronidazole treatment-failure in travellers returning to the United Kingdom [11] , and toxicity associated with most nitroheterocyclics [6] , highlight the need for continued development of anti-giardial drugs . This in turn requires a thorough understanding of the molecular biology of the parasite . Aside from its medical importance , G . duodenalis is thought to belong to one of the earliest eukaryotic lineages , and therefore serves as a useful model for studies of eukaryotic features such as secretory and organellar protein trafficking [12] , cellular differentiation [13 , 14] and RNA interference [15–17] . G . duodenalis can be cultured in complex , host cell-free media , which is a rarity among parasitic protists and of great advantage for conducting molecular research . Axenic culture provides an excellent system in which to explore the biology of G . duodenalis over time and in response to external stimuli , drug perturbation [18–21] and other stressors [22 , 23] . System-level transcriptomic investigations based on microarray or serial analysis of gene expression , have established that G . duodenalis trophozoites exhibit clear transcriptional responses to encystation medium [13] , protein folding stress [23] and the presence of intestinal epithelial cells [24 , 25] . More recently , RNA sequencing ( RNA-seq ) has been used to identify transcriptional start-sites [26] , 3’ un-translated regions and polyadenylation variants in mRNA [27] , and to compare transcription between different G . duodenalis assemblages [27] . RNA-seq has also recently been applied to investigate the transcriptional response to oxidative stress in trophozoites [28] , and to ultraviolet irradiation in trophozoites and cysts [29] . However , considering the complex nature of the standard culture medium ( TYI-S33 ) for G . duodenalis trophozoites , and variation between laboratories in how this medium is prepared , comparing studies is challenging , particularly given that each study represents a static time-point observation . Understanding how transcription varies over time in TYI-S33 medium is important to provide context to single time-point studies . In terms of the axenic growth of the parasite , such research can also provide insight into the changes in the metabolic behaviour and demands of G . duodenalis during different growth phases . A major challenge for genomic investigations of divergent organisms such as G . duodenalis relates to the vast numbers of functionally un-annotated gene products . Indeed , around 50% of the proteins predicted in this protist lack functional information . In lieu of in vitro characterization , computational protein structure-prediction approaches can provide substantial insight into the putative function of hypothetical proteins [30] . Here , we used RNA-seq coupled with structural homology-based protein annotation , to investigate the longitudinal transcriptional behavior of G . duodenalis assemblage A ( WB isolate ) trophozoites under standard laboratory conditions in TYI-S33 medium . This represents the first high-resolution , longitudinal transcriptional data set for this protist . We hypothesize that differential transcription will be evident between log , stationary , and declining growth phases , and that these changes will reflect the metabolic preferences of G . duodenalis and the pressures of resource exhaustion .
Giardia duodenalis trophozoites ( assemblage A , strain WB-1B; [31] ) were generously provided by Drs Jaqueline Upcroft and Peter Upcroft and maintained in axenic culture in filtered , complete modified TYI-S33 medium in close-capped t25 flasks ( Falcon ) according to standard protocols [32] . The growth kinetics of attached trophozoites was charted over 96 hours ( h; S1 Fig ) , from which the following growth phases were estimated: lag ( 0–24 h ) , log ( 24–60 h ) , stationary ( 60–72 h ) and declining ( 72–96 h ) . As attached and suspended populations exhibited generally similar growth dynamics , we focused on the attached population in order to enrich samples for viable trophozoites and minimize the risk of contamination with degraded mRNA from dead cells . In our hands the generation time of WB1B was 5 . 4 ±1 . 2 h during log phase . Samples for sequencing were generated on four different weeks as follows . Nine t25 flasks ( 64 mL total capacity ) were filled with 56 mL of medium , inoculated with 105 trophozoites from confluent t25 flasks , and incubated at 37°C . At 48 , 60 and 96 h after inoculation , three flasks were selected at random , and supernatant and suspended cells were discarded and replaced with ice-cold phosphate-buffered saline ( PBS ) . Flasks were incubated on ice for no more than 5 minutes to ensure detachment of trophozoites , and the suspensions were transferred to 50 mL falcon tubes and pelleted at 680 x g for 5 min at 4°C . Supernatants were discarded , and pellets were combined through re-suspension in 1 mL of PBS before transfer to a 1 . 5 mL Eppendorf tube . The suspension was pelleted ( 770 x g , 2 min , 22–24°C ) , re-suspended in 1 mL of TriPure reagent ( Roche ) , and stored at -80°C . RNA was extracted from TriPure reagent according to the manufacturer’s instructions within four weeks of sample preparation . The dried RNA pellet was re-suspended in reverse-osmosis deionized water ( H2O ) and treated with Turbo DNAse ( Ambion ) according to the manufacturer’s instructions . The DNAse-treated RNA was electrophoresed , and large and small subunits of nuclear rRNA bands were examined as a proxy for RNA integrity . RNA concentration was estimated by fluorometry ( Qubit ) and further quality control was performed using a BioAnalyzer ( Agilent ) . Polyadenylated RNA was purified from 10 μg of total RNA using Sera-mag oligo ( dT ) beads , fragmented to a length of 100–500 bases , reverse transcribed using random hexamers , end-repaired , and adaptor-ligated , according to the manufacturer's instructions ( Illumina ) . Ligated products ( ~300 bp ) were excised from agarose and PCR-amplified . Products were purified over a MinElute column ( Qiagen ) and paired-end sequenced ( 100 bp; non-normalised cDNA ) using the Ilumina HiSeq 2000 ( Yourgene Biosciences , Taiwan ) . Adapters were trimmed from raw reads using Trimmomatic [33] ( sliding window: 4 bp , minimum average PHRED quality: 20; leading and trailing: 3 bp; minimum read length: 40 bp ) , and overlapping read pairs were merged using SeqPrep ( downloaded 2 June 2014 https://github . com/jstjohn/SeqPrep ) with default parameters . The merged reads were combined with unpaired and non-overlapping paired reads from Trimmomatic output , and all were mapped as single-ended reads to the accepted G . duodenalis gene models ( assemblage A genome , WB strain , release 3 . 1; GiardiaDB . org; [27][34] ) , using RSEM [35] . Transcripts-per-million ( TPM ) for each gene were averaged across replicates from each growth phase , and used to rank genes according to relative transcriptional abundance . Expected counts for each gene were submitted to EBSeq [36] , incorporating median normalization , and DTGs were identified using a false discovery rate ( FDR ) of <0 . 05 . Fold-change in transcription between growth phases was calculated using the normalized mean expected counts output from EBSeq . As a further filter , only those genes with at least 10 mean expected counts in at least one growth phase were included in further analysis . Feature detection was calculated as a function of mapped read depth , using the counts module in QualiMap ( v1 . 0 ) with the–k 10 flag ( denoting a minimum mapped read threshold of 10 ) [37] . Saturation plots were displayed in Excel ( Microsoft ) . Heat maps were generated in R ( v3 . 0 . 2 ) using the heatmap module . KEGG BRITE terms associated with peptides in the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) database ( release 69 . 0 ) , were transferred to the closest homolog in G . duodenalis using BLASTp ( lower expect threshold of 10−5; [38] ) . Gene ontology ( GO ) terms for the predicted G . duodenalis proteome were retrieved from GiardiaDB . org; and sensitive structure-based homology searches were performed for DTGs annotated as ‘hypothetical’ or ‘deprecated , ’ and for peptides encoded by highly transcribed ( top 100 ) genes , using I-TASSER software ( v3 . 0; [30 , 39] ) . Briefly , I-TASSER generates putative three-dimensional structural models from amino acid sequences , incorporating predicted secondary structure and the consensus model from multiple threading programs , followed by iterative molecular dynamics simulation to minimize free energy [30] . The closest structural homolog available in the Research Collaboratory for Structural Bioinformatics Protein Data Bank ( RCSB PDB; rcsb . org ) , and consensus GO terms associated with the ten best structural matches , are then inferred for the putative model . Bar charts and box plots describing transcriptional abundance were generated using Excel ( Microsoft ) and Prism software ( GraphPad ) . For bar charts , mean normalized expected counts are plotted with standard error derived from pre-normalised expected counts for biological quadruplicates . Putative transcription factor ( TF ) -binding motifs in the promoter regions of DTGs were identified within 400 bp upstream of the start codon in non-overlapping ( i . e . , non-coding ) regions using DREME [40]; cf [41] . Promoters from a DTG group of interest were interrogated using promoters from another DTG group as the background . Interacting TFs for homologous motifs in yeast ( all available databases ) were predicted using TOMTOM [42] . For each motif , the density of the 5’ nucleotide position ( both forward , and reverse complement ) was calculated as a function of promoter length , and displayed together with a histogram of the corresponding promoter lengths using R . The coefficient of variation ( SD ÷ average; denoting variation in transcript abundance between biological replicates ) was calculated for all transcribed genes encoding variant-specific surface proteins ( VSPs ) . Pearson correlation was used to compare transcriptional abundance ( FPKM and RPKM ) values from independently generated transcriptomic data sets . Fisher exact tests were used to determine significantly over-represented ( i . e . , enriched ) KEGG BRITE terms within DTG groups . GO enrichment analysis was performed using the BinGO module in Cytoscape [43 , 44] by firstly providing a background of GO terms specific to G . duodenalis , incorporating all GO terms from GiardiaDB . org and those terms from I-TASSER with confidence scores ≥0 . 3 . The enriched Biological Process GO terms within DTG groups were then identified using Fisher exact testing ( FDR < 0 . 05 ) . To further minimize false positives in this analysis , resultant GO terms with fewer than ten associated genes in the background , were discarded . Fisher permutation tests were also used to compare the average transcription level of genes of interest between growth phases . Open reading frames for genes in this article , quoted according to GiardiaDB . org: GL50803_87577; GL50803_7195; GL50803_10403; GL50803_33769; GL50803_27266; GL50803_23756; GL50803_16568; GL50803_27266 .
GO enrichment analysis revealed significant over-representation of 113 ‘Biological Process’ terms in the down-regulated gene group , of which 49 were unique to this group , including ‘energy derivation by oxidation of organic compounds , ’ ‘signaling , ’ and ‘locomotion’ ( S3 Table ) . Enriched KEGG BRITE annotations included the NEK kinase family , threonine peptidases , ubiquitin conjugating enzymes ( E2 ) and the T1 proteasome family ( S4 Table ) . The down-regulated genes annotated with oxidoreductase activity ( GO:0055114 and/or GO:0016491 ) included glutamate synthase , 6-phosphogluconate dehydrogenase , alcohol dehydrogenase and nitroreductase-1 as well as a number of hypothetical proteins with predicted structural similarity to glutamate synthase , hydroxylamine oxidoreductase , thiol-cycling enzymes ( protein disulfide isomerase and thiol:disulfide protein dsbA ) , a ferretin-like Dps-like peroxide resistance protein and a nickel-binding superoxide dismutase ( Table 1 ) . Further investigation of down-regulated genes associated with ubiquitin-conjugating and protease activity , revealed four ubiquitinylating enzymes ( two paralogs of a 28 . 4 kDa E2 , a 17 kDa E2 and the ubiquitin ligase UBC3 ) , and four beta-subunits of the 20S proteasome ( S4 Fig ) . We identified an annotated and a hypothetical glutamate synthase in the down-regulated gene group . Interrogation of putative structures for each protein revealed that the hypothetical glutamate synthase ( GL50803_87577 ) was most structurally similar to a bacterial glutamate synthase beta sub-unit ( PDB code: 2VDC ) , whereas surprisingly , the annotated glutamate synthase ( GL50803_7195 ) was most similar in structure to trimethylamine ( TMA ) dehydrogenase from Methylophilus methylotrophus ( PDB code: 1DJQ ) . Putative homologs of a TMA sensor protein , a Rap modulator protein ( C-terminal domain only ) , and a phosphotransfer protein were also present in the down-regulated gene group . The former two are present as single-copy orthologs in other G . duodenalis assemblages ( B and E; GiardiaDB . org ) . These findings suggest the presence of a two-component-like signaling system in G . duodenalis . Histidine kinases are integral to two-component systems , and further interrogation of putative structures for hypothetical proteins revealed putative histidine kinase activity for GL50803_10403 ( Fig 2 ) . The up-regulated gene group was enriched for 107 ‘Biological Process’ GO terms ( 43 unique ) , including ‘gene silencing , ’ ‘mitosis , ’ and ‘microtubule-based process’ ( S3 Table ) . Enriched KEGG BRITE annotations in this group related to carbon fixation , oxidoreductases and the cytoskeleton ( S4 Table ) . Further investigation of oxidoreductase-related genes in the up-regulated group revealed two pyruvate:ferredoxin oxidoreductase ( PFOR ) paralogs , and a hypothetical protein with structural homology to a GntR transcription factor ( S2 Table ) . To contextualize the oxidoreductase-related genes that were differentially transcribed between growth phases , we investigated the transcription of other genes involved in the antioxidant system and glycolysis . Progressive but non-significant decreases in transcriptional abundance were identified for thioredoxin reductase , three putative thioredoxins , three peroxidredoxins and five thioredoxin domain-containing protein disulfide isomerases . Similar patterns were observed for transcripts encoding the oxygen-consuming enzymes NADH oxidase ( GL50803_33769; [45] ) and flavodiiron protein [46 , 47] ( Fig 3A ) . The collective transcription of annotated antioxidant enzymes was significantly lower in the declining phase compared to earlier phases ( Fisher permutation test , 1000 iterations , p = 0 . 037; Fig 3A ) . The presence of genes encoding glycolytic oxidoreductases in both down- and up-regulated gene groups suggested changes in central carbon metabolism during in vitro growth . A hexose transporter , and the non-oxidative glycolytic enzyme glucose-6-phosphate isomerase , were also down-regulated over time . Indeed , transcription of the majority of genes representing glycolytic enzymes in both the pentose phosphate and Embden-Meyerhoff pathways , was lowest in the declining phase ( Fig 4 ) . Conversely , transcription of a number of glycolytic enzymes downstream of pyruvate increased over time . In addition to significant up-regulation of PFOR-coding genes in the declining phase , genes encoding one of three ferredoxins ( GL50803_27266 ) and a glutamate dehydrogenase also showed greatest transcriptional abundance in this phase ( Fig 4 ) . Given the robust transcriptional changes in the antioxidant system , we compared the transcriptome for each growth phase with independently generated transcriptomes for WB strain trophozoites cultured under aerobic and anaerobic conditions , as reported by Ma’ayeh et al [28] . When all transcribed genes were considered , our data correlated most closely with the anaerobic transcriptional profile . The transcriptional abundance of annotated antioxidant genes at log and stationary phase , however , correlated best with the aerobic transcriptome , and in the declining phase we observed a shift to stronger correlation with the anaerobic transcriptome ( S5 Fig , panels A & B ) . When glycolytic genes were investigated , there was a trend of increasing correlation with the anaerobic profile over time , and the correlation with the aerobic profile declined after stationary phase . Further dissection of this result revealed separate underlying trends , wherein the genes upstream of pyruvate diverged markedly from the aerobic profile after stationary phase , but little change was seen in genes downstream of pyruvate ( S5 Fig , panels C-E ) . Eleven genes encoding VSPs were identified in the up-regulated group as opposed to only two such genes in the down-regulated group . Further investigation of the transcription levels of 193 transcribed VSPs in our data revealed seven prominently transcribed genes , whereas the rest of the population was relatively lowly transcribed ( S6 Fig ) . Aggregate VSP transcription increased over time , driven by increases in the seven most highly transcribed genes ( Fig 3B and 3C ) , which were consistently highly transcribed in all replicates ( S7 Fig ) . Interestingly , a marked decrease in inter-experimental variation was evident for highly transcribed VSPs in the declining phase relative to earlier phases ( S8 Fig ) . These results prompted investigation of the other major class of membrane proteins in G . duodenalis , the 60 high-cysteine membrane proteins ( HCMPs ) , whose aggregate transcription in contrast to the VSPs , decreased progressively over time , driven by declining abundance of the most highly transcribed gene quartile ( Fig 3B ) . We hypothesize that DNA-binding transcription factors ( TFs ) may mediate the dynamic variation in gene transcription evident in G . duodenalis during axenic growth . The down-regulated gene group did not contain annotated TFs , but did include a putative homolog of the Neisseria gonorrhoeae MtrR repressor among three hypothetical protein-coding genes with GO annotations relating to transcriptional regulation ( GO:0001071; Fig 2 and S2 Table ) . The up-regulated group contained an E2F-like TF ( GL50803_23756; [48] ) , a putative TF ( GL50803_16568 ) and four hypothetical proteins annotated with GO:0001071 including the aforementioned GntR homolog ( S2 Table ) . In order to identify putative TF-binding motifs within the promoter regions of genes in the two large DTG groups , we used a similar method to Xu et al . [41] for analysis of the related diplomonad Spironucleus salmonicida . Totals of 283 and 178 non-overlapping promoter regions of 8 bp to 400 bp were available for the down- and up-regulated gene groups , respectively . The motif AWTTW was significantly over-represented in the promoters of down-regulated genes relative to promoters of up-regulated genes , and the motif GRGGTM was over-represented in the up-regulated gene promoters in the same way ( Table 2 ) . After correction for multiple comparisons , no significant matches to known TF-binding motifs in yeast were found for either motif using TOMTOM . An analysis of the position of each motif within promoter regions revealed robust positional conservation for the AWTTW motif within 100 bp of the start codon , but not for GRGGTM . There was no evidence to suggest that these results are biased by the proportion of non-overlapping genes available for analysis in each group ( 76% and 72% , respectively ) ; moreover , the motif positional densities remain when only the ( artificially truncated ) 400 bp promoter regions are considered , indicating little effect of promoter length on motif location ( Fig 5 ) .
G . duodenalis features an elaborate antioxidant system that utilizes NAD ( P ) H to reduce intracellular oxygen and associated reactive oxygen species ( ROS ) . This system both protects iron-containing enzymes , such as PFORs , from oxidative inactivation [51 , 52] and allows maximal ATP production from glycolysis [53] . As a ‘microaerophile’ , G . duodenalis thrives under dissolved oxygen ( dO2 ) concentrations between 5–25 μM , above which dO2 becomes cytotoxic [54] . In accordance with the standard trophozoite maintenance protocol [32] , we did not sparge dO2 from the TYI-S33 medium for this experiment , and medium is expected to contain dO2 , particularly during the log phase of trophozoite growth . The abundant transcription of genes encoding peroxiredoxin , oxygen-consuming NADH oxidases , thioredoxins and other protein disulfide isomerases is consistent with previous reports [27–29 , 55] . However , the dynamics of antioxidant transcription in G . duodenalis under standard culture conditions has not been studied previously . It is likely that the antioxidant system is constitutively highly transcribed to manage transient increases in gut dO2 [28 , 56] . Nevertheless , against this background , we observed down-regulation of the vast majority of antioxidant-coding genes over time , suggesting global regulation of antioxidant transcription . A number of our results indicate that G . duodenalis trophozoites may be under oxidative stress at early stages of in vitro culture . Firstly , although it was not possible to directly measure dO2 without perturbing the standard culture system , correlations with independently generated transcriptional profiles for trophozoites under aerobic and anaerobic conditions [28] can be considered as a proxy measure of dO2 tension . At successive growth phases , antioxidant and glycolytic gene transcription shifted from resembling the aerobic transcriptional profile , to the anaerobic profile . The correlation was influenced more strongly by antioxidant transcription than glycolytic enzyme transcription , however glycolytic genes upstream of pyruvate appeared most sensitive to changes in oxygen tension . This strongly suggests that oxygen tension decreases progressively in axenic culture . Furthermore , genes that were down-regulated in our data at the declining phase , encode putative antioxidant proteins , such as an iron-independent superoxide dismutase , and a ferritin-like ( iron-sequestering ) protein . TYI-S33 medium is supplemented with ammonium ferric citrate , as iron is essential for the activity of enzymes such as PFOR . However iron can also react with dO2 to generate ROS . Thus the greater transcriptional abundance of putative iron-sequestering and iron-independent antioxidant proteins at early growth phases , may represent a response to iron- and oxygen-induced oxidative stress . In addition , PFOR enzymes are sensitive to oxidative inactivation [51 , 52 , 57] , and both PFOR paralogs showed a significant increase in transcription in the declining phase , which is consistent with lower oxygen tension . Lastly , the greater abundance of transcripts encoding elements of the ubiquitin system and proteasomal components at earlier phases , may reflect heightened turn-over of oxidized proteins [58] . However as the proteasome is also important for the demands of protein folding in actively dividing cells , this result requires further investigation . In the context of global changes in antioxidant transcription , and a suggested decline in dO2 , we observed inverse transcriptional patterns for the high-cysteine membrane proteins ( HCMPs ) , and variant-specific surface proteins ( VSPs ) , which may indicate differential sensitivity to dO2 . HCMPs contain disulfide motifs that are common in antioxidant proteins and can both oxidize and reduce substrates such as misfolded proteins , and reduce ROS [59] . HCMPs localize to the nuclear envelope , endoplasmic reticulum , and possibly to the trophozoite plasma membrane [24 , 59] . The greater aggregate transcription of HCMPs early during in vitro growth might relate to protecting membranes from peroxidation [59] . Conversely , aggregate VSP transcription increased over time , which was largely due to progressively greater transcription of seven highly abundant VSP-coding genes , rather than induction of new genes . G . duodenalis trophozoites transcribe multiple VSP-coding genes , all but one of which are degraded by RNA-interference [17] , and thus only a single VSP gene product is displayed on the trophozoite membrane at any time . Our results support previous reports that WB trophozoite populations express relatively few VSPs [60] , which may be due to slower VSP turnover in this strain [61] . Specific VSPs are proposed to modulate trophozoite sensitivity to host defenses such as intestinal proteases [62] , and VSP suppression is associated with nitroimidazole resistance [19] . Given that the variation in VSP transcription between replicates is particularly low for highly transcribed VSPs in the declining phase , it would be interesting to test whether VSPs are under selection in TYI-S33 medium , or whether the transcriptional increase merely reflects , for example , lower competition from HCMPs for membrane occupancy . The functionally reduced mitochondria , or mitosomes , of G . duodenalis do not contain enzymes for the tricarboxylic acid cycle or oxidative phosphorylation [34] , and this protist is dependent on glycolysis and fermentative metabolism to generate energy from glucose . ATP is generated by direct phosphorylation of AMP and ADP [63] . Electrons liberated during glycolysis are accepted by NAD and NADP , forming NAD ( P ) H , which must be re-oxidized . Under anaerobic conditions , pyruvate is diverted to ethanol to regenerate NAD , whereas NADP is regenerated through a ‘shunt’ incorporating glutamate dehydrogenase and alanine aminotransferase . Conversely , in the presence of dO2 , oxidoreductases in the antioxidant system consume NAD ( P ) H to neutralize dO2 , ROS , and oxidized biomolecules . Under these conditions , pyruvate is not required for NAD ( P ) regeneration , and can be further oxidized to acetate . This ‘micro-aerobic’ metabolism maximizes the ATP that is generated from glycolysis . Here , we observed a trend of decreasing transcriptional abundance for the majority of genes encoding enzymes in the Embden-Meyerhoff and pentose phosphate glycolytic pathways , that convert glucose to pyruvate ( Fig 4 ) . TYI-S33 medium contains very high glucose concentrations ( 55 mM ) [32] , and thus it is highly unlikely that glucose is exhausted at the declining phase . Instead , the apparent down-regulation of glycolytic pathways may be due to declining dO2 availability , which could limit the efficiency of glycolysis . If glycolysis is progressively down-regulated as dO2 declines , G . duodenalis may rely on alternative energy sources . This protist is capable of converting arginine , aspartate and alanine to pyruvate [64 , 65] . The arginine dihydrolase pathway provides a ready source of ATP in G . duodenalis and is highly transcribed and stable across all time points studied here . In contrast to glycolysis , which must be coupled to NAD ( P ) + regeneration mechanisms , the catabolism of aspartate to pyruvate is effectively redox-neutral in that it does not generate excess NAD ( P ) H [65] . We observed progressive , but non-significant increases in transcription of two genes involved in aspartate catabolism ( Fig 4 ) , which may reflect greater reliance on aspartate for energy as dO2 declines . Significant up-regulation of PFORs at the declining phase , and the concomitant down-regulation of alcohol dehydrogenase-coding genes , suggests that a substantial amount of pyruvate is converted to acetate for ATP production even under limited dO2 availability ( Fig 4 ) . This is supported by reports of acetate production under complete anaerobiasis in G . duodenalis [64] , indicating that pyruvate flux through acetyl-CoA synthase is a resilient mechanism of ATP generation . The identification of a putative trimethylamine ( TMA ) dehydrogenase and elements of a putative TMA-NO sensing system ( discussed below ) in the down-regulated genes , raise the possibility that G . duodenalis can utilize TMA-NO as a terminal electron acceptor . TMA-NO metabolism has been demonstrated for a variety of gut commensals [66] , and the acquisition of key metabolic genes in G . duodenalis via horizontal gene transfer is well documented [34 , 67 , 68] . Lastly , the significant up-regulation of glycerol kinase at the stationary and declining phases relative to log phase , could indicate a glycerol-dependent ATP generation pathway in G . duodenalis . The metabolically similar protist Entamoeba histolytica has been shown to divert glycolytic intermediates to glycerol when central glycolytic enzymes are experimentally inactivated , and glycerol kinase is suggested to function in ATP generation [69] . Thus , it would be interesting to investigate the potential of this ‘glycerol shunt’ as an alternative source of ATP in G . duodenalis . The major shift in transcription between the log and stationary , and the declining growth phases , is likely to involve signaling between cytosolic proteins and nuclear transcription factors . Intriguingly , the observed transcriptional down-regulation of genes related to glycolysis might be mediated by redox-dependent TFs . A putative GntR homolog is up-regulated at the declining phase . In Corynebacterium glutamicum , GntR is reported to repress transcription of the gene encoding 6-phosphogluconate dehydrogenase , which is associated with the production of NADPH in the pentose-phosphate pathway . Notably , the gene encoding 6-phosphogluconate dehydrogenase is transcriptionally down-regulated at the declining phase in our data-set , and as mentioned above , transcription of glycolytic genes upstream of pyruvate , such as 6-PGDH , seem to vary more greatly in response to changes in dO2 tension ( S5 Fig , panel D ) . Taken together , these findings suggest that declining dO2 may inhibit glycolysis and lead to the accumulation of intracellular NAD ( P ) H . The apparent metabolic shift away from glycolysis may be mediated , at least in part , by redox-sensitive TFs such as the GntR homolog . Conserved kinases have been classified in G . duodenalis , many of which localize to the cytoskeleton and might participate in regulation of cell structure or motility . Other kinases , particularly within the massively expanded NEK kinase family , are predicted to lack catalytic activity [70] . Although complete signaling pathways have not been resolved in G . duodenalis , here we have identified several differentially transcribed structural homologs of proteins that participate in two-component signal transduction . Two-component systems feature histidine kinases and associated sensor domains that detect changes in cytoplasmic or extracellular environmental conditions . Conformational changes in the sensor domain induce histidine autophosphorylation proximal to the kinase domain , and the charged phosphate is subsequently transferred to an effector protein either directly , or via phosphotransferase intermediates . In many cases , the effector translocates to the nucleus to elicit a transcriptional response [71] . Two-component systems are prevalent in bacteria , fungi , plants and free-living protozoa [72] , but are little documented in parasitic protozoa and reportedly absent from Plasmodium falciparum [73–75] . The putative sensor protein in our data is structurally similar to a transmembrane protein that detects trimethylamine ( TMA ) in the bacterial periplasm . The predicted G . duodenalis structure is truncated however , which may indicate a role in intra-membrane or cytosolic chemotaxis . We also identified a histidine kinase homolog , which could represent a kinase class that was previously thought to be absent in G . duodenalis [70] . Furthermore , the down-regulated gene group included a gene encoding a hypothetical protein with putative structural homology to the MtrR repressor , a TF that is regulated by two-component signaling in Neisseria gonorrheae [76] ( Fig 2 ) . Although we did not identify a dimerization domain in the putative histidine kinase or a candidate for the response regulator , the sensor domain , Rap modulator and MtrR homologs are conserved between assemblages B and E of G . duodenalis , emphasizing the functional importance of these gene products among members of the species complex ( Fig 4 ) . Given the absence of two-component systems from metazoans [74] , this putative pathway warrants detailed investigation as a possible target for chemotherapeutic intervention . In further support of coordinated transcriptional regulation , we performed the most comprehensive and sensitive promoter motif search on this species to date , and identified motifs that are enriched in the promoter regions of down- and up-regulated DTG groups . The motif enriched in the down-regulated group ( AWTTTW ) , occurs close to the translation start codon and is likely to be related to the AT-rich transcription initiator motifs reported in previous studies [26 , 77 , 78] . At present , little is known about TFs in G . duodenalis other than those involved in inducing encystation [48 , 79–82] . It is conceivable that , in the presence of abundant nutrients , transcription is relatively tightly regulated via TF binding to initiator regions . Subsequently , in the declining phase when trophozoites appear to be metabolically stressed , and display physiological changes such as a loss of cytoadherence , less energy might be expended on transcriptional regulation . The GRGGTM motif , which is enriched in the promoters of up-regulated genes , exhibits little positional conservation , perhaps due to the relatively low number of positive promoter sequences ( 64 vs 209 for the AWTTTW motif ) . Although neither the of the motifs identified in down- and up-regulated genes matched to known TF-binding motifs in yeast , the MtrR and GntR putative TFs identified in these groups are worthy candidates for further investigation , which could link redox/metabolite sensing and transcriptional regulation . By characterizing the dynamics of the mRNA transcriptome of axenically cultured G . duodenalis trophozoites across growth phases , we identified major changes in gene transcription that relate to central carbon metabolism and the antioxidant system . We also identified a putative signaling pathway and promoter motifs upstream of DTGs that might contribute to transcriptional regulation . We show that transcriptional behaviour of G . duodenalis trophozoites differs markedly over time in axenic culture , likely reflecting the exploitation and depletion of essential nutrients in a closed culture system . Importantly , the present data indicate that culturing G . duodenalis under micro-aerobic or entirely anaerobic conditions ( through sparging medium ) is likely to have a significant impact on transcriptional behaviour , particularly in relation to the oxidative stress response . Therefore , we suggest that the modularity of antioxidant transcription in this protist be further tested under precisely defined atmospheres , together with metabolomic profiling . The consistently high transcription of these pathways during log and stationary phases—when trophozoites are typically harvested for experimentation—is also relevant for contextualizing single time-point studies in these parasites . Consistent with previous findings that G . duodenalis exhibits specific stress responses and considerable transcriptional flexibility , the present study indicates that major shifts in transcription in G . duodenalis might be regulated at least in part through key transcription factors ( i . e . , MtrR , GntR ) , and a number of distinct motifs consistent with TF-binding sites , that are enriched in the promoter regions of down- and up-regulated DTGs respectively . The possible role of a two-component-like signal transduction system is particularly interesting and could link cytosolic redox or metabolite sensing and transcriptional changes . This work has been greatly enhanced by the large-scale prediction of putative structures and structural homologs for hypothetical and deprecated proteins , among which were novel antioxidant and signaling proteins among many others . This approach has great potential for illuminating the functions of vast numbers of under-annotated and un-annotated gene products in other important pathogens . The present study also provides a starting point for re-examination of the constituents of the standard trophozoite culture medium , and a reference against which future studies of targeted alterations could be compared . For example , the reaction of dissolved oxygen ( dO2 ) with iron may be a source of oxidative stress at early phases during in vitro culture of G . duodenalis trophozoites , and thus concentrations of ammonium ferric citrate and ascorbic acid might need to be reconsidered , particularly as work in other systems has linked ascorbic acid with intracellular iron concentrations [83] . A major finding is that glucose is likely to be in excess in the standard culture media , but its utilization as a carbon source might be limited by the availability of dO2 , inducing trophozoites to rely on less efficient energy generation pathways in the declining phase . In the future these findings and the detailed longitudinal transcriptomic information presented here , should be used in conjunction with targeted metabolomic investigations in G . duodenalis , with the aim of creating a completely defined medium . Whereas the present study has used the G . duodenalis assemblage A genome strain ( WB ) , single time-point profiling of assemblages B [84] and E [85] indicate different transcriptional patterns , which could result from different genomic organization [27 , 60] . Therefore , similar longitudinal transcriptional investigations of other assemblages are required to better characterize the degree of transcriptional flexibility and the drivers and mediators of transcriptional responses across the species . This work should facilitate a better understanding of the transcriptional flexibility and metabolic preferences of G . duodenalis under standard culture conditions , and contribute to the development of a completely defined medium for more refined investigations into the biology of this important model eukaryote and pathogen . | Giardia is the most common gastrointestinal protozoan parasite of humans . This parasite causes diarrheal disease and is correlated with post-infectious conditions such as irritable bowel syndrome . In the absence of a vaccine , treatment is limited to drugs such as metronidazole , against which clinical resistance is reported . Effective control of Giardia requires a detailed understanding of its biology , and in turn , complete characterization of the standard in vitro culture system . Using RNA sequencing assisted by informatics to functionally annotate hypothetical proteins , we investigated transcriptional changes in axenic Giardia trophozoites at three growth phases over 96 hours . We found two large groups of differentially transcribed genes that indicate changes in the antioxidant system and central carbon metabolism over time . A putative novel signaling pathway may act together with putative transcription factor-binding motifs to regulate these transcriptional changes . Our results suggest that dissolved oxygen in Giardia culture medium may cause oxidative stress early during in vitro growth and that oxygen depletion may limit the efficiency of glycolysis in the declining phase . This work enhances our understanding of the transcriptional flexibility and metabolism of Giardia in vitro . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Time-Dependent Transcriptional Changes in Axenic Giardia duodenalis Trophozoites |
In this study a new computational method is developed to quantify decision making errors in cells , caused by noise and signaling failures . Analysis of tumor necrosis factor ( TNF ) signaling pathway which regulates the transcription factor Nuclear Factor κB ( NF-κB ) using this method identifies two types of incorrect cell decisions called false alarm and miss . These two events represent , respectively , declaring a signal which is not present and missing a signal that does exist . Using single cell experimental data and the developed method , we compute false alarm and miss error probabilities in wild-type cells and provide a formulation which shows how these metrics depend on the signal transduction noise level . We also show that in the presence of abnormalities in a cell , decision making processes can be significantly affected , compared to a wild-type cell , and the method is able to model and measure such effects . In the TNF—NF-κB pathway , the method computes and reveals changes in false alarm and miss probabilities in A20-deficient cells , caused by cell’s inability to inhibit TNF-induced NF-κB response . In biological terms , a higher false alarm metric in this abnormal TNF signaling system indicates perceiving more cytokine signals which in fact do not exist at the system input , whereas a higher miss metric indicates that it is highly likely to miss signals that actually exist . Overall , this study demonstrates the ability of the developed method for modeling cell decision making errors under normal and abnormal conditions , and in the presence of transduction noise uncertainty . Compared to the previously reported pathway capacity metric , our results suggest that the introduced decision error metrics characterize signaling failures more accurately . This is mainly because while capacity is a useful metric to study information transmission in signaling pathways , it does not capture the overlap between TNF-induced noisy response curves .
Each individual cell receives signals from the surrounding environment and is supposed to respond properly through a variety of biochemical interactions among its signaling molecules . Single cell studies and modeling approaches have emerged in recent years [1 , 2 , 3] , to understand the biochemical processes in each individual cell , as opposed to a large population of cells and their average behavior . Due to signal transduction noise , a cell can respond differently to the same input , which may result in incorrect ( unexpected ) cell decisions and responses [2] . Upon providing an input signal , however , it is not clear whether the cell is going to make a correct decision or not . Due to the random nature of the transduction noise , this decision making becomes somewhat probabilistic [2] . Here we introduce a method for characterization and quantification of decision making processes in cells , using statistical signal processing and decision theory concepts [4] used in radar and sonar systems . The basic goal of such systems is the ability to correctly decide on the presence or absence of an object . For example , in a radar system it is of interest to decide if there is an object transmitting a constant signal , while noise is present . If the received signal is much stronger than noise , the system can correctly declare the presence of the object . However , if the received signal is much weaker than noise , the system will miss the presence of the object . This erroneous decision is called a miss event . The radar system can make another type of erroneous decision , called a false alarm event , where there is no object but noise misleads the system to falsely declare the presence of an object . A mathematical model for this example [4] , including received signal and noise models , the decision making algorithm , probabilities for making incorrect decisions and some numerical results are presented in Materials and Methods . To explain the method in a practical way and in the context of molecular computational biology , we use the tumor necrosis factor ( TNF ) signaling pathway [2] which regulates the transcription factor nuclear factor κB ( NF-κB ) ( Fig 1A ) . NF-κB is a nuclear transcription factor that regulates numerous genes which play important roles in cell survival , apoptosis , viral replication , and is involved in pathological processes such as inflammation , various cancers and autoimmune diseases . In the TNF signaling pathway ( Fig 1A ) , the molecule A20 has an inhibitory feedback effect , whereas TRC stands for the TNF receptor complex [2] . TNF is a cytokine that can mediate both pro-apoptotic and anti-apoptotic signals [5] . In wild-type cells and upon binding of TNF ligands , NF-κB translocates to the nucleus , temporarily increasing the level of nuclear NF-κB . NF-κB activation rescues the cell from apoptosis . Then due to the negative feedback of A20 , the nuclear NF-κB level decreases . This short period of NF-κB activity is sufficient to activate transcription of the so called early genes , including numerous cytokines and its inhibitor A20 . In A20-deficient cells , the level of nuclear NF-κB remains relatively high for several hours . Loss or mutation of A20 can result in chronic inflammation and can promote cancer [6 , 7] . The signal transduction noise considered in our analysis encompasses all factors that make cell responses to the same signal variable or heterogeneous . In reference [3] it is demonstrated that both intrinsic and extrinsic noise contribute to the transduction noise in the NF-κB pathway . Extrinsic noise results from the fact that at the time of stimulation , cells are not identical and may have different levels of TNF receptors and other components of the signal transduction cascade . Intrinsic noise , on the other hand , results from the randomness of the biochemical reactions that involve a small number of molecules .
Recent information theoretical analysis of single cell data has demonstrated that in the TNF signaling pathway , cell can only decide whether TNF level at the system input is high or low [2] . In other words , based on the nuclear NF-κB level , cell can only tell if there is high TNF level at the input or not [2] . During this process , we formulate that cell can make two types of incorrect decisions: deciding that TNF is high at the system input whereas in fact it is low , or missing TNF’s high level when it is actually high . These two incorrect decisions can be called false alarm and miss events , respectively , similarly to the terminology used in radar and sonar [4] . The likelihood of occurrence of these incorrect decisions depends on the signal transduction noise . To understand how cell makes a decision on whether TNF is high or low , we first studied two TNF concentrations of 8 and 0 . 0021 ng/mL , respectively ( other TNF levels are discussed later ) . The histograms representing NF-κB responses of hundreds of cells to each TNF stimulus after 30 minutes are shown in Fig 1B . By using a probability distribution such as Gaussian ( Fig 1C ) ( see Materials and Methods ) for histograms , we specified the regions associated with incorrect decisions ( Fig 1C ) ( see Materials and Methods ) . These regions are determined by the optimal decision threshold obtained using the maximum likelihood principle4 ( see Materials and Methods ) , which simply indicates that the best decision on some possible scenarios is selecting the one that has the highest likelihood of occurring [4] . The area to the right of the decision threshold under the low TNF response curve is the false alarm region ( Fig 1C ) , meaning that nuclear NF-κB level could be greater than the threshold due to the noise , which falsely indicates a high level of TNF at the system input . The size of this shaded area specifies PFA , the false alarm probability . On the other hand , the area to the left of the decision threshold under the high TNF response curve is the miss region ( Fig 1C ) , meaning that due to the noise , nuclear NF-κB level could be smaller than the threshold , which results in missing the presence of high TNF level at the system input . The size of this shaded area is PM , the miss probability . Using the single cell experimental data we calculated PFA = 0 . 04 and PM = 0 . 1 ( see Materials and Methods ) . The higher value for PM can be attributed to the broader response curve when TNF is high ( Fig 1C ) . The overall probability of error Pe for making a decision is given by Pe = ( PFA + PM ) /2 = 0 . 07 ( see Materials and Methods ) , which is the average of false alarm and miss probabilities . We also collected the histograms of NF-κB responses of hundreds of cells to each TNF stimulus after 4 hours ( Fig 1D ) , which seem to have more overlap , compared to the response histograms collected at 30 min . This can be better understood by looking at the two response curves and the larger false alarm and miss regions ( Fig 1E ) . In fact , we observed higher values for false alarm and miss probabilities , i . e . , PFA = 0 . 2 and PM = 0 . 29 ( see Materials and Methods ) . These higher values for false alarm and miss probabilities , as well as the higher overall probability of error Pe = ( 0 . 2 + 0 . 29 ) /2 = 0 . 245 can be due to the negative feedback of A20 ( Fig 1A ) , which reduced the level of nuclear NF-κB in 4 hours , when TNF was high ( notice the considerable shift of the TNF-high response curve to the left that we observe in Fig 1E , compared to Fig 1C ) . To understand the decision making process based on both early and late responses , we computed ( see Materials and Methods ) high and low TNF joint response curves of the nuclear NF-κB at 30 minutes and 4 hours ( Fig 1F ) . The top view of the response curves ( Fig 1G ) shows that while high and low TNF concentrations produce relatively distinct distribution patterns in the early response domain , they have a higher degree of overlap in the late response domain . Using a more sophisticated approach to determine decision thresholds and decision probabilities based on joint early and late response data ( see Materials and Methods ) , we calculated PFA = 0 . 03 , PM = 0 . 1 and Pe = 0 . 065 . These results turned out to be about the same as early decision probabilities , i . e . , PFA = 0 . 04 , PM = 0 . 1 and Pe = 0 . 07 . It appears that in this signaling pathway , maximum likelihood decisions based on joint early/late events and early event alone provide the same finding on whether TNF level at the system input is high or low . In the presence of abnormalities in a cell , such decision making processes can significantly change , compared to a wild-type cell . For example , in the absence of A20 , a cell is unable to inhibit the TNF-induced NF-κB response [2 , 8] . Under this condition , response curves of hundreds of A20-/- cells to high and low TNF levels after 30 minutes ( Fig 2A ) show significant overlap , compared to the response of wild-type cells ( Fig 1C ) . This is because the negative feedback was no longer present in A20-/- cells , which resulted in the broadening of the TNF-low response curve and the increase in its mean value ( Fig 2A ) . Therefore , the false alarm and miss regions in A20-/- cells turned out to be much larger ( Fig 2A ) , for which we computed PFA = 0 . 37 and PM = 0 . 15 ( see Materials and Methods ) . Both false alarm and miss probabilities were greater than those of wild-type cells ( Fig 2B ) . In biological terms , the higher false alarm rate in this abnormal TNF signaling system means perceiving more signals which in fact do not exist at the system input , whereas the higher miss rate indicates that it is more likely to miss signals that actually exist . Using the response curves after 4 hours in A20-/- cells ( Fig 2C ) , we computed PFA = 0 . 73 and PM = 0 . 12 ( see Materials and Methods ) . The increase in PFA and decrease in PM , compared to the wild-type cells , reflected a more profound effect of the lack of negative feedback after 4 hours in A20-/- cells , which resulted in an increase in the mean nuclear NF-κB level for both low and high TNFs ( Fig 2C ) . Computations using both early and late response data ( see Materials and Methods ) revealed that in this signaling pathway , decisions based on joint early/late events and early events in A20-/- cells provide about the same results and probabilities on whether TNF level at the system input is high or low ( Fig 2B ) . To study the impact of different TNF concentrations on cell decisions , we computed the overall probability of error Pe in making decisions after 30 minutes and 4 hours in both wild-type and A20-/- cells ( Fig 2D ) , after treatment with six different TNF concentrations . This analysis shows that in wild-type cells a higher decision error rate Pe is observed over time for all TNF concentrations . Also in wild-type cells Pe decreases as TNF concentration increases up to about 3 ng/mL , and then becomes less sensitive to the higher concentrations of TNF . On the other hand , depletion of A20 increases the decision error rate Pe , compared to the wild-type cells , after 30 minute treatment ( Fig 2D ) . Interestingly , A20-/- cells show higher Pe after the 4 hour treatment that is nearly insensitive to the increase in TNF concentration . Overall , for each time course , there is a significant increase in Pe in A20-/- cells , compared to wild-type cells ( Fig 2D ) . This is because of the failure of the signaling pathway due to A20 deficiency , where cells fail to stop TNF-induced NF-κB response . This observation further confirms the usefulness of the decision error rate Pe as a metric and method for modeling and measuring cell decision making processes under normal and abnormal conditions and in the presence of transduction noise uncertainty .
The developed approach can be extended to more complex and larger signaling networks , where inputs could be ligands or secondary messengers , and outputs could be several transcription factors that produce certain cellular functions [9] . Then by analyzing the concentration levels of these transcription factors at single or multiple time points using the proposed approach , probabilities of various cell fates in response to the input signals can be computed . In a broader context , one notes that in various organisms ranging from simple ones such as viruses to bacteria , yeast , lower metazoans and finally complex organisms such as mammals , various decisions are made in the presence of noise [10] . Depending on the concentration levels of certain molecules and their changes , regulated by some intracellular molecular networks , a cell may select from several possible fates . For example , in embryonic stem cells in mammals , the Nanog transcription factor expression level , which might be affected by molecular noise , is a determinant of cell differentiation , if proper signals are present [10] . In this context , one can use the approach presented here to compute false alarm and miss probabilities at different time instants , to better understand how precise or erroneous the decision to differentiate is ( given that noise is present ) , and how it changes over time . In a broader context , one may envision studying cell decision making processes in other organisms , such as those reviewed in [10] , using the developed approach .
This study shows that compared to the overall probability of error Pe introduced in this paper for signaling systems , the signaling capacity defined as the maximum amount of information between the system input and output , may not be a convenient metric for revealing dysfunctionalities in the system . The rationale is that while in the TNF—NF-κB pathway ( Fig 1A ) a reduction in capacity is observed in A20-/- cells in 30 minutes , compared to wild-type cells , an opposite effect , i . e . , capacity increase , is observed after 4 hours [2] . Therefore , the impact of A20 deficiency on the pathway capacity appears in different directions over time . The introduced error probability metric , on the other hand , consistently shows the increased level of erroneous behavior of this signaling pathway , in both short and long terms . The difference between decision error probability and capacity in the context of dysfunctionalities can be anticipated . This is because decision error probability is a metric defined such that it directly reflects departure of the pathway from normal behavior and its expected response . Capacity , on the other hand , is defined to measure the maximum amount of information that can flow from the pathway input to its output . While , in general , one may expect that a higher capacity in a pathway is a desired outcome , one can also note that the increased capacity might be caused by an alteration or loss of some otherwise important molecular functions in the pathway . In the TNF—NF-κB pathway , it has indeed been observed [2] that after 4 hours , A20-deficient cells exhibit a higher capacity , compared to wild-type cells . The point we are making here is that the higher amount of information that can travel from TNF to NF-κB in A20-deficient cells may not necessarily reflect biologically appropriate functioning of the pathway . To be able to understand dysfunctionalities in a pathway and how they affect cell decision makings , one can therefore benefit from a complementary metric and approach to characterize cell decision making errors in abnormal pathways , which we have studied here . In summary , capacity is a useful metric for studying information transmission in signaling pathways , whereas the introduced metrics of false alarm , miss and overall error rates are suitable for modeling decision making errors caused by noise and signaling failures . The goal of dynamical modeling is to use tools such as differential equations or stochastic processes , to model changes in the concentration levels of molecules with time . On the other hand , our approach aims at statistical characterization of decision making processes in cells , based on the concentration levels of certain molecules that control cell decisions , using statistical signal processing and decision theory tools . The concentration levels can be obtained via either experiments or stochastic simulations . As an example , in reference [3] a stochastic dynamical model is developed , which mimics nuclear NF-κB level changes with time , in response to a given TNF dose . The model is designed to assess the kinetics of molecular activities in a representative cell , provides information about single cell responses , and can also be used to simulate distributions of given protein levels across a population . It does not quantify the chance of missing a signal . The proposed approach provides methods to analyze single cell data in the context of cell decision making . For example , TNF high level of 8 ng/mL indicates the presence of a strong signal . However , due to noise , there is a chance for a cell to miss this signal . The approach presented here addresses probabilistic decision making , and the fidelity of decision making in noisy signaling networks . In the particular example of TNF = 8 ng/mL , our approach reveals that there is a 10% chance for a cell not to respond to the signal , based on the measured nuclear NF-κB levels after 30 minutes . We also note that while our approach is not meant to provide tools to model temporal variations of concentration levels , it allows to analyze and quantify the dynamics of signaling pathways and helps to understand cell decision making processes . In the above example , our approach shows that based on the measured nuclear NF-κB levels after 4 hours of TNF stimulation , the chance for missing the strong signal increases to 29% . This observation agrees with the dynamics of the TNF- NF-κB pathway activity , where due to the negative feedback of A20 , the level of nuclear NF-κB decreases after 4 hours , as discussed in the paper . To further relate the developed approach to the dynamics of signaling , here we have also developed a more sophisticated method to determine cell decision making probabilities , if a cell can make decisions based on the nuclear NF-κB level at the two time points jointly , compared to deciding based on 30 minute or 4 hour levels only . Our results show that in this example , joint decision based on the two time points has a 10% chance of missing the signal . As discussed in the paper , for this specific pathway , our results suggest that decisions based on joint early/late signaling events versus the early event alone show similar chance for missing the presence of the signal . In other pathways and signaling systems , however , this does not have to be the case , and the presented method can still be used to determine the probability of missing a signal and taking a certain cell fate road , based on multiple observations at different time points . Overall , the approach complements dynamic modeling by providing quantitative results for assessing the dynamical decision-making performed by a cell in the presence of an external stimulus . In contrast to the more common dynamical modeling analysis , the approach presented here does not explicitly characterize changes in the concentration levels of molecules with time . These approaches are compatible , as a stochastic dynamical model can yield distributions of input-conditioned output levels , expressed in the form of the concentration of a singling molecule of interest . Then our approach can use the simulated concentration level distributions to determine decision thresholds , false alarm and miss probabilities , etc . While it is preferred to use experimental data directly to understand cell decisions , it may be advantageous to use data generated by dynamical models , including those that were developed to describe the TNF-stimulated NF-κB signaling [11] . Furthermore , by perturbing kinetic parameters of a dynamical model , one can investigate the sensitivity of both the concentration level distributions and false alarm and miss probabilities to those parameters . This analysis may reveal that some kinetic parameters can significantly affect cell decisions , while others may play less important roles .
In summary , the proposed method of the analysis of possible cellular decisions , as applied to the TNF—NF-κB pathway , yields insights that are biologically meaningful and are in agreement with the known pathway functionality . NF-κB is a potent transcription factor regulating expression of numerous genes controlling cell fate decisions , including those regulating proliferation , apoptosis , or transition to the antiviral state . The accuracy of transmitting information between TNF stimulation and NF-κB activation is therefore crucial for proper fate decisions . Based on our analysis we found that the pathway can transmit within 30 minutes the information about the increase of TNF concentration , from a very low level to a high value of 8 ng/mL , with the transmission error of 0 . 07 . Interestingly , when the NF-κB translocation is measured at 4 hours post-stimulation , the transmission error increases to 0 . 245 . This finding reflects the presence of a negative feedback that attenuates the strength of the response at longer times and shifts the TNF-high response histogram to the left ( Fig 1D ) . This causes a greater overlap between the two response histograms after 4 hours ( Fig 1D ) and therefore results in a higher decision error probability , compared to that corresponding to the lower overlap between the response histograms after 30 minutes ( Fig 1B ) . Consistent with this result , our analysis also indicates a dramatic increase in the decision error in the feedback deficient cells , lacking expression of A20 . This implies that cells are not able to compensate for the loss of A20 feedback controlling NF-κB activity . This finding can help account for experimental observations that a loss or mutation of A20 can lead to chronic inflammation and can promote cancer due to the persistent activation of anti-apoptotic genes induced by NF-κB [12] . The decision is expected to become less uncertain with an increasing input dose . Our method can help analyze and quantify this effect . For instance , increasing the TNF dose from 0 . 2 to 0 . 51 ng/mL reduces the decision error probability from 0 . 25 to 0 . 11 in 30 minute data . The same behavior is observed for 4 hour data . The method described here can be expanded to describe the performance of more complex and larger signaling networks , including those with multiple ligands or second messengers as network inputs and several transcription factors involved in certain cellular functions as network outputs . By analyzing the concentration levels of these transcription factors using the proposed approach , probabilities of various cell fates in response to the input signals can be computed . We also note that the proposed decision error metrics complement the previously introduced analysis of the information capacity of signaling pathways and networks [2] . The information capacity is a useful metric to study information transmission in signaling pathways , but it does not address how the information transmitted by a signaling network can be converted into cellular decision making . Our results show that the introduced metrics of false alarm , miss and overall error rates can on the other hand be used for modeling decision making errors caused by noise and signaling failures . Overall , our analysis presents a powerful and widely applicable methodology to evaluate the expected fidelity of cellular decision making that can be used to further evaluate the performance of cellular signaling and communication .
This radar example is presented for illustrative purposes to show how statistical signal processing and decision theory concepts and tools are used in an engineering discipline . It paves the way for understanding the proposed methods and concepts in the context of molecular computational biology and cellular decision making . In radar systems , the system makes a decision based on samples of the received input waveform x[n] , where n is the time index . Based on the N samples x[0] , x[1] , … , x[N−1] , the system should decide between two hypotheses about x[n]: H0 which indicates that only noise is received , i . e . , no object is present , and H1 which represents that signal plus noise is received , i . e . , an object is present . With w[n] and A representing noise and constant amplitude signal , respectively , these two hypotheses can be written as H0:x[n]=w[n] , n=0 , 1 , … , N−1 , H1:x[n]=A+w[n] , n=0 , 1 , … , N−1 . , ( 1 ) To simplify the notation for computing the optimal decision metric , typically it is reasonable to assume both hypotheses have the same probability , i . e . , P ( H0 ) = P ( H1 ) = 1/2 , especially when we do not have a priori information about these probabilities ( the case of non-equal probabilities is discussed in the next section ) . It can be proved [4] that the optimal decision making system which minimizes the decision error probability is the one that compares probabilities of x under H0 and H1 . More specifically , let p ( x|H0 ) and p ( x|H1 ) represent conditional probability density functions ( PDFs ) of x under H0 and H1 , respectively . Then the optimal system decides H1 if p ( x|H1 ) > p ( x|H0 ) , otherwise decides H0 . This simply means that the optimal decision making system , after observing the input data , picks up the hypothesis which is more probable . This decision strategy is also called the maximum likelihood [4] decision , since it chooses the hypothesis with the highest likelihood . To compute p ( x|H0 ) and p ( x|H1 ) , we need the PDF of noise w[n] . Upon using a Gaussian noise model with zero mean and variance σ2 in ( 1 ) , the univariate conditional PDFs of x[n] for each n under H0 and H1 can be written as p ( x[n]|H0 ) = ( 2πσ2 ) −1/2 exp[− ( x[n] ) 2/ ( 2σ2 ) ] and p ( x[n]|H1 ) = ( 2πσ2 ) −1/2 exp[− ( x[n] − A ) 2/ ( 2σ2 ) ] , respectively . These two PDFs are graphed in S1 Fig for A = 2 and σ = 1 . When noise samples are independent , joint PDF of x[0] , x[1] , … , x[N−1] becomes the product of individual univariate PDFs . This results in the following expressions for p ( x|H0 ) and p ( x|H1 ) p ( x|H0 ) =p ( x[0] , x[1] , … , x[N−1]|H0 ) = ( 2πσ2 ) −N/2exp[−∑n=0N−1 ( x[n] ) 2/ ( 2σ2 ) ] , p ( x|H1 ) =p ( x[0] , x[1] , … , x[N−1]|H1 ) = ( 2πσ2 ) −N/2exp[−∑n=0N−1 ( x[n]−A ) 2/ ( 2σ2 ) ] . ( 2 ) To compare the above two PDFs , we need to set them equal , to find the optimal decision metric , as well the optimal decision threshold p ( x|H0 ) =p ( x|H1 ) , → ( 2πσ2 ) −N/2exp[−∑n=0N−1 ( x[n] ) 2/ ( 2σ2 ) ]= ( 2πσ2 ) −N/2exp[−∑n=0N−1 ( x[n]−A ) 2/ ( 2σ2 ) ] , →exp[−∑n=0N−1 ( x[n] ) 2/ ( 2σ2 ) ]=exp[−∑n=0N−1 ( x[n]−A ) 2/ ( 2σ2 ) ] , →−∑n=0N−1 ( x[n] ) 2/ ( 2σ2 ) =−∑n=0N−1 ( x[n]−A ) 2/ ( 2σ2 ) , →∑n=0N−1 ( x[n]−A ) 2−∑n=0N−1 ( x[n] ) 2=0 , →−2A∑n=0N−1x[n]+NA2=0 , →N−1∑n=0N−1x[n]=A/2 . The above equation indicates that the radar system makes an optimal decision , by comparing the average of N observed samples with the optimal threshold A/2 . It decides H1 , an object generating a constant signal with amplitude A is present , if the average of observed samples is greater than A/2 x¯=x[0]+x[1]+…+x[N−1]N>A2 , decideH1 . ( 3 ) Otherwise , the radar decides H0 , i . e . , no object is present and there is only noise . This optimal radar system still may make mistakes in its decisions due to noise , although the probability of its incorrect decisions is minimized . To calculate the probability of error in making decisions , first we need to calculate probability of deciding H1 when H0 is true , false alarm probability , and probability of deciding H0 when H1 is true , i . e . , miss probability PFA=P ( decidingH1|H0 ) , PM=P ( decidingH0|H1 ) . To compute the above probabilities , we need to determine the PDF of the decision variable x¯=N−1∑n=0N−1x[n] introduced earlier , under the two hypotheses . As discussed previously and under H0 , x[0] , x[1] , … , x[N−1] are noise samples , independent and Gaussian with zero mean and variance σ2 . Using properties of Gaussian random variables , it can be shown that x¯ here is Gaussian with zero mean and variance σ2/N p ( x¯|H0 ) = ( 2πσ2/N ) −1/2exp[−x¯2/ ( 2σ2/N ) ] . Under H1 , on the other hand , x[0] , x[1] , … , x[N−1] are signal plus noise samples , independent and Gaussian with mean A and variance σ2 . Using properties of the sum of Gaussian random variables , it can be shown that now x¯ is Gaussian with mean A and variance σ2/N p ( x¯|H1 ) = ( 2πσ2/N ) −1/2exp[− ( x¯−A ) 2/ ( 2σ2/N ) ] . To compute PFA , we note that false alarm occurs when H0 is true , but according to Eq ( 3 ) we have x¯>x¯th , where x¯th=A/2 . This results in PFA=P ( x¯>x¯th|H0 ) =∫x¯th∞p ( x¯|H0 ) dx¯ . Integrating the expression for p ( x¯|H0 ) , derived earlier , provides us with the following formula for the false alarm probability PFA=Q ( Nx¯thσ ) , where Q is a commonly-used Gaussian probability function Q ( η ) = ( 2π ) −1/2∫η∞exp ( −u2/2 ) du . To compute PM , we similarly note that miss occurs when H1 is true , but we have x¯<x¯th . This results in PM=P ( x¯<x¯th|H1 ) =∫−∞x¯thp ( x¯|H1 ) dx¯ . Integration of the expression for p ( x¯|H1 ) , derived earlier , gives the following formula for the miss probability in terms of the Q function PM=Q ( N ( A−x¯th ) σ ) . The overall probability of error in making decisions by the radar system is a mixture of false alarm and miss probabilities Pe=P ( H0 ) P ( decidingH1|H0 ) +P ( H1 ) P ( decidingH0|H1 ) =P ( H0 ) PFA+P ( H1 ) PM . By substituting P ( H0 ) = P ( H1 ) = 1/2 , and PFA and PM formulas , finally the probability of error can be written as Pe=12Q ( Nx¯thσ ) +12Q ( N ( A−x¯th ) σ ) . The above formula holds true for the optimal threshold x¯th=A/2 , as well as other choices for x¯th . To understand the importance of the decision threshold and how it affects Pe , the above formula is graphed in S2 Fig versus x¯th , for A = 2 , σ = 1 and N = 4 . We observe that the probability of error is minimal when x¯th is the optimal threshold of A/2 = 1 , and departure of the decision threshold from the optimal value increases Pe . With the choice of the optimal threshold , x¯th=A/2 , the above Pe formula simplifies to Pe=Q ( NA2σ ) . This formula is graphed in S3 Fig versus the signal-to-noise ratio A/σ , for N = 4 . We observe that the probability of error in making decisions decreases as signal-to-noise ratio increases , as expected . Making a decision on whether TNF level at the signaling system input is high or low is a binary hypothesis testing problem . The two hypotheses are H1: TNF is high , and H0: TNF is low . Due to the signal transduction noise or signaling malfunctions in a cell , it can respond differently to the same input , which may result in incorrect ( unexpected ) cell decisions and responses . Cell can make two types of incorrect decisions: deciding that TNF is high at the system input whereas in fact it is low ( deciding H1 when H0 is true ) , and missing TNF’s high level when it is actually high ( deciding H0 when H1 is true ) . These two incorrect decisions can be called false alarm and miss events , respectively . Let x be the measured quantity based on which the decision is going to be made . With p ( x|H0 ) and p ( x|H1 ) as the conditional probability density functions ( PDFs ) of x under H0 and H1 , respectively , false alarm and miss probabilities can be written as [4] PFA=∫x∈falsealarmregionp ( x|H0 ) dx , ( 4 ) PM=∫x∈missregionp ( x|H1 ) dx , ( 5 ) where false alarm and miss regions will be specified later . The overall probability of error Pe for making a decision is given by Pe=P ( H0 ) PFA+P ( H1 ) PM , ( 6 ) where P ( H0 ) and P ( H1 ) are probabilities of H0 and H1 , respectively . It can be shown [4] the optimal decision making system that minimizes the decision error probability Pe is the one that compares the conditional likelihood ratio L ( x ) = p ( x|H1 ) /p ( x|H0 ) with the ratio γ = P ( H0 ) /P ( H1 ) . The optimal system decides H1 if L ( x ) > γ . When H0 and H1 are equi-probable , P ( H0 ) = P ( H1 ) = 1/2 , the optimal decision decides H1 if L ( x ) > 1 , which means comparing the two conditional PDFs p ( x|H1 ) >p ( x|H0 ) , decideH1 . ( 7 ) This decision rule is called the maximum likelihood [4] decision , since it chooses the hypothesis with the highest likelihood . The choice of P ( H0 ) = P ( H1 ) = 1/2 represents the case where a priori knowledge on the probabilities of H0 and H1 is not available . This is considered just to demonstrate the proposed method . When P ( H0 ) and P ( H1 ) are known , the maximum likelihood decision rule simply changes to P ( H1 ) p ( x|H1 ) > P ( H0 ) p ( x|H0 ) , to decide H1 . To evaluate the performance of the maximum likelihood decision , we need to compute its false alarm and miss probabilities in the signaling system , which according to Eqs ( 4 ) and ( 5 ) can be written as PFA=∫{x:p ( x|H1 ) >p ( x|H0 ) }p ( x|H0 ) dx , ( 8 ) PM=∫{x:p ( x|H0 ) >p ( x|H1 ) }p ( x|H1 ) dx . ( 9 ) In these formulas the PDFs p ( x|H0 ) and p ( x|H1 ) represent the response probabilities of NF-κB nuclear translocation when TNF level is low and high , respectively . Similarly to Cheong et al . [2] we consider the Gaussian PDF p ( x ) = ( 2πσ2 ) −1/2 exp[− ( x−μ ) 2/ ( 2σ2 ) ] for the nuclear NF-κB level ( Fig 1C , Fig 1E ) , where μ and σ2 are the mean and variance , respectively . We symbolically represent this by x ∼ N ( μ , σ2 ) , where N stands for the Normal or Gaussian PDF . To determine PFA and PM , false alarm and miss integration regions in Eqs ( 8 ) and ( 9 ) should be specified , by solving the equation p ( x|H0 ) = p ( x|H1 ) . Since these two PDFs are N ( μ0 , σ02 ) and N ( μ1 , σ12 ) , respectively , equating them provides the following equation ( 2πσ02 ) −1/2exp[− ( x−μ0 ) 2/ ( 2σ02 ) ]= ( 2πσ12 ) −1/2exp[− ( x−μ1 ) 2/ ( 2σ12 ) ] , →exp[− ( x−μ0 ) 2/ ( 2σ02 ) ]exp[− ( x−μ1 ) 2/ ( 2σ12 ) ]= ( 2πσ12 ) −1/2 ( 2πσ02 ) −1/2 , →exp[− ( x−μ0 ) 2/ ( 2σ02 ) + ( x−μ1 ) 2/ ( 2σ12 ) ]=σ0/σ1 . By taking the natural logarithm of both sides of the above last equation we obtain − ( x−μ0 ) 2/ ( 2σ02 ) + ( x−μ1 ) 2/ ( 2σ12 ) =ln ( σ0/σ1 ) , →σ02 ( x−μ1 ) 2−σ12 ( x−μ0 ) 22σ02σ12=ln ( σ0/σ1 ) , →σ02 ( x−μ1 ) 2−σ12 ( x−μ0 ) 2=2σ02σ12ln ( σ0/σ1 ) , which can be re-written in the form of the following quadratic equation ( σ02−σ12 ) x2+2 ( σ12μ0−σ02μ1 ) x+σ02μ12−σ12μ02−2σ02σ12ln ( σ0/σ1 ) =0 , ( 10 ) where ln ( . ) is the natural logarithm . As mentioned previously , Eq ( 10 ) is derived assuming P ( H0 ) = P ( H1 ) = 1/2 , i . e . , equal probabilities for having low and high TNF levels , and considering a Gaussian model for the nuclear NF-κB level . For other prior probabilities and distribution models , the threshold can be similarly obtained , by solving the equation P ( H0 ) p ( x|H0 ) = P ( H1 ) p ( x|H1 ) for x . The solution to the quadratic Eq ( 10 ) gives NFκBth , the threshold value of NF-κB , such that p ( NFκBth|H0 ) = p ( NFκBth|H1 ) ( Fig 1C , Fig 1E ) . By computing the integrals in Eqs ( 8 ) and ( 9 ) , as shown below , we obtain the following results for false alarm and miss probabilities PFA=∫NFκBth∞p ( x|H0 ) dx=Q ( NFκBth−μ0σ0 ) , ( 11 ) PM=∫−∞NFκBthp ( x|H1 ) dx=Q ( μ1−NFκBthσ1 ) , ( 12 ) where Q function is defined as Q ( η ) = ( 2π ) −1/2∫η∞exp ( −u2/2 ) du . ( 13 ) To measure PFA and PM , we used single cell data collected from hundreds of cells [2] , to estimate ( μ0 , σ02 ) and ( μ1 , σ12 ) of nuclear NF-κB readouts after 30 minutes ( early events ) , for low and high TNF levels , 0 . 0021 ng/mL and 8 ng/mL , respectively . Then using Eq ( 10 ) we estimated the decision threshold NFκBth ( Fig 1C ) which upon substituting into Eqs ( 11 ) and ( 12 ) resulted in the false alarm and miss probabilities PFA = 0 . 04 and PM = 0 . 1 , respectively . Repeating the same steps for nuclear NF-κB readouts after 4 hours ( late events ) resulted in a decision threshold NFκBth ( Fig 1E ) which after substitution into Eqs ( 11 ) and ( 12 ) provided PFA = 0 . 2 and PM = 0 . 29 , respectively . Overall , in this study we have made the following assumptions , which can be relaxed , as explained below: Probabilities of having different input signals , i . e . , low and high TNF levels herein , are equal; and , concentration level of interest , which is nuclear NF-κB level in our work , has a Gaussian distribution . The first assumption is for cases where a priori knowledge on these probabilities is not available . The developed method , however , is not limited to this assumption and can incorporate non-equal prior probabilities , if they become available . If a priori probabilities are not equal , the threshold can be determined by comparing P ( H1 ) p ( x|H1 ) and P ( H0 ) p ( x|H0 ) , rather than p ( x|H1 ) and p ( x|H0 ) . The overall probability of error in making decisions also changes from Pe = ( 1/2 ) PFA + ( 1/2 ) PM to Pe = P ( H0 ) PFA + P ( H1 ) PM . The second assumption is made following the study of Cheong et al . [2] , which has considered a Gaussian model for the nuclear NF-κB level . This model reasonably represents the data . For other data sets and other distribution models , one can still use the developed approach , using modified mathematical formulas for the decision threshold , false alarm and miss probabilities , obtained by integrating the probability distribution of interest . More specifically , we have obtained the decision threshold by solving the equation p ( x|H0 ) = p ( x|H1 ) for x . When they are both Gaussian , the equation simplifies to the quadratic Eq ( 10 ) . For a non-Gaussian distribution , we will obtain another equation to compute the threshold , still by solving the equation p ( x|H0 ) = p ( x|H1 ) for x . Additionally , integration of a non-Gaussian distribution to obtain false alarm and miss probabilities using Eqs ( 11 ) and ( 12 ) will give us results that will be different from the Q function . If the data is not easily characterized by a well-known distribution , one can model the data using various probability density function estimators . Alternatively , one can estimate threshold value and false alarm and miss probabilities directly from empirical histograms . The derived formulas for false alarm and miss error probabilities in the NF-κB pathway , Eqs ( 11 ) and ( 12 ) , show some biological factors such as mean expression levels of NF-κB and its noise-induced variances that affect decision makings . For example , since the Q function is inversely related to its argument , we note that as variances increase , the overall decision error probability can increase . This is biologically relevant , as larger variances broaden NF-κB response curves , which in turn cause more overlap between the response curves , therefore resulting in a higher decision error probability . To understand the effect of various components of the pathway on decision making , one can knockout or knockdown these components and calculate decision error probabilities in the modified system , as we did in A20-/- cells . Maximum likelihood decision based on the data at two time points needs the joint PDF of x and y , which represent the nuclear NF-κB level after 30 minutes and 4 hours , respectively . The joint Gaussian PDF is given by [13] p ( x , y ) =12πσxσy1−ρ2exp ( −12 ( 1−ρ2 ) [ ( x−μx ) 2σx2−2ρ ( x−μx ) ( y−μy ) σxσy+ ( y−μy ) 2σy2] ) , ( 14 ) where ρ is the correlation coefficient between x and y , whereas ( μx , σx2 ) and ( μy , σy2 ) are the mean and variance of x and y , respectively . Upon defining the following mean vector μ and covariance matrix Σ for x and y μ=[μxμy] , Σ=[σx2ρσxσyρσxσyσy2] , ( 15 ) we succinctly represent the joint Normal or Gaussian PDF in Eq ( 14 ) for ( x , y ) by the notation ( x , y ) ∼ N ( μ , Σ ) . To determine ρ , we used an experimentally-verified simulator [3] whose accuracy is verified by single cell data [3] . To evaluate the performance of the maximum likelihood decision based on early and late event data , we need to compute its false alarm and miss probabilities in the signaling system , by extending Eqs ( 8 ) and ( 9 ) to two variables PFA=∬{x , y:p ( x , y|H1 ) >p ( x , y|H0 ) }p ( x , y|H0 ) dxdy , ( 16 ) PM=∬{x , y:p ( x , y|H0 ) >p ( x , y|H1 ) }p ( x , y|H1 ) dxdy , ( 17 ) where the bivariate PDFs p ( x , y|H0 ) = N ( μ0 , Σ0 ) and p ( x , y|H1 ) = N ( μ1 , Σ1 ) represent the joint early/late response probabilities of NF-κB nuclear translocation when TNF level is low and high , respectively ( Fig 1F ) . To find the integration regions in Eqs ( 16 ) and ( 17 ) , we need to solve the equation p ( x , y|H0 ) = p ( x , y|H1 ) . The solution is a threshold curve in the ( x , y ) plane . Performing the double integrations in Eqs ( 16 ) and ( 17 ) , however , is not straightforward either analytically or numerically . Therefore , we resorted to Monte Carlo integration which resulted in PFA = 0 . 03 and PM = 0 . 1 . Similarly to wild-type cells , we considered Gaussian PDF for the nuclear NF-κB level in A20-/- cells ( Fig 2A , Fig 2C ) . Upon using the same steps and equations and thresholds as wild-type cells , we computed PFA and PM in A20-/- cells ( Fig 2B ) . | Cell continuously receives signals from the surrounding environment and is supposed to make correct decisions , i . e . , respond properly to various signals and initiate certain cellular functions . Modeling and quantification of decision making processes in a cell have emerged as important areas of research in recent years . Due to signal transduction noise , cells respond differently to similar inputs , which may result in incorrect cell decisions . Here we develop a novel method for characterization of decision making processes in cells , using statistical signal processing and decision theory concepts . To demonstrate the utility of the method , we apply it to an important signaling pathway that regulates molecules which play key roles in cell survival . Our method reveals that cells can make two types of incorrect decisions , namely , false alarm and miss events . We measure the likelihood of these decisions using single cell experimental data , and demonstrate how these incorrect decisions are related to the signal transduction noise or absence of certain molecular functions . Using our method , decision making errors in other molecular systems can be modeled . Such models are useful for understanding and developing treatments for pathological processes such as inflammation , various cancers and autoimmune diseases . | [
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"... | 2017 | Computation and measurement of cell decision making errors using single cell data |
Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes . The effect sizes between studies in a meta-analysis may differ and these differences , or heterogeneity , can be caused by many factors . If heterogeneity is observed in the results of a meta-analysis , interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study . However , interpreting heterogeneous results is difficult . The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study . In this paper , we propose a framework facilitating the interpretation of the results of a meta-analysis . Our framework is based on a new statistic representing the posterior probability that the effect exists in each study , which is estimated utilizing cross-study information . Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect , the studies predicted to not have an effect , and the ambiguous studies that are underpowered . In addition to helping interpretation , the new framework also allows us to develop a new association testing procedure taking into account the existence of effect .
Meta-analysis is a tool for aggregating information from multiple independent studies [1]–[3] . In genome-wide association studies ( GWASs ) [4] , the use of meta-analysis is becoming more and more popular because one can virtually collect tens of thousands of individuals that will provide power to identify associated variants with small effect sizes [5]–[7] . Several large scale meta-analyses have been performed for diseases including type 1 diabetes [8] , type 2 diabetes [9]–[11] , bipolar disorder [12] , Crohns disease [13] , and rheumatoid arthritis [14] , and have identified associations not revealed in the individual studies . In meta-analyses , the effect size between studies may differ and this difference , or heterogeneity , can be caused by many factors [15]–[18] . If the populations are different between studies , the genetic factors can cause heterogeneity [19] , [20] . If the subjects are from different regions , the environmental factors can cause heterogeneity [21] . Even if the true effect size is invariant , the design factors can also cause a phenomenon that looks like heterogeneity , what is often called the statistical heterogeneity [22] . If the linkage disequilibrium structures are different between studies , the collected marker can show heterogeneity [23] . If the studies use different genotyping platforms , different imputation accuracies and different genotyping errors can cause heterogeneity [24] . In current meta-analyses of genome-wide association studies , heterogeneity is often observed in the results [9]–[11] , [13] , [17] . Interpreting the cause of such heterogeneity is important . If the heterogeneity is caused by either genetic or environmental factors , understanding the cause of heterogeneity can help our understanding of the disease mechanism . If the heterogeneity is statistical heterogeneity caused by the design factors , understanding the cause of heterogeneity is crucial in designing a replication study so that we can eliminate the design factors that can hinder the revelation of the true effect in the replication study . However , interpreting heterogeneous results is difficult . One standard approach is to examine the association p-values of the studies . The inherent limitation of this approach is that a non-significant p-value is not evidence of the absence of an effect . Thus , a p-value does not provide the full information necessary for the interpretation whether or not there is an effect in the study . Another standard approach is to plot observed effect sizes and their confidence intervals of all studies [17] , [25] , [26] . This plot can be overly complicated when the number of studies is large and does not provide a single estimate that represents the existence of an effect in each study . The limitation of both approaches is that they use classical estimates that are calculated using only the data of each single study . That is , they utilize only within-study information . In this paper , we propose a framework facilitating the interpretation of the results of a meta-analysis . Our framework is based on a new statistic termed the m-value which is the posterior probability that the effect exists in each study . Plotting the new statistic together with p-values in a two-dimensional space helps us distinguish between the studies predicted to have an effect , the studies predicted to not have an effect , and the ambiguous studies that are underpowered . We name this plot a P-M plot . In this framework , the outlier studies showing distinct characteristics from the other studies are easily identified , as we demonstrate using data from type 2 diabetes and Crohns disease meta-analyses [10] , [13] . Our new statistic is fundamentally different from traditional estimates based on the data of single studies . We use all studies simultaneously to calculate the new statistic based on the assumption that the effect sizes are similar if the effect exists . Thus , we utilize cross-study information as well as within-study information . In addition to helping interpretation , the new framework allows us to develop a new association testing procedure which takes into account the presence or absence of the effect . The new method called the binary effects model is a weighted sum of z-scores method [5] assigning a greater weight to the studies predicted to have an effect and a smaller weight to the studies predicted to not have an effect . Application to the Crohns disease data [13] shows that the new method gives more significant p-values than previous methods at certain loci already identified as associated . The new method is available at http://genetics . cs . ucla . edu/meta .
In our framework , we use a simplified model to describe heterogeneity among the studies which makes two assumptions . The first assumption is that effect is either present or absent in the studies . This assumption is different from the traditional assumption assuming normally distributed effect sizes [27]–[29] . Our assumption is inspired by the phenomenon that the effect sizes are sometimes observed to be much smaller in some studies than in the others . It is reported that different populations can cause such phenomenon [19] , [20] , [30] , [31] . For example , the homozygosity for APOE 4 variant is known to confer fivefold smaller risk of Alzheimer disease in African Americans than in Asians [19] , [30] . The HapK haplotype spanning the LTA4H gene is shown to confer threefold smaller risk of myocardial infraction in the populations of Europeans decent than in African Americans [31] . The HNF4A P2 promoter variants are shown to be associated with type 2 diabetes in Ashkenazi and the results have been replicated [20] . However , in the same study , the same variants did not show associations in four different cohorts of UK population suggesting a heterogeneous effect . Gene-environmental interactions can also cause such phenomenon . If a study lacks an environmental factor necessary for the interaction , the observed effect size can be much smaller in that study . It is generally agreed that the gene-environmental interactions exist in many diseases such as cardio vascular diseases [32] , respiratory diseases [33] , and mental disorders [34] . The second assumption is that if the effect exists , the effect sizes are similar between studies . We call these two assumptions together the binary effects assumption . While other types of heterogeneity structures are possible such as arbitrary effect sizes , for identifying which studies have an effect and which studies do not have an effect , we expect that this model will be appropriate . We propose a statistic called the m-value which is the posterior probability that the effect exists in each study of a meta-analysis . Suppose that we analyze studies together in a meta-analysis . Let ( ) be the observed effect size of study and let be the estimated variance of . It is a common practice to consider the true variance . In the current GWASs , the distribution of is well approximated by a normal distribution due to the large sample sizes . Let denote the observed data . If there is no effect in study , where is the probability density function of a normal distribution whose mean is and the variance is . If there is effect in study , where is the unknown true effect size . Since we want a posterior probability , the Bayesian framework is a good fit . We assume that the prior for the effect size isA possible choice for in GWASs is 0 . 2 for small effect and 0 . 4 for large effect [35] , [36] . Let be a random variable which has a value 1 if study has an effect and a value 0 if study does not have an effect . Let be the prior probability that each study will have an effect such thatThen we assume a beta prior on Through this paper , we use the uniform distribution prior ( and ) , but other priors can also be chosen . Let be the vector indicating the existence of effect in all studies . can have different values . Let be the set of those values . Our goal is to estimate the m-value , the posterior probability that the effect exists in study . By the Bayes' theorem , ( 1 ) where is a subset of whose elements' th value is 1 . Thus , we only need to know for each the posterior probability of , consisting of the probability of given and the prior probability of . The prior probability of iswhere is the number of 1's in and is the beta function . And the probability of given is ( 2 ) where is the indices of 0 in and is the indices of 1 in . We can analytically work on the integration to obtainwherewhere is the inverse variance or precision . The summations are all with respect to . is a scaling factor such thatThe details of the derivation is in Text S1 in Supporting Information S1 . As a result , we can calculate for every and therefore obtain for each study . We propose plotting the studies' p-values and m-values together in two dimensions . This plot , which we call the P-M plot , can help interpreting the results of a meta-analysis . Figure 1 shows that how to interpret such a plot . The right-most ( pink ) region is where the studies are predicted to have an effect . Often , a study can be in this region even if the p-value is not very significant . The left-most ( light-blue ) region is where the studies are predicted to not have an effect . This suggests that the sample size is large but the observed effect size is close to zero , suggesting a possibility that there exists no effect in that study . The middle ( green ) region is where the prediction is ambiguous . A study can be in this region because the study is underpowered due to a small sample size . If the sample size increases , the study will be drawn to either the left or the right side . If the binary effects assumption does not hold , a study can sit in an unexpected region and a careful interpretation is necessary . For example , if the effects are significant but the effect sizes are in opposite direction in some studies , the studies can sit in the unusual top left region . However , such case will be rare and may be a result of the strand errors . We propose a new type of random effects model meta-analysis approach called the binary effects model . If the binary effects assumption holds , that is , if the effect is either present or absent in the studies , taking into account this pattern of heterogeneity in the association testing procedure can increase power compared to the general RE approach [23] . The binary effects model we propose is the weighted sum of z-scores method [5] where the m-values are incorporated into the weights . Intuitively , this is equivalent to assigning a greater weight to the studies predicted to have an effect and a smaller weight to the studies predicted to not have an effect . Let be the z-score of study . The common form of the weighted sum of z-scores statistic for the fixed effects model isIn many cases , the weight approximates to the form where is the sample size and is the minor allele frequency [23] . When the minor allele frequency is similar between studies , the weight approximates to the popular form of [5] . The binary effects model statistic we propose isOur method is an empirical approach that uses estimated from the data as the prior weight for each study . Since the m-value is estimated using all studies , our approach can be thought of as gathering information from all studies and distributing back to each study in the form of weight . We choose this approach because of its simple formulation . Since is not independent of , the statistic does not follow a normal distribution . Thus , the p-value is obtained using sampling which can be inefficient . We use two ideas to expedite the sampling . First , we propose an importance sampling procedure which is more efficient than the standard sampling . Second , we use an efficient approximation of m-value . See Text S2 and S3 in Supporting Information S1 for details . In order to evaluate our methods , we use the following simulation approach . Assuming a minor allele frequency , a relative risk , and the number of individuals of cases and controls , a straightforward simulation approach is to sample alleles for cases and alleles for controls according to the expected minor allele frequencies in the cases and controls respectively [38] . However , since we perform extensive simulations assuming thousands of individuals , we use an approximation approach that samples the minor allele count from a normal distribution and rounds it to the nearest non-negative integer . The URL for methods presented herein is as follows: http://genetics . cs . ucla . edu/meta
We demonstrate a simple simulation example showing how m-value behaves depending on the presence and absence of the effect and the sample size . First , we make the following assumptions throughout all of the experiments in this paper . We assume that the minor allele frequency of the collected marker is 0 . 3 . We assume that the equal number of cases and controls are collected and refers to the total number of individuals as sample size . We also assume a very small disease prevalence when we calculate the expected minor allele frequencies for cases and controls given a relative risk . For the details how the expected values are calculated , see Han and Eskin [23] . Note that these assumptions are not critical factors affecting our simulation results . In all experiments , the random effects model ( RE ) denotes the RE method of Han and Eskin [23] . We omit the results of the conventional RE method [15] because they are highly conservative [23] . Throughout this paper , we use the following priors for calculating m-values . We use for the prior of the effect size ( ) . We use the uniform distribution prior , , for the prior of the existence of effect ( ) . In this simulation example , we assume four different types of studies . The first type is a large study having an effect ( and ) . The second type is a small study having an effect ( and ) . The third type is a large study not having an effect ( and ) . The fourth type is a small study not having an effect ( and ) . We generate two studies per each type , constructing a simulated meta-analysis set of total eight studies . We accept this simulation set only if none of eight studies' p-values exceeds the genome-wide threshold ( ) but the meta-analysis p-value calculated by the RE approach exceeds the genome-wide threshold . Otherwise , we repeat . We construct 1 , 000 meta-analysis sets . Given this simulated data , we plot the histogram of m-values for each type of studies separately in Figure 2 . Figure 2A shows that almost all ( 99 . 9% ) of large studies with an effect are concentrated on large m-values ( ) , showing that the m-values effectively predict that the effect exists in the studies . Figure 2C shows that a large amount ( 78 . 6% ) of large studies without an effect are concentrated on small m-values ( ) . Figure 2B and 2D show that when the sample size is small , m-value tends to the mid-range regardless of the effect , suggesting that the studies are underpowered to determine the presence of an effect . In this experiment , we compare the p-value , m-value , and BF by measuring how well they predict which studies have an effect and which studies do not have an effect . We assume a meta-analysis of 10 studies where the effect is either present ( ) or not . We randomly pick the number of studies having an effect ( ) from a uniform distribution ranging from 1 to 9 , and randomly decide which studies have an effect . We randomly pick the sample size of each study from a uniform distribution between 500 and 2 , 000 . Given the sample sizes and the effect sizes , we generate a meta-analysis study set . We accept the meta-analysis set only if none of the studies' p-values exceeds the genome-wide threshold ( ) and the meta-analysis p-value exceeds the genome-wide threshold . We repeat until we construct 1 , 000 meta-analysis sets . We examine each of 10 , 000 studies included in the simulated 1 , 000 meta-analysis sets . For each study , we calculate the p-value , m-value , and BF . We use the asymptotic BF of Wakefield [39] assuming the same prior distribution about the effect size as the m-value . Then we evaluate the performance of each statistic as follows . To evaluate the performance of m-value , we fix an arbitrary threshold so that we predict the studies having m-value to have an effect . Since we know the underlying truth if the effect exists or not in each study , we can measure what proportion of the studies actually having an effect is correctly predicted to have an effect ( true prediction rate ) and what proportion of the studies actually not having an effect is incorrectly predicted to have an effect ( false prediction rate ) . Then we change the threshold to draw a curve between the true prediction rate and the false prediction rate , which is often called the receiver-operating-characteristic ( ROC ) curve . We do the same analysis for p-value and BF . Figure 3A shows that m-value is superior to p-value and BF in predicting the studies having an effect . This is because m-value can utilize the cross-study information when the binary effects assumption holds . The performances of p-value and BF are almost identical . Next , we evaluate the performance of the statistics in predicting studies not having an effect . The experiment is exactly the same as the previous experiment except that , given a threshold , we predict the studies having m-value to not have an effect . We similarly draw the ROC curves for the three statistics . True and false prediction rates are defined similarly for the objective of predicting the studies not having an effect . Figure 3B shows that the m-value is even more superior to the other statistics in this experiment than in the previous experiment . The p-value shows the most inferior performance . This is expected because p-value is designed for detecting the presence of an effect but not for detecting the absence of an effect . That is , a non-significant p-value is not evidence of the absence of an effect but can be the result of a small sample size . On the other hand , the BF testing for the absence of an effect is just the reciprocal of the BF testing for the presence of an effect . Thus , the same BF can be used for both purposes . Although the BF performs better than the p-value , the m-value is even more superior . The relative performance gain of the m-value compared to the BF is due to the cross-study information utilized . We apply our P-M plot framework to the real data of the meta-analysis of type 2 Diabetes ( T2D ) of Scott et al . [10] . The meta-analysis consists of three different GWAS investigations , the Finland-United States Investigation on NIDDM Genetics ( FUSION ) [10] , the Diabetes Genetics Initiative ( DGI ) [11] , and the WTCCC [9] , [40] . In their analysis , two SNPs are shown to have a heterogeneous effect , rs8050136 and rs9300039 . Ioannidis et al . [17] provide an insightful explanation about the heterogeneity at rs8050136 . The WTCCC/UKT2D groups identified evidence for T2D and body mass index ( BMI ) associations with a set of SNPs including rs8050136 in the FTO region [40] . On the other hand , in the DGI study , the SNP rs8050136 was not significant . The explanation that Ioannidis et al . suggest is that the observed association at rs8050136 ( FTO ) may be mediated by its association with obesity . In fact , DGI is the only study where the BMI is matched between cases and controls , and the T2D association appears to be mediated through a primary effect on adiposity [11] . Thus , although the truth is unknown , the explanation of Ioannidis et al . is reasonable . Compared to rs8050136 , the cause of heterogeneity at rs9300039 is less understood . It is suggested that the heterogeneity might reflect the different tag polymorphisms used in the studies [17] . To gain insights on these studies , we apply our P-M plot . Figure 4A shows the forest plot , the plot showing only the p-values , and the P-M plot for rs8050136 . In the P-M plot , DGI appears to be well separated from the other two studies , even though its m-value ( ) is not below the threshold ( ) . Thus , the P-M plot visualizes that DGI can have a different characteristic from the others . Such a separation is not clear in the plot showing only the p-values . In the plot showing only the p-values , DGI is close to FUSION since FUSION is also not very significant ( ) . However , the m-value of FUSION is much greater ( ) than that of DGI . This suggests that the effect is much more likely to exist in the FUSION study than in the DGI study . Figure 4B shows the plots for rs9300039 . The P-M plot shows a different pattern from the P-M plot of rs8050136 . In this P-M plot , every study has an m-value greater than 0 . 5 . Thus , no study shows evidence of no effect . Comparing the plots of rs8050136 and rs9300039 gives an interesting observation . In the plot showing only the p-values , both SNPs show a specific pattern of p-values that a single study is considerably more significant than the other two . However , despite of this similarity in the pattern of p-values , the two SNPs' P-M plots look different enough that can lead us to different interpretations . This shows that our P-M plot can provide information that is not apparent in the analysis of only the p-values . We apply our plotting framework to the data of the recent meta-analysis of Crohns disease of Franke et al . [13] . This meta-analysis consists of six different GWAS comprising 6 , 333 cases and 15 , 056 controls , and even more samples in the replication stage . In this study , 39 associated loci are newly identified increasing the number of associated loci to 71 . We apply our framework to six loci where a high level of heterogeneity is observed . Han and Eskin [23] showed that at these six loci , RE gave more significant p-values than the fixed effects model ( FE ) . Figure 5 shows the P-M plots of two loci . See Figure S1 for the plots of all six loci . The names of the studies follow the names used in Franke et al . [13] . At these two loci , rs3024505 and rs17293632 , the m-value of WTCCC is close to the threshold for predicting no effect . A possible explanation is that the different marker sets could have caused the statistical heterogeneity at these loci . WTCCC [40] used the Affymetrix platform while others used the Illumina platform . Although we do not further investigate this hypothesis , it is true that the P-M plots visualize an interesting outlier behavior of WTCCC at these loci . Such an observation is not clear in both the forest plot and the plot showing only p-values . In the plot showing only p-values , studies having non-significant p-values are all clustered and WTCCC is only one of them . In the forest plot , WTCCC is not the only study showing a small effect size at both loci . For example , at rs3024505 , NIDDKNJ shows a smaller effect size than WTCCC . However , the m-value of WTCCC is much smaller than NIDDKNJ's because of the large sample size . Such an interaction between the sample size and the prediction can also be inferred from the forest plot since the forest plot includes the confidence interval . However , it is difficult to numerically quantify the effect of sample size on the prediction by visually examining the forest plot . We estimate the false positive rate of the new binary effects model ( BE ) . Assuming the null hypothesis of no association , we construct 5 studies of sample size 1 , 000 to build a meta-analysis set . We calculate the meta-analysis p-value of BE using our importance sampling procedure with 10 , 000 samples . We also calculate the meta-analysis p-values of FE and RE . We build 100 million sets of meta-analysis and estimate the false positive rate as the proportion of the simulated sets whose p-value exceeds a threshold . We vary the threshold levels from 0 . 05 to . Table 1 shows that all methods including BE control the false positive rates accurately , at all threshold levels examined . When we increase the number of studies from 5 to 10 , the results are essentially the same and the false positive rates are controlled ( Data not shown ) . We compare the power of BE to the powers of FE and RE . Assuming a meta-analysis of five studies of an equal sample size 1 , 000 , we construct 10 , 000 meta-analysis sets . The power of each method is estimated as the proportion of the meta-analysis sets whose meta-analysis p-value calculated by each method exceeds the genome-wide threshold ( ) . We measure power in two different situations . First , we assume a situation that the effect is either present or absent . We decrease the number of studies having an effect ( ) from 5 to 2 . We increase the relative risk as decreases , using for respectively , in order to show the relative performance between methods . Figure 6 shows that except for the case that there is no heterogeneity ( ) , BE is the most powerful among all methods . BE is more powerful than RE , even though both are a random effects model , possibly because it learns the fact that some studies do not have an effect from the data . When there is no heterogeneity ( ) , FE achieves the highest power and BE achieves the lowest power . Second , we assume a classical setting where the effect sizes follow a normal distribution . Assuming that the mean effect size of , we sample the log of effect size of each study from a normal distribution having the mean and the standard deviation where is the parameter we vary . As increases , the heterogeneity increases . We measure the power of each method varying from zero to one . Figure 7 shows that in this situation , BE is generally less powerful than RE . The power difference between BE and RE is the greatest when the heterogeneity is small . As the heterogeneity increases , BE shows a similar power to RE . We apply BE to the real data of Crohns disease of Franke et al . [13] . Han and Eskin [23] showed that out of 69 associated loci analyzed , RE gave more significant p-values than FE at six loci where high level of heterogeneity is observed . We calculate the p-values at these loci using BE and compare to the p-values of FE and RE . Table 2 shows that at all six loci where RE gave more significant p-values than FE , BE gives even more significant p-values . The reason why BE gives more significant p-values can be explained by examining the P-M plots of these loci in Figure 5 and Figure S1 . The P-M plots show that at these loci , some studies show high m-values and some studies show low m-values , suggesting a bimodal distribution of effect size . Thus , the situation is very similar to the case that the effect is either present or not , in which case BE achieves higher power than RE as shown in Figure 6 . We measure how accurately the importance sampling procedure of BE estimates the p-value depending on the number of samples used . We calculate the BE p-value for the same dataset in 100 different runs to estimate the variance of the p-value estimate . Our criterion of interest is the ratio between the standard deviation of our estimate and the target p-value . For this , we use the 69 associated loci in the Crohns disease data of Franke et al . [13] that were previously analyzed in Han and Eskin [23] . We measure the ratio for each locus and average over all loci . We do this varying the number of samples from 1 , 000 to 1 , 000 , 000 . Table 3 shows that as the number of samples used for importance sampling increases , the accuracy increases . The pattern of accuracy increase is what we would usually expect in a sampling procedure; standard deviation is decreased approximately by the square root of the sample size increase . When the number of samples is 1 , 000 , the ratio is roughly 0 . 5 . A ratio of 0 . 5 is large , but can be enough for initial screening if we would apply an adaptive sampling that samples larger number of samples only for loci that are at least moderately significant ( e . g . ) . We measure the computational efficiency of the importance sampling procedure of BE . In our software , we implemented an adaptive sampling procedure that samples smaller number first ( ) and then larger number ( ) for the loci that are at least moderately significant . In the machine equipped with Intel Xeon 1 . 68 GHz CPU , when we use 1 , 000 samples in the importance sampling , calculating BE p-values of 1 , 000 loci for the meta-analysis of 10 studies takes 100 seconds . Thus , to calculate BE p-values of one million loci assuming that 1 , 000 loci among them are moderately significant , it will take approximately 30 hours which is a feasible amount of time . If the number of samples is increased to achieve better accuracy , such as and , the procedure will still be efficient if one uses multiple computers or a cluster since the procedure is parallelizable .
We introduce a framework facilitating the interpretation of meta-analysis results based on a new statistic representing the posterior probability that the effect exists in each study . Our framework utilizes cross-study information and is shown to help interpretations in the simulations and the real data . The new statistic also allows us to develop a new association testing procedure called the binary effects model . In the current meta-analyses of genome-wide association studies , heterogeneity is often observed and our framework will be a useful tool for interpreting such results . We expect that our framework will be even more useful in the future meta-analyses . As the number of studies in a meta-analysis grows , the chance of heterogeneity will increase [6] . Also , a meta-analytic approach can often be applied to a broader area such as to multiple diseases with similar etiology , in which case the heterogeneity is more likely to occur . Moreover , the majority of the current meta-analyses only use the fixed effects model ( FE ) . The use of a random effects model ( RE ) approach [23] such as the binary effects model presented herein will increase the number of identified associations showing heterogeneity , since an RE approach is more powerful than FE for detecting associations with heterogeneity . One limitation of our approach is that although the new statistic can predict the studies having an effect and the studies not having an effect , it does not distinguish the true heterogeneity and the statistical heterogeneity [22] . Discriminating between the two can be very difficult based on the observed data and might often be possible only by external data such as the replication studies . In that sense , our method can help discriminating them because one can come up with a hypothesis based on m-values that the heterogeneity is caused by specific design factors and then control the factors in the replication stage . The heterogeneity will disappear in the replication stage if it was due to the design factors . Similarly to other Bayesian approaches [35] , [36] , the prior choice in our method can have a non-negligible effect on the predictions . For the prior of the effect size , it is important to set a reasonable value based on the prior information about the effect size . See Stephens and Balding [35] for the general guideline for this choice . For the prior of the probability that the effect exists , we used the uniform distribution ( ) in this paper . However , different priors can also be used for different situations . If one expects that most of the studies have an effect , an asymmetric prior such as can be used . If one is certain that the studies having an effect and the studies not having an effect are mixed , a bell-shape prior such as can be used . See Figure S2 for the plots of the possible choices of priors . | Genome-wide association studies are an effective means of identifying genetic variants that are associated with diseases . Although many associated loci have been identified , those loci account for only a small fraction of the genetic contribution to the disease . The remaining contribution may be accounted by loci with very small effect sizes , so small that tens of thousands of samples are needed to identify them . Since it is costly to conduct a study collecting such a large sample , a practical alternative is to combine multiple independent studies in a single analysis called meta-analysis . However , many factors , such as genetic or environmental factors , can differ between the studies combined in a meta-analysis . These factors can cause the effect size of the causal variant to differ between the studies , a phenomenon called heterogeneity . If heterogeneity exists in the data of a meta-analysis , interpreting the meta-analysis results is an important but difficult task . In this paper , we propose a method that helps such interpretation , in addition to a new association testing procedure that is powerful when heterogeneity exists . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"mathematics",
"statistics",
"genetics",
"biology",
"genetics",
"and",
"genomics"
] | 2012 | Interpreting Meta-Analyses of Genome-Wide Association Studies |
Tissue morphogenesis relies on proper differentiation of morphogenetic domains , adopting specific cell behaviours . Yet , how signalling pathways interact to determine and coordinate these domains remains poorly understood . Dorsal closure ( DC ) of the Drosophila embryo represents a powerful model to study epithelial cell sheet sealing . In this process , JNK ( JUN N-terminal Kinase ) signalling controls leading edge ( LE ) differentiation generating local forces and cell shape changes essential for DC . The LE represents a key morphogenetic domain in which , in addition to JNK , a number of signalling pathways converges and interacts ( anterior/posterior -AP- determination; segmentation genes , such as Wnt/Wingless; TGFβ/Decapentaplegic ) . To better characterize properties of the LE morphogenetic domain , we sought out new JNK target genes through a genomic approach: 25 were identified of which 8 are specifically expressed in the LE , similarly to decapentaplegic or puckered . Quantitative in situ gene profiling of this new set of LE genes reveals complex patterning of the LE along the AP axis , involving a three-way interplay between the JNK pathway , segmentation and HOX genes . Patterning of the LE into discrete domains appears essential for coordination of tissue sealing dynamics . Loss of anterior or posterior HOX gene function leads to strongly delayed and asymmetric DC , due to incorrect zipping in their respective functional domain . Therefore , in addition to significantly increasing the number of JNK target genes identified so far , our results reveal that the LE is a highly heterogeneous morphogenetic organizer , sculpted through crosstalk between JNK , segmental and AP signalling . This fine-tuning regulatory mechanism is essential to coordinate morphogenesis and dynamics of tissue sealing .
Epithelial morphogenesis is orchestrated at the cellular level through local shape changes and tension-based dynamics . In this process , cell-cell signalling plays an essential role in coordinating gene expression programs with tissue behaviour . One of the best-studied morphogenetic movements is embryonic dorsal closure ( DC ) in Drosophila . Following germ band retraction , the embryo is only partly enveloped by the epidermis , leaving a large dorsal area covered by the transient squamous amnioserosa . DC uses a combination of signalling pathways to generate mechanical forces , cell shape changes and cell adhesion rearrangements causing the two lateral epidermal sheets to spread dorsalwards and fuse at the dorsal midline through a zipping mechanism . At the onset of DC , the dorsal-most epidermal cells , a . k . a leading edge cells or LE cells , polarise under the influence of the canonical Wingless ( Wg ) pathway and elongate along the dorso-ventral axis [1–3] . LE polarization leads to a particular organization of the dorsal cell membrane ( that in contact with the amnioserosa ) , which in particular loses adherens junction markers ( such as E-Cadherin , ECad ) and septate junction markers ( such as Discs-large , Dlg ) [2 , 4 , 5] . This redistribution results in the formation of actin-nucleating centers ( ANC ) from either side of the dorsal membrane that will transmit , along with the adherens junctions , mechanical forces [2 , 5 , 6] . LE cells start synthesising an ANC-linked acto-myosin cable that generates a contractile force allowing their alignment during dorsal migration [7 , 8] . The acto-myosin cable also participates in a ratchet-like process induced by the asynchronous pulsation of the amnioserosa cells [9] . By progressively contracting and disappearing by cell engulfment and apoptosis , the amnioserosa supplies an additional positive force , whereas the stretching lateral ectoderm opposes a resistance force to tissue progression [8 , 10] . The two contralateral ectodermal sheets then adhere to each other starting from the two canthi of the hole in a process called zipping , providing a fourth force to DC [6 , 8 , 11] . Non-muscle myosin II is involved in the generation of the four forces thanks to its motor and contractile activities [12] . The LE is the organizing centre of DC and is specified by JNK signalling . Mutations in components of the JNK pathway such as DJNKK/hemipterous ( hep ) or DJNK/basket ( bsk ) strongly affect DC , leaving embryos with dorsal holes in their cuticle [1 , 13 , 14] . JNK signalling is specifically activated in the LE cells , controlling the expression of the target genes puckered ( puc , as revealed by the lacZ-containing enhancer-trap puc-Z ) , decapentaplegic ( dpp ) and scarface ( scaf ) [1 , 15–17] . Recently , a feed-forward loop between the JNK and Dpp activities was shown to regulate the expression in the LE of three proteins , the myosin VI homologue Jaguar , the microtubule-binding protein Jupiter and the integrin-linked Zasp52 [18] . Other JNK target genes , whose expression is not limited to the LE during DC , have also been described , such as the profilin-coding gene chickadee , the transcription factor cabut , the integrin-coding genes scab and myospheroid , and the trafficking gene Rab30 [19–22] . A specific feature of DC is that it occurs in a field of cells that is not uniform along the anterior-posterior ( AP ) axis , encompassing the thoracic and abdominal regions . In addition , the ectodermal cells ( LE and lateral ectoderm ) are divided in repeated , segmental units ( T1-T3 and A1-A8 segments ) . Whereas the HOX genes define the identity of the segments along the AP axis [23] , the segment polarity genes are responsible for the elaboration of cellular patterning within each segment of the Drosophila embryo leading to the formation of anterior and posterior compartments [24] . Therefore , it is important to characterize the interaction between these orthogonal signalling events ( JNK vs . AP/segmentation signalling ) and determine how this interaction controls tissue morphogenesis . Previous studies have shown that the Wg pathway collaborates with JNK to induce dpp expression in the LE [3 , 25] . It was also demonstrated that the dynamics of closure presents robust asymmetric properties along the AP axis [10 , 26]; for instance , the anterior speed of closure is faster than the posterior one , which could be due to localized apoptotic forces present in the anterior amnioserosa [10] . Yet , whether and how segmentation and AP cues impact on DC is currently unknown . In this study , we characterized the genomic response of the JNK pathway during DC , revealing a whole set of new target genes , several of them being specifically transcribed in the LE . Quantification of these new intra-LE expression profiles uncovers a complex organization of the LE that depends on crosstalk between JNK , HOX and segmentation pathways . In this network , HOX genes can have positive or negative activities , regulating segmental features during closure . For instance , loss of the posterior HOX gene abdominal-A ( abd-A ) or Abdominal-B ( Abd-B ) leads to closure delays of the most posterior segments , indicating that they control the timing of closure . Thus , crosstalk between the AP and JNK systems shapes the LE organizing centre for proper tissue sealing dynamics .
To better characterize the LE and its role during DC , we used microarrays to identify genes expressed under the control of the JNK pathway in the Drosophila embryo ( Figs 1A and S1A ) . Three different conditions for JNK activity were tested: wild-type ( WT; used as reference ) , loss-of-function ( LOF ) and gain-of-function ( GOF ) . GOF embryos were obtained by ectopically expressing the active form of the DJNKK/Hep ( Hepact ) protein in the ectoderm using the 69B-GAL4 driver , while LOF embryos were generated using a hepr75 / hep1 hetero-allelic combination [1] . The extent of JNK activity and sample homogeneity in the three groups of embryos were assessed through in situ hybridization using a dpp probe ( S1A Fig ) . Total RNAs were prepared from carefully-staged embryos and analysed by microarray . The statistical comparison of WT and GOF conditions identified 1648 independent genes ( corresponding to 1679 probes ) which are regulated by a factor equal to or higher than 1 . 5 , of which 1001 ( 1023 probes ) are activated by JNK and 647 ( 656 probes ) are inhibited ( S1B Fig ) . For the WT-LOF comparison , 113 distinct genes ( 117 probes ) are uncovered with a fold change limit of 1 . 5 , with 35 ( 37 probes ) activated in the hep mutant ( i . e . inhibited by JNK ) and 78 ( 80 probes ) inhibited ( S1B Fig ) . These results revealed a smaller number of genes controlled in LOF compared to GOF . This difference is not surprising when comparing the extent of JNK activity ( evaluated through the expression of dpp ) in GOF/LOF vs . wild-type embryos . Indeed , JNK activity is strongly increased in GOF embryos , while only LE cells ( representing approximately a mere 200 cells of the whole embryo ) lose dpp mRNA in LOF embryos ( S1C Fig ) . This difference in amplitude between GOF and LOF likely explains the fact that only a few genes are found in common between the two lists ( S1 Table ) . For this reason , we focused on the WT-GOF comparison , and in particular on the genes that are activated by the JNK pathway . We initially performed a functional classification of 1648 GOF genes based on Gene Ontology ( GO ) terms , using the David bioinformatics tool which has the advantage of making family groups from GO terms and ranking them according to enrichment scores [27] . Functional classification of the 1001 up-regulated genes ( S2 Table ) and 647 down-regulated genes ( S3 Table ) revealed that during DC , JNK signalling controls a wide range of biological functions including cell cycle , cytoskeleton , proteolysis , mRNA transport , and regulation of transcription . To further identify bona fide JNK target genes , we performed in situ hybridization on en-GAL4/UAS-hepact embryos , in which the JNK pathway is activated in a striped pattern , as seen with the puc control ( Fig 1B ) . Because of the great number of identified genes , we first made a selection of genes based on their potential localization and function ( membrane , secreted , motors , etc… ) and on available expression data from public databases ( such as FlyExpress ) . In addition , comparison of our embryonic data set with Drosophila Schneider-2 cells induced by the lipopolysaccharide JNK activator , allowed the extraction of a list of genes having a common transcriptional response to JNK activity [28 , 29] . In total , we selected and tested 194 genes by in situ hybridization and identified 31 bona fide JNK target genes ( Fig 1B; S4 Table ) . The response of these 31 genes to JNK signalling was further confirmed by quantitative RT-PCR ( Fig 1C ) . Of the 31 genes identified , some have already been described as transcriptional targets of the JNK pathway ( jra/jun , scab , Zasp52 , scaf , Rab30 and reaper ) [17 , 18 , 21 , 22 , 30 , 31] , while others have been linked to the JNK pathway and/or to DC without being described as transcriptional targets of JNK ( Pak , icarus and ALiX ) [32–34] , thus validating our approach and genomic data . Interestingly , 10 out of 31 genes show specific expression in the LE , which is dependent on JNK activity as their expression is lost in the hep mutant , similarly to puc or dpp ( Fig 2 ) . Therefore , transcriptional profiling of embryos undergoing DC allowed the identification of several new JNK target genes ( representing 25 genes plus the previously known ones: jra/jun , scab , Zasp52 , reaper , scaf and Rab30; i . e . 31 genes in total ) , including 10 ( 8 new in addition to scaf and Zasp52 ) with a specific expression in the LE , like the well-known LE JNK target genes puc and dpp ( hereafter referred to as “LE genes” ) . Surprisingly , we noticed that the expression of most of the LE JNK target genes was not spatially and temporally homogeneous along the LE . First , the onset and arrest of gene expression show temporal regulation during DC . Genes can start to be expressed from either stage 11 ( corresponding to full germ-band extension; puc-Z , CG34417 , dpp , al , Zasp52 ) , stage 12 ( during germ-band retraction; scaf , sick ) or at the onset of DC at stage 13 ( CG5835 , hui , yb ) ( Fig 3A ) . Termination of expression also varies from gene to gene . The expression of dpp , al and Zasp52 decreases very rapidly after the start of DC and is no longer visible at stage 14 while DC is still in progress . In contrast , other genes lose their LE expression after the complete fusion of the lateral sheets of the epidermis . To our knowledge , this is the first time that early , intermediate and late JNK-target gene expression is described in a developmental process ( Fig 3A ) , whereas it is known that the JNK pathway has distinct phases of strength and duration [35–38] . Second , our initial in situ hybridization experiments suggested heterogeneous expression along the LE ( Fig 2 ) . In order to precisely define the expression pattern of each LE gene , we developed CurvedPeriodicity , a program to quantify the mRNA levels specifically in the LE from fluorescent in situ hybridizations ( FISH ) . The FISH experiments were coupled to immuno-fluorescence ( FISH-IF ) revealing the En protein to delineate the LE borders and position the segment boundaries . We extracted from CurvePeriodicity the fluorescence intensity per segment , showing the existence of 4 intra-LE profiles with either uniform expression along the entire length of the LE ( dpp and CG34417 ) , or expression domains biased towards the anterior ( puc-Z , al , sick , Zasp52 and Pvf2 ) , posterior ( hui and yb ) or central ( scaf , CG5835 , CG4199 ) portions of the LE ( Fig 3B ) . Pvf2 is a remarkable example of the ‘anterior’ group , being only expressed in the three thoracic segments ( posterior expression in Fig 2 corresponds to cardiac cells ) . In the central domain , expression can be either lowered ( scaf ) or increased ( CG5835 , CG4199 ) compared to more terminal parts . In addition to this global LE heterogeneity along the AP axis , we also found striking differences in the segmental expression of JNK target genes ( Fig 3C ) . Quantitative analysis revealed five different categories of segmental profiles: i ) a linear profile , with no significant change along the segment ( puc , CG34417 , CG4199 ) , ii ) anterior and iii ) posterior profiles , showing stronger expression in the anterior ( scaf ) or posterior ( CG5835 , Zasp52 ) compartment , respectively , iv ) a parasegmental boundary profile in which highest expression is observed between the anterior and posterior compartments of the same segment ( dpp , hui ) and v ) a segmental boundary profile in which highest expression is found at the segment boundary ( sick , al , yb , Pvf2 ) . Altogether , these results identify a highly complex , spatio-temporal regulation of the JNK transcriptional response during DC both along the anterior-posterior axis and within segments , raising the question of the mechanisms setting these different transcriptional programs at the LE . To address this question , we used the JNK target gene scaf as readout because it exhibits a strong expression in the LE that undergoes both AP and segmental regulations ( Figs 3B , 3C and 4A ) . Quantification in the A and P compartments from each segment revealed that scaf is down-regulated in the P compartments of the central abdominal segments , from A1 to A6 ( Fig 4C ) . The posterior factor En has been shown to have both activating and repressive transcriptional activities [39] , making it a potential scaf regulator in this process . In en null mutant embryos , scaf expression is no longer segmented ( Figs 4A and S4A ) . Moreover , the overexpression of en with the pannier ( pnr ) -GAL4 driver in the whole dorsal ectoderm leads to down-regulation of scaf expression , but strikingly , this inhibition only occurs in the abdominal segments . Of note , en had no influence on the non-segmented expression of the puc-Z JNK reporter gene ( Fig 4A ) . Altogether , these results indicate that en is a LE negative regulator , repressing the posterior expression of a subset of JNK target genes , including scaf . To assess if En acts directly or through a relay mechanism , we expressed a mutant form of En in which the repressor domain has been replaced by the VP16 transactivation domain ( VP16En ) [39] . In contrast to the wild-type form , the overexpression of VP16En has no inhibitory effect on scaf expression , supporting a direct repressive activity of En ( Fig 4B and 4C ) . This result also indicates that the effect of en occurs independently of the auto-regulatory loop involving wg and hh . Additionally , we observed that in VP16En-overexpressing embryos , the scaf expression level in the posterior compartments of central segments is higher than in WT embryos , indicating that VP16En can compete with endogenous En ( Fig 4C ) . Altogether these results indicate a direct role of the segment polarity gene en in patterning the LE during DC , shown by the control of the JNK target gene scaf ( Fig 4D ) . Despite being expressed in all segments , the repressor activity of En is restricted to central abdominal segments , indicating the existence of additional regulatory mechanisms to circumscribe En action and refine LE patterning . We thus tested the role of the HOX genes , which control segment identity along the AP axis [23] . The HOX gene abd-A is expressed and acts in the abdominal segments . In abd-A null mutant embryos , the expression of scaf is increased in segments A1-A7 ( i . e . in the abd-A domain; Figs 5A and S4B ) , indicating that abd-A negatively regulates scaf . Interestingly , quantification of scaf expression at the segmental compartment level shows that it is no longer repressed in the P compartments of central segments ( A1-A6 ) . This abd-A mutant phenotype is therefore similar to the en mutant phenotype , suggesting that both genes work together to control scaf in the LE . When overexpressed , abd-A leads to decreased scaf expression in the posterior segments ( Figs 5B and S4C ) . In the thoracic segments where abd-A is normally not expressed , we observed scaf down-regulation in the P compartments where en is also present , indicating that the inhibition of scaf expression in the P compartment requires both En and Abd-A . This phenotype explains why the overexpression of En has a restricted effect in the abdominal segments where abd-A is expressed , with no effect in the thoracic segments where abd-A is absent ( Fig 4A ) . Therefore , En and Abd-A collaborate to down-regulate scaf in the central segments , where their pattern of expression overlaps . In conclusion , En and Abd-A are two repressors regulating LE gene expression ( Fig 5C ) . We extended our study to the other HOX genes that are expressed in the dorsal ectoderm during DC ( Fig 5F ) by testing the role of Sex combs reduced ( Scr; expressed in the T1 segment ) , Antennapedia ( Antp; expressed in the T1-T3 ) , Ultrabithorax ( Ubx; strongest expression in A1 , declining until A7 ) and Abd-B ( strongest expression in A8 , declining towards A6 ) [23] . Null mutants for either Hox gene showed reduced scaf mRNA levels in their expression domain , suggesting they all have a positive role on scaf transcription ( Figs 5D and S4D ) . Consistently , upon overexpression , these HOX genes were all able to ectopically up-regulate scaf expression ( Fig 5E ) . Therefore , in contrast to abd-A which acts as a repressor , Scr , Antp , Ubx and Abd-B activate the expression of scaf in their respective functional domain . All these results reveal that key transcription factors controlling AP axis and segmentation work together generating a complex gene regulatory network to control gene expression along the LE . This LE network , which involves both activating ( JNK , Scr , Antp , Ubx and Abd-B ) and repressive ( abd-A and en ) activities , leads to a remarkable transcription profile of scaf in the LE , with high expression at the poles of the embryo and low expression in the P compartments of A1-A6 segments ( Fig 5F ) . HOX proteins cooperate with cofactors to activate transcription [40] . Homothorax ( Hth ) and Extradenticle ( Exd ) are known to interact with each other and with HOX proteins to form a transcriptional complex binding specific cis-regulatory sequences [41] . Hth contains a homeodomain ( HD ) in its C-terminal part that is responsible for DNA binding . In addition , Hth promotes Exd nuclear translocation and its binding to DNA [40 , 42–44] . In order to inquire the role of HOX cofactors on scaf expression , we thus analysed embryos mutants for hthP2 , a strong hypomorphic allele resulting in an absence of Hth protein expression [45] . As a result , Exd cannot be translocated into the nucleus . In absence of cofactor function , scaf expression decreases ( Fig 5G ) . This is especially the case in the more extreme segments , whereas the effect is only moderate in the central segments . Indeed , we still observe in the region A1-A6 of the hthP2 mutant the segmented pattern of scaf expression , with the same level of transcripts in the posterior compartments than wild-type embryos ( Fig 5G , right panel and S4E Fig ) . These results indicate that the HOX cofactors Hth and Exd are required for the positive regulation of scaf expression in the LE by Scr , Antp , Ubx and Abd-B , but do not participate in the negative action of En and Abd-A . Therefore , in addition to the core LE network involving the transcription factors AP1 , HOX and En , the HOX cofactors have a distinctive contribution along the AP axis according to the identity of the segments , further refining LE patterning . To determine whether HOX genes have a more general role on JNK target genes , we investigated the profiles of hui , yb and Pvf2 genes , which like scaf display a complex pattern of expression along the LE . hui and yb exhibit a stronger expression in the posterior part of the embryo ( ‘posterior’ group ) , whereas Pvf2 expression is limited to the thoracic segments ( ‘anterior’ group; see Fig 3B ) . In abd-A and Abd-B mutants , the expression of hui and yb is diminished in the respective HOX domains , indicating that both genes activate the expression of hui and yb , which is responsible for their stronger expression at the posterior pole of the embryo ( Fig 6A and 6B ) . For Pvf2 , we observed a strong expression increase from T3 to A6 segments in the abd-A mutant and a weak up-regulation in the A6/A7 segments in the Abd-B mutant ( Fig 6C ) . Thus , the restricted expression of Pvf2 in the thoracic segments is due to the inhibitory action of the two abdominal genes , especially abd-A . These results indicate that abd-A and Abd-B can either act as repressors or activators to shape the asymmetric expression profiles of JNK target genes in the LE . In order to assess the functional relevance of Hox gene regulation during DC , we analysed the phenotype of HOX gain- and loss-of-function embryos . Overexpression of Abd-B in the dorsal ectoderm results in a strong DC phenotype , as revealed by holes present in the embryonic dorsal cuticles ( Fig 7A ) . Therefore , ‘posteriorisation’ of the LE through Abd-B overexpression , which leads to ectopic activation of JNK target genes ( Fig 5E and 5F ) , is detrimental to DC , indicating the importance of proper patterning of the LE along the AP axis . In contrast , abd-A overexpression does not create cuticle holes ( Fig 7A ) , which might be due to the regulation of a different set of target genes compared to Abd-B . To characterize the role of the HOX genes in their normal domain of expression , we analysed the effect of their loss-of-function . First , we observed the cuticles of the abd-A and Abd-B mutants , which resemble that of WT embryos ( Fig 7A ) . The hth mutant exhibits a strong DC phenotype , with holes located in the anterior part . This regionalisation of the phenotype is likely reflecting the higher expression of hth in the thoracic part of the embryo [45] . In addition , this strong phenotype is probably due to the effect on several HOX genes , in particular the thoracic ones ( Scr , Antp and Ubx ) , as previously shown ( Fig 5G ) . We then decided to analyse in more details the DC phenotype of the HOX genes by analysing segment closure during early DC . In the WT embryo , closure of the posterior segment A8 and closure of the anterior segment T1 take place approximately at the same time ( Fig 7B ) . In the hth mutant , when A8 has just closed , the segment T1 is far from being closed , revealing a strong delay in the closure of the anterior part of the embryo ( Fig 7B and 7C ) . We also analysed the abd-A and Abd-B mutants , and both show a delay in the closure of the posterior segment A8 ( Fig 7B and 7C ) . Therefore loss of HOX gene function triggers DC defects in their respective field of action , and the severity of the phenotype depends on the number of HOX genes affected . To get better insight into the role of individual HOX genes on the dynamics of DC , we performed live imaging on Abd-B mutant embryos , which display a clear delay in DC ( Fig 7D; S1 Movie ) . Despite this delay , they eventually close and show no hole in their dorsal cuticles ( Fig 7A ) , reflecting the high degree of adaptability and robustness of DC in challenged conditions [8 , 46 , 47] . Analysis of the point of closure at the LE indicated that closure of the Abd-B mutant was shifted towards the posterior , compared to WT embryos whose final point of closure is located at the middle of the LE ( Fig 7E ) . Early delay observed in fixed embryos ( Fig 7B and 7C ) is thus maintained throughout DC to trigger an asymmetric closure , indicating that no compensatory mechanism takes place . The posterior shift and closure delay seen in Abd-B embryos can be explained by a progressive reduction of the posterior speed of closure . Indeed , this speed shows a clear reduction of 59% ( mid DC ) and 78% ( late DC ) ( S5A Fig ) . The anterior speed also decreases , but to a less extent ( 58% in late DC; see Discussion ) . The DC defect of the hth mutant seems to be generated by an inefficient zipping in the anterior part , but the strong phenotype makes it difficult to interpret . We therefore analysed the zipping zone of the abd-A and Abd-B mutant embryos . Antibody stainings indicated that the establishment and positioning of LE markers and of the zipping zone are correct , such as the ANC-localised protein Enabled ( Ena ) , the adherens junction protein ECad and the septate junction protein Dlg ( Figs 7F and S5B ) . However we noticed a reduction in the length of the posterior zipping zone in both mutants ( Fig 7F and 7G; S5B and S5C Fig ) . The WT zipping zone , in the anterior or posterior part , is about 11 μm long on average , which corresponds to the length previously published [6] . Whereas the anterior zipping zone of the posterior HOX mutants does not vary , there is a reduction of the posterior one ( between 7 and 8 μm ) . As the zipping zone provides a force that is important for DC [6 , 8 , 11] , reduction of its length is likely to affect the dynamics of closure , as observed with the abd-A and Abd-B mutants . We finally tested whether the action of the HOX genes takes place in the dorsal most cells of the ectoderm . Reducing Abd-B expression by RNAi ( Abd-Bi ) with pnr-GAL4 ( expression in the dorsal ectoderm and amnioserosa ) induces a phenotype of posterior closure delay , whereas the AS-GAL4 driver ( expression in the amnioserosa only ) does not give any DC phenotype ( S5D and S5E Fig ) . This result indicates that the activity of Abd-B in the dorsal most cells , and not in the amnioserosa , is required to control DC . Altogether , our results indicate that the HOX genes are essential for the correct timing of closure of the segments of the Drosophila embryo by influencing the formation of the zipping zone and thus the efficacy of zipping . They further demonstrate the importance of the interplay between the HOX genes and the JNK pathway in the LE to control the dynamics of DC along the AP axis .
Our identification of several new JNK target genes during DC and analysis of their quantitative expression pattern uncover the complex transcriptional response taking place in the LE morphogenetic domain . Results reveal an intricate regulatory network integrating multiple signalling layers . In this process , AP positional information and JNK signalling cooperate to generate a highly patterned , yet apparently smooth and regular LE . Mutant analysis shows that LE partitioning into discrete domains is important to control the coordination , hence dynamics of the whole closure process . The LE is a major component of DC , being the site of JNK activity and actin cable assembly; it also provides an active boundary with the amnioserosa , driving epidermal spreading and seamless tissue sealing . Therefore , it is important to determine its morphogenetic and signalling features and how these are dynamically controlled . To this end , first we identified a new set of target genes whose expression in the dorsal ectoderm is dependent on JNK activity during DC . Transcriptome analysis allowed us to identify 1648 independent genes which are up- or down-regulated in JNK activated embryos . Filtering of this large set led us to focus on a group of 194 genes whose expression was analysed by quantitative in situ hybridization in different genetic conditions . Transcriptional profiling unveiled 31 Drosophila JNK target genes of which only a fraction was already known , including jra/jun , reaper , Zasp52 and scab [21 , 30 , 31] . Amongst novel targets were also Scaf and Rab30 the role of which we have previously described during DC [17 , 22] . Two categories of JNK target genes can be distinguished: genes that are specifically expressed in the LE and genes whose expression is more ubiquitous in the dorsal ectoderm . Genes belonging to the latter category may play a general role in the ectoderm under the control of different pathways , for example in the case of Rab30 . In contrast , LE-specific genes likely play a specific role during DC , as is the case for puc , dpp and scaf . However , it is also possible that some of the new genes , despite being expressed in the embryo in a JNK-dependent manner , are not involved in DC . These target genes thus remain under the control of JNK , but are functionally ‘silent’ during DC . This behaviour is best illustrated by the reaper gene , whose expression is JNK-dependent in the embryo ( Fig 1B and 1C ) , but which does not seem to have any function in the LE , only later during development or at the adult stage . Surprisingly , quantitative analysis of LE-specific gene expression profiles showed a variety of previously uncharacterized expression patterns along the LE , with two levels of regulation , AP and segmental . These observations reveal a new property of the LE which appears highly patterned along the AP axis , contrasting with the homogenous and linear structure previously envisioned . In addition , the higher order regulation that emerges from these results provides every LE cell with its own identity through the cross-talk between JNK , AP and segmental information . Such cell-level patterning through signalling crosstalk [48] is likely essential for coordination and robustness of closure as well as segment matching . In this view , recent work showed that Wg and JNK interact at the LE to control the formation of specific mixer cells at segment boundaries [49] . Previous work showed that , instead of acting independently , HOX and segmentation genes can be coupled to regulate target genes in the embryo [50] . Here , we reveal an additional layer of regulation involving the ‘morphogenetic’ JNK signalling pathway . During DC , JNK acts as a tissue-specific switch whose activity can be regulated by HOX and segmentation pathways , providing positional information and segmental organization to a moving tissue boundary . Thus , a multi-layered or ‘onion-like’ regulatory model allows for several levels of regulation/information to pile up in order to regulate individual cellular behaviours important for tissue morphogenesis . Each layer can act positively or negatively on LE target gene expression , generating a complex repertoire of regulatory pathways . Distinct categories of expression profiles have been identified in this study through the analysis of individual target genes , with likely more gene-specific patterns to be anticipated . For example , the same HOX gene ( abd-A or Abd-B ) can have activating or repressive activity according to the target gene , as is the case for the transcription factor En [39] . Molecular functional characterization of cis-regulatory elements controlling LE gene expression will bring a more detailed view of how transcription factor complexes are formed , how specificity of DNA recognition is achieved and how activating or repressive activities are regulated to generate LE patterning . scaf proves to be a remarkable case among the JNK target genes , summarizing the different levels of regulation that can be integrated into a single promoter . Not only is it strongly expressed in the LE in a JNK-dependent manner , but it is also regulated by both the segmentation gene en and the HOX genes . In particular scaf displays a transcriptional response induced by all the trunk HOX genes tested , being positively controlled by Scr , Antp , Ubx and Abd-B and negatively by abd-A . It can therefore be considered as a general HOX target gene , i . e . regulated by most Hox paralogs , as previously defined [40] . Another example of a general target is the Drosophila gene optix , which is activated by the head HOX genes labial and Deformed ( Dfd ) and inhibited by the trunk HOX genes [51] . Nonetheless the general HOX target genes do not represent the majority . A genomic analysis in the Drosophila embryo identified more than 1500 genes regulated by at least one of the six HOX paralogs tested ( Dfd , Scr , Antp , Ubx , abd-A , Abd-B ) [52] . Only 1 . 3% of these genes are regulated by the six paralogs and 1 . 5% by the five paralogs that we used in our study . Interestingly more than 40% of the ~1500 HOX target genes are also present in the JNK genomic data set that we obtained . This strong overlap well reflects the fact that the LE runs along most of the body AP axis encompassing the thorax and abdomen . More importantly , it also indicates that AP patterning plays a crucial role in the regulation of DC , as shown in this study . Live imaging and mathematical modelling revealed asymmetries in the geometry and zipping process along the AP axis [8 , 53] , which can be attributed to local constraints induced by head involution and apoptosis [10 , 26] . Head involution is concomitant with DC and induces tension in the anterior part of the embryo , explaining why the DC phenotypes are almost exclusively observed in the anterior part , leading to the so-called ‘anterior-open phenotype’ . The exception to this rule is the experimental manipulation of the posterior zipping rate through localized laser ablation of the amnioserosa close to the canthus , which induces a strong delay of posterior closure [26] . Our results with the abd-A and Abd-B mutants show that posterior delay can also be obtained in genetically-perturbed embryos . However , while anterior zipping is slightly up-regulated when posterior zipping is laser-targeted [26] , we showed that the anterior speed of closure is diminished in the Abd-B embryo . Thus , compensatory mechanisms may only appear when tissue integrity is severely impaired . Apoptosis was also proposed to participate in the asymmetric properties of DC [10] . Delamination of apoptotic cells in the anterior amnioserosa produces forces that are responsible for a higher rate of anterior zipping . However , the phenotype that we observed with the abd-A or Abd-B mutation cannot be attributed to defects in this mechanism , as the rate of apoptosis is already very low in the posterior amnioserosa . Therefore , our data reveal a genetic control of zipping through precise transcriptional regulation in the LE . Overall , our work provides a framework for apprehending how the HOX selector genes and their cofactors collaborate with other signalling pathways to generate specific transcriptional responses allowing morphogenetic patterning and proper coordinated development .
The following fly stocks were used in this study: w1118 ( BDSC#3601 ) as WT flies , y w hep1 and y w hepr75 [1] , UAS-hepact ( BDSC#9306 ) , pucE69 ( puc-Z ) [54] , 69B-GAL4 ( BDSC#1774 ) , pnr-GAL4 ( BDSC#3039 ) , en-GAL4 ( gift from A . Brand ) , AS-GAL4 ( c381-GAL4; BDSC#3734 ) , enX31 [55] , UAS-en [56] , UAS-HA::VP16::en [39] , Scr17 ( BDSC#3400 ) , Antp[Ns-rvC4] ( BDSC#1830 ) , Ubx1 ( BDSC#626 ) , abdAM1 and AbdBD18 [57] , UAS-Scr::HA , UAS-Ubx::HA and UAS-abdA::HA [58] , UAS-Antp [59] , UAS-AbdBm ( BDSC#913 ) , UAS-AbdBi ( VDRC#12024 ) [60] , UAS-Dicer2 ( BDSC#24650 ) , hthP2 [61] , TM3 , dfd-lacZ [62] . For live imaging , we used arm::GFP ( BDSC#8556 on chromosome II; BDSC#8555 on chromosome III ) and created the recombinant line arm::GFP , Abd-BD18 that we balanced with TM3 , twi-GAL4 , UAS-GFP ( BDSC#6663 ) to select the homozygote mutant embryos . We also created the following line for the Abd-B RNAi: UAS-Dicer2;UAS-CD8::RFP , UAS-Abd-Bi , allowing the selection of AbdBi embryos thanks to the expression of CD8::RFP . Three genetic conditions were compared: wild-type ( WT , w1118 ) , gain of function ( GOF , 69B-GAL4 > hepact ) and loss of function ( LOF , hep1/hepr75 ) . The 69B-GAL4 driver allows a uniform expression in the ectoderm . For the LOF embryos , we crossed y w hepr75/FM6 virgin females to y w hep1/Y males to obtain y w hepr75/y w hep1 virgin females that were then crossed to y w hep1/Y males . 100% of the embryos coming from this last cross are mutant for DJNKK because hep1 behaves as a total loss of maternal function while being zygotically viable [1] . Two hour egg collections were incubated at 25°C during the time necessary to obtain 85 to 90% embryos undergoing DC ( ~9 hrs ) . Because of a certain variability of development that we were not able to control , a fraction of each embryo was systematically tested by dpp in situ hybridization , and the collections that did not correspond to the definite criterion ( above 85% of stage 13–14 embryos ) were eliminated . Embryos from three biological replicates for each genetic condition were dechorionated with 50% bleach , frozen in nitrogen liquid and stored at -80C . Embryos were homogenized in RLT buffer ( QIAGEN ) /beta-mercaptoethanol using a conventional rotor–stator homogenizer . Total RNA was prepared with the Qiagen RNeasy mini kit for animal tissues according to the manufacturer's instructions . Biotinylated cRNA were prepared according to the standard Affymetrix protocol from 2 μg total RNA . The biotinylation , the hybridization and the scan were done at the Affymetrix Platform located at the Institut Curie , Paris . Bioinformatics analysis was performed using RMA ( "Robust Multi-array Average" ) and SAM ( "Significance Analysis Microarray ) under R/Bioconductor . The false discovery rate ( FDR ) of the GOF analysis is excellent ( 0 . 85% ) , evaluating the number of false positive to 14 out of the 1648 genes identified . The FDR associated with the LOF comparison is 3 . 19% , i . e . corresponding to 3 or 4 false positive out of the 113 isolated genes . The enrichment scores were obtained using the DAVID Bioinformatics Resources [27] . Comparison of microarray data sets was performed with THEA [29] . Microarray data have been deposited in the Gene Expression Omnibus ( accession number GSE21805 ) . The embryos were prepared using a standard protocol . Briefly , embryos resulting from 16hr collections at 25°C , or 29°C for the overexpression experiments , were dechorionated in 50% bleach , fixed in 4% formaldehyde and devitellinized in a heptane:methanol ( 1:1 ) mix . Embryos were then freshly used or kept at -20°C for a few weeks . Digoxigenin ( DIG ) -labelled RNA probe synthesis was performed with T3 , T7 or Sp6 RNA polymerases as recommended ( Promega , New England Biolabs ) from cDNA collections ( Drosophila Genomics Resource Center ) . Classical in situ hybridization ( ISH ) experiments were done using a standard protocol with the anti-DIG antibody conjugated to alkaline phosphatase ( 1/2000; Roche Diagnostics ) . Staining was performed with NBT/BCIP reagent ( Sigma ) . For fluorescent ISH coupled to immuno-fluorescence ( FISH-IF ) , the second fixation step was accomplished using freshly prepared paraformaldehyde and the proteinase K treatment was omitted . Primary antibodies were: rabbit anti-En ( 1/100; Santa Cruz ) , mouse anti-Scr ( 1/50; DSHB ) , mouse anti-Antp ( 1/50; DSHB ) , mouse anti-Ubx ( 1/50; DSHB ) , mouse anti-abd-A ( 1/100; DSHB ) , mouse anti-Abd-B ( 1/50; DSHB ) , mouse anti-HA ( 1/500; Covance ) , chicken anti-β Galactosidase ( 1/500; GeneTex ) . Sheep anti-DIG antibody coupled to horseradish peroxidase ( 1/500; Roche Diagnostics ) was joined to the secondary antibodies: anti-rabbit Al488 ( 1/400; Molecular Probe ) , anti-mouse Cy5 ( 1/100; Jackson ImmunoResearch ) , anti-chicken DyLight649 ( 1/200; Life Technologies ) . Revelation was done twice for 5–10 minutes with Tyramide Signal Amplification ( PerkinElmer ) . Embryos were mounted in Mowiol 4–88 Reagent ( Calbiochem ) . Images were acquired with a LSM 710 Zeiss confocal microscope using a 40X Objective . Confocal Z sections that entirely encompass the LE of 15 to 20 embryos per genotype ( except for the en mutant: n = 10 ) were acquired as 2048X2048–12 bit images and maximum intensity projections were created . To quantify the fluorescence signal in the LE , we developed CurvedPeriodicity , a standalone user-friendly program coded in Matlab ( the Mathworks ) . The steps of the image pre-treatment are 1 ) to correct the background measured in the amnioserosa to linearize the intensity signal to fluorescence , 2 ) to extract the ribbon containing the LE and to make it linear , 3 ) to split the ribbon by semi-automatic demarcation of the segment boundaries , and 4 ) to normalize each segment in length . From these data , the software quantifies the mRNA signal as the mean intensity projections along the LE . It yields both the signal distribution per segment along the LE ( dLE ) and the mean distribution along a Normalized Segment ( dNS ) . Segments are composed , on average , of 10 cells with 7 in the anterior compartment and 3 in the posterior compartment . Therefore dNS was divided in 10 regions and the parasegmental boundary was set between the 7th and 8th cells . CurvedPeriodicity calculates the average signal from each cell of each segment , and also the AP ratio per segment . Taking advantage of the normalization , the software is able to pool the confocal images per condition , synthetizing the data and extracting the mean and s . d . for dLE and dNS , and to export it directly into an Excel file . In case of negative numbers ( i . e . expression below background ) , the expression level was set to 0 . For the AP analysis , the mean of the signals from each segment from T1 to A7 ( n embryos; n = 15 to 20 ) was calculated . For the compartmental analysis , the signals for a given cell of all the segments ( n segments = 150 to 200 , with 10 segments per embryo ) were averaged . We used the same RNA samples ( control: w1118 , GOF: 69B-GAL4 > hepact , LOF: hep1/hepr75 ) than the ones prepared for the microarray analysis . Reverse-transcription was performed with SuperScript III Reverse Transcriptase ( Invitrogen Life Technology ) after DNase I digestion with a mix of oligo-dT and random primers . Q-PCR was performed with the Mastermix Plus for SYBR Green containing Rox ( Eurogentec ) with the endogenous rp49 gene for normalization . The list of primers that were used is available upon request . Standard curves of all the couples of primers presented an efficacy of amplification comprised between 95% and 110% with a coefficient of determination R2 of at least 0 . 995 . For each condition we did three technical replicates from one biological sample . Results were analysed with the StepOne software v2 . 1 ( Applied Biosystems ) . Embryos were collected for 24 hours and incubated at 25°C or 29°C to let the wild-type larvae crawl away . The cuticles of unhatched ( dead ) embryos were prepared as following: embryos were dechorionated , mounted in 100 μl of a mix ( 1:1 ) made of lactic acid and Hoyer’s mounting medium ( 30 g of gum Arabic , 50 ml of distilled water , 200 g of chloral hydrate , 20 ml of glycerol ) , and incubated overnight at 65°C . Images were taken with a Nikon Coolpix 990 camera under a Leica DMR microscope . Embryos resulting from 16hr collections at 25°C were dechorionated in 50% bleach , fixed in 4% formaldehyde and devitellinized in a heptane:methanol ( 1:1 ) mix . Embryos were then quickly rinsed with PBS-triton X-100 0 . 1% and the following primary antibodies were then used: rabbit anti-En ( 1/200; Santa Cruz ) , mouse anti-Ena ( 1/100 , DSHB ) , rat anti-ECad ( 1/100 , DSHB ) and/or mouse anti-Dlg ( 1/200 , DSHB ) . After washes , secondary antibodies were added: anti-rabbit Al488 ( 1/400; Molecular Probe ) , anti-mouse Al488 ( 1/400; Molecular Probe ) , anti-mouse Al546 ( 1/400; Molecular Probe ) , anti-rat Al546 ( 1/400; Molecular Probe ) and/or anti-rat Al647 ( 1/400; Jackson ImmunoResearch ) . After washes , embryos were mounted in Mowiol 4–88 Reagent ( Calbiochem ) . Images were acquired with a LSM 780 Zeiss confocal microscope using a 25X or 63X Objective . Closure delay was quantified by measuring with ImageJ the distance between the matching posterior compartments ( stained with En ) of the T1 ( D-A ) or A7 ( D-P ) segment when the opposing segment ( A8 or T1 , respectively ) just closed . For the zipping zone , quantification was done with ImageJ by tracing a line from the point where the two opposing LE are in close contact to the point where a stable junction ( adherens junction with ECad or septate junction with Dlg ) is formed . Embryos collected from overnight egg-laying were dechorionated ( 50% bleach ) and stage 13 embryos ( at the onset of DC ) were selected under the binocular . They were mounted in Halocarbon 95 oil between two cover slips separated by spacers , glued on their ventral part . Two hydrating chambers ( watered cotton ) were positioned on the sides . 1024X1024 Z-stacks ( 1 to 2 μm/image ) were acquired over 4 hours with a Zeiss 780 confocal microscope . Distances necessary for calculation of the speed of closure , H and L ( Fig 7 ) were measured from maximum intensity projections of optimized Z sections with FIJI . Stages of DC ( expressed as % of DC ) were estimated by calculating the ratio of the seam ( fused LE ) and L ( projected LE at the midline ) . L was divided in 8 sub-domains ( corresponding approximately to 8 segments ) , as previously described [63] . | Dorsal closure of the Drosophila embryo is used as a paradigm to study epithelial sealing and is related to wound healing . This vital process relies on the dorsal migration of the two lateral ectodermal sheets and is necessary for the protective epidermis to completely envelop the embryo . The row of cells located at the front of migration , called the leading edge , is the organizing center of the process , where key signaling pathways turn on specific gene expression . Here we used a genomic approach to identify new genes whose expression is restricted to the leading edge . A quantitative analysis revealed differential expression along the anterior-posterior axis of the leading edge , which was considered for a long time as homogeneous or amorphous . We demonstrate that anterior-posterior cues provide an orthogonal coordinate system specifying cell identity along the whole leading edge , making it a highly patterned morphogenetic center . We further show that these anterior-posterior cues are functionally important , controlling the dynamics of dorsal closure and participating to the robustness of the process . Our work sheds new light on the role of anterior-posterior cues in epithelial tissue sealing related to wound-healing . | [
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"biology... | 2017 | Signalling crosstalk at the leading edge controls tissue closure dynamics in the Drosophila embryo |
Micrurus snake bites can cause death by muscle paralysis and respiratory arrest , few hours after envenomation . The specific treatment for coral snake envenomation is the intravenous application of heterologous antivenom and , in Brazil , it is produced by horse immunization with a mixture of M . corallinus and M . frontalis venoms , snakes that inhabit the South and Southeastern regions of the country . However , this antivenom might be inefficient , considering the existence of intra- and inter-specific variations in the composition of the venoms . Therefore , the aim of the present study was to investigate the toxic properties of venoms from nine species of Micrurus: eight present in different geographic regions of Brazil ( M . frontalis , M . corallinus , M . hemprichii , M . spixii , M . altirostris , M . surinamensis , M . ibiboboca , M . lemniscatus ) and one ( M . fulvius ) with large distribution in Southeastern United States and Mexico . This study also analyzed the antigenic cross-reactivity and the neutralizing potential of the Brazilian coral snake antivenom against these Micrurus venoms . Analysis of protein composition and toxicity revealed a large diversity of venoms from the nine Micrurus species . ELISA and Western blot assays showed a varied capability of the therapeutic antivenom to recognize the diverse species venom components . In vivo and in vitro neutralization assays indicated that the antivenom is not able to fully neutralize the toxic activities of all venoms . These results indicate the existence of a large range of both qualitative and quantitative variations in Micrurus venoms , probably reflecting the adaptation of the snakes from this genus to vastly dissimilar habitats . The data also show that the antivenom used for human therapy in Brazil is not fully able to neutralize the main toxic activities present in the venoms from all Micrurus species occurring in the country . It suggests that modifications in the immunization scheme , with the inclusion of other venoms in the antigenic mixture , should occur in order to generate effective therapeutic coral snake antivenom .
The Elapidae family has about 250 species , distributed from the Southeastern and Southwestern United States , through Mexico , Central America and South America , and are also found in Asia , Africa and Australia [1] . In the Americas , there is a group of more than 120 species and subspecies , divided into three genera: Micruroides , with one species; Leptomicrurus with three , and Micrurus , with almost 70 species [1] , [2] , [3] , [4] . Coral snakes have a large geographical distribution in Americas , inhabiting extremely diverse environments , from lowland rainforests and deserts to highland cloudy forests [1] , [5] . Most of the snakes of the Micrurus genus has terrestrial to subfossorial habits , however , some species are semi-aquatic , such as M . surinamensis and M . lemniscatus [1] . Most coral snakes have a color pattern of some combination of the red , yellow or white , and black , usually disposed in rings . They are proterogliphous animals , presenting the fixed small teeth at the forefront of the mouth . Food is generally composed of small snakes , but may also include lizards and amphisbaenians . Certain species have specialized nutritional habits , feeding on caecilians , swamp eels , and other type of fishes and even onycophorans and other invertebrates [1] . Snakes such as M . lemniscatus and M . surinamensis feed on fish and M . hemprichii of peripatus [6] , [7] , [5] . The Micrurus species of public health importance are M . fulvius in the United States and Mexico , M . alleni , M . diastema and M . nigrocintus in Central America and M . altirostris , M . corallinus , M . dumerilii , M . frontalis , M . mipartitus , M . spixii , M . surinamensis and M . isozonus in South America [8] , [9] , [10] . In Brazil , some species are quite common and widespread in large areas of the territory , such as M . corallinus , M . frontalis , M . ibiboboca , M . lemniscastus , M . spixii and M . surinamensis . Human envenomations by coral snakes are relatively rare due to their subfossorial habits; however , the case fatality , attributable to respiratory paralysis , may be high [11] . A variety of local and systemic manifestations of envenoming has been described in patients bitten by different species of coral snakes [5] , [12] , [11] . The main feature of the venom action is the neurotoxicity , although , experimentally , it has been reported that some Micrurus venoms may induce myotoxicity and local lesions [13] , [14] . Neurotoxicity can be produced by a post-synaptic action , through blockage of the end-plate receptors by alpha neurotoxins , as determined for M . frontalis venom , or by a pre-synaptic-like activity , which causes inhibition of acetylcholine release at the motor nerve endings , as induced by M . corallinus venom . There are also venom toxins , such as cardiotoxins and myotoxic phospholipases A2 from M . nigrocinctus and M . fulvius , which block the end-plate receptors and depolarize the muscle fiber membrane [15] . Experimental studies have shown that Micrurus venoms are cardiotoxic , myotoxic , hemolytic , hemorrhagic and edematogenic [13] , [14] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] . Furthermore , many enzymatic activities have been detected including those derived from phospholipase A2 , hyaluronidase , phosphodiesterase , leucine amino peptidase , L-amino acid dehydrogenase , acetylcholinesterase , alkaline phosphomonoesterase and L-amino acid oxidase actions [25] , [26] . Anticoagulant action was also identified in some coral venom species , but none or little proteolytic activity was detected [26] . Therefore , common characteristics , as well as variability of some biological properties , have been demonstrated in comparative studies of Micrurus venoms [23] , [25] , [26] , [27] , [28] . The transcriptomic analysis of a Micrurus snake venom gland ( M . corallinus ) was recently described [29] . Toxin transcripts represented 46% of the total ESTs and the main toxin classes were neurotoxins , i . e , three-finger toxins ( 3FTx ) and phospholipases A2 ( PLA2s ) . It was also showed that the post-synaptic components ( 3FTx ) were very diverse in terms of sequences , possibly aiming to achieve different types of receptors , whereas the pre-synaptic component ( PLA2 ) was more conserved . The high expression of both types of these neurotoxins is in agreement with the known presence of pre- and post-synaptic activities in the Micrurus venoms . However , eight other classes of toxins were found , including C-type lectins , natriuretic peptide precursors and high-molecular mass components such as metalloproteases and L-amino acid oxidases . The specific treatment for Micrurus envenomation is the intravenous application of heterologous antivenom . In Brazil , the coral snake therapeutic antivenom produced by Butantan Institute is obtained by the immunization of horses with a mixture containing equivalent amounts of M . corallinus and M . frontalis venoms [30] . In view of the fact that Micrurus venoms can exhibit a diversity of composition and toxicity , the therapeutic antivenom may not be capable to fully recognize all the major components of the distinct venom species occurring in the country . Therefore , the aim of this study was to characterize some biological properties of venoms from nine species of Micrurus , including those used for serum preparation , i . e . , M . frontalis and M . corallinus , evaluate their antigenic cross-reactivity , using the Brazilian coral snake antivenom , as well as to test the ability of this antivenom to neutralize the main toxic activities of these venoms .
Triton X-100 , Tween 20 , bovine serum albumin ( BSA ) , L-α-phosphatidylcholine , ortho-phenylenediamine ( OPD ) , hyaluronic acid and goat anti-horse ( GAH ) IgG horseradish peroxidase labeled ( IgG-HRPO ) were purchased from Sigma ( St . Louis , Missouri , USA ) . Goat anti-horse ( GAH ) IgG labeled with alkaline phosphatase ( IgG-AP ) , 5-bromo-4-chloro-3-indolyl-phosphate ( BCIP ) and nitroblue tetrazolium ( NBT ) were from Promega Corp . ( Madison , Wisconsin , USA ) . The P2E Fluorescent Resonance Energy Transfer ( Abz-FEPFRQ-EDnp ) substrate was synthesized and purified as described in Hirata et al . [31] . Venoms from Micrurus frontalis , M . corallinus , M . ibiboboca , M . hemprichii , M . spixii , M . fulvius , M . altirostris , M . surinamensis and M . lemniscatus were supplied by Herpetology Laboratory from Butantan Institute , SP , Brazil . Stock solutions were prepared in PBS ( 10 mM sodium phosphate containing 150 mM NaCl , pH 7 . 2 ) at 1 . 0 mg/mL . The Brazilian therapeutic coral snake antivenom , produced by hyperimmunization of horses with venoms from M . corallinus ( 50% ) and M . frontalis ( 50% ) , was obtained from Butantan Institute , SP , Brazil . Samples of 20 µg of Micrurus venoms were solubilised in non-reducing sample buffer and run on 7 . 5 to 15% SDS-PAGE gradient gels [32] . Gels were stained with silver [33] or blotted onto nitrocellulose [34] . After transfering , the membrane was blocked with PBS containing 5% BSA and incubated with the coral snake antivenom ( diluted 1∶2 , 000 ) for 1 h at room temperature . The membrane was washed 3 times for 10 min with PBS/0 . 05% Tween 20 , and incubated with GAH/IgG-AP ( 1∶7 , 500 ) in PBS/1% BSA for 1 h at room temperature . After washing 3 times for 10 min with PBS/0 . 05% Tween 20 , the blot was developed using NBT/BCIP according to the manufacturer's instructions ( Promega ) . The lethal potential of Micrurus venoms was assessed in Swiss mice by intraperitoneal injection of different amounts of venoms in 500 µL of PBS . Four animals were used for each venom dose ( five doses ) . The LD50 was calculated by probit analysis of death occurring within 48 h of venom injection [35] . All animal experiments were approved in advance by the Laboratory Animal Ethics Committee of Butantan Institute . The phospholipase A2 activity of Micrurus venoms was determined as described by Price III [36] , with some modifications . Samples of the venoms ( 4 µg ) and PBS were added to a final volume of 200 µL . Samples of 180 µL of the mixture containing: 5 mM Triton X-100 , 5 mM phosphatidylcholine ( Sigma ) , 2 mM HEPES , 10 mM calcium chloride and 0 , 124% ( wt/vol ) bromothymol blue dye in water , at pH 7 . 5 and at 37°C , were added . After a pre-incubation of 5 min at 37°C , the absorbance of the samples was determined at λ 620 nm in a Multiskan spectrophotometer EX ( Labsystems , Finland ) . Results were expressed in nanomoles of acid per minute per µg of venom ( compared on pH changes in standard curves of the reaction mixture using HCl ) . Samples of the Micrurus venoms ( 50 µg ) were mixed with 5 µM of the Fluorescent Resonance Energy Transfer ( FRET ) substrate , Abz-FEPFRQ-EDnp , and PBS , for a final volume of 100 µL , and the reactions monitored by measuring the fluorescence ( λem 420 nm and λex 320 nm ) in a spectrofluorimeter ( Victor 3™ , Perkin-Elmer , USA ) at 37°C , as described by Araújo et al . [37] . The specific proteolytic activity was expressed as units of free fluorescence per minute per µg of venom ( UF/min/µg ) . Hyaluronidase activity was measured as described [38] , with slight modifications . Samples of Micrurus venoms ( 30 µg ) were added to 100 µL of the hyaluronic acid substrate ( 1 mg/mL ) and acetate buffer ( pH 6 . 0 ) for a final volume of 500 µL . The mixtures were incubated for 15 min at 37°C . After the incubation , it was added to the samples 1 mL of cetyltrimethylammonium bromide 2 . 5% in NaOH 2% , to develop the turbidity in the mixtures , and the absorbance measured in a spectrophotometer ( Multiskan EX ) at λem 405 nm . Results were expressed in units of turbidity reduction ( UTR ) per mg of venom . Microtitre plates were coated with 100 µL of Micrurus venoms ( 10 µg/mL; overnight at 4°C ) . Plates were blocked with 5% BSA in PBS and increased dilutions of the therapeutic coral snake antivenom were added . After 1 h of incubation at room temperature , plates were washed with PBS/0 . 05% Tween 20 and incubated with GAH-IgG-HRPO diluted 1∶3 , 000 , for 1 h at room temperature . Plates were washed and the reactions developed with OPD substrate according to the manufacturers conditions ( Sigma ) . The absorbances were recorded in an ELISA reader ( Multiskan spectrophotometer EX ) at λ 492 nm . The titer was established as the highest antivenom dilution , in which an absorbance five times greater than that determined for the normal horse serum was measured . The ability of the therapeutic Brazilian coral snake antivenom to neutralize the venoms phospholipase , hyaluronidase and proteolytic activities was estimated by incubating Micrurus venoms with the antivenom . The antivenom volume , amount of venoms and the pre-incubation time , for each tested enzymatic activity , was standardized using the immunization pool , composed by 50% of M . corallinus and 50% of M . frontalis venoms . For serum neutralization measurements of the phospholipase activity , samples of 4 µg of the venoms were incubated with the antivenom , diluted 1∶10; for the hyaluronidase activity , samples of Micrurus venoms ( 30 µg ) and the antivenom ( 1∶20 ) were incubated for 20 min at room temperature; for the proteolytic activity , samples of Micrurus venoms ( 50 µg ) and the antivenom ( 1∶4 ) were incubated for 10 min at room temperature . Venoms residual toxic activities were measured as described above . The capacity of the therapeutic coral snake antivenom to neutralize the lethal activity of Micrurus venoms was determined by mixing the venoms , corresponding to 2 LD50 , with serial dilutions of the horse antivenom . The mixtures were incubated for 30 min at 37°C and the animals received 0 . 5 mL by the intraperitoneal route . The effective dose ( ED50 ) was calculated from the number of deaths within 48 h of injection of the venom/antivenom mixture using probit analysis , as described above . The ED50 was expressed as mL of antivenom per µg of venom .
The protein profiles of Micrurus venoms were analyzed by SDS-PAGE followed by silver staining . Figure 1 shows that the venoms from the nine coral species differ in composition , number and intensity of bands . The majority of the components of these venoms present Mr inferior to 64 kDa . Venom from M . surinamensis differs more from the others , by the presence of a few number of components with Mr lower than 20 kDa . In order to assess whether the venoms of Micrurus displayed the same biological activities , some functional assays were carried out . The LD50 , used as a parameter of the Micrurus venom neurotoxicity , was tested in groups of mice , after intraperitoneal injection of different concentrations of the venoms , and the number of deaths recorded during 48 h . The LD50 values , calculated by probit analysis at 95% confidence , were variable among Micrurus venoms , being the most lethal those from M . lemniscatus , M . altirostris , M . spixii , M . corallinus and M . frontalis ( Table 1 ) . Figure 2 shows that venoms contain variable levels of PLA2 activity , toxin also associated with Micrurus envenomation neurotoxicity . M . ibiboboca , M . lemniscatus , M . fulvius , M . altirostris , M . spixii , M . frontalis , M . hemprichii and the mixture of M . corallinus and M . frontalis venoms present an intense hydrolytic activity . In the same experimental conditions , the venoms from M . corallinus and M . surinamensis showed , respectively , low and none phospholipase activity . The proteolytic activity of the Micrurus venoms was tested using a FRET substrate , Abz-FEPFRQ-EDnp . Figure 3 demonstrates that the venoms from M . ibiboboca , M . lemniscatus , M . fulvius , M . altirostris , M . spixii , M . corallinus , M . frontalis and M . hemprichii present hydrolytic activity on this substrate . However , no proteolytic could be measured in the venom from M . surinamensis . The hyaluronidase activity was also analyzed and Figure 4 illustrates that it is high in the venoms of M . lemniscatus , M . corallinus , M . hemprichii and mixture of M . corallinus and M . frontalis . M . ibiboboca , M . altirostris , M . surinamensis , M . frontalis and M . spixii displayed intermediate activity followed by M . fulvius venom , which presents low hyaluronidase activity . The coral snake antivenom , produced by Butantan Institute and used in Brazil for human serum therapy , is obtained by the immunization of horses with a mixture of M . corallinus and M . frontalis venoms . In an ELISA , this antivenom was tested for cross-reactivity , using Micrurus spp venoms as antigens . Figure 5A shows the antivenom was mostly effective in detecting components of M . corallinus and M . ibiboboca venoms . This antivenom presented intermediary titers for M . lemniscatus , M . fulvius , M . altirostris , M . frontalis , M . spixii and M . hemprichii venoms , being the one from M . surinamensis poorly recognized . By western blotting , it was demonstrated that the coral snake antivenom could recognize several but not all components present in the Micrurus spp venoms . M . surinamensis venom components were weakly detected . The antivenom was also unable to recognize molecules with Mr above 64 kDa present in the majority of the venoms ( Fig . 5B ) . In order to analyze if the Brazilian coral snake antivenom could neutralize the enzymatic activities present in the Micrurus spp venoms , some in vitro assays were performed . Figure 6A shows that the antivenom , used in a dilution capable of neutralizing 100% of the phospholipase activity present in the mixture of M . frontalis and M . corallinus venoms ( positive control ) , was able to completely inhibit this activity in M . ibiboboca , M . lemniscatus , M . altirostris , M . corallinus and M . hemprichii venoms . However , it could partially neutralize the venoms from M . fulvius ( 57 . 4% ) , M . spixii ( 59% ) and M . frontalis ( 76 . 5% ) . Figure 6B showed that the antivenom was also able to fully neutralize the proteolytic activity present in M . fulvius , M . altirostris and M . corallinus venoms . On the other hand , this activity was partially neutralized in the venoms from M . ibiboboca , M . lemniscatus , M . spixii , M . frontalis and M . hemprichii . The hyaluronidase activity could be totally inhibited by the therapeutic antivenom in most of the Micrurus venoms , with the exception of M . lemniscatus and M . hemprichii ( Fig . 6C ) . Some coral snake venoms were chosen for further in vivo antivenom neutralization analysis based on their lethal toxicity . Figure 7 shows that the coral snake antivenom was able to neutralize , although with different potencies , the venoms from M . spixii , M . frontalis and M . corallinus . However , it could poorly neutralize the venoms from M . altirostris and M . lemniscatus , with potency values inferior to 100 µg of venom per mL of antivenom .
Biochemical studies concerning the Micrurus venoms are very scarce , due to difficulties in the correct identification of the species , extraction of the venom and maintenance of the animals in captivity . Previous studies have demonstrated that snakes venoms from Micrurus genus present individual variations in composition , related to their geographic distribution , age , gender and diet [22] , [39] , [40] . In the present study , we have investigated the toxic properties of venoms from nine species of Micrurus , the antigenic cross-reactivity and the neutralizing potential of the Brazilian therapeutic coral snake antivenom against these venoms . Results are summarized in Table 2 . Analysis of the Micrurus spp biological properties , as performed by testing the phospholipase , proteolytic , hyaluronidase and lethal activities showed a great variability in the venoms composition . Thus , data presented here showed that the majority of the venoms present intense PLA2 activity , although it is lacking in M . surinamensis venom . Venoms from Micrurus genus have been characterized as possessing low or no proteolytic activity [41] . In the present study , using the FRET substrate Abz-FEPFRQ-EDnp , we could identify proteolytic activity in the majority of Micrurus venoms , being the exception the M . surinamensis venom . Moreover , it was also demonstrated that Micrurus venoms posses varied levels of hyaluronidase activity . The analysis of the lethal potential also showed a large range of toxicity variation among coral snake venoms from Micrurus genus . The ones from M . lemniscatus , M . altirostris , M . spixii , M . corallinus and M . frontalis were the most poisonous . These results are in accordance with those reported by others authors , who studied the lethal toxicities of M . altirostris ( LD50 = 10 µg/kg ) , M . spixii ( LD50 = 6 . 7 µg/kg ) , M . corallinus ( LD50 = 7 . 1 µg/kg ) and M . frontalis ( LD50 = 19 . 3 µg/kg ) [42] , [43] . Micrurus venom is primarily neurotoxic , causing little local tissue reaction or pain at the bite site . Once clinical signs of coral snake envenomation appear they progress with alarming rapidity and are difficult to reverse . In a recent clinical report , it was observed unusual features of coral snake ( M . lemniscatus helleri ) envenomation , with the patient presenting persistent severe local pain , very slow evolution of neurotoxic envenoming , which after 60 h culminated with respiratory failure [11] . These data reinforce the idea that differences in venoms composition may be responsible for the variety of systemic and local manifestations of coral envenoming . Our results showed that the Brazilian coral snake antivenom presents a variable capability of recognizing venoms antigens , as demonstrated by differences in the antibody titers , as measured by ELISA , being the lowest the one obtained for M . surinamensis venom . By Western blotting , it was revealed that the antivenom recognized components of Mr from 64 . 2 to 14 . 8 kDa , presented in the majority of the venoms . In contrast , this antivenom , as also demonstrated by ELISA , weakly recognized proteins from M . surinamensis venom . Some in vitro tests were established and performed in order to analyze the neutralization potential of the therapeutic antivenom . It was possible to show that the antivenom was not capable to fully neutralize the phospholipase activity from M . fulvius , M . spixii and M . frontalis , the proteolytic activity from M . ibiboboca , M . lemniscatus , M . spixii , M . frontalis and M . hemprichii and the hyaluronidase activity of the M . lemniscatus and M . hemprichii venoms . Moreover , neutralization tests performed in vivo demonstrated that the coral snake antivenom was capable to neutralize the most lethal Micrurus venoms , such as the ones from M . spixii , M . corallinus and M . frontalis . However , this antivenom had low efficacy in neutralizing the high lethal activity of M . altirostris and M . lemniscatus venoms . Higashi et al . [43] have demonstrated that anti-M . corallinus antivenom was able to neutralize , in vivo , the lethal effect of M . corallinus venom , but not from M . frontalis , M . ibiboboca and M . spixii venoms . In contrast , antivenom against M . frontalis could neutralize the lethal effect of M . frontalis , M . ibiboboca and M . spixii venoms , but not from M . corallinus . Abreu et al . [44] showed that the commercial and experimental coral antivenoms have low efficacy in neutralizing the M . altirostris venom neurotoxicity as measured in in vitro and in vivo ( inhibition of the lethality ) assays . Table 2 shows that the antivenom antibody titers had no positive correlation with its neutralization potential , indicating that in vitro and in vivo neutralization tests are fundamental to determine the efficacy of the therapeutic antivenom . Multivalent coral snake antivenom has been also prepared , in horses , against a mixture of venoms from M . nigrocinctus , M . mipartitus and M . frontalis species [45] . In this study it was suggested that it would be useful in treating bites from most of the important coral snake species in North and South America , such as M . fulvius , M . alleni , M . carinicaudus dumerilii , M . corallinus , M . frontalis , M . lemniscatus , M . mipartitus , M . nigrocinctus and M . spixii . They also note that M . surinamensis venom was not significantly neutralized by the antivenom . Table 2 shows the existence of a large range of both qualitative and quantitative variations in Micrurus venoms , probably reflecting the adaptation of the snakes from this genus , to vastly dissimilar habitats . Thus , the comparative analysis of distinct phenotypes , particularly the venom constituents and their toxic activities , reveals the heterogeneous complexity of the Micrurus venoms ascertaining that both the structural and the ecological evolutions constrain specific characters for adaptive values . The most striking example is given by M . surinamensis , a snake that inhabits an extremely distinct environment , whose venom expresses limited composition . Besides , it was showed that the antivenom used for human therapy in Brazil is not fully able to neutralize the main toxic activities present in all Micrurus spp venoms , indicating that , for the preparation of the Brazilian coral snake antivenom , other venoms should be included in the immunization mixture . Taking into account the decision made by PAHO/WHO [46] and the countries of the Americas to promote strategies to diminish the health burden of accidents involving poisonous animals in the countries of Latin America , it would be lawful to consider the possibility to prepare a continental coral snake antivenom , thus contributing to countries where national production is insufficient or where it does not have manufacturing laboratories . The appropriate cooperation by scientists in various countries in order to prepare multivalent coral snake antivenom has already been proposed by Bolaños et al . [46] , as early as the 1970's , but until now this relevant aim for the public health of the Americas has not been achieved . Data present in the literature , and results obtained in this study , should encourage PAHO to coordinate a regional cooperative effort to produce multivalent continental Micrurus antivenom that would have an important impact in the treatment of accidents involving coral snakes over the entire continent . | The Elapidae family is represented in America by three genera of coral snakes: Micruroides , Leptomicrurus and Micrurus , the latter being the most abundant and diversified group . Micrurus bites can cause death by muscle paralysis and respiratory arrest few hours after envenomation . The specific treatment for Micrurus envenomation is the application of heterologous antivenom . The aim of this study was to compare the toxicity of venoms from nine species of coral snakes and analyze the neutralization potential of the Brazilian coral snake antivenom . In vitro assays showed that the majority of the Micrurus venoms are endowed with phospholipase and hyaluronidase and low proteolytic activities . These enzymes are not equally neutralized in all venoms by the therapeutic antivenom . Moreover , in vivo assays showed that some of the Micrurus venoms are extremely lethal , such as the ones from M . altirostris , M . corallinus , M . frontalis , M . lemniscatus and M . spixii . Neutralization tests , performed in vivo , showed that the therapeutic antivenom was able to neutralize better the venoms from M . frontalis , M . corallinus , and M . spixii but not from M . altirostris and M . lemniscatus . Taken together , these results suggest that modifications in the immunization antigenic mixture should occur in order to generate more comprehensive therapeutic antivenom . | [
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"biotechnology",
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] | 2010 | Diversity of Micrurus Snake Species Related to Their Venom Toxic Effects and the Prospective of Antivenom Neutralization |
The neglected tropical disease Buruli ulcer ( BU ) caused by Mycobacterium ulcerans is an infection of the subcutaneous tissue leading to chronic ulcerative skin lesions . Histopathological features are progressive tissue necrosis , extracellular clusters of acid fast bacilli ( AFB ) and poor inflammatory responses at the site of infection . After the recommended eight weeks standard treatment with rifampicin and streptomycin , a reversal of the local immunosuppression caused by the macrolide toxin mycolactone of M . ulcerans is observed . We have conducted a detailed histopathological and immunohistochemical analysis of tissue specimens from two patients developing multiple new skin lesions 12 to 409 days after completion of antibiotic treatment . Lesions exhibited characteristic histopathological hallmarks of Buruli ulcer and AFB with degenerated appearance were found in several of them . However , other than in active disease , lesions contained massive leukocyte infiltrates including large B-cell clusters , as typically found in cured lesions . Our histopathological findings demonstrate that the skin lesions emerging several months after completion of antibiotic treatment were associated with M . ulcerans infection . During antibiotic therapy of Buruli ulcer development of new skin lesions may be caused by immune response-mediated paradoxical reactions . These seem to be triggered by mycobacterial antigens and immunostimulators released from clinically unrecognized bacterial foci . However , in particular the lesions that appeared more than one year after completion of antibiotic treatment may have been associated with new infection foci resolved by immune responses primed by the successful treatment of the initial lesion .
Buruli ulcer ( BU ) is a chronic necrotizing infection of subcutaneous tissue caused by Mycobacterium ulcerans [1]-[4] . BU seems to start usually as a movable subcutaneous nodule or papule and may later progress to a plaque or edema . After destruction of subcutaneous tissue , the skin may break down centrally leading to the development of largely painless necrotic skin ulcers with characteristic undermined edges . These may progress to large necrotic lesions . M . ulcerans is unique among mycobacterial pathogens in that it resides in advanced lesions mainly extracellularly . A histopathological hallmark of progressing BU is a poor local inflammatory response in the presence of clusters of extracellular acid-fast bacilli surrounded by areas of necrosis [5]–[7] . M . ulcerans produces a toxin with a polyketide-derived macrolide structure , named mycolactone , which plays a central role in tissue destruction and local immunosuppression . Observations both in cell culture and infection models indicate that cells infiltrating BU lesions are killed due to the cytotoxic and apoptosis inducing activity of mycolactone [7]–[10] . While M . ulcerans may be captured by phagocytes during initial stages of infection , it appears to persist only transiently inside these host cells [11] , [12] . After killing of the phagocytes , extracellular growth leads to the development of extracellular mycolactone-producing bacterial foci in areas of coagulating necrosis . Thermosensitivity of M . ulcerans seems to favor development of skin lesions of the limbs [13]–[15] . Clinical diagnosis of BU can be confirmed by insertion sequence 2404 ( IS2404 ) PCR [16]–[18] , microscopic detection of acid-fast bacilli ( AFB ) , culture of M . ulcerans [19] and histopathological examination of lesions [6] , [20]–[22] . While surgery has traditionally been the only recommended treatment for BU [23] , [24] , WHO recommends currently as a first-line treatment a combination therapy with rifampicin and streptomycin ( R/S ) for eight weeks for all forms of the active disease [25] , [26] . After a pilot study assessing treatment of BU with R/S [25] , a case-series in Benin showed that of 224 patients 215 were successfully treated [27] , with 47% of them receiving antibiotics only . More recently , studies by Nienhuis et al . , Kibadi et al . and Sarfo et al . [28]–[30] reconfirmed efficacy of R/S treatment . However , débridement , surgery and skin grafting may be used as an adjunct to the antimicrobial therapy , mainly to remove necrotic tissue , cover skin defects and correct deformities . Reported rates of recurrence after surgical treatment alone range between 6% and 47% because even wide surgical excision of lesions may not remove all bacilli [31]–[34] . Recurrences may be caused by small numbers of M . ulcerans that have spread to healthy tissue surrounding the primary lesion [5] . Also lymphohematogenous spread of the mycobacteria may occur , since subsets of BU patients develop multiple skin lesions or metastatic osteomyelitis [35]–[39] . Although clinical trials indicate that some bacilli may survive the recommended eight week course of antibiotic treatment [28] , [30] , recurrence rates after R/S treatment are as low as 1–2% [27] , [29] . In active BU disease , a protective cloud of mycolactone around the mycobacterial clusters is thought to both destroy infiltrating leukocytes and hinder them from passing pro-inflammatory signals to other cells . It is most likely , but still remains to be formally proven , that mycolactone production is reduced or abolished early after the onset of R/S chemotherapy due to impairment of mycolactone synthesis , bacterial growth arrest and/or bacterial cell death , reflected by ‘beaded’ appearance of AFBs ( MT Ruf; unpublished results ) . Declining toxin levels allow leukocytes to reach the extracellular mycobacteria , leading to their phagocytosis and destruction [40] . Chronic leukocyte infiltration cumulates in the development of ectopic lymphoid structures [20] . After eight weeks of R/S chemotherapy , antigen presenting cells as well as B and T lymphocyte foci are found in large numbers inside the BU lesions [20] indicating that antigen recognition and processing is leading to active M . ulcerans specific immune responses . Vigorous local immune responses during R/S treatment may lead in some of the patients to the development of clinical deteriorations , ‘paradoxical reactions’ [41] . For this study we conducted detailed immunohistochemical analyses of secondary lesions which had occurred at extended periods of time after effective R/S treatment at different body sites .
Ethical approval for analyzing patient specimens was obtained from the ethical review board of the Ministry of Health of Benin . Written informed consent from the guardians of the patients was obtained before surgical specimens were used for reconfirmation of BU as well as a detailed immunohistological analysis . Both patients , two six year old boys , included in this study were laboratory-confirmed BU cases with one primary lesion . Both received a combination of rifampicin ( 10 mg/kg body weight ) and streptomycin ( 15 mg/kg body weight ) administered daily over 8 weeks at the Centre de Diagnostic et de Traitement de l'Ulcère de Buruli ( CDTUB ) in Pobè , Benin according to the WHO recommendations . Both patients developed several new lesions at different parts of the body , 12 – 409 days after completion of antibiotic treatment . These lesions were removed by limited excision and no additional antibiotic treatment was administered . Excised tissue from a number of these new lesions became available for histopathological analysis ( Table 1 ) . Both patients were tested negative for HIV , shistosomiasis , hepatitis B and syphilis . Blood values tested and the nutritional status was within the limits typically found in children in rural Africa . Only patient 2 presented with a BCG scar . Tissue specimens analyzed are listed in Table 1 . Samples were fixed in 4% neutral-buffered paraformaldehyde for 24 h and subsequently transferred to 70% ethanol for storage and transport . Afterwards biopsies were dehydrated , embedded in paraffin , and cut into 5 µm thin sections . After deparaffinization and rehydration sections were either directly stained with haematoxylin/eosin ( HE ) or Ziehl-Neelsen/methylenblue ( ZN ) according to WHO standard protocols [42] or further processed for immunohistochemistry ( IHC ) . For IHC antigen retrieval was performed according to standard protocols either with citrate buffer , EDTA buffer or by enzymatic trypsin digestion ( Dako® Education guide: Immunohistochemical Staining methods ) . Afterwards endogenous peroxidase was inactivated with 0 . 3% H202 for 20 min and prevention of unspecific binding was achieved by incubation with blocking serum matching the secondary antibody host . Primary antibodies specific for N-Elastase ( polymorphonuclear neutrophils [PMNs]; Dako clone NP57 ) , CD3 ( T-lymphocytes; Dako clone F7 . 2 . 38 ) , CD8 ( CD8+ T-lymphocytes; Serotec clone 4B11 ) , CD14 ( Monocytes/macrophages; Novocastra clone 7 ) and CD20 ( B-lymphocytes; Novocastra clone7D1 ) were appropriately diluted in phosphate buffered saline ( PBS ) containing 0 . 1% Tween-20 and added to the slides for 1 h at room temperature or over night at 4°C . After incubation with a matching biotin-conjugated secondary antibody staining was performed using the Vector NovaRED system . Haematoxylin was used as a counter stain .
In the present report we describe clinical and histopathological observations in two BU patients that have developed series of new skin lesions ( Table 1 ) after effective anti-mycobacterial chemotherapy . Patient 1 , a six year old boy , presented at the Centre de Diagnostic et de Traitement de l'Ulcère de Buruli ( CDTUB ) in Pobè , Benin with a 15×15 cm ulcerated plaque lesion at the right forearm and elbow with undermined edges characteristic for BU ( Figure 1A ) . First BU symptoms had been noticed eight month before and the lesion had been treated afterwards with traditional medication . After admission to the hospital clinical diagnosis was confirmed by a positive IS2404 PCR result of a fine needle aspirate , whereas culture was negative . As recommended in the WHO guidance on the role of specific antibiotics in the management of BU [26] the patient received for 8 weeks daily oral rifampicin ( 10 mg/kg body weight ) and intramuscular streptomycin ( 15 mg/kg body weight ) . 37 and 65 days after start of this standard R/S chemotherapy wound débridement was performed , 18 days after the last excision skin grafting was done and 83 days after grafting the primary lesion had healed . 75 days after completion of chemotherapy a first new ulceration 0 , 5×0 , 5 cm ( ulcer 1 ) in the axilla of the right arm emerged . After performing some débridement , this lesion had healed 35 days later and the patient was discharged from hospital . 275 days after completion of chemotherapy the patient was readmitted with a non ulcerated fluctuant nodule 1 , 5×1 , 5 cm ( nodule 1 ) on the back ( Figure 1B ) , which was excised with primary skin closure one day later . 409 days after completion of chemotherapy two more lesions developed , a 1 , 5×1 , 5 cm nodule ( nodule 2 ) on the thorax ( Figure 1C ) and an ulcerated plaque 3×3 cm on the right shoulder ( ulcer 2 ) ( Figure 1D ) . Both lesions were excised two days after admission . From both lesions specimen taken were IS2404 PCR as well as AFB positive , whereas culture was negative . 28 days after the surgical intervention , the patient was discharged from hospital . No further relapses were observed after 10 months of follow-up ( February 2011 ) . Patient 2 , also a six year old boy , presented at the CDTUB with a 20×15 cm ulcerated lesion on the interior side of his right upper leg and knee . Undermined edges as well as ‘cotton wool’ appearance of necrotic tissue at the center of the lesion were characteristic for BU [42] . Clinical diagnosis was confirmed by positive IS2404 PCR results and microscopic detection of AFB in swab samples . Surgical débridement was performed 29 days after start of standard R/S chemotherapy followed 10 days later by skin grafting . Twelve days after completion of antibiotic treatment , a nodule ( nodule 1 ) 2×2 cm; had emerged about 5 cm proximal of the border of the primary lesion at the upper right leg and was excised 7 days later . The initial lesion as well as the lesion at the excision site had healed 39 days after completion of the antibiotic treatment ( i . e 57 days after skin grafting ) and the patient was discharged from hospital . One week after discharge ( 46 days after completion of antibiotic treatment ) the patient was readmitted with a second nodule ( 1 , 5×1 , 5 cm ) located at the lower right leg about 15 cm distal of the border of the primary lesion . Again eight days later ( 54 days after completion of antibiotic treatment ) a third nodule ( nodule 3 ) ( 3×2 cm ) had emerged at the upper right leg located 5 cm proximal of the initial wound . These two nodules were excised 93 days after completion of the antibiotic therapy . While AFB staining , as well as IS2404 PCR confirmed the presence of M . ulcerans , both nodules were culture negative . After surgical excision and healing of the satellite lesions the patient was discharged , but re-admitted 176 days after completion of antibiotic treatment with a fourth nodule ( nodule 4 ) 2×2 cm on the right foot . A minimal surgical intervention was performed and the patient was discharged 10 days later and no further relapses were observed after 10 months of follow-up ( February 2011 ) . Histopathological and immunohistochemical analyses were performed with nodule 1 , nodule 2 and ulcer 2 from patient 1 , and nodule 2 and 3 of patient 2 ( Table 1 ) . These lesions appeared 275 to 409 days and 46 to 54 days , respectively , after completion of chemotherapy . Analysis yielded comparable results for all specimens analyzed . Typical data are shown below . Features characteristic for BU pathology , such as fat cell ghosts , necrotic soft tissue , hemorrhages , and epidermal hyperplasia were present in all specimens analyzed . As shown in Figure 2A , necrotic areas were massively infiltrated with leucocytes , a feature , which is characteristic for successfully treated lesions [20] . Immunohistochemical analysis revealed mixed cellular infiltrations ( Region 1 ) composed of large numbers of CD14 positive macrophages/monocytes ( Figure 2B ) and CD3 positive T-cells ( Figure 2C ) . In contrast , intact N-elastase positive neutrophils were rare ( Figure 2D ) . As described previously [20] , some areas , such as the AFB containing region 2 in Figure 2A contained N-elastase positive debris ( Figure 2E ) , which appears to represent remains of an early wave of neutrophilic infiltration . ZN staining revealed AFBs and globi like structures in the necrotic areas in the tissue from nodule 1 of patient 1 and in nodules 2 and 3 of patient 2 ( Figure 2F , G ) . AFBs had a ‘beaded’ appearance ( Figure 2G ) , which has been shown to be an indicator for loss of viability in the case of M . leprae [43] . Clusters of CD20 positive B-cells , another hallmark of ectopic lymphoid tissue developing in BU lesions after successful treatment [20] , were also found in the tissue specimens analyzed . These clusters varied in size ranging from very large accumulations forming a band throughout the whole tissue ( Figure 3A ) to small dense B cell accumulations ( Figure 3B ) surrounded by CD14 positive macrophages/monocytes ( Figure 3C ) and few interspersed CD3 positive T-cells ( Figure 3D ) , mostly CD8 negative ( Figure 3E ) , mainly at the border of the dense B-cell cluster . Higher magnifications confirmed the dense packaging of B-cells ( Figure 3F ) and more dispersed distribution of other leucocytes ( Figure 3G–H ) . In some parts of the lesions small uninfiltrated necrotic areas surrounded by belts of leucocytes still remained . Immunohistochemical analyses gave indications for sequential infiltration of these areas by different types of leucocytes ( Figure 4 ) . Necrotic areas were surrounded by an inner dense belt of CD14 positive macrophages/monocytes ( Figure 4A ) , which thus seem to constitute the first line of infiltration after decline of cytotoxic mycolactone levels . While a belt containing large numbers of CD3 positive T-cells representing a second line of infiltration were found in direct neighborhood to the macrophages ( Figure 4B ) , intact N-elastase positive neutrophils ( Figure 4C ) and CD20 positive B-cells ( Figure 4D ) were comparatively rare in these settings . However a strong staining of N-elastase positive neutrophilic debris was observed inside the necrotic areas ( Figure 4C ) . Higher magnification revealed no intact cells in this location ( Figure 4C insert ) .
In this report we describe the development of series of new skin lesions in two BU patients 12 – 409 days after completion of antibiotic treatment . The newly emerging nodules and ulcerations were located either at some distance from the initial lesion at the same extremity or at other body locations . Detection of M . ulcerans DNA by IS2404 PCR , microscopic detection of AFBs and the presence of histopathological features characteristic for BU demonstrated that the new lesions were associated with M . ulcerans infection . Degenerated appearance of the AFBs and the presence of massive immune cell infiltrates in most parts of the lesions were on the other hand characteristic for treated BU lesions [20] . Detailed immunohistochemical analyses showed that residual necrotic areas were surrounded by an outer belt of T-lymphocytes and an inner belt of macrophages/monocytes with appendices reaching into the necrotic tissue . These belts of intact leucocytes seem to reflect ongoing efforts of the immune system to resolve the necrotic areas . In contrast , remains of neutrophils found inside the necrotic areas seem to be leftovers of early acute neutrophilic infiltration waves . These are also observed in early phases of M . ulcerans infection in experimentally infected mice ( MT Ruf et al . , unpublished results ) . Apart from these residual necrotic regions , the destroyed adipose and dermal connective tissue layers showed angiogenesis and contained abundant leukocyte infiltrates . It is thought that such chronic infiltrates can only develop once the concentration of the cytopathic M . ulcerans macrolide toxin mycolactone has diminished [20] , [44] . Imbedded in the diffuse infiltrates , more structured leukocyte accumulations , such as B-cell clusters indicative for humoral immune responses [45]–[47] and first granulomas were found . In BU granulomas may function primarily as a place for antigen presentation and adaptive immune response , rather than for sequestration of the mycobacteria [20] . Recently O'Brien et al have described the occurrence of paradoxical reaction in two Australian BU patients during R/S treatment of BU [41] . After a first clinical improvement worsening of the clinical appearance occurred . For one patient incomplete excised wound margins showed paradoxical reaction whereas for the other patient a more distant secondary lesion opened , before end of treatment was reached . Worsening of lesions motivated a change in the treatment regimen and additional surgery . After detailed evaluation , data have been interpreted as immune-mediated reactions rather than treatment failures , as it has been shown that antibiotic therapy for M . ulcerans leads to a reversal of local immunosuppression [20] , [41] , [48] . The observed vigorous local immune responses are most likely caused by bacterial antigens and immunostimulators released from the killed mycobacteria . Similar paradoxical reactions have been well described for M . tuberculosis , M . leprae and in particular in immunocompromised HIV patients who commence HAART [49]–[52] . In tuberculosis an elevation of the TNF-α level , stimulated by lipoarabinomanan and other lipopolysaccharides present in the cell wall , has been postulated as an initial step in the development of paradoxical reaction [53] , [54] . Limited surgical excision may help to resolve paradoxical reactions by reducing the burden of mycobacterial antigens and in some clinical settings corticosteroids have been used for down regulation of immune responses [55]–[58] . In the case of the two patients described here , new lesions developed at prolonged periods of time after completion of antibiotic treatment . These lesions may represent secondary M . ulcerans infection foci that were already present without clinical signs and symptoms during antibiotic treatment and development of new lesions may be the consequence of delayed paradoxical reactions . However , in particular the lesions that appeared more than one year after completion of antibiotic treatment may also have been associated with new infection foci caused by new M . ulcerans infections or by mycobacteria that had survived the eight week course of R/S treatment [28] , [29] . These may have been resolved by immune responses primed by the successful treatment of the primary lesion . If this is the case , detailed analysis of immune responses in more patients developing such secondary lesions may provide important insights into immune protection against M . ulcerans and support vaccine design [59] . | Buruli ulcer ( BU ) is a chronic necrotizing skin disease presenting with extensive tissue destruction and local immunosuppression . Standard treatment recommended by the WHO includes 8 weeks of rifampicin/streptomycin and , if necessary , wound debridement and skin grafting . In some patients satellite lesions develop close to the primary lesion or occasionally also at distant sites during effective antibiotic treatment of the primary lesion . We performed a detailed analysis of tissue specimens from lesions that emerged in two BU patients from Benin 12 to 409 days after completion of chemotherapy . Histopathology revealed features of tissue destruction typically seen in BU and degenerated acid-fast bacilli . In addition , lesions contained organized immune infiltrates typically found in successfully treated BU lesions . Secondary lesions emerging many months after completion of chemotherapy may have been caused by immune response-mediated paradoxical reactions . However , the late onset may also indicate that they were associated with new infection foci spontaneously resolved by adaptive immune responses primed by antibiotic treatment of the primary lesions . | [
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"immune",
"r... | 2011 | Secondary Buruli Ulcer Skin Lesions Emerging Several Months after Completion of Chemotherapy: Paradoxical Reaction or Evidence for Immune Protection? |
Viruses are by definition fully dependent on the cellular translation machinery , and develop diverse mechanisms to co-opt this machinery for their own benefit . Unlike many viruses , human cytomegalovirus ( HCMV ) does suppress the host translation machinery , and the extent to which translation machinery contributes to the overall pattern of viral replication and pathogenesis remains elusive . Here , we combine RNA sequencing and ribosomal profiling analyses to systematically address this question . By simultaneously examining the changes in transcription and translation along HCMV infection , we uncover extensive transcriptional control that dominates the response to infection , but also diverse and dynamic translational regulation for subsets of host genes . We were also able to show that , at late time points in infection , translation of viral mRNAs is higher than that of cellular mRNAs . Lastly , integration of our translation measurements with recent measurements of protein abundance enabled comprehensive identification of dozens of host proteins that are targeted for degradation during HCMV infection . Since targeted degradation indicates a strong biological importance , this approach should be applicable for discovering central host functions during viral infection . Our work provides a framework for studying the contribution of transcription , translation and degradation during infection with any virus .
Human cytomegalovirus ( HCMV ) is a ubiquitous pathogen , infecting the majority of the human population worldwide , leading to severe diseases in newborns and immunocompromised adults . The HCMV genome contains almost 240kb , making it the largest known human virus . Its genome was traditionally estimated to code for approximately 200 open reading frames [1 , 2] , but our recent study showed that many additional , mostly short open reading frames are also translated during infection [3] . During viral infection , cellular gene expression is subjected to rapid alterations induced by both viral and antiviral mechanisms . The differential regulation of cellular transcription and translation distinguishes host pathways that the virus either relies on or actively subverts and can open new therapeutic opportunities and reveal novel principles in cell biology . Over the years , a large body of work was conducted to decipher these changes in a global manner by examining the temporal changes in RNA levels [4–11] . The use of microarrays revealed many biological pathways that are significantly altered during infection and established an important progress in our understanding of how HCMV exploits cellular pathways during infection [6–11] . These studies helped to reveal numerous pathways that are elevated during infection and are important for viral propagation such as cell cycle , DNA damage , transcription and translation factors and energy production . In addition pathways that were reduced during infection were also mapped , such as cell adhesion , cytoskeletal regulators and apoptosis and extracellular matrix . Recently , advancement in mass-spectrometry methods had been used to quantify the cell proteome along HCMV infection giving a wider view on the pathways that are altered during infection [12] . More specifically , this method was used to predict natural killer ( NK ) and T cells ligands by identifying cell surface molecules which are downregulated during HCMV infection [12] . However , these approaches could not delineate transcriptional , translational and post-translational layers of regulation . It had been shown that unlike many other viruses ( including several Herpes viruses , e . g . Herpes simplex virus 1 and 2 ) , the overall impact of HCMV is stimulation of host RNA and protein synthesis [13–15] . Still , fundamental questions such as how and to what extent HCMV changes the spectrum of host mRNAs translation , and whether the virus possess mechanisms to ensure more effective translation of its own mRNAs , have just begun to be addressed [16 , 17] . In a recent study , changes in host genes translation at 48 hours post HCMV infection were examined by analyzing the fraction of mRNAs associated with polysomes . This study revealed that a significant fraction of cellular mRNAs are translationally activated or repressed by HCMV [17] . In addition , expression of pUL38 , a virally encoded mTORC1-activator sufficed to partially recapitulate these translational alterations in uninfected cells , demonstrating that some of the effect is mediated by mTORC1 activation [17] . Here , we have used RNA sequencing ( RNA-seq ) and ribosome profiling ( deep sequencing of ribosomes-protected fragments ) to globally map the changes in host genes transcription and translation during HCMV replication . These comprehensive and simultaneous measurements revealed how HCMV orchestrates cellular gene expression at both the transcription and translation levels . We identified several novel pathways that are upregulated during infection and are central for viral propagation . We show that most of the regulation of cellular genes along infection occurs on the level of transcription but our experiments also uncover extensive and dynamic translational regulation of subsets of cellular genes . In addition , our measurements enabled the comparison between translation of viral and host genes , revealing that late in infection viral genes are translated more efficiently than their host counterparts . Finally , by integrating our measurements of protein synthesis rate with recent measurements of protein levels [12] we were able to globally and unbiasedly identify host proteins that are actively targeted for degradation during HCMV infection . We show that BTN2A1 and IGSF8 , two cell surface proteins that belong to the immunoglobulin ( Ig ) superfamily are degraded during HCMV infection . We also reveal that few cytosolic proteins including ROCK1 , a key regulator of actin cytoskeleton , are degraded during HCMV infection .
To gain a detailed view of the changes that occur in host genes transcription and translation over the course of HCMV infection , we infected human foreskin fibroblasts ( HFF ) with the Merlin HCMV strain and harvested cells at 5 , 12 , 24 and 72 hours post infection ( hpi ) . We also used cells treated with type I interferon ( IFN ) or cells infected with an irradiated virus , in which viral DNA is inactivated [18] , preventing viral gene transcription . We designed our experiment to simultaneously monitor both RNA levels and translation ( Fig 1A ) . Deep sequencing of mRNA ( RNA-seq ) allows a detailed mapping of transcript levels during infection and these were paired with ribosome footprints ( deep sequencing of ribosome-protected mRNA fragments ) , which allow accurate measurement of protein synthesis by capturing the overall in vivo distribution of ribosomes on a given message [19] . In order to assess the reproducibility of our experiments we have prepared two independent biological replicates for the 5hpi and 72hpi time points . Both the mRNA and footprints read density measurements were highly reproducible ( correlation coefficient [R2] = 0 . 97 and 0 . 92; SD in ratio between biological replicates corresponded to a 0 . 18- and 0 . 3-fold change , respectively ) demonstrating consistency in our experimental methods ( Figs 1B and S1 ) . We quantitatively assessed the expression pattern of 10 , 354 genes . Interestingly , 73% of the transcripts changed by more than 3-fold in their footprints densities along infection , reflecting the drastic changes that occur in cells during HCMV infection ( S1 Table ) . In order to identify patterns of specific cellular pathways that were influenced during infection , we compared the expression of infected samples to mock sample and clustered the mRNA and footprints ratios using partitioning clustering . This approach allowed clustering of the cellular transcripts into ten distinct classes based on similarities in temporal expression profiles in the RNA-seq and ribosome profiling data . Overall we found that changes in ribosome footprints tracked the changes in transcripts abundance ( Figs 2A and S2A ) , which indicates that most of the regulation of host gene expression occurs at the level of transcription . To identify biological processes that are altered during HCMV infection we applied the DAVID software [20] . Most of the time-related clusters showed enrichment for specific biological functions supporting their biological relevance ( Fig 2A ) . Many of these biological functions were captured by previous transcriptomic studies [4 , 14 , 15 , 8 , 9] and were reported to be affected by HCMV infection such as cell cycle , DNA damage , sterol biosynthesis , ribosome biogenesis and the proteasome [21–25] . The increased sensitivity of deep sequencing approaches allowed us to also identify significant changes in novel biological functions such as RNA processing , protein intracellular transport and transcription regulation . The temporal profile of candidate genes from each of these clusters was confirmed by real-time PCR or western blot analysis ( S2B Fig ) . The full DAVID enrichment analysis is presented in S2 Table . It is likely that many of the genes in the same temporal profile are co-regulated by common transcriptional activators or repressors . Therefore , to find potential regulators that drive the expression of these clusters and to better focus on well-characterized canonical pathways , we turned to Ingenuity Pathway Analysis ( IPA ) ( Ingenuity Systems , www . ingenuity . com ) . This analysis helped to reveal functional pathways that are significantly changing during infection and identifying upstream molecules that may control the expression of the genes in each cluster . HCMV is known to induce the cellular DNA damage response ( DDR ) , that includes activation of the ATM , H2AX , NBS1 , CHK2 , CHK1 and p53 genes [21] . Indeed , we found multiple pathways related to DNA damage that were significantly enriched in cluster 1 ( S3 Table ) . Interestingly , we identified significant upregulation of genes that promote homologous recombination repair ( HR ) , such as Fanconi anemia complementation group D2 protein ( FANCD2 ) , BRCA1 and BARD1 ( S3 Fig , P-val = 1 . 25E-16 ) . Recently , it had been suggested that FANCD2 upregulation during HSV1 infection is important for promoting HR on the expanse of non- homologous end-joining [26] . Our results , therefore , suggest that similar strategy may be used in HCMV-infected cells . We also identified significant upregulation of DNA mismatch repair complex that is required for the repair of DNA replication errors ( S4 Fig , P-val = 6 . 3E-12 ) . Mismatch Repair Proteins were shown to be elevated and required for efficient HSV1 replication [27] . The significant elevation we mapped during HCMV infection suggests that the mismatch repair may also be important during HCMV infection and it will be of interest to determine whether mismatch repair proteins contribute to the fidelity of herpesvirus DNA replication . An important upstream regulator of genes in cluster 1 was E2F1 ( P-val = 3 . 14E-16 ) which was previously shown to be important for HCMV replication [28] . Cholesterol levels are significantly elevated during HCMV infection and cholesterol import was shown to play an important role in this process [23] . Our results also reveal direct upregulation of cholesterol biosynthesis enzymes implying an increase in cholesterol de novo synthesis ( P-val = 1 . 7E-6 , cluster1 , S3 Table ) . In agreement with the overall upregulation in translation in HCMV infected cells [17] , we found translation initiation factors to be significantly elevated ( S5 Fig , P-val = 7 . 94E-11 ) . Cluster 2 is also enriched in genes related to assembly of RNA polymerase II ( S6 Fig , P-val = 2 . 4E-5 ) , suggesting a simultaneous upregulation of both the transcription and translation machineries . In addition , we identified upregulation of genes related to tRNA charging and tRNA modifications ( P-val = 3 . 3E10-5 , cluster 4 , S3 Table ) that probably support the higher demand for tRNAs due to the increase in translation . We have also mapped a significant enrichment in genes involved in proteins quality control ( P-val = 5 . 03E-13 , S7 Fig ) , including upregulation of many of the cell’s chaperones , the proteasome components and deubiquitinating enzymes ( DUBs ) . DUBs form a large family of proteases that cleave ubiquitin chains from target proteins and their up regulation can effect the stability , localization and function of the proteome [29] . Interestingly , DUBs are also encoded by herpesviruses and HCMV encoded DUB ( UL48 ) was shown to influence viral replication [30] . It is possible that the increased cellular DUB activity can increase the stability of target proteins during the progression of infection by inhibiting polyubiquitination . NRF2 is a transcription factor that plays a key role in cellular defense against oxidative stress . We observed upregulation in NRF2 targets , that included the induction of few detoxifying enzymes and stress response proteins ( P-val = 6 . 6E-5 , cluster 5 , S3 Table ) . These results extend previous observations showing NRF2 was elevated during HCMV infection[31] . Downregulated genes ( Clusters 3 and 8 ) were significantly enriched ( P-values 1 . 4E-8 and 1 . 25E-14 respectively ) for genes related to the synthesis of the extra cellular matrix ( ECM ) ( S8 Fig ) and for metalloproteinase inhibition ( P-values 1 . 2E-3 and 8 . 3E-6 , S9 Fig ) , in agreement with previous microarray measurements [9] . Interestingly , collagen was found to restrict HSV-1 infectivity in healthy tissues [32] , suggesting a potential motivation to downregulate ECM production . Reduction in metalloproteinase inhibition may have additional effect on reducing ECM protein levels but can also affect proteolytic degradation of cell surface molecules . Indeed , increased proteolytic degradation was demonstrated for the cell surface associated low density lipoprotein related receptor 1 ( LRP1 ) at late phase of infection[23] . In addition proteolytic degradation of cell surface molecules could be employed by the virus to increase shedding of immune stimulatory molecules [33] . Important upstream regulators of genes in these downregulated clusters are β-catenin ( Pval = 3 . 96E-11 ) and TP63 ( Pval = 3 . 14E-11 ) , in agreement with recent studies showing dysregulation of Wnt/β-catenin signaling pathway during HCMV infection[34] . The enrichment of all pathways and upstream regulators in the different clusters are presented in S3 Table and S4 Table . In order to test the functional importance of genes we identified as elevated during infection , we tested the effect of knockdown ( KD ) of few candidate genes on viral replication . We chose genes that fall in different functional categories that were not connected before to HCMV infection . Since we observed elevation in genes responsible for protein transport and localization we targeted two genes that are involved in these processes; SEC11C- a component of the microsomal signal peptidase complex that removes signal peptides from nascent proteins and PEX3- which is involved in peroxisome biosynthesis and integrity; We also chose two genes that are involved in RNA processing; TRMT1-an enzyme that dimethylates a guanine residue on most tRNAs but its importance is poorly defined and METTL3- N6-methyltransferase that methylates adenosine residues in mRNAs and was recently shown to be important for RNA stability and translation [35] . Lastly we chose HPRT1- which is a central enzyme in the purine salvage pathway . Since the expression of these genes could be essential for cell survival , we confirmed that the various KDs did not cause significant cell death and that siRNA-mediated ablation reestablished mRNA levels to these observed in uninfected cells ( S10A and S10B Fig ) . Importantly , KD of SEC11C , PEX3 and HPRT1 significantly reduced viral titers ( Fig 2B ) . In order to preclude off target effects we confirmed that similar effects were obtained using distinct siRNAs that target the same gene ( S10C Fig ) . These results strongly indicate that the elevation in the secretory pathway proteins , the integrity of peroxisomes and the purine salvage pathway are all contributing to viral propagation . Importantly , both PEX3 and HPRT1 KD blocked HCMV at early stages of infection as the expression of early-proteins IE2 and UL44 was reduced ( Figs 2C and S10D ) . In contrast , SEC11C blocked only the late stage of infection as only the expression of the late-protein , pp28 was reduced ( Figs 2C and S10D ) . Although the TRMT1 KD did not significantly affect viral titers we observed a reproducible reduction in pp28 expression ( Figs 2C and S10D ) suggesting that the TRMT1 dimethylation activity might be playing a subtler role during HCMV infection . Overall , our results increase the breadth of knowledge about the changes that occur in various cellular processes during HCMV infection . To quantitatively evaluate the role of translational control along HCMV infection , we calculated relative translation efficiency ( TE ) across our time course . TE is defined as the ratio of ribosome-associated RNA ( footprints ) to total mRNAs for a given gene ( Fig 3A ) . Replicates indicated high reproducibility ( SD of ratio between biological replicates corresponded to a 0 . 29 fold change ) , which allowed sensitive measurement of dynamic translational control ( Figs 3A and S11A ) . In order to focus on cases in which translational regulation might play a substantial role in viral replication , we centered on genes that showed more than a 3-fold difference in their TE between any two-time points . In addition , we required that the change in TE would be accompanied by a change in a similar direction in the footprints densities . Based on these criteria we obtained 731 transcripts that showed significant changes in their TE during infection ( S5 Table ) . For each of these genes we calculated log2 TE versus the mock sample and clustered them into five classes based on similarity in their temporal TE profiles . The heat-map of the footprints and mRNA temporal profiles of these genes exemplifies the translation regulation; the changes in footprints are much more pronounced than the changes in mRNA levels ( Fig 3B ) . To characterize the parameters involved in HCMV-dependent translational regulation , we examined specific characteristics of the 5’ untranslated regions ( 5’UTRs ) of the corresponding transcripts , including their length and percent of G+C content . We observed few unique features that define genes from each of these clusters ( S11B and S11C Fig ) , suggesting that several features of 5’UTR may contribute to the TE along infection . In addition , we examined whether the TE clusters show strong enrichment for specific biological processes . Interestingly , cluster 4 ( in which translation is induced at 5hpi ) is enriched in genes related to the translation machinery , including many of the ribosomal proteins and translation initiation factors . This enrichment suggested that elevated translation of these genes is mediated by mTORC1 activation , as many of mTORC1 targets are related to protein synthesis [36 , 37] . Supporting this notion , there is the significant overlap ( Pval = 5 . 432e-21 ) between mRNAs whose translation is repressed by mTOR inhibitors [38 , 39] and genes found in cluster 4 . Genes in cluster 5 ( in which translation is induced at 24hpi ) were significantly enriched in functions related to cell cycle regulation . Since HCMV is known to arrest the cell cycle at a “pre-S” phase we speculated that translation regulation of this cluster could be attributed to the cell cycle arrest . Indeed , mRNAs whose translation was upregulated in this cluster significantly overlapped with genes that showed enhanced translation at G1 and S phases of the cell cycle ( Pval = 9 . 03e-07 and Pval = 0 . 0007 , respectively ) [40] . Further clusters showed interesting patterns of translation regulation but were only weakly enriched for particular biological process ( S6 Table ) . We confirmed the translational regulation for HSP90AB1 , one of the genes identified in cluster 5 , by showing that protein amounts increase throughout infection while no significant changes in mRNA as measured by real-time PCR are observed ( Fig 3C ) . In order to further validate our translation measurements and to generate a platform that will enable identification of cis-regulatory elements that control translation during HCMV infection , we used a fluorescence-based reporter system to analyze translation of individual transcripts in single , living cells ( Figs 3D , 3E , and S11D and [41 , 42] ) . The 5’UTRs of translationally regulated genes from clusters 4 and 5 were inserted into the fluorescent reporter and compared with 5’ UTRs of controls mRNAs that did not show HCMV related translational regulation . While the 5’ UTRs of control transcripts did not show significant changes in translation after HCMV infection , the transcripts with 5’UTRs from translationally regulated genes ( RPS19 , SMC2 and RAD50 ) showed increased translation after HCMV infection ( Fig 3F ) . These results provide validation of our TE calculations and demonstrate that the regulatory elements required for translational activation of these genes are present in their 5’UTRs . Overall , our data demonstrates that multifaceted translational control of gene expression is carried out during HCMV infection . Replication of viruses is completely dependent on the host translational apparatus and many viruses commandeer this machinery to translate their own mRNAs on the expense of cellular mRNAs . In order to evaluate if HCMV evolved mechanisms to co-opt the cells' ribosomes we compared the TE of human genes to that of viral genes at each of the time points along infection . Interestingly , at 5hpi , on average , viral genes are translated less efficiently than human genes ( Fig 4A , Pval = 0 . 0027 ) , this suggests a successful host defense mechanism that lessens the translation of viral mRNAs compared to host mRNAs at the beginning of HCMV infection . However , this effect is diminished later during infection , as at 12hpi and 24hpi human and viral genes do not show any significant difference in their TE . Interestingly , at 72hpi viral mRNAs are , on average , translated more efficiently than human mRNAs ( Fig 4A , Pval = 1 . 29E-05 ) . These same effects were observed in independent biological replicates ( S12 Fig ) . Thus , translation rates of viral mRNAs late in infection are higher than expected from their mRNA prevalence . This effect can also be seen when the fraction of mRNA and footprints reads that map to the virus are plotted along infection ( Fig 4B ) . The molecular mechanism underlying this phenomenon is yet to be studied . HCMV is a paradigm for viral immune evasion and several immune ligands were shown to be targeted for proteosomal degradation by specific viral proteins [43] . One prediction is that molecules that play a role in marking infected cells for immune recognition will be induced at the transcriptional and translational levels ( rate of protein synthesis ) as a cellular antiviral response , while the virus will stimulate their degradation , causing their protein levels to drop . A recently published quantitative proteomic analysis of host proteins levels during HCMV infection [12] identified many immune molecules that are down regulated during infection . However , down regulation of a given protein could be merely a mirror of transcriptional changes and therefore , does not necessarily indicate degradation . We reasoned that comparing our footprints measurements with these quantitative proteomic measurements , would allow us to systematically identify immune molecules that are targeted for degradation . Indeed , when we examined the profiles of proteins that were shown to be targeted for degradation by HCMV , such as; HLA-A ( targeted by US2 and US11 proteins [44 , 45] ) and PVRL2 ( a ligand for the activating NK receptor DNAM-1 which is targeted by UL141 [46] ) , we observed the expected profiles ( Fig 5A ) . We next examined the temporal profiles of the known NK and T cells activating or co-stimulatory ligands for which we had quantitative measurements , including protocadherins that were recently suggested to act as novel activating ligands for NK cells [12] . However , unlike HLA-A and PVRL2 we observed that the regulation of these immune ligands occurs mostly at the transcriptional level as both footprints and mRNA levels were downregulated along infection ( Figs 5B and S13 ) . Since active degradation during HCMV infection may indicate biological importance , we looked for inverse correlation between the footprints and protein temporal profiles in a list of potential immune ligands composed of proteins that belong to a few immune-related protein families [12] . This search led us to identify two proteins , BTN2A1 and IGSF8 , which belong to the immunoglobulin superfamily and present a profile that suggests degradation ( Fig 5C and 5E ) . Interestingly a recent study that performed plasma membrane profiling showed that the relative abundance of both BTN2A1 and IGSF8 was higher in cells infected with HCMV ΔUS2 compared to those infected with WT virus , further supporting the possibility that these proteins are actively degraded [47] . Real-time PCR measurements from cell lysates along HCMV infection supported elevation in BTN2A1 and IGSF8 mRNA levels ( Fig 5D and 5F , upper panels ) . Since we were not able to obtain specific detection of BTN2A1 and IGSF8 using commercially available antibodies , we expressed these proteins fused to an HA tag in fibroblasts using lentiviral vectors . The vectors also contained green fluorescent protein ( GFP ) that was expressed from the same transcript using an internal ribosome entry site ( IRES ) and was used as an internal control . These cells were then infected with HCMV and the kinetics of BTN2A1 and IGSF8 expression along infection was evaluated by western blot analysis . These experiments demonstrated that BTN2A1 and IGSF8 are targeted for degradation since their protein levels were downregulated during HCMV infection ( Fig 5D and 5F , middle panels ) , whereas the levels of the GFP which was expressed from the same transcript was elevated ( Fig 5D and 5F , lower panels ) . Since these proteins were suggested to be effected by the viral US2 protein [47] , we tested if US2 affect BTN2A1 and IGSF8 expression by ectopically expressing US2 in fibroblasts expressing either tagged BTN2A1 or tagged IGSF8 . Importantly , US2 ectopic expression was sufficient to downregulate BTN2A1 and to lesser extent IGSF8 in uninfected fibroblasts ( Fig 6A ) . We next tested the expression of BTN2A1 and IGSF8 during infection with the AD169VarL virus and a BAC derived AD169VarL virus , in which the US2-US6 region was deleted [48] ( the region in which the BAC cassette was inserted ) . Similar to the results obtained with Merlin strain , both BTN2A1 and IGSF8 were degraded when cells were infected with the AD169VarL parental virus ( Figs 6B , S14A , and S14B ) . In accordance with the plasma profiling results [47] , in cells that were infected with the AD169VarL-BAC virus ( that is US2-deleted ) the expression of BTN2A1 was elevated and resembled the expression pattern of the GFP that was expressed from the same plasmid , strongly suggesting that US2 is essential and sufficient for BTN2A1 degradation ( Figs 6B and S14A ) . Interestingly , however , although in the absence of US2 some of IGSF8 expression was restored , IGSF8 levels are still reduced during infection with the AD169VarL-BAC virus ( Figs 6B ( second panel ) and S14B ) , indicating that additional viral proteins mediate IGSF8 degradation . In order to identify the viral protein/s that are involved in IGSF8 degradation we performed a pull-down on cells expressing HA tagged-IGSF8 and that were infected with HCMV for 48hr . Isolated viral and host proteins were resolved by electrophoresis and identified by mass spectrometry . Two HCMV proteins were identified in this capture experiment . The first was US9 , which was recently shown to selectively target MICA*008 to proteasomal degradation [49] and the second was UL40 , which possesses a signal peptide that mimics cellular signal peptides from HLA molecules and regulates the cell surface expression of the NK cell ligand HLA-E [50] . In order to test if these proteins are involved in IGSF8 degradation , we tested the expression of IGSF8 during infection with AD169VarL-BAC derived virus in which US9 or UL40 were deleted . In cells that were infected with the AD169VarL-BAC delta US9 virus , the expression of IGSF8 was elevated and better resembled the GFP levels that was expressed from the same plasmid , suggesting that US9 is contributing to IGSF8 degradation ( Figs 6B and S14B ) . The effect of US9 seemed specific for IGSF8 since US9 deletion had no additional effect on BTN2A1 expression ( Figs 6B and S14A , and S14B ) . In contrast to US9 , the deletion of UL40 had no obvious effect on BTN2A1 or IGSF8 expression ( Figs 6B , fourth panel , S14A , and S14B ) . Finally we tested if US9 affect IGSF8 by ectopically expressing US9 in fibroblasts expressing epitope tagged IGSF8 . In accordance with the results obtained with US2 , ectopic expression of US9 had mild but reproducible effect on IGSF8 expression in uninfected fibroblasts ( Fig 6A , middle and right panel ) . Overall , these results illustrate how our ribosome profiling data in conjunction with recent mass spectrometry measurements [12] allowed us to identify two Ig superfamily proteins that are degraded during HCMV infection . Since targeted degradation of central host proteins by the virus is not limited to immune ligands , we expanded our search and looked for anti-correlations between synthesis rate and steady state protein levels for all human genes for which we had both footprints and proteomic measurements [12] . We limited our search to the simplest profile in which both the footprints measurements and protein levels temporal profiles fit a linear regression ( R2 > 0 . 8 ) and we focused on cases in which the footprints were upregulated ( positive slope ) , whereas steady state protein levels were downregulated ( negative slope ) . These criteria generated a list of 65 proteins that presented profiles that suggest active degradation ( S7 Table , Figs 7A and S15A–S15C , left panels ) . When the same search was conducted with opposite criteria ( requiring that the footprints will be downregulated and protein levels will be upregulated ) we found no proteins that passed these conditions , supporting the notion that the list we obtained is biologically meaningful . We focused on five proteins from this list that reflect diverse biological functions; 1 . ROCK1—a Rho-associated kinase that is a central regulator of actin cytoskeleton [51] . 2 . ERC1—a regulatory subunit of the IκB kinase ( IKK ) complex [52] . 3 . CDC37- a co-chaperone that binds to numerous kinases and promotes their interaction with the HSP90 complex [53] . 4 . WDR61- a subunit of the human polymerase associated factor ( PAF ) and SKI complexes that regulate transcription[54] . 5 . TIPRL- an inhibitory regulator of protein phosphatase-2A ( PP2A ) . We conducted simultaneous real-time PCR and western blot analysis on cell extracts along HCMV infection and could confirm profiles that suggest active degradation of ROCK1 and ERC1 ( Figs 7B and S16A ) . CDC37 , WDR61 and TIPRL also presented profiles that support the premise they might be degraded , but their downregulation was less prominent ( S15A–S15C Fig , right panels ) . To further establish the increased degradation of these proteins during HCMV infection , we examined their half-lives by cycloheximide ( CHX ) chase assays . As shown in Fig 7C , both ROCK1 and ERC1 show a long half-life in uninfected cells , whereas , in HCMV infected cells their half-lives are considerably decreased ( Figs 7C and S16B ) . Similar results were obtained for CDC37 , WDR61 and TIPRL ( S15D Fig ) . We next tested if the proteins we identified are still degraded when cells are infected with a HCMV laboratory-adapted strain , AD169 , in which a 15 kb composing the ULb’ region ( genes UL133–UL150 ) is deleted . Importantly , ERC1 , CDC37 , WDR61 and TIPRL showed similar reduction in protein levels during infection with the AD169 virus ( Figs 7D and S15E ) . However , ROCK1 levels were significantly elevated during AD169 infection ( Fig 7D ) , suggesting that its degradation might depend on a protein/s that are encoded in the ULb’ region . ROCK1 plays a central role in actin regulation and its KD resulted in rounding up of cells ( S17 Fig ) . pUL135 which is encoded in the ULb’ region was recently shown to remodel the actin cytoskeleton [55] . We therefore tested the effect of pUL135 on ROCK1 expression . We expressed pUL135 in fibroblasts using a lentiviral vector . The vector also contained GFP that was expressed from the same transcript using IRES and was used to identify cells that express the pUL135 protein . As a control , we expressed another viral protein pUL26 in the same manner . As was previously reported [55] , expression of pUL135 in fibroblasts induced dramatic changes in cell morphology and cells became rounded up ( Fig 8A , GFP expressing cells ) . We examined ROCK1 expression by immunofluorescence and observed a significant reduction in ROCK1 levels only in cells that express pUL135 ( Figs 8A and S18 ) . Similar reduction in ROCK1 levels following pUL135 expression was observed by immunoblotting ( Fig 8B ) and we confirmed that pUL135 expression did not affect ROCK1 mRNA levels ( Fig 8C ) . Although pUL135 clearly affects ROCK1 expression the magnitude of the observed reduction strongly suggests that additional proteins contribute to ROCK1 degradation . Overall , our results demonstrate that integration between dynamic translation and proteome measurements enables systematic identification of proteins that are targeted for degradation during infection . Since targeted degradation indicates a strong biological importance this approach could facilitate the discovery of central host functions during viral infection .
In this study , we present a comprehensive resource describing temporal changes in cellular gene transcription and translation along HCMV infection . During infection , HCMV extensively manipulates cellular gene expression to maintain conditions favorable for efficient viral propagation . Analysis of transcription profiles has been the focus of systematic characterization of gene expression during infection [6 , 8 , 9 , 5 , 56] . However , control of protein production reflects both regulation of mRNA levels and the efficiency with which these messages are translated into proteins . Systematically measuring translation and mRNA levels allowed us to quantitatively evaluate their relative contributions and to reveal novel insights into the viral life cycle . Our results show that the majority of cellular transcripts , changed by more than 3-fold along infection , exemplifying the radical changes that occur in cells during lytic HCMV infection . Our measurements show that this extensive gene regulation is dominated by changes in mRNA levels , but translational control also regulated the magnitude and timing of protein production during HCMV infection ( discussed below ) . Although the majority of the pathways that significantly changed during infection were previously mapped , our deep measurements and siRNA experiments allowed us to reveal that peroxisomes might be playing an unrecognized role during HCMV infection . Peroxisomes participate in central pathways of cellular metabolism such as β-oxidation of fatty acids ( especially fatty acid chains that are too long to be handled by the mitochondria ) , amino acid catabolism and detoxification of reactive oxygen species [57] . It will be important to test which of these peroxisome functions are important for HCMV progression . A more global look at the pathways that are elevated during infection reveals upregulation of modules that are important for cell proliferation ( cell cycle , translation and transcription ) , whereas downregulated modules are related to development and cell-to-cell communication . Since an important dichotomy in the life of a cell is between proliferation and differentiation , these gene expression profiles illustrate that the cell program in infected cells is shifted , and although infected cells are not dividing , their cellular program resembles the cell program of a proliferating transformed cell . Our simultaneous measurements of mRNA levels and translation rates along infection allowed us to quantitatively evaluate the role of translation regulation in controlling cellular genes expression and to expand recent findings [17] . Using stringent criteria we identified significant changes in TE for almost 10% of cellular genes . By clustering translationally regulated genes based on their TE values , we revealed several distinct temporal profiles suggesting that several independent molecular mechanisms are responsible for the observed translational changes . We were able to connect two of these clusters to known cellular modules; one cluster of genes was significantly enriched in mTORC1 targets implying that the increased translation of mRNAs from this cluster is attributed to mTORC1 activation by the virus [58] . This observation is also supported by the finding that ectopic expression of UL38 ( mTORC1-activator ) recapitulates a large fraction of the genes whose translation was stimulated by HCMV [17] . A second cluster we identified was enriched in genes involved in cell cycle progression and in mRNAs that showed enhanced translation at G1 and S phases of the cell cycle [40] . Since HCMV infection arrests the cells in a unique G1/S phase it is likely that the induced translation of these mRNAs is the consequence of cell cycle arrest . We observed additional translation regulation profiles , but these were not significantly enriched for any annotated biological process and additional work is needed to delineate the cellular mechanism that drives these changes and their relation to HCMV infection . Overall , our results suggest that functionally related groups of genes are translationally co-regulated and this provides an additional mean to control the expression of a particular subset of mRNAs . Transcription studies have enabled the identification of cis- and trans-transcriptional elements that control diverse cellular processes , whereas a similarly broad understanding of the importance and mechanisms of translational control remains much more elusive . Our data set and clustering approach provides a valuable basis for identifying such cis- and trans-translational regulators . Unlike many viruses ( including several Herpesviruses ) , HCMV does not completely suppress the synthesis of host proteins in infected cells [1 , 2] . Our approach allowed , for the first time , to test whether the virus manages to manipulate the cellular translation machinery to preferably translate its own mRNA . By globally comparing the TE of host genes to that of viral genes we made two novel observations; 1 . When infection starts , viral mRNAs are translated less efficiently than host mRNAs . This observation intriguingly suggests that the cell possess intrinsic means to distinguish between cellular and viral mRNAs . 2 . At 72 hpi a subtle but significant advantage for viral mRNAs translation is observed , suggesting that late in infection the virus does deploy the translation machinery to biasedly translate its mRNAs . Even though this virus does not fully co-opt the translation machinery like other viruses , elucidating the regulatory mechanisms underlying translational reprogramming of both the virus and the host can reveal novel modules the virus relies on , which could ultimately lead to the development of novel therapeutic strategies . Our work also demonstrates that mass spectrometry and ribosome profiling represent highly complementary approaches; our comparison between changes in rate of protein synthesis , measured by ribosome profiling , and protein abundance , measured by mass spectrometry , revealed novel examples of regulated degradation of cellular proteins during HCMV infection . This integration of ribosome profiling with mass spectrometry measurements along a dynamic process , presents a novel unbiased approach to map protein degradation . HCMV has evolved a variety of mechanisms to evade the immune response to survive in infected hosts , including elimination of cellular immune ligands from being presented on the cell surface . We reveal here that BTN2A1 and IGSF8 , members of the Ig superfamily , are degraded during HCMV infection . BTN2A1 is a cell surface glycoprotein related to the extended family of B7 costimulatory molecules . It was shown to act as a ligand for DC-SIGN [59] , a specific dendritic cell receptor , but its role in immune response was never investigated . IGSF8 was identified as a major tetraspanin ( CD9 and CD81 ) -associated protein [60] and was shown to regulate the formation and maintenance of immune synapses [61] . It is therefore possible that degradation of IGSF8 allows protection against cytotoxic immune effector cells by interfering with immune synapse formation . A recent study revealed that US2 , a viral protein that was originally defined by its capacity to target MHC molecules for degradation [44] , is a pleotropic modulator of cell surface receptors [47] . Hsu et al . [47] unbiased measurements suggested that the US2 protein downregulates BTN2A1 and IGSF8 cell surface expression . Indeed , we show that BTN2A1 degradation is dependent on US2 expression but our analysis indicate that IGSF8 is still degraded even when US2 is not expressed . Our results suggest that US9 probably also plays a role in mediating IGSF8 degradation . Therefore , presenting an additional example for the redundancy in mechanisms HCMV is using to escape immune recognition . Interestingly , US9 was recently shown to selectively target MICA*008 , a highly prevalent stress-induced ligand , to proteasomal degradation [49] . Our results suggest that like US2 [47] , US9 probably targets a broader set of proteins and future work will have to address the specificity of this viral protein . More broadly , our ability to unbiasedly identify two immune ligands that are degraded during infection strongly argues for the validity of our approach . Our analysis revealed additional cellular proteins that are degraded during infection . ROCK1 plays a critical role in mediating the effects of small GTPase RhoA on stress fiber formation , focal adhesion and cell motility [62] . Interestingly , these structures were shown to be modulated during HCMV infection [63 , 64] and the possible role of ROCK1 in modulation of these processes could be studied . We also show that ERC1 is degraded during HCMV infection . ERC1 was shown to be critical for NF-κB activation following ATM induction by genotoxic stress [65] . Since it is known that ATM is activated during HCMV infection [28 , 66] , targeted degradation of ERC1 could serve as an elegant way to antagonize innate immunity response through attenuation of NF-κB signaling . Finally , our approach is applicable to other viruses or any other pathogen and is useful to gain mechanistic insights into pathogen interference with regulation of mRNA expression , translation and protein degradation .
Human fibroblasts ( CRL-1634 ) and the HCMV Merlin strain ( VR-1590 ) were obtained from American Type Culture Collection ( ATCC ) . The virus was propagated twice on HFF cells before the preparations of samples for sequencing . Cells were grown on 15cm plates and were infected at a multiplicity of infection ( MOI ) of 5 . The AD169 , AD169VarL , AD169VarL-BAC , AD169VarL-BAC deltaUS9 and the AD169VarL-BAC deltaUL40 were previously described [49 , 67 , 68] . US2 , US9 and UL135 were amplified from cDNA derived from HCMV-infected cells and cloned into the lentiviral vector pHAGE-DsRED ( − ) -ZsGreen ( + ) . UL135 was cloned under the doxycycline inducible promoter . Lentivirus was packaged by co-transfection of constructs with the 2nd generation packaging plasmids pMD2 . G and PsPax using jetPEI ( Polyplus-transfection ) into 6-well plates with 293T cells according to protocol . 60 hours post transfection supernatants were collected and centrifuged at 1500 rpm for 5 minutes and filtered through a 0 . 45 μm filter . HFF expressing BTN2A1 , IGSF8 or UL135 cell lines were generated by lentiviral transduction . 12 hours after infection fresh media was added , for UL135 cell line , the medium was supplemented with tet-free serum ( Biological industries ) . Cells were analyzed for expression of proteins using fluorescent microscopy for GFP positive cells . Cylcoheximide treatment was carried out as previously described [3] . Cells were lysed in lysis buffer ( 20mM Tris 7 . 5 , 150mM NaCl , 5mM MgCl2 , 1mM dithiothreitol , 8% glycerol ) supplemented with 0 . 5% triton , 30 U/ml Turbo DNase ( Ambion ) and 100μg/ml cycloheximide , ribosome protected fragments were then generated as previously described [3] . Total RNA was isolated from infected cells using Tri-Reagent ( Sigma ) . Polyadenylated RNA was purified from total RNA sample using Oligotex mRNA mini kit ( Qiagen ) . The resulting mRNA was modestly fragmented by partial hydrolysis in bicarbonate buffer so that the average size of fragments would be ~ 80bp . The fragmented mRNA was separated by denaturating PAGE and fragments 50–80 nt were selected as previously described [3] Prior to alignment , linker and polyA sequences were removed from the 3’ ends of reads . Bowtie v0 . 12 . 7 ( 5 ) ( allowing up to 2 mismatches ) was used to perform the alignments . First , reads that aligned to human rRNA sequences were discarded . All remaining reads were aligned to the concatenated viral ( NC_006273 . 2 ) and human ( hg19 ) genomes . Finally , still-unaligned reads were aligned to 200bp sequences that spanned splice junctions . Reads with unique alignments were used to compute the total number of reads at each position . Footprints and mRNA densities were calculated in units of reads per kilobase per million ( RPKM ) in order to normalize for gene length and total reads per sequencing run . TE was calculated for genes that had more than uniquely aligned 150 reads of mRNA and footprints . For the comparison of between the virus and the host TE only genes with TE >1 were included . For clustering only genes with calculated expression > 3 RPKM in at least one of the condition and a change greater than 3-fold were used . Partitioning clustering was performed using Partek Genomic suits across mRNA , footprints , and TE data . Where indicated , gene lists were analyzed by Ingenuity Pathway Analysis ( Ingenuity Systems , Redwood City , CA , USA ) using default settings . The 5′UTRs were obtained using the known gene ID from the UCSC Genome Browser ( GRCh37/hg19 ) . For Each Cluster the 5′UTR length , %G+C content and Gibbs free energy was calculated and compared to background list using Wilcoxon two-sided test . The translationally regulated clusters we identified ( Fig 3B ) were compared to genes that were identified as transitionally upregulated during different phases of the cell cycle [40] and to genes that were translationally repressed after mTOR inhibition ( log2 ratio <-1 , Thoreen et al . [38] ) and the enrichment was calculated using hyper Geometric test . Cells were lysed using RIPA buffer . Lysates were nutated at 4°C for 10 min , then centrifuged at 20 , 000 × g for 15 min at 4°C . Samples were then separated by 4–12% polyacrylamide Bis-tris gel electrophoesis ( Invitrogen ) , blotted onto nitrocellulose membranes and immunoblotted with primary antibodies ( αROCK1 ab134181; αERC1 ab180507; αCDC37 ab108305; αWDR61 ab57840; αB7H6 ab138588; αTIPRL ab70795; αGFP ( ZsGreen ) 632474 ( Takara-Clontech ) ; αIE1/IE2 ( CH160 ) ab53495 ( Abcam ) ; αHA 3F10 ( Roche ) ; αGAPDH 2118S ( Cell signaling ) ; αATG3 A3231 ( Sigma ) ; αUL44 ( CMV ICP36 ) CA006 ( Virusys ) ; αpp28 CA004 ( Eastcoast ) . Secondary antibodies used were Goat anti-rabbit , Goat anti-mouse ( IRDye 800CW or IRDye 680RD , Licor ) , or Goat anti Rat ( Alexa Fluor 680 , ab175778 , Abcam ) . Reactive bands were detected by Odyssey CLx infrared imaging system ( Licor ) . Protein concentration was measured by Bradford assay ( Sigma cat no . B6916 ) . Protein quantification was performed on Licor software . Total RNA was extracted using Tri-Reagent ( Sigma ) according to protocol . cDNA was prepared using High-Capacity cDNA Reverse Transcription Kit ( ABI ) according to protocol . Real time PCR was performed using the SYBR Green PCR master-mix ( ABI ) on a real-time PCR system StepOnePlus ( life technologies ) with the following primers ( forward , reverse ) : RAB12; GCCGTCATGGAAGGTTATTT , CCCTTAGGAAGCCATGAGAG IRAK1; CAGACAGGGAAGGGAAACAT , AATCACTGTGAAGCCTGTGC HSP90AB1; GCAGACATCTCCATGATTGG , AAGGAACCTCCAGCAGAAGA SIX5; CAGTCACCACATCCTTCTGC , GGGAGGGCTGTAACAGAGAG FAT1; CATCATTGTTGCCAAACCTC , GAGGACGATGGTCATTTGTG FAT4; AGTGGTGGAACCTGTCACAA , CTCTGCAGGCACTCATTGAT LAMA2; GGCCTGACTGGGAAATTAAA , CTCGGAAATTCCACAAACCT ATP5J: GGTGTTACAGCAGTGGCATT , CCTCTCCAGCTCTTGCTGAT NCOA6; GTCCTGGGTCCAGTAGGAAA , GAGGAGTGGGACTGACCAAT TSEN54; CCAAGACCTGCCACTGTCTA , GGACAGAGCTTGGTTGGAAT POLR2L; AGGAGAGCCTTCCATCTCG , ATCTGGCTCTTCAGATTCCG CDC37; GGTAAATACCAAGCCCGAGA , ATGCCAAAGTGCTTGATCTG B7H6; ACCCTGGGACTGTCTACCAG , TGAAATAGGCCACCAATGAA ERC1; TGCAAATCAGAAAGCTGACC , TGGTGGTAGAGGTGGTC TIPRL; TCCCTGAAATGATGTTTGGA , CTTCAGCACAGGCCACTTTA ROCK1; TTGGTAGAGGTGCATTTGGA , AAAGCCATGATGTCCCTTTC WDR61; TGCTCATATTCGTCTTTGGG , ACTTTCCCGACATGAGTTCC MFGE8; CACTCTGCGCTTTGAGCTAC , TCCAGCTGAAGAGATGCAAG IGSF8; ACCCTATTTGTGCCTCTGCT , ACAGTCGACACCTGCAAGAC BTN2A1; AGAGGAATCCACAGGACCAC , GGGACTTAGCCACCCTTACA RAB12; GCCGTCATGGAAGGTTATTT , CCCTTAGGAAGCCATGAGAG BTN2A1; AGAGGAATCCACAGGACCAC , GGGACTTAGCCACCCTTACA Cells were transfected with siRNA validated for each of the target genes or negative control ( TriFECTa Kit DsiRNA Duplex , IDT ) in the presence of Lipofectamine RNAiMAX reagent ( Life Technologies ) , according to manufacturer's standard protocol . 24 hours after transfection , cells were infected with HCMV ( Merlin strain , MOI 3 ) . All experiments were performed in triplicate , and representative results are reported . 104 HFF were plated in 96-well plates and cells were infected with 10-fold serial dilutions of supernatant from knocked-down infected cells , collected 5 days post infection . At 10 days post infection the dilutions showing cytopathic effect were evaluated by light microscopy . The TCID50/ml was calculated using the Spearman-Kaerber method [69] . Experiments were performed at least 3 times and representative figure is presented . The fluorescence-based translation reporter was cloned using fusion pcr of three parts; 1 . DHFR ( Y100I ) 2 . sfGFP-NLS-P2A 3 . NLS-mCherry and cloned into the pHR lentiviral expression vector using BstXI-NotI . 5’ UTRs were inserted using BstX1-BsiWI . Primers to amplify the UTRs used in this study were based on the RNA-seq data to represent the most common UTR splice variant in HFF cells . All live cell-imaging experiments were performed at 37°C on a AxioObserver Z1 widefield microscope using a 20x air objective and Axiocam 506 mono camera . Cells were grown and imaged in 24-well glass bottom plates and 1 hr before imaging normal growth medium was replaced with DMEM without phenol red , supplemented with 10% FCS and antibiotics . Image analysis was done in Imaris software . For image quantification , images were first corrected for background subtraction using default settings . Segmentation and tracking of each field was performed on the mCherry channel and the GFP mean intensity over time for each segment was measured . The average GFP slope for all segments for each sample was calculated ( 10<n ) . Cells were plated on ibidi slides and fixed in 4% paraformaldehyde for 30 min , washed in PBS ( pH 7 . 4 ) and permeabilized with 0 . 2% Triton X-100 in PBS for 10 min , then blocked with 3% BSA in PBS for 30 min . Detection of ROCK1 was performed by immunostaining with anti-ROCK1 antibodies ( abcam 156284 , 1:200 in PBS ) 1hr , RT . Cells were washed 3 times with PBS and labeled with anti-rabbit Rhodamine Red-X-conjugated secondary antibody ( Jackson ImmunoResearch 711-295-152 , 1:200 in PBS ) 1 hr , RT . Imaging was performed on a AxioObserver Z1 widefield microscope using a 63x oil objective and Axiocam 506 mono camera . Samples were digested by trypsin , analyzed by LC-MS/MS on Q Exactive ( Thermo ) . The data was analyzed with Protein Discoverer 1 . 4 versus Human and HCMV Uniprot database and against decoy databases ( in order to determine the false discovery rate -FDR ) , using the Sequest search engine . The data was also analyzed vs the specific sequences of HCMV Merlin strain . Identifications were filtered with high identification confidence refers to 0 . 01 FDR , top rank , mass accuracy , and a minimum of 2 identified peptides in the human proteins . Semi-quantitation was done by calculating the peak area of each peptide . The area of the protein is the average of the three most intense peptides from each protein . HFF stably expressing IGSF8-HA or empty vector were collected from 2x15 cm tissue culture plates 48 hours after mock infection or infection with HCMV Merlin strain at MOI ~ 5 . 12 hours before harvesting , the proteosomal inhibitor MG-132 was added in final concentration of 10μM . Cells were washed twice in PBS and lysed in 1 ml lysis buffer ( 150mM NaCl , 2mM CaCl2 , 2mM MgCl2 , 1% NP-40 in PBS , supplemented with Roche complete protease inhibitor cocktail ) . Lysis was facilitated by nutating the cells 1 hr at 4°C . Cells were then centrifuged for 15 min , 20 , 000 rpm 4°C and supernatant was separated and incubated with pre-equilibrated anti-HA magnetic beads ( Pierce ) . Tagged protein binding to beads was performed by nutating cells-beads mixture at 4°C for 1 hr . Tagged protein bound to anti-HA magnetic beads was separated using magnetic stand , beads were washed 3 times in wash buffer ( same composition as lysis buffer with 0 . 1% NP-40 ) . Elution was performed by incubating the beads with 0 . 1M glycine pH 2 . 5 with gentle mixing . Eluate was neutralized with 0 . 1M Tris pH 8 . 5 . Protein sample buffer was added to eluates and samples were resolved on Bis-Tris-SDS gel 4–12% and stained with Instant Blue staining . The gel was sent to Mass-spectrometry analysis for identification of interacting proteins . | Viruses are fully dependent on the cellular translation machinery , and develop diverse mechanisms to co-opt it for their own benefit . However , fundamental questions such as: what is the effect that infection has on the spectrum of host mRNAs that are being translated , and whether , and to what extent , a virus possesses mechanisms to commandeer the translation machinery are still hard to address . Here we show that by simultaneously examining the changes in transcription and translation along Human cytomegalovirus ( HCMV ) infection , we can uncover extensive transcriptional regulation , but also diverse and dynamic translational control . We were also able to show that , at late time points in infection , translation of viral mRNAs is higher than that of cellular mRNAs . Lastly , we take advantage of our measurements of translation ( protein synthesis rate ) and integrate these with mass spectrometry measurements ( protein abundance ) . This integration allowed us to unbiasedly reveal dozens of cellular proteins that are being degraded during HCMV infection . Since targeted degradation indicates a strong biological importance , this approach should be applicable for discovering central host functions during viral infection . Our work provides a framework for studying the contribution of transcription , translation and degradation during infection with any virus . | [
"Abstract",
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"Methods"
] | [] | 2015 | The Transcription and Translation Landscapes during Human Cytomegalovirus Infection Reveal Novel Host-Pathogen Interactions |
The Core Binding Factor ( CBF ) protein RUNX1 is a master regulator of definitive hematopoiesis , crucial for hematopoietic stem cell ( HSC ) emergence during ontogeny . RUNX1 also plays vital roles in adult mice , in regulating the correct specification of numerous blood lineages . Akin to the other mammalian Runx genes , Runx1 has two promoters P1 ( distal ) and P2 ( proximal ) which generate distinct protein isoforms . The activities and specific relevance of these two promoters in adult hematopoiesis remain to be fully elucidated . Utilizing a dual reporter mouse model we demonstrate that the distal P1 promoter is broadly active in adult hematopoietic stem and progenitor cell ( HSPC ) populations . By contrast the activity of the proximal P2 promoter is more restricted and its upregulation , in both the immature Lineage- Sca1high cKithigh ( LSK ) and bipotential Pre-Megakaryocytic/Erythroid Progenitor ( PreMegE ) populations , coincides with a loss of erythroid ( Ery ) specification . Accordingly the PreMegE population can be prospectively separated into “pro-erythroid” and “pro-megakaryocyte” populations based on Runx1 P2 activity . Comparative gene expression analyses between Runx1 P2+ and P2- populations indicated that levels of CD34 expression could substitute for P2 activity to distinguish these two cell populations in wild type ( WT ) bone marrow ( BM ) . Prospective isolation of these two populations will enable the further investigation of molecular mechanisms involved in megakaryocytic/erythroid ( Mk/Ery ) cell fate decisions . Having characterized the extensive activity of P1 , we utilized a P1-GFP homozygous mouse model to analyze the impact of the complete absence of Runx1 P1 expression in adult mice and observed strong defects in the T cell lineage . Finally , we investigated how the leukemic fusion protein AML1-ETO9a might influence Runx1 promoter usage . Short-term AML1-ETO9a induction in BM resulted in preferential P2 upregulation , suggesting its expression may be important to establish a pre-leukemic environment .
Adult hematopoiesis is orchestrated by a series of lineage fate decisions that control the specification of mature erythroid , myeloid and lymphoid blood cells from pluripotent HSCs . RUNX transcription factors play key roles at different stages , activating or repressing transcriptional targets through DNA binding in association with other lineage-specific and ubiquitous transcription factors and cofactors [1 , 2] . RUNX1 ( also known as Acute Myeloid Leukemia 1 or AML1 ) is a master regulator of definitive hematopoiesis , broadly expressed in HSCs , progenitors and mature populations , with the exception of terminally differentiated erythrocytes [3–5] . RUNX1 activity is vital for the embryonic establishment of normal adult hematopoiesis through the regulation of HSPC emergence in a process termed endothelial-to-hematopoietic transition ( EHT ) [6–12] . Conditional deletion of Runx1 in adult mice , meanwhile , results in hematological imbalances such as decrease of peripheral blood lymphocytes , expansion of monocytes and granulocytes and impaired T cell maturation [13–15] . RUNX1 is also critical in megakaryocytic maturation and platelet production [16 , 17] . The requirement for RUNX1 in adult HSC maintenance is more controversial , with assertions of impaired long-term repopulating ability in Runx1-null HSCs due to increased stem cell exhaustion being increasingly challenged [6 , 18 , 19] . The importance of normal CBF function extends to malignant hematopoiesis , with RUNX1 or CBFB mutations found in over 20% of acute myeloid and lymphoid leukemia cases [20] . Although impaired RUNX1 activity is frequently important for establishing a pre-leukemic stage , WT RUNX1 protein is nonetheless necessary for maintaining AML1-ETO Acute Myeloid Leukemia ( AML ) [21 , 22] . Consequently , the investigation of RUNX1’s expression and function in hematopoiesis is of considerable interest to developmental biologists and clinical researchers alike . All vertebrate Runx genes contain two alternative promoters , a distal P1 promoter and a proximal P2 promoter thought to represent the initial “primitive” promoter [23–25] . The major protein isoforms produced from the P1 and P2 promoters , RUNX1C and RUNX1B respectively , differ in their N-terminal amino acid sequences; RUNX1C is 14 amino acids longer and begins with the MASDS sequence whereas RUNX1B begins with MRIPV , a feature conserved in mice and humans [26 , 27] . P2 is the more active promoter at the onset of definitive hematopoiesis in the E7 . 5 embryo [28 , 29] . P1 activity is subsequently upregulated , enriched in definitive hematopoietic culture colony-forming unit ( CFU-C ) populations from E8 . 5 onwards [29] . Analyses on whole cell populations revealed a remarkable switch to P1-dominant Runx1 expression at the fetal liver stage that is maintained in adult BM populations [28 , 29] . At this stage P2 activity is only detected in some specific adult hematopoietic subsets . However , the exact cell populations defined by the activities of P1 and P2 remain largely unknown . To define the activities of the Runx1 promoters in adult HSPCs we utilized a previously described distal-Green Fluorescent Protein ( GFP ) , proximal-truncated human CD4 ( hCD4 ) ( P1-GFP::P2-hCD4 ) dual reporter knock-in mouse line [29] . We observed that all Runx1‐positive adult BM populations expressed P1‐GFP , whereas P2‐hCD4 expression was highly restricted . Phenotypic HSCs expressed solely P1‐GFP , with upregulation of P2‐hCD4 in CD48‐positive multipotent progenitors ( MPPs ) coinciding with a significant downregulation of erythroid output . We also found that the PreMegE population could be prospectively separated into P2‐hCD4‐ “pro‐erythroid” and P2‐hCD4+ “pro‐megakaryocyte” populations . Global gene expression analyses identified various candidate cell surface markers which were differentially expressed between the two PreMegE subpopulations . Among them , differential expression of the hematopoietic cell antigen CD34 enabled the prospective isolation of CD34- “pro‐erythroid” and CD34+ “pro‐megakaryocyte” PreMegEs from WT BM . To further investigate the potential functional significance of the dominance of RUNX1C in adult hematopoiesis , we investigated the impact of its absence in adult mice and found it to recapitulate certain phenotypes observed in complete Runx1 knockout mouse models . We observed perturbations in platelet versus erythroid output and altered splenic CD4 SP and CD8 SP specification , suggesting certain lineages were more dependent on specific RUNX1C-associated activity than others . Finally , we probed the potential specific RUNX1 isoform requirements in AML by analyzing the impact of AML1-ETO oncogene expression on Runx1 promoter usage . Interestingly , AML1-ETO expression appeared to promote Runx1 P2 over P1 expression in several HSPC populations , suggesting that the Runx1 isoforms may have specific functions both in normal and malignant hematopoiesis .
Utilizing the P1-GFP::P2-hCD4 reporter mouse model [29] , we traced Runx1 expression for both promoters in vivo at a single cell level in adult mice ( with flow cytometry gates based on the WT control tissues ) ( Fig 1A and 1B ) . We observed substantial heterogeneity of Runx1 expression within adult BM; approximately 55% of all BM cells were P1-GFP positive , almost 21% co-expressing P2-hCD4 ( Fig 1B ) . Red blood cell lysis ( using Ammonium-Chloride-Potassium ( ACK ) buffer ) led to the depletion of P1-GFP- P2-hCD4- cells; 97% of remaining cells expressed P1-GFP with 20% co-expressing P2-hCD4 . No P1-GFP- P2-hCD4+ cells were observed . In the spleen , approximately 70% of cells expressed P1-GFP but only 1% co-expressed P2-hCD4 , whereas in the thymus almost 100% of cells expressed P1-GFP , a quarter of which also expressed P2-hCD4 . Altogether these results establish , in line with other reports , that P1 is the dominant Runx1 promoter in adult hematopoietic populations and that the activity of P2 is much more restricted [28] . Taking advantage of our reporter model , we pursued a detailed examination of P1-GFP and P2-hCD4 expression in mature lymphoid and erythro-myeloid populations ( Figs 2 and S1 ) . Runx1 is expressed in definitive erythroid precursors , where it is involved in the regulation of erythroid gene expression as part of a core transcription factor complex , but is subsequently downregulated in mature erythrocytes [3–5 , 28 , 30] . Correspondingly , P1-GFP expression was restricted to 26% of the proerythroblast ( CD71high Ter119int , ProE ) , 56% of the basophilic erythroblast ( CD71high Ter119high FSChigh , EryA ) and 4% of the late basophilic/polychromatic erythroblast ( CD71high Ter119high FSClow , EryB ) fractions whilst being completely absent in the most mature CD71low Ter119high FSClow ( orthochromatic erythroblasts , reticulocytes , red blood cells , EryC ) compartment ( Figs 2A , 2B , and S1A ) . P2-hCD4 was expressed in less than 1% of Ter119+ erythroid cells , being apparently entirely dispensable for adult erythropoiesis . The low level of expression from both Runx1 P1 and P2 promoters , particularly the latter , in WT erythroid lineage cells was confirmed at the RNA level by qPCR ( S2A Fig ) . By contrast to the restricted expression observed in the erythroid lineage , Runx1 P1-GFP was expressed in almost 100% of mature myeloid CD11b+ BM cells ( Figs 2C , 2D and S1B ) . Of these , P2-hCD4 was co-expressed in 12% of Gr1high granulocytes , 39% of Gr1-/low monocytic/immature granulocyte cells and 17% of F4/80+ macrophages . The decreased P2 activity in the more mature granulocytic/macrophage ( GM ) fractions suggests a diminished role for RUNX1B as myeloid differentiation progresses . This also appears to be the case for terminal lymphoid differentiation , as P2-hCD4 co-expression with P1-GFP was restricted in the B-cell lineage to 58% of the BM Pre-pro-B , almost half ( 49% ) of the Pro-B and just 8% of the Pre-B progenitors ( Figs 2E–2H , S1C and S1D ) . P1-GFP was expressed in over 90% of BM B cell progenitors but was reduced to approximately 80% of mature BM and spleen B cells . Finally , thymic T cells were highly enriched in the P1-GFP+ P2-hCD4- fraction but P2-hCD4 activity appeared to peak in the CD4 CD8 double negative 2 ( DN2 ) fraction at approximately 61% ( Figs 2I , 2J and S1E ) . Interestingly , the more mature spleen CD4 and CD8 single positive ( SP ) T cell subsets displayed greater heterogeneity than their thymic counterparts; almost 100% of CD4 SP cells express P1-GFP whilst this is the case for only 20% of CD8 SP cells ( Figs 2K , 2L and S1F ) . Relative quantitation of the Runx1 isoforms’ expression revealed comparatively high P1 and P2 activity in the GM , B and T lineages , peaking in the early thymic T cell CD4 CD8 DN population and provides direct evidence that the P1-GFP::P2-hCD4 reporters faithfully represent WT Runx1 expression throughout adult hematopoiesis ( S2A–S2C Fig ) . Overall , P1 clearly dominates , accounting for over 80% of Runx1 expression in all analyzed lineage positive populations . Nonetheless , strong P2 expression was observed in CD11b+ GR1+ GM cells , Pre-pro/pro/pre-B cells and CD4 CD8 DN T cells , decreasing substantially in the more mature IgM+ B and CD4/CD8+ T cells . These results indicate that P1 is the dominant Runx1 promoter in terminally differentiated hematopoietic cells and suggest that downregulation of P2 is required for maturation to occur . We therefore decided to determine whether P2 expression has a greater prominence and significance in immature HSPC subsets . To examine the relative activities of the two Runx1 promoters in the most immature hematopoietic compartments , we separated the LSK fraction into phenotypic HSC and MPP fractions ( Fig 3A ) . We observed that only the P1 promoter was active in the HSCs and CD48- MPPs ( Fig 3C ) . The upregulation of CD48 expression coincides with the loss of long-term repopulating ability , the LSK CD48+ fraction consisting of a mixture of lymphoid and myeloid progenitors with varying multipotentiality . FMS-Like Tyrosine Kinase 3 ( FLT3 ) expression marks a commitment to the GM and lymphoid lineages at the expense of Mk/Ery specification [31 , 32] . Increased GM/lymphoid lineage commitment appears to coincide with increased P2 activity , as the majority of cells in the lymphoid-primed multipotent progenitor ( LMPP ) -enriched FLT3+ and the common lymphoid progenitor ( CLP , Fig 3B ) subsets co-expressed P1-GFP and P2-hCD4 ( Fig 3C ) . Therefore , although P1 is the dominant Runx1 promoter at the onset of adult hematopoiesis , our results suggest that P2 expression imparts or at least reflects distinct lineage commitment decisions in these immature hematopoietic compartments . Consistent with this theory , Runx1 P1 activity , as measured by quantitative RT PCR in WT BM HSPCs , peaked in the WT HSCs and decreased by approximately 50% in the FLT3+ MPPs , coinciding with the substantial increase in Runx1 P2 expression ( S3 Fig ) . The overall result is that total Runx1 expression decreases only modestly in MPPs compared to HSCs but the relative contribution by P1 compared to P2 decreases substantially . When the differences in biological potential were directly assessed in FLT3+ MPPs , P2-hCD4- and P2-hCD4+ subpopulations were capable of relatively similar levels of lymphoid and myeloid differentiation ( Fig 3D and 3E bottom ) , although the increased GM:M CFU-C ratio in the P2-hCD4- subset may suggest it represents a more immature population than its P2-hCD4+ counterpart . However , the difference in lineage output by the FLT3- subsets was more marked; P2-hCD4- LSK CD48+ FLT3- ( LSK48F- ) MPPs appeared to have reduced T cell output ( Fig 3D top ) but enhanced multilineage myeloid colony-forming unit potential at the expense of CFU-M output ( Fig 3E and 3F ) . Co-culturing the LSK48F- progenitors with the OP9 murine stromal cell line in myeloid differentiation media revealed that CD11b+/Gr1+ GM output was significantly decreased and CD41+ megakaryocytic ( Mk ) cell production was slightly increased in the P2-hCD4- fraction as a proportion of total cells ( Fig 3G and 3H ) . As a proportion of non-GM ( CD11b- ) cells , CD41+ Mk cell output was in fact significantly increased in the P2-hCD4+ LSK48F- fraction . Most strikingly , Ter119+ erythroid cell output was almost entirely restricted to the P2-hCD4- fraction . Our phenotypic characterization of BM HSPCs therefore demonstrate that upregulation of Runx1 P2 not only occurs after loss of HSC activity but also coincides with a substantial decrease in erythroid specification . To determine whether LSK48F- P2-hCD4- and P2-hCD4+ progenitors arise sequentially or independently in the hematopoietic hierarchy , sorted cells were cultured with pro-myeloid cytokines for up to 18 hours and immunophenotypically characterized ( S4A and S4B Fig ) . Whereas the P2-hCD4+ fraction solely produced P2-hCD4+ LSK cells , P2-hCD4- cultures yielded P2-hCD4- and P2-hCD4+ LSK cells ( S4B Fig ) . In addition , LSK48F- P2-hCD4- cultures produced more phenotypic erythroid ( pre-erythroid colony-forming unit , PreCFUe or erythroid colony-forming unit , CFUe ) or bi-potential PreMegE progenitors and fewer GM ( Pre- Granulocyte-Macrophage progenitor , PreGM or Granulocyte-Macrophage Progenitor , GMP ) and megakaryocyte progenitor ( MkP ) cells compared to LSK48F- P2-hCD4+ cells . Altogether these data demonstrate a hierarchical relationship between an erythroid-biased P2-hCD4- MPP population and increasingly pro-GM/Mk P2-hCD4+ progeny . Although Runx1 P2 expression appears to decrease as GM maturation proceeds , its expression in the earliest identified GM-restricted progenitors remained unknown . We found that approximately 80% of PreGMs and 70% of GMPs co-express P1-GFP and P2-hCD4 ( Fig 4A and 4B ) . Moreover , we found P2 expression ( as determined by qRT-PCR ) to be higher in the PreGM and GMP than other analyzed WT BM HSPC populations but P1 expression was only 50% and 25% of the level observed in HSCs ( S3 Fig ) . High P2 activity therefore appeared to be important for GM lineage commitment and we decided to investigate the functionality of the P2-hCD4+ and minority P2-hCD4- GM progenitor populations . Interestingly , CFU-C activity was significantly higher in the P2-hCD4+ fractions , compared to the P2-hCD4- populations , of both PreGM and GMP populations , reflecting higher CFU-M and CFU-GM frequencies ( Fig 4C–4E ) . In particular , the P2-hCD4- GMP fraction appeared to consist of monopotent granulocytic and monocytic/macrophage progenitors rather than bipotential GM progenitors . Liquid culture of the progenitors confirms an apparent bias against macrophage specification as F4/80+ cell numbers were significantly diminished in P2-hCD4- PreGM and GMP cultures , whereas Gr1high granulocyte output was unaltered ( S5A and S5B Fig ) . Interestingly , production of CD11b- cKit+ FcεR1α+ mast cells was also elevated in P2-hCD4- cultures and more detailed analyses confirmed the absence of P2-hCD4 expression in immunophenotypic mast cell progenitors ( MCp , S5A–S5C Fig ) . The decreased CFU-GM activity of the P2-hCD4- GM progenitors implies that they reside later in the hematopoietic hierarchy than the P2-hCD4+ populations , but in vitro lineage tracing revealed that P2-hCD4- GMPs gave rise to P2-hCD4+ GMPs ( S5E Fig ) . P2-hCD4- PreGMs gave rise to P2-hCD4+ PreGMs and subsequently to P2-hCD4- and P2-hCD4+ GMPs , the latter dominating ( S5D Fig ) . Therefore even within the GM lineage , differential Runx1 promoter activity appears to play a role in or at least correlate with crucial cell fate decisions . Erythropoiesis and megakaryopoiesis are highly similar developmental pathways , sharing numerous regulatory factors particularly at the point of lineage specification [33 , 34] . However , there are key differences and the specificity of a megakaryocyte maturation defect in Runx1-null adult BM implicates RUNX1 as a central player in Mk/Ery lineage determination [14] . Moreover , our observation that P2-hCD4- and P2-hCD4+ MPPs have distinct Mk/Ery potential led us to investigate Runx1 promoter activity in Mk/Ery-restricted progenitors further ( Fig 5A ) . We observed that erythroid restricted PreCFUe and CFUe progenitors expressed solely P1-GFP whereas the MkP was chiefly P1-GFP+ P2-hCD4+ ( Fig 5B ) . Because mature megakaryocytes are scarce in adult mice , BM-derived megakaryocytes were obtained by culturing purified MkPs in vitro . CD41-expressing megakaryocytes expressed P2-hCD4 and a large fraction ( 60% ) co-expressed P1-GFP ( Fig 5C ) . Whilst lineage-restricted megakaryocytic and erythroid progenitors were highly homogeneous , the PreMegE fraction , which generates the MkP and PreCFUe populations , was markedly more heterogeneous; approximately 75% express solely P1-GFP whereas the remaining 25% were P1-GFP+ P2-hCD4+ ( Fig 5B ) . When compared to the relative homogeneity of the monopotent MkP , PreCFUe and CFUe populations , the heterogeneity of the PreMegE led us to consider the possibility of two functionally distinct and prospectively isolatable PreMegE subsets . We subsequently observed that erythroid CFU-C activity ( CFUes and erythroid blast-forming units ( BFUes ) ) was significantly enriched in the P2-hCD4- PreMegE fraction compared to the P2-hCD4+ population ( Fig 5D and 5E ) . By comparison , megakaryocyte CFU-C potential was highly enriched in the P2-hCD4+ fraction ( Fig 5D–5H ) . In the MkP population , the P2-hCD4+ fraction possessed similar megakaryocytic CFU-C activity to its WT counterpart ( S6A Fig ) , suggesting Runx1 haploinsufficiency did not significantly impair megakaryocyte colony formation . In addition to being more numerous , CFU-Mks derived from P2-hCD4+ PreMegEs were also larger than those derived from the P2-hCD4- fraction , the median number of cells per colony being doubled ( Fig 5E , 5G and 5H ) . To determine whether this was a result of increased proliferation in the P2-hCD4+ PreMegE fraction , we analyzed their cell cycle status by measuring 5’ethynyl-2’-deoxyuridine ( EdU ) incorporation and DNA content ( S6B Fig ) . More P2-hCD4+ PreMegE cells were in the EdU+ DNA Synthesis ( S ) phase compared to their P2-hCD4- counterparts , suggesting P2-driven RUNX1B expression may confer a proliferative advantage on PreMegE cells . The distinct megakaryocytic and erythroid potential of the two PreMegE fractions was further confirmed following co-culture with OP9 cells . After 7 days , P2-hCD4+ PreMegE cultures contained significantly more CD41+ megakaryocytes and significantly fewer Ter119+ erythroid cells than the P2-hCD4- PreMegE cultures ( Fig 5I and 5J ) . In addition , we performed clonal analyses by plating single P2-hCD4- and P2-hCD4+ PreMegEs with OP9 ( S6C Fig ) . This demonstrated that although the two fractions had similar clonal output ( 28% and 24% positive wells respectively ) the P2-hCD4+ fraction contained more bi-potent megakaryocytic/erythroid progenitors ( 42 . 9% versus 25% “Mk + Ery” ) and more monopotent megakaryocyte-producing progenitors ( 31 . 4% versus 12 . 5% “Mk only” ) than the P2-hCD4- fraction . The P2-hCD4- fraction was highly enriched for monopotent erythroid-producing progenitors ( 62 . 5% “Ery only” compared to 25 . 7% in the P2-hCD4+ co-cultures ) . Therefore , as in the immature LSK HSPC compartment , upregulation of P2 expression in PreMegEs appeared to coincide with a loss of erythroid and an enrichment of megakaryocytic specification . To decipher their relative positions in the hematopoietic hierarchy , P2-hCD4- and P2-hCD4+ PreMegE cells were cultured for up to 12 hours and analyzed ( Fig 5K ) . We observed that P2-hCD4+ PreMegEs made a more rapid transition to an MkP immunophenotype than P2-hCD4- cells . In addition P2-hCD4- PreMegEs gave rise to both P2-hCD4- and P2-hCD4+ fractions in vitro , whereas the P2-hCD4+ fraction did not appear to downregulate P2-hCD4 . Taken together , these data suggest the P2-hCD4- PreMegE can be placed earlier in the hematopoietic hierarchy , giving rise to the P2-hCD4+ PreMegE . Interestingly , we also observed that cultured P2-hCD4+ LSK48F- cells produced only P2-hCD4+ immunophenotypic PreMegEs ( S4B Fig ) . The differences in megakaryocytic and erythroid lineage potential in the P2-hCD4 negative and positive LSK48F- fractions may therefore be due to the preferential downstream specification of distinct PreMegE subpopulations . To explore the distinct gene regulatory mechanisms involved in the bifurcation of the Mk/Ery pathway , and to identify candidate genes which may serve as markers to isolate the progenitors in WT BM , we performed global gene expression analysis by RNA-Seq ( Fig 6A and S4 Table ) . The expression patterns of WT , P2-hCD4+ ( P2+ ) and P2-hCD4- ( P2- ) PreMegE samples were clearly separated based on principal component analysis , with WT cells clustering between the P2+ and P2- samples ( S7A Fig ) . When directly comparing the P2+ and P2- populations , 4876 genes were found to be at least 2-fold differentially expressed ( false discovery rate <0 . 05 ) , 2681 being upregulated in P2+ and 2195 in P2- PreMegEs ( Fig 6A and S4 Table ) . Gene Set Enrichment Analyses ( GSEA ) revealed a significant correlation between P2-hCD4 expression and activation of the Thrombopoietin ( TPO ) and Integrin pathways , both of which are crucial for megakaryopoiesis ( Figs 6B and S7B–S7D ) [35–37] . In line with the observed increased proliferative capacity of P2-hCD4+ PreMegEs , cell cycle regulators were also enriched in this population . Ingenuity pathway analysis ( IPA ) identified cell migration and blood cell recruitment as highly enriched functions and integrin signaling as the most significant activated pathway in P2-hCD4+ PreMegEs ( S7E and S7G Fig ) . By contrast , functions and pathways associated with cell death and cell cycle inhibition were highly enriched in P2-hCD4- PreMegEs ( S7F and S7H Fig ) . To further validate the distinct “pro-megakaryocytic” and “pro-erythroid” phenotypes of each PreMegE population , we screened , and validated by qPCR , the RNA Seq data for the expression of known Mk/Ery regulators and markers ( Fig 6C and 6D ) . Early erythroid-associated factors , including Kruppel-like factor 1 ( Klf1 ) and the Erythropoietin receptor ( Epor ) were significantly upregulated in P2-hCD4- PreMegEs whereas numerous megakaryocyte-specific markers ( Integrin alpha 2b ( Itga2b or Cd41 ) , Integrin beta 3 ( Itgb3 or Cd61 ) , Myeloproliferative Leukemia Virus Oncogene ( Mpl ) and Platelet Factor 4 ( Pf4 ) ) were enriched in the P2-hCD4+ fraction . Interestingly , the transcription factors GATA binding protein 1 ( Gata1 ) and Growth Factor Independent 1B ( Gfi1b ) were upregulated in P2-hCD4- PreMegEs . Both factors are crucial for the normal development of both megakaryocytic and erythroid lineages: deletion of either Gata1 or Gfi1b results in an early block in erythropoiesis at the PreCFUe stage whereas megakaryocytic maturation is impaired resulting in the accumulation of undifferentiated megakaryoblasts [38–41] . It would therefore appear that high Gfi1b and/or Gata1 expression promote erythroid specification whereas lower levels would favor megakaryocytic commitment , but ultimately an increase of both would be required for megakaryocytic maturation and thrombopoiesis . It is therefore highly likely that differential expression of Gata1 and Gfi1b at the PreMegE stage plays a role in Mk/Ery lineage determination and their differential expression may be driven by P2-driven RUNX1B . In order to distinguish “pro-megakaryocytic” and “pro-erythroid” PreMegE subsets in WT mice by alternative means to our reporter line , we screened the list of differentially expressed genes in P2-hCD4+ and P2-hCD4- PreMegEs for cell surface markers with commercially available antibodies validated for use in flow cytometry ( S8A Fig ) . The majority of selected markers had low RPKM values , with the exception of Itgb3 ( Cd61 ) and Cd34 ( Figs 6C–6E and S8B ) . However , CD61 protein expression was not detected on P2-hCD4+ and P2-hCD4- PreMegE cells by flow cytometry ( S8C and S8D Fig ) . By contrast , CD34 expression was approximately 2-fold higher in P2-hCD4+ PreMegEs compared to P2-hCD4- cells , both in terms of numbers of positive cells and median fluorescence intensity ( MFI; Figs 6E and S8D ) . WT CD34+ and CD34- PreMegEs were therefore FACS sorted to >95% purity ( Fig 6E ) and their lineage output and Runx1 isoform expression elucidated . Importantly , Runx1 P2 expression was substantially higher in the CD34+ PreMegEs compared to the CD34- fraction ( S3 Fig ) . Interestingly , P1 activity was also increased in the CD34+ cells , resulting in a 20% increase in total Runx1 expression . It is therefore unclear how important expression of the RUNX1B isoform is for the promotion of megakaryopoiesis compared to enhanced RUNX1 expression overall . Indeed , P2 transcripts were even more highly expressed in WT MkPs , contributing to the highest levels of total Runx1 in all analyzed HSPCs ( S3C Fig ) . However , P1 expression was in fact decreased in MkPs compared to PreMegEs , offering additional evidence in favor of a specific pro-megakaryopoiesis role for the P2-specified RUNX1B protein . Akin to P2-hCD4+ PreMegEs , WT CD34+ PreMegEs had enhanced CFU-Mk and diminished BFUe activity compared to CD34- cells ( Fig 6F ) . Myeloid co-culture with OP9 stromal cells confirmed these phenotypes , as CD34+ PreMegEs produced substantially more CD41+ megakaryocytes and fewer Ter119+ erythroid cells ( Fig 6G and 6H ) . Single-cell OP9 co-culture revealed the CD34+ PreMegE compartment was highly enriched for monopotent “Mk only” progenitors ( 79 . 2% versus 41 . 7% ) and bipotent “Mk + Ery” progenitors ( 20 . 8% versus 8 . 3% ) compared to the CD34- fraction ( S8E Fig ) . By contrast , monopotent “Ery-only” progenitors accounted for 50% of the CD34- PreMegE cultures but were apparently absent from the CD34+ fraction . We have therefore established the existence of prospectively isolatable “pro-megakaryocytic” CD34+ and “pro-erythroid” CD34- PreMegE cells in WT mice . Having established that the P1-directed RUNX1C isoform is expressed throughout adult hematopoiesis , we decided to determine how its absence would impact the overall homeostasis of the adult blood system . Previously , we utilized the P1-GFP homozygous mouse to investigate the requirement for RUNX1C at the onset of hematopoiesis and found it to be dispensable for hematopoietic commitment [29] . However , this may be due to P2 being the dominant promoter at this stage . We therefore analyzed hematopoietic populations in adult WT , RUNX1C heterozygous ( P1-GFP/+ ) and homozygous knockout ( P1-GFP/GFP ) mice ( Fig 7A ) . Despite the high expression of P1 in erythroid , myeloid and lymphoid progenitors , we observed no significant perturbation of circulating red or white blood cell numbers upon performing automated cell counts ( Fig 7B ) . Erythroid differentiation appeared to be normal , as peripheral blood hematocrit , hemoglobin concentration and the reticulocyte counts of the RUNX1C null mice were unaltered compared to their WT and heterozygous littermates ( S9A–S9C Fig ) . Similarly , myelo-lymphoid cell fate decisions did not appear to be significantly affected , as the proportions of circulating monocytes , neutrophils and lymphocytes were unaffected ( S9D-F ) . However , a modest but significant decrease in platelet numbers was observed in RUNX1C null mice compared to both the WT and heterozygous animals ( Fig 7B ) . Their plateletcrit was also slightly decreased ( albeit not to a significant extent ) but the mean platelet volume was unaltered ( S9G and S9H Fig ) . This suggests that , unlike in the conditional total Runx1 null adult mouse model , platelet maturation is not impaired but specification may be hampered . FACS analysis of circulating blood cells and BM confirmed the presence of equal proportions of CD11b+ Gr1+ GM lineage and B220+ CD19+ B lymphoid cells in WT , P1-GFP/+ and P1-GFP/GFP mice ( Figs 7C and S10 ) . However , the numbers of CD3ε+ T cells were significantly reduced , suggesting that the absence of RUNX1C partially impairs T cell specification . We therefore analyzed the thymic T cell populations in greater detail and found that CD4/8 DN , DP and SP population numbers were not altered in P1-GFP/GFP mice ( Fig 7F ) . However , the ratio of CD4 SP:CD8 SP T cells in the spleen was severely perturbed , as P1-GFP/GFP mice had considerably fewer CD4 SP and more CD8 SP T cells compared to WT littermates ( Fig 7G and 7H ) . This therefore suggests that RUNX1C is dispensable for the DN to DP transition , observed to be blocked in total Runx1 deficient mice [13] . Nonetheless , the RUNX1C knockout recapitulates the defect in CD4 SP and CD8 SP T cell specification observed in total Runx1+/- mice , clearly demonstrating an important role for P1-driven RUNX1 activity in the T cell lineage [4 , 42 , 43] . To determine whether the absence of P1-directed RUNX1C expression impacts adult colony-forming HSPC populations , we performed myeloid CFU-C assays on unfractionated BM from WT , P1-GFP/+ and P1-GFP/GFP mice ( Fig 7D ) . GM , MkE and multilineage GEMM colony numbers were unaffected , but RUNX1C null BM cells produced significantly more erythroid CFUe colonies than either the WT or P1-GFP/+ cultures . FACS analysis of unlysed BM revealed a significant expansion of the EryC population in the RUNX1C null mice , a stage which coincides with almost complete silencing of both the Runx1 P1 and P2 promoters ( Fig 7E ) . In combination with the observed mild thrombocytopenia , it appears that the absence of RUNX1C may favor erythroid specification over megakaryopoiesis , a phenotype observed recently in mouse and human HSPCs depleted for total RUNX1 [44] . Overall , P1-directed RUNX1C activity may be dispensable for normal adult hematopoiesis but its absence nonetheless results in defects reminiscent of total RUNX1 deficiency . Increasingly it is becoming apparent that , in addition to a more classically defined tumor suppressor role , WT RUNX1 is required for the promotion of leukemogenesis in certain leukemia subtypes . Notably , AML1-ETO-driven CBF AML appears to be dependent on maintaining WT RUNX1 activity [21 , 22] . However , although AML1-ETO appears to promote RUNX1 expression , it is unclear whether AML1-ETO oncogene expression promotes the expression of one Runx1 promoter over another [45] . To address this question , we utilized a novel mouse model expressing a Doxycycline-inducible AML1-ETO9a transgene under the control of a Tetracycline Responsive Element ( TRE , Fig 8A ) . The AML1-ETO9a oncogenic transcript is expressed in a majority of t ( 8;21 ) AML patients studied and encodes a truncated AML1-ETO protein with enhanced leukemogenic potential [46 , 47] . We therefore took advantage of our ability to induce AML1-ETO expression in adult mice ( by administering Doxycycline in the food for 8 days ) and studied the impact on Runx1 isoform expression in vivo by isolating AML1-ETO-expressing ( AML1-ETO9a-IRES-GFP+ ) and non-expressing ( AML1-ETO9a-IRES-GFP- ) BM HSPCs and quantitating Runx1 expression through qRT-PCR ( Fig 8A ) . This allowed us to study the effect of AML1-ETO expression on WT Runx1 expression as one of the earliest events at the initiation of leukemogenesis . Firstly , we confirmed the presence and absence of AML1-ETO9a expression in BM GFP+ and GFP- HSPCs respectively ( Fig 8B ) . We chose to analyze LSK , PreGM and GMP cells as the immature HSPC and GM-lineage progenitors contain the leukemia propagating cell fraction in numerous AML patient samples and in a previously described AML1-ETO mouse model [48 , 49] . Whilst in the PreGM , and GMP Runx1 P1 expression was unperturbed by the expression of AML1-ETO , it was in fact decreased by approximately 40% in LSK GFP+ cells compared to GFP- ( Fig 8D ) . In all three HSPC populations , however , the presence of AML1-ETO resulted in an upregulation of Runx1 P2 expression ( Fig 8E ) , albeit not to a significant extent in PreGM cells . This resulted in an increase in total Runx1 expression in the GMP fraction ( Fig 8C ) but also a significant decrease in the P1:P2 ratio in all three populations , particularly in the LSK compartment , a phenotype associated with enhanced CFU-C activity , particularly in the GM lineage ( Fig 8F ) .
Our understanding of the hematopoietic hierarchy , and of the complexity of cell fate decisions in this system , has been increasingly refined in recent years . For a long time , it was assumed that the most mature shared ancestor for all myeloid populations was the Common Myeloid Progenitor ( CMP ) , until this population was subsequently dissected and shown to be a heterogeneous population containing the PreGM and PreMegE fractions [50 , 51] . Using the Runx1 P1-GFP::P2-hCD4 dual reporter mouse model , we have now similarly demonstrated further heterogeneity in the PreMegE fraction , prospectively isolating “pro-erythroid” P2- and “pro-megakaryocytic” P2+ fractions ( Fig 9 ) . Moreover , we have successfully identified their equivalents in WT BM as being CD34- and CD34+ respectively . CD34 , a cell-cell adhesion factor previously characterized as a direct RUNX1 transcriptional target [52] and expressed on vascular-associated tissue and selected HSPCs , was previously used to distinguish the CMP from the Megakaryocyte/Erythroid Progenitor ( MEP ) [50] . By in vitro cell tracing experiments , we have determined that the P2+ PreMegE lies directly downstream of the P2- PreMegE , apparently contradicting a CMP-based model as this involves downregulation of CD34 expression prior to Mk/Ery lineage commitment . Moreover , we have demonstrated immunophenotypic P2- PreMegEs can be directly derived from P2- LSK48F- MPPs , lending weight to the argument that progenitors lose Mk/Ery potential before separation of the GM and lymphoid pathways [32] . In fact , our model goes further , proposing that erythroid potential is downregulated prior even to megakaryocytic potential , either coinciding with or as a direct result of Runx1 P2 upregulation . Interestingly , comparative analysis of transcription factor binding motifs by rVISTA [53–56] in the vicinity of the P1 and P2 regions revealed the presence of conserved erythroid transcription factor EKLF ( KLF1 ) motifs in the P1 region but none surrounding P2 ( S11A and S11B Fig ) . By contrast , FLI1-binding motifs are present in both regions . This is interesting as RUNX1 has recently been implicated in regulating the balance of EKLF and FLI1 activity , which promote Ery and Mk output respectively [44] . In addition , EKLF and FLI1 may in fact act upstream of RUNX1 , for example with EKLF directly activating Runx1 P1 but not P2 expression , a state which is reinforced by the enhanced EKLF expression in P1+P2- pro-erythroid PreMegEs compared to P1+P2+ pro-megakaryocytic PreMegEs . Analysis of ChIP-Seq data from the mouse ENCODE project [53 , 54 , 57 , 58] also reveal some interesting differences in GATA1 and SCL ( TAL1 ) binding to the P1 and P2 promoter regions in megakaryocytes and erythroblasts ( S11C Fig ) . GATA1 and TAL1 binding appear largely unchanged in the vicinity of the P1 promoter in both cell types . By contrast , GATA1 binding is observed at P2 and GATA1+TAL1 binding approximately 15kb upstream in erythroblasts but not megakaryocytes . It is conceivable , therefore , that GATA1-mediated transcriptional repression of the P2 promoter occurs in the erythroid lineage , whereas the absence of a GATA1-containing complex enables its derepression and recruitment of activating factors instead . The high number of differentially expressed genes ( >4000 ) in the P2- and P2+ PreMegEs lends credence to the hypothesis that they are derived from distinct progenitor ancestors . Commitment to megakaryocytic or erythroid lineages may even occur earlier , at the HSC level; the P2- and P2+ MPPs may themselves be derived from pro-erythroid and pro-megakaryocyte HSCs respectively as previously described [59–61] . Regardless of this , the increased purification of phenotypically distinct progenitors within the hematopoietic hierarchy afforded by our model will enable the investigation of molecular mechanisms involved in cell fate decisions with significantly greater precision . In fact the role of RUNX1 in lineage commitment was recently expanded to include promotion of megakaryopoiesis over erythropoiesis through repression of KLF1 [44] . Overexpression studies were performed solely using a RUNX1B construct and knockdown was non-isoform specific , so it remained unclear how important the isoform specificity is to the process of megakaryocytic or erythroid lineage commitment . Our investigation of the P1-GFP/GFP model suggests RUNX1C plays a specific role in these lineages , as its absence means circulating platelet numbers are decreased whereas BM CFUes and EryCs are increased . However , we cannot discount the fact that this phenotype may be due to an overall decrease in RUNX1 protein as opposed to the specific loss of RUNX1C and therefore further studies utilizing either targeted mutagenesis of the two Runx1 promoters separately or isoform-specific knockdown whilst not impacting the overall level of RUNX1 would be required to explore this possibility . As previously mentioned , P1 is the dominant promoter in adult hematopoiesis , being active in all Runx1-expressing populations . P2 expression is far more heterogeneous , confined to immature/progenitor subsets of the GM and lymphoid lineages and megakaryocytes . With the exception of megakaryocytes , it appears that downregulation of P2 is a prerequisite of terminal differentiation of these lineages . We also observed that , at least in myeloid lineages , P2-expression correlates with enhanced CFU-C activity and in the PreMegE specifically with increased proliferation . Numerous cell cycle regulators are upregulated in P2+ PreMegEs , several of which have previously been identified as putative RUNX1-targets . A unique feature of megakaryocytic differentiation is polyploidisation achieved through undergoing numerous abortive cell cycles . Cell cycle activators are therefore highly expressed in these cells , as is Runx1 P2 . It is also of interest that P2 expression has previously been observed in newly emerging embryonic HSC-containing hematopoietic clusters but not in the more quiescent BM HSC populations [28] . Despite their distinct roles in hematopoiesis , many parallels have been drawn between the specification of HSCs and megakaryopoiesis [62] . HSCs and MkPs share similar cell surface marker profiles and have numerous regulatory pathways in common [63 , 64] . These include critical dependence on TPO signaling and hematopoietic transcription factors , including the CBF complex , Ets and HOX-related genes , several of which are upregulated in P2+ PreMegEs ( S4 Table ) [62 , 65] . Megakaryocytes appear to have more in common with embryonic than adult HSCs , their production being characterized by CD41 expression and RUNX1-dependency [7 , 66] . Our observations therefore suggest that expression of P2-driven RUNX1B may actively promote cell cycling , with a role in expanding HSPC numbers and is then downregulated to allow terminal differentiation of the B/T/GM and erythroid lineages . It would be of interest to investigate to what extent RUNX1B , and also RUNX1C , indeed directly regulate different transcriptional targets and the mechanisms through which they may achieve this . In addition to erythroid progenitors , mast cell progenitor specification did not appear to require P2 expression . Intriguingly , both are lineages which do not appear to be adversely affected by the absence of Runx1; complete ablation of Runx1 in adult mice has no impact on peripheral red blood cell numbers , whereas mast cell development is normal in Runx1 P1-null mice [14 , 67] . Whether this suggests Runx1 expression is entirely incidental in these lineages will need to be investigated further . The requirement for WT RUNX1 activity in AML has been extensively studied in recent years . In AML1-ETO CBF AML in particular , a balance of AML1-ETO and RUNX1 expression must be maintained to promote stem cell gene expression and repress differentiation-associated gene expression [68] . Moreover , it appears AML1-ETO may directly regulate Runx1 expression , as depletion of AML1-ETO leads to a decrease in RUNX1 levels in Kasumi1 cells [45] . However , whether expression of P1 or P2 was favored in this context had not been investigated . By utilizing an inducible AML1-ETO mouse model , we were able to establish that AML1-ETO expression resulted in a specific upregulation of Runx1 P2 . We have found P2 expression coincides with enhanced CFU activity and proliferation in HSPCs . Ben-Ami et al . previously demonstrated RUNX1 enhances the viability of preleukemic AML1-ETO-expressing cells [21] , therefore it may be that RUNX1B activity specifically enhances a preleukemic phenotype in emerging CBF AML leukemia propagating cells . Interestingly , Trombly et al . observed the recruitment of AML1-ETO to P1 and the +23 enhancer but not to P2 in Kasumi1 cells [45] . Therefore , the mechanism of AML1-ETO’s activation of P2 is of considerable interest . AML1-ETO may directly activate P2 , potentially via the +23 enhancer or it may instead promote expression of other transcriptional activators which enhance P2 activity . Alternatively , AML1-ETO may directly repress P1 , resulting in a compensatory upregulation of P2 by a secondary mechanism . These possibilities will all need to be explored further .
P1-GFP::P2-hCD4 and P1-GFP mice have previously been described [29] . The AML1-ETO9a-IRES-GFP/rtTA mice were generated as follows: HA-tagged AML1-ETO9a cDNAs ( provided by the Zhang laboratory [47] ) were subcloned into a tet-ON vector in front of an IRES-GFP as described [69] . Ainv18 ES cells [69] ( which constitutively express the rtTA under the control of the Rosa26 promoter ) were then transfected with this tetracycline-inducible AML1-ETO9a construct by electroporation and stably transfected clones were selected with G418 ( 0 . 5mg/ml , Life Technologies ) for 10–14 days . Chimeric mice were then generated by injecting AML1-ETO9a-IRES-GFP/rtTA ES cells into C57BL6J blastocysts . To induce AML1-ETO9a-IRES-GFP expression , 12 week-old mice were fed irradiated diet supplemented with 545mg/kg Doxycycline ( ssniff Spezialdiäten GmbH ) for 8 days prior to humane culling and tissue collection . All animal work was performed under regulations governed by UK Home Office Legislation under the Animals ( Scientific Procedures ) Act 1986 . Details of animal husbandry and tissue collection are listed in S1 File . Dead cells were excluded using either 0 . 5μg/ml 7-Aminoactinomycin D ( 7-AAD , eBioscience ) or 1μg/ml Hoechst 33258 ( Life Technologies ) . Biotinylated antibody staining was detected by a secondary incubation step with fluorochrome-conjugated Streptavidin . Prior to flow sorting of HSPCs , bone marrow cells stained with biotinylated anti-lineage antibodies were lineage-depleted using anti-biotin-conjugated magnetic beads ( Miltenyi ) and then stained with additional antibodies , including conjugated streptavidin . Red blood cell depletion was performed by treatment with ACK lysis buffer ( 154mM ammonium chloride , 9 . 99mM potassium bicarbonate , 0 . 110mM EDTA ) for 5 minutes at room temperature , followed by quenching with Phosphate-Buffered Saline ( PBS ) . Details of flow cytometry antibodies and reagents are listed in S1 Table . Details of flow cytometry antibody combinations used for each analysis or sort are listed in S2 Table . For cell cycle analysis , in vivo EdU incorporation was performed by injecting 1 . 125mg EdU dissolved in PBS intraperitoneally into adult mice . After two hours , bone marrow was harvested and stained with hematopoietic stem and progenitor cell surface markers as detailed in S2 Table . Cells were then stained using the Click-iT EdU Alexa Fluor 647 Flow Cytometry Assay Kit ( Life Technologies ) . Total DNA was stained with 1μg/ml FxCycle Violet Stain ( Life Technologies ) . Cells were analyzed using a LSR-II or LSR-II Fortessa analyzer , a FACSAria-II cell sorter or a FACSAria-III cell sorter ( BD ) . Tail vein blood ( no more than 50μl per mouse ) was sampled from 12 week old mice using heparinized end-to-end Micro Pipettes ( Vitrex ) and analyzed on a Sysmex XT 2000i analyzer , according to the manufacturer’s instructions . Flow cytometry plots display the mean values of each indicated population . Unless otherwise indicated , data were evaluated using an Ordinary 2-way ANOVA and expressed as mean ± standard error of the mean ( SEM ) . P<0 . 05 was considered statistically significant . *P<0 . 05 , **P<0 . 01 , ***P<0 . 001 , ****P<0 . 0001 | The transcription factor RUNX1 is considered a master regulator of adult and embryonic blood cell production . Mutations in RUNX1 cause defects in different blood lineages in human patients and mouse models , including leukemia and blood clotting defects due to a shortage of platelet-producing megakaryocytes . Together with the other RUNX genes present in mammals , RUNX1 is expressed from two promoters , which produce several distinct RNA transcripts and protein isoforms . To investigate the timing and localization of the expression of these two promoters ( termed distal and proximal ) , we created a mouse model with reporter genes expressed under the control of the Runx1 promoters . We previously described the activities of the Runx1 promoters at the initiation of blood production in the developing embryo . We now investigate the output from the two promoters in adult organs , including bone marrow , spleen and thymus . We show here that the distal Runx1 promoter is highly expressed but the proximal promoter is more restricted and in particular marks the point in adult blood production where the red blood cell and megakaryocyte pathways separate . The different proteins produced by these two Runx1 promoters may therefore have different roles in driving the production of these two distinct cell types . | [
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"a... | 2016 | RUNX1B Expression Is Highly Heterogeneous and Distinguishes Megakaryocytic and Erythroid Lineage Fate in Adult Mouse Hematopoiesis |
The outbreak of Zika virus ( ZIKV ) in the Americas has transformed a previously obscure mosquito-transmitted arbovirus of the Flaviviridae family into a major public health concern . Little is currently known about the evolution and biology of ZIKV and the factors that contribute to the associated pathogenesis . Determining genomic sequences of clinical viral isolates and characterization of elements within these are an important prerequisite to advance our understanding of viral replicative processes and virus-host interactions . We obtained a ZIKV isolate from a patient who presented with classical ZIKV-associated symptoms , and used high throughput sequencing and other molecular biology approaches to determine its full genome sequence , including non-coding regions . Genome regions were characterized and compared to the sequences of other isolates where available . Furthermore , we identified a subgenomic flavivirus RNA ( sfRNA ) in ZIKV-infected cells that has antagonist activity against RIG-I induced type I interferon induction , with a lesser effect on MDA-5 mediated action . The full-length genome sequence including non-coding regions of a South American ZIKV isolate from a patient with classical symptoms will support efforts to develop genetic tools for this virus . Detection of sfRNA that counteracts interferon responses is likely to be important for further understanding of pathogenesis and virus-host interactions .
Zika virus ( ZIKV ) is a mosquito-transmitted arbovirus in the Flavivirus genus , Flaviviridae family . This previously obscure virus has recently caused large scale outbreaks in French Polynesia in 2013 [1 , 2] , New Caledonia [3] , the Cook Islands [4] and Easter Island [5] in 2014 and the Americas in May 2015 , beginning in Brazil [6 , 7] . These outbreaks have been characterized by an increased prevalence of neurological syndromes , such as Guillain-Barré syndrome and microcephaly [8–13] , which has heightened public concern . As of April 2016 the World Health Organization ( WHO ) announced that 60 countries had reported autochthonous transmission in the escalating epidemic originating in Bahia , Brazil in 2015 that has so far resulted in over 1 . 5 million suspected cases [14] . This unprecedented spread combined with the associated neurological conditions resulted in WHO declaring a global public health emergency in February 2016 . Brazil has the greatest burden of dengue virus ( DENV ) , a related flavivirus , in the world and the ongoing ZIKV epidemic is occurring in areas where such mosquito-borne arboviruses are a major public health problem . This is due to widespread arbovirus vectors such as Aedes aegypti and Ae . albopictus which are important vectors of DENV and chikungunya virus ( CHIKV , Togaviridae ) , as well as ZIKV [15–19] . Clinical manifestations of ZIKV are similar to symptoms of DENV or CHIKV infections making misdiagnosis common [3 , 20] . Only 20% of ZIKV infections are thought to progress to clinical symptoms , which present as an acute , self-limiting illness comprising fever , myalgia , headache , polyarthralgia , nonpurulent conjunctivitis and maculopapular rash . The largest public health risk from ZIKV is its association with neurological conditions such as Guillain-Barré syndrome and microcephaly which place substantial strains on local communities and healthcare providers . As is characteristic of flaviviruses , ZIKV possesses a linear single-stranded , positive-sense RNA genome . The flavivirus genome has a single open reading frame that encodes all structural and non-structural proteins flanked by 5´ and 3´ untranslated regions ( UTRs ) [21] . Phylogenetic analysis of partial ZIKV sequence data revealed isolates may be categorised into African and Asian lineages , of which the African lineage is further subdivided into Nigerian and MR766 prototype strain clades [22 , 23] . Recently obtained sequences from the current epidemic are of Asian lineage and are most closely related to strains from the French Polynesian outbreak in 2013 [5 , 6 , 24] . However , there are currently few full-length complete sequences that include the genome termini . One of these is from the Americas and was derived from a microcephaly case [10] . Nonetheless , such information is important given the relevance of the genome termini and non-coding regions in virus translation , replication and pathogenesis . The 5’ and 3’ non-translated regions of flavivirus genomes have been shown to demonstrate conserved secondary structures , cyclization elements , and are important for binding to several host proteins in addition to proteins involved in viral replication complexes [25 , 26] . Furthermore , the 3’UTR encodes subgenomic flavivirus RNA ( sfRNA ) which is produced by the incomplete degradation of viral RNA by a cellular 5’-3’ exoribonuclease [27 , 28] . These molecules have been shown to be more than a by-product and are involved in viral interference with innate immune responses in both vertebrates and invertebrates through antagonizing type I interferon and RNA interference responses respectively [29–36] . Herein we present the complete genome sequence of a ZIKV isolate derived from a patient in Brazil with classical disease symptoms . This will be important for future studies and the development of reagents , such as reverse genetics systems , for ZIKV . We also identified ZIKV-derived sfRNA in infected cells and show that it functions as an antagonist of RIG-I mediated induction of type I interferon , while a lesser effect on MDA-5 mediated induction was observed . The production of sfRNA in ZIKV infection may be an important contributor to associated pathogenesis .
This study was approved by the Brazilian Ethics Committee , Process number: IMIP Human Ethics Research Committee Approval number 4232 , PlatBr580 . 333 and 44462915 . 8 . 2004 . 5190 . The virus reported here , ZIKV/H . sapiens/Brazil/PE243/2015 ( abbreviated to ZIKV PE243 ) , was isolated in Recife ( Brazil ) in 2015 from a patient ( rash on face and limbs; arthralgia hands , fist/wrist , ankle; edema on hands , fist/wrist; no neurological symptoms ) . All patients who agreed to participate in this study were asked to sign an informed consent form . ZIKV from positive serum samples was isolated at Fundação Oswaldo Cruz ( FIOCRUZ ) , Recife ( Brazil ) by amplification in C6/36 Ae . albopictus cells . then Vero cells , which are frequently used for virus isolation and were obtained from collections at FIOCRUZ . Briefly , 50 μl of positive serum was incubated for 1 h at room temperature on monolayers of C6/36 cells . The cells were then further incubated for 7 days . Following this , ZIKV infection was confirmed by RT-PCR as described below . Viral RNA was extracted from serum of suspected acute DENV/ZIKV cases using the QIAmp Viral RNA Mini kit ( Qiagen ) following the manufacturer’s instructions . RNA was extracted from 140 μl of the sample and stored at -70°C prior to downstream applications . RT-PCR was carried out using the QIAGEN OneStep RT-PCR kit in a final volume of 25 μl following previously established protocols and primers [22] . Vero E6 cells , a commonly used cell line for the growth of viruses [37] were infected with ZIKV PE243 for the preparation of virus stocks which were collected upon detection of cytopathic effect . ZIKV PE243 infected cells tested positive with mouse anti-ZIKV serum ( provided by G . Fall and A . A . Sall , Institut Pasteur de Dakar , Senegal ) as well as with commercially obtained ZIKV E protein-specific antibodies by western blotting and immunofluorescence ( S1 File ) . For titration , Vero E6 cells were infected with serial dilutions of virus and incubated under an overlay consisting of DMEM supplemented with 2% FCS and 0 . 6% Avicel ( FMC BioPolymer ) at 37°C for 5–7 days . Cell monolayers were fixed with 4% formaldehyde . Following fixation , cell monolayers were stained with Giemsa to visualize plaques . Plaque assays for plaque size comparisons were also performed using A549 and A549/BVDV-Npro cell lines ( provided by R . E . Randall , University of St Andrews , UK ) [37–39] . Denaturated total RNA ( 3 . 5 μg per sample; isolated from Vero E6 cells infected with ZIKV PE243 at an multiplicity of infection [MOI] of 1 by Trizol followed by Direct-zol RNA purification ) was separated on a denaturating formaldehyde agarose gel ( 1 . 5% agarose , 1x MOPS buffer [Fisher Scientific] , 12 . 3 M formaldehyde ) in 1x MOPS running buffer . RNA was transferred onto a Hybond-N+ membrane ( GE Healthcare Life Sciences ) via capillary transfer action using 10x SSC ( 1 . 5 M NaCl , 150 mM trisodium citrate ) . RNA was crosslinked to the membrane by UV ( 120 mJ/cm2 ) . Following transfer , the membrane was prehybridized for 2 h in PerfectHyb Plus Hybridization buffer ( Sigma-Aldrich ) at 65°C . Specific oligonucleotides for the sfRNA region of the ZIKV PE243 3’UTR ( forward: AGCTGGGAAACCAAGCCTAT , reverse: GTGGTGGAAACTCATGGAGTCT ) were used to amplify a fragment by PCR with KOD polymerase ( Merck Millipore ) . Following this 250 ng of the PCR product was end-labelled with 32P using T4 Polynucleotide Kinase ( NEB ) and [γ-32P]Adenosine 5’-triphosphate ( PerkinElmer ) to produce a probe . The probe was denatured for 5 min at 95°C and added to prehybridization mixture which was incubated on the membrane overnight at 65°C . The membrane was then washed twice for 15 min at 65°C with each of the following three buffers: 2x SSC and 0 . 5% SDS , 2x SSC and 0 . 2% SDS , 0 . 2x SSC and 0 . 1% SDS . RNA species were detected by phosphorimaging . The Gateway cloning system was used for cloning the 3’UTR of ZIKV , potentially containing the sfRNA sequence , fused to hepatitis delta virus ribozyme ( HDVr ) into pDEST40 ( mammalian expression vector [Invitrogen] ) . The 3’UTR of ZIKV PE243 was amplified by PCR using 1 μl of the 3' end RACE reaction as a template . Subsequently , fusion PCR was performed using the primers described in Table 1 . The resulting fragment was inserted into the pDONR207 using BP Clonase II kit ( Invitrogen ) and sequenced using the pDONR201 forward primer . LR Clonase II kit ( Invitrogen ) was used for the recombination of pDONR207-ZIKV PE243-3’UTR ( entry vector ) and the empty pDEST40 resulting in pDEST40-ZIKV PE243-3’UTR . The sequence of pDEST40-ZIKV PE243-3’UTR was validated using the T7 promoter forward primer . Similar cloning strategies have been used for other flavivirus 3’UTRs containing sfRNA [29 , 30] . In vitro type I interferon assays were performed using the human A549 cell line [37] to analyze the activity of the IFN-β promoter in the presence of plasmids expressing flavivirus 3’UTRs containing the sfRNA sequence . A549 cells were grown in DMEM ( supplemented with 10% FBS , 1000 units/ml penicillin and 1 mg/ml streptomycin ) at 37°C with 5% CO2 . Briefly , 24 h prior to transfection , A549 cells were seeded in 24 well plates at a density of 1 . 2x105 cells/well to reach 70% confluency the following day . Cells were first co-transfected with 400 ng p125Luc IFN-β promoter reporter vector expressing Firefly luciferase [40] , 2 ng pRL-CMV ( internal control , expressing Renilla luciferase ) , and 500 ng of either pDEST40 expressing DENV [29] or ZIKV 3’UTRs ( constructs described in this study ) or a MBP-HDVr ( maltose-binding protein-HDVr ) control using Opti-MEM and Lipofectamine2000 ( Invitrogen ) according to the manufacturer’s protocol . Following a further 24 h incubation , type I interferon induction was stimulated by transfecting the cells a second time with either 10 μg/well poly I:C , 50 ng Vero cell produced EMCV RNA or 50 ng Neo1-99 IVT-RNA ( universal , MDA-5 specific and RIG-I specific type I interferon agonists respectively ) [41 , 42] . Cells were lysed in 1x passive lysis buffer ( Promega ) 24 h after the second transfection and Firefly and Renilla luciferase activities determined using a Dual-Luciferase reporter assay kit ( Promega ) in a GloMax luminometer . Vero E6 cells were infected with ZIKV at an MOI of 0 . 001 in triplicate . At 48 h post infection ( p . i . ) , cell culture supernatant was harvested and clarified by low speed centrifugation . Following clarification , 6 ml of infected cell supernatant was concentrated to 250 μl using an Ultra-15 Centrifugal Filter Units with 100 kDa molecular weight cut-off ( Amicon ) . Concentrated supernatant was then added to Direct-zol solution and RNA extracted using a Direct-zol RNA mini kit ( Zymogen ) according to the manufacturer’s instructions . Purified RNA was then stored at -80°C for further downstream processing . Sequencing of the 5’ and 3’ termini of the viral genome was performed using a 5’/3’ RACE kit ( Roche ) following the manufacturer’s protocol . All primers used are described in Table 2 . To obtain the 5’ end sequence of the ZIKV genome 5’ RACE was performed . Briefly , 1 μg total RNA was extracted from ZIKV-infected Vero E6 cells using a Direct-zol RNA mini kit and reverse transcribed using the ZIKV specific primer , SP1 . The synthesized cDNA was purified using the illustra GFX PCR DNA and Gel Band Purification kit ( GE Healthcare ) according to the manufacturer’s instructions . This was prior to polyadenylation at the 3’ end and amplification using the PCR anchor primer and a ZIKV specific primer ( 5’ PCR ) . 3’ RACE was carried out to obtain the 3’ end sequence using 1 μg total RNA extracted from ZIKV infected Vero cells which was polyadenylated at the 3’ end using Poly ( A ) polymerase ( New England Biolabs ) following the manufacturer’s guidelines . cDNA synthesis was performed by reverse transcribing the RNA using the oligo ( dT ) anchor primer . Amplification of the cDNA was achieved by using the PCR anchor primer and a ZIKV specific primer ( 3’ PCR ) . The PCR cycling conditions were 95°C for 2 min then 35 cycles of 95°C 20 sec , 56°C ( 5’ RACE ) or 68°C ( 3’ RACE ) for 10 sec , 70°C for 15 sec and 70°C for 7 min . A volume of 25 μl of cell culture supernatant was treated with RNase-free DNase I ( Ambion ) , purified with RNAClean XP magnetic beads ( Beckman Coulter ) and eluted in 11 μl of water . In parallel , an equivalent sample was concentrated from 25 to 11 μl using magnetic beads as indicated above , in the absence of DNase I treatment . In addition , 45 μl of extracted total cellular nucleic acid was treated with RNase-free DNase I and purified as above . Half of the volume was further depleted of ribosomal RNA ( RiboZero Gold ) according to the manufacturer's protocol . All samples were reverse-transcribed using Superscript III ( Invitrogen ) followed by dsDNA synthesis with NEB Next ( r ) mRNA Second Strand Synthesis Module ( New England Biolabs ) . Libraries were prepared using a KAPA DNA Library Preparation Kit ( KAPA Biosystems ) , utilizing a modified protocol that includes ligation of the NEBnext adapter for Illumina ( New England Biolabs ) , followed by indexing with TruGrade oligonucleotides ( Integrated DNA Technologies ) to eliminate tag crossover . Resulting libraries were quantified using a Qubit 3 . 0 fluorometer ( Invitrogen ) and their size determined using a 2200 TapeStation ( Agilent ) . Libraries were pooled in equimolar concentrations . Samples from different passages were sequenced on a NextSeq500 platform ( Ilumina ) . This obtained 24 , 275 , 098 read pairs ( 2x150bp ) and 88 . 8% of reads had a quality score of >Q30 . Reads were first checked for quality using FASTQC ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) and trimmed for adapter sequences and quality filtered using trim_galore ( http://www . bioinformatics . babraham . ac . uk/projects/trim_galore/ ) . These were subsequently mapped to the ZIKV complete genome KU321639 using two different aligners: Tanoti ( http://www . bioinformatics . cvr . ac . uk/tanoti . php ) and Bowtie2 [43] . The assembly was parsed using customized scripts to determine the frequency of nucleotides at each site and reconstruct a consensus with nucleotides above 50% . The complete genome was extended at the 5’ and 3’UTRs by extracting additional reads that overlapped with the terminal ends of the consensus sequence . The sequence of the ZIKV PE243 genome has been deposited in GenBank with the accession number KX197192 . Phylogenetic and comparison analyses were carried out using full coding sequence alignments that were generated using MUSCLE [44] within the program suite Geneious ( version 7 . 1 . 8: http://www . geneious . com ) [45] . These alignments were created using our ZIKV PE243 sequence in addition to publicly available coding sequences on GenBank . All Asian and African lineage ZIKV sequences used for the analysis are described in S1 Table . A single African sequence ( MR-766 , accession NC_012532 ) was used as an outgroup . Before generating phylogenies , the data set was analyzed for the presence of recombination . The Recombination Detection Program version 4 ( RDP4 ) [46] software was utilized , specifically the programs RDP , Chimaera , BootScan , 3Seq , GENECONV , MacChi & SiScan . Phylogenies were generated with both maximum likelihood and Bayesian inference methods using the software packages PhyML [47] and MrBayes ( version 3 . 2 . 6 ) [48] respectively . Support for the maximum likelihood tree topology was generated by 1 , 000 non-parametric bootstrap replicates . For the Bayesian analysis one MCMC run of four heated chains of length 1 , 000 , 000 was utilized to ensure an effective sample size of at least 200 . The run was sampled every 200th generation and the first 10% of samples were discarded as burn-in . The generalized time reversible ( GTR ) substitution model with gamma distribution ( +G ) was found to suit the data set best , as selected by both jModel Test [49] and HyPhy [50] software packages . The topologies of both the Bayesian and maximum likelihood trees were identical; here we present only the Bayesian tree . All data were analysed using Prism 5 software ( GraphPad ) and presented as mean ± standard error . Statistical significance for the comparison of means between groups was determined by a two-way ANOVA; p values ≤0 . 05 were considered significant .
At the time of writing , 62 ZIKV genomes are available on GenBank , of which 37 are published . Of these only 11 showed both 5’ and 3’ complete UTRs ( accessed 16th April 2016 ) . A summary of currently available strain information and accession numbers is presented in S1 Table . ZIKV PE243 was isolated from a patient presenting with classical symptoms associated with ZIKV infection and the complete viral genome sequence including the non-coding regions was determined . The UTRs are largely missing in many sequences from the Americas , with some exceptions including the Natal isolate derived from a case presenting with microcephaly [10] . Only recently have more full-length ZIKV sequences been described [51 , 52] . Our phylogenetic analysis uses the entire protein-coding region and the position of our isolate was supported by a posterior probability node support of 1 . Recombination screening prior to analysis also produced no signals . The sequence of ZIKV PE243 used for further analysis ( as deposited in GenBank ) derives from virus that had been passaged five times in Vero E6 cells upon receipt by the Centre for Virus Research ( Glasgow , UK ) on a NextSeq500 ( average depth of coverage of 5637 , range 52–13691 ) . Three nucleotide substitutions were observed following the sequencing of this virus compared to a previous passage of the isolate ( passage two ) that had been sequenced on a MiSeq platform ( these earlier data did not generate complete coverage; average depth of coverage of 1158 , range 2–2944 ) . The mutations observed are as follows: site 2784 , 1149 out of 1159 reads had A in the MiSeq run ( after two passages ) and 3508 out of 3910 reads had G in the NextSeq run after a further three passages . The mutation A2784G corresponds to the amino acid substitution R893G in NS1 . The mutations observed in NS3 ( U5231C: 1727/1730 Ts in passage two versus 7031/7623 Cs in passage five ) and NS4B ( A7637G: 1835/1846 As in passage two versus 9578/10587 Gs in passage five ) were synonymous . These three substitutions represent mutations obtained during adaptation in cell culture between passage two and passage five . The mutations A2784G and U5231C are unique to ZIKV PE243 and are not found in any other strains published to date . Phylogenetic analysis based on the entire protein coding region grouped the ZIKV PE243 isolate with another 2015 Brazilian isolate ( KU321639 , ‘ZikaSPH2015’ ) with 100% posterior support ( Fig 1 ) . As expected , our isolate clusters with other strains from the Americas which belong to the Asian lineage that is attributed to the epidemic in French Polynesia in 2013 ( Fig 1 ) . Previous findings have shown that American isolates are genetically very comparable , with approximately 99% homology at the nucleotide level , and there is less than 12% diversity between strains from both African and Asian lineages [24 , 53] . Our data are in agreement with this as ZIKV PE243 demonstrates a strong degree of conservation at amino acid level ( 98 . 3% pairwise identity ) with sequences from 62 isolates ( Fig 2 ) . ZIKV PE243 shares the greatest level of similarity with the Brazilian isolate ZikaSPH2015 ( 99 . 9% at the nucleotide level and 99 . 97% at amino acid level ) [54] and the passage two isolate matched the coding region precisely . There is no obvious virological explanation , based upon our sequence analysis , for the increased occurrence of neurological disease cases associated with the outbreak in Brazil . This is in accordance with other findings which have similarly suggested that there are no specific mutations in the viral genome associated with severe cases [54] . However , the role of mutations in ZIKV isolates needs to be assessed by reverse genetics approaches to provide conclusive evidence . We also successfully sequenced both the 5’ and 3’ non-coding regions ( Figs 3 and 4 ) . Of the 62 sequences publicly available ( as of 16th April 2016 ) , 48 sequences with 5’UTR information are shown in the consensus alignment ( Fig 3 ) . ZIKV strains ZIKV/Homo sapiens/NGA/ibH-30656_SM21V1-V3/1968 and ZIKV/Macaca mulatta/UGA/MR-766_SM150-V8/1947 contain large insertions and were subsequently excluded from 5’UTR analysis . The 5’UTR of ZIKV PE243 shares 100% sequence identity with the consensus sequence ( the most common bases between all sequences analyzed ) and overall very few mismatches are detected across all 48 sequences studied . The 5’UTR is largely conserved between isolates of the same lineage and is approximately 107 nucleotides long in isolates from the Asian lineage , similar to the length shown for MR766 strain and other African lineage viruses . There was strong similarity between ZIKV PE243 and Natal RGN , a Brazilian isolate associated with microcephaly [10] , while ZIKV PE243 was associated with classical symptoms . Similarly , there are few mismatches between known 3’UTRs ( Fig 4 ) . These non-coding regions are expected to be approximately 428 nucleotides in length as seen for many Asian and African isolates . The host interferon response is known to be essential for fighting viral infections and preventing virus replication , including mosquito-borne flaviviruses [55–58] . This has been specifically illustrated for ZIKV as in vivo pathogenesis studies require murine models lacking type I interferon [59] , while type III interferon has been shown to have a protective role against ZIKV infection in human placental cells [60] . Furthermore , ZIKV NS5 has recently been described as a type I IFN signaling antagonist that targets STAT2 [61] . Indeed , ZIKV PE243 was also susceptible to type I interferon responses and produced much larger plaque sizes in the type I interferon incompetent A549/BVDV-Npro cell line than in A549 cells ( S1 Fig ) . However , viruses also employ mechanisms that allow them to counteract the host’s interferon responses in order to replicate efficiently . Mosquito- and tick-borne flaviviruses express sfRNA derived from the 3’ terminus , which is resistant to RNase ( XRN1 ) -mediated virus genome degradation due to RNA stem loop structures and pseudoknots in this region [27 , 28] . Interestingly , sfRNA has been implicated in pathogenesis , immune evasion and inhibition of small RNA-based responses [29–34] . Thus , a similar subgenomic RNA produced during ZIKV infection could be important in the development of disease and virus-host interactions . Based on our sequence data and comparisons to other mosquito-borne flavivirus 3’UTRs , we predicted the structure of ZIKV PE243 sfRNA ( Fig 5 ) . Secondary structures , specific for flavivirus 3’UTRs , were detected in the 3’UTR of ZIKV PE243 by Clustal alignments of the 3’UTR of ZIKV PE243 , yellow fever virus ( X03700 , K02749 ) , DENV2 ( M19197 ) , Kunjin virus ( AY274504 ) , Japanese encephalitis virus ( AF014161 ) and Murray Valley encephalitis virus ( AF161266 ) in combination with Mfold . Putative pseudoknot interactions were determined by hand . Further analysis was also carried out to compare the 3’UTR sequences between ZIKV PE243 and 3 African strain isolates ( two MR766 isolates [AY632535 , KX377335] and another African isolate [KU955592] ) . Our comparisons suggest that the sequence differences between these Asian and African isolates do not , or are unlikely to , affect the predicted sfRNA structure ( S2 Fig , S3 Fig and S2 Table ) . Our sequence data for ZIKV PE243 and predictive analysis suggested that the ZIKV sfRNA molecule begins 15 nt after the stop codon of the open reading frame and is 413 nt in length . This was further confirmed by northern blot analysis , which indicates a band at the anticipated size present only in ZIKV PE243 infected cell lysate ( Fig 6 ) . It is important to determine whether this molecule is involved in inhibition of type I IFN production as previously described for other flavivirus sfRNAs [27] . To test this hypothesis , cells were co-transfected with a reporter plasmid ( p125Luc ) expressing Firefly luciferase under the control of the IFN-β promoter as well as plasmids expressing either ZIKV or DENV 3’UTRs which contain the sfRNA sequences . The IFN-β promoter was stimulated by treating with poly I:C ( Fig 7 ) . As demonstrated in Fig 7 , ZIKV PE243 sfRNA reduced activation of the IFN-β promoter to the same level as DENV sfRNA compared to MBP-HDVr control . This shows that ZIKV sfRNA functions in a similar manner to other flavivirus sfRNA molecules and interacts with important innate immune responses that may impact on virus replication and thus the severity of the clinical outcome . To further understand the mechanism of action ZIKV sfRNA molecules use to antagonize the interferon response , the above assay was repeated this time using specific inducers of type I interferon induction components , RIG-I and MDA-5 [41 , 42] . Receptors such as RIG-I and MDA-5 signal for the induction of IFN-α/β production through the detection of viral nucleic acid [62 , 63] . As shown in Fig 8 , stimulation of RIG-I ( Fig 8A ) results in a significant decrease in IFN-β promoter activity in the presence of both DENV and ZIKV sfRNAs compared to the control . In contrast MDA-5 ( Fig 8B ) stimulation did not alter the activity of the IFN-β promoter in the presence of DENV sfRNA , although a weak but significant decrease was observed in ZIKV sfRNA expressing cells . These data suggest that both ZIKV and DENV antagonize RIG-I mediated type I interferon induction . Our data is consistent with previous findings for DENV sfRNA which found that DENV sfRNA binds TRIM25 interfering with its deubiquitylation , consequently hindering RIG-I mediated interferon induction [34] . Only ZIKV sfRNA antagonized MDA-5 activity in this assay , although the biological significance of this is yet to be clarified . Over the past 40 years there has be an upsurge in the number of cases of important arbovirus infections such as DENV , CHIKV and West Nile virus ( WNV ) , and ZIKV is now another emerging arbovirus of significant clinical importance . The factors involved in the emergence of ZIKV from a rarely detected pathogen to a major epidemic are yet to be determined and could include genetic adaptation , environmental influences , interactions with other pathogens within infected individuals and changes in population dynamics of the virus . To date , the northeast region of Brazil has reported a significant increase in cases of microcephaly and it is important to understand the determinants that lead to this clinical outcome . It has been suggested that alterations in codon usage in the NS1 gene may have facilitated an adaptation towards improved fitness for human infections in the Asian lineage over the African [64] . These changes , combined with the geographical ranges throughout the Americas of its vector population , may have contributed to its accelerated spread . More work is required to analyze these possibilities , and reverse genetics systems in particular will be key to studying mutations and genetic diversity within viral populations . The 5’ and 3’UTRs are important for virus replication and are therefore required for the development of such reverse genetic systems [65] that may be used in vaccine development or to advance knowledge of virus-host interactions . In order to understand not only ZIKV evolution and pathogenesis but also to support the development of virus-based tools , it is imperative to generate full virus genome sequences from ZIKV isolates in the Americas and elsewhere associated with classical and non-classical symptoms . Although new scientific information about ZIKV is published on a near daily basis , many avenues of research are yet to be fully explored in order to understand the clinical manifestations surrounding this outbreak . Characterization of the full sequence of ZIKV PE243 from a patient with symptoms classically associated with infection adds to our understanding of the virus genetics . We have also shown that ZIKV , like other pathogenic flaviviruses infecting humans , encodes sfRNA which inhibits type I interferon induction and thus is likely to contribute to viral pathogenesis . Our interferon induction assays suggest that ZIKV sfRNA may have broader antagonist activity compared to DENV sfRNA , which could contribute to disease outcome and requires further investigation . The data shown here give important insights into virus-host interactions that will help guide future research efforts in this field . | The current ZIKV outbreak is a major public health concern in the Americas . To further understand the virus , and to develop tools and potentially vaccines , more information on the virus strains circulating in the Americas is required . Here we describe the full-length sequence of a ZIKV isolate from a patient with classical symptoms , including the complete non-coding regions which are missing from many currently available sequences , and put these in context . Moreover , we also demonstrate the production of an RNA molecule derived from the 3’ untranslated region that counteracts interferon responses and may therefore be important for understanding the pathogenesis of ZIKV infection . | [
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"technique... | 2016 | Full Genome Sequence and sfRNA Interferon Antagonist Activity of Zika Virus from Recife, Brazil |
Porcine circovirus 2 ( PCV2 ) is a circular single-stranded DNA virus responsible for a group of diseases collectively known as PCV2 Associated Diseases ( PCVAD ) . Variation in the incidence and severity of PCVAD exists between pigs suggesting a host genetic component involved in pathogenesis . A large-scale genome-wide association study of experimentally infected pigs ( n = 974 ) , provided evidence of a host genetic role in PCV2 viremia , immune response and growth during challenge . Host genotype explained 64% of the phenotypic variation for overall viral load , with two major Quantitative Trait Loci ( QTL ) identified on chromosome 7 ( SSC7 ) near the swine leukocyte antigen complex class II locus and on the proximal end of chromosome 12 ( SSC12 ) . The SNP having the strongest association , ALGA0110477 ( SSC12 ) , explained 9 . 3% of the genetic and 6 . 2% of the phenotypic variance for viral load . Dissection of the SSC12 QTL based on gene annotation , genomic and RNA-sequencing , suggested that a missense mutation in the SYNGR2 ( SYNGR2 p . Arg63Cys ) gene is potentially responsible for the variation in viremia . This polymorphism , located within a protein domain conserved across mammals , results in an amino acid variant SYNGR2 p . 63Cys only observed in swine . PCV2 titer in PK15 cells decreased when the expression of SYNGR2 was silenced by specific-siRNA , indicating a role of SYNGR2 in viral replication . Additionally , a PK15 edited clone generated by CRISPR-Cas9 , carrying a partial deletion of the second exon that harbors a key domain and the SYNGR2 p . Arg63Cys , was associated with a lower viral titer compared to wildtype PK15 cells ( >24 hpi ) and supernatant ( >48hpi ) ( P < 0 . 05 ) . Identification of a non-conservative substitution in this key domain of SYNGR2 suggests that the SYNGR2 p . Arg63Cys variant may underlie the observed genetic effect on viral load .
Porcine Circovirus 2 ( PCV2 ) is a member of the Circoviridae family and the smallest virus known to infect mammalian cells . Despite its small size , this single-stranded circular DNA virus has been identified as the causative source of a set of systemic disorders known as Porcine Circovirus Associated Diseases ( PCVAD ) , which includes Post-Weaning Multi-systemic Wasting Syndrome ( PMWS ) . PMWS is characterized by severe weight loss , respiratory and enteritic conditions that can lead to mortality [1] . Other symptoms associated with PCVAD include nephritis , dermatitis , reproductive failure , interstitial pneumonia , and lymphoid depletion . PCV2 infection can be detected in all domestic populations of pigs , but most infections are subclinical and only a subset of pigs that experience various triggering factors develop clinical disease [2] . The frequency of subclinical infection , combined with environmental stability of the virus , has enabled PCV2 to spread worldwide and persist undetected for generations . For example , the first documented PMWS outbreak occurred in 1991 , but PCV2 was identified in archival semen samples collected in the early 1970s [3] . Current PCV2 isolates display consistent variation in a 9 bp region of the capsid gene , associated with increased virulence in experimental infection of gnotobiotic pigs , compared to archival PCV2 isolates , indicating viral genetic variation associated with virulence [3] . Anecdotal field data and initial experimental evidence [4 , 5] described differences between breeds in both incidence and severity of PCVAD [6] , supporting the role for host genetic variation in the etiology of the disease . In our first genome-wide association study ( GWAS ) , we found that host genetics influenced PCV2 titer and accounted for an important proportion of the phenotypic variation ( ~45% ) for viral load [7] . In this study , we integrated two datasets of pigs experimentally infected with a PCV2b strain [7 , 8] and in vitro siRNA and gene editing validation models to elucidate the role of host genetics in pathogenesis by identifying genes and genetic variants that could influence PCV2 susceptibility .
Substantial variation in the timing and magnitude of immune response , and in the efficiency of PCV2 replication , was reported in our previous studies of experimental infection with a PCV2b strain [7 , 8] . The present study extends that work by examining the influence of host genetics on the process of infection , based on a combination of the two previous study populations ( n = 974 F1 crossbred pigs originating from 14 genetic lines ) challenged by experimental infection with PCV2b and genotyped with 56 , 557 SNPs ( Porcine SNP60 BeadArray ) . The population structure provides substantial variation in linkage disequilibrium ( LD ) decay in order to identify genomic regions that influence the phenotype using a GWAS approach . Key phenotypes included viremia and PCV2-specific antibodies at specific time points , overall viral load across the study , and growth rate as a measure of impact of infection . The proportion of phenotypic variation accounted for by SNP genotypes was limited early in the infection , but increased after the surge in viral replication and associated immune response . Specifically , SNP genotypes explained from 19% of phenotypic variation in PCV2 viremia at 7 days post infection ( 7 dpi ) to 52% at 14 dpi ( Table 1 ) . Similarly , SNP genotypes explained 14% and 3% of PCV2-specific IgM and IgG variation at 7 dpi , respectively , but this increased to 60% ( IgM ) and 44% ( IgG ) at 21 dpi . Overall , SNP genotypes explained 64% of the variation in viral load calculated across time points . In comparison , the contribution of SNP genotypes to variation in Average Daily Gain ( ADG , monitoring growth rate through body weight ) during the study period was limited , explaining 16% of the variation in overall ADG with 13% explained at 7 dpi and 7% at both 21 and 28 dpi . The initial GWAS was performed using a BayesB-based approach where individual SNPs and successive 1 Mb windows of the genome were evaluated for association with phenotypic variation [7] . Bayesian regression models fit multiple SNPs in genome-wide associations , assuming that the marker effects result from a mixture of a point mass distribution whereby SNP have null effects and a distribution of non-zero effects ( e . g . , normal , heavy tailed ) . Prior assumptions are made relative to the genetic and environmental variances and the proportion of markers that have a null effect on a specific trait of interest . These models are implemented via a Markov chain Monte Carlo ( MCMC ) sampling algorithm . The posterior means are averaged over the number of samples from the MCMC [9] . Genome-wide average posterior distribution for the genetic variance was used to estimate the probability of each 1 Mb window having greater than the average genetic variance explained across PCV2-related traits ( S1 Table ) . The analysis identified two windows with greater than average window effect associated with both viral replication and immune response phenotypes ( Pr > 0 . 90 ) , assumed to represent Quantitative Trait Loci ( QTL ) ( Fig 1 ) . One window associated with viral load was found on SSC7 in the vicinity of the swine leukocyte antigen complex class II ( SLAII ) at 24–25 Mb while the other was located near the proximal end of SSC12 , at 3–4 Mb . The SNPs associated with the largest genetic variance , ALGA0039682 and ALGA0110477 , in each of these two windows explained 65 . 1% and 99 . 7% , respectively of the genetic variance explained by their respective windows . The SNP ( ALGA0110477 ) associated with the largest effect on viral load explained 9 . 3% of the genetic variance and 6 . 2% of the phenotypic variance for PCV2 viral load ( S1 Fig ) . This SNP was initially located on an unplaced scaffold in the previous 10 . 2 reference assembly . Estimating LD between ALGA0110477 and all other SNPs on the Porcine SNP60 BeadArray provided weak evidence of its location at the proximal end of the SSC12 reference sequence . Specifically , SNPs ALGA0122316 , ASGA0089708 , and ASGA0090188 had the highest LD estimates with ALGA0110477 ( r2 = 0 . 28–0 . 33 ) . Interestingly , SNPs in the genomic region encompassing these markers did not show strong evidence of association with viral load despite LD with ALGA0110477 . A more nuanced analysis fitting haplotypes across the region rather than individual SNPs using BayesIM [10] detected an effect in this genomic region ( S2 Fig ) , without the inclusion of the previously unmapped ALGA0110477 , providing support for the initial discovery based only on ALGA0110477 . The unplaced scaffold containing the SSC12 marker , ALGA0110477 , did not contain any annotated candidate genes that might underlie the observed effects , and the available sequence surrounding the marker only extended for 84 bp . Using inverse PCR ( iPCR ) , the proximal sequence was extended to 1 , 252 bp . This extended sequence was used to interrogate contigs from an early version of a long read-based genome assembly of a pig ( accession NPJO00000000 ) , [11] which identified a 19 Mb scaffold that provided precise location and context for ALGA0110477 used for identification of candidates genes described below . The recent release of a long read-based improved reference assembly , Sscrofa 11 . 1 ( GenBank accession GCA_000003025 ) , supported more accurate ordering and placement of markers , including ALGA0110477 ( SSC12 , 3 , 673 , 576 bp ) . Profiling of the loci associated with PCV2-related phenotypes across time points following infection was performed in order to distinguish the role of host genetics in innate and adaptive immunity . One of the outputs of Bayesian analyses is model frequency , which provides the proportion of post-burn-in samplings that included a particular SNP covariate in the model . Model frequency can also be used to compare loci effects across multiple traits , despite differences in phenotypic and genetic variances . This analysis based on BayesIM supported the previous result , with the highest model frequency for viral load occurring within the previously identified locations on SSC7 and SSC12 ( Fig 2 ) . In addition , haplotype effects estimated across the proximal end of SSC12 ( 0–10 Mb ) , provided evidence of haplotypes with divergent effects in viral load , with a peak detected at 3 . 7 Mb ( S3 Fig ) . The two major QTLs were consistently observed for other targeted traits , including viremia ( S4 Fig ) . Both SSC7 and SSC12 QTLs had similar model frequencies at 14 dpi , while the QTL on SSC7 showed an increasing effect on viremia at 21 and 28 dpi . It should be noted that an additional QTL on the proximal end of SSC8 was detected for viremia at 14 dpi; this QTL had not been observed for viral load , and represented the largest effect for 14 dpi . The QTLs located on SSC7 and SSC12 were also observed for PCV2-specific antibody variation . Specifically , these QTLs were associated with IgM variation , indicative of active infection , starting at 14 dpi and with IgG variation , representing previous PCV2 exposure or vaccination , starting at 21 dpi ( S5 and S6 Figs ) , supporting the hypothesis that they represent host variation affecting PCV2 infection including immune response . One of the genomic regions associated with viral load was located on SSC7 , in the vicinity of SLAII locus . The SNPs associated with the largest effects ( ALGA0039682 and ALGA0039710 , at 24 . 5 and 24 . 8 Mb , respectively ) are located at the proximal end of SLAII with DRA being the closest gene ( 24 . 8 Mb ) from the SLAII complex . Combined , these two SNPs explained 3 . 8% of the genetic variance for viral load . While the role of SLAII in antigen recognition and immune response in a variety of infectious diseases is well established , highly polymorphic genes and extended LD are the main factors limiting the discovery of functional variants . The SNP ALGA0039710 , was still associated with the largest effect in viral load in an analysis of a subset of samples ( n = 268 ) with extreme phenotypes that included novel SNPs located in genes in the QTL such as DRA , C2 , CFB , NELFE , SKIV2L [12] . Previous to the recent release of the Sscrofa 11 . 1 , we used the un-annotated long-read scaffold to identify the genes surrounding the SSC12 marker ALGA0110477 . Ab initio gene prediction [13] and pBLAST combined with RNA-seq of peripheral blood were used for annotation of this QTL region . Thirteen potential genes with an e value > 7e-64 and a pBLAST score > 200 were identified . Five of these genes were found to be expressed in RNA-seq data of peripheral blood from pigs subjected to PCV2 . These genes are involved in immune response and cytokine signaling ( SOCS3 ) , inhibition of apoptosis and promotion of cell proliferation ( BIRC5 ) , membrane trafficking and transport ( SYNGR2 ) and transmembrane ion channels ( TMC6 and TMC8 ) . The number of isoforms observed across these genes varied from one ( SOCS3 ) to more than 10 ( TMC6 ) . RNA-seq analysis of alternate ALGA0110477 homozygotes exhibiting extreme viral load following PCV2 challenge uncovered missense ( n = 4 ) , synonymous ( n = 11 ) , and UTR ( n = 10 ) SNPs and an UTR indel across the 5 candidate genes located in the QTL region . In addition , 1–2 kb sequencing upstream of the Transcription Start Site ( TSS ) for BIRC5 , SOCS3 and SYNGR2 uncovered 32 SNPs and 4 short indels . These novel polymorphisms and 580 SNPs from the Porcine SNP60 BeadArray were mapped to the 19 Mb scaffold using BLAT . The highest LD between ALGA0110477 and the polymorphisms mapped on the scaffold was with a SNP from the Porcine SNP60 BeadArray ( ASGA0086395 , r2 = 0 . 55 ) located 24 . 5 kb away followed by a group of 3 SNPs from SYNGR2 ( r2 = 0 . 42–0 . 48 ) including the missense polymorphism SYNGR2 p . Arg63Cys located 123 . 7 kb away . Using an additive linear mixed model and a subset of pigs with extreme high and low viral loads ( n = 268 ) genotyped for all polymorphisms mapped to the scaffold ( n = 629 ) , we found that the SYNGR2 p . Arg63Cys SNP and a 1bp indel located 343 bp upstream of BIRC5 TSS were associated with the largest effects on PCV2 viral load ( Fig 3 , F-ratio > 47 , P < 0 . 0001 ) . The phenotypic variance explained by each of these novel polymorphisms was substantially larger ( 21–23% +/- 6 . 1–6 . 4% ) compared to the original QTL SNP ALGA0110477 ( 12 . 6 +/- 4 . 8% ) . As expected , these polymorphisms were associated with large effects on all weekly viremia measures ( P < 0 . 0001 ) , and on PCV2-specific antibodies , starting from 14 dpi for IgM and 21 dpi for IgG ( p < 0 . 0001 ) . The effects on growth during the challenge were most evident after 14 dpi as well as during the entire challenge period ( 0–28 dpi , P < 0 . 0005 ) . The SYNGR2 p . Arg63Cys SNP ( SSC12 , 3 , 797 , 516 bp ) is located in the first loop of synaptogyrin-2 ( SYNGR2 ) in a region conserved across mammals [14] known to be crucial for formation of microvesicle membrane fraction [15] . The Arg residue is prevalent in other species ( e . g . , human , rat , cow , horse ) sometimes being replaced by His ( Rhesus macaque , dog ) , Lys ( prairie vole , Chinese hamster ) or Gln ( mouse , golden hamster ) while the Cys residue appears to be specific to swine ( Fig 4 ) . The substitution of Arg to Cys determines a change in charge and hydrophobicity of the loop ( Fig 5 ) . The SYNGR2 p . 63Cys allele is favorable with the viral load of the homozygous genotype ( Least Square Mean = 54 . 3 units ) being lower compared to the heterozygote ( 67 . 03 units , P = 0 . 005 ) and alternate homozygote ( p . 63Arg , 79 . 54 units , P < 0 . 0001 ) . The favorable homozygous genotype was also associated with lower weekly viremia ( P < 0 . 0001 , Fig 6 ) , IgM ( > 14 dpi , P < 0 . 0001 , S7 Fig ) , IgG ( > 21 dpi , P < 0 . 0001 , S8 Fig ) and higher growth ( overall 0–28 dpi and > 14 dpi , P < 0 . 001 ) compared to the alternate homozygote . We hypothesize that the effects on growth and PCV2 specific antibodies are a result of the variation in viremia modulated by SYNGR2 . Expression of SYNGR2 did not differ across SYNGR2 p . Arg63Cys genotypes or time points following in vivo PCV2 challenge . No interaction ( P > 0 . 30 ) was detected between SYNGR2 p . Arg63Cys and the SNPs associated with the largest effects from the QTL detected on SSC7 ( ALGA0039682 and ALGA0039710 ) . The effect of this SNP was confirmed in an independent validation data set consisting of 71 pigs infected with the same PCV2b strain and representing all three SYNGR2 p . Arg63Cys genotypes . This SNP had an effect on viremia starting from 14 dpi to 42 dpi ( P < 0 . 05 ) . The viremia of the homozygous genotype for SYNGR2 p . 63Cys allele was lower than the alternate homozygote ( 14–28 dpi , P < 0 . 05 ) and the heterozygote ( 14 dpi , P < 0 . 05 ) . Our inability to uncover the QTL located on SSC12 in our previous report [7] was based on 1 ) a genetic structure with a very limited number of homozygotes for SYNGR2 p . 63Cys allele ( Q = 1 . 2% ) compared to Engle et al . ( 2014 ) dataset ( Q = 18 . 3% ) , which is less favorable for detecting associations in additive statistical models , and 2 ) lower ability of the ALGA0110477 to capture the low viremic effects of SYNGR2 p . 63Cys . While in the Engle et al . ( 2014 ) dataset the presence of the ALGA0110477 C variant had a probability of 65% to be located on the same haplotype with SYNGR2 p . 63Cys , in McKnite et al . ( 2014 ) this variant is found in similar proportions in haplotypes that carry different SYNGR2 alleles ( e . g . SYNGR2 p . 63Cys; P = 55 . 9% ) . The 1bp deletion located 343 bp upstream of the TSS of BIRC5 ( BIRC5 g . -343delA ) was found to be in high LD ( r2 = 0 . 83 ) with SYNGR2 p . 63Cys allele and as expected was associated with low viral load ( P < 0 . 0001 ) . The deletion was predicted to affect a potential motif for NR5A2 , a DNA-binding zinc finger transcription factor . However , no significant difference in expression was observed between BIRC5 genotypes across time points following in vivo PCV2 challenge ( P < 0 . 17 ) . At 14 dpi the homozygotes for the insertion exhibited an elevated nominal expression compared to the other genotypes , but the difference was not significant ( P = 0 . 061 ) . Located 41 . 9 kb apart , the high LD observed between SYNGR2 p . Arg63Cys and BIRC5 g . -343delA ( r2 = 0 . 83 ) hampered the ability to distinguish their individual effects in the in vivo challenge dataset . In contrast , the LD between SYNGR2 p . Arg63Cys and other SYNGR2 SNPs was limited ( r2 < 0 . 26 ) , as well as the LD between BIRC5 g . -343delA and other BIRC5 polymorphisms ( r2 < 0 . 16 ) . A very defined LD block exists from ALGA0110477 to SYNGR2 that includes 16 DNA polymorphisms . Within this block , there were 9 haplotypes with individual frequencies greater than 1% that accounted for 85% of the haplotypes present . A single haplotype ( Hap 1 ) carried the SYNGR2 p . 63Cys allele . The frequency of this haplotype in our resource population was 0 . 28 . The remaining eight haplotypes carried the SYNGR2 p . 63Arg allele . A haplotype substitution effect demonstrates that Hap 1 was associated with the lowest viral load ( P < 0 . 0001 , S2 Table ) substantiating the potential role of SYNGR2 p . Arg63Cys in PCV2 susceptibility . An analysis of the Sequence Read Archive and Whole-genome Shotgun Sequences revealed that the SYNGR2 p . 63Cys allele is only present in Duroc and Pietrain , while SYNGR2 p . 63Arg was present in the rest of the Western ( Large White , Landrace , Hampshire and Berkshire ) and indigenous Chinese breeds ( Bamei , Jinhua , Meishan , Rongchang and Tibetan ) , as well as wild pigs such as common Warthog ( Phacochoerus africanus ) , Java ( Sus verrucosus ) and Visayan ( Sus cebifrons ) warty pigs . Phylogenetic analysis based on the polymorphisms from this haplotype block separated the breeds into paternal ( Pietrain , Duroc , Hampshire and Duroc ) and indigenous Asian breeds while the maternal breeds ( Large White and Landrace ) were located in between these two groups ( Fig 7 ) . Geographical location has an important role in the genetic relationships between indigenous Chinese breeds [16] . The breeds originating from the Tibetan plateau ( Tibetan pigs from Sichuan region and Bamei pigs ) share the same haplotype , which is similar with the haplotype of the pigs from the neighboring Rongchang region . In contrast , Meishan and Jinhua are from the middle-lower belt of Yangtze River and their haplotypes share more similarities . The haplotype that carried the SYNGR2 p . 63Cys allele ( Hap1 ) was the closest to Pietrain and Duroc . The other haplotypes present in our resource population were similar to those identified in Berkshire ( Hap 4 ) , Hampshire ( Hap 5 ) , Meishan ( Hap 2 ) , or Jinhua ( Hap 9 ) . This finding suggests that SYNGR2 p . 63Cys is the predominant allele in Duroc and Pietrain , breeds in which more emphasis is placed on growth related traits compared to breeds such as Large White and Yorkshire . Our analysis of SYNGR2 p . Arg63Cys in pure breeds showed that SYNGR2 p . 63Cys has a frequency of 0 . 25 in Yorkshire , 0 . 53 in Landrace and 0 . 78 in Duroc . Considering that the location of the SYNGR2 p . Arg63Cys substitution is in a conserved domain involved in vesicle formation [15] and recent literature support of SYNGR2 affecting replication of a tick-borne human RNA virus [17] , we hypothesized that SYNGR2 may play a role in the internalization and early release of PCV2 from endosomes influencing its replication . The Porcine kidney 15 cell line ( PK15 ) has an epithelial origin and is a well-established model system for PCV2 innate immunity and cellular pathogenesis [2] . We found that PK15 cells carry both the SYNGR2 p . 63Arg and the insertion of BIRC5 g . -343delA variants associated with high-viremia . Expression of SYNGR2 did not differ across time points following PCV2 infection of PK15 cells , corroborating in vivo findings . In order to validate a role of SYNGR2 in PCV2 replication , we transfected PK15 with siRNA targeting the mRNA of SYNGR2 . We evaluated two siRNA ( siRNA-01 and siRNA-03 ) at two different concentrations ( 10 nM and 20 nM ) and found that siRNA-01 was the most efficient to knock-down mRNA level of SYNGR2 compared to the cells subjected to a scramble siRNA control . A substantial reduction ( >75% ) in SYNGR2 mRNA level was observed starting 24 hours after transfection ( Fig 8 ) . PK15 cells with the expression of SYNGR2 silenced were then infected with PCV2 24 hours after transfection . A reduction in viral titer was observed in the SYNGR2 silenced cells subjected to PCV2b starting at 48 hours post infection ( hpi ) when compared to scramble siRNA and non-transfected control cells , indicating a role of SYNGR2 in viral replication ( Fig 9 ) ( P < 0 . 05 ) . The viral titer across time points was not statistically different between the scramble siRNA and non-transfected control cells ( P > 0 . 54 ) . PK15 edited clones were generated by CRISPR-Cas9 Ribonucleoprotein ( RNP ) complex approach with a pair of guide RNAs ( 31_AC/40_AC ) targeting the second exon of SYNGR2 to cause a partial deletion of this exon and removal of the region containing the SYNGR2 p . Arg63Cys polymorphism ( Fig 10 ) . Sequencing of the mRNA from selected PK15 edited clones revealed a single clone homozygous for the same 106 bp deletion ( E1 ) . This deletion is predicted to cause a shift in the reading frame and an altered protein ( 195 residues ) beginning at amino acid residue 42 compared to the wildtype SYNGR2 sequence ( 224 residues ) . The deleted fragment included the conserved motif located in the first loop while the shift in the reading frame affected the C-terminus of SYNGR2 . A significant reduction in viral titer starting at 24 hpi in cells ( Fig 11 ) and 48 hpi in supernatant ( Fig 12 ) was observed in the E1 edited clone compared to wildtype PK15 ( P < 0 . 05 ) . The induced changes resulted in a potential nonsense-mediated mRNA decay , since a nominal reduction in expression of SYNGR2 was observed in E1 cells compared to wildtype PK15 cells with a significant difference observed at 24 hpi ( P <0 . 05 , S9 Fig ) .
Substantial variation in efficiency of viral replication and specific immune response was reported in our previous studies of experimental infections with PCV2 [7 , 8] . Host genotype explained a substantial proportion of the phenotypic variation for viremia , viral load and immune response , with two major QTL identified on SSC7 and SSC12 . Despite the presence of these two major loci , GWAS across time points following infection underlined the quantitative nature of the phenotypic variation of the targeted traits . The genetic complexity of PCV2 susceptibility has been augmented by the presence of a QTL in the vicinity of the SLAII complex of genes ( SSC7 ) . Despite the known role of this region in antigen recognition and immune response , high LD and genetic diversity have limited discovery of functional variants . Dissection of the SSC12 QTL based on gene annotation , genomic and RNA-sequencing uncovered a non-conservative substitution in a key domain of the SYNGR2 gene associated with PCV2 viremia and immune response . SYNGR2 is a non-neural member of the synaptogyrin family , a group of genes primarily expressed in the membrane of synaptic vesicles of neuronal cells with roles in vesicle biogenesis , exocytosis and recycling via endocytosis [15 , 18] . There is limited information about the functional role of this member of the gene family . Recently , SYNGR2 was implicated as an active player in promoting viral RNA replication and immune evasion of severe fever with thrombocytopenia syndrome virus ( SFTSV ) , a novel tick-borne bunyavirus in humans [17] . SYNGR2 interacted with non-structural viral proteins to promote the formation of lipid–based inclusion bodies , which become virus factories within the cytoplasm of infected cells . SYNGR2 mRNA had been upregulated more than 200-fold at 36 hpi with SFTSV . In vitro silencing of SYNGR2 resulted in a decrease in viral replication and a reduction in the number and size of the inclusion bodies , further substantiating the role of SYNGR2 in facilitating SFTSV infection [17] . Similarly , our study showed that silencing the expression of SYNGR2 in PK15 cells was associated with a significant reduction in PCV2 titer , indicating a role of SYNGR2 in promoting viral replication . SYNGR2 p . Arg63Cys , the only missense polymorphism identified in SYNGR2 , is characterized by a predicted change in charge and hydrophobicity of the first loop that connects two essential transmembrane domains , and is located in a region conserved across mammals . In rats , the first intraluminal loop and the C-terminus of SYNGR2 were found to be crucial for successful incorporation of the protein into vesicular membranes and vesicle formation [15] . Replacement of residues 67–73 in the first loop led to protein degradation , with residues 70–73 having the largest impact [15] . In pigs , this segment of four residues is analogous to amino acids 60–63 and identical with the rat sequence ( Val-Phe-Asn-Arg ) corresponding to SYNGR2 p . 63Arg allele ( Fig 4 ) . Since the SYNGR2 p . Arg63Cys substitution is located within this crucial region , we hypothesize that SYNGR2 p . 63Cys allele could influence incorporation of SYNGR2 into vesicular membranes , impact vesicle formation , and efficient trafficking of PCV2 to nucleus for replication . Using CRISPR-Cas9 that targeted the second exon which encodes this important motif , we generated a PK15 SYNGR2 edited clone . A reduction in viral titer observed in the edited clone , underlined the critical role of this gene in PCV2 susceptibility . Predominance of the SYNGR2 p . 63Cys allele in Pietrain and Duroc compared to other domestic and wild pig breeds could be a result of the subclinical effects that early PCV2 strains had on growth prior to the surge in PMWS in the early 1990’s and the emphasis on growth in selection of these two breeds . In the Danish pig breeding program , five times more emphasis is placed on ADG ( 30–100 kg ) in Duroc than in Large White or Landrace ( Bolhom , 2010 , personal communication; http://docplayer . net/20998319-Danish-pig-production . html ) . This selection pressure and the presence of mild PCV2 strains could have resulted in a rapid increase in the frequency of the SYNGR2 p . 63Cys allele in both Pietrain and Duroc . In our research we did not observe an increase in SYNGR2 mRNA levels following in vitro or in vivo infection with PCV2 . This may reflect important distinctions between SFTSV and PCV2 . For instance , SFTSV is an RNA virus with the capacity to replicate within intracytoplasmic inclusion bodies , or viral factories . As Sun et al . ( 2016 ) demonstrated , SYNGR2 is a component of these vesicles and necessary for their formation [17] . As SFTSV replicates , more viral factories will be required for viral proliferation , resulting in increased levels of SYNGR2 . PCV2 , on the other hand , is a DNA virus and can only replicate in the nucleus of host cells . Therefore , the potential role of SYNGR2 in PCV2 infection likely takes place prior to viral replication and may not require such an increase in SYNGR2 expression , but rather specific SYNGR2-PCV2 interactions . Since the position of this substitution is clearly located in a loop and not part of the transmembrane regions indicated by Janz and Sudhof ( 1998 ) and predicted by PSIPRED [19] ( S10 and S11 Figs ) , an interaction between SYNGR2 and a ligand is favored compared to the potential impact of SYNGR2 p . Arg63Cys on overall protein folding or conformation of the first loop . A shift in the position of the second transmembrane helix as a result of the substitution in the loop region was predicted by HMMTOP software [20] , but not supported by others ( e . g . , TMHMM , DisEMBL , PSIPRED ) . In this study , decreased viral titer by 1 ) exogenous reduction of SYNGR2 expression by siRNA along with 2 ) partial deletion of a key domain by gene editing , provided evidence of the involvement of SYNGR2 in PCV2 infection . Since the SYNGR2 p . Arg63Cys polymorphism is the only missense mutation within the entire gene and located in this key domain , it is a plausible QTN ( Quantitative Trait Nucleotide ) candidate for PCV2 susceptibility . However , future studies of the mechanistic role of SYNGR2 and specifically of SYNGR2 p . Arg63Cys substitution will be required to provide additional experimental evidence of their role in PCV2 replication and pathogenesis .
The experimental design and procedures used during this research project were approved by the Institutional Animal Care and Use Committee of the University of Nebraska -Lincoln . Experimental PCV2b challenge was conducted in nine batches that varied in size from 81 to 141 pigs , with a total of 974 pigs . The genetic makeup of this resource population consisted of crossbred pigs produced by 14 genetic lines generated by seven genetic programs . The dams of the experimental pigs had been vaccinated for PCV2 at 3 weeks of age with a single dose of Ingelvac CircoFLEX vaccine ( Boehringer Ingelheim ) . The suppliers of the pigs also had vaccination programs for Porcine parvovirus , Erysipelothrix rhusiopathiae , Clostridium perfringens , Leptospirosis and Colibacillosis and tested negative for Porcine Reproductive and Respiratory Syndrome Virus ( PRRSV ) . Prior to experimental infection , the pigs tested negative for presence of PCV2 in peripheral blood by real time quantitative PCR ( qPCR ) and had a sample/positive ratio ( S/P ) lower than 0 . 4 for IgM and 0 . 3 for passive IgG , the PCV2-specific antibodies [7] . Following infection , experimental pigs were examined daily for clinical signs of disease; weights and blood samples were collected at 0 , 7 , 14 , 21 and 28 days post infection ( dpi ) . Details of the experimental procedures , phenotypic and sample collection are described in Engle et al . ( 2014 ) and McKnite et al . ( 2014 ) . A validation dataset consisting of a group of 71 pigs representing all three SYNGR2 p . Arg63Cys genotypes infected with the same PCV2b strain at 5 weeks of age was generated using the same experimental conditions . A group of 40 pigs ( SYNGR2 p . 63Arg/63Cys and 63Arg/63Arg ) vaccinated for PCV2 at 3 weeks of age were used as controls . The vaccinated pigs were housed in the same room with the experimentally infected pigs , but in different pens . The PCV2b strain ( UNL2014001 ) used for the experimental infection was obtained from a pig with symptoms characteristic to Post-weaning Multisystemic Wasting Syndrome ( PMWS ) , which is the most common PCVAD syndrome . The strain was sequenced ( accession KP016747 . 1 ) using dye terminators and the sequence was compared to PCV2 strains available in GenBank [8] . The strain was cultured in swine testicular cell lines as described [7] . At an average of 36 d all the pigs were inoculated with the UNL2014001 PCV2b strain with a titer of 104 . 0 50% tissue culture infection dose ( TCID50 ) intranasally and intramuscularly . PCV2 specific antibodies , IgM and IgG , were profiled weekly from serum using ELISA ( Ingenasa ) as described in McKnite et al . ( 2014 ) . Estimates of the number of PCV2b copies , or viremia , was performed using viral genomic DNA isolated by QIAamp DNA Minikit ( Qiagen ) and quantified by qPCR using TaqMan Master Mix and ABI 7900 Real Time PCR System ( Thermo Scientific ) . The viral load for each pig during the entire challenge was represented as area under the curve ( AUC ) based on an algorithm that takes into account viral levels observed at each time point following infection ( 0 , 7 , 14 , 21 , and 28 dpi ) fitting a smooth curve over the 28 days and summing the areas in 0 . 01 time increments [21] . The DNA was isolated from ear and tail tissue clips using DNeasy or Puregene blood and tissue kits ( Qiagen ) . The experimental animals were genotyped using either the first or second generation of the Porcine SNP60 BeadArray ( Illumina ) that contain 62 , 183 and 61 , 565 SNPs , respectively . Only the common SNPs present in both BeadArray versions ( 91 . 6% , 61 , 177 ) were mapped on Sscrofa 11 . 1 porcine reference genome assembly and used in GWAS via GenSel software package [22] . DNA samples and SNP assays with a genotyping call rate below 80% were excluded from the analyses . A GenCall quality score of 0 . 40 was used as a minimum threshold for genotype quality [23] . Targeted DNA sequencing of candidate genes in the SSC12 QTL region including SOCS3 , BIRC5 and SYNGR2 and their 2–4 kb region upstream of the transcription start sites ( TSS ) was performed using dye terminators and ABI PRISM 3100 Genetic Analyzer ( Thermo Scientific ) on high and low viremic samples . Discovery and validation of the polymorphisms detected by RNA-seq was based on alignment of DNA sequences using Sequencher software ( Gene Codes ) . Potential impact of the polymorphisms located in the proximal promoter on important regulatory motifs was evaluated using FIMO ( version 4 . 11 . 3 ) [24] and the JASPAR transcription profile database ( version 2016 ) . Genotyping of polymorphisms located in the transcribed regions and proximal promoters of SOCS3 , BIRC5 , SYNGR2 , THA1 , TMC6 and TMC8 was performed by multiplex assays using Sequenom MassARRAY platform and Sequenom iPLEX chemistry based on the manufacturer protocols ( Sequenom , San Diego , CA ) . The proportion of phenotypic variance explained by host genetics for PCV2-viremia , PCV2-specific antibodies ( IgM and IgG ) and average daily gain ( ADGi ) during experimental infection was estimated based on Porcine SNP60 BeadArray genotypes using a BayesB model [25] and GenSel software [22] . The statistical model included litter , pen and batch as class variables and passive IgG and age at infection as covariates . Bayesian analyses were based on π equal to 0 . 99 that assumed a prior probability of 1% of the SNPs having a non-zero effect . The Markov chain included 40 , 000 samples with the first 1 , 000 being removed as burn-in . Markov chain was set to use every 40th sample to estimate posterior distribution for the genetic variance explained by each 1 Mb window of the reference genome . This distribution was used to estimate the probabilities of each 1Mb window having a variance greater than 0 or greater than the average variance explained by each 1Mb window as described in McKnite et al . ( 2014 ) . Bayes Interval Mapping ( BayesIM ) was implemented to derive haplotype effects across the genome on PCVAD-related traits as described in Kachman ( 2015 ) and Wilson-Wells and Kachman ( 2016 ) [11] . Briefly , a hidden Markov model was used to generate 8 haplotype states based on SNP genotypes [26] . Phenotypic variation of the targeted traits was analyzed with a hierarchical Bayesian model . QTL were placed every 50 kb across the genome while average haplotype size was set to 500 kb . Genetic variances , haplotype effects , and model frequencies were estimated at each locus . There were 42 , 000 MCMC samples collected with the first 2 , 000 used for burn-in . The model included batch , litter and pen as random effects and IgG and age at infection used as covariates . If a locus had an effect , haplotype effects for each cluster were modeled as independent normal random variables . Associations between the single marker genotypes and phenotypic variation were tested using a linear mixed model fitted by JMP 10 . 0 ( SAS Inst . Inc . ) that included marker genotype and batch as fixed effects , litter and pen as random effects while age at infection and IgG were used as covariates . Additive and dominance effects were estimated for each of the targeted DNA polymorphisms . A similar model was used to estimate the interaction between SNPs . The potential effect on viral load of the haplotypes in the defined LD block from ALGA0110477 to SYNGR2 that includes 16 DNA polymorphisms , was estimated as haplotype substitution effects . Contrasts between haplotypes were estimated using a linear mixed model as described above including one variable for each haplotype with values 0 , 1 , and 2 corresponding to the animal having 0 , 1 , or 2 copies of the haplotype in question . The haplotype substitution effects were presented as deviations from the mean of the haplotypes . Inverse PCR ( iPCR ) , using four ( AciI , AluI , HaeIII , HpaII , RsaII ) and six cutter ( EcoRI , HaeII , HincII , HindIII , KpnI , MfeI , MspA1l ) restriction enzymes ( New England Biolabs ) , T4 DNA ligase ( New England Biolabs ) and nested PCR using AmpliTaq Gold 360 DNA polymerase ( Thermo Scientific ) , was employed to expand the genomic DNA sequence surrounding the short ALGA0110477 sequence , a SNP previously unmapped on the Sscrofa 10 . 2 reference genome . A genomic scaffold ( 19 Mb ) of the proximal end of SSC12 was constructed based on Pacific Biosciences sequencing reads [11] . The position of the extended ALGA0110477 sequence and all SSC12 mapped and unmapped SNPs were determined on the genomic scaffold using BLAT . Annotation of the QTL region on the SSC12 scaffold was based on RNA-seq alignments and BLAST but also using ab initio approaches such as GenScan [13 , 27] in combination with pBLAST . In order to profile transcriptome changes and sequence variation related to PCV2 infection , peripheral blood samples collected from the validation group of pigs that exhibited high ( NTT = 6 ) and low ( NCC = 5 ) viremic genotypes for ALGA0110477 at 0 , 7 and 14 dpi were subjected to RNA sequencing . RNA was extracted from peripheral blood collected in Tempus tubes using the Tempus Spin RNA Isolation Reagent Kit ( Thermo Scientific ) . RNA samples were sequenced using Ion Proton technology as described in the manufacturer protocol ( Thermo Fisher Scientific Inc . ) . The adaptor-free sequencing reads were trimmed and filtered using Trim galore ( version 0 . 4 ) [28] with low-quality bases in the 5’ end being removed and nucleotides with quality call less than 22 being trimmed from the 3’ end . The filtered reads were initially aligned to the SSC12 scaffold ( 19 Mb ) using the two-step alignment approach used for Ion Proton transcriptome data that includes both Tophat and local-Bowtie [29] . The reads were later also aligned to the new pig assembly Sscrofa 11 . 1 . The number of reads mapped to each gene in the annotated QTL region was obtained using HTSeq ( version 0 . 6 . 1p1 ) [30] . Expression of the candidate genes SOCS3 , BIRC5 and SYNGR2 across time points following PCV2 infection was quantified using TaqMan Master Mix and CFX384 Real Time PCR ( BioRad ) . The qPCR assays were designed using IDT Realtime PCR Tool software ( www . idt . com ) and sequences generated based on RNA-seq alignments . RNA was extracted from peripheral blood samples collected in Tempus tubes from a subset of pigs representing all genotypes from the validation data set that displayed extreme viral load ( high vs low ) ( n = 40 ) from 0 to 21 dpi using the Tempus Spin RNA Isolation Reagent Kit ( Thermo Scientific ) . Complementary DNA ( cDNA ) was obtained using a mix of random hexamers and poly dT primers using First strand cDNA Synthesis Kit ( GE Healthcare Bio-Sciences ) . Expression of ribosomal protein L32 ( Rpl32 ) gene was used for normalization . Mean normalized expression ( MNE ) values were calculated based on cycle crossing thresholds ( CT ) obtained for the technical triplicates taking qPCR efficiencies into account [31] . MNE values for ALGA0110477 , SYNGR2 p . Arg63Cys or BIRC5 g . -343delA genotypes and time point following infection were log10 transformed and compared by t-test . The porcine kidney cell line ( PK15 ) was grown in DMEM high glucose media supplemented with 10% FBS and 1% Penicillin-Streptomycin ( 5 , 000 U/mL ) . Cells were cultured in 12-well plates ( 4 cm2 ) with 5 . 0x105 cells per well and infected with UNL2014001 PCV2b strain ( TCID50 = 104 ) when 80–100% confluent at MOI = 0 . 00025 . One hour following infection , cells were washed and fresh media was added ( DMEM high glucose and 2% FBS ) . The cells were incubated at 37 °C with 5% CO2 for up to 5 days . Control cells were maintained the same way and mock-inoculated with plain DMEM high glucose media . Supernatants and cells were collected at specific time points and frozen at −80 °C . Viral DNA was extracted from supernatants using QIAamp DNA Mini kit ( Qiagen ) . RNA , viral and host DNA was extracted from PK15 cells using AllPrep DNA/RNA Mini kit ( Qiagen ) . TaqMan Master Mix and CFX384 Real Time PCR Detection System were used for quantification of PCV2 and expression profiling of BIRC5 and SYNGR2 from PK15 cells . Dideoxy sequencing of the cDNA and genomic DNA was used to profile the sequences and to genotype BIRC5 and SYNGR2 variants in PK15 cells . PK15 cells were transfected 24 hours after plating in 12-well plates ( 4 cm2 ) with 2 . 5x105 cells per well when ~80% confluent with two siRNA oligos ( siRNA-01: sense 5’-CUACAAGGCCGGAGUGGAUUU-3’ , and antisense 5’-AUCCACUCCGGCCUUGUAGUU-3’; siRNA-03: sense 5’-CCACAAGUCCGGAGAGCAGUU 3’ , and antisense , 5’-CUGCUCUCCGGACUUGUGGUU-3’ , Dharmacon Research ) targeting SYNGR2 mRNA and the AllStars Negative Control siRNA ( scramble , Qiagen ) at 10nM and 20nM concentrations . Transfection was performed using Lipofectamine RNAiMAX transfection reagent ( Invitrogen ) following the manufacturer’s protocol . Cell samples were collected 24 , 48 , 72 , and 96 hours post transfection in PBS and centrifuged at 16 , 000xg for 1 minute to pellet the cells . RNA was extracted using RNAeasy Mini kit ( Qiagen ) . Real Time PCR was used to profile SYNGR2 expression . siRNA oligo 01 and the AllStars Negative Control siRNA ( 20nM ) were used for subsequent transfections prior to infection . The siRNA transfected cells were inoculated 24 hr after transfection following the same infection protocol described above . Statistical differences in viral titer between cell lines across time points were tested using a linear model fitted by JMP 10 . 0 ( SAS Inst . Inc . ) that included batch and cell line as fixed effects . Pairwise comparisons between least-squares means of the viral titers were based on the Tukey test . Six potential guide RNAs targeting the second exon of SYNGR2 were designed and ordered ( IDT ) , three located upstream ( 5’ ) and three located downstream ( 3’ ) of the SYNGR2 p . Arg63Cys polymorphism . Each guide RNA was hybridized with fluorescently labeled Alt-R CRISPR-Cas9 tracrRNA ATTO 550 ( IDT ) and Alt-R S . p . Hifi Cas9 Nuclease V3 ( IDT ) following the manufacturer’s protocol to form Ribonucleoprotein ( RNP ) complexes . These RNP complexes were reverse transfected into PK15 cells using Lipofectamine RNAiMAX transfection reagent ( Invitrogren ) at a final concentration of 10nM . After 48 hours post transfection , genomic DNA was extracted using QIAamp Blood DNA Mini Kit ( Qiagen ) and amplified via PCR using LongAmp Taq DNA polymerase ( NEB ) with primers located in the introns flanking the second exon of SYNGR2 ( 5’-AGAAGGGAGAGACAGCACCA-3’ , 5’- CACCAGCACATCTTCCACCT-3’ ) . The amplicons were subjected to T7 endonuclease I ( NEB ) digestion following the manufacturer’s protocol and visualized by agarose gel electrophoresis to assess cutting efficiency of each individual guide RNA . The ability of guide RNA pairs ( upstream/downstream ) to generate partial deletions of the second exon was assessed following the same RNP transfection protocol with a final RNP concentration of 20nM ( 10nM/guide RNA ) . After 48 hours post transfection , genomic DNA was extracted , amplified , and visualized via agarose gel electrophoresis . A single guide RNA pair ( sg31_AC/sg40_AC ) was selected to generate PK15 edited clones . After 24 hours post transfection , the cells were collected and sorted using Fluorescence Activated Cell Sorting ( FACS ) into 96 well plates to generate single cell clones . Genomic DNA from each single cell clone was extracted using QuickExtract DNA extraction solution ( Lucigen ) following the manufacturer’s protocol and genotyped by PCR amplification and gel electrophoresis . RNA was extracted from selected clones using All Prep DNA/RNA Mini kit ( Qiagen ) and PCR was performed with primers located in the 5’ and 3’ UTRs of the SYNGR2 mRNA sequence ( 5’-ACGGCGACAATGGAGAGCGG-3’ , 5’-GGGAAACAAGAGGGGCCAGCA-3’ ) to amplify full-length transcripts , which were sequenced using Dideoxy sequencing . A single clone ( E1 ) homozygous for a 106bp deletion was plated and infected with PCV2b inoculate as previously described . Wildtype PK15 cells were concurrently infected and served as a control . To determine if induced changes in the mRNA sequence of SYNGR2 led to nonsense-mediated mRNA decay , expression of SYNGR2 was profiled by qPCR at 0 , 24 and 48 hrs in edited and wildtype PK15 cells . | The cost of managing Porcine Circovirus 2 ( PCV2 ) associated diseases in the US alone costs the swine industry more than $250 million a year . This virus is found in all swine populations in the US , but only a few pigs get sick and show signs of disease . Previous anecdotal field data showed differences between pig breeds in both incidence and severity of PCV2-associated diseases , supporting the role of host genetics in disease susceptibility . This research , including over 1 , 000 experimentally infected pigs with PCV2 , is the largest study ever conducted to understand the role of host genetics in PCV2 related illnesses . We found that a pig’s own genetics can impact the ability of PCV2 to multiply and cause disease . Specifically , we found a mutation in the SYNGR2 gene that influences the ability of the PCV2 virus to replicate , which can affect pig growth and immune system following infection . This research will aid in the development of genetic tests with the ability to predict PCV2 susceptibility . Early identification of pigs that are susceptible to PCV2 infection will result in an improvement in the general health and welfare of pigs . | [
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... | 2018 | Synaptogyrin-2 influences replication of Porcine circovirus 2 |
The “arms race” relationship between transposable elements ( TEs ) and their host has promoted a series of epigenetic silencing mechanisms directed against TEs . Retrotransposons , a class of TEs , are often located in repressed regions and are thought to induce heterochromatin formation and spreading . However , direct evidence for TE–induced local heterochromatin in mammals is surprisingly scarce . To examine this phenomenon , we chose two mouse embryonic stem ( ES ) cell lines that possess insertionally polymorphic retrotransposons ( IAP , ETn/MusD , and LINE elements ) at specific loci in one cell line but not the other . Employing ChIP-seq data for these cell lines , we show that IAP elements robustly induce H3K9me3 and H4K20me3 marks in flanking genomic DNA . In contrast , such heterochromatin is not induced by LINE copies and only by a minority of polymorphic ETn/MusD copies . DNA methylation is independent of the presence of IAP copies , since it is present in flanking regions of both full and empty sites . Finally , such spreading into genes appears to be rare , since the transcriptional start sites of very few genes are less than one Kb from an IAP . However , the B3galtl gene is subject to transcriptional silencing via IAP-induced heterochromatin . Hence , although rare , IAP-induced local heterochromatin spreading into nearby genes may influence expression and , in turn , host fitness .
Transposable elements ( TEs ) are major constituents of eukaryotic genomes and are important catalysts of evolution [1] , [2] . Indeed , TEs may cause negative , neutral or positive effects upon insertion , increase genomic instability by chromosomal rearrangements [3] and act as central collaborators in genome-wide regulatory network creation and renewal [4] . TEs are able to move throughout the genome either directly ( DNA transposons ) or by an RNA intermediate ( Retrotransposons ) . Autonomous copies code for the necessary machinery for host invasion while non-autonomous copies will depend upon the former . The well-known arms-race between TEs and the host genome [5] has resulted in several regulatory pathways , including a combination of various epigenetic mechanisms i . e . DNA methylation , small RNAs and histone post-translational modifications . In plants , invertebrate species and vertebrates , DNA methylation has been described as an important epigenetic silencing mechanism . In mouse , IAPs ( Intracisternal A-type Particle elements ) , long terminal repeats ( LTR ) retrotransposons ( also termed endogenous retroviruses ( ERVs ) ) , are highly DNA methylated and the disruption of enzymes responsible for such methylation ( DNA methyltransferases Dnmt1 and Dnmt3L ) causes global derepression of IAP copies [6] , [7] , albeit only in particular tissues . In Arabidopsis thaliana , TEs are strictly silenced by DNA methylation , which is often guided by small RNAs [8] . In Drosophila melanogaster , rasiRNAs and piRNAs ( repeat-associated small interfering RNAs and piwi-interacting RNAs respectively ) are responsible for the silencing of many TE copies [9] , [10] . Small RNAs may trigger local heterochromatin [11] and histone post-translational modifications are also involved in TE silencing . Indeed , the repressive histone modifications H3K9me3 and H4K20me3 are associated with ERVs in mouse ES cells [12] , [13] . Moreover , knock out of a histone methyltransferase ( SETDB1 ) [14] or the protein that recruits it ( KAP-1 ) [15] in mouse ES cells causes reduction of H3K9me3 at ERVs and induces high expression of several ERV classes as well as genes controlled by cryptic ERV promoters [16] . TEs are therefore often observed in regions associated with repressive histone marks and hence trapped into local heterochromatin . The analysis of euchromatin/heterochromatin boundaries has shown that an increase in TE density is co-localized with heterochromatin delimitation [17] . D . melanogaster mitotic chromosome analysis and the genome sequencing projects have also shown that TEs are abundant in heterochromatic regions [18] , [19] . Such phenomena might be: 1 . the consequence of insertional preferences of TEs into heterochromatin; 2 . positive selection of TE maintenance into heterochromatin for genomic stability [20]; or 3 . an induction of heterochromatin by TE sequences . The Sleeping beauty ( SB ) transposase has been shown to have an affinity for heterochromatin when transfected into mouse ES cells , however SB transposons do not seem to prefer heterochromatin over euchromatin [21] . In Drosophila , HeT-A , TAHRE and TART elements are found in telomeres but never in other heterochromatic regions , such as centromeres or interspersed heterochromatin , suggesting therefore an inclination for telomere specific sequences and not heterochromatin [22] . In general , no global heterochromatic insertional preference has been described for TEs with the exception of the yeast Ty5 retrotransposon that integrates into telomeres and the silent mating loci ( the only two heterochromatic regions of the yeast genome ) when the targeting domain of the Integrase is phosphorylated [23] . Some studies have found that only multiple tandem copies of a transgene ( or even TEs ) are able to induce heterochromatin but not a single copy [24]–[26] . Repetitive sequences are hypothesized and often claimed to induce heterochromatin and local region silencing [27] , [28] . However , very few reports describe induction of heterochromatin at individual loci by a specific copy of a TE ( see the sexual determination in melon by heterochromatin spreading from a DNA transposon for example [29] ) . While genome-wide studies on histone post-translational modifications have revealed the repressive chromatin environment of several TE types in mouse ES cells [12] , [16] , no study has determined if TE insertion per se can induce the spreading of a repressive chromatin environment into flanking genomic regions . To determine if , indeed , TE families can induce local heterochromatin in a natural system , we surveyed two mouse strains where insertionally polymorphic TE copies have been documented [30] , [31] . For the same genomic location , in the same cell type , we can distinguish a “full site” in one strain from an “empty site” in the other . Comparisons of the profiles of repressive epigenetic marks at both classes of sites allow us to determine the capacities of TEs to induce local heterochromatin . We report robust induction of H3K9me3-H4K20me3-chromatin spreading into nearby regions for the IAP family of LTR retrotransposons . Intriguingly , induction of such chromatin by other families of active LTR retrotransposons , including ETn/MusD ( Early transposons ) , is much more variable . We found that transcription of one gene is impacted by the spread of IAP-induced heterochromatin in ES cells , but these effects on genes are likely rare , as such insertions are likely subject to strong negative selection .
We chose three different families of TEs for this study based on the data available on insertionally polymorphic copies , namely IAPs , ETn/MusD elements and LINEs ( Long interspersed nuclear elements ) [30] , [31] . IAPs and ETn/MusDs are active families of mouse ERVs/LTR retrotransposons [30] , together accounting for 10%–12% of spontaneous mutations in inbred mice [32]–[34] . ETn/MusD are a non-autonomous/autonomous pair of ERVs respectively [35] , [36] , where MusD appears to be more efficiently repressed by the host compared to their non-autonomous ETn cousins [37] . LINEs are non-LTR retrotransposons , abundant in the mouse genome with many active copies , although most are 5′ truncated due to their transposition mechanisms ( dead-on-arrival copies ) [38] . IAPs and ETn/MusD are highly associated with H4K20me3 and H3K9me3 in ES cells [12]–[14] , [37] whereas LINEs are not associated with these marks [13] . No strong insertional biases have been described for these TE families apart from AT rich regions for LINEs [38] , and analysis of the distribution of all three families , including common and polymorphic copies , reveals no obvious preference for heterochromatic regions or regions near genes ( Figure S1 ) . The use of our previous genome-wide analysis of H3K9me3 distribution in two ES cell lines ( TT2 and J1 ) originating from different mouse backgrounds ( C57Bl/6 x CBA F1 hybrid , 129S4/SvJae respectively ) [16] allowed us to determine if TEs indeed induce local heterochromatin . Note that heterochromatin is herein defined according to the presence of the repressive histone post-translational modification H3K9me3 and H4K20me3 . No significant differences have been observed in the overall load of TEs between B6 and 129 strains ( our unpublished results ) . Two sets of copies were chosen: copies present in both ES cell lines ( common copies ) or copies present only in TT2 and absent in J1 cells ( insertionally polymorphic copies of ETn/MusDs and IAPs ) . The inverse analysis , i . e . studying copies present in J1 and absent from TT2 was not performed since TT2 is a hybrid of CBA with B6 and our insertionally polymorphic data set does not include the CBA strain [30] . Total average density of H3K9me3 was first calculated in regions flanking specific LINE , ETn/MusD and IAP copies present in both ES cell lines ( common copies ) ( Figure 1A ) . It is important to note that the location of each copy was precisely known and the copies were present in the sequenced C57BL/6 reference genome [30] , hence , all ChIP-seq reads matching the TE insertions themselves were excluded , allowing us to specifically examine the chromatin state of the flanking regions . Furthermore , since the ChIP-seq H3K9me3 data was generated using native-ChIP and the sequencing of DNA fragments were predominantly of mono-nucleosome size [16] ( Figure S2 ) , we could specifically observe the chromatin status of flanking regions with minimal background from H3K9me3 enriched TE copies . H3K9me3 is absent from the flanks of LINE elements ( Figure 1A ) , consistent with previous analyses showing that these elements are themselves generally not marked by H3K9me3 [12] , [13] ( Figure S3A ) . However , ETn/MusDs and IAPs are associated with H3K9me3 enriched flanking regions in both ES cell lines , with the latter being particularly enriched ( Figure 1A ) . Next we analyzed genomic loci harboring ERVs in TT2 cells but not in J1 cells ( insertion site polymorphic copies ) and observed that the average density of H3K9me3 is higher when an ETn/MusD or IAP is present , with IAPs again being most striking ( Figure 1B ) . This analysis suggests that insertion of these ERVs causes deposition and spreading of this histone mark into flanking genomic DNA . To determine if such tendency was a general phenomenon and not the result of only a few very enriched regions in TT2 cells ( full site ) , we calculated the RPKM asymmetry ( reads per kilobase per million mapped reads – see Materials and Methods ) between flanking regions ( 1 Kb ) of both strains , allowing us to distinguish regions differently enriched , i . e . regions where the RPKM asymmetries is near – 1 or +1 ( see Figure 1C and Materials and Methods for data normalization and asymmetry calculation ) . Common copies of ETn/MusDs and IAPs show very similar marking of their flanking regions by H3K9me3 in both ES cell lines , as illustrated by a high frequency of copies near 0 RPKM asymmetry ( Figure 1C and Figure S4 ) . However , for polymorphic IAP copies , there is marked skewing towards high H3K9me3 in flanking regions of full sites ( TT2 ) compared to empty sites ( J1 ) ( Figure 1C – skewness of -1 . 009 ) . Hence , nearly all IAP copies induce local H3K9me3 . ETn/MusD elements show less pronounced skewing among polymorphic copies towards more H3K9me3 flanking full sites , but do show a different pattern when compared to common copies ( Figure 1C ) . A minority of copies do indeed induce H3K9me3 deposition while the majority of the flanking regions do not seem to differ between full and empty sites . The limited number of polymorphic copies does not appear to be responsible for such a pattern since equivalently small sets of IAP polymorphic copies chosen randomly still show higher H3K9me3 in full sites ( Figure S5 ) . ETn/MusDs are highly expressed in ES cells ( Figure S3A and S3B ) and the copies identified by our study as being expressed ( Figure S3 – note that uniquely aligned reads are biased towards old copies and therefore our analysis is an underrepresentation of expressed copies ) are devoid of H3K9me3 and therefore do not promote spreading into the flanking regions . We have also observed ETn/MusD copies devoid of detectable expression and flanking H3K9me3 marking . We are unable to determine if individual IAP and ETn/MusD elements with equivalent levels of H3K9me3 are promoting different degrees of heterochromatin spreading into their flanking regions since the mappability of uniquely aligned reads is very low for single ERV copies ( Figure S3C ) . Since the pattern of ETn/MusD H3K9me3 RPKM asymmetry is variable between copies it is possible that intrinsic characteristics of each copy may or may not trigger H3K9me3 deposition ( see Figure S6 and Text S1 ) . Since very different scenarios were observed for ETn/MusDs and IAPs , we asked if other ERVs could also induce H3K9me3 chromatin in their flanking regions . We analyzed the flanking regions of full-length elements of four ERV families known to be regulated by H3K9me3 and one family ( MTD ) lacking H3K9me3 [16] . Note that we do not have insertional polymorphic data for these families but the vast majority are expected to be present in both J1 and TT2 since there is little evidence for recent retrotranspositional activity as judged by ERV-induced germ line mutations [34] . Most ERVs marked and regulated by H3K9me3 [16] do spread this mark into flanking regions but again , to different degrees ( Figure 2 ) . ERVK10C and RLTR1B robustly induce H3K9me3 while RLTR45 and RLTR10 present a modest enrichment , and MTD elements , as expected , are not associated with H3K9me3 in the flanking regions . Apart from ETn/MusD , the ERV families studied show low levels of overall expression ( Figure S3A ) . However , analysis at the level of individual copies reveals that the few copies that are expressed are devoid of H3K9me3 ( Figure S3B ) . As the percentage of expressed ERVs within each of these ERV families ( as well as LINE elements ) is low in ES cells , no correlation can be drawn between their expression and the different degrees of H3K9me3 spreading into their flanking regions . The presence of high H3K9me3 enrichment within the ERV sequences themselves is observed for all ERVs with the exception of MTD ( Figure S3A ) . However , due to the mappability limitation ( Figure S3C ) , we are unable to determine if single ERV copies marked with H3K9me3 from different families equally promote spreading towards flanking regions . Therefore , while regulation by H3K9me3 seems correlated with deposition of this mark in the flanks , it does not correlate with the robustness or level of such deposition , which seems to be either ERV-specific and/or single copy specific . Out of all the TEs analyzed , IAPs induce the highest level of H3K9me3 enrichment in their nearby flanking sequences . To better characterize the chromatin induced by IAPs , we chose five copies in neighborhoods devoid of genes and if possible other repeats in order to observe an unbiased environment far from potential selective pressures ( Figure S7 ) . We used genomic PCR to confirm the presence of IAP copies in both alleles of the TT2 cells ( C57BL/6 x CBA F1 hybrid ) and their absence in J1 cells ( 129 origin ) ( Table 1 ) . We also confirmed the presence of H3K9me3 in their flanking regions ( Figure S8 ) and assayed for the presence of H3K4me3 ( permissive modification ) , H3K27me3 ( repressive , but observed in bivalent domains together with H3K4me3 in ES cells [39] ) and H4K20me3 ( repressive ) ( Figure 3 and Figure S9 , see Figure S10 for ChIP controls ) . The only histone post-translational modification that spreads into genomic DNA flanking IAP copies together with H3K9me3 is H4K20me3 . These post-translational modifications often target the same regions and were shown previously to be associated with IAPs and LTR retrotransposons in general [12]–[14] . All other marks analyzed are absent from both the empty and full sites . It is important to note that Histone 3 is equally present in both sites , eliminating the possibility that full sites have more nucleosomes and hence are more enriched in histone modifications than empty sites ( Figure 3 ) . Polycomb group proteins ( PcG ) mediate the methylation of Histone 3 at lysine 27 [40] . Knock out of both PcG complexes induces loss of DNA methylation and upregulation of IAP copies [41] . Nevertheless no specific association of H3K27me3 with IAP was observed in our study which may be a consequence of our choice of copies being far from genes . Alternatively , induction of IAP expression in PcG depleted cells may be the result of indirect effects . As described above , silencing of TEs is often associated with DNA methylation . It is important to note that 70–80% of all CpGs in the mouse genome are methylated , half in repeats [42] . CpG Islands seem to be the primary exception , as they generally remain unmethylated [43] , [44] . This fact increases the probability that TEs insert into DNA methylated regions . Bisulfite sequencing of the five flanking regions of both full sites and empty sites show methylation regardless of the presence of an IAP copy ( Figure 4A and Figure S11 ) . A significant increase in DNA methylation compared to the empty site was observed on only one side of two copies ( Figure S11 ) . To obtain a global view of DNA methylation status , we performed a genome wide DNA methylation analysis ( MeDIP-seq ) in TT2 and J1 cells . No significant difference was observed between full and empty sites , in agreement with our bisulfite data ( Figure 4B ) . The presence of IAP copies is therefore not necessary for DNA methylation of the flanking regions analyzed . Also , together with many other reports [6] , [45] , [46] we show that IAP copies are indeed methylated ( Figure 4A and Figure S11 – red circles ) . Spreading of heterochromatin was first described in Drosophila melanogaster as a phenomenon called “position effect variegation” ( PEV ) where a transgene may be silenced if near heterochromatin ( for a review see [47] ) . However , there are few documented examples of spreading of heterochromatin into flanking sequences and genes from TEs . In one case , two mouse B1 sequences were described as playing a crucial role in the establishment of a specific DNA methylation signal which appeared to be spreading towards flanking sequences but not reaching the nearby gene Aprt [48] . In mouse ES cells , 78% of sites comprising both H3K9me3 and H4K20me3 are near a satellite repeat or an IAP/ETn copy ( maximum distance of 2Kb ) [12] . In plants , S1 retrotransposons may lead to DNA methylation spreading into flanking sequences [49] . Finally , the most interesting and well-documented case of spreading of DNA methylation was also observed in plants , in which a DNA transposon is responsible for the spreading of DNA methylation into the CmWIP1 promoter leading to sexual determination in the melon [29] . Despite such few documented examples , spreading of repressive chromatin is nevertheless often cited as a potential consequence of TE presence [50] , [51] . From the insertionally polymorphic families that we analyzed , IAPs would likely be the only family of TEs capable of robustly spreading heterochromatin since they are able to consistently induce its formation in their proximal neighborhood . We measured the extent of IAP induced spreading of the H3K9me3 mark into flanking sequences by examining non-overlapping windows of 2 . 5 kb from the insertion sites of polymorphic copies ( Figure 5A and 5B ) . Only the first one kb surrounding IAP copies is markedly affected by the IAP insertion . Even though the following 1 . 5 kb is still biased towards higher H3K9me3 associated with the IAP , the skewing is not as obvious as in the first one kb . However , it is important to note that some IAP copies are able to induce spreading of the H3K9me3 mark for almost 5 kb ( Figure 5 , Figure 1B and Figures S12 and S13 ) . The analysis of other ERV families , discussed above , shows a similar degree of spreading for ERVK10C but spreading from members of the other ERV families appears more limited in extent ( Figure 2 ) . Therefore , only the closest regions seem to be robustly marked by H3K9me3 as a result of a nearby ERV and as observed above . Given that H3K9me3 and H4K20me3 , both of which have been shown to act as repressive marks , frequently extend at least one kb into genomic regions flanking IAP elements , we next determined whether such spreading could have a consequence on expression of neighboring host genes . We filtered the IAP polymorphic database for copies near transcription start sites ( TSS – maximum distance of 5 kb ) where H3K9me3 was detected at the IAP copy but also at the associated gene promoter . We required the gene promoter/TSS near the empty site not to be enriched in H3K9me3 . Using RNA-seq data for both ES cell lines [16] , we asked if the presence of a heterochromatic IAP copy might influence gene expression by comparing levels of RNA-seq reads in both lines . We found only one gene that matched these criteria . It is important to note , however , that IAPs near the TSS of genes are rare and therefore the number of IAP copies capable of disrupting gene expression is much lower than the total number of IAP copies analyzed . Indeed , from our dataset of polymorphic IAP copies [30] , we found only four genes that harbor an upstream IAP copy less than 1 kb away ( 102 copies less than 5 kb away ) , and that differ in presence between strains C57BL/6 ( TT2 cells ) and 129 ( J1 cells ) . Of these 102 cases , only one gene , B3galtl , a beta 1 , 3-galactosyltransferase-like gene , is differentially expressed in these two ES cell lines and this gene appears to be affected by heterochromatin spreading according to the criteria outlined above ( see Figure S14 for genome browser view of this region ) . The B3galtl gene has a solitary antisense IAP LTR just 368bp upstream of the TSS in J1 ES cells ( 129 origin ) but not in either allele of TT2 cells ( C57BL/6 x CBA F1 hybrid ) ( Table 1 ) . We studied chromatin post-translational modifications in the full site and empty site and in the CpG island promoter in both ES cell lines . At the full site only , we observe the appearance of more repressive marks , namely H3K9me3 and H4K20me3 , associated with the presence of the IAP copy ( Figure 6A and Figure S14 ) . Furthermore , this gene has a CpG island promoter that is likely normally unmethylated , so we predict that spreading of DNA methylation into the CpG Island might accompany the repressive histone marks and have an important impact . The empty site ( TT2 ) has no DNA methylation in the upstream region nor in the CpG Island ( Figure 6A ) . There is significant H3K4me3 enrichment in the CpG Island in TT2 cells , as expected since this gene is expressed in these cells . On the contrary , the full site analysis ( J1 ) shows that the IAP element itself is methylated in nearly all molecules sequenced . Strikingly , in a subset of these molecules , methylation spreads into the CpG Island . Since we are comparing two cell lines of different mouse strains , we wanted to ensure that the differences observed were indeed caused by the IAP and not by a different genetic background . Therefore we studied DNA methylation in B6/129 hybrid ES cells so both alleles are in the same background . Again , DNA was methylated in the CpG Island only in the full site ( Figure S15 ) . In both bisulfite analyses , the IAP copy was heavily methylated , however the spacer region between the IAP and the gene's CpG island can be methylated or unmethylated . Since the spacer region harbors different methylation patterns , we hypothesize that this region functions as a buffer that generally limits DNA methylation spreading from the IAP . Nevertheless , it appears that if the spacer region becomes methylated then the adjoining CpG Island will also be fully methylated . Since we observe spreading of repressive marks into the CpG island of B3Galtl , we wondered if any expression differences could be observed between both strains as suggested by the RNA-seq data . Indeed , the presence of the IAP insertion is associated with a decrease in the RNA expression of B3galtl ( Figure 6B ) . Allelic quantification in the hybrid cell line also shows a decrease in expression of the 129 allele ( Figure 6B ) suggesting that spreading of heterochromatin from the IAP copy is impeding expression from this allele . Nevertheless , no detectable difference in protein expression was observed between the cell lines ( Figure 6C and Figure S16 ) . Taken together , these observations reveal that an IAP element insertion near a gene can indeed induce local heterochromatin ( DNA methylation , H3K9me3 and H4K20me3 ) and modify gene transcription . The lack of a significant difference in protein abundance suggests that posttranscriptional mechanisms compensate for the lower RNA levels in J1 . The strength of our model system to study induction of chromatin marks is the exploitation of natural insertional polymorphisms of TEs , which has advantages over an artificial system of introduced vectors that may not mimic natural loci . Using these polymorphic TEs , we demonstrate that , out of three abundant families of repeats in mouse , only IAPs consistently promote spreading of H3K9me3-H4K20me3-chromatin , robustly in the first 1 kb ( Figure 7A ) . Indeed , the strong association of such chromatin and IAP copies allowed us to find new copies in the 129 mouse genome , but lacking in the sequenced B6 genome , by examining H3K9me3 regions differently enriched between both cell lines ( Text S2 ) . In contrast , LINE copies are not enriched in H3K9me3 and ETn/MusD are able to induce H3K9me3 chromatin only in some cases . The mechanisms and the nature of the IAP-induced heterochromatin are most likely responsible for the differences observed between IAPs , ETn/MusDs and LINEs as explained above . Indeed , the mechanisms regulating ERVs or LINEs are different in ES cells . IAPs and ETn/MusD are upregulated in mutants associated with H3K9me3 heterochromatin formation ( ESET/Setdb1 [14] ) , the TRIM28/KAP1 pathway [15] , and PcG complexes [41] ) while LINEs are only modestly upregulated in such mutants [15] and mainly upregulated in DNA methylation mutants [7] , [52] . DNA methylation influence on IAP copies in ES cells remains poorly understood as mutants of DNA methylation pathways ( DNMT total KO ) do not induce transcriptional upregulation of IAP copies while treatment with 5-azacytidine induces IAP over expression [15] , [53] . Such a discrepancy could be explained by a recent report that the H3K9me3 genome-wide pattern in human cells is dramatically disturbed after treatment with 5-azacytidine [54] . As we observed , insertion sites for IAP copies are methylated at empty sites , suggesting that DNA methylation is not dependent on the presence of the IAP . Since the mouse genome is thought to be broadly methylated , one might extrapolate the results obtained for IAP empty sites to all TE empty sites . Henceforth , the global impact on the host genome of L1 copies probably involves other mechanisms than spreading of DNA methylation . The analysis of other ERVs annotated in the sequenced genome shows that spreading of H3K9me3 towards flanking sequences is associated with regulation of the ERV family by this mark . However , the degree of deposition of this mark and also the spreading distance of such repressive chromatin seems to be unique for each of the families analyzed . There are two possible explanations for this observation . First , the degree of spreading of a TE family could be dependent on the number of copies that are actually targeted by H3K9me3 , with copies marked with equivalent levels of H3K9me3 promoting equivalent spreading of heterochromatin . Alternatively , the degree of spreading of a TE family may be dependent on specific characteristics of each family , such that copies with equivalent H3K9me3 marking , if belonging to different families , would differently spread heterochromatin towards flanking regions . Our data cannot distinguish between these possibilities . The mechanisms responsible for heterochromatin initiation in ERV copies in ES cells were recently studied . Small RNAs have been reported to act as central players in the formation and spreading of heterochromatin in several other species such as fission yeast and fruit flies , however in mammals such a role for small RNAs remains uncertain . It has been shown that DICER related pathways are not responsible for IAP repression [53] . Dicer-independent small RNAs , such as piRNAs , have only been described in the male germline of mouse [55] . Nevertheless , the influence of such small RNAs on the spreading of chromatin induced by IAPs and other ERVs should not be ruled out . Furthermore , we and others have shown that KAP1/Trim28 recruitment of SETDB1 is necessary for H3K9me3 silencing of ERVs in mouse ES cells [14] , [15] . KAP1 along with KRAB-zinc finger proteins are able to induce spreading of repressive chromatin within 10 kb from the heterochromatin initiation site in humans [56] and such a mechanism might therefore be responsible for IAP induced-heterochromatin spreading in mouse . Transposable elements have different life cycles and are expressed in different tissues and stages of development . It is well known that ETns are highly expressed in early development and then silenced [57] . Transgenic introduced IAPs are transcribed nearly exclusively in the male germ line [46] but expression of endogenous copies can be detected in thymocytes and other tissues [58] . Moreover , IAPs may become active in somatic tissues of old mice by demethylation of their sequences [45] . Mouse L1s have different expression patterns and even produce protein in testis for instance [59] . Therefore the time and place where the spreading of heterochromatin from these different families occurs may be different . Further analysis of other cell types , developmental stages and also other mouse strains would be of interest to compare to the results described here . Furthermore , epigenetic regulation of TEs may be influenced by environmental factors as already observed for several TEs [60]–[62] . Therefore , the induction and spreading of heterochromatin from a TE may be labile to environment and should be further studied in stressed conditions and during development . TEs may therefore provide cryptic sites for heterochromatin formation and also spreading . We show that IAP induced repressive chromatin can affect the CpG island promoter of a neighboring mouse gene in cis , and in turn reduce expression of the genic mRNA . The paucity of examples of such a phenomenon is likely due to the fact that insertions of ERVs which attract and spread repressive chromatin and which occur very close to gene transcriptional start sites are strongly selected against unless such spreading is blocked ( Figure 7B ) . Intriguingly , there are numerous situations where ERV LTRs are actually co-opted as constitutive , tissue-specific or developmental-specific promoters or enhancers for genes [63]–[65] , indicating that the relationship between ERVs and genes is complex and multi-faceted . Even if the impact of ERV-induced heterochromatin is rare , it may participate in malleability of the host genome as epigenetic regulation of IAP copies and other TEs may be tissue or developmental-stage specific but also susceptible to environmental changes [66] ( Figure 7B ) . The impact of TEs in genome evolution and speciation is being increasingly appreciated [2] and our report suggests that some TEs may have an indirect impact on host adaptive potential by spreading of epigenetic marks . As described for the sexual determination of melon [29] , IAPs and other ERVs may have played a role in the genome evolution of Mus musculus through fine-tuning of genes by ERV-induced-heterochromatin . Indeed , since new insertions of IAPs continue to bombard the mouse genome , this fine-tuning of gene expression is likely ongoing and may contribute to phenotypic differences between strains .
J1 and TT2 ES cells were passaged every 48–72 hours in DMEM supplemented with 15% FBS ( HyClone ) , 20 mM HEPES , 0 . 1 mM nonessential amino acids , 0 . 1 mM 2-mercaptoethanol , 100 units/ml penicillin , 0 . 05 mM streptomycin , leukemia inhibitory factor ( LIF ) and 2mM glutamine on gelatinized plates . Common and polymorphic copies of ETn/MusD and IAP were obtained from our previous analysis of different strains of mouse [30] . In our analysis , ETn and IAP subtypes are grouped as a major family . Coordinates for regions containing LINE copies were obtained from [16] and include only the L1MdA subfamily . All coordinates depicted in figures and bioinformatics analyses refer to the sequenced mouse genome , July 2007 ( NCBI37/mm9 ) . Methods and details on H3K9me3 ChIP-seq can be found in Karimi et al . [16] . For the layout of the experiments please see Figure S2 . MeDIP-seq libraries were constructed as described in Harris et al . [67] , from 1 µg of genomic DNA using an anti-5-Methylcytidine monoclonal antibody obtained from Eurogentec ( cat# BI-MECY-0100 , lot#080808 ) and sequenced on an Illumina Genome Analyzeriix following the manufacturer's recommended protocol ( Illumina Inc . , Hayward , CA ) . The resulting sequence reads were aligned using BWA v0 . 5 . 7 [68] using default parameters to the mouse reference genome ( mm9 ) . Uniquely placed sequence reads with a mapping quality of > = 10 were passed to FindPeaks v4 . 1 [69] for segmentation and wig [70] track generation with -dist_type = 0 [200] , -duplicatefilter and no thresholding . After filtering , 23 , 293 , 703 and 23 , 672 , 774 reads remained for the TT2 and J1 libraries respectively . Custom Java program was used to calculate RPKM values for genomic regions of interest . RPKM was calculated using normalized genome coverage . To compare total average density of H3K9me3 between two cell lines we used only full-length elements including flanking LTRs . For example out of 7 , 666 IAPE elements annotated in UCSC ( mm9 ) , only 1 , 318 ( 945 common and 373 polymorphic ) satisfied the length selection criteria ( total length > = 4000 bp ) and were used in the analysis . Using the strand information , for every copy we identified the 5′ and 3′ 5 kb flanking regions and calculated coverage profiles in these regions for BWA aligned H3K9me3 reads directionally extended by 150 bp . To calculate total average density we agglomerated 5′ profiles and 3′ profiles for all elements for a given family and normalized them by 1 ) total number of copies in the family and 2 ) relative number of aligned reads in the library between TT2 and J1 cell lines . Reads were filtered by the BWA alignment quality ( QC> = 7 ) . If there were more than one sequenced read aligned to the same location , it was considered only once . Reads mapped to multiple locations were ignored . In order to compare H3K9me3 or MeDIP coverage in specific regions between the two ES cell lines , a normalization factor has to be taken into account . We calculated the Reads per Kilobase per million mapped reads ( denoted RPKM [71] ) in all regions of interest for ChIP-seq samples . The following formula was used to calculate RPKM for these regions: where n is a fractional number of reads aligned to the region , L is the length of the region in Kb , and N is the total number of aligned reads for a given sample , in millions . For pair-wise H3K9me3 comparisons , we calculated the RPKM asymmetry across samples: where RPKMTT2 and RPKMJ1 are RPKMs in the region of interest of TT2 and J1 samples respectively , and is a very small number to avoid dividing by zero . The asymmetry calculation gives us a number comprised between -1 ( enriched only in J1 ) and +1 ( enriched only in TT2 ) allowing us to directly compare H3K9me3 enrichment in specific regions between both cell lines . For further details on the ChIP-seq and RNA-seq data analysis refer to [16] . The skewness is a measure of the asymmetry of a distribution . If the skewness is equal to 0 the distribution is symmetric . When the skewness is negative , the left tail of the distribution is longer . When applied to our dataset it means that if the peak of frequency is >0 and the skewness is negative than the TT2 cells are more enriched than J1 at the loci analyzed . The skewness and other statistical tests ( Kruskal-Wallis and Dunn comparison tests ) were calculated using GraphPad Prism version 5 . 00 for Windows , GraphPad Software , San Diego California USA . To plot heat maps of H3K9me3 , we used the same coverage profiles as described above . Color indicates coverage at every base in the flank of individual copies . Rows corresponding to individual IAP elements were sorted according to the total coverage of H3K9me3 in the 5′ flank of the copies present in TT2 in the descending order . Same ordering of elements was preserved in the heat map showing H3K9me3 in J1 . To evaluate a mappability of a given genomic region we averaged genomic mappability ( CRG Alignability ) profiles that we downloaded in a form of bedGraph from the UCSC browser ( http://genome . ucsc . edu [72] ) and converted into a wig file . The value of mappability for every base in the genome for a given read length ( 50 bp in our case ) gives a fraction of reads covering a given base and aligned uniquely to this genomic position even when up to two mismatches are allowed and ranges from 0 to 1 . Two biological replicates of 107 TT2 and J1 cells were used for Native ChIP . Cells were homogenized in a douncing buffer ( 10 mM Tris-HCl pH 7 . 5 , 4 mM MgCl2 , 1 mM CaCl2 and protease inhibitors ) and disrupted using a p1000 tip and a syringe . Micrococcal nuclease digestion ( 150 U/ml ) was performed for 7 min at 37°C and stopped with 10 mM EDTA . Correct digestion of the chromatin was verified by gel electrophoresis . Further lysis of the digested chromatin was done by incubating it 1 hour on ice with 1 ml of hypotonic lysis buffer ( 0 . 2 mM EDTA pH 8 , 0 . 1 mM benzamidine , 0 . 1 mM PMSF , 1 . 5 mM DTT and protease inhibitors ) and vortexing every 10 min . The digested chromatin was centrifuged at 3000g for 5 min and the supernatant was pre-cleared at 4°C for 2 hours in a rotating wheel with 100 µl of 50% solution of pre-blocked Protein A beads . Pre-cleared chromatin was aliquoted into 7 fractions ( 6 IP and 1 Input ) and all IP fraction volumes were brought to 325 µl using IP Buffer ( 10 mM Tris-HCl , 1% Triton , 0 . 1% Deoxycholate , 0 . 1% SDS , 90 mM NaCl , 2 mM EDTA and protease inhibitors ) . We used antibodies recognizing total Histone H3 ( Sigma , H9289 ) , and histone modifications as H3K4me3 ( Millipore , 17–614 ) , H3K27me3 ( Millipore , 07–449 ) , H3K9me3 ( Millipore , 07–442 ) , H4K20me3 ( Millipore , 17–671 ) and also IgG ( 12–370 ) as a negative control . After 1 hour at 4°C in a rotating wheel we added 20 µl of 50% solution of pre-blocked Protein A beads and rotated at 4°C overnight . Washes were held the next day by adding 400 µl of washing buffer to the beads ( 20 mM Tris-HCl pH 8 , 0 . 1% SDS , 1% Triton X-100 , 2 mM EDTA , 150 mM NaCl and protease inhibitors ) , rotating the samples for 3 min at 4°C and spinning at 4000 rpm . The amount of NaCl was increased in the second wash step ( 500 mM NaCl ) and performed as described above . The INPUT and IP fractions were eluted with 100 µl of 100 mM NaHCO3 - 1% SDS . RNase treatment was performed for 2 hours at 68°C with gentle vortexing . The IP samples were spun at 4000 rpm for 2 min and the supernatant recovered ( twice ) . All samples were purified with the Qiaquick PCR purification kit ( Qiagen ) and quantified by using the PicoGreen system from Invitrogen . 0 . 05 ng/µl of ChIP material was analyzed in technical duplicates through quantitative PCR ( Fast SYBR Green Master Mix from Applied Biosystems ) by comparing the amplification of Input DNA relative to immunoprecipitated DNA ( IP ) using the formula “Efficiency of primers∧ ( CtInput – CtIP ) ” where the efficiency is calculated through serial dilutions of Input DNA ( primers efficiency were all comprised between 1 . 9 and 2 . 1 ) . ChIP enrichment for each specific antibody was tested with control regions for each antibody used for both ES cells ( Figure S10 ) . For H4K20me3 , primers located more than one nucleosome away ( 150 bp ) were tested to confirm spreading of this mark ( Figure S9 ) . All primers are listed in Table S1 . Bisulfite conversion , PCR , cloning and sequencing were carried out as described previously [73] . All the sequences included in the analyses either displayed unique methylation patterns or unique C to T non-conversion errors ( remaining C's not belonging to a CpG dinucleotide ) after bisulfite treatment of the genomic DNA . This avoids considering several PCR-amplified sequences resulting from the same template molecule ( provided by a single cell ) . All sequences had a conversion rate >95% . Sequences were analyzed with the Quma free online software [74] . Total RNA was extracted ( two biological replicates for each ES cell line ) with the All Prep DNA/RNA mini kit from Qiagen . RNA was treated with the Turbo DNA-free kit from Ambion in order to remove DNA . One µg of total RNA extracts was reverse transcribed with SuperScript II reverse transcriptase system ( Invitrogen ) . We synthesized two different cDNAs ( 65°C for 5 min , 25°C for 5 min , 50°C for 60 min and 70°C for 15 min ) : a control reaction with no reverse transcriptase to test DNA contamination , and a pool of total cDNA synthesized with random primers . The cDNA samples were diluted 10 fold , and PCR was carried out using Fast SYBR Green Master Mix ( Applied Biosystems ) using specific primers for each gene analyzed . Primers were chosen surrounding introns in order to amplify 100-250 bp fragments of cDNA . Quantitative PCR cycling conditions were 20 s at 95°C ( 1 cycle ) , and then 3 s at 95°C , followed by 30 s at 60°C ( 45cycles ) . Reactions were done in duplicate , and standard curves were calculated from serial dilutions of cDNA . The quantity of the transcripts was estimated relative to the expression of tubulin , actin and TBP ( TATA binding protein ) ; chosen as the most stable genes out of 6 reference genes tested using the GeNorm method [75]; with the equation “Absolute quantity = “Efficiency of primers∧ ( -Ct ) ” . Primers efficiency were equivalent and chosen between 1 . 9 and 2 . All primers are listed in Table S1 . For determining allele-specific expression , RNA extraction and cDNA synthesis was carried as described above . Allelic expression was determined with the PeakPicker software [76] based on chromatogram analysis of cDNA and genomic PCR fragments . Western blot was done with biological triplicates . Cells were washed in PBS and solubilised in RIPA buffer ( 150 mM NaCl , Triton-X 1% , 50 mM Tris-HCl pH8 , 0 . 1% SDS and 50 mM sodium deoxycholate ) for 20 min at 4°C . For performing Western blot analysis , total cell extracts ( 40 µg/lane ) were size-fractionated by NuPAGE 4-12% Bis-Tris Gel ( Invitrogen ) and transferred to polyvinylidene difluoride membranes . The membranes were blocked for one hour and then incubated with first antibodies against B3GALTL ( Santa Cruz sc-67610 ) and ACTIN ( Sigma A2066 ) overnight . After washing , blots were incubated with the secondary antibodies for one hour ( 1:8000 Peroxidase rabbit anti-goat ( Sigma A5420 ) and 1∶10000 goat anti-rabbit ( Sigma A0545 ) , and specifically bound antibodies were detected with the ECL ( Thermo Scientific ) . The films were then scanned and the quantification of B3GALTL protein was performed using Adobe Photoshop ( San Jose , CA ) with three biological replicates as previously described [77] . For each lane , the B3GALTL level was normalized to that of ACTIN ( Figure S16 ) . | Transposable elements ( TEs ) are often thought to be harmful because of their potential to spread heterochromatin ( repressive chromatin ) into nearby sequences . However , there are few examples of spreading of heterochromatin caused by TEs , even though they are often found within repressive chromatin . We exploited natural variation in TE integrations to study heterochromatin induction . Specifically , we compared chromatin states of two mouse embryonic stem cell lines harboring polymorphic retrotransposons of three families , such that one line possesses a particular TE copy ( full site ) while the other does not ( empty site ) . Nearly all IAP copies , a family of retroviral-like elements , are able to strongly induce repressive chromatin surrounding their insertion sites , with repressive histone modifications extending at least one kb from the IAP . This heterochromatin induction was not observed for the LINE family of non-viral retrotransposons and for only a minority of copies of the ETn/MusD retroviral-like family . We found only one gene that was partly silenced by IAP-induced chromatin . Therefore , while induction of repressive chromatin occurs after IAP insertion , measurable impacts on host gene expression are rare . Nonetheless , this phenomenon may play a role in rapid change in gene expression and therefore in host adaptive potential . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"genomics",
"genetics",
"epigenetics",
"biology",
"computational",
"biology",
"genetics",
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"genomics"
] | 2011 | Retrotransposon-Induced Heterochromatin Spreading in the Mouse Revealed by Insertional Polymorphisms |
In the mammalian cochlea , small vibrations of the sensory epithelium are amplified due to active electro-mechanical feedback of the outer hair cells . The level of amplification is greater in the base than in the apex of the cochlea . Theoretical studies have used longitudinally varying active feedback properties to reproduce the location-dependent amplification . The active feedback force has been considered to be proportional to the basilar membrane displacement or velocity . An underlying assumption was that organ of Corti mechanics are governed by rigid body kinematics . However , recent progress in vibration measurement techniques reveals that organ of Corti mechanics are too complicated to be fully represented with rigid body kinematics . In this study , two components of the active feedback are considered explicitly—organ of Corti mechanics , and outer hair cell electro-mechanics . Physiological properties for the outer hair cells were incorporated , such as the active force gain , mechano-transduction properties , and membrane RC time constant . Instead of a kinematical model , a fully deformable 3D finite element model was used . We show that the organ of Corti mechanics dictate the longitudinal trend of cochlear amplification . Specifically , our results suggest that two mechanical conditions are responsible for location-dependent cochlear amplification . First , the phase of the outer hair cell’s somatic force with respect to its elongation rate varies along the cochlear length . Second , the local stiffness of the organ of Corti complex felt by individual outer hair cells varies along the cochlear length . We describe how these two mechanical conditions result in greater amplification toward the base of the cochlea .
The mammalian cochlea encodes sounds with pressure levels ranging over six orders of magnitude into neural signals . This wide dynamic range of the cochlea is achieved by the amplification of low amplitude sounds . The outer hair cells have been identified as the mechanical actuators that generate the forces needed for cochlear amplification [1] . Cochlear amplification is dependent on location along the cochlear length . For example , according to measurements of the chinchilla cochlea , the amplification factor of basilar membrane ( BM ) vibrations was about 40 dB in basal locations while it was 15 dB in apical locations [2–4] . Theoretical studies have reproduced location-dependent cochlear amplification by adopting tonotopic electrophysiological properties , such as the active feedback gain of the outer hair cells [5 , 6] , or the mechano-transduction properties of the outer hair cell stereocilia [7 , 8] . These studies are based on experimental reports concerning the tonotopy of the outer hair cells’ electrophysiological properties [e . g . , 9 , 10–12] . On the other hand , recent experimental observations suggest that organ of Corti mechanics may play a role in cochlear amplification . For example , organ of Corti micro-structures vibrate either in phase or out of phase depending on stimulation level and frequency [13–16] . These observations challenge a long-standing framework for modeling the organ of Corti mechanics—rigid body kinematics , introduced by ter Kuile [17] . A fully deformable organ of Corti may have implications for cochlear amplification . Micro-mechanical aspects of cochlear power amplification were investigated in our previous study , using a computational model of the cochlea [18] . The model features detailed organ of Corti mechanics analyzed using a 3-D finite element method , and up-to-date outer hair cell physiology . In that previous work [18] , it was shown that the stiffness of the organ of Corti complex ( OCC ) felt by the outer hair cells remains comparable to the outer hair cell stiffness , independent of location . An intriguing observation was that even though the same active force gain was used for all outer hair cells , the model reproduced greater amplification toward the base . However , the specific model aspects responsible for the location-dependence were not identified in that paper . In this study , by analyzing power generation in individual hair cells , by observing different micro-mechanical transfer functions of the organ of Corti , and through a series of parametric studies , we identify passive mechanical aspects that are responsible for the location-dependent amplification .
Two sets of fluid dynamical , structural and electro-physiological responses are presented in Fig 1 and Fig 2 that represent the active and the passive responses , respectively . When the stimulating frequency was 18 . 6 , 4 . 4 and 0 . 78 kHz , the BM vibrations of the active cochlea peaked at x = 2 , 6 and 10 mm , respectively ( Fig 1 ) . For the same stimulating frequencies , the peak responding location shifted toward the base when passive ( x = 1 . 2 , 5 . 4 , 9 . 7 mm , Fig 2 ) . This shift of peak responding location due to the outer hair cell active feedback is consistent with experimental observations [e . g . , 19] , and other model studies [e . g . , 20 , 21] . The pressure amplitude plots show one or two peaks and notches as the pressure propagates from the oval window toward the apex . This is similar to the pressure peaks/notches observed experimentally by Kale and Olson [22] . As was discussed in their work , the pressure peaks/notches were generated by the interference between fast and slow pressure components . For example , the slow ( differential pressure across the OCC ) component showed no pressure notch . When the amplification is large ( Fig 1A ) , the local pressure near the peak responding location was large enough to mask the pressure pattern created by the oval window motion . Despite changes in the outer hair cell active force , the pressure at the stapes remains relatively constant at about 80 mPa for a 1 nm/ms stapes velocity amplitude . After considering the stapes footplate area of 0 . 8 mm2 [23] , our simulated result corresponds to a cochlear input impedance of 100 GPa·s/m3 . This value is comparable to measured values ranging between 50 and 300 GPa·s/m3 [24–26] . Explicit computation of the interactions between the outer hair cells and OCC fine structures is both an opportunity and a challenge of our continuum mechanics-based approach . Any poorly determined parameters can affect the result , and there are a large set of model parameters . However , as more OCC mechanical data accumulate , well defined parameters can serve as rigorous constraints on the model . For example , the geometrical information of the OCC is well known , but underused . The elastic moduli or stiffness of different OCC structures have been measured as summarized in [27] . This study takes advantage of such existing data . As a result , the vibration amplitude ratios and phase relationships between the micro mechanical structures vary depending on location , simulating frequency , and active force feedback . The resulting micro-mechanical responses compare well with experimental observations . For example , the vibration pattern ( the relative motion between different structural components ) changes depending on the outer hair cell’s active feedback: when active , the TM ( tectorial membrane ) vibrations lead the BM vibrations by 15 to 60 degrees ( Fig 1B ) , but they vibrate in phase when passive ( Fig 2B ) . This dependence of vibration patterns on the outer hair cell motility has been observed experimentally in the gerbil cochlea [13] . Our model predicts that the micro-mechanical response characteristics are location-dependent: at middle to apical locations , the TM vibrates less than the BM , but the opposite is true in the basal turn ( x < 4 mm ) . The relative motion between the BM and other OCC structures , caused by the outer hair cell’s active feedback , indicates that the top and the bottom of the OCC are effectively decoupled . In the field of vibration measurement , the vibration patterns due to internal forces are referred to as the operational deflection shapes . This decoupling due to the outer hair cell action persists along the entire cochlear length [28] . Up-to-date physiological properties of outer hair cell mechano-transduction and electromotility are incorporated into our model to predict the electro-mechanical feedback of the cells to acoustic stimulations . The amplitudes of the mechano-transduction current and receptor potential are presented in Fig 1D , and Fig 2D . Independent of location or outer hair cell feedback force , mechano-transduction current is nearly in phase with the BM displacement . Using these first-hand results ( fluid pressure , vibration amplitudes of micro-structures , and electrical responses of the outer hair cells ) , we analyzed how the outer hair cells’ power generation is modulated by the organ of Corti mechanics . Model responses at three different locations ( x = 9 , 6 , and 3 mm ) are presented together with experimental results in Fig 3 . There are measurements of the gerbil cochlear vibrations at different locations [e . g . , 19 , 29] . Cooper and Rhode’s experiment with the chinchilla cochlea [4] is also pertinent to this study , because the mechanical amplification measurements from different cochlear locations were reported in a single paper . Our simulated results for two key quantitative measures that represent cochlear performance are in reasonable agreement with experimental observations [4 , 19 , 29] . First , the BM vibrations are amplified by 30–50 dB in high-frequency locations ( x < 4 mm ) , and 10–20 dB in low-frequency locations ( x > 8 mm ) . Second , the tuning quality represented by Q10dB are > 3 in the base , and ~1 in the apex . While gain and phase curve shapes are in reasonable agreement with the experiment , there are differences between our model response and experimental results in absolute values . As compared to Ren and Nuttall’s measurements [19] , the gain was lower by 17 dB , and the phase was different by a half cycle . The difference in gain may be ascribed to different reference input conditions: our kinematic boundary condition at x = 0 and 0 < y < H , does not exactly represent the stapes excitation . The half a cycle phase difference between simulation and experimental results could be due to different definitions of positive stapes displacement . In this study , the stapes velocity is positive when moving into the cochlea . The phase of BM vibration versus stimulating frequency has been extensively measured and analyzed because it characterizes the cochlear traveling waves [e . g . , 30 , 31] . Three key characteristics are reproduced by our simulations in Fig 3B: First , there exist more than two cycles of total phase accumulation as the frequency increases . Second , the phase accumulation at the peak responding location is between 1 and 3 cycles . Finally , the slope of the phase versus frequency curve is similar for the active and passive cases . The amplification and tuning quality of the cochlea decrease toward the apex ( Fig 3C and 3D ) . Because the amplification is the primary consequence of the outer hair cell’s active feedback , the location-dependent amplification implies that the outer hair cells provide more power in the base . One possible approach to model this location-dependent amplification is to assume that the outer hair cells in the basal cochlea have greater active force gain than those in the apex . Alternatively , the passive mechanics of the OCC may be responsible for the location-dependent amplification . Note that our model adopted a constant force gain ( gOHC of 0 . 1 nN , active force per mV membrane potential change after [11] ) independent of location . We investigated which location-dependent properties could be responsible for the greater amplification in the base . To investigate the origin of the location-dependent amplification , power generation by individual outer hair cells was analyzed ( Fig 4 ) . The power provided by an outer hair cell to its external system is defined as the product of the active force generated by the cell ( fOHC ) and the rate of cell’s length change ( vOHC , see Eq ( 6 ) in Methods ) . In Fig 4A , fOHC and vOHC at a moment of time are presented for high , mid and low frequency stimulations ( 18 . 6 , 4 . 4 , and 0 . 78 kHz ) . For a stapes velocity of 1 nm/ms , the peak values of fOHC and vOHC are 0 . 65 nN and 1 . 2 mm/s at 18 . 6 kHz , and 0 . 11 nN and 21 μm/s at 0 . 78 kHz . This trend of decreasing force and velocity toward lower stimulating frequency is consistent with decreased amplification toward the apex . The timing ( phase ) of outer hair cell force generation also contributes to the location-dependent amplification in the cochlea . The phase of vOHC with respect to fOHC depends on stimulating frequency ( Fig 4B ) . At the peak-responding location , fOHC lags vOHC by 17 , 61 and 83 degrees for stimulating frequencies of 18 . 6 , 4 . 4 and 0 . 78 kHz , respectively . For given fOHC and vOHC amplitudes , the power generation by an outer hair cell is greatest when fOHC is in phase with vOHC , and zero when they are 90 degrees out of phase . In other words , the fOHC-vOHC phase causes the outer hair cells in the base to be 8 times more efficient in generating power than those in the apex ( cos ( 17° ) /cos ( 83° ) ≈ 8 ) . The power generation per cycle of individual outer hair cells ( computed from Eq ( 6 ) in Methods ) is shown in Fig 4C . For a stapes vibration amplitude of 1 nm/ms , an individual outer hair cell at the peak responding location generates 377 , 12 , and 0 . 36 fW for the three stimulating frequencies of 18 . 6 , 4 . 4 and 0 . 78 kHz , respectively . The apical value is comparable to values reported by Wang et al . [32] , but the basal value is about two to three orders of magnitude greater . This discrepancy could be due to the difference in model species ( Wang et al . modeled the mouse cochlea ) . As Wang et al . discussed , small difference in vibration amplitude can result in different estimations of outer hair cell power generation ( i . e . , the gerbil cochlea may vibrate greater than the mouse cochlea ) . Alternatively , the difference may be ascribed to difference in model assumptions such as dissipating mechanisms . While we lumped the effect of power loss due to the viscous fluid with the viscous damping of the OCC structures , Wang and her colleagues incorporated the fluid viscosity explicitly and considered the damping within the OCC negligible . Further investigation of the OCC mechanical impedance may be required to better understand the difference . The power flux ( computed using Eq ( 1 ) in Methods ) represents how much energy is transferred by the scala fluid along the cochlear length . In the passive system , the power is provided through the stapes . As a result , the longitudinally transmitted power is dissipated as the traveling waves propagate . That is , the power flux decreases monotonically toward the apex when passive ( dashed curves , Fig 4D ) . However , for the active cochlea ( comparable to the case of weak sound stimulation to healthy ears ) , the power flux pattern is non-monotonic—the power flux increases until it culminates at the best responding location ( solid curves , Fig 4D ) . A similar trend of power flux was shown in other theoretical studies [32 , 33] . In Figs 1 and 2 , it was shown that the TM and BM vibrate in phase when passive , but the TM leads the BM displacement by 15 to 60 degrees when active . A plausible theory is that the active feedback of the outer hair cells modulates the OCC mechanics to facilitate outer hair cell power generation . We examined whether the outer hair cell active feedback also modulates the phase between fOHC and vOHC . Different levels of amplification were simulated using different values for the active gain gOHC ( between 0 and 0 . 1 nN/mV , constant through the cochlear length ) . The amplification level and the phase between fOHC and vOHC were analyzed ( Fig 5 ) . As expected , the power generated by the outer hair cell , and the level of amplification increases as gOHC increases ( Fig 5A ) . The phase between fOHC and vOHC is minimally affected by the level of outer hair cell active feedback ( Fig 5B ) . For high values of gOHC , the basal location is amplified more than the apical location . This location-dependent amplification is consistent with the phase relationships between fOHC and vOHC: fOHC is approximately in phase with vOHC in the basal location , but fOHC is roughly in phase with the outer hair cell displacement in the apex ( panel B ) , regardless of amplification level . According to this result , the active feedback does not ‘correct’ the phase relationship toward more favorable amplification . That is , the fOHC-vOHC phase becomes less favorable for amplification as the active gain increases . We investigated which aspect of OCC micro-mechanics is responsible for the location-dependent phase relationship . The amplitudes and phases of six variables with respect to the BM displacement are shown in Fig 6 . They are the transverse ( y ) and radial ( z ) displacement of the TM , the mechano-transduction current and receptor potential of the outer hair cells , and the stereocilia and somatic displacement of the outer hair cells . These responses were obtained at various distances from the base , for each location’s best-responding frequency . The following observations were made: First , the gain of most variables decreases toward the apex ( top panels of Fig 6 ) . Second , the phases of most variables are approximately flat over the distance ( bottom panels of Fig 6 ) . Third , these gain and phase trends are similar with and without outer hair cell feedback ( i . e . , the plots of the left and the right columns are similar ) . Many of the mechanical responses were approximately in phase with the BM vibrations ( within ±10 degrees ) when the mechanics were passive ( gray curves in Fig 6B bottom panel ) , and were up to 60 degrees out of phase when active ( gray curves in Fig 6A bottom panel ) . There are two variables that deviate from the general trends . The gain of the TM radial displacement ( zTM , curves with □ markers ) varies non-monotonically with distance from the base . The gain of zTM has a minimum value near x = 4 mm , and its phase relative to the BM shifts 180 degrees near this location . Although the 180 degree-shift is reminiscent of a resonator , it is not due to resonance . For example , a change of TM stiffness or mass does not change the characteristic location of the phase shift . An explanation for this phase shift is given in the next section . The phase of the outer hair cell somatic displacement ( dOHC , curves with ° markers ) increases toward the apex by about 90 degrees with or without outer hair cell feedback . Considering that the phase of the receptor potential ( Vm , curves with * markers ) remains near -60 degrees over the distance when active , the dOHC phase is primarily responsible for the location-dependent phase difference between fOHC and vOHC in Fig 5 , since vOHC = jωdOHC . Note that the trend of the dOHC phase with distance is not affected by the active feedback of the outer hair cells . To conclude , the extent of the outer hair cell power generation is passive mechanically regulated . The vibration pattern of the OCC represented by the phase of dOHC is more favorable for amplification in basal locations . In the present model , the point of attachment between the TM and the spiral limbus is above the reticular lamina in the base ( indicated by the dimension e , Fig 7A ) , but it is below the reticular lamina in the apex ( Fig 7B ) . This variation of the TM attachment geometry at different locations is modeled after available anatomical data of the gerbil cochlea [34 , 35] . For example , the length of an inner pillar cell is greater than the height of the TM attachment ( c > e ) in the apex , but the opposite is true in the base ( c < e ) [35] . The variation of the zTM phase with respect to the BM displacement is determined by geometry , specifically by the attachment angle of the TM ( θ in Fig 7 ) . The TM motion trajectories are shown in Fig 7—the red and black curves for active and passive simulations , respectively . The blue curve normal to the BM is the BM trajectory to which the TM trajectory is referenced . For example , the TM to BM amplitude ratio is greater in the basal location than in the apical location . In the basal location , the TM to BM amplitude ratio is greater when active than when passive . The opposite is true in the apical location . The BM vibrates minimally in the radial direction . Roughly speaking , the TM rotates about its attachment point . Because the TM is subject to axial deformation in addition to bending ( rotational ) deformation , the TM motion trajectory is not exactly normal to the TM . When the OCC kinematics is dominated by the bending deformation , the sign of the attachment angle ( θ ) determines the direction of zTM . The Meaud-Grosh model is consistent with this interpretation in that it incorporates the bending and the axial motions of the TM explicitly [20 , 36] . Further investigation is necessary to learn the functional implications of the bending and axial motion of the TM . In Fig 7A and 7B , the TM radial displacements are in the positive and negative directions , respectively , for the peak BM upward displacement . In agreement with experimental observations , an angle difference as small as 10 degrees can result in this approximately 180-degree phase reversal of zTM . Although this TM geometry affects the nominal direction of the TM radial motion ( zTM ) , we do not find a functional consequence of the geometry , i . e . , the zTM phase is not correlated with parameters related to cochlear amplification such as the phase of dOHC . Our model is complex with many independent parameters . Different parameter sets can result in similar functional characteristics , including the level of cochlear amplification . To identify parameters critical for cochlear amplification , a series of sensitivity analyses were performed ( Fig 8 ) . With only one model parameter altered from its standard value , amplification factors were obtained . Active and passive responses to a 4 kHz pure tone were used to define the amplification factor . For 4 kHz stimulation , the traveling waves peak in the middle of the model ( ~6 mm from the base ) . The elastic properties of the outer hair cell’s body and hair bundle affect the amplification more prominently than other supporting structures ( Fig 8A ) . As expected , as the level of damping increases the amplification decreases ( Fig 8B ) . Note that this study uses inviscid fluids . Any viscous dissipation in the fluids or in the OCC is approximated with the Rayleigh damping term . The active gain of the somatic motility ( gOHC ) has a strong effect on the amplification , but the active gain of stereocilia motility ( gMET ) has negligible effect on the amplification ( Fig 8C ) . This suggests that , in the present model , the somatic motility is the primary active component for amplification . This series of sensitivity analyses reveals that there exist different sets of parameters that result in the same amplification level . For example , the same level of amplification can be achieved by reducing both OCC damping and active gain , or by decreasing outer hair cell stiffness while increasing OCC damping . The outer hair cell stiffness , the damping imposed on the BM , and the active gain of the outer hair cells are the three most sensitive parameters in our model . As the model parameter increases by a factor of two near the standard value , the amplification level changes approximately by -20 dB , -10 dB and +30 dB , for the outer hair cell stiffness , OCC damping and outer hair cell active force gain , respectively . Consistent with a previous study ( Meaud , Grosh , 2011 ) and our previous report ( Liu et al . , 2015 ) , the active hair bundle force minimally affects the OCC mechanics . The somatic force is one to two orders of magnitude greater than the hair bundle force according to available physiological data ( Nam , Fettiplace , 2012 ) . For a 1 nm BM vibration amplitude , the fOHC amplitude ranged between 172 and 11 pN and the fMET amplitude ranged between 13 and 0 . 17 pN , over the range of x between 2 and 10 mm . The minimal contribution of fMET to amplification may be ascribed to its small force magnitude . However , even when the active force gain of fMET ( fMET , max in Table 1 ) was increased by a factor of 10 , the contribution of hair bundle forces to amplification remained minimal . This suggests that the somatic force is more favorably situated to deliver power for cochlear amplification . Although we did not observe a significant effect of fMET on amplification , conditions may exist when the hair bundle force can modulate the OCC mechanics more effectively ( e . g . , Ó Maoiléidigh , Hudspeth , 2013 ) . In theory , the trend of greater amplification in higher frequency locations can be reversed by adjusting model parameters . To determine which parameters have the greatest effect on the location-dependent amplification , we attempted to reverse the location-dependent amplification trend . Although damping or active gain can also affect the trend , we could not find a set of values that completely reverses the amplification trend by adjusting only these parameters . In contrast , the location-dependent amplification trend is readily modulated by adjusting the relative stiffness of the OCC felt by individual outer hair cells . The stiffness of the OCC felt by each outer hair cell has consequences for cochlear amplification [18 , 36] . It is the relative stiffness of the OCC as compared to the outer hair cell stiffness that is relevant to power generation by the outer hair cells [37 , 38] . To compute the relative OCC stiffness , a set of equal-and-opposite forces was applied to the outer hair cell body ( fC ) or the stereocilia ( fB ) , and the corresponding static displacements ( δC or δB ) were obtained ( Fig 9A and 9B ) . The relative OCC stiffness felt by an outer hair cell is defined as rOHC = ( fC/δC—kOHC ) /kOHC , where kOHC and fC/δC—kOHC are the stiffness of the outer hair cell body and the stiffness of the OCC felt by the outer hair cell , respectively . Likewise , the relative OCC stiffness felt by the outer hair cell bundle is defined as rOHB = ( fB/δB—kOHB ) /kOHB , where kOHB and fB/δB—kOHB are the stiffness of the outer hair cell bundle and the stiffness of the OCC felt by the outer hair cell hair bundle , respectively . We introduce the parameters rOHC and rOHB for two reasons . First , the stiffness of outer hair cell or its stereocilia bundle has a much greater effect on cochlear amplification than other OCC structures ( Fig 8 ) . Second , according to the theory of impedance matching [37 , 38] , it is the impedance ratio between an actuator and the actuated system that determines the efficiency of power transmission . We hypothesized that the trend of greater amplification toward the base is a consequence of rOHC or rOHB monotonically decreasing toward the apex . To test this hypothesis , while all other model parameters remained the same , either kOHC or kOHB was adjusted so that the longitudinal trend of rOHC or rOHB was reflected with respect to the center at x = 6 mm ( curves with dot symbols , Fig 9C and 9F ) . kOHB or kOHC was adjusted instead of other OCC mechanical properties , because a change in the other OCC structures such as the BM or TM stiffness can change the fundamental tonotopy ( i . e . , the definition of base and apex will become obscured if the BM stiffness is reversed ) . Although we simulated a wide range of outer hair cell stiffness values , it is not because their properties are poorly grounded . The mechanical properties of the outer hair cell and the stereocilia are better understood than other fine structures in the organ of Corti . For example , the hair bundle stiffness has been measured to be ~3 mN/m for 4–5 μm tall outer hair cell hair bundles [39] . Our standard properties ( 40 and 4 . 5 mN/m for 2 and 6 μm-tall hair bundles , respectively ) are within a reasonable range . The axial stiffness of the outer hair cell body has been measured to be 500 nN per unit strain independent of location [11] . Our standard property is 950 nN per unit strain . We used conservative ( greater ) kOHC and kOHB values than the measured values after considering experimental factors that could influence measured values [e . g . , 40] . To reverse the longitudinal trend of rOHC or rOHB , the ratio between kOHB values at x = 2 and 10 mm is increased from 9 to 140 or the kOHC ratio is increased from 2 . 4 to 50 . These large variations in stiffness seem unlikely , but are used here as a simple way to illustrate the effect of rOHC and rOHB . Reversing the longitudinal profile of rOHC or rOHB results in a reversed amplification trend along the cochlear length ( Fig 10 ) . As a result of this adjustment , the active tuning curves at three locations show less amplification and blunt tuning in the base , greater amplification and sharp tuning in the apex ( Fig 10 A and 10D ) . In contrast to active responses , passive responses were affected minimally by the reversal of rOHC or rOHB ( Fig 10B and 10E ) . The amplification level versus location shows that the location-dependent amplification trend is reversed with the adjusted rOHC or rOHB ( Fig 10C and 10F ) . The OCC transfer functions of the test cases with a reversed longitudinal pattern of rOHC or rOHB reveal that the two parameters modulate cochlear amplification differently . Figs 11 and 12 present how reversed rOHC and rOHB affect the OCC transfer functions . Reversing the spatial pattern of rOHC affected the outer hair cell length change ( the curves with filled circles , top panels of Fig 11 ) , but barely affected the phase relationship . Reversing the spatial pattern of rOHB affected both the amplitude and phase of the radial TM motion with respect to the BM motion ( the curves with ■ , Fig 12 ) . The characteristic phase shift of zTM near x = 4 mm disappears because the TM no longer behaves like a rigid bar hinged at the attachment point when the hair bundle stiffness becomes comparable to or greater than the TM axial stiffness ( Fig 7C ) . Despite similar outcomes , the mechanisms by which rOHC and rOHB affect cochlear amplification are different . Fig 13 summarizes the differences . The change of rOHC affects the amplification in two ways . First , as the stiffness of the outer hair cell body increases , the effective active force that is used to deform the OCC other than the cell itself decreases . Second , because the outer hair cells act as an elastic coupler between the TM and the BM , the modulation of their stiffness affects the vibration pattern . When the longitudinal dependence of rOHC is reversed , the phase between vOHC and fOHC becomes more favorable for apical power generation as compared to the standard case ( the curve with ○ , Fig 13A ) . Unlike the case of reversed rOHC , the reversed rOHB hardly affected the vOHC-fOHC phase relationship ( the curve with ■ , Fig 13A ) . The change of rOHB affected the amplification by changing the mechanical gain of the hair bundle displacement ( Fig 13B ) . For example , as a result of decreased kHB ( or increased rOHB ) , the hair bundle displacement gain ( dHB/yBM ) was increased from 0 . 25 to 0 . 55 at x = 10 mm . Because dHB is a part of the loop determining the active feedback gain , doubling the hair bundle gain ( dHB/yBM ) is comparable to doubling the active gain ( gOHC ) . According to Fig 8C , doubling gOHC resulted in an approximately 40 dB increase in amplification . Unlike the case of reversed rOHB , reversing rOHC hardly affects the hair bundle displacement gain .
Because location-dependent amplification is a well-known characteristic of cochlear physiology , theoretical studies have reproduced this characteristic for validation . However , the origin of location-dependent amplification is quite different from theory to theory , revealing the lack of agreement for the mechanism of cochlear amplification . There are three major aspects to consider: 1 ) the active force gain of the outer hair cell; 2 ) the limiting speed of outer hair cell force generation , often referred to as the RC time constant issue; and 3 ) the timing ( phase ) of active force application . Some theoretical studies assumed that the force gain of cochlear actuators varies with location . For example , Mammano and Nobili [5] made two assumptions—the outer hair cell force cancels the damping of BM vibrations , and its amplitude is proportional to the BM stiffness ( their Eq ( 10 ) ) . Lu et al . [6] used a gain that exponentially varies over the cochlear length to represent the outer hair cell force ( parameter kf in their Table 1 ) . The location-dependent amplification of these studies may represent a graded capacity of the feedback force with phase-locked force application . Although our study used a constant outer hair cell electro-mechanical gain ( gOHC of 0 . 1 nN/mV ) , when referenced to the BM displacement like the previous studies , the mechanical and electrical gain of the present model varies with location ( Fig 6 ) . In that sense , our study is not inconsistent with the previous studies . Instead , our study divides the active gain into two components: the OCC mechanical gain , and the electro-mechanical gain of the outer hair cells . In this study , we demonstrated that the OCC mechanical gain is location-dependent , and that location-dependent gain affects cochlear amplification . Recent theoretical studies tend to use an outer hair cell electro-mechanical gain that is independent of location . The Meaud-Grosh model [8 , 36] , and the Liu-Neely model [7 , 41] did not assume increased electromotility of the outer hair cells toward the basal locations . The Liu-Neely model achieves amplification over the physiological frequency range by incorporating high-pass filtering of the mechano-transduction current that compensates for the cell membrane’s low-pass filter . Meaud and Grosh [8] incorporated higher outer hair cell RC filter frequencies ( similar to the present study ) . They used greater mechano-transduction conductance toward high frequency locations , which effectively increased the active feedback gain of the outer hair cell . To summarize , the recent models that include electro-physiological details of the outer hair cells considered either mechano-transduction as a high-pass filter [7 , 41] or a location-dependent amplitude modulator [8 , 36] . Although the present study focuses on the effect of passive mechanics , it is still worthwhile to further explore this possible role of mechano-transduction . The experimental study by Dong and Olson [16] share a conclusion with our study—the passive mechanics modulate cochlear amplification . Their measurements are valuable in that both electrical and mechanical responses of intact OCC at the site of amplification were presented . Similar to our results ( Fig 6 ) , the action of outer hair cells only modestly affects the phase relationship between electrical and mechanical responses . At first glance , Dong and Olson’s results seem incongruent with Chen et al . ’s measurements [13]: the TM-BM phase relationship was observed to vary with stimulus level . According to our results , those two observations are not necessarily incongruent . For example , the TM vibration phase with respect to the BM varies depending on the outer hair cell feedback ( Figs 1 and 2 ) similar to Chen et al . ’s observation . The timing of outer hair cell’s force generation ( vOHC-fOHC phase relationship ) is passive mechanically determined ( Figs 5 , 6 and 11 ) in line with Dong and Olson’s conclusion . Recent advances in measurements of the OCC micro-mechanics began to provide data that previously unavailable such as tissue vibrations in the apical turn , and relative motions between OCC fine structures . Ren and his colleagues have refined low-coherence heterodyne laser interferometry to measure vibrations of both the top and the bottom surfaces of the OCC in live mouse cochlea [15 , 42] . When the cochlea was insensitive the motions of the OCC fine structures were in-phase . In contrast , the motions of OCC structures showed frequency-dependent phase differences in sensitive cochlea . Oghalai and his colleagues used optical coherence tomography to measure the OCC vibrations of live mouse cochlea [14 , 43] . Similar to Ren et al . ’s observation , the OCC structures tended to vibrate more in unison when the cochlea is passive ( dead ) than when it is active ( sensitive ) . Our results are in qualitative agreement with those measurements in that active responses show more complex vibration patterns than the passive case as seen from the phase differences in Fig 6 . For now , however , direct comparison may need caution because of the differences in animal models . Those recent measurements are from the mouse cochlea which has a higher frequency range than our subject ( the gerbil cochlea ) . We chose the gerbil cochlea because its mechanical properties are well-known . For example , for model studies , the BM stiffness is an essential parameter that determines the frequency-location relationship . The BM stiffness at different locations has been available only for the gerbil cochlea [44 , 45] until recent measurements for the mouse cochlea [46] . Our simulated results compare well with measurements from the gerbil cochlea by Chen et al . ( 2011 ) . For example , the TM vibrations leads the BM vibrations as the outer hair cells’ feedback becomes more prominent ( Figs 1 and 2 ) . The amplitude ratio between the TM and the BM transverse vibrations is greater when active than passive . There may be a consequence of the relative stiffness rOHC or rOHB values being near unity . We investigated how different values of rOHC or rOHB affect amplification ( Fig 14 ) . To focus on the effect of the magnitudes of these two parameters instead of their longitudinal variations , in this series of simulations , the rOHC or rOHB values were set constant along the cochlear length . Although our model is linear ( e . g . , the transduction current does not saturate ) , the amplification level saturates as rOHC or rOHB increases by an order of magnitude ( or the outer hair cell stiffness is reduced by one order of magnitude ) . For example , when the outer hair cells are very compliant ( rOHC >>1 ) or stiff ( rOHC <<1 ) as compared to their surrounding structure , a small change in outer hair cell stiffness does not affect the level of amplification . There exists an optimal range where changes of rOHC or rOHB have the greatest influence on amplification . Our result suggests that , if the active motility of the stereocilia or the cell body modulates its stiffness , those actions will result in level-dependent amplification . If such a level-dependent modulation exists , it is most effective when rOHC or rOHB is near unity where the modulation sensitivity ( the slope of the curves in Fig 14B and 14D ) is greatest . Previous studies foresaw a similar condition ( unity rOHC ) for efficient power generation by outer hair cells [37 , 38] . According to physiological evidence , the stiffness of the outer hair cell’s hair bundle is dependent on the stimulation level [39 , 47] .
The same cochlear mechano-electrical model as Liu et al . ’s [18] was used for this study with some adjustments of parameters ( Fig 15 , Table 1 ) . When possible , physiological parameters were obtained from existing gerbil cochlea data . Three dynamic systems were solved simultaneously—the fluid dynamics of the cochlear scalae , structural mechanics of the OCC , and hair cell electro-physiology . The cochlear fluid domain is represented by a 2-D rectangular space separated by the OCC into two compartments ( Fig 15A ) . The basal end of the two compartments ( x = 0 ) represents the oval window ( 0 < z < H ) and the round window ( -H < z < 0 ) , where H is the compartment height . Throughout this work , the oval window was subjected to a constant velocity amplitude ( 1 nm/ms , corresponding to ~50 dB SPL in the ear canal [24 , 48] ) independent of stimulating frequency . The round window is considered to be a pressure-release ( zero pressure ) boundary . All other external fluid boundaries are considered rigid . At the end of the cochlear coil , an opening called the helicotrema connects the top and the bottom fluid domains . Fluids in the top and the bottom compartments interact separately with the OCC through its top and the bottom surfaces . The bottom interacting surface is represented by the mid-line of the BM . The top interacting surface is represented by the TM edge located over the outer hair cell hair bundles . When the cochlea is passive , because the two interacting surfaces vibrate in phase , the predicted response is similar to models using a single interacting surface . The structural mechanics of the OCC is solved using a 3-D finite element method . Structurally significant components of the OCC such as the TM , BM , outer hair cells , pillar cells , Deiters’ cells , and reticular lamina are incorporated [27] . Because each fine structure of the OCC has a clearly defined primary axis ( the direction with which microtubules , actin fibers or collagen fibers are aligned ) , beam elements are used to represent these micro-structures . Simplifications of the OCC mechanics include: three rows of the outer hair cells are merged into one; and non-structural supporting cells and inner hair cells are neglected . The fine structures of the OCC reflect their anatomical dimensions , configurations , and mechanical properties that vary along the cochlear length . The BM is hinged along the spiral lamina and clamped along the spiral ligament . The edge of the TM along the spiral limbus is clamped . The apical and the basal extremities of the OCC ( x = 0 and 12 mm ) are clamped . This boundary conditions at x = 0 and 12 mm have negligible effect on the results . The OCC is subjected to two different types of stimulating force . One is the hydrodynamic pressures acting on the top and the bottom surfaces of the OCC . The other is the active forces from the outer hair cells . Viscous damping matrix is defined by multiplying the stiffness matrix by a coefficient ( stiffness-proportional Rayleigh damping coefficient ) . The damping coefficient varies independently with longitudinal position . The coefficient value is chosen to obtain a reasonable tuning-quality factor with the passive model . The Rayleigh damping coefficient was αC = 0 . 963exp ( 0 . 413x ) μsec , where the distance x is in mm . With this value , the passive cochlea is slightly under-damped ( Q10dB 1 . 1 at the base and 0 . 9 at the apex ) . This value is comparable to 2 . 2–0 . 23 kN·s/m/m2 when considered per unit BM area . The damping coefficients for two components are independently determined: First , the viscous dissipation coefficient for the sub-tectorial space is analytically approximated , assuming Newtonian viscous friction , by cSTS = μLw/h , where L , w and h are the length , width and height of the space . The dynamic viscosity μ was 0 . 7 mPa·s . This value was added to the dissipating component of the outer hair cell stereocilia bundles . Second , viscoelastic properties of the outer hair cells were determined based on the literature ( 13 ) . The assigned value of the outer hair damping coefficient ranges between 0 . 3 μN·s/m in the base and 0 . 75 μN·s/m in the apex . The active mechanical feedback of the outer hair cells is represented by two electro-mechanical components: mechano-transduction of the stereocilia and electro-mechanical motility of the basolateral membrane . The mechano-transduction in the outer hair cell stereocilia is driven by the relative displacement between the TM and the reticular lamina which is equal to the shear displacement of the hair bundle . The transduction channel kinetics are based on previous studies [39 , 49] . The product of the gating swing ( 0 . 7 nm ) and gating spring stiffness ( 6 mN/m ) determines the transduction sensitivity . The electrical circuit of the outer hair cell comprises the conductance of the stereocilia ( Gs ) and the lateral membrane ( Gm ) , the capacitance of the stereocilia ( Cs ) and the membrane ( Cm ) , and collective equilibrium potential ( EK ) . The electrical conductance of outer hair cell’s basolateral membrane are based on the literature [50] . The resultant resistor-capacitor ( RC ) corner frequency of the outer hair cell membrane ranges from 0 . 2 kHz in the apical end to 15 kHz in the basal end . As this RC corner frequency is lower than the best responding frequency along the cochlear length , the receptor potential lags the mechano-transduction current by about a quarter cycle ( cf . the phase difference between iMET and Vm in Figs 1D and 2D and Fig 6 ) , indicating that the outer hair cell impedance is largely capacitive . The outer hair cell membrane generates a force proportional to membrane potential change . A constant electro-mechanical gain of 0 . 1 nN/mV [11] is used . Model parameters are obtained primarily from the experimental data of the gerbil cochlea ( Table 1 ) . The simulation code was written and implemented in Matlab ( Mathworks , Natick , MA ) . The longitudinal ( along the x-axis ) grid size is 10 μm matching the spacing between the outer hair cells . To match the structural domain grid , the mesh grid size of the fluid domain is also 10 μm . The problem size of the combined system was about 0 . 23 million degrees-of-freedom . When run on an IBM PC ( Intel i7-4790 processor , 16 GB RAM ) , it takes 3 minutes to assemble matrices and 20 seconds to solve for each stimulating frequency . The simulation code is available through the research webpage of the corresponding author . The power flux in the fluid through a cross-section at the distance x averaged over a cycle is Pflux ( x ) =0 . 5w∫−HHRe ( pslowvfCT ) dz , ( 1 ) where w is the width of cochlear scalae ( compartment ) , vf is the fluid velocity magnitude in the longitudinal direction , the superscript CT indicates the complex conjugate , and pslow represents the pressure component caused by the slow traveling waves on the OCC . A positive value of Pflux indicates the flow toward the apex of the cochlea . The same definition was used in a previous study [51] . Little energy from the slow traveling wave reaches the helicotrema , when the wave peaks and decays before reaching the helicotrema . That is , the pressure at the helicotrema p ( L , 0 ) approximates the fast wave pressure . This fast wave pressure is subtracted from the total pressure to obtain the slow wave pressure , or pslow ( x , z ) = p ( x , z ) − p ( L , 0 ) . pslow is equivalent to the fluid pressure of other studies that assume an anti-symmetric pressure . Because the fluid velocity is not explicitly obtained in our inviscid fluid model , vf is obtained from the pressure field . The present cochlear system has two power sources . The stapes vibrations excite the system . In addition , the outer hair cells supply power to locally amplify the OCC responses . As we assumed that the fluid is inviscid , all the provided power is dissipated within the OCC through the viscous damping represented by the damping matrix C in Eq ( A3 ) in S1 Appendix . The dissipated power in the cochlea is calculated from the OCC velocity v , Ploss=0 . 5vCTCv . ( 3 ) The power provided by the stapes is calculated from the pressure and velocity at the stapes ( x = 0 , 0 < z < H ) , Pstapes=0 . 5wstapes∫0HRe ( p ( 0 , z ) vfCT ( 0 , z ) ) dz , ( 4 ) where wstapes is the effective width of stapes in the radial direction assuming the fluid velocity component vf is uniform in the radial direction . Meanwhile , the power provided by the fluid to the OCC is Pf2s=0 . 5∫0LRe ( ffluid ( x ) vCPCT ( x ) ) dx , ( 5 ) where vCP is vector containing the transverse velocities at the fluid-structure interfaces ( i . e . , the TM and the BM velocity for the top and the bottom surfaces , respectively ) . When there are no active forces of the outer hair cells , the power delivered through the stapes is equal to the power provided by the fluid to the structure , or Pstapes = Pf2s . The effective width of the stapes ( wstapes ) is obtained from this equality and used for the active cochlea cases , too . The power provided by the somatic motility of a single outer hair cell is the product of the outer hair cell force ( fOHC ) , and the rate of outer hair cell elongation ( vOHC ) , POHC=0 . 5Re ( fOHCvOHCCT ) . ( 6 ) There are 3 outer hair cells per a 10 μm section , and there are total of 1201 sections . | The mammalian cochlea encodes sound pressure levels over six orders of magnitude . This wide dynamic range is achieved by amplifying weak sounds . The outer hair cells , one of two types of receptor cells in the cochlea , are known as the cellular actuators that provide power for the amplification . It is well known that high frequency sounds encoded in the basal turn of the cochlea are amplified more than low frequency sounds encoded in the apical turn of the cochlea . This difference in amplification has been ascribed to a difference in electrophysiological properties , such as the membrane capacitance and conductance of the outer hair cells at different locations . Whether the outer hair cells have a sufficient range of electrophysiological properties to explain the location dependent amplification has long been a topic of scientific debate . In this study , we present an alternative explanation for how the low and high frequency sounds are amplified differently . Using a detailed computational model of the cochlear epithelium ( the organ of Corti ) , we demonstrate that the micro-mechanics of the organ of Corti can explain the variation of amplification with longitudinal location in the cochlea . | [
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... | 2017 | Two passive mechanical conditions modulate power generation by the outer hair cells |
Natural sensory stimuli frequently consist of a fast time-varying waveform whose amplitude or contrast varies more slowly . While changes in contrast carry behaviorally relevant information necessary for sensory perception , their processing by the brain remains poorly understood to this day . Here , we investigated the mechanisms that enable neural responses to and perception of low-contrast stimuli in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus . We found that fish reliably detected such stimuli via robust behavioral responses . Recordings from peripheral electrosensory neurons revealed stimulus-induced changes in firing activity ( i . e . , phase locking ) but not in their overall firing rate . However , central electrosensory neurons receiving input from the periphery responded robustly via both phase locking and increases in firing rate . Pharmacological inactivation of feedback input onto central electrosensory neurons eliminated increases in firing rate but did not affect phase locking for central electrosensory neurons in response to low-contrast stimuli . As feedback inactivation eliminated behavioral responses to these stimuli as well , our results show that it is changes in central electrosensory neuron firing rate that are relevant for behavior , rather than phase locking . Finally , recordings from neurons projecting directly via feedback to central electrosensory neurons revealed that they provide the necessary input to cause increases in firing rate . Our results thus provide the first experimental evidence that feedback generates both neural and behavioral responses to low-contrast stimuli that are commonly found in the natural environment .
Understanding how sensory information is processed by the brain in order to give rise to perception and behavior ( i . e . , the neural code ) remains a central problem in systems neuroscience . Such understanding is complicated by the fact that natural sensory stimuli have complex spatiotemporal characteristics . Specifically , these frequently consist of a fast time-varying waveform whose amplitude ( i . e . , the “envelope” or contrast ) varies more slowly [1–3] . Envelopes are critical for perception [4 , 5] , yet their neural encoding continues to pose a challenge to investigators because their extraction ( i . e . , signal demodulation ) requires a nonlinear transformation [6 , 7] . It is generally thought that peripheral sensory neurons implement such demodulation through phase locking , in which action potentials only occur during a restricted portion of the stimulus cycle , and that such signals are further refined downstream to give rise to perception . Indeed , in the auditory system , peripheral auditory fibers respond to amplitude-modulated sounds because of phase locking [8] , with the most sensitive units displaying detection thresholds similar to those of the organism ( see [3] for review ) . Sensitivity to amplitude modulations ( AMs ) increases in higher-level areas ( e . g . , cochlear nuclei , inferior colliculus , auditory cortex ) , thereby exceeding that seen at the periphery , but the underlying mechanisms remain poorly understood [3 , 9–12] . The common wisdom is that these are feedforward in nature and involve integration of afferent input from the sensory periphery . Here , we show that refinement of neural sensitivity to AMs that occurs in central brain areas is not due to integration of afferent input but is rather mediated by feedback pathways , thereby mediating perception and behavior . Wave-type weakly electric fish generate a quasi-sinusoidal signal called the electric organ discharge ( EOD ) around their body , which allows exploration of the environment and communication . During interactions with conspecifics , each fish experiences sinusoidal AMs as well as phase modulations ( PMs ) of its EOD ( i . e . , a beat ) . This beat can interfere with electrolocation of other objects when the frequency is low . Specifically , such stimuli elicit a jamming avoidance response ( JAR ) in which both fish shift their EOD frequencies in order to increase the beat frequency to higher values that do not interfere with electrolocation . The neural circuitry giving rise to the JAR is well understood and involves feedforward integration of AM and PM information that is processed in parallel by separate neural pathways that later converge ( see [13] for review ) , although JAR behavior can sometimes be elicited by stimuli consisting of AMs or PMs only [14] . In particular , neural sensitivities to AM and PM components increase in higher-level areas , thereby explaining the animal’s remarkable behavioral acuity [15] . Experiments focusing on the JAR have typically but not always used beats with constant depth of modulation ( i . e . , the envelope or contrast ) . More recent studies have focused on studying how time-varying contrasts , which carry information as to the distance and relative orientation between both fish [16 , 17] , are processed by the AM neural pathway to give rise to behavioral responses that consist of the animal’s EOD frequency tracking the detailed time course of the envelope [7 , 18–28] . P-type peripheral electrosensory afferents ( EAs ) scattered over the animal’s skin surface encode EOD amplitude , but not PMs , and synapse onto pyramidal cells ( PCells ) within the electrosensory lateral line lobe ( ELL ) . PCells are the sole output neurons of the ELL and project to higher brain areas that mediate behavior . Moreover , PCells receive large amounts of input from descending pathways ( i . e . , feedback ) [29–31] that have important functional roles such as gain control [32 , 33] , adaptive stimulus cancellation [34–41] , coding of natural electro-communication signals [42] , and synthesizing neural codes for moving objects [43] , as well as shifting the tuning properties of PCells contingent on the stimulus’s spatial extent [44–46] . However , whether and how feedback input determines PCell responses to time-varying contrasts have not been investigated to date . Moreover , while previous studies have focused on studying neural and behavioral responses to high-contrast stimuli [7 , 18–28] , we instead focused on low-contrast stimuli that are more commonly found in the natural environment [17] .
We first investigated behavioral responses to increasing contrast ( Fig 2A ) . To do so , we first quantified behavioral responses in the absence of stimulation by looking at the time-varying EOD frequency ( Fig 2B , top ) . Plotting the EOD spectrogram ( i . e . , the time-varying power spectrum of the measured EOD trace ) revealed that the frequency at which there was maximum power ( i . e . , the EOD frequency ) fluctuated slightly ( Fig 2B , top ) around a mean value . We used these fluctuations to compute a probability distribution and to determine the interval of values that contains 95% of this distribution ( Fig 2B , white dashed lines , see Materials and methods ) . During stimulation , we found that the animal’s EOD frequency increased more or less linearly as a function of time ( Fig 2B , bottom ) . The detection threshold was computed as the contrast corresponding to the smallest time after stimulus onset for which the EOD frequency was outside the range of values determined in the absence of stimulation ( Fig 2B , bottom , black circle and white dashed lines ) . We found that fish could reliably detect weak contrasts as evidenced from low detection thresholds ( n = 35 fish , 8 . 8% ± 1 . 1% , min: 1 . 1% , max: 27 . 6% , Fig 2B , bottom , inset ) . The detection threshold values obtained were furthermore robust to large changes in filter settings ( S1 Fig ) . Our behavioral results show that electrosensory neural circuits must extract the time-varying stimulus contrast ( i . e . , implement signal demodulation ) . We thus investigated next how electrosensory neurons respond to increasing contrast . We first recorded from peripheral EAs ( Fig 3A ) . EAs are characterized by high-baseline firing rates in the absence of stimulation within the range of 200–600 spk s−1 [19 , 47] . Our dataset confirms these previous results as the baseline firing rates were all within this range ( population average: 400 . 8 ± 18 . 0 spk s−1 , n = 54 , N = 5 fish ) . As done for behavior , we used the baseline activity of EAs to determine whether the observed neural activity was due to stimulation . We note that this is physiologically realistic as , in order to be detected , a stimulus must perturb the ongoing baseline activity of EAs . Overall , we found that EA activity was phase locked to the stimulus waveform for both low ( Fig 3B , left inset ) and high ( Fig 3B , right inset ) contrasts . Notably , for high contrasts , we observed stronger phase locking in that there was cessation of firing activity during some phases of the stimulus cycle ( Fig 3B , right inset ) . We quantified EA responses to stimulation using standard measures of firing rate ( see Materials and methods ) and phase locking ( i . e . , the vector strength [VS] , see Materials and methods ) . Overall , the time-varying VS quickly became significantly different from baseline ( i . e . , in the absence of stimulation ) after stimulus onset ( Fig 3B , dashed blue ) , leading to low phase locking detection threshold values ( Fig 3B , left black circle ) . However , the mean firing rate ( Fig 3B , solid blue ) only became significantly different from baseline for larger contrasts , leading to higher firing rate detection threshold values ( Fig 3B , right black circle ) . We note that , while there is no complete dichotomy between phase locking and firing rate , our results above do show that it is possible to increase phase locking without increasing firing rate for low contrasts . Similar results were seen across our dataset in that EA phase locking thresholds were low and comparable to behavioral values ( EA VS: 9 . 1% ± 1 . 1% , behavior: 8 . 8% ± 1 . 1% , Kruskal-Wallis , df = 2 , p = 0 . 99 ) , whereas those computed from firing rate were much higher than behavioral thresholds ( 38 . 2% ± 3 . 1%; Kruskal-Wallis , df = 2 , p = 8 . 9 × 10−5; Fig 3B , inset ) . Neural detection threshold values obtained were also robust to large changes in filter settings ( S1 Fig ) . Thus , our results show that , for low contrasts ( i . e . , <15% ) , EA firing rate modulations ( i . e . , phase locking ) carry the information necessary to implement signal demodulation . However , such demodulation must occur downstream of EAs , as their mean firing rates were effectively unchanged relative to baseline conditions . For high contrasts ( i . e . , >40% ) , our results show that EAs implement signal demodulation , as their firing rates are then different from baseline . We next recorded from the downstream targets of EAs: PCells within the ELL ( Fig 4A ) . ELL PCells have much lower baseline firing rates than EAs , which are typically within the 5–45 Hz range [48] . Baseline firing rates of our PCell data were all within this range ( population average: 13 . 2 ± 0 . 8 spk s−1 , n = 59 , N = 27 fish ) . We found that , like EAs , ELL PCell spiking activity was phase locked to the stimulus shortly after stimulus onset ( Fig 4B , dashed green curve and Fig 4B , insets ) . Phase locking was seen for both low and high contrasts in that spiking only occurred during a restricted portion of the stimulus cycle ( Fig 4B , compare left and right panels ) . However , PCells responded in a qualitatively different fashion than EAs in that their firing rates also became significantly different from baseline shortly after onset ( Fig 4B , solid green curve ) . Thus , firing rate detection threshold values for PCells were comparable to those found for behavior ( PCells firing rate: 7 . 0% ± 0 . 9%; Kruskal-Wallis , df = 2 , p = 0 . 23; Fig 4B , bottom inset ) , whereas phase locking detection thresholds for PCells were significantly lower than firing rate and behavioral detection thresholds ( VS: 3 . 9% ± 0 . 6%; Kruskal-Wallis , df = 2 , Firing Rate–VS: p = 0 . 0054; VS-Behavior: p = 1 . 1 × 10−5; Fig 4B , bottom inset ) . We further tested the relationship between neural and behavioral detection thresholds by plotting values obtained from neurons in different individual fish . Overall , there was a strong correlation between neural and behavioral detection threshold values ( S2A Fig , n = 10 , N = 10 fish , 3 repetitions each; r = 0 . 93; p = 4 . 6 × 10−7 ) , indicating that neurons with low detection thresholds were primarily found in fish with low behavioral detection thresholds . There was , however , no correlation between the trial-to-trial variabilities of neural and behavioral detection thresholds to repeated stimulus presentations ( S2B Fig , n = 10 , N = 10 fish , 3 repetitions each; r = −0 . 14; p = 0 . 48 ) , indicating that fluctuations in the activity of a single ELL PCell do not significantly influence behavior . Overall , our results show that , for low contrasts ( i . e . , <15% ) , PCell phase locking and firing rate both carry information about contrast . As such , either phase locking or firing rate could be decoded by downstream brain areas in order to give rise to behavior . Perhaps the simplest explanation for why PCells phase lock to stimuli with low contrasts is that they simply linearly integrate feedforward input from EAs , which are already phase locked to these . What then causes PCells to increase their firing rates in response to stimuli with low contrasts ? Unlike the explanation above for increased phase locking , this cannot be due to linear integration of feedforward input from EAs . This is because our results show that , for low contrasts , EA firing rates are not significantly different from baseline values . One possibility is that increases in PCell firing rate result from nonlinear integration ( e . g . , half-wave rectification ) of feedforward input from EAs . Another possibility is that increases in firing rate are due to feedback input . To determine the relative roles of feedforward and feedback inputs , we pharmacologically inactivated all feedback input onto ELL PCells by injecting lidocaine , a sodium channel antagonist , bilaterally into nP ( n = 10 cells , N = 4 fish , see Materials and methods , Fig 5A ) . Importantly , this manipulation does not alter feedforward input onto ELL PCells , since EAs do not receive feedback input . Thus , if increases in firing rate are due to feedforward input , then we would expect that PCell responses will be relatively unaffected and that the firing rate detection threshold will remain the same as under control conditions . If , on the other hand , increases in firing rate are due to feedback input , then we would expect that , after complete feedback inactivation , PCells will no longer respond to low-contrast stimuli through increases in firing rate , thereby significantly increasing the firing rate detection threshold . We found that complete feedback inactivation strongly altered ELL PCell responses to stimuli with increasing contrast ( Fig 5B ) . Indeed , PCell firing rate only became significantly different from baseline for much higher contrasts ( Fig 5B , middle , compare dark and light solid green curves ) . Consequently , PCell firing rate detection threshold values were much higher after feedback inactivation ( control: 8 . 4% ± 2 . 9%; lidocaine: 29 . 7% ± 6 . 3% , Fig 5B , middle inset ) . We note that this was not due to changes in the integration of feedforward input , as phase locking was unaffected ( Fig 5B , bottom , compare dark and light dashed green curves ) . Indeed , phase locking threshold values were similar before and after complete feedback inactivation ( control: 5 . 2% ± 2 . 1%; lidocaine: 5 . 1% ± 2 . 0% , Fig 5B , bottom inset ) . We note that vehicle injection ( i . e . , saline ) did not affect ELL PCell firing rate ( n = 7 , N = 3 fish , control: 9 . 4% ± 1 . 0%; saline: 9 . 3% ± 0 . 7% , S3 Fig ) or phase locking ( control: 5 . 4% ± 1 . 7%; saline: 4 . 8% ± 1 . 5% , S3 Fig ) detection thresholds . Thus , while increases in PCell firing rate were no longer observed for low ( <15% ) contrasts after complete feedback inactivation , such inactivation did not affect phase locking . These results show that it is possible to alter firing rate without altering phase locking . We conclude that , during low-contrast stimulation , increased PCell firing rate is due to feedback input , while increased phase locking is instead due to feedforward input from EAs . Our results so far show that the increase in PCell firing rate shortly after stimulus onset ( i . e . , to low contrasts ) is due to feedback , while increased phase locking is instead due to feedforward input from EAs . In theory , either PCell firing rate or phase locking could be used to determine behavioral responses . If the former , then increases in EOD frequency shortly after stimulus onset are due to increases in PCell firing rate . If the latter , then nonlinear integration of PCell input by downstream neurons would give the information necessary to drive behavior . To test which of PCell firing rate or phase locking is relevant for determining behavior , we investigated how complete feedback inactivation affected behavioral responses , as this manipulation does not affect ascending pathways from TS to higher brain areas mediating behavior ( Fig 6A ) . On the one hand , if phase locking is necessary to elicit behavior , then we would expect that feedback inactivation will not affect behavioral responses to low contrasts and thus that behavioral detection threshold values will be largely unaffected . On the other hand , if changes in PCell firing rate are necessary to elicit behavioral responses , then we would expect that feedback inactivation will cause cessation of behavioral responses to low contrasts , thereby increasing the behavioral detection threshold . We found that complete feedback inactivation gave rise to significant changes in behavioral responses ( N = 15 fish ) . Indeed , behavioral responses to low contrasts ( <15% ) were no longer present , as the EOD frequency remained below the response level ( Fig 6B , compare light and dark brown curves ) . EOD frequency only became significantly different from baseline for much larger contrasts than under control conditions , leading to significantly larger behavioral detection threshold values ( control: 12 . 2% ± 2 . 3%; lidocaine: 34 . 2% ± 6 . 3% , Wilcoxon sign rank test , p = 6 . 1 × 10−5 , Fig 6C , brown boxes ) . We note that vehicle injection ( i . e . , saline ) did not significantly affect behavioral detection thresholds ( N = 10 fish , control: 13 . 9% ± 1 . 5%; saline: 14 . 1% ± 1 . 9% , S3 Fig ) . Thus , our results show that , for low contrasts , the information carried by PCell phase locking is not decoded by downstream areas to determine behavior . Rather , it is the increase in PCell firing rate that is necessary to elicit behavioral responses . Interestingly , complete feedback inactivation increased PCell firing rate and behavioral detection thresholds to values that were similar to those obtained for the firing rate of single EAs ( Fig 6C ) . Thus , not only do our results show that feedforward input from EAs is sufficient to elicit changes in PCell firing rate for high ( >40% ) contrasts , but they also suggest that it is the changes in EA firing rate that are then necessary to elicit behavioral responses to high contrasts , rather than phase locking . ELL PCells receive two sources of feedback input . One source originates directly from nP and forms a closed loop with ELL PCells , while the other instead originates indirectly from nP and goes through the eminentia granularis posterior ( EGP ) ( S4 Fig ) . To test which pathway mediates changes in PCell firing rate responses , we performed two additional manipulations . The first was selectively blocking the indirect pathway by injection of 6-cyano-7-nitroquinoxaline-2 , 3-dione ( CNQX ) within the ELL , which did not significantly alter PCell firing rate or phase locking , as well as behavioral responses ( S4 Fig ) . The second was to selectively inactivate direct feedback by injecting lidocaine unilaterally within the TS , which gave rise to similar changes in PCell activity as those observed with complete feedback inactivation ( compare S5 Fig to Fig 5 ) . Thus , these results show that it is closed-loop feedback that causes increases in PCell firing rate in response to low-contrast stimuli . We will return to this point in the Discussion section . How does closed-loop feedback input enable increases in PCell firing rate for low-stimulus contrasts ? To answer this question , we recorded from nP STCells ( n = 10 , N = 3 fish ) that provide direct feedback input to ELL PCells in response to increasing contrast ( Fig 7A ) . We used previously established criteria [30] to identify STCells ( S6 Fig ) . Overall , STCells were mostly silent in the absence of stimulation and started firing shortly after stimulus onset ( Fig 7B , bottom , solid orange line ) . Overall , their firing rate detection thresholds were comparable to those of PCells under control conditions as well as behavior ( STCells: 4 . 6% ± 0 . 7% , min: 2 . 0% , max: 7 . 6%; Fig 7B , inset ) . We also found that STCells phase locked to the stimulus at the onset of firing ( Fig 7B , bottom , dashed orange line ) . Consequently , their phase locking detection thresholds were also low ( 6 . 5% ± 0 . 9%; Fig 7B , inset ) . Thus , our results show that STCells , by increasing their firing activity in response to low-contrast stimuli , provide the necessary input to drive increases in ELL PCell firing rates . Fig 8 shows the proposed contributions of feedforward and feedback inputs toward determining behavioral responses to increasing stimulus contrast . Overall , EAs phase lock to low contrasts , which causes ELL PCells to in turn phase lock to these . While the information carried by PCell phase locking is necessary to extract the contrast ( i . e . , implement signal demodulation ) , our results show that this information is not directly decoded by downstream brain areas to give rise to behavior ( Fig 8A ) . Rather , PCell phase locking is integrated via a closed feedback loop that is necessary to elicit increases in PCell firing rate for low contrasts , which in turn elicit behavioral responses . For high contrasts , and in the absence of feedback , our results suggest that it is changes in EA firing rate that are carried over to ELL PCells , which in turn elicit behavioral responses ( Fig 8B ) .
Here , we investigated how weakly electric fish process and perceive stimuli with different contrasts . Contrary to previous studies , we focused specifically on behavioral responses and the underlying neural mechanisms to low ( <15% ) contrasts . We found that behavioral detection thresholds were low on average ( 9% ) . Overall , peripheral EAs responded through phase locking and thus transmitted the necessary information to extract contrast to downstream areas . However , changes in EA firing rate were only elicited for much higher ( approximately 40% ) contrasts . ELL PCells receiving input from EAs responded to low contrasts through both increased phase locking as well as firing rate: the detection threshold values computed from either were lower than those for EAs ( 7% and 4% , respectively ) and matched behavior ( 9% ) . Pharmacological inactivation of feedback input revealed that , while such input was necessary to elicit increases in firing rate for low contrasts , increases in phase locking were caused by feedforward input from EAs . Analysis of behavioral responses after feedback inactivation revealed that it was changes in PCell firing rate and not phase locking that determined behavior . Finally , we recorded from nP STCells that provide direct feedback input to PCells . STCells increase their firing activity shortly after stimulus onset and thus displayed low detection thresholds ( 5% ) that matched those of PCells and behavior under control conditions . Our results thus provide the first experimental evidence showing that feedback is necessary to give rise to neural and behavioral responses to weak sensory input that would not be detected otherwise . Our results show that behavioral and ELL PCell firing rate responses to low contrasts are generated because of closed-loop feedback . These results have strong implications for the electrosensory system as well as other systems , as described below . We note that information about low-stimulus contrast is carried by PCell phase locking and is due to feedforward input from EAs and does not require feedback . Theoretical studies have shown that it is possible to directly extract this information ( e . g . , by performing a nonlinear operation such as half-wave rectification followed by low-pass filtering [6] ) . However , our results show that downstream brain areas that mediate behavior do not decode information carried by PCell phase locking . This is because we showed that feedback inactivation strongly increased behavioral thresholds but did not alter PCell phase locking . We also note that some EAs displayed firing rate detection thresholds ( approximately 2% ) that are lower than those obtained at the organismal level ( approximately 9% ) . The input from these EAs could theoretically be used to elicit behavioral responses to low contrasts . Moreover , as EAs display negligible correlations between their baseline firing rate variabilities [49–51] , it is then theoretically possible to improve the signal-to-noise ratio ( SNR ) by linearly integrating their activities [52] . Anatomical studies have shown that the PCells considered here receive input from many ( 600–1 , 400 ) EAs [53] , which should give rise to substantial improvement in SNR , according to theory . However , it is unlikely that the lower firing rate thresholds of PCells are due to either selectively responding to input from the most sensitive EAs or to improving the SNR . This is because our results show that , under complete feedback inactivation , PCell firing rate threshold values were similar to those of single EAs ( 40% ) . We hypothesize that this is because heterogeneities within the EA population counteract the potential beneficial effects of summing afferent activities . Indeed , previous studies have shown that EAs display large heterogeneities , particularly in terms of their baseline firing rates [47] , which can strongly influence how they respond to envelopes [19] . It is well known that ELL PCells receive both direct and indirect sources of feedback [29] . However , the functional role of the direct pathway has remained largely unknown until recently [43] . Indeed , previous studies have hypothesized that this pathway could act as a sensory searchlight that enhances salient features of sensory input , as originally hypothesized by Crick [54] . Our results provide the first experimental evidence that such feedback serves to generate sensory neural responses and perception of behaviorally relevant features of sensory input that would otherwise not be processed in the brain , which is in line with this hypothesis . In particular , nP STCells providing direct feedback input to ELL PCells have firing properties that are ideally suited for detecting low contrasts . Indeed , these cells display little to no spiking activity in the absence of stimulation [30] , which is unlike ELL PCells [55 , 56] or multipolar cells that instead give rise to indirect feedback input onto ELL PCells [31] . Our results suggest that it is the increase in firing rate of STCells that is likely needed to increase PCell firing rate to low contrasts , thereby eliciting behavioral responses . We note that our results show that feedback plays an active role in generating increases in PCell firing rate , rather than changing how they integrate feedforward input . This is because PCell phase locking was unaffected by feedback inactivation , strongly suggesting that the response to feedforward input is similar under both conditions . This novel function for feedback is thus quite different than previously uncovered functions for feedback input such as gain control [32] . We further note that our stimuli consisting of a sinusoidal waveform whose amplitude increases with time will roughly mimic the spatially diffuse AM stimulation caused by a looming conspecific [16] . The resulting stimulation is quite different than that caused by a looming object ( e . g . , a prey ) , which instead gives rise to spatially localized stimulation consisting of changes in EOD amplitude with no envelope . However , we note that a spatially localized envelope would be also generated if an oscillating motion would be superimposed on top of the looming motion . A previous study has shown that the direct feedback pathway played an important role in determining neural responses to receding objects [43] , strongly suggesting that responses to looming objects are primarily determined by feedforward input . Here , we have instead shown that the direct feedback pathway generates neural and behavioral responses to stimuli mimicking a looming conspecific . While previous studies have shown that lateral motion can give rise to changes in EOD frequency [14] , how looming objects affect EOD frequency should be the focus of future studies . An important question pertains to how feedback generates increased PCell firing rate responses to low contrasts . Such studies will require recording from the TS neurons that receive input from ELL PCells and project back to nP STCells . Previous studies have shown that there are about 50 cell types within the TS [57 , 58] that display highly heterogeneous responses to electrosensory stimulation [59–65] . In particular , some cell types in TS ( i . e . , so-called ON-OFF neurons ) respond selectively to stimulus contrast because of balanced input from ON- and OFF-type ELL PCells [24] . Specifically , these neurons respond to both increases and decreases in the stimulus and are thus ideal to generate behavioral responses . This is because they will simply increase their firing rates with increasing contrast ( see [7] for review ) . Other cell types within TS respond to contrast in a manner similar to that of ELL PCells ( i . e . , through changes in phase locking and firing rate ) [24] . We hypothesize that it is these latter neurons that project back to nP and provide input to STCells . It is , however , important to note that all previous studies of TS neural responses used high contrasts . As such , future studies that are beyond the scope of this paper are needed to understand how different cell types within TS respond to the low contrasts considered here and mediate both ELL PCell and behavioral responses to these . Such studies should focus on brain areas downstream of TS , where it is expected that variability in the responses of single neurons would correlate with behavior , as observed in other sensory modalities [66] . What is the relationship between our observed behavioral responses to stimuli with time-varying contrasts and those previously observed using stimuli with constant contrasts ? Previous studies have focused on studying the JAR behavior in response to stimuli with constant contrast . In particular , the JAR and the underlying neural circuitry have been extensively studied in the weakly electric fish species Eigenmannia virescens [13] . This species shows exquisite sensitivity to AM stimuli , as these generate behavioral responses with contrasts as low as 0 . 1% [14] . The JAR behavior in Apteronotus is less sensitive than for Eigenmannia [67] , which is most likely due to the fact that the former species is less gregarious than the latter [68–70] . Further , there are important differences between the JAR behavior as well as the underlying neural circuitry in Apteronotus and Eigenmannia that have been reviewed extensively [71–73] . Most notably , Apteronotus tend to always increase their EOD frequency in response to low-frequency jamming stimuli with constant amplitude , which does not require the presence of PMs [74 , 75] . Specifically , the EOD frequency will rise and saturate to a higher value . What is the role of feedback input onto ELL PCells in determining the JAR behavior ? Previous studies have shown that lesioning both indirect and direct feedback onto ELL PCells increases their phase locking responses to low-frequency sinusoidal stimuli for high but not for low contrasts [32] . Further studies have shown that this effect was mediated primarily , if not exclusively , by the indirect feedback pathway [34 , 45] . Our results showing that selectively blocking the direct pathway does not alter phase locking in ELL PCells are consistent with these . Although the effects of complete feedback inactivation on the JAR have , to our knowledge , not been tested in Apteronotus , manipulations that enhanced phase locking by ELL PCells to low-frequency stimuli also led to an enhanced JAR ( i . e . , a greater increase in EOD frequency ) [76] . We thus predict that complete feedback inactivation would enhance the JAR and that this would be primarily , if not exclusively , due to the indirect pathway . If true , then this would imply that the role of feedback in determining JAR behavior in response to stimuli with constant contrast and our observed behavioral responses to stimuli with time-varying contrasts are qualitatively different . While the indirect feedback pathway is involved in determining the JAR magnitude via gain control , we have instead shown here that the direct feedback pathway is necessary in order to elicit increases in ELL PCell firing rate that in turn elicit increases in EOD frequency . It is nevertheless possible that the direct pathway could play a role in generating the initial increase in EOD frequency during the JAR , or in setting the latency . Further studies are needed to test these predictions . Finally , our results show that feedback is only necessary to generate neural and behavioral responses to low contrasts . Indeed , our results show that EAs will change their firing rates for high ( >40% ) contrasts , which are then sufficient to elicit changes in PCell firing rate and , in turn , behavioral responses . An important question is thus: why generate responses to low contrasts through feedback when such responses could , in theory , be generated by feedforward pathways ? To answer this question , one must first consider that the sinusoidal stimuli with different contrasts considered here , while behaviorally relevant , are by no means the only behaviorally relevant stimuli that must be encoded by the electrosensory system . For example , prey stimuli [77] as well as intraspecific communication stimuli [78] must also be encoded . Secondly , one must consider the actual mechanism by which EAs can encode contrast , which involves static nonlinearities ( e . g . , rectification and/or saturation ) during which the firing activity is constant and thus cannot encode sensory input . Thus , responses to envelopes in EAs comes at a cost . This is because these neurons then cannot respond as well to other sensory input , as the firing rate is constant ( either at zero or at its maximum value ) for some portion of the stimulus . We thus hypothesize that generating responses to low contrast at the level of feedback pathways does not compromise ELL PCell responses to other behaviorally relevant sensory input ( e . g . , caused by prey ) . While further studies are needed to test this hypothesis , we note that ELL PCells display large heterogeneities , with some PCells receiving much less feedback than others [34 , 55] . It is also conceivable that these latter PCells , which also project to higher brain areas , help mediate perception of other behaviorally relevant stimuli . Processing of AMs is behaviorally relevant in multiple sensory modalities ( auditory: [3]; visual: [79]; vestibular: [50 , 80]; somatosensory: [81 , 82] ) . As mentioned above , AMs found in natural auditory stimuli ( e . g . , speech ) are particularly necessary for perception [4 , 5] . There exist important parallels between processing of amplitude-modulated stimuli in both the auditory and electrosensory systems . Our results show that behavioral detection thresholds in weakly electric fish ( approximately 9% ) are similar to those found in the auditory system ( approximately 4% ) [83–85] . The processing of amplitude-modulated sounds by the auditory system has been extensively studied . In particular , single peripheral auditory fibers will respond to AMs because of phase locking [8] with the most sensitive neurons displaying detection thresholds that are similar to perceptual values [83] ( see [3] for review ) . Sensitivity to AMs also increases in higher-level areas ( e . g . , cochlear nuclei , inferior colliculus , auditory cortex ) [3 , 9–12] . Thus , it has been commonly assumed in the auditory system that the lower detection thresholds seen centrally are the result of integration of afferent input from the periphery , as predicted from mathematical models [86 , 87] . We hypothesize that the lower detection thresholds seen in more central areas are instead due to feedback . Further studies investigating the effects of feedback onto central auditory neurons are needed to validate this hypothesis . Finally , we note that it is frequently assumed that behavioral responses are determined by feedforward integration of afferent input [66 , 88 , 89] . However , anatomical studies in several systems have shown that feedback projections from higher centers often vastly outnumber feedforward projections from the periphery [90–93] , and a recent review has highlighted the need for further studies focusing on the role of feedback projections in determining how sensory information gives rise to behavioral responses [94] . Previous studies have demonstrated that feedback is involved in predictive coding [95–97] ( see [98] for review ) or combined with feedforward input in order to amplify neuronal responses [99] . Instead , we provide here the first experimental evidence that closed-loop feedback actually generates responses to and perception of weak or low-intensity sensory input . Our results are thus timely in that they show for the first time how feedback pathways mediate sensory neural responses to and perception of behaviorally relevant stimulus features . Important commonalities between the electrosensory system and the visual , auditory , and vestibular systems of mammals ( see [100 , 101] for review ) suggest that similar mechanisms will be found in these systems as well .
All animal procedures were approved by McGill University’s animal care committee and were performed in accordance with the guidelines of the Canadian Council on Animal Care under protocol 5285 . The wave-type weakly electric fish A . leptorhynchus was used exclusively in this study . Animals of either sex were purchased from tropical fish suppliers and were housed in groups ( 2–10 ) at controlled water temperatures ( 26–29°C ) and conductivities ( 300–800 μS cm−1 ) according to published guidelines [102] . Surgical procedures have been described in detail previously [44 , 50 , 103] . Briefly , 0 . 1–0 . 5 mg of tubocurarine ( Sigma ) was injected intramuscularly to immobilize the fish for electrophysiology and behavioral experiments . The fish was then transferred to an experimental tank ( 30 cm × 30 cm × 10 cm ) containing water from the animal’s home tank and respired by a constant flow of oxygenated water through their mouth at a flow rate of 10 mL min–1 . Subsequently , the animal’s head was locally anesthetized with lidocaine ointment ( 5%; AstraZeneca , Mississauga , ON , Canada ) , the skull was partly exposed , and a small window was opened over the recording region ( hindbrain for ELL or midbrain for nP ) . The EOD of A . leptorhynchus is neurogenic and therefore is not affected by injection of curare . All stimuli consisting of AMs of the animal’s own EOD were produced by triggering a function generator to emit 1 cycle of a sine wave for each zero crossing of the EOD , as done previously [104] . The frequency of the emitted sine wave was set slightly higher ( 30 Hz ) than that of the EOD , which allowed the output of the function generator to be synchronized to the animal’s discharge . The emitted sine wave was subsequently multiplied with the desired AM waveform ( MT3 multiplier; Tucker Davis Technologies ) , and the resulting signal was isolated from the ground ( A395 linear stimulus isolator; World Precision Instruments ) . The isolated signal was then delivered through a pair of chloridized silver wire electrodes placed 15 cm away from the animal on either side of the recording tank perpendicular to the fish’s rostro-caudal axis . Depending on polarity , the isolated signal either added or subtracted from the animal’s own discharge . It is important to realize that these stimuli mimic the EOD AMs but not the PMs generated during encounters with conspecifics . This is not an issue here , as these FMs do not elicit responses from the neurons considered here . Further , previous studies have shown that the behavioral responses considered here ( see below ) do not require PMs [28] . In order to obtain behavioral and neural ( periphery: EAs; hindbrain: PCells; midbrain: STCells ) detection thresholds , we used a stimulus consisting of either a 5 Hz sinusoidal or a 5–15 Hz noise ( fourth-order Butterworth ) carrier waveform whose depth of modulation computed with respect to the animal’s unperturbed EOD amplitude increased from 0% to 100% . We found that EA detection thresholds were similar for both sinusoidal ( n = 15 ) and noisy ( n = 39 ) stimulus waveforms ( Kruskal-Wallis , df = 2 , p = 0 . 11 ) . Thus , detection threshold values for EAs were pooled . We only used the 5 Hz sinusoidal waveform for determining detection thresholds for ELL PCells , nP STCells , and behavior . We characterized each ELL PCell as either “ON” or “OFF” type using a noisy AM stimulus with frequency content of 0–120 Hz , as done previously [25 , 105] . In this case , the standard deviation of the AM was adjusted as in previous studies [51 , 76 , 106 , 107] , as measured using a small dipole placed close to the animal’s skin in the middle of the animal’s rostro-caudal and dorsoventral axes ( typically 0 . 2 mV cm−1 ) . We note that it is likely that some of the variations in threshold values obtained for EAs are due to the location of the pore on the animal’s skin relative to the stimulus . The composition of the vehicle/control saline was as follows ( all chemicals were obtained from Sigma ) : 111 mM NaCl , 2 mM KCl , 2 mM CaCl2 , 1 mM MgSO4 , 1 mM NaHCO3 , and 0 . 5 mM NaH2PO4 . The pH of the saline solution was 6 . 8 . Glutamate ( Sigma ) , lidocaine ( Astra Pharmaceuticals ) , and CNQX ( Sigma ) was dissolved in saline before application , as done previously [23] . Drug application electrodes were made using two-barrel KG-33 glass micropipettes ( OD 1 . 5 mm , ID 0 . 86 mm , A-M Systems ) and pulled by a vertical micropipette puller ( Stoelting ) to a fine tip and subsequently broken to attain a tip diameter of approximately 5 μm for each barrel . The two barrels were used for separate application of either lidocaine ( 1 mM ) or CNQX ( 1 mM ) , as well as glutamate ( 1 mM ) or saline . During ELL recordings for which the EGP indirect feedback was blocked with CNQX , we first used excitatory responses to glutamate application to confirm that we were within proximity of the pyramidal neuron we were recording from , as done previously [76] . CNQX was then applied to the neuron to ensure a local effect . Complete feedback inactivation was achieved by inserting 2 pipettes containing lidocaine bilaterally into nP . In order to block the direct feedback from the midbrain area TS , we performed unilateral injections of lidocaine on the contralateral TS while recording from PCells within the ipsilateral ELL . Injection locations were guided by the Apteronotus brain atlas [108] and determined based on somatotopic mappings . We inserted a glass pipette ( 20–30 μm tip ) and pressure injected lidocaine at a few depths between 1 , 000–1 , 500 μm with 4–5 puffs each at a pressure of 15–20 psi and 130 ms of injection time , as done previously [43] . We note that this manipulation also blocks ascending input to higher-order brain areas mediating behavior . As such , we did not investigate the effects of injecting lidocaine within TS on behavioral responses . For behavioral recordings , injections of lidocaine , saline , and CNQX were performed bilaterally in nP and ELL , respectively , as done previously [23 , 76] . All pharmacological injections were performed using a duration of 130 ms at 15–20 psi using a Picospritzer ( General Valve ) . Indirect feedback inactivation was assessed by comparing the baseline firing rates of PCells before and after drug application , as shown in a previous study [55] . Sharp glass micropipette electrodes ( 20–40 MΩ ) backfilled with 3 M KCl were used to record in vivo from EAs within the deep fiber layer of ELL , as described in previous studies [20 , 49 , 109] . EAs can be easily identified based on their high baseline ( i . e . , in the absence of stimulation ) firing rates as well as from the fact that their probability of firing increases with increasing EOD amplitude [47 , 110] . The recording electrode was advanced into the ELL with a motorized microdrive ( IW-711; Kopf ) . We used well-established techniques to perform extracellular recordings with Woods metal electrodes from PCells [111] located within the lateral segment of the ELL based on recording depth and mediolateral placement of the electrode on the brain surface , as done previously [21 , 48 , 105] . Similarly , we performed extracellular recordings with Woods metal electrodes from STCells in nP . STCells were confirmed based on the recording depth as well as their low spontaneous firing rate and response-tuning curves to sinusoidal AMs based on previous characterization ( see S5 Fig ) [30] . All recordings were digitized at a 10 kHz sampling rate using CED 1401 plus hardware and Spike2 software ( Cambridge Electronic Design ) and stored on a computer hard disk for offline analysis . Animals were immobilized by an intramuscular injection of 0 . 1–0 . 5 mg tubocurarine and set up in the recording tank , similarly to the method described in the Recording section . Depending on which feedback pathway was pharmacologically inactivated , different surgeries were performed . Briefly , to inactivate the nP direct feedback pathway , both sides of the midbrain were exposed rostrally to T0 [108] , and double-barrel pipettes containing saline and lidocaine were inserted into the nP ( 1 , 000–1 , 250 μm ) . To inactivate the EGP indirect feedback pathway , both sides of the hindbrain ELL were exposed to the caudal-lateral edge , where pipettes containing CNQX were inserted superficially ( 100–400 μm ) . Multiple injections ( typically 3–5 ) were performed to ensure that both hemispheres of nP and ELL were sufficiently affected by the pharmacological agents . Stimuli were then presented as in the Recording section in order to elicit behavioral responses . The animal’s behavior was recorded through a pair of electrodes located at the rostrum and tail of the animal . The fish’s time-varying EOD frequency was extracted either by computing a spectrogram of the recorded signal or from the zero-crossings of the recorded EOD signal . For the former , the EOD frequency was then determined as the frequency with the highest power near the fourth harmonic of the fish’s baseline EOD frequency , and the extracted frequency was then divided by 4 in order to get the true EOD frequency of the fish . For the latter , the zero-crossings were used to generate a binary sequence ( as described in the Recording section ) that was low-pass filtered ( second-order Butterworth filter with 0 . 05 Hz cutoff frequency ) to obtain the time-varying EOD frequency . Quantitatively similar results were obtained using either methodology . All data analysis was performed offline using custom-written codes in MATLAB software ( MathWorks ) . The recorded membrane potentials were first high-pass filtered ( 100 Hz; eighth-order Butterworth ) . Spike times were defined as the times at which the signal crossed a given threshold value from below . A binary sequence R ( t ) was then constructed from the spike times of each P-unit in the following manner: time was first discretized into bins of width dt = 0 . 1 ms . The value of bin i was set to 1 if there was a spike at time tj such that i × dt < tj < ( i + 1 ) × dt and to 0 otherwise . Note that since the bin width dt is smaller than the absolute refractory period of the neuron , there can be at most 1 spike time that can occur within any given bin . The firing rates were obtained by filtering the binary sequence using a second-order Butterworth filter with 0 . 05 Hz cutoff frequency . Both neural and behavioral response detection threshold values to the stimulus were characterized by the intensity at which the firing rate or EOD frequency first became significantly different at the p = 0 . 05 level from those observed in the absence of stimulation . Specifically , the threshold was determined as the intensity for which either the neural or behavioral response was first outside the range of values that contains 95% of the probability distribution in the absence of stimulation . We note that changing the percentage value did not alter the qualitative nature of our results ( S7 Fig ) ( Kruskal-Wallis , df = 2 , Behavior: p = 0 . 99; EAs: p = 0 . 99; PCs: p > 0 . 66 ) . Statistical significance was assessed through a nonparametric Kruskal-Wallis test with Bonferroni correction or Wilcoxon sign rank test for paired measures at the p = 0 . 05 level . Values are reported as box plots unless otherwise stated . Error bars indicate ± SEM . On each box , the central mark indicates the median , and the bottom and top edges of the box indicate the 25th and 75th percentiles , respectively . The whiskers extend to the most extreme data points not considered outliers , and the outliers are plotted individually using the “•” symbol . | Feedback input from more central to more peripheral brain areas is found ubiquitously in the central nervous system of vertebrates . In this study , we used a combination of electrophysiological , behavioral , and pharmacological approaches to reveal a novel function for feedback pathways in generating neural and behavioral responses to weak sensory input in the weakly electric fish . We first determined that weak sensory input gives rise to responses that are phase locked in both peripheral sensory neurons and in the central neurons that are their downstream targets . However , central neurons also responded to weak sensory inputs that were not relayed via a feedforward input from the periphery , because complete inactivation of the feedback pathway abolished increases in firing rate but not the phase locking in response to weak sensory input . Because such inactivation also abolished the behavioral responses , our results show that the increases in firing rate in central neurons , and not the phase locking , are decoded downstream to give rise to perception . Finally , we discovered that the neurons providing feedback input were also activated by weak sensory input , thereby offering further evidence that feedback is necessary to elicit increases in firing rate that are needed for perception . | [
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... | 2018 | Descending pathways generate perception of and neural responses to weak sensory input |
The high mortality of melanoma is caused by rapid spread of cancer cells , which occurs unusually early in tumour evolution . Unlike most solid tumours , thickness rather than cytological markers or differentiation is the best guide to metastatic potential . Multiple stimuli that drive melanoma cell migration have been described , but it is not clear which are responsible for invasion , nor if chemotactic gradients exist in real tumours . In a chamber-based assay for melanoma dispersal , we find that cells migrate efficiently away from one another , even in initially homogeneous medium . This dispersal is driven by positive chemotaxis rather than chemorepulsion or contact inhibition . The principal chemoattractant , unexpectedly active across all tumour stages , is the lipid agonist lysophosphatidic acid ( LPA ) acting through the LPA receptor LPAR1 . LPA induces chemotaxis of remarkable accuracy , and is both necessary and sufficient for chemotaxis and invasion in 2-D and 3-D assays . Growth factors , often described as tumour attractants , cause negligible chemotaxis themselves , but potentiate chemotaxis to LPA . Cells rapidly break down LPA present at substantial levels in culture medium and normal skin to generate outward-facing gradients . We measure LPA gradients across the margins of melanomas in vivo , confirming the physiological importance of our results . We conclude that LPA chemotaxis provides a strong drive for melanoma cells to invade outwards . Cells create their own gradients by acting as a sink , breaking down locally present LPA , and thus forming a gradient that is low in the tumour and high in the surrounding areas . The key step is not acquisition of sensitivity to the chemoattractant , but rather the tumour growing to break down enough LPA to form a gradient . Thus the stimulus that drives cell dispersal is not the presence of LPA itself , but the self-generated , outward-directed gradient .
Melanoma is an unusually aggressive cancer , which often metastasizes early during tumour development [1] . Tumours that have not clinically metastasized are frequently curable , but patients are far less likely to survive if tumours have metastasized before they are surgically removed , and metastasis is the principal cause of cancer mortality [2] . The most influential prognostic factor in predicting metastasis and survival is the thickness of the tumour ( the “Breslow depth” ) [3] . There is a dramatic increase in the risk of metastasis with only millimeter increases in Breslow depth [3] . This characteristic is unlike most solid tumours , in which the cytological morphology of the tumour cells and the individual genes mutated in the cancer are more important than size alone . Metastasis is therefore an important , and undermedicated , potential target for cancer therapy [4] , [5] . One principal reason behind the aggressiveness of melanoma derives from the developmental history of melanocytes , the pigment producing cells in the skin that mutate to form melanomas . During mammalian development melanoblasts , the melanocyte precursors , emerge from a restricted location at the neural crest , and migrate rapidly from there throughout the developing dermis , before maturing into melanocytes on the basement membrane of the epidermis [6] . Thus a substantial level of cell migration is required for even skin pigmentation . Even in adults—for example following treatment for vitiligo—melanocytes can spread significant distances from the hair follicles to repopulate the surrounding skin . The melanocyte lineage is thus inherently migratory . However , several questions about melanoma progression remain unanswered . The first is what drives melanomas to change from the relatively benign radial growth phase ( RGP ) to the far more invasive vertical growth phase ( VGP ) ( see schematic diagram in Figure 1A ) . In RGP melanomas , cells only spread horizontally along the basement membrane , compared to VGP melanoma cells , which are also capable of spreading both upwards into the epidermis ( Pagetoid spread ) and downwards , into and through the dermis ( invasion ) . This spread raises the related question , of what drives cells to migrate away from the primary tumour . Simple , random migration is an extremely inefficient way of dispersing cells and also unlikely to drive cells to invade through matrix and basement membranes . Chemotaxis—cell migration directed by gradients of soluble signalling molecules—is implicated as an important driver of metastasis by a wide range of data [7] , [8] , and is considered necessary to drive efficient invasion . In breast cancer , for example , some tumour cells migrate towards epidermal growth factor ( EGF ) [9] . However , EGF gradients have only been inferred in vivo , never measured , and their sources are usually unclear . In the case of breast cancer , the EGF is thought to be secreted by macrophages recruited in a paracrine loop by the tumour [10] , but for other attractants and cell types the sources of chemotactic signals are not known . In the melanoma literature , most chemotaxis is attributed to growth factors such as platelet-derived growth factor ( PDGF ) and EGF [11] and the CXCR4 ligand SDF-1 [12] , though a wide variety of potential attractants have been discussed [13] . Gradients of growth factor or SDF-1 have not been identified in vivo , they can only be inferred from the cells' behaviour or pattern of responses in vitro . Chemotaxis assays are typically performed in transwell chambers , in which cells are grown on one side of a membrane filter and potential attractants are added to the other side . Chemotaxis is assayed by the number of cells observed on the far side of the filter after a fixed interval . These assays are subject to a wide range of artifacts . Cells' behaviour during chemotaxis cannot be studied , which makes it extremely difficult to distinguish chemotaxis from directionless changes in migratory behaviour ( i . e . , chemokinesis [14] ) . Potential attractants form extremely steep and rather short-lived concentration gradients , unlike the physiological conditions the assay aims to reproduce . More seriously still , conditions either side of the filter may be discretely different; cells may grow , survive , or adhere better on one side of the filter than the other , giving changes in the numbers of cells that can be artifactually interpreted as chemotaxis . Direct viewing chambers , such as Dunn , Zigmond , or Insall chambers , are more laborious to use but yield a far higher quality of data , with fewer artifacts [15]–[17] . In work described here , we use direct-viewing chambers to identify lysophosphatidic acid ( LPA ) as a far more potent chemoattractant for melanoma cells than other previously described attractants . We have developed and refined two direct-viewing assays to assess mechanisms of cell dispersal and chemotaxis , allowing us to distinguish chemotactic from chemokinetic and contact-driven responses under defined conditions that minimize artifacts . Furthermore , the use of direct-viewing chambers makes comparison of attractants' relative efficiencies practical . The suggested role of chemoattractants in cancer dispersal—whether growth factors , chemokines , or LPA—raises the crucial question of how gradients are generated . Chemotaxis will only work with signals that are presented as gradients—homogeneous signals contain no directional information—and the steeper the gradient , the more efficient the chemotaxis . Chemical gradients are typically effective over distances of less than a millimetre—limits on the efficiency of diffusion make larger gradients impractical [18] . Thus for a gradient to be formed there must be a gradient source that is close to the tumour . Alternatively , local gradients may be formed from signals that are widely produced , but are absorbed or broken down locally . This local depletion mechanism is potentially just as effective as local production , but less often invoked . In the cancer literature , only localised sources are typically invoked , for example individual macrophages within the vasculature attracting cancer cells within the tumour [10] . If cells that are responding to a stimulus are also responsible for breaking it down , the result is a self-generated gradient . Under these conditions the gradient is always oriented away from the current location of the cells . One such example has been shown during the development of the zebrafish lateral line primordium [19]–[21] , in which a dummy receptor locally absorbs an SDF-1 stimulus to set up a gradient that is detected by a different receptor . In this work we find that melanoma cells self-generate chemotactic gradients from unlocalised , exogenous LPA . These gradients tend to direct cells to disperse outwards from tumours , thus directly promoting metastasis . Furthermore , we measure LPA gradients across real melanomas in vivo . Since melanomas of sufficient size both generate their own LPA gradients and respond to them , chemotaxis-steered spread of melanomas is almost inevitable .
To examine the signals that drive the spread of melanoma cells , we set up 2-D assays for tumour cell spread using a direct-viewing chemotaxis chamber that allows detailed analysis of cell migration [15] . The chamber contains two wells , connected by a bridge that allows diffusion of attractants but not flow . Both cells were homogeneously filled with complete medium , but cultured melanoma cells [22] were only seeded in one well , at a range of different densities . Our initial results were surprising: Cells consistently spread outwards from the well in which they started , even in uniform medium without an externally applied gradient ( Figure 1B; Movie S1 ) . This effect was density-dependent; cells plated at 2×103 or 6×103 cells/well barely migrated , while 2×104 cells/well migrated up to 350 µm in 24 hours ( Figure 1C and 1D ) . This behaviour strikingly resembles the behaviour of real melanomas , in which the chance of metastasis is more correlated with tumour thickness than any other parameter [3] . This type of density-dependent spreading requires individual cells ( or small clusters of cells ) to migrate away from the bulk population . This dispersal occurred in our assays; cells moved directly away from the well they resided in with unprecedented accuracy ( Movie S1 ) . This directed , non-random migration can only occur if the moving cells perceive a directional cue from the bulk population of the cells to spread . We therefore analyzed the nature of the signal that was directing cells away from the bulk population . The most probable signalling mechanisms are contact inhibition of migration [23] or chemotaxis . We therefore examined these potential mechanisms in turn . Contact inhibition ( of migration , as opposed to the more frequently described contact inhibition of growth ) is an effective mechanism for short-range dispersal in which cell∶cell contact directs cells away from one another . It has been shown in other neural crest-derived cell types [24] . However we found no evidence to suggest it drives cell dispersal in our assays . Movie S2 shows one example in which cells spread both individually and while contacting one another . Some cells steer accurately outwards through multiple cycles of new pseudopods independently of cell∶cell contact . Others continue to migrate outwards when contacting the cell in front , where contact inhibition predicts these cells should reverse into the space behind them . Analysis of the paths of individual cells ( Figure S1 ) shows that cell-cell contact is not steering cells; the paths of cells that are contacting others , have recently contacted others , and are not in contact are strikingly similar . The one apparent example of contact inhibition ( Movie S2 , cell 2 ) changed the cell's direction but did not improve its outward accuracy . Thus while these cells may experience contact inhibition , we considered chemotaxis as the most likely mechanism steering them away from the main population . Cells could generate chemotactic gradients to drive dispersal by either of two mechanisms . They could secrete an autocrine chemorepellent and migrate away from it . We have previously shown this to be a key driver of Entamoeba pathogenesis [25] , in which chemotaxis away from ethanol generated by the amoebas themselves causes cells to migrate from the lumen of the gut into the walls of the gut and eventually the liver of the patient . Alternatively , the melanoma cells could locally break down or consume a chemoattractant that is produced externally , but spatially homogeneously [26] , [27] , as seen in the zebrafish lateral line primordium [19] , [21] . In either case , dense populations of cells create a gradient that consistently directs migration away from themselves . We considered that homogeneous attractants would most likely derive from the serum added to full medium . To find if dispersal used a repellent or a consumed attractant , we compared cell dispersal in serum-free and normal medium . Cells in serum-free medium are healthy and motile in control movies , but do not migrate away from one another ( Figure 1E ) , demonstrating that the cells do not secrete chemorepellents . We also compared cells moving out of fresh medium into serum-free and full medium . Cells dispersed far more efficiently into the rich medium ( Figure 1F ) , implying that they are driven by attractants in fresh medium rather than an inhibitor whose production depends on serum . To test whether consumption of a component of serum produces a positive chemotaxis response , we compared migration in uniform serum to an assay in which cells are exposed to a gradient between serum-free medium and medium supplemented with 10% serum ( Movie S3 ) . We found that both assays produced similar directed migratory responses; cells migrated towards the opposite well with or without a preformed serum gradient ( Figure 1G ) . This finding further supports the concept that the outward migration is driven by positive chemotaxis , most likely towards a chemoattractant globally present in the serum but depleted around the cells . We tested this hypothesis using a more traditional chemotaxis assay , in which cells are spread homogeneously over the field at the start of the assay , giving the cells the opportunity to move in any direction [14] . We loaded cells into the chamber in complete medium that had been conditioned by melanoma cells for 48 hours , then replaced the medium in one well with fresh medium containing 10% serum . The cells migrated towards the well containing fresh medium very efficiently ( Figure 2A and 2B ) , showing that an attractant in fresh medium is consumed by the melanoma cells . We confirmed that chemoattractants are present in normal serum by exposing melanoma cells—again homogeneously seeded in the chemotaxis chamber—to exogenous gradients of serum . In homogeneous serum-free medium the cells were healthy , and migrated , but randomly ( Figure 2C ) . When a gradient of serum was applied , the cells migrated towards the higher concentrations with unprecedented precision ( Figure 2D ) ; their paths are overwhelmingly oriented up-gradient , in a manner more usually associated with neutrophils and Dictyostelium [28] than cancer cells , which typically chemotax less accurately [29] . The high chemotactic index was maintained throughout a sustained period , with narrow and accurate confidence interval , and strongly significant Rayleigh test [30] for directional migration ( Figure 2E ) . Thus serum contains a remarkably potent chemoattractant for melanoma cells . We therefore conclude that melanoma dispersal across the chamber is driven by positive chemotaxis towards an attractant that is present in serum . The attractant is broken down by the cells themselves into a gradient that efficiently disperses cells . One potential explanation for cancer cells becoming metastatic is that they evolve chemotactic competence as the tumours develop [13] , [31] , [32] , and thus move from unsteered to steered migration . We therefore examined the ability of a panel of cell lines isolated from different tumour stages and selected for physiologically appropriate behaviour ( Figure 3A ) [22] . Surprisingly , all the lines we examined responded chemotactically to serum gradients ( Figure 3B ) . Cells from metastases were more motile than cells from earlier stages ( Figure 3C ) ; highly invasive ( VGP ) cells were slightly more accurate , but not significantly faster than the biologically earlier , RGP cells . Cells from more advanced tumours responded more robustly , but the progression from nonmetastatic to metastatic was not marked by the cells newly acquiring responsiveness—all lines examined were chemotactic enough to spread away from the tumour efficiently in the presence of an appropriate gradient . Several lines of data suggest that genetic and epigenetic changes during progression from RGP to VGP increase cells' ability to survive [33]; our data imply that it is cell survival , rather than chemotactic sensitivity , that defines the difference . The increase in migratory ability could modulate cells' ability to escape from a primary tumour , but our principal conclusion is that melanoma cells from all stages are chemotactic . There are multiple reports of chemotaxis driving metastasis of melanoma and other tumour cells , in particular breast cancer . Published accounts of chemotactic invasion most often describe growth factors as the attractants—for example EGF for solid tumours [34] , and EGF , hepatocyte growth factor ( HGF ) , and stem cell factor ( SCF ) /KitL for melanoma [13] . However these attractants were often identified in transwell chambers , which as earlier discussed are subject to a range of artifacts , in particular false positive . For example , the positive well might promote survival , growth , or adhesion of cells that move randomly across the membrane . Our direct-viewing chambers provide a far more rigorous analysis . We therefore tested a broad range of attractants in our assays . To our surprise , no growth factor acted as an attractant to any measurable degree ( Figure 4A ) ; steep or shallow gradients gave no obvious movement upgradient , and no significant chemotactic index towards any growth factor tested ( Figure 4B ) . We therefore conclude that the chemotaxis towards serum we observed was unlikely to be towards growth factors . This does not , of course , demonstrate that melanoma cells are never chemotactic towards growth factors; but it clearly shows the surprising and efficient chemotaxis towards serum observed earlier is mediated by another molecule . EGF and PDGF did increase cells' speed ( Figure 4C ) , but they did not provide directional specificity . They therefore acted as chemokines , regulating overall cell behaviour , rather than as chemoattractants that could steer the cells . The striking accuracy of chemotaxis demonstrated by melanoma cells towards serum was more reminiscent of neutrophil chemotaxis towards formyl peptides , or Dictyostelium towards cAMP , which signal through G-protein coupled receptors ( GPCRs ) rather than growth factor receptors like EGFR and PDGFR . We therefore investigated SDF-1 , the ligand for the GPCR CXCR4 , which has been associated with poor prognosis and malignancy of melanoma [35]; but again , it was not measurably attractive to cells in our assays ( Figure 4B , compare with strong response to serum ) . However , LPA , another well-known component of serum that signals through GPCRs , was strikingly attractive to melanoma cells . A gradient from 0 to 1 µM LPA across the chamber ( consistent with the approximate concentration of LPA in serum; see below ) induced chemotaxis almost as effectively as 0%–10% serum ( Figure 4D ) , yielding a comparable chemotactic index ( Figure 4E ) . This was a surprise: LPA is more typically described as an inflammatory mitogen , acting on haematopoietic cells such as macrophages . It appears frequently in the cancer literature , but more often as a mitogen and chemokine for cancer cells , acting via autotaxin , which catalyzes the production of LPA from lysophosphatidylcholine [36] . However in our assays the chemotaxis of melanoma to LPA was again remarkably accurate compared with the weaker chemotaxis typically seen in cancer cells [37] . To examine whether LPA was the principal attractive component of serum , we assayed chemotaxis in the presence of the antagonist Ki16425 , which specifically inhibits binding to LPA receptors 1 and 3 [38] . The effects were again remarkably clear . 10 µM Ki16425 blocked cell spread in our original , density-dependent assay ( Movie S4 ) and chemotaxis towards 10% serum ( Figure 5A; Movie S5 ) , reducing the chemotactic index from more than +0 . 4 to zero ( Figure 5B ) . Ki16425-treated cells were obviously healthy , and moved similarly to untreated cells , with similar track lengths , showing that the treatment was not making the cells nonspecifically sick or non-motile . Knockdown of LPAR1 by siRNA had a similar effect ( Figure S2A ) , showing that LPAR1 is the key receptor for this process , and 10 µM Ki16425 also blocked chemotaxis towards pure LPA ( Figure S2B ) . Again , LPA chemotaxis is not tumour stage-specific; Ki16425 blocked chemotaxis in all cell lines from all stages of cancer progression ( Figure 5C ) . RGP and VGP cell lines were completely inhibited , and the highly motile metastatic lines were substantially inhibited . The residual chemotaxis in the presence of inhibitor could represent either incomplete inhibition by the antagonist , or a small amount of chemotaxis to another agent . From these data , we conclude that LPA is overwhelmingly the dominant chemoattractant in serum for all lines examined . While chamber-based assays are optimized to allow accurate and detailed recording , they provide a 2-D view of a process that more often happens in 3-D tissues in vivo [39] . We therefore examined the role of LPA in a widely used organotypic tumour cell invasion model [40] . In this system melanoma cells are added to the top of a plug of collagen in which fibroblasts are growing , and over time they migrate vertically downwards into the 3-D matrix . During the course of the assay , the collagen plug is set so only its bottom face contacts the medium , at which point malignant melanoma cells invade downwards [41] . We hypothesized that the melanoma cells were driven by a self-generated LPA gradient as in Figure 1B , once fresh LPA could only be supplied from the bottom . This hypothesis is supported by assays in which the collagen plugs remain submerged , and no invasion is seen ( Figure S3 ) , further rejecting contact inhibition of migration as a mechanism of invasion . When the gels were treated with Ki16425 , the melanoma cells did not invade downwards into the gel ( despite comparable numbers of cells at the end , showing no change in growth or survival ) . Quantitative analysis confirms that Ki16425 strongly inhibited invasion in both cell lines that were invasive in this assay ( Figure 5D and 5E ) . Thus LPA is a dominant steering system for 3-D organotypic assays , as well as for 2-D chamber assays . Our earlier data ( Figures 1B and 2A , in particular ) showed that melanoma cells disperse by depleting a chemoattractant from serum . We therefore tested whether melanoma cells are able to deplete LPA from their surroundings . Full medium with and without serum was incubated with different densities of melanoma cells for different times , then LPA was extracted from the conditioned medium and analyzed by mass spectrometry [42] . This confirms that the melanoma cells effectively break down LPA; the conditioned medium was depleted in a density-dependent manner ( Figure 6A ) and in a timescale that correlates with the medium conditioning experiments in Figure 2A and 2B . One advantage of using mass spectrometry is the identification of molecular subspecies . The biological activity of LPA is known to vary with its structure [43] , [44] . In particular , there is a strong correlation between biological activity and the degree of polyunsaturation , and also acyl chain length [45] . Melanoma cells broke down the biologically active species more rapidly than the others ( Figure 6B ) , ensuring that the most active species also formed the steepest gradients . The results we have obtained conflict with the established dogma that growth factors are primary melanoma chemoattractants [13] . To reconcile these accounts with our data , we examined the role of growth factors during chemotaxis towards LPA . As shown previously ( Figure 4C ) , EGF and ( particularly ) PDGF increased the basal speed of cells . Gradients of EGF and PDGF , and mixtures of both , enhanced the accuracy of chemotaxis to LPA ( Figure 7 ) ; LPA , EGF , and PDGF together in serum-free minimal medium were as effective as 10% serum . Most tellingly , however , when cells were presented with LPA and growth factor gradients oriented in opposite directions , they chemotaxed towards the LPA not the growth factors; if anything they migrated towards the LPA with enhanced efficiency ( Figure 7B , bottom two lines ) . Thus when examined in the high levels of detail afforded by our chambers , the growth factors are potentially important accessory factors that increase cell speed and efficiency of chemotaxis , but they do not themselves act as chemoattractants . These results are reminiscent of observations of development in vivo , in which the growth factor SCF promotes migration but not direction of melanoblast migration [46] . It is possible that the melanoma chemotaxis to growth factors observed in other work [13] is due to changes in speed alone , which as discussed earlier can cause a false positive in transwell assays . It has also been shown that growth factors can cause cancer cells to secrete LPA [47] , which could also provide an element of indirect chemotaxis in many types of assay . We have clearly shown that LPA is a potent chemoattractant for melanoma cells of all biological stages . To determine whether this chemotaxis was an important driver of melanoma chemotaxis in vivo , we investigated whether the tissue surrounding real melanomas contained LPA gradients that would direct cells out of tumours . Mice that are heterozygotes for the driver mutation BrafV600E ( the most prevalent driver of human melanomas ) and deletion of the tumour suppressor PTEN develop sporadic melanomas ( Figure 8A ) genetically and cytologically comparable to human tumours ( Figure 8B ) . We took punch biopsies from the tissue in and across melanomas ( Figure 8C ) from several mice , extracted total lipids , and examined LPA levels using mass spectrometry . In all non-ulcerated melanomas we examined , LPA levels were low inside the tumour , higher at the edges , and higher still in the tissues immediately outside the tumour ( Figure 8D ) . Cells at the edges of the tumour are therefore experiencing an outward-oriented LPA gradient tending to drive them out into surrounding tissues and vasculature . We further examined the LPA species in the tissue . Forms that are strongly associated with signalling , in particular 18∶2-LPA and 20∶4-LPA [48] , formed the steepest gradients ( Figure 8E ) , while gradients of non-signalling forms such as 18∶0-LPA were flatter . This finding further supports the idea that the gradients of LPA are specifically produced as signals targeted at LPA receptors . This study is , to our knowledge , the first time a chemotactic gradient has been directly measured around tumours in vivo . There are a number of situations where the presence of a gradient has been inferred from cellular behaviour , most prominently in the paracrine loops shown by Segall and others [10] . However , such gradients must by definition be local and tend to be transient . The gradients we observe in melanomas are clear , large-scale , and provide a convincing driver for cell dispersal , and one highly plausible explanation of why melanomas above a certain size , and hence Breslow thickness , always tend to be metastatic .
The source of LPA around melanomas is unknown . In many tumours , including melanoma , expression of autotaxin and thus autocrine production of LPA has been associated with tumour progression [50] . This LPA production appears to be a mechanism for promoting melanoma growth , rather than driving chemotaxis and invasion . LPA generated by the tumour itself would be found at a higher level in the tumour than outside it , which would oppose outward dispersal and thus metastasis . Rather , we find that the melanoma cells in culture and in tissues break down externally generated LPA , making outward-facing gradients . LPA is therefore more likely to be generated through inflammatory processes—haematopoietic cells , in particular , are a principal source of LPA in tissues [51] —or by inducing LPA production from stromal cells . In metastatic breast cancer xenografts , expression of LPA receptor promotes cell growth and metastasis , but the LPA is made locally by platelets , which are in turn recruited by many tumours [52] . Platelets are also a rich source of growth factors [53] . Our data therefore implicate inflammation in initiating melanoma spread . This finding has important implications for therapy . Interventions that promote inflammation without removing the entire tumour could be extremely dangerous—diagnostic punch biopsies , in particular , could promote a wave of metastasis in response to LPA released by inflammation . From a therapeutic perspective , data from epidemiological studies suggests the anti-inflammatory drug aspirin can protect against metastasis [54] . The increased speed of the metastatic cells may be important , but may also be an artifact of selection . It remains unclear whether the increased speed of migration is clinically important , or whether the fastest strains will metastasize earlier , and thus be the first to be identified . Our data suggest that even less invasive cells move rapidly and accurately enough to metastasize , but our assays may miss factors that retard cell migration . We have shown that cultured melanoma cells from throughout tumour evolution are chemotactic towards LPA in transwell assays . A recent paper has reported the opposite , that LPA is a chemorepellent for B16 cells [37] . This seems a cell-line specific effect , as these highly derived and divergent cells do not express the LPAR1 and LPAR3 receptors , which are usually highly expressed and dominate LPA chemotaxis in our assays ( Figure S2A ) . We have found that melanomas generate their own chemotactic gradients from homogeneous LPA that is exogenously provided . LPA chemotaxis is an essential feature driving melanoma invasion in 3-D organotypic assays . We have also shown that real tumours create a chemotactic gradient of LPA in vivo . Taken together , these lines of evidence suggest a model of chemotaxis towards self-generated LPA gradients is a major driving force for melanoma dispersal ( Figure 9 ) . One unforeseen advantage of this model is that it also provides a simple unifying explanation for upward or pagetoid spread , which is a hallmark of the invasive VGP stage melanoma . We have measured actual LPA gradients in animals with experimentally induced melanomas . We have also shown that all the melanoma cells we tested perform chemotaxis towards LPA gradients , in both 2-D and 3-D assays . It is thus reasonable to conclude that LPA gradients are sufficient signals to mediate melanoma cell dispersal . To test whether LPA is necessary for melanoma metastasis in vivo will be very difficult . Our hypothesis is that LPA gradients drive intravasation from the tumour towards local blood vessels . Many widely used metastasis assays , for example tail-vein injection , completely miss this step . Slower assays , for example subcutaneously injected xenografts , metastasize impractically slowly , and to nonphysiological targets , in particular the lymph nodes . Pharmacological approaches , for example blockade of the LPA signalling system by LPA antagonists , are confounded by the importance of LPA to the vascular and haematopoietic systems . A mouse model of melanoma that metastasizes through a physiological route and can be crossed with inducible LPA receptor knockouts does not currently exist; when it is developed , such a model will be the ideal system for testing our model in vivo . The most important message from this work is that it is the gradient of LPA—not the presence of LPA per se—that contains the information . LPA is a very prevalent molecule . It is present at high levels in serum , and may be generated within tumours by cancer cells or exogenously by , for example , platelet activation . Interestingly , cells ahead of the main group do not respond even when an external gradient is applied ( in Movie S3 , for example ) . Presumably these cells reach a region where LPA levels are homogeneously high , at which point there is little or no guidance information available to them . Likewise , if too few cells are used in the spread cell assay , no chemotaxis is observed , suggesting that LPA breakdown is important even in classical chamber assays . We suspect that LPA is not a chemoattractant for low densities of cells , because they cannot break it down rapidly enough to form an appropriate local gradient . In our invasion assays , LPA becomes an attractant when—counterintuitively—cells are present at high enough densities to break down most of it . This means that the LPA gradient is self-generated by the melanoma . Self-generated gradients are currently highly topical . Recent papers showing the detailed roles of the CXCR4 and CXCR7 receptors ( which respond to and deplete SDF-1 , respectively ) during the formation of the zebrafish lateral line have caused a spike in interest , but other methods whereby cells drive creation of attractant gradients then respond to them occur in multiple systems , especially during embryonic development [26] , [27] , [55] , [56] . More generally self-generation provides a means whereby cells can maintain a directional cue over distances that are far too large for premade gradients . Furthermore , with externally formed gradients , the information that specifies the gradient must come from somewhere else . If an external gradient attracts cells during development , the secret to understanding the process lies with understanding where and by whom the attractant is being made . Self-generated gradients are different; there is no need for external information . The gradient is generated as an emergent property of the interaction between the cells and their environment . Thisconclusion is perhaps the most interesting feature of this work . In LPA chemotaxis during melanoma metastasis , there is no need for any other cell type to set up a local gradient . The melanoma cells first generate a gradient—once the tumour is thick enough—and then respond to it by migrating away . Thus the melanoma drives its own metastasis .
All mice used were control cohorts from other studies . Before they were humanely killed , all mice had reached the primary or secondary end-points of their designated study . All melanoma cell lines used are listed by biological stage of derivation and were transferred from the Wellcome Trust Functional Genomics Cell Bank ( Biomedical Sciences Research Centre , St . George's , University of London ) . Cells were maintained in Roswell Park Memorial Institute ( RPMI , Invitrogen ) 1640 medium , supplemented with 10% fetal bovine serum ( FBS ) ( PAA Labs ) , 2 mM L-Glutamine ( Gibco , Invitrogen ) , and 1% penicillin and streptomycin ( Gibco , Invitrogen ) . siRNA constructs were obtained from QIAGEN and transfected as per instructions . WM239A cells were challenged twice with siRNA , 48 hours apart , then used in the assay 48 hours after the second transfection . Insall chambers were manufactured and used as described [15] . The chambers were drilled in advance with a 1 . 3 mm drill bit using an overhead drill press . During drilling , the chamber was secured in a small machine vice sitting inside a V-block at 45° and a hole was drilled into each “rabbit ear” of the outer well to allow reverse filling . Cells were starved in PBS for 12 hours then seeded at a density of 5 . 5×104 cells/ml in CGM . Each cover slip was coated with 2 ml of the seeding suspension . After seeding cells , the six-well dish was shaken in the x and then y planes for 5 seconds each and placed in a CO2 incubator at 37°C on top of a shock absorbent base to prevent vibration induced patterns of cell accumulation . VALAP sealant ( vaseline , lanolin , and paraffin ) was prepared by combining the three components together in a weight ratio 1∶1∶1 and melting at 100°C on a heat block . A fine artist's paint brush was used to apply the VALAP . Cover slips were treated with human fibronectin ( BD Biosciences ) 1 mg/ml throughout , generating an adsorbed concentration of 4 . 17 µg/cm2 in the range of 1–5 µg/cm2 as suggested by the manufacturer . Following fibronectin coverslips were passivated with 0 . 5% ( w/v ) heat-treated BSA solution in PBS . Chemoattractants were added to serum-free RPMI medium as required . Addition of 5 mM HEPES to the media in the sealed chamber is essential to buffer the pH of the media throughout the experiment . LPA ( Sigma ) was dissolved in a 1∶1 ratio of distilled water: absolute ethanol to generate a 1 mM stock solution and stored at −20°C . To use this as a chemoattractant , BSA was diluted to a final concentration of 0 . 05% ( w/v ) to SFM-H ( SFM-HB ) and then 1 µl LPA was added to 1 ml to generate a 1 µM LPA solution . EGF ( Peprotech ) , PDGF , BB Homodimer ( Calbiochem ) , HGF/Scatter Factor ( Peprotech ) , and SDF-1α/CXCL12 ( Peprotech ) were dissolved in PBS to a stock concentration of 10–100 µg/ml , stored at −20°C and used as indicated . Ki16425 ( Cambridge Bio ) was stored in absolute ethanol at a stock concentration of 10 mM as per the manufacturer's instructions . In Insall chamber assays , cells were pre-incubated for 5 minutes with a 10 µM solution before combining with reagents in the chamber at the same concentration . We used a Nikon TE2000-E inverted time-lapse microscope equipped with a motorised stage ( Prior ) and Perfect Focus System ( PFS ) to prevent focal drift due to thermal fluctuations . The entire microscope was enclosed in a plexiglass box , humidified and maintained at 37°C with 5% CO2 . The Insall chamber experiments did not require the addition of supplementary CO2 . Our microscope system was driven by Metamorph software ( Molecular Devices ) and the x , y positions were manually selected and pre-loaded . Images were processed using ImageJ ( http://rsb . info . nih . gov/ij/ ) , if necessary using the Image stabilizer plugin ( http://www . kangli . org/code/Image_Stabilizer . html ) to correct for drift . Cells were tracked using MtrackJ ( http://www . imagescience . org/meijering/software/mtrackj/ ) to follow the path of the cell nucleus For consistency , we attempted to track a minimum of 40 cells in every chamber assay; in most cases this was sufficient to ensure statistical significance . The following criteria were used for deciding which cells to track: cells that moved more than 1 cell length in 24 hours; cells that tracked continuously until the end of the experiment or until the cell migrated off the bridge or rounded up in preparation for mitosis; cells were excluded that migrated onto the bridge during the experiment; avoided tracking post-mitotic cells . We developed an Excel spreadsheet ( written by DMV and AJM-M ) to facilitate the processing , analysis , and quantification . This spreadsheet automatically produces spider plots , speed , and chemotaxis index data over time . A time window was selected ( e . g . , 6–12 hours for melanoma cells ) and values zeroed within this window to produce end-point data . Chemotaxis index ( cosθ ) plots are presented as mean ± standard error of the mean ( SEM ) . Cosθ is a function of the distance migrated in the direction of the gradient divided by the euclidian distance ( the linear distance between the start and end position of the cell ) . These data were also processed in the Circstat toolbox for MATLAB by GK [30] . This process generated rose and polar plots with 95% confidence intervals and a Rayleigh test . Conditioned media were generated as follows . A sub-confluent 10 cm petri dish of WM239A cells was washed 3× with PBS then cells were split in a 1∶5 ratio into five new 10 cm petri dishes and combined with fresh CGM to a final volume of 10 ml . Conditioned medium was then harvested from one dish per time-point , staggered between 0–48 hours ( Marked T0 , T6 , etc . ) . All 10 ml was aliquoted into 10×1 ml eppendorf tubes . The samples were immediately frozen on dry ice before storing at −80°C . The cells in each dish were then counted . When needed aliquots of conditioned media were thawed at 37°C and centrifuged for 10 min using a lab top centrifuge , then filtering with a sterile 0 . 2 µm filter . Collagen gels were prepared by combining 2 mg/ml rat tail collagen solution , 10× Minimum Essential Medium ( MEM , Invitrogen ) , and 0 . 22 M NaOH in a ratio 8∶1∶1 . The pH was finely adjusted to pH 7 . 2 with the 0 . 22M NaOH . One volume of FBS containing 7 . 5×105 primary human skin fibroblasts ( passage 5–7 ) was immediately combined with 10 ml of the gel mixture on ice . After pipetting well , 2 . 5 ml of the gel and cell mixture was added to each 35 mm petri dish . The gels were then placed in a humidified incubator with 5% CO2 to set for 15–30 minutes . A further 1 ml MEM was added to each petri dish and the gels were carefully detached to enable gel contraction in the same incubator . The media was changed every 3 days . After 6–7 days the gels measured approximately 1 . 5 cm in diameter and were transferred to a 24-well dish ready for tumour cell seeding . 1–2×105 tumour cells were then counted and allowed to seed on the surface of each gel . The gel was carefully transferred with forceps to an elevated stainless steel grid ( Sigma , screens for CD-1 , size: 40 mesh ) and placed in a 6 cm petri dish and this was denoted day 0 . CGM was added to cover the grid and was then carefully aspirated to leave a meniscus around the base of each gel , thereby generating an air-liquid interface . Three gels were loaded onto each grid and the medium was changed three times weekly . In experiments using Ki16425 , the gels with adherent cells were pre-incubated for 5 minutes with 10 µM Ki16425 in the CGM before raising the gels to the air-liquid interface . 10 µM Ki16425 was maintained in the CGM throughout the experiment with thrice weekly media changes as before . A typical experiment lasted 7–12 days . At the end of the invasion assay , each gel was divided into two with a scalpel , fixed in 4% formaldehyde at 4°C and sectioned before being stained with haematoxylin and eosin . We used the inducible Tyr::CreERT2 BRAFV600E/+ PTENlox/− melanoma model [57] , in which the melanomas were all generated in mixed background mice from 6–12 weeks of age . Animals were treated with 2 mg tamoxifen topically to shaved back skin daily for 5 days . There was no discernable phenotype until naevi or primary melanomas started developing 6–8 weeks after induction predominantly on the treated area . Typical grooming behaviour spread the tamoxifen to other parts of the skin and/or was ingested leading to activation in other cutaneous regions . All mice used were control cohorts from other studies . Before they were killed , all mice had reached the primary or secondary end-points of their designated study . Suitable mice were identified with at least one and up to four tumours , ideally located on the back . The smallest tumour size was 4×4 mm to enable at least two areas to be sampled . Skin containing the tumours was rapidly dissected off the back and pinned slightly taut to paper overlying a corkboard . Sterile Punch Biopsy tools ( Stiefel ) were used to punch circular samples from the tumour and surrounding skin . The size of punch biopsy depended on the tumour size and varied from 3–6 mm in diameter . Samples were taken at various locations across the tumour and were coded as follows: within the tumour ( A ) , across the margin ( B ) , 5 mm from the margin ( C ) , and 10 mm from the margin ( D ) . Samples were immediately snap frozen in liquid nitrogen and transferred to a −80°C freezer for storage . Control samples of normal appearing skin in the same melanoma model activated with tamoxifen were used to calculate the basal level of LPA . Each section of mouse skin underwent a series of nine punch biopsies ( A , B , and C in three replicate series ) . Mice and human melanoma/skin samples ( 1–20 mg ) were pulverised after thoroughly cooling with liquid nitrogen . The pulverised powder was suspended in 750 µl water then used for LPA extraction . For cell culture media samples , 750 µl of cell culture media was used for LPA extraction . Media or tissue samples were spiked with 50 ng of 17∶0-LPA as an internal standard before extraction . LPA was extracted with 1 ml n-butanol three times at room temperature . The combined LPA extract was dried under vacuum with SpeedVac ( Thermo ) and re-dissolved in 60 µl chloroform/methanol/water 2∶5∶1 . 14 µl was injected for liquid-chromatography with tandem mass spectrometry ( LC-MS/MS ) analysis . For LC-MS/MS analysis , we used a Thermo Orbitrap Elite system ( Thermo Fisher ) hyphenated with a five-channel online degasser , four-pump , column oven , and autosampler with cooler Shimadzu Prominence HPLC system ( Shimadzu ) for lipids analysis . High resolution/accurate mass and tandem MS were used for molecular species identification and quantification . The identity of the lipid subspecies was further confirmed by reference to appropriate lipids standards . All the solvents used for lipid extraction and LC-MS/MS analysis were LC-MS grade from Fisher Scientific . The final amount of LPA ( ng ) is presented as a concentration per 750 ml of conditioned media analysed or per mg tissue . The data are represented graphically plotting mean ± SEM for the concentration of LPA versus conditioning time ( for conditioned media samples ) ; and distance from tumour margin ( for tumour samples ) . Samples were normalised to position “A” for comparison between tissue samples . | Melanoma is feared because it spreads very rapidly when tumours are relatively small . It is not known why this metastasis is so efficient and aggressive . In particular , it is not known what drives melanoma cells to start to migrate out from the tumour . Here , we have studied the chemical signals that guide the migration of melanoma cells . We find that a component of serum , lysophosphatidic acid ( LPA ) , functions as a remarkably strong attractant for all of the melanoma cells that we examined . We also observe that melanoma cells rapidly break down LPA . We conclude that melanomas create their own gradients of LPA , with low LPA in the tumour and high LPA outside . Since melanoma cells are attracted by LPA , this LPA gradient around the melanomas serves as a signal that drives the tumour cells out into the surrounding skin and blood vessels . Finally , we show that such gradients exist in a mouse model of melanoma . Self-generated LPA gradients are therefore an intriguing new driver for melanoma dispersal . | [
"Abstract",
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"cell",... | 2014 | Melanoma Cells Break Down LPA to Establish Local Gradients That Drive Chemotactic Dispersal |
Hemophagocytosis is a phenomenon in which macrophages phagocytose blood cells . There are reports on up-regulated hemophagocytosis in patients with infectious diseases including typhoid fever , tuberculosis , influenza and visceral leishmaniasis ( VL ) . However , mechanisms of infection-associated hemophagocytosis remained elusive due to a lack of appropriate animal models . Here , we have established a mouse model of VL with hemophagocytosis . At 24 weeks after infection with 1 x 107 Leishmania donovani promastigotes , BALB/cA mice exhibited splenomegaly with an average tissue weight per body weight of 2 . 96% . In the tissues , 28 . 6% of macrophages contained phagocytosed erythrocytes . All of the hemophagocytosing macrophages were parasitized by L . donovani , and higher levels of hemophagocytosis was observed in heavily infected cells . Furthermore , more than half of these hemophagocytes had two or more macrophage-derived nuclei , whereas only 15 . 0% of splenic macrophages were bi- or multi-nuclear . These results suggest that direct infection by L . donovani causes hyper-activation of host macrophages to engulf blood cells . To our knowledge , this is the first report on hemophagocytosis in experimental Leishmania infections and may be useful for further understanding of the pathogenesis .
Visceral leishmaniasis ( VL ) , also known as kala-azar , is caused by parasitic protozoa of the genus Leishmania . Countries endemic for VL include India , Bangladesh , Nepal , Brazil , Ethiopia and Sudan . It is estimated that there are 300 , 000 new cases of VL and 20 , 000 deaths annually ( WHO , 2012 ) . VL is characterized by clinical manifestations such as fever , weight loss , hepatosplenomegaly , and anemia . Leishmania parasites develop as promastigotes in the midgut of the infected sand fly . Once transmitted from sand fly to mammalian host through blood feeding , these parasites proliferate as amastigotes within macrophages in the spleen , liver , and bone marrow . Hemophagocytosis is a phenomenon that macrophages or histiocytes engulf erythrocytes and/or leukocytes in the bone marrow , liver or spleen [1] . Under normal conditions , macrophages phagocytose only senescent or injured blood cells . In some cases , however , engulfment of new and intact blood cells by hyper-activated macrophages or histiocytes can be observed at high frequency [1] . This uncommon hemophagocytosis has been reported in auto-immune disease [2 , 3] , malignancy [4–6] and infections with virus including Epstein-Barr virus and influenza virus [7 , 8] , bacteria including Salmonella and Mycobacterium [9 , 10] , and protozoa including Babesia and Leishmania [11 , 12] . Patients showing hemophagocytosis are often accompanied with other manifestations such as fever , pancytopenia , and splenomegaly , which are diagnostic criteria for hemophagocytic syndrome ( HPS ) or hemophagocytic lymphohistiocytosis ( HLH ) ( 1 ) . There are reports on hemophagocytosis in human VL patients [13–18] . In those reports , hemophagocytes engulfing red blood cells were observed in the bone marrow of VL patients [13–18] . Those hemophagocytes can be cleared after treatment with anti-leishmanial drugs , such as amphotericin B and sodium stibogluconate [13 , 15–18] . VL patients representing hemophagocytosis are often diagnosed as HPS since the typical symptoms of VL , including fever , splenomegaly and lymphadenopathy , are also common in HPS [14–16] . This misdiagnosis sometimes delays the treatment of VL [14 , 16] . In some infectious diseases hyper-activation of macrophages can be induced by the high levels of cytokines , such as interferon-γ ( IFN-γ ) , produced in response to the causative pathogen [19 , 20] . There is also a report that macrophages stimulated with IFN-γ and LPS become hemophagocytic [21] . However , the mechanisms that lead macrophages of VL patients to engulf their own blood cells have not been studied well . Examination of these cells in human VL patients is difficult because biopsy of bone marrow or spleen is a highly invasive procedure of some risk to the patients . Therefore , establishment of an animal model representing hemophagocytosis will facilitate an understanding of the underlying mechanisms of the phenomenon in VL cases . Indeed , animal models representing HPS have been established for some diseases and helped elucidate the mechanisms of hemophagocytosis [22–26] . However , reports on hemophagocytosis in experimental VL are lacking . Here , we report the establishment of a mouse model of VL exhibiting hemophagocytosis in which we analyzed the infection status of the phagocytes . These results demonstrate the importance of direct infection of parasites leading to hyper-activation of the macrophages and resulting in acquisition of the hemophagocytic character . These results serve as the first step in elucidating the process of hemophagocytosis during VL .
All animal experiments were reviewed and approved by the Animal Experiment Committee at the University of Tokyo ( Approval No . P14-930 ) . The experiments were performed in accordance with the Regulations for Animal Care and Use of the University of Tokyo , which were based on the Law for the Humane Treatment and Management of Animals , Standards Relating to the Care and Management of Laboratory Animals and Relief of Pain ( the Ministry of the Environment ) , Fundamental Guidelines for Proper Conduct of Animal Experiment and Related Activities in Academic Research Institutions ( the Ministry of Education , Culture , Sports , Science and Technology ) and the Guidelines for Proper Conduct of Animal Experiments ( the Science Council of Japan ) . Collection of peripheral blood was performed under anesthesia with isoflurane . At the end of the experiments , the animals were euthanized by exsanguination under anesthesia with isoflurane followed by cervical dislocation . Male BALB/cA mice were purchased from Japan Clea , Tokyo , Japan . All mice were maintained under specific pathogen-free conditions . The mice were used for experiments at the age of 6–8 weeks . Leishmania donovani promastigotes ( MHOM/NP/03/D10; a gift from the National BioResource Project at Nagasaki University [27] ) were cultured in medium TC199 ( Nissui Pharmaceutical , Tokyo , Japan ) supplemented with 10% heat-inactivated fetal bovine serum ( Thermo Scientific , Waltham , USA ) and 25 mM HEPES buffer ( MD Biomedicals , France ) at 25°C . L . donovani promastigotes in late log or stationary phase were washed with phosphate-buffered saline ( PBS: Nissui Pharm ) by centrifugation at 1 , 600×g for 10 min and were resuspended with PBS at the concentration of 1 × 108 cells/ml . Mice were infected with 1 × 107 L . donovani promastigotes by intravenous injection into the tail vein . Blood was collected from the orbital sinus of mice under anesthesia with isoflurane ( Pfizer Japan Inc . , Tokyo , Japan ) both 12 and 24 weeks after infection using heparinized capillary tubes ( TERUMO , Tokyo , Japan ) . Hematocrit was determined by centrifuging the tubes at 15 , 000 × g for 10 min . Hemoglobin was measured following Zander‘s procedure [28] , and the number of blood cells were counted by microscopic examination . For analysis of polychromatic erythrocytes , thin blood smears were prepared using the heparinized blood , followed by fixation with methanol ( WAKO , Osaka , Japan ) for 5 min and staining with 5% Giemsa solution ( Merck KGaA , Parmstadt , Germany ) for 25 min . The ratio of polychromatic erythrocytes to total erythrocytes was calculated through microscopic observation of the stained smears at 200× magnification . After the blood collection for hematological analyses , cardiac puncture was performed on those same mice under isoflurane anesthesia to collect the whole blood . The mice were then sacrificed by cervical dislocation to collect the spleen , liver , and bone marrow . Serum was collected from the blood after centrifuging at 5 , 000 × g for 10 min and analyzed for erythropoietin level using a Mouse Erythropoietin Quantikine ELISA Kit ( R&D Systems , Minneapolis , USA ) . Stamp smears of the spleen and liver were fixed for 5 min in methanol and stained for 25 min with 5% Giemsa solution . Amastigotes were counted by microscopic observation of the stained smear at 1 , 000× magnification , and Leishman-Donovan Units ( LDU ) were enumerated as the number of amastigotes per 1 , 000 host nuclei times the tissue weight in grams as performed in a previous study [29] . The tissues collected at the time of sacrifice were fixed with 20% buffered formalin ( Sumitani Shoten Co . , Ltd , Osaka , Japan ) and embedded in paraffin . Four-micrometer thick sections were prepared from the paraffin-embedded tissues . The sections were dewaxed and stained with Mayer’s hematoxylin solution ( WAKO ) for 90 sec and rinsed in running tap water for 1 hour . Next , the sections were stained with eosin solution ( MUTO PURE CHEMICALS CO . , Ltd . , Tokyo , Japan ) for 2 min . In the HE-stained splenic sections , the number of infected macrophages , hemophagocytes , and multinucleated giant cells and the total number of splenic macrophages were counted in 5 random microscopic fields of the red pulp at 1 , 000× magnification . Also , around 100 hemophagocytes were individually analyzed in each section of the red pulp ( 1 , 000× magnification ) for the number of host nuclei as well as the number of L . donovani amastigotes present . In this study , a macrophage was defined as a large cell ( ~20 μm in size , except for multinucleated giant cells ) with large cytoplasm and round non-polymorphic nucleus . In contrast , hemophagocytes and multinucleated cells were defined as those cells containing engulfed red blood cells inside their phagosomal compartments and those cells with multiple round nuclei , respectively . Immunohistochemical staining was performed to characterize the subpopulation of heavily infected and multi-nucleated macrophages . Paraffin-embedded tissues were dewaxed and boiled in Tris-EDTA buffer ( 10 mM Tris Base , 1 mM EDTA solution , 0 . 05% Tween 20 , pH 9 . 0 ) or 10 mM sodium citrate buffer ( pH 6 . 0 ) for 20 min , followed by washing with tap water . Endogenous peroxidase was inactivated with 0 . 3% H2O2 in methanol for 30 min . After blocking with Block Ace ( DS Pharm . , Osaka , Japan ) , rabbit anti-mouse CD11b ( Abcam , Cambridge , UK ) , rat anti-mouse F4/80 ( AbD Serotec , Oxford , UK ) or MOMA-2 antibody ( rat monoclonal; Abcam ) antibody was applied to the serial sections of spleens , and the sections were incubated for 1 h at room temperature and washed with PBS . Horseradish peroxidase ( HRP ) -conjugated anti-rat IgG ( Nichirei Bioscience , Tokyo , Japan ) or biotinylated anti-rabbit IgG ( Nichirei ) was applied , and the sections were incubated for 1 h at room temperature and washed with PBS . For the CD11b staining , alkaline phosphatase-conjugated streptavidin ( Nichirei ) was applied , and the sections were incubated for 1 h at room temperature . After enzymatic color development was performed using 3 , 3'-diaminobenzidine ( Nichirei ) or 4- [ ( 4-amino-m-tolyl ) ( 4-imino-3-methylcyclohexa-2 , 5-dien-1-ylidene ) methyl]-o-toluidine monohydrochloride ( new fuchsine , Nichirei ) , the sections were counterstained with Mayer’s hematoxylin solution for 1 min and rinsed with tap water . Statistical comparisons of means between naive and infected mice were performed by two-way ANOVA followed by Bonferroni’s multiple comparison test or unpaired t test with GraphPad Prism 6 software ( GraphPad Software , Inc . , La Jolla , USA ) . A difference between groups was considered as statistically significant when the P value was less than 0 . 05 .
L . donovani infection induced hepatosplenomegaly in BALB/cA mice . The spleen and liver of the infected mice became significantly larger in size over time than those of uninfected mice . Mean weight per body weight ± SD of the spleen and liver from those infected mice were 0 . 72 ± 0 . 18% and 2 . 96 ± 0 . 16% at 12 weeks post-infection ( p . i . ) , and 6 . 95 ± 0 . 25% and 6 . 81 ± 0 . 11% at 24 weeks p . i , respectively ( Fig 1A and 1C ) . In contrast , those of the uninfected mice ( age-matched to the 24 week-infected mice ) were 0 . 27 ± 0 . 02% and 5 . 40 ± 0 . 20% , respectively . Parasite burdens in both tissues also showed increases from 12 to 24 weeks p . i . Means ± SD of LDU for the spleen were 58 . 9 ± 37 . 0 at 12 weeks p . i . and 796 ± 159 at 24 weeks p . i . , and for the liver the means ± SD were 551 ± 282 at 12 weeks p . i . and 2 , 291 ± 279 at 24 weeks p . i . ( Fig 1B and 1D ) . Microscopic observation of HE-stained spleen at 24 weeks-infected mice revealed pathological changes compared with that of the uninfected tissues . The enlarged spleens from the infected mice were coupled with expansion of both red pulp and white pulp; nevertheless , both red pulp and white pulp were structurally intact . The red pulp showed more significant expansion than white pulp and was filled with an increased number of macrophages/histiocytes with a characteristic of diffuse histiocytosis . There were other myeloid cells including monocytes and neutrophils observed in the red pulp of the infected spleen , with macrophages/histiocytes constituting the major myeloid cells in the area and the predominant host cells for the observed amastigotes . In addition to hepatosplenomegaly , the 24 week-infected mice exhibited anemia with lower hematocrit , hemoglobin and red blood cell counts ( 38 . 7 ± 2 . 16% , 13 . 9 ± 1 . 49 g/dl and 6 . 73 x 106/μl , respectively ) than the naive mice ( 45 . 2 ± 1 . 48% , 17 . 3 ± 0 . 83 g/dl and 8 . 53 x 106/μl , respectively ) ( Fig 1E to 1G ) . Those infected mice had higher levels of serum erythropoietin compared with the naïve mice ( 1 , 589 ± 712 pg/ml vs . 94 . 3 ± 100 pg/ml ) ( Fig 1H ) . Furthermore , a higher frequency of polychromatic erythrocytes was observed in the peripheral blood of infected mice than of naïve mice ( 4 . 70 ± 1 . 76% vs . 1 . 39 ± 0 . 30% , respectively ) ( Fig 1J ) . At 12 weeks post-infection , no significant decrease in the above hematological parameters was found ( Fig 1E ) . Next , the spleen of the 24 week-infected mice was examined for hemophagocytosis by microscopic observation of the HE-stained section . Erythrocytes were observed to be internalized in phagosomal compartments of the splenic macrophages in the red pulp ( Fig 2 ) . At 24 weeks , hemophagocytes accounted for 28 . 6% of the total splenic macrophages in the infected mice . In contrast , such macrophages were not observed in the spleen of uninfected mice . The liver and bone marrow of the 24 week-infected mice were also examined for the presence of hemophagocytes . Although amastigotes were detected in those tissues , there were less frequency of hemophagocytes in the bone marrow than the spleen and no detectable levels of hemophagocytes in the liver ( S1 Fig ) . At 12 weeks of post-infection , hemophagocytes were less frequently observed; they were only 9 . 41% of the total splenic macrophage population . Histological analyses on the spleens from the 24 week-infected mice also revealed that all of the hemophagocytes were infected with amastigotes although only about one-half ( 50 . 2% ) of splenic macrophages were parasite infected ( Fig 3 ) . Furthermore , hemophagocytosis was observed more often in heavily infected macrophages . The infection status was also categorized based on the number of parasites per macrophage . The percentages of splenic macrophages with low ( 1–10 amastigotes ) , moderate ( 11–20 amastigotes ) , and high ( more than 20 amastigotes ) parasite infections , were found to be comparable ( 16 . 0 ± 1 . 7% , 19 . 4 ± 2 . 4% and 14 . 7 ± 1 . 5% , respectively ) ( Fig 3 ) . In contrast , the majority of hemophagocytes were categorized in the high infection group ( 66 . 5 ± 6 . 2% ) , followed by moderate ( 26 . 2 ± 7 . 9% ) and low ( 11 . 7 ± 3 . 7% ) ( Fig 3 ) . Besides having a heavy infection , the multinucleated giant cell ( MGC ) phenotype was prominent in those hemophagocytes ( Fig 4A ) . The multinucleated macrophages accounted for 15 . 0 ± 6 . 2% of the total splenic macrophages ( Fig 4B ) . No MGC were observed in spleens of uninfected mice . Although a few multinucleated macrophages were found in the liver or bone marrow of the infected mice , the ratio in those tissues was lower than that in spleen . The multinuclear phenotype was even more prominent in hemophagocytes where 60 . 4 ± 5 . 8% of splenic hemophagocytes of the infected mice were multinucleated ( Fig 4B ) . Immunohistochemical staining was performed on the spleen from uninfected or 24 week-infected mice by using anti-F4/80 , anti-CD11b or MOMA-2 antibodies . F4/80 and CD11b signals were observed broadly in the red pulp of the uninfected spleen , whereas a MOMA-2 signal was detected only in a limited population of cells in both red pulp and white pulp ( S2A , S2C and S2E Fig ) . MOMA-2-positive cells increased after 24 weeks of infection , and the increase was more prominent in the red pulp , whereas such an increase was not evident for F4/80 and CD11b ( S2B , S2D and S2F Fig ) . The F4/80 staining of MGCs harboring amastigotes was positive and of similar intensity to the other surrounding cells , while MGCs were actually less intensely stained with anti-CD11b than other nearby positive cells ( Fig 5A and 5C ) . Infected MGCs were MOMA-2-positive with a more concentrated staining pattern compared to surrounding non-MGCs ( Fig 5B ) .
We have established for the first time an experimental animal model of Leishmania infection representing hemophagocytosis ( Fig 2 ) , a phenomenon observed in human VL cases . The reason for our success could be because few observers have monitored Leishmania infection over such an extended period , or it could be a phenomenon that is more readily seen using the parasite strain we used in our study . Limits placed on sampling in human cases make it difficult to parse out how Leishmania infection induces hemophagocytosis . Hemophagocytosis sometimes accompanies pancytopenia , splenomegaly and anemia , three major symptoms of VL . Therefore , hemophagocytosis may have a role in the occurrence of each of these VL symptoms . There are only isolated reports investigating anemia in rodent models of Leishmania infection . The only report on murine anemia deals with L . tropica infection [30] , not infection by the L . donovani complex . L . donovani-induced anemia has only been reported in hamster models [31] . Although hamsters are regarded as a better model for VL pathology than mice , there are disadvantages to this model: a paucity of immunological reagents ( e . g . , antibodies ) and animal handling and welfare ( e . g . , housing , generation of transgenic animals ) . In the present study , for the first time we demonstrated anemia in mice infected with L . donovani ( Fig 1 ) . The fact that anemia and hemophagocytosis , two clinical manifestations observed during human VL , can be achieved in a mouse model provides an avenue to investigate immunopathological mechanisms . In this study , we used L . donovani D10 strain , which is different from strains such as LV9 and LV82 that have been used by other researchers [31 , 32] . If anemia/hemophagocytosis is D10-specific , a comparative study of those strains that induce anemia with those that do not may facilitate identification of parasite factors responsible for VL-associated anemia . Animal models have been reported for Salmonella- [21] , EB virus- [26] and Trypanosoma brucei [33]-associated hemophagocytosis . However , mechanisms underlying infection-associated hemophagocytosis appear to vary among these diseases . For example , infection of mice with T . brucei , a related trypanosomatid parasite and a causative agent of sleeping sickness , induces hemophagocytosis by macrophages [33] . However , unlike leishmaniasis , T . brucei remains extracellular in its mammalian hosts and infection-associated hemophagocytosis in trypanosomiasis is distinct from that observed with leishmaniasis since we demonstrated that direct infection by Leishmania amastigotes is an important factor leading macrophages to be hemophagocytic . This is supported by our finding that heavily infected macrophages were more phagocytic than those with no or low infection ( Fig 3 ) . In this respect , hemophagocytosis in VL seems to differ from that in EB virus or T . brucei infection where direct infection is a dispensable step in hemophagocytogenesis . On the other hand , Pilonieta et al . have reported that , in the case of Salmonella infection , the bacteria can be found in precisely the same types of hemophagocytes as those seen during L . donovani infection [21] . The authors speculated that Salmonella in hemophagocytes derive nutrient iron from commandeered erythrocytes . This speculation may be applied to Leishmania parasites because they do not have a heme synthetic pathway [34] . The finding that hemophagocytosis is most prominent in heavily infected macrophages provides an argument against the idea that damages/changes to erythrocytes contribute significantly to this phenomenon . Peripheral erythrocytes from the 24-week infected mice showed no apparent damage when examined by microscopy , and there was no apparent difference in osmotic fragility of erythrocytes from uninfected mice ( S3 Fig ) . The other suggested major cause of hemophagocytosis is opsonization by autoantibodies [3] . The emergence of anti-erythrocyte antibodies has been reported in human VL patients [35 , 36] . To test for this , we performed a direct agglutination test using anti-IgG to detect autoantibodies bound to the murine erythrocytes from Leishmania-infected mice . No agglutination was observed by the test ( S4 Fig ) . Identification of MGCs as the major hemophagocytes ( Fig 4 ) also supports the idea that a macrophage abnormality , not an erythrocyte abnormality , is a principle factor leading to hemophagocytosis in infected mice and possibly human VL . There are several types of MGCs including Langhans giant cell and foreign body giant cell . Cytokines and T cells play key roles in MGC development: IFN-γ is a key factor for development Langhans giant cells [37] , whereas IL-4 and IL-13 seem important for foreign body giant cell formation [38 , 39] . Involvement of cytokines and lymphocytes in hemophagocytosis has been reported for various infectious diseases . IFN-γ and CD8+ T cells are central in hemophagocytosis during lymphocytic choriomeningitic virus infection [22] . Also , IFN-γ-deficient mice failed to manifest hemophagocytosis during T . brucei infection [33] . Both IFN-γ and IL-4 can cause anemia/hemophagocytosis through different pathways [40 , 41] . L . donovani-infected macrophages may also receive extra signals including cytokines embarking them down the hemophagocytic pathway . Positive signals of F4/80 confirmed the macrophage lineage of MGCs , whereas the MGCs had a reduced CD11b signal compared with non-infected CD11b+ macrophages ( Fig 5 ) . A recent study demonstrated that CD11blow F4/80+ cells are involved in efferocytosis [42] , which seems to be related to phagocytosis of unopsonized erythrocytes [43] . In fact , indications are that IFN-γ-induced hemophagocytosis follows a similar process as efferocytosis [40] . Also , MGCs were strongly stained with MOMA-2 antibody . Although the target molecule of MOMA-2 antibody has not been identified yet , the antibody seems to stain a small subpopulation of macrophages [44] . Lang et al . have previously reported that macrophages stained with MOMA-2 are the major host macrophages for L . donovani in the spleen [45] , which is consistent our findings . Together , characterization of MCGs as F4/80+/CD11blow/MOMA-2+ may be useful for isolation of these cells by cell sorting , providing the means to further study their activation status and mechanisms involved in hemophagocytogenesis and hemophagocytosis . | Anemia is one of the major clinical manifestations during visceral leishmaniasis ( VL ) , whereas mechanisms behind this symptom remain elusive . To get a better understanding of the responsible mechanism ( s ) , we have developed for the first time a mouse model of VL exhibiting anemia . Mice chronically infected with L . donovani had low hematocrit , hemoglobin and erythrocyte counts while having up-regulated erythropoiesis , suggesting hemolytic events due to infection . We propose here that hemophagocytosis is one of the hemolytic events associated with anemia in the infected mice . The spleen is the major place for hemophagocytosis; there , multinucleated giant cells heavily infected with amastigotes are markedly observed and are the major cell type phagocytosing erythrocytes . These results suggest that heavy infection of macrophages with Leishmania parasites triggers phagocytosis of erythrocytes resulting in anemia during murine VL . Because hemophagocytosis has been reported in human VL cases , reproduction of the pathology in mice may facilitate an understanding of the mechanisms leading to anemia during VL . | [
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... | 2016 | Hemophagocytosis in Experimental Visceral Leishmaniasis by Leishmania donovani |
The tegument protein U14 of human herpesvirus 6B ( HHV-6B ) constitutes the viral virion structure and is essential for viral growth . To define the characteristics and functions of U14 , we determined the crystal structure of the N-terminal domain of HHV-6B U14 ( U14-NTD ) at 1 . 85 Å resolution . U14-NTD forms an elongated helix-rich fold with a protruding β hairpin . U14-NTD exists as a dimer exhibiting broad electrostatic interactions and a network of hydrogen bonds . This is first report of the crystal structure and dimerization of HHV-6B U14 . The surface of the U14-NTD dimer reveals multiple clusters of negatively- and positively-charged residues that coincide with potential functional sites of U14 . Three successive residues , L424 , E425 and V426 , which relate to viral growth , reside on the β hairpin close to the dimer's two-fold axis . The hydrophobic side-chains of L424 and V426 that constitute a part of a hydrophobic patch are solvent-exposed , indicating the possibility that the β hairpin region is a key functional site of HHV-6 U14 . Structure-based sequence comparison suggests that U14-NTD corresponds to the core fold conserved among U14 homologs , human herpesvirus 7 U14 , and human cytomegalovirus UL25 and UL35 , although dimerization appears to be a specific feature of the U14 group .
Human herpesvirus 6B ( HHV-6B ) and the closely-related virus HHV-6A are classified as Roseolovirus genus of beta herpesvirus subfamily [1] [2] [3] [4] , which also includes human herpesvirus 7 ( HHV-7 ) and human cytomegalovirus ( HCMV ) . HHV-6B is a causative agent of exanthema subitum for children [5] [6] by primary infection and for immunocompromised adults by reactivation of the latent virus . Diseases induced by HHV-6 primary or reactivated infection are sometimes severe , causing encephalitis [7] [8] . Herpesviruses share a common architecture of the virion that is enveloped and contains the double-stranded DNA genome in a protein shell known as capsid . The space between the envelope and the capsid is filled with a pool of tegument proteins [9] [10] . The composition of tegument proteins differs among herpesviruses , and numerous tegument proteins have been identified for HHV-6B [11] . Tegument proteins are versatile proteins suggested to have additional functions other than acting as structural components of the viral tegument [12–14] . Thus , the characteristics and function of each tegument protein remain to be defined and could have a role in understanding herpesvirus pathogenesis . HHV-6B U14 is a tegument protein that is 604 amino acid residues in length . U14 belongs to the herpes pp85 superfamily that is shared among beta herpesviruses , and has no homologs in alpha and gamma herpesvirus [11] [15] [16] [17] . HHV-6A , HHV-6B , and HHV-7 have U14 with relatively high sequence homology . Other members of the beta herpesvirus subfamily , including HCMV , have two tegument proteins belonging to the pp85 superfamily , UL25 and UL35 [18] , although their sequence identities with U14 is less than 20% . Recently , HHV-6A U14 was revealed as an essential factor in the viral life cycle because a three amino-acid deletion in the U14 sequence resulted in a defect in viral growth [19] . In addition , U14 of HHV-6A and HHV-6B associate with the tumor suppressor protein p53 in the nucleus and cytoplasm , finally being incorporated into virions with p53 [20] . Furthermore , we found that HHV-6A U14 induces cell cycle arrest in G2/M phase by associating with a cellular protein , EDD during early phase of infection [21] . These results indicate that HHV-6 U14 functions not only as a virion tegument protein , but also in viral DNA replication cycles , suggesting that it is a multi-functional protein . Structure determination of tegument proteins is an effective approach , providing information about their structural characteristics as well as a basis for mapping the results of biochemical experiments . In this study , we solved the crystal structure of the N-terminal region of U14 protein derived from HHV-6B . The structure represents a characteristic dimer form with potential functional sites . Through sequence comparison with HHV-6A U14 , HHV-7 U14 , and HCMV UL25 and UL35 , shared and specific features among these homologs are discussed .
Full-length HHV-6B U14 ( 603 amino acids ) was expressed in E . coli with MBP at the N-terminus ( U14-MBP; Fig 1A ) . During purification , a fraction of U14-MBP was degraded to a smaller size , indicating that the C-terminal region of U14-MBP is unstable in E . coli ( S1 Fig ) . Thus , a new construct was designed to express the U14-NTD corresponding to the N-terminal region ( residues 2–458 ) in the form of an N-terminal MBP fusion ( Fig 1A ) . MBP-U14-NTD was not degraded significantly during purification ( S1 Fig ) , supporting the assumption that C-terminal region of MBP-U14 was degraded . Actually , the size of MBP-U14-NTD was similar to the degradation product of U14-MBP ( S1 Fig ) . In the size-exclusion column chromatography experiment , the retention time of U14-NTD was shorter than expected from its size ( 50 kDa ) , indicating that U14-NTD forms a multimer in solution ( Fig 1B ) . The size of U14-NTD estimated from the calibration curve was 118 kDa , which is slightly higher than the calculated size 100 kDa for a U14-NTD dimer . Because there was no available structural information of any protein with high sequence homology to HHV-6B U14 , a SeMet-derivative of U14-NTD was prepared to solve the phase problem by anomalous dispersion method . The structures of native U14-NTD and the SeMet-derivative were determined at 1 . 85 Å and 2 . 3 å resolutions , respectively ( Table 1 ) . There were two almost identical U14-NTD molecules in the asymmetric unit . Their RMS deviation for main-chain atoms and heavy atoms were 0 . 51 å and 1 . 09 å , respectively . Almost all of the U14-NTD residues were assigned to the electron density , with the exception of N-terminal residues 1–7 and C-terminal residues 456–458 , indicating that the designed U14-NTD represents an actual structural domain of U14 . U14-NTD has an elongated helix-rich structure composed of sixteen α helices , four 310 helices and two β strands . To facilitate structure description , the U14-NTD structure was divided into four subdomains ( SDs ) based on secondary structure topology and spatial arrangement ( Fig 2 ) . The four-helix bundle SD2 , which is composed of the N-terminal half of α4 , the C-terminal half of α10 , α11 and α12 , forms a central part of U14-NTD ( Fig 2 , cyan ) . At the preceding N-terminal region , SD1 forms a compact fold including helices η1 , α1 , α2 and α3 ( Fig 2 , magenta ) , and is associated with SD2 . SD3 is located at one side of the elongated long axis of U14-NTD ( Fig 2 , green ) . The C-terminal half of α4 and the N-terminal half of α10 are surrounded by five α helices ( α5 , α6 , α7 , α8 , and α9 ) and form a compact fold of SD3 . At the opposite side of the long axis , the C-terminal region of U14-NTD folds as SD4 composed of α13 , α14 , η2 , η3 , η4 , η5 , β1 , β2 , α15 and α16 ( Fig 2 , yellow ) . The β1 and β2 form a recognizable β hairpin that protrudes from the core overall fold . Analysis by the DALI program [22] with the latest set of Protein Data Bank ( PDB , www . rcsb . org , [23] ) entries revealed that SD2 is similar to a variety of proteins characterized by four-helix bundles ( Table 2 ) . In addition , SD3 showed marginal similarity to Unc-51-like kinase 3 and other proteins ( Table 2 ) . For SD1 and SD4 , no significant homology to known proteins was detected . In the crystal structure , a dimer is formed along the long axis of U14-NTD in an antiparallel orientation ( Fig 3 ) . The dimer can be regarded as two right hands shaking one another with the protruding β hairpins forming the “thumbs” . A two-fold axis is located by the side of the β hairpin , resulting in an arrangement of crossed hairpins . All of the SDs are involved in the dimer interface . The calculated buried surface area per monomer is 4146 Å2 ( Fig 3B , red ) , which is a relatively large value compared with those of known homodimer structures of similar size , at approximately 2800 Å2 [24] . One noticeable characteristic of the U14-NTD dimer is an internal cavity within the dimer interface ( Fig 3C ) . The two-fold axis of the dimer penetrates the cavity . The volume of the cavity is approximately 1200 Å3 and corresponds to 2 . 1% of the volume of the monomer ( 57400 Å3 ) . The internal cavity is enclosed with α4 and α10 of SD2 and α13 of SD4 from each monomer . The β hairpins of SD4s also face this cavity , forming a lid that separates it from the outer solvent . Broadly spanning electrostatic interactions contribute to the dimerization ( Fig 4A ) . At the dimer interface , a monomeric U14-NTD shows a negatively-charged surface between SD2 and SD4 . On the other hand , a positively-charged surface is found between SD2 and SD3 in the same monomer . In the dimer form , the negatively-charged area of each monomer faces the positively-charged area of the opposite monomer . There are a lot of hydrogen bonds within and around the electrostatically attracting areas . Four clusters were found and named as interaction sites a , b , c , and d ( Fig 4B ) . A total of 40 hydrogen bonds were formed in these areas , indicating tight and specific dimerization . Their distribution is summarized in S1 Table and the detailed interaction modes are shown in S2 Fig . The U14-NTD dimer shows characteristic multiple clusters of positive and negative electrostatic potential on the surface ( Fig 5A ) . At the β hairpin side , an extended negatively-charged area is formed across the two-fold symmetry axis ( front side , Fig 5A , left ) . The dimer surface of this side is composed primarily of SD4 . The β hairpin of each monomer contains six negatively-charged residues ( S3A Fig ) . The region 342–378 of SD4 , which corresponds to the outermost part of the long axis of U14-NTD , includes 11 negatively-charged residues ( S3B Fig ) . On the opposite side ( back side , Fig 5A right ) , the area around the two-fold axis is surrounded by seven positively-charged residues from each monomer ( S3D Fig ) . At this same back side , a negatively-charged cluster consisting of ten negatively-charged residues is observed at the peripheral area distant from the two-fold axis ( S3C Fig ) . The three amino acids , L424 , E425 , and V426 , of which deletion or substitutions to alanines caused a defect in viral growth [19] , were mapped to the β hairpin ( Fig 5B ) . The side-chains of L424 and V426 face the solvent side and constitute a continuous hydrophobic patch with the side-chain of I414 on the opposite β strand and the side-chain of L297 on SD2 of the partner monomer ( Fig 5B ) . In contrast to the flat surface of the front side , the back side has deep grooves along the dimer interface due to the staggered arrangement of monomers ( Fig 5C ) . One side of the groove is exclusively composed of SD3 , with SD4 and SD2 of the partner monomer forming the opposite wall . The length , depth , and width ( distance between monomers ) were roughly estimated to be ~30 Å , ~20 Å , and ~20 Å , respectively ( S4 Fig ) . To address issues of similarity and difference between HHV-6B U14 and its homologs , multiple sequence alignment was performed for HHV-6B U14 , HHV-6A U14 , HHV-7 U14 , and HCMV UL35 ( Fig 6 ) . The alignment combined with the structural information of U14-NTD showed that U14-NTD is a core part conserved among all members . In the core region , HHV-6A U14 and HHV-7 U14 are well aligned with HHV-6B U14 across all SDs . HCMV UL35 was also aligned in the core part , except for the SD4 , where short gaps are required for the alignment ( Fig 6 ) . For HCMV UL25 , another pp85 family protein , similar alignment was obtained , although the pattern is different from that of HCMV UL35 ( S5 Fig ) . These alignments suggest that these U14 homologs have a similar helix-rich fold at the core region . Next , we examined the conservation of the residues involved in the hydrogen bond network in the HHV-6B U14-NTD dimer . Most of the HHV-6B U14-NTD dimer interaction sites are occupied by identical residues in HHV-6A U14 and HHV-7 U14 , indicating that these homologs also dimerize in a similar manner . Of the 19 residues whose side-chains involved in the interaction , 17 and 16 residues are identical for HHV-6A U14 and HHV-7 U14 , respectively ( Fig 6 ) . By contrast , the residues participating in the dimer interface are different from HHV-6B U14 in HCMV UL25 and UL35 . For HCMV UL25 and UL35 , only 4 and 3 residues are identical to HHV-6B , respectively . It suggests that HCMV UL25 and UL35 takes a different form to that of the HHV-6B U14-NTD dimer .
The HHV-6B U14-NTD structure , comprised of residues 2–458 , reveals a helix-rich fold forming a compact homodimer . The broad and intricate interactions between each monomer ( Figs 3 and 4 ) , as well as the retention time of the size-exclusion column chromatography ( Fig 1B ) , support the suggestion that a dimer is the natural form for U14-NTD . Multimerization of viral proteins has been frequently reported , particularly for structural proteins constituting the capsid and associated proteins . A number of tegument proteins have also been shown to form self-associated multimers , such as HSV-1 UL36 [26] and VP22 [27] , HCMV pp65 [28] and pp28 [29] , and murine gammaherpesvirus 68 ORF52 [30] . Compared with these , the ~50 kDa U14-NTD is relatively large as a dimerization domain with a broad interface in which all four SDs are included . Although the viral matrix is considered to be an amorphous/disordered protein pool in general , multimerization of its constituents would impose local order to some extent as a corollary to the symmetries of their own and of their interaction sites for other partners . Such local order in the viral matrix has been suggested for matrix proteins of RNA virus; multimerization of matrix proteins relates to the formation of a protein lattice in the matrix and contributes to the membrane deformation required for the budding process [31] , [32] . Thus , the dimerization of U14 revealed in this research implies a role for this protein as a scaffold in the viral matrix . Analyzing the expression amount of U14 protein in virions would be required . As far as we know , the expression amount of HHV-6 U14 has not been investigated , hence it should be addressed in a future research . In the case of HHV-7 , U14 is known as a major antigen pp85 [33] , and U14 is thought to be relatively expressed abundantly . On the other hand , one of predominant major antigens of HHV-6 has been shown to be U11 [34] , [35] , which has been revealed to interact with U14 [36] . It may be noteworthy to mention that the HCMV UL25 was expressed abundantly especially in the dense body [37] . The HHV-6B U14 structure suggests multiple structural features as potential function sites , such as the negatively- and positively-charged clusters ( Fig 5A ) , the β hairpin flanked by the hydrophobic patch ( Fig 5B ) , and the grooves formed along the dimer interface ( Fig 5C ) . These distinct structural features would be consistent with the multiple functions of U14 . U14 is observed in at least three different locations during the protein's life cycle , namely in the nucleus of a host cell at an early phase of infection , in the cytoplasm at a late phase , and in the virion [20] . At each location , U14 could have a different role via interaction with different host/viral factors . Thus far , at least two associated host proteins are reported for U14: the tumor suppressor p53 [20] and EDD [21] . Experiments using deletion mutants of U14 revealed that three amino acids on the β hairpin and the C-terminal region outside U14-NTD are implicated in the interaction with p53 and EDD , respectively [21] . Among the potential function sites , the area around the β hairpin is of importance because substitution or deletion of three amino acids ( L424 , E425 , and V426 ) on the β hairpin results in a defect in viral multiplication [19] . It is expected that the deletion of the three amino acids strongly affects the β hairpin structure due to the imbalanced length of the two β strands . The β hairpin contributes to the dimer interaction ( Fig 4 and S2D Fig ) ; such deletion could change either the fold of U14-NTD or its dimerization and function . On the other hand , substitutions probably maintain the β hairpin structure because the original side chains are not involved in folding and easily simulated to be substituted without any necessity to change its structure . To further assess the importance of the β hairpin , we performed immunoprecipitation assay with HHV-6A U14 mutants in which residues on the β hairpin were substituted ( S6 Fig ) . The p53 interaction was abolished by the deletion of three amino acids corresponding to L424 , E425 and V426 as reported previously [21] . In contrast , a single alanine substitution at the corresponding position to I414 ( Fig 5B ) did not affect the interaction . Therefore , p53 is suggested to be sensitive to the change in β hairpin structure due to the deletion . Another possibility is that the β hairpin is involved in binding to other viral proteins , such as tegument proteins to form the tegument structure or capsid or envelope proteins to form the virion structure . Recently , we identified a major tegument U11 as the binding partner of U14 [36] , then the effect of the mutation/deletion on the β hairpin was also analyzed by the immunoprecipitation assay ( S6 Fig ) . The interaction between U14 and U11 was abolished by the deletion of the corresponding residues of the L424 , E425 and V426 . Moreover , in contrast to p53 , a single substitution at the corresponding residue of I414 ( I414A ) caused impaired interaction with U11 . Thus , we suggest that the β hairpin is the binding site for U11 and the exposed hydrophobic sidechain of I414 observed in the U14-NTD structure is likely to be recognized by U11 . Because U11 is an abundant and essential tegument protein of HHV-6 [36] , further research focused on the interaction via the β hairpin will provide more information about the functionality of U14 . SD3 of U14-NTD showed structural similarity with the MIT ( microtubule interacting and trafficking ) domain of Unc-51-like kinase 3 ( ULK3 , PDB ID: 4wzx , [38] ) by DALI analysis ( Table 2 and Fig 7A ) . Recent research revealed that the ULK3-MIT domain interacts with the MIM2 motif of ESCRT-III and phosphorylates the site , resulting in inhibition of cytokinesis during the cell division process [38] . The MIM2 binding site of the ULK3-MIT domain was superposed to a part in the deep groove observed around U14-NTD SD3 , and partially opened to the solvent ( Fig 7B ) . Thus , it is tempting to speculate that SD3 and the nearby groove of U14-NTD dimer serve as the binding site for ESCRT-III or related proteins , thereby contributing to their transporting function . Considering that ESCRT-III is involved in the viral maturation/budding step [39] [40] , further experiments are required to examine the relationship between U14 and the ESCRT system , thus further elucidating U14 function . Structure-based sequence analysis revealed that the HHV-6B U14-NTD corresponds to the core part conserved between U14 and UL25/UL35 ( Fig 6 and S5 Fig ) . The similarity across this region indicates that HHV-6A U14 , HHV-7 U14 , and HCMV UL25 and UL35 adopt the same elongated helix-rich fold . However , the absence of homology in HCMV UL25 and UL35 at the dimer interface region of HHV-6B U14 suggests that the dimer form is specific to U14 proteins . Because most of the structural features that likely constitute the functional sites of U14 depend on the dimer form , this information is not applicable to HCMV UL25 and UL35 . The C-terminal region outside U14-NTD contains a large proportion of hydrophilic and glycine residues ( S2 Table ) . This indicates that U14 consists of the core part with an intrinsically disordered tail [41] . The C-terminal region following the core fold differs in length among U14 and U25/U35 proteins ( HHV-6B U14: 147 residues , HHV-6A U14: 146 residues , HHV-7 U14: 190 residues , HCMV UL25: 13 residues , and HCMV U35: 169 residues ) , posing difficulty in obtaining valid sequence alignments . The variation in the C-terminal region has been indicated from the alignment between HHV-6A and HHV-7 U14 [33] . HCMV UL25 has a long extension of 180 residues that precede the core part instead of the C-terminal region observed in other homologs . The amino acid compositions of these extensions share a common propensity . Similar to the HHV-6B U14 C-terminal region , those of other homologs are dominantly composed of hydrophilic and glycine residues ( S2 Table ) . This indicates that these regions are intrinsically disordered without a stable structure in solution [41] , and thus the construction , a core fold followed by an unstructured tail ( s ) , is common for these proteins of the pp85 family . Interestingly , the proportion of serine residues is unusually high , around 20% , in these tail regions ( S2 Table ) , which suggests that the tail could be the site of post-translational modifications such as phosphorylation and glycosylation . The importance of the C-terminal region has been established for U14 and UL35 . The C-terminal region of HHV-6 U14 is required for interaction with EDD and , subsequently , cell cycle arrest [21] . HCMV UL35 has an isoform , UL35A , corresponding to the C-terminal 193 amino acids of UL35 . UL35A functions to modulate expression of immediate early genes [18] . Alignment between HHV-6B U14 and HCMV UL35 showed that UL35A includes only a part of α15 and α16 in SD4 , suggesting that UL35A is unlikely to form a stable fold on its own . The structural information derived from U14-NTD provides the basis for further structure-based analyses necessary for addressing the roles of these similar , but significantly different , tegument proteins .
The coding sequences for the U14 N-Terminal Domain ( U14-NTD ) was amplified by PCR from optimized viral DNA ( optimized by GeneOptimizer ) of HHV-6B strain HST using the U14 forward primer , with the HRV 3C protease site underlined , ( 5’- ACAGGATCCCTGGAGGTGCTGTTCCAGGGCCCCGAAGGCAGCAAGACCTTC-3’ ) and the U14 reverse primer ( 3’-ACAGTCGACTTACTCGTTCTGGTTCAGC-5’ ) . The PCR product was subcloned into pMAL-C2 using BamHI and SalI restriction sites . The cloned DNA fragment was sequenced with a 3130 Genetic Analyzer ( Applied Biosystems ) . For native U14-NTD , freshly transformed Escherichia coli strain BL21 cells were incubated at 37°C overnight in 10 ml lysogeny broth ( LB ) starter culture supplemented with 50 μg ml-1 carbenicillin . The starter culture was diluted into 1 liter LB medium supplemented with 50 μg ml-1 carbenicillin and grown at 37°C until an OD600 of 0 . 6–0 . 7 . Then the temperature was shifted to 20°C and the cells were induced with 0 . 3 mM isopropyl-β-D-thiogalactopyranoside ( IPTG ) . The expression was induced for 24 h . To prepare a selenomethionine ( SeMet ) derivative of U14-NTD for phase determination , Escherichia coli strain B834 was used as a host . Cells grown overnight in 10 ml LB medium were then diluted into 400 ml LB medium supplemented with 50 μg ml-1 carbenicillin and grown at 37°C until an OD600 of 0 . 9–1 . 0 . Cells were harvested by centrifugation and suspended in SeMet M9 medium supplemented with 50 μg ml-1 carbenicillin . The final volume of medium was 1 liter when the main culture was started . Cells were grown at 37°C until the OD600 reached 0 . 6–0 . 7 before being induced with 0 . 3 mM IPTG . The expression was induced for 16–20 h . Cells containing native U14-NTD or SeMet U14-NTD were harvested by centrifugation at 8000 ×g for 12 min at 4°C and suspended in column buffer ( 20 mM TrisHCl pH 7 . 4 , 200 mM NaCl , and 0 . 1 mM DTT ) with 0 . 5% v/v TritonX-100 . The lysate was stored at -80°C for 12–14 h , and then disrupted by sonication . Insoluble proteins were removed by centrifugation at 11000 ×g for 15 min at 4°C . As the pMAL-C2-encoded U14-NTD contained an N-terminal maltose-binding protein ( MBP ) tag , Amylose Resin ( NEW ENGLAND BioLabs ) was added to the supernatant and gently rocked at 4°C for 10–12 h . The Amylose Resin was spun down by centrifugation at 500 ×g for 5 min at 4°C and washed with cold column buffer five times before being applied to a 20 ml column ( BioRad ) . The column was washed with five column volumes of column buffer . The U14-NTD was eluted with column buffer containing 10 mM maltose . The MBP tag was removed by adding PreScission Protease ( GE Healthcare; 15 U mg-1 U14-NTD with 0 . 4 mM DTT ) using the HRV 3C protease site as described above . Further purification was carried out by size-exclusion chromatography using a Superdex 200pg column ( GE Healthcare ) . The column was calibrated by HWM Calibration Kit ( GE Healthcare ) . The protein was concentrated to 2 . 0–2 . 5 mg ml-1 using an Amicon Centrifugal Filter ( molecular weight cut-off 30 KDa , Millipore ) and the purity was assessed by SDS-polyacrylamide gel electrophoresis and Western blot using an antibody against MBP . Purified U14-NTD was passed through a 0 . 22 μm Ultrafree Centrifugal Filter ( Millipore ) to remove aggregate . The concentration of the protein was estimated based on an A280 of 0 . 75 for 1 mg ml-1 , calculated from the amino acid composition . Initial crystallization screening of U14-NTD was executed in 96-well plates at 4°C by the sitting-drop vapor-diffusion technique using the screening kit Index HTTM ( Hampton Research ) . Each drop was prepared by mixing 0 . 5 μl of protein solution ( both 2 . 5 mg and 1 . 25 mg ml-1 U14-NTD , 20 mM TrisHCl pH 8 . 0 , 100 mM NaCl , and 0 . 1 mM DTT ) with 0 . 5 μl reservoir solution , and was then equilibrated against 60 μl reservoir solution . Crystallization conditions were optimized by varying the pH , salt and precipitant concentration in 24-well plates . Finally , crystals suitable for X-ray analysis were obtained from drops prepared by mixing 1 . 0 μl protein solution ( 1 . 25 mg ml-1 ) with 1 . 0 μl reservoir solution consisting of 0 . 2 M Potassium sodium tartrate tetrahydrate and 16–18% w/v Polyethylene glycol 3 , 350 at 4°C . The crystals described here formed in 3–5 days and were harvested 30–40 days later to reach a size suitable for data collection . X-ray diffraction data were collected on beamline BL26B1 and BL26B2 at SPring-8 , Harima , Japan [42] . For data collection , crystals were transferred into a solution consisting of the reservoir solution supplemented with 25% glycerol prior to being flash frozen in liquid nitrogen . The data were processed using XDS [43] and indexed in space group P21212 . Dataset of SeMet-U14-NTD was collected at the peak wavelength of Se K-edge and used for the experimental phasing by Phenix . autoSol [44 , 45] . Dataset of native U14-NTD was solved by molecular replacement method with Phenix . phaser [46] . The SeMet-U14-NTD model was used as the search model . Structural refinement was performed with Phenix . refine [44] [47] and Coot [48] . Structural analysis and image depiction were performed using MolMol [49] and UCSF Chimera [50] . The synchrotron radiation experiments were performed at BL26b1 and BL26b2 in SPring-8 with the approval of RIKEN ( Proposal No . 2014B1234 , 2015A1070 , and 2015A1101 ) . The coordinates and structure factors for the U14-NTD structure has been deposited in the Protein Data Bank under the accession number 5B1Q . | Human herpesvirus 6B ( HHV-6B ) , a causative agent of exanthema subitum for children and immunocompromised adults , encodes numerous tegument proteins that constitute the viral matrix . HHV-6B U14 is a tegument protein essential for viral propagation , and additionally it interacts with host factors such as tumor suppressor p53 and cellular protein EDD , thereby regulating host cell responses . Here , we report the molecular structure of HHV-6B U14 at an atomic resolution . The N-terminal domain of U14 ( U14-NTD ) adopts an elongated , helix-rich fold without any significant overall similarity to known structures . U14-NTD forms a 100 kDa homodimer through electrostatic interactions and a wide hydrogen bond network . The U14-NTD homodimer displays four clusters of electrostatic potential with deep grooves , implying multiple binding sites for other viral or host proteins . U14-NTD corresponds to the core fold shared by homologous proteins of human herpesvirus 7 ( HHV-7 ) and of human cytomegalovirus , although dimerization seems to be specific to HHV-6 and HHV-7 . The U14-NTD structure provides clues to promote further analysis on the role and behavior of U14 in the pathogenesis of HHV-6 . It also leads to a comprehensive understanding of the U14 homologs in beta herpesviruses , and furthermore contributes to the overall knowledge about tegument proteins in herpesviruses . | [
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"biolo... | 2016 | Crystal Structure of Human Herpesvirus 6B Tegument Protein U14 |
In recent years there has been a strong development of computational approaches to mechanistically understand organ growth regulation in plants . In this study , simulation methods were used to explore which regulatory mechanisms can lead to realistic output at the cell and whole organ scale and which other possibilities must be discarded as they result in cellular patterns and kinematic characteristics that are not consistent with experimental observations for the Arabidopsis thaliana primary root . To aid in this analysis , a ‘Uniform Longitudinal Strain Rule’ ( ULSR ) was formulated as a necessary condition for stable , unidirectional , symplastic growth . Our simulations indicate that symplastic structures are robust to differences in longitudinal strain rates along the growth axis only if these differences are small and short-lived . Whereas simple cell-autonomous regulatory rules based on counters and timers can produce stable growth , it was found that steady developmental zones and smooth transitions in cell lengths are not feasible . By introducing spatial cues into growth regulation , those inadequacies could be avoided and experimental data could be faithfully reproduced . Nevertheless , a root growth model based on previous polar auxin-transport mechanisms violates the proposed ULSR due to the presence of lateral gradients . Models with layer-specific regulation or layer-driven growth offer potential solutions . Alternatively , a model representing the known cross-talk between auxin , as the cell proliferation promoting factor , and cytokinin , as the cell differentiation promoting factor , predicts the effect of hormone-perturbations on meristem size . By down-regulating PIN-mediated transport through the transcription factor SHY2 , cytokinin effectively flattens the lateral auxin gradient , at the basal boundary of the division zone , ( thereby imposing the ULSR ) to signal the exit of proliferation and start of elongation . This model exploration underlines the value of generating virtual root growth kinematics to dissect and understand the mechanisms controlling this biological system .
Regulation of plant growth and development has been the subject of intensive research for over a century and this will likely continue , given the increasing need for crop production to sustain a growing world population [1] . By virtue of new experimental approaches Arabidopsis thaliana has surpassed classical crops like wheat , tobacco and maize as the main model system to study the underlying molecular processes , thereby considerably advancing the field [2] . The principal processes that determine the growth of plant organs , including the primary root , are cell division and cell growth . Detailed analyses of these processes at high spatial and temporal resolution can be obtained by means of kinematic analyses providing a link with whole organ growth measurements [3] , [4] . Under consistent growth conditions , typically around six days after germination the Arabidopsis seedling root has established a steady root growth rate . At the distal end of the root a stable zone of cell proliferation ( division or proliferation zone: DZ or PZ , respectively ) precedes a delineated zone of accelerated growth ( elongation zone , EZ ) . Cells in the elongation zone grow anisotropically reaching 20-fold length increases in as little as six hours , by massive vacuolar expansion , before reaching their final length ( in the mature zone , MZ ) , where root hairs in specific epidermal cell files ( trichoblasts ) indicate a final differentiation [5] , [6] . This relatively simple outline of the growing root apex originates from a more complex underlying organisation . Arranged concentrically around the longitudinal axis , the root apex contains well-defined cell layers originating from stem cell-like initials organized in a tiered arrangement around a zone with low mitotic activity ( the quiescent centre , QC; [7] ) . These initials give rise to 6 distinct cell layers in the proximal direction: stele , pericycle , endodermis and cortex ( from initials proximal to the QC ) , epidermis and lateral root cap cells ( from initials lateral to the QC ) , and one in the distal direction: columella cells ( from initials distal to the QC ) . How the underlying tissue structure is established and maintained remains the subject of extensive investigation [8] . As ample evidence demonstrates , plant hormones play a central role in growth and development and they enable plants to respond to changing circumstances [9] . The wealth of molecular knowledge has inspired various attempts to capture aspects of those complex phenomena in mathematical models ( reviewed in [10] , [11] ) . Some modelling studies have focused on root growth . The landmark study of Grieneisen et al . [12] used experimental findings on PIN auxin-exporter distributions to reproduce realistic auxin patterns in the root apex that govern cell division and growth . Laskowski and coworkers [13] extended this model to account for lateral root initiation based on auxin maxima induced by root bending . Swarup et al . [14] and Band et al . [15] used modelling approaches to link asymmetric auxin redistribution at the root tip to the gravitropic response . Mironova et al . [16] presented the so called ‘reflected flow’ mechanism as an alternative to the ‘reverse fountain’ mechanism [12] to explain auxin self-organisation , especially during the very early stages of root development . Cruz-Ramírez et al . [17] proposed models that integrate radial patterns of the transcription factors SHR and SCR with longitudinal information from an auxin gradient to control asymmetric cell divisions required for differentiation within the Arabidopsis root . With a recent spatial model of the root tip Santuari et al . [18] implicate differential endocytosis in explaining how the auxin gradient is interpreted throughout the meristem . Band et al . [19] have modelled root growth as a single file of cells with growth-driven dilution of gibberellins providing a mechanism for growth arrest toward the end of the elongation zone . Although some of those models comprise cell growth and division , these processes are typically implemented in an arbitrary and artificial way since their relationship with hormone action has not been sufficiently elucidated . Moreover , plant cells are connected mechanically through their cell walls , which by their stiffness counteract the hydrostatic turgor pressure driving cell growth . The collective framework of growing cell walls ( apoplast ) , imposes major constraints on organ growth , preventing neighbouring cells to become separated ( precluding for instance ‘cell sliding’ ) . This type of growth , designated ‘symplastic’ , [20] , has only recently been represented in a vertex-based computational model of root growth [21] . By creating mechanical stresses , in principle the apoplast operates as an integrative function in coordinating organ growth [22]–[24] . In this study we explore the prowess of previously proposed mechanisms for regulation and coordination of growth and division in a symplastic framework by building and testing computational models within the simulation platform VirtualLeaf [25] . The simulation results are discussed in relation to classical kinematic studies of Arabidopsis thaliana .
Kinematic growth analyses aim to infer properties of cell growth and division from microscopic time series of the growing root , yielding detailed profiles of cell expansion ( extension ) and cell division ( partitioning ) along the principal axis of growth [3] . Among others Green [26] developed and advocated this method . Interestingly , Green also derived how proposed elemental growth models would be manifested in a kinematic framework . Based on our current concepts of growth regulation we have extended this analysis that relates changes in cell size along the root axis to relative rates of cell division and expansion . To this end we used the vertex-based plant modelling software VirtualLeaf [25] to directly simulate a selection of distinct growth models ( see Methods ) . These models evolve a two-dimensional cellular grid that represents an axial bisection of the growing root apex ( Figure 1A and 1B ) . Cells are defined as polygons and cell wall segments correspond to the edges acting as linear springs . Cells and cell walls are endowed with biochemical properties represented by reaction and transport equations and logical rules . Specifying regulatory mechanisms ( = input ) amounts to specifying rules that minimally determine ( i ) cellular growth rates ( via changing target areas ) , ( ii ) cell division rates and orientation ( here strictly horizontal ) , ( iii ) the transition at the border of the QC and the proximal meristem , ( iv ) the transition between division and elongation zone ( DZ and EZ , respectively ) , and ( v ) the transition to mature ( differentiated ) cells . By modifying those rules and analysing the resulting virtual phenotypes in terms of microscopic and kinematic characteristics we then delineate the requirements for cell growth and division needed to produce a realistic and stably growing root . We have opted here to look at proposed elementary mechanisms and gradually increase the degree of realism compared to real data . Cell division in plants is spatially confined to meristematic zones , which has inspired discussions on whether its regulation takes place at the supra-cellular ( ‘organismal’ ) level instead of the level of individual cells ( ‘cellular’ ) [27] . Although as often the truth probably lies somewhere in between [28]–[30] , we here classify regulatory mechanisms as cell-autonomous or non-cell-autonomous [31] . In the first case cells behave as pre-programmed automata and there is no role for spatial signalling . In the second case regulation relies on spatial cues that are a function of the organisation of the symplast and that feed back into the cellular decision centres . Here we will define and evaluate the outcome of simulations of models corresponding to those two classes of mechanisms . A consequence of the symplastic growth of the root is that at a given distance from the tip all cells have the same relative expansion rate [32] . As stated by Ivanov [33] , any observed difference in cell lengths between tissues must therefore reflect differences in cell proliferation ( see also [26] ) . Inversely , any form of growth regulation that results in different elongation rates for cells at the same distance from the tip would disrupt symplastic growth ( Figure 1A ) . For instance , suppose all cells at the same ( vertical ) position in a downward growing root have the same absolute ( areal ) expansion rate , irrespective of their size ( Model 1 , Tables 1 and S1 ) . With inner cell files narrower than outer cell files ( similar to the real root ) this fixed size increment results in consistently larger relative elongation rates for the inner tissue layers leading to tissue distortion and unbalanced distribution of mechanical stresses ( Figure 1B and C ) . Note that the same situation would occur when adjacent files contain cells of similar width , but different lengths growing at the same absolute rates . Hence , non-uniform relative strain rates at some position along the principal growth axis eventually lead to malformations . Disruption can be manifested in different forms ranging from small local cell or tissue deformations up to changes of whole organ growth . In fact , ‘disruption’ may be an overly negative term as it could be argued that a carefully-coordinated breach of that principle , as for instance in the root gravitropic response , can be beneficial to the plant . Furthermore , the stated necessary condition might be too stringent since small and short-lived random perturbations are likely to yield no significant distortions since they can cancel each other out to some extend as demonstrated in the next section . The apoplast , by its ability to transmit mechanical stress , may effectively function as a buffer to those small and random perturbations . Nevertheless , it seems evident that systematic differences in strain rates will eventually result in geometrical changes to the organ structure . In order to evaluate whether growth mechanisms allow for stable root growth we therefore reformulate the previously mentioned findings of Ivanov [33] and Green [26]: If in a uni-directionally growing root at least two points at the same axial position ( with respect to the growth direction ) have a persistently different longitudinal strain rate ( relative elongation rate ) then the symplastic structure will be distorted . ‘Persistent’ should here be interpreted as present during a minimum time interval sufficient to produce an arbitrary distortion based on that local strain rate difference . From here on , we will refer to this formulation as the ‘Uniform Longitudinal Strain Rule’ ( in short ULSR ) to emphasise its application over a finite time interval . Although root growth of the Arabidopsis seedling is in general not steady [5] in some conditions during development this is approximately the case [34] . Here we will investigate also how steady growth can be attained and maintained . Like embryonic roots we will therefore start the models from a minimal set of precursor cells ( Figure 1B ) . Subsequently the models will evolve to a situation where a larger population of cells occupies the meristem , elongation and mature part of the roots . In this process the growth of the root will accelerate initially , but then ideally converges to a state where a constant size of the meristem and elongation zone is obtained and the root growth rate becomes constant . For a uni-directionally growing root the following definition is proposed . The growing root is in a steady state if all points at each position along the growth axis have a constant strain rate . For practical reasons in the next sections our evaluation of stable and realistic growth will be largely based on three simplified criteria: We start off by considering strictly cell-autonomous regulation of cell growth and division as this appears to be the most elementary form of regulation . Cell-autonomous regulation implies here that essential developmental transitions are only governed by endogenous cellular programs independent of any external signals ( excluding their direct physical connection in the symplast ) . Whereas we know this is unlikely the case , some plant growth studies point towards regulation based on cells acting as autonomous , pre-programmed units . Typically , cell behaviour is proposed to be a function of the number of events or the time passed with respect to some reference point ( a defined cellular event ) . These types of regulation are called counters and timers , respectively . A counter keeping track of the number of cell divisions of a cell before exiting the proliferative phase has been repeatedly proposed [34]–[36] . Timers have been used in various growth models for instance on vertebrate segmentation [37] or the cell cycle [12] , [38] , [39] . It has indeed been found that in wild type roots under diverse treatments , cells spend about the same time traversing elongation zones of very different sizes ( even during accelerated growth ) suggesting that cells enter the EZ with a pre-programmed duration for expansion activity [5] . In his seminal study Green [26] includes a meristem doubling model in which the meristem consists of a number of cells that undergo one cell cycle after which the proximal half of them enters the non-dividing state . To test whether cell-autonomous regulation can produce realistic root apex structures we constructed a set of models in which cells in contact with the columella/QC maintain their capacity to divide . Upon release from the QC , these cells are allowed to divide another two or three times , based on cells keeping track of the number of divisions or the time passed since release from the columella/QC ( Models 2–7 , Tables 1 and S1 , Figures 3 and 4 ) . The loss of proliferative capacity is then followed by a fixed time interval of accelerated growth . In Model 2 ( Table 1 and S1 ) the exit from the proliferative stage is determined by a timer , with cell division synchronous among cells at the same longitudinal position . Starting from a simple grid , after a transient phase , a roughly linear increase in length is achieved in accordance with the first of the proposed criteria ( Figure 3A ) . The synchronized cell division results in regular stepwise increases in cell numbers ( Figure 3B ) . All cells in one file derive from one cell ( clones ) occurring in groups ( of increasing multiples of 2 ) with the same developmental state ( i . e . number of divisions and time since QC release ) . Consequently , discrete sets of developmentally synchronous cells with similar size and growth rate arise ( see Figures 3C and 3D ) leading to sharp developmental transitions . The use of a counter instead of a timer to decide when cells exit proliferation as in Model 3 ( Table 1 and S1 ) , produces essentially the same outcome ( Figure S1 ) . Similarly to what Green described with his meristem doubling model , the size of the proliferation zone periodically varies ( Figures 3C and S2 ) , growing exponentially and losing half of its size as a complete group of clonal cells exits proliferation and enters accelerated growth . Importantly , in vivo such patterns of synchronised behaviour have not been observed . Contrary to the third ( ULSR ) , the second criterion is breached since cell length distributions along the zones are not smooth ( contrary to [5] ) . Indeed , some aspects of realistic root growth can be reproduced , yet , others not based on this type of model . Simulating perfectly synchronized cell divisions produces highly regular cellular grids dissimilar to root micrographs ( compare Figure S1 with anatomical sections in for instance [7] ) . After adding random uniform noise ( within +/−25% of reference ) to each individual cell cycle time a more naturally looking outcome results ( Model 4 , Figure 4A ) , with a smoother cell number increase in time ( Figure S3A ) . Importantly , in a spatial context the cell length distributions remain discontinuous , yet smeared out more ( Figure S3C ) . Logically with the partial loss of synchronicity , cells at the same position along the growth axis will not necessarily grow at the same relative rate since their number of divisions or the time since release from the QC varies . Hence , the proposed ULSR is violated sensu stricto . We have found , however , through our simulations that , to some extent , the symplast acts as a stabilising framework in that local , random mechanical perturbations are dissipated through its structure ( Figure 4A , with colouring as a function of the ratio actual area/target area , with red colours representing an outgoing or cell-expanding potential: for details see Methods ) . However , if even more cell cycle time noise is added or if perturbations are systematic , then growth defects eventually arise . For instance in Model 5 and Model 6 ( Tables 1 and S1 ) a systematically shorter cell cycle time of the outer cell layers is propagated over multiple generations . Through timer or counter based rules the cells in the outer layers undergo the fast acceleration phase much earlier , leading to tissue structure defects ( Figure 4B and 4C ) . Cell-autonomous regulation is prone to this since a local defect is easily clonally amplified , especially if occurring at an early stage in development , without external signals to constrain the effect within spatial bounds . Likely , robust regulation also includes well-coordinated cell division and cell growth , leading to a stable cell size distribution over time . Contrary to a simple pre-programmed cell cycle timer , which does not respond to fluctuations in growth rate , a size-based cell division mechanism ( ‘sizer’ ) might provide this extra stability . Size-based control mechanisms are well-established in ( plant ) cell cycle modelling ( e . g . [40] , [41] . If cell division is implemented to happen at an absolute cell size ( or length rather as in Model 7 ) cell sizes ( or lengths ) are kept in strict bounds in the DZ , yet synchronicity of cell division is reduced ( Figure S3B ) . We conclude that despite a quasi-steady growth , in the absence of spatial cues stable developmental zones and smooth gradients in cell lengths are not feasible based on the investigated types of strict cell-autonomous regulation . It makes sense that to have cells of the same generation behave according to a smooth positional gradient requires a spatial signal , even in the presence of noise . Model 8 ( Tables 1 and S1 ) was constructed to test whether including spatial signals ( non-cell-autonomous regulation ) can improve resemblance with experimental data . Counter and timer-based rules for exit of proliferation , start of accelerated growth and maturation were replaced by a fully independent spatial signal that marks these transitions at fixed positions from the QC ( like a ‘ruler’ ) . Simulations with this concept model lead to growth , which to a good approximation can be described as steady and linear ( Figure 5; Video S1 ) . The spatial boundaries directly limit the total mass production occurring within those predefined zones . Importantly , the simulated data of ( epidermal ) cell lengths along the root apex are similar to what we obtained experimentally ( Figure 2 ) , and the fitted cell length distributions are similar to what was reported in previous kinematics studies as well [5] . The strain rates are either low ( in the DZ ) or high ( in the EZ ) changing rather abruptly going from DZ to EZ for a single simulated root ( Figures S4A ) . Using averaged strain rates from multiple simulations ( with different random seeds for mechanical equilibration ) shows that the strain rate curves become more smooth and bell-shaped ( although still skewed ) as was observed in various studies [42] , [5] , [43] ( Figure S4B ) . Accordingly , Beemster and Baskin [5] described sharp changes in the slope of the velocity profiles for individual root samples versus averaged data ( compare their Figure 2A ( including inset ) to Figures S4C and S4D ) . This rapid change was also found by van der Weele et al . [44] . Furthermore , since no conflicts with the ULSR appear to exist , non-cell-autonomous regulation can effectively fulfil all proposed criteria for realistic root growth . From a biochemical point of view the most likely spatial signal to act as a ‘ruler’ would be a phytohormone or combination of phytohormones undergoing long-distance transport through the root tissue resulting in a morphogenetic gradient . Auxin is arguably the most prominent root morphogen . It has been demonstrated that a stable auxin gradient can be formed which could act as the primary signal determining growth and cell division [12] . In the simulations reported with that model the growing root tip is regularly truncated providing an auxin source at a constant distance from the root tip . Via Model 9 , which is a vertex-based variant of the latter model ( Table 1 and S1 ) we investigated whether auxin patterns effectively reach a steady state with a source at a fixed position that becomes displaced further and further from the root tip due to growth . We specifically explored how spatially regulated growth and division ( as in Figure 5 ) affect the auxin gradient ( not conversely ) . It followed that an important factor is the definition of the auxin source: as a ( constant ) net import from the upper plant parts ( and ) or as some form of local production . In the first variant of Model 9 ( in the presence of first order auxin degradation ) only the total auxin level of the root slowly converges to a steady state ( Figure S5A ) , whereas the concentration is steadily diluted ( after an initial increase ) by the growing root ( Figure S5B and S5C ) . If we define developmental transitions with a stable spatial signal , therefore this type of auxin source in principle does not support steady root growth . Rather , it might be suitable to produce temporary responses . A different behaviour emerges with variants of Model 9 that instead use local ( root-based ) auxin production: either with cellular production proportional to size or with a constant production rate per cell . In both cases the total auxin level ( Figure S5D and S5G ) increases proportionally to the area increase ( Figure S5E and S5H ) , and the auxin concentration over the total root slowly ( especially for the area-based production mode ) converges to a steady state ( Figure S5F and S5I ) . Production modes can be combined as well , yet this does not change the picture with typically one mode being ( or becoming ) dominant ( results not shown ) . In accordance with previous work , the two-dimensional shape of the auxin gradient depends on whether a-polar ( diffusive or AUX/LAX importer-mediated ) or polarized ( PIN-mediated ) transport is dominant , with in the first case a gradual auxin concentration increase towards the lower ( distal ) root combined with a smooth auxin peak near the ‘QC’ cells ( Figure S6 ) . If PIN-transport is dominant a much more pronounced local auxin maximum occurs at the central cell lines just proximal to the ‘QC’ cells . The dominance of diffusive versus a-polar transport is determined in this model by the relative values of the diffusion and PIN transport kinetic constants rather than their absolute values ( see for instance the similarity of Figures S6A and S6E , and Figures S6B and S6F ) . With the model variant based on constant auxin import that sharp peak shifts in time , connected to the growing root apex . However , it eventually disappears by growth dilution ( Figure 6A ) . Local production modes again show the shifting of the sharp peak , however , in those cases the sharp peak slowly but gradually becomes stable ( Figure 6B and 6C ) . We conclude that typical auxin-based models can produce stable gradients if auxin production scales with the growing root . Importantly , unless auxin transport is completely a-polar , there is always an additional horizontal ( transversal ) concentration gradient besides the longitudinal gradient ( Figures 6D , S6 , S7 , S8 , S9 ) . This corresponds to what can be seen in various reporter studies [18] , [45] . How this relates to growth regulation is investigated in the next section . Having obtained a steady and stable spatial auxin gradient , the question remains how it is interpreted by the cells as they move through it . In Model 10 ( Tables 1 and S1 ) cell growth and division are directly determined by the local auxin concentration , e . g . according to specific step functions ( division above a minimum size and above an auxin threshold , slow growth above the same auxin threshold , fast growth above a lower threshold ) . In this case no steady state was obtained with severely distorted root growth ( Figure 7A ) . Because of the radial gradient , cells at the same position along the growth axis are instructed to consistently produce different strain rates thereby violating the ULSR . With auxin concentration determining cell division , cell size distributions are not arranged in parallel along the longitudinal axis but follow the peaks and valleys in the auxin landscape ( to some extent this has been observed , ( e . g . [46] ) . When tuning the auxin transport parameters to a more dominant diffusion regime the transversal gradient remains present still precluding stable growth , although the distortions of the cellular grid are less pronounced . Some tissue locations ( for instance the vascular layers near the QC ) have a much lower tendency to grow ( blue colours ) compared to others ( red coloured outer layers near the QC ) and inhibit and distort growth ( Figure 7B ) . Alternative scenarios based on spatial or temporal derivatives of the auxin concentration pattern ( as proposed for instance for Drosophila organ development [47] ) do not provide more regularly spaced developmental cues ( results not shown ) . We conclude that in itself a strictly auxin-based growth model is unlikely to produce stable and realistic root growth . Whereas a simple diffusion-based auxin transport mechanism ( e . g . with a source near the ‘QC’ ) could resolve this issue , this spatial complexity resulting from polar auxin transport is important for other purposes , like auxin's role in regulating formative divisions around the ‘QC’ [17] . In the following we investigate two opposing theories proposed to circumvent this differential growth problem in response to a lateral ( radial ) auxin gradient . It has been recently highlighted that hormones seem to regulate root growth in a layer ( /tissue ) -specific way [9] . Whereas short-range ( paracrine ) signals have been put forward as a hypothetical solution to transmit developmental signals transversally through the layers [9] we have tested whether it is sufficient for cell layers to ‘communicate’ through their mechanical connections . Model 11 ( Tables 1 and S1 ) represents this concept of ‘layer ( /tissue ) -driven growth’ by selecting one arbitrary cell file ( represented by 2 cell columns ) in which the cells undergo turgor-driven growth . The other cell files are forced to passively follow ( Figure 8 , see legend and Dataset S1 for implementation ) . Simulations show that one cell file can effectively drive steady growth of the whole organ , yielding smooth cell length distributions ( Figures 8B and 8C ) . Strictly , a conflict exists with the ULSR since one cell file is consistently growing faster . Nevertheless , if the cells in the other layers can respond rapidly ( adjusting their target areas or ‘turgor pressures’ ) then the difference in strain rate apparently remains sufficiently low to cause much overall tissue distortion . This rapid response requires transversal communication between the layers . Biologically , signal transduction could potentially be mediated by the mechanical connections between the layers or otherwise a secondary chemical signal would have to be transmitted . Irrespectively , we conclude a layer-driven mechanism can solve the problem of layer-specific differences in the instructive hormone ( here auxin ) gradient . A different solution to reconcile the central role of auxin in root growth with the ULSR may consist of introducing regulatory cross-talk into the models . In other words to complement or modulate the auxin signalling other spatially non-uniform signals , like transcription factors [17] , [48] or other hormones [49]–[52] are implicated . Based on proposed regulatory interactions [53] , [54] , we have implemented a model ( Model 12 , Tables 1 and S1 , Text S1 ) centred around the cross-talk between auxin as the cell proliferation promoting factor and cytokinin as the cell differentiation promoting factor ( Figure 9A ) . Auxin transport was described as both diffusive and PIN-mediated , with local production and an external source as in the previously discussed models . Cytokinin transport was described as strictly diffusion-based , also with local and external sources , the local production rate being repressed by auxin . This led to a shallow gradient , the concentration decreasing towards the tip ( as in [55] ) . In this simplified model cytokinin up-regulates the transcription factor SHY2 which is typically strongly expressed at the so called transition zone between DZ and EZ [54] . Increasing SHY2 leads to repression of PIN transporter levels and a resulting auxin signal drop that reduces the meristem size . Accordingly a local auxin response minimum has been observed before [18] . Auxin on the other hand has been proposed to counteract this effect by mediating SHY2 degradation , in part through gibberellin ( GA ) repressing ARR1 via the DELLA family protein RGA . In our simulation model we did not include any ARRs as variables . In accordance with others ( see for instance [56] ) auxin was assumed to directly inhibit cellular cytokinin production . GA was only included as a variable that determines where the EZ ends due to simple growth dilution ( in accordance with [19] ) . The exit from proliferation and start of accelerated growth was determined by crossing a SHY2 concentration threshold ( cf . Text S1 ) . Interestingly , because of its inhibition of PIN expression , the steep increase in the SHY2 concentration coincides with a dip in the auxin concentration as well as the transition from a strongly polarized to a predominantly a-polar transport regime ( Figures 9B and S10A ) leading in general to much more linear spatial gradients in accordance with the ULSR ( Figures 9C–E and S11 ) . Since the first two proposed criteria for realistic growth were fulfilled too ( Figures S10B , C ) , this mechanism in principle allows for the required regular and stable zonation of the growing root apex , and can therefore potentially reconcile multiple roles for auxin in patterning along different directions . Furthermore , with Model 12 we could qualitatively predict how adding external auxin and cytokinin affects the meristem size ( increasing and decreasing in size , respectively ) as demonstrated experimentally by Beemster and Baskin ( [34]; Figure 9C–E ) . We conclude that this computational model most effectively captures the basic growth characteristics of the Arabidopsis root and represents an ideal starting point to develop more advanced computational kinematic models which can predict root growth under more diverse conditions and perturbations .
We have constructed and simulated different models that represent steady symplastic growth of the Arabidopsis root tip . We compared diverse regulatory mechanisms and found out which of them can adequately reproduce crucial properties of primary root growth , according to three well-defined criteria ( steady-state , realistic cell length distribution , and ULSR ) that allow rigorous comparison with experimental observations of in vivo growing roots of Arabidopsis thaliana . A necessary condition for stable unidirectional symplastic growth [33] , [26] was re-interpreted and re-formulated as a strain ( rate ) rule ( ‘ULSR’ ) for those mechanisms to conform to . It is mainly this third criterion that warrants the use of our sophisticated vertex-based simulations rather than the more simple approach of modelling a single cell file such as in [19] , which could potentially produce kinematic output such as steady state length growth and cell length profiles . As soon as differences in strain rates or complex transport phenomena occur , the use of a two-dimensional tissue representation becomes necessary . Our simulations do suggest that symplastic structures are resilient to differences in strain rates as long as they are small and short-lived . How far or fast perturbations are precisely transmitted through plant tissue and to which extend this affects organ growth are intriguing questions that go beyond the scope of this study . A more accurate representation of cell wall mechanical properties ( such as [21] ) and of tissue and organ structure ( possibly in 3D ) would then be desirable and would further enhance kinematic and microscopic resemblance with real root tissues . Considering the complexity of molecular interactions implicated in growth regulation of the Arabidopsis root apex and considering the presence of many gaps in our understanding , we have opted rather to define rules for cell growth and division inspired by previous studies and enriched them with molecular interactions whenever useful . Whether regulation takes place at the supra-cellular ( ‘organismal’ ) level versus at the level of individual cells ( ‘cellular’ ) forms the subject of a long standing discussion [27] . This distinction is probably overly polarized as indicated by experimental observations [29]–[31] . In fact , signals that vary on the cellular , tissue- and organ-scale are known to affect pattern formation considerably . We have kept this conceptual distinction nevertheless and classified the proposed regulatory mechanisms as cell-autonomous ( based on local pre-programmed rules ) or non-cell-autonomous ( affected by external , spatial signals ) . Cell-autonomous mechanisms like timers , counters and sizers are readily implemented with the logical expressions of a computer program . In a biological context such mechanisms typically require more complex designs [57]–[59] . Various sizer and timer based cell cycle models have been reported , making it reasonable representations of cell behaviour [38]–[40] . Given the conserved nature of the eukaryotic cell cycle machinery these types of mechanisms are likely operating in the plant cell cycle [41] , [60] . Timers and counters operating at different spatial and/or temporal scales are even more obscure . For instance , a developmental counter that determines the exit of proliferation would have to keep track of the number of cell divisions a cell has undergone . The epigenetic state of a plant cell can reflect this history [61] . In the context of plant development , telomere shortening could play such a role [62] . After cells exit the DZ , DNA duplication cycles are expected to continue through the process of endoreduplication . The number of DNA copies could therefore serve as a direct developmental marker to the plant cell . We have shown that stable growth is possible based strictly on counter and timer mechanisms . However , the absence of spatial cues precludes realistic primary root growth . Indeed , such strictly cell-autonomous mechanisms will logically lead to groups of ( nearly ) synchronously growing cells . Cell packages derived from each division of the initial cells will behave in a similar way irrespective of their position along the growth axis and produce growth zones changing periodically in size ( when such a package leaves the DZ and enters the EZ ) . Even taking into account the inherent noise in cell behaviour , fixed developmental zones and smooth transitions in cell lengths are not feasible based on this type of regulation . On the other hand it cannot be excluded that some indirect mechanisms exist by which cells can circumvent the need for a spatial signal by deriving spatial information in an autonomous way . An example of this hypothesis could be the gravity-sensing columella cells that can extract spatial information through statholits in the root gravitropic response as modelled in [15] . Considering the inherent limitations of strictly cell-autonomous mechanisms and given the extensive evidence on the importance of phytohormones , it seems unlikely that those mechanisms are the main determinants of developmental zonation in the Arabidopsis root . We have demonstrated that spatial signals ( for instance stemming from a biochemical gradient ) can be a direct and ( based on the visual and kinematic comparison ) effective manner of instructing morphogenesis . This has been shown for various other life forms like Arthropoda and Vertebrata [63] , [47] , [37] . Our simulations regarding the effectiveness of auxin as the primary signal controlling root growth show that based on local auxin production a stable auxin pattern can be produced , however this potentially a slow process . A constant production per cell seems the most effective to this end . A faster breakdown [12] or efflux rate might aid in faster convergence . In any case , with an external auxin source such a pattern gradually fades out through growth dilution . Polar transport results in a lateral ( radial ) concentration gradient which conflicts with the ‘ULSR’ . In fact our model does not even capture the extra volumetric dilution of auxin from the inner to the outer layers in three dimensions . Similar patterns were obtained by other studies like Grieneisen et al . [12] and Santuari et al . ( [18]; their model did also not include the apoplastic compartment ) and supported by various reporter studies ( e . g . [64] , [45] ) . Although Grieneisen et al . [12] have simulated stable ( 2D ) growth of the root tip , the lateral gradients were not recognized as problematic probably due to the fact that relative movement of cells ( cell sliding ) is possible with their ( cellular Potts instead of vertex-based ) framework . We have proposed various strategies to circumvent the ULSR conflict . It has been pointed out that several hormones are exerting their regulatory effect on the root in a cell-layer specific way [9] , [14] . This provides a way out of the ‘ULSR’ conundrum if accompanied by rapid transversal transmission to the other tissue layers . Candidate molecules to act as secondary transported signal are only just surfacing . On the other hand , even layer-driven growth by direct mechanical transduction was successful in producing a realistic root phenotype according to the three defined criteria . The role of auxin must by all means be understood in the complex context of various downstream response factors ( with variations in levels , localisation , etc . ( e . g . [48] , [18] ) and also of other hormones that interfere via their respective signalling pathway components . We constructed a model based on the antagonistic role of auxin and cytokinin in root development , with the SHY2 transcription factor as a central regulator of meristem size [65]–[67] , [53] , [54] and gibberellin ( GA ) dilution determining cell maturation . Simulations with this model were in accordance with the ULSR and reproduced visual and kinematic observations as well as the expected increase and decrease of meristem size upon addition of auxin and cytokinin [34] . By down-regulating PIN-mediated transport through the transcription factor SHY2 , cytokinin effectively flattens the lateral auxin gradient , at the basal end of the division zone ( ‘transition zone’ [6] ) , thereby signalling the exit of proliferation and start of differentiation without conflicting the ULSR . GA dilution has been proposed before as part of a more intricate mechanism ( including cell compartments and DELLA proteins , [19] ) determining the exit of the elongation phase in a single cell row of the Arabidopsis root . We have used growth dilution of GA in a simplified form with a GA production term proportional to ( initial ) cell width to account for layer-specific differences in GA concentration . Cell elongation is defined to stop as soon as the GA concentration drops below a specific GA minimum , which depends on reaching a certain cell size ( and accordingly length ) . This could account for the similarity in cell lengths at the end of the EZ which has been observed under different conditions in the Arabidopsis root [34] . Various definitions exist for the term ‘robustness’ [68] . We define it here in a broad sense as a property that allows a system to maintain its functions in the context of internal and external perturbations [69] . ‘Function’ pertains to maintaining stable growth upon internal changes or changing growth conditions ( external ) . Some of our simulations showed that , in the absence of feedback from spatial cues , and driven by cell proliferation , cellular defects ( for instance leading to differences in cell cycle time ) can propagate . In contrast to cell-autonomous regulation a more dictatorial or hierarchical form of regulation ( in this case via organ-scale spatial signals ) , is more robust to local perturbations . A possible trade-off of such a dictatorial system might be its sensitivity to mutations in the controlling network itself [70] as supported by experimental studies on PIN mutants [71] , [72] . At the same time , such a high sensitivity to modulations in the control system may be beneficial for plants , being sedentary and therefore requiring a great deal of developmental plasticity . This trade-off also applies to the degree of dependence of cell growth and division . In theory , a completely independent regulation of these processes allows for a much wider morphological space to be accessible . Evidently , reaching stable balanced growth would become far more challenging . As the mechanistic coupling of growth and division is still poorly characterized [73] , we opted here to couple those processes to various degrees in an artificial way , for instance as synchronized counters and timers , or concurring spatial transitions . Integrating an explicit model of the cell cycle in our models would undoubtedly further progress our understanding of how the coordination of those processes affects root morphogenesis . Starting from a simple grid and by stepwise evaluation of regulatory mechanisms for cell division and expansion , we have obtained a model that produces realistic root growth according to well-defined criteria . In that respect spatial regulation appears to be essential , presumably with auxin as the central signal that is modulated by feedback interaction with cytokinin . This represents an important step towards predicting the Arabidopsis root phenotype under various conditions using vertex-based modelling . As indicated above , more advanced representations of cell wall mechanics , cell cycle regulation , etc . would benefit this model , and are currently being developed .
In several instances visualization of simulation output consisted of colouring the cells according to either: ‘AS’ - Areal strain ( rate ) : To obtain a simple , approximate measure of the local expansion of the cells , the change of the area relative to the area in the previous simulation step was accounted for . ( 3 ) Red colouring typically indicates a positive strain rate and blue colouring a negative strain rate . Or: ‘GP’ - Growth potential: The ratio was used to colour the cellular grids in specific instances as a measure of the cellular growth potential ( broadly interpreted as the ‘turgor pressure’ ) . Red colouring indicates an energy driving areal growth , blue colouring an energy growth driving cell shrinking . Or: ‘AU’ - Arbitrary concentration Units , with red to yellow colouring indicating increasing concentration values . | The growth of a plant root is driven by cell division and cell expansion occurring in spatially distinct developmental zones . Although these zones are in principle stable , depending on the conditions , their size and properties can be modulated . This has been meticulously described by kinematic studies , which have led to the proposal of mechanisms underpinning those observations . At the same time , much knowledge of the identities and interactions of molecules involved in these mechanisms has accumulated , in particular from the model species Arabidopsis thaliana . Here we attempt to resolve the longstanding question whether observed growth patterns can be explained by autonomous decision-making at the level of individual cells or if the aid of some external signal is required . We then ask , building on the accumulated molecular information , which minimal models can provide for stable growth while keeping sufficient flexibility to regulate growth . Therefore , we constructed computational models for different growth mechanisms operating in a virtual two-dimensional Arabidopsis root and compared their behaviour with biological experiments . The simulations provide strong indications that spatial signals are required for realistic and flexible root growth , likely orchestrated by the plant hormones auxin and cytokinin . | [
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"physiology"
] | 2014 | Putting Theory to the Test: Which Regulatory Mechanisms Can Drive Realistic Growth of a Root? |
Drought is a major threat to plant growth and crop productivity . The phytohormone abscisic acid ( ABA ) plays a critical role in plant response to drought stress . Although ABA signaling-mediated drought tolerance has been widely investigated in Arabidopsis thaliana , the feedback mechanism and components negatively regulating this pathway are less well understood . Here we identified a member of Arabidopsis HD-ZIP transcription factors HAT1 which can interacts with and be phosphorylated by SnRK2s . hat1hat3 , loss-of-function mutant of HAT1 and its homolog HAT3 , was hypersensitive to ABA in primary root inhibition , ABA-responsive genes expression , and displayed enhanced drought tolerance , whereas HAT1 overexpressing lines were hyposensitive to ABA and less tolerant to drought stress , suggesting that HAT1 functions as a negative regulator in ABA signaling-mediated drought response . Furthermore , expression levels of ABA biosynthesis genes ABA3 and NCED3 were repressed by HAT1 directly binding to their promoters , resulting in the ABA level was increased in hat1hat3 and reduced in HAT1OX lines . Further evidence showed that both protein stability and binding activity of HAT1 was repressed by SnRK2 . 3 phosphorylation . Overexpressing SnRK2 . 3 in HAT1OX transgenic plant made a reduced HAT1 protein level and suppressed the HAT1OX phenotypes in ABA and drought response . Our results thus establish a new negative regulation mechanism of HAT1 which helps plants fine-tune their drought responses .
As sessile organisms , plants need to respond and adapt to environmental stress to survive adverse conditions . Plants respond and adapt to stresses through a complex network of factors involved in stress hormone signaling and regulation of gene expression . The phytohormone abscisic acid ( ABA ) plays a key role in plant responses to biotic and abiotic stress , in particular drought and salinity [1–3] . Since the discovery of ABA receptors , PYRABACTINRESISTANCE1 ( PYR1 ) /PYR1-LIKE ( PYL ) /REGULATORYCOMPONENTS OF ABA RECEPTORS ( RCAR ) [4 , 5] , a core ABA signaling pathway has been proposed . In the absence of ABA , group A protein phosphatases type 2C ( PP2Cs ) interact with subclass III SNF1-related protein kinases ( SnRK2 . 2 , 2 . 3 and 2 . 6 ) which keeps the kinases inactive by blocking their catalytic cleft and by dephosphorylating the activation loop [6] . In the presence of ABA , ABA binds to the PYL receptors , forming a PYLs-ABA-PP2C complex and inhibiting phosphatase activity of PP2C [7 , 8] . This binding and inhibition of the PP2Cs releases the SnRK2s from PP2C-SnRK2 complexes , and the released SnRK2s are activated through autophosphorylation . The activated SnRK2s can then phosphorylate downstream effectors and activate ABA signaling [7 , 9 , 10] . Various transcription factors function in ABA signaling-mediated drought response [2 , 11] . The basic leucine zipper ( bzip ) family transcription factors including ABF1 , ABF2 ( AREB1 ) , ABF3 , and ABF4 ( AREB2 ) , which bind directly to ABREs of stress-responsive genes and stimulate their transcriptional activities , function in the ABA-dependent pathway and are major targets of SnRK2 protein kinases in the ABA core signaling pathway [12–14] . Additionally , some members of the MYB and MYC ( bHLH ) classes , the No Apical Meristem/Cup-Shaped Cotyledon ( NAC ) , and WRKY families have also been shown to be induced by ABA or abiotic stress or to regulate stress responses , underscoring the importance of transcriptional regulation in plant stress responses [2 , 15 , 16] . Transcriptional regulation is one of the most essential mechanisms in the acquisition of stress tolerance [2 , 17] . However , in many cases , stress adaptation is exchanged for growth and productivity , therefore , it is necessary for plants to develop a resilient system to obtain the optimal trade-off for survival and growth . To this end , plants use elaborate mechanisms associated with posttranscriptional modulation [18] and posttranslational regulation [19 , 20] , as well as transcriptional regulation . In particular , the appropriate control of transcription factors regulating plant growth and development genes is important , because these transcription factors negatively affect plant stress tolerance while being essential for increased productivity . The environmental conditions surrounding plants are constantly changing; thus , posttranslational regulation to control the protein levels of these transcription factors is considered an important mechanism to avoid adverse effects on plant survival . However , the negative components involved in regulation to efficiently coordinate ABA-dependent stress responses are less well known . The homeodomain-leucine zipper protein ( HD-ZIP ) family constitute a large family of transcription factors that are unique to plants and is divided into four subfamilies ( HD-ZIP I–IV ) on the basis of the additional conserved domains , structures and physiological functions [21–23] . HD-ZIP proteins contain homeodomain ( HD ) that is responsible for specific DNA binding and the closely associated leucine zipper ( LZ ) domain which acts as a dimerization motif [24] . HD-ZIP proteins can bind to partially inverted repeats such as CAAT ( A/T ) ATTG ( BS1 site ) , CAAT ( C/G ) ATTG ( BS2 site ) or as lightly modified version TAAT ( C/T ) ATTA for AtHB2/HAT4 [25] . Arabidopsis thaliana homeodomain-leucine zipper protein 1 ( HAT1 ) and its close homologs belong to Class II HD-ZIP of transcription factors that mainly act as repressors by binding to their target genes promoters and play important roles in plant development and in response to the environment [25 , 26] . Previous works have shown that several members of the family , HAT1 , HAT4/AtHB2 and AtHB4 , are induced by shade avoidance and overexpression of HAT1 or HAT4 resulted in a similar effect in promoting cell elongation [23 , 25–27] . HAT2 expression is rapidly induced in response to auxin , and AtHB4 was also reported to modulate auxin , BRs and gibberellin responses [28 , 29] . It was recently reported that HAT1 is a substrate of BIN2 ( BRASSINOSTEROID-INSENSITIVE 2 ) kinase and appears to function redundantly with other family members such as HAT3 to positively mediate BR responses [30] . HAT1 was also reported to participate in anti-CMV defense response in Arabidopsis and negatively regulates this process [31] . Collectively , these studies indicate that HAT1 is involved in the complex signaling and transcriptional networks coordinating plant growth and stress response . HAT1 promotes plant growth and development by BR signaling or other pathway . In this study , we demonstrate that HAT1 , which was previously reported as a critical regulator in BR-mediated plant growth and in viral defense response , is involved in ABA regulation of drought response by suppressing the ABA biosynthesis and signaling . We found that HAT1 and its homolog HAT3 act redundantly , as the expression of both HAT1 and HAT3 were repressed by ABA and drought , and the double mutant hat1hat3 displayed a reduced ABA sensitivity and enhanced drought tolerance phenotype that was stronger than the single mutants alone . HAT1-overexpressing transgenic plants exhibit a hyposensitive response to ABA and drought . Furthermore , we found that HAT1 physically interacts with and can be phosphorylated by SnRK2 . 3 in vitro and in vivo . SnRK2 . 3 phosphorylation of HAT1 decreased its protein stability and binding activity . Overexpressing SnRK2 . 3 in HAT1OX transgenic plant can suppress its phenotype in ABA and drought responses . Therefore we identified a new substrate of SnRK2 . 3 and established a novel negative regulation mechanism by which plants can efficiently coordinate drought responses .
From public data ( http://bbc . botany . utoronto . ca/efp/cgi-bin/efpWeb . cgi ) , we found that HAT1 expression was reduced after ABA and osmotic stress treatment in seedlings , implying that HAT1 may be implicated in ABA and stress responses . To test the hypothesis , we examined the expression of HAT1 in different tissues and in seedlings treated with exogenous ABA or osmotic stress . Consistent with the public data , the expression level of HAT1 was highest in root , and lower in stem , leaf , and inflorescence ( Fig 1A ) , and was significantly repressed by exogenous ABA and osmotic stress ( Fig 1B ) . We further generated GUS reporter lines using HAT1 native promoter and examined the responsiveness of HAT1 expression in the presence of ABA and osmotic stress . As shown in S1A Fig , after ABA and osmotic stress treatments , GUS signals were reduced in cotyledons , leaves and roots as well as guard cells . To determine the subcellular localization of HAT1 , we generated constructs that introduced the GFP sequence at the C-terminus of HAT1 . The 35S:GFP and 35S:HAT1-GFP constructs were used to transfect Arabidopsis thaliana protoplasts . As shown in S1B Fig , 35S:GFP fluorescence was observed in the entire cell , while HAT1-GFP fusion protein localized in the nucleus . The expression of HAT3 , its homolog , was similarly regulated , whereas the expression of HAT2 was not changed by ABA and osmotic stress ( S3A and S3B Fig ) . To investigate the role of HAT1 in the ABA response and in osmotic stress tolerance , we obtained T-DNA insertion mutants of HAT1 , HAT2 and HAT3 , hat1 , hat2 and hat3 , respectively . Then we created the double mutant hat1hat2 , hat1hat3 and triple mutants hat1hat2hat3 . The RT-PCR results showed that HAT1 expression was hardly detected in hat1 , hat1hat2 , hat1hat3 and hat1hat2hat3 mutants . Similarly , transcript of HAT2 was not observed in hat2 , hat1hat2 and hat1hat2hat3 mutants , and HAT3 transcript was abolished in hat3 , hat1hat3 or triple ( hat1hat2hat3 ) mutants ( S2A Fig ) . Western blotting using an anti-GFP antibody showed that HAT1-GFP accumulated in the two HAT1OX lines ( S2B Fig ) . Next , we analyzed ABA sensitivity with regard to seedlings growth in Col-0 , HAT1OX lines and knockout mutants . The 4-day-old seedlings grown on 1/2 MS medium were transferred to 10 μM ABA-containing medium for 10 days . As shown in Fig 1C and 1D and S3C and S3D Fig , root growth of double mutant hat1hat3 or triple mutant hat1hat2hat3 was dramatically retarded under ABA conditions compared with that of wide-type Columbia-0 ( Col-0 ) and was similar among Col-0 , hat1 , hat2 , hat3 and hat1hat2 with or without ABA treatment . To analyze the function of HAT1 in osmotic stress tolerance , 4-day-old Col-0 and knockout mutants were treated with mannitol , a stress treatment commonly used to mimic osmotic stress tolerance in the laboratory . The double mutant hat1hat3 or triple mutant hat1hat2hat3 displayed less inhibition on growth in the medium containing mannitol compared with Col-0 , while the single mutant hat1 , hat2 , hat3 and double mutant hat1hat2 showed little difference after mannitol treatment in comparison with Col-0 ( Fig 1C , 1E and 1F and S3C , S3E and S3F Fig ) . In contrast , the two HAT1-overexpressing lines ( HAT1OX#11 and HAT1OX#13 ) showed significantly reduced ABA sensitivity and osmotic stress tolerance ( Fig 1C bottom , Fig 1D , 1E and 1F ) . Together , these data indicate that HAT1 plays a negative role in ABA signaling and in osmotic stress tolerance , and it is functionally redundant with HAT3 in ABA and osmotic stress response . ABA regulation of stomatal movements is a well established model system for the study of plants response to drought stress . Thus , we measured the stomatal aperture from epidermal peels of Col-0 , HAT1OX lines and knockout mutants . Overexpression of HAT1 suppressed ABA-mediated stomatal closure , while double mutant hat1hat3 and triple mutant hat1hat2hat3 exhibited an accelerated ABA sensitivity in stomatal closure and single mutants ( hat1 , hat2 , hat3 ) or hat1hat2 showed little difference after ABA treatment in comparison with Col-0 ( Fig 2A and 2B and S4A and S4B Fig ) , indicating that HAT1 and HAT3 function redundantly in regulating ABA-mediated stomatal closure . As H2O2 acts as an important signal molecular in ABA-induced stomatal closure , H2O2 accumulation in guard cells was measured by a fluorescence dye , 2 , 7-dichlorodihydro fluorescein diacetate ( H2DCF-DA ) [32 , 33] . As shown in Fig 2C and 2D , H2O2 accumulation in guard cells was less in HAT1OX lines , more in hat1hat3 double mutant , compared to Col-0 and hat1 after ABA treatment , suggesting that HAT1-impaired stomatal closure may be caused by changed H2O2 in guard cells . Next , we tested whether HAT1 plays a role in the drought stress response . When exposed to dehydration stress by withholding water for 10 days , HAT1OX lines displayed a withered phenotype , while the hat1hat3 double mutant largely remained turgid and single mutant hat1 showed little difference in comparison with Col-0 ( Fig 3A ) . Measurement of leaves water loss showed that HAT1OX lines lost water much faster , while hat1hat3 displayed reduced water-loss rate than Col-0 and hat1 ( Fig 3B ) . As a result , overexpression of HAT1 markly reduced plant survival under drought stress , whereas hat1hat3 showed enhanced survival compared to Col-0 and hat1 ( S5A–S5C Fig ) . To study the responses of different genotypes to controlled soil water deficit drought , Col-0 , HAT1OX lines and knock-out mutants ( hat1 , hat1hat3 ) were grown for 3 weeks under well-water condition ( 2 . 2g H2O/g dry soil ) and then subjected to mild drought stress ( Fig 3D ) . After grown under mild drought condition ( 0 . 7g H2O/g dry soil ) for 9days , the biomass of both drought-treated and well-watered plants was measured and then the change in biomass was calculated . As shown in Fig 3C and 3E , HAT1OX lines showed more reduction in biomass compared to the Col-0 which is considered drought sensitive genotype , while the double mutant hat1hat3 displayed less reduction in biomass and hat1 showed similar reduction in biomass in comparison to Col-0 . Altogether , these data demonstrate that HAT1 and HAT3 function redundantly and negatively to regulate ABA-mediated stomatal closure and drought response . As HAT1 is a negative regulator of ABA signaling and drought response ( Fig 1 and Fig 2 ) , the expression of ABA or drought stress inducible marker genes were tested in different genotypes . We first determined the transcript levels of ABA response maker genes which were also ABA biosynthesis genes . These genes include ABA1 [34] , AAO3 [35] , ABA3 [36] , and NCED3 [37] . Among the four genes , the expression of ABA3 and NCED3 were significantly reduced in HAT1OX lines and up-regulated in hat1hat3 double mutant under both control and osmotic stress conditions ( Fig 4A and 4B ) . To determine whether or not ABA levels were affected , we quantified the ABA content in different genotypes . Under normal conditions , ABA level in HAT1OX seedlings was found to be lower than that in Col-0 and hat1 single mutants , whereas it was elevated in hat1hat3 double mutant . When exposed to 15% polyethylene glycol ( PEG ) 6000 that mimics a drought stress , HAT1OX lines had a reduced ABA level , and the hat1hat3 double mutants accumulated higher level of ABA compared with Col-0 and hat1 ( Fig 4E ) . In addition , the induction of RD29A and RD22 , which are well established drought-induced marker genes [16 , 38] , was also tested in different genotypes . As expected , the expression of these two genes were reduced in HAT1OX lines and elevated in hat1hat3 double mutant compared with Col-0 and hat1 ( Fig 4C and 4D ) . Furthermore , the expression of HAI1 , HAI2 and PP2CA , which belong to PP2Cs , negative regulators of ABA signaling , was diminished by down-regulation of both HAT1 and HAT3 and enhanced by HAT1 overexpression ( Fig 4F–4H ) . Taken together , these results indicate that HAT1 repressed drought-responsive genes and induced PP2C genes , which may account for the repressed drought tolerance in HAT1OX lines . The SnRK2 kinases are integral positive component of ABA signaling , and phosphorylate S/T residues in the RXXS/T motif in their substrates . There are 7 potential phosphorylation sites for SnRK2 kinases in the predicted HAT1 protein , which prompted us to test whether HAT1 was a substrate of SnRK2 kinases . First , we tested if subclass III SnRK2s could physically interact with HAT1 . Bimolecular fluorescence complementation ( BiFC ) analysis was performed to examine the interaction of HAT1 with SnRK2 . 2 , SnRK2 . 3 , and SnRK2 . 6 in plants . We found that HAT1 interacts with all subgroup III SnRK2s in the nucleus and no fluorescence signal was detected in the negative controls ( Fig 5A ) . Quantitative analyses of BiFC signals showed strong SnRK2 . 3-HAT1 interactions and weak signals for HAT1 interaction with other subgroup III SnRK2s ( Fig 5B ) . GST pull-down experiment confirmed this interaction in vitro ( Fig 5C ) . GST-SnRK2s , but not GST alone , pulled down a significant amount of MBP-HAT1 protein , demonstrating a direct interaction between SnRK2s and HAT1 . Consistent with the result of BiFC assays , the interaction between SnRK2 . 3 and HAT1 is the strongest ( Fig 5C ) . The in vivo interaction of SnRK2s with HAT1 were corroborated by co-immunoprecipitation ( Co-IP ) assay using Arabidopsis protoplasts co-expressing Myc-SnRK2s and HAT1-Flag fusion constructs ( Fig 5D ) . We also generated a series of truncated HAT1 fragments ( HAT1-1F ( 135–282 ) , HAT1-2F ( 192–282 ) , HAT1-3F ( 234–282 ) ) which were fused with the C-terminal half of YFP and transformed them individually with SnRK2 . 3-nYFP into tobacco leaves . When deleted to amino acid 134 in HAT1 , only a weak fluorescent signal was detected , while deletions to amino acid 191 and 233 in HAT1 totally abolished the interaction with SnRK2 . 3 ( S6B Fig ) . Several truncated MBP-HAT1 ( N-terminal region , HD , LZ , and C-terminal region of HAT1 ) were further used to map the specific domain of HAT1 required for the interaction with SnRK2 . 3 . As shown in S6C Fig , HAT1 interacts with Snrk2 . 3 with its N-terminal region . Taken together , the N-terminal region in HAT1 mediates the interaction between HAT1 and SnRK2 . 3 . Further , we conducted in vitro kinase assays to test whether SnRK2 . 3 can phosphorylate MBP-fusion HAT1 protein and found that SnRK2 . 3 can phosphorylate HAT1 , but not MBP ( Fig 5E ) . The kinase dead form of SnRK2 . 3 ( SnRK2 . 3K51N ) was used as a negative control and it totally abolished the phosphorylation of SnRK2 . 3 on HAT1 ( Fig 5E ) . We further found that the homeodomain of HAT1 ( MBP-HAT1-HD ) can be phosphorylated by SnRK2 . 3 rather than the other regions ( Fig 5F ) . In addition , the interaction of SnRK2 . 3 with HAT3 was also examined by BiFC analysis . As shown in S7A Fig , HAT3 interacts with SnRK2 . 3 in the nucleus , suggesting that SnRK2 . 3 may regulate HAT3 through a similar manner as HAT1 . To test whether phosphorylation of HAT1 by SnRK2 . 3 in vivo , the HAT1-GFP was immunoprecipitated from HAT1OX or SnRK2 . 3OX/HAT1OX transgenic seedlings treated with/without ABA or MG132 and detect the phosphorylation/dephosphorylation form using phos-tag gel blot analysis with an anti-GFP antibody ( Fig 6A ) . Two faster-migrating bands can be detected in untreated plants . We found that ABA treatment or SnRK2 . 3 overexpression resulted in the appearance of a slower-migrating HAT1 in HATOX transgenic plants ( Fig 6A ) . When subjected to phosphatase [calf-intestinal alkaline phosphatase ( CIP ) ] treatment , all three bands disappeared and a new lower band which is likely the unphosphorylated form of HAT1 appeared , indicating that HAT1 exists mostly as phosphorylated forms in plants and an elevated phosphorylation of HAT1 is formed by ABA treatment or SnRK2 . 3 overexpression ( Fig 6A and 6B ) . Furthermore , when treated with MG132 , the phosphorylation level of HAT1 was significantly increased in ABA-treated HAT1OX or SnRK2 . 3OX/HAT1OX seedlings , indicating that super-phosphorylation form of HAT1 was instable ( Fig 6A ) . To investigate the function of the SnRK2 . 3 phosphorylation on HAT1 protein stability , we detect HAT1-GFP protein level in transgenic plants . First , we expressed HAT1-GFP fusion proteins in Nicotiana benthamiana epidermal cells and examined the effects of ABA and the proteasome inhibitor MG132 on GFP fluorescence . Time-course microscopic observation revealed that the HAT1-GFP fluorescence intensity was substantially reduced in leaves treated with ABA alone , whereas HAT1-GFP was more stable after application of ABA plus MG132 ( Fig 6C ) . Similarly , HAT3-GFP fluorescence intensity was also rapidly reduced in response to ABA treatment and only slightly altered in response to the control stimulus ( solvent used for ABA ) and combined ABA and MG132 ( S7B Fig ) . We then examined HAT1-GFP protein level in HAT1-GFP transgenic plants . As shown in Fig 6D , HAT1 protein increased in the liquid one-half MS ( Murashige and Skoog ) medium without ABA treatment ( Fig 6D top panel ) . However , in the presence of ABA , HAT1-GFP protein clearly decreased in relation to the mock treatment after 3 h of treatment ( Fig 6D middle panel ) . When we treated plants with ABA and MG132 together , the HAT1 protein level significantly increased as mock ( Fig 6D bottom panel ) , suggesting that ABA triggers proteasome-mediated HAT1 degradation . To investigate whether ABA-induced HAT1 degradation is mediated by SnRK2 . 3 phosphorylation in plant , we detected HAT1-GFP protein level in HAT1OX or SnRK2 . 3OX/HAT1OX transgenic seedlings . The transcriptional level of HAT1 was same in HAT1OX and SnRK2 . 3OX/HAT1OX ( S8 Fig ) . As shown in Fig 6E , HAT1 protein was clearly degraded in SnRK2 . 3OX/HAT1OX transgenic plants , while this degradation was blocked by addition of MG132 . We further examined the ubiquitination level of HAT1 in ABA-treated HAT1OX and in SnRK2 . 3OX/HAT1OX transgenic plants . As shown in Fig 6F , the ubiquitinated level of HAT1 was significantly increased in HAT1OX plants after treatment with ABA , or in SnRK2 . 3OX/HAT1OX transgenic plants . Taken together , these results indicated that SnRK2 . 3-mediated HAT1 phosphorylation facilitates the degradation of HAT1 via stimulating its ubiquitination . HAT1 acts as a regulator by binding to HB site within its target genes promoters . First , we analyzed promoter sequences of four ABA or drought-responsive genes ( ABA3 , NCED3 , RD29A , RD22 ) and found that there were two HB-binding sites within the ABA3 and NCED3 promoter regions respectively ( Fig 7A ) . To determine whether or not HAT1 bind to the ABA3 and NCED3 promoter , electrophoresis mobility shift assays ( EMSAs ) were conducted . The MBP-HAT1 fusion protein can bind to A1 fragment of ABA3 promoter and N1 fragment of NCED3 promoter , but this binding was abolished by mutation of HB sites in the probes ( Fig 7B and 7C ) . The addition of GST-SnRK2 . 3 fusion protein was able to slightly inhibit the ability of HAT1 binding to the A1 fragment and N1 fragment ( Fig 7B and 7C ) . When HAT1 was phosphorylated by SnRK2 . 3 in vitro , the binding affinity of phosphorylated HAT1 was dramatically reduced ( Fig 7B and 7C ) . These data indicate that HAT1 protein can bind to the A1 fragment of ABA3 promoter and N1 fragment of NCED3 in vitro , and its binding ability is repressed by SnRK2 . 3 phosphorylation . To further test the effect of SnRK2 . 3 on the binding ability of HAT1 in vivo , we performed chromatin immunoprecipitation ( ChIP ) assays . We immunoprecipited HAT1-GFP protein from HAT1OX transgenic seedlings treated with/without ABA or ABA in combination with MG132 with anti-GFP antibody . TA3 , a retrotransposable element , was used as the internal control [39] . ChIP-qPCR results indicated that HAT1 specifically bound to the A1 region of ABA3 and N1 region of NCED3 , and other genomic fragments containing HB sites were not targeted by HAT1 ( Fig 7D and 7E ) . The binding ability of HAT1 was reduced by both ABA treatment and ABA plus MG132 treatment ( Fig 7D and 7E ) . Furthermore , HAT1 binding ability was significantly diminished by SnRK2 . 3 overexpression , and it cannot be recovered by addition of MG132 ( Fig 7D and 7E ) . Altogether , these results support that SnRK2 . 3 represses the binding ability of HAT1 by phosphorylation . To confirm the regulation of HAT1 by SnRK2 . 3 , we examined whether or not overexpression of SnRK2 . 3 can suppress HAT1OX phenotypes in ABA and drought responses . SnRK2 . 3OX/HAT1OX double overexpressing line displayed an enhanced ABA sensitivity in seedlings growth and was more tolerant to drought stress compared with HAT1OX , which was similar to Col-0 ( Fig 8A–8D and S9 Fig ) . Moreover , SnRK2 . 3OX/HAT1OX showed less reduction in biomass under mild drought conditions compared to HAT1OX ( Fig 8E and 8F ) . Then , we tested the influence of SnRK2 . 3 overexpression on HAT1 in the regulation of ABA or drought inducible marker genes expression . As shown in Fig 8G–8J , the expression of ABA3 , NCED3 , RD29A , and , RD22 , were significantly up-regulated in SnRK2 . 3OX/HAT1OX , compared to HAT1OX , which reached to the expression level of Col-0 . These data together with phenotype tests indicated that SnRK2 . 3 overexpression suppressed the ABA-insensitivity and drought-hypersensitivity of HAT1OX .
Currently , the most thoroughly understood in transcriptional regulation of ABA-mediated drought responses is AREB/ ABFs pathway , which activated the expression of drought-responsive genes in an ABA-dependent manner [40] , however , the components involved in compromising drought response were less well studied . In this study , we identified SnRK2 . 3 interaction transcription factors HAT1 and HAT3 as important components to regulate ABA-mediated drought response . As negative regulators , HAT1 and HAT3 suppressed ABA sensitivity and drought tolerance . Furthermore , we found HAT1 was a substrate of SnRK2 . 3 and SnRK2 . 3 phosphorylation decreased HAT1 protein stability and binding activity . Our results identified a new negative component that regulates ABA signaling in Arabidopsis in response to drought and established a novel mechanism to attenuate stress response . HAT1 plays important roles in phytohormone-regulated developmental processes and stress response [23 , 25] . HAT1 interacts with BES1 , a central regulator in BR signaling pathway , and functions as a BES1 co-repressor to inhibit BR-repressed genes and thus optimizes BR-regulated plant growth [30] . In addition , HAT1 acts as a repressor in plant defense response to CMV infection [31] . Thus , HAT1 may function as a transcriptional regulator to modulate plant growth and stress response . Several lines of evidence support the role of HAT1 as a negative regulator in ABA-mediated drought response . First , the expression of both HAT1 and its close homologs HAT3 is repressed by ABA and osmotic stress , indicating that these genes are ABA or stress-responsive factors . Second , HAT1 can bind to specific DNA sequences ( HB binding sites ) on promoter of NCED3 and ABA3 , two key ABA biosynthesis genes , and represses these genes expression , leading to a reduction of ABA synthesis . In addition , drought-responsive genes like RD22 and RD29A , were also suppressed by HAT1 . Third , consistent with the role of negative regulators for ABA signaling under stress conditions , HAT1OX displayed reduced sensitivity to ABA and less tolerance to drought stress , whereas the double knockout mutant hat1hat3 showed an enhanced ABA sensitivity and increased drought tolerance phenotypes . Finally , the modulation of HAT1 by SnRK2 . 3 kinase further suggests that HAT1 forms part of ABA signaling network to regulate ABA-dependent stress response . Besides the repression by ABA at transcription level , HAT1 is regulated by ABA-activated SnRK2 kinases through a post-transcriptional modification mechanism . Post-translational modifications of transcription factors fine-tune their functions to effectively and precisely implement the stress response . SnRK2s-mediated phosphorylation of target proteins triggers most of the molecular actions of ABA signaling pathway [14 , 41 , 42] . In addition to the originally identified bZIP transcription factors AREBs ( ABA-Responsive Element Binding factors ) that function in ABA-responsive gene regulation , 58 putative substrates of ABA-activated SnRK2s were identified through mass spectrometry-based global phosphorylation profiling , which include components involved in flowering time regulation , RNA and DNA binding , miRNA and epigenetic regulation , signal transduction , chloroplast function , and many other cellular processes [41] . In this study , we identified an additional substrate for SnRK2 . 3 kinase . In contrast to bZIP transcription factors AREBs , which are stabilized by SnRK2s phosphorylation [43 , 44] , SnRK2 . 3 phosphorylation promotes the degradation of HAT1 . In addition to destabilizing HAT1 protein , we found that SnRK2 . 3 phosphorylated HAT1 on its homeodomain , which is responsible for specific DNA binding , leading to the reduction of its binding ability to the HB sites on the promoter of target genes . Our results thus suggest that SnRK2 . 3 phosphorylation of HAT1 can have different functional consequences , inhibiting both its DNA binding and protein accumulation . However , the mechanisms how phosphorylation by SnRKs mediates HAT1 degradation remain to be determined in future studies . HAT1 belongs to Class II HD-ZIP transcription factors , which have been shown to regulate plant growth and development [45–47] . For example , ATHB4 , ATHB2 and HAT3 are required for normal leaf development and blade growth [45] . ATHB4 , a shade signaling component , acts redundantly to other members of the HD-Zip class-II subfamily to integrating shade perception and hormone-mediated growth [29] . HAT2 is an auxin inducible gene and modulates auxin-mediated morphogenesis [48] . In addition to the regulation of plant growth and development , several of the class II HD-ZIP transcription factors have been also reported to participate in plant responding to exogenous ABA and drought stress . ATHB17 has been characterized as a positive regulator of ABA response and multiple stress responses [46 , 49] . ABIG1/HAT22 is induced by ABA and drought stress , and relays ABA signaled growth inhibition and drought induced senescence [50] . HDG11 can promote main root elongation and lateral root formation in Arabidopsis and was able to confer drought tolerance in Arabidopsis , tobacco , rice , sweet potato , cotton and woody plant poplar ( Populus tomentosa Carr . ) [51–55] . It seems likely that a general role for HD-ZIP II proteins is to link environmental and developmental signals to growth control . As noted above , these class II HD-ZIP transcription factors share many similar characteristics though they have different expression patterns . Expression pattern of HAT1 and HAT3 in response to BR and ABA is analogous and functions in BR-mediated hypocotyl elongation and ABA-induced drought stress tolerance are redundant . So it proposed that HAT1 together with HAT3 played essential roles in balancing plant growth and stress responses . However whether ABA regulates HAT1 and HAT3 function and stability in a similar manner is unclear and further study will be needed . Our results strongly indicate that HAT1 is an important part of mechanisms that functions to control basal ABA signaling and drought response . HAT1 can suppress ABA synthesis and signaling through down-regulating the expression of ABA3 and NCED3 via directly binding to their promoters , and ABA/drought-responsive genes , RD29A and RD22 . In contrast , HAT1 promotes the expression of PP2Cs which negatively regulate the ABA response , enhancing the negative regulation of ABA signaling ( Fig 9 ) . When exposed to drought conditions , stress-induced ABA led to activation of SnRK2s , which in turn negatively regulates HAT1 functions by posttranslational regulation of its stability and binding ability . The suppression of HAT1 at both transcriptional and protein level appears to be an adaptive strategy of plant responses to water deficit , facilitating plants survival under drought conditions ( Fig 9 ) . When the environmental conditions are favorable , HAT1 and its homologous function to suppress drought response , prevent unnecessary activation of stress response , and ensure the normal growth of plants . HAT1 thus can be considered as a brake to fine tune ABA signaling and drought response ( Fig 9 ) . In summary , this study revealed the mechanism of the negative regulatory function of HAT1 in ABA-mediated drought response ( Fig 9 ) . We found that ABA biosynthesis and signaling were repressed by HAT1 . We also establish that HAT1 is phosphorylated by SnRK2 . 3 kinase and that SnRK2 . 3 phosphorylation promotes the proteasome-mediated HAT1 degradation and represses the binding ability of HAT1 . The identification of negative regulators , like HAT1 , and elucidation of the regulatory mechanism will lead to a better understanding of ABA signaling mechanism and drought response , which has potential in manipulating crop plants for drought tolerance .
Arabidopsis thaliana ecotype Columbia-0 ( Col-0 ) was used as the WT control . The HAT1-overexpressing lines ( HAT1-OX#11 and HAT1-OX#13 ) were described previously [30] . T-DNA insertion mutants hat1 , hat2 and hat3 were obtained from ABRC ( Arabidopsis Biological Resource Center ) [56] , corresponding to line SALK_059835 , SALK _091887 and SALK_056541 . We performed cross to create the double mutant hat1hat2 , hat1hat3 and triple mutant hat1hat2hat3 . HAT1OX and mutants were identified ( S2 Fig ) . All the plants were grown on half-strength MS plates and/or in soil under long-day conditions ( 16 h light/8 h dark ) at 22°C . Gene-specific primers HAT1 were used to isolate HAT1 , from a cDNA library by PCR . To generate the pZP211-HAT1-GFP , full-length HAT1 was amplified and cloned into the pZP211 vector with a GFP tag using the BamHI and SalI sites [57] . To generate the Myc-SnRK2 . 2/2 . 3/2 . 6 , the coding regions of SnRK2 . 2/2 . 3/2 . 6 were cloned into pCAMBIA1307-63 Myc vector [58] . To generate HAT1-Promoter::GUS , 1 . 3-kb fragments upstream of HAT1 were amplified by PCR using primers HAT1p-F/R and inserted into the binary vector pBI121-GUS using HindIII and BamHI sites [59] . For BiFC assays , SnRK2 . 2/2 . 3/2 . 6 were cloned into the pXY103 vector fused to the C terminus of YFP , and HAT1 and its fragments were fused into the pXY104 vector fused to the N terminus of YFP [60] . For the recombinant protein and GST pull-down assay , the HAT1 coding region was amplified from Col-0 cDNA and various deletion constructs were incorporated into the pETMALc-H vector ( MBP , BamHI/SalI ) [61] . The coding regions of SnRK2s were inserted into the binary vector PGEX-6P-1 ( GST , BamHI/SalI ) . All primers are listed in S1 Table . The construct of HAT1-GFP driven by 35S promoter were transformed into Agrobacterium tumefaciens ( strain GV3101 ) , which were used to transform plants by the floral dip method . Transgenic lines were selected on half-strength MS medium that contained 50 μg ml-1 kanamycin . Transgene expression was analyzed by western blotting . Rosette leaves of 4-week-old A . thaliana plants grown under short day conditions were used for the isolation of protoplasts [62] . The relevant vectors 35S:HAT1–GFP , and 35S:GFP were used for protoplast transformation . A fluorescence microscope was used to observe GFP signals ( Kim et al . , 2001; Bae et al . , 2008 ) . For GUS staining , the transgenic plants with or without ABA and osmotic stress treatment were immersed in a staining solution ( 100 mM sodium-phosphate buffer , pH 7 , 1 mM K4Fe ( CN ) 6 , 1 mM K3Fe ( CN ) 6 , 0 . 1% Triton X-100 , 2 mM X-Gluc ) overnight at 37°C in the dark followed by two times washes with 70% ethanol to remove chlorophyll . Samples were photographed using a stereoscope ( Leica ) equipped with a CCD camera . To test for GUS expression before and after ABA and osmotic stress , plants were treated with 100 μM ABA for 3 h and mannitol treatment for 6 h , respectively . For ABA sensitivity , different genotype seeds were grown vertically on 1/2 MS medium for 3–5 days and then transplanted to normal 1/2 MS medium or 1/2 MS medium containing 10μM ABA . The root growth was observed after about 10 days [63] . For the osmotic stress treatment , 4-day-old seedlings grown on half-strength MS medium ( 0 . 5% agar ) were transferred to new agar plates containing 200 mM mannitol , and the primary root length and 30-seedlings fresh weight were measured after 10 days . The primary root lengths were measured with ImageJ ( National Institutes of Health , Bethesda , MD , USA ) . Three independent experiments were performed . To study the promotion of stomatal closure by ABA , fully expanded young leaves of 4-week-old Arabidopsis plants were harvested and incubated in MES-KCl buffer ( 50 mM KCl , 10 mM MES-KOH , pH 6 . 15 ) , at 22°C and exposed to light for 2 h . Once the stomata were fully open , leaves were incubated in MES-KCl buffer alone or containing 50 μM ABA . Control treatments involved the addition of DMSO , an appropriate solvent with ABA . After treatment for 3h under light conditions , the epidermal strips were immediately peeled carefully from the abaxial surface of leaves , and stomatal apertures were measured with an optical microscope ( Nikon , Optiphot-2 ) fitted with a camera lucida and a digitizing table linked to a personal computer [64] . The stomatal aperture sizes were analysed by the software image J . To avoid any potential rhythmic effects on stomatal aperture , experiments were always started at the same time of the day . Blinded stomatal aperture experiments were conducted by another group in the laboratory who are not aware of any information about the control group ( WT ) and test group ( mutants and transgenic plants ) ( S2 Data Blinded experiments ) . For the ROS accumulation assay in guard cells , prepared epidermal peels with or without ABA treatment were loaded with 50 μM 2 , 7-dichlorofluorescin diacetate for 10 min ( H2DCF-DA; Sigma-Aldrich ) in dark , as described previously [65] . Fluorescence emission of guard cells was analyzed using image J . Three independent experiments were performed . To measure leaf water loss , rosette leaves of similar developmental stages from 4-week-old plants were excised from their roots , placed in open Petri dishes , and kept on the lab bench for the indicated time , and then their fresh weights were monitored , with three replicates per time-point [66] . Water loss was expressed as a percentage of weight loss at the indicated time versus initial fresh weight . For the progressive drought treatment experiment , 10-day-old plants were transferred from 1/2 MS medium to water-saturated soil and the plants were grown in the same glasshouse with 120 μmol m-2 s-1 under a 16 h: 8 h , light: dark photoperiod ( 23°C ) for 2 weeks , then the plants were deprived of water for 14 days and the survival rates of plants were determined 5 d after re-watering ( rehydration ) [67] . Relative electrolyte leakage rates were measured as described by Julieta V . Cabello et al . [68] . Three independent experiments were performed . The mild drought treatment was conducted as previously described [69 , 70] , with a slight modification . Briefly , 12-day-old Arabidopsis seedlings of different genotypes grown on 1/2 MS medium were transferred to pots . Before transfer , the relative water content of the pots was set at 2 . 2 g water g-1 dry soil . The plants were kept to grow for 10 days . During this growth period , the water content of the soil was kept constant until 10 days , after which it was lowered daily to target 0 . 7 g water g-1 dry soil and mild drought stress treatment began . Control soil water content ( well water ) was maintained at a constant value of 2 . 2 g water g-1 dry soil during the entire experiment . Fig 3D showed the water loss from the peat pellets during the duration of the experiment . After mild drought treatment for 9 days , images of each genotype were taken . To quantify the biomass change of each genotype , the dry weights of detached rosettes of both the drought-treated and the well-watered control were measured . The reduction in biomass was calculated using the following equation: Reduction in Biomass ( RB ) = ( Biomass of Well Watered Control–Biomass of Drought Treated ) / ( Biomass of Well Watered Control ) Polyethylene glycol ( PEG ) 6000 was used to mimic drought stress [66] . Arabidopsis seedlings grown on 1/2 MS medium plates were transferred to 1/2 MS liquid medium ( CK ) and 1/2MS liquid medium containing 15% PEG ( drought stress treatment ) for indicated time , and then the seedlings were harvested for gene expression analysis or ABA content assay . For ABA content assay , 0 . 5g 12-day-old seedlings with or without 15% PEG treatment were homogenized in 2 mL of 80% methanol , and incubated with additional 3 mL of 80% methanol overnight at 4°C . After centrifugation ( 4000 r/min for 10 min , 4°C ) , the supernatant was passed through a C18-SepPak classic cartridge ( Waters , Milford , USA ) [71] . ABA content measurement was performed by using a Plant hormone abscisic acid ( ABA ) ELISA Kit ( BIOSAMITE , CK-E90047 ) . Three independent experiments with different biological repeats were done . 12-day-old seedlings grown under long-day conditions were used for qRT-PCR analysis of ABA or drought stress-responsive genes . Total RNA extraction , cDNA synthesis and qRT-PCR were performed as described by Zhang et al . ( 2010 ) [72] . Briefly , total RNAs were extracted using RNAprep pure Plant Kit ( from Transgene Biotech Co . Ltd . of Qiagen , Beijing ) according to the manufactures’ protocols . Total RNAs treated with DNase I ( Transgene Biotech Co . Ltd . of Qiagen , Beijing ) were converted into cDNAs using M-MLV Reverse Transcriptase Kit ( Invitrogen , USA ) . Real-time qPCR analysis was carried out using the SYBR® Premix Ex TaqTM II ( TAKARA ) on a BIO-RAD CFX ConnectTM Real-Time System , following the manufacturer’s instruction . Three independent experiments were performed , and three technical replicates of each experiment were performed . Actin2 genes was used as an internal control for normalization of transcript levels [73] . All primers used for gene expression analysis are shown in S1 Table . For GST pull-down assay , HAT1 and HAT1 fragments fused with MBP were purified with amylose resin ( NEB ) . SnRK2 . 3 fused with GST was purified with glutathione beads ( Sigma , G4510 ) . GST pull-down assays were performed as described Yin et al . [74] . The assays were repeated three times with similar results . For the BiFC assay , SnRK2s were cloned into the pXY103 vector and fused to the C terminus of YFP , and HAT1 and its fragments were fused into the pXY104 vector and fused to the N terminus of YFP . The resulting plasmids were introduced into Agrobacterium tumefaciens ( strain GV3101 ) , and then infiltrated into young leaves of Nicotiana benthamiana . Infected leaves were analyzed 48h after infiltration . YFP fluorescence was observed under a fluorescence microscope ( Leica ) . For the Co-IP assays in the Arabidopsis protoplasts , full-length coding sequences of HAT1 and SnRK2 . 3 were individually cloned into tagging plasmids behind Flag or Myc tag sequences in the sense orientation behind the cauliflower mosaic virus 35S promoter . Flag-fused HAT1 and Myc-fused SnRK2s were then transformed into Arabidopsis protoplasts . After overnight incubation at 23°C , the protoplasts were lysed , sonicated , and centrifuged . Co-IP assays were performed using transiently expressed proteins as described previously [75] . Briefly , the protein extracts were mixed with Myc agarose beads ( Sigma-Aldrich ) and then incubated at 4°C for 2 h . After being was hed at least five times , the agarose beads were recovered and mixed with the SDS sample buffer . The samples were detected by immunoblotusing anti-Myc antibody , and the coimmunoprecipitated protein was then detected using an anti-Flag antibody . The in vitro kinase assay was performed as previously described as Yin et al . [74] . MBP , MBP-HAT1 , and truncated MBP-HAT1 were incubated with GST-SnRK2 . 3 kinase in 20 μL of kinase buffer [20 mM Tris ( pH 7 . 5 ) , 100 mM NaCl , and 12 mM MgCl2] and 10 μCi 32P ATP . After incubation at 37°C for 60 min , the reactions were stopped by adding 20 μL of 2×sodium dodecyl sulfate ( SDS ) buffer and boiling for 5 min . Proteins were resolved by polyacrylamide gel electrophoresis ( PAGE ) and phosphorylation was detected by exposing to a phosphor screen , and signals were obtained by a Typhoon 9410 phosphor imager . The in vivo phosphorylated HAT1 was examined by Phostag reagent ( NARD Institute ) with or without CIP treatment as described Guan et al [76] . Total protein was extracted from Arabidopsis using extraction buffer as described previously [77] . Briefly , plant material was ground in the Eppendorf tube using 2×sodium dodecyl sulfate ( SDS ) sample buffer , centrifuged at 13 , 000g for 10 min , and the supernatant was saved . For immunoblot analysis , total protein was separated by 10% SDS-polyacrylamide gel electrophoresis ( PAGE ) and transferred to PVDF membranes . The membrane was blocked for 1 h in TBST buffer ( 10 mM Tris , pH 7 . 6 , 150 mM NaCl , 1 . 0% Tween20 ) with 5% skim milk powder at room temperature and then incubated with specific primary antibodies in TBST buffer for 1 h . After the membrane washed by TBST buffer for several times , the blot was incubated with horseradish peroxide-conjugated secondary antibody ( goat anti-rabbit IgG , Thermo fisher ) at a dilution of 1/10000 for detection by the enhanced chemilumine scence assay . EMSA was performed using an Electrophoretic Mobility-Shift Assay ( EMSA ) Kit *with SYBR Green and SYPRO Ruby EMSA stains* ( MolcularprobesTM , E33075 ) . The binding reactions were carried out in 20 μL binding buffer [25 mM HEPES-KOH pH 8 . 0 , 50 mM KCl , 1 mM dithiothreitol ( DTT ) and 10% glycerol] with approximately 1 ng probe ( 10000 cpm ) and recombinant proteins purified from E . coli . After 30 min incubation on ice , the reactions were resolved by 5% native polyacrylamide gels with 1×TGE buffer ( 6 . 6 g L-1Tris , 28 . 6 g L-1 glycine , 0 . 78 g L-1EDTA , pH 8 . 7 ) . The assays were repeated three times with similar results . ChIP was performed as previously described [78] . Briefly , 14-day-old seedlings of HAT1OX and SnRK2 . 3OX/HAT1OX seedlings were treated as above described . 1 . 5 g of the samples were cross-linked with formaldehyde and nuclei were isolated using sucrose gradients . Chromatin was sonicated to generate fragments with the average size of 300 bp and precipitated using anti-GFP antibody . Immunocomplexes were harvested by protein A beads , washed and reverse cross-linked by boiling in the presence of Chelex resin ( Bio-Rad , http://www . bio-rad . com/ ) . The level of precipitated DNA fragments was quantified by RT-qPCR using specific primer sets ( S1 Table ) . Col-0 was the negative control and the values in control plants were set to 1 after normalization against TA3 for qPCR analysis . Three biological replicates were carried out through the whole process . | Drought stress is a key environmental factor that severely reduces crop yield all over the world . The phytohormone abscisic acid ( ABA ) is known to mediate drought responses through regulating drought-responsive genes expression and stomatal closure , but the mechanisms that negatively regulate this process and prevent the adverse effects of excess drought responses on plant growth is less well studied . Here , we show that a HD-ZIP II transcription factor HAT1 negatively regulates ABA-mediated drought responses through suppressing ABA biosynthesis and signaling . The hat1hat3 mutant showed ABA-hypersensitive and drought-tolerant phenotypes , whereas the HAT1-overexpressing lines were insensitive to ABA and less tolerant to drought . Furthermore , we found SnRK2 . 3 kinase , a positive component of ABA signaling , interacts with and phosphorylates HAT1 to destabilize and suppress its binding activity . Overexpression of SnRK2 . 3 reduces HAT1 protein level and inhibits HAT1OX phenotypes in ABA and drought responses . Our results revealed a HAT1-mediated negative regulatory mechanism in attenuating the ABA signaling and drought response . | [
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"organisms"... | 2018 | Transcription factor HAT1 is a substrate of SnRK2.3 kinase and negatively regulates ABA synthesis and signaling in Arabidopsis responding to drought |
HIV-1 Vpu prevents incorporation of tetherin ( BST2/ CD317 ) into budding virions and targets it for ESCRT-dependent endosomal degradation via a clathrin-dependent process . This requires a variant acidic dileucine-sorting motif ( ExxxLV ) in Vpu . Structural studies demonstrate that recombinant Vpu/tetherin fusions can form a ternary complex with the clathrin adaptor AP-1 . However , open questions still exist about Vpu’s mechanism of action . Particularly , whether endosomal degradation and the recruitment of the E3 ubiquitin ligase SCFβTRCP1/2 to a conserved phosphorylated binding site , DSGNES , are required for antagonism . Re-evaluation of the phenotype of Vpu phosphorylation mutants and naturally occurring allelic variants reveals that the requirement for the Vpu phosphoserine motif in tetherin antagonism is dissociable from SCFβTRCP1/2 and ESCRT-dependent tetherin degradation . Vpu phospho-mutants phenocopy ExxxLV mutants , and can be rescued by direct clathrin interaction in the absence of SCFβTRCP1/2 recruitment . Moreover , we demonstrate physical interaction between Vpu and AP-1 or AP-2 in cells . This requires Vpu/tetherin transmembrane domain interactions as well as the ExxxLV motif . Importantly , it also requires the Vpu phosphoserine motif and adjacent acidic residues . Taken together these data explain the discordance between the role of SCFβTRCP1/2 and Vpu phosphorylation in tetherin antagonism , and indicate that phosphorylation of Vpu in Vpu/tetherin complexes regulates promiscuous recruitment of adaptors , implicating clathrin-dependent sorting as an essential first step in tetherin antagonism .
Counteraction of the antiviral membrane protein tetherin ( BST2/ CD317 ) is an essential attribute of primate lentiviruses , and is mediated by either the Vpu or Nef accessory proteins , or occasionally the viral envelope glycoprotein ( reviewed in [1] ) . In their absence , tetherin restricts the release of virions assembling at the cell surface [2–6] . By virtue of its N-terminal transmembrane ( TM ) domain and C-terminal GPI anchor , partitioning of tetherin dimers into budding virions allows them to simultaneously span host and viral membranes resulting in accumulation of cross-linked virions on the plasma membrane ( PM ) [7 , 8] . In addition to physically limiting virion release , tetherin’s activity sensitizes infected cells to antibody-dependent cellular cytotoxicity [9–12] , targets virions for endosomal degradation , and in the case of great ape tetherins , can directly induce the activation of proinflammatory NF-κB signaling [13–16] . Tetherin recycles to the PM via the trans-Golgi network ( TGN ) [17] . This requires a dual tyrosine-based sorting signal ( YDYCRV in humans ) , which can interact with the clathrin adaptor AP-1 . Lentiviral countermeasures physically interact with tetherin , often in a highly species-specific manner [1] . Through their action , tetherin incorporation into virions is blocked , and this is associated with its reduced cell surface levels . In the case of HIV-1 Vpu , a small membrane phospho-protein , physical interaction is mediated by the TM domains themselves [18–20] . HIV-1 Vpu targets human tetherin into an ESCRT-dependent endosomal degradation pathway [21 , 22] . This is an ubiquitin driven process and requires a highly conserved DSGNES motif in the Vpu cytoplasmic tail [23–25] . Phosphorylation of the serine residues ( S52/53 and S56/57 in subtype B depending on the isolate ) by casein kinase II ( CKII ) [26 , 27] recruits the β-TrCP1/2 subunits of a Skp1-Cullin1-F-Box ( SCF ) E3 ubiquitin ligase [28] that mediates direct ubiquitination of various residues in the tetherin cytoplasmic tail including an STS motif [29] . However , there is still debate as to whether the recruitment of the SCFβTRCP1/2 to the DSGNES motif in Vpu is required for counteraction of physical retention of virions by tetherin ( hereafter also termed antagonism ) as well as its final endosomal degradation . Much of this discrepancy may be attributable to whether assays are performed in virally infected cells or those transiently transfected with Vpu , tetherin or both [30] . While ESCRT-I appears to be dispensable in infected cells [21] , evidence that the ESCRT-0 component HRS is required for tetherin antagonism suggests targeting to endosomal degradation plays a role [22] . Furthermore , mutations of the Vpu serine residues ( so called 2/6 mutations ) have intermediate phenotypes in tetherin antagonism suggesting degradation does not fully explain Vpu function[24 , 25 , 31] . Moreover this defect in antagonism is not recapitulated by siRNA depletion of β-TrCP1/2 [32] . Indeed evidence that the DSGNES motif might have a dual function in tetherin trafficking has been proposed [33] . This is consistent with our recent study of Vpu variation in patients where we found that naturally occurring variants in the NE of the DSGNES imparted tetherin-specific defects to Vpu without blocking its other SCF-dependent activity , dislocation of CD4 from the endoplasmic reticulum [34] . Vpu has been shown to block newly synthesized and/or recycling tetherin from trafficking to the cell surface [33 , 35] . This requires a variant of an acidic dileucine motif , ExxxLV , in the second alpha helix of the cytoplasmic tail of most HIV-1 group M clade Vpu [36] . Acidic dileucine sorting signals bind to the σ subunits of the major cellular clathrin adaptors AP-1 ( trafficking from TGN to endosomes and vice versa ) and AP-2 ( clathrin-dependent endocytosis from the PM ) ( reviewed in [37] ) . In keeping with this , Vpu-mediated tetherin antagonism is entirely clathrin-dependent [36 , 38] . Mutation of the ExxxLV motif does not block Vpu/tetherin interactions , but reduces the efficiency of counteraction and inhibits degradation [36] . In particular ExxxLV is essential for counteraction of tetherin in CD4+ T cells upon interferon upregulation , and mutant phenotypes are exacerbated when tetherin lacks the YDYCRV motif [36] . A recent structural and biochemical study has demonstrated that the ExxxLV motif can bind canonically to the σ subunit of AP-1 , whereas the YXXθ motif of tetherin can bind to the μ subunit of AP-1 [39] . In fusions of Vpu and tetherin cytoplasmic tails both motifs can occupy their respective binding sites simultaneously [39] . Some density in the structure also indicated other contacts between Vpu and AP-1μ , and together implied a mechanism whereby the formation of this ternary complex would modulate AP-1-dependent trafficking of tetherin to endosomes . However , whilst the localization of Vpu to the TGN suggested AP-1 as the major target , siRNA-mediated knockdown of AP-1 or expression in AP-1 -/- murine fibroblasts did not inhibit Vpu function [36] . Neither has physical interaction between AP-1 and the wild-type Vpu protein been demonstrated in living cells . Expression of tetherin fused at its N-terminus to the second helix of Vpu is excluded from budding virions at the PM in an ExxxLV-dependent manner [18] . Added to this , tetherin can be expressed as two isoforms , one of which lacks the YDYCRV motif and can be antagonized by Vpu to a certain extent without cell surface downregulation [13 , 40] . Likewise , Vpu has only a modest effect on tetherin endocytosis [25 , 35] , and AP-2 knockdown also has little impact on antagonism , contrasting sharply with SIV Nef and HIV-2 envelopes [38 , 41 , 42] . AP1 binding to a non-canonical acidic dileucine motif in CI-M6PR has been associated with upstream serine phosphorylation by CKII previously [43] . Thus we hypothesized that the DSGNES in Vpu might regulate clathrin adaptor interaction independently of SCF recruitment . Here we provide evidence that this is indeed the case .
The importance of the SCFβTRCP1/2 E3 ligase and the ultimate degradation of tetherin to the counteraction of its physical antiviral activity by Vpu has been controversial . Since the discrepant studies were mostly performed under conditions of transient transfection of tetherin , provirus or both , and which have been shown previously to lead to artifactual effects on tetherin degradation [30] , we re-examined these issues in HIV-1 infected 293T cells stably expressing surface tetherin at levels similar to those induced by type 1 interferon ( Fig 1A ) . We have previously shown that an endosomal sorting-specific subunit of ESCRT-I , UBAP1 , is essential for tetherin’s degradation but not for antagonism [21 , 36] . Despite efficient levels of knockdown , similarly efficient siRNA knockdowns of HRS ( ESCRT-0 ) or UBAP1 had only minor effects on one-round yield of wild-type HIV-1 ( HIV-1 wt ) from 293T tetherin cells at an MOI of 0 . 8 ( Fig 1B and 1C ) . As expected , knockdown of the core ESCRT-I subunit TSG101 destablized UBAP1 [44] and blocked all virion release because of its essential late-domain function [45] , and all siRNA treatments also stabilized Vpu expression ( Fig 1B ) . In keeping with this , cells infected at an MOI of 2 , to ensure at least 90% infection , demonstrated that Vpu-induced degradation was blocked by all siRNA knockdowns ( Fig 1D and 1E ) . These data therefore indicate that in infected cells expressing physiological levels of Vpu from an integrated HIV-1 provirus , the core ESCRT pathway and HRS are essential for Vpu-mediated tetherin degradation , but dispensable for counteraction of tetherin’s physical antiviral activity . A previous study indicated that HRS interacted with Vpu in immuno-precipitates [22] . We confirmed this in transfected cells using myc-tagged HRS , and found that HRS truncations that removed its double-ubiquitin interaction motif ( DUIM ) inhibited this interaction ( S1A and S1B Fig ) . Furthermore , point mutations in the DUIM that abolish ubiquitin-interaction ( A266Q/ A228Q ) [46] , not putative ubiquitin-binding mutants in the VHS domain , completely abolished HRS/Vpu interactions in co-IPs ( S1C Fig ) . Whilst formally possible that the DUIM is a direct binding site for Vpu , these data likely suggest that Vpu interactions with HRS are mediated indirectly through ubiquitination either of cargo , or associated factors in the degradation pathway . We next similarly re-evaluated the effect of simultaneously knocking down β-TrCP1 and 2 on Vpu-mediated tetherin-degradation and tetherin-counteraction in infected cells . Again despite efficient knockdown , we saw little effect of this treatment on HIV-1 WT release ( Fig 1F–1H ) . Of note , there was no evidence that β-TrCP1/2 knockdown reduced wild-type release to that of a viral mutant lacking the phosphorylated serines at positions 52 and 56 that are essential for β-TrCP1/2 recruitment ( HIV-1 Vpu 2/6A ) . This was in contrast to a complete reversal of Vpu-mediated tetherin degradation by β-TrCP1/2 siRNAs in cells infected at an MOI of 2 ( Fig 1H ) . Therefore whilst tetherin degradation by Vpu requires the SCFβTRCP1/2 complex , under conditions when it is sufficiently depleted to block this , there is no effect on Vpu-mediated tetherin antagonism . Since the phospho-mutant of Vpu , Vpu 2/6A , has been shown to be partially defective for tetherin antagonism [23–25] , we revisited whether this impairment could be uncoupled from the ubiquitin ligase . We recently showed that mutants of clade B Vpu lacking a conserved ExxxLV sorting signal ( Vpu ELV ) were also partially defective for tetherin antagonism because they could not traffic tetherin/Vpu complexes for endosomal degradation [36] . Notably , ELV mutant Vpu loses all residual activity against tetherin lacking the dual-tyrosine recycling motif , and a recent study demonstrated that the tetherin and Vpu cytoplasmic tails can assemble into a ternary complex with clathrin adaptor AP-1 [39] . In addition , hints in the structure suggested that residues 42 and 43 of the first helix of the cytoplasmic tail make a non-canonical contact with AP-1μ . We found similar Vpu mutants with tetherin-defective phenotypes in our patient cohort [34] , and mutation of conserved L41I42/L45I46 in the first alpha helix to alanines in the NL4 . 3 provirus led to a profound defect in tetherin antagonism and degradation without preventing interaction ( S2A–S2E Fig ) . Since the DSGNES motif is located in an acidic patch between helix 1 and the ExxxLV site , we hypothesized that Vpu phosphomutants may also be similarly defective for mis-trafficking tetherin . In one round virus infection assays in 293T/tetherin cells , LI/LI , ELV and 2/6A mutants all had similarly defective phenotypes for tetherin antagonism ( Fig 2A and 2B ) . Interestingly , like the ELV mutant [36 , 39] , both LI/LI and 2/6A mutants lost all their residual activity in cells expressing tetherin Y6 , 8A whereas release of the wild-type virus was only slightly affected . Moreover , as expected , all mutants were defective for tetherin degradation ( Fig 2C ) . Examination of the localization of the three mutants in transfected HeLa cells revealed that , unlike the wild-type , 2/6A and LILI localized prominently to peripheral endosomal structures as well as the TGN ( Fig 2D ) . This was similar to the localization expected for the ELV mutant [36] , and quantification of coincidence with TGN46 revealed that all three mutants had a significantly reduced localization to the TGN consistent with a trafficking defect ( Fig 2E ) . Importantly there was no significant additive effect of combined 2/6 and ELV mutations in full-length virus release from either the 293T/tetherin cells or primary CD4+ T cells ( S3A–S3C Fig ) . Also these data could be recapitulated using a highly active primary Vpu ( Vpu 2_87 ) isolate from our previous patient study [34] ( S4A–S4D Fig ) . Treatment of 293T tetherin cells infected with wild-type HIV-1 with a CKII inhibitor , Tyrphostin , to mimic the 2/6A mutation showed a reduction of virus release only in the presence of tetherin , or more prominently , the Y6 , 8A mutant ( Fig 2F and 2G ) . Western blot analysis of cell lysates transfected with HA-tagged Vpu expression vectors and run on an 8% PhosTag gel showed that in the presence of Tyrphostin , the smear of phosphorylated Vpu was reduced indicating inhibition of Vpu phosphorylation ( Fig 2H ) . Together , these data therefore suggested that the defective tetherin antagonism of Vpu 2/6A may be due to phosphorylation-regulated trafficking of Vpu rather than ubiquitin ligase recruitment and degradation . The current model for Vpu function is that it prevents tetherin trafficking to the PM from the TGN and sorts it into a clathrin-dependent endosomal trafficking pathway [1 , 47] . If our above hypothesis was the case , we reasoned that bypassing clathrin adaptors and linking Vpu directly to clathrin itself could functionally rescue all ELV , LI/LI and 2/6A mutants . To do this we appended the AQLISFD clathrin box ( CB ) from HRS or a mutated sequence , AQAASFD , lacking the leucine and isoleucines essential for clathrin interaction , to the C-termini of Vpu and the respective mutants ( Fig 3A ) . Transient transfection of increasing doses of Vpu into 293T tetherin cells effectively rescued Vpu-defective HIV-1 viral release , and neither the clathrin box nor its mutant impaired wild-type Vpu function ( Fig 3B and 3C ) . Remarkably , however , Vpu 2/6A , Vpu ELV or Vpu LI/LI function was almost fully restored by fusion of the clathrin box , whereas grafting the mutated sequence had no effect . All Vpu chimeras were well expressed , although as shown in Fig 3C , the apparent molecular weight of Vpu and its chimeras in SDS-PAGE did not reflect amino acid length . Similar results were obtained for a heterologous clathrin box ( RNLLDLL ) derived from GGA2 ( available on request ) . The clathrin box also fully restored downregulation of tetherin from the surface of transiently transfected HeLa-TZMbl cells to all the mutants ( Figs 3D and S5A–S5D ) . To show that this rescue of function was clathrin-dependent , we depleted clathrin membrane binding with the C-terminal fragment of the neuronal clathrin-adaptor AP180 ( AP180c ) . As expected , rescue of wild-type Vpu-dependent virus release was inhibited by AP180c whereas residual viral release in the presence of tetherin was not [36] . In all cases , the same held true for clathrin box fusions ( Fig 4A ) . Thus , direct linkage to the clathrin machinery was sufficient to rescue both Vpu 2/6A and the trafficking mutants . Moreover , in cells stably expressing the Vpu chimeras , no reduction of tetherin steady state levels was observed upon CB fusion to any of the chimeras ( Fig 4B ) , nor was β-TrCP interaction restored to the 2/6A mutant fusion ( Fig 4C ) , indicating this was independent of SCF and ESCRT function . Wild-type subcellular localization was restored to all mutants; 2/6A , ELV and LI/LI localization was significantly restored to TGN-associated compartments upon CB fusion ( Fig 4D and 4E ) . To further characterize these Vpu chimeras , we next examined whether they were functional against tetherin bearing tyrosine ( trafficking ) and serine/threonine ( the proposed SCFβTRCP ubiquitination site [29] ) mutations in the cytoplasmic tail . In the case of 293T tetherin-STS-AAA cells , the Vpu CB chimeras behaved as they did against the wild-type protein , effectively fully rescuing the 2/6A , LILI or ELV lesion ( Fig 5A ) . Importantly , stable expression of an STS mutant tetherin had no detectable effect on the efficiency of counteraction by wild-type Vpu , and the CB addition had no effect , indicating that there is no reduction in Vpu antagonism when tetherin lacks the residues proposed to be important for ubiquitination . However , in the case of 293T tetherin Y6 , 8A cells , whilst Vpu wild-type and CB fusions remained active , the mutant chimeras remained completely defective ( Fig 5B ) . These data imply that unlike the ExxxLV motif , the clathrin box addition is not dominant over the tetherin tyrosine-based sorting motif . This therefore suggests that tetherin sorting into clathrin-rich domains in the recycling compartment is essential for clathrin box chimera rescue , which then anchors the Vpu/tetherin complex . Subsequent endosomal trafficking , and importantly , any requirement for serine/threonine ubiquitination are downstream of this event . It also further reinforces the notion that the primary lesion in tetherin antagonism of the 2/6A mutant , like ELV and LI/LI , is at the level of clathrin-dependent sorting , not ubiquitin ligase recruitment . Finally we examined mutations within the DSGNES motif itself . The consensus for a β-TrCP-binding site is DSGxxS , yet the N55/E56 in group M Vpu is almost universally conserved . We found rare mutations ( N55H/E56G ) in patients that displayed impaired tetherin antagonism despite retaining β-TrCP interaction [34] . Similarly , examination of a Vpu N55H/E56G mutation in the context of the NL4 . 3 Vpu revealed defects in tetherin counteraction in 293T tetherin cells ( S3 and S4 Figs ) , which again could be rescued by a clathrin box fusion unless tetherin itself contained tyrosine mutations ( Fig 5C and 5D ) . Together with the above data , these observations suggest that structural constraints or flexibility within the phosphoserine motif may underlie the reason why the 2/6A mutant is defective for tetherin mis-trafficking . Our previous characterization of the ExxxLV motif and the data presented herein indicate that clathrin-dependent sorting of Vpu/tetherin complexes is an essential step in tetherin antagonism , prior to ubiquitin-dependent degradation . The demonstration that the ExxxLV motif of Vpu and the YDYCRV site in tetherin can form a ternary complex with AP-1 [39] is consistent with the cell biological observations that Vpu primarily blocks tetherin recycling and transit to the PM rather than stimulating its endocytosis [33 , 35] . However , demonstration that Vpu can interact with AP-1 in cells is lacking , and neither siRNA depletion of AP-1 , nor deletion of γ-adaptin in murine fibroblasts , affects tetherin antagonism [36] . Clathrin adaptor interactions with their cargoes can sometimes ( but not universally ) be detected in yeast 2 or 3-hybrid assays or with recombinant proteins , but the relative weakness of their affinities often precludes direct demonstration of their interactions in vivo by conventional immunoprecipitations . To examine Vpu interaction with AP-1 , we initially employed a proximity-based biotin ligase assay ( S6A Fig ) . A consenus clade B Vpu or indicated mutant ( note the phosphomutant S53 , 57A is labeled S3/7A ) , was fused to a myc-tagged E coli biotin ligase BirA-R113G , which itself does not compromise Vpu activity ( S6B Fig ) . These constructs were then transfected into 293T or 293T tetherin cells . 6 hours after transfection the cells were incubated with free-biotin overnight in the presence of concanamycin A to block any tetherin degradation by the wild-type Vpu protein . Cell lysates were precipitated with streptavidin beads , and recovered proteins analyzed by Western blotting . Such treatment will lead to promiscuous biotinylation of proteins in close proximity with Vpu , potentially allowing us to detect interacting factors with weak affinities . As shown in S6C Fig , addition of biotin led to an accumulation of biotinylated proteins in cell lysates , including a strong band that is auto-biotinylation Vpu-BirA fusion itself . Importantly , β-TrCP was detected for all mutants tested in both 293T and 293T tetherin cells except the 2/6A mutant . Interestingly AP-1 γ-adaptin was detected only in streptavidin precipitates from 293T tetherin cells transfected with wild-type Vpu-BirA fusion , and not cells lacking tetherin expression . Furthermore , in 293T tetherin cells both ELV and LI/LI mutants failed to biotinylate AP-1 . Interestingly , this was observed for the 2/6A mutant and also a Vpu A14L/W22A mutant that lacks tetherin binding . Thus , proximity-based tagging suggested Vpu does indeed interact with AP-1 in living cells . This appears to be dependent on tetherin binding and requires both the predicted AP-1σ binding site in Vpu , ExxxLV , and the non-canonical AP-1μ contact proposed to imparted by LI/LI . Furthermore , the lack of the 2/6A mutant to biotinylate AP-1γ suggests that Vpu phosphorylation is required to promote interaction , consistent with its cellular phenotype . Whilst this data is strongly suggestive , it does not rule out that conformational changes in the mutants position the BirA in a context where AP-1 cannot be biotinylated . To strengthen these observations , we performed cross-linking immunoprecipitations in 293T tetherin cells transfected with HA-tagged Vpu or all of the above Vpu mutants . This revealed that AP-1γ could be detected in immunoprecipitates of Vpu-HA ( Fig 6A ) . This was not detected for the A14L/W22A mutant , again indicating a requirement for tetherin interaction . A reduced amount of AP-1γ was detected in the 2/6A and ELV mutant immunoprecipitates , and this varied between replicates ( see histogram below blot ) . Since tetherin’s YDYCRV motif also binds to AP-1 ( Jia et al . , 2014 ) , we repeated the immunoprecipitations in 293T tetherin Y6 , 8A cells ( Fig 6B ) . Whilst AP-1 precipitation was preserved for the wild-type protein , this effectively removed all detectable AP-1 interactions with any of the mutants , including the NE mutation between the two serines , indicating the reduced detection was due to tetherin/AP-1 interactions . To confirm these data , we also performed the same precipitations in 293T cells expressing a rhesus macaque tetherin to which HIV-1 Vpu cannot bind ( Fig 6C ) , or parental 293T cells ( S7A Fig ) and found that no AP-1 could be detected under any conditions . These data also held true for the patient isolate Vpu 2_87 ( S7B Fig ) . Therefore , these data demonstrate for the first time that Vpu does interact with AP-1 in vivo . Tetherin/Vpu TM-domain interactions are essential for this interaction , as are the predicted AP-1 binding sites in Vpu . Moreover , the lack of interaction of the 2/6A mutant indicates that phosphorylation of Vpu upstream of the ExxxLV regulates AP-1 interaction , and these data correlate well will the clathrin dependency presented in Fig 4 . The ExxxLV motif has the potential to bind to other clathrin adaptor σ subunits[39] . Since AP-1 depletion does not block Vpu function , we wondered whether Vpu interaction with the clathrin machinery might also occur through AP-2 . We therefore analyzed the precipitations from cells expressing tetherin Y6 , 8A for the AP-2α adaptin subunit ( Figs 6A , 6B and S7B ) . Surprisingly this could also be detected with the wild-type protein , but was absent for all the mutants , indicating ExxxLV also regulates this interaction . Thus , Vpu interacts promiscuously with both major cellular clathrin adaptors in a manner dependent on its ability to bind to tetherin . This is likely to account for why individual adaptor knockdowns fail to block Vpu function , and suggest that AP-2 might represent a compensatory clathrin-dependent trafficking mechanism for counteracting tetherin . Finally , to provide direct evidence that it was phosphorylation of Vpu that permitted AP1/AP2 interactions , we repeated these immunoprecipitations in 293T tetherin Y6 , 8A cells treated with Tyrphostin ( Fig 7 ) . Under these conditions the ability of wildtype Vpu to interact with AP1 or AP2 was abolished , indicating that CKII-mediated phosphorylation for Vpu is required for recruitment of clathrin transport machinery .
In this study we have re-evaluated discrepancies in the literature regarding the role of SCFβTRCP1/2 and ESCRT in Vpu-mediated tetherin degradation and antagonism of its physical antiviral activity . We find that whilst essential for the former , they are dispensable for the latter in HIV-1 infected cells . We further show that phospho-serine mutants of Vpu have a distinct phenotype , displaying defects in tetherin antagonism because they cannot engage with clathrin-dependent trafficking pathways . We demonstrate that in cellulo Vpu/tetherin TM interactions induce Vpu binding to either clathrin adaptors AP-1 or AP-2 . This interaction requires the ExxxLV trafficking motif , validating the recent structural study [39] . Importantly , phosphomutants of Vpu are also defective for clathrin adaptor engagement , implying that CKII-mediated phosphorylation not only regulates SCFβTRCP1/2 recruitment , but also regulates Vpu trafficking . Together these data clarify the role of the Vpu DSGNES motif in tetherin counteraction and provide strong evidence that sorting of Vpu/tetherin complexes into clathrin-rich domains of the endocytic pathway is the critical event in efficient tetherin antagonism . Furthermore , the observation that Vpu can interact both with AP-1 or AP-2 suggests a redundancy in adaptor protein requirement for tetherin counteraction that provides a plausible explanation for why depletion of either AP-1 or AP-2 is not sufficient to compromise Vpu function[36] . Thus potentially , tetherin/Vpu complexes that escape AP-1 in the TGN , and which traffic to the PM , can be retrieved by AP-2 . Such a model would also rationalize why in some cases tetherin counteraction by Vpu can be observed with minimal evidence of surface downregulation [18 , 48] . Much of the discrepant literature regarding the mechanism of Vpu-mediated tetherin antagonism comes from experiments where tetherin , provirus and/or Vpu are transiently transfected into cells . Whilst these experiments are useful for understanding much of the biology of tetherin/HIV interactions , they are prone to artifacts when interpreting the cell biology and importance of Vpu-mediated degradation . Overexpression of tetherin or Vpu at non-physiological levels has been shown to induce ER-associated degradation [30] . This is not observed in infected cells , where tetherin is degraded in endosomes . Also , because of the nature of transient transfections , there is a huge variability of expression levels of the transfected components between cells within the culture . Under these conditions strong blocks to degradation may lead to tetherin accumulation , and an overwhelming of the endosomal system , giving the appearance of a direct inhibition of counteraction . By infecting tetherin-expressing cells at relevant multiplicities of infection , to ensure each cell has on average one productive infection event , these issues can be mitigated and this has allowed us to separate the requirement of the phospho-serine motif in counteraction from the recruitment of SCFβTRCP1/2 and the ESCRT machinery for degradation . Our in cellulo data validates the structural and biochemical studies by Jia et al [39] , in which AP-1 interaction requires the ExxxLV motif that occupies the acidic-dileucine binding site in AP-1σ . We also provide evidence that in cells , this motif can also bind to AP-2 . Furthermore , the phenotype of our LI/LI mutant is consistent with the proposed non-canonical interaction of R44/L45 with AP-1μ suggested by densities in the crystal . However , because the constructs used by the authors to determine the structural requirements for AP-1/tetherin/Vpu interaction required artificial Vpu/tetherin fusions , they may not faithfully represent how AP-1 is initially recruited . Thus , the requirements for the DSGNES and Vpu/tetherin transmembrane domain interactions that we have uncovered in cells were not previously observed . We propose a model whereby phosphorylation of Vpu regulates the AP interaction with the ELV motif ( Fig 8 ) . Whilst we cannot formally rule out that the phosphoserine directly contributes to AP-1 interaction itself , the lack of a significant additive phenotype in terms of virus release and AP-1 interaction makes this the most consistent explanation of our data . Furthermore there is precedence for phosphorylation upstream of certain acidic dileucine motifs interactions with the clathrin transport machinery [43] . In particular , a CKII phosphorylation upstream of a non-canonical RDDHLL site in the cation-independent mannose-6-phosphate receptor regulates its interaction with AP1 . Another context-dependent feature of acidic dileucine signals is an adjacent acidic patch [37] . Interestingly , this feature is present in HIV-1 Vpu . Furthermore , the laboratory strain NL4 . 3 Vpu , which has a reduced anti-tetherin activity compared to most primary isolates , has a shorter acidic patch between the DSGNES and ExxxLV motifs [34] . The requirement for TM interactions in addition to the phospho-serines in “priming” Vpu for clathrin adaptor interaction would imply that tetherin binding contributes to conformational changes that are required for antagonism . Since β-TrCP binding does not require the presence of tetherin ( or CD4 ) , phosphorylation must be an independent event . However , whether β-TrCP and AP-1/2 binding can occur simultaneously or are mutually exclusive is unknown . Another interesting point to note is that the LI/LI mutation is more severely compromised than either the 2/6 or the ELV mutations in some contexts . As it also compromises AP binding , the non-canonical interaction of the R45 , L46 with AP-1μ may also play an essential contextual role in positioning the ELV motif . This interaction may also explain why the residual activities of 2/6 and ELV mutations are sensitive to clathrin depletion . Structural information on the Vpu cytoplasmic tail is limited at present . Partial NMR structures in solution and associated with lipids have been determined [49–52] . In a lipid environment , the ExxxLV is embedded within helix 2 of the cytoplasmic tail [52] , but adopts an extended conformation in solution [51] . To bind to AP-1 , the ExxxLV site cannot be helical . However , the lipid-associated structure has a very interesting feature: a highly conserved C-terminal tryptophan residue appears to pack against the DSGNES , almost as if locking the structure . Mutations in the W residue have context-dependent defects in tetherin antagonism depending on the Vpu used [34 , 53] . Importantly , NMR studies on the effects of serine phosphorylation suggests that it leads to conformational changes within the C-terminal region of the Vpu cytoplasmic tail that promotes βTRCP binding . In some studies [49 , 50] , but not others [54] , these conformational changes are consistent with an opening up of the ELV site . However , all these studies have thusfar been performed in the absence of target binding using soluble Vpu cytoplasmic tails , and so how representative they are of the wildtype protein is unclear . Furthermore , upregulation of SCYL2 , a clathrin associated protein that modulates protein phosphatase 2A ( PP2A ) , induces Vpu de-phosphorylation and reduces tetherin antagonism [55] . Thus , there is scope for regulated phosphorylation and subsequent dephosphorylation cycles in regulation of Vpu activity . We suggest that this would occur at the level of clathrin-dependent transport rather than SCFβTRCP1/2 interactions . There is much indirect evidence consistent with AP-1 being the major clathrin adaptor used by Vpu . The block to tetherin transport to the surface , the predominant localization to the TGN , and of course the recent structure discussed above [33 , 35 , 36 , 39] . However , AP-1 knockdown is difficult to efficiently achieve and does not compromise Vpu function [36] . AP-1 has multiple orthologs for some of its subunits , and there is potential redundancy in the adaptor machinery allowing the cell to compensate for its absence [37] . Our observation that Vpu can interact also with AP-2 in an ExxxLV-dependent manner is therefore an important observation for several reasons . Firstly , it suggests that Vpu is promiscuous and if one adaptor is compromised , another can be used , explaining why neither AP-1 nor AP-2 have been unambiguously identified as Vpu cofactors [25 , 36] . Secondly , it might explain why in some studies , Vpu has been observed to induce a weak enhancement of tetherin endocytosis [35] . Artificial tetherin/Vpu linked chimeric proteins are excluded from budding virions , and this is dependent on the ExxxLV motif [18] , which would be consistent with anchoring by AP-2 into clathrin-rich domains at the plasma membrane . The YDYCRV motif of tetherin cannot interact with AP-2μ as a YXXθ signal because of a steric clash of Y6 in the binding pocket [39] . The YDYCRV motif is essential for the “slow” , AP-1-dependent recycling of tetherin to the PM via the TGN [17 , 33] . Therefore , Vpu is likely to meet the majority of its target ( newly synthesized and recycling tetherin ) in the TGN . Since AP-1 has been proposed to regulate bidirectional traffic between the TGN and endosomal compartments [37] , AP-1 is likely to be the major player in tetherin counteraction . However , the ExxxLV motif is dominant over the tetherin recycling motif [36] . Therefore we would predict that tetherin/Vpu complexes that escape re-routing in the TGN and make it to the PM would be excluded from virions and AP-2 would promote their endocytosis , much in the same way that SIV Nef proteins and HIV-2 envelopes antagonize tetherin [6 , 38 , 56] . More importantly , it also accounts for why Vpu still has some activity against the short tetherin isoform without appreciable cell surface downregulation [13 , 40] . The relative role of AP-1 and AP-2 will reflect the kinetics of their respective activities in different cell types . We suggest the combination of some or all of the above accounts for the variable importance that downregulation of tetherin from the PM has been given to its antagonism . The requirement for the ExxxLV and DSGNES motifs is not absolute when tetherin levels are low . At higher expression levels , such as upon IFN treatment of primary CD4+ T cells , they become essential for tetherin antagonism [36] . This residual function requires tetherin’s sorting motif , suggestive of competition between the clathrin-dependent trafficking and virion retention . Tetherin/Vpu interaction may simply tip this balance , reducing tetherin partitioning into virions sufficiently when its expression levels are low . It is this that we propose to augment via our clathrin box fusion rescue , locking the tetherin/Vpu complex into clathrin-rich domains in the recycling pathway from where they cannot be transited to the PM . Decoupling tetherin degradation ( which amongst primate lentiviruses is so far peculiar to HIV-1 group M Vpu ) from subversion of trafficking ( counteraction ) suggests that the importance of the former might reflect downstream consequences of tetherin restriction . Enhanced antagonism of the long tetherin isoform by Vpu could be required because of its signal transduction or its ability to deliver retained virions to endosomes [14 , 40] . Our data shows that in stable tetherin expressing cells , STS mutations impart little resistance to Vpu and that they are still sensitive to Vpu-clathrin box fusions . Since neither LI/LI nor ELV mutations block binding of Vpu to β-TrCP or tetherin , ubiquitination may still occur on serine and threonine residues . However , its effect is likely to be subsequent to antagonism by clathrin-dependent mis-trafficking . Strong reduction of tetherin at the cell surface by Vpu coupled to endosomal degradation would therefore be a potent way of suppressing signal transduction , or blocking the routing of virions for degradation where they may encounter other host pattern recognition receptors or antigen processing machinery . These will be important attributes to maintain in vivo without necessarily being essential for physical antagonism of tetherin .
HEK293T cells were obtained from ATCC ( American Tissue Culture Collection ) . 293T tetherin cell lines stably expressing human tetherin and mutants were previously described [4 , 57] . The HeLa-TZMbl reporter cell line , was kindly provided by John Kappes through the NIH AIDS Reagents Repository Program ( ARRP ) . Cells were maintained in Dulbecco’s modified Eagle medium ( DMEM ) supplemented with 10% fetal calf serum and Gentamycin ( Invitrogen , UK ) . Wildtype HIV-1 NL4 . 3 ( obtained from NIH-ARRP ) , a Vpu-defective counterpart and a codon optimized pCR3 . 1 Vpu-HA has been described previously [58] . The Vpu A14L/W22A , ELV , 2/6A , LILI and NE mutants in pCR3 . 1 Vpu-HA and in the NL4 . 3 proviral genome were generated by Quick-change site-directed mutagenesis PCR according to standard protocols using Phusion-II polymerase ( New England Biolabs ) . A codon-optimised version of the previously described primary wild-type HIV-1 Vpu 2_87 [34] was HA-tagged and cloned into pCR3 . 1 . The Vpu A15L/W23A , ELV , 2/6A , LILI and NE mutants were generated in pCR3 . 1 Vpu 2_87-HA by Quick-change site-directed mutagenesis as described above . Consensus B codon-optimised Vpu-myc-BirA-R188G fusion was synthesized ( Life Technologies ) and cloned into the lentiviral vector pAIP ( kindly provided by A Cimarelli ) . The Vpu A15L/W23A , ELV , 2/6A and LILI mutants were generated by Quick-change site-directed mutagenesis as described above . The pCR3 . 1 myc-β-TrCP2 was previously described by [36] and the pCR3 . 1 myc-HRS expression vector was kindly provided by Juan Martin-Serrano [59] . Primary human CD4+ T cells were isolated from fresh venous blood drawn from healthy volunteers . CD4+ T cells were purified from total peripheral blood mononuclear cells ( PBMC ) isolated by lymphoprep ( AXIS-SHIELD ) gradient centrifugation using a CD4+ T cell Dynabeads isolation kit ( Invitrogen ) . T cells were then activated for 48 h using anti-CD3/anti-CD28 magnetic beads ( Invitrogen ) . The beads were then removed cells were then maintained in rhIL-2 ( 20 U/ml ) ( Roche ) . For full-length HIV-1 WT , HIV-1 ΔVpu , HIV-1 Vpu LILI , HIV-1 Vpu ELV , HIV-1 Vpu LILI/ELV , HIV-1 Vpu 2/6A , HIV-1 Vpu 2/6A/ELV virus stocks pseudotyped with the Vesicular Stomatitis Virus Glycoprotein ( VSV-G ) , 293T cells were transfected with 2 μg of proviral plasmid in combination with 200 ng of pCMV VSV-G . 48 hours post-transfection , the supernatant containing virions was harvested and endpoint titers were determined on HeLa-TZMbl cells as described previously [3] . For virus release assays using transient transfection , subconfluent 293T cells or derivatives were plated in 24 well plates and transfected with 500 ng of NL4 . 3 proviral plasmid , in combination with increasing concentrations of tetherin ( 0 ng , 25 ng , 50 ng and 100 ng ) and fixed 25 ng of Vpu-HA or mutants using 1 μg/ml polyethyleneimine ( Polysciences ) . Medium was replaced 8 hours post-transfection and cells and supernatants were harvested after 48 hours . The infectivity of viral supernatants was determined by infecting HeLa-TZMbl and assayed for β-galactosidase activity as previously described [36] . For biochemical analysis of physical virus particle release , supernatants were filtered ( 0 . 22 μm ) ( Merck Millipore ) and pelleted through a 20% sucrose/ PBS cushion at 20 , 000 x g for 90 min at 4°C . Virion and cell lysates were subjected to SDS-PAGE and Western blotted for rabbit anti-HSP90 ( Santa Cruz Biotechnologies ) , HIV-1 p24CA ( monoclonal antibody 183-H12-5C; kindly provided by B Chesebro through the NIH ARRP ) , monoclonal mouse anti-HA . 11 ( Covance ) , polyclonal rabbit anti-HA ( Rockland ) and/ or Vpu ( rabbit polyclonal; kindly provided by K . Strebel through the NIH ARRP [60] . For CK-II inhibition , we used Tyrphostin AG1112 ( Sigma ) reconstituted in DMSO at a concentration of 50 μM . Where indicated , Phos-tag ( Wako Chemicals , Japan ) and MnCl2 ( Sigma ) were added to the composition of 8% polyacrylamide gels to induce mobility shifts in phosphorylated proteins , to final concentrations of 25 μM and 50 μM , respectively . 1 . 5 x 105 293T tetherin cells were infected with VSV-G-pseudotyped HIV-1 WT , HIV-1 ΔVpu , HIV-1 Vpu LILI , HIV-1 Vpu ELV or HIV-1 Vpu 2/6A at an MOI of 2 . The medium was replaced 4 hours after infection . 48 hours post infection cell lysates were harvested and subjected to SDS-PAGE and Western blotted for rabbit anti-HSP90 ( Santa Cruz Biotechnologies ) and polyclonal rabbit anti-tetherin antibody ( kindly provided by K Strebel through the NIH ARRP ) [48] , and processed as described above . 293T tetherin cells were seeded at a density of 2 x 105 cells per well in a 12 well plate . After 6 hours , the first transfection was performed . For each well , 2 μl Dharmafect ( Thermo Scientific ) was added to 98 μl of Opti-MEM ( Life Technologies ) , this solution was added to 5 μl of 20 μM siRNA in 95 μl of Opti-MEM according to manufactures protocol . For HRS knockdown , siRNA oligonucleotide against HGS targeting the CCGGAACGAGCCCAAGTACAA sequence ( Qiagen ) was used . For UBAP1 knockdown , siRNA oligonucleotide against UBAP1 targeting CTCGACTATCTCTTTGCACAT ( Qiagen ) was used . For TSG101 knockdown , siRNA oligonucleotide sequence CCUCCAGUCUUCUCUCGUCUU ( Thermo Scientific ) was used . For β-TrCP1 and 2 knockdown , SMARTpool siRNA against human BTRC and FBXW11 ( Thermo Scientific ) were used . A non-targeting siRNA was used as control ( Thermo Scientific ) . The cells were re-seeded into a 24 well plate on day 2 and a second transfection was performed according to manufactures protocol . The cells were infected 3 hours post transfection with VSV-G-pseudotyped HIV-1 WT , HIV-1 ΔVpu at an MOI of 0 . 8 . The infectivity of viral supernatants was determined by infecting HeLa-TZMbl as described above . Cell lysates and viral particles were subjected to SDS-PAGE , and Western blot assays were performed using a rabbit polyclonal anti-HRS ( HGS ) antibody ( Millipore ) , a polyclonal rabbit anti-UBAP1 antibody ( Proteintech ) and a monoclonal mouse anti-TSG101 antibody ( Abcam ) . HeLa-TZMbl cells were transfected with 400 ng of pCR3 . 1 GFP and 400 ng of pCR3 . 1 Vpu-HA or indicated mutants . 48 hours post transfection the cells were harvested and stained for surface tetherin using a monoclonal anti-BST2 IgG2a antibody ( Abnova ) and a goat-anti-mouse IgG2a-Alexa633 conjugated secondary antibody ( Molecular Probes , Invitrogen , UK ) . Tetherin expression on GFP positive cells was then analyzed using a BD FACSCanto II flow-cytometer ( Becton Dickinson ) and the FlowJo software . For Vpu/HRS coIP , 293T tetherin cells were transfected with 700 ng of pCR3 . 1 myc-HRS or indicated mutants/truncations in combination with pCR3 . 1 Vpu-HA or indicated mutant or pCR3 . 1 GFP expression plasmids . 48 hours post transfection the cells were lysed in buffer containing 50 mM Tris pH 7 . 4 , 150 mM NaCl , 200 μM sodium ortho-vanadate , 5 mM NEM , complete protease inhibitors ( Roche ) and 1% digitonin . After removal of the nuclei , the supernatants were immunoprecipitated with 5 μg/ml monoclonal mouse anti-myc antibody previously described ( Kueck and Neil , 2012 ) . Western blot assays were performed using a polyclonal rabbit anti-HA antibody ( Rockland ) and rabbit polyclonal anti-HRS ( HGS ) antibody ( Millipore ) . For Vpu/tetherin coIP , 293T cells were transfected twice over 48 hours with siRNA oligonucleotide against UBAP1 targeting CTCGACTATCTCTTTGCACAT or Non-targeting siRNA was used as control ( Dharmacon ) . The cells were then infected with VSV-G-pseudotyped HIV-1 WT , HIV-1 ΔVpu , HIV-1 Vpu LILI or HIV-1 Vpu A14L W22A at an MOI of 2 . 48 hours post infection the cells were lysed on ice for 30 min in buffer containing 50 mM Tris pH 7 . 4 , 150 mM NaCl , complete protease inhibitors ( Roche ) and 1% digitonin ( Calbiochem ) . Immunoprecipitation was performed as previously described [36] and Western blot assays were performed using a rabbit anti-Vpu antibody polyclonal rabbit anti-tetherin antibody and polyclonal rabbit anti-UBAP1 antibody ( Proteintech ) , and visualized by ImageQuant using corresponding HRP-linked secondary antibodies ( New England Biolabs , UK ) . Hela cells were grown on coverslips , transfected with 50 ng of pCR3 . 1 Vpu-HA or indicated mutant . 16 hours later cells were fixed in 4% paraformaldehyde/ PBS , washed with 10 mM glycine/ PBS , and permeabilized in 1% bovine serum albumin/ 0 . 1% Triton-X100/ PBS for 15 min . Cells were stained using anti-rabbit polyclonal HA antibody ( Rockland ) in combination with sheep anti-human TGN46 ( AbD Serotec ) , followed by the appropriate secondary antibodies conjugated to Alexa 488 or 594 fluorophores ( Molecular Probes , Invitrogen ) . Cells were mounted on glass slides using ProLong AntiFade- 4’ , 6-diamidino-2-phenylindole ( DAPI ) mounting solution ( Molecular Probes , Invitrogen ) and images were captured with a Nikon ESCLIPSE Ti inverted microscope . Z stacks were taken of all cells , images deconvolved using AutoQuant X3 and analyzed using the ImageJ software . 293T , 293T tetherin , 293T tetherin Y6 , 8A or 293 Rhesus tetherin cells were transfected with 8 μg GFP expression construct , pCR3 . 1 Vpu-HA or mutant thereof . Transfection media was changed 6 hours post transfection and cells incubated with 50 nM concanamycin . In the case of CKII inhibitor treatment , cells were treated with 50 μM final Tyrphostin 24 hours prior to harvesting . 48 hours post transfection , cells were trypsinised and washed in PBS . Cells were cross-linked with 0 . 05% HCHO/PBS for 10 min at 37°C . The cross-linking reaction was then quenched by incubating cells in 0 . 25 M glycine for 5 min . Cells were washed once in PBS before resuspension in lysis buffer ( 10 mM Hepes pH 7 , 150 mM NaCl , 6 mM MgCl2 , 2 mM DTT , 10% glycerol , 0 . 5% NP40 , 200 μM sodium orthvanadate and 1x Complete protease inhibitors ( Roche ) ) . Cells were lysed on ice for 10 min followed by repeated sonication ( 3 x 10 s cycles with 20 s rests ) . The cell lysates were clarified by centrifugation at 1000 x g for 10 min and supernatants were immunoprecipitated with 5 μg/ml mouse monoclonal anti-HA . 11 antibody ( Covance ) or rabbit polyclonal anti-HA antibody ( Rockland ) on Dynabeads protein G beads ( Life Technologies ) for 4 hours at 4°C . Beads were collected post incubation and washed 5 times in lysis buffer before cross-links were reversed in 1% SDS , 10 mM EDTA and 5 mm DTT at 65°C for 45 min . Western blot assays were performed using rabbit polyclonal anti-HA antibody ( Rockland ) , polyclonal rabbit anti-tetherin antibody , mouse monoclonal anti-HA . 11 antibody , mouse monoclonal anti-AP-1γ1 antibody ( Sigma ) and mouse monoclonal anti-AP-2α antibody ( Sigma ) . Vpu/β-TrCP2 cross-linking IP was previously described by [36] . [61] 293T or 293T tetherin cells were transiently transfected with 8 μg empty BirA vector , Vpu-myc-BirA or relevant mutant constructs using polyethylenimine ( PEI ) . Cells were incubated for 8 hours prior to changing medium and treated overnight with 100 nM Concanamycin A ( Invitrogen ) and 150 μM biotin ( Invitrogen ) . Cells were washed twice in PBS and lysed in 1 ml lysis buffer ( 50 mM Tris pH 7 . 4 , 500 mM NaCl , 0 . 4% SDS , 5 mM EDTA , 1 mM DTT and 1x Complete protease inhibitor ( Roche ) ) before sonication . Triton-X-100 was added to a final concentration of 2% before further sonication and an equal volume of 50 mM Tris pH 7 . 4 was added to the cell lysates before clarification at 14 , 000 rpm for 5 minutes . Supernatants were incubated with 200 μl avidin agarose ( Pierce ) for 4 hours at 4°C . Beads were collected and washed four times in 1 ml lysis buffer before resuspension in 100 μl Laemmli-SDS sample buffer supplemented with free biotin . Cell lysates and precipitates were analysed by Western blot using HRP-conjugated streptavidin ( Invitrogen ) , mouse monoclonal anti-myc antibody ( Covance ) , rabbit monoclonal β-TrCP antibody ( Cell signaling Technology ) and mouse monoclonal anti-AP-1γ1 antibody ( Sigma ) . Permission to isolate primary human CD4+ T cells from healthy consenting donors was provided by the KCL Infectious Disease BioBank Local Research Ethics Committee , reference SN-1/6/7/9 . | Counteraction of tetherin , a host antiviral protein that blocks viral release from infected cells , is an essential attribute of HIV-1 and its related viruses . The HIV-1 accessory protein Vpu binds to tetherin , preventing its incorporation into viral particles , and targets it for ubiquitin-dependent degradation . This involves mis-trafficking of tetherin by a Vpu-dependent mechanism through the engagement of clathrin adaptor proteins . Although structural evidence exists for Vpu and tetherin interacting with clathrin adaptor 1 ( AP-1 ) , evidence that it is required for Vpu-mediated tetherin counteraction is still lacking . Tetherin degradation by Vpu also requires an E3 ubiquitin ligase , SCFβTRCP1/2 that binds to phosphorylated serine residues in the Vpu cytoplasmic tail . Again , discrepancies exist about the importance of this interaction in tetherin’s counteraction . Here we show that Vpu phosphorylation , in combination with its physical interaction with tetherin , regulates interaction with both AP-1 and the other major cellular clathrin adaptor , AP-2 . These interactions can be decoupled from SCFβTRCP1/2 recruitment , thus indicating clathrin-dependent mis-trafficking as a critical step in tetherin antagonism by Vpu . Additionally , the ability to interact both with AP-1 and AP-2 in a tetherin-dependent manner indicates a redundancy in host cofactors used by Vpu that explains disparate previous observations of its mechanism of action . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Serine Phosphorylation of HIV-1 Vpu and Its Binding to Tetherin Regulates Interaction with Clathrin Adaptors |
Dengue infection is one of the most important mosquito-borne diseases . More data regarding the disease burden and the prevalence of each clinical spectrum among symptomatic infections and the clinical manifestations are needed . This study aims to describe the incidence and clinical manifestations of symptomatic dengue infection in Thai children during 2006 through 2008 . This study is a school-based prospective open cohort study with a 9 , 448 person-year follow-up in children aged 3–14 years . Active surveillance for febrile illnesses was done in the studied subjects . Subjects who had febrile illness were asked to visit the study hospital for clinical and laboratory evaluation , treatment , and serological tests for dengue infection . The clinical data from medical records , diary cards , and data collection forms were collected and analyzed . Dengue infections were the causes of 12 . 1% of febrile illnesses attending the hospital , including undifferentiated fever ( UF ) ( 49 . 8% ) , dengue fever ( DF ) ( 39 . 3% ) and dengue hemorrhagic fever ( DHF ) ( 10 . 9% ) . Headache , anorexia , nausea/vomiting and myalgia were common symptoms occurring in more than half of the patients . The more severe dengue spectrum ( i . e . , DHF ) had higher temperature , higher prevalence of nausea/vomiting , abdominal pain , rash , diarrhea , petechiae , hepatomegaly and lower platelet count . DHF cases also had significantly higher prevalence of anorexia , nausea/vomiting and abdominal pain during day 3–6 and diarrhea during day 4–6 of illness . The absence of nausea/vomiting , abdominal pain , diarrhea , petechiae , hepatomegaly and positive tourniquet test may predict non-DHF . Among symptomatic dengue infection , UF is most common followed by DF and DHF . Some clinical manifestations may be useful to predict the more severe disease ( i . e . , DHF ) . This study presents additional information in the clinical spectra of symptomatic dengue infection .
Dengue infection is one of the most important mosquito-borne viral diseases especially in tropical and subtropical regions of the world . World Health Organization ( WHO ) estimated that some 2 . 5 billion people – two-fifths of the world population are at risk from dengue and there may be 50 million dengue infections worldwide every year [1] . Its clinical spectrum ranges from asymptomatic infection to undifferentiated fever ( UF ) , dengue fever ( DF ) , and dengue hemorrhagic fever ( DHF ) [2] . In two studies , infected Thai children , ranging from 49 [3] to 87% [4] , were asymptomatic . The available previous studies on the clinical manifestations in Thai children were a hospital-based study [5] which studied more severe disease , and a school-based study [3] which provided less detailed data on clinical manifestations . More data regarding the disease burden and the prevalence of each clinical spectrum ( UF , DF , DHF ) among symptomatic infections and the clinical manifestations are important for health care providers and policymakers to better understand the disease [6] . In 2005 , a cohort epidemiological study of dengue infection in school-children aged 3–14 years was conducted in Ratchaburi Province , Thailand . There were 481 subjects recruited in 2005 and then 3056 subjects were enrolled in February , 2006 . The initial plan was to conduct the study up to the end of 2008 . In 2009 , the surveillance for dengue epidemiology was extended . However , because many subjects withdrew from the study to participate a phase 2b dengue vaccine trial , the data on clinical manifestations was collected only up to the end of 2008 . This article aims to describe the incidence and clinical manifestations of symptomatic dengue infection occurring over the 3-year study of this cohort , from 2006 to 2008 .
Ratchaburi Province is approximately 100 km west of Bangkok , the capital province of Thailand . It was ranked among the top ten provinces of Thailand for dengue incidence rate as reported to the Thai Ministry of Public Health . The study site is Muang District which is the capital district of the province . The population in this area is quite stable . There are low rates of migration and high ethnic homogeneity . The total population and number of children aged 5–14 years in Muang District was approximately 188 , 000 and 24 , 000 , respectively . The data from Ratchaburi Provincial Health Office ( unpublished ) showed that the incidences of dengue diseases during 2006–2008 were 1 . 05 , 1 . 15 , and 0 . 24% in children aged 5–9 years , 10–14 years and total population , respectively . The principal medical facility in this area is Ratchaburi Hospital ( RH ) , which is an 855-bed hospital providing tertiary medical care . The hospital has 90 pediatric beds and 12 pediatricians on staff . School-children enrolled in 7 schools in Muang District were invited to participate in this cohort study . The inclusion criteria were healthy boys and girls aged 3–10 years at the time of recruitment , living in Muang District or nearby villages . Exclusion criteria included children suffering from serious or chronic severe diseases such as asthma , malignancy , hepatic , renal , cardiac disease or disease associated with insufficiency of immune system , and planning to move to another school within 48 months . This study was a school-based prospective open cohort study . It is a part of the epidemiologic study of dengue infection in school children in Ratchaburi Province , Thailand . After explanation and obtaining informed consent , active surveillance for febrile episodes and laboratory confirmation for dengue infection were then conducted throughout the study period . Each year , new subjects were recruited to replace those who prematurely withdrew . All subjects/parent were provided with digital thermometers and were instructed to use the thermometer to measure and record the subjects' body temperature . The subjects were asked to visit RH if they had fever , defined as oral temperature ≥37 . 5°C . At the hospital , the study pediatrician performed physical examination and recorded the detail of illness . Tourniquet test was done by inflating a blood pressure cuff on the patient's upper arm to the point midway between the systolic and diastolic blood pressure for 5 minutes . The test was positive if 20 or more petechiae per square inch were observed . Blood samples for dengue diagnostic tests were collected from all febrile subjects irrespective of clinical diagnoses . Other laboratory tests such as complete blood count , liver function test , etc . were done when clinically indicated . Treatment was provided at the discretion of the pediatrician . In the subject who dengue infection was clinically diagnosed or suspected , or had no definite localizing sign of infection ( e . g . purulent tonsillitis , otitis media , pneumonia , bacterial meningitis , etc . ) and dengue infection could not be excluded , the pediatrician gave a diary card and asked the parent to record daily symptoms of the subject until recovery . The investigator checked and verified all diary cards on the day of convalescent blood drawing and asked the parent/caretaker to complete the missing data . If hospitalization was indicated , the pediatrician in charge recorded daily symptoms and physical findings , and laboratory results on a data collection form . In addition to self-reporting febrile episode , an active surveillance for febrile episode based on subjects' school absence was also done . During school session , teachers reported school absentees every day and then project field coordinators called the subject's parent or visited the subject's home to verify the reason of school absence . If it was caused by fever , the subject was asked to visit RH as mentioned above . During school vacation and holidays , project field coordinators called the subjects' parents at least twice a week or performed home visit to ask whether the subject had febrile illness and remind the parent to take the subject to RH if he/she had febrile illness . At RH , the pediatrician made clinical diagnosis without awareness of confirmatory laboratory test for dengue . Clinical diagnosis of DF , DHF and grading of DHF were made using WHO's criteria [2] after the subject recovered . Undifferentiated fever ( UF ) was defined if the subject did not meet the clinical criteria for DF ( i . e . acute febrile illness with at least two of the following symptoms: headache , retro-orbital pain , myalgia , arthralgia , rash , hemorrhagic manifestations , leucopenia ) or DHF ( i . e . fever with hemorrhagic tendency , thrombocytopenia and evidence of plasma leakage ) but was serologically proved to have dengue infection . Each subject was bled for acute and convalescent blood samples . The interval for the blood samplings was at least 7 days . The paired sera were tested for IgM and IgG against dengue using the enzyme-linked immunosorbent assay ( ELISA ) described by Innis et al [7] . Acute dengue infection was diagnosed if there was an IgM level higher than the cut-off value or there was a seroconversion of IgG level . IgM and IgG levels against Japanese encephalitis were also measured to exclude cross serologic reaction due to this infection . In addition to ELISA test which was use as primary diagnostic criterion for acute dengue infection , acute samples of the ELISA positive cases were further tested for dengue virus serotypes . In 2006 , mosquito inoculation in Toxorhynchites splendens [8] was used for serotype identification . In 2007–8 , the serotype diagnostic test used detection of viral RNA by a modified nested serotype-specific reverse-transcriptase polymerase chain reaction ( RT-PCR ) [9] . Although this study design was a school-based prospective open cohort study , most of the data in this article were cross-sectional data . Only symptoms were obtained from all days of illness from each subject and were analyzed longitudinally . The data were analyzed using SAS version 9 . 1 . 3 and Epi Info version 3 . 5 . 1 . Frequency and median were used where appropriate to describe the data . Chi-square test or Fischer-exact test was used for comparing categorical variables and Kruskal-Wallis test or ANOVA-Scheffe test was used for comparing continuous variable as appropriate . The statistical level was considered significant when the p-value was <0 . 05 . This study was approved by the Ethical Review Committee for Research in Human Subjects , Ministry of Public Health , Thailand . The informed consent signed by at least one parent or other legal guardian and assent form signed by the children if their age >7 years were obtained before recruiting the subject into the study .
A total of 9 , 448 ( male 4 , 759 , female 4 , 689 ) person-years were followed in the 3 years study period . There were 2 , 591 febrile episodes attended RH during this period . These represented 71% of the total febrile episodes occurring in the recruited subjects . Fifty-two percent of febrile episodes not attending RH were mild and were self-treated without medical attention . The remaining febrile patients who did not attend RH had attended either private clinics or hospitals . None of them were diagnosed as dengue infection because a study requirement was that a diagnosis of dengue outside RH would prompt a visit to RH . Our study follow-up team tracked all febrile patients to a conclusion and prompted all with a dengue diagnosis to visit RH . Among those who attended RH , 313 febrile episodes ( 12 . 1% ) were due to dengue infections as diagnosed by ELISA test , including 41 ( 13 . 1% ) primary antibody responses and 272 ( 86 . 9% ) secondary antibody responses . Dengue serotypes were identified by either mosquito inoculation or RT-PCR in 259 cases ( 82 . 7% ) including 115 ( 36 . 7% ) , 80 ( 25 . 6% ) , 41 ( 13 . 1% ) , and 23 ( 7 . 3% ) cases of DEN1 , 2 , 3 , and 4 , respectively . There were 156 cases ( 49 . 8% ) of UF , 123 cases ( 39 . 3% ) of DF , and 34 cases ( 10 . 9% ) of DHF including 22 cases ( 7 . 0% ) , 5 cases ( 1 . 6% ) , and 7 cases ( 2 . 2% ) of DHF grade 1 , 2 and 3 , respectively . Among 41 cases with primary antibody responses , 26 cases had UF ( 63 . 4% ) , 14 cases had DF ( 34 . 2% ) and 1 case had DHF grade 1 ( 2 . 4% ) . There was no significant association among infecting dengue serotypes and types of antibody response ( data not shown ) . The detailed data on clinical manifestations ( i . e . daily symptoms until recovery ) of 71 UF cases were not obtained . Therefore only 85 UF cases are used for analysis . The clinical diagnosis of these 71 and 85 UF cases are shown in table 1 . Significantly more cases in the group whose data were obtained were diagnosed as acute febrile illness/suspected viral infection ( p<0 . 001 ) . However , there was no statistically significant difference in other clinical diagnosis between groups . There was also no statistically significant difference by age and gender [8 . 9 ( SD 2 . 2 ) vs 9 . 2 ( SD 2 . 2 ) years and 46 . 5% vs 61 . 2% male gender , respectively] although the male proportions were rather skewed . The median age ( interquatile range [IQR] ) and percentage of males in the cases who had UF , DF and DHF were 9 . 3 ( 3 . 3 ) , 9 . 6 ( 3 . 3 ) , 10 . 0 ( 3 . 3 ) years , and 55 . 1 , 55 . 3 , 58 . 8 percent , respectively . There was no statistically significant difference in the age and gender among different disease spectra . The symptoms of overall dengue infection and each specific disease spectrum ( UF , DF , DHF ) are presented in table 2 . Headache , anorexia , nausea/vomiting and myalgia were common symptoms occurring in more than half of the patients . It was found that nausea/vomiting , abdominal pain , rash , diarrhea and petechiae were statistically more common in DHF compared to DF and UF . Table 3 shows symptoms and disease severity from different dengue serotypes . DEN4 was found to cause only UF , DF and DHF grade 1 and seemed to have less headache , anorexia , nausea/vomiting and rash compared to other DEN serotypes . However , these differences are not statistically significant . Regarding fever , the median ( IQR ) value of the peak temperature and duration of fever in UF , DF , and DHF were 38 . 4 ( 1 . 6 ) , 39 . 0 ( 1 . 5 ) , and 39 . 0 ( 1 . 7 ) degree Celsius and 5 ( 3 ) , 6 ( 2 ) , and 6 ( 1 ) days , respectively . It was found that UF had both lower peak temperature and shorter duration of fever compared to DF and DHF ( p<0 . 001 ) . The prevalence of common symptoms in each day of illness is shown in figure 1 . It was noted that the prevalence of most of the symptoms was highest during the first 2 days of illness and then slowly declined . The exceptions are for anorexia , nausea/vomiting , abdominal pain and diarrhea that tended to increase in prevalence in DHF during day 3–5 of illness . It was also revealed that most symptoms were more common in DHF and , specifically , DHF cases had significantly higher prevalence of anorexia , nausea/vomiting and abdominal pain during day 3–6 and diarrhea during day 4–6 of illness ( Chi-square test; p<0 . 05 ) . Conversely , drowsiness/lethargy was significantly more common in UF during day 2–5 of illness . It was also revealed that a higher proportion of DHF had prolonged ( 3 or more days ) anorexia , nausea/vomiting , abdominal pain , retro-orbital pain and petechiae . In contrast , a higher proportion of UF had prolonged drowsiness/lethargy . It is worth noting that very few cases had prolonged hemorrhagic manifestation ( Table 4 ) . Table 5 shows physical findings in dengue infection . Positive tourniquet test was found in 72% and flushed face was found in approximately half of the dengue infected patients . Hepatomegaly was found in approximately 40% of DHF . This proportion was significantly higher than that found in UF and DF . Clinical jaundice was found only in one DHF ( 2 . 9% ) . Table 6 shows the occurrence of selected clinical manifestations and their predictive value for DHF among symptomatic dengue infected cases . It was found that all of these clinical manifestations had low positive predictive value but high negative predictive value for DHF . Complete blood count were done in 43 , 11 , 8 , 2 , 2 , 2 UF and in 113 , 69 , 32 , 20 , 7 , 4 DF , and 32 , 19 , 12 , 8 , 2 , 2 DHF cases on day 1 , 2 , 3 , 4 , 5 , 6 of illness , respectively . Figure 2 shows the median value of hematocrit , peripheral white blood cell and platelet count in these dengue-infected patients . It was revealed that DHF cases had higher median hematocrit during the later days of illness and the median hematocrit in DHF was significantly higher than DF and UF during day 4–6 of illness . All of the 3 spectra of dengue infection had lower median peripheral white blood cell ( WBC ) count during the later days of illness and cases with UF had significantly higher median WBC count compared to DF and DHF . All spectra of dengue also had lower median peripheral platelet count during the later days of illness , although the median value in DF and UF did not meet the WHO's criterion for thrombocytopenia , i . e . lower than 100 , 000/mm3 . Moreover , the level of platelet count showed reverse correlation with the disease severity , i . e . highest in UF and lowest in DHF . The median platelet count in DHF was significantly lower than the other 2 groups during day 3–6 of illness , while DF had significantly lower median platelet count compared to UF only during day 3–5 of illness .
This was a prospective cohort study and the data represent mild to severe dengue illnesses . The data have been prospectively collected , diagnoses of dengue were serologically proven and the observations were representative of a defined pre-illness population . Because confirmatory laboratory results were available after convalescence , the pediatricians made diagnosis solely based on WHO's criteria [2] without bias from the etiologic investigation results . This provides us the opportunity to make the diagnosis of UF; one clinical spectrum of dengue infection that has been mentioned but poorly defined [2] and describe its clinical manifestations . Although there have been many published data on clinical manifestations of dengue , this study addresses the large proportion of clinical dengue that is often unrecognized by physicians . This provides useful information for policy-makers in endemic countries because the impact of dengue has likely been underestimated , based on the more standard clinical definitions . Recently , prospective cohort studies on the dengue incidence in Cambodia and Nicaragua using community-based enhanced passive surveillance were published [10]–[11] . Similar to this study , they reported a high incidence of dengue infection in these endemic areas . However , the detailed clinical manifestations of dengue-infected cases were not mentioned . This study describes the prevalence of each clinical symptom day by day starting from the onset of illness , which has never been reported in outpatient setting . It was found that some clinical symptoms , for example , nausea/vomiting , diarrhea , abdominal pain are significantly more common in DHF compared with non-DHF . Although these clinical manifestations have low positive predictive value for DHF that might be due to much higher incidence of non-DHF cases ( Table 6 ) , they have quite high negative predictive value . The absence of these clinical manifestations suggests that the patient should have non-DHF while the presence of them should alert the physician to be watchful for DHF . The high incidence of UF cases that did not meet the clinical criteria for DF or DHF was not anticipated in the study design . In a large proportion of laboratory confirmed cases of UF dengue ( 71 or 45 . 5% ) , the clinical manifestations of these cases were not obtained because the doctors on duty did not think that the patients might have dengue infection and did not ask the patients to record their symptoms . Moreover , many patients especially those who had UF and DF , had mild illness . They visited the out-patient department only once during the febrile phase and therefore many data on physical findings and laboratory investigations were not obtained . These may be sources of bias resulting in overestimating or underestimating the clinical manifestations of UF dengue infection and is a weakness in this study . Nevertheless , the largely similar clinical diagnoses and demographics between the UF patients whose clinical data were available and the patients whose data were missed implied that the available clinical data are generally representative of UF cases . The difference in the diagnosis of acute febrile illness/suspected viral infection is probably reflective of an overestimate of clinical severity among UF patients since the patients whose clinical data was collected were more likely to have a clinical presentation common to many viral illnesses . This would suggest that UF may be even milder than we report here . Nonetheless , these data demonstrate several interesting aspects of mild dengue infection , which we classified as UF , that do not meet case criteria for DF . There may be argument that these UF cases may be asymptomatic dengue with co-infection by another agent . Although this is possible , we estimated that these events might not be very common because the time these cases presented was not the outbreak period of other infection . In addition , the high degree of similarity in many clinical symptoms in UF patients compared to DF and DHF patients ( Table 2 ) , suggests that most of the UF patients really had symptomatic dengue disease . The prevalence of overall clinical manifestations of dengue infection presented in this report is within the range of that previously reported , including much older reports ( Table 7 ) . However , it is worth emphasizing that the data from each study presented in table 7 are not comparable due to different study design , population ( age and ethnicity ) , settings ( i . e . community vs . hospital-based and in- vs . out-patient setting ) , which all could lead to biases in the clinical manifestations . Data from inpatients ( usually DHF ) are more reliable , have more detail , and are therefore less biased when compared to outpatients ( usually DF and UF ) . However , among the subjects who were given diary cards , the 100% return rate of diary cards and verification of the diary card data with parent/caretaker on the day of convalescent blood drawing would decrease the bias in outpatient data . Because of the lower proportion of viral syndrome presented in the UF cases without diary card , we feel there was a conservative bias for the UF cases , i . e . the illness profile was likely even milder in this UF group than seen in the data we were able to collect . It is not surprising that the more severe disease spectrum ( i . e . DHF ) had higher prevalence and longer duration of symptoms . The exceptions were found in headache and drowsiness/lethargy . We do not have an explanation for this finding . Further studies are needed to clarify this issue . It is interesting that rhinorrhea was commonly found in this study . Whether this symptom is one manifestation of dengue infection or is caused by co-incidental respiratory infection is still unclear . However , it has been suggested that dengue infection should be included in the differential diagnosis of acute infection of the upper respiratory tract [14] . Facial flushing was also commonly found . This distinct clinical feature was used as an enrolment criterion for suspected dengue infection in one study [5] . It is worth noting that some clinical manifestations ( e . g . rash , positive tourniquet test ) were considered as more specific to DF/DHF and are included in clinical diagnostic criteria for DF . This can explain why these clinical manifestations were more common in DF/DHF . The same reason is also applicable to the finding that hepatomegaly was more common in DHF . Mild dengue infection is quite similar to many other infections in that the prevalence of most of the symptoms was highest during the first few days of illness and then slowly declined . On the other hand , in severe dengue infection ( i . e . DHF ) , the prevalence of anorexia , nausea/vomiting , abdominal pain and diarrhea during day 3–5 of illness was found to increase instead of decrease and it will indicate the risk for more severe disease . The presence of diarrhea in dengue infection has been mentioned in many previous studies [3] , [12]–[14] . However , there has been no study on its mechanism . This study , to the best of our knowledge , is the first study suggesting that diarrhea may be a predictor for DHF . Its highest prevalence just before the stage of shock suggests that it may be related to plasma leakage that may cause malabsorption . Further study to clearly define the association between diarrhea and DHF and its mechanism is warranted . This study shows that a decrease in peripheral platelet count occurs not only in DHF , but also in DF and UF , with a respectively less degree . It confirms that the cut off level for platelet count of 100 , 000/mm3 is appropriate for differentiation between DHF and non-DHF . Nevertheless , complete blood counts were done selectively by clinicians based on clinical need and may not be accurately extrapolated to all patients with UF and DF . In addition , there were variations around this median value and may reduce predictive value for individual patient . Infecting dengue virus serotype has been postulated as a risk factor for severe disease . For example , DEN2 and 3 have been shown to be associated with more severe disease [16] , [17] and DEN4 with mild disease [18] . This study also found that DEN4 did not cause severe disease . However , we could not show the statistically significant association between specific virus serotypes and disease severity . While the WHO's new guidelines for dengue diagnosis [19] classifies dengue infection into severe and non-severe infection and has a potential for being of practical use in the clinicians' decision and management , this new guideline as well as the earlier WHO classification [2] may easily miss the UF cases because the clinician will not diagnose dengue when seeing patients who present in the fashion of the UF group described here , and therefore confirmatory diagnostic tests for dengue will not be done . These UF cases may have important role in spreading of infection and may explain the failure of disease control in some endemic area where vector control is implemented only around the house of index DF and DHF , but not UF patients . It is stated in the WHO's new guideline for dengue diagnosis [19] that the presence of abdominal pain or tenderness , persistent vomiting , clinical fluid accumulation , mucosal bleed , lethargy , restlessness , liver enlargement are clinical warning signs of severe dengue disease . The finding in this study that DHF cases had higher prevalence of nausea/vomiting and abdominal pain supports this guideline . On the other hand , we found that the presence of lethargy or drowsiness in the first few days of illness may not necessarily indicate severe dengue infection . The significance of drowsiness/lethargy needs to be further defined . In conclusion , this study describes the clinical manifestations of all spectra of symptomatic dengue infection as well as some possible early clinical predictors for DHF . It also reveals the epidemiological importance of UF as the most common spectrum of symptomatic dengue infection . This study presents additional information in the clinical spectra of symptomatic dengue infection . | Dengue infection is one of the most important diseases transmitted to human by mosquito bite . The disease may be mild or severe . This study reveals the occurrence and clinical features of diseases caused by dengue infection in a 3-year follow-up in school-children aged 3–14 years in Ratchaburi Province , Thailand using an active surveillance for the episodes of fever . Children who had fever were laboratory tested for the evidence of dengue infection and recorded for clinical features . It was found that most of dengue infected patients had headache , anorexia , nausea/vomiting , and muscle ache . About half of the patients had clinical symptoms that closely mimic other diseases , especially respiratory tract infection , and were incorrectly diagnosed by pediatricians . Only 11% of the patients had more a severe disease called “dengue hemorrhagic fever . ” This severe disease may be predicted by the presence of anorexia , nausea/vomiting , and abdominal pain after the second day of illness . This study provides better understanding in this disease . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine"
] | 2012 | Dengue Infection in Children in Ratchaburi, Thailand: A Cohort Study. II. Clinical Manifestations |
Chromatin accessibility is an important functional genomics phenotype that influences transcription factor binding and gene expression . Genome-scale technologies allow chromatin accessibility to be mapped with high-resolution , facilitating detailed analyses into the genetic architecture and evolution of chromatin structure within and between species . We performed Formaldehyde-Assisted Isolation of Regulatory Elements sequencing ( FAIRE-Seq ) to map chromatin accessibility in two parental haploid yeast species , Saccharomyces cerevisiae and Saccharomyces paradoxus and their diploid hybrid . We show that although broad-scale characteristics of the chromatin landscape are well conserved between these species , accessibility is significantly different for 947 regions upstream of genes that are enriched for GO terms such as intracellular transport and protein localization exhibit . We also develop new statistical methods to investigate the genetic architecture of variation in chromatin accessibility between species , and find that cis effects are more common and of greater magnitude than trans effects . Interestingly , we find that cis and trans effects at individual genes are often negatively correlated , suggesting widespread compensatory evolution to stabilize levels of chromatin accessibility . Finally , we demonstrate that the relationship between chromatin accessibility and gene expression levels is complex , and a significant proportion of differences in chromatin accessibility might be functionally benign .
Changes in gene regulation have long been hypothesized to be an important mechanism of evolutionary diversification [1]–[3] and to contribute to phenotypic variation [4]–[7] . An increasing catalog of adaptive regulatory changes has been identified , such as lactase persistence [8] , [9] and the effect of the Duffy blood group chemokine receptor on malaria resistance in humans [10] , [11] and beak morphology in Darwin's finches [12] . Furthermore , it has also been suggested that a substantial fraction of SNPs associated with human diseases through genome-wide association studies may act through regulatory changes with genes [13] , [14] . On a genome-wide scale , molecular studies have uncovered pervasive transcriptional variation within and between species [15]–[20] . A substantial amount of gene expression variation is heritable , and thousands of regulatory QTL have been mapped in numerous organisms [17] , [21]–[24] . In general , regulatory variation can act in cis or trans . Cis-acting regulatory QTL influence transcript levels in an allele-specific manner , typically from variation located within or near the gene being studied . In contrast , trans-acting regulatory QTL does not result in allelic differences in expression and arises from variation that is usually located at a position distinct from the gene being studied [7] . Although both cis and trans regulatory variation make important contributions to heritable variation of transcript abundance , cis-acting variants are thought to be more numerous , have larger effect sizes , and accumulate at a faster rate between species [21] , [25] . Despite the progress in mapping cis and trans-acting regulatory QTL , the mechanisms they act through are less well understood . Chromatin structure is a fundamentally important determinant of gene regulation , and changes in the position and number of nucleosomes can affect transcript abundance [26]–[29] . New technologies have enabled genome-wide maps of chromatin architecture to be constructed across different cell types [30] , [31] individuals [32]–[34] and species [20] , [35] . Although these studies have revealed extensive variation in chromatin structure , many outstanding issues remain , including how much of variation in chromatin accessibility is heritable , the relative contributions of cis and trans-acting regulatory variation to differences in chromatin architecture [32] , and how often variation in chromatin structure results in gene expression variation [22] , [36] . To address these issues , we describe a genome-wide analysis of chromatin accessibility between two closely related Saccharomyces sensu stricto yeast species , Saccharomyces cerevisiae and Saccharomyces paradoxus , and their hybrid . S . cerevisiae is the yeast model species and has been extensively studied . S . paradoxus is the most closely related species to S . cerevisiae , with an estimated divergence time of 5 million years ago [37] . Chromatin structure in S . cerevisiae has been studied previously [38] , [39] and across a single genome , open chromatin regions are weakly associated with increased expression [39] . In addition , nucleosome locations have been compared across multiple yeast species , including S . cerevisiae and S . paradoxus , and cis changes , such as anti-nucleosomal sequences and binding sites for general regulatory factors , were found to contribute to differences in nucleosome location [20] . Within species , the genetic architecture of chromatin accessibility has been studied using QTL mapping [34]; however , this has not been addressed between species . We assessed chromatin accessibility using FAIRE-Seq and found considerable divergence in chromatin structure between S . cerevisiae and S . paradoxus . Moreover , we developed a novel statistical approach to identify cis and trans-acting effects on chromatin accessibility in hybrids and found cis effects on chromatin structure are more common than trans effects , are of greater magnitude , and that the direction of cis and trans effects are often in opposite directions suggesting compensatory evolution . Finally , we show that the relationship between chromatin structure and transcript levels in S . cerevisiae and S . paradoxus is complex , and a significant proportion of differences in chromatin accessibility might be functionally benign .
We first assessed differences in chromatin structure between haploid strains of S . cerevisiae and S . paradoxus . We generated FAIRE-Seq ( Formaldehyde-Assisted Isolation of Regulatory Elements ) data [40] for two biological replicates for two strains of S . cerevisiae ( DBVPG1373 , a wine strain , and UWOPS05_217_3 , a wild isolate ) and one strain of the sister species S . paradoxus , CBS432 ( see Methods ) . FAIRE isolates DNA that is not bound to proteins , resulting in increased signal in regions with increased chromatin accessibility . We sequenced FAIRE DNA samples to approximately 10× coverage using short read sequencing ( see Methods ) . As expected , sequencing reads were enriched in intergenic regions ( mean of 2 . 4× enrichment compared to coding regions ) . We first asked which specific areas of the genome have undergone changes in chromatin accessibility between species . We focused on the nucleosome-free region ( NFR ) found upstream of the transcription start site of many yeast genes because this region is known to harbor important regulatory information; this was also where the dominant FAIRE signal was found in our data [41] , ( see Figure S1 ) . We computationally identified the nucleosome-free region from the FAIRE data ( see Methods ) by identifying the peak in FAIRE signal found upstream of each gene and extended the region in either direction until a background level of signal was observed . We then merged NFR calls across the two species ( see Methods ) . We also carried out extensive filtering to eliminate peaks whose differences might be caused by duplications between species or mapping issues ( see Methods ) . In total , we identified 3 , 498 NFRs that passed our filtering and had an average size of 253 bp . We first compared one strain of S . paradoxus , CBS432 , and one strain of S . cerevisiae , UWOPS05_217_3 . Overall , the locations of NFRs called were well-conserved across species , and on average the location of 42% of NFRs overlapped between the two species . As a complementary analysis , we compared levels of chromatin accessibility in the set of all 3 , 498 NFRs , and found them to be strongly correlated ( R2 = 0 . 68 between species , p<2 . 2×10−16 ) suggesting that broad-scale patterns of accessibility are conserved over time . Next , we tested each of the 3 , 498 NFRs for differences in chromatin accessibility between the two parental haploid species , S . cerevisiae and S . paradoxus , and used the R package DESeq to test for significant differences . We found 947 NFRs showed significant differences in FAIRE signal ( FDR = 0 . 05 , Figure 1 , see Methods ) . Furthermore , by analyzing the distribution of p-values [42] , we estimate that π0 ( the proportion of NFRs with no differences in chromatin accessibility ) is 0 . 53 , suggesting that 47 percent of NFRs are differentially accessible between species . These 947 NFRs were upstream of 1 , 149 distinct genes and on average resulted in a 2 . 17-fold difference in FAIRE signal between the two species . 483 of the NFRs showed higher accessibility in UWOPS05_217_3 , while 464 NFRs showed higher accessibility in CBS432 . We carried out a test for GO enrichment at the genes downstream of differentially accessible peaks and found that several GO biological process terms were enriched compared to the genome as a whole ( corrected p<0 . 05 ) , specifically intracellular transport , protein localization , protein transport , and establishment of protein localization [43] . To assess the robustness of these results , we also generated FAIRE-Seq data for a second strain of S . cerevisiae ( DBVPG1373 , a wine strain ) . Divergence at synonymous sites between these species is estimated to be 0 . 29 [37] . Levels of chromatin accessibility in NFRs were highly similar between the two S . cerevisiae strains ( Figure 1; R2 = 0 . 84; p<2 . 2×10−16 ) , and of similar magnitude between species ( Figure 1; mean R2 = 0 . 63; p<2 . 2×10−16 ) . Similarly , of the 947 NFRs that showed differential accessibility between UWOPS05_217_3 and CBS432 , 515 were also significantly different between DBVPG1373 and CBS432 . Thus , patterns of chromatin accessibility are highly reproducible between genetically diverse strains of S . cerevisiae and S . paradoxus . To better understand the genetic architecture of the widespread differences in chromatin accessibility observed between S . cerevisiae and S . paradoxus , we developed novel statistical tests for the presence of cis and trans effects ( see Methods; Figure 2 ) . Simulations showed that these tests had high power and maintained correct false positive rates over a range of parameters ( see Methods; Table S1 ) . Briefly , we tested for allele-specific chromatin accessibility within the hybrid to identify cis effects and tested for differences between the ratio of chromatin accessibility in the two parental species and the ratio of chromatin accessibility observed in the hybrid to identify trans effects ( Figure 2 ) . Over 99% of all NFRs identified in the parental strains contained one or more variants ( median = 32 ) and could therefore be assessed for cis and trans effects . We identified 2 , 256 NFRs showing a significant cis effect ( posterior probability >0 . 95 , see Figure 3A ) and 1 , 020 NFRs showing a significant trans effect ( posterior probability >0 . 95 , see Figure 3B ) . Interestingly , 782 NFRs showed both significant cis and significant trans effects . Cis effects were both more numerous as well as of greater magnitude on average compared to trans effects ( 1 . 8 and 1 . 6-fold difference in chromatin accessibility for cis and trans effects , respectively; Mann Whitney test , p<2 . 2×10−16 , Figure 3C ) . Strikingly , we found that cis and trans effects were negatively correlated ( r = −0 . 32 , p<1×10−16 ) , which suggests a widespread role for compensatory evolution to stabilize chromatin structure ( Figure 3D ) . To test the hypothesis that cis-acting chromatin QTL result from variation in regulatory motifs , we identified motifs independently in the two species and computationally inferred whether sequence differences abrogated motif usage . Specifically , we define disrupted motifs as those that were called in only one of the two species ( see Methods ) . Disrupted motifs were strongly enriched in NFRs with significant cis-acting chromatin QTL ( p = 2 . 4×10−7 ) . We also found that overall nucleotide divergence was higher at NFRs with significant cis effects compared to regions without significant cis effects ( Mann Whitney test , p = 3 . 48×10−6 ) . Note , this observation parallels previous findings that polymorphism is higher for genes that show significant allele-specific expression in S . cerevisiae hybrids [44] . We next asked if any of the 106 motifs were overrepresented for being disrupted in the set of significant cis-acting chromatin QTL . We found two overrepresented motifs , GCN4 and GZF3 ( FDR = 0 . 10; Figure 4A ) . GCN4 is an activator of amino acid biosynthetic genes , which itself is a tightly regulated pathway [45] . GZF3 is a negative regulator of nitrogen catabolic gene expression [46] . While it is not immediately clear why disruption of these two genes is associated with changes in chromatin structure , it is interesting that both play an important role in metabolism , which is a highly regulated process . To identify factors contributing to trans effects , we searched for cases where there was no disruption to the motif but the occupancy of the site changed between species . Such patterns could result from mutations that either alter the binding specificity of a trans-acting regulatory protein or change its regulation . We used the FAIRE data surrounding each motif to determine occupancy , analogous to a DNase I footprint [39] . We then tested whether there was a significant difference in the pattern of occupancy between species by fitting splines to the mean occupancy across conserved sites in trans regions and testing whether the splines were significantly different in a 100 bp window surrounding the motif using bootstrapping ( see Methods ) . We identified four motifs whose pattern of occupancy had significantly ( p<0 . 05 ) changed between species ( Figure 4B ) . SPT2 , a transcription factor that interacts with histones and the SWI/SNF complex , showed a clear footprint in S . paradoxus , but nearly the opposite pattern in S . cerevisiae , implying decreased occupancy in S . cerevisiae at these trans regions . Similarly , TEA1 , a Ty enhancer activator , and RGT1 , a glucose-responsive transcription factor , showed increased occupancy in S . paradoxus . Conversely , CBF1 , a centromere binding factor also involved in stress response , showed higher FAIRE signals in S . paradoxus than S . cerevisiae , implying increased occupancy in S . cerevisiae . To examine the relationship between differences in chromatin accessibility and transcriptional divergence between S . cerevisiae and S . paradoxus , we performed RNA-Seq on the haploid parents and interspecific hybrid and tested for the cis and trans effects on gene expression values . Out of the 4 , 899 genes that could be aligned between species , 4 , 181 exhibited significant cis effects and 3 , 117 showed significant trans effects . Overall , cis and trans effects on gene expression levels were smaller than those on chromatin accessibility , ( Spearman rank-sum test , p<2 . 2×10−16 for both cis and trans effects , Figure 5A ) . We next tested whether genes with a significant cis or trans effect in chromatin were more likely to have a significant cis or trans effect in transcript abundance . Specifically , we divided genes into categories of those downstream of an NFR with a cis effect on chromatin versus those downstream of an NFR without a cis effect on chromatin . We then compared the percentage of genes showing cis effects on RNA in these two categories . Surprisingly , we did not find evidence that cis or trans effects in NFRs were more likely to be upstream of cis or trans effects on RNA , as would be expected if there was a simple correspondence between cis and trans effects in NFRs and RNA ( see Figure 5B , Table S2 ) . This was true even when using varying cutoffs for the cis and trans effects , including ones that took into account the magnitude of effect sizes ( Table S2 ) . The relationship of cis and trans effects observed in gene expression and chromatin structure may be complicated by differences in statistical power . For example , 85% of all genes show significant cis effects on RNA . Thus , even if cis effects in NFRs are not more likely to be found upstream of cis effects on RNA , they could still contribute to gene expression variation between S . cerevisiae and S . paradoxus . To this end , we assessed whether expression differences between species could be modeled as a function of the cis and trans effects found upstream of each gene . Specifically , we fit the simple linear model: expression difference = Intercept + cis effect + trans effect + cis * trans effect + error , using the lm function in R . We found that both cis effects and trans effects on chromatin were significantly related to expression differences between species ( p = 0 . 002 , p = 4 . 18×10−5 respectively ) though they explained a very small proportion of the total variance in expression between species ( 0 . 8% combined ) . The interaction term of cis and trans effects was not significant ( p>0 . 05 ) . The motif for GZF3 , which is significantly overrepresented in cis NFRs , was overrepresented in cis NFRs upstream of genes with cis effects on RNA . Finally , we found no significant correlation between the magnitude of differences in chromatin accessibility and differences in gene expression between the parental species ( Spearman rank-sum test , p = 0 . 11 , Figure 5C ) . However , for a subset of NFRs , differences in chromatin accessibility and gene expression do appear to be highly correlated . To identify these regions , we compared the log2 ( S . paradoxus/S . cerevisiae ) for NFRs and gene expression at downstream genes and identified those whose absolute value of the difference between the two ratios was less than 0 . 25 . We identified 701 such regions; one example is shown in Figure 5D .
The ability to assay chromatin accessibility at high-resolution and on a genome-wide scale has enabled comprehensive insights into the structure and function of chromatin in many cell types , developmental stages , and organisms . Here , we were particularly interested in the evolutionary dynamics of changes in chromatin accessibility between two closely related yeast species . Broad-scale patterns of chromatin accessibility have been well conserved between S . cerevisiae and S . paradoxus ( Figure 1 ) , but superimposed on this background of conservation , we estimate that nearly 50% of NFRs exhibit differential accessibility . To better understand the relative contributions of cis and trans effects on differences in chromatin accessibility observed between S . cerevisiae and S . paradoxus , we developed novel statistical methods to analyze FAIRE-Seq data from diploid hybrids . Similar to previous findings on RNA levels [17] , [21] , [22] , [25] , differences in chromatin accessibility are caused by changes both in cis and in trans . In our data , cis effects were of greater magnitude and were more abundant . Recently , Lee et al . performed a study similar to ours and assessed cis and trans effects on chromatin structure in a cross between two strains of S . cerevisiae [34] . In contrast to our observations , they found that trans QTL were more pervasive than cis QTL ( 92 . 1% of associations versus 7 . 9% of associations ) [34] . We hypothesize that these disparate observations are primarily the consequence of differences in the evolutionary trajectory of chromatin accessibility QTL in within versus between species data . In particular , trans-acting chromatin QTL are likely to be subject to more intense purifying selection due to their potential pleiotropic effects , and tend to be eliminated over longer time periods [47] . This hypothesis is consistent with findings for expression QTL studies , which showed that trans-eQTL were more common within species and cis-eQTL were more common between species [22] , [23] . Consistent with this hypothesis , we found that cis and trans effects were significantly negatively correlated , indicating that chromatin accessibility in each species is subject to stabilizing selection and perturbations of chromatin structure are , on average , deleterious . We estimated cis and trans effects for both chromatin accessibility and gene expression levels . Unexpectedly , the presence of cis or trans effects on chromatin accessibility in NFRs was not significantly associated with cis or trans effects on RNA . In other words , gene expression levels with significant cis or trans effects were not more likely to have an NFR with significant cis or trans effects on chromatin accessibility . Thus , it appears that many of the changes in chromatin accessibility in NFRs between S . cerevisiae and S . paradoxus do not necessarily have transcriptional consequences . One factor that may contribute to this observation is that compensatory changes downstream of chromatin accessibility , such as mutations that influence mRNA stability , may evolve to maintain levels of gene expression . In addition , many changes in chromatin accessibility may simply be functionally benign . Furthermore , an important caveat is that our data was obtained from a single environmental condition , and it is plausible that stronger correlations between chromatin and gene expression QTL may exist when analyzing data from either a different environment or across multiple environments . Nonetheless , the lack of a clear relationship between chromatin and gene expression QTL in our data is interesting in light of recent observations from the ENCODE Project [48] that have found a large proportion of the human genome has reproducible biochemical activity . Our results suggest caution in assuming all , or perhaps even most , of such sequences are functionally important .
65 ml of each of 2 biological replicates of the S . paradoxus strain CBS432 and the two S . cerevisiae strains DBVPG1373 and UWOPS05_217_3 were grown to mid-log phase . 15 ml were used for RNA-seq and 50 ml were used for FAIRE . We performed FAIRE as described in Simon et al . [40] , with some modification . The cells were fixed with 1% formaldehyde for 35 minutes with mixing . Cells were sonicated using the Fisher Scientific Sonic Dismembrator Model 100 for three cycles of 15 one-second bursts with 1 second rest in between , keeping the cells on ice for at least 30 seconds between cycles . The remainder of the protocol was followed as in Simon et al . [40] . RNA isolation was performed using the hot phenol protocol [49] , and RNA was treated with Turbo DNAse before library construction . Libraries were constructed for the FAIRE samples using the Illumina TruSeq DNA kit , starting with approximately 200 ng FAIRE DNA , following their standard kit protocol but omitting the fragmentation step . RNA libraries were prepared using the Illumina TruSeq RNA kit , following their standard protocol . Libraries were pooled into two lanes , one for the FAIRE samples and one for the RNA samples , and were sequenced on the HiSeq 2000 . Raw sequence data and processed files are available at the GEO database with accession number GSE55717 . Reads were mapped to genomes assembled in Skelly et al . [50] for the S . cerevisiae haploid samples using bwa and samtools [51] , [52] . For the S . paradoxus strain CBS432 , we used the last updated reference version from the SGRP [53] . For the diploid samples , we mapped to a combined FASTA containing both genomes . We tested whether mapping to each genome separately for the diploid samples resulted in increased mapping; it did not . For the diploid samples , we generated simulated reads and mapped to the combined FASTA . For all further analyses , we restricted analysis to NFRs for which greater than 90 percent of simulated reads mapped back to the correct region . We also sequenced a genomic DNA sample . We also filtered out NFRs where the absolute value of the log2 ( ratio of reads between the two species ) for the genomic DNA was greater than 0 . 3 . We identified NFRs as follows: specifically , starting at the beginning of the coding region of the gene , we looked for the peak of chromatin accessibility within 300 bp upstream of the start codon . We then defined the edges of the NFR as the base-pair after which at least 3 bases had had a chromatin accessibility count of less than 10 . We did this separately for each biological replicate and each species . For each gene separately , we then merged NFRs if they were within 200 bp . In order to convert between the two species coordinates , we created a multiple alignment between the two species using LASTZ and TBA [54] , [55] . We inferred scoring parameters using the two species of interest . Using this multiple alignment , we then converted the NFRs called in CBS432 to S . cerevisiae coordinates , and found the union of all NFRs called across the samples . We used this union of NFRs for further tests . We also filtered the NFRs based on a reciprocal alignment filter , where we required that NFRs align to only one region in the other species , based on the multiple alignment . This allowed us to filter out regions with duplications or deletions between the two species . Using samtools , we summed the count of reads mapping in each species across each NFR or gene in both biological replicates . Note that we did this in the native coordinates for each species , filtering out sites that were called as indels in the multiple alignment . We then used the R package DESeq [56] to assess differential FAIRE signal between species . This method takes into account biological replicates , and models the count distribution using a negative binomial distribution . We used the R package qvalue [42] to estimate q-values . We used a significance threshold of FDR = 0 . 05 unless otherwise noted . If differences in chromatin accessibility between S . cerevisiae and S . paradoxus are due to trans-acting factors , the relative chromatin accessibility in the haploid parents will be different than the relative chromatin accessibility in the diploid hybrid ( Fig . 2 ) . We leveraged the FAIRE-Seq data to detect differences in the relative levels of chromatin accessibility between F1 hybrids and the parental species . Specifically , let Nc and Np be the total number of reads across the genome mapping to polymorphic sites in the S . cerevisiae and S . paradoxus haploid parents , respectively . For a particular locus j , Yc and Yp denote the observed number of reads mapping to S . cerevisiae and S . paradoxus , respectively . Then assume: Yc|rc∼Binomial ( Nc , rc ) and Yp|rp∼Binomial ( Np , rp ) , where rc and rp denote the probabilities of observing a read mapping to S . cerevisiae or S . paradoxus for a particular locus , respectively . Since Nc and Np are large , and rc and rp are small , we can approximate these binomials by Poissons to give: Yc|rc∼Poisson ( Nc rc ) and Yp|rp∼Poisson ( Np rp ) . We define θP = rc/rp to be the ratio of these probabilities in the parents and R = Nc/Np to be the ratio of the total numbers of reads in each parent . Then , Yc|Yc+Yp , sc∼Binomial ( Yc+Yp , sc ) , where sc = Ncrc/ ( Ncrc+Nprp ) = RθP/ ( RθP+1 ) is the probability of observing a read map to S . cerevisiae , without adjusting for differences in the total number of reads mapping to each species . We can thus write log ( Sc/1−Sc ) = log R+log θP , such that θP is the odds of observing a read map to S . cerevisiae compared to S . paradoxus for a particular locus in the haploid parents , adjusted for differences in the total number of reads mapping to each species . For the diploid hybrid , let Zc and Zp denote the number of reads mapping to S . cerevisiae and S . paradoxus SNPs within locus j , respectively . Thus , Zc|Zc+Zp , pc∼Binomial ( Zc+Zp , pc ) , where pc is the probability of observing a read map to the S . cerevisiae allele for a particular locus . The odds of observing a read map to S . cerevisiae in the hybrid for a particular gene is θH = pc/ ( 1−pc ) . In the following , let Ycj , Ypj , Zcj , and Zpj represent the data as defined above , but with j = [1] , [2] indexing biological replicate . Thus , the locus specific models are: Ycj|Ycj+Ypj , scj∼Binomial ( Ycj+Ypj , scj ) , Zcj|Zcj+Zpj , pcj∼Binomial ( Zcj+Zpj , pcj ) logit scj = log Rj+log θP+δj logit pcj = log θP+Δ+εj where Rj = Ncj/Npj , δj∼N ( 0 , σ2 ) and εj∼N ( 0 , σ2 ) represent random effects that allow for excess-binomial variation . Here , Δ is the parameter of interest and provides an estimate of the difference between log ( θP ) and log ( θH ) , as described above . The above framework is an example of a generalized linear mixed model ( GLMM ) and we used a Bayesian approach to inference with relatively flat hyperpriors . One computationally intensive method for summarizing the posterior would be Markov chain Monte Carlo ( MCMC ) but the integrated nested Laplace approximation ( INLA ) as described in [57] provides an efficient alternative for GLMMs [58] . We used the R implementation of INLA to estimate Δ . We examined a 95% posterior interval estimate for Δ and recorded whether this interval contained 0 or not . If the interval does not contain 0 it indicates that chromatin accessibility differs . To detect cis effects , we developed a model to test for differential accessibility between alleles within the diploid hybrid . Let Zcj , and Zpj represent the data as defined above . We can therefore write: Zcj|Zcj+Zpj , pcj∼Binomial ( Zcj+Zpj , pcj ) logit pcj = log θH+εj with εj∼N ( 0 , σ2 ) representing random effects that allow for excess-binomial variation . In this model , θH is the parameter of interest and provides an estimate of the odds of a read mapping to the S . cerevisiae allele compared to the S . paradoxus allele in the diploid hybrid for a particular gene . We again used the R program INLA to estimate the posterior for log ( θH ) and in particular examine whether the 95% posterior interval estimate contains 0 . We carried out extensive simulations to evaluate the operating characteristics of our model . Specifically , for the trans model , we set the total number of reads mapping to polymorphic sites for species 1 ( Nc1 ) equal to 5×106 , and drew the total number of reads mapping to polymorphic sites for the other species and replicate from a normal distribution with mean Nc1 and standard deviation Nc1 . We then drew the value for rc , the probability of a read mapping to S . cerevisiae for a particular locus from an exponential distribution with rate 10 , 000 . For Nc1 = 5×106 , this results in a mean of 500 reads mapping to a locus , with most having less than 500 reads , consistent with the observed data . We drew the value for rp , the probability of a read mapping to S . paradoxus for a particular locus , from a normal distribution with mean rc and standard deviation rc and took the absolute value to ensure rp was greater than zero . Using these values , we derived Yc and Yp , the number of reads mapping to S . cerevisiae and S . paradoxus , respectively , for a particular locus , for two biological replicates as specified by the model . For Zc and Zp , the number of reads mapping to the S . cerevisiae and S . paradoxus alleles in the hybrid summed across polymorphic sites in a particular locus , we either derived these using the same rc and rp values as above , to simulate a locus which showed no trans effect , or we set the value of log2 ( θP ) −log2 ( θH ) equal to 0 . 1 , 0 . 5 , or 0 . 8 , to simulate a locus with a trans effect . Note , this spans the range of detected trans effects . For 100 replicates , we simulated a collection of 6000 loci , 5000 of which did not show a trans effect and 1000 or which did show a trans effect . For each of the 100 replicates , we then used the method described above to test whether the 95% posterior interval estimate for Δ for each locus contained zero . To evaluate the cis test , we again started with the same values for the total number of reads . To simulate a locus with no cis effect , we set the value of log2 ( Zc/Zp ) equal to zero , and to simulate a locus with a cis effect , we set the value of log2 ( Zc/Zp ) equal to 0 . 1 , 0 . 5 , or 0 . 8 . Again , for 100 simulations , we simulated a collection of 6000 loci , 5000 showing no cis effect and 1000 showing a cis effect . For each simulated set of loci , we then used the statistical method above to test whether the 95% posterior interval estimate for log ( θH ) for each locus contained zero to test for a significant cis effect . We found that the false discovery rate for both cis and trans based on a test based on a 95% interval was 0 . 05 . Moreover , we found that the trans test has reduced power compared to the cis test , as expected because there were more parameters that could vary across biological replicates . However , with an effect size = 0 . 5 for both the cis and trans tests , there was significant power to detect the cis or trans effects ( Table S1 ) . We called motifs separately in both species , using MEME , using their standard p value cutoff of p<10−4 [59] . This results in the same cutoffs used for both species . Motifs that were not called in both species were considered polymorphic . We filtered out motifs where the polymorphism was due to indels in order to mitigate alignment errors . The motif calls used for this analysis are available as supplementary data on our website ( http://akeylab . gs . washington . edu/downloads . shtml ) . We compared the proportion of disrupted motifs ( those that were called in only one species ) in cis NFRs to non-cis NFRs using the Fisher exact test . We determined significance by permutations; we permuted the assignment of cis or not cis NFRs 1000 times and obtained p values from the permutations . We then used the positive False Discovery Rate approach to determine significance [42] . We obtained the RPKM in a 200 bp window surrounding motifs that were conserved across species in trans NFR regions for each of the two species . We filtered out motifs that did not have at least five instances of conserved motifs . We fit a cubic smoothing spline to the mean coverage using the R function spline . We then bootstrapped the data 1000 times by resampling from the motifs for each species . At five bp intervals across the region , we then tested whether the coverage was significantly different between the species , using the confidence intervals obtained from the bootstrapping . We then manually inspected the significant motifs ( p<0 . 05 ) to identify those which appeared to affect the FAIRE signal at or the near the motif . | Inside the nucleus of a cell , DNA is associated with proteins to form a complex three-dimensional structure referred to as chromatin . The structure of chromatin influences how accessible specific DNA sequences are to transcription factors , and therefore chromatin accessibility is an important determinant of gene expression . To better understand how patterns of chromatin accessibility change over time , we quantitatively measured levels of chromatin accessibility in two yeast species and their diploid hybrid . We show that significant differences in chromatin accessibility exist between these two species and occur upstream of genes that are enriched for specific biological functions . We also develop new statistical methods to understand the genetics of variation in chromatin accessibility . Finally , we show that the relationship between chromatin accessibility and gene expression is complex , and many of the observed differences in chromatin accessibility between these two species may not influence gene expression levels . Thus , our work highlights the need to develop additional experimental and statistical methods to distinguish between functionally significant and benign changes in chromatin accessibility . | [
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] | 2014 | Evolution and Genetic Architecture of Chromatin Accessibility and Function in Yeast |
Alternative cleavage and polyadenylation influence the coding and regulatory potential of mRNAs and where transcription termination occurs . Although widespread , few regulators of this process are known . The Arabidopsis thaliana protein FPA is a rare example of a trans-acting regulator of poly ( A ) site choice . Analysing fpa mutants therefore provides an opportunity to reveal generic consequences of disrupting this process . We used direct RNA sequencing to quantify shifts in RNA 3′ formation in fpa mutants . Here we show that specific chimeric RNAs formed between the exons of otherwise separate genes are a striking consequence of loss of FPA function . We define intergenic read-through transcripts resulting from defective RNA 3′ end formation in fpa mutants and detail cryptic splicing and antisense transcription associated with these read-through RNAs . We identify alternative polyadenylation within introns that is sensitive to FPA and show FPA-dependent shifts in IBM1 poly ( A ) site selection that differ from those recently defined in mutants defective in intragenic heterochromatin and DNA methylation . Finally , we show that defective termination at specific loci in fpa mutants is shared with dicer-like 1 ( dcl1 ) or dcl4 mutants , leading us to develop alternative explanations for some silencing roles of these proteins . We relate our findings to the impact that altered patterns of 3′ end formation can have on gene and genome organisation .
Eukaryotic mRNA 3′ ends are defined by a protein complex that cleaves pre-mRNA in close association with RNA polymerase II ( Pol II ) and adds a poly ( A ) tail to the free 3′ end [1] , [2] . This event is closely associated with transcription termination , since cleavage exposes the 5′ end of the nascent RNA to a 5′–3′ exonuclease that degrades the RNA up to the exit channel of Pol II , hence contributing to termination [3] . However , termination is the least understood aspect of the transcription cycle [3] and at a sub-set of mammalian genes , cleavage and polyadenylation occur post-transcriptionally because rapid cleavage of nascent RNA at co-transcriptional cleavage ( CoTC ) sites downstream of the poly ( A ) signal promotes termination and the release of pre-mRNA from the chromatin template [4] . The selection of alternative cleavage and polyadenylation sites defines different 3′ ends within pre-mRNAs transcribed from a single gene and can therefore affect function by determining coding potential and the inclusion of regulatory sequence elements . Termination efficiency can also affect transcript levels , possibly because termination facilitates recycling of transcription complexes [5]–[7] . Therefore , the processes of cleavage , polyadenylation and termination are important stages at which gene expression can be regulated . However , the widespread nature of this control has only become apparent relatively recently [1] , [2] . A less well-studied phenomenon , which suggests an additional role for regulated 3′ end formation and transcription termination , is the existence of chimeric transcripts formed between exons of neighbouring genes encoded on the same chromosomal strand [8]–[10] . Specific chimeric RNAs are conserved in vertebrates [10] , regulated under certain conditions [11] and occur recurrently in cancerous tissues [12] , [13] . However , regulatory processes controlling chimeric RNA formation are poorly understood . The spen family protein FPA is a trans-acting regulator of RNA 3′ end formation , but is not a conserved component of either the splicing or the cleavage and polyadenylation apparatus [14] . First identified as a regulator of flower development , FPA enables flowering by ultimately limiting the expression of the floral repressor FLC [15] . Intriguingly , several viable A . thaliana mutants disrupted in factors that mediate RNA 3′ end formation are late flowering as a result of the specific misregulation of FLC [16]–[18] . The mechanism by which FPA controls FLC is unknown , but increased FLC transcription in fpa mutants is accompanied by increased levels of alternatively processed non-coding antisense RNAs ( asRNAs ) at the FLC locus [14] . The biological role of FPA is not restricted to flowering , since FPA also influences other aspects of development [19] , [20] . In addition , fpa mutants were isolated in a screen designed to identify factors required for RNA silencing [21] . However , the involvement of FPA in endogenous RNA silencing pathways is currently unclear because the apparent misregulation of an endogenous RNA silencing target ( the SINE retroelement AtSN1 ) in fpa mutants can be explained by read-through resulting from defective 3′ end formation at an upstream gene [14] . There is intense interest in determining how the widespread alternative patterns of RNA 3′ end formation can be regulated and what the consequences of disrupting specific regulators may be . We recently defined genome-wide patterns of cleavage and polyadenylation in A . thaliana using direct RNA sequencing ( DRS ) , thereby refining our understanding of 3′ end formation in this model organism [22] . DRS can define the site of RNA cleavage and polyadenylation with an accuracy of ±2 nt in the absence of errors induced by reverse transcriptase , internal priming , ligation or amplification [22] , [23] . In this study , we set out to answer two questions by quantifying shifts in RNA 3′ end formation between wild-type ( WT ) and fpa mutants with DRS: ( 1 ) could we clarify the roles of FPA in plant biology ( particularly in relation to flowering and RNA silencing ) ? ; and ( 2 ) could we define generic consequences of defective RNA 3′ end regulation that would be of broad relevance ? Here we identify the abundance and sites of 3′ end formation of RNAs transcribed antisense to the floral regulator FLC , but do not detect evidence of a widespread role for FPA in RNA-mediated chromatin silencing . We identify the generic consequences of disrupting regulated RNA 3′ end formation , prominent among which is the formation of specific chimeric RNAs between exons of otherwise separate and well-characterised genes . In addition , we make the unexpected but related discovery that transcription termination defects in fpa are shared at some of the same loci in both dcl1 and dcl4 mutants . Consequently , we suggest an alternative explanation involving defective upstream termination for the previously reported DCL1-mediated silencing of overlapping gene pairs [24] .
We subjected total RNA purified from three biological replicates of WT A . thaliana [Columbia-0 ( Col-0 ) accession] and fpa-7 loss-of-function mutants to DRS . RNA was prepared from 14-day-old whole seedlings . A total of 22 , 560 , 508 WT and 24 , 383 , 585 fpa-7 reads that map polyadenylated RNA 3′ ends were aligned uniquely to the most recent A . thaliana genome release ( currently TAIR10 ) . A summary of the read statistics is given in Table S1 . In each genotype , the vast majority of reads mapped to 3′ untranslated regions ( UTRs ) of protein-coding genes ( Figure 1A–B ) . The DRS data can be visualised using aligned reads available at www . compbio . dundee . ac . uk/polyADB/ . We first asked whether DRS could reveal changed patterns of gene expression between genotypes by measuring the difference in read counts mapped to annotated protein-coding genes . We used DESeq [25] to detect differential gene expression between the WT and fpa mutant DRS datasets . The expression of 18 , 406 protein-coding genes was detected in WT A . thaliana; DESeq analysis suggested that 1 , 114 genes were differentially expressed in the fpa-7 mutant ( Table S2 ) , with the vast majority being up- or down-regulated by less than two-fold ( Figure 2A ) . Since fpa mutants flower late , a number of gene expression changes in fpa are either predictable or already established . For example ( and consistent with our expectations ) , down-regulation of the floral pathway integrator SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 ( SOC1; fold change = 0 . 13 , P = 10e−96 ) and up-regulation of the floral repressor FLC ( fold change = 27 , P = 3 . 10e−139 ) were readily detected ( Figures 2B–C and S1A ) . We previously reported that increased read-through of asRNA transcripts through the FLC locus in fpa mutants correlates with increased sense strand transcription [14]; this seemingly counterintuitive finding was confirmed here by DRS ( Figures 2C and S1A ) . DRS identified the preferred sites of asRNA cleavage and polyadenylation ( Figure 2C ) , indicated that asRNA expression is approximately 100-fold lower than FLC sense strand expression ( Figure 2C ) and showed that , in fpa mutants , increased levels of sense strand FLC expression are associated with increased levels of asRNAs cleaved and polyadenylated antisense to the FLC promoter ( Figures 2C and S1A ) . In contrast to another report [18] , DRS data did not indicate reduced levels of cleavage and polyadenylation at proximal sites in the asRNAs in fpa mutants , although this interpretation would benefit from a greater sequencing depth ( Figure 2C ) . A single nucleotide polymorphism ( SNP ) associated with variation in flowering time and asRNA expression level [26] maps to the distal poly ( A ) signal of preferred cleavage sites antisense to the FLC promoter ( Figure S1B ) . These DRS data clarify multiple 3′ end processing events at the FLC locus for the first time and are therefore valuable for understanding how the recurrent identification of 3′ end processing factors might affect FLC expression [14] , [16]–[18] . Although FPA has been reported to play a widespread role in RNA-mediated chromatin silencing [21] , we found statistically significant increases in fpa-7 DRS counts at only 28 of the 31 , 189 transposons and 3 , 903 transposable element genes annotated in TAIR10 ( Table S3 ) . Since we had previously found that the apparent involvement of FPA in silencing the SINE retroelement AtSN1 could be explained by read-through resulting from defective termination at an upstream Pol II gene [14] , [21] , we asked whether reads mapping to annotated transposons reflect a genuine loss of silencing or whether they could also be explained by read-through . We found , for example , that DRS reads mapping to an apparently up-regulated transposable element gene ( At5g10670 ) did indeed result from read-through from the upstream protein-coding gene At5g10690 ( Figure 3A–C ) . Therefore , at least some of the relatively small number of reads mapping to transposons in this study may also be explained by read-through events . Clearly , not all misregulated transposons will be polyadenylated and so they will not be detected here , but the results of this genome-wide analysis are inconsistent with the suggestion that FPA plays a widespread role in RNA-mediated chromatin silencing . This conclusion is supported by a recent DNA methylation analysis of A . thaliana silencing mutants that included fpa-7 and found no evidence of FPA affecting RNA-dependent DNA methylation ( RdDM ) target sites [27] . The number of DRS reads mapping to the transposable element At5g35935 was significantly different between WT and fpa-7 ( Figure 3D; fold change = 33 , P = 2 . 63e−32 ) . Recent re-annotation of At5g35935 defined a newly arisen pseudogene psORF ( pseudogene small open reading frame ) within this sequence [28] , [29] , and it is the increased expression in fpa-7 of psORF , rather than the transposon , that is detected by DRS ( Figure 3D–E ) . According to recently published DNA methylation data [27] , psORF is demethylated in fpa-7 mutants ( Figure S2A ) , and we found no evidence that read-through from an upstream gene could account for the increased number of DRS reads detected here in fpa-7 . These findings therefore raised the possibility that FPA functions to silence this newly arisen pseudogene . However , we did not detect misregulation of psORF in a second allele , fpa-8 ( Figure 3E ) . It has recently been shown that de novo originated A . thaliana genes might be prone to epigenetic variation in the early stages of their formation [30] . Accordingly , apparent changes in the DNA methylation or expression of such sequences may be a coincidence of the different genetic backgrounds analysed [28] , [29] . Such epigenetic variation might also explain why misregulation of antisense RNAs at the newly acquired helitron transposable element At1TE93275 [28] , previously reported in fpa mutants [31] , was not detected in this fpa-7 dataset ( Figure S2B ) . Consequently , particularly careful analysis is required for the identification of authentic factors mediating the silencing of such newly originated sequences . Besides refining our understanding of previously proposed roles for FPA in flowering and RNA silencing , we sought to identify the generic consequences of disrupting a regulator of RNA 3′ end formation . For example , one might predict that 3′ end formation within intronic sites and conventional 3′UTRs would be altered in fpa mutants . Cleavage and polyadenylation within intronic sequences outside the 3′UTR can have profound consequences on gene function by truncating mRNA coding potential . FPA effects autoregulation in this way by mediating selection of the promoter proximal intronic cleavage site within its own pre-mRNA [14] . We therefore asked whether FPA controls alternative polyadenylation at other intronic sites . We applied our data-smoothing and peak-finding algorithms to define cleavage sites [22] and estimated differential usage of these sites using DESeq . The validity of this approach was supported by the finding that previously identified intronic alternative polyadenylation events within FPA ( Figure 4A; P = 7 . 10e−11 ) , but not FCA were dependent on FPA function [14] , [22] . Unexpectedly , additional intronic cleavage sites were also detected in FPA RNA itself , but only in the transfer ( T ) -DNA-induced fpa-7 allele ( Figure 4A ) and not in ethyl methanesulfonate ( EMS ) -induced alleles such as fpa-8 [21] , suggesting that T-DNA insertions can trigger cryptic cleavage and polyadenylation ( Figure S3A–F ) . These allele-dependent distinctions in patterns of FPA polyadenylation were indicated by previous RNA gel blot hybridisations [14] . Reduced selection of intronic cleavage sites in 13 genes and increased selection of intronic cleavage sites in another 25 genes were found in fpa-7 ( Table S4 ) , indicating that FPA ultimately promotes cleavage at some sites , but represses it at others . In order to validate this data analysis , we investigated some of the deduced intronic alternative polyadenylation changes in more detail ( Figure S4 ) . For example , we detected shifts in alternative polyadenylation at IBM1 ( Increase in BONSAI Methylation 1 ) , a gene encoding a histone demethylase specific for H3K9 dimethylation and monomethylation [32] . Expression of the active enzyme was recently shown to be controlled by alternative polyadenylation , which in turn is dependent on DNA methylation at this locus ( Figure S4A ) [33] since 3′ end formation occurs exclusively at proximal poly ( A ) sites in mutants that disrupt CG and CHG DNA methylation [33] . In contrast , our DRS data suggest that cleavage occurs almost exclusively at the IBM1 distal site in fpa mutants ( Figure S4B; P = 0 . 03 ) ; this was confirmed by RNA gel blot hybridisation ( Figure S4C ) . FPA and DNA methylation mediated by METHYLTRANSFERASE 1 ( MET1 ) and the plant-specific chromomethylase CMT3 therefore appear to have opposing effects on poly ( A ) site choice in IBM1 pre-mRNA transcribed through intragenic heterochromatin . We previously showed that FPA affects 3′ end formation not only within introns but also at conventional 3′UTRs , thus causing intergenic read-through of Pol II transcripts [14] . This discovery was recently extended by a tiling array analysis of fpa fca double mutants [31] . Consistent with these previous studies , our DRS analysis mapped reads to intergenic sequences ( defined here as regions between protein-coding genes ) in fpa mutants . DESeq analysis identified 61 up-regulated intergenic regions downstream of down-regulated genes and 109 up-regulated intergenic regions downstream of genes with unchanged expression in fpa-7 ( Table S5 ) . The amount of polyadenylated read-through RNAs relative to upstream annotated genes varied ( Figure S5A ) . We validated potential intergenic read-through events in detail ( Figures S5B–Q and S6 ) and identified three different types of events: first , extended 3′UTRs ( Figure S5F–N ) ; second , cryptic splicing event ( s ) generating altered 3′UTR sequences ( Figure S6 ) ; and third , read-through accompanied by cryptic splicing that alter the protein-coding sequence of the upstream gene , revealing that intergenic read-through is not necessarily benign ( Figure S5O–Q ) . DRS extends previous studies by identifying the cleavage and polyadenylation sites of read-through RNAs . We analysed the sequence features of intergenic cleavage sites and , although the relatively small size of the fpa read-through dataset makes the nucleotide profile surrounding intergenic cleavage sites appear somewhat noisy , an alternating pattern of A- and U-rich regions flanking the cleavage site and a prominent A-rich region around −20 was detected ( Figure 4B–C ) . We previously showed that the distinguishing feature of preferred and non-preferred cleavage sites within the same 3′UTR is the relative prominence of the A-rich peak located approximately 20 nt upstream of the cleavage site [22] ( Figure 4B ) . Because the corresponding A-rich peak is prominent here , we suggest that 3′ end formation of read-through RNAs in fpa mutants takes place at strong poly ( A ) signals located within intergenic regions . We identified 14 read-through RNAs in fpa mutants that result in transcription of novel RNAs antisense to expressed protein-coding genes ( Table S6 ) , but in no case was this associated with down-regulation of sense strand mRNA expression . For example , although we could validate read-through from At1g29530 that generated new antisense RNA against the sense strand-encoded At1g29520 , there was no change in the sense strand expression of At1g29520 ( Figure 4D–F ) . Since increased expression of RNA cleaved and polyadenylated antisense to the FLC promoter correlates with increased sense strand FLC mRNA expression , we next asked whether other genes differentially expressed in fpa-7 are associated with increased detectable cleavage and polyadenylation of RNA antisense to their promoters . This analysis ( Table S7 ) identified four cases ( including FLC ) in which sense strand gene expression was increased and 13 cases in which it was decreased when increased DRS reads mapping antisense to promoters in fpa-7 were detected . Overall , we conclude that novel asRNAs generated in fpa mutants are not necessarily associated with gene silencing . However , we also identify loci where detailed experimental analysis is required to uncover potentially distinct regulatory consequences of novel asRNAs . Among those RNAs affected by FPA , we identified cases in which tandem genes showed reciprocal changes in read abundance . For example , in the fpa-7 mutant we found a reduction in the number of reads aligning to the 3′ end of At3g59060 ( PHYTOCHROME INTERACTING FACTOR 5 , PIF5 ) compared to WT , but the number of reads mapping to the 3′ end of the downstream gene At3g59050 ( POLYAMINE OXIDASE 3 , PA03 ) was increased ( Figure 5A ) . We asked whether these read differences could be explained by defective 3′ end formation at PIF5 in fpa mutants , leading to chimeric RNAs being cleaved and polyadenylated within the 3′UTR of PA03 . Consistent with this idea , RT-PCR analysis using primers anchored in PIF5 and PA03 ( and thus spanning the intergenic region ) detected the formation of chimeric RNAs specifically in fpa mutants ( Figure 5B–C ) . RNA gel blot analysis confirmed this , with a probe to the 5′ end of PIF5 revealing 60% read-through into RNAs of increased size relative to PIF5 ( Figure 5D ) . Three major hybridising signals specific to fpa mutants were detected using probes spanning the intergenic region , the PA03 3′UTR and the different exons and UTRs of PIF5 ( Figure 5D–E ) . A combination of RT-PCR , 5′ rapid amplification of cDNA ends ( RACE ) , RNA gel blot and cloning approaches identified two of these ( α and β ) as chimeric RNAs that differ as a result of a cryptic splicing event ( Figure 5E–G ) , while the third comprised two similar sized RNAs with 5′ ends mapping to either the 3′UTR of PIF5 ( γ ) or the intergenic sequence ( γ′ ) ( Figure 5E–G ) . The differential sensitivities of these RNAs to tobacco acid pyrophosphatase ( TAP ) suggest that γ is capped , but γ′ is not ( Figure 5F ) . None of the chimeric RNAs altered the deduced PIF5 open reading frame , but they did effectively extend the 3′UTR from 211 nt to 2 , 720 nt in α and to 1 , 680 nt in β chimeric transcripts . In contrast , native PA03 expression is undetectable in fpa mutants ( Figure 5D ) . The 5′ end of γ RNAs aligned to the PIF5 3′UTR ( Figure 5F , G ) but , as judged by 5′RACE , was distinct from all of the 3′ ends that mapped to the PIF5 3′UTR ( Figure 5G ) . This suggests that γ RNAs did not result from cleavage of chimeric RNAs followed by capping . Instead , we found differences between WT and fpa mutants in H3K4me3 , a chromatin modification associated with transcription start sites [34] , across PIF5 and PA03 , with a decrease in H3K4me3 at the 5′ end of PA03 and an additional H3K4me3 peak detected at the 3′ end of PIF5 in fpa-8 ( Figure 6A ) . These data are consistent with a shift in the PA03 transcription start site accompanying the chimeric RNAs detected here in fpa mutants . In addition to being detected in single fpa-7 and fpa-8 mutant alleles , chimeric PIF5–PA03 RNAs were detected in an early-flowering flc-3 fpa-7 double mutant but were generally not found in other late-flowering mutants ( Figure S7A–C ) , revealing that they result from a specific lack of FPA and not indirectly from late flowering . We did detect low levels of chimeric RNAs in late-flowering pcfs4 mutants ( Figure S7C ) in which a protein related to the core cleavage , polyadenylation and termination factor Pcf11 is disrupted [17] . We investigated whether FPA may mediate this effect indirectly by determining whether splicing of PIF5 pre-mRNA is perturbed , since splicing is intimately connected to RNA 3′ end formation [1] . However , we found no evidence for changes in either the fidelity or efficiency of PIF5 pre-mRNA splicing in fpa mutants ( Figure S7D–F ) . We also investigated whether FPA affects the turnover of chimeric RNAs , since they comprise long 3′UTRs with multiple introns downstream of in-frame stop codons that might normally be degraded by nonsense-mediated RNA decay ( NMD ) [35] . However , we found no evidence of stabilised read-through RNAs in mutant backgrounds defective in the NMD factor UPF1 [36] ( Figure 6B–C ) , and indeed found that chimeric RNAs appear to escape NMD ( Figure 6C ) . PIF5 is one of a family of critical growth regulators in A . thaliana and is closely related to PIF4 , with which it shares some functions [37] . PIF4 and PIF5 may have arisen from a duplication event since both have related downstream polyamine oxidase loci ( PIF4-PA02 and PIF5-PA03 ) that have conserved peptide open reading frames in their 5′UTRs ( CPuORF17 and CPuORF18 , respectively ) . Despite these similarities , chimeric RNAs were found at PIF5 , but not PIF4 ( Figure S7G ) in fpa mutants , and not dcl1 mutants ( Figure S7H ) underscoring the specificity of FPA-mediated effects on chimeric RNA formation . Chimeric RNA formation was detected at four other tandem gene pairs ( Figures S8–S11 ) following the development of an algorithm based on reciprocal DRS read abundance at tandem protein-coding genes ( Tables S8 , S9 ) . In each case , the resulting chimeric RNA encodes the open reading frame for the same upstream gene combined with a different effective 3′UTR ( Figure S8C ) . Remarkably , one chimeric RNA isoform appears to result from a splicing event that excised almost the entire coding sequence of the downstream At1g02470 gene from the chimeric transcript . In all cases , while there was evidence of chimeric RNA in WT , the level of chimeric RNAs was increased in fpa mutants . Although our algorithm probably underestimates the number of chimeric RNAs formed in fpa mutants because it only considers neighbouring protein-coding genes , it is clear that FPA affects specific chimeric RNA formation at a limited number of sites in the genome . Read-through was detected at the 3′ end of the flowering regulator FCA ( At4g16280 ) in fpa mutants . Failsafe termination at this site was recently shown to depend upon DCL4 [38] . FCA read-through RNAs similar to those previously found in dc14 mutants were detected here in fpa mutants ( Figure 7A–C ) , revealing that , even in the presence of DCL4 , failsafe termination fails in the absence of FPA . The expression of DCL4 itself is unaffected by FPA ( Table S2 ) . We next asked whether this connection between FPA and DCL proteins extends to other loci . DRS indicated that fpa read-through RNAs are found at genomic regions where DCL-dependent small RNAs , described as natural antisense transcript siRNAs ( nat-siRNAs ) , are also found [24] . We first validated the existence of these read-through RNAs in fpa mutants , using RT-PCR to reveal read-through at the 3′ end of At1g51390 , for example ( Figure 7D ) . A DCL1-dependent nat-siRNA1g51400 has recently been mapped to this genomic region [24] . Using RT-PCR analysis of poly ( A ) + RNA , we identified similar read-through RNAs in dc11–11 downstream of At1g51390 ( Figure 7D–F ) . This finding is consistent with DCL1 ultimately controlling transcription termination . Expression of the annotated downstream gene At1g51402 has previously been reported to increase in dc11 mutants , which has been taken as evidence of nat-siRNA-mediated gene silencing [24] . However , our findings suggest that this detectable increase in At1g51402 expression can be explained instead by read-through from the upstream gene . We also detected differences in 3′ end formation at other loci in fpa and dc11 mutants , although read-through at FCA was unaffected by DCL1 ( Figure 7B ) . We therefore conclude that FPA and DCL proteins control termination at overlapping sets of target genes .
The impact of alternative polyadenylation and transcription termination is underexplored , but widespread changes in poly ( A ) site choice reveal it to be an important level at which gene expression can be regulated . Here we used DRS to assess the consequences of disrupting regulated 3′ end formation dependent on the spen family protein FPA . The function of FPA in flowering ultimately depends on its control of the floral repressor FLC [15] . The recurrent identification of late-flowering mutants with elevated FLC expression , in which other factors that mediate RNA cleavage and polyadenylation are also disrupted , suggests a critical role for RNA 3′ end formation in FLC regulation [16]–[18] . However , 3′ end formation of FLC mRNA itself appears to be largely unaffected by these mutations . Instead , attention has focused on a potential role for asRNA processing in FLC regulation because polyadenylated asRNA expression at the FLC locus is also changed in these mutants [14] , [18] , [39] . Importantly , it is unclear whether these asRNAs are the cause of , the consequence of or simply coincide with FLC regulation . For example , the positive correlation of sense and asRNA expression detected here may be a consequence of gene-looping events that juxtapose promoters and terminators that are each characterised by nucleosome-free regions [40] . The embedded nature of these asRNAs within the FLC locus makes experimental separation of these events particularly challenging . DRS sheds new light on these complex issues because it can unequivocally score the strand of origin and simultaneously define and quantify multiple sites of RNA 3′ end formation [22] , [23] . DRS analysis provided little evidence of altered asRNA proximal site cleavage between WT and fpa mutants , while increased levels of asRNAs that read-through to cleavage sites antisense to the FLC promoter were clearly detected in fpa mutants . These findings are not easily reconciled with a model that suggests proximal processing of asRNA triggers FLC chromatin silencing [18] . Clearly , much remains to be explained about FLC regulation and , while the DRS data might restrict some models of interpretation , greater sequencing depth and characterisation of further RNA species are also required . For example , it is unclear whether RNAs engaged in R-loops at this locus ( within a region that overlaps the proximally polyadenylated FLC asRNAs ) [41] would be detected by DRS . A second proposed function for FPA is in RNA-mediated chromatin silencing [21] . Our DRS data , together with recently reported DNA methylation data [27] , show that FPA does not play a widespread role in RNA-mediated chromatin silencing at RdDM sites . This clarification is important because a perceived role in RNA silencing has influenced interpretations of how FPA might function . Our data do not explain why FPA was identified in a mutant screen for factors required for RNA silencing [21] . We could show that a novel asRNA , which may be generated by defective termination in fpa mutants , was not obligatorily associated with reduced sense strand expression , but we also identified cases in which increased cleavage and polyadenylation antisense to promoter regions in fpa-7 mutants was associated with either increased or reduced sense strand expression . Therefore , one explanation for the identification of FPA as a factor required for RNA silencing may be that defective 3′ end formation , arising either within the transgenes used for the screen or at endogenous genes close to the site of transgene insertion , affects the efficacy of the silencing trigger from the transgene itself [21] . We did identify misregulation of psORF , a recently acquired pseudogene [28] , [29] , in one fpa mutant allele . The silencing of such recently duplicated sequences in different backgrounds is poorly studied , but recent work suggests that epialleles may exist in either different accessions or different generations . In other words , de novo originated genes might be prone to epigenetic variation in the early stages of their formation [30] . Since we only detected misregulation of psORF in fpa-7 and not in fpa-8 , and also did not detect misregulation of asRNAs at the newly acquired At1TE93275 transposable element in this fpa-7 dataset , it is unclear whether FPA is genuinely involved in the control of such newly arisen sequences . Besides addressing specific biological roles of FPA in A . thaliana , we also asked whether this analysis could reveal generic consequences of defective RNA 3′ end formation . We detected changes in intronic cleavage sites , intergenic read-through and chimeric RNA formation , thus defining a range of events that inform future analyses of the impact of such regulators in other species . These findings also have broader implications because they suggest how 3′ end formation might affect the evolution of gene structure and gene order through regulated 3′UTR sequences , new exon combinations or de novo evolution of new genes . We ( and others [31] ) discovered that intergenic read-through to new poly ( A ) sites in fpa mutants is often associated with cryptic splicing events , consequently generating mRNAs with a diversity of potential 3′UTRs . Furthermore , in some cases read-through was associated with cryptic splicing events that disrupted upstream protein-coding exons , resulting in an impact on protein function that may be more immediately obvious . The strong intergenic poly ( A ) signals ( activated here in fpa mutants ) may derive from genuine alternative polyadenylation , unannotated genes or transposons or may instead reflect the evolution of high quality intergenic poly ( A ) sites to trap runaway read-through RNAs . Such read-through to intergenic poly ( A ) signals may contribute to the de novo evolution of new or orphan genes from non-coding genomic regions [42] . One strikingly clear shift in poly ( A ) site choice was detected at IBM1 . Alternative polyadenylation of IBM1 pre-mRNA controls the activity of the histone demethylase encoded by this gene [33] . IBM1 poly ( A ) site choice is also dependent on DNA methylation: the IBM1 intronic DNA sequence downstream of the proximal poly ( A ) site is heavily methylated and alternative polyadenylation is profoundly altered in met1 and cmt3 mutants [33] . Relatively little is known about the impact of DNA methylation and intragenic heterochromatin on co-transcriptional pre-mRNA processing , but alternative splicing was recently shown to be affected by DNA methylation [43] . In addition , alternative polyadenylation of the imprinted H13 locus in mice involves interplay between two poly ( A ) sites and DNA methylation at an internal promoter that separates them [44] . How directly involved DNA methylation and FPA are in poly ( A ) site choice at IBM1 still needs to be established . However , the opposing alternative polyadenylation phenotypes of met1 and cmt3 , on the one hand , and fpa mutants , on the other , suggest that A . thaliana IBM1 could be a genetically tractable model system to dissect the interplay between DNA methylation , intragenic heterochromatin and poly ( A ) site selection . It will be interesting to determine whether other FPA-dependent alternative polyadenylation events are also related to DNA methylation . It may be relevant that methyl-CpG binding domain protein 9 ( MDB9 ) binds to regions of FLC chromatin coincident with the sites of alternative polyadenylation of FLC asRNAs and influences FLC expression and DNA methylation [45] . A striking consequence of read-through we discovered in fpa mutants was the formation of chimeric RNAs . Conceptually related chimeric RNAs have previously been discovered [8]–[10] , but no specific trans-acting factor mediating their formation has been found . Therefore , one of the advances we make here is to reveal that a consequence of losing regulated 3′ end formation can be the transcription of specific chimeric RNAs . We do not yet know whether FPA-dependent chimeric RNAs are regulated in vivo or are simply the consequence of loss-of-function in fpa mutants . Regardless , chimeric RNAs have been overlooked in genome-wide studies of 3′ end formation until now [46]–[48] . However , the formation of chimeric RNAs may account for phenotypes resulting from defects in mediators of this process or be a consequence of global changes in poly ( A ) site choice that distinguish quiescent from proliferating cells [49] , [50] . Since chimeric RNAs bring together different exon combinations , they have the potential to generate novel biological functions . Consistent with this idea , specific chimeric RNAs are conserved in different vertebrates [10] . An additional function or consequence of chimeric RNA formation that we detected here may be transient silencing of downstream genes by interference with transcription [51] or pre-mRNA processing . By discovering a trans-acting factor that affects specific chimeric RNAs , it should now also become possible to design switches that reversibly convert two genes into one in a controllable way and that may have biotechnological applications . Although it is clear that chimeric RNAs were formed at only a limited number of sites in fpa mutants , it is likely that our algorithm identifying reciprocal changes in expression at tandem protein-coding genes underestimated the number of potential chimeric RNAs . For example , Pol II read-through could result in complex transcripts comprised of more than two genes ( including non-protein-coding RNAs ) , which terminate at a site other than the 3′ end of an annotated protein-coding gene . Indeed , a transcript previously detected in fpa fca double mutants [31] , which can also be described as a chimeric RNA , results from read-through at the 3′ end of At1g55805 into At1g55800 and is cleaved within a downstream intergenic space . A clear molecular phenotype associated with loss-of-function fpa mutants is the stable accumulation of RNAs that are cleaved and polyadenylated in intergenic regions , revealing that FPA ultimately promotes transcription termination . Our analysis of these read-through events led us to make the unexpected discovery that FPA and DCL proteins share termination targets . DCL4 , an RNase III-like protein previously established to function in processing small RNAs , was recently also shown to function in transcription termination at the FCA locus [38] . One interpretation of these data is that DCL4 mediates ‘failsafe’ termination by cleaving RNA downstream of the poly ( A ) site ( s ) at potential CoTC sites and thus facilitates access of the 5′–3′ exonuclease that disrupts the transcribing Pol II . We discovered the same read-through events at the FCA locus in fpa mutants , revealing that termination at this locus requires not only DCL4 but also FPA . Since FPA does not affect DCL4 expression , this suggests that termination at certain loci requires multiple , specific trans-acting factors in addition to the torpedo exonuclease and the cleavage and polyadenylation machinery [3] , [52] , [53] . We discovered that the connection between FPA , DCL proteins and termination was not limited to DCL4 , since DCL1 may ultimately affect termination at some loci that are also regulated by FPA . Notably , we detected read-through downstream of genes with validated DCL1-dependent antisense nat-siRNAs [24] . DCL proteins may directly cleave nascent RNA to mediate termination , as recently suggested [38] ( with small RNAs such as nat-siRNAs simply being the by-products of this cleavage ) , or may cleave antisense RNAs and thus generate siRNAs that guide subsequent nascent sense strand cleavage and Pol II termination by Argonaute proteins . Notably , read-through at the FCA locus in dc14 mutants also occurs antisense to an overlapping gene , At4g16270 [38] . Here we clarify the suggestion that nat-siRNAs function in trans to silence At1g51402 gene expression [24] by revealing that defective termination at the upstream gene in dc11 mutants can explain these findings . This set of discoveries raises the general question of whether some aspects of dcl mutant phenotypes are a direct consequence of defects in Pol II transcription termination . By focusing our analysis on polyadenylated RNA 3′ ends , we set out to investigate the generic consequences of disrupting a regulator of alternative polyadenylation . We have documented examples of such alterations here , but it will now be interesting to study the mechanism by which FPA mediates this control . Crucial to this is the identification of the immediate , not simply ultimate , targets of FPA function . Furthermore , sequencing RNA from individual cells of different tissues and across circadian rhythms of expression is likely to be required for the comprehensive identification of transcripts affected by FPA . We recently proposed that the 3′ ends of thousands of A . thaliana genes require re-annotation [22] . The more in-depth DRS data we present here reinforces this finding . Although we developed an algorithm designed to assign DRS reads to incompletely annotated gene models , this automated approach used in isolation is not meant to be definitive [22] . Ultimately , the analysis of gene expression would benefit from the development of a technology that directly sequences entire RNA molecules at depth . In the current absence of such a technique , it is likely that accurately revising A . thaliana genome annotation will require diverse datasets that include DRS and , for example , RNA-seq and chromatin modification data . The meta-analysis of data from these different technologies nevertheless requires appropriate alignment software specific for each sequencing technology . Single molecule sequencing is more susceptible to mismatches and deletions and so requires indel-aware alignment software . A recent analysis of DRS data is fundamentally flawed because it used a version of the Bowtie alignment software with parameters that are wholly inappropriate for DRS data analysis [54] . Although global analyses of pre-mRNA processing require an improved understanding and annotation of the A . thaliana transcriptome , it is likely that de novo annotation of control transcriptomes prepared in parallel , rather than improved genome annotation alone , will be required to comprehensively analyse changes in the sequences and levels of RNA molecules resulting from altered RNA processing .
The T-DNA insertion line fpa-7 ( SALK_021959 ) and the flc-3 mutant were provided by R . Amasino ( Madison ) . The fpa-8 mutant ( induced by EMS and containing a point mutation leading to a premature stop codon ) and fy-2 were provided by C . Dean ( John Innes Centre ) , and fve-3 was provided by J . Martínez-Zapater ( Madrid ) . The WT strain Col-0 and the T-DNA insertion lines upf1–5 ( SALK_112922 ) , sr45–1 ( SALK_004132 ) and flk-1 ( SALK_007750 ) , as well as ld-1 , were obtained from NASC ( UK ) . fld-3 and ref6–3 were provided by S . Michaels ( Indiana University ) and Y . S . Noh ( Seoul National University ) , respectively . A . thaliana WT Col-0 , fpa-7 and fpa-8 mutant seeds were sown in MS10 plates , stratified for 2 days at 4°C and germinated in a controlled environment at a constant temperature of 24°C under 16 h light/8 h dark conditions . Seedlings were harvested 14 days after transfer to 24°C . Samples were prepared for DRS as described previously [22] and RNA gel blot analyses were carried out as described [14] . For RT-PCR and RT-qPCR , RNA was isolated using TRI Reagent ( Sigma-Aldrich ) followed by DNase I treatment ( Invitrogen ) , and reverse transcription was primed with oligo ( dT ) 15 using M-MLV reverse transcriptase ( Promega ) . RT-qPCR was carried out as previously described [14] . For validation of read-through transcripts , fragments were cloned into the pGEM-T Easy ( Promega ) vector and then sequenced . 5′RACE was performed on 250 ng of poly ( A ) + RNA from WT and fpa-8 backgrounds using the FirstChoice RLM-RACE Kit ( Ambion ) , with or without TAP treatment , according to the manufacturer's instructions . Fragments obtained by 5′RACE were cloned into the pGEM-T Easy ( Promega ) vector and then sequenced . Sequencing datasets described in this study have been deposited at the European Nucleotide Archive ( ENA ) : Study , PRJEB3993; accession no , ERP003245 . Raw DRS sequences were aligned using open source HeliSphere software ( version 1 . 1 . 498 . 63 , available free from http://sourceforge . net/projects/openhelisphere/files/helisphere/ ) . The indexDPgenomic aligner was run with the following parameters: seed_size = 18; num_errors = 1; weight = 16; best_only = 1; max_hit_duplication = 25; percent_error = 0 . 2; read_step = 4; min_norm_score = 4 . 2; and strands = both . We discarded globally non-unique alignments and selected one alignment randomly if there were several non-unique local alignments mapped to a genetic region . Reads with more than four indels were deleted , and read alignments were refined using an iterative multiple alignment procedure . DRS reads containing low complexity genomic regions , identified by DustMasker from the Blast+ 2 . 2 . 24 package , were discarded , as previously described [22] . In order to compare WT and fpa mutant data , we used read-per-million ( RPM ) normalisation , i . e . each DRS read was assigned a weight in such a way that the sum of all weights in a sample condition was equal to 1×106 . We called the weights ‘normalised reads’ . All images of normalised read alignments were made using the Integrated Genome Browser [55] and correspond to combined reads from the three sequenced biological replicates for each genotype . An algorithm that accommodates intergenic DRS reads in automated gene re-annotation was carried out as previously described [22] , but based on the WT data described in this study . The DESeq ( version 1 . 8 . 2 ) package [25] was used to search for differentially expressed ( DE ) genes and poly ( A ) peaks . DESeq estimates the variance of expression levels for a set of genomic features ( genes , intergenic regions or poly ( A ) peaks ) based on read count data within the features in several biological replicates from two different genotypes . This package then calculates P values for the features to be non-DE based on the hypothesis that replicated expression levels of the features are distributed according to negative binomial distribution . These P values were adjusted using Benjamini-Hochberg multiple testing corrections [56] . We prepared raw un-normalised DRS read counts for all replicates of the two genotypes . In order to curb uncertainty due to low read counts , we set a minimal read count per replicate of 15 raw reads for protein-coding genes and transposons , 11 raw reads for intergenic regions , and 8 raw reads for poly ( A ) peaks . Reads with counts lower than the limit in different replicates were excluded from the DE analyses . These cutoffs were established by maximising the number of DE features identified between WT and fpa-7 . Counts of DRS reads mapping to re-annotated protein-coding genes were prepared for three replicates of each genotype ( WT and fpa ) . A total of 18 , 406 protein-coding genes were identified . Once the requirement of a minimum raw read count of 15 was applied to all replicates , 15 , 081 protein-coding genes remained for the DE analysis . Table S2 summarises normalised mean expression values for both genotypes , as well as fold change and P values calculated by DESeq for each coding gene with a P value of <0 . 01 to be considered as differentially expressed . Table S3 shows the same data for transposons and transposable gene elements . We defined promoter regions as the 2 Kb regions upstream of the transcription start site of protein-coding genes . The analysis was based on prepared poly ( A ) peaks for intergenic read-through analysis and our list of DE coding genes ( Table S2 ) . P values between WT and fpa were calculated for the regions antisense to DE gene promoter , with at least 11 raw reads in a replicate . So-called promoter regions with P values<0 . 05 in the fpa mutants were classified as DE genes with antisense transcripts that overlap with their promoters . Uniquely aligned and filtered reads and re-annotated coding genes were used for this analysis . We prepared a list of neighbouring genes with normalised read counts for every gene for both genotypes ( WT and fpa ) . We selected gene pairs with expression levels higher than 0 . 5 RPM ( this corresponds to roughly 11–12 raw reads within a gene ) for both genes in the pair and for both genotypes . Pairs with a DESeq P value of <0 . 01 for which the upstream gene was down-regulated and the downstream gene was up-regulated in the fpa mutant were considered as candidates for chimeric RNA formation . We prepared a dataset of poly ( A ) sites , corresponding to the 3′ ends of aligned and filtered DRS reads , by applying smoothing and peak-finding algorithms as previously described [22] . We defined intergenic regions as regions between protein-coding genes and excluded genomic regions of transposable gene elements , pseudogenes and non-coding genes . The analysis was based on uniquely aligned , filtered and smoothed poly ( A ) sites and our re-annotation of coding genes . We separately prepared poly ( A ) peaks for each replicate , clustered poly ( A ) peaks within a 4-bp window and selected poly ( A ) peaks with at least 0 . 5 RPM expression . We removed poly ( A ) peaks within protein-coding genes , transposable gene elements , pseudogenes and non-coding genes . Since we did not re-annotate the transposable gene elements , pseudogenes and non-coding genes , we also excluded poly ( A ) peaks within 50 bp downstream of the 3′ ends of these genomic features . We assigned two parameters to every intergenic region: the total sum of poly ( A ) peaks within the region ( and for each replicate ) ; and the position of the centroid of the poly ( A ) peaks in a region as Pcentroid = ΣEiPi/ΣEi , where Ei is the expression level of ith poly ( A ) peak within each region and Pi is the genomic coordinate of the peak . P values between WT and fpa were calculated for intergenic regions with at least 11 raw reads in a replicate . At the final step , intergenic regions with P values<0 . 05 and centroid positions not within 30 bp downstream of the coding gene 3′ end in the fpa mutants were classified as DE . The 30-bp offset corresponds to the median length of DRS reads; by applying this additional filter we therefore excluded situations where intergenic reads were mainly grouped near the 3′ end of the upstream gene and hence may belong to the gene . In this analysis , we compared expression levels of individual poly ( A ) peaks in the two genotypes rather than the total reads aligned to a gene . The comparison of peaks is complicated by over-calling peaks ( i . e . peaks being identified where there should be no peak ) and the fact that the centre of biologically equivalent peaks may be called in slightly different positions in different genotypes as a consequence of the varied read depth and the peak-calling algorithm . Figure S12 summarises the process we developed to overcome these issues and systematically identify equivalent peaks between WT and fpa-7 . Having identified peak pairs , we applied DESeq to all pairs with at least 8 raw reads in a replicate . This gave 86 , 699 poly ( A ) peak pairs . Peak pairs with a P value of <0 . 01 were considered to be DE in the fpa-7 mutant . These comprised 6% of the selected poly ( A ) peaks: 2 , 538 down-regulated and 2 , 671 up-regulated . Due to the data partition between two neighbouring peaks in one genotype and/or incorrect peak matching , our algorithms can produce ‘false positives’ . For example , in Figure S13 , if we examine the most highly expressed poly ( A ) peak in the gene , we see there is another poly ( A ) peak downstream of the peak in WT and nothing in fpa-7 . The peak-finding algorithm incorrectly combined poly ( A ) sites belonging to the bump on the right-hand slope of the peak to the most highly expressed poly ( A ) peak in fpa-7 and created a new peak in WT . Since this WT peak was matched to 0 in fpa-7 , it was wrongly classified as DE by DESeq . We therefore designed an algorithm to identify and exclude these ‘false positive’ peaks: if a poly ( A ) peak was up-regulated in one genotype , we looked for poly ( A ) peaks within an 8-bp window in the other genotype; if we found poly ( A ) peaks in the genotype that were not associated with the DE poly ( A ) peak and the summed expression of the peaks was significantly different ( 20% higher than the associated poly ( A ) peak only ) , then we defined this DE poly ( A ) peak as a ‘false positive’ . This led to 37% of down-regulated and 32% of up-regulated DE peaks being excluded from further analysis . We also applied this method to select intronic cleavage sites that were differentially used in fpa-7 compared to WT . We then only considered peaks that comprised >10% of expression at a particular gene and manually excluded sites that mapped to 3′UTR introns . 3′RACE was performed using the FirstChoice RLM-RACE Kit Protocol ( Ambion ) according to the manufacturer's instructions . The reaction was started with 250 ng poly ( A ) + RNA . Multiple PCR products were purified , cloned into the pGEM-T Easy vector ( Promega ) and sequenced . ChIP was performed as previously described [57] . Anti-H3K4me3 monoclonal antibodies were obtained from Diagenode ( MAb-152–050 ) . All primers used in this study are listed in Table S10 . | The ends of almost all eukaryotic protein-coding genes are defined by a poly ( A ) signal . When genes are transcribed into mRNA by RNA polymerase II , the poly ( A ) signal guides cleavage of the precursor mRNA at a particular site; this is accompanied by the addition of a poly ( A ) tail to the mRNA and termination of transcription . Many genes have more than one poly ( A ) signal and the regulated choice of which to select can effectively determine what the gene will code for , how the gene can be regulated and where transcription termination occurs . We discovered a rare example of a regulator of poly ( A ) site choice , called FPA , while studying flower development in the model plant Arabidopsis thaliana . Studying FPA therefore provides an opportunity to understand not only its roles in plant biology but also the generic consequences of disrupting alternative polyadenylation . In this study , we use a technique called direct RNA sequencing to quantify genome-wide shifts in poly ( A ) site selection in plants that lack FPA function . One of our most striking findings is that in the absence of FPA we detect chimeric RNAs formed between two otherwise separate and well-characterised genes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Transcription Termination and Chimeric RNA Formation Controlled by Arabidopsis thaliana FPA |
The control of seed germination and seed dormancy are critical for the successful propagation of plant species , and are important agricultural traits . Seed germination is tightly controlled by the balance of gibberellin ( GA ) and abscisic acid ( ABA ) , and is influenced by environmental factors . The COP9 Signalosome ( CSN ) is a conserved multi-subunit protein complex that is best known as a regulator of the Cullin-RING family of ubiquitin E3 ligases ( CRLs ) . Multiple viable mutants of the CSN showed poor germination , except for csn5b-1 . Detailed analyses showed that csn1-10 has a stronger seed dormancy , while csn5a-1 mutants exhibit retarded seed germination in addition to hyperdormancy . Both csn5a-1 and csn1-10 plants show defects in the timely removal of the germination inhibitors: RGL2 , a repressor of GA signaling , and ABI5 , an effector of ABA responses . We provide genetic evidence to demonstrate that the germination phenotype of csn1-10 is caused by over-accumulation of RGL2 , a substrate of the SCF ( CRL1 ) ubiquitin E3 ligase , while the csn5a-1 phenotype is caused by over-accumulation of RGL2 as well as ABI5 . The genetic data are consistent with the hypothesis that CSN5A regulates ABI5 by a mechanism that may not involve CSN1 . Transcriptome analyses suggest that CSN1 has a more prominent role than CSN5A during seed maturation , but CSN5A plays a more important role than CSN1 during seed germination , further supporting the functional distinction of these two CSN genes . Our study delineates the molecular targets of the CSN complex in seed germination , and reveals that CSN5 has additional functions in regulating ABI5 , thus the ABA signaling pathway .
Seed germination launches the active growth phase of a plant , while seed dormancy prevents germination even under optimal growth conditions . The decision and the processes of seed germination are modulated by many factors but predominantly by gibberellin ( GA ) and abscisic acid ( ABA ) , two phytohormones which act antagonistically on seed germination [1 , 2] . ABA levels become elevated during seed maturation to establish and maintain seed dormancy , and its levels drop sharply upon imbibition of seeds . On the other hand , GA biosynthesis starts upon seed imbibition , and GA is necessary to release seed dormancy and stimulate germination [3] . In Arabidopsis , the GA biosynthetic mutant ga1-3 cannot germinate without an exogenous supply of GA , demonstrating the necessity of GA in seed germination [4 , 5] . Various environmental factors such as light , moisture , temperature , and nutrients ( e . g . nitrate ) can affect germination both during seed maturation and during seed imbibition . Those environmental factors modulate germination in a large part through altering the levels of GA and ABA [6–8] . In the laboratory , seed dormancy is released by a period of dry storage ( termed after-ripening ) or by cold stratification . The GA response pathway is negatively controlled by the DELLA proteins , consisting of five members in Arabidopsis: GA-INSENSITIVE ( GAI ) , REPRESSOR OF ga1-3 ( RGA ) , RGA-LIKE1 ( RGL1 ) , RGA-LIKE2 ( RGL2 ) , and RGA-LIKE3 ( RGL3 ) [9] . In response to GA , the DELLA proteins are rapidly degraded by the ubiquitin-proteasome system via SCFSLY1/2 , which results in GA-stimulated growth and development [10 , 11] . Among the DELLA proteins , RGL2 plays a major role as a GA-regulated repressor in seed germination , as rgl2 can rescue the germination defect of ga1-3 in the absence of exogenous GA [12 , 13] . In addition , RGA and GAI , together with PIL5/PIF1 regulate light-mediated control of seed germination [14 , 15] . Under white light , RGL2 plays a predominant role in endosperm tissue , and it also has a central function in the crosstalk with ABA signaling during seed germination [16–18] . ABA induces a number of effectors , including the bZIP transcription factor ABA INSENSITIVE5 ( ABI5 ) . ABI5 accumulates during seed maturation and in dry seeds [19 , 20] . During the normal course of seed germination , ABA and concomitantly ABI5 levels rapidly decline following imbibition and GA biosynthesis , enabling seed germination . ABI5 has been implicated as the final inhibitor of seed germination , possibly acting downstream of the GA repressor RGL2 [16 , 17] . The COP9 signalosome ( CSN ) is a conserved heteromeric protein complex known to regulate the CULLIN-RING family of ubiquitin E3 ligases ( CRLs ) , including the SCF sub-family of E3s [21] . Biochemically , CSN inhibits CRL E3 activity by removing the NEDD8 ( RUB1 ) modification on the CULLIN subunit ( a process known as de-neddylation or de-rubylation ) [22 , 23] , and by direct interaction with the CRL core components [24–26] . However , genetic studies in several organisms including Arabidopsis have shown that CSN promotes the functions of the CRLs [22 , 27] . In a number of cases , CSN activity has been shown to protect components of the CRL E3s against their autoubiquitination activity [28 , 29] . Still , several studies also indicate that not all substrates of CRL and SCF E3s are regulated by CSN , since some of the SCF substrates display normal signal dependent degradation in CSN-deficient cells [30 , 31] . Our understanding of the specific roles of the CSN in SCF-mediated substrate ubiquitination remains incomplete . In Arabidopsis where the CSN was initially identified , complete loss of any one subunit destabilizes the entire complex [32] . As a result , all of the null csn mutants exhibit characteristic purple seeds ( the fusca phenotype ) and developmental arrest soon after germination [33 , 34] . Since CSN5 and CSN6 are each encoded by two functionally redundant genes , CSN5A vs . CSN5B , and CSN6A vs . CSN6B , respectively , a null mutation in either of the CSN5 or CSN6 genes are viable , while knocking out both genes of either CSN5 ( csn5a-1 5b-1 ) or CSN6 ( csn6a-1 6b-1 ) lead to the lethal fusca phenotype , like that of other csn null mutants [35–37] . Studies using both lethal and weak mutants of the CSN have shown that the CSN is involved in multifaceted developmental processes and physiological responses . For examples , CSN works with SCFTIR1 in auxin responses , with SCFUFO in flower development , and with SCFCOI1 in JA responses . CSN also affects GA signaling , cell divisions , stem cell functions , root patterning , and defenses [22 , 27 , 38–42] . However , in many of these cases , the specific substrates of the SCF for the corresponding process have not been clearly identified . It has been observed that lethal mutants of the CSN require extended cold stratification to germinate . The precise germination rates of the lethal mutants were difficult to measure , because the mutants can only be maintained as a heterozygous population . Weak mutants such as csn5a-2 also show defects in germination [39 , 43] . However , the specific targets and the mechanisms underlying this phenotype remain obscure . In this study , we carried out a systematic study of the germination and dormancy phenotypes using viable csn mutants , including csn5a-1 and csn1-10 . We demonstrate here that CSN regulates seed germination by modulating the levels of RGL2 and ABI5 in the GA and ABA pathways , respectively .
In recent years , the availability of viable and fertile mutants of Arabidopsis CSN , which can produce homozygous mutant seeds , has provided a feasible genetic material for a systematic analysis of the role of the CSN in seed germination . First , we conducted a basic molecular comparison of a number of viable csn mutants for the levels of CSN subunits in plants ( Fig 1 and S1 Fig ) . csn5a-1 and csn5b-1 are null mutants of the respective genes , while csn5a-2 is a weak allele of CSN5A [37 , 44] . The CSN5A gene is considered to predominate over CSN5B , based on multiple microarray datasets [36] as well as the observation that csn5a-1 mutants showed a more severe growth defect than those of csn5b-1 mutants [35 , 37] ( S1A Fig ) . The anti-CSN5A antibody could readily detect endogenous CSN5A , but not CSN5B ( Fig 1A and S1B Fig ) , while the anti-CSN5B antibody could detect both CSN5A and CSN5B , and showed that endogenous CSN5B proteins migrate more slowly than CSN5A in SDS-PAGE ( Fig 1B ) . It should be mentioned that under our growth conditions , csn5a-2 plants can tolerate a considerable reduction of CSN5A levels ( S1B Fig ) , without showing obvious growth defects , although the same csn5a-2 allele was previously reported to show a noticeable growth defect [35 , 37] . For the other viable mutants used , csn1-10 , which contains a point mutation that reduces the level of CSN1 expression [45] , exhibited a clear growth defect , while csn3-3 [46] and csn2-5 [Landsberg erecta ( Ler ) background] [47] appeared to be phenotypically similar to the respective wild type plants ( S1A Fig ) . In Arabidopsis , CSN subunits are stabilized through assembly of the complex [32 , 35 , 48] . Immunoblot analyses showed that this group of viable csn mutants contained variably lowered protein levels of other CSN subunits ( Fig 1 and S1 Fig ) . In particular , csn5a-1 and csn1-10 displayed the most noticeable reductions in the steady state level of CSN3 and CSN5 ( Fig 1 ) , suggesting that the level of the CSN complex was lower in these mutants . Consistent with previous reports , csn5a-1 , csn5a-2 , csn1-10 , and csn2-5 , but not csn5b-1 or csn3-3 , caused hyperneddylation of CUL1 , indicating that these mutants had lower CSN-mediated deneddylation activity ( Fig 1 ) . Therefore , csn5a-1 , csn5a-2 , csn1-10 , and csn2-5 are definitively partial loss-of-function mutants of the CSN . We examined germination of the csn mutant seeds with or without cold stratification . When Arabidopsis Columbia ( Col ) seeds were cold stratified at 4°C for 3 days or more , germination was accelerated by 12–24 hours over unstratified seeds under our growth and planting conditions ( Fig 2A and 2B ) , indicating that those Col ( wild type ) seeds exhibited a weak dormancy response . Without cold stratification , csn1-10 , csn3-3 , 5a-1 and 5a-2 exhibited noticeably delayed and poor germination compared to Col . csn2-5 also exhibited a slight but consistently poor seed germination compared to the corresponding wild type ( Ler ) . The germination defects could be nearly completely alleviated by cold stratification in csn1-10 , csn3-3 , and csn2-5 seeds ( Fig 2A and 2B ) . However , in csn5a-1 and csn5a-2 , cold stratification could significantly , but not completely , alleviate their germination defects ( Fig 2B , S2 Fig ) . These observations suggest that most of the csn mutants had stronger seed dormancy . The exception was csn5b-1 , which appeared to have a weaker dormancy than Col , as it germinated equally well regardless whether or not the seeds had been cold stratified . Since our medium contained 1% sucrose , we tested mutant germination on plates without sucrose , and found that csn1-10 and csn5a-1 showed similar germination phenotype regardless of whether sucrose was present or not ( S2A Fig ) . To further confirm the seed dormancy phenotype , we examined the germination rates in relation to different after-ripening storage ages of the seeds in csn1-10 , csn5a-1 , and the Col control . As shown in Fig 2C and 2D , freshly collected seeds ( 1 or 2 days after collection ) of either Col or the mutants showed low and heterogeneous germination . While the Col seeds showed significant dormancy-release after two weeks , csn1-10 and csn5a-1 seeds continued to germinate slowly , consistent with the stronger seed dormancy of the mutants . After extended dry storage ( 11 weeks ) , csn1-10 mutants could catch up with Col in germination , but csn5a-1 still displayed a delay in germination compared to Col even in fully after-ripened seeds ( 1-year ) ( Fig 2C and 2D ) . This result , as well as the observation that cold stratification could not fully alleviate the germination defect of the csn5a mutants , suggest that while csn1-10 has a seed hyperdormancy phenotype , csn5a-1 shows a delay in germination in addition to hyperdormancy . As shown in Fig 2D , the germination phenotype of the mutants is best displayed in partially after-ripened seeds , typically between 1–4 weeks after seed collection , although the window of phenotypic alteration varies depending on different batches of seeds . Since most of the csn mutants have been reported to have mild photomorphogenic phenotypes , we tested the response of the mutants to phytochrome B ( phyB ) -controlled seed germination . To distinguish the light response from other aspects of dormancy responses , cold stratified seeds were used . Seeds were then subjected to a 5-min pulse of red ( R ) light , far red ( FR ) light , or alternating R and FR treatments in a sequential order as indicated in S3 Fig . The det1-1 mutant , which lacks a phyB-controlled seed germination response and germinates regardless of light treatments [49] , was used as a control . The data showed clearly that all of the tested csn mutants , including csn1-10 , csn3-3 , csn5a-1 and csn5b-1 , displayed normal responses in phyB-mediated seed germination ( S3 Fig ) . Thus , for the rest of the studies on the mechanisms of the CSN-regulated seed germination , we carried out the experiments under white light . When not specified , un-stratified seeds were used for the germination and biochemical analyses . The seed coat plays an important role in seed dormancy in many plant species including Arabidopsis [50] . The Arabidopsis seed coat consists of an outer layer of maternally derived material known as the testa . Underneath the testa is a single cell layer of endosperm that encloses the embryo . In the hyperdormant ecotypes Cvi and C24 , or in non-germinating ga1-3 mutants , removal of the seed coat can break dormancy and allow the embryo to grow [51] . To determine whether the germination defects in csn1-10 and 5a-1 were imposed by the seed coat , we removed the seed coat by dissection , and observed the growth of the embryos with regard to radicle ( embryonic root ) growth and cotyledon greening and expansion . Without the seed coat , both csn5a-1 ( Fig 3A ) and csn1-10 ( Fig 3B ) mutant embryos displayed radicle growth on day-2 , and exhibited greening and cotyledon expansion on day-3 , similar to the developmental time line of comparably treated Col embryos and seeds . Remarkably , the removal of the seed-coat even rescued the germination of csn1-1 ( or fus6-1 , cop11-1 ) ( Fig 3C ) , the null mutant of csn1 , which were otherwise extremely dormant such that mutant seeds rarely germinate without cold stratification . After germination , csn1-1 mutants arrested further development , and they died as purple seedlings ( Fig 3C bottom right corner panel ) , similar to the final morphology of the seedlings germinated from intact seeds [34] . These results showed that csn1 and csn5a mutant embryos have the intrinsic capacity to initiate growth on a similar time scale to the Col embryos , and that the hyperdormancy phenotype of the mutant seeds is dependent on the seed coat . Seed coat-dependent inhibition of seed germination has been shown to be mediated at least in part by RGL2 in the endosperm , where it stimulates ABA synthesis , and ultimately ABI5 activity [16 , 51] . GA stimulate seed germination by removal of GA inhibitors known as DELLA proteins . Among the five DELLA proteins in Arabidopsis , RGA and GAI function mostly in the dark and in the embryos , while RGL2 is the main inhibitor of seed germination under light [12 , 18] . RGL2 also plays a central role in seed coat-dependent inhibition of seed germination , which likely involves CSN . RGL2 is predominantly regulated at level of protein turnover via SCFSLY1 E3 ligase mediated protein degradation through the ubiquitin-proteasome system [10 , 11] . We examined RGL2 protein levels in the csn mutants by anti-RGL2 immunoblotting in a time course analysis . Both csn1-10 ( Fig 4A ) and csn5a-1 ( Fig 4B ) displayed clear defects in timely degradation of RGL2 . Abnormal accumulation of RGL2 is consistent with the idea that degradation of RGL2 via an SCF E3 ubiquitin ligase requires a fully active CSN complex . Another major inhibitor of seed germination is ABI5 , which accumulates in dry seeds and in response to low GA or high ABA conditions . We next examined ABI5 protein levels by anti-ABI5 immunoblotting over the course of germination . While ABI5 proteins were depleted by day-3 in Col wild type and csn5b-1 after imbibition , they remained high in csn1-10 and csn5a-1 mutants , and showed slower kinetics of the decline in csn5a-2 ( Fig 4C ) . These results implied that csn1 and csn5a mutants are defective in ABI5 downregulation following imbibition of the seeds . Taken together , csn1-10 and csn5a mutants exhibited defects in the timely removal of RGL2 and ABI5 , two key inhibitors of seed germination in Arabidopsis , which correlated with the deficiency of the mutants in their timely germination . If accumulation of RGL2 is responsible for the germination phenotype of the csn mutants , genetic removal of the RGL2 gene should be able to rescue the defects of the csn mutants . To test this , we crossed the csn mutants with the null mutant of RGL2 , rgl2-13 ( Col ) [13] , and generated the csn1-10 rgl2-13 and csn5a-1 rgl2-13 double mutants . Germination tests showed that introduction of rgl2-13 into the csn1-10 mutant effectively rescued its germination phenotype ( Fig 5A ) . Moreover , rgl2-13 could significantly alleviate the poor germination of the csn1-1 lethal allele , enabling the un-stratified csn1-1 rgl2-13 double mutant seeds to germinate , albeit at a slower rate ( S4 Fig ) . In contrast to csn1 , the germination defect of csn5a-1 could not be rescued by rgl2-13 ( Fig 5B ) . The csn5a-1 rgl2-13 double mutant showed a poor germination profile that was similar to that of csn5a-1 ( Fig 5B ) . These results suggest that , while over-accumulation of RGL2 is responsible for the seed hyperdormancy of csn1-10 , additional components may be involved in the germination defects of csn5a-1 . The csn1-10 and csn5a-1 mutants also differ in their responses to low GA conditions induced by PAC ( paclobutrazol ) , an inhibitor of GA biosynthesis . To test the sensitivity to PAC , cold stratified seeds were used to factor out the dormancy effect . Treatment with PAC inhibited germination of wild type seeds , and the inhibition could be reversed by simultaneously supplying GA3 ( PAC + GA , Fig 5C ) . As previously reported [12 , 13] , rgl2-13 was resistant to PAC in germination assays . Both csn1-10 and csn5a-1 were sensitive to PAC as Col , and the germination of Col and csn1-10 , but not of csn5a-1 , could be significantly restored by addition of GA3 ( Fig 5C ) , indicating that csn5a-1 was hypersensitive to PAC-induced low GA condition . We next examined the double mutants for their responses to PAC . As shown in Fig 5D , rgl2 and csn1 rgl2 mutants were able to germinate well in PAC , indicating that removal of RGL2 is sufficient to overcome the effect of PAC even when CSN1 is deficient . This result is in agreement with the notion that the main function of CSN1 in germination is to modulate RGL2 turnover . However , the rgl2 mutation had only a slight effect on overcoming PAC treatment in the background of csn5a-1 , as the germination rates of csn5a rgl2 double mutants remained very low in PAC ( Fig 5D ) , suggesting that removal of RGL2 was insufficient to allow germination in the absence of CSN5A . Thus , it appeared that an additional factor ( s ) apart from RGL2 was affected by csn5a-1 , that prevented timely germination or for germinating under low GA conditions . Time-course immunoblotting showed that , as in the csn5a-1 single mutant , high levels of ABI5 still accumulated in csn5a-1 rgl2-13 on day-4 post imbibition ( Fig 5E ) . This result pointed to ABI5 as a factor that is potentially regulated by CSN5A . ABI5 is thought to act downstream of RGL2 to inhibit seed germination under unfavorable environments [16 , 17 , 52] . To address whether the characteristic over-accumulation of ABI5 protein in the csn mutants was responsible for their germination phenotypes , double mutants of abi5-4 with csn1-10 or csn5a were generated . Since the abi5-4 mutant is in the Wassilewskija ( Ws ) ecotype [20] while csn5a-1 and csn1-10 mutants are in the Col background , we used segregating sibling lines of different genotypes in all of the germination comparisons , including WT , csn1-10 , and csn5a-1 , abi5-4 , and the double mutants ( Fig 6 ) , in which “WT”was a segregating sibling line with a wild type genotype . Clearly , abi5-4 rescued the germination phenotype of both csn1-10 and csn5a-1 , as demonstrated by strongly improved germination in the respective double mutants , i . e . csn1-10 abi5-4 ( Fig 6A ) , or csn5a-1 abi5-4 ( Fig 6B ) compared to csn1-10 or csn5a-1 , respectively . In addition , the double mutant of abi5-1 csn5a-2 also rescued the slow germination of csn5a-2 , a weaker mutant of csn5a ( S5 Fig ) . The finding that abi5-4 can rescue the germination phenotype of csn1-10 , or its over-accumulation of RGL2 , is in agreement with the report that the abi5 mutant can suppress the RGL2-mediated block of germination such as in PAC treatment [16] . These data showed that ABI5 is epistatic to CSN1 in the same pathway during seed germination , and that accumulation of ABI5 is responsible for the germination defects of csn5a-1 . Associated with the rescue in germination , time-course immunoblots showed , somewhat surprisingly , that timely removal of RGL2 has also been restored in the double mutants of csn1-10 abi5-4 as well as csn5a-1 abi5-4 following germination ( Fig 6C and 6D ) . Although the mechanism is unclear as to how RGL2 turnover can be rescued in the absence of ABI5 , these results are in agreement with previous reports that low ABI5 is associated with germination in low GA conditions ( for example treatment of PAC ) [16] . Together , our data further reinforce the notion that ABI5 functions downstream of RGL2 in seed germination , and that proper regulation of both germination inhibitors requires CSN5A . To understand the transcriptional changes in the csn mutants during germination , we conducted a transcriptome analysis on dry seeds and 2-day imbibed seeds of csn5a-1 ( or 5a-1 ) and csn1-10 along with the Col control . We determined the number of genes whose expression was significantly changed ( SSTF , statistically significant two-fold change ) in csn1-10 or csn5a-1 compared to the Col controls in dry seeds ( Fig 7A ) or in 2-day imbibed seeds ( Fig 7B ) . csn1-10 affected more genes ( 919 SSTF genes ) than csn5a-1 ( 644 SSTF genes ) in dry seeds , suggesting that CSN1 plays a greater role than CSN5A in seed maturation . However , upon seed imbibition for two days , we observed a robust expansion in the number of genes whose expression was affected by the csn5a-1 mutation ( 1502 SSTF genes ) in comparison to those affected by csn1-10 ( 640 SSTF genes ) . This may indicate that CSN5A has more critical functions than CSN1 during germination and the active growth phase of Arabidopsis . This idea is also consistent with the expression value profiling analyses shown in heat-map plots ( S6 Fig ) . In dry seeds , csn1-10 displayed greater abnormalities than csn5a-1 in overall expression profiles when compared to that of Col , but in 2-day imbibed seeds , the profile of csn5a-1 altered from that of Col more than csn1-10 did . Also , csn5a-1 exhibited an expression profile that appeared to have departed further from the dry seeds ( S6 Fig ) . The top GO genes that are down-regulated in Col 2-day imbibed seeds compared to the dry seeds are genes responsive to various stimuli , and approximately the same GO catagory genes were further down-regulated in csn5a-1 at the 2-day timepoint ( S1 Table ) . csn1-10 , on the other hand , did not show strongly enriched GO groups at the 2-day timepoint according to the p-values , but in dry seeds , it showed strong misregulation of temperature responsive genes ( S1 Table ) . The transcriptome results showed that , from dry seeds to the 2-day time-point , several GA biosynthetic genes were strongly induced in Col , and ABA biosynthetic genes appeared to be active in dry and 2-day imbibed seeds ( S7 Fig ) . However , we were unable to find drastic differences in the mutants . We also examined a panel of genes that have been shown to regulate dormancy and seed germination , but the csn mutants exhibited a similar pattern of expression changes as Col ( S7C Fig ) . Since the seed germination conditions used for transcriptome analysis were not identical to that used for the germination tests , the kinetics of germination could differ slightly . We thus conducted qRT-PCR analysis of several ABA-related genes using the material from our standard germination testing conditions . At day2 and day3 post-imbibition , the ABA biosynthetic gene AAO1 appeared to be expressed at higher levels in csn1-10 , while the ABA catabolic gene CYP707A1 , whose expression rose following seed imbibition , appeared to be lower in csn5a-1 ( Fig 7B ) . The ABI5 transcript was significantly down-regulated from dry seeds after 2days imbibition in Col as well as in both csn mutants ( S7C Fig ) . On day2 and day3 post seed imbibition , the levels of ABI5 were still moderately higher in both csn mutants compared to Col ( Fig 7B ) , although not as substantial as the difference in its protein accumulation . We tested the sensitivity of the mutants to exogenous ABA during germination . On ABA-containing plates , both csn5a1 and csn1-10 were more sensitive to ABA than Col ( Fig 7C ) . In addition , the double mutant csn5a-1 rgl2-13 was hyper-sensitive to ABA , whereas csn5a-1 abi5-4 was hypo-sensitive to ABA . In a positive correlation , the mutants that were hypersensitive to ABA also showed abnormal accumulation of ABI5 ( Fig 4 and Fig 5E ) . We were interested in the causes of the sustained ABI5 accumulation , a key inhibitor that has prevented timely germination of both csn1-10 and csn5a-1 mutants . It has been shown that accumulation of RGL2 during germination can drive ABA biosynthesis , which would induce ABI5 expression and inhibit germination [16] . To investigate whether de novo ABA synthesis is required for the germination phenotypes of the csn mutants , we applied ABA biosynthetic inhibitor norflurazon to test its effect on germination . As shown in Fig 7D , norflurazon treatment nearly completely restored the germination of csn1-10 . While it considerably alleviated the germination defects of csn5a-1 , norflurazon treatment could not rescue the delayed germination of csn5a-1 ( Fig 7D ) . This result shows that csn1-10-induced hyperdormancy requires ABA synthesis after seed imbibition , which suggest that the extended ABI5 accumulation in csn1-10 likely resulted from RGL2-induced ABA biosynthesis . By contrast , csn5a-1 appeared to cause a defect downstream of ABA synthesis , which would support a role of CSN5A in facilitating ABI5 protein degradation following seed imbibition . How CSN5A regulates ABI5 protein degradation is unclear . To this end , we found that CSN5A , or CSN5B , can interact with ABI5 in a yeast-two-hybrid assay ( Fig 7E ) . The precise mechanism by which CSN5A regulates ABI5 will be a challenge for future studies . Taken together , we propose a model illustrating the hierarchical functions of the CSN in seed germination ( Fig 8 ) . CSN1 and CSN5A , as parts of the CSN complex that regulates SCF ubiquitin E3s , are necessary for timely degradation of DELLA inhibitors , mainly RGL2 . RGL2 inhibits germination by inducing ABA synthesis and promoting ABI5 expression , based on previous reports . CSN5A additionally plays a role in the timely removal of ABI5 proteins to facilitate seed germination .
The CSN has been shown to regulate SCF family of ubiquitin E3 ligases . In fact , csn1-10 and csn3-3 were isolated as enhancers of tir1-1 , a mutation of the auxin receptor that acts as the F-box component of SCFTIR1 [45 , 46] . Thus , the CSN genetically promotes the function of SCFTIR1 in mediating auxin responses . Similarly , a csn1 hypomorphic line showed genetic interactions with F-box proteins UFO and COI1 overexpression lines , supporting the role of the CSN in facilitating the functions of the corresponding SCF E3s [27 , 40] . Moreover , csn5a and several lethal csn mutants were shown to abnormally accumulate RGA , a DELLA protein and a substrate of SCFSLY1/2 that has an important role in seedling development [43] . In the same report , the germination defect of the csn mutants were described , but the molecular targets responsible for their germination phenotypes were not determined . In this study , we show that the inability to timely degrade RGL2 , a key inhibitor of germination for dormancy response and a substrate of SCFSLY1/2 , can fully account for the hyper-dormant phenotype of csn1-10 . Considering what is known about the function of the CSN , we suggest that CSN1 , or the CSN complex , most likely plays an important role in SCFSLY1/2-mediated RGL2 ubiquitination during the course of seed germination ( Fig 8 ) . In Arabidopsis , complete loss of any one of the eight canonical subunits of the CSN leads to the destruction of the complex [32] . This explains why null mutants of any one of the CSN subunits all cause early seedling lethality , with a similar “fusca” phenotype . Probably as a consequence of this similarity , all CSN subunits have largely been indiscriminately viewed to have more or less same physiological functions in plants . In a number of other species , differences in phenotypes and functions for different subunits of the CSN have been observed [53] . In fungi , deletion mutants of different CSN subunits cause distinct phenotypes [54 , 55] . In mammalian cells , knock-down of CSN3 or CSN8 in cultured cells can accelerate cell proliferation [56 , 57] , whereas knock-down of CSN5 decreases cell proliferation and causes cell senescence [57 , 58] . The human CSN5 has been extensively studied due to its association with many types of cancers [59] . Originally isolated in human cells as a coactivator of c-Jun , Jab1 ( c-Jun activation domain-binding protein 1 ) [60] , CSN5/JAB1 has been reported to bind to an array of transcription factors or other critical cellular regulators to stabilize the target protein or to facilitate its degradation . As an integral subunit of the CSN complex , CSN5 carries the catalytic center of the CSN deneddylase that is active only when assembled into the complex [61] . It remains unclear how the regulatory activities of CSN5 on its binding partners are related to the role of CSN5 in deneddylation of the CRL E3 ligases . Notably , in a highly conserved manner from yeast to plants and mammals , CSN5 has been found to exist both as part of the holo-CSN complex and in a free form unbound to the CSN [53] . Studies in mammalian systems have shown that CSN5 has functions independent of the CSN holocomplex [58] . Whether the Arabidopsis CSN5 , like its mammalian counterpart , has functions apart from that of the CSN complex , has not been reported . In Arabidopsis , csn1-10 and csn5a-1 both exhibit poor germination . However , careful analyses indicate that the two mutants differ in several aspects . First , csn1-10 shows deeper seed dormancy , such that its germination can be restored by cold stratification or extended after-ripening period , whereas csn5a-1 additionally exhibits a delayed germination that is resistant to these dormancy-breaking measures ( Fig 2 and S2 Fig ) . Second , the csn1-10 germination phenotype can be suppressed by loss of RGL2 , while csn5a-1 cannot ( Fig 5A and 5B ) . Third , the responses to GA-synthesis inhibitor PAC and to ABA-synthesis inhibitor norflurazon are different . In particular , norflurazon completely rescues the germination of csn1-10 , but cannot rescue the delayed germination of csn5a-1 ( Fig 7D ) . We suggest that the different behaviors between the two mutants can be explained by csn5a-1-specific deficiency in protein degradation of ABI5 , which occurs downstream of RGL2 in the germination pathway ( Fig 8 ) . PAC treatment has been shown to stabilize RGL2 and stimulate accumulation of ABI5 [16] . That csn5a-1 mutants failed to down-regulate ABI5 protein would render the mutant hypersensitive to PAC . As a consequence , even though rgl2 can confer resistance to PAC in wild type background , csn5a-1 rgl2 double mutant still accumulate ABI5 and consequently remain hypersensitive to PAC . The fact that rgl2 can fully rescue the germination of csn1-10 rgl2 suggests that CSN1 does not play a significant role in direct regulation of ABI5 . The prolonged ABI5 accumulation in csn1-10 mutant is most likely caused by its extended accumulation of RGL2 , which induce ABA biosynthesis and promote ABI5 expression . The fourth difference between the csn1-10 and csn5a-1 is implied by their transcriptome profiles , which indicate that the two mutations have differential impacts on plants at different developmental stages . Relative to each other , csn1-10 strongly affected seed maturation , while csn5a-1 strongly affected seed germination and seedling establishment ( Fig 7A ) . We also found a peculiar behavior of csn5b-1 , which has a weaker seed dormancy than Col ( Fig 2 ) , opposite to the phenotype of the rest of the csn mutants . Similar phenomena have been reported regarding the root phenotype . It was observed that csn5b-1 has more adventitious roots , opposite to that of other csn mutants which develop fewer ( csn1-10 and csn3-3 ) or no ( csn5a-1 and csn5a-2 ) adventitious roots [42] . It is possible that CSN5B has a different function from CSN5A , which results in the difference of the phenotype . Alternatively , it is possible that csn5b-1 only slightly reduces the activity of CSN to the extent that could not be definitively detected by immunoblotting . Since CSN’s de-neddylation activity biochemically inhibits SCF , the slight reduction of the CSN might increase the SCF activity without creating a hyperactive SCF that auto-ubiquitinates its own components , as in severe csn mutants . This might enhance the SCF activity in targeting RGL2 , resulting in the reduced dormancy in csn5b-1 . This idea is admittedly highly speculative . With this study , we have for the first time revealed that CSN5A can directly or indirectly regulate protein stability of the b-ZIP transcription factor ABI5 ( Fig 8 ) , a key transcription factor that mediate the response to ABA , especially in seed germination [20 , 62] . Elevated ABI5 may attribute to the hypersensitivity of the csn1-10 , csn5a-1 and csn5a rgl2 in ABA-mediated inhibition of germination ( Fig 7C ) . It has been reported that ABA receptor is targeted for degradation by the CRL4-CDDD E3 complex [63] . Given that CSN can regulate CRL4 , it seems possible that CRL4’s activity in targeting the ABA receptor be compromised in the csn mutants , which might also contribute to the ABA hypersensitivity . ABI5 transcripts are drastically down-regulated following seed imbibition in the csn mutants ( S7C Fig ) , although the levels are still moderately higher than in the wild type at day2 and day3 after imbibition ( Fig 7B ) . Nonetheless , the elevated level of ABI5 transcripts cannot fully account for the sustained high-level accumulation of its protein in the mutants ( Fig 4 ) . Moreover , inhibiting ABA biosynthesis , and thus ABA-induced ABI5 expression , cannot rescue the delayed germination of csn5a-1 . These results suggest that the sustained ABI5 accumulation is probably caused by defective ABI5 degradation in csn5a-1 , which results in a delay of the germination . ABI5 protein stability has been shown to be regulated by several factors and ubiquitin E3 ligases , including KEEP ON GOING ( KEG ) [64] , SALT- AND DROUGHT-INDUCIBLE RING FINGER 1 ( SDIR1 ) [65] , ABI FIVE BINDING PROTEIN ( AFP ) [66] , CRL4ABD and CRL4DWA1/2 [67] [68] . In these studies , KEG and CRL4s ubiquitin E3 ligases are shown to target ABI5 in post-germination events . Our data suggest that CSN5A facilitates protein degradation of ABI5 during germination , but it is unclear which of those known ABI5 E3 ligases CSN5A works with . We also cannot exclude that there may be a different E3 yet to be identified that targets ABI5 specifically during germination . Furthermore , ABI5 protein undergoes various post-translational modifications including phosphorylation , SUMOylation and S-nitrosylation , all of which can modulate its protein stability [62 , 69 , 70] . It cannot be ruled out that CSN5A may modulate ABI5 protein stability by affecting those modifications on ABI5 . Our findings raise additional questions as to whether CSN5A may affect other aspects of ABI5 functions or other ABA responses . Regardless , the observations that ABI5 is specifically affected in csn5a but not in csn1 suggest that this function might represent the first CSN5-specific activity in plants . With this function of CSN5A , we can now add ABA responses to the repertoire of CSN/CSN5 regulated pathways .
The Arabidopsis thaliana mutants csn5a-1 , csn5a-2 , csn5b-1 [35] , csn1-10 [45] , csn3-3 [46] , and rgl2-13 [13] are in the Col ecotype . csn2-5 [47] is in Langsberg eracte ( Ler ) background . The abi5-4 and csn1-1 ( fus6-1 ) mutants are in Wassilewskija ( Ws ) ecotype . After the seeds were germinated and grown on GM plates ( see below ) for 6 to 9 days , the seedlings were transferred to soiled pots . The soil mix was composed of one part Vermiculite and one part Farfard#2 soil mix , which were soak in water with fertilizer ( 0 . 25 g/L water ) . The fertilizer is from Scotts ( Peters Professional 20-10-20 #99250 from Scotts ) . Plants were grown in a walk-in growth room in 22°C long day light cycle . When not specified , seeds between 1 week to 6 weeks were used and un-stratified in the germination tests or immunoblot analyses . For experiments requiring cold stratification to break the dormancy , such as testing sensitivity to PAC or ABA , seeds from 1 week to up to 6 month were used , and the seeds were cold stratified for 2–3 days regardless of the collecting time . Seeds to be used for direct comparisons were usually collected on the same day from the parental plants grown side-by-side under the same conditions . This was strictly the case for Col and csn1-10 . However , since csn5a-1 plants is late–flowering , it was matched with later planted Col that had a same or close seed collection dates . Seeds were surface sterilized with a solution containing 30% bleach and 0 . 1% TritonX-100 , washed . When not specified , seeds are saw on solid growth medium ( GM ) containing MS salt 4 . 4 g/L ( M0404 , Sigma ) , MES 0 . 5g/L ( M5287 , Sigma ) , 1% sucrose and 0 . 8% ( w/v ) Bacto-Agar . The plates were divided to 4 areas to aid in counting and scoring seed germination rates . To determine the germination rates , approximately 20–60 seeds in 4 repeats were examined daily under a Nikon/Zeiss dissecting microscope , and germination were scored based on radicle protrusion from endosperm . The germination rates were measured in DAI ( days after imbibition ) . Seeds to be tested for germination were routinely carried out in pairs: one set of the seeds were un-stratified and the germination rates were presented unless otherwise noted; the other matching set of seeds were cold stratified ( 4°C ) in the dark for 3–4 days , and their germination rates were recorded to make sure that the batch of the seeds were of high quality . Plates were incubated in a Percival growth chamber for 22°C with constant white light . For PAC , GA , and ABA sensitivity test , cold stratified seeds were used . The medium was supplemented with PAC ( paclobutrazol , sc-236284 , Santa Cruz Biotechnology ) , GA3 ( G7645 Sigma ) . For ABA sensitivity teste , solid GM medium described above without sucrose were used that contained ABA ( A1049 , Sigma-Aldrich ) in indicated concentration . For testing of norflurazon ( Sigma-Aldrich 34364 ) effect , unstratified seeds were used on no sucrose GM plates . For light controlled seed germination experiment , seeds sow on GM plates were cold stratified for two days in the dark , then seeds were exposed to 5 min red light ( 13 . 4 watts . m-2 ) or far-red light ( 8 . 2 watts . m-2 ) , or followed by another 5 min of light treatment as indicated . Plates containing the seeds were then wrapped in foil and kept in the dark at 22°C incubator for two days before counting for germination . After sterilizing and washing , seeds were imbibed in water for 3 hours before dissection using a fine syringe needle according to a previously described procedure [51 , 52] . Dissected embryos were placed on a water agar ( 1% ) plate alone with corresponding seed controls , and were incubated in a 22°C growth chamber with constant white light . Embryos and seeds were examined through a Leica dissecting microscope and photographs were taken daily . For every 100 mg of fresh weight imbibed seeds or germinating seedlings , the sample was mixed with 180 microliter of chilled extraction buffer ( 50mM Tris-HCL , pH7 . 5 , 150mM NaCl , 10mM MgCl2 , 2 . 5mM EDTA , 1mM DTT , 0 . 1% Nonidet P-40 , and freshly added 1mM protease inhibitors phenylmethylsulfonyl fluoride ( PMSF ) and 1x complete protease inhibitor cocktail ( Roche Molecular Biochemicals ) was added and mixture was homogenized . Then , 100 microliter of 4x sample buffer was added and the mixture was vortexed . Samples were then boiled for 10 min and span in a microfuge for 10 min . The supernatant was transferred to a new tube , from which samples were loaded onto SDS-PAGE for immunoblotting . Antibodies used for this study include anti-CSN5A and anti-CSN3 [35] , anti-CUL1 [48] , anti-RPN6 [71] , anti-RGL2 [72] , and anti-ABI5 ( Abcam , ab98831 ) . The anti-CSN5B polyclonal antibodies were made by Beijing Protein Innovation Co . , Ltd . ( BPI ) . Briefly , an EcoRI/XhoI fragment containing the full-length CSN5B open reading frame was cloned into the EcoRI/XhoI sites of the pET-28a vector , so as to express 6×His-tagged CSN5B protein . The fusion protein was expressed in Escherichia coli , then purified and used as antigen to immunize rabbits for the production of polyclonal antiserum . Antigen affinity purified anti- CSN5B antibodies were used in immunoblots . Col-0 , csn1-10 , and csn5a-1 seeds were collected from plants grown side-by-side in the growth room . Seeds for Col-0 and csn1-10 were 1 . 5wks in storage , and csn5a-1 seeds were 4wk in storage . Approximately 100 microliter of settled seeds were used for RNA extraction for each sample . The 2-day imbibed seeds , prepared with the same volume of dry seeds as above , were sterilized , washed , and incubate on cell culture wells with 1ml of 0 . 5X liquid MS medium at 22°C under constant light for 2 days . Each sample points had three biological repeats . Seeds were centrifuged to remove the liquid , and were frozen in liquid nitrogen . The frozen seeds were ground to a fine powder using mortar and pestle in the presence of liquid nitrogen and small quantity of sterile quartz powder . RNA extraction was performed according to a published procedure [73] . High-throughput RNA-seq was carried out at Yale Center for Genome Analysis . The Single-End RNA-sequencing was carried out with Illumina Hi-seq 2000 platform ( Genome Center , Yale West Campus ) . Specifically , libraries were analyzed with a Bioanalyzer 2100 instrument ( Agilent , Santa Clara , CA ) , quantified by Qubit fluorometer ( Life Technologies , Carlsbad , CA ) . The Arabidopsis thaliana genome obtained from TAIR10 ( https://www . arabidopsis . org ) was used as the genome reference . After adaptor trimming and contaminate sequence removing by fastqc ( www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) and fastx-toolkits ( hannonlab . cshl . edu/fastx_toolkit/ ) , Bowtie2 ( http://bowtie-bio . sourceforge . net/bowtie2/index . shtml ) was used for genome mapping and followed by the tophat ( https://ccb . jhu . edu/software/tophat/index . shtml ) transcript assembling . Gene differentially expression profiling was accomplished by cufflink and cuffdiff software package ( cole-trapnell-lab . github . io/cufflinks/cuffdiff/ ) with default parameters and cutoffs , fold change cutoff was set to 2 . The data set is accessible at NCBI GEO under accession number GSE106223 . Col , csn5a-1 and csn1-10 seeds ( 5-days in storage ) were sterilized and sow on the solid medium plate as described above in Germination assay . Total RNAs were extracted from 2- or 3- day germinating Col , csn5a-1 and csn1-10 seeds following the procedures as described previously [73] . Then 1 μg of total RNA was used for reverse transcription reaction using SuperScript III Reverse Transcriptase ( Invitrogen ) and quantitative PCR ( qPCR ) reaction was performed in Bio-Rad CFX96 real-time system using iQ SYBR Green mix ( Bio-Rad ) . Gene expression was normalized to IPA-like1 ( AT1G17210 ) . Full-length cDNA coding region of CSN5A and CSN5B were each subcloned into the EcoRI/SalI and EcoRI/XhoI sites of pB42AD ( AD ) vector ( Clontech ) . ABI5 was subcloned into the EcoRI/XhoI site of pLexA ( BD ) vector ( Clontech ) . Yeast two-hybrid assay was performed according to the Matchmaker LexA Two-Hybrid System manual ( Clontech , K1609-1 ) . Briefly , all constructs were co-transformed into yeast strain EGY48 containing p8op-LacZ . Transformants were grown on SD/-His/-Trp/-Ura plates containing X-Gal for blue color development . | The control of seed germination and seed dormancy are critical for successful propagation of plant species , and manipulation of these processes is important for agriculture . The COP9 Signalosome ( CSN ) is a multi-subunit protein complex that regulates proteasome-mediated protein degradation in part as a regulator of SCF ubiquitin E3 ligases . The CSN is important for timely germination of seeds , but its molecular targets in this process is unclear . In this study , we demonstrate that the CSN regulates protein stabilities of two different targets from two antagonistic hormonal pathways , RGL2 of the GA pathway and ABI5 of the ABA pathway . Our genetic and transcriptome analyses showed that , although csn1-10 and csn5a-1 exhibit similar defects in timely germination , the mechanisms of how the mutations affect seed germination differ . Since RGL2 is known to be targeted by SCF during germination , the defect in the timely degradation of RGL2 in csn1-10 and csn5a-1 is consistent with the role of CSN as a regulator of the SCF . In addition , we show that CSN5A , but not CSN1 , has an additional function in regulating ABI5 , a downstream inhibitor of germination . | [
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"... | 2018 | The COP9 Signalosome regulates seed germination by facilitating protein degradation of RGL2 and ABI5 |
Efficient assimilation of alternative carbon sources in glucose-limited host niches is critical for colonization of Candida albicans , a commensal yeast that frequently causes opportunistic infection in human . C . albicans evolved mechanistically to regulate alternative carbon assimilation for the promotion of fungal growth and commensalism in mammalian hosts . However , this highly adaptive mechanism that C . albicans employs to cope with alternative carbon assimilation has yet to be clearly understood . Here we identified a novel role of C . albicans mitochondrial complex I ( CI ) in regulating assimilation of alternative carbon sources such as mannitol . Our data demonstrate that CI dysfunction by deleting the subunit Nuo2 decreases the level of NAD+ , downregulates the NAD+-dependent mannitol dehydrogenase activity , and consequently inhibits hyphal growth and biofilm formation in conditions when the carbon source is mannitol , but not fermentative sugars like glucose . Mannitol-dependent morphogenesis is controlled by a ROS-induced signaling pathway involving Hog1 activation and Brg1 repression . In vivo studies show that nuo2Δ/Δ mutant cells are severely compromised in gastrointestinal colonization and the defect can be rescued by a glucose-rich diet . Thus , our findings unravel a mechanism by which C . albicans regulates carbon flexibility and commensalism . Alternative carbon assimilation might represent a fitness advantage for commensal fungi in successful colonization of host niches .
Candida albicans is by far the most prevalent commensal and pathogenic Candida species . In mammals , this polymorphic fungus most commonly resides as a lifelong , harmless commensal on mucosal surfaces of the oropharynx , gastrointestinal and genitourinary tracts in 30–70% of healthy individuals [1–3] . The mucosa surface provides a natural barrier in preventing the invasion of C . albicans . Successful colonization by the fungus requires a homeostasis between C . albicans and host immunity [4] . Once the balance is disrupted , e . g . , unbalanced microbial flora after antibiotic treatment , weakened host immune response or impaired proliferation of epithelial cells , C . albicans rapidly transits from being a commensal to a pathogen and therefore causes serious and life-threatening systemic infections [5] . For clinically important microbial pathogens like C . albicans , assimilation of locally available nutrients is important for their survival , proliferation and infection in the diverse host niches . Interestingly , nutrients available in the host are mostly different from the ones supplied in the laboratory culture medium . Most fermentative sugars including glucose , fructose and galactose , although routinely used in laboratory cell culture medium , are actually only present at very low levels and even absent in many host niches . For example , compared to 2% glucose often used for culturing C . albicans in vitro , only 4–7 mM ( 0 . 07–0 . 13% ) glucose exists in the bloodstream and its concentration in vaginal fluids is about 28 mM ( 0 . 5% ) [6 , 7] . Whereas in human intestinal tract , glucose concentrations are thought to be extremely low because glucose derived either from hydrolysis of starch or from sucrose is rapidly taken up into the epithelial cells by glucose transporters [8] . Therefore , C . albicans colonization in these glucose-poor niches must rely on alternative , non-fermentative carbon sources . For example , physiologically relevant carbon sources , including amino acids , fatty acids , carboxylic acids , glycerol , mannitol and N-acetylglucosamine ( GlcNAc ) , are found at varying concentrations in different host niches and constitute major relevant nutrients for C . albicans upon infection . A classical example supporting the considerable metabolic flexibility of C . albicans is the glyoxylate cycle , a metabolic pathway that permits the use of two-carbon compounds as carbon sources and is required for C . albicans virulence in the mouse model of systemic candidiasis [9] . Disruption of the GlcNAc catabolic pathway significantly causes morphological changes and attenuated virulence in C . albicans [10] . Lactate is the carboxylic acid highly enriched in the gastrointestinal tracts [11] . Inhibition of lactate assimilation by deleting CYB2 , a gene encoding L-lactate dehydrogenase , compromises the ability of Candida glabrata to colonize in the GI tract [12] . Moreover , lactate influences C . albicans recognition and phagocytosis by immune cells [13] . These studies attest to the distinct effect of alternative carbon assimilation on the host-pathogen interaction , particularly its contribution to virulence and commensalism . As previously stated , mechanisms that C . albicans employs to cope with alternative carbon assimilation are largely unknown . It remains unclear whether the regulation of carbon flexibility contributes to colonization of C . albicans in different host niches . Mitochondria are the organelles generating most energy supply for the eukaryotic cells , converting oxygen and nutrients into the coenzyme adenosine triphosphate ( ATP ) , which is primarily synthesized by the process of oxidative phosphorylation ( OXPHOS ) and the OXPHOS-electron transport chain ( ETC ) . The ETC harbors five integral protein complexes in the inner mitochondrial membrane: the first four transfer high-energy electrons from NADH to molecular oxygen , and potential energy is established by a proton gradient and finally dissipated through complex V to synthesize ATP [14] . Among these complexes , mitochondrial complex I ( CI ) , also referred to as NADH:ubiquinone oxidoreductase , is the first enzyme of ETC catalyzing NADH oxidation by regenerating NAD+ [15] . In addition to the primary role in ATP generation from nutrients , CI has other important cellular functions . For example , CI activity has been found to be important in regulating the yeast-to-hypha morphogenesis and pathogenicity of C . albicans . NDH51 encodes the NADH dehydrogenase protein of CI and deletion of NDH51 in C . albicans results in a filamentation defect even at low level of glucose [16] . Growth and Oxidation Adaption protein 1 ( Goa1 ) is a mitochondrial protein uniquely present in the CTG subclade of the Saccharomycotina including C . albicans , deletion of GOA1 in C . albicans significantly impairs the enzymatic function of CI [17] and therefore exhibits increased reactive oxygen species ( ROS ) production and cell death , heightened sensitivity to oxidants and neutrophil killing , and avirulent in a murine systemic infection model [18] . Similarly , deletion of either NUO1 or NUO2 , each encoding a subunit of NADH:ubiquinone oxidoreductase , causes CI disassembly and a number of physiological defects including reduced oxygen consumption , decreased mitochondrial redox potential , decreased CI activity , increased ROS and decreased chronological aging in vitro , as well as significantly attenuated virulence in a murine systemic infection model [19 , 20] . Importantly , a link between the mitochondrial activity and intracellular signals appears to exist and decides the fate of cellular morphology in C . albicans . Post-transcriptional regulation by the RNA binding protein Puf3 and the mRNA deadenylase Ccr4 is crucial for mitochondrial biogenesis in C . albicans biofilms and this regulatory mechanism links metabolic adaptation to biofilm maturation [21] . Environmental factors such as methylene blue ( MB ) treatment interfere with mitochondrial activity and consequently down-regulate ATP production , which significantly decreases signaling through the Ras1-cAMP-PKA pathway and inhibits the Ras1-dependent yeast-to-hypha switch [22] . Of significance to our study , these data reflect a fact that most of the identified mitochondria-associated cellular events appear to rely on carbon sources . Indeed , searching for genes essential for utilization of GlcNAc identified a mitochondrial protein Mcu1 and deletion of MCU1 leads to defects in utilizing a variety of non-fermentative carbon sources such as GlcNAc , sodium citrate , sodium pyruvate , acetate , ethanol and glycerol [23] . CI dysfunction by either NUO1 or NUO2 deletion results in severe growth defects in conditions when glycerol or oleic acid is used as the sole carbon source . Given that alternative carbon sources , rather than fermentative sugars , are highly enriched in mammalian host niches such as GI tract [24] , these studies strongly suggest that mitochondrial activity may actively promote C . albicans colonization in host niches by regulating alternative carbon assimilation . However , the exact regulatory mechanisms , especially signaling pathways that incorporate input from mitochondria to sense and respond to alternative carbon assimilation , have not been fully elucidated . In this study , we identified an uncharacterized carbon source-dependent function of CI in regulating hyphal morphogenesis and biofilm development of C . albicans . Both in vitro and in vivo studies demonstrate that CI triggers a specific , carbon source-dependent signaling pathway to allow C . albicans to adjust to disparate environments with varied availability of carbon sources , highlighting its role in modulating morphological changes of C . albicans by manipulating its flexibility to respond to local changes in intracellular environment and metabolism .
A standard set of biofilm-inducing condition is to expose the C . albicans cells on either the silicone square substrate or directly on the bottom of 12-well bovine serum-recoated polystyrene plates , and incubate for 24 to 48 hours in Spider media , with gentle shaking of samples at 37°C [25] . Using this method , we screened the homozygous gene deletion library [26] for mutants defective in biofilm formation . Our screen identified a list of important regulators ( S4 Table ) . Among them , four genes ( NUO1/Orf19 . 6607 , NUO2/Orf19 . 287 , Orf19 . 1625 , and Orf19 . 2570 ) , each encoding the subunit of mitochondrial complex I ( CI ) , were chosen for further analysis . Fig 1A showed that deletion of any one of the four CI subunits severely abolished biofilm formation . We did not observe biofilm formation in these mutants even when the incubation time was extended up to 14 days ( S1A Fig ) . The biofilm defects of these mutants were also confirmed by dry weight measurements ( S1B and S1C Fig ) . Reintroducing an ectopic copy of the wild type allele back into each mutant rescued the biofilm defect of each mutant , indicating that the phenotype is due to the deleted genes . Hence , all of the four CI subunits are required for wild type biofilm formation in vitro , at least under this set of biofilm-inducing condition . To further ascertain the importance of these CI subunits in biofilm formation , we subjected the same set of strains to a different type of biofilm assay , where we kept all manipulations unchanged except that the cells were incubated in RPMI 1640 , a medium previously known to promote biofilm formation in C . albicans [27] . To our surprise , we found that in RPMI 1640 medium , these four mutants formed biofilm that were indistinguishable from the wild type ( Fig 1B ) , suggesting that nutrients differed in Spider and RPMI 1640 media may account for the opposite biofilm phenotypes . Nutritional comparison of these two media revealed differences in the source of carbon , that is , Spider medium uses mannitol while RPMI 1640 uses glucose as the sole carbon source . This led us to hypothesize that source of carbon might be contributing to the nature of biofilm development observed in these CI mutants . Therefore , we evaluated biofilm formation of these CI mutants in a carbon source-switching assay by adding one of three carbon sources , including glucose , mannitol and mannose , to different biofilm-inducing media . The reason we use mannose in this assay is because mannitol degradation produces D-fructose-6-phosphate via a conversion of mannitol to mannose and mannose is converted to mannose-6-phosphate which can enter the glycolysis pathway ( Details can be found in Candida genome database ) . Because all four identified genes belong to the same family of CI subunits and each mutant displays a similar phenotype , we chose NUO2 as a representative and carried out the rest of experiments . As shown in Fig 1C , nuo2Δ/Δ mutant displayed regular biofilm formation in Spider medium when mannitol was replaced by either glucose or mannose . In contrast , adding mannitol to glucose-free YEP or RPMI 1640 medium significantly compromised the biofilm growth of nuo2Δ/Δ mutant . Considering all these explicit results , we propose that a functional CI positively regulates biofilm development by imposing its effect on assimilation of mannitol , a sugar normally recognized as an alternative carbon source . Our findings that deleting NUO2 impairs C . albicans biofilm development in a carbon source-dependent manner prompted us to investigate the role of this CI subunit in hyphal morphogenesis , given that hyphal development is an important step in normal biofilm development [28] . A standard hyphal induction assay was performed at 37°C in Spider or YEP medium supplemented with different carbon sources . Cells from wild type , nuo2Δ/Δ mutant and nuo2Δ/Δ+NUO2 complemented strains ( NUO2 AB ) were harvested from overnight cultures ( Fig 2A ) and re-inoculated to indicated medium . Aliquots of the cells were microscopically visualized at different time points . Apparently , when mannitol was used as the sole carbon source , massive C . albicans hyphae were observed in wild type , whereas nuo2Δ/Δ mutant cells were completely devoid of hyphae and remained in yeast form ( Fig 2B–2D ) . Intriguingly , hyphal inhibition was still evident in nuo2Δ/Δ cells even for a prolonged incubation ( up to 14 days ) ( S2A Fig ) . Additionally , the hyphae-defective phenotype of nuo2Δ/Δ mutant can be recapitulated in solid media ( Fig 2E and S2B Fig ) . This phenotype was complemented by re-introduction of the wild type NUO2 gene into the mutant genome . By comparison , defective hyphal growth of nuo2Δ/Δ mutant could be partially rescued by replacement of carbon source with either glucose ( S3 Fig ) or mannose ( S4 Fig ) , and its germination rate and the hyphal elongation were much slower than wild type strain . These results support the proposition that hyphal morphology can also be influenced by carbon source , and necessarily in relation to CI function . A recent study done by She et al . [20] has validated Nuo2 as a NADH:ubiquinone oxidoreductase and annotated its gene function . Mutant cells lacking NUO2 displayed reduced oxygen consumption , decreased mitochondrial redox potential , decreased CI activity , increased reactive oxidant species ( ROS ) and attenuated virulence in a murine systemic infection model . In line with these reported findings , we also observed a significant reduction of intracellular ATP levels in nuo2Δ/Δ mutant regardless of carbon sources , although the effect of mannitol appears to be more evident ( S5 Fig ) . Taking into consideration the in vitro phenotypic characteristics of nuo2Δ/Δ mutant , our results suggest that CI dysfunction results in defective biofilm formation and hyphal growth in a carbon source-dependent manner , reflecting the possibility that the mutant cells may have an intrinsic inability to sense and utilize the alternative carbon sources such as mannitol . The role of mitochondrion as a power station in generating ATP undoubtedly could have important effects on cell growth . Indeed , severe growth defects were observed in nuo2Δ/Δ mutant when mannitol , rather than glucose or mannose , was used as the sole carbon source ( Fig 3A and S6A–S6C Fig ) . Here , incubation on different carbon sources was at 30°C instead of 37°C . Notably , cells lacking NUO2 still presented in the yeast form ( Fig 2 and S7A Fig ) after an increase in temperature , a condition that stimulates filamentous growth in wild type and complemented strains , and we found that the null mutant strain exhibited indistinguishable patterns of growth under both temperature conditions ( S6 and S7B Figs ) . Therefore , the restrictions of assaying morphology and cell proliferation of nuo2Δ/Δ mutant at two different temperatures ( 37°C vs 30°C ) could be neglected . Furthermore , our observation that nuo2Δ/Δ cells displayed severe growth defects in a mannitol-dependent manner raised an immediate question of whether inability of this mutant to trigger hyphal growth in mannitol-containing medium is due to a general , inherent defect in vegetative growth . To answer this , we assessed hyphal morphogenesis of the nuo2Δ/Δ cells under different conditions . Firstly , we examined its growth under hypoxia ( 0 . 2% O2 ) , a condition that C . albicans cells grow exclusively as hyphae [29 , 30] . Cells were inoculated on mannitol-containing YEP medium and incubated at 37°C for 7 days under normoxic and hypoxic conditions , respectively . We found that although nuo2Δ/Δ mutant cells grew even more slowly , hypoxia restored regular but shorter hyphae ( Fig 3B , note that pictures were taken after 7 days incubation ) , indicating that lack of hyphae formation in the medium using mannitol as the sole carbon source is not due to a general growth defect of the nuo2Δ/Δ mutant . In contrast , normoxic condition failed to stimulate hyphae formation even the incubation time was extended up to 30 days ( S2B Fig ) . Secondly , we evaluated hyphal morphology of nuo2Δ/Δ mutant when glucose was added to mannitol-containing medium . Cells were grown at 37°C in YEP medium supplemented with different combinations of carbon sources and hyphal morphologies were monitored under microscopy . Although hyphal formation was completely suppressed by adding 2% of mannitol to YEP medium , we found in Fig 3C that adding an extremely low concentration of glucose or mannose ( 0 . 1% ) to the mannitol medium is sufficient to restore hyphal growth , however , the treatment only leads to a modest growth improvement , as mutant cells propagated more slowly than the wild type and NUO2 AB strains ( Fig 3D and S6D–S6G Fig ) . These results further support the notion that filamentation defect of nuo2Δ/Δ mutant is independent from its growth defect , that is , impaired growth appears to play a minor role in hyphal suppression . In addition , a previous study in the C . albicans strain BWP17 showed that CI dysfunction by deleting the CI subunit-encoding gene NDH51 caused a severe defect in filamentous growth at 37°C in the basal salts medium ( BSM ) , a condition known to induce filamentation [16] . Moreover , treating the wild type cells with rotenone , a potent CI inhibitor that inhibits the transfer of electrons from Fe-S centers in CI to ubiquinone [31] , significantly decreases filamentation without affecting the doubling times [32] . These results implied that the hyphal defect of ndh51Δ/Δ cells was due to inhibition of the respiratory pathway and not to impaired growth . Using a similar strategy , we evaluated the effect of rotenone on proliferation and hyphal morphology of wild type ( SN250 ) cells . After exposure to rotenone for 6h , cells proliferated at almost the same rate as untreated ( control ) ( S8A Fig ) , but the rate of filamentation was significantly decreased to only 20% ( S8B Fig; note that 95% of cells undergo filamentous growth in the absence of rotenone ) . Further filamentation decreases occurred at later time points ( S8B Fig ) but cell proliferation was only mildly slowed down ( S8A Fig ) . These results match the observed decrease in the NUO2 transcript level after rotenone treatment ( S8C Fig ) . Our data , therefore , provide indirect evidence that hyphal inhibition by CI dysfunction could not be ascribed to impaired growth . However , in BSM , we observed that CI dysfunction by deleting NUO2 substantially blocked hyphal development but only marginally impaired the cell proliferation ( S8D Fig ) , implying that the unknown composition of the medium may somehow compensate for the mannitol-dependent growth defect in nuo2Δ/Δ mutant . Taken together , our data argue against the notion that a defect in growth is the predominant mechanism by which CI dysfunction blocks hyphal development in a carbon source-dependent manner . It is likely that defects in hyphal formation and proliferation may be uncoupled when CI mutant cells are exposed to alternative carbon sources such as mannitol . Previous studies have established that the addition of mannitol to the culture medium could impose effects on cells by either the osmotic effect [33] or the role of mannitol as a carbon source [34] . Because the nuo2Δ/Δ mutant cells failed to efficiently utilize mannitol present in the medium , we next ask whether an osmotic effect due to extracellular accumulation of unassimilated mannitol could contribute to the defective hyphal growth of this mutant . Given that glucose has been found to repress the uptake of mannitol in C . albicans [35] , we monitored hyphal growth of nuo2Δ/Δ cells in conditions when 0 . 1% glucose was added to YEP medium containing either 2% mannitol or a high concentration of salt ( 1M NaCl ) , an osmotic condition that has been reported to completely inhibit the switch of C . albicans yeast cells to hyphal form [36] . As shown in Fig 3C , we observed regular hyphal growth after 0 . 1% glucose was added to the medium containing mannitol but not salt , arguing against a role for mannitol-induced osmotic stress in this phenotype . Therefore , it is more evident that CI functions to mediate mannitol-induced biofilm development and hyphal growth by regulating its assimilation and an uncharacterized signal transduction pathway might be involved . Mannitol metabolism requires an enzyme named mannitol dehydrogenase which mediates the conversion of mannitol to mannose by catalyzing the chemical reaction: Mannitol + NAD+—Mannose + NADH + H+ [37] . Mannitol dehydrogenase belongs to the family of NAD+-dependent oxidoreductases that specifically act on the CH-OH group of donor with NAD+ as acceptor [38] . Since replacing mannitol with mannose is able to restore the hyphal growth and biofilm formation of nuo2Δ/Δ mutant , we hypothesized that CI dysfunction may have an impact on enzymatic activity of the mannitol dehydrogenase . Cells from wild type , nuo2Δ/Δ , and NUO2 AB strains were cultured in YEP medium supplemented with either glucose or mannitol , and mannitol dehydrogenase activity was determined by an assay previously described in Sacchromyces cerevisiae [39] . As expected , mannitol , but not glucose , triggers a significant increase of enzymatic activity in the wild type and complemented strains ( Fig 4A ) . However , mannitol-induced enzymatic activity was significantly diminished in the nuo2Δ/Δ mutant , indicating that Nuo2 is required for upregulation of mannitol dehydrogenase activity . Considering the fact that NUO2 encodes a subunit of NADH:ubiquinone oxidoreductase catalyzing the production of NAD+ from NADH and enzymatic activation of mannitol dehydrogenase is NAD+-dependent , we hypothesized that downregulation of mannitol dehydrogenase activity in nuo2Δ/Δ mutant could be due to a reduction of NAD+ level . To test this , we measured the ratio of cellular NADH/NAD+ following a carbon source shift . Cells were originally grown in the presence of glucose and then shift to a fresh medium using mannitol as the sole carbon source . As expected , deletion of NUO2 led to about 40% reduction of NAD+ level ( Fig 4B ) . Thus , our results clearly indicate that CI dysfunction by deleting NUO2 significantly reduces the overall level of NAD+ and thereby downregulates the activity of NAD+-dependent mannitol dehydrogenase , a key enzyme responsible for mannitol metabolism . As a result , mannitol cannot be converted to mannose and glycolytic pathway through mannose metabolism is blocked . To test whether the defects in hyphal morphology and biofilm formation in nuo2Δ/Δ were due to reduced NAD+ levels , we examined morphological changes of nuo2Δ/Δ cells in mannitol-containing YEP medium supplemented with different concentrations of NAD+ . Strikingly , NAD+ supplementation is able to rescue the defects in hyphae and biofilm formation ( Fig 4C and 4D ) , supporting that glycolytic inhibition by decreasing availability of NAD+ contributes to impaired hyphae and biofilm formation of nuo2Δ/Δ cells in mannitol condition . It has been documented that CI inhibition impedes the reoxidation of NADH and thus free electrons react with ambient oxygen to produce reactive oxygen species ( ROS ) [40 , 41] . Previous studies in C . albicans showed that treatment with rotenone , a specific CI inhibitor , significantly induces ROS generation [42] . In line with these observations , we and She et al . [20] also confirmed that deletion of NUO2 results in significant accumulation of ROS in glucose condition ( Fig 5A ) . Remarkably , mannitol treatment further increased the already elevated ROS levels ( Fig 5A and S9 Fig; note that similar results were obtained using two different ROS detection methods ) , suggesting that glucose-triggered ROS production might be insufficient to impose a harmful effect on hyphal morphology of nuo2Δ/Δ cells , instead , defective hyphal growth necessitates accumulation of ROS to higher levels in the presence of mannitol . Given that ROS are no longer recognized as just a toxic by-product of mitochondrial respiration , but are now appreciated for their roles as important and common secondary messengers that are poised at the core of signaling pathways maintaining normal metabolic fluxes and different cellular functions [43 , 44] , we speculated that an uncharacterized ROS-mediated signaling pathway may contribute to the carbon source-dependent phenotypes observed in CI mutants . We considered a possibility of the classic Hog1 MAPK signaling cascade since this pathway has been found to play a crucial role in sensing and responding to oxidative stress , and in repressing hyphal formation in C . albicans [45 , 46] . To test this , we performed two independent immunoblotting analyses by detecting activation of Hog1 under different growth conditions . After cells from WT , nuo2Δ/Δ and NUO2 AB strains were grown to exponential phase in YEP medium supplemented with different carbon sources , cell extracts were prepared and subjected to immunoblotting analysis , using an antibody that specifically recognizes the phosphorylated form of Hog1 since Hog1 is activated in response to cationic stress via phosphorylation at its conserved TGY motif [46] . Compared to the wild type and complemented strains , we observed significantly higher levels of phosphorylated Hog1 in the nuo2Δ/Δ mutant cells under the condition when mannitol , rather than glucose or mannose , was the sole carbon source ( Fig 5B ) . As controls , analysis of Hog1 in wild type and mutant strains showed that the total Hog1 protein levels did not change upon treatment with different carbon sources . We further investigated the time course of Hog1 phosphorylation in both wild type and nuo2Δ/Δ mutant cells after shifting the carbon source from glucose to mannitol , glucose to glucose or glucose to mannose . Among these conditions , we found that a strong induction of phosphorylated Hog1 was only observed in the nuo2Δ/Δ cells after a glucose to mannitol shift ( Fig 5C and S10 Fig ) . Taken together , our data demonstrate that CI dysfunction stimulates a signal from ROS to flow through the Hog1 MAPK system in a mannitol-dependent manner . Hyphal formation is an important feature of biofilms as mutants deficient in hyphal growth are often impaired in biofilm development [47] . Because Hog1 activation has been previously documented to inhibit the yeast-to-hypha switch via the Sko1-dependent repression of BRG1 expression [46] , we hypothesized that the transcription factor Brg1 might act downstream of Hog1 signaling to mediate inhibition of hyphae and biofilm formation in nuo2Δ/Δ mutant when mannitol was used as the sole carbon source . To test this hypothesis , we first measured transcript levels of BRG1 responding to varied carbon sources ( Fig 5D ) . In the glucose condition , expression of BRG1 remained at low levels and displayed no significant differences among the wild type , nuo2Δ/Δ mutant and NUO2AB strains . In marked contrast , its expression was abolished in the nuo2Δ/Δ mutant when mannitol was used as the sole carbon source , suggesting that impaired hyphae and biofilm formation of nuo2Δ/Δ cells could be due to mannitol-dependent BRG1 downregulation . Second , inhibitory effect of CI dysfunction on BRG1 expression was further evaluated by measuring its protein levels . An epitope-tagged version of Brg1 was utilized in which 13 copies of the Myc epitope were fused in-frame at the C-terminus and this fusion protein is fully functional since recombinant strain expressing Brg1-Myc had no effect on the yeast-to-hypha transition ( S11 Fig ) . A glucose-mannitol shifting assay was carried out and we found that although the steady state level of Brg1-Myc in wild type was substantially induced by mannitol ( Fig 5E , top panel ) , Brg1 induction was significantly abolished by deleting NUO2 ( Fig 5E , bottom panel ) . These results demonstrate that mannitol-dependent morphological defects in nuo2Δ/Δ involves decreased levels of Brg1 . Neverthless , a recent study showed that Brg1 , together with other 5 transcription regulators ( Bcr1 , Efg1 , Ndt80 , Rob1 and Tec1 ) , constitutes a core transcriptional network that regulates biofilm formation and lack of any one of these regulators significantly blocked biofilm in vitro [25] . This prompted us to investigate whether CI dysfunction by deleting NUO2 could specifically impact BRG1 expression or have a general role in affecting expression of all genes in the network . Transcript levels of genes encoding the remaining 5 transcriptional regulators , including BCR1 , EFG1 , NDT80 , ROB1 and TEC1 , were quantified by RT-qPCR . Our results indicated that unlike BRG1 , the five genes had similar expression patterns between the wild type and nuo2Δ/Δ mutant in a carbon source-independent manner ( S12 Fig ) , highlightling a specific role of Brg1 in CI-regulated , mannitol-dependent hyphal growth and biofilm development in C . albicans . Interestingly , a puzzling pattern from Western blots was observed in the two samples labeled as time point 0: a rapid induction of Brg1-Myc during the shift from glucose to mannitol ( Fig 5E ) . A possibility we considered is that the irregular induction might be due to the samples cultured at different temperatures , given the fact that the glucose sample at time point 0 was cultured at 30°C , whereas the mannitol sample at time point 0 was actually maintained at 37°C after a carbon source shift . As for a higher level of Brg1 in the mutant than in the wild type at time point 0 , a possible explanation could be that CI dysfunction may confer increased susceptibility to a rapid temperature change . A previous study has shown that a temperature increase in wild type C . albicans leads to decreased level of phosphorylated Hog1 and we therefore speculate that this effect might be strengthened by CI dysfunction , which could induce a higher level of Brg1 , given an inverse relationship between Hog1 phosphorylation and Brg1 expression . The above results had indicated a mannitol-dependent Brg1 downregulation as a consequence of CI dysfunction . To further confirm this finding , we sought to overexpress BRG1 in nuo2Δ/Δ cells with the prediction that BRG1 overexpression could result in a rescue of hyphae and biofilm defects . The endogenous promoter of BRG1 was replaced with a constitutively active TDH3 promoter in nuo2Δ/Δ strain ( nuo2Δ/Δ + BRG1OE ) . Increased level of BRG1 mRNA was confirmed by RT-qPCR ( S13 Fig ) . As expected , overexpressed BRG1 substantially rescued the defects in hyphae and biofilm formation ( Fig 5F and S14 Fig ) , supporting the idea that Brg1 plays a critical role in CI-mediated cellular activities that sense and utilize available carbon sources to promote hyphal growth and biofilm formation . Surprisingly , overexpression of BRG1 was unable to rescue the growth defect of nuo2Δ/Δ mutant ( S15 Fig ) even though the failure of the mutant to form hyphae and biofilm could be recovered , providing additional evidence for our conclusion that the mannitol-dependent defects in hyphae and biofilm formation in nuo2Δ/Δ mutant are unlikely to be attributable to the impaired growth . In view of our results , we next ask whether the signaling pathway involving Hog1 activation and Brg1 repression specifically participates in the response to ROS . To this end , we treated the nuo2Δ/Δ mutant cells with N-acetyl-L-cysteine ( NAC ) , a potent antioxidant . Strikingly , we found that 10mM NAC was sufficient to restore high levels of Brg1 expression and coincidently diminished the level of phosphorylated Hog1 ( Fig 5G ) . As a control , we observed that NAC treatment in the wild type cells led to downregulation of Brg1 expression in a dose-dependent manner , whereas it had no effect on the level of phosphorylated Hog1 ( S16 Fig ) . To our surprise , we found that NAC treatment only partially complemented the hyphae and biofilm defects of nuo2Δ/Δ cells ( Fig 5H and S17 Fig ) , which could be due to the observation that although Brg1 was induced by NAC in a dose-dependent manner , its peak level in nuo2Δ/Δ mutant was still lower than that attained in untreated wild type ( S18 Fig; compare lanes 9 and 10 to lane 1 ) . Importantly , like the case with BRG1 overexpression , NAC treatment also did not improve the growth of mutant cells ( S19 Fig ) , further suggesting a disconnection between the morphological defect and impaired growth in nuo2Δ/Δ mutant under the mannitol condition . Interestingly , NAC treatment seems to have a negative impact on wild type biofilm formation ( S17 Fig ) , possibly due to downregulation of Brg1 expression ( S16 Fig ) . Taken together , these results establish that CI dysfunction reduces the level of NAD+ and blocks the glycolytic pathway by downregulating enzymatic activity of the mannitol dehydrogenase , that NUO2 disruption impedes the reoxidation of NADH and thus free electrons react with ambient oxygen to produce ROS , and that inhibition of hyphal growth and biofilm development occurs through a signal from ROS to flow through a specific , mannitol-dependent signaling pathway involving Hog1 activation and Brg1 repression . Intriguingly , the glycolytic inhibition appears to act independently from the ROS signaling as treating the nuo2Δ/Δ cells with NAD+ had no effect on Brg1 expression ( S20 Fig ) . It is more likely that exogenous NAD+ bypasses the inhibitory effect of CI dysfunction on the activity of the NAD+-dependent mannitol dehydrogenase and thus enables the cells to enter the glycolysis , a pathway which contributes to hyphal growth and biofilm formation through an as yet uncharacterized , Brg1-independent mechanism . Brg1 promotes hyphae-specific gene expression by directly binding to hyphal gene promoters and sustains hyphae elongation [46 , 48] . Since Brg1 expression was repressed when nuo2Δ/Δ mutant cells were treated with mannitol , we determined whether , under these conditions , Brg1 repression specifically had an effect on expression of hyphae-specific genes ( HSGs ) . Cells from the wild type , nuo2Δ/Δ mutant and NUO2 AB strains were grown at 37°C in YEP medium supplemented with either glucose or mannitol and quantitative expression of HSGs were measured by RT-qPCR . Compared to the condition that glucose was the sole carbon source , mannitol strongly induced expression of three well-characterized HSGs ( ECE1 , HWP1 and ALS3 ) in the wild type but the induction was abolished in the nuo2Δ/Δ mutant ( S21A Fig ) . Interestingly , it appears that CI dysfunction does not influence the expression of all HSGs because HYR1 shows similar levels of expression in both wild type and nuo2Δ/Δ mutant . In a similar manner we analyzed the expression of these four genes in nuo2Δ/Δ mutant treated with or without NAC ( S21B Fig ) . After exposed to NAC , the HWP1 transcript was largely increased , a slight but significant upregulation of ECE1 was detected , but transcript levels of HYR1 and ALS3 remained unchanged . Importantly , their transcript expression modes nicely fit the morphological pattern showing a partial restoration of hyphae and biofilm formation after a NAC treatment . Thus , the results reinforce an essential role of a functional CI in regulating mannitol-induced hyphal growth and biofilm formation of C . albicans . C . albicans thrives as a commensal in the gastrointestinal ( GI ) tract , a niche with lower proportion of energy-rich carbon sources ( e . g . glucose , fructose and galactose ) , indicating that its colonization in GI tract must rely on alternative carbon sources such as glycerol , GlcNAc , lactic acid and mannitol . Moreover , this fungus uses transcriptional programs associated with the yeast-to-hypha transition to maintain attachment , to invade tissue and to promote survival and immune evasion , as well as to regulate the behavior during GI tract colonization [49 , 50] . Our in vitro results suggest that CI dysfunction severely disrupts mannitol assimilation and consequently blocks hyphal morphogenesis and biofilm development . Therefore , we decided to address the role of CI in GI colonization . For this purpose , we first analyze if CI activity holds a general feature of affecting utilization of alternative carbon sources . To this end , we compared the in vitro proliferation rates of the wild type , nuo2Δ/Δ mutant and NUO2 AB strains by growing them on YEP medium supplemented with either fermentative or alternative carbon sources . As shown in Fig 6A , growth defects of the nuo2Δ/Δ mutant responding to a variety of alternative carbon sources ( GlcNAc , lactic acid , sorbitol or glycerol ) were almost as strong as the defect shown by the mutant to mannitol . This observation is in contrast to the phenotype displayed by the nuo2Δ/Δ mutant responding to fermentative sugars such as glucose , mannose and fructose , which significantly compromise the growth defects ( Fig 6B ) . In order to testify whether the CI-dependent Hog1 pathway has a universal role in response to alternative carbon sources , we examined morphogenesis of the wild type and nuo2Δ/Δ cells in conditions when the sole carbon source is either glycerol or GlcNAc , considering their abundance in GI tract . Apparently , both glycerol and GlcNAc induce robust filaments in the wild type ( S22A Fig ) . However , opposite effects were observed for the nuo2Δ/Δ mutant , that is , filamentation was blocked in glycerol but maintained in GlcNAc ( S22B Fig ) . To determine if these distinct morphological changes in response to the two carbon sources reflect their different impacts on activation of Hog1 signaling , we analyzed the expression levels of Hog1 and Brg1 in nuo2Δ/Δ mutant after a carbon source shift from glucose to glycerol or GlcNAc . Indeed , we note that similar to mannitol , treating the nuo2Δ/Δ cells with glycerol also leads to an inverse correlation between Hog1 and Brg1 expression , as illustrated by the pattern that increased levels of phosphorylated Hog1 correlates with the spontaneous reduction of Brg1 expression during the course of glycerol ( Fig 6C ) . This appears to be specific for the mutant cells since we observed a different pattern in the wild type showing that a glycerol shift induced robust Brg1 expression while the level of phosphorylated Hog1 was almost undetected ( S23A Fig ) . Interestingly , unlike the effects of mannitol and glycerol on this signaling pathway , we found that under GlcNAc , the total amount of Brg1 and of phosphorylated Hog1 was strongly increased in both wild type and nuo2Δ/Δ mutant cells ( Fig 6D and S23B Fig ) , suggesting that CI activity appears to have no role in sensing and utilizing this alternative carbon source . Furthermore , it is very likely that in addition to Brg1 , other transcription factor ( s ) may operate to sense signal through Hog1 , depending on which carbon source is available . Our in vitro data highlight the importance of CI activity in promoting hyphal growth and biofilm development by regulating utilization of alternative carbon sources known to be highly enriched in GI tract . Since defects of hyphal growth , biofilm formation and utilization of alternative carbon sources were observed in the nuo2Δ/Δ mutant , we ask whether these defects could have profound effects on the ability of C . albicans cells to function as a commensal in a mammalian host . To this end , we assessed the influence of CI dysfunction on in vivo colonization of C . albicans in a mouse model of gastrointestinal infection . Following a protocol described previously [51] , we inoculated 7–10 antibiotic-treated BALB/c mice by oral gavage with 1:1 mixtures of wild type and nuo2Δ/Δ mutant ( 1x108 CFU/mouse ) . Fecal pellets were collected at specified intervals and plated on Sabouraud Dextrose Agar ( SDA ) medium . Because the nuo2Δ/Δ mutant displays a severe growth delay than the wild type , we can easily distinguish these two types of cells based on the size of colonies . A representative image that shows C . albicans cell colonies recovered from fecal samples at day 5 post-inoculation is displayed in S24 Fig . To our surprise , we found that the plate is fully occupied by wild type cells with only few small colonies representing the nuo2Δ/Δ mutant cells ( S24 Fig , typified with arrows ) . Genetic backgrounds of these tiny colonies were further confirmed by PCR . However , it turns out to be problematic for the next step to measure the abundance of each strain in the innoculum and after recovery from fecal pellets by real-time PCR . Using primers specifically designed for identifying mutant cells , we did not detect any PCR signals when genomic DNAs derived from recovered cells were used as template . The most parsimonious explanation for our observed results is that wild type cells completely outcompete the nuo2Δ/Δ mutant in GI tract; however , we cannot exclude another possibility that competitive advantage of the wild type cells over mutant occurs only after the mixed cells from fecal pellets are patched on SDA plate . It is likely that overgrowth of the wild type on SDA plates significantly inhibits proliferation of the nuo2Δ/Δ mutant cells since our in vitro studies indicate that the mutant has severe growth defect and grows more slowly than the wild type ( Fig 3A and S6 Fig ) . The results obtained from this mixed infection assay may not truly reflect the competitive relationship between wild type and the nuo2Δ/Δ mutant in GI tract . To assess the role of CI in commensalism more accurately , we decided to inoculate mice with a single strain other than a 1:1 mixture . Cells of WT , nuo2Δ/Δ mutant or NUO2 AB strain were inoculated by oral gavage into the antibiotic-treated BALB/c mice ( 1x108 CFU/mouse ) . Each strain shown here was evaluated in 7–10 mice , experiments were repeated twice independently . GI tract colonization was measured in fresh fecal pellets on days 3 , 5 and 8 post-inoculation . On day 3 post-inoculation , the nuo2Δ/Δ mutant colonization was moderately lower than either wild type or NUO2 AB . The colonization defects of nuo2Δ/Δ mutant were even more significant on day 5 and 8 post-inoculation ( Fig 6E ) . Thus , these results suggest that Nuo2 subunit is implicated in the murine gastrointestinal colonization by C . ablicans , possibly through its important roles in regulating utilization of alternative carbon sources which are abundant in GI tract . To further verify that CI-regulated carbon flexibility is the key factor contributing to C . albicans gut commensalism , we re-test the ability of nuo2Δ/Δ mutant to colonize the GI tract by changing the diet . We added 5% glucose to the drinking water together with antibiotics and persistently fed the mice throughout the experiment . Colonization of the nuo2Δ/Δ mutant in GI tract was monitored on day 3 , 5 and 8 post-inoculation . We found that colonization defects of the nuo2Δ/Δ mutant were substantially rescued by glucose treatment ( Fig 6F ) . In contrast , a glucose diet did not influence the gastrointestinal colonization of the wild type C . albican cells ( S25 Fig ) . Therefore , these results highlight the importance of CI activity in metabolism being crucial to compete in the GI tract and CI dysfunction entails a disadvantage that affects the capability of C . albicans to efficiently assimilate alternative carbon sources which are highly enriched in this niche . Collectively , our data suggest that CI activity integrates its roles in sensing and utilizing available carbon sources to promote hyphal growth and biofilm formation , a process that may contribute to C . albicans commensalism in the GI tract .
We propose a model ( Fig 7 ) for the role of CI activity in promoting hyphal morphogenesis , biofilm formation and gastrointestinal commensalism in C . albicans . In conditions when the sole carbon source is mannitol , CI acts to provide sufficient levels of NAD+ to sustain the enzymatic activity of NAD+-dependent mannitol dehydrogenase and advance the glycolysis by converting mannitol to mannose . Accordingly , CI dysfunction by deleting NUO2 significantly inhibits hyphae and biofilm formation in a mannitol-dependent manner , and the morphological defects are attributable to at least two distinct mechanisms . First , glycolytic inhibition by decreasing availability of NAD+ contributes to defective hyphae and biofilm formation of nuo2Δ/Δ cells because the defects can be rescued by NAD+ addition ( Fig 4C–4E ) . Second , CI dysfunction triggers production of high levels of ROS which stimulates a mannitol-dependent signaling pathway involving Hog1 phosphorylation and the transcription factor Brg1 repression , leading to transcriptional downregulation of HSGs and subsequent inhibition of hyphal growth and biofilm development ( Fig 5 and S21 Fig ) . Most importantly , glycolytic inhibition appear to be independent from activation of ROS signaling because NAD+ addition could rescue the morphological defects of nuo2Δ/Δ cells but had no effect on Brg1 expression ( Fig 4 and S20 Fig ) . Given the impact of glycolysis on glycerol production and the regulatory role of glycerol on biofilm formation of C . albicans [52] , it is likely that glycolytic inhibition in nuo2Δ/Δ mutant may affect the production of glycerol which has been known to play a regulatory role in biofilm formation by regulating expression of numerous biofilm-associated genes , in addition to its role in metabolism [52] . In addition , our observations that small amount of glucose or mannose seems to fully restore hyphal growth of nuo2Δ/Δ mutant imply the presence of the alternative mechanisms ( Fig 3C ) , which warrant future studies . In this work , we provide sufficient evidence that defects in filamentation and growth are uncoupled in the nuo2Δ/Δ mutant . Our data support the conclusion that a defect in growth may not be the predominant mechanism by which CI dysfunction blocks hyphae and biofilm formation in a mannitol-dependent manner . However , the contribution of growth defect could not be ruled out , but may play a relatively minor role in the morphological defects of nuo2Δ/Δ mutant . This conclusion was drawn upon two experimental points . First , treating the nuo2Δ/Δ cells with 0 . 1% glucose or mannose only partially improves its growth but almost fully restores hyphae formation ( Fig 3D and S6D–S6G Fig ) . Second , the mutant cells exhibited shorter filaments than the wild type after 7 days under a hypoxic condition ( Fig 3B ) , however , they remained in yeast form even after 30 days under a normoxic condition ( S2 Fig ) . Moreover , the observation that mannitol-dependent hyphal defect in nuo2Δ/Δ mutant could be rescued under hypoxia suggests the presence of a distinct signaling pathway that may override the effect of mannitol ( Fig 3B ) . A recent study in C . albicans [29] has illustrated that under hypoxia , filamentous growth could be highly induced by activation of the Cek1 MAPK pathway and a number of transcription factors , including the activator Ace2 and two repressors like Efg1 and Bcr1 , are involved . Interestingly , filamentation ( 37°C , hypoxia ) requires Brg1 only in the absence of CO2; in its presence , Brg1 was dispensable . Our analysis showed that a ROS-triggered signaling pathway , which involves Hog1 activation and Brg1 downregulation , contributes to the defective hyphae and biofilm formation of nuo2Δ/Δ mutant . More importantly , this ROS-responsive pathway seems to operate in a CI- and carbon source-dependent manner . For example , glycerol treatment also results in an inverse correlation between Hog1 activation and Brg1 repression in the nuo2Δ/Δ mutant but not wild type ( Fig 6C and S23A Fig ) , a pattern that was characteristic of mannitol effects . In contrast , GlcNAc appears to act in a CI-independent way , as we observed simultaneous induction of Hog1 phosphorylation and Brg1 expression in both wild type and nuo2Δ/Δ mutant ( Fig 6D and S23B Fig ) . These results suggest that additional , as yet uncharacterized , mechanisms may exist , depending on available carbon sources . We speculate that the differences could be due to the fact that the glycerol catabolic pathway in the yeast starts with the oxidation of glycerol to dihydroxyacetone ( DHA ) via a NAD+-dependent glycerol dehydrogenase [53] , however , NAD+ appears to be dispensable for activities of GlcNAc metabolic enzymes [54] . An interesting question was raised as to why nuo2Δ/Δ cells trigger ROS accumulation when glucose is the sole carbon source [20] , yet defects in hyphae and biofilm formation were not observed under such conditions ? In order to tackle this question , we measured intracellular ROS production of different strains in conditions when glucose or mannitol was used as the sole carbon source . Although the nuo2Δ/Δ cells exhibited relatively higher levels of ROS than wild type in glucose condition , we surprisingly observed a continued increase of endogenous ROS after exposure to mannitol ( Fig 5A , S9 Fig ) . Since NUO2 encodes the subunit of NADH:ubiquinone oxidoreductase catalyzing the transfer of electrons from NADH to ubiquinone in a reaction involving proton translocation across the membrane [15] , it is plausible that in the mannitol condition , loss of NUO2 blocks glycolysis via downregulation of the NAD+-dependent mannitol dehydrogenase and thus might redirect the carbohydrate flux to an as yet unknown pathway resulting in a further accumulation of electrons that react with ambient oxygen to produce even higher levels of ROS [40 , 41] . In addition to being the major systemic fungal pathogen of humans , C . albicans is predominantly considered as part of the commensal microbiome on the mucosal surfaces of the oral and vaginal cavities and gastrointestinal ( GI ) tract . Environmental cues , such as changes in carbon source , were found to be vital for the variety and dynamism of the niches that C . albicans inhabits in the human host niches [55] . It has been well documented that fermentative carbon hydrates such as glucose , fructose , or galactose , although are widely used in our in vitro assays for culturing fungal cells , are not available or present at low concentrations in host niches mentioned above [6 , 9 , 56] . Instead , alternative carbon sources , such as amino acids , glycerol , mannitol and organic acids , provide the main nutrients that support the in vivo growth of the infecting fungus . A previous study in humans has shown that 74% of mannitol passes through the small intestine and reaches the large intestine , where it is utilized by microbes like beneficial bacteria to produce organic acids , which can be used by the host [57] . Similarly , mannitol in rats was found to escape metabolism in the small intestine and reaches the cecum intact , where it is fermented by local microbes and promotes absorption of calcium and magnesium in the large intestine [58] . Therefore , metabolic flexibility of C . albicans reflects its ability to utilize a variety of different nutrients available in the diverse microenvironments of the host . If so , it makes sense that a successful colonization of C . albicans in GI tract may rely on its ability to assimilate locally available carbon sources such as mannitol . Given a severe defect of the nuo2Δ/Δ cells in assimilation of alternative carbon sources , it is not surprising that CI dysfunction may have profound effects on C . albicans commensalism in a mammalian host . Indeed , our commensalism experiments revealed that the nuo2Δ/Δ cells , but not wild type , conferred a significant defect in fitness in the mammalian GI tract ( Fig 6E and S25 Fig ) . Remarkably , we note that this commensal defect can be rescued by changing a glucose-rich diet ( Fig 6F ) . We conclude from this set of experiments that CI activity plays a major role in metabolism being crucial to compete in the GI tract and CI dysfunction entails a disadvantage that affects the capability of C . albicans to efficiently assimilate locally available nutrients , in particular , alternative carbon sources which are highly enriched and crucial for its colonization in this niche . Although a growing body of literature has validated the necessity of regulation of alternative carbon source assimilation in promoting C . albicans pathogenicity [9 , 22 , 59] , studies about its role in gastrointestinal commensalism are scarce . The results presented here , to our knowledge , for the first time unravel a mechanism underlying the interplay between CI-regulated utilization of alternative carbon sources and C . albicans commensalism . We propose that CI executes a carbon-dependent strategy to allow C . albicans to adjust to disparate environments with varied availability of carbon sources , highlighting its important role in bridging a connection between carbon flexibility regulation and gastrointestinal commensalism . Future work will aim to decipher the comprehensive picture of this signaling pathway responding to different alternative sources and if possible , to evaluate its impact on C . albicans colonization in other host niches such as skin and genital mucosa .
Female BALB/c mice were purchased from Shanghai Laboratory Animal Center ( Shanghai , China ) . Mice that were 6–8 weeks old and weighed 16-20g were used . These mice were maintained in a pathogen-free animal facility at Institut Pasteur of Shanghai . Infections were performed under SPF conditions . Animals were inoculated by oral gavage with the indicated dose of C . albicans cells in 100μl PBS buffer . All animal experiments were carried out in strict accordance with the regulations in the Guide for the Care and Use of Laboratory Animals issued by the Ministry of Science and Technology of the People's Republic of China . All efforts were made to minimize suffering . The protocol was approved by IACUC at the Institut Pasteur of Shanghai , Chinese Academy of Sciences ( Permit Number: A150291 ) C . albicans strains were routinely propagated at 30°C in YPD . For growth phenotypes , yeast cells were grown at 37°C in YEP ( 1% yeast extract , 2% Facto-Peptone ) , Spider [60] or RPMI 1640 cell culture medium ( Gibco corp . ) supplemented with the appropriate carbon source including each of fermentable sugars ( glucose , mannose , fructose and galactose ) or alternative carbon sources ( mannitol , GlcNAc , lactic acid , sorbitol , glycerol ) at indicated concentrations . All carbons were purchased from Sigma-Aldrich . For carbon source-shift assays , logarithmically growing cells were diluted 1:250 in YPD medium and incubated at 37°C for 6 hours to reach an OD600 of 0 . 5 , and sequentially washed twice with pre-warmed sterile water and reinoculated to equal volume of either YPD ( glucose as carbon source ) or YPM ( mannitol as carbon source ) . Cells were continued to grow at 37°C and harvested for immunoblotting assays at indicated time points . SC5314 genomic DNA was used as the template for all PCR amplifications of C . albicans genes . The C . albicans strains used in this study are listed in S1 Table . The primers used for PCR amplification are listed in S2 Table . Plasmids used for Brg1-Myc tagging and knockout gene complementation are listed in S3 Table . Construction of C . albicans knockout mutants , complemented strains , strains expressing Myc-tagged Brg1 fusion protein , and overexpression strain for BRG1 was performed as previously described [51] . The in vitro biofilm growth assays were carried out using a previously established protocol with minor modifications [25] . In brief , overnight cultures of C . albicans strains were grown in YPD at 30°C . After wash twice with phosphate-buffered saline ( PBS ) , the cells were diluted to an optical density at OD600 of 0 . 5 in 2ml of Spider medium . The 12-well polystyrene plate alone or 12-well plate with silicone square were previously treated with 10% fetal bovine serum overnight and washed with 2 ml of PBS . After inoculating each of indicated C . albicans strains , the plates were incubated at 37°C for 90 min at 100 rpm to allow initial adhesion of cells . Each well was washed once with 2ml of PBS to remove any nonadhering cells , and 2ml of fresh spider media was added to each well and biofilms were grown for another 48 h as described above . After removing the medium , each well was washed with 2ml of PBS , dried , and photographed or cells in the biofilm resuspended and quantified . For dry-mass measurements , the silicone squares were dried overnight and weighed the following day . The average total biomass of each strain was calculated from six independent samples after subtracting the mass of square with no cells added . Statistical significance ( P values ) was calculated using the Student’s two-tailed paired t test . Hyphal induction was manipulated using a standard method . Strains were grown overnight in a liquid YPD at 30°C , pelleted , washed twice in PBS , resuspended in an equal volume of PBS , and diluted 1:250 in different growth media . Cells were incubated at 37°C , harvested and hyphal morphologies were visualized under microscopy at indicated time points . For hyphal induction in hypoxia , experiments were conducted using Thermo Scientific Forma CO2 incubator ( Thermo Scientific ) by maintaining the oxygen concentration less than 0 . 2% O2 . Following strain inoculation , the plate was placed into the chamber at 37°C immediately and photographed after 7 days . Saturated overnight cultures of wild type , nuo2Δ/Δ , nuo2Δ/Δ+NUO2 complemented strains were inoculated into YPD to OD600 = 10−4 and incubated with shaking at 30°C . The next morning , logarithmically growing cells were diluted 1:250 in YPD medium and incubated at 30°C to reach an OD600 = ~0 . 4–0 . 5 . Cells were collected , washed twice with pre-warmed water , resuspended in equal volume of freshly prepared YPD or YPM medium . After 2 h of incubation at 37°C , cells were harvested by centrifugation and methods for RNA isolation were carried out using a hot phenol method [61] . 1–2μg of each RNA was treated with RNase-free DNase I ( Promega , Madison WI , USA ) and reverse transcribed using the GoScript Reverse Transcription System ( Promega , Madison WI , USA ) . qPCR was performed using the SYBR Green Master Mix ( High ROX Premixed ) ( Vazyme , Nanjing , China ) using the primers CBO278 and CBO279 for BCR1 , CBO280 and CBO281 for BRG1 , CBO282 and CBO283 for EFG1 , CBO284 and CBO285 for TEC1 , CBO286 and CBO287 for NDT80 , CBO288 and CBO289 for ROB1 , CBO290 and CBO291 for HYR1 , CBO292 and CBO293 for HWP1 , CBO294 and CBO295 for ECE1 , CBO296 and CBO297 for ALS3 . Normalization of expression levels was carried out using the ACT1 genes and the primers for ACT1 was used as previously described [61] . At least three biological replicates were performed per strain per condition . A previously described protocol was used to prepare C . albicans protein extracts [51] . Lysates corresponding to 1 OD600 of cells were analyzed by SDS-PAGE and immunoblotted with the antibodies including the anti-c-Myc ( 9E10 , Covance Research ) for Myc-tagged proteins , the anti-phospho-p38 MAPK ( Thr180/Tyr182 ) ( 3D7 ) antibody 9215S ( Cell Signaling , MA , USA ) for phosphorylated Hog1 proteins , the anti-Hog1 ( y-215 ) antibody sc-9079 ( Santa Cruz Biotechnology , CA , USA ) for total Hog1 proteins . Immunoblots were also probed with anti-α-tubulin antibody NB100-1639 ( Novus Biologicals , CO , USA ) as a loading control . Cell extracts for C . albicans mannitol dehydrogenase activity assays were prepared using a protocol adapted from a method previously described in S . cerevisiae [39] . Briefly , cells of wild type , nuo2Δ/Δ and NUO2 AB strains were grown at 37°C in 50ml of YPD ( YEP + 2% of glucose ) or YPM ( YEP + 2% of mannitol ) for 2 days with continuous shaking at the speed of 95 rpm . After washed once with sterile water , half of the cells were resuspended in 750μl of 50 mM sodium phosphate ( pH 6 . 5 ) and disrupted with acid-washed glass beads ( Sigma-Aldrich ) using a high-throughput tissue homogenizer scientz-192 ( settings: 70MHZ/s , 8 x 30s with an 30s interval on ice ) . Cell lysates were centrifuged at 20 , 000xg for 10 min at 4°C and the supernatant was used for enzymatic assays . Mannitol dehydrogenase activity was assayed at 30°C using a standard method described previously [62] . The assay mixture contained 100 mM mannitol , 0 . 36 mM NAD in 25 mM Na 2- ( N-cyclohexylamino ) ethanesulfonic ( CHES ) buffer ( pH 9 . 0 ) , and cell extract ( 0 . 5 to 10g ) . Reactions were initiated with enzyme and NADH formation was monitored at 340 nm by using a SmartSpec Plus spectrophotometer ( Bio-Rad ) . One unit of enzyme activity was defined as 1 . 0 mol of NADH produced in 1 min at 30°C . A single colony of nuo2Δ/Δ strain was inoculated to YPD and incubated at 30°C . Logarithmically growing cells were washed three times with pre-warmed sterile water , resuspended in the same volume of YEP supplemented with 2% of mannitol , and continued to incubate at 37°C . Cell lysates were prepared with one-third volume of 0 . 5mm acid-washed glass beads ( Sigma-Aldrich ) using a high-throughput tissue homogenizer scientz-192 ( Scientz , Ningbo , China ) , and NAD+ and NADH contents were measured using a NAD+/NADH Quantification Kit ( Sigma-Aldrich ) . Intracellular ROS production was detected by a dihydrorhodamine 123 ( DHR 123 ) staining method previously described [63] . Briefly , Cells of each strain were grown to mid-log stage in either YPD or YPM medium . After incubation , cells were harvested , washed , and resuspended in sterile PBS to a concentration of 2x107 CFU/ml and stained with DHR123 ( 10mM ) in the dark for 60min . After stained cells were collected by centrifugation and washed twice with PBS , total fluorescence of each sample was measured using a Cytoflow 2300 fluorescence spectrometer ( Millipore Co . , Billerica , MA ) with excitation at 488 nm and emission at 525 nm . We carried out two independent in vivo approaches to explore the effect of CI activity on C . albicans commensalism in murine GI tract . First , a competitive infection assay was performed using the mouse model of C . albicans commensalism , based on a previously reported protocol [51] . Briefly , groups of 6–8 week female BALB/c mice ( Shanghai Laboratory Animal Center , Shanghai , China ) were treated with penicillin ( 1500 μm/ml ) and streptomycin ( 2 mg/ml ) added to drinking water containing with or without 5% of glucose throughout the experiment beginning 3 d prior to inoculation . Mice were inoculated by oral gavage with 1x108 CFUs of 1:1 mix of wild type and nuo2Δ/Δ . Fecal pellets were collected at various days post-inoculation and C . albicans was recovered by plating homogenates of mouse feces onto Sabouraud agar medium ( with ampicillin 50 μm/ml , gentamicin 15 μm/ml ) . Relative abundances of strains in the infecting inoculum and after recovery from murine fecal pellets were determined by qPCR , using strain-specific primers ( S2 Table ) . A second approach was carried out using a protocol described previously [64] . In this assay , most of procedures are exactly the same as the first one , unless each group of mice was inoculated by gavage with each of the three strains ( wild type , nuo2Δ/Δ or NUO2AB; 108 CFUs cells/mouse ) . Facal pellets were collected at various days post-inoculation , homogenized in PBS buffer , diluted and plated on Sabouraud agar medium ( with ampicillin 50 μm/ml , gentamicin 15 μm/ml ) . Colonization was determined by counting the number of C . albicans cells grown on each plate . Data were analyzed using R and statistical significance ( P values ) was calculated using the post-hoc pairwise t-tests . | Most fermentative sugars like glucose , although routinely used in laboratory cell culture medium , are in fact only present at very low levels and even absent in many host niches . Therefore , assimilation of alternative nutrients is essential for the survival , proliferation and infection of most clinically important microbial pathogens like C . albicans in their hosts . In this study , we show that mitochondrial complex I ( CI ) is indispensable for proper hyphal growth and biofilm formation of C . albicans cells when mannitol , but not fermentative sugars like glucose or mannose , is used as the sole carbon source . We also find that a specific signaling pathway that senses and responds to the alternative carbon source incorporates input from mitochondrially-derived molecules like reactive oxygen species ( ROS ) to influence activation of the Hog1 MAPK and expression of the biofilm-regulator Brg1 . Our findings further demonstrate that CI dysfunction confers a severe defect of C . albicans in gastrointestinal colonization and changing the diet with glucose is able to significantly rescue the commensal defect . Our study suggests that C . albicans has a unique regulatory system to sense and utilize the alternative carbon sources abundant in the GI tract and to promote commensalism . Significantly , CI activity appears to play a vital role in this highly adaptive system to regulate commensalism , in addition to its well-characterized role in virulence . | [
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"experimental"... | 2017 | Mitochondrial complex I bridges a connection between regulation of carbon flexibility and gastrointestinal commensalism in the human fungal pathogen Candida albicans |
Worldwide , the theory and practice of agricultural extension system have been dominated for almost half a century by Rogers' “diffusion of innovation theory” . In particular , the success of integrated pest management ( IPM ) extension programs depends on the effectiveness of IPM information diffusion from trained farmers to other farmers , an important assumption which underpins funding from development organizations . Here we developed an innovative approach through an agent-based model ( ABM ) combining social ( diffusion theory ) and biological ( pest population dynamics ) models to study the role of cooperation among small-scale farmers to share IPM information for controlling an invasive pest . The model was implemented with field data , including learning processes and control efficiency , from large scale surveys in the Ecuadorian Andes . Our results predict that although cooperation had short-term costs for individual farmers , it paid in the long run as it decreased pest infestation at the community scale . However , the slow learning process placed restrictions on the knowledge that could be generated within farmer communities over time , giving rise to natural lags in IPM diffusion and applications . We further showed that if individuals learn from others about the benefits of early prevention of new pests , then educational effort may have a sustainable long-run impact . Consistent with models of information diffusion theory , our results demonstrate how an integrated approach combining ecological and social systems would help better predict the success of IPM programs . This approach has potential beyond pest management as it could be applied to any resource management program seeking to spread innovations across populations .
In view of the growing number of challenges related to controlling agricultural pests , the promotion of Integrated Pest Management practices ( IPM; a range of methods used for responsible pest control ) has a larger place than ever on the international policy agenda [1] , [2] . The participation of local communities and other stakeholders in such management processes has long been advocated as an essential step to achieve sustainable development [3] . Over the past decades , extension science has developed many types of participatory approaches towards farmers [4] to promote knowledge of agro-ecological concepts , apply IPM practices , reduce the use of pesticides and improve crop yields [5] . As budget and manpower constraints do generally not allow for direct interaction with every member of the target population , the strategy of most participative IPM programs is to train a limited number of farmers in the community who commit themselves to share the information they learn with other farmers [6] . Following Rogers' “diffusion of innovation theory” [7] , the success of extension practices depends on the effectiveness of cooperation among farmers which determines IPM information diffusion from trained farmers ( graduate farmers ) to other farmers ( exposed farmers ) . Funding from international development organizations often relies on the important , but poorly studied , assumption that farmers cooperate with their peers , neighbors , or friends [8] . Increasing our understanding of farmers' cooperation theory and practice is a timely issue as field-level interactions among small-scale farmers are increasingly limited in a world of intense social reorganizations associated with land distribution , privatization of ownership , and market-oriented society [9] . A collective action problem that requires farmers to cooperate in information diffusion is exemplified by invasive pest control in fragmented agro-ecosystems [10] . If neighbors of graduate farmers do not adopt IPM measures , then the invasive pests from their fields can re-infest the graduate farmers' fields even if they apply IPM principles [11] . Moreover , in the case of emergent invasive species , farmers cannot rely on preexisting local knowledge , which makes them even more dependent on externally based experience . In farmer communities , IPM for invasive species is therefore characterized by a conflict of interest between individual and group benefit leading to cooperation dilemma [12] , [13] . On the one hand , cooperation by graduate farmers to share IPM information is expected , in the end , to benefit the whole community of farmers ( including themselves ) by an area-wide suppression of the pest . On the other hand , under the assumption that graduate farmers want to prioritize control in their fields instead of training other farmers , theory predicts that individuals might have little incentive to cooperate and will not contribute to the public good [12] . Both types of behaviors have been classically observed in a wide array of agricultural situations [1] . In the specific case of IPM , farmers' decisions about whether to disseminate or not pest control practices will be closely dependent on pest infestation levels in their own field [1] . This means that farmers' dilemma to train others or not will be tightly linked to pest dynamics at the landscape level , itself depending on landscape characteristics , pest ecology and control behaviors of other famers . Exploring the relative merits of helping others vs . self interest in IPM information diffusion therefore requires the coupling of ecological and sociological models , an approach which has , to our knowledge , never been performed in the context of IPM . The objective of our study was to develop a methodological framework to explore the relevance of participative IPM extension programs for pest control . We carried out these investigations in the context of an IPM program launched to help small scale farmers facing the arrival of an invasive insect pest , the potato tuber moth ( Tecia solanivora Povolny ) in the Ecuadorian Andes [14] . This region was highly relevant for our study as there is a long history of social reciprocity in the Andes that extends to pre-Incan times and has been one of the keystones for why farmers have been able to successfully farm for centuries in such harsh conditions [15] . We then built an agent-based model ( ABM , [16] , [17] ) merging a spatially explicit pest population dynamic model through a cellular automaton ( CA ) with a field-based multi-agent system describing farmer features and behaviors ( Fig . 1A ) . The global output of our ABM was determined from pest–landscape interactions , pest-farmer interactions , and inter-farmer interactions . To mimic real-world patterns of farmer behaviors as closely as possible , our ABM was implemented with field data , including learning processes and control efficiency , from large scale surveys from c . a . 300 farmer households in the Ecuadorian Andes . In our model , the agricultural landscape was modeled as a lattice composed of cells that represented various land plots of groups of farmers ( hereafter named agents ) within the same community ( in total , 6 neighbor agents in the same community representing about 220 people , Fig . 1B ) . Pest dynamics was driven by the intrinsic population growth , migration , and pest control practiced by agents depending on their IPM knowledge . Under our IPM program , one agent was trained to control pest infestation in his fields . In return , this graduate agent was required to diffuse the IPM information to other agents so that they can increase their IPM knowledge and implement efficient practices . Agent decision to diffuse the information to others mainly depended on pest infestation level in his fields but also on social and economic factors included in the diffusion process of IPM information among farmers . Therefore , pest control at the community level was modeled as emerging from IPM information acquired by one graduate agent and spreading through exposed agents ( see Text S1 ) . We believe that the relevance of our study stands in two main points . First , recent works on collective actions of IPM diffusion have reported that because behaviors and perceptions towards new information and technology can vary widely among farmers , farmers' behavioral heterogeneity is a key issue to understand and predict the success of pest control information diffusion throughout the community , and therefore the success of the IPM program at a large scale [14] , [18] . In this context , ABMs may reveal ideal tools to better understand and predict the sustainable development of farmers' control practices [19]–[21] as they allow simulating the actions and interactions of autonomous agents ( either individual or collective entities such as organizations or groups of farmers ) with a view to assessing their effects on the system as a whole . Using ABM therefore allows integrating behavioral complexity of farmers and performing theoretical experiments ( e . g . , varying the level of farmer cooperation ) which could not be performed in the real world ( for time , ethical or financial reasons ) . Although ABM have increasingly been applied to physical , biological , medical , social , and economic problems [22] , [23] , [16] it has been , to our knowledge , completely disregarded by IPM theory and practice . Second , our study proposes an innovative computational framework merging recent advances in contagion-like model of knowledge diffusion through human populations [24] , [25] and coupled land management models with spatially explicit species spread models ( see papers presented at LandMod 2010 or Global Land Project 2010 ) . Such a framework combining two approaches which developed in relative independence likely has potential beyond pest management as it could be applied to any resource management program seeking to spread innovations across populations .
The field survey revealed that , at the beginning of our program , a majority of farmers ( 87% ) had a low IPM knowledge ( score ranging between 0 and 2 ) regarding potato moth control ( Fig . 2A ) . Our data further showed that although this knowledge could be greatly increased through training ( graduate farmers reached an IPM knowledge of 4 . 39±0 . 61 ) , those skills were not easily diffused to exposed farmers by informal training sessions ( Fig . 2B ) . After having graduate farmers shared information with exposed farmers the mean knowledge score of the 64 surveyed exposed farmers increased only slightly when compared to control , from 0 . 96±0 . 80 to 1 . 65±0 . 53 ( Student t-test , t = −1 . 717 , P = 0 . 111 ) . Interestingly , although moth control gradually increased with increasing IPM knowledge scores ( linear model fit , R2 = 0 . 51 , P<0 . 001 ) , there were a few cases in which farmers with relatively high IPM knowledge had also poorly efficient pest control in their fields , probably due to contamination from neighboring fields ( Fig . 2C ) . Once the ABM was set up with these real-world data , we explored on a 20-year time scale the influence of the level of cooperation among agents ( i . e . how often graduate agents did share their information with others ) on pest infestation levels . Our model predicted that knowledge acquisition by exposed agents would follow a logistic regression through time ( R2 = 0 . 50±0 . 11 , P<0 . 05 , Fig . 3A ) . Our simulations further predicted that both IPM knowledge diffusion and spillover after training would significantly decrease moth infestation by 60 to 70% from their initial levels ( Fig . 3B ) . Time dedicated by graduate agents to train exposed agents instead of controlling pest had the short term consequence of increasing pest infestation in his own land ( interviews with farmers revealed that training others would demand time and compromise of coordination with consequences in terms of pest control in their own field . ) . However , as exposed agents were being trained , graduate agents were less solicited thereby being able to dedicate more time to pest control . Importantly , the patterns of IPM information diffusion among agents predicted by our ABM was consistent with the Bass model ( F-test , P<0 . 001 , Fig . 4 ) , a model traditionally used in diffusion of innovations [24] . The ability of our ABM to reproduce Bass model predictions therefore provided a validation of the correctness of information adoption patterns among agents , mainly through internal ( “word-of-mouth” ) influences . Results of our simulation of the effect of farmer's cooperation level on pest control showed that within the first 6–7 years , pest infestation levels in both graduate and exposed agents' lands remained higher than those expected in the lands of a non-cooperating agent , whatever the cooperation levels . After 6–7 years , cooperating graduate agents had lower pest infestation level than non-cooperating ones , and therefore received the benefit of cooperating . Finally , for high levels of cooperation among agents ( >0 . 5 ) , our model predicted that after 6–7 years , pest infestation levels at the scale of the entire community ( i . e . in all lands of agents ) would be lower than levels expected in the fields of a non-cooperating graduate agent . The benefit of cooperation had therefore scaled up at the level of the whole community of agents ( Fig . 5 ) .
Since the emergence of the concept of knowledge based economy [26] , the analysis of information diffusion has become a key issue to organization research [27] . Our results showed that the slow IPM learning process measured in Andean farmer communities placed restrictions on the amount of information that could be diffused within the community over time , giving rise to natural lags in IPM applications . This reinforces the view that IPM outcome at the community level will be achieved on a relatively long-term scale for the farmer , a feature which may be common to many agriculture programs . In an influential study that spawned an enormous diffusion of literature in rural sociology , [28] , estimated that it took 14 years before hybrid seed corn was completely adopted in two Iowa communities . Rogers [7] also reported slow adoption in crop protection management in the Colombian Andes and Berger [21] showed that behavioral heterogeneity among Chilean farmers , delayed for almost 10 years the use of new irrigation methods . In our study , the six year delay in benefits of cooperation was mainly due to the limited spread of IPM information from graduate to exposed farmers which itself may have been a consequence of high IPM knowledge heterogeneity among farmers . Information is indeed expected to flow less smoothly in a heterogeneous population , particularly when the performance of new practices is sensitive to imperfectly transmitted information [29] . Our simulations also showed that there were short-term costs for the diffusion of IPM information resulting from our assumption that farmers cannot control pests in their own fields when they share IMP information with other farmers . Indeed , “lack of time” is a common motive invoked by farmers when they are questioned why they do not share IPM practices they learned with neighboring farmers [30] . As farmers often believe that there is a trade-off between diffusing and practicing IPM information , we think that an important outcome of our study was to show that , even if such a trade-off is included in the model , cooperating farmers would still benefit from IPM information diffusion in the long run . It is also likely that , in some cases , farmers may practice and diffuse new information simultaneously [1] . Cooperating farmers would then not suffer from short-term costs , potentially increasing their cooperation will , thereby speeding up information transfer throughout the community . Obviously , our modeling approach made a series of simplifications which may be important to consider . For example , farmers usually tend to make high contributions initially but over time contributions dwindle to low levels . Many people are conditional cooperators , who in principle are willing to cooperate if others do so as well , but get frustrated if others do not pull their weight [31] . In agricultural systems personal networks , where trusted people ( prestigious individuals , people of authority or holding otherwise vested power and influence ) often play a key role in decision making , are difficult to integrate into models due to their dynamic , multi-directional , and non-symmetric nature [32] . Moreover the spread of behaviors may arise from the spread of social norms or from other psychosocial processes , such as various types of innate mimicry [33] . A recent study has shown that cooperative behaviors can cascade in human social networks even when people interact with strangers or when reciprocity is not possible; people simply mimic the behavior they observe , and this mimicking can cause behaviors to spread from person to person to person [34] . In this case , the rate of diffusion is largely dependent upon the knowledge ( i . e . , relative advantage , compatibility within the social setting , observability , and simplicity ) . Finally , another limitation may arise from the use of a behavioral reciprocity model . Theoretically , the adoption of IPM cooperative behavior among farmers could be favored as the reciprocated benefit outweighed the immediate cost [27] . However , in practice , the delay between the cost of a cooperative act and the benefit of reciprocated cooperation ( from 7 to 20 years for graduate agents in our study ) would introduce a number of cognitive challenges . For example , temporal discounting ( for example devaluing of future rewards in the case of shift in crop type produced ) , often results in a preference for smaller , immediate rewards over larger , delayed rewards [35] . Variation in human discounting and cooperation validate the view that a preference for immediate rewards may inhibit reciprocity [35] . Despite these limits the ability of our model to capture real-world patterns of pest control ( Fig . S5 in Text S1 ) and information diffusion ( Fig . 4 ) indicates that our findings may yield important insights for IPM science and policies . First , IPM programs worldwide are confronting the reality of increasingly subdivided habitats managed as smaller areas , reducing the likelihood that pest population will be controlled , thereby requiring higher levels of cooperation among farmers [10] . We showed that when farmers make control decisions based on lower levels of damages occurring on their own land , they can increase information spread and the speed with which the whole community can control pest populations . Second , our study stresses the need to develop a comprehensive and empirically-based framework for linking the social and ecological disciplines across space and time [19] . In our model , predictions of the coupled dynamic of pests and farmer behavior show the evidence that farmer to farmer training can help the broader community control pest infestation in the long term . Third , as institutions increasingly seek to help communities sustainably providing local public goods themselves rather than depend on external assistance , the idea that development projects should aim at financial sustainability through local cooperative actions has had tremendous influence on funders . Our study shows that sustainable approaches to providing local public goods concerning invasive pest control would be possible despite a challenging delay between the cost of a communal act and the benefit of reciprocated cooperation . However , if individuals learn from others about the benefits of early prevention of invasive pests ( i . e . cooperation takes from low levels of pest populations ) , then a temporary educational effort may have a sustainable long-run impact .
We addressed the issue of the importance of farmer cooperation in invasive pest management in the socio-agricultural system of the Ecuadorian highlands where potatoes ( Solanum tuberosum L ) , are a major staple [36] . In 1996 a new pest , T . solanivora , invaded the country attacking potato tubers in the field and in storage and becoming one of the most damaging crop pests in the region [37] . Under the climatic conditions of the Ecuadorian highlands ( sierra ) potatoes are grown at any time of the year between elevations of 2400 m and 3800 m elevation [38] . The agricultural landscape of the highlands is made up of a mosaic of small potato fields ( <1 ha ) at various stages of maturation in which potato moths are active all year round . IPM programs have been implemented for about 10 years by the INIAP ( Ecuador's National Institute for Agronomy Research ) and the CIP ( International Potato Center ) , through the Farmer Field School methodology [39] . In the North Andean region , collaborative work in the form of “mingas” and “Aynis” is necessary among small groups of farmers in order to realize hard tasks like sowing or harvesting . These labor force exchanges , despite of being very hierarchical , share common practices [40]–[42] . We built a representation of socio-agronomical landscapes of the central Andes at an altitude of 3000 m , which corresponds to the zone where most farmers cultivate potato . This landscape comprised three key elements: the socio-agricultural landscape , the potato moth population , and the groups of farmers ( Fig . 1B ) . First , characteristics of the socio-agricultural landscape were set up using data from published field surveys: 1 ) the median community size in the study area was about 150 people [14] which roughly corresponded to 6 household units ( i . e . a group of fields cultivated by one group of farmers ) . 2 ) The size of elemental cells was set up to 500 m×500 m in order to accurately model pest dispersion among cells with regards to insect's flight capability [43] . 3 ) Seasonal variability in climatic features ( both temperature and rainfall ) for each cell was obtained using the Worldclim data set [44] . Second , potato moth dynamics were simulated through a cellular automaton ( CA ) recently developed by our team [43] . Briefly , the CA is spatially explicit , stage-structured , and based on biological and ecological rules derived from field and laboratory data for T . solanivora's physiological responses to climate ( temperature and rainfall ) . Main processes include moth survival ( climate dependent ) , dispersal to neighbor cells through diffusion processes ( density dependent ) , and reproduction ( climate dependent ) ( see Fig . S1 in Text S1 ) . In each time step ( equivalent to one moth generation , about 2 months ) the infestation grows and spread over household units . A Mathematical presentation of the underlying principles of the pest model , along with general results identifying the important simulation details and their consequences , are given in [45] . Third , to transfer the pest model into an ABM we populated the agricultural landscape with artificial agents acting individually upon pest dynamics ( see Fig . 1A and Appendix for a complete description of the model structure ) . Briefly , each agent represented a group of farmers and was set with a behavioral model that guided his or her decisions . Potato moth control at the community level was modeled as emerging from IPM information spreading through agents that composed the community . The ability to learn IPM recommendations was considered as an adaptive trait that indirectly contributed to agent's fitness by improving their capability of controlling pest populations ( and therefore assuring their crop production ) . Agents with different IPM knowledge interacted directly with each other to exchange information ( agents with less information learned from other agents ) . We used a reciprocity model for cooperation in which agents paid a short term cost of cooperation for the future benefit of a community member's reciprocated cooperation [35] . Agents indeed perform multiple roles which constrict the amount of time and energy they may allot to any single activity . They perceived and controlled pest infestation levels in their field depending on their IPM knowledge ( see below and Protocol S1 , S2 ) . To explore the profitability of our IPM program as a function of the coupled dynamics of agent behaviors ( and learning spillover ) and pest population , we needed three pieces of field information: 1 ) the initial IPM knowledge of each agent in the community , 2 ) the relationship between IPM knowledge and pest control , and 3 ) the efficiency of IPM information diffusion between graduate and exposed agents ( including a wide range of social factors influencing innovation diffusion ) . We acquired these data through a farm-level empirical survey from nationally representative samples of farmers in rural Highland Ecuador . Our database was obtained through a three-year household survey conducted in 2006–2008 in four provinces of the Ecuadorian highlands ( Bolivar , Tungurahua , Cotopaxi , and Chimborazo ) using standard household survey techniques [46] . Survey zones had not been covered by any educational program regarding potato moth management . In total , 293 potato grower families from about 100 different communities were interviewed , gathering data on IPM knowledge in communities and pest control . The efficiency of IPM learning and dissemination processes was assessed through farmer field schools as described in details by [30] . Briefly in each target community , we first performed a baseline study of IPM knowledge for as many community members as possible . Farmers interested in IPM extension were then trained through FFS procedures during eight one-day sessions over the duration of potato crop cycle ( about 4 months ) . Each graduate farmer committed himself in training at least five other farmers . Informal discussion with trained framers revealed that the amount of time they dedicated in training other farmers varied greatly , between several hours to several days . Exposed farmers were then interviewed to measure their IPM knowledge and the efficiency of the IPM information diffusion process . In each community , the IPM knowledge of agents were set up according to the frequency distribution presented in Fig . 2A ( one agent with a score of 0 , two with a score of 1 , two with a score of 2 , and one with a score of 3 ) . We then increased the knowledge of the agent with a score of 3 to a score of 5 as if it had participated in a FFS ( see Fig . 2B ) . This agent became the graduate agent of the community . According to FFS recommendations , this agent ( in the case he or she was eager to cooperate ) shared his information with exposed agents of his community ( defined as an agent with a lower IPM knowledge ) . Once other exposed agents achieved , in turn , a higher IPM knowledge , they could also share their information with neighbor agents . An agent could share information with only one agent with a lower IPM knowledge ( during this time the farmer could not control pest in his fields ) . When not sharing their information each agent was able to control pest in his field with an efficiency which depended on their IPM knowledge ( following Fig . 2C ) . Again , the pest level in each cell was driven by both intrinsic population growth and diffusion from neighbor cells ( see above ) . Once the ABM was set up and sensitivity analysis performed ( Fig . S2–S4 in Text S1 ) , we further explored how agents' level of cooperation ( i . e . how available agents were to share their information with others ) would influence the benefits of our IPM program at both individual farmer and community levels . Because decision of poor farmers to cooperate for crop protection is likely to be driven by self-interest rather than altruism [14] , [15] , we assumed that farmers would be more prone to cooperate in IPM information diffusion when they perceive that a pest represents a danger for themselves . In our model , varying levels of cooperation were obtained by changing the pest infestation level that triggered a control action by agents ( see Text S1 ) . Each simulation was repeated 100 times over 120 time steps ( i . e . about 20 years ) and pest infestation levels were given for exposed agents , graduate agents , and the whole farmer community . | Food security of millions of people in the third world has faced a growing number of challenges in recent years including risks associated with emergent agricultural pests . Worldwide , the promotion of integrated pest management practices has been heavily promoted through participative methodologies relying on farmer cooperation to share pest control information . Recent studies have put into doubt the efficiency of such methodologies evoking our poor knowledge of farmers' perceptions , behavioral heterogeneity , and complex interaction with pest dynamics . While pest management programs have a larger place than ever on the international policy agenda , the debate concerning their efficiency at large scales has remained unresolved . Here , we developed an innovative modeling approach coupling pest control information diffusion and pest population dynamics to study the role of cooperation among farmers to share the information . We found that the slow learning process placed restrictions on the knowledge that could be generated within farmer communities over time , giving rise to natural lags in pest control diffusion and applications . However , our model also predicts that if individuals learn from others about the benefits of early prevention of invasive pests , then a temporary educational effort may have a sustainable long-run impact . | [
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] | 2011 | Coupled Information Diffusion–Pest Dynamics Models Predict Delayed Benefits of Farmer Cooperation in Pest Management Programs |
Comparing gene expression profiles over many different conditions has led to insights that were not obvious from single experiments . In the same way , comparing patterns of natural selection across a set of ecologically distinct species may extend what can be learned from individual genome-wide surveys . Toward this end , we show how variation in protein evolutionary rates , after correcting for genome-wide effects such as mutation rate and demographic factors , can be used to estimate the level and types of natural selection acting on genes across different species . We identify unusually rapidly and slowly evolving genes , relative to empirically derived genome-wide and gene family-specific background rates for 744 core protein families in 30 γ-proteobacterial species . We describe the pattern of fast or slow evolution across species as the “selective signature” of a gene . Selective signatures represent a profile of selection across species that is predictive of gene function: pairs of genes with correlated selective signatures are more likely to share the same cellular function , and genes in the same pathway can evolve in concert . For example , glycolysis and phenylalanine metabolism genes evolve rapidly in Idiomarina loihiensis , mirroring an ecological shift in carbon source from sugars to amino acids . In a broader context , our results suggest that the genomic landscape is organized into functional modules even at the level of natural selection , and thus it may be easier than expected to understand the complex evolutionary pressures on a cell .
An enormous genetic diversity exists on earth , particularly in the microbial domains of life - yet how much diversity is functional , and what are the important adaptations that serve to partition species into different niches ? Adaptive differences can be identified in genes subject to positive Darwinian selection - the evolutionary force that causes advantageous genetic traits to spread in populations , allowing species to differentiate ecologically . Natural selection acts not just on individual proteins , but on the complex assemblage of proteins specified by an organism's genome . Thus , looking for natural selection across the entire genome is valuable for two reasons . First , it allows us to identify systems-level patterns of adaptation - for example , selection on consecutive enzymes in a metabolic pathway . Secondly , it provides a built-in empirical distribution against which outliers ( candidates for selection ) can be evaluated . In addition , by simultaneously considering multiple genomes , we can compare relative amounts of selection on a gene in different species subject to different ecological constraints . Much recent work has focused on genome-wide scans for positive selection in human [1 , 2] and other eukaryotic species ( e . g . Drosophila , Plasmodium [3 , 4] ) . Many of these scans rely on skews in polymorphism patterns as selectively favored alleles become fixed in a population [5] . Most such tests for selection assume that neutral polymorphism patterns at each locus are unlinked from the rest of the genome , making selected loci stand out as regions of reduced variation , or unexpectedly long haplotypes [6] . It is thus unclear whether any of these ‘diversity-based' tests ( e . g . , Tajima's D [7] , Fay & Wu's H [8] ) for positive selection on sexual genomes - which rely on the assumption that recombination occurs between genomic loci - will be amenable to bacteria , in which recombination is decoupled from reproduction , occurring infrequently , and sometimes across species boundaries ( horizontal gene transfer; HGT ) . Alternative ‘rate-based' approaches to detecting positive selection ( in both sexual and asexual species ) include finding genes with high rates of amino acid substitution - relative to ( i ) the rate of evolution in other lineages ( relative rates ) , or ( ii ) the number of silent substitutions in the gene ( nonsynonymous : synonymous substitution ratio; dN/dS ) [9] . These approaches may lack power when positive selection affects a small number of sites [6 , 10] , and the latter may be inappropriate as dS becomes saturated with multiple substitutions on long branches . Both approaches may have difficulty distinguishing between positive selection ( fixation of beneficial mutations ) and relaxed purifying selection ( loss of constraint , fixation of neutral or deleterious mutations , for example during population bottlenecks ) . These two types of selection can , however , be better distinguished by normalizing out demographic effects , and when polymorphism data is available , using independent methods such as the McDonald-Kreitman ( MK ) test , which compares the ratio of synonymous and nonsynonymous substitution rates within and between groups [11] . In this study , we focus on relative evolutionary rates because our model system , the γ-proteobacteria , span a considerable evolutionary time period over which synonymous substitution rates are saturated in many branches , and because polymorphism data from Escherichia coli provide an independent means to estimate the relative contributions of positive selection and relaxed negative selection to elevated evolutionary rates . Nonetheless , we show results from dN/dS profiling for comparison . The biological factors driving protein evolutionary rates are complex and widely debated [12–16] ( for recent reviews see [17 , 18] ) . In addition , selection may lead to subtle lineage-specific variation in evolutionary rates . To identify potentially important rate variation from the background of gene family and genome-specific rates , we factor evolutionary rates into three components that contribute to the total evolutionary distance ( amino acid substitutions per site ) as defined in Equation 1 ( where r is the total evolutionary rate , and t is time ) : The first and most significant background component ( ρ in Equation 1 ) is related to the protein family: for example , the ribosomal machinery is known to evolve slowly across all sequenced microbes , while surface-exposed proteins often evolve rapidly to avoid predation . The second major contribution ( β in Equation 1 ) is the background rate of evolution that results from the ‘molecular clock' associated with each lineage , perhaps due to between-species differences in population size , generation time , constraint on codon usage , or environmental factors such as UV light exposure [19] . For example , genes from the intracellular parasites of the Buchnera genus evolve more rapidly than those in other Enterobacteria . This may be due to frequent population bottlenecks , allowing fixation of neutral or slightly deleterious alleles , or an increased mutation rate [20 , 21] . Of course , ρ and β are not always independent , and are expected to interact , resulting in evolutionary rate variation that is both gene-specific and species-specific ( ν in Equation 1 ) . When a gene evolves at the rate predicted by its gene family and genome , ν will be equal to one . However , when ν deviates from one , this may represent natural selection on different functionality in different genomic/ecological milieus , Deviations from the ‘expected' rate of protein evolution can be used to detect positive selection and functional diversification between orthologous proteins [22–24] , and the ‘expected' background is best estimated empirically , by measuring rates across the entire genome . A recent study demonstrated global differences in evolutionary rate between environments [19] , but did not attempt to identify patterns of natural selection on genes in different genomes . The growing number of organisms with fully sequenced genomes provides an opportunity to look for patterns of selection on genomes , and to begin to address a question of fundamental interest: to what extent does differentiation in core , ‘housekeeping' genes drive functional divergence between species across the tree of life ? And can we identify genes under selection , and make predictions about their biological/ecological significance ?
In addition to the anecdotal cases described above , we examined more generally whether genes of common function tend to experience similar regimes of selection . Indeed , in our overall dataset , pairs of genes sharing the same COG ( clusters of orthologous groups [27] ) functional annotation have significantly more correlated selective signatures ( the vector of ν across all species ) than pairs with different functions ( Kolmogorov-Smirnov ( KS ) test , D = 0 . 12 , p < 2 . 2e-16 ) ; conversely , genes with similar selective signatures are more likely to share a common function ( Figure 3A ) . This indicates that selection can act coherently at the level of function , and across levels of organization larger than single genes . Considering each functional category in isolation , we find that most functions ( 11 of 16 COG function categories , excluding ‘general' and ‘unknown' categories ) contribute significantly to this effect . Thus , selective signatures are a surprisingly good predictor of common function – a feature that could be useful in the annotation of genes of unknown function , provided that they have orthologs in several species . Correlation in ν is also a significantly better predictor of function than correlation in dN/dS ( Figure 3A ) , or raw evolutionary distance , and the predictive power remains strong even after removing genes used to construct the species tree or genes on the same operon ( Figure S3 ) . When dN/dS is normalized by its median for each ortholog and genome to produce a ‘relative' dN/dS measure , it correlates much better with function , almost equal to ν , highlighting the generality of the empirical multi-species approach used in this study . Our dataset of 744 genes is enriched in highly conserved ‘housekeeping' genes ( median dN/dS = 0 . 047 , with 70% of dN/dS values ( within 1 standard deviation on a log2 scale ) ranging from 0 . 005 to 0 . 26 ) . Despite this uniformly low range of dN/dS , the subtle rate variation captured by selective signatures is able to identify co-dependencies between genes of related functions . We explicitly tested the ability to detect co-dependencies between genes by simulating codon data for 30 species under 36 different models of evolution , half of which allowed dN/dS to vary on different branches , chosen at random . All models allowed dN/dS to vary among sites . However , for any site , dN/dS was only allowed to range within 1 standard deviation of the mean of the observed data ( 0 . 005 to 0 . 26 ) . For each of the 36 models , 5 replicate datasets were generated , and we treated replicates as genes with known evolutionary co-dependence . We computed ν for each of the resulting 180 simulated genes , and found that in models with branch variation in dN/dS , replicates of the same model had significantly more correlated ν across species than expected ( KS test versus all models , D = 0 . 58 , p < 2 . 2e-16; Figure 3B ) . Thus , when at least some branch variation is present , selective signatures are able to uncover genes with similar evolutionary patterns , even amidst a strong background of purifying selection . The relationship between selective signatures and gene function is borne out in several genomes in our study . For example , evolution of flagellar proteins appears to be most rapid in some species of Enterobacteria , perhaps reflecting evolutionary ‘arms races' with hosts or predators . In contrast , ion transport/metabolism proteins , especially those involving sulfur , are slowest evolving in Buchnera aphidicola APS ( Table S3A and S3B ) , indicating the importance of these proteins in the lifestyle of this intracellular symbiont . A deep-sea bacterium that lives at the periphery of hydrothermal vents , Idiomarina loihiensis , presents a particularly interesting case study . Having lost many genes essential for sugar metabolism , it relies instead on amino acids as its primary source of energy and carbon [28] . Consistent with disuse of sugar metabolism , we find that glycolysis genes , as well as an upstream phosphotransferase system component ( COG2190 ) have some of the highest values of ν in the Idiomarina genome , suggesting relaxed negative selection on this pathway ( Figure 4 ) . Moreover , carbohydrate transporters and key glycolytic enzymes in the pentose phosphate and Entner-Doudoroff pathways have been lost in Idiomarina , and two of these relatively rapidly evolving enzymes have been lost ( COG166 and COG2190 ) in Colwellia , the most closely related sister-taxon of Idiomarina in our study . Taken together , these results suggest the relaxation of purifying ( negative ) selection on this pathway resulting from the disuse of sugars as a carbon source . By contrast , the relatively rapid evolution of amino acid metabolic enzymes in Idiomarina might reflect adaptation to growth on amino acids , particularly phenylalanine ( Figure 4 ) . Further supporting the idea of a species-specific adaptation in Idiomarina , none of the rapidly evolving phenylalanine metabolism genes are also rapidly evolving in Colwellia , nor have they been lost in this sister species . The 7 glycolysis genes and 3 phenylalanine biosynthesis genes were also analyzed in PAML [29 , 30] , using models allowing dN/dS to vary among sites and branches , or branches only ( Table S4 ) . In the branch-only models , none of these genes had significantly high average dN/dS in Idiomarina , but the branch-site models found evidence for a few sites in each gene with unusually high dN/dS in Idiomarina . While selective signatures cannot distinguish positive from relaxed negative selection on these genes , the known ecology and genome dynamics suggest positive selection on phenylalanine metabolism and relaxed negative selection on sugar metabolism . Although the true patterns of selection may be more complex , our results paint a broad picture of how the Idiomarina core metabolism has been optimized for a diet of amino acids rather than sugars , and lay a path for more targeted follow-up studies . For the cases above , we used biological intuition to discriminate the roles of positive and negative selection on gene evolutionary rates . In general though , natural selection may act to accelerate changes in a protein's sequence ( positive selection; ν > 1 ) or to slow down and constrain its rate of change ( negative selection; ν < 1 ) . Alternatively , when negative selection is relaxed , the apparent rate of evolution may increase due to fixation of slightly deleterious mutations ( relaxed negative selection; ν > 1 ) . Because these scenarios cannot be distinguished by relative rates methods alone , we employed an independent test for selection ( the McDonald-Kreitman ( MK ) test [11] ) using polymorphism data from 473 genes from 24 fully sequenced E . coli strains , with Salmonella enterica as an outgroup . In the MK test , rather than normalizing according to a sample of distantly related species ( as in the selective signatures approach ) , we normalize according to the expected dN/dS from a within-species polymorphism sample . Specifically , the ratio of synonymous ( S ) and nonsynonymous ( NS ) changes at polymorphic sites ( within the 24 strains ) is compared to the ratio at ( nonpolymorphic ) divergent sites ( comparing E . coli to S . enterica ) . The Fixation Index is calculated as FI = ( divergent NS/S ) / ( polymorphic NS/S ) [3] . Under neutral evolution , FI is expected to equal 1; under positive selection it may exceed 1 , and under negative selection it may be less than 1 . We compared the FI values of the 473 genes to their corresponding selective signatures ( ν ) in E . coli and found a significant positive correlation ( Pearson's correlation = 0 . 23 , p = 6 . 5e-7 ) . Although relaxation of negative selection on the branch leading to S . enterica could result in high values of FI , at least some of the genes with the highest values of FI are expected to be under positive selection [31] . This demonstrates that relative rate acceleration is often associated with positive selection , and deceleration with purifying selection ( for a complete list of selected genes identified by both methods , see Table S5 ) . The correlation between ν and FI is striking because , although the same set of gene families were used to calculate relative rates and the FI , the former used protein sequence while the latter used DNA , and the alignments were performed independently using different sets of species . These results imply that many genes have experienced positive selection since the divergence of E . coli and Salmonella , despite low overall values of dN/dS . When the distributions of FI values are compared between genes with fast ( ν > 2 ) versus slow ( ν < 0 . 5 ) relative rates ( Figure 5A ) , the difference is very clear . Fast-evolving genes have significantly higher FI values than slow-evolving genes ( one-sided KS test; D = 0 . 43 , p = 4 . 1e-6 ) . The fast and slow subsets are also both significantly different from the mid-range ( 0 . 5 < ν < 2 ) subset of genes ( one-sided KS tests: D = 0 . 17; p = 0 . 04 , and D = 0 . 30; p = 2 . 7e-5 , respectively for fast and slow ) . Moreover , the distribution of FI values for fast-evolving genes has a broad shoulder with mean slightly less than 1 , and a sharper peak with mean greater than 1 ( note the log2 scale in the figure ) . The simplest interpretation of these results is that increased relative rate reflects both relaxed negative selection and positive selection . Interestingly , the two hypothesized distributions appear to contain a similar number of genes , suggesting that positive selection is about as common as relaxed negative selection as a cause for acceleration of evolutionary rate . This result is largely in agreement with the previous finding that ∼50% of amino acid substitutions between E . coli and S . enterica were fixed by positive selection [31] , with the remaining substitutions due to genetic drift , perhaps resulting from relaxed negative selection or hitchhiking with positively selected mutations ( discussed below ) . Unusually slowly evolving genes ( ν < 0 . 5 ) , on the other hand , show greater levels of negative selection ( low FI ) than normal genes ( 0 . 5 < ν < 2 ) . While these results may seem unsurprising at first , it is important to note that our evolutionary rates have been normalized for gene family-specific effects , thus even the fastest evolving genes ( in terms of ‘raw' rate ) will appear ‘slow-evolving' ( ν < 1 ) in about half of the genomes . Conversely , the slowest evolving genes ( e . g . , the ribosomal machinery ) will appear to be ‘fast-evolving' ( ν > 1 ) in about half of the genomes . To further investigate the role of negative selection , we used gene deletions within a clade as evidence of relaxed negative selection , with the expectation that genes under relaxed selective constraint are lost more frequently . Consistent with a significant role for negative selection in constraining rate variation , genes evolving much more slowly than expected ( ν < 0 . 25 ) were less likely to have undergone deletion in a sister clade ( Figure 5B ) . Conversely , genes evolving much faster than expected ( ν > 4 . 0 ) were more likely to have lost their ortholog in a sister clade , pointing toward relaxed negative selection . In sexually recombining organisms , positively selected mutations are thought to sweep rapidly through the population , lowering effective population size and decreasing the effectiveness of negative selection at linked loci . When sweeps occur faster than recombination can separate the beneficial allele from ‘hitchhikers' , clusters of rapidly evolving genes ( i . e . , one gene under positive selection , and linked genes under relaxed negative selection ) can arise [6] . Perhaps unexpectedly for an asexual species , selective sweeps and genetic hitchhiking between linked ( ∼30 kb apart ) , but not unlinked loci , have been documented in E . coli [32] . Theoretically , there exist regimes of selection and recombination in prokaryotes that would be able to produce a pattern of genetic hitchhiking [33] . Early work on variation across ∼1700 strains of E . coli showed genetic linkage between loci separated by ∼45 kb [34] - an estimate largely supported by recent whole-genome scans , which find recombinational segments of up to 100 kb [35] . To determine whether genetic hitchhiking was detectable among fast-evolving genes in this study , we examined proximal pairs of genes ( separated on the chromosome by 0–5 genes ) and asked whether they showed a tendency to co-evolve - either both ‘fast' ( ν > 1 ) , or both ‘slow' ( ν < 1 ) . Proximal genes are frequently encoded on the same operon , and are thus expected to be under similar selective pressures due to co-expression and common function . Indeed , we find that pairs of genes predicted to be on the same operon [36] co-evolve in the same direction ( either both genes with ν > 1 , or both with ν < 1 , Z-score > 1; Fisher's Exact Test: Odds Ratio = 3 . 1 , p < 2 . 2e-16 ) . In fact , selective signature ( correlation in ν across species ) is a better predictor of operons than dN/dS , and about as accurate as a small compendium of gene expression data from E . coli under different experimental conditions ( Figure S2 ) . Because these operon effects could confound the detection of hitchhiking , we restricted our analysis to pairs of genes on different operons , transcribed on opposite strands of DNA or separated by at least one gene on the opposite strand . In this operon-free dataset , we observe a slight but statistically significant tendency for fast-evolving genes ( ν > 1 ) , but not slow-evolving genes ( ν < 1 ) , to cluster together in a genome , not only at distances of 0–5 intervening genes , but even as far as 20–100 genes apart ( Figure 5C ) . Assuming an average gene length of ∼1 kb in prokaryotes [37] , clustering of fast-evolving genes up to 100 genes apart ( Figure 5C ) is very much consistent with earlier predictions [32–35] . Alternatively , genomic mutational hotspots might explain the observed clustering , but this hypothesis is currently difficult to test . Therefore , we tentatively conclude that selective sweeps are occurring in a significant fraction of the 30 species analyzed in this study , and that these sweeps leave a detectable signal in the form of accelerated evolutionary rates . Taken together , the observed correlations between ν and the Fixation Index ( MK test ) , deletion frequency , and the inferred footprint of genetic ‘hitchhiking' lead us to conclude that ν is reflective of both positive and negative natural selection on core genes .
Even for detecting selection in single genomes , the selective signatures approach can be powerful because it can identify positive ( or relaxed negative ) selection for genes with low values of dN/dS , while in some other cases selection is more easily detected using dN/dS with a variable branch or branch-site model . To illustrate this , we simulated codon data for 180 genes families under different models of natural selection across our tree of 30 γ-proteobacteria , and calculated dN/dS and ν in each branch ( Methods ) . In cases with elevated dN/dS in all branches ( Model 1 in Figure 6 ) , PAML is able to correctly identify all branches under selection . Because there is very little variation among branches , ν is uninformative , despite positive selection in all lineages . When branch variation is present , and selection is strong in some branches but not others ( Model 2 in Figure 6 ) , both ν and dN/dS are able to correctly identify the species under selection . Yet when branch variation is present but the branch under selection is only weakly selected ( few sites and dN/dS only slightly higher than background ) , it is identified correctly by ν but not dN/dS ( Model 3 in Figure 6 ) . Therefore , ν is well-suited to detect subtle cases of species-specific selection , but is powerless to detect uniform positive selection in all species . This is further demonstrated in an example from a gene family in our dataset: PstC ( COG573 ) , which encodes a permease involved in phosphate transport . This gene is highly conserved across 18 species , with dN/dS near zero in most species except Xylella fastidiosa and Xanthomonas campestris , which have among the highest genome-wide average dN/dS , suggesting the high dN/dS of PstC may be due in part to demographic effects . Despite the lack of information from dN/dS , this gene shows substantial variation in ν across species ( Figure 6 ) , which may be related to species-specific ecological factors . Like the Fixation Index computed in the MK test , selective signatures measure selection relative to a baseline . While the MK identifies selection relative to a baseline of within-population polymorphism , selective signatures test for selection relative to a baseline established by related species . Despite their contrasting and independent data and analytical methods , the two measures tend to overlap significantly in their predictions of natural selection . Moreover , the positive association between them ( Figure 7; Odds ratio > 1 ) persists at high , intermediate , and low levels of dN/dS . The association may be slightly stronger when dN/dS is very high , due to correct identification of strong positive selection by all three methods . Yet even when absolute dN/dS is low , the FI and ν often agree that evolutionary rate is relatively fast , suggesting positive or relaxed negative selection ( or strong negative selection , when both FI and ν are low ) , perhaps on just a few sites . While the MK test may wrongly predict selection after a population bottleneck , leading to between-species fixation of slightly deleterious mutations [10] , selective signatures explicitly normalize out such genome-wide effects . On the other hand , if demographic effects are not significant , the MK test has the advantage of distinguishing positive selection from relaxed negative selection , which is not possible with selective signatures . In addition , HGT ( e . g . , from S . enterica to E . coli ) is expected to reduce the observed divergence , lowering ν without affecting FI or dN/dS . Thus , the intersection of genes predicted by both high FI and ν ( see Table S5 ) provides a more robust prediction of positive selection . Because selective signatures are also lineage-specific , they represent a measure of niche-specific changes in selection , and have the advantage of being sensitive to substitutions in just a few amino acid sites , provided these are unexpected relative to the gene-family and genome-specific background rates . For example , we identified several Idiomarina genes with high values of ν , which corresponded to only a few sites with high dN/dS , while average dN/dS across each gene was low ( Table S4 ) . Even if rate acceleration is due to relaxed negative selection rather than positive selection , the change in selection detected by ν is both gene- and lineage-specific , and thus may be relevant to ecological differentiation among species . Genes with similar values of ν in the same species may be part of a co-evolving functional module , and correlations in ν are able to identify such sets of genes ( Figures 3 , 4 , and S2 ) . Can horizontal transfer alter effective protein evolutionary rates , thereby affecting selective signatures ? HGT is prevalent in prokaryotes [38 , 39] , especially among closely related taxa [40] . For example , we suspect that homologous recombination ( or HGT between close relatives ) within ‘species' contributes to the observed clustering of rapidly evolving genes ( Figure 5C ) . HGT can also complicate inferred evolutionary rates in two qualitatively different ways: ( i ) transfer from distant lineages ( or replacement with paralogs ) can make distances to sister taxa appear long ( and disrupt tree topology ) ; and ( ii ) transfer between sister taxa does not affect tree topology , but can shorten observed distances . Thus , some of our observed rate variation is likely due to lateral gene flow . We investigated the extent to which HGT affects our results by repeating our analyses with a set of genes more likely to include horizontal gene flow , and concluded that our main findings are not easily attributable to artifacts of HGT ( Figures S4–S6 ) . Moreover , our main findings are supported by methods not directly biased by HGT ( MK and dN/dS tests ) . Species are believed to diverge only when they gain the ability to exploit a new ecological niche [41] , and this may come about through mutations in existing ( core ) genes , or acquisition of new genes . It is gaining widespread acceptance that the latter is responsible for many , if not most adaptations [39 , 42] , and possibly ensuing speciation events . Yet , as we demonstrate , core genes are also subject to selection , and likely contribute to differentiation between species over long time spans . Much of this selection is positive , leading to novel adaptations in core genes . Thus , core genes , which are by definition retained in genomes over long periods of time , may be quite dynamic in terms of their precise molecular functionality . The coherence of selective patterns across genes of similar function ( those with the same operon , functional annotation , or in the same pathway ) is exciting because it suggests that the genomic landscape is organized into functional modules even at the level of natural selection . Thus , it may be easier than anticipated to understand the complex evolutionary pressures acting on genomes . Correlations in selective signatures could be used to identify fitness co-dependencies among genes in much the same way that correlated mRNA expression profiles are used to identify genes connected in the physical or regulatory networks of the cell .
To calculate relative evolutionary rates ( ν ) , normalized to remove protein-specific ‘scaffold' constraints ( ρ ) and species-specific ‘molecular clock' ( β ) effects , we first constructed a ‘species tree' for 30 species of γ-proteobacteria ( see Table S2 for species names and taxonomy IDs ) . Our tree is based on a concatenation of amino acid sequences for 80 housekeeping genes that occur in single-copy in each genome ( Table S1 ) , and have previously been shown to be orthologous and consistent with a single organismal phylogeny [43] . Gene trees were then constructed for 977 putative ‘core' gene families ( members of the same cluster of orthologous genes [27] , retrieved from the MicrobesOnline database [44] ) , each occurring as a single copy in at least 16 of the 30 genomes . Multiple sequence alignments ( MSAs ) were performed using MUSCLE [45] , and all gaps were removed , along with one flanking residue on either side . Gene trees were constructed from the resulting MSAs using Tree-Puzzle [46] with a JTT amino acid substitution model [47] and 8 γ-distributed rate categories . Estimation of ν proved to be independent of the substitution model used ( see Figure S7 for comparison with WAG model [48] ) . Gene trees were screened to remove genes that may have resulted from horizontal transfer by excluding all gene families with topologies that conflicted with the species tree topology according to a Kishino-Hasegawa ( K-H ) test [49] ( p < 0 . 05 ) . Of the remaining 744 ‘core' gene families , 99% of the top BLAST hits were to a member of the same Genus , or to a neighboring branch on the species tree . For the 744 gene families consistent with the species tree phylogeny , trees were re-built using the consensus ‘species tree' topology , but with branch lengths estimated separately for each gene . These gene trees were first normalized to remove gene family-specific contributions ( ρ ) by re-scaling each tree such that the sum of all branch lengths in the tree matched that expected by the species tree ( considering only those branches of the species tree that are present in the gene tree ) . Gene trees were further normalized to remove ‘molecular clock'-type effects ( β · t ) by dividing each branch by the corresponding branch length in the species tree ( Figure S1 ) . Only terminal branches ( those leading directly to extant species ) were used in this study , and branches with near-zero sequence changes were excluded from the analysis . Finally , the resulting relative rates were median centered within each genome , leaving an estimate of ν in which values greater than 1 . 0 indicate faster than expected evolution ( e . g . , due to positive or relaxed negative selection ) , and values smaller than 1 . 0 indicate slower than expected evolution ( e . g . , due to increased negative selection ) . To estimate the significance of the deviation from 1 . 0 ( no unusual selective pressures ) , we computed 100 replicates of our estimate for ν by nonparametric sequence bootstrapping , and computed a ‘Z-score' as the ratio of the observed log2 ( ν ) to the square root of its variance over the bootstrap replicates . We used the codeml program from the PAML 4 . 0 package [29] to estimate dN and dS , allowing their ratio to vary freely along branches of the species tree ( ‘free-ratio' model ) . Estimates of dN , dS and dN/dS were made for each of the 744 core orthologs described above . To generate ‘relative' values of dN , dS and dN/dS , each of these values was first normalized by its median value for each genome , then by the median for each ortholog . Note the order of normalization steps is reversed from that for relative rates , because there is no prior expectation that dN/dS values across the tree are proportional to evolutionary time/distance . We used the evolver program from the PAML 4 . 0 package [29] to simulate gene families of 300 codons in 30 species , using the γ-proteobacteria species tree topology . In the first set of simulations ( Figure 3B ) , we used two classes of sites ( occurring at frequency 0 . 1 and 0 . 9 , respectively ) , each with a different value of dN/dS , randomly chosen from within ±1 standard deviation of the mean of the observed distribution of dN/dS in our dataset of 744 genes across 30 species . In 18 of the models , dN/dS was not allowed to vary among branches; in the remaining 18 a different dN/dS value was chosen at random for each site class and each branch . For each model , we generated 5 replicate codon sequences in 5 independent runs of evolver . In the second set of simulations ( Figure 6 ) , we used either 2 or 3 classes of sites ( with frequency chosen within the range of 0 . 1 to 0 . 9 ) , each with dN/dS of either 2 . 0 , 1 . 5 , 1 . 1 , 1 . 0 , 0 . 5 or 0 . We generated 180 different models , 45 of which did not allow branch variation , and the remaining 135 with 1 to 5 branches under selection , with one site class having a higher dN/dS than the other branches . We generated 12 replicate sequences for each model . For both sets of simulations , we translated the codons to amino acid sequence in order to calculate ν , treating each replicate of each model as a protein family . We also estimated dN/dS in each branch using the free-ratio model in PAML . Gene families were retrieved from 24 strains of E . coli ( including strains of Shigella; see Table S2B ) , and an outgroup , S . enterica . Each gene had exactly one representative in each strain . Genes were assigned to orthologous families using OrthoMCL [50] . Only the 473 gene families corresponding to COGs in the relative rates dataset , and not violating the K-H test , were retained for analysis . We tried excluding genes with a large number of divergent sites relative to polymorphic sites , which might reflect HGT from closely related species , but this did not significantly affect results . Nucleotide sequences were aligned and trimmed using MUSCLE , as described above . Polymorphic substitutions ( within the 24 strains of E . coli ) and divergent substitutions ( fixed between E . coli and Salmonella ) were counted , and assigned to synonymous or nonsynonymous categories , as previously described [11] . Only codons for which there were no more than two states were retained for analysis , and we always chose the pathway between codons that minimized the number of nonsynonymous changes . An Odds Ratio statistic , the Fixation Index ( FI ) , was then calculated as described in the main text . | Natural selection promotes the survival of the fittest individuals within a species . Over many generations , this may result in the maintenance of ancestral traits ( conservation through purifying selection ) , or the emergence of newly beneficial traits ( adaptation through positive selection ) . At the genetic level , long-term purifying or positive selection can cause genes to evolve more slowly , or more rapidly , providing a way to identify these evolutionary forces . While some genes are subject to consistent purifying or positive selection in most species , other genes show unexpected levels of selection in a particular species or group of species—a pattern we refer to as the “selective signature” of the gene . In this work , we demonstrate that these patterns of natural selection can be mined for information about gene function and species ecology . In the future , this method could be applied to any set of related species with fully sequenced genomes to better understand the genetic basis of ecological divergence . | [
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] | 2008 | Comparing Patterns of Natural Selection across Species Using Selective Signatures |
The filamentous fungus Chromocrea spinulosa ( Trichoderma spinulosum ) exhibits both self-fertile ( homothallic ) and self-sterile ( heterothallic ) sexual reproductive behavior . Self-fertile strains produce progeny cohorts that are 50% homothallic , 50% heterothallic . Heterothallic progeny can mate only with homothallic strains , and progeny also segregate 50% homothallic , 50% heterothallic . Sequencing of the mating type ( MAT ) region of homothallic and heterothallic strains revealed that both carry an intact MAT1-1 locus with three MAT1-1 genes ( MAT1-1-1 , MAT1-1-2 , MAT1-1-3 ) , as previously described for the Sordariomycete group of filamentous fungi . Homothallic strains , however , have a second version of MAT with the MAT1-2 locus genetically linked to MAT1-1 . In this version , the MAT1-1-1 open reading frame is split into a large and small fragment and the truncated ends are bordered by 115bp direct repeats ( DR ) . The MAT1-2-1 gene and additional sequences are inserted between the repeats . To understand the mechanism whereby C . spinulosa can exhibit both homothallic and heterothallic behavior , we utilized molecular manipulation to delete one of the DRs from a homothallic strain and insert MAT1-2 into a heterothallic strain . Mating assays indicated that: i ) the DRs are key to homothallic behavior , ii ) looping out of MAT1-2-1 via intra-molecular homologous recombination between the DRs in self-fertile strains results in two nuclear types in an individual ( one carrying both MAT1-1 and MAT1-2 and one carrying MAT1-1 only ) , iii ) self-fertility is achieved by inter-nuclear recognition between these two nuclear types before meiosis , iv ) the two types of nuclei are in unequal proportion , v ) having both an intact MAT1-1-1 and MAT1-2-1 gene in a single nucleus is not sufficient for self-fertility , and vi ) the large truncated MAT1-1-1 fragment is expressed . Comparisons with MAT regions of Trichoderma reesei and Trichoderma virens suggest that several crossovers between misaligned parental MAT chromosomes may have led to the MAT architecture of homothallic C . spinulosa .
Most fungi use one of two sexual reproductive strategies , i . e . , heterothallism ( self-sterility ) or homothallism ( self-fertility ) . A heterothallic fungus requires a genetically distinct partner to complete the sexual process , whereas a homothallic one does not require a partner [1] . In ascomycetes , sexual reproduction of both heterothallic and homothallic species is controlled by a single master regulatory locus called the mating-type ( MAT ) locus [1] . All heterothallic ascomycetes examined to date carry one of two MAT forms ( idiomorphs [2] ) per nucleus , that encode apparently unrelated , but , in fact , ancestrally related transcription factors [3] . Most homothallic species carry both MAT forms in a single nucleus [1 , 4 , 5] . There is a significant difference in the use of the term “homothallism” in filamentous fungi versus in yeasts such as Saccharomyces cerevisiae . Homothallism in the latter refers to a change in mating type/identity of some cells within a culture of a formerly uniform mating identity , followed by mating of “switched” with “unswitched” cells [6] . In S . cerevisiae , three MAT loci ( one active and two silent loci containing opposite MAT genes ) are linked on the same chromosome . Switching is achieved by homologous intramolecular recombination-mediated replacement of the active copy at MAT with a formerly silent copy of opposite mating type [6 , 7] . Methylotrophic yeasts , such as Hansenula polymorpha and Pichia pastoris have a simpler switching system , achieved by an inversion between two MAT loci ( one active , one silent ) , mediated by inverted repeats located nearby [8 , 9] . This mechanism silences the formerly active MAT , thus altering mating type [10] . Although switching of mating type observed in the yeasts has not been demonstrated in filamentous ascomycetes , mating type instability has been reported for several , including Chromocrea ( = Hypocrea ) spinulosa ( Trichoderma spinulosum ) [11] ( S1 Fig ) , Glomerella cingulata [12] , various Ceratocystis species [13 , 14] , Sclerotinia trifoliorum [15] , Fusarium subglutinans [16] , and Botrytinia fuckeliana [17] . Interestingly , only unidirectional switching of mating type has been observed in these cases [18] . For example , selfing of C . spinulosa and S . trifoliorum , both of which are assumed to be haploid , yields equal ratios of large and small ascospores ( 8:8 in C . spinulosa and 4:4 in S . trifoliorum ) in a single ascus; mating ability segregates with size . Colonies derived from large spores ( L ) are self-fertile whereas those from small spores ( S ) are self-sterile; selfing of the former results in asci containing , again , large and small spores in equal ratio [11 , 19] . Unlike the well-described mechanism of mating-type switching characterized for e . g . , S . cerevisiae , the molecular details of unidirectional switching in fungi such as C . spinulosa , Ceratocystis fimbriata , and S . trifoliorum are unclear , however deletion of MAT1-2-1 is involved in all three cases [20–23] . To explore this question in depth in C . spinulosa , we employed sequence and expression analyses of the MAT region , plus functional manipulation of genes . We present evidence supporting our hypothesis that , in order to self , homothallic strains must generate two versions of the MAT locus . One is the well-described MAT1-1 locus of Sordariomycetes encoding three genes , MAT1-1-1 , MAT1-1-2 and MAT1-1-3 , while the other is an altered MAT1-1 locus in which MAT1-2 is situated between truncated fragments of MAT1-1-1 ( MAT1-1;MAT1-2 ) . An 115 bp stretch of the MAT1-1-1 coding sequence is duplicated in each truncated MAT1-1-1 fragment . We argue that an intramolecular recombination between these repeats occurs pre-meiotically in chromosomes of nuclei carrying the MAT1-1;MAT1-2 locus , and results in a majority of nuclei with chromosomes carrying the typical MAT1-1 locus . We propose that recognition between these two types of nuclei occurs prior to karyogamy , after which a typical meiosis produces self-fertile ( MAT1-1;MAT1-2 ) :self-sterile ( MAT1-1 ) progeny in a ratio of 1:1 . These structural discoveries and molecular manipulation allowed us to propose a mechanism whereby homothallic strains can yield both homothallic and heterothallic progeny .
TAIL and inverse PCR-based chromosome walking strategies generated 21 . 9-kb and 18 . 3-kb contiguous DNA fragments from Cs23 and Cs27 strains respectively ( Fig 1 , S1 and S2 Figs , S1 and S2 Tables ) . Both Cs23 and Cs27 carry MAT1-1 encoding the three canonical MAT1-1 genes ( MAT1-1-1 , MAT1-1-2 , and MAT1-1-3 ) . Cs23 also carries a second version of MAT ( MAT1-1;MAT1-2 ) with MAT1-2 tightly linked to , and interrupting MAT1-1-1 ( described in detail below ) . Six additional ORFs were identified in the MAT flanks and noted to be conserved and syntenic when compared to MAT flanks of closely related heterothallic Trichoderma reesei and Trichoderma virens ( Fig 1 ) . These genes are also conserved in homothallic F . graminearum , although with some rearrangement ( Fig 1 , bottom line ) . Interestingly , the T . reesei QM6a MAT1-2 strain , which carries an intact MAT1-2-1 ORF , contains a partial MAT1-1-1 sequence ( corresponding to 131 amino acids of the 3’ end ) translationally fused to a hypothetical protein with similarity to F . graminearum MAT1-2-3 ( 28% over 55 amino acids out of 264 aa ) [24] whose function is dispensable for self-fertility [25] ( Fig 1 ) . This MAT1-1 locus structure is conserved among MAT1-1 strains of Hypocrea jecorina , the teleomorphic stage of T . reesei [26] . In contrast , MAT1-2-3 is present as a standalone ORF near MAT in T . virens strain Gv29-8 ( protein ID 221797 JGI MycoCosm database ( http://genome . jgi . doe . gov/TriviGv29_8_2/TriviGv29_8_2 . home . html ) . Identification of two different architectures of MAT1-2-3 in Trichoderma allowed us to speculate about MAT evolution in C . spinulosa self-fertile strain Cs23 ( Discussion ) . As noted above , two versions of Cs23 MAT are found; one version ( MAT1-1 ) includes the three canonical MAT1-1 genes while the second ( MAT1-1;MAT1-2 ) includes four MAT genes , three of which , MAT1-2-1 , MAT1-1-2 , and MAT1-1-3 are intact . The fourth , MAT1-1-1 , is in two fragments , separated by ~3 . 5 kb of DNA that includes MAT1-2-1 . The 5’ fragment of the MAT1-1-1 ORF ( MAT1-1-1L ) is the largest ( 1140bp/380 amino acids ) and contains the entire alpha 1 domain box . The 3’ fragment ( MAT1-1-1S ) is much smaller ( 76 aa ) . Two identical 115-bp DNA stretches ( designated DR1 and DR2 ) reading in the same direction were identified . DR1 is 126 bp from the 3’ end of MAT1-1-1L ( Fig 2 ) . DR2 is at the 5’ end of MAT1-1-1S . Additionally , a homopolymeric tract ( poly T ) , 155 bp from DR2 and an ORF encoding MAT1-2-3 are between MAT1-1-1S and MAT1-2-1 ( Figs 1 and 2 ) . In contrast , in heterothallic strain Cs27 , only one ( DR1 ) of the two repeats is present at MAT1-1 , MAT1-1-1 is not fragmented , and no poly T tract or MAT1-2-3 protein are present ( Figs 1 and 2 ) . Primer sets matching MAT1-2-1 ( CP1/CP2 ) , MAT1-1-1L ( CP5/CP6 ) and bridging DR1 ( CP3/CP4 ) ( S2 Table ) amplified both MAT1-2-1 and MAT1-1-1 fragments and the DR1 region from DNA of C23 and self-fertile progeny , but only the MAT1-1-1 fragment from DNA of C27 and self-sterile progeny ( Fig 2A and 2B ) . In addition , diagnostic MAT fragments hybridized to DNA gel blots of self-fertile Cs23 and self-sterile Cs27 strains in a manner consistent with the PCR amplification pattern ( Fig 2C and 2D ) . In Cs23 , two fragments , 7 . 1 and 3 . 5 kb , corresponding to the MAT1-1;MAT1-2 and MAT1-1 versions of MAT , respectively , were visible when probed with MAT1-1-1 , while only the 3 . 5 kb fragment was visible in Cs27 ( Fig 2A , 2C and 2D ) . Notably , in the Cs23 lane , the intensity of the 7 . 1 kb signal ( arrow ) was much lower than that of the 3 . 5 kb signal ( Fig 2C ) . The same gel , when probed with MAT1-2-1 , hybridized to the 7 . 1 kb band in Cs23 , also with low intensity ( Fig 2D , arrow ) . The MAT1-2-1 probe did not hybridize to Cs27 . This is compelling evidence that the 7 . 1 kb band carries MAT1-1-1 ( MAT1-1-1L ) and MAT1-2-1 and that the copy number of MAT1-1;MAT1-2 is less than that of MAT1-1-1 on the 3 . 5 kb fragment . If the 3 . 5 kb MAT1-1-1 and 7 . 1 kb MAT1-1-1 and MAT 1-2-1 signals were from DNA on the same chromosome in the same nucleus , or if Cs23 were heterokaryotic and the two types of nuclei were in equal numbers , the signals would be expected to be approximately the same intensity . We hypothesize that these unequal signals arise from DNA at the MAT locus and that the MAT1-1;MAT1-2 locus and the MAT1-1 locus reside in different nuclei that are unequally distributed in the population . To achieve this configuration , we propose that an intramolecular recombination occurs between the direct repeats in homothallic Cs23 , eliminating MAT1-2 and leaving an intact MAT1-1-1 ORF as shown in S3 Fig . Note that the 3’ end of MAT1-1-1L extends 126 nucleotides beyond DR1 , and this extra sequence would be eliminated in the loop out ( S3 Fig ) . Following the loop out , the combined MAT1-1-1 protein aligns well with other MAT1-1-1 proteins particularly those from Trichoderma species . Whether or not the complete MAT1-1-1 protein is required for homothallic function or the MAT1-1-1L fragment is sufficient is addressed below . To determine if the direct repeats play a role in homothallic capability in Cs23 , we deleted DR2 using the strategy shown in Fig 3A . DNA gel blot hybridization confirmed targeted replacement of the DR2 region with the hygB cassette via double crossover homologous recombination ( Fig 3B ) . Specifically , BamHI-digested genomic DNA of candidate deletion strain T10 showed a single 8 . 9-kb band hybridizing to both MAT1-1-1 and MAT1-2-1 ( Fig 3B , lane 2 ) instead of the 7 . 1-kb band in progenitor Cs23 ( Fig 3B , lane 1 , arrow ) , confirming that DR2 had been replaced by hygB . Remarkably , the signal in T10 DNA hybridizing to either MAT1-1-1 or MAT1-2-1 was equally strong in contrast to the faint 7 . 1 kb signals in DNA of Cs23 ( Fig 3B , arrows ) . Note also that the intense MAT1-1-1 3 . 5 kb signal in progenitor Cs23 is missing in T10 ( Fig 3B ) , which suggests that T10 lacks the version of MAT1-1 that is intact . The T10 strain maintained resistance to hygromycin B through 10 successive transfers on drug-free medium , indicating that it was mitotically stable . Unexpectedly , however , and in contrast to self-fertile Cs23 , T10 was self-sterile on both PDA and corn meal agar ( CMA ) media [11] , although it did form white , rounded hyphal aggregates ( Fig 4A , top , insert ) . The T10 aggregates did not differentiate into stroma bearing sexual fruiting bodies ( perithecia ) as did the control Cs23 strain ( contrast Fig 4A left and right , top row ) . These findings suggest that the DRs are a key feature of the molecular mechanism generating the two versions of MAT in Cs23 . Our demonstration that the DNA hybridization signals are unequal supports the notion that the two versions of MAT are in different nuclei . Two different Cs23 DNA fragments carrying MAT1-2 were introduced into the genome of Cs27 which carries only MAT1-1 . In the first case , a plasmid ( pMAT2 ) bearing the MAT1-2-1 ORF , truncated MAT1-1-1S/DR2/poly T/MAT1-2-3 and 1 . 0 and 0 . 5-kb of the 5’ and 3’ sequences flanking these regions , respectively , was introduced ( Fig 5A ) . In the second case , a plasmid ( pM2M1 ) carrying all three MAT1-1 genes ( truncated MAT1-1-1L , MAT1-1-2 , MAT1-1-3 ) , as well as MAT1-2-1 , was inserted ( Fig 5C ) . Both homologous and ectopic integrants were obtained ( Fig 5B and 5D ) . DNA gel blot analysis confirmed insertion of MAT1-2-1 into the Cs27 genome ( Fig 5B and 5D ) . Homologous integrants generated using each strategy are represented by T27M12a-H1 ( Fig 5A , Fig 5B , lane 7 , asterisk , Table 1 ) and T27M12b-H3 ( Fig 5C , Fig 5D lane 4 , asterisk , Table 1 ) , respectively . Unlike T10 derived by deleting DR2 from Cs23 , all transgenic strains generated by insertion of MAT1-2-1 by homologous recombination into the MAT region of Cs27 , were mitotically unstable . For example , hygBR strain T27M12b-H3 ( Fig 5C ) completely lost resistance to hygromycin B after three successive transfers on PDA without the drug ( S4 Fig ) . In the first transfer , these strains formed brownish stroma-like structures , similar in morphology to those formed by self-fertile Cs23 , but no perithecia . In subsequent transfers , stroma-like structures no longer developed , although white hyphal aggregates , similar to those formed by T10 ( Fig 4A top ) , were produced occasionally . T27M12a-H1 ( Fig 5A ) showed phenotypes similar to those of the T27M12b-H3 strain . In summary , all Cs27 transformants generated by homologous recombination , were mitotically unstable for hygB and did not produce normal-looking stroma ( S4 Fig ) . We consider it likely that these are unstable because our introduced constructs created nearby repeated regions upon homologous integration . In contrast , all of Cs27 transgenic strains examined ( Fig 5B , lanes 1–4 ) carrying an intact copy of MAT1-2-1 at an ectopic position ( e . g . T27M12a-E5 in Fig 4A , Fig 5B , lane 1 , asterisk ) were mitotically stable , based on resistance to hygromycin B , and produced normal-looking stroma similar to stroma produced by self-fertile Cs23 ( Fig 4A , bottom ) . However , none of these strains developed fertile perithecia on the stroma . One of these strains ( T27M12a-E25 , Fig 4 , Fig 5B , lane 2 , asterisk ) , produced black perithecia partially immersed in the stroma ( Fig 4A , bottom ) , but no asci/ascospores ( Fig 4B , bottom ) . Thus , overall , all hygB stable transgenic C . spinulosa strains examined , whether from Cs23 or Cs27 , were self-sterile . In the following , we focus on analyses of T10 , the isogenic strain of Cs23 lacking DR2 , and also on progeny from crosses in which T10 was a parent . Although strain T10 carrying both MAT1-1 and MAT1-2 was self-sterile , when out-crossed to self-sterile strain Cs27 carrying only MAT1-1 , sexual progeny were produced ( Fig 6 ) . Four out-crosses were set up using the original T10 strain or T10-type progeny as one parent and Cs27 or geneticin-resistant ( genR ) Cs27 strain TC27G-1 as the other parent ( Table 1 ) . The first cross was between the original T10 strain and Cs27 , the second and third were between T10-type progeny obtained from the first out-cross , and Cs27 , and the fourth was between T10-type progeny from the second out-cross and the genR Cs27 strain TC27G-1 carrying gen in the 3’ flank of MAT1-1-3 ( Table 1 ) . As all self-sterile progeny were similar in both sexual development and colony morphology to the original parental T10 ( hygBR , some hyphal aggregates ) or Cs27 ( hygBS , no stroma ) strains , while the self-fertile progeny were identical to Cs23 ( hygBS , perithecia ) , we designated these progeny T10-type , Cs27-type , and Cs23 type , respectively . The first out-cross produced progeny with phenotypes that were of four types . These included the parental types- hygBR and self-sterile , like T10 , and hygBS and self-sterile , like Cs27 , and two recombinant types- hygBR and self-fertile and hygBS and self-fertile . The fourth out-cross also produced progeny with four phenotypes . These included the parental types- hygBR and self-sterile , like T10 , and genR and self-sterile , like Cs27-G ( Cs27 ) , and two recombinant types- genR and self-fertile ( Cs23 type ) and hygBRgenR and self-fertile ( Cs23 type ) . The second and third crosses produced progeny with three phenotypes-hygBR and self-sterile , like T10 , hygBS and self-sterile ( like Cs27 ) , hygBR and self-fertile . Note that in all four crosses between self-sterile parents a low percentage of self-fertile progeny were recovered . No self-fertile progeny would be expected from a typical cross between self-sterile heterothallic parents . The ratio of hygBR to hygBS progeny varied but was approximately 2:1 , 1:2 , and 1:2 , in crosses 1–3 , respectively . Self-fertile ( Cs23 type ) progeny were produced at frequencies of 21% , 5 . 7% , and 6 . 3% ( Table 1 ) . In the fourth out-cross , each parental phenotype ( hygBR;self-sterile , and genR;self-sterile ) segregated approximately 1:1 ( 47:59 ) , and recombinant phenotypes ( hygBR;genR;self-fertile and genR;self-fertile ) occurred with a frequency of 7% . In addition to the outcrosses described above , selfs of two hygBR Cs23 type progeny obtained from outcross 1 produced both self-sterile ( belonging to either the T10- or Cs27-type ) and self-fertile progeny ( Cs23 type ) . Self-fertile progeny were recovered at a much lower frequency ( 4/30 = 13 . 3% ) , than in selfs of the original Cs23 strain ( ~50% self-fertile and 50% self-sterile ) in one cross only ( Table 2 ) . In addition , overall ascospore numbers were significantly lower than from selfs of the wild-type ( WT ) strain ( ~ 10% of Cs23 ) . Finally , unlike the T10-derived strains , none of the Cs27-derived transformants that carried MAT1-2-1 at the MAT1-1 locus or an ectopic position in the genome was able to produce fertile perithecia in outcrosses to Cs27 . The genetic event ( s ) responsible for the occurrence of self-fertile progeny in any of the above outcrosses remains unclear . We analyzed the structure of MAT loci in progeny of out-crosses described above , using DNA gel blots ( S5 Fig ) and quantitative real-time PCR ( qPCR ) ( Figs 7 and 8 ) . The appearance of a single hybridizing 2 . 6 kb-SacI-digested band ( ‘b’ in S5 Fig ) in the DNA of T10-type progeny , probed with MAT1-1-1 or a DR region , demonstrated that all T10-type progeny examined carried MAT1-1-1L and one DR near MAT1-1-1L as in the original T10 strain ( S5A–S5C Fig ) . Additionally , the 6 . 1 kb-MAT1-2-1-hybridizing band ( ‘a’ in S5D Fig ) showed intensities in T10-type progeny similar to those hybridizing to MAT1-1-1L ( S4A and S4C Fig ) , supporting the genomic architecture of the MAT loci depicted in S5A Fig ( top panel ) . Note that the 2 . 6 and 6 . 1 kb bands were not visible in progenitor Cs23 DNA , again supporting the notion that nuclei containing MAT1-2-1 are in the minority in this strain . The 3 . 3-kb hybridizing band ( ‘c’ in S5B and S5C Fig ) confirmed the presence of only MAT1-1 in Cs27-type progeny , as in the original Cs27 strain . This band is also visible in Cs23 as expected . In contrast , in some Cs23-type self-fertile progeny ( P12 , P26 , P30 , and P60 ) , two SacI bands ( 3 . 3 and 2 . 6 kb ) were visible ( S5B and S5C Fig ) demonstrating that these progeny carried both versions of MAT ( S5A Fig , middle ) . The ratio of intensities of the two hybridizing bands varied , suggestive of unequal numbers of nuclei carrying each MAT structure , as proposed for the original Cs23 strain ( Fig 2 ) . We performed qPCR on genomic DNA to determine copy number of individual MAT genes in the progeny that we examined by Southern hybridization ( Fig 7 ) . In Cs23 and Cs23-type progeny , the relative amounts of MAT1-1-2 and MAT1-1-3 did not differ significantly from each other in any of the strains examined . MAT1-1-1 was less abundant than these two MAT1-1 genes , but the largest fold-changes among the three MAT1-1 genes in the same strain were less than ~1 . 7 ( e . g . , Cs23 type progeny P28 and P26 , Fig 7 ) . In contrast , they were ~15- to ~25-fold-higher than the relative amount ( 1 . 0 ) of MAT1-2-1 in several Cs23 types ( e . g . , Cs23 , P12 and P28 ) . The relative amounts of MAT1-2-1 in T10 and T10-type progeny did not differ significantly ( less than a two-fold change ) from those of the MAT1-1 genes in the same strain . In particular , T10 and progeny P17 , P48 , and P102 showed MAT1-2-1 levels similar to those of the MAT1-1 genes within the same strain ( e . g . , ~0 . 8- to ~1 . 1-fold changes compared with MAT1-1-3 ) . This is in marked contrast to the MAT1-2-1 levels in Cs23 type progeny which were much lower than those of the MAT1-1 genes , ranging from ~0 . 04- to ~0 . 4-fold compared with MAT1-2-1 in the same progeny . No MAT1-2-1 signal was detected in Cs27 and Cs27 type progeny and the profiles of all progeny mirrored those of Cs27 ( Fig 7 ) . We examined expression patterns of each MAT gene in a subset of the progeny shown in Fig 7 and S5 Fig by qPCR using total RNAs from progeny grown on PDA ( Fig 8 ) . In all strains examined , the MAT1-1-2 transcript accumulated at the lowest levels among the three MAT1-1 transcripts at the MAT1-1 locus , which is consistent with the expression pattern of the three MAT1-1 transcripts in self-fertile F . graminearum [25] . The levels of the MAT1-2-1 transcript varied among the strains examined . In Cs23 and Cs23-type progeny the signal was lower than those of the MAT1-1 genes ( lower than those of all three MAT1-1 transcripts in Cs23 and P28 strains , and lower than those of the MAT1-1-1 and MAT1-1-3 transcripts in P12 and P26 strains ) . In contrast , MAT1-2-1 transcripts in the T10-type progeny showed the highest accumulation levels among all MAT transcripts , ranging from ~4- to ~13-fold higher than MAT1-1-2 transcripts . The expression pattern of MAT1-2-1 in the T10-type progeny was consistent with that of MAT1-2-1 in F . graminearum , where the transcript level of MAT1-2-1 was the highest of all MAT genes [25] . In contrast , no significant MAT1-2-1 expression was detected in Cs27 and Cs27-type strains . To determine whether or not MAT1-1-1L was expressed , we used qPCR analysis of total RNA with PCR primers ( S6 Fig , S2 Table ) that could distinguish MAT1-1-1L from intact MAT1-1-1 ( S7 Fig ) . These experiments revealed that the MAT1-1-1L fragment was indeed expressed in Cs23 and Cs23-type progeny ( MAT1-2-1 was set to as reference ) because the expression level of MAT1-1-1L was almost same as that of MAT1-2-1 . In contrast , MAT1-1-1L transcript levels in T10 or T10-type progeny were much higher than those in Cs23 strains ( S7A Fig ) . qPCR on genomic DNA determined that copy number of MAT1-1-1L was less than MAT1-1-1 . MAT1-1-1L signals from genomic DNA were not significantly different from those of MAT1-2-1 ( set to 1 as reference ) in Cs23 and Cs23-type progeny , whereas MAT1-1-1L signals in T10 strains were higher but also similar to those of MAT1-2-1 ( S7B Fig ) . To determine if Cs23 MAT1-1-1L is functional , we inserted it and MAT1-1-1 ( as a positive control ) , separately , into a F . graminearum ΔMAT1-1-1 strain [25] ( S8A Fig ) . PCR amplification analysis revealed that all candidate hygBR , genR strains , transformed with C . spinulosa MAT1-1-1 ( designated FgΔMAT1-1-1::CsMAT1-1-1 ) , MAT1-1-1L ( FgΔMAT1-1-1::CsMAT1-1-1L ) or F . graminearum MAT1-1-1 carried the transgenes at ectopic positions ( S8A Fig ) . Wild-type self-fertile F . graminearum positive control strain Z3643 and all transgenic ( FgΔMAT1-1-1::FgMAT1-1-1 strains , began to form protoperithecia at 3 dai ( days after inoculation ) and were fully fertile after 6–7 dai , containing asci with eight ascospores . The negative control strain , F . graminearum ΔMAT1-1-1 formed perithecia that were 4~5 times smaller than WT perithecia and barren even four weeks after perithecial induction ( S8B and S8C Fig ) . Transgenic FgΔMAT1-1-1::CsMAT1-1-1 and FgΔMAT1-1-1::CsMAT1-1-1L strains also produced small , barren perithecia , but these were at least two times bigger than those of the negative control , and were not significantly different from each other ( S8B and S8C Fig ) . Although they could not completely complement function , both C . spinulosa MAT1-1-1L and MAT1-1-1 performed similarly , thus we conclude that both are able of promoting perithecial development in F . graminearum and that MAT1-1-1L is functional . We attempted to determine whether nuclei in large versus small spores could be distinguished using DAPI staining . Ascospores generated by selfing Cs23 revealed that only immature asci in which ascospores could not be seen clearly or were just starting to be visible were stained well; mature tetrads ( carrying 16 ascospores of two sizes ) were barely stained ( S9A and S9B Fig ) . Immature asci carrying nuclei in various stages of division ( ranging from nuclei in the diploid zygote to those in complete tetrads ) were distinguishable ( S9A andS9B Fig ) . The presence of several types of ascospore arrangement within an ascus ( 8L:8S , 4L:8S:4L , 4S:8L:4S , 4L:4S:4L:4S; large = L , small = S ) indicates that second , as well as first meiotic division segregations occurred frequently . At least 2 to 3 nuclei were present per single ascospore , though the exact number was unclear ( S10 Fig ) . As they matured , nuclei in small ascospores were less likely to be stained than in large ( S10 C , D , leftmost ascus ) , which may suggest that small ascospores mature earlier than large ones . Nevertheless , no significant difference between large and small ascospores within as ascus ( number/or intensity of nuclei ) was found . Asci from an outcross between T10 and Cs27 were not significantly different from those from the Cs23 self in terms of spore number and size segregation ( S9C and S9D Fig ) . However , the frequency of asci segregating for size in the first meiotic division was different between the Cs23 self ( 22 . 9% , 8 out of 35 asci ) and the outcross ( 60 . 5% , 17 out of 25 asci ) .
The presence of 115-bp direct repeats bordering the MAT1-2 locus in Cs23 suggested a possible mechanism for elimination of MAT1-2-1 , based on homologous recombination between the DRs , as pointed out by us in preliminary work [22] . Such an event would loop out MAT1-2-1 , leaving only the MAT1-1 locus , now with an intact MAT1-1-1 ORF . Importantly , this would result in two types of nuclei in a common cytoplasm , one type carrying MAT1-1;MAT1-2 and the other type carrying MAT1-1 only , as is actually found in homothallic Cs23 ( Fig 9 ) . The weak intensity of the band hybridizing to MAT1-2-1 compared with the band hybridizing to MAT1-1-1 on DNA gel blots of Cs23 ( Fig 2 ) supports a model in which a minority of nuclei carry both MAT versions during mitotic growth ( Fig 9B , S10 Fig ) . That the DRs are important in elimination of MAT1-2-1 and subsequent unevenness of nuclei carrying this MAT gene was confirmed by comparing DNA blots of WT Cs23 to those of transgenic strains derived from it ( T10 and T10-type progeny ) that lack DR1 and are self-sterile . Unlike Cs23 , which shows two hybridizing bands when hybridized with MAT1-1-1 , one of which also hybridizes to MAT1-2-1 , T10 strains showed only one , and the same , band , when hybridized separately with MAT1-1-1 and MAT1-2-1 ( Fig 3 ) . Furthermore , in contrast to WT Cs23 , the intensities of bands hybridizing to MAT1-1-1 or MAT1-2-1 in T10 were similar ( Fig 3 , S4 Fig ) . This indicates that the MAT1-1;MAT1-2 organization in T10 was stably maintained during vegetative growth and sexual development . T10 strains were no longer self-fertile , which bolsters our hypothesis that removal of one repeat from Cs23 would prevent looping out of MAT1-2-1 , and abolish capability for unidirectional mating-type alteration and self-mating . Although MAT1-1-1 at the MAT1-1;MAT1-2 locus is split to a large ( MAT1-1-1L ) and small ( MAT1-1-1S ) fragment , this may not lead to functional disruption . MAT1-1-1L , which consists of ~83% of MAT1-1-1 including the alpha-box ( S3 Fig ) , is expressed ( S7 Fig ) and capable of promoting perithecium formation as well as the intact CsMAT1-1-1 in a MAT1-1-1-deletion strain of F . graminearum ( S8 Fig ) . Therefore , it is likely that Cs23 carries two different types of nuclei , one expressing three functional MAT1-1 genes ( including MAT1-1-1L ) and MAT1-2-1 , and the other expressing only MAT1-1 genes ( including MAT1-1-1 ) , and that karyogamy between these two different nuclei is necessary for sexual development ( see details below ) . To date , the involvement of DNA rearrangement in mating-type switching has been elucidated in depth for only a few yeast species that show bi-directional switching . S . cerevisiae , which carries three MAT loci ( an active MAT locus and two silent MAT loci ) on the same chromosome , can switch mating type through a mitotic recombination-dependent gene conversion between the silent and active copies [7] . The heavily studied yeast model , S . pombe operates similarly [28] . Beyond the Saccharomycetaceae with three MAT loci [29 , 30] , methylotropic yeasts such as H . polymorpha and P . pastoris , which carry two linked MAT loci , undergo mating-type inversion mediated by inverted repeats [8 , 9] , that silences one of the two MAT loci and thus changes mating type [10] . Our in depth probing of MAT alteration with the filamentous Sordariomycete C . spinulosa supports our earlier hypotheses [22] and has parallels with recently described features of mating behavior of the Leotiomycete , S . trifoliorum [21] and the Sordariomycete , C . fimbriata [20] Although the gene complement at MAT in homothallic strains of these species varies , both carry MAT1-1 and MAT1-2 , and have structural features ( two direct repeats flanking MAT1-2 ) that testify to a mechanism by which the MAT1-2 region is eliminated via homologous recombination between repeats ( Fig 9A ) . S3 Table summarizes the similarities and differences among C . spinulosa , S . trifoliorum , and C . fimbriata MAT features . To our knowledge no functional analyses by gene manipulation have been reported for S . trifoliorum or C . fimbriata . Through DR and MAT-manipulation , crossing experiments with transgenic strains , and examination of MAT gene copy number and expression levels in WT strains and progeny , we provide experimental evidence for the looping mechanism and demonstrate that the MAT1-1;MAT1-2 region with associated DRs is unstable during vegetative growth . This feature is central to the mechanism of homothallism . Crucially , this process also leaves two types of nuclei , in unequal numbers , in a common cytoplasm . An unexpected result of this study was that transgenic C . spinulosa strains carrying both MAT1-1 and MAT1-2 loci in a single nucleus were self-sterile . Transgenic T10 and T10-type progeny , with MAT1-1;MAT1-2 architecture identical to that of Cs23 except for the absence of DR1 ( replaced with hygB ) were unable even to produce stroma , although all four MAT genes were transcribed to levels similar to levels in self-fertile Cs23 on PDA medium . Transgenic Cs27 strains carrying an intact MAT1-2-1 gene at an ectopic position produced normal-looking stroma with occasional perithecia , however all were barren . These results strongly suggest that presence of opposite forms of MAT in a single nucleus is not sufficient for self-fertility in C . spinulosa , even though this appears to be sufficient for many fungi that self successfully [22 , 27 , 31 , 32] . Supporting our findings are several reports that heterothallic species carrying both the native MAT locus and an introduced MAT locus of opposite mating type are not fertile [33–36] . Based on the unequal intensity of MAT1-1-1- and MAT1-2-1-hybridizing bands on DNA gel blots of Cs23 , we suggest that Cs23 hyphae carry two different types of nuclei in unequal numbers . One type contains the MAT1-1;MAT1-2 structure and the other , MAT1-1 only ( Fig 9 , S10 Fig ) . We propose that this unusual MAT organization is achieved by deletion of MAT1-2-1 from a majority of the nuclei within a common cytoplasm during mitotic growth , via a DR-mediated looping out mechanism that reconstructs the full MAT1-1-1 ORF and leaves the MAT1-1 locus only . Because the MAT1-1-1L fragment is expressed and appears to be as functional as the intact MAT1-1-1 ORF in a MAT1-1-1-deletion strain of F . graminearum , we hypothesize that this loop out step is required for subsequent karyogamy . Given that T10-type strains , constructed in the Cs23 genetic background , but carrying only one version of MAT with both MAT1-1 and MAT1-2 , were completely self-sterile , we propose that karyogamy in WT Cs23 occurs only between the two types of nuclei during sexual development ( Fig 9B ) . Subsequent meiosis would result in production of progeny segregating 1:1 for parental type nuclei ( Fig 9B ) . Specifically , 50% of these progeny would carry both MAT loci and be self-fertile and large [11] , while the other 50% would carry only MAT1-1 and be self-sterile and small . When self-fertile progeny are selfed , the same genetic event ( i . e . , the loss of MAT1-2-1 during mitotic growth ) is repeated . It remains unclear , and a subject of further study , how the two types of nuclei recognize each other . Do they behave as functionally heterothallic , perhaps by a mechanism that allows for activation of MAT1-2-1 expression and repression of expression of the three MAT1-1 genes at a critical stage in nuclei with MAT1-1;MAT1-2 ? Differential expression and epigenetic inactivation of MAT have been hypothesized [1 , 2 , 37] . Also unknown are the particulars of the molecular mechanism underlying the DR-mediated homologous recombination in C . spinulosa . MAT1-2-1 deletion occurs at high frequency in Cs23 during mitotic growth ( most nuclei do not carry it , Fig 9 , S10 Fig ) . Given that DR1 and DR2 are relatively short ( 115-bp ) compared with in the size of inverted repeats ( > 2 kb ) in methylotropic yeasts [8 , 9] , it is likely that additional genetic element ( s ) ( e . g . , site-specific recombinases or transposases ) are required for enhancing this recombination event . In addition , it is difficult at this point , to explain the low frequency of self-fertile progeny in out-crosses between T10 ( or T10-type progeny ) and Cs27 ( Table 1 ) . When selfed ( Table 2 ) , one of these self-fertile progeny ( P28 ) produced both self-fertile and self-sterile progeny , but the other ( P26 ) did not . One clue might be that the copy number of MAT1-2-1 in P28 matched that of Cs23 , while in P26 , MAT1-2-1 copy number was higher ( Table 2 , Fig 7 ) . Note , however , that the ratio of self-sterile to self-fertile progeny in the P28 self was not 1:1 as in self-fertile WT control Cs23 ( Table 2 ) . The connection between ascospore size and MAT genotype remains a mystery . Although the molecular mechanism underpinning the evolutionary origin of C . spinulosa DRs located at the Cs23 MAT locus remains unclear , the availability of MAT sequences of several closely related Trichoderma species allows us to propose a mechanism for evolution of C . spinulosa MAT . Our starting point is the MAT1-1-1/MAT1-2-3 fusion adjacent to MAT1-2 in T . reesei strain QM6a ( Fig 1 ) . This strain is capable of mating with H . jercorina MAT1-1 field isolate CBS999 . 97 [26] . We speculate that the fused ORF resulted from a misalignment of parental MAT chromosomes ( i . e . , between regions at the 3’ end of MAT1-2-3 and within MAT1-1-1 ) during meiosis ( S11A Fig ) . Indeed , a putative recombination site can be identified for this event ( S11B Fig ) . To achieve the C . spinulosa Cs23 MAT1-1;MAT1-2 structure , we need to postulate that at least three different crossover events occurred in crosses between the putative ancestors of C . spinulosa ( S12 Fig ) . In the first ( S12A Fig ) , an unequal crossover would occur as described above , resulting in a MAT1-2 progeny carrying a fused MAT1-1-1/MAT1-2-3 gene ( S12A Fig , asterisk ) and a MAT1-1-1 progeny , as described in S11A Fig for Trichoderma . In this cross , we assume that DR1 is included in the partial MAT1-1-1 sequence . In the second crossover ( S12B Fig ) , we postulate a similar event on the other side of DR1 , resulting in a MAT1-1 progeny ( S12B Fig , asterisk ) . The third crossover would be between progeny of the first and second events ( S12A and S12B Fig , asterisks ) and would yield a progeny carrying the MAT1-1;MAT1-2 structure present in C . spinulosa Cs23 ( S12C Fig ) . Considering the high frequency of recombination ( in the form of gene conversion ) within the unusually large MAT locus ( >100 kb ) in the human pathogenic fungus Cryptococcus neoformans [38 , 39] , this inference warrants further investigation . Our data clearly support a mechanism in which a DR-mediated looping out of MAT1-2-1 occurs during mitotic growth ( i . e . , premeiotic ) resulting in a strain with two different types of nuclei ( genotypes MAT1-1;MAT1-2 and MAT1-1 ) within a common cytoplasm in self-fertile C . spinulosa . Subsequently , in sexual reproduction , recognition and karyogamy occur between these two types of nuclei , and meiotic progeny are produced that segregate 1:1 for parental nucleus type [11] . Those with MAT1-1;MAT1-2 are self-fertile and large , while those with MAT1-1 only are self-sterile and smaller . We argue , therefore , that C . spinulosa is not primarily homothallic as are true homothallic species ( e . g . , F . graminearum ) , but employs a heterothallic mating strategy at the level of the nucleus ( not cell ) , as previously suggested [19] . The MAT1-2 deletion and imbalance of nuclear types are key to unidirectional mating-mode alteration . This hypothesis can also be applied to explain unidirectional mating-type alteration in C . fimbriata and S . trifoliorum [20 , 21] . Our study is the first to confirm , by MAT manipulation , that a chromosomal looping out-based mechanism underpins irreversible unidirectional mating-type alteration in filamentous fungi . Additionally , our results expand the repertoire of molecular mechanisms underlying homothallism and evolution of MAT loci in fungi .
C . spinulosa strains Cs23 and Cs27 , which are self-fertile and self-sterile respectively , were kindly provided by Dr . John R . S . Fincham ( S1 Fig ) in 1997 and were used as the WT strains in this study . Transgenic strains derived from Cs23 and Cs27 mentioned in this study are listed in S1 Table . The WT and transgenic strains were maintained on potato dextrose agar ( PDA; Difco Laboratories , Detroit , MI , USA ) and stored in 20% glycerol at –70°C . Sexual development was induced on cornmeal agar ( CMA , Difco ) medium , as described previously [40] . Because of mitotic instability of Cs27-derived transformants carrying MAT1-2-1 at the MAT1-1 locus , ability to self was assayed with mycelia taken directly from transformation plates . For genomic DNA extraction , each strain was grown in 50 mL PD broth at 25°C for 72 h on a rotary shaker ( 150 rpm ) . The self-fertile F . graminearum WT strain Z3643 and its self-sterile transgenic MAT1-1-1-deletion strain [25] , were maintained on PDA . Sexual development was induced on carrot agar , as previously described [25 , 41] . Fungal nuclei were stained with 4′ , 6-diamidino-2-phenylindole ( DAPI ) ( Invitrogen , Carlsbad , CA ) and examined as previously described [42] . To isolate genomic DNA , fungal strains grown in PD broth medium for 4 days at 25°C were harvested and lyophilized , as described previously [43] . DNA gel blots were hybridized with biotinylated DNA probes prepared using the BioPrime DNA labeling system ( Invitrogen , Carlsbad , CA , USA ) and developed using the BrightStar BioDetect Kit ( Ambion , Austin , TX , USA ) . Other general procedures for nucleic acid manipulation were performed as described previously [44] . Total RNA was extracted using the Easy-Spin Total RNA Extraction Kit ( iNtRON Biotechnology , Seongnam , Korea ) and first-strand cDNA was synthesized from total RNA using ReverTra Ace qPCR RT Master mix ( Toyobo , Osaka , Japan ) . All PCR primers ( S2 Table ) used in this study were synthesized by the Bioneer Corporation ( Chungwon , Korea ) , diluted to 100 μM in sterilized water , and stored at −20°C . Quantitative real-time PCR ( qPCR ) was performed with SYBR Green Super Mix ( Bio-Rad , Hercules , CA , USA ) using first-strand cDNA synthesized from total RNA or genomic DNA from strains grown on PDA for 5 days . Amplification efficiencies of all genes were determined as described previously [45] . Gene expression was measured in three biological replicates from each time point . Statistical analysis was performed by ANOVA by Duncan's multiple range test . The C . spinulosa EF1A gene was used as endogenous control for data normalization [45] . The DNA construct for deletion of DR1 from strain Cs23 was created using the double-joint ( DJ ) PCR procedure , as described previously [46] . To delete the DR1 region , the 5′- and 3′-flanking regions of the DR1 sequence ( Fig 3 ) were amplified using the primer pairs CoHo5F/CoHo5RT and CoHo3FT/CoHo3R , respectively , and these were fused to a hygromycin B resistance gene cassette ( hygB ) amplified from pBCATPH [47] using primers hygB-For and hygB-Rev . The resulting PCR products were used as template for the final PCR to generate the gene deletion , using the primers CoHo5N/pUH-BC/H3 and CoHo3N/pUH-H2 . Similarly , the DNA constructs for the generation of the transgenic strain TC27G-1 ( S1 Table ) were created as described above . Two DNA regions at the 3’ end of MAT1-1-3 corresponding to nucleotide positions 11 , 702 in the Cs27 MAT and 15 , 291 in the Cs23 MAT locus , respectively , which start at the 3’ end of MAT1-1-3 , were amplified by primer sets CoHoG5F/CoHoG5RT and CoHoG3FT/CoHoG3R , respectively , and fused to the geneticin resistance gene ( gen ) cassette using the primers CoHoG5N/GenForN and CoHoG3N/GenRenN . For insertion of MAT1-2 into Cs27 , two different plasmid DNAs ( pMAT2 and pM2M1 , Fig 5 ) , both carrying MAT1-2-1 , were constructed . For pMAT2 , the 5 . 3 kb-region carrying the entire MAT1-2-1 ORF and its 5’ flank ( 1 . 0 kb ) was amplified from Cs23 and put into the pGEMT vector , followed by hygB insertion at a SalI site in pGEMT , as described in pM2M1 . The 8 . 4-kb DNA region carrying MAT1-2-1 and all three MAT1-1 genes , which was amplified from Cs23 , was inserted into the pGEMT vector ( Promega ) , followed by the insertion of the hygB cassette at a SalI site in pGEMT , resulting in pM2M1 . For heterologous expression of Cs23 MAT1-1-1 in a MAT1-1-1-deletion strain of F . graminearum , the entire ORF of each MAT1-1-1 version amplified from Cs23 [MAT1-1-1L and MAT1-1-1 with primer pairs Cs27forE/Cs23revE and Cs27forE/Cs27revE2 ( S2 Table ) , respectively] was fused to DNA ( ~ 1 kb ) both 5' and 3' of the F . graminearum MAT1-1-1 ( annotated as FGSG_08892 . 3 in the F . graminearum genome database ) as described above [46] . The 5′- and 3′-flanking regions of the F . graminearum MAT1-1-1 sequence were amplified using the primer pairs FgM1-1-1F5/FgM1-1-1Rt5Cs27 and Fgmat1-1-1Rt5Cs23 or FgM1-1-1Ft3Cs27 /FgM1-1-1R3Cs27 ( S2 Table ) , respectively , and these were fused to either Cs23 MAT1-1-1L or MAT1-1-1 , followed by the final PCR amplification using nested primers FgM1-1-1FNCs27 and FgM1-1-1RNCs27 ( S2 Table ) . Nucleotide sequences of Cs23 and Cs27 MAT loci ( Fig 1 ) were obtained using conventional PCR amplification followed by a combination of TAIL-PCR and inverse PCR amplification as chromosome walking strategies ( S2 Fig ) . First , a 270-bp fragment of the High Mobility Group ( HMG ) box of MAT1-2-1 was amplified from genomic DNA of Cs23 using degenerate HMG primers ( NcHMG1 and NcHMG2 [48] , designated P1 and P2 in S2 Table , S2 Fig ) , after which TAIL-PCR [49] was performed to obtain sequence beyond the HMG box using combinations of arbitrary and specific primers , such as P4 [49]/P3 for the 3’ flank and P5 [49]/P6 for the 5’ flank . To recover the rest of the MAT region , we employed an inverse PCR strategy , as described previously [31] . Genomic DNA from Cs23 was digested with BamHI , SphI , ClaI , NheI , and SacII , self-ligated , and used as a template with appropriate primer pairs as shown in S2 Fig . Subsequently , sequencing was extended using primers corresponding to previously determined sequences . Sequence assembly ( 21 . 9 kb ) revealed that the C . spinulosa Cs23 MAT chromosome included MAT1-2-1 , three MAT1-1 genes and additional ORFs ( S2 Fig , Fig 1 ) . Similarly , a total of 18 . 3 kb of the MAT chromosomal region was recovered from Cs27 using the inverse PCR strategy and conventional PCR with the primers derived from the Cs23 MAT region , as shown in S2 Fig . Nucleotide sequences were assembled ( Cs23 , Cs27: GenBank accessions # KY624604 and # KY624603 ) and analyzed using the DNASTAR software package ( DNAStar Inc . , Madison , WI , USA ) . BLAST [50] searches were performed against the NCBI/GenBank databases . The C . spinulosa Cs23 , Cs27 , and T10 strains were transformed using a polyethylene glycol ( PEG ) -mediated transformation procedure newly developed in this study . For preparation of young mycelia for protoplasting , approximately 50 agar blocks ( 3 × 3 mm ) from a 7-day old PDA culture of a strain were inoculated into 100 ml PD broth and incubated for 24 h on a shaker ( 200 rpm ) . Mycelia , harvested by centrifugation , were re-suspended in 100 ml fresh PD broth in a 500 ml flask and incubated for an additional 24 h under the same conditions . Note that C . spinulosa was unable to produce asexual spores ( conidia ) under all growth conditions examined . Young mycelia were recovered by centrifugation and suspended in 25 ml osmoticum ( 0 . 7 M KCl ) containing 800 mg of lysing enzyme from Trichoderma harzianum and 8 mg of cellulase from Trichoderma sp . ( Sigma ) . The suspension was incubated for 3 h at 30°C on a shaker ( 50 rpm ) . Protoplasts , which were collected by filtering through three layers of cheesecloth followed by centrifugation , were suspended in 10 ml STC ( 1 . 2 M Sorbitol , 10 mM Tris-HCl pH 8 , and 50 mM CaCl2 ) . All other transformation steps using protoplasts were performed as described previously [43] . Two genes conferring resistance to hygromycin B ( hygB ) or geneticin ( gen ) were used as selectable markers for fungal transformation in this study . To determine if Cs23 MAT1-1-1 L is functional , we inserted it and MAT1-1-1 , separately , into a F . graminearum ΔMAT1-1-1 strain [25] . PCR constructs were directly added to protoplasts of the F . graminearum ΔMAT1-1-1 strain along with the plasmid DNA pSSK660 carrying a geneticin resistance gene ( gen ) [51] ( S8A Fig ) . As a positive control , the F . graminearum MAT1-1-1 region ( carrying the entire MAT1-1-1 along with its 5' and 3' flanking regions ) was added using the same strategy . | Fungi employ one of two mating tactics for sexual reproduction: self-sterile/heterothallic species can mate only with a genetically distinct partner while self-fertile/homothallic species do not require a partner . In ascomycetes , sexual reproduction is controlled by master regulators encoded by the mating-type ( MAT ) locus . The architecture of MAT differs in heterothallic versus homothallic species; heterothallics carry one of two forms ( MAT1-1 or MAT1-2 ) per nucleus , whereas most homothallics carry both MAT forms in a single nucleus . There are intriguing exceptions . For example , the yeast models , Saccharomyces cerevisiae , and Schizosaccharomyces pombe undergo reversible MAT switching , not demonstrated in filamentous fungi . Here , we describe the mating mechanism in Chromocrea spinulosa ( Trichoderma spinulosum ) , a filamentous ascomycete that exhibits both homothallic and heterothallic behavior . Self-fertile strains produce progeny cohorts that are 50% homothallic , 50% heterothallic . Self-sterile strains can mate only with homothallic strains , and when this occurs , homothallic and heterothallic progeny are also produced in a 1:1 ratio . By MAT sequencing and manipulation , we discovered unique MAT architecture and determined that self-fertility is achieved by deletion of MAT1-2 from most homothallic nuclei and subsequent inter-nuclear recognition between the resulting two , unevenly present , nuclear types in a common cytoplasm . | [
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"saccharomyce... | 2017 | Self-fertility in Chromocrea spinulosa is a consequence of direct repeat-mediated loss of MAT1-2, subsequent imbalance of nuclei differing in mating type, and recognition between unlike nuclei in a common cytoplasm |
The ecdysone receptor is a heterodimer of two nuclear receptors , the Ecdysone receptor ( EcR ) and Ultraspiracle ( USP ) . In Drosophila melanogaster , three EcR isoforms share common DNA and ligand-binding domains , but these proteins differ in their most N-terminal regions and , consequently , in the activation domains ( AF1s ) contained therein . The transcriptional coactivators for these domains , which impart unique transcriptional regulatory properties to the EcR isoforms , are unknown . Activating transcription factor 4 ( ATF4 ) is a basic-leucine zipper transcription factor that plays a central role in the stress response of mammals . Here we show that Cryptocephal ( CRC ) , the Drosophila homolog of ATF4 , is an ecdysone receptor coactivator that is specific for isoform B2 . CRC interacts with EcR-B2 to promote ecdysone-dependent expression of ecdysis-triggering hormone ( ETH ) , an essential regulator of insect molting behavior . We propose that this interaction explains some of the differences in transcriptional properties that are displayed by the EcR isoforms , and similar interactions may underlie the differential activities of other nuclear receptors with distinct AF1-coactivators .
Nuclear receptors are multifunctional transcription factors that mediate responses to steroids and other small hydrophobic signaling molecules . Most nuclear receptors have two transcriptional activation functions ( AF1 and AF2 ) . AF2 is formed by ligand-induced folding of the ligand-binding domain , and the structural basis of its interaction with coactivators is becoming known . AF1 designates a second , ligand-independent activation function often present in the N-terminal region of the receptor . AF1 sequences are not conserved , and the existence of an AF1 must be inferred from functional assays . Although the AF1s are of considerable interest , because they often differentiate receptor isoforms and because some have been shown to interact with general transcription factors , comparatively few AF1-coactivator interactions have been characterized . The relative contributions of AF1 and AF2 to transcriptional activation vary among receptors , and for any given receptor the relative contributions may depend upon the promoter context [1] . The three isoforms of EcR ( FlyBase ID: FBgn0000546 ) have unrelated AF1 regions , each capable of mediating transcriptional activation in some contexts [2]–[6] . Although several coactivators and corepressors for the AF2 of EcR have been identified [7]–[13] , the interacting factors for the unique AF1 domains remain unknown . The 17-residue AF1 region of isoform B2 is capable of strong transcriptional activation on a standard test promoter and is required for ecdysone-regulated differentiation in a few fly tissues [2] , [4] . Here , we show that the bZIP transcription factor , CRC ( FBgn0000370 ) , binds the AF1 of isoform B2 to promote steroid-dependent expression of the peptide molting hormone , ETH ( FBgn0028738; [14] ) .
We performed a yeast two-hybrid screen using the N-terminal region of EcR-B2 as bait and recovered a plasmid containing the complete coding sequence of the predominant CRC isoform , CRC-A [15] . Figure 1 illustrates the salient features of these assays . Interaction ( as judged by reporter activation ) required the presence of both CRC-A and the AF1 of EcR-B2 ( Figure 1A ) . The EcR-B2 mutation E9K , which sharply reduces transcriptional activation in vivo [4] , also abolished the two-hybrid interaction ( Figure 1A ) . The interaction surface provided by CRC was contained within the C-terminal three-quarters of the protein , a region that includes its bZIP and PEST domains , and C-terminal truncation of the protein to remove just the leucine zipper domain abolished the interaction with EcR-B2 ( Figure 1B ) . Binding of CRC to the amino-terminal region of EcR-B2 was confirmed in vitro ( Figure 2A ) . Radiolabeled CRC-A bound to the EcR-B2 amino-terminus and to full-length EcR-B2 , but not to EcR-B1 or to EcR-B2 carrying the E9K mutation . The AF1 region of the EcR-B2 N-terminus contains sequences suggestive of a short amphipathic helix , and it is known that the acidic-to-basic substitution E9K ( within the proposed helix ) abolishes AF1 function in vivo [4] . We think it likely that this helix is unstructured in solution and infer that both ionic and hydrophobic interactions play roles in its dimerization , probably with the leucine zipper region of CRC . To test this idea further , we made several individual basic-to-acidic mutations within the CRC leucine zipper – at sites predicted to determine the dimerization specificity of the bZIP domain [16] – and tested the binding of the mutant CRC proteins to wild-type and E9K mutant EcR-B2 ( Figure 2B ) . The binding properties of CRC-R347E and CRC-R353E were indistinguishable from those of wild-type CRC , but CRC-R361E bound EcR-B2-E9K . That an alteration in CRC reversed the effect of an EcR mutation strongly implies direct interaction . Neither USP ( FBgn0003964 ) nor the hormone ecdysone affected the CRC-EcR-B2 interaction as measured in our biochemical tests ( Figure 2A ) . While AF1 activity is hormone-dependent in vivo , that is probably due to the effects of corepressors ( e . g . SMRTER ) that bind unliganded EcR/USP and suppress the activity of AF1 [4] . Both the crc mutant phenotype , which includes molting defects that result in supernumerary mouthparts in larvae and failure to evert the adult head at pupal ecdysis , and the pattern of crc expression suggest a role for CRC in the ecdysone response [15] . We used a genetic interaction test to determine whether CRC functions as a modulator of EcR-B2 function in flies . We examined the effects of a single copy of crc1 ( a spontaneous mutation , Q171R; FBal0001818 ) [15] , [17] on the phenotype produced by targeted expression of the dominant-negative mutant EcR-B1-F645A [2] , [4] . EcR-B1-F645A is normal in transcriptional repression ( the effect of unliganded receptor ) , but it fails to mediate transcriptional activation . Because the EcR isoforms do not display isoform specificity in DNA binding [5] , the EcR-B1-F645A mutant is thought to competitively inhibit all three endogenous EcR isoforms [18] . Hence , a reduction in EcR-B2 coactivator titer should selectively enhance the effects caused by EcR-F645A expression only in tissues requiring the EcR-B2 isoform . Targeted expression of the dominant-negative receptor EcR-B1-F645A permits an examination of the properties of EcR function in specific tissues in the context of an otherwise normal animal [2] . crc1 is a recessive mutation , and crc1/crc+ heterozygous cells are phenotypically normal . We generated sensitized tissues by targeting expression of EcR-B1-F645A to five different developmental domains , using specific GAL4 drivers [2] . As shown in Table 1 , crc1 was a dominant enhancer of the EcR dominant negative phenotype in the Eip domain ( largely larval epidermis ) and in the slbo domain ( specialized portions of the follicular epithelium of the egg chamber ) , but it had no significant effect in the GMR domain ( primarily retinal epithelium ) , the dpp domain ( primarily A/P disc boundaries ) , or the Lsp2 domain ( fat body ) . We have previously described the EcR isoform requirements in each of these domains [2] . There was a remarkable correlation: Where EcR-B2 is required for development , wild-type crc function was also required , and where normal development does not require EcR-B2 , reduction of the CRC titer had little or no effect . In homozygous or hemizygous crc1 mutant larvae , expression of ETH is markedly reduced [19] . The loss of crc has similar effects on expression of an ETH-EGFP reporter gene , which contains 382 bp of the ETH promoter and precisely recapitulates the native pattern of ETH expression [14] , [19] . Thus , CRC up-regulates ETH expression . CRC is expressed in many larval tissues , including the endocrine source of ecdysone [15] . Therefore , to test for cell-autonomous regulation of ETH expression by CRC , we used an ETH-GeneSwitch driver to drive transgenic crc RNAi ( UAS-crc-RNAi ) specifically in the Inka cells ( Figure S1 ) , the site of ETH synthesis [14] . GeneSwitch is a conditional GAL4 protein that is activated by addition of the progesterone antagonist RU486 to the food [20] . Compared to the control larvae , larvae with crc RNAi showed a 15-fold or greater reduction in ETH transcript levels ( Figure 3A ) . Thus , CRC was cell-autonomously required in the Inka cells for full ETH expression . The Drosophila melanogaster ETH promoter contains a putative ecdysone response element [19] , [21] , and ETH expression in the tobacco hawkmoth ( Manduca sexta ) fluctuates during the molts and is elevated in response to circulating ecdysteroids [22] . Therefore , we examined whether ETH expression is ecdysone-dependent . In larval and pupal Drosophila , expression of the ETH peptide hormone is restricted to 14 endocrine Inka cells located on the trachea [14] . We performed ETH in situ hybridization and found that ETH transcript levels increased gradually during the first few hours after metamorphosis was initiated ( Figure 4A ) . The ETH transcript levels peaked 6–8 hr after the pulse of ecdysone that occurs at pupariation ( Figure 4A ) , suggesting that ETH was transcribed in response to elevation of the circulating ecdysone titer . We tested for direct ecdysone-dependence of ETH expression in young third instar stage larvae , when circulating ecdysone and ETH transcript and protein levels are low , by feeding them the major active form of ecdysone , 20-hydroxyecdysone ( 20E ) [23] . These larvae carried the ETH-EGFP reporter gene . By 12 hr after the onset of the 20E treatment , the level of ETH-EGFP fluorescence was markedly elevated ( Figure 4B ) . Thus , ETH expression was strongly up-regulated by circulating ecdysone . To test for a direct , cell autonomous effect of ecdysone on ETH expression , we targeted EcR-F645A dominant negative proteins specifically to the Inka cells with the ETH-GeneSwitch driver . Following Inka cell expression of the EcR dominant negative proteins , ETH transcripts were still present but at levels that were 2–6 fold lower than in wild-type larvae ( Figure 3B ) . Thus , ETH expression was strongly stimulated by ecdysone and required EcR expression in the Inka cells . The EcR-B1-F645A mutant is effective as a dominant negative when it is expressed in excess of the wild-type isoforms ( Table 1 ) [2] , [4] . However , the dominant negative EcR-B1-F645A protein competes poorly with wild-type EcR when both are expressed from identical promoters [2] , [4] . Therefore , to determine which EcR isoforms support up-regulation of ETH expression in the Inka cells , we performed competition experiments in which EcR-B1-F645A and individual wild-type isoforms were coexpressed under the control of the ETH-GeneSwitch driver . The ability of a wild-type EcR isoform to mitigate the effects of the dominant negative is indirect evidence in support of transcriptional activation of the ETH promoter by that isoform . Supernumerary mouthparts result when larvae fail to complete ecdysis to either the second or the third larval instar , and they are a characteristic feature of the ETH and crc mutant phenotypes [14] , [15] . Over 95% of larvae with Inka cell-targeted EcR-B1-F645A expression had multiple mouthparts , and ∼70% of these animals died as larvae ( Figure 5A ) . Simultaneous expression of wild-type EcR-B2 or EcR-B2-E9K with EcR-B1-F645A fully rescued lethality and ecdysis of the larval mouthparts , whereas EcR-B1 and EcR-A produced only partial rescue ( Figure 5A ) . Within the Inka cells , ETH transcript levels were fully rescued by EcR-B2 , but EcR-B1 and EcR-B2-E9K were ineffective at rescue ( Figure 5B ) . Thus , of the three EcR isoforms , only wild-type EcR-B2 was capable of supporting full ETH expression and successful ecdysis . The E9K mutant of EcR-B2 failed to rescue ETH transcript levels , suggesting a model in which dimerization of EcR-B2 with CRC is required for ETH mRNA expression . In M . sexta , 20E regulates ETH synthesis as well as the competency of the Inka cells to secrete ETH [24] . The ability of EcR-B2-E9K , and to a lesser extent EcR-B1 and EcR-A , to rescue ecdysis and lethality ( Figure 5A ) indicates that EcR likely regulates other Inka cell processes , such as ETH protein accumulation or secretory competence , that are necessary for signaling by ETH . We tested this hypothesis by performing ETH immunostaining in EcR-B2-E9K rescue animals both before and after secretion at pupal ecdysis . Although EcR-B2-E9K did not stimulate ETH transcription ( Figure 5B ) , it drove ETH protein accumulation in the Inka cells ( Figure S2A ) . Consistent with the predicted role of EcR in the development of secretory competence , we also observed a marked decrease in accumulated ETH at pupal ecdysis ( Figure S2B ) . These results show that EcR—likely through different sets of EcR isoforms and transcriptional coactivators—regulates ETH protein accumulation independently of ETH mRNA expression .
Our experiments suggest that the 17-residue B2-specific N-terminus binds to the bZIP region of CRC , that an ionic interaction between EcR-B2-E9 and CRC-R361 plays some role in the binding , and that the interaction of the two proteins plays a crucial role in those tissues where EcR-B2 is essential . These tissues include the endocrine Inka cells , which display ecdysone-dependent upregulation of ETH transcripts and which require EcR-B2 and CRC for full ETH expression . Taken together , these findings implicate CRC as an isoform-specific transcriptional activator for EcR-B2 . In diverse systems , bZIP proteins interact with dyadic or palindromic promoter sequences as homodimers or heterodimers with other bZIP partners [25] . Dimerization involves regularly spaced hydrophobic amino acids that form a coiled-coil between two leucine zipper domains [26] . Other bZIP transcription factors are known to interact with nuclear receptors , modulating the activities of either AF1 or AF2 [27]–[29] , but in the cases reported previously , bZIP proteins bind either to the DNA-binding domain or to the hinge domain of the nuclear receptor . By contrast , CRC ( through its bZIP domain ) appears to bind directly to the EcR-B2 AF1 region , and its interaction is specific to one EcR isoform . ATF4 , the mammalian homolog of CRC , plays a central role in stress responses [30] . The role of CRC in ecdysone signaling suggests the possibility of interesting and unexpected connections between stress responses and the control of developmental timing and metamorphosis . The ETH promoter contains sequences matching the consensus half-sites for binding of ATF4 and EcR to DNA . These half-sites are separated by 4 nucleotides , and they are located within a highly conserved sequence ( comparing D . melanogaster to several other Drosophila species ) that is 138–171 nucleotides upstream of the ETH transcriptional start site [19] . Since bZIP proteins may bind first sequentially as monomers and then dimerize while bound to DNA [26] , [31] , these observations suggest a model in which CRC participates in the stabilization of EcR-B2 binding to the ETH promoter . This interaction provides a basis for understanding some of the differences in transcriptional properties that are displayed by the EcR isoforms and perhaps other nuclear receptors with distinct AF1-coactivators .
Yeast two-hybrid assays were carried out using the Clontech Matchmaker yeast two-hybrid kit ( Clontech , Mountain View , CA ) and yeast strain Hf7C . The bait was a fusion of the GAL4 DNA-binding domain to either the 17-residue AF1 domain of EcR-B2 , or the same fragment containing the mutation E9K . Binding was assayed as expression of β-galactosidase from a UAS-lacZ reporter . Proteins were synthesized in vitro using the TNT reticulocyte lysate kit ( Promega , Madison , WI ) ; template plasmids were described previously or were generated by a similar procedure [4] . Binding reactions ( 50 µl ) contained 3 µl of each indicated translation mix in buffer A ( 20 mM NaH2PO4 , 150 mM NaCl , pH 8 . 0 ) were incubated at 4° for 30 min . Then , 25 µl of a 50% slurry of Sepharose-protein A ( Sigma ) loaded with the indicated antibody was added and the incubation continued for 30 min . Beads were washed 3 times in buffer A and then boiled in SDS-PAGE sample buffer . The eluted proteins were separated by SDS-PAGE and radiolabeled proteins detected by autoradiography . For precipitation of full length EcRs , a mixture of the EcR-common region monoclonal antibodies , AG 10 . 2 and DDA 2 . 7 [6] , was used with each at a 1∶10 , 000 dilution of an ascites fluid . For precipitation of GBT-B2-NS , we used a commercial antibody to the GAL4 DNA-binding domain ( 1∶1000 , Santa Cruz Biotechnology , Santa Cruz , CA ) . Drosophila melanogaster were reared on standard cornmeal-yeast-agar media at 22–25° unless otherwise noted . Oregon-R was used as the wild-type strain . Larvae at the onset of metamorphosis were scored based on the blue color intensity observed in the gut of third instar larvae fed with cornmeal-yeast-agar food supplemented with 0 . 1% bromophenol blue . We collected blue gut larvae ( 18 hours before pupariation ) and clear gut larvae ( 4 hours before pupariation ) [32] . Prepupae and pupae were selected based on the criteria reported by Bainbridge and Bownes [33] at the following stages: white puparium ( P1 stage; at puparium formation ) , buoyant prepupa ( P4i stage; 6 . 5–8 hours after puparium formation ) , and moving bubble prepupa ( P4ii stage; 12–13 . 5 hours after puparium formation ) . The ETH-GeneSwitch ( ETH-GSW ) line was a kind gift from Michael Adams ( University of California , Riverside ) and Yoonseung Park ( Kansas State University ) . It expresses a conditional , RU486-dependent GAL4 protein chimera [20] under the control of the 382 bp ETH promoter region [19] , [21] . First instar larvae carrying ETH-GSW and selected UAS constructs were transferred after hatching to cornmeal-yeast-agar media supplemented with 500 mM RU486 [34] . In larvae , the expression of a reporter gene under ETH-GSW/RU486 control was restricted to just the Inka cells ( Figure S1 ) . The CRC and EcR loss-of-function transgenes included UAS-Crc-RNAi ( Vienna Drosophila RNAi Center ( VDRC ) line #2935 , FBti0084038 ) [35] , UAS-EcR-B1-W650A ( FBti0026963 ) , UAS-EcR-B2-W650A , UAS-EcR-B1-F645A ( FBti0026961 ) , and UAS-EcR-B2-E9K . The UAS-EcR-B1 ( FBti0023086 ) , UAS-EcR-B2 ( FBti0023085 ) , and UAS-EcR-A ( FBti0023087 ) transgenes contain the three wild-type EcR isoforms [2] . Freshly ecdysed ETH-EGFP ( FBal0136020 ) third instar larvae were transferred on cornmeal-yeast-agar media supplemented with 0 . 08 mg/ml 20E [36] and collected 12 hours later for analysis of EGFP fluorescence in the Inka cells . Digoxigenin-labeled DNA probe preparation , whole-mount larval in situ hybridization , ETH in situ hybridization , anti-PETH immunostaining , and ETH-EGFP imaging was performed as described [19] . In larvae , the Inka cells are identified by the tracheal metameres ( TMs ) on which they are located , and the TMs are numbered 1 to 10 , starting with the anterior end of the animal . To quantify the intensity of EGFP , immunostaining , and in situ hybridization signals , we measured the Intensity Index*Area = S*[ ( I–B ) /B] where ( S ) is the surface area covered by the signal , ( I ) is the mean pixel intensity of the signal within this area , and ( B ) is the background signal intensity [19] , [37] . This method takes into consideration the density of the signal distributed over the cell area , and it therefore normalizes for the angle at which the Inka cell is photographed and for heterogeneity in the spatial distribution of the signal . The measurements were taken using Adobe Photoshop ( San Jose , CA , USA ) . Statistical tests were performed using the NCSS 2001 software package ( Kaysville , UT ) . Bonferroni corrections were performed to minimize type I errors in multiple pair-wise comparisons ( Rice , 1989 ) . We used parametric statistics because the data generally followed a normal distribution . All values are means ± s . e . m . , except as indicated . | Nuclear receptors are proteins that regulate gene expression in response to steroid and thyroid hormones and other small lipid-soluble signaling molecules . In many cases , nuclear receptor genes encode multiple variants ( isoforms ) that direct tissue- and stage-specific hormonal responses . The sequence differences among isoforms are often found at the protein N-terminus , which mediates hormone-independent interactions with unknown regulatory partners to control target gene expression . Here , we show that the fruit fly Cryptocephal ( CRC ) protein is a specific coactivator for one of three isoforms of the receptor for the insect molting steroid , ecdysone . Our findings reveal a mechanism for differential activation of gene expression in response to ecdysone during insect molting and metamorphosis , and contribute to our understanding of isoform-specific functions of nuclear hormone receptors . | [
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"signaling",
"pathways",
"neuroscience"
] | 2012 | Cryptocephal, the Drosophila melanogaster ATF4, Is a Specific Coactivator for Ecdysone Receptor Isoform B2 |
Adenocarcinoma ( AC ) and squamous cell carcinoma ( SqCC ) are two major histological subtypes of lung cancer . Genome-wide association studies ( GWAS ) have made considerable advances in the understanding of lung cancer susceptibility . Obvious heterogeneity has been observed between different histological subtypes of lung cancer , but genetic determinants in specific to lung SqCC have not been systematically investigated . Here , we performed the GWAS analysis specifically for lung SqCC in 833 SqCC cases and 3 , 094 controls followed by a two-stage replication in additional 2 , 223 lung SqCC cases and 6 , 409 controls from Chinese populations . We found that rs12296850 in SLC17A8-NR1H4 gene region at12q23 . 1 was significantly associated with risk of lung SqCC at genome-wide significance level [additive model: odds ratio ( OR ) = 0 . 78 , 95% confidence interval ( CI ) = 0 . 72–0 . 84 , P = 1 . 19×10−10] . Subjects carrying AG or GG genotype had a 26% ( OR = 0 . 74 , 95% CI = 0 . 67–0 . 81 ) or 32% ( OR = 0 . 68 , 95% CI = 0 . 56–0 . 83 ) decreased risk of lung SqCC , respectively , as compared with AA genotype . However , we did not observe significant association between rs12296850 and risk of lung AC in a total of 4 , 368 cases with lung AC and 9 , 486 controls ( OR = 0 . 96 , 95% CI = 0 . 90–1 . 02 , P = 0 . 173 ) . These results indicate that genetic variations on chromosome 12q23 . 1 may specifically contribute to lung SqCC susceptibility in Chinese population .
Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer death around the world [1] . Adenocarcinoma ( AC ) and squamous cell carcinoma ( SqCC ) are two major histological subtypes of lung cancer [2] . Although tobacco smoking increases the risk of all major histological subtypes of lung cancer , it appears to be stronger for SqCC than AC [3] . Different spectra and frequencies of “driver” mutations have been described between lung AC and SqCC and result in a histology-specific therapy [4] . These evidences support a histology-specific pathogenesis process and biological characteristics of lung cancer , and studies specifically focused on individual histological subtype are required for understanding lung carcinogenesis . Several large genome-wide association studies ( GWAS ) of lung cancer have been conducted to uncover genetic factors associated with lung cancer risk [5]–[15] ( Table S1 ) . Three loci at 5p15 , 6p21 and 15q25 were initially identified to contribute to the susceptibility to lung cancer in populations of European ancestry [5] , [6] , [16]–[18] . These findings have provided new clues for the mechanism of lung cancer development . Interestingly , some of these loci reflected different associations across lung cancer histology . For example , the 5p15 locus defined by rs2736100 showed stronger association with AC in populations of both European [7] and Asian [19] ancestries . However , most of lung cancer GWAS combined lung cancer cases with multiple subtypes of histology together when compared with controls in the discovery stage , making it difficult to identify histology-specific susceptibility loci due to dilution of effect . With efforts to determine genetic variants associated with a specific type of lung cancer , two GWAS of lung AC have been conducted in populations of eastern Asian . Hsiung et al . performed a GWAS of AC and subsequent replications in never-smoking females and further confirmed that rs2736100 at 5p15 is associated with risk of lung AC [20] . Recently , Miki et al . carried out a GWAS of lung AC in Japanese and Korean populations and identified a new susceptibility locus at TP63 on 3q28 [13] , which have also been confirmed by following studies [11] , [21] . Interestingly , Landi et al . conducted a lung cancer histology-specific association study in 917 selected genes with 19 , 802 SNPs in the HuGE-defined “inflammation” pathway using available GWAS data from populations of European descent , and identified a locus at 12p13 . 33 associated with SqCC risk [15] . These evidences suggest the importance of exploring susceptibility loci by subtypes in lung cancer . Recently , we conducted a three-stage GWAS for overall lung cancer in the Han Chinese populations and identified two new loci at 13q12 . 12 and 22q12 . 2 that were consistently associated with multiple subtypes of lung cancer [11] . Here , in order to identify genetic variants across whole genome specifically related to lung SqCC risk , we carried out the GWAS analysis in 833 cases with lung SqCC and 3 , 094 controls ( Nanjing study: 428 cases and 1 , 977 controls; and Beijing study: 405 cases and 1 , 117 controls ) , and further evaluated suggestive associations involving lung SqCC risk by a two-stage replication with a total of 2 , 223 cases with lung SqCC and 6 , 409 controls in the Han Chinese populations .
After filtering by standard quality-control procedures , a total of 3 , 927 subjects ( 833 lung SqCC cases and 3 , 094 controls ) with 570 , 009 SNPs were qualified for further GWAS analysis ( Table S2 ) . A quantile-quantile plot using P values from additive model showed a relatively low inflation factor ( λ = 1 . 04 ) , suggesting a low possibility of false-positive associations due to population substructure ( Figure S1 ) . After excluding the SNPs at reported loci of our previous study [11] , P-value on a -log scale for each SNP was plotted by location on chromosome ( i . e . , Manhattan plot; Figure S2 ) . We determined promising SNPs associated with risk of lung SqCC based on P value of ≤1×10−4 in additive model and consistent associations between Nanjing and Beijing studies ( P<0 . 01 with the same direction of associations ) . After linkage disequilibrium ( LD ) analysis ( excluding 9 SNPs at r2 of 0 . 8; Table S4 ) , 14 autosome SNPs were selected to be further evaluated in the first replication stage ( Replication I ) including 822 cases with lung SqCC and 2 , 243 controls ( Table S3 ) . Three SNPs at 6p22 . 2 ( rs16889835 ) , 11p15 . 1 ( rs7112278 ) and 12q23 . 1 ( rs12296850 ) that were confirmed in the Replication I were further assessed in the second replication stage ( Replication II ) using additional 1 , 401 cases and 4 , 166 controls ( Tables S5 ) . In the Replication II , rs12296850 at 12q23 . 1 remained to be significantly associated with risk of lung SqCC ( OR = 0 . 82 , 95%CI = 0 . 74–0 . 91 , P = 3 . 47×10−4 ) , consistent with those observed in the GWAS stage ( OR = 0 . 73 , 95%CI = 0 . 63–0 . 86 , P = 9 . 30×10−5 ) and the fist replication stage ( OR = 0 . 75 , 95%CI = 0 . 63–0 . 88 , P = 5 . 08×10−4 ) ( Table S5; Table 1 ) . After combining results from the GWAS and two-stage replications , rs12296850 was associated with the risk of lung SqCC at genome-wide significance level ( P<5 . 0×10−8 ) , and the OR for additive model is 0 . 78 ( 95%CI = 0 . 72–0 . 84 , Pcombined = 1 . 19×10−10 ) . The combined ORs for the heterozygote ( AG ) and minor homozygote ( GG ) are 0 . 74 ( 95% CI = 0 . 67–0 . 81 ) and 0 . 68 ( 95%CI = 0 . 56–0 . 83 ) , respectively , as compared with major homozygote ( AA ) ( Table 1 ) . To further characterize the association of genetic variants at 12q23 . 1 with lung SqCC risk , we performed imputation analyses based on CHB+JPT data of 1000 Genomes Project ( released at June 2010 ) . In a 300-kb region around rs12296850 , 243 imputed SNPs at imputed r2>0 . 5 and MAF>0 . 05 were evaluated with association of lung SqCC risk . As shown in Figure 1 and Table S6 , two SNPs , rs17030141 and rs11568535 having strong LD ( r2>0 . 9 ) with rs12296850 , showed similar associations with risk of lung SqCC at a P value of 6 . 46×10−5 and 7 . 43×10−5 , respectively . We further conducted stratification analysis on the association between rs12296850 at 12q23 . 1 and lung SqCC risk by age , gender and smoking dose . As shown in Table S7 , none of different associations were significantly observed between subgroups . In addition , we did not detect significant interaction between rs12296850 and smoking on lung SqCC risk . Similar associations were observed among populations of Nanjing and Shanghai , Beijing , and Shenyang , and no significant heterogeneity between populations was detected for the association , though a non-significant association was shown in Guangzhou population ( Figure S3 ) . To investigate whether the variant rs12296850 was SqCC-specific , we further evaluated the association between rs12296850 and the risk of lung AC and small cell carcinoma ( SCC ) using the shared controls as SqCC study for each stage . We found that rs12296850 was not consistently associated with risk of lung AC in the three stages ( GWAS: OR = 0 . 85 , 95%CI = 0 . 76–0 . 95; Replication I: OR = 1 . 08 , 95%CI = 0 . 96–1 . 22; Replication II: OR = 0 . 96 , 95%CI = 0 . 88–1 . 05 ) ( Table 2 ) . After combining three stages , rs12296850 was not significantly associated with lung AC risk ( OR = 0 . 96 , 95%CI = 0 . 90–1 . 02 , P = 0 . 173 ) . Similarly , rs12296850 was not consistently associated with lung SCC risk with a combined OR of 0 . 89 ( 95%CI = 0 . 79–1 . 01; P = 0 . 073 ) ( Table 2 ) . These results indicate that rs12296850 at 12q23 . 1 may be a specific susceptibility locus to lung SqCC in Chinese population . To characterize the functional relevance of the rs12296850 , we further evaluated the relationship of this variant with the expression levels of two surrounding genes ( NRIH4 and SLC17A8 ) . We examined NRIH4 mRNA levels in 46 paired lung cancer tumor and adjacent non-tumor tissues using quantitative RT-PCR , and observed that the relative expression of NRIH4 in adjacent non-tumor tissues was significantly higher in subjects with G allele of rs12296850 ( n = 18 ) as compared with those carrying AA genotype ( n = 28 ) ( AG/GG: 0 . 54±0 . 25 versus AA: 0 . 36±0 . 19 , P = 0 . 008 ) ( Figure S4 ) . Similar but non-significant results were also observed in tumor tissues ( AG/GG: 0 . 50±0 . 22 versus AA: 0 . 39±0 . 26 , P = 0 . 143 ) . However , the mRNA expression level of SLC17A8 could not be detectable ( Ct>40 ) in all of the adjacent non-tumor tissues ( n = 46 ) and most of tumor tissues ( n = 43 ) whereas only 3 subjects were measured with low expression levels in tumor tissues ( Ct = 33 . 7 , 36 . 1 and 39 . 0 ) .
In this study , we conducted a GWAS analysis in specific to lung SqCC in Chinese populations and identified a novel locus at 12q23 . 1 ( lead SNP: rs12296850 ) that was specifically associated with lung SqCC . In our prior GWAS on overall lung cancer , we also showed genome-wide significant associations of loci at 3q28 , 5p15 . 33 , 13q12 . 12 , and 22q12 . 2 with lung SqCC in stratification analysis [11] . Unlike previous study designed for overall lung cancer followed by a ‘post-hoc’ analysis on lung SqCC , the current study directly evaluated genetic variants across genome that might be specifically associated with lung SqCC risk . The identified locus was further assessed whether it was also associated with lung AC or SCC risk . This study represents an improved approach on exploring subtype-specific susceptibility loci for diseases with heterogeneous phenotypes , such as lung cancer . We also evaluated the association of the SNP rs12296850 with SqCC risk in lung cancer GWAS data of European descent from MD Anderson Cancer Center ( MDACC ) [16] . After imputation based on HapMap 2 CEU population , rs12296850 was not significantly associated with SqCC risk ( OR = 0 . 80 , 95%CI: 0 . 52–1 . 24; P = 0 . 325 ) in 306 SqCC cases and 1 , 135 controls from the MDACC GWAS . The inconsistent results may be due to small sample size of MDACC study and different genetic backgrounds between Chinese and European descents . The minor allele ( G ) frequency of rs12296850 in Chinese population ( >0 . 20 for all three stages ) is more common than that in MDACC ( 0 . 053 ) . The relative small sample size and low frequency may result in a negative result due to limited statistical power . In addition , the subjects of MDACC GWAS were all smokers , which may not represent the similar target population used in our study . However , at this stage , we have no substantial evidence to extend our findings to other populations , and further studies in other populations are required to further confirm our findings . Genomic alterations on chromsome12q23 have been frequently linked to a spectrum of cancers , including non-small cell lung cancer ( NSCLC ) , prostate cancer , adenoid cystic carcinoma and oligodendrogliomas and colorectal carcinoma [22]–[26] . For NSCLC , cigarette smoking dose has been associated with copy number alterations in12q23 [26] . In addition , chromosomal gains at 12q23–24 . 3 facilitated tumour progression and metastasis of lung SqCC and may serve as potential predictors for this disease [27] . These evidences as well as our findings collectively suggested the importance of chromosome 12q23 in the development of lung cancer , especially for SqCC . At 12q23 . 1 , the lead SNP rs12296850 is located in 4 . 2 kb downstream of SLC17A8 ( encoding vesicular glutamate transporter 3 ) and 47 . 6 kb upstream of NR1H4 ( encoding a ligand-activated transcription factor ) . Correlation analysis results indicate that this SNP may be associated with the expression of NR1H4 , a gene known as nuclear farnesoid X receptor ( FXR ) . FXR is a member of the nuclear receptor family of transcription factors and highly expressed in the entero-hepatic system where it transcriptionally regulates bile acid and lipid metabolism [28] . Bile acids are natural ligands for the FXR , and the bile acid-FXR interaction has been suggested to be involved in the pathophysiology of a number of inflammatory-associated cancers [29] , [30] . Loss of FXR increased tumor progression via promoting Wnt signaling by infiltrating neutrophils and macrophages , and elevated the tumor necrosis factor α ( TNFα ) production in vivo [30] . Furthermore , FXR was involved in CYP regulation through mutual repression with NF-kappaB which indirectly regulates the transcription of CYP genes [31] . Further studies are required to elucidate the potential role of NR1H4 on SqCC development . SLC17A8 ( also known as Vesicular Glutamate Transporter Type 3 , VGLUT3 ) is a member of the solute carrier ( SLC ) superfamily encoding multiple transmembrane transporters that may involve in the development and progression of a number of diseases , including cancers [32] . Genetic variants in the urea transporter ( UT ) gene SLC14A were reported to be significantly associated with susceptibility to urinary bladder cancer in a GWAS of European population , whereas SLC5A8 may function as a tumor suppressor gene whose silencing by epigenetic changes may contribute to carcinogenesis and progression of pancreatic cancer [33] , [34] . However , the expression levels of SLC17A8 were very low in lung cancer tumor and adjacent non-tumor tissues . Whether this gene involves in SqCC development is still unclear to date . In addition , SCYL2 and GAS2L3 were another two genes around the SNP rs12296850 in a relatively long distance . SCYL2 ( also known as CVAK104 ) is located at 86 . 2 kb upstream of rs12296850 , encoding a coated vesicle-associated kinase of 104 kDa . SCYL2 can regulate the levels of frizzled 5 ( Fzd5 ) via inducing lysosomal degradation , which probably inhibit the Wnt signaling pathway [35] . GAS2L3 , encoding proteins with putative actins and microtubule binding domains , is located at 147 . 4 kb downstream of rs12296850 . GAS2L3 was reported to localize to the spindle midzone and the midbody during anaphase and cytokinesis , respectively , and to act as a novel target of DREAM and play an important role in accurate cell division [36] . However , expression quantitative trait loci ( eQTL ) analysis did not reveal any significant correlation between rs12296850 and the expressions of these two genes . In this GWAS of lung SqCC in Chinese , we reported evidence that common genetic variants at 12q23 . 1 are implicated in the development of lung SqCC . Our findings highlight the importance of studying subtype of lung cancer and may provide new insight into the mechanism of SqCC . Further studies , such as resequencing this region followed by fine-mapping study and eQTL analysis in lung tissues as well as biochemical assays , may affiliate to determine causal variants at 12q23 . 1 that directly regulate the development of lung SqCC . In addition , the moderate sample size in GWAS scan stage may have decreased statistical power in the current study , and further studies with larger sample size or pooling multiple studies may promise to identify more SqCC-specific loci .
A three-stage case-control study was designed to evaluate the associations between genetic variants across human genome and the risk of lung SqCC . Study subjects for GWAS scan of lung cancer and two-stage replication have been described elsewhere [11] . Briefly , the cases newly diagnosed with lung cancer were recruited from hospitals . The histology for each case was histopathologically or cytologically confirmed by at least two local pathologists . Cancer-free control subjects were recruited in local hospitals for individuals receiving routine physical examinations or in the communities for those participating screening of noncommunicable diseases . The controls were frequency-matched to lung cancer cases for age , gender and geographic regions . Demographic information was collected using standard questionnaire through interviews . Individuals were defined as smokers if they had smoked at an average of one cigarette or more per day and for at least one year in their lifetime; otherwise , subjects were considered as non-smokers . Smokers were considered as former smokers who quit for at least one year before recruitment . Both current and former smokers were divided into light and heavy smokers according to the threshold of 25 pack-year ( median value among the controls ) . The patients with lung SqCC and all of the controls that were included in previous GWAS of overall lung cancer [11] were considered as the cases and controls in the current study . As a result , 833 SqCC cases and 3 , 094 controls were included in the GWAS scan stage , including 428 cases and 1977 controls from Nanjing and Shanghai ( Nanjing Study ) , and 405 cases and 1 , 117 controls from Beijing ( Beijing Study ) . The first replication stage ( Replication I ) included 822 SqCC cases and 2 , 243 controls that were from Nanjing and Shanghai ( 235 cases and 754 controls ) and Beijing ( 587 cases and 1 , 489 controls ) . The second replication stage ( Replication II ) included 1 , 401 SqCC cases and 4 , 166 controls that were from Nanjing and Shanghai ( 238 cases and 1 , 069 controls ) , Beijing ( 362 cases and 936 controls ) , Shenyang ( 306 cases and 1 , 027 controls ) and Guangzhou ( 495 cases and 1 , 134 controls ) . All study subjects provided informed consent and each study was approved by its respective institution's IRB . A total of 906 , 703 SNPs were genotyped in the GWAS scan in 844 lung SqCC cases and 3 , 160 controls by using Affymetrix Genome-Wide Human SNP Array 6 . 0 chips as described previously [11] . A systematic quality control ( QC ) procedure was applied to both SNPs and samples before association analysis . SNPs were excluded if they ( i ) did not map on autosomal chromosomes; ( ii ) had a call rate <95%; ( iii ) had a minor allele frequency ( MAF ) <0 . 05; or ( iv ) deviated from Hardy-Weinberg equilibrium ( P<1×10−5 in all GWAS samples or P<1×10−4 in either of the Nanjing Study or the Beijing Study samples ) . We removed samples with low genotype call rates <0 . 95 ( 3 subjects ) and ambiguous gender ( 4 subjects ) . Unexpected duplicates or probable relatives ( 52 subjects ) identified by pairwise identity-by-state comparisons were also excluded according to their PI_HAT value in PLINK ( all PI_HAT>0 . 25 ) . Heterozygosity rates were calculated , and samples were excluded if they were more than 6 s . d . away from the mean ( 12 subjects were excluded ) . We detected population outliers using a method based on principle component analysis and 6 subjects were removed . As a result , 833 lung SqCC cases and 3 , 094 controls with 570 , 009 SNPs remained after QC . After genome-wide association analyses , we selected SNPs for the first stage replication based on the following criteria: ( i ) SNPs had P≤1 . 0×10−4 for all GWAS samples; ( ii ) they showed consistent associations between the Nanjing study and the Beijing study at P≤1 . 0×10−2; ( iii ) they are not located in the same chromosome regions or genes of SNPs reported in previous GWAS; ( iv ) they had clear genotyping clusters; ( v ) only the SNP with the lowest P value was selected when multiple SNPs were observed in a strong linkage disequilibrium ( LD ) ( r2≥0 . 8 ) . As s results , a total of 23 SNPs satisfied the criteria ( i ) , ( ii ) , ( iii ) and ( iv ) , and 14 SNPs survived according to criterion ( v ) . Therefore , we genotyped these 14 SNPs in the first replication stage ( Table S3 ) and the other 9 SNPs that were in strong LD with 14 selected SNPs were excluded from further analysis ( Table S4 ) . The SNPs showed significant associations with lung SqCC risk with P<0 . 05 in the first stage replication were selected for the second replication stage . Genotyping were performed by using the TaqMan OpenArray Genotyping Platform ( Applied Biosystems , Inc . ) and the iPLEX Sequenom MassARRAY platform ( Sequenom , Inc ) for SNPs selected in the first replication stage , and TaqMan allelic discrimination Assay ( Applied Biosystems , Inc . ) for SNPs selected in the second replication stage . A series of methods was used to control the quality of genotyping: ( i ) case and control samples were mixed on each plate and genotyped without knowing the case or control status; ( ii ) two water controls in each plate were used as blank controls; ( iii ) five percent of the samples were randomly selected to repeat the genotyping , as blind duplicates , and the reproducibility was 100%; ( iv ) 1 , 347 samples were randomly selected and detected using both TaqMan Openarray platform and TaqMan assay for rs12296850 , yielding a concordance rate of 99 . 97% . The statistical analysis methodology of our lung cancer GWAS was described previously [11] . In brief , genome-wide association analysis was performed using logistic regression analysis in additive model as implemented in PLINK 1 . 07 ( see URLs ) . EIGENSTRAT 3 . 0 was used for the principal component analysis of population structure . Minimac software ( see URLs ) was used to impute untyped SNPs using the CHB+JPT data from the hg18/1000 Genomes database ( released at June 2010 ) as reference set . Regional plot was generated using the LocusZoom 1 . 1 ( see URLs ) . R software ( version 2 . 11 . 1; The R Foundation for Statistical Computing ) was also used for statistical analysis and generating plots , including Q-Q plot and Manhattan plot . To determine the expression levels of NRIH4 and SLC17A8 , we collected 46 paired lung cancer tissues from the patients who had undergone resection between June 2009 and April 2010 from the Nantong Cancer Hospital . All cases were histopathologically diagnosed lung cancer without radiotherapy or chemotherapy before surgical operation . Quantitative reverse transcription polymerase chain reaction ( qRT-PCR ) was performed to determine the mRNA expressions of NRIH4 and SLC17A8 . RNAs from lung cancer tumor and adjacent non-tumor tissues were isolated with the Trizol reagent ( Invitrogen ) . We used TaqMan gene expression probes ( Applied Biosystems Inc . ) to perform qRT-PCR assay . All real-time PCR reactions , including no-template controls and real-time minus controls , were run by using the ABI7900 Real-Time PCR System ( Applied Biosystems Inc . ) and performed in triplicate . β-actin gene was used to normalize the expression levels . A relative expression was calculated using the equation 2−ΔCt ( Ct , Cycle Threshold ) , in which ΔCt = Ct gene−Ct β-actin . We applied the publicly available data from GTEx ( Genotype-Tissue Expression ) eQTL Browser , eQTL . Chicago . edu and Gene Expression Analysis Based on Imputed Genotypes ( see URLs ) to perform cis-eQTL analysis and evaluated the cis association between rs12296850 and the expression of nearby genes in a variety of cells/tissues , including lymphoblastoid cell lines [37]–[42] , monocytes [43] , fibroblasts [42] , liver [44] and brain tissues [45] . PLINK1 . 07 , http://pngu . mgh . harvard . edu/~purcell/plink/; R 2 . 11 . 1 statistical environment , http://www . cran . r-project . org/; Minimac , http://genome . sph . umich . edu/wiki/Minimac ;LocusZoom 1 . 1 , http://csg . sph . umich . edu/locuszoom/; GTEx ( Genotype-Tissue Expression ) eQTL Browser , http://www . ncbi . nlm . nih . gov/gtex/test/GTEX2/gtex . cgi ; eQTL . Chicago . edu , http://eqtl . uchicago . edu/cgi-bin/gbrowse/eqtl/; Gene Expression Analysis Based on Imputed Genotypes , http://www . sph . umich . edu/csg/liang/imputation/ . | Previous genome-wide association studies ( GWAS ) strongly suggested the importance of genetic susceptibility for lung cancer . However , the studies specific to different histological subtypes of lung cancer were limited . We performed the GWAS analysis specifically for lung squamous cell carcinoma ( SqCC ) with 570 , 009 autosomal SNPs in 833 SqCC cases and 3 , 094 controls and replicated in additional 2 , 223 lung SqCC cases and 6 , 409 controls from Chinese populations ( 822 SqCC cases and 2 , 243 controls for the first replication stage and 1 , 401 SqCC cases and 4 , 166 controls for the second replication stage ) . We found a novel association at rs12296850 ( SLC17A8-NR1H4 ) on12q23 . 1 . However , rs12296850 didn't show significant association with risk of lung adenocacinoma ( AC ) in 4 , 368 lung AC cases and 9 , 486 controls . These results indicate that genetic variations on chromosome 12q23 . 1 may specifically contribute to lung SqCC susceptibility in Chinese population . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"oncology",
"medicine",
"cancer",
"genetics",
"squamous",
"cell",
"lung",
"carcinoma",
"lung",
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"intrathoracic",
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"biology",
"cancers",
"and",
"neoplasms",
"genetics",
"and",
"genomics"
] | 2013 | Genome-Wide Association Study Identifies a Novel Susceptibility Locus at 12q23.1 for Lung Squamous Cell Carcinoma in Han Chinese |
Telomerase is a telomere dedicated reverse transcriptase that replicates the very ends of eukaryotic chromosomes . Saccharomyces cerevisiae telomerase consists of TLC1 ( the RNA template ) , Est2 ( the catalytic subunit ) , and two accessory proteins , Est1 and Est3 , that are essential in vivo for telomerase activity but are dispensable for catalysis in vitro . Est1 functions in both recruitment and activation of telomerase . The association of Est3 with telomeres occurred largely in late S/G2 phase , the time when telomerase acts and Est1 telomere binding occurs . Est3 telomere binding was Est1-dependent . This dependence is likely due to a direct interaction between the two proteins , as purified recombinant Est1 and Est3 interacted in vitro . Est3 abundance was neither cell cycle–regulated nor Est1-dependent . Est3 was the most abundant of the three Est proteins ( 84 . 3±13 . 3 molecules per cell versus 71 . 1±19 . 2 for Est1 and 37 . 2±6 . 5 for Est2 ) , so its telomere association and/or activity is unlikely to be limited by its relative abundance . Est2 and Est1 telomere binding was unaffected by the absence of Est3 . Taken together , these data indicate that Est3 acts downstream of both Est2 and Est1 and that the putative activation function of Est1 can be explained by its role in recruiting Est3 to telomeres .
Telomeres , the DNA-protein structures at the ends of most eukaryotic chromosomes are essential for genome integrity: they protect chromosomes from degradation and end-to-end fusions , distinguish chromosome ends from DNA breaks , position chromosomes for pairing in meiosis and ensure the complete replication of chromosome ends ( reviewed in [1]–[3] ) . Telomeric sequences are comprised of highly repetitive DNA in which the strand running 5′ to 3′ towards the chromosome end is G-rich and extended to form a 3′ single stranded tail . For example , throughout most of the cell cycle , each Saccharomyces cerevisiae telomere has ∼300 bps of duplex C1-3A/TG1-3 DNA ending with a short ∼12–14 base TG1-3-tail [4] . In most eukaryotes , a specialized reverse transcriptase called telomerase provides the basis for an RNA templated replication mechanism that elongates the G-rich strand of telomeric DNA . The S . cerevisiae telomerase consists of the catalytic reverse transcriptase subunit , Est2 [5] , the templating RNA component , TLC1 [6] , and two regulatory proteins Est1 [7] and Est3 [8] , [9] . Eliminating any one of these four gene products results in the est ( ever shorter telomeres ) phenotype , characterized by gradual telomere shortening and death in most cells after ∼50–100 generations [6]–[8] . In addition , certain alleles of CDC13 , such as cdc13-2 , a gene that encodes an essential protein that binds the 3′ single stranded TG1-3 tails in vivo [10] , [11] , are telomerase defective [12] . Cdc13 is a multi-functional telomere binding protein that is essential to protect chromosome ends from degradation [13] and has a key function in telomerase recruitment [14] , [15] . Most of the yeast telomere is replicated by standard semi-conservative DNA replication , which occurs at the end of S phase [16] , [17] . This replication is followed by C-strand resection , which generates long ( ∼50–100 base ) transient single-stranded G-tails [17]–[19] . Telomerase action is also restricted to late S/G2 phase [20] , [21] , even though Est2 is telomere associated throughout most of the cell cycle with peak binding in both G1 and late S/G2 phase [22] . Est2 telomere association during G1 and early S phase requires a specific interaction between TLC1 and the heterodimeric Ku complex [23] . Est2 telomere association in late S/G2 phase is low in cdc13-2 cells [22] , requires a specific interaction between a stem-bulge region on TLC1 RNA and Est1 [24] , and is lost entirely in tlc1Δ cells [22] , [24] . Est1 telomere binding , which occurs only in late S/G2 phase , coincident with telomerase action [22] , is low when it cannot interact with TLC1 RNA or in cdc13-2 cells and is eliminated altogether in est2Δ cells [24] . Moreover , Est1 abundance is cell cycle regulated , low in G1 and early S phase , and peaking in late S/G2 phase [22] , [25] Although both Est1 and Est3 are essential for telomerase action in vivo [8] , the requirement for Est1 ( but not Est3 ) can be bypassed by expressing a DBDCdc13-Est2 fusion protein ( DBD , DNA binding domain ) [14] . This result is consistent with a model in which a Cdc13-Est1 interaction recruits the telomerase holoenzyme to the telomere , an interpretation supported by biochemical and genetic data that show that the two proteins interact in vivo [26] , [27] . However , Est1 has a role other than recruitment as it is needed for the hyper-elongation of telomeres that occurs in cells expressing a DBDCdc13-Est2 fusion [14] . This extra function can be seen in vitro as well: Est1 is required for long extension products in a PCR based in vitro assay [28] , and its addition to a primer extension assay increases the amount of product [29] . In Candida albicans , Est1 affects both initiation and processivity of telomerase in vitro in a primer-specific manner [30] . Thus , Est1 appears to function in both recruitment and activation of telomerase . The telomeric role of Est3 is separable from that of Est1 as an Est3-DBDCdc13 fusion cannot bypass the requirement for Est1 and an Est1-DBDCdc13 fusion cannot rescue the telomerase defect of an est3Δ strain [9] . Nonetheless , the two proteins are interconnected . In C . albicans , Est3 and Est1 mutually depend on each other for assembly into the telomerase holoenzyme [30] . However , the situation in S . cerevisiae is unclear as using co-immunoprecipitation , one group found that Est3 association with Est2/TLC1 is Est1 dependent [25] while one did not [9] , [31] . In vitro , extracts prepared from a C . albicans est3Δ strain show the same initiation and processivity defects in telomerase assays as extracts from est1Δ cells [30] , while all primers are extended less efficiently in extracts from an est3Δ S . castellii strain [31] . Est3 from both S . cerevisiae and C . albicans has structural similarity to TPP1 within an OB-fold domain [32] , [33] , a mammalian telomere structural protein that has roles in both telomere end protection and promoting telomerase activity [34]–[36] . Here we used chromatin immuno-precipitation ( ChIP ) in mutant and WT cells to determine the temporal pattern and genetic dependencies for S . cerevisiae Est3 telomere binding . We show that Est3 telomere binding occurred mainly in late S/G2 phase and was at background or close to background levels in tlc1Δ , est1Δ and est2Δ cells . In contrast , the late S/G2 phase association of both Est1 and Est2 was not reduced in est3Δ cells , making est3Δ the first telomerase deficient strain where the temporal and quantitative pattern of Est2 telomere binding is indistinguishable from that in WT cells . As purified Est1 and Est3 interact in vitro , the putative activation role of Est1 can be explained by its role in recruiting Est3 to telomeres . We also determined the absolute copy number for each of the three Est proteins , the first such determination for any protein subunit of telomerase in fungi .
As in previous work from our lab , we used chromatin immuno-precipitation ( ChIP ) to determine protein association with telomeres in vivo ( e . g . [22] ) . Previous studies from other labs used an HA3-tagged version of Est3 [9] , [25] to study its association with other telomerase subunits , but this protein was not detectable at telomeres by ChIP ( our unpublished results ) . Est3 directly tagged with nine Myc-epitopes was not functional ( data not shown ) . Therefore , we epitope tagged Est3 at its carboxyl-terminus with a glycine linker ( G8 ) , which improves the functionality of epitope tagged proteins [37] , followed by either 9 or 18 Myc epitopes . As with all of the epitope tagged proteins used in this paper , Est3 was expressed from its own promoter as the only copy of EST3 in the strain . Cells expressing these Est3 alleles did not senesce and maintained stable telomere length , although as in the HA3-tagged strain [9] , [25] , telomeres were shorter than in WT cells ( see methods and Figure S1A for more details ) . Both Myc-tagged proteins were detectable by an anti-Myc antibody in western blotting of whole cell extracts ( Figure 1C , Figure S1B ) , but only Est3-G8-Myc18 gave reliable results in a ChIP assay . We used real-time PCR quantitation to evaluate the association of Est3-G8-Myc18 to two native telomeres , the right arm of chromosome VI ( TEL-VI-R ) and the left arm of chromosome XV ( TEL-XV-L ) throughout a synchronized cell cycle ( Figure 1 , Figure 2 ) . For all synchrony experiments , cells were arrested in late G1 phase with alpha factor and then released into the cell cycle . The quality of each synchrony was evaluated by flow cytometry , which revealed no major reproducible differences in cell cycle progression among the various strains used in this study ( Figure 1A , Figure 2A ) . We used real-time PCR to determine the amount of telomeric DNA in the immuno-precipitate . Synchronies were done at least three times with the data presented as the average telomere association +/− one standard deviation . At each time point , we normalized the telomeric signal to the signal at the non-telomeric ARO1 locus in the same sample . At both telomeres , the profile of Est3 telomere association in WT cells was bi-phasic , with a small peak in G1 phase ( 0 and 15 min ) and 2-2 . 5-fold higher binding in late S/G2 phase ( 60 min ) ( Figure 1B ) . This late S/G2 binding was ∼10-fold above the no tag control . This biphasic binding pattern was reminiscent of Est2 telomere association except that for Est2 , the peaks in G1 and late S/G2 phases were of similar magnitude [22] . Compared to the untagged strain , the G1 telomere binding was significant at TEL-VI-R ( P = 0 . 020 ) but not at TEL-XV-L ( P = 0 . 078 ) , while the late S/G2 phase Est3-G8-Myc18 association was significant at both telomeres ( P = 0 . 0086 , TEL-VI-R; P = 0 . 0079 , TEL-XV-L ) . Differential binding throughout the cell cycle was not due to cell cycle variations in protein abundance as levels of tagged Est3 were constant throughout the cell cycle ( Figure 1C; Figure S1C . ) . We conclude that the telomere association of Est3 occurs mainly at late S/G2 phase , which coincides temporally with the peak of Est1 telomere association , the second peak of Est2 telomere binding , and the time of telomerase action ( see Introduction ) . Next , we determined if Est3-G8-Myc18 telomere binding requires the presence of other telomerase components by examining its telomere association in synchronized est2Δ , tlc1Δ ( Figure 1B ) , and est1Δ ( Figure 2B ) cells . In the absence of either Est2 or TLC1 RNA , Est3-G8-Myc18 telomere association was not detected at any point in the cell cycle at either TEL-VI-R or TEL-XV-L ( see legend of Figure 1 and Figure 2 for P values ) . The reduced Est3 telomere binding in est2Δ and tlc1Δ cells was not due to reduced Est3 abundance ( Figure 1D ) . Therefore , both Est2 and TLC1 RNA are absolutely required for Est3 telomere association . In est1Δ cells , the amount of telomere associated Est3-G8-Myc18 at late S/G2 phase was significantly reduced at TEL VI-R ( 60 min , P = 0 . 0007 ) and at TEL XV-L ( 60 min , P = 0 . 0028 ) compared to the level of binding in WT cells ( Figure 2B ) . When compared to the no tag control , the level of Est3-G8-Myc18 binding from 45 to 75 minutes at telomere VI-R was low but still significant while the level of binding to XV-L was not significant ( see Figure 2 legend for P values for each time point ) . Est3-G8-Myc18 association in G1 and early S phase ( Figure 2B , 0 to 30 min ) was also reduced but was significantly higher than in the no tag control at both telomeres ( see Figure 2 legend for P values ) . Although there was a small amount of Est3-G8-Myc18 telomere binding in the absence of Est1 , Est3 telomere association was largely Est1 dependent . Est1 could affect Est3 telomere binding directly or indirectly . To distinguish between the two possibilities , we purified C-terminally strep-tagged [38] Est1 from S . cerevisiae and N-terminally strep-tagged Est3 from E . coli , and removed the Est3 strep-tag post-purification . Both proteins were purified to near homogeneity ( Figure 3A ) , and their identities verified by MS/MS mass spectrometry . When expressed from its own promoter on a CEN plasmid as the only copy in the cell , strep-tagged Est1 supported WT length telomeres ( data not shown ) . We tested the ability of the two purified proteins to interact using a magnetic bead pull down experiment ( Figure 3B ) . Purified Est1 was mixed with streptavidin-coated magnetic beads that capture the C-terminal affinity tag of Est1 ( lane 4 ) . Est3 was not pulled down by the beads in the absence of Est1 ( lane 5 ) . However , in the presence of Est1 , Est3 was bead-associated ( lane 6 ) . BSA , which was used as a negative control , was not bead associated either in the presence or absence of Est1 . Further evidence for specificity is provided by similar assays where Est1 did not interact with the DBD region of Cdc13 , and Est3 did not interact with full length Cdc13 ( YW and VAZ , in preparation ) . As monitored by whole cell and immuno-precipitate western experiments [22] , [23] , [25] , [39] , Est1 abundance is cell cycle regulated , low in alpha factor arrested G1 phase cells and peaking at late S/G2 phase . Consistent with our previous studies and coincident with its peak in abundance , Est1 telomere binding occurred at late S/G2 phase ( 60 min ) at both telomeres in WT cells ( Figure 4B ) . In est3Δ cells , Est1 binding at TEL-VI-R was indistinguishable from WT ( 60 min , P = 0 . 68 ) . Est1 association at TEL-XV-L was marginally lower in est3Δ compare to WT cells ( 60 min ) , but this difference was not significant ( P = 0 . 26 ) . Est1 abundance was also not Est3 dependent ( Figure 4C ) . We conclude that the telomere association of Est1 is Est3 independent . Likewise , Est2 telomere binding ( Figure 4E ) and its abundance ( Figure 4F ) were very similar in WT and est3Δ cells . As shown previously [22] , in WT cells , Est2 bound to TEL-VI-R and TEL-XV-L throughout the cell cycle with peak binding in G1 ( 0 , 15 min ) and late S/G2 phases ( 60 min ) ( Figure 4E ) . In est3Δ cells , Est2 binding was indistinguishable from WT throughout the cell cycle at both telomeres except at 30 min ( p = 0 . 05 ) at TEL VI-R . We conclude that the telomere association of the catalytic subunit Est2 is also Est3 independent . Telomerase action is cell-cycle regulated [20] , [21] , and Cdc13 and each of the three Est proteins binds telomeres in a cell cycle dependent manner ( [22] and Figure 1 ) . Nonetheless , Est1 is the only one of these proteins whose abundance is cell cycle regulated [22] , [25] . The checkpoint kinase Tel1 binds telomeres in a cell cycle dependent manner , and this binding is necessary for preferential association of Est2 and Est1 with a short VII-L telomere [40] . Cdk1/Cdc28 activity is required for C-strand degradation [41] , [42] , and Cdc13 is phosphorylated by Cdk1 in a cell cycle dependent manner that affects the level of Est1 telomere association [43] , [44] . Since Est1 , Est2 and Est3 contain one or more candidate Tel1 and Cdk1 phosphorylation sites , one possibility is that their cell cycle regulated telomere binding is due to cell cycle regulated phosphorylation . However , there is no evidence for slower migrating species for any of the three Est proteins when analyzed by conventional polyacrylamide gels ( [22] and Figure 1 , Figure 4 ) . To address in more detail the possibility that Est proteins are phosphorylated , we prepared protein extracts from synchronized cells expressing either Est1-Myc9 , Myc9-Est2 , or Est3-G8-Myc9 and separated the extracts in gels containing Phos-tag ( Wako ) , a reagent that binds phosphate groups resulting in slower mobility of phosphorylated proteins [45] . In the extracts containing Est1-Myc9 or Myc9-Est2 , there was no detectable fraction of the protein with reduced mobility in Phos-tag gels ( Figure S2 ) . This pattern was seen for extracts resolved in 25 µM Phos-tag ( as shown in Figure S2 ) as well as in 100 µM phos-tag ( data not shown ) . Likewise , the mobility of Est3-G8-Myc9 was not affected by 25 or 50 µM Phos-tag ( data not shown ) . However , extracts from cells expressing Est3-G8-Myc9 and resolved in gels containing 100 µM Phos-tag had about equal amounts of a slower migrating form of Est3 ( Figure 5 , right panels ) that was not detected in the absence of Phos-tag ( Figure 5 , left panels ) . The two species were of similar levels in extracts from asynchronous ( Figure 5A ) , G1 arrested ( Figure 5B , 0 min ) or from throughout a synchronous cell cycle ( Figure 5B , 15–90 min ) . Thus , the apparent phosphorylation of Est3 revealed in the presence of Phos-tag was not cell cycle regulated . As part of our efforts to understand Est protein function , we generated Myc9-tagged versions of each of the three Est proteins ( here and [23] ) . We used these tagged alleles to generate a strain in which Est1 , Est2 , and Est3 were each marked with nine Myc epitopes . The triply tagged strain maintained stable telomeres that were ∼75–125 bps shorter than WT telomeres ( Figure 6A ) and showed no evidence of senescence even after >6 restreaks ( data not shown ) . We measured the absolute abundance of each of the Myc9-tagged proteins using quantitative western blot analyses . In order to provide a standard to convert western signals to absolute protein levels , a Myc9-tagged Cdc13 protein was fused to a C-terminal tandem 5X Strep-Tag II , over-expressed in S . cerevisiae , and purified to homogeneity ( Figure 6B ) . Untagged yeast extract containing serial dilutions of purified Myc9-tagged Cdc13 protein ranging from 0 . 5 to 10 femtomoles were run on a gel along with whole cell extracts from the triply tagged strain ( Figure 6C ) . Comparison of the signals of Myc9-tagged Est proteins to the known standards allowed us to determine that there are 1 . 18±0 . 32×10−22 , 0 . 62±0 . 11×10−22 , and 1 . 40±0 . 22×10−22 moles ( or 71 . 1±19 . 2 , 37 . 2±6 . 5 , and 84 . 3±13 . 3 molecules ) of Est1 , Est2 , and Est3 per cell , respectively ( Figure 6D ) . These results were statistically identical to those obtained from singly Myc9-tagged strains ( data not shown ) .
Although Est1 and Est3 were discovered over 15 years ago [7] , [8] , their exact roles in telomerase-mediated telomere maintenance have been difficult to establish . Evidence from diverse approaches indicates that Est1 has a key role in recruiting telomerase to DNA ends by virtue of its ability to interact with Cdc13 [14] , [15] , [26] , [27] . This step is likely direct as purified Cdc13 and Est1 interact in vitro , and this interaction facilitates Est1 association with telomeric DNA ( YW and VAZ , in preparation ) . However , as summarized in the introduction , current data suggest that Est1 also has a telomerase activation function that is poorly understood . Even less is known about the function ( s ) of Est3 except that it is clearly essential in vivo , and its role cannot be bypassed by a variety of fusion proteins ( see Introduction ) . Using ChIP , we find that while Est3 telomere binding was bi-phasic , occurring in both G1 and late S/G2 phase , late S/G2 binding was 2 to 3 times higher than G1 phase association . Thus , peak Est3 binding correlated with the time in the cell cycle when telomerase is active ( Figure 1B ) . In contrast , Est3 abundance was not cell cycle regulated ( Figure 1C , Figure S1C ) . Even though at least two kinases , Tel1 and Cdk1/Cdc28 are important for telomerase action [39] , [40] , [42]–[44] , [46]–[48] , cell cycle regulated telomere binding of Est3 ( Figure 1B ) , as well as Est1 and Est2 [22] , is probably not due to their being phosphorylated in a cell cycle dependent manner . By the criterion of phos-tag induced changes in protein mobility , we found no evidence for phosphorylation of Est1 or Est2 ( Figure S2 ) , and although Est3 appeared to be phosphorylated , this modification was not cell cycle regulated ( Figure 5 ) . In addition , mutation of the single Cdk1/Cdc28 ( S56A ) or the single Tel1 ( S96A ) consensus site in Est3 did not affect telomere length or senescence ( CTT and VAZ , data not shown ) . Therefore , we found no evidence that phosphorylation of Est1 , Est2 , or Est3 is important for their telomere functions . We also examined Est3 telomere binding in the absence of other telomerase components . Est3 telomere binding was at background levels in both tlc1Δ and est2Δ cells ( Figure 1B ) and very low in est1Δ cells ( Figure 2B ) , even though none of these mutations affected Est3 abundance ( Figure 1D , Figure 2C ) . Neither Est1 nor Est2 is telomere associated in tlc1Δ or est2Δ cells [24] . Therefore , the lack of Est3 telomere binding in these backgrounds could be due to the absence of either Est1 or Est2 . However , in est1Δ cells , Est2 telomere binding in G1 phase is at wild type levels and reduced but still high ( 40–50% of wild type binding ) in late S/G2 phase [24] . The simplest explanation for these data is that an Est1-Est3 interaction is critical for Est3 telomere binding , especially in late S/G2 phase , since Est3 telomere binding was very low in the absence of Est1 , even in situations where there are substantial levels of telomere associated Est2 . This interpretation is particularly appealing given our demonstration that purified Est1 and Est3 interact in vitro ( Figure 3B ) . Together , these results provide one of the most important mechanistic implications of our data because they suggest that the activation function of Est1 is due to its recruitment of Est3 to telomeres , as proposed previously [30] . Indeed structural considerations have led to the proposal that Est3 is a homologue of the mammalian TPP1 protein [32] , [33] . Human TPP1 cooperates with POT1 , the human G-strand telomere binding protein , to increase telomerase processivity [35] , [36] It has been proposed that the direct interaction between Est3/TPP1 and the G-strand binding protein was lost in S . cerevisiae , requiring a new link , Est1 , to allow Cdc13-Est3 cooperation [49] . Our data support this hypothesis . Recent in vitro data using S . castellii Est3 are also consistent with Est3 having a positive effect on telomerase processivity [31] . However , a role for Est3 in promoting processivity is not sufficient to explain all of the Est3 data as Est3 activity seems to be required for even a minimal level of telomere repeat addition in vivo . In the case of the mammalian system , in the absence of TPP1 , POT1 alone inhibits telomerase activity in vitro [35] , [50] . Thus , like TPP1 , Est3 may function in facilitating telomerase to overcome the inhibition by Cdc13 [51] , explaining the complete lack of telomerase activity in the est3Δ cell . Although our data argue that Est1 is the main factor recruiting Est3 to telomeres , our results also suggest that there is a secondary pathway for Est3 recruitment , which is likely Est2-mediated . A minor role for Est2 in Est3 recruitment can explain the low levels of Est3 at telomeres in G1 arrested cells ( Figure 1 , Figure 2 ) , when Est2 binding is high but Est1 telomere binding is not detected [22] . It can also explain why Est3 was found at low but detectable levels at telomeres in both G1 and late S/G2 phase est1Δ cells ( Figure 2B ) . In support of this interpretation , while the Est3 binding at the 60 min time point at both telomeres was reduced over 80% in est1Δ cells , telomere binding was reduced only 30% at both telomeres in G1 arrested cells ( 0 time point ) in this background ( Figure 2B ) . Genetic evidence provides strong support for interaction between the TEN domain of Est2 and Est3 , although a direct interaction has not yet been established [52] . Our data can also help resolve a discrepancy where one group finds that Est3 association with the holoenzyme is Est1 dependent [25] and one finds that it is Est2 , not Est1 , dependent [9] , [31] . Our data argue that Est1 has a key role and Est2 a more minor role in recruiting Est3 to telomeres ( Figure 1 , Figure 2 ) . Unlike our telomere binding studies which used synchronous cultures , these other studies were done with asynchronous cells . However , the Est1/Est3 interaction that brings Est3 to telomeres in late S/G2 phase occurred in a relatively narrow window of the cell cycle ( Figure 2B ) , and it is easy to imagine that this dependence could be missed in asynchronous cells . It is possible that the study that found that Est1 was not needed for Est3 to co-immunoprecipitate with TLC1 RNA had a larger fraction of G1 phase cells than in the other study , and therefore , the Est3-TLC1 interaction they detect is Est2 , not Est1 mediated . Another key mechanistic finding from our study is that Est3 acts downstream of both Est1 and Est2 . This interpretation is based on the finding that the temporal and quantitative patterns of both Est1 ( Figure 4B ) and Est2 ( Figure 4E ) telomere association were not altered in est3Δ cells . These findings indicate that Est3′s essential role in telomere maintenance is not to support telomerase binding to Cdc13 coated telomeric DNA , although it might be needed for correct positioning or engagement of the holoenzyme at the very end of the G-tail . Thus , the presence of normal levels of telomere bound Est2/TLC1 RNA , the catalytic core of telomerase , at the appropriate time in the cell cycle , is not sufficient in vivo for telomere maintenance even though it supports telomerase action in vitro . Finally our analysis of the abundance of the three Est proteins puts limits on models for how Est3 regulates telomerase . Previous studies that monitored the levels of all yeast proteins as fusions to a GFP or TAP tag [53] detected no signal for any of the Est proteins . Indeed the only core subunit of yeast telomerase whose abundance is known is TLC1 , the telomerase RNA , which is estimated to be present in 29 . 9±3 . 6 molecules per haploid cell [54] . By using a strain expressing Myc9 tagged versions of Est1 , Est2 , and Est3 , we confirmed that all three Est proteins were present in low amounts , with Est2 being the least ( ∼37 ) and Est3 the most ( ∼84 molecules per cell ) abundant ( Figure 6D ) . Telomeres in the triply tagged strain were short but stable ( Figure 6A ) , and the protein levels obtained from this strain were indistinguishable from those obtained in three strains where only one of the three Est proteins was Myc-tagged , and telomere length was less affected ( YW and VAZ , data not shown ) . Therefore , these low abundances are probably not an artifact of partially active subunits , although we cannot rule out this possibility . So far Est2 is the only subunit whose abundance is known to be lower in the absence of another subunit ( TLC1 ) [22] while Est1 is the only subunit whose abundance is cell cycle regulated [22] , [25] . Even if Est3 acts as a dimer , as suggested by an earlier study [55] , Est3 is probably not the limiting protein subunit . This interpretation is supported by the effects of subunit over-expression on telomere length . While Est3 over-expression does not cause telomere lengthening [52] , [56] , Est1 over-expression does [56] , [57] . These effects can be explained by Est1 dependent , cell cycle and concentration limited recruitment of Est3 to telomeres with a resulting increase in telomerase processivity . Thus , the protein abundance data presented here make it clear that Est3′s unique and essential role in telomerase mediated telomere lengthening is unlikely due to its being the limiting telomerase component .
All experiments , unless noted otherwise , were conducted in the YPH background [58] ( see Table S1 for strain list ) . For cell cycle synchrony and chromatin immuno-precipitation ( ChIP ) experiments , the BAR1 gene was deleted and replaced with KanMX6 . All epitope tagged genes were expressed from their own promoters at their endogenous loci . Epitope tagged EST1 and EST2 were previously described [22] , [23] , [37] . Est3 was similarly tagged at its carboxyl terminus with a flexible linker and 9 or 18 Myc epitopes . Telomere lengths were stable and cells did not senesce in either EST3-G8-MYC9 or Est3-G8-MYC18 cells but telomeres were shorter than WT in both strains ( Est3-G8-Myc9 , 50-75 bp shorter and 75 to 125 bps shorter in Est3-G8-Myc18; Figure S1A ) . Although both Est3- G8-Myc tagged proteins were detectable in whole cell westerns ( Figure S1B ) , only Est3-G8-Myc18 telomere binding was reliably detected by ChIP . Therefore , the EST3-G8-MYC18 allele was used for ChIP ( Figure 1B , Figure 2B ) and the EST3-G8-MYC9 allele was for westerns ( Figure 1C , Figure 5 , Figure 6 ) . Both chromosomal constructs were verified by sequencing , and thus , the larger apparent molecular weight of the fusion proteins in SDS-PAGE was not due to incorrectly fused proteins . In addition , the tagged loci segregated 2 2 with the TRP1 marker used to select its integration into the genome , indicating that an unknown protein was not accidently tagged . The est1Δ , est2Δ and tlc1Δ mutations were complete gene deletions and were generated as heterozygous diploids expressing EST3-G8-MYC18 tagged protein . Likewise , the est3Δ mutation was generated in a heterozygous diploid expressing EST1-MYC9 or EST2-G8-MYC18 tagged proteins . For all experiments in telomerase deficient strains , newly dissected estΔ or tlc1Δ spores were replica plated to verify the genotype and then cultured for immediate use so that analysis could be done before cells began to senesce . Cell cycle synchrony experiments were carried out as previously described [22] , [23] , [39] . Briefly , cells were cultured in rich media to an OD660 ∼0 . 15 and arrested with 0 . 01 µg/ml alpha factor for 3 . 5 hr at 24°C with shaking . Aliquots of cells were removed from the alpha factor arrested culture ( 0 min time point ) and at 15 min intervals after release from G1 arrest and processed for fluorescence-activated cell sorting ( FACS ) analysis and ChIP at each time point . ChIP was performed as described [22] , [23] , [39] and quantitated on an iCycler iQ Real-Time PCR detection system ( Bio-Rad Laboratories ) . The relative fold enrichment of a protein with telomeres was determined by ( TELIP/TELIN ) / ( ARO1IP/AROIN ) , where IP is the amount of DNA sequence that was amplified from the anti-Myc immuno-precipitate and IN is the amount of DNA sequence that was amplified in the input DNA prior to immuno-precipitation . Each synchrony was repeated at least three times; error bars represent one standard deviation . Where applicable , a two-tailed Student's t test was used to determine statistical significance ( P values ≤0 . 05 were considered significant ) . Whole cell extracts from epitope tagged strains were probed with an anti-Myc monoclonal antibody ( 9E10 , Clontech ) as previously described [22] , [23] , [39] . Briefly , cells were grown in rich medium to mid-log phase for asynchronous cell growth or to early log phase as described for cell cycle synchrony . Cells were pelleted , resuspended in CE lysis buffer ( 50 mM HEPES pH 7 . 5 , 140 mM NaCl , 1 mM EDTA pH 8 . 0 , 10% glycerol , 0 . 1% IGEPAL CA-630 , 1 mM DTT , 1 mM PMSF and 1 tablet protease inhibitor EDTA-free/10mL ) and frozen in liquid nitrogen . Cells were thawed quickly then lysed for 1 min by mechanical disruption with the addition of 425–600 µm glass beads using a beat beater at 4°C . Total cell extracts were pre-cleared by centrifugation at 10 , 000 g for 30 min at 4°C . Protein concentration was determined by Coomassie Plus protein reagent ( Pierce ) and equivalent protein levels were separated in an 8% SDS-PAGE gel . Proteins were transferred to Immobilon PVDF ( 0 . 45 µM ) membranes ( Millipore ) , probed with an α-Myc primary antibody and goat-anti-mouse-HRP secondary antibody , and exposed to film . Myc9-tagged Cdc13 and Est1 were purified from yeast BCY123 carrying an arc1-K86R mutation . Cdc13-Myc9 and Est1 were cloned into a pYES2 vector ( Invitrogen ) with a carboxyl terminal tag consisting of a G8 linker , 5x Strep-Tag II , and a HAT tag ( Clontech ) . Protein over-expression was induced with 2% galactose at 30°C for 12 hr . Cdc13-Myc9 was purified by 0 . 1% polyethyleneimine precipitation , streptactin agarose ( Novagen ) , and Talon Metal Affinity resin ( Clontech ) and was concentrated and buffer exchanged to TDEG/100 buffer ( 25 mM Tris-Cl , pH 7 . 5 , 0 . 1 mM DTT , 0 . 1 mM EDTA , 10% glycerol , 100 mM NaCl ) on an Amicon Ultra-4 ( MWCO 50 kDa ) concentrator . Concentration was determined using an extinction coefficient of 87 , 050 M−1cm−1 at 280 nm . Est1 was purified by 0 . 1% polyethyleneimine precipitation , 45% ammonium sulfate precipitation , and a streptactin column . Fractions from the streptactin column were pooled and buffer exchanged to Est1 storage buffer ( 25 mM Na-HEPES , pH 7 . 0 , 200 mM NaCl , 0 . 1 mM EDTA , 0 . 1 mM DTT , 0 . 05% Triton X-100 , 20% glycerol ) using a PD-10 column ( GE Healthcare ) . Protein concentration was determined by comparison to known quantities of tagged Cdc13 in western blot analysis against anti-streptag II antibody ( Novagen ) . Est3 was corrected for its natural +1 frameshifting [59] and cloned into a pET21d vector ( Novagen ) fused to an amino terminal tag consisting of a HAT tag , 4x streptag II , a G8 linker , and a HRV 3C site . Fresh E . coli Rosetta2 ( DE3 ) ( Novagen ) transformants were grown at 18°C , and protein over-expression was induced with 0 . 1 mM isopropyl β-D-1-thiogalactopyranoside for 24 hr . Cells were lysed by sonication and Est3 was purified by streptactin and Talon columns . The N-terminal Strep-tag was cleaved off by HRV 3C protease and the tag-removed Est3 was concentrated-buffer exchanged to TDEG/100 buffer using an Amicon Ultra-4 ( MWCO 10 kDa ) concentrator . Protein concentration was determined using an extinction coefficient of 37 , 410 M−1 cm−1 at 280 nm . A complete reaction contained 1 µM each of Est1 and Est3 in 20 mM Tris-HCl , pH 7 . 5 , 100 mM NaCl , 0 . 2% Triton X-100 , 20 µg/mL BSA , and protease inhibitors ( Roche ) . Reactions were pre-incubated for 15 min on ice before addition of Dynabeads streptavidin M280 ( Invitrogen ) . Reactions were incubated with the beads on a rotary shaker at 4°C for 30 min before separating the unbound from bound proteins on a magnet . The beads were washed 3 times with 200 µl of washing buffer ( 20 mM Tris-HCl , pH 7 . 5 , 50 mM NaCl , 0 . 2% Triton X-100 , 5% glycerol ) before being resuspended in 20 µl loading buffer ( 36 mM Tris HCl , pH 6 . 9 , 1% SDS , 1% β-mercaptoethanol , 6% glycerol , 0 . 05% bromophenol blue ) , boiled , and loaded onto a 10% SDS-PAGE gels along with 15% of input materials . The gel was visualized by Coomassie brilliant blue staining . Extracts were prepared from cells expressing either EST1-Myc9 , Myc9-EST2 , or EST3-G8-Myc9 that were grown asynchronously at 30°C until an OD660 of 0 . 5 or synchronized as described above . Extracts were prepared by TCA precipitation as described [60] . Briefly , ∼10 mL of culture was centrifuged and washed once with 20% TCA . Cells were resuspended in 20% TCA , glass beads ( 400-600 µm ) were added and cells were lysed in a bead beater for 2 minutes at 4°C ( FastPrep 96; MPBiomedical ) . Extracts were pre-cleared by centrifugation at 3 , 000 rpm for 10 minutes at 4°C . The pellets were resuspended in buffer and run on western gels . Extracts from cells expressing either Est1 or Est2 were separated on 7 . 5% SDS-PAGE with or without acrylamide-pendant Phos-tag ( Phos-tag AAL-107 , Wako ) [45] . Extracts from cells expressing Est3 were separated on 10% SDS-PAGE with or without Phos-tag . Two different positive controls were used: S . pombe cells expressing Chk1-3HA treated ( or not ) with 40 µM Camptothecin to induce Chk1 phosphorylation or S . cerevisiae cells expressing Rfa1-Myc13 UV irradiated ( or not ) using a Strategene Stratalinker 1800 at 60 J/m2 . HA monoclonal antibody ( Santa Cruz ) was used to detect Chk1 . The α-Tubulin loading control was detected with monoclonal antibody ( Abcam ) . Cells were grown in rich medium to early-log phase , briefly sonicated , and the cell density determined on a Beckman Coutler Z2 cell counter . To achieve complete protein extraction , whole cell extracts were prepared as described [61] . Indicated amount of purified standard protein ( 10 , 6 , 4 , 2 , 1 , and 0 . 5 femtomoles ) was mixed with extracts from 2×107 cells of untagged strain and heated at 95°C for 3 min before loaded alongside with extracts from 4 , 2 , 1×107 cells of the triply tagged strain on a 9% SDS-PAGE for anti-Myc Western blot analysis . The tagged proteins were detected using chemiluminescence by a FluroChem CCD camera and quantified by AlphaView software ( Alpha Innotech ) . | Owing to the biochemical properties of DNA polymerases , the free ends of linear chromosomes , called telomeres , cannot be replicated by the same mechanisms that suffice for the rest of the chromosome . Instead they are maintained by a telomere-dedicated reverse transcriptase called telomerase that uses its integral RNA component as the template to make more telomeric DNA . In baker's yeast , telomerase is composed of a catalytic subunit ( Est2 ) , the templating RNA ( TLC1 ) , and two accessory proteins , Est1 and Est3 . Here we show that Est3 associates with telomeres late in the cell cycle , at the same time when telomerase is active , and this binding was Est1-dependent , even though Est3 abundance was neither cell cycle–regulated nor Est1-dependent . Since purified Est3 and Est1interacted in vitro , Est1-dependent recruitment of Est3 is probably due to direct protein–protein interaction . Neither Est1 nor Est2 telomere binding was Est3-dependent . Thus , Est3 acts downstream of telomerase recruitment to promote telomerase activity , and the telomerase activation functions of Est1 can be explained by its recruiting Est3 to telomeres . | [
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... | 2011 | The Saccharomyces cerevisiae Telomerase Subunit Est3 Binds Telomeres in a Cell Cycle– and Est1–Dependent Manner and Interacts Directly with Est1 In Vitro |
The lack of capacity in laboratory systems is a major barrier to achieving the aims of the London Declaration ( 2012 ) on neglected tropical diseases ( NTDs ) . To counter this , capacity strengthening initiatives have been carried out in NTD laboratories worldwide . Many of these initiatives focus on individuals' skills or institutional processes and structures ignoring the crucial interactions between the laboratory and the wider national and international context . Furthermore , rigorous methods to assess these initiatives once they have been implemented are scarce . To address these gaps we developed a set of assessment and monitoring tools that can be used to determine the capacities required and achieved by laboratory systems at the individual , organizational , and national/international levels to support the control of NTDs . We developed a set of qualitative and quantitative assessment and monitoring tools based on published evidence on optimal laboratory capacity . We implemented the tools with laboratory managers in Ghana , Malawi , Kenya , and Sri Lanka . Using the tools enabled us to identify strengths and gaps in the laboratory systems from the following perspectives: laboratory quality benchmarked against ISO 15189 standards , the potential for the laboratories to provide support to national and regional NTD control programmes , and the laboratory's position within relevant national and international networks and collaborations . We have developed a set of mixed methods assessment and monitoring tools based on evidence derived from the components needed to strengthen the capacity of laboratory systems to control NTDs . Our tools help to systematically assess and monitor individual , organizational , and wider system level capacity of laboratory systems for NTD control and can be applied in different country contexts .
Effective prevention and treatment of neglected tropical diseases ( NTDs ) requires reliable and efficient laboratories for diagnosis and for supporting disease and entomological mapping surveys and yet laboratory systems are often weak in low and middle-income countries ( LMICs ) where the majority of this testing is carried out [1] , [2] . Neglected tropical diseases consist of 17 microbiological diseases ( see Table S1 for a list of the 17 Neglected Tropical Diseases as Classified by WHO ) that affect the poorest people in the world . Current estimates suggest that over one billion people are infected with at least one NTD , and that these diseases cause approximately 534 , 000 deaths and 57 million disability adjusted life years ( DALYs ) each year [3] . In January 2012 , as part of the London Declaration , a number of charities , pharmaceutical companies , and other businesses pledged to work together to improve the lives of people affected by NTDs and ultimately progress towards the elimination or control of 10 NTDs by 2020 . The lack of capacity in NTD laboratory systems in LMICs is a major barrier to monitoring and evaluation of interventions used for control and elimination of NTDs . The DFID funded Centre for Neglected Tropical Disease ( CNTD ) in the UK is monitoring the impact of mass drug administration ( MDA ) on the incidence of NTDs . The programme has found that lack of laboratory capacity in the CNTD supported countries is a critical bottleneck to implementing and monitoring community-based elimination interventions . To help the laboratories perform more effectively , the CNTD requested support from the Liverpool School of Tropical Medicine's ( LSTM ) Capacity Research Unit to design , monitor , and evaluate the capacity development of four laboratories in Ghana , Kenya , Malawi , and Sri Lanka . Definitions of capacity development vary depending on the sector or particular programme focus , but a common definition is “ability of individuals , organisations or systems to perform appropriate functions effectively , efficiently and sustainably” [4] . Laboratory capacity strengthening is complex; it can require investment in specialised equipment , the support of all cadres of staff including laboratory scientists and researchers , as well as the leadership of the organisation in which the laboratory is housed , and sufficient time for training and embedding new processes , systems and equipment . Our aim was to develop a capacity strengthening programme which used a common approach to assessment and monitoring , but which could be tailored to take account of the different ways laboratories were financed , managed , and operated and their interactions with national programmes and regional collaborators . There are many capacity strengthening initiatives being carried out with laboratories in LMICs [5]; however , many of these initiatives focus on individuals' skills ( e . g . , technical skill of using microscope ) [6] or institutional systems and processes ( e . g . , quality control office ) [7] ignoring wider national and international structures ( e . g . , national and regional health systems ) integral to establishing sustainable capacity . In addition to the dearth of literature on organizational and national or international structures integral to capacity strengthening , rigorous approaches and methods to evaluate capacity strengthening initiatives are scarce [8] . Measuring the progress and impact of these capacity strengthening efforts is a priority for the international development community [9] , but donors and scientists alike are struggling with how to do this well [5] . Evidence-based tools have been developed to help evaluate health research capacity strengthening [8] but in the area of laboratory capacity strengthening for NTD control and elimination specifically , no such tools exist . The CNTD's goal in relation to laboratory capacity is to strengthen one laboratory in each of the four countries to support intervention activities that aimed to control and eliminate NTDs by 2020 . To support this goal , our project aimed to describe and measure the capacities required by each laboratory at the individual ( e . g . , technicians , students , researchers ) , organizational ( e . g . , universities , research institutions , clinical facilities ) , , and national and international levels . To achieve CNTD's goal , our specific objectives were to a ) use available evidence to describe the optimal capacities needed at each of the three levels for each laboratory if they were to achieve the goal , b ) develop a set of assessment and monitoring collection tools that would enable us to assess what capacity gaps needed addressing if laboratories were to achieve optimal capacity and c ) develop a capacity strengthening action plan to address the gaps and indicators that would enable us to monitor progress as capacity gaps were addressed .
We used a validated framework and theory of change principles to guide the development of our capacity strengthening tools . The framework for designing and evaluating a health research capacity-building programme is based on four phases of capacity strengthening ( see Table 1 ) - awareness , experiential , expansion , and consolidation [10] . Based on this framework an important first step in the awareness phase is to carefully review current capacity against a set of optimal standards and conduct a needs assessment to identify capacity gaps . We focused efforts on engaging all relevant stakeholders to determine the objectives of the capacity strengthening programme , identify capacity gaps and needs , and jointly develop a capacity development action plan . Our approach enabled stakeholders to be actively involved in the assessment and monitoring process . To carry out these activities we recognized that we would require specific assessment and monitoring collection tools and would need to consult various data sources within each laboratory system . We also draw on theory-based evaluation methods , particularly theory of change evaluation , to develop our approach to laboratory capacity strengthening . We define theory of change as “An on-going process of reflection to explore change and how it happens – and what that means for the part organisations play in a particular context , sector and/or group of people” [11] . Using a theory of change approach involves specifying an explicit theory of how and why a capacity strengthening intervention might cause an effect , and this is used to guide the evaluation [12] . Guided by this , our theory was that strengthening laboratories for NTD control is a complex and non-linear process involving wider systems and actors beyond the institution; we also assumed strengthening capacity in the laboratories would involve strengthening partnerships , organisational development , empowering people , and open communication . We purposely choose to incorporate theory of change in our work in order to determine indicators that could help us explore the relationship between the programme inputs , activities , and outcomes . Prior to our research , no tools existed for specifically examining the capacities required by laboratory systems at the individual , organizational , and national and international levels to support the control of NTDs , or for capturing information from various data sources within laboratory systems . Therefore we developed our own tools based on evidence concerning the components ( i . e . , people , skills , systems , resources ) needed to strengthen the capacity of laboratory systems . We used a three-stage approach to develop the assessment and monitoring tools . First we searched published evidence concerning laboratory capacity strengthening at the individual , organisation , and national and international system level . We searched the electronic databases of PubMed and Google Scholar , using the keywords “laboratory” , “NTD” and “capacity strengthening” . We also consulted books and published reports concerning capacity strengthening initiatives conducted with medical laboratories . From this information we were able to generate a list of all the components that were necessary for an optimal laboratory system in the domain of NTDs and used this to inform the design of our tools . Specifically , the following documents guided the development of our assessment and monitoring tools; the Global Laboratory Initiative Stepwise Process towards TB Laboratory Accreditation [13] and adapted for NTD laboratories , the EFQM excellence model [14] , the SIDA evaluation model of HEPNet [15] and the UNDP Measuring Capacity document [16] . Using all the components in the list of optimal capacities we developed a questionnaire for laboratory managers , a semi-structured interview guide for use with laboratory stakeholders , a capacity gap checklist for use with the laboratory manager and laboratory staff , and a checklist for ISO 15189 to be used for on-site observations ( see Table 2 ) . Our intention was to use these tools during a site visit to collect data that would allow us , in collaboration with local stakeholders ( e . g . , laboratory technicians , laboratory managers , NTD scientists , directors of institutions , Ministry of Health representatives , etc . ) , to identify capacity gaps and to create a comprehensive capacity development action plan to address the gaps . We analyse the data generated from all the tools using content and thematic analysis . Specifically , we use an analytic framework to help guide thematic data analysis of the interview and focus group data . The analytic framework consists of a range of apriori codes that help to organize the data generated and includes codes pertaining to quality assurance , institutional collaboration , funding , NTD coverage or focus , research capacity , and organizational resources . Data from the checklists and questionnaire are analysed using content analysis . We use the findings of the capacity gap analysis to jointly develop with laboratory managers their own unique five-year capacity development strategy to improve their capacity to conduct research and analysis to support NTD control . Gaps in capacity that need to be filled to achieve the strategy are agreed upon during a consensus meeting with invited stakeholders . Priority gaps that require action in the first year are proposed by stakeholders and amalgamated into a one year capacity development action plan with measurable indicators and targets to drive capacity strengthening . The plans are then finalised through Skype and email discussions ( e . g . , details concerning completion dates ) after the completion of each of the visits . These capacity development action plans can also be used to mobilize donor funding as they highlight and provide justification for the priority areas where funding needs to be invested . Following development of the tools , we implemented them in four of the CNTD/LF programme ( 2012-16 ) funded laboratories , including Ghana , Kenya , Malawi , and Sri Lanka . The laboratories in each country were initially selected by CNTD to be a part of their MDA programme because it had been identified that a lack of capacity globally in laboratory systems was a major bottleneck in the monitoring of MDA . Of all of the laboratories in the MDA programme , the laboratories in Ghana , Kenya , Malawi , and Sri Lanka were chosen to be a part of the pilot study because each were seen to be potential regional leaders in the control of NTD and had a potential ability to support NTD laboratories in other countries . See Table 3 for a description of each laboratory involved in the study . Implementation of the tools occurred throughout 2012 during a 5–10 day visit at each institution , with two complementary members ( e . g . , laboratory specialist , social scientist ) of the Capacity Research Unit leading each visit . A total of 62 semi-structured interviews were conducted , 17 in Malawi , 11 in Ghana , 16 in Kenya , and 18 in Sri Lanka . We interviewed stakeholders from a range of institutions and levels including laboratory scientists , laboratory directors , research staff , WHO staff , ministry representatives , students , human resource and financial staff , donors , and senior academics . For example , key NTD stakeholders in Kenya were drawn from the Eastern and Southern Africa Centre of International Parasite Control NTD laboratory located in the Kenyan Medical Research Institute and the National NTD programme through the office of the Department of Disease Prevention and Control in the Ministry of Health . In addition to the semi-structured interviews , in each country one pre-visit questionnaire and ISO checklist were completed , 2–4 capacity gap checklists were completed , and one focus group was held . We revised the tools after their implementation in each country by conducting a retrospective analysis of how the tools contributed or not to the awareness phase in the framework for designing and evaluating a health research capacity-building programme that guided the design of our capacity strengthening tools . The analysis was developed through collaborative and candid dialogue by the research partners , using the framework as the basis for deliberation . These analysis meetings with the entire research team reviewing the findings were an important step in establishing rigour in the refinement of the tools . Throughout the analysis , questions were asked such as; “Were all relevant stakeholders at organisation and policy level as well as individuals involved in implementing capacity strengthening cycle engaged ? ” and “Was there an emphasis on local ownership with defined role for external input ? ” Results of the retrospective analysis shed light on factors such as how some stakeholders were not participating in the capacity assessment possibly as a result of the work being carried out in a context where being critical could be considered inappropriate , particularly for a junior member of staff . To address this particular issue , we adapted the methods to include focus group discussions specifically for laboratory staff , where laboratory managers did not participate . These refinements enabled us to gain an increasingly greater depth and breadth of information from laboratory staff . The retrospective analysis also illuminated that the laboratories held varying capacity strengths and gaps and the tools needed to be able to be tailored accordingly . For example , following the work in Malawi , modifications of the tools included re-designing the ISO checklist to enable laboratory staff to bypass sections of questions that were not relevant to their laboratory's stage of development . By analyzing the implementation of the tools in succession in different countries we had time to use systematically lessons we had learnt to revise the tools between each evaluation . We obtained ethics approval for the capacity strengthening component of the work from the LSTM Research Ethics Committee . The wider DFID funded CNTD programme has ethics approval for all monitoring and evaluation activities scheduled to be implemented in the country laboratories .
Using the rich information collected with the tools we were able to identify strengths and gaps in NTD laboratories' systems capacity ( see Table 4 ) . The identified strengths and gaps varied amongst the countries; however , inter-laboratory comparison revealed some similarities . For example , all laboratory systems mentioned that NTDs being recognized as a national priority was a specific strength , which resulted in greater availability of national funding and human resource support for laboratories . The following quote from a stakeholder in Kenya illustrates this finding , “A national multi-year strategic plan for control of NTD was published in 2011” . Furthermore , in all countries the laboratories had strong links to policymakers and existing national and regional collaborations . In regards to capacity gaps , one common gap was the lack of funding for NTD research , as allocating funding for research was seen as less of a priority than operations and management when health sector funding decisions were being made . Also common to all of the laboratories was a lack of quality assurance documentation and safety systems , a lack of formalized agreements with national NTD programmes , and reliance on external funds . There also was a specific disease focus in each laboratory , without consideration of the broader NTD focus , creating a need for each laboratory to consider how they move beyond their specific focus on malaria or lymphatic filariasis etc . to NTDs as a whole . Finally , there was a lack of research and biostatistics capacity in all of the laboratories , partially due to the fact that research training courses were not accessible to all staff . Activities were identified for each country to undertake to work towards achieving ISO 15189 . As with the strengths and gaps , the identified activities varied amongst the countries; however , inter-laboratory comparison revealed some similarities . The checklist revealed that none of the countries had written safety systems in place ( e . g . , procedures to follow in event of a biohazardous incident that are essential to achieve quality assurance ) . Therefore , similar activities that needed to be undertaken in each country included the drafting of full standard operating procedures for all experimental processes , safety , and equipment in the laboratory . Additional gaps in relation to ISO standards included the need to appoint and assign a safety officer and to have job descriptions available for all staff . The tools generated information about how the NTD laboratories could support national NTD programmes in the region with achieving their aims . The NTD laboratories were found to provide timely and helpful input on country specific issues for topics related to NTDs such as sample diagnostics , vector analysis , and the efficacy of control programmes . For example , in Kenya the tools helped identify the potential for the laboratory to provide support to regional LF control programmes in Tanzania , Zimbabwe , Botswana , and Zambia . Additional potential activities that were identified through our process include confirmation of NTD elimination through implementation of monitoring and evaluation activities , quality control , processing of samples collected through operational research carried out in hotspot areas where transmission of NTD is persisting even after several mass interventions , and support other operational research activities aimed to support implementation . Furthermore , in each country the laboratories were found to provide robust scientific data to support national and regional NTD control programmes , enabling policy makers to make informed decisions that contributed to control and elimination of NTDs in their country and region . Information about each NTD laboratory's position within national and international networks and collaborations was generated from the set of tools . Findings indicate that the level of technical expertise and experience within the laboratory system enhanced a laboratory's position within their networks as with this expertise the laboratory was seen to be a preferential collaborator . Technical expertise was perceived by stakeholders to be more essential to a laboratory's position within networks than other factors such as geographic proximity . For example , the laboratory scientists in Ghana are highly skilled in using real-time polymerase chain reaction ( RT-PCR ) . Given their expertise the Ghanaian scientists were identified as being able to provide training to other laboratories within the CNTD network .
Our novel set of assessment and monitoring tools provide a practical and field-tested approach for assessing laboratory capacity strengthening initiatives . We have implemented the tools for laboratory system strengthening in NTD laboratory systems in three countries in Africa and one country in South East Asia , but they could be adapted for use in other geographical and laboratory contexts . | Capacity strengthening activities such as technical training for staff , student research project supervision , and equipment provision are being carried out in laboratories worldwide as part of the global effort to control neglected tropical diseases ( NTDs ) . However , these activities often focus on developing the skill sets of an individual and are not being thoroughly monitored and assessed . To address these gaps we developed a set of monitoring and assessment tools that can be used to determine the capacities required and achieved by laboratory systems to support the control of NTDs . The tools simultaneously focus on individuals ( e . g . , technicians , students , researchers ) , organisations ( e . g . , universities , research institutions , clinical facilities ) , national governments , and international agencies . Using the tools highlighted the strengths and limitations of each laboratory system in addition to the role of the laboratory regionally and internationally . We used the tools in Kenya , Ghana , Malawi and Sri Lanka , and concluded that our tools can be adapted and tailored to use in other countries and laboratories . | [
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] | 2014 | A Systematic Approach to Capacity Strengthening of Laboratory Systems for Control of Neglected Tropical Diseases in Ghana, Kenya, Malawi and Sri Lanka |
Mobile group II introns are bacterial retrotransposons that are thought to have invaded early eukaryotes and evolved into introns and retroelements in higher organisms . In bacteria , group II introns typically retrohome via full reverse splicing of an excised intron lariat RNA into a DNA site , where it is reverse transcribed by the intron-encoded protein . Recently , we showed that linear group II intron RNAs , which can result from hydrolytic splicing or debranching of lariat RNAs , can retrohome in eukaryotes by performing only the first step of reverse splicing , ligating their 3′ end to the downstream DNA exon . Reverse transcription then yields an intron cDNA , whose free end is linked to the upstream DNA exon by an error-prone process that yields junctions similar to those formed by non-homologous end joining ( NHEJ ) . Here , by using Drosophila melanogaster NHEJ mutants , we show that linear intron RNA retrohoming occurs by major Lig4-dependent and minor Lig4-independent mechanisms , which appear to be related to classical and alternate NHEJ , respectively . The DNA repair polymerase θ plays a crucial role in both pathways . Surprisingly , however , mutations in Ku70 , which functions in capping chromosome ends during NHEJ , have only moderate , possibly indirect effects , suggesting that both Lig4 and the alternate end-joining ligase act in some retrohoming events independently of Ku . Another potential Lig4-independent mechanism , reverse transcriptase template switching from the intron RNA to the upstream exon DNA , occurs in vitro , but gives junctions differing from the majority in vivo . Our results show that group II introns can utilize cellular NHEJ enzymes for retromobility in higher organisms , possibly exploiting mechanisms that contribute to retrotransposition and mitigate DNA damage by resident retrotransposons . Additionally , our results reveal novel activities of group II intron reverse transcriptases , with implications for retrohoming mechanisms and potential biotechnological applications .
Mobile group II introns are site-specific retrotransposons that consist of a catalytically active intron RNA ( ribozyme ) and an intron-encoded protein ( IEP ) , with reverse transcriptase ( RT ) activity [1] . Although they are found mainly in bacterial and organellar genomes , group II introns are thought to have played a major role in eukaryotic genome evolution as evolutionary ancestors of nuclear spliceosomal introns and retrotransposons in higher organisms [2]–[4] . Group II intron RNAs catalyze their own splicing via two sequential transesterification reactions that are the same as those for spliceosomal introns and yield an excised intron lariat with a branched 2′-5′ phosphodiester linkage [5] , [6] . For mobile group II introns , the splicing reactions are assisted by the IEP , which binds specifically to the intron RNA and stabilizes the catalytically active RNA structure [7] , [8] . The IEP then remains bound to the excised intron lariat RNA in a ribonucleoprotein particle ( RNP ) that promotes intron integration into new DNA sites [9] , [10] . Intron integration is targeted to the ligated-exon junction in an intronless alleles in a process called “retrohoming” , but can also occur at lower frequency into ectopic sites that resemble the homing site in a process called “retrotransposition” or “ectopic retrohoming” . In both cases , the intron inserts into the new DNA site by a novel mechanism in which the excised intron lariat RNA fully reverse splices into a DNA strand and is reverse transcribed by the IEP , yielding an intron cDNA that is integrated into the genome by host enzymes [1] , [10]–[14] . Retrohoming leads to the expansion of intron-containing alleles in a population , while ectopic retrohoming provides a means of intron dispersal to new sites . Group II intron RNAs can also splice without branching by an alternate pathway , termed “hydrolytic splicing” [1] . In this pathway , the first transesterification , 5′-splice site cleavage , occurs by hydrolysis rather than branching , and the second transesterification yields ligated exons and an excised linear intron RNA . Hydrolytic splicing was first observed as a side reaction of group II intron self-splicing under non-physiological conditions [15] , [16] and was demonstrated to occur in vivo by using a mutant yeast mitochondrial intron that was deleted for the branch-point A residue [17] . Some subclasses of group II introns lack the branch-point A residue and rely entirely on the hydrolytic mechanism for splicing in vivo [18] , [19] . Linear group II intron RNAs can also be generated from excised intron lariat RNAs by debranching , which is believed to accelerate RNA turnover [20] . However , the physiological and evolutionary significance of hydrolytic splicing and linear group II intron RNAs have remained largely unclear . The Lactococcus lactis Ll . LtrB intron , which has been used extensively as a model system for studying group II intron mobility mechanisms , has a broad host range and is actively mobile in Escherichia coli , making it possible to use E . coli genetic approaches to dissect mobility pathways [11] , [21] . The major retrohoming pathway used by the Ll . LtrB intron in E . coli is shown in Figure 1A . After promoting splicing , the Ll . LtrB IEP , denoted LtrA protein , remains bound to the excised intron lariat RNA in RNPs that recognize a DNA target site at the ligated-exon junction ( denoted E1–E2 ) of an intronless allele . The intron lariat RNA initiates retrohoming by fully reverse splicing into the top DNA strand , leading to insertion of the intron RNA between the two DNA exons . The IEP then uses a DNA endonuclease activity to cleave the bottom strand and uses the 3′ DNA end at the cleavage site as a primer for reverse transcription of the inserted intron RNA . In E . coli , the resulting intron cDNA is integrated into the host genome by a mechanism that involves degradation of the intron RNA template strand by a host RNase H and second-strand DNA synthesis by a host DNA polymerase [11] , [21] . In variations of this mechanism , Ll . LtrB and other group II introns can also retrohome without bottom-strand DNA cleavage by using a nascent strand at a DNA replication fork to prime reverse transcription of the intron RNA [14] , [22] , [23] , and yeast mitochondrial group II introns retrohome by using recombination rather than DNA repair for cDNA integration [24] . Recently , while carrying out experiments to test whether microinjected group II intron RNPs could be used for gene targeting in Xenopus laevis oocyte nuclei and Drosophila melanogaster embryos , we found that linear as well as lariat group II intron RNAs can retrohome in vivo [25] . This finding was surprising because , unlike lariat RNAs , linear group II intron RNAs can carry out only the first reverse-splicing step , ligation of the 3′ end of the intron RNA to the 5′ end of the 3′- exon DNA [26] , [27] . While reverse transcription of fully reverse-spliced intron RNA yields an intron cDNA that can be extended directly by continued DNA synthesis into the upstream exon ( Figure 1A ) , reverse transcription of a partially reverse-spliced intron RNA yields an intron cDNA with an unattached 3′ end that must be linked to the upstream exon DNA in a separate step ( Figure 1B ) . Sequencing of 5′-integration junctions showed that this step occurs by an error-prone process . Although some events result in the precise insertion of the intron between the two DNA exons , most give 5′-integration junctions with 5′-exon deletions , intron 5′-end truncations , insertion of extra nucleotides at the intron-exon junction , or indications of DNA repair via base pairing of microhomologies on opposite sides of the break [25] , similar to ligation junctions resulting from double-strand break repair by non-homologous end joining ( NHEJ ) [28]–[31] . NHEJ activities have been found to contribute to the retrotransposition of LINE elements and other retrotransposons in eukaryotes [32] , [33] and may be exploited preferentially by retrotransposons to gain advantage in genetic conflict with their hosts , which rely on these enzymes for survival [34] . Thus , although group II introns are alien to Xenopus and Drosophila , they could be utilizing mechanisms that contribute to the retrotransposition of resident retroelements in eukaryotes and could be subject to host defenses that evolved to counter or mitigate such retrotransposition . Although NHEJ seemed the most likely mechanism for attachment of the free cDNA to the 5′ exon in linear intron RNA retrohoming , an alternate possibility was that the RT template switches to the 5′-exon DNA , either directly or following incorporation of extra nucleotide residues at the end of the cDNA , resulting in synthesis of a continuous DNA bottom strand containing intron and 5′-exon sequences . Both template switching and incorporation of extra nucleotide residues at the ends of cDNA have been found for other non-LTR-retroelement RTs [35]–[37] . Although we thought this possibility unlikely because group II intron RTs appeared to have low DNA-dependent DNA polymerase activity in vitro [21] , template switching and non-templated nucleotide addition by group II intron RTs have not been investigated previously . Here , we used Drosophila melanogaster mutants to investigate the contribution of NHEJ activities to linear intron RNA retrohoming and assessed the involvement of template switching by comparing junctions formed by this mechanism in vitro with those formed during linear intron RNA retrohoming in vivo . Our results indicate that linear intron RNA retrohoming occurs primarily by a novel variation of NHEJ that uses host enzymes , including DNA ligase 4 ( Lig4 ) and DNA repair polymerase θ ( PolQ ) , but is minimally dependent upon Ku .
To investigate the involvement of NHEJ factors , we compared lariat and linear group II intron retrohoming in D . melanogaster embryos with mutations in the genes encoding DNA ligase 4 ( Lig4 ) , Ku70 , and the DNA repair polymerase θ ( PolQ ) [38] , [39] . For these experiments , we used a plasmid-based retrohoming assay in which an Ll . LtrB-ΔORF intron with a phage T7 promoter sequence inserted near its 3′ end integrates into a target site cloned in an AmpR-recipient plasmid upstream of a promoterless tetR reporter gene , thereby activating that gene ( Figure 2A ) [25] , [26] , [40] . The recipient plasmid was injected into the posterior of precellular blastoderm stage embryos , followed within 5 min by injection of lariat or linear RNPs , which were reconstituted in vitro from the purified IEP and intron RNA ( see Materials and Methods ) . After incubating the embryos for 1 h at 30°C , nucleic acids were extracted and transformed into an E . coli strain ( HMS174 ( DE3 ) ) , which expresses T7 RNA polymerase . The transformed bacteria were then plated on medium containing ampicillin or ampicillin and tetracycline , and mobility efficiencies were quantified as the ratio of ( TetR+AmpR ) /AmpR colonies . Figure 2B compares the retrohoming efficiencies of lariat and linear intron RNAs in wild-type and mutant embryos , based on parallel assays in ten separate experiments ( summarized in Table S1 ) . For each strain in each experiment , 80 injected embryos were pooled prior to extracting nucleic acids and transforming them into E . coli . The results for the lig4 mutant show the retrohoming efficiency of the lariat intron was unchanged , whereas the retrohoming efficiency of the linear intron was decreased strongly ( 18% wild type ) , but could be restored to wild-type levels by ectopic expression of Lig4 from an integrated P-element ( lig4−; P{lig4+} embryos ) . In the polQ mutant , the retrohoming efficiency of the linear intron was decreased to ≤0 . 5% of wild type , compared to 27% wild type for lariat RNPs . Finally , the ku70 mutant , a trans-heterozygote of two putative null alleles ( see Materials and Methods ) , showed only moderately decreased retrohoming efficiencies for both lariat and linear intron retrohoming ( 67% and 46% wild type , respectively ) . The latter result was surprising because Ku and Lig4 ordinarily function together in the same NHEJ pathway [41] , [42] . The strong differential inhibition of linear compared to lariat RNA retrohoming in the lig4 and polQ mutants supports models in which these enzymes function directly at unique steps in this process , presumably by providing the DNA ligase and repair DNA polymerase activities needed to link the intron cDNA to the upstream exon . The similar moderate decreases in lariat and linear intron retrohoming efficiency in the ku70 mutant could reflect that Ku functions at a common step in both pathways or could be an indirect effect ( see Discussion ) . D . melanogaster uses at least two NHEJ pathways to repair double-strand breaks: classical NHEJ ( C-NHEJ ) , which is dependent upon Lig4 and Ku70 , and alternate end-joining ( alt-EJ ) , which operates without either factor and could be a mixture of different pathways [31] , [38] , [39] , [43] . In a genetic assay for repair of double-strand breaks induced in the germline by the meganuclease I-SceI , lig4 and ku70 mutants inhibited NHEJ activity by 76–78% , leaving 22–24% residual activity that was attributed to alt-EJ [43] . Our finding above that the lig4 mutant shows similar degrees of inhibition and residual activity for linear intron retrohoming ( 82% and 18% , respectively ) , most simply suggests that Lig4-independent retrohoming occurs by using the alt-EJ pathway or components thereof . Previous studies showed that the DNA repair junctions resulting from alt-EJ in Drosophila ku70 and lig4 mutants are generally similar to those for C-NHEJ [31] , although in some assays , the lig4 mutant gave somewhat increased frequencies of junctions with extra nucleotide additions ( 55–63% compared to 30–36% for wild type [38] , [39] ) . To further investigate whether Lig4-independent retrohoming occurs via alt-EJ , we compared 5′- and 3′-intron integration junctions resulting from linear intron retrohoming in the mutant and two commonly used wild-type strains ( w1118 and Or-R ) by PCR using primers flanking the junctions , followed by cloning and sequencing of the PCR products ( Figure 3 and Figure 4 ) . The 5′- and 3′-integration junctions from lariat intron retrohoming and the 3′-integration junctions from linear intron retrohoming result from reverse-splicing reactions ( see Figure 1 ) , and as expected , the PCRs for these junctions gave single prominent products , with no differences between the wild-type and mutant strains ( Figure 3 , bottom gels; in each case , the expected precise junction sequence was confirmed by sequencing; see legend for details ) . By contrast , the 5′-integration junctions resulting from linear intron RNA retrohoming were heterogeneous in all strains , with a major band of the size expected for full-length intron insertion and smaller bands , which appeared most prominent in the lig4−; P{lig4+} and polQ− embryos ( Figure 3 , top gels ) . DNA sequences of the 5′-integration junctions resulting from linear intron retrohoming in wild-type w1118 and lig4− , ku70− , and lig4−; P{lig4+} embryos are summarized in Figure 4A–4D , and their characteristics are compared by the bar graphs in Figure 5 . As found previously [25] , the 5′-integration junctions for linear intron RNA retrohoming in the wild-type embryos were heterogeneous with different combinations of 5′-exon deletions , 5′-intron truncations , and extra nucleotide additions ( Figure 4A ) . Some of the junctions show evidence of DNA repair at regions of microhomology ( parentheses ) , and in some cases , the extra nucleotides inserted at the junctions match and were presumably copied from neighboring sequences in the 5′ exon or intron ( underlined ) . The 5′ junctions resulting from linear intron retrohoming in the lig4− , ku70− , and lig4−; P{lig4+} mutants were generally similar to those in the wild type w1118 , the more closely related wild-type strain , with no large differences in the percentage of junctions with 5′-exon deletions , 5′-intron truncations , extra nucleotide additions , or microhomologies ( Figure 4 and Figure 5 ) . Compared to the other strains assayed in parallel , the proportion of long 5′-intron truncations appears to be somewhat lower in the ku70− embryos and higher in the lig4− and lig4−; P{lig4+} embryos , but the significance of these findings is unclear , as the differences were not large and the proportions of full-length and shorter 5′-junction products in each stock were somewhat variable in different experiments . The similarity of the junction sequences resulting from linear intron RNA retrohoming in the lig4 and ku70 mutants to those resulting from double-strand break repair in these mutants [31] , [39] supports the hypothesis that Lig4-independent linear intron retrohoming occurs predominantly by using components of the alt-EJ pathway . The DNA repair polymerase θ ( PolQ ) has been shown to function in DNA end-joining repair in Drosophila , including a role for extra nucleotide addition at the repaired junctions [38] . To investigate the function of PolQ in linear intron RNA retrohoming , we compared the sequences of 5′-intregration junctions from parallel assays of this process in wild-type Or-R and polQ− embryos ( Figure 4E and 4F , Figure 5 ) . Because the number of unique junction sequences recovered from the polQ mutant was lower than those for the other strains and some of these junctions were represented multiple times , we calculated the proportion of junctions with different characteristics in Figure 5 relative to both the total number of junctions ( left bars ) and the total number of unique junctions sequences ( right bars , asterisks ) . Both comparisons show that that the polQ mutation decreases the proportion of junctions containing extra nucleotide residues ( 4% of total junctions and 20% of unique junctions compared to >64% of junctions in wild-type Or-R and >48% in all other strains analyzed ) , as expected from the known function of PolQ . Further , the 5′ junctions from the polQ− embryos have a higher frequency of long ( ≥15 bp ) 5′-exon deletions ( 72% of total junctions and 80% of unique junctions compared to 29% in wild-type Or-R ) and a dramatically increased frequency of long ( ≥5 nt ) microhomologies between exon and intron sequences ( 56% of total junctions and 40% of unique junctions compared to none among 170 total junctions from all the other strains analyzed ) . This increased frequency of long microhomologies may reflect that they are more stringently required for annealing of the 3′ end of the cDNA to the upstream exon in the absence of PolQ . We note that among the unique junction sequences from the polQ mutant , two with large deletions were recovered ≥10 times each . Although we cannot exclude that the repeated recovery of these junctions reflects differential amplification by PCR , both have ≥5 nt microhomologies that could have been used preferentially for annealing in multiple events , and indeed one of these junctions ( Figure 4F bottom sequence ) comprised 6 of 12 recovered junctions in an additional , separate experiment ( data not included in Figure 4F ) . Considered together , the junction sequences indicate that PolQ functions in extra nucleotide addition to the 3′ end of the cDNA during linear intron RNA retrohoming and that this extra nucleotide addition may be critical for generating microhomologies that enable annealing between the 3′ end of the cDNA and the upstream exon DNA . Further , the strongly decreased frequency of linear intron RNA retrohoming in the polQ mutant indicates that PolQ functions in both the Lig4-dependent and Lig4-independent retrohoming pathways . Although the residual linear intron RNA retrohoming events in the lig4 mutant can be accounted for by Lig4-independent ( alt-EJ ) NHEJ , it remained possible that template switching of the RT from the 5′ end of the intron RNA directly to the 3′ end of the 5′-exon DNA contributes to this process . Previous studies have shown that other non-LTR retroelement RTs are proficient at template switching directly to the 3′ end of a template strand with little or no complementarity to the cDNA end and that these events can be accompanied by extra nucleotide addition at the junctions , as found for NHEJ [35] , [36] , [37] , [44] . To determine if a template-switching mechanism could be responsible for the manner of 5′ junctions observed during linear intron retrohoming , we carried out biochemical assays using small artificial substrates that simulate the situation at the 5′-integration junction just prior to completion of intron cDNA synthesis ( Figure 6 ) . The primary substrate consists of a 60-nt RNA template ( Ll . LtrB RNA ) , whose 5′ end corresponds to that of the Ll . LtrB intron , with a 45-nt DNA primer representing the nascent cDNA ( primer c ) annealed to its 3′ end . The Ll . LtrB RT ( LtrA ) initiates reverse transcription of the intron RNA template from the annealed DNA primer and extends it to the 5′ end of the Ll . LtrB RNA template , where it can then switch to a second 40-nt DNA or RNA template with the nucleotide sequence of exon 1 ( E1 RNA or DNA , red and black , lanes 5 and 6 , respectively ) . The 3′ end of the Ll . LtrB RNA has an aminoblock to impede the RT from switching to a second molecule of the initial template . Figure 6 , lanes 5 and 6 show that the Ll . LtrB RT efficiently extends the annealed primer c ( Pri c ) to the end of the intron RNA template , yielding major labeled products of ∼60-nt , which were resolved as a doublet , along with smaller amounts of larger products of the size expected for template switching to the exon 1 ( E1 ) DNA or RNA ( ∼100 nt ) or to a second molecule of Ll . LtrB RNA despite the presence of the aminoblock ( ∼120 nt ) . Controls show that no labeled products were detected after incubating the RT with primer c in the presence or absence of the exon 1 RNA or DNA ( lanes 2–4 ) . Cloning and sequencing of the gel bands confirmed that the major ∼60-nt products ( bands a and b in lane 5 and h and i in lanes 6 ) correspond to cDNAs extending to or near the 5′ end of the intron RNA , with the doublet reflecting the addition of extra nucleotide residues , mostly A-residues , to the 3′ end of the cDNA upon reaching the end of the Ll . LtrB RNA template ( Figure 7A and 7B ) . Such non-templated nucleotide addition is a common property of DNA polymerases and RTs [35] , [36] , [45]–[48] . The first set of larger gel bands ( 90–110 nts; band c–e in lane 5 and j–l in lane 6 ) corresponds to products resulting from template switching from the 5′ end of the intron to the 3′ end of exon 1 DNA or RNA ( Figure 7A and 7B ) , as well as products resulting from template switching to the 3′ end or internal regions of the Ll . LtrB intron ( Figure S1 ) . Many of the template switches to exon 1 DNA occurred seamlessly , but small numbers of extra nucleotide residues , mostly A residues , were found at some junctions , as well as at the 3′ end of the cDNAs ( Figure 7A; bands c–e ) . The template switches to exon 1 RNA showed similar characteristics , but with a higher proportion of junctions containing extra nucleotide residues ( 61% compared to 33% for exon 1 DNA; Figure 7B; bands j–l ) . The second set of larger bands ( 120–140 nts; bands f and g in lane 5 and m and n in lane 6 ) contains products resulting from two sequential template switches to exon 1 DNA or RNA ( Figure 7A and 7B , respectively ) and/or the Ll . LtrB RNA ( Figure S1 ) . These products of multiple template switches have characteristics similar to those resulting from a single template switch , including addition of extra nucleotide residues , mostly A residues , at some template-switching junctions and at the 3′ ends of the cDNAs . The above results were obtained under reaction optimized for reverse transcription by the Ll . LtrB RT in vitro ( 450 mM NaCl , 5 mM Mg2+ ) , the high salt concentration helping to stabilize free protein and minimize aggregation of this RT [9] . However , similar results were obtained for template-switching reactions under near-physiological salt conditions ( 100 or 200 mM KCl , 5 mM Mg2+ ) . Although the RT activity of the protein was lower under these conditions , the gel profiles show roughly equal levels of template switching to exon 1 RNA and DNA ( Figure S2 ) , and sequencing of the products showed similar template-switching junctions and patterns of non-templated nucleotide addition ( Figure S3 ) . Finally , we tested whether the Ll . LtrB RT could template switch to double-stranded exon 1 DNA or RNA with an annealed bottom-strand DNA leaving either a blunt end or a 5′ bottom-strand overhang identical to that generated in vivo by the staggered double-strand break accompanying group II intron insertion ( Figure 1; complete annealing confirmed by native gel analysis; Figure S4 ) . Neither of these configurations significantly decreased the formation of the 100-nt product resulting from template switching to exon 1 DNA or RNA ( Figure 6 , lanes 7–9 ) . DNA sequencing confirmed the template switch to double-stranded E1 DNA with a 5′-bottom-strand overhang and showed that this template switch was seamless in most cases ( Figure 7C ) . The sequencing also showed several instances in which the template switch occurred to the penultimate rather than the 3′ terminal residue of exon 1 ( Figure 7C ) , as well as template switches to Ll . LtrB RNA and the bottom-strand overhang oligonucleotide ( Figure S5 ) . Template switching to the penultimate nucleotide residue was not seen for single-strand acceptor DNA templates and could be related to the presence of the complementary DNA strand . Together , the biochemical assays show that the Ll . LtrB RT can template switch from the 5′ end of the intron RNA to exon 1 and surprisingly that template switching is similarly efficient regardless of whether the exon 1 template is RNA or DNA or single- or double-stranded . However , the junctions resulting from template switching differ from those generated during retrohoming of linear intron RNA in vivo in that extra nucleotide additions are uniformly short and mostly A-residues .
Considered together , our results lead to the model shown in Figure 8 for the key steps in linear intron RNA retrohoming . The finding of strong differential inhibition of linear relative to lariat intron retrohoming in D . melanogaster mutants indicates that the NHEJ factor Lig4 is the predominant enzyme involved in ligating the intron cDNA to the upstream exon and that extra nucleotide addition by the DNA repair polymerase θ ( PolQ ) also plays a crucial role . Although Lig4 and PolQ appear to be the major enzymes playing these roles in D . melanogaster , residual linear intron RNA retrohoming with extra nucleotide addition occurs in both the lig4 and polQ mutants , indicating that other DNA ligases and polymerases can serve as backups that perform the same functions at lower efficiency . Biochemical experiments show that another possible Lig4-independent mechanism , template switching by the group II intron RT from the 5′ end of the intron RNA directly to the upstream exon DNA , is possible but gives junctions differing from the majority of those in vivo . It seems likely that the mechanism elucidated here involving host DNA ligases and repair polymerases is also used for linear intron RNA retrohoming in Xenopus laevis , where we observed similar 5′-integration junctions [25] , and more generally , in other eukaryotes , including mammalian cells , where it could have implications for group II intron-based gene targeting . This mechanism also provides a possible means for proliferation of non-branching group II introns in prokaryotes , some of which encode a Ku homolog and ATP-dependent DNA ligases along with DNA repair polymerases and use them in NHEJ pathways related to those of higher organisms [49] . Additionally , features of this mechanism , including the use of both Lig4-dependent and alt-EJ and the requirement for extra nucleotide addition to the cDNA end by a DNA repair polymerase , may be used to promote retrotransposition and mitigate DNA damage caused by LINE elements and other retrotransposons [33] , [50]–[52] . The involvement of Lig4 in linear intron RNA retrohoming in Drosophila is indicated by the findings that a lig4 mutation decreases the retrohoming efficiency of linear intron RNA by ∼80% while having no effect on the retrohoming of lariat RNA , and that the decreased retrohoming efficiency of the linear intron in the mutant could be restored to wild-type levels by ectopic expression of Lig4 from a P-element insertion . Most if not all of the residual linear intron RNA retrohoming in the lig4 mutant appears to occur by using components of the alt-EJ pathway , as judged both by similar levels of activity and characteristics of the cDNA ligation junction , particularly patterns of extra nucleotide and the use of microhomologies ( see Results ) . In Drosophila , C-NHEJ and alt-EJ give generally similar double-strand break repair junctions , albeit with quantitative differences in the frequency of extra nucleotide addition in some assays [31] , [38] , [39] , whereas in yeast or mammalian cells , alt-EJ junctions show increased deletion lengths and use of microhomologies [53]–[55] . The involvement of PolQ in linear intron RNA retrohoming is indicated by the findings that a PolQ mutation decreases the retrohoming efficiency by >99% and substantially decreases the frequency of 5′-integration junctions having extra nucleotide residues ( 4–20% of junctions compared to 67% for wild-type Or-R assayed in parallel and >48% in all other strains; Figure 4 and Figure 5 ) . The mutation also increases the frequency of junctions with long ( ≥15 bp ) 5′-exon deletions and long ( ≥5 nt ) microhomologies . The latter increase is particularly striking , as such long microhomologies were found at 56% of the total and 40% of the unique junction sequences from the polQ mutant , but were not found at junctions ( 170 total ) from any of the other strains analyzed ( Figure 4 and Figure 5 ) . The residual extra nucleotide addition in the polQ mutant , which was also seen at 15–20% of junctions in end-joining assays [38] , [39] , could be due to small amounts of the enzyme remaining in the mutant , which has an unidentified expression defect , or to an alternate DNA polymerase . The very strong decrease in linear intron RNA retrohoming efficiency in the polQ mutant ( >99% ) indicates that PolQ functions in both the Lig4-dependent and Lig4-independent retrohoming pathways . PolQ could potentially play at least two roles in linear intron RNA retrohoming . First , extra nucleotide addition to the 3′ end of the cDNA by PolQ may be critical for generating microhomologies that can base pair with the upstream exon to facilitate DNA ligation . Second , PolQ contains a putative DNA helicase domain that could also contribute to retrohoming by promoting base pairing between microhomologies at the cDNA end and the upstream exon , either by annealing the cDNA end to complementary exon sequences or by unwinding the exon DNA strands , making the top strand more accessible to base pairing [38] . The increased frequency of long 5′-exon deletions in the polQ mutant may reflect a delay in cDNA attachment due to lack of suitable microhomologies and/or impaired annealing of complementary cDNA ends to the top strand . The striking increase in the frequency of long microhomologies at the 5′ junctions in the polQ mutant ( see above ) indicates that an alternate annealing mechanism exists in the polQ mutants , but that it is more dependent upon longer microhomologies between exon and intron sequences than the PolQ-assisted mechanism . The function , if any , of Ku in linear intron RNA retrohoming is unclear . The finding that ku70 mutations moderately inhibit retrohoming of both linear and lariat intron RNA ( 54 and 33% inhibition , respectively ) could reflect either that Ku contributes to both pathways or that Ku mutations affect one or both pathways indirectly . The Ku protein interacts with a stem-loop region of the RNA component of yeast and human telomerase [56]–[58] , and it is possible that Ku may similarly bind to linear or lariat group II intron RNAs to protect them from degradation and/or recruit other DNA repair enzymes to the site . An alternate possibility is that Ku affects retrohoming efficiency indirectly by contributing to the repair of double-strand breaks induced by the intron RNP in the recipient plasmids . In yeast mitochondria , double-strand breaks resulting from abortive retrohoming events are substantially more frequent than completed integrations [59] . If not repaired correctly , such double-strand breaks could lead to loss of functional recipient plasmid target sites , which would appear as decreased retrohoming efficiencies in our assay . A similar indirect effect , involving repair of double-strand breaks in the recipient plasmid could also account for the moderate inhibitory effect of the polQ mutation on lariat intron RNA retrohoming . Lig4 is ordinarily recruited to DNA breaks by Ku , and D . melanogaster mutations in either lig4 or ku70 give similar decreases in NHEJ efficiency , suggesting that Lig4 acts exclusively in Ku-dependent NHEJ [41]–[43] . By contrast , we find that linear intron RNA retrohoming is more strongly inhibited by a lig4 mutation than by putative null mutations in ku70 ( 82 and 54% inhibition , respectively ) . Even assuming that the inhibition by the ku70 mutations is a direct effect , these findings most simply suggest that a substantial proportion of linear intron RNA retrohoming events are promoted by Lig4 in the absence of Ku . Unlike a conventional double-strand break , the double-strand break formed during linear intron RNA retrohoming has an RNA attached to one of the DNA ends , and this difference could potentially affect the recruitment and use of NHEJ activities . We noted previously that group II intron RNPs bind to both the 5′- and 3′-exons during retrohoming , and such bridging of the ligation junction could impede access and decrease the need for Ku to cap the broken DNA ends [25] . Additionally , the attached intron RNA could contribute directly to the recruitment of NHEJ activities . The interaction of Ku with telomerase RNA noted above is thought to help recruit telomerase to double-strand breaks [57] , and it is possible that a similar interaction between Ku and the attached group II intron RNA contributes to the recruitment of Lig4 for some linear intron RNA retrohoming events . More generally , such a mechanism involving the interaction of Ku with RNA could also be used by LINE elements and other retrotransposons to recruit Lig4 and other NHEJ activities for cDNA integration and repair of DNA breaks . Finally , our biochemical experiments demonstrate that template switching by the group II intron RT from the 5′ end of the intron RNA directly to the 3′ end of the upstream DNA exon is a potential alternate mechanism for cDNA attachment during linear intron RNA retrohoming . Although we found previously that the Ll . LtrB RT has low DNA-dependent DNA polymerase activity in vitro [21] , we find here that it template switches and copies 5′-exon DNA templates as well as 5′-exon RNA templates ( Figure 6 ) , possibly reflecting that reverse transcription favors a conformation of the enzyme that can initiate more efficiently on DNA templates . In many cases , the template-switching junctions to DNA or RNA templates are seamless , but some have a small number of extra nucleotide residues , predominantly A-residues ( corresponding to T-residues in the top strand ) that were added by the RT to the 3′ end of the cDNA prior to the template switch . This pattern of extra nucleotide addition , which we found under both enzyme optimal and near-physiological conditions , differs from the majority of 5′-integration junctions resulting from linear intron retrohoming in vivo , where the extra nucleotide residues do not show a similar bias and sometimes correspond to copies of neighboring DNA sequences . It remains possible , however , that template switching by the Ll . LtrB RT could give different junctions in vivo , and that template switching and extra nucleotide addition by this enzyme contributes to some retrohoming events . We note that the ability of the group II intron RT's template switching activity to efficiently link sequences in two different templates could potentially be used to directly attach linker sequences containing primer-binding sites to the ends of cDNAs for cDNA cloning and sequencing applications .
Flies were raised in standard fly media at 22°C . The lig4169 mutant , obtained from Mitch McVey ( Tufts University , Medford , MA ) , has a deletion that removes the start codon and most of the region encoding the ATPase and adenylation domains [31] . The ku707B2 and ku70Ex8 mutants were obtained from William Engels ( University of Wisconsin , Madison , WI ) . The ku707B2 allele lacks 1 , 359 bp at the 3′ end of the 2 , 393-bp gene , including most of the DNA and Ku80-interaction domains [43] . The ku70Ex8 allele lacks at least 1 kb , including all of exon 1 and the start codon [43] . The ku707B2/ku70Ex8 genotype , generated by crosses between trans-heterozygous ku707B2/ku70Ex8 parents , is the same as that used previously to study double-strand break repair pathways [39] , [43] . The mus308D2 stock [60] was obtained from the Drosophila Stock Center ( Bloomington , IA ) . The mutation lies outside of the coding region and results in undetectable levels of PolQ protein expression [38] . To obtain transgenic flies harboring a lig4 rescue fragment , a 6-kb DNA segment containing the lig4 gene was amplified from w1118 genomic DNA by using the Expand High Fidelity PCR System ( Roche Applied Science , Indianapolis , IN ) , with primers Lig4 F1 BamHI ( 5′-AAGAGGATCCAGTAGCTGTAGAAGCAGCCAAC ) and Lig4 R1 XhoI 5′-AAGACTCGAGCAGCAGTTCCTCCGACATGAAG ) . This PCR product was inserted between BamHI and XhoI sites of the P-element transformation vector pCaSpeR4 [61] , and transgenic flies were produced by GenetiVision ( Houston , TX ) . A transgene insertion on chromosome 2 was recombined with lig4169 to generate the fly stock used in P{lig4+} rescue experiments . Except for the trans-heterozygous ku707B2/ku70Ex8 embryos ( see above ) , embryos used for microinjection were obtained from crosses between isogenic wild-type or homozygous mutant parents . For all stocks , precellular blastoderm embryos were collected in egg laying chambers in under 40 min , microinjected with recipient plasmids and group II intron RNPs , and incubated for 1 h at 30°C prior to DNA extraction . pACD5C , which was used for synthesis of lariat and linear intron Ll . LtrB intron RNAs , is a derivative intron-donor plasmid pACD4C with a T7 promoter sequence inserted in the sense orientation at the SalI site in intron DIV [25] , [62] . pBRR3-ltrB , the target plasmid for intron-integration assays , contains the Ll . LtrB intron homing site ( ligated exon 1 and 2 sequences of the ltrB gene from positions −178 upstream to +91 downstream of the intron-insertion site ) cloned upstream of a promoterless tetR gene in an AmpR pBR322-based vector [63] . pIMP-1P , used for expression of the LtrA protein for RNP reconstitution , contains the LtrA ORF cloned downstream of a tac promoter and Φ10 Shine-Dalgarno sequence in the expression vector pCYB2 ( New England BioLabs , Ipswich , MA ) [9] . LtrA is expressed from this plasmid as a fusion protein with a C-terminal tag containing an intein-linked chitin-binding domain , enabling LtrA purification via a chitin-affinity column , followed by intein-cleavage [9] . pMAL-LtrA , used for expression of the LtrA protein for biochemical assays , contains the LtrA ORF [64] cloned downstream of a tac promoter and Φ10 Shine-Dalgarno sequence between BamHI and HindIII of the protein-expression vector pMAL-c2t . The latter is a derivative of pMal-c2x ( New England BioLabs , Ipswich MA ) with a TEV protease-cleavage site in place of the factor Xa site [65] . LtrA is expressed from this plasmid with an N-terminal fusion to maltose-binding protein ( MalE ) , enabling its purification via an amylose-affinity column , followed by TEV-protease cleavage to remove the tag ( see below ) . Ll . LtrB-ΔORF intron RNAs were transcribed from DNA templates generated by PCR of plasmid pACD5C with primers that append a phage T3 promoter sequence ( underlined in sequences below ) [26] . For the lariat precursor RNA , the PCR primers were pACD-T3 ( 5′-GGAGTCTAGAAATTAACCCTCACTAAAGGGAATTGTGAGCG ) and NheIR ( 5′-CTAGCAGCACGCCATAGTGACTGGCG ) , and for linear intron RNA , the PCR primers were T3LIS-1G ( 5′-AATTAACCCTCACTAAAGTGCGCCCAGATAGGGTGTTAAGTCAAG ) and HPLC-purified LtrB940a ( 5′-GTGAAGTAGGGAGGTACCGCCTTGTTC ) . The PCR products were purified by using the Wizard SV Gel and PCR Clean-up System ( Promega ) , extracted with phenol-chloroform-isoamyl alcohol ( phenol-CIA; 25∶24∶1 by volume ) , ethanol precipitated , and dissolved in nuclease-free water . In vitro transcription and the preparation of lariat and linear intron RNAs were as described [26] . The LtrA protein used for RNP reconstitution was expressed in E . coli BL21 ( DE3 ) from the intein-based expression vector pImp-1P and purified via a chitin-affinity column and intein cleavage , as described [9] , except that the column buffer contained 50 mM Tris-HCl , pH 8 . 0 , 0 . 1 mM EDTA , and 0 . 1% NP-40 . Ll . LtrB RNPs were reconstituted with the purified LtrA protein and in vitro-synthesized lariat or linear Ll . LtrB-ΔORF intron RNA , as described [26] , except that the final RNP pellet was dissolved in 10 mM KCl , 5 mM MgCl2 , and 40 mM HEPES , pH 8 . 0 . The LtrA protein used in biochemical assays was expressed in E . coli BL21 ( DE3 ) from the plasmid pMAL-LtrA . Cells were grown in starter cultures of LB medium overnight at 37°C , inoculated into 0 . 5-l LB medium in ultra-yield flasks , and autoinduced by growing at 37°C for 3 h , followed by 18°C for 24 h [66] . Cells were harvested by centrifugation ( Beckman JLA-8 . 1000; 4 , 000× g , 15 min , 4°C ) , resuspended in 1 M NaCl , 20 mM Tris-HCl , pH 7 . 5 , 20% glycerol , and 0 . 1 mg/ml lysozyme ( Sigma-Aldrich , St . Louis , MO ) , kept on ice for 15 min , and lysed by 3 freeze-thaw cycles on dry ice followed by sonication ( Branson 450 Sonifier , Branson Ultrasonics , Danbury , CT; three or four 10 sec bursts on ice at an amplitude of 60% , with 10 sec between bursts ) . After pelleting cell debris ( Beckman JA-14 rotor , 10 , 000 rpm , 30 min , 4°C ) , nucleic acids were precipitated from the supernatant with 0 . 4% polyethylenimine ( PEI ) and constant stirring for 20 min at 4°C , followed by centrifugation ( Beckman JA-14 rotor , 14 , 000 rpm , 30 min , 4°C ) . Proteins were precipitated from the supernatant by adding ammonium sulfate to 50% saturation with constant stirring for 1 h at 4°C . The precipitated proteins were pelleted ( Beckman JA-14 rotor , 14 , 000 rpm , 30 min , 4°C ) , dissolved in 500 mM NaCl , 20 mM Tris-HCl , pH 7 . 5 , 10% glycerol , and run through a 10-ml amylose column ( FPLC; Amylose High-Flow resin; New England BioLabs , Ipswich , MA ) , which was washed with 3 column volumes of 500 mM NaCl , 20 mM Tris-HCl , pH 7 . 5 , 10% glycerol and eluted with 500 mM NaCl , 20 mM Tris-HCl , pH 7 . 5 , 10% glycerol containing 10 mM maltose . Fractions containing the MalE-LtrA fusion were incubated with TEV protease ( 80 µg/ml , 18 h , at 4°C ) , and imidazole was added to a final concentration of 40 mM . LtrA freed of the MalE tag was then purified by FPLC through a Ni-NTA equilibrated with 500 mM NaCl , 20 mM Tris-HCl , pH 7 . 5 , 10% glycerol , 40 mM imidazole . The Ni-NTA column , which takes advantage of endogenous histidine residues in LtrA's C-terminal domain , was washed with 3 column volumes of 500 mM NaCl , 20 mM Tris-HCl , pH 7 . 5 , 10% glycerol , 40 mM imidazole , and eluted in 500 mM NaCl , 20 mM Tris-HCl , pH 7 . 5 , 10% glycerol , 300 mM imidazole . Finally , the peak LtrA fractions from the Ni-NTA column were further purified through two tandem 1-ml heparin Sepharose columns ( New England BioLabs ) . The columns were equilibrated with 500 mM NaCl , 20 mM Tris-HCl , pH 7 . 5 , 10% glycerol , loaded directly with LtrA protein from the Ni-NTA column , washed with 5-column volumes of 500 mM NaCl , 20 mM Tris-HCl , pH 7 . 5 , 10% glycerol , and eluted with a 20-column volume gradient of 0 . 5 to 1 M NaCl , 20 mM Tris-HCl , pH 7 . 5 , 10% glycerol . The protein elutes approximately midway through the gradient at ∼750 mM NaCl . The purified protein was concentrated to 30 µM , exchanged into 100 mM NaCl , 20 mM Tris-HCl , pH 7 . 5 , 10% glycerol by dialysis , flash-frozen in liquid nitrogen , and stored at −80°C . Drosophila embryos were microinjected with ∼300 pl recipient plasmid pBRR3-ltrB at 1 . 4 mg/ml in solution with 500 mM MgCl2 and 17 mM dNTPs , followed within 5 min by ∼300 pl of Ll . LtrB lariat or linear RNPs at 2 . 6 mg/ml in 10 mM KCl , 5 mM MgCl2 , 40 mM HEPES , pH 8 . 0 . The RNPs consist of LtrA protein bound to Ll . LtrB lariat or linear intron RNA with a phage T7 promoter sequence inserted in intron domain IV , and the recipient plasmid contains the Ll . LtrB intron target site ( ligated exon 1 and 2 sequences of the ltrB gene; E1 and E2 ) cloned upstream of a promoterless tetR gene in a pBR322-based vector carrying an AmpR marker . Site-specific integration of the intron into the target site introduces the T7 promoter upstream of the promoterless tetR gene , thereby activating that gene . Eighty embryos were injected and incubated at 30°C for 1 h for each assay . The pooled embryos were incubated in lysis buffer ( 20 mM Tris-HCl , pH 8 . 0 , 5 mM EDTA , 400 mM NaCl , 1% SDS ( w/v ) ) with 400 µg/ml proteinase K ( Molecular Biology Grade; Sigma-Aldrich ) for 1 h at 55°C , and then extracted with phenol-CIA . Nucleic acids were ethanol precipitated and dissolved in 12 µl of distilled water . For assays of retrohoming efficiency , a 4-µl portion of the nucleic acid preparation was electroporated into electrocompetent E . coli HMS174 ( DE3 ) F− , hsdR , recA , rifr ( Novagen , EMD Chemicals , Gibbstown , NJ ) , which expresses T7 RNA polymerase . Cells were plated at different dilutions on 2% agar containing LB medium with ampicillin ( 50 µg/ml ) plus tetracycline ( 25 µg/ml ) or the same concentration of ampicillin alone . Colonies were counted after overnight incubation at 37°C , and the integration efficiency was calculated as the ratio of ( AmpR+TetR ) /AmpR colonies . For analysis of intron-integration junctions , a 1-µl portion of the nucleic acid preparation was used as template for PCR using Phusion High Fidelity PCR Master Mix with HF buffer ( New England BioLabs ) . The 5′-junction PCRs were done with primers P1 ( 5′-CTGATCGATAGCTGAAACGC ) and LtrB933a ( 5′-AGGGAGGTACCGCCTTGTTCACATTAC ) , and the 3′ junction PCRs were done with primers P3 ( 5′-CAGTGAATTTTTACGAACGAACAATAAC ) and P4 ( 5′-AATGGACGATATCCCGCA ) . The PCR was done for 25 cycles for all strains , except for parallel assays of wild-type Or-R and the polQ mutant , which required 35 PCR cycles to obtain sufficient PCR product from the mutant . The PCR products were purified using a MinElute PCR purification Kit ( Qiagen ) , cloned into a TOPO TA cloning vector ( pCRII-TOPO; Invitrogen , Carlsbad , CA ) , and transformed into chemically competent E . coli ( One Shot TOP10; Invitrogen ) . The cloned PCR products were then amplified from randomly picked colonies by colony PCR using Phusion High Fidelity PCR Master Mix with HF buffer and primers M13 F ( -20 ) ( 5′- GTAAAACGACGGCCAGT ) and M13 R ( -26 ) ( 5′-CAGGAAACAGCTATGAC ) for 25 cycles , and sequenced using primers M13 R ( -24 ) ( 5′-GGAAACAGCTATGACCATG ) or M13 F ( -20 ) [67] . Biochemical assays were done by incubating purified LtrA protein ( 40 nM ) with synthetic oligonucleotide substrates that correspond to the 5′ end of the Ll . LtrB intron ( 60-nt Ll . LtrB RNA; 40 nM ) with an annealed 5′-32P-labeled DNA primer corresponding to nascent cDNA ( 45-nt Pri c; 44 nM ) in the presence of exon 1 RNA or DNA ( 40-nt E1; 40 nM ) in 20 µl of reaction medium containing 450 mM NaCl , 5 mM MgCl2 , 20 mM Tris-HCl , pH 7 . 5 , 1 mM dithiothreitol ( DTT ) and 200 µM dNTPs . The reaction components were assembled on ice with substrate added last and then incubated at 30°C for 30 min . Reactions were terminated by phenol-CIA extraction . Portions of the reaction product ( 3 µl ) were added to an equal volume of gel loading buffer II ( 95% formamide , 18 mM EDTA and 0 . 025% each of SDS , xylene cyanol , and bromophenol blue; Ambion , Austin , TX ) , denatured at 98°C for 7 min , and analyzed by electrophoresis in a denaturing 10 or 15% polyacrylamide gel , which was visualized by scanning with a PhosphorImager ( Typhoon Trio , GE Healthcare , Piscataway , NJ ) . 32P-labeled DNA products were excised from the gel , amplified by PCR , as described ( Sabine Mohr , Scott Kuersten , and A . M . L . , manuscript in preparation ) , and cloned into the TOPO-TA pCR2 . 1 vector ( Invitrogen ) , according to the manufacturer's protocol . Random colonies were picked and the cloned PCR products were amplified by colony PCR using Phusion High Fidelity PCR Master Mix/HF buffer with primers M13 F ( -20 ) and M13 R ( -26 ) , and sequenced using the M13 R ( -24 ) primer ( see above ) . The oligonucleotides used in the biochemical assays were Ll . LtrB RNA [LtrB5'S20Anchor6 , 5 RNA] ( ( 5′- GUGCGCCCAGAUAGGGUGUUCUCGUUGGCAAUGGUGUCCAACUUGUGCUGCCAGUGCUCG ) , with an aminoblock on its 3′ end ) ; annealed primer c ( 5′- CGAGCACTGGCAGCACAAG-deoxyuridine-TGGACACCATTGCCAACGAGAACAC ) ; and exon 1 DNA ( 5′-TGTGATTGCAACCCACGTCGATCGTGAACACATCCATAAC ) or RNA ( 5′-UGUGAUUGCAACCCACGUCGAUCGUGAACACAUCCAUAAC ) . Oligonucleotides complementary to exon 1 DNA or RNA were: exon 1 AS ( 5′-GTTATGGATGTGTTCACGATCGACGTGGGTTGCAATCACA ) and exon 1 AS+9 ( 5′-AATGATATGGTTATGGATGTGTTCACGATCGACGTGGGTTGCAATCACA ) . DNA and RNA oligonucleotides used in the assays were obtained from Integrated DNA Technologies ( IDT; Coralville , IA ) and purified in a denaturing 10% ( w/v ) polyacrylamide gel . DNA primers were 5′-end labeled with [γ-32P]-ATP ( 10 Ci/mmol; Perkin-Elmer , Waltham , MA ) by using phage T4 polynucleotide kinase ( New England BioLabs ) according to the manufacturer's protocol . Complementary oligonucleotides were annealed at ratios of 1∶1 ( E1 oligonucleotides ) or 1∶1 . 1 Ll . LtrB/primer c by mixing at 20 times the final concentration in annealing buffer ( 100 mM Tris-HCl , pH 7 . 5 , and 5 mM EDTA ) , heating to 82°C , and slowly cooling to 25°C for 45 min . The efficiency of annealing was assessed by electrophoresis in a non-denaturing 6% polyacrylamide gel containing Tris-borate-EDTA ( 90 mM Tris , 90 mM boric acid , 2 mM EDTA ) at 30°C [67] . | Group II introns are bacterial mobile elements thought to be ancestors of introns and retrotransposons in higher organisms . They consist of a catalytically active intron RNA and an intron-encoded reverse transcriptase , which function together to promote intron integration into new DNA sites in a process called “retrohoming . ” In bacteria , retrohoming occurs by the excised intron lariat RNA fully reverse splicing into a DNA site , where it is reverse transcribed , yielding an intron cDNA that is copied directly into the host genome . However , little is known about how group II introns behave in higher organisms . Here , we find that linear group II intron RNAs , which cannot fully reverse splice , retrohome in Drosophila melanogaster by attaching themselves to only one end of a DNA site . Reverse transcription then yields an intron cDNA , which is integrated into the recipient DNA by host enzymes that function in non-homologous end joining , a critical cellular DNA–repair pathway . Biochemical experiments exploring alternate mechanisms show that group II intron reverse transcriptases can also template switch efficiently from one RNA template to a second RNA or DNA template , thereby directly linking the two template sequences . Our findings have implications for retotransposition and DNA repair mechanisms and potential biotechnological applications . | [
"Abstract",
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] | 2012 | The Retrohoming of Linear Group II Intron RNAs in Drosophila melanogaster Occurs by Both DNA Ligase 4–Dependent and –Independent Mechanisms |
Models describing the process of stem-cell differentiation are plentiful , and may offer insights into the underlying mechanisms and experimentally observed behaviour . Waddington’s epigenetic landscape has been providing a conceptual framework for differentiation processes since its inception . It also allows , however , for detailed mathematical and quantitative analyses , as the landscape can , at least in principle , be related to mathematical models of dynamical systems . Here we focus on a set of dynamical systems features that are intimately linked to cell differentiation , by considering exemplar dynamical models that capture important aspects of stem cell differentiation dynamics . These models allow us to map the paths that cells take through gene expression space as they move from one fate to another , e . g . from a stem-cell to a more specialized cell type . Our analysis highlights the role of the transition state ( TS ) that separates distinct cell fates , and how the nature of the TS changes as the underlying landscape changes—change that can be induced by e . g . cellular signaling . We demonstrate that models for stem cell differentiation may be interpreted in terms of either a static or transitory landscape . For the static case the TS represents a particular transcriptional profile that all cells approach during differentiation . Alternatively , the TS may refer to the commonly observed period of heterogeneity as cells undergo stochastic transitions .
Cells are not inert objects . They have finite lifetimes with typically well defined origins and ends . And over the course of their lifetime—which lasts anything from minutes to many years—change in response to environmental , physiological and , potentially , developmental signals [1] . Some of these changes are minor , e . g . changing the expression of certain proteins in response to an environmental signal , or the activity of an enzyme as part of metabolism . Others relate to longer-term , less reversible , or more profound changes in cell state; including commitment to replication , apoptosis , or differentiation . The former set of changes can be viewed as tactical decisions which are made in response to ( typically transient ) changes in a cell’s environment [2] , whereas the latter are of more strategic importance for the cell and , where relevant , potentially the organisms as a whole [3 , 4] . In humans , a single fertilized egg cell eventually gives rise to some 35 trillion cells in the adult . How many cell types there are remains an unanswered question , but some aspects of the process by which an omni-potent stem cell differentiates into a more specialized cell are now becoming clearer . Remodeling of the gene regulatory networks—typically in response to signaling events—change the transcriptional program of the cell , thereby leading to a concomitant change in cell phenotype/state . We will here assume for ease of argumentation , that the molecular state of a cell can reflect the true state of the cell , its phenotype , but stress that this may only be a poor substitute for a more direct biological or phenotypic characterization . The popular metaphor of the Waddington’s epigenetic landscape has come to predominate much of the discussion about cell differentiation processes: cells are described as marbles rolling through a landscape of hills and valleys drawn towards local points of minimum elevation [1] . An individual ball starts its journey in a valley at the back of the landscape and as it progresses forward ( the passage of time in the original formalism ) and downward; it might face branching points along the path , representing the series of ( typically binary ) fate choices made by a developing cell . Every point a ball travels through represents a cellular state , for example a specific level of expressed RNA or protein . Although the number of possible states is theoretically infinite , the number of phenotypes observed in actual cells are often very limited . In this view , the final basins of low elevation where a high proportion of cells end up correspond to these experimentally observable , terminal cell types . The key insight offered by the landscape is to illuminate how genetically identical cells can attain distinct phenotypes following differentiation , and furthermore how these phenotypes persist in daughter cells . While such persistence and memory effects are understood to result from the epigenetic and proteomic state of the cells , the landscape itself is widely regarded to be shaped by the underlying gene regulatory network [5 , 6] . In this way , models describing the co-regulation and interactions between different genes may also be understood to describe the epigenetic mechanisms underlying persistent stem cell differentiation . The landscape notation has been widely used as a qualitative way of understanding and illustrating the dynamics driving development [7–9] . Moreover , even though Waddington may not have intended his landscapes to be any more than a conceptual tool , landscapes can be given quantitative meaning: exploring the behaviour of the underlying network in terms of a probabilistic landscape framework . A succession of studies have recently proposed different approximations to potential functions that can serve as mathematical descriptions of the epigenetic landscape for a cell fate regulatory network . Here the elevation of the surface reflects the probability of observing a particular state in phase space [6 , 10–14]: states that have the highest probability locally will have lower potential and hence will act as the valley-bottoms on the landscape , surrounded by a basin of attraction , which in this picture would correspond to cells with slightly different states but exhibiting the same phenotype . Even though the landscape depiction might be adopted to many decision making processes over the lifetime of a cell , its major uses are still in describing development and stem cell differentiation processes [15 , 16] . In this context the final attractors of the landscape are differentiated cell types with well-defined patterns of robust gene expression , and differentiation occurs through transitions along the surface , while some cells might reside or return to the original basin of the pluripotent phenotype . Mathematically , we can draw on a vast body of work to characterize the cell states defined in terms of steady-states of molecular concentrations . For the sake of clarity , we consider X to denote the state of the system ( e . g . the whole set of mRNA and/or protein abundances ) , which evolves according to the stochastic differential equation ( SDE ) d X = f ( X ; θ ) d t + g ( X ; η ) d W t , ( 1 ) where f ( X; θ ) describes the deterministic dynamics of the system , and g ( X; η ) dWt captures the stochastic components of the dynamics; these functions being parametrized by ( typically vector-valued ) θ and η , respectively . If the latter can be ignored we recover a more conventional ordinary differential equation ( ODE ) , commonly written as d X d t = f ( X ; θ ) . ( 2 ) This , setting the left-hand side equal to zero , allows us to solve for the ( deterministically ) stationary states of the system , which if they are stable , i . e . if they are attractors , we assume correspond to distinct cell states . While many stationary states of the system may describe the recognizable , robust cell phenotypes , others may correspond to the so-called intermediate cell states ( ICS ) [17] . These are states with a particular molecular phenotype , but without the particular function that usually accompanies traditionally defined cell types . ICS have been observed experimentally in epithelial-mesenchymal transition [18] and hematopoietic differentiation [19 , 20] . Here , by examining models for stem cell differentiation from a dynamical systems perspective , we explore how the related concept of transition states ( TS ) may arise , and how their properties are affected by external signals . If the set {X1 , … , Xq} denotes the set of stable stationary/attractor states of ( 2 ) , we identify them with the valleys/local minima in the corresponding epigenetic landscape for the system , which in turn correspond to robustly defined cellular phenotypes . Classifying the stability of these solutions ( e . g . in the presence of stochastic effects ) , and the basins of attraction has been one of the long-standing problems in dynamical systems theory . Closely linked to it is the question of how different stationary points can be reached from one another: is there a particularly favored path that the system traverses as it moves from , say Xi to Xj ( 1 ≤ i , j ≤ q ) ? In general , any two stable stationary states of a dynamical system must be separated by an unstable stationary state . As will be discussed , such transition states may sometimes correspond to the experimentally observed ICS . In the context of stochastic dynamical systems , the most probable paths between stationary states correspond to those that minimize the action associated with them [21]: the so-called minimum action paths ( MAP ) . This concept has allowed several previous investigations to shed light on the typical routes taken through gene expression space between phenotypes in stochastic models of cellular differentiation . For example [22] obtained the most probable paths between phenotypes in a 52 gene network describing stem cell behaviour . These paths were obtained via the method detailed in [23] , wherein a kinetic path framework is developed both to obtain most likely paths and to calculate a landscape . Similarly , in [14] , the geometric minimum action method [24] is used to obtain the most probable transition paths in a stem cell differentiation model , while [25] obtained paths for a model of epithelial-mesenchymal transition . In this work we use a similar approach , obtaining the most probable paths in the forward ( differentiation ) and reverse ( reprogramming ) directions , but with a particular emphasis on the transition states through which these paths pass . Below we will use a set of illustrative examples that allow us to study the transitions between stationary solutions of ( stochastic ) differential equations , with an emphasis on dynamical systems in the context of stem cell differentiation . In particular we shall be investigating the concept of transition states ( TS ) , as recently discussed and reviewed by [5] . For representative model systems we discuss two competing definitions for the transition state in the context of static and transitory landscapes . For static landscapes we shall employ the concept of the MAP to examine typical transition paths . We first discuss the properties of transition states for an exemplar “toy” system before applying the same analysis to a developmental model . In particular we will aim to answer four linked questions:
In order to exemplify some key properties of stochastic dynamical systems in the context of potential landscapes we provide a simple model that exhibits some of the hallmarks found in real developmental systems . The model we provide is , however , not intended to represent any particular biological process , but serves as a simple example in which potential landscapes and transition states may be described . We will use this model to firstly demonstrate the distinction between gradient and non-gradient dynamics , before examining two definitions for the concept of the transition state . With the insights derived from this illustrative model , we continue our analysis with a model that incorporates some of the dynamic relationships involved in stem cell differentiation [43] . The model focuses on four key players: Nanog ( N ) , the complex Oct4-Sox2 ( O ) , Fgf4 ( F ) , and Gata6 , ( G ) a typical differentiation marker . A schematic representation of their network is shown in Fig 4A; all arrows stand for non-linear interactions that account for implicitly modelled species and processes , like the formation of the Oct4-Sox2 complex , or Nanog dimerization . In addition , Leukaemia inhibitory factor ( LIF ) , is also included in the model: the concentration of LIF ( L ) is treated as an external control , through which the environment of the cells is modified . Full details of the model are given in the methods section , while further analysis may be found in [44] . The behaviour illustrated above suggests the existence of a transition state in the static landscape , similar to the saddle point in the toy model discussed above . However , the transition state may also be defined for a transitory landscape , which in this case may be achieved by varying the level of LIF , quantified by the parameter L . Before proceeding to simulations of a transitory system , it is first insightful to examine analytically how the fixed points ( Xi ) of the system vary with L . This is achieved by finding the expression levels at which the rate equations are equal to zero . Three such fixed points are displayed in Fig 7A for varying L . The three identified fixed points may be associated with each of the stem-cell , transition and differentiated states of the system . Of these three states it is only the stem-cell state that is strongly influenced , moving to higher values of Nanog as L is increased . In addition to the location of the fixed points , we may also examine the eigenvalues of the Jacobian at each of them . These eigenvalues , over a range of LIF concentrations , are displayed in Fig 7B as a scatter plot in the complex plane . Because the system is four-dimensional , each fixed point has four eigenvalues associated with it , although these may sometimes be very close together . The eigenvalues for the stem cell and differentiated state can be seen to reside entirely in the left half plane , a characteristic indicative of the linear stability of these states . In contrast , the transition state is seen to always have at least one eigenvalue with positive real part , and is therefore unstable . The stem-cell and differentiated states are therefore attractors of the system while the transition state is a saddle-point in the landscape . With increasing L , the magnitude of the positive real eigenvalue of the transition state increases . This implies both that the system will leave this state more rapidly if it passes through it , but also that arriving at this state is less probable , consistent with the observation that high L inhibits ( initiation of ) differentiation . In terms of the underlying landscape , the increasing positive eigenvalue corresponds to an increasingly steep roll-off away from the saddle point , just as for the toy model displayed in Fig 2 . The eigenvalues of the stem-cell state also vary with LIF concentration , in this case transitioning to complex values with increasingly negative real part . This implies increased stability of this system and , together with the variation of the transition state eigenvalues , is consistent with the behaviour of the system under increasing L: increased stability of the stem cell state . The eigenvalues of the differentiated state do not vary with L , but all the values are seen to be more negative than some of those of the transition and stem cell states . This is consistent with the greater stability of the differentiated state and the tendency for the system to remain there once it has arrived . Furthermore , these properties of the different states may be related to the shape of the probabilistic landscapes shown in Fig 7C . As L varies the landscape is seen to transition from one with a single valley around the stem-cell state to one with two valleys of varying depth . Further to this , for the case of L = 50 the differentiated state is surrounded by more tightly spaced contours than is the stem-cell state , and hence higher local curvature; this is consistent with the relative size of the eigenvalues . Given the observed variation of the system behaviour , we run simulations in which the parameter L is varied linearly over time . As for the toy model in section 1 , we examine the variance across a large ensemble of simulations ( 10 , 000 ) . This is displayed in Fig 8 . The ensemble starts out with relatively low variance before displaying a period of high variance at intermediate times . In the context of real experimental data , such high variance would be observed as large heterogeneity across the sampled cells . This heterogeneity occurs because for this particular model , there is a distribution of times at which the stochastic switch is made from the stem-cell to differentiated state . Therefore even though all cells follow a similar path through gene expression space with very similar start and end points , there is high variability between cells over a particular period of time .
Models describing stem-cell differentiation are plentiful [48] . In this work we have examined one such model , that of [43] , from a landscape perspective . Starting our analysis from an illustrative model we see that the landscape is strongly linked to the probability distribution over the state space , and gives an intuitive description of the dynamics . In the stem-cell differentiation model we are able to obtain a landscape that describes the typical system behavior . Moreover , the landscape may be linked to the dynamics in a quantitative manner via the fixed points Xi and the eigenvalues of the linearized dynamics at these locations: the fixed points occur at minima , maxima or saddle points in the landscape while the eigenvalues describe the local curvature . While the potential landscape describes the tendency of the system to move towards particular stable states , it cannot fully capture all of the system’s dynamics . A non-gradient contribution to the dynamics is also generally present , known as the curl . A curl component is thought to be indicative of non-equilibrium systems , or those which are not closed to external influences [26] . This is because closed systems always have an available energy such as the Gibbs free energy which may be quantified and is directly related to the dynamics . For a developing stem cell , there is a constant exchange of both energy and nutrients via the diffusion of heat and mass through the membranes of the cell . Such a system is therefore clearly not closed in a thermodynamic sense , and non-equilibrium effects are bound to be present . An alternative viewpoint on the presence of curl , is that a curl-free or purely gradient-based system requires complete symmetry in the interactions between all state variables [37] . In the context of gene regulatory networks , a necessary condition is that the influence of gene i on the expression of gene j is exactly the same as in the opposite direction . However even when this is the case , only in cases where these influences are of a particular functional form will the necessary symmetry be present . The precise requirement is that ∂ f i ∂ x j = ∂ f j ∂ x i , since both are equal to ∂ 2 U ∂ x i ∂ x j . For systems such as a symmetric toggle switch in which there is mutual repression between two gene products , the standard Hill function form of the interaction does not lead to this symmetry . Therefore in general , with the exception cases in which the regulatory equations are specially chosen [49] , we may expect a curl component to the dynamics of the system . Although the potential landscape cannot describe all of the dynamics of cell differentiation , it can elucidate some pertinent features of the process . One such feature is the static transition state . Given a static landscape description for a developing cell , we may define transition states as the saddles in the landscape , corresponding to unstable fixed points of the associated dynamical system . The behavior of the system at these transition states is linked to the eigenvalues of the linearized dynamics , evaluated at these states . Such saddle points will always have eigenvalues with a positive real part , the magnitude of which is linked to the speed at which the transition states may be traversed . In the case that the transition speed is quick , the transition states may rarely be observed and measured trajectories will appear discontinuous , while if transitions are slow these states may correspond to the experimentally observed intermediate cell states . The static transition states may also be inferred without recourse to the landscape , utilizing the concept of the minimum action path . Such paths have been evaluated for similar developmental models before [14] , and are in general agreement with those found here . The MAPs describe the most probable routes through state space between any two fixed points . For purely gradient-based systems these routes simply follow that of steepest climb/descent and are therefore identical in both directions between any two points . For two-dimensional systems with an additional curl component such as the toy model examined above , the paths differ between the two directions but will meet at the saddle point between adjacent attractors . Such a saddle point may therefore be termed a static transition state . For higher dimensional systems such as the developmental model , the paths are again different but still come close together near to one of the unstable fixed points . The transcriptional region around this static transition state is therefore of significance for both differentiation and reprogramming . An alternative to the static landscape description of the system is one of a transitory landscape , in which the potential changes under the influence of an external input or time-varying parameter . In this framework we may therefore consider an alternative definition for the transition state as those periods during development in which large heterogeneity is observed . Such heterogeneity may arise from two effects , firstly from the broad distribution of times for the transition from one fixed point to another , and secondly from a temporary flattening of the landscape under the influence of the changing conditions . In the models discussed here , both effects are present . The variation in switching times is typical of any stochastic system in which the escape from an attractor occurs due to random perturbations [41] and is therefore an inherent feature of many processes governed by a gene regulatory network . The temporary flattening of the landscape is dependent on the particular nature of the interactions between genes , but there is increasing experimental evidence for such a phenomenon [50] . The observation of cell-cell variability has become particularly apparent with the recent use of single cell experimental techniques . For example [51] observed high variability between cells undergoing transitions from one state to another , while [52] observed an increase in the variability between cells exiting pluripotency . Similar conclusions have also been drawn from the experimental work of [53] who suggested that cell fate transitions are linked to a critical change in the underlying landscape . When forming any gene regulatory model , the choice between a static and transitory landscape may often be an issue of model complexity . For a gene regulatory network this choice is akin to that between modelling all of the relevant network interactions , or simply taking a subset of the network and treating the influence of other genes as ( time-varying ) parameters in the model . In the particular application of stem-cell development there is evidence that a relatively limited number of genes may be sufficient to describe typical behaviour , although external inputs are still required [54] . Such a system may therefore be described by a transitory landscape . Elucidating the subtle relationship between transition states and intermediate cell states holds the key to linking mathematical models with data . TS are mathematically well defined unstable fixed points of a dynamical system; ICS are empirically determined accumulation points in gene expression space . Most likely they correspond to transition states with ‘flat’ local neighborhoods . Such landscape features will result in slower progress through the transition state/transitory region which may allow more substantial remodeling of the underlying transcriptional network [17 , 39] . We can thus in principle use characteristics of the transition state or transitory region ( see Fig 9 ) to demarcate two scenarios: ( i ) for a peaked TS we will see cells rapidly moving through the TS and being easily missed in single cell transcriptomic assays; ( ii ) for a flat TS we will observe ICS behavior [17] and are likely to capture some of the cells in the vicinity of the TS . This would clearly have implications for further analysis and network inference [55 , 56] , and will reflect the extent of network remodeling during differentiation , or the means by which this is achieved , e . g . 3D genome structure or epigenetic silencing/activation versus signaling events rapidly affecting transcription factor activity . An important final consideration , irrespective of the transition state scenarios highlighted above , is that the presence of curl places limits on the identifiability of the dynamics . Most experimental data consists of purely static snapshots , from which instantaneous distributions can be obtained . Methods such as scRNA-seq only allow measurements of transcriptional profiles at single instances , and cannot provide the temporal variation of any individual cell . While this will enable measurement of the typical distribution of cell states , sometimes including the intermediate cell states , it will not readily allow measurement of certain characteristics of the cell paths , as discussed in [37] . The full identification of regulatory dynamics may therefore additionally require some measure of derivatives , as may be afforded by future experimental and analytical techniques [57]; this will still leave considerable challenges to identifying model structures [58] .
The two-dimensional toy model is chosen to exemplify some key properties of nonlinear stochastic dynamical systems . It is a two-dimensional extension of a well studied one-dimensional bistable system [41] , known to exhibit a wide range of phenomena . The governing equations are , d X t = [ 2 α x 1 - 4 λ x 1 3 - β + 4 c λ x 2 3 2 c α x 1 - 4 c λ x 1 3 - c β - 4 λ x 2 3 ] d t + [ σ 0 0 σ ] d W t . ( 7 ) Here dWt refers to increments of a Wiener process , which can be interpreted as the time integral of Gaussian distributed white noise [59] . Unless otherwise stated , the parameter values used in the model were [ α λ β c σ ] = [ 0 . 5 0 . 25 - 0 . 05 0 . 5 0 . 4 ] The developmental model consists of four key players: Nanog ( N ) the complex Oct4-Sox2 ( O ) , Fgf4 ( F ) and Gata6 ( G ) . We additionally include the typical cell media LIF ( L ) , which is a controllable parameter . Applying the quasi-equilibrium assumption , we treat all modifying interactions as factors in the same birth process , with their associated rates acting as a weighting on the net production rate . A first order degradation reaction parametrized by kd is also included for each molecular species , leading to eight reactions with the following propensities: a 1= k 0 O ( k 1 + k 2 N 2 + k 0 O + k 3 L ) 1 + k 0 O ( k 2 N 2 + k 0 O + k 3 L + k 4 F 2 ) + k 5 O G 2 ( 8 ) a 2= k 6 + k 7 O 1 + k 7 O + k 8 G 2 ( 9 ) a 1= k 9 + k 10 O 1 + k 10 O ( 10 ) a 1= k 11 + k 12 G 2 + k 14 O 1 + k 12 G 2 + k 13 N 2 + k 14 O ( 11 ) a 5= k d N ( 12 ) a 6= k d O ( 13 ) a 7= k d F ( 14 ) a 8= k d G . ( 15 ) The evolution of the system is then described by the combination of these eight reactions and a stoichiometry matrix S , that defines the integer changes to the copy number of each species . This is given as , S = [ 1 0 0 0 - 1 0 0 0 0 1 0 0 0 - 1 0 0 0 0 1 0 0 0 - 1 0 0 0 0 1 0 0 0 - 1 . ] ( 16 ) The parameter values used in the model were [ k 0 k 1 k 2 k 3 k 4 k 5 k 6 k 7 k 8 k 9 k 10 k 11 k 12 k 13 k 14 k d ] = [ 0 . 005 0 . 01 0 . 4 1 0 . 1 0 . 00135 0 . 01 0 . 01 1 1 0 . 01 5 1 0 . 005 1 1 ] . Simulations of the developmental model were performed using the Gillespie algorithm [60] , as is standard for chemical reaction systems . For the analysis of the fixed points , linearized dynamics and minimum action paths , the system was transformed into the approximate SDE format . Defining the vector X = [N , O , F , G]⊤ , and the reaction vector a = [ai]⊤ , the SDE approximation is given as [61] d X t = S a d t + S diag ( a ) d W t . ( 17 ) Here diag ( v ) stands for the R N × N matrix with the elements of vector v in its diagonal and zeros elsewhere . The deterministic fixed points of the system may be obtained by solving the equation , f ( X ) = S a ( X ) = 0 . ( 18 ) Due to the nonlinear nature of the equations , analytical solutions are difficult to obtain . Solutions were therefore found numerically using the DifferentialEquations . jl package in Julia [62] . Analysis of the Jacobians was performed symbolically using the SymPy . jl package . The minimum action paths were computed via the optimization approach detailed in the supplementary information of [47] . The MAP are those paths through the state space that minimize the Freidlin-Wentzell action functional [21] . | Current emphasis on single cell analysis , especially in the context of the human and mouse cell atlas projects , is on characterizing the transcriptomic signatures of different cell states . This is clearly of great importance , as even the number of different cell types , e . g . in humans , is not known with any satisfying degree of certainty . There are enormous challenges in mapping these states , but this will still only provide a partial answer . Importantly , the way in which cells differentiate , and the way in which gene expression changes over the course of differentiation will still be unknown . Here we use a dynamical systems perspective to consider the nature of , and dynamics during , the transition between different cell types ( or cell states ) . We show how the developmental landscape ( in Waddington’s sense ) and the nature of the transition states change in response to external stimuli and discuss this in the context of stem cell differentiation ( as well as its potential reversal ) . In particular , we discuss how the nature of the landscape at the transition state , as well as the presence of non-gradient dynamics , has strong implications for the identifiability of differentiation dynamics from experimental data . | [
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"... | 2018 | Transition state characteristics during cell differentiation |
Cellular restriction factors responding to herpesvirus infection include the ND10 components PML , Sp100 and hDaxx . During the initial stages of HSV-1 infection , novel sub-nuclear structures containing these ND10 proteins form in association with incoming viral genomes . We report that several cellular DNA damage response proteins also relocate to sites associated with incoming viral genomes where they contribute to the cellular front line defense . We show that recruitment of DNA repair proteins to these sites is independent of ND10 components , and instead is coordinated by the cellular ubiquitin ligases RNF8 and RNF168 . The viral protein ICP0 targets RNF8 and RNF168 for degradation , thereby preventing the deposition of repressive ubiquitin marks and counteracting this repair protein recruitment . This study highlights important parallels between recognition of cellular DNA damage and recognition of viral genomes , and adds RNF8 and RNF168 to the list of factors contributing to the intrinsic antiviral defense against herpesvirus infection .
Mammalian cells have evolved complex defenses to protect themselves from viral infections . Innate and adaptive immune responses are well-characterized , but resistance mediated by pre-existing cellular factors has recently emerged as another important arm of antiviral defense . In contrast to the canonical immune responses , which are slower acting and initiated by virus-induced signaling cascades , the pre-existing cellular factors are poised to protect the cell before the virus has even entered [1] . This mechanism of resistance is called intrinsic antiviral defense , and is characterized by the fact that the antiviral proteins are intracellular and constitutively expressed , and that the restrictive factors can be overcome by viral countermeasures . These intrinsic defense pathways provide a primary protective mechanism in the first cell infected in an immunologically naive host , making them an important front line of defense against viruses . Herpes simplex virus type 1 ( HSV-1 ) is a common human pathogen that causes life-long recurrent disease . Lytic HSV-1 infection is characterized by transcription in a temporal cascade of immediate-early ( IE ) , early ( E ) , and late ( L ) gene products . The immediate early ( IE ) genes create a favorable intracellular environment for the virus , and regulate the expression of the E and L genes . The IE protein ICP0 is one of the first viral proteins expressed during HSV-1 infection ( reviewed in [2] ) . Although ICP0 is a not an essential viral protein , its deletion significantly impairs productive replication , especially at low multiplicity of infection ( MOI ) [3]–[6] . ICP0 is a RING finger E3 ubiquitin ligase that induces degradation of several cellular proteins including the catalytic subunit of DNA-dependent protein kinase ( DNA-PKcs ) [7] , the cellular DNA damage ubiquitin ligases RNF8 and RNF168 [8] , components of the nuclear domain structures known as ND10 ( or PML nuclear bodies ) [9] , [10] , and centromeric proteins [11]–[13] . Prototypic intrinsic antiviral defense proteins , such as APOBEC3 proteins , are known to be active against a variety of viruses [1] . However , to date , the only proteins demonstrated to mediate intrinsic defense against herpesviruses are all components of ND10 . The first evidence that these proteins may mediate intrinsic immune defense against herpesviruses came from the observation that depletion of PML increased the plaque-forming efficiency of both human cytomegalovirus ( HCMV ) [14] and ICP0-null HSV-1 [15] . Similarly , it was found that the ND10 proteins hDaxx and ATRX induce a repressive viral chromatin structure on incoming HCMV genomes that is prevented by the viral tegument protein pp71 targeting hDaxx for degradation [16]–[21] . Depletion of either hDaxx or ATRX also improves the plaque-forming efficiency of ICP0-null HSV-1 , providing further evidence that ND10 proteins have a general role in mediating intrinsic antiviral defense against herpesviruses [22] . In the case of HSV-1 , the repressive ND10 proteins have been detected at sites juxtaposed to incoming viral genomes [22]–[24] . During wild-type HSV-1 infection ICP0 rapidly disperses these inhibitory proteins , ensuring that replication can proceed . In the absence of ICP0 , the recruitment of the ND10 proteins into novel structures associated with the viral genomes is readily observable as a very early cellular response , detectable within the first 30 minutes of infection [25] . ICP0 has therefore emerged as one of the key viral counterattacks to the cellular attempt to limit the early stages of infection . Cells have elaborate machinery in place to monitor damage to genomic DNA and ensure the fidelity of replication [26] . Recent work has demonstrated that the cellular DNA repair machinery can also recognize viral genetic material [27] . HSV-1 has a complex relationship with the DNA damage response , in that it appears to activate many components of the ATM-dependent arm of the signaling pathway , while inhibiting the DNA-PKcs- and ATR-dependent arms [7] , [28]–[30] . During lytic infection , HSV-1 recruits several cellular DNA repair proteins into viral replication compartments where they enhance viral replication [28]–[31] . Despite global activation of the ATM-dependent signaling pathway , we recently reported that RNF8 and RNF168 , which are key mediators in this pathway , are targeted for proteasome-mediated degradation by ICP0 [8] . During HSV-1 infection , the viral capsid docks at the nuclear pore and the linear viral genome is released into the nucleus [32] . In this study , we asked whether the cellular DNA repair machinery recognizes this incoming viral DNA , and we explored the significance of ICP0-mediated degradation of RNF8 and RNF168 for the virus . We report that cellular DNA repair proteins respond to incoming HSV-1 genomes and we identify RNF8 and RNF168 as novel components of the intrinsic antiviral defense against HSV-1 .
In order to investigate effects of incoming HSV-1 genomes on localization of DNA damage proteins , we utilized a previously described assay to visualize nuclei at the earliest stages of infection [23] , [24] . In this assay , cells are infected at low multiplicity so that directional viral spread through developing plaques can be analyzed . This directionality , combined with the fact that incoming viruses often congregate near the microtubule organizing center , means that nuclei of cells at the edge of plaques frequently display an asymmetric arc of incoming viral genomes [23] , [24] . Human foreskin fibroblast ( HFF ) cells were infected at low MOI with wild-type or ICP0-null HSV-1 , fixed 24 hours post-infection ( hpi ) and processed for immunofluorescence . Sites of incoming viral genomes were detected by staining with antiserum to the viral DNA binding protein , ICP4 , which has been previously shown to co-localize with viral genomes in this assay [23] , and the localization of certain cellular DNA repair proteins ( Figure S1A ) was assessed . In mock infected cells there was minimal γH2AX staining , and the damage checkpoint mediators Mdc1 , 53BP1 , and BRCA1 were localized in a diffuse nuclear pattern . In cells infected with ICP0-null virus , we detected that γH2AX , Mdc1 , 53BP1 , and BRCA1 accumulated in distinct asymmetric arcs in close proximity to incoming viral genomes ( Figure 1A ) . In cells infected with wild-type virus , γH2AX and Mdc1 still re-localized to sites associated with viral DNA , but 53BP1 and BRCA1 remained diffusely nuclear ( Figure 1B , Figure S1C ) . 53BP1 accumulated at sites associated with incoming ICP0-null viral genomes when high MOI infection was performed in the presence of α-amanitin , suggesting that viral transcription may not be essential ( Figure S1B ) . These data indicate that redistribution of 53BP1 and BRCA1 in response to incoming viral genomes is an early response to HSV-1 infection that is inhibited by ICP0 . We quantified the effect using 53BP1 and γH2AX as examples of DNA repair proteins that accumulated near incoming HSV-1 genomes . We observed that γH2AX accumulated near incoming HSV-1 genomes in over 80% of cells in both the presence and absence of ICP0 ( Figure S1C ) . In contrast , while 53BP1 accumulated near incoming HSV-1 genomes in approximately 90% of cells infected with ICP0-null virus , this was reduced to approximately 25% of cells in the presence of ICP0 ( Figure S1C ) . It has previously been reported that components of ND10 , including hDaxx , PML , ATRX , and Sp100 accumulate at sites overlapping , but not precisely co-localizing with , incoming HSV-1 genomes [22]–[24] . We wished to determine if the virus-induced accumulation of DNA repair proteins we observed co-localized with either viral genomes or ND10 proteins . We found that while the γH2AX and 53BP1 staining co-localized , these DNA repair proteins did not co-localize with either ICP4 ( representing viral genomes ) or PML ( representing ND10 proteins ) ( Figure 2A; see Figure S2A for the corresponding cytofluorograms ) . Despite this lack of co-localization , we observed a degree of overlap between the different structures . To analyze this , a Manders' overlap co-efficient [33] was determined for each image ( Figure S2B ) . We observed that on average , approximately 50% of the PML signal overlapped with the ICP4 signal , whereas only 20% of the 53BP1 or γH2AX signal overlapped with the ICP4 signal . These data suggest that incoming viral genomes are more closely associated with ND10 proteins than DNA repair proteins , and that all three structures have subtly distinct sub-nuclear localizations . Next , we investigated whether the accumulation of DNA repair proteins at sites of incoming viral genomes was dependent on major ND10 proteins . HepaRG cells depleted of PML or Sp100 [34] were infected with wild-type or ICP0-null HSV-1 and processed for immunofluoresence at 24 hpi . Infections in cells depleted for PML or Sp100 were indistinguishable from control cells with respect to γH2AX accumulation near incoming viral genomes in both the presence and absence of ICP0 ( data not shown ) , while 53BP1 accumulated only in the absence of ICP0 ( Figure 2B ) . Therefore , the recruitment of 53BP1 to incoming HSV-1 genomes and the ability of ICP0 to block this process are not dependent on either PML or Sp100 . Taken together , these observations suggest that accumulation of ND10 proteins and DNA repair proteins are independent events occurring at distinct physical locations . We recently reported that ICP0 expression leads to proteasome-mediated degradation of the cellular DNA repair proteins and histone ubiquitin ligases RNF8 and RNF168 [8] . We therefore investigated whether these proteins were responsible for coordinating the recruitment of 53BP1 to sites associated with ICP0-null viral genomes . We infected RNF8 depleted cells ( Figure S3 ) , or cells derived from a patient who has a biallelic mutation in RNF168 ( RIDDLE cells , [35] ) with wild-type or ICP0-null HSV-1 and assessed the recruitment of DNA repair proteins to incoming viral genomes ( Figure 3A and B and Figure S4 ) . During infection with wild-type virus , ICP0 expression prevented 53BP1 recruitment in the presence or absence of RNF8 and RNF168 . However , in cells infected with ICP0-null virus , 53BP1 was not recruited to sites associated with incoming viral genomes in the absence of RNF8 or RNF168 ( Figure 3A and B ) , despite the fact that γH2AX still accumulated ( Figure S4 ) . To determine if RNF8 and RNF168 themselves were recruited to sites associated with incoming viral genomes , we generated a cell line that could be induced to express GFP-tagged RNF8 , or utilized RIDDLE cells complemented with a cDNA expressing HA-tagged RNF168 . We observed that RNF168 clearly accumulated near incoming ICP0-null viral genomes ( Figure 3C ) . Redistribution of RNF8 to the vicinity of HSV-1 genomes was also detectable , although this was weaker and more variable than recruitment of RNF168 ( Figure 3C ) . Together , these data suggest that accumulation of RNF8 and RNF168 at sites associated with incoming viral genomes coordinates 53BP1 recruitment . This implies that the reason ICP0 targets RNF8 and RNF168 for degradation is to prevent recruitment of specific DNA repair factors to viral genomes , suggesting that this recruitment is detrimental to incoming virus during early stages of lytic infection . In uninfected mammalian cells , a tightly controlled hierarchy of events occurs following the induction of DNA double strand breaks [36] , [37] . RNF8 and RNF168 coordinate the recruitment of 53BP1 to sites of cellular damage [38] and also to sites associated with incoming viral genomes . We therefore predicted that the latter process would be disrupted by depletion of factors upstream of RNF8 and RNF168 in the DNA damage response pathway . Phosphorylation of the histone variant H2AX is one of the first events to occur after induction of a double stranded DNA break [39] , [40] and it is required for sustained accumulation of factors such as 53BP1 at damage sites [41] , [42] . Phosphorylated H2AX binds MDC1 , which in turn recruits RNF8 in a phosphorylation-dependent manner , and this interaction tethers 53BP1 and other downstream mediators at damage sites [38] . H2AX is therefore upstream of RNF8 and RNF168 , and stable foci of 53BP1 do not form in H2AX-null cells . We infected cells from mice deleted for H2AX or matched control cells [43] with wild-type and ICP0-null HSV-1 , and examined cells at the edge of developing plaques . As predicted , 53BP1 was recruited to ICP0-null viral genomes in wild-type mouse embryonic fibroblasts ( MEFs ) , but did not accumulate during infection of cells lacking H2AX ( Figure 4A ) . ATM and the Mre11 complex are also upstream regulators of the cellular response to DNA damage . The Mre11 complex senses DNA double strand breaks and facilitates activation of ATM by recruiting it to the break sites [44]–[46] . However , despite this upstream role , 53BP1 still accumulates at sites of cellular DNA damage in cells deficient in Mre11 complex members or ATM [47] . We therefore assessed the requirement for ATM and Mre11 in coordinating the recruitment of 53BP1 to sites associated with ICP0-null viral genomes . We infected cells from patients with ataxia telangiectasia ( A–T ) and ataxia telangiectasia-like disorder ( A-TLD ) that lack functional ATM and Mre11 respectively , and compared them to matched controls in which ATM or Mre11 had been reconstituted . We observed that neither ATM ( Figure 4B ) or Mre11 ( Figure 4C ) were required for the accumulation of 53BP1 at sites associated with incoming ICP0-null viral genomes . These data demonstrate that H2AX , RNF8 and RNF168 are required for accumulation of 53BP1 at sites associated with incoming viral genomes , but ATM and Mre11 are not required . This hierarchy of signaling and recruitment events in response to viral genomes parallels the response to cellular DNA damage . Our data therefore suggest that the host cell recognizes either the incoming viral genomes themselves , or the resultant changes in local chromatin structure induced by incoming viral genomes , as DNA damage . RNF8 and RNF168 are ubiquitin ligases for the histone H2A [35] , [48]–[51] , and we have previously reported that ICP0 expression leads to loss of uH2A , concomitant with the degradation of these two ligases [8] . We therefore examined ubiquitin conjugation at the sites associated with incoming ICP0-null viral genomes . We infected RNF8-null MEFs , RIDDLE cells , and matched controls , with ICP0-null virus and examined conjugated ubiquitin staining ( FK2 ) at sites associated with incoming viral genomes at 24 hpi ( Figure 5A ) . Asymmetric FK2 staining was detectable only in cells expressing RNF8 and RNF168 , suggesting that this represents uH2A , which we also detected associated with incoming ICP0-null viral genomes ( Figure S5A ) . The FK2 signal co-localized with 53BP1 , but not PML , at sites associated with incoming viral genomes , suggesting that conjugated ubiquitin was a marker for sites of DNA repair protein accumulation rather than sites of ND10 protein accumulation ( Figure S5B ) . SUMO modification has also recently emerged as an important regulator of cellular DNA damage signaling [52] , [53] and SUMO conjugates have been detected at sites associated with incoming ICP0-null genomes ( Cuchet-Lourenco , Boutell and Everett , unpublished observations ) . In the case of cellular DNA double strand breaks , SUMO1 and SUMO2/3 recruitment is dependent on RNF8 and RNF168 [52] . We therefore determined whether SUMO recruitment to sites associated with incoming ICP0-null genomes was also dependent on RNF8 and RNF168 . We infected cells depleted for RNF8 or lacking functional RNF168 , and their matched controls , with ICP0-null virus and analyzed cells at the edges of developing plaques for asymmetric accumulations of SUMO . Both SUMO1 and SUMO2/3 were recruited to sites associated with incoming ICP0-null genomes even in the absence of RNF8 or RNF168 ( Figure 6A and B ) . ND10 proteins are heavily SUMOylated , and SUMO modified forms of PML and Sp100 are known to be targets of ICP0 [15] . We therefore speculate that at least some of the SUMO conjugates we detected in the absence of RNF8 and RNF168 may represent sites of ND10 protein accumulation rather than DNA repair proteins , an idea supported by the observation that PML is still recruited to these sites in cells depleted for RNF8 and RNF168 ( Figure 6C and D ) . Together , these data show that recruitment of ND10 components and DNA repair proteins are independent events , sharing the common themes of being disrupted by ICP0 and likely being coordinated by SUMO modification events . Accumulation of cellular factors at sites associated with incoming HSV-1 genomes has been strongly linked to restricting the invading virus [22] , [34] . We therefore wished to determine the biological significance of the accumulation of specific DNA repair proteins at sites associated with incoming viral genomes . First , we assessed the ability of wild-type or ICP0-null virus to form plaques on cells deficient for H2AX or matched control cells expressing wild-type H2AX . We observed that both wild-type and ICP0-null HSV-1 were approximately 10-fold more likely to form plaques in the presence of H2AX ( Figure 7A ) . This is similar to our previous data demonstrating that certain DNA repair proteins , such as ATM and Mre11 , are beneficial for HSV-1 replication [28] , possibly via processing of intermediates generated during viral replication/recombination [54] . Even though γH2AX is excluded from viral replication compartments [55] , this histone variant is one of the master regulators of DNA damage signaling , and it is likely that H2AX phosphorylation is required to activate or recruit specific downstream proteins required during viral replication . Our FK2 data ( Figure 5 ) suggested that ubiquitination events at sites associated with incoming viral genomes are regulated by RNF8 and RNF168 . Since uH2A has well-characterized roles in silencing [56]–[58] and ICP0 is a known transcriptional activator , we hypothesized that one reason for ICP0 to target RNF8 and RNF168 is to limit transcriptional repression of incoming viral genomes . To test this hypothesis , we compared the transcriptional competence of viral genomes in the presence and absence of RNF8 ( Figure 7B ) . Cells from RNF8 null mice transduced with empty retrovirus or retrovirus expressing human WT RNF8 [8] were infected with wild-type or ICP0-null HSV-1 and harvested at 2 and 5 hpi . RNA was isolated and reverse transcribed , and qPCR was performed to detect ICP27 transcripts as a marker of viral transcription . We confirmed that input DNA was similar in all infections ( data not shown ) and analyzed the data by comparing transcription in the presence of RNF8 to transcription in the absence of RNF8 ( Figure 7B; see Figure S6A for transcript levels across all samples ) . We observed that a ) both viruses were transcriptionally repressed by RNF8 , b ) this repression was more significant in the absence of ICP0 and c ) RNF8-mediated repression decreased over time during wild-type but not ICP0-null virus infection , presumably as a consequence of RNF8 degradation ( Figure 7B ) . These data indicate that RNF8 is transcriptionally repressive to HSV-1 genomes and explains why HSV-1 forms plaques less efficiently in the presence of RNF8 [8] and/or RNF168 ( Figure S6B and C ) .
In this study we discovered that RNF8 and RNF168 coordinate a repressive barrier to incoming HSV-1 genomes , and that ICP0 targets these cellular ubiquitin ligases to overcome this host antiviral effect . We describe structures marked by DNA repair proteins and conjugated ubiquitin that form de novo in response to incoming viral genomes . These DNA repair structures are associated with , but are independent of , similar ND10-like structures that also form near incoming viral genomes . Our studies highlight the complexity of the interface between HSV-1 and the cellular DNA damage response . Previous work demonstrated that certain recombination and repair proteins , such as Mre11 , ATM , ATR/ATRIP , and WRN are beneficial for HSV-1 replication [28] , [29] , [31] . Here we show that H2AX is also required for optimal replication of HSV-1 , as previously suggested [59] . In contrast , the NHEJ proteins , DNA-PKcs and Ku70 , have been reported to be detrimental to HSV-1 replication [7] , [31] . We found that RNF8 and RNF168 also inhibit replication , likely by creating a repressive environment at the nuclear sites of incoming viral genomes . Together , these observations suggest that HSV-1 temporally dissects the DNA repair pathway; this ensures that repressive proteins are degraded , while repair proteins required to coordinate signaling and facilitate replication or processing of viral genomes are retained . Although accumulation of many cellular factors has been strongly linked to restricting the incoming viral genomes [15] , [22] , [34] , recruitment does not necessarily always correlate with repression . For example , some PML isoforms accumulate at sites associated with incoming HSV-1 genomes but do not inhibit the plaque-forming ability of ICP0-null virus ( Cuchet-Lourenco , Boutell and Everett , unpublished observations ) . Similarly , we observe γH2AX accumulation at sites associated with incoming viral genomes , but find that H2AX is required for optimal HSV-1 replication . In contrast , proteins involved in intrinsic antiviral defense are not only recruited to incoming genomes , but limit viral progression , and are therefore inactivated by the virus during the earliest stages of infection . Our data identify RNF8 and RNF168 as new members of the host cell antiviral arsenal against incoming HSV-1 . When HSV-1 genomes enter the nucleus , they do so as naked DNA . However , the cell responds by depositing repressive chromatin marks on the incoming nucleic acid [60]–[64] . In turn , the virus recruits modification complexes containing histone demethylases and methyltransferases , and installs positive marks to facilitate IE transcription [65] . These demethylases may act in concert with histone deacetylases , such as HDAC1 , which bind the transcriptionally repressive coREST/REST complex in the absence of ICP0 [66] , [67] . We observed γH2AX and uH2A in association with sites of incoming viral genomes at the earliest detectable stages of infection , suggesting that these post-translational histone modifications are a very early response to incoming viral DNA . Our co-localization studies raise the possibility that these modified histones may be deposited on the displaced host chromatin around the incoming viral genomes . Our data highlight the emerging parallels between cellular recognition of viral DNA and the cellular response to DNA damage . In both cases , γH2AX is activated , Mdc1 accumulates , and downstream repair factors such as 53BP1 are recruited . Furthermore , both processes are coordinated by the ubiquitin ligases RNF8 and RNF168 , and ICP0 is thus able to disrupt both by inducing the degradation of these cellular proteins ( Figure 7C ) . However , in contrast to the situation at sites of cellular DNA damage , we observed that SUMO conjugates still accumulate near incoming viral genomes even in the absence of RNF8 or RNF168 . This accumulation likely reflects SUMO modification of ND10 components , which we show are still recruited in the absence of RNF8 or RNF168 . Recent work has demonstrated that the SIMs of PML , hDaxx and Sp100 are essential for their recruitment to virus-induced foci ( Cuchet-Lourenco , Boutell and Everett , unpublished observations ) raising the possibility that these ND10 components are recruited in response to upstream SUMO-dependent events at these sites . RNF168 is known to contain SIMs and its recruitment to sites of cellular damage depends on the SUMO ligase PIAS4 [52] . It will therefore be interesting to determine whether the SIMs in RNF168 are required for its accumulation near incoming viral genomes , and whether disrupting the SUMO pathway can abrogate accumulation of both ND10 proteins and DNA repair proteins at these sites . Conversely , it will be interesting to see if the accumulation of ND10 components at sites of cellular DNA damage [68] , [69] is SUMO-dependent and whether this still occurs in the absence of RNF8 and RNF168 . It has recently been shown that sites of cellular DNA damage are characterized by transcriptional repression [70] . The parallels we have uncovered between recruitment of DNA repair proteins to sites of cellular DNA damage and to incoming viral genomes raise the possibility that silencing is a defining characteristic of both sites . We propose that the accumulation of SUMO conjugates , ND10 components and DNA repair proteins are hallmarks of a repressive cellular response to both damaged and foreign DNA .
Vero and U20S cells were purchased from the American Tissue Culture Collection . MEFs from RNF8-/- knockout mice and matched wild-type controls were obtained from Razq Hakem [71] or Junjie Chen [72] and for some experiments RNF8-/- MEFs were complemented with human RNF8 [8] were used . Human foreskin fibroblasts ( HFFs ) , obtained from the University of California San Diego Medical Center , were kindly provided by Debbie Spector . Cells were maintained in Dulbecco modified Eagle's medium ( DMEM ) containing 100 U/ml of penicillin and 100 µg/ml of streptomycin , supplemented with 10% fetal bovine serum ( FBS ) and selection antibiotics as appropriate . Cells were grown at 37°C in a humidified atmosphere containing 5% CO2 . HepaRG hepatocyte cells [73] were grown in William's medium E supplemented with 2 mM glutamine , 5 µg/ml insulin , and 0 . 5 µM hydrocortisone . H2AX-/- MEFs were obtained from Andre Nussenzweig [43] . A-T cells ( AT22IJE-T ) and matched ATM put-back cells were obtained from Yosef Shiloh [74] . A-TLD-1 cells and matched cells with Mre11 reconstituted were described previously [75] . The inducible RNF8-GFP cell line was constructed by cloning RNF8 into the previously described tet-inducible pLKO based expression system [76] . Cells were induced with 0 . 1 µg/ml tetracycline for 8 hrs . shRNA targeting RNF8 was 5′ ACATGAAGCCGTTATGAAT 3′ as previously described [49] . This sequence was incorporated into a pLKO based ( for HepaRG cells ) or GFP-tagged HIV vector plasmids [77] . Parental virus HSV-1 strain was 17 syn+ and the matched ICP0 deletion mutant was dl1403 [5] . Viruses were grown in Vero cells and titered in U2OS cells , in which ICP0 is not required for efficient plaque formation . Infections were performed on monolayers of cells in DMEM with 0% FBS . After 1 hr at 37°C , virus was removed and media containing 10% FBS was added . For plaque edge experiments , this media was supplemented with 1% human serum to limit spread of the virus . For plaque assays , 24 well dishes were infected with three-fold dilutions of wild-type or ICP0-null HSV-1 . After adsorption , the cells were overlaid with medium containing 10% FBS and 1% human serum . Plaques were stained with crystal violet 24–36 h post-infection . Pseudotyped lentiviral stocks were generated by transfecting 293T cells with the appropriate vector plasmid and pVSV-G , pRev and pMDL plasmids as previously described [77] . Primary antibodies were purchased from Bethyl ( PML ) , Abcam ( SUMO1 and SUMO2/3 ) , Rockland ( ATM S1981-P ) , Santa Cruz ( BRCA1 , 53BP1 ) , Millipore ( H2AX S139 , FK2 , H2A , uH2A ) , Calbiochem ( BRCA1 ) , Research Diagnostics Inc . ( GAPDH ) , Covance ( HA ) , Transduction Laboratories ( DNA-PKcs ) , and Sigma ( FLAG ) . Rabbit antisera to Mdc1 was from J . Chen . The 58S monoclonal antibody to ICP4 was generated from an ATCC hybridoma cell line [78] . All secondary antibodies were from Jackson Laboratories or Invitrogen . For immunoblotting , lysates prepared by standard methods . For immunofluorescence , cells were fixed with 4% paraformaldehyde for 15 min and extracted with 0 . 5% Triton X-100 in PBS for 10 min . For certain antibodies , cells were pre-treated with 0 . 5% Triton X-100 in PBS for 10 min prior to fixation . Nuclei were visualized by staining with DAPI . Images were acquired using a Leica TCS SP2 confocal microscope . 2X106 cells were infected with WT or ICP0-null virus at an MOI of 0 . 01 and harvested at 2 and 5 hpi . 75% of the cell pellet was used for RNA extraction and 25% for DNA purification . 1 µg RNA was reverse transcribed using SuperScriptIII RT ( Invitrogen ) and oligo dT in a 20 µl reaction . qPCR was run in triplicate with 3 µl cDNA or 100 ng genomic DNA using SYBR Green PCR master mix ( ABI ) on an ABI 7900HT system . ICP27 transcript was detected using primers GCATCCTTCGTGTTTGTCATT ( F ) and GCATCTTCTCTCCGACCCCG ( R ) [65] and normalized to endogenous RPLPO transcript detected using primers CTGGAAGTCCAACTACTTCC ( F ) and TGCTGCATCTGCTTGGAGCC ( R ) . | The cellular DNA damage response pathway monitors damage to genomic DNA . We investigated whether cellular DNA damage response proteins can also respond to incoming viral genetic material and how they impact virus growth . Using Herpes Simplex Virus type 1 ( HSV-1 ) , we present evidence that DNA repair proteins are activated at the earliest times post-infection , and that they physically accumulate at sites associated with incoming viral genomes . A subset of these DNA repair proteins deposit repressive ubiquitin marks , recruit other DNA repair proteins , and limit transcription from the viral genomes . We demonstrate that the virus overcomes this anti-viral defense by targeting key DNA repair proteins for degradation . Our study adds these DNA repair protein mediators to the list of intrinsic antiviral defense factors active against HSV-1 , and demonstrates that many aspects of the cellular recognition of foreign DNA parallel the recognition and response to cellular damage . | [
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] | 2011 | The Intrinsic Antiviral Defense to Incoming HSV-1 Genomes Includes Specific DNA Repair Proteins and Is Counteracted by the Viral Protein ICP0 |
The O-mannosyltransferase Pmt4 has emerged as crucial for fungal virulence in the animal pathogens Candida albicans or Cryptococcus neoformans as well as in the phytopathogenic fungus Ustilago maydis . Pmt4 O-mannosylates specific target proteins at the Endoplasmic Reticulum . Therefore a deficient O-mannosylation of these target proteins must be responsible for the loss of pathogenicity in pmt4 mutants . Taking advantage of the characteristics described for Pmt4 substrates in Saccharomyces cerevisiae , we performed a proteome-wide bioinformatic approach to identify putative Pmt4 targets in the corn smut fungus U . maydis and validated Pmt4-mediated glycosylation of candidate proteins by electrophoretic mobility shift assays . We found that the signalling mucin Msb2 , which regulates appressorium differentiation upstream of the pathogenicity-related MAP kinase cascade , is O-mannosylated by Pmt4 . The epistatic relationship of pmt4 and msb2 showed that both are likely to act in the same pathway . Furthermore , constitutive activation of the MAP kinase cascade restored appressorium development in pmt4 mutants , suggesting that during the initial phase of infection the failure to O-mannosylate Msb2 is responsible for the virulence defect of pmt4 mutants . On the other hand we demonstrate that during later stages of pathogenic development Pmt4 affects virulence independently of Msb2 , probably by modifying secreted effector proteins . Pit1 , a protein required for fungal spreading inside the infected leaf , was also identified as a Pmt4 target . Thus , O-mannosylation of different target proteins affects various stages of pathogenic development in U . maydis .
O-mannosylation is an essential posttranslational protein modification in fungal cells [1] . This type of protein O-glycosylation , which adds mannoses to the nascent glycoproteins at the Endoplasmic Reticulum ( ER ) and Golgi Apparatus ( AG ) , is required for correct protein conformation and stabilization [2] . In pathogenic fungi , such as Candida albicans or Cryptococcus neoformans , O-mannosylation is crucial for virulence [3]–[5] . The first step of the O-mannosylation pathway is catalyzed by protein O-manosyltransferases ( Pmts ) , which add the first mannose to hydroxyl groups of serine and threonine in ER resident target proteins [6] . In fungi , the Pmt family is grouped into three subfamilies: Pmt1 , Pmt2 and Pmt4 , which mannosylate specific target proteins [7] , [8] . The number of members in these subfamilies differs in each organism; for example , in Saccharomyces cerevisiae or C . albicans the Pmt1 subfamily contains two members [9] , [10] , while in Schizosaccharomyces pombe , C . neoformans , Aspergillus nidulans or in Ustilago maydis , the Pmt1 family contains only one member [11]–[14] . PMT orthologs have also been found in humans , rat and Drosophila melanogaster but not in nematodes ( Caenorhabditis elegans ) or plants ( Arabidopsis thaliana and Oryza sativa ) [6] , [15]–[18] . Interestingly , the Pmt4 subfamily contains only one member in all organisms analyzed [6] . The protein O-mannosyltransferase Pmt4 has been characterized in several pathogenic fungi and was shown to be essential for virulence of fungal animal pathogens such as C . albicans and C . neoformans [4] , [5] , [10] , [19] . In addition , Pmt4 is relevant for virulence in the phytopahogenic fungus U . maydis [11] . It has been proposed that the loss of Pmt4 in fungal pathogens causes an alteration in their ability to adhere to the host , which might be caused by affecting the glycosylation of secreted proteins and cell wall proteins [5] , [20] . Despite their proposed relevance , Pmt4 target proteins have only been described in S . cerevisiae and C . albicans [21]–[23] . The lack of a defined consensus sequence that is targeted by Pmt4 , coupled with the transient interaction of this O-mannosyltransferase with its target proteins , makes identification of Pmt4 substrates difficult [21] . The only knowledge regarding Pmt4 target proteins is that they should contain a membrane anchor to facilitate their glycosylation in the ER lumen . This characteristic feature has been used to perform a search for Pmt4 substrates in S . cerevisiae [8] . However this type of in silico screening has not been carried out in pathogenic fungi , which would allow the identification of fungal virulence factors . In this work , we have performed an in silico screening of putative Pmt4 target proteins in the plant pathogen U . maydis with a role during fungal pathogenesis . U . maydis is a basidiomycete fungus causing smut disease in maize . For infection two yeast-like compatible haploid cells need to mate and to form a filamentous dikaryon [24]–[26] . On the leaf surface U . maydis senses a combination of physical-chemical plant-derived signals and differentiates into an appressorium , a morphogenetic structure which mediates fungal penetration into the plant tissues [27] . Appressorium formation and penetration is controlled by two MAP kinases , Kpp2/Ubc3 and Kpp6 [28]–[30] . Moreover , it is known that the perception of the physical surface signal required for appressorium formation depends specifically on the Kpp2/Ubc3 MAP kinase [27] , and that its activation is likely to involve the signalling mucin Msb2 [31] . Msb2 has been placed upstream of the MAP kinase pathway , consisting of the MAPKK kinase Kpp4/Ubc4 , the MAPK kinase Fuz7/Ubc5 and the MAP kinase Kpp2/Ubc3 [28] , [30] , [32]–[34] . After penetration of the plant epidermis , U . maydis proliferates inside the plant as a mycelium . During this process the fungus produces high amounts of putative secreted proteins which are only found in related smut fungi and are important for virulence [25] , [35] . Many of the respective effector genes are organized in gene clusters [25] . At late stages of the fungal invasion process , U . maydis induces the formation of prominent tumors in the plant which will finally contain the diploid spores [24] , [36] , [37] . The Pmt4 protein is the only O-mannosyltransferase required for pathogenesis in U . maydis . The deletion of pmt4 causes a drastic reduction in appressorium formation and , more importantly , a total loss of fungal capacity for plant penetration [11] ( Figure S1 ) . In this work , we present novel Pmt4 target proteins , identified by an in silico screening , with an essential role during U . maydis pathogenic development . Among them , we detect the signalling mucin Msb2 as a Pmt4 substrate , suggesting that an altered glycosylation pattern of this sensor membrane protein could be the cause for the defects in appressoria production in Δpmt4 cells .
Since the role of Pmt4 in virulence must be mediated via its target proteins , we performed a search for such substrates in order to explain the role of Pmt4 in appressorium formation and penetration . Thus , we devised a bioinformatic approach to identify putative Pmt4 targets from the 6787 proteins contained in the U . maydis proteome . As Pmt4 mediates O-mannosylation of Ser/Thr-rich membrane-associated proteins [8] , we carried out three sequential in silico searches . First , we identified membrane proteins containing a region of at least 20 aa in which the percentage of Ser/Thr was ≥40% . With these parameters we found 826 proteins ( 12 . 17% of the U . maydis proteome ) ( Table S1 ) . Then , we performed a more restrictive search on the U . maydis proteome for membrane attached proteins containing a wider region of 40 aa , where the percentage of Ser/Thr was ≥40% . With this bioinformatic approach we identified 306 proteins from the U . maydis database ( 4 . 51% of the U . maydis proteome ) ( Table S2 ) . Since it has been described that the presence of a Ser/Thr rich region in the amino terminal part of single-pass transmembrane proteins triggers the recognition by Pmt4 [8] , this was the search criterion for the third round . 64 out of the 306 previously identified sequences met these criteria ( 0 . 94% of the U . maydis genome ) ( Table S3 ) ( see Figure 1A ) . The obtained putative Pmt4 substrates were categorized based on their probable functions using FunCatDB from the MIPS U . maydis database ( http://mips . helmholtz-muenchen . de/genre/proj/ustilago/ ) . We found that the initially identified group of 826 proteins was mainly enriched in proteins associated with transport , i . e . ion transport , detoxification by export as well as homeostasis of cations . The more restricted group of 306 proteins contained mostly unclassified proteins ( 52 . 1% of the proteins contained in the list ) . Other enriched functional categories were associated with inorganic chemical agent resistance and detoxification by export . Finally , the most restrictive group of 64 proteins was also mainly enriched in unclassified proteins ( Figure S2 ) . In the genome of U . maydis many of the genes encoding small secreted proteins are organized in gene clusters , most of them implicated in virulence [25] , [35] . Interestingly , we found in our screening four proteins included in these clusters: Um01235 , Um03746 and Um03749 ( identified in group 1 , Table S1 ) ; and Um01374 ( belonging to group 2 , Table S2 ) . The Um01235 protein is part of the cluster 2A . The deletion of all eight genes from cluster 2A causes a hypervirulent phenotype [25] . On the other hand , Um03746 and Um03749 reside in cluster 10A , which contains 10 genes for effector proteins in total . The deletion of cluster 10A reduces virulence [25] . Finally , Um01374 ( Pit1 ) is one of four proteins encoded by the pit ( proteins important for tumors ) cluster . The deletion of the transmembrane gene pit1 or the effector gene pit2 strongly reduces pathogenic development at the level of tumor formation [38] . To confirm our bioinformatic results , we performed a biochemical approach to study the role of Pmt4 in posttranslational modification of its putative target proteins by western blot . For this purpose we isolated candidate proteins from wild-type and Δpmt4 strains and compared the mobility of these proteins during SDS polyacrylamide gel electrophoresis ( SDS-PAGE ) . Given the critical role of cluster 10A during U . maydis virulence , we decided to analyse the role of Pmt4 in the posttranslational modification of Um03746 and Um03749 . In addition we included the protein Pit1 . Finally , we also randomly selected an until now uncharacterized protein , Um04580 , which contains a stretch of 40 aa where the percentage of Ser/Thr is ≥40% . The deletion of um04580 did not affect U . maydis virulence ( Figure S3 ) . Since some of the candidate genes are expressed exclusively during biotrophic development , i . e . um03746 , um03749 and um01374 [25] , [38] , we placed the open reading frames ( ORFs ) of the candidate genes under the control of the constitutive active otef promoter [39] . A C-terminal fusion to gfp allowed visualization of the produced proteins by western blot . The constructs were integrated into the ip locus of the solopathogenic SG200 strain and its derivate SG200Δpmt4 . Cells were grown in YEPSL liquid medium , total cell extracts prepared and proteins were separated by SDS-PAGE . In western blot analysis Um03749-GFP , Pit1-GFP and Um04580-GFP , but not Um03746-GFP showed differences in mobility when isolated from the two strains ( Figure 1B ) . The higher mobility observed when pmt4 is deleted suggests that these proteins could be direct Pmt4 targets . Thus , in analogy with S . cerevisiae , the bioinformatic approach is a useful tool to identify new Pmt4 target proteins . In order to substantiate the role of protein glycosylation on the O-mannosylated virulence factors identified , we investigated if the novel Pmt4 target proteins , Pit1 and Um03749 , are also N-glycoproteins . N-glycosylation consists in the addition of an oligosaccharide core to nascent protein in the consensus sequence Asn-X-Ser/Thr ( where X could be any acid amino except proline ) . Similarly to O-mannosylation , N-glycosylation is also required for full virulence of U . maydis [40] . In Um03746 and Um03749 no putative glycosylation sites could be identified . In Pit1 we identified the putative N-glycosylation site NGTF ( amino acids 330–333 ) . To know if Pit1 is also modified by N-glycosylation we analysed the Pit1-GFP protein in the SG200Δcwh41 background . Cwh41 encodes glucosidase I , an upstream component of the N-glycosylation machinery in U . maydis [41] . Pit1-GFP isolated from Δcwh41 strains showed a similar mobility shift during SDS-PAGE as Pit1-GFP isolated from Δpmt4 strains . This observation indicates that the glycosylated isoform of Pit1 is also N-glycosylated ( Figure S4 ) . To understand the specific role of pmt4 during U . maydis appressorium formation and penetration we searched our list of putative Pmt4 target proteins for those for which a link to appressoria had previously been established . Interestingly , in group 3 we identified Um00480 ( Msb2 ) , a transmembrane mucin , which plays an important role during early pathogenic development in U . maydis [31] . Recently , homologs of Msb2 have been also linked to virulence in the plant pathogenic fungi Magnaporthe oryzae and Fusarium oxysporum [42] , [43] . Msb2 meets the most restrictive criteria for Pmt4 targets by displaying a window of 40 aa with a percentage of ≥40% Ser/Thr located in the amino terminal part of the protein ( Table S3 ) . Furthermore , S . cerevisiae Msb2p has been shown to be glycosylated by Pmt4p [44] . Although U . maydis Msb2 is unable to functionally complement the MSB2 mutant of S . cerevisiae and displays only weak similarity to S . cerevisiae Msb2p [31] , we took this as a strong indication that Pmt4 glycosylates Msb2 in U . maydis . In S . cerevisiae , Msb2p is cleaved upstream of the transmembrane domain releasing an extracellular N-terminal part and a cellular C-terminal fragment [45] . To analyse the Msb2 protein in U . maydis we used a differentially tagged protein , which was C-terminal fused to GFP and carried an internal HA-epitope between amino acids 709 and 710 in the extracellular region ( Figure 2A ) . The msb2-HA-GFP construct was placed under the control of the otef promoter and integrated into the ip locus of SG200Δmsb2 . Western blot analysis revealed that the Msb2 protein is processed into two distinct fragments . The extracellular domain , detected by the anti-HA-antibody , migrated at a molecular weight of >250 kDa , while the anti-GFP-antibody detected a product of approximately 65 kDa . The size of the latter fragment allowed us to predict a cleavage site , situated between the Ser/Thr rich region and the transmembrane domain ( Figure 2 ) . In S . cerevisae , cleavage of Msb2 results in the secretion of the N-terminal extracellular domain [45] . Similar to the situation in S . cerevisiae we could detect the extracellular N-terminal domain of U . maydis Msb2 in the culture supernatant , while the C-terminal fragment was exclusively detected in the cellular fraction ( Figure S5 ) . To confirm that Msb2 is a Pmt4 target protein in U . maydis , we deleted pmt4 in SG200Δmsb2/msb2-HA-GFP and analysed whether pmt4 deletion influences the migration of the Msb2 protein in SDS-PAGE . The extracellular part of Msb2 isolated from Δpmt4 strains migrated faster in polyacrylamide gel electrophoresis than the extracellular part of Msb2 isolated from wild-type strains ( Figure 2B ) . Thus , we can conclude that Msb2 is a mannosylated substrate of Pmt4 and that mannosylation occurs in the extracellular domain of Msb2 . However , the extracellular fragments from wild-type and Δpmt4 strains migrated at positions >250 kDa , which exceeded by far the predicted size of the full length Msb2 protein of ∼145 kDa . This indicates that Msb2 is also postranslationally modified by Pmt4-independent mechanisms . We analysed the Msb2 protein in the N-glycosylation deficient Δcwh41 strain . We found that in SDS-PAGE the extracellular part of Msb2 migrated similarly in both , wild-type and Δcwh41 background ( Figure S4 ) . Thus , our data suggest that , similarly to the situation described in budding yeast [44] , Msb2 in U . maydis is subject to a non-characterized posttranslational modification process . Considering that the extracellular domain of Msb2 is mannosylated by Pmt4 , we assumed that this domain might be needed for the function of Msb2 . We therefore deleted the coding region of the Ser/Thr rich domain of Msb2 and integrated the construct into the ip locus of SG200Δmsb2 generating the variant SG200Δmsb2/msb2ΔSTR-GFP ( Figure 3A ) . The loss of this region did not alter the normal localization of Msb2 in the plasma membrane as revealed by treatment with the endocytosis inhibitor Latrunculin A [31] ( Figure 3B ) . Western blot analysis revealed that processing of the truncated Msb2 protein still occurred since the C-terminal 65 kDa fragment could be detected for both full length Msb2-HA-GFP and truncated Msb2ΔSTR-GFP ( Figure 3C ) . However , in plant infections no complementation was observed when the truncated Msb2 protein was expressed , while virulence could be completely restored when Msb2 was expressed as full-length protein ( Figure 3D ) . This indicates that , contrary to the situation in S . cerevisiae , where the Ser/Thr rich region of Msb2p possesses a negative regulatory function [46] , the mannosylated Ser/Thr-rich region of Msb2 in U . maydis has a positive role during virulence . In U . maydis , appressorium formation requires a set of combined physical-chemical plant-derived signals [27] . Δmsb2 strains are capable of recognizing the chemical signal ( hydroxy fatty acids ) but show defects in responding to the physical stimulus ( hydrophobic surface ) . Hence , the msb2 mutant is severely affected in appressorium differentiation [31] . To investigate whether the defect in appressorium formation described for the pmt4 mutant might be due to a defective sensing activity of Msb2 , we analysed the capability of Δpmt4 cells , as well as the double mutant Δpmt4Δmsb2 to respond to physical-chemical signals . To test the chemical signal perception , we used 16-hydroxy hexadecanoic acid , a main component of the maize cuticle . Exposure of SG200 to this fatty acid induces filamentous growth [27] . Filament formation of SG200Δpmt4 , SG200Δmsb2 and SG200Δmsb2Δpmt4 cells in response to 16-hydroxy hexadecanoic acid was indistinguishable from SG200 cells ( Figure 4A ) . Thus , the defect in appressorium formation described for Δpmt4 cells is not likely to be linked to a defective perception of the chemical signal . To study the perception of the physical signal , we added 16-hydroxy hexadecanoic acid to U . maydis cell suspensions and sprayed the cells onto parafilm M , which constitutes a hydrophobic and hard surface inducing differentiation of hyphae into appressoria [27] . To facilitate quantification of appressoria , we used strains harbouring a gfp reporter , AM1 , which is specifically expressed in those hyphae that have formed an appressorium [27] . We observed comparable filament formation of SG200AM1 and the respective derivative mutants . However , with respect to appressorium formation , msb2 , pmt4 and the double msb2 pmt4 mutants were severely reduced ( Figure 4B ) . Interestingly , appressorium formation in the various mutants was impaired to a similar extent ( Figure 4C ) suggesting that the defect in appressorium development in the Δpmt4 strain might be a consequence of a defect in glycosylation of the Msb2 protein . In U . maydis Msb2 has been placed upstream of the MAP kinases Kpp2 and Kpp6 implicated in appressorium development and penetration respectively [27] , [29] , [31] , [34] . In fact , expression of a hyperactive allele of the MAP kinase kinase Fuz7 , Fuz7DD [34] , which activates Kpp2 and Kpp6 , restores plant cuticle penetration in sho1 msb2 double deletion strains [31] , while the sho1 msb2 double deletion strain is non-pathogenic and unable to form appressoria on the plant surface [31] . The genetic interaction analysis between msb2 and pmt4 may suggest a role of pmt4 upstream of the MAP kinase cascade . However , we cannot exclude the possibility that Pmt4 is acting downstream of the MAP kinase pathway controlling glycosylation of proteins implicated in the appressorium morphogenesis . To test our hypothesis that Pmt4 acts above the MAP kinase cascade we used the hyperactive allele fuz7DD cloned under the control of the crg promoter , which is repressed by glucose and induced by arabinose [31] , [47] . This construct was integrated in the ip locus of SG200 , SG200Δpmt4 and SG200Δmsb2 . To analyse appressorium formation , we infected leaves from seven days old maize seedlings with SG200 , SG200fuz7DD , SG200Δpmt4 , SG200fuz7DDΔpmt4 , SG200Δmsb2 and SG200fuz7DDΔmsb2 in the presence of arabinose . To quantify appressorium formation , cells were stained with calcofluor white 15 hours after plant inoculation . Induction of the fuz7DD allele restored appressoria production in the pmt4 and msb2 mutants ( Figure 5A and Figure S6A ) . We also analysed the appressorium-mediated penetration under these conditions . To this end , we used the chlorazol black E staining procedure to visualize invading hyphae [29] . Remarkably , the appressorium-mediated penetration capability of Δpmt4 cells was also restored upon expression of the constitutively active fuz7DD allele , including the formation of clamp-like cells , the structures associated with fungal progression inside the plant [48] ( Figure 5B , 5C and 5D ) . Analysis of disease progression 12 days after infection revealed an increased virulence capacity of SG200fuz7DDΔpmt4 and SG200fuz7DDΔmsb2 when compared to SG200Δpmt4 and SG200Δmsb2 , respectively ( Figure 6 and Figure S6B ) . Thus , our data suggest that the role of Pmt4 on appressorium biology might take place upstream of MAP kinase cascade . If Pmt4 and Msb2 activate the MAP kinase cascade during appressorium formation , Kpp2 phosphorylation in the respective deletion mutants is expected to be reduced during this developmental stage . To examine the phosphorylation of Kpp2 we incubated SG200 , SG200Δpmt4 and SG200Δmsb2 for 10 h on parafilm M . At this time point about 15% of the SG200 filaments begin to form appressoria ( data not shown ) . As shown in Figure S7 , the hydrophobic surface induced Kpp2 phosphorylation in SG200 as well as in the mutants . We observed a slight reduction in Kpp2 phosphorylation in the Δpmt4 ( 84±11% ) and Δmsb2 ( 76±7% ) strains when compared to SG200 ( 100% ) ( Figure S7 ) . However , compared to SG200 only the Δmsb2 strain showed a statistically significant reduction in Kpp2 phosphorylation ( P<0 . 05 ) , while differences in the Δpmt4 strain were not statistically significant ( P>0 . 05; Figure S7 ) . This experiment shows that both , Pmt4 and Msb2 , are marginally involved in the activation of the MAP kinase Kpp2 . We could previously observe that the penetration of Δpmt4 appressoria was arrested [11] . To find out more about this phenotype , we carried out an analysis of appressorium penetration of wild-type strains and pmt4 mutants using scanning electron microscopy ( SEM ) . With this aim , we infected maize plants with a mixture of the compatible FB1 and FB2 as well as compatible FB1Δpmt4 and FB2Δpmt4 strains . 15 hours after infection maize leaves were fixed and visualized by SEM . In infections with compatible wild-type strains filaments differentiated into appressoria and penetrated inside the plant tissue . By contrast , the few appressoria formed by the Δpmt4 strain were unable to penetrate the cuticle ( Figure 7 ) , and instead the hyphae continued to grow on the plant surface . In order to verify that this aberrant morphology is a consequence but not the cause of the deficiency to penetrate the plant cuticle , we studied appressorium formation of FB1 and FB2 , as well as FB1Δpmt4 and FB2Δpmt4 strains on a non-penetrable surface such as parafilm M . In the in vitro conditions post-appressorial outgrowth of hyphae was observed in both , pmt4 mutants and wild-type strains , resembling the phenotype of Δpmt4 strains on the plant surface ( Figure 7 ) . This suggests that the Δpmt4 appressoria are unable to penetrate the plant cuticle and as consequence continue to grow on the plant surface . We have shown that the deletion of pmt4 affects the perception of physical signals required for appressorium development as well as the appressorium-mediated penetration on plant surface . In the rice blast fungus M . oryzae , cell adhesion to surfaces has been shown to be needed for the detection of the physical plant-derived signals and for the penetration of the plant cuticle [49] , [50] . Thus we wondered whether pmt4 mutants were able to properly adhere to solid surfaces . To analyse adhesion capacity of Δpmt4 cells , we spotted exponentially growing cells of FB1 wild-type and FB1Δpmt4 strains on starch agar plates and incubated them for three days at 28°C . After this time period plates were washed with water . As shown in Figure 8 , deletion of pmt4 significantly reduces U . maydis cellular adhesion capacity since the colonies of FB1Δpmt4 were easily removed from the plates after a gentle washing . By contrast , wild-type colonies as well as Δpmt1 ( control ) colonies remained attached to the surface . This result shows that Pmt4 plays a role in cellular adhesion in U . maydis which may be connected to the appressorium formation and/or penetration defects . To distinguish between both possibilities , we studied the cellular adhesion capacity in FB1Δmsb2 strain . We observed a similar behaviour to wild-type cells ( Figure S8 ) . Because the loss of Msb2 affects to appressorium formation but not penetration , our data suggest a link between cellular adhesion capacity and appressorium penetration in pmt4 mutant cells . We have observed that although the plant infections with the SG200fuz7DDΔpmt4 strain were able to induce small tumors , the virulence of this strain was severely reduced when compared to SG200fuz7DD suggesting a role for Pmt4 during fungal progression inside the plant tissues . To confirm this result , we cloned the pmt4 ORF under the control of the otef promoter which is active during early pathogenic development , but turned off during the late biotrophic stage [51] . This construct was integrated in the ip locus of the SG200Δpmt4 strain . To address if Pmt4 plays a role after plant penetration , we inoculated maize plants with SG200 , SG200Δpmt4 and SG200Δpmt4Potef:pmt4 . Disease progression was scored 12 days after infection . Although appressorium penetration was completely restored in plants infected with SG200Δpmt4Potef:pmt4 , tumor formation was severely reduced compared to infections with SG200 ( Figure 9 ) . This demonstrates , that Pmt4 is not only needed for early pathogenic development , but has additional roles during the in planta development , most likely by posttranslational modification of fungal effectors required for tumor formation .
In this paper we have performed in silico screening for putative Pmt4 target proteins in the phytopathogen U . maydis . We have found a high percentage of proteins containing Ser/Thr rich regions with most of them having unknown functions . A screening approach searching for transmembrane anchored proteins , containing a stretch of 20 aa in which the percentage of Ser/Thr was ≥40% , identified 826 proteins ( 12 . 17% of the U . maydis proteome ) . By contrast , using similar parameters in S . cerevisiae 51 candidates ( 0 . 87% of the proteome ) could be identified [8] . This difference may be due to the high number of putative secreted proteins present in the U . maydis proteome , which may be linked to the biotrophic relationship with the host plant [25] . In order to obtain a more restrictive group of Pmt4 target proteins we screened the proteome of U . maydis for single-pass transmembrane proteins containing a Ser/Thr-rich region facing the ER lumen . Under these conditions , we found 64 proteins . The validation of our screening retrieved novel Pmt4 substrates such as Um04580 , or Pit1 , which is essential for pathogenic development of U . maydis [38] . Moreover , we have found that Um03749 , which belongs to the cluster 10A , required for tumor formation [25] , is also a novel Pmt4 target protein . One of the most intriguing phenotypes associated with the pmt4 deletion in U . maydis is the strong reduction in appressorium formation and appressorium-mediated penetration of the plant cuticle [11] . Both processes are controlled by a MAP kinase pathway , consisting of the MAPKK kinase Kpp4/Ubc4 , the MAPK kinase Fuz7/Ubc5 and the MAP kinases Kpp2/Ubc3 and Kpp6 [28]–[30] , [32]–[34] . While Kpp2 is needed for appressorium formation [30] , Kpp6 has a specific role during the penetration process [29] . Our results suggest that the role of Pmt4 during appressorium formation consists probably not in the glycosylation of downstream components of this pathway rather Pmt4 modifies proteins placed upstream of the MAP kinase pathway . In U . maydis the signalling mucin Msb2 is likely an activator of the MAP kinase cascade and the deletion of msb2 leads to a strong reduction in appressorium formation due to defects in perception of plant surface signals [31] . We have identified Msb2 in our in silico screening and confirmed that it is substrate of Pmt4 in U . maydis , as it has been described for S . cerevisiae [44] . Our epistatic studies comprising msb2 and pmt4 suggest that during appressorium development both proteins act in the same pathway . A critical question was if Pmt4 and Msb2 are needed for the activation of the MAP kinase Kpp2 , which is essential for filamentation and appressorium formation in U . maydis [30] . Here we could show for the first time that Kpp2 is phosphorylated during appressorium formation in U . maydis . However , in pmt4 and msb2 mutants phosphorylation of Kpp2 was only slightly reduced . In view of the fact that under our experimental conditions only a minor fraction of U . maydis filaments differentiate appressoria and that pmt4 and msb2 mutants exhibit normal filament formation , a process that depends also on Kpp2 , we did not expect major differences in Kpp2 phosphorylation . We do currently not know in which spatial and temporal context Kpp2 phosphorylation induces either filament formation or appressorium formation . It is conceivable that appressorium formation requires a temporal coordinated threshold of MAP kinase activity and that this process involves Msb2 and its glycosylation status . A strong indication that Msb2 and Pmt4 act above the MAP kinase cascade is inferred from our observation that expression of the constitutive active fuz7DD allele restored appressorium formation in the Δmsb2 as well as Δpmt4 strains . Overall our data suggest that Msb2 and Pmt4 are needed for the activation of the MAP kinase pathway , although formal proof for this needs further experimentation . In gel mobility shift analysis we found that Pmt4 specifically glycosylates the extracellular domain of Msb2 . In S . cerevisae the extracellular domain of Msb2p has a negative regulatory function , since deletion of this domain leads to the activation of the FG ( filamentous growth ) -pathway specific MAP kinase Kss1p [46] . It is therefore assumed , that in yeast the release of the extracellular glycosylated domain of Msb2p activates the FG-pathway [45] , [46] . This release can be mimicked by the deletion of pmt4 , which leads to underglycosylated Msb2p and to the activation of the FG-pathway in an Msb2p-dependent manner [44] . Thus , in S . cerevisiae underglycosylated Msb2p activates the MAP kinase pathway . Our data from U . maydis demonstrates that the extracellular domain of Msb2 has a positive function , since deletion of the Ser/Thr-rich region causes a decrease in pathogenicity similar to the deletion of the entire msb2 gene . This observation is consistent with the reduced appressorium formation in pmt4 deletion mutants . Accordingly , Pmt4-mediated mannosylation of Msb2 seems to be necessary for activation of the MAP kinase cascade and the reduced virulence of Δpmt4 strains can be partially explained by defective glycosylation of the Msb2 sensor . Recent studies showed that the Ser/Thr-rich extracellular domain of M . oryzae Msb2 is like in U . maydis Msb2 and in contrast to Msb2p from S . cerevisae also essential for function [42] . This suggests that the model proposed for the Pmt4-Msb2 relationship , regulating appressorium development is conserved among phytopathogenic fungi ( see Figure S9 ) . The defect in appressorium formation can be explained by a defective Msb2 glycosylation . However , since the few appressoria produced in Δmsb2 strains are not affected in penetration [31] , the penetration defect of Δpmt4 appressoria is likely caused by insufficient glycosylation of yet unidentified Pmt4 target proteins . Our observations using scanning electron microscopy showed that the few appressoria produced by the Δpmt4 strain were unable to penetrate the plant cuticle and instead continued growth of the hyphae on the plant surface . This phenotype has also been observed in strains carrying a non-phosphorylatable allele of the MAP kinase Kpp6 [29] . These findings , together with the observation that expression of the fuz7DD allele restores the penetration defect of Δpmt4 appressoria suggest that Pmt4 target proteins implicated in appressorium-mediated penetration are also likely to be placed upstream of the MAP kinase pathway ( Figure 10 ) . To better analyse the outgrowing of the hypha observed in the pmt4 mutant , we studied appressoria developed by wild-type strains on non-penetrable surfaces such as parafilm M , finding a similar behaviour . This data strongly suggest that this hyphal outgrowth is the consequence but not the cause of its incapability to penetrate the plant cuticle . The penetration failure of Δpmt4 appressoria could be caused by an insufficient adhesion of the fungus to the plant surface . For the rice blast fungus M . oryzae it has been demonstrated that proper adhesion is critical for appressoria to penetrate [50] . Interestingly , we have shown that Pmt4 plays an important role in cellular adhesion to surfaces which might be related to the appressorium penetration defect . Our previous studies as well as this work demonstrated a prominent role of Pmt4 in the appressorium biology . In addition , we have now shown that Pmt4 is also necessary for tumor induction in planta . We observed that pmt4 expression under the control of the otef promoter , which is turned off during the infection process [51] , is not sufficient to restore normal tumor formation in the SG200Δpmt4 strain . Likewise , expression of fuz7DD in Δpmt4 strains did not rescue the virulence defect , although initial appressorium formation and penetration were restored . This suggests that the Pmt4 protein is also required during fungal proliferation inside the plant tissue . For C . albicans it has been demonstrated that the deletion of pmt genes is associated with defects in cell wall integrity and secretion of fungal effectors , probably disturbing the interaction with the host [5] , [10] , [52] , [53] . In U . maydis , the biotrophic interaction between the plant and the fungus is also mediated by fungal effectors [24] . The role of Pmt4 during this developmental stage could be related to the O-mannosylation of Pit1 and Um03749 ( see Figure 10 ) , since both are important for the biotrophic development [25] , [38] . Um03749 is a putative secreted protein which is directly modified by Pmt4 . The underglycosylation of Um03749 in Δpmt4 strains might affect the function of this effector . Pit1 is a plasma membrane protein that is genetically linked to the secreted effector Pit2 . Both proteins are specifically needed for tumor formation [38] . Although the connection between these two proteins is currently unclear , it is assumed , that they act in the same pathway [38] . Therefore , mannosylation of Pit1 by Pmt4 could indirectly affect the function of the secreted effector Pit2 . This points to a role of Pmt4 in the regulation of secreted effector proteins , needed for the biotrophic development . In this context , considering the large number of putative Pmt4 targets obtained in this study , it is tempting to speculate that more Pmt4 targets exist , which contribute to the establishment of biotrophic interaction between plant and fungus . The bioinformatic search for Pmt4 target proteins in other pathogenic fungi might highlight the role of Pmt4 during fungal pathogenic development and allow the identification of novel Pmt4 substrates required for fungus-host interactions .
Escherichia coli DH5α and pGEM-T easy ( Promega ) were used for cloning purposes . pING , a derivative of pONG [31] where mcherry is exchanged by gfp , was used for cloning of ORFs from pmt4 , um03746 , um03749 and um04580 . To clone these ORFs , U . maydis genomic DNA was used as template amplifying with the primers Pmt4ORF-5 and Pmt4ORF-3 , 03746ORF-5 and 03746ORF-3 , 03749ORF-5 and 03749ORF-3 , 04580ORF-5 and 04580ORF-3 , respectively ( sequences in Table S4 ) . The fragments were cut with SfiI and ligated with the 6 . 3 kb SfiI fragment of pING . Expression of ORFs from pING derivatives is controlled by the otef promoter . To generate the msb2-HA-GFP construct , the primer combinations oDL81/oDL125 and oDL82/oDL124 ( Table S4 ) using U . maydis genomic DNA as template generated two PCR products , 1 . 4 kb and 2 . 1 kb in length , respectively . Both fragments were cut with SfiI and ligated with the 6 . 3 kb SfiI fragment of pING , resulting in pPotef-msb2-HA-GFP . In this plasmid the msb2 gene with an internal HA tag ( corresponding to amino acid 709 ) is C-terminally fused to GFP . Expression of the fusion gene is driven by the otef promoter . To generate the truncated msb2ΔSTR-GFP allele , the primer combinations oDL124/oDL171 and oDL204/oDL125 using pPotef-msb2-HA-GFP as template generated two PCR products , 0 . 1 kb and 1 . 3 kb in length , respectively . Both fragments were cut with SfiI and XmaI and ligated with the 6 . 3 kb SfiI fragment of pING resulting in pPotef-msb2ΔSTR-GFP . In this plasmid the entire Ser/Thr-rich region of msb2 , corresponding to amino acids 33 to 713 is deleted . All plasmids were linearized with SspI and integrated into the ip locus . U . maydis strains used in this study are listed in Table 1 . Deletion constructs were generated as described previously [25] . To generate single deletion mutants of um04580 and pmt4 genes , fragments of the 5′ and 3′ flank of their open reading frames were generated by PCR on U . maydis FB1 genomic DNA with the following primer combinations: Um04580KO5-1/Um04580KO5-2 and Um04580KO3-1/Um4580KO3-2; UmPMT4KO5-1/UmPMT4KO5-2 and UmPMT4KO3-1/UmPMT4KO3-2; ( Sequences in Table S4 ) . These fragments were digested with SfiI and ligated with the 1 . 4 kb SfiI nourseothricin ( ClonNAT ) resistance cassette [54] . Wild-type U . maydis strains FB1 ( a1 b1 ) and FB2 ( a2 b2 ) [55] , solopathogenic strain SG200 ( a1:mfa2 bE1 bW2 ) [56] cells were grown at 28°C in liquid YEPSL ( 0 . 4% bactopeptone , 1% yeast extract and 0 . 4% saccharose ) medium . Pathogenicity assays were performed as described [25] . U . maydis cultures were grown to exponential phase and concentrated to an OD600 of 3 , washed two times in water and injected into one week old maize ( Zea mays ) seedlings ( Golden Bantam ) . Disease symptoms were quantified 7 to 25 days post infection . Virulence tests were repeated at least three times using the number of plants indicated in each figure . Molecular biology techniques were used as described by [57] . U . maydis DNA was isolated following the protocol of [58] . Standard U . maydis transformation procedure was used [59] . The U . maydis proteome was downloaded from the MIPS FTP server at ftp://ftpmips . gsf . de/ustilago/Umaydis_valid/Umaydis_valid_orf_prot_121009 , and the identifiers of proteins annotated as transmembrane were downloaded from the MIPS web server . A program to filter the protein sequences was written using the Perl programming language . The program searches for proteins with a fixed frequency of Ser/Thr within a window of selected length . The most restrictive screening was carried out selecting only transmembrane proteins , avoiding GPI-anchored and multipass membrane proteins . Protein extracts were prepared from exponentially growing cells , collected by centrifugation , washed with stop buffer ( 0 . 9% NaCl , 1 mM NaN3 , 10 mM EDTA , 50 mM NaF ) , and frozen on dry ice . All manipulations were done on ice or in the cold room ( 4° ) . For isolation of total protein extracts from surface-attached hyphae , cell suspensions were incubated on parafilm M as described below . Cells not attached to the surface were removed in a water bath . Attached hyphae were harvested in Thorner buffer ( 8 M urea , 5% SDS , 0 . 1 mM EDTA , 0 . 01% bromophenol blue , 100 mM DTT , and 100 mM Tris-HCl , pH 6 . 8 ) using a cell scraper ( Greiner , Frickenhausen/Germany ) . Total protein extracts were prepared by Fast-prep vortexing with glass beads ( Sigma ) [60] . For isolation of secreted proteins , U . maydis cells were grown in 50 mL of CM medium supplemented with 1% glucose to an OD 600 of 0 . 4 . Free-cell culture supernatant was obtained by centrifugation at 3 . 000 g for ten minutes at 4°C . Secreted proteins were isolated by incubation for 30 minutes on ice with 0 . 02% sodium deoxycholate and further addition of 10% Trichloroacetic acid . Samples were incubated overnight at 4°C and precipitated proteins were collected by centrifugation at 12 . 000 g for 15 minutes at 4°C . The pellet was air dried and resuspended in 200 µl of loading buffer 2X ( 0 . 1 mM Tris-HCl pH 6 . 8 , 4% SDS , 20% glycerol , 2 . 98 mM bromophenol blue , 0 . 2 M DTT ) . Purified proteins were separated on SDS–PAGE ( 6–12% of polyacrylamide ) . Blots were probed with anti-HA antibodies ( Roche ) or anti-GFP mouse IgG1 k antibodies ( Roche ) . To detect phosphorylated Kpp2 anti-phospho-p42/44 antibody ( cell signaling , Danvers/USA ) was used . To detect total Kpp2 a polyclonal anti-Kpp2 antibody was generated in rabbits using the Kpp2 antigen sequence N-CLTFSPRKRITVEEAL-C ( Eurogentec , Cologne/Germany ) . Anti-alpha tubulin was routinely used as loading control . As secondary antibodies horseradish peroxidase–conjugated anti-mouse IgG ( Sigma ) or anti-rabbit IgG ( Cell Signaling ) were used . Supersignal ( Pierce ) was used to detect the proteins analysed . Quantification of western blots was performed using a chemocam imager ( INTAS , Göttingen/Germany ) and ImageJ software . The in vitro system for inducing filaments and appressoria in U . maydis was applied as describes previously [27] with minor modifications [31] . Cell cultures ( 2% YEPSL ) were sprayed ( EcoSpray Labo Chimie , France ) on Parafilm M ( Pechiney Plastic Packaging , Chicago , USA ) and , if applicable , treated with 100 µM ( f . c . ) 16-hydroxyhexadecanoic acid ( Sigma ) . To quantify filaments relative to yeast-like cells samples were directly analysed by light microscopy . To quantify appressoria , surfaces were rinsed with water and later , stained with calcofluor white ( 1 µg ml−1 ) ( Sigma ) . To quantify the production of appressoria , filaments stained with calcofluor white were counted relative to filaments showing eGFP fluorescence . Data are expressed as means ±SEM of triplicate samples . Statistical significance was assessed using Statistical Calculators ( http://www . graphpad . com/quickcalcs/index . cfm ) and considered significant if P values were <0 . 05 . The SEM analyses were performed on PHILIPS XL30 microscope . Infected leaves from maize two days post-infection were fixed with 4% glutaraldehyde in cacodylate ( 0 . 1 M pH 7 . 4 ) at 4°C . The samples were washed several times in cacodylate . Afterwards , a second fixation in 1% tetraoxide osmium was carried out for two hours at 4°C . This was washed in cacodylate containing 7 . 5% of sucrose . Dehydration with acetone at 4°C: 30 minutes with 70% acetone , 30 minutes with 80% acetone , 30 minutes 90% acetone and finally , one hour with 100% acetone . The samples were then introduced into an SPI-Dry Critical Point Drying apparatus and coated in gold . To ascertain the effects of the deletion of pmt4 and msb2 on cellular adhesion , we grew the strains to exponential phase in YEPSL at 28°C and the cells were spotted on starch agar plates . Starch medium contains , 0 . 25% starch from potato , 0 . 1% ammonium sulphate , 0 . 1% sucrose and phosphate buffer 25 mM pH 7 . Plates were incubated for three days at 28°C and cellular colonies were rinsed with water to analyse their adhesion properties . To analyze the pre-penetration stages of U . maydis using fluorescence microscopy , cells were stained with calcofluor white . Post-penetration stages were studied by optical microscopy of chlorazol black E stained leaf samples as previously described [29] . Cells were examined using a Leica fluorescence microscope , equipped with a PlanApo ×100 lens . Analysis of the pre-penetration stages was done using a Deltavision widefield microscope ( Applied Precision , Issaquah , WA ) . Image deconvolution was performed using z-series of between 7 and 23 focal planes , acquired at 0 . 5 µm intervals . Image processing was carried out using Adobe Photoshop CS5 and ImageJ . U . maydis sequence data can be found in the UniProt data library under accession numbers UniProt:Q4P380 for Pmt4 , UniProt:Q4P140 for Pmt1 , UniProt:Q4PAX9 for Cwh41 , UniProt:Q4PHD3 for Msb2 , UniProt:Q9UQY5 for Kpp2/Ubc3 , UniProt:Q4PC32 for Kpp6 , UniProt:Q8J230 for Kpp4/Ubc4 , UniProt:Q99078 for Fuz7/Ubc5 , UniProt:Q4PET9 for Pit1 , UniProt:Q4PF78 for Um01235 , UniProt:Q4P817 for Um03746 , UniProt:Q4P814 for Um03749 and UniProt:Q4P5N3 for Um04580 . | The O-mannosyltransferase Pmt4 is essential for virulence of animal and plant pathogenic fungi . This protein attaches one mannose at serine/threonine residues of cell wall and secreted proteins modulating their location and function . Thus , the crucial role of Pmt4 in fungal pathogenic development is probably caused by a defective glycosylation of its target proteins altering host-fungus interaction . In this paper , we performed a screen for Pmt4 target proteins employing the fungus Ustilago maydis , which causes smut disease in maize plants . This allowed identifying novel Pmt4 target proteins having a crucial role on its virulence . One of these targets is the signalling mucin Msb2 , a conserved protein which acts upstream of MAP kinase cascades in various fungi and regulates early pathogenic development in U . maydis . We propose that Pmt4-dependent glycosylation of the extracellular domain of Msb2 is required for Msb2 activity and hence pathogenic development of U . maydis . This is divergent to the situation in S . cerevisiae where the mannosylated extracellular region of Msb2p possesses a negative regulatory function . In addition , we demonstrate important roles of Pmt4 during later stages of plant infection and identified Pmt4 target proteins which could be responsible for the virulence defect of pmt4 mutants during tumor formation . | [
"Abstract",
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] | 2012 | Identification of O-mannosylated Virulence Factors in Ustilago maydis |
Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks . However , transmission trees from one outbreak do not generalize to future outbreaks . Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission . In a survival analysis framework , estimation of transmission parameters is based on sums or averages over the possible transmission trees . A phylogeny can increase the precision of these estimates by providing partial information about who infected whom . The leaves of the phylogeny represent sampled pathogens , which have known hosts . The interior nodes represent common ancestors of sampled pathogens , which have unknown hosts . Starting from assumptions about disease biology and epidemiologic study design , we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person , place , and time . We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny . A simulation study confirms that this leads to more efficient estimates of hazard ratios for infectiousness and baseline hazards of infectious contact , and we use these methods to analyze data from a foot-and-mouth disease virus outbreak in the United Kingdom in 2001 . These results demonstrate the importance of data on individuals who escape infection , which is often overlooked . The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular infectious disease epidemiology .
The earliest use of phylogenetics in infectious disease epidemiology was to confirm or rule out a suspected source of the human immunodeficiency virus ( HIV ) . Phylogenetic analyses were used to confirm that five HIV patients were infected at a dental practice in Florida between 1987 and 1989 [19] and to rule out infection of a Baltimore patient in 1985 by an HIV-positive surgeon [20] . A more ambitious use of phylogenetics is to reconstruct a transmission tree , which is a directed graph with an edge from node i to node j if person i infected person j . An analysis by Leitner et al . [21 , 22] of an HIV-1 transmission cluster in Sweden from the early 1980s compared reconstructed phylogenies based on HIV genetic sequences to a true phylogeny based on the known transmission tree , times of transmission , and times of sequence sampling . The reconstructed phylogenies accurately reflected the topology of the true phylogeny , and the accuracy increased when sequences from different regions of the HIV genome were combined . The increasing availability of whole-genome sequence data has renewed interest in combining pathogen genetic sequence data with epidemiologic data to reconstruct transmission trees . One approach to this problem is to reconstruct the transmission tree using genetic distances . Spada et al . [23] reconstructed the transmission tree linking five children infected with hepatitis C virus ( HCV ) by finding the spanning tree linking the HCV genetic sequences that minimized the sum of the genetic distances across its edges , excluding edges inconsistent with the epidemiologic data . The SeqTrack algorithm of Jombart et al . [24] generalizes this approach . It constructs a transmission tree by finding the spanning tree linking the sampled sequences that minimizes ( or maximizes ) a set of edge weights . Snitkin et al . [25] used this algorithm to investigate a 2011 outbreak of carbapenem-resistant Klebsiella pneumoniae in the NIH Clinical Center , penalizing edges with large genetic distances , between patients who did not overlap in the same ward , or that required a long silent colonization . Wertheim et al . [26] constructed a network among HIV patients in San Diego by linking individuals whose sequences were <1% distant . This was used to estimate community-level effects of HIV prevention and treatment . A second approach to transmission tree reconstruction is to weight possible infector-infectee links using a pseudolikelihood based on genetic and epidemiologic data . Ypma et al . [27] analyzed a 2003 influenza A ( H7N7 ) outbreak among poultry farms in the Netherlands by combining data on the times of infection and culling at each farm , the distances between the farms , and RNA consensus sequences . The weight of each possible transmission link was the product of components based on temporal , geographic , and genetic data . The weight of a complete transmission tree was the product of the edge weights . The R package outbreaker implements an extension of this approach that allows multiple introductions of infection and unobserved cases [28] . Like the spanning tree methods above , these methods model pathogen evolution as a process that occurs at the moment of transmission . Morelli et al . [29] proposed a variation of these methods that allows within-host pathogen evolution by incorporating the times of infection and observation into the likelihood component for the genetic sequence data . A third approach is to reconstruct the transmission tree by combining a phylogeny with epidemiologic data , which was first done by Cottam et al . [30 , 31] in an investigation of a 2001 foot-and-mouth disease virus ( FMDV ) outbreak among farms in the United Kingdom ( UK ) . The phylogeny and the transmission tree were linked by considering possible locations of the most recent common ancestors ( MRCAs ) of viruses sampled from the farms . The probability pij that farm i infected farm j was calculated using epidemiologic data on the oldest detected FMDV lesion and the dates of sampling and culling on each farm . The weight of each possible transmission network was proportional to the product of the pij for all edges i → j . Similar methods were used to track farm-to-farm spread of a 2007 FMDV outbreak [32] . Gardy et al . [33] combined social network analysis with a phylogeny based on whole-genome sequences to construct a transmission tree for a tuberculosis outbreak in British Columbia . Didelot et al . used the time of the most recent common ancestor ( TMRCA ) to identify possible person-to-person transmission events in studies of Clostridium difficile transmission in the UK [34] and Helicobacter pylori transmission in South Africa [35] . In a study of Mycobacterium tuberculosis transmission in the Netherlands , Bryant et al . [36] ruled out transmission between individuals whose samples did not share a parent in the phylogeny . Recent research has identified problems with using genetic sequence data to reconstruct transmission trees . Simulations by Worby et al . [37 , 38] found that pairwise genetic distances cannot reliably identify sources of infection . Methods based on phylogenies often underestimate the complexity of the relationship between the phylogenetic and transmission trees . Branching events in a phylogeny do not necessarily correspond to transmissions , and the topology of the phylogenetic tree need not be the same as the topology of the transmission tree [39–41] . These differences are especially important for diseases with significant within-host pathogen diversity and long latent or infectious periods [41 , 42] . Ypma et al . [40] and Didelot et al . [42] have developed Bayesian methods that enforce consistency between phylogenetic and transmission trees in Markov chain Monte Carlo ( MCMC ) iterations . More recently , Lau et al . [43] have outlined a Bayesian integration of epidemiologic and genetic sequence data that uses likelihoods based on survival analysis , but their approach does not use pathogen phylogenies directly , assuming that a single dominant lineage within each host can be transmitted . Here , we build a systematic understanding of the relationship between pathogen phylogenies and transmission trees under much weaker assumptions about within-host evolution , allowing the incorporation of genetic sequence data into frequentist and Bayesian survival analysis of infectious disease transmission data . Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission . The transmission tree from one outbreak does not generalize to future outbreaks , but a phylogeny provides partial information about who-infected-whom . Survival analysis provides a rigorous but flexible statistical framework for infectious disease transmission data that explicitly links parameter estimation to the set of possible transmission trees [44–46] . In this framework , estimates of transmission parameters such as covariate effects on infectiousness and susceptibility and evolution of infectiousness over time in infectious individuals are based on sums or averages over all possible transmission trees . Since a phylogeny linking pathogen samples from infected individuals constrains the set of possible transmission trees , pathogen genetic sequence data can be combined with epidemiologic data to obtain more efficient estimates of transmission parameters .
At any time , each individual i ∈ {1 , … , n} is in one of four states: susceptible ( S ) , exposed ( E ) , infectious ( I ) , or removed ( R ) . Person i moves from S to E at his or her infection time ti , with ti = ∞ if i is never infected . After infection , i has a latent period of length εi during which he or she is infected but not infectious . At time ti + εi , i moves from E to I , beginning an infectious period of length ιi . At time ti + εi + ιi , i moves from I to R , where he or she can no longer infect others or be infected . The latent period εi is a nonnegative random variable , the infectious period ιi is a strictly positive random variable , and both have finite mean and variance . If person i is infected , the time elapsed since the onset of infectiousness at time ti + εi is the infectious age of i . After becoming infectious at time ti + εi , person i makes infectious contact with j ≠ i at time t i j = t i + ε i + τ i j * . We define infectious contact to be sufficient to cause infection in a susceptible person , so tj ≤ tij . The infectious contact interval τ i j * is a strictly positive random variable with τ i j * = ∞ if infectious contact never occurs . Since infectious contact must occur while i is infectious or never , τ i j * ∈ ( 0 , ι i ] or τ i j * = ∞ . For each ordered pair ij , let Cij = 1 if infectious contact from i to j is possible and Cij = 0 otherwise . For example , the Cij could be the entries in the adjacency matrix for a contact network . However , we do not require that Cij = Cji . We assume the infectious contact interval τ i j * is generated in the following way: A contact interval τij is drawn from a distribution with hazard function hij ( τ ) . If τij ≤ ιi and Cij = 1 , then τ i j * = τ i j . Otherwise , τ i j * = ∞ . Survival analysis of infectious disease transmission data can be viewed as a generalization of discrete-time chain binomial models [47] to continuous time , and it includes parametric methods [44] , nonparametric methods [45] , and semiparametric relative-risk regression models [46] . For simplicity , we use parametric methods and assume that exogenous infections are known . Let the hazard of infectious contact from i to j at time τ after the onset of infectiousness in i be h i j ( τ ) = exp β 0 ⊤ X i j ( τ ) h 0 ( τ ) , ( 1 ) where β0 is an unknown coefficient vector , Xij ( τ ) is a covariate vector , and h0 ( τ ) is a baseline hazard function . The vector Xij ( τ ) can include individual-level covariates affecting the infectiousness of i or the susceptibility of j as well as pairwise covariates ( e . g . , membership in the same household ) . The coefficient vector β0 captures covariate effects on the hazard of transmission , and the baseline hazard function h0 ( τ ) captures the evolution of infectiousness over time in infectious individuals . We assume that τij can be observed only if j is infected by i at time ti + εi + τij . The contact interval τij will be unobserved if i recovers from infectiousness before making infectious contact with j , if j is infected by a someone other than i , or if observation of j has stopped . Let Ii ( τ ) = 1τ ∈ ( 0 , ιi] be a left-continuous process indicating whether i remains infectious at infectious age τ . Let Sij ( τ ) = 1ti+εi+τ ≤ tj be a left-continuous process indicating whether j remains susceptible when i reaches infectious age τ . Assume that the population is under observation until a stopping time T and let Oij ( τ ) = 1ti+εi+τ ≤ T be a left-continuous process indicating whether j is under observation when i reaches infectious age τ . Then Y i j ( τ ) = C i j I i ( τ ) S i j ( τ ) O i j ( τ ) ( 2 ) is a left-continuous process indicating whether infectious contact from i to j can be observed at infectious age τ of i . The assumptions above ensure that censoring of τij is independent for all ij , and they can be relaxed if independent censoring is preserved . Let θ be a parameter vector for a family of hazard functions h ( τ , θ ) such that h0 ( τ ) = h ( τ , θ0 ) for an unknown θ0 . To allow maximum likelihood estimation , we assume that h ( τ , θ ) has continuous second derivatives with respect to θ . Let h i j ( τ , β , θ ) = exp β ⊤ X i j ( τ ) h ( τ , θ ) . ( 3 ) Let W j = { i : t i + ε i < t j and C i j = 1 } denote the set of all infectious individuals to whom j was exposed while susceptible , which we call the exposure set of j . When we observe who-infected-whom ( i . e . , v is known ) , the likelihood is L v ( β , θ ) = ∏ j = 1 n h v j j ( t j - t v j - ε v j , β , θ ) 1 v j ∉ { 0 , ∞ } ∏ i ∈ W j e - ∫ 0 ι i h i j ( τ , β , θ ) Y i j ( τ ) d τ . ( 4 ) The hazard terms depend on v , but the survival terms do not [44] . When we do not observe who-infected-whom , the likelihood is a sum over all possible transmission trees: L ( β , θ ) = ∑ v ∈ V L v ( β , θ ) [44] . Each v ∈ V can be generated by choosing a v j ∈ V j for each endogenous infection j . Given the epidemiologic data , each vj can be chosen independently [48] . This leads to the sum-product factorization L ( β , θ ) = ∏ j = 1 n ∑ i ∈ V j h i j ( t j - t i - ε i , β , θ ) 1 v j ∉ { 0 , ∞ } ∏ i ∈ W j e - ∫ 0 ι i h i j ( τ , β , θ ) Y i j ( τ ) d τ . ( 5 ) The probability of a particular transmission tree v is Pr ( v | β , θ ) = L v ( β , θ ) L ( β , θ ) = ∏ j : v j ∉ { 0 , ∞ } h v j j ( t j - t v j - ε v j , β , θ ) ∑ i ∈ V j h i j ( t j - t i - ε i , β , θ ) , ( 6 ) and Lv ( β , θ ) = Pr ( v|β , θ ) L ( β , θ ) . In this framework , estimation of ( β , θ ) , and the probabilities of possible transmission trees is simultaneous . An interesting special case is when hij ( τ , β , θ ) = λ for all ij . Then Pr ( v|β , θ ) does not depend on λ , so the transmission tree is an ancillary statistic [44] . The relationship between phylogenies and transmission trees we develop here is similar to the approach taken by Cottam et al . [31] who linked phylogenetic and transmission trees via the locations of common ancestors . It is logically equivalent to the approaches of Ypma et al . [40] who joined the within-host phylogenies of infectors and infectees into a single phylogeny , Didelot et al . [42] who colored lineages in the phylogeny with a unique color for each individual , and Hall and Rambaut [49] who represented transmission trees as partitions of phylogenies . We begin with these assumptions: The first two assumptions concern the biology of disease . The last three assumptions concern the resolution of the epidemiologic data , which can be controlled through study design . Initially , we use only the topology of the pathogen phylogeny to infer the set of possible transmission trees . Later , we consider how branching times at interior nodes further restrict the set of possible transmission trees . To study the impact of a phylogeny on the efficiency of transmission parameter estimates , we conducted a series of 1 , 000 simulations . In each simulation , there were 100 independent households of size 6 . Each household had an index case with an infection time chosen from an exponential distribution with mean one . Each individual i had a binary covariate Xi that could affect infectiousness and susceptibility . Given a parameter vector β = ( βinf , βsus ) , the hazard of infectious contact from i to j at infectious age τ of i is h i j ( τ , β ) = exp ( β inf X i + β sus X j ) λ 0 . ( 16 ) In each simulation , βinf and βsus were independently chosen from a uniform distribution on ( −1 , 1 ) . In all simulations , the baseline hazard was λ0 = 1 and the infectious periods were independent exponential random variables with mean one . In each simulation , we analyzed data from the first 200 infections in three ways: using only epidemiologic data via the likelihood in Eq ( 5 ) , using epidemiologic data with who-infected-whom via the likelihood in Eq ( 4 ) , and using epidemiologic data with a phylogeny via the likelihood in Eq ( 7 ) . In the phylogenetic analysis , we assumed a single pathogen sample from each infected individual . The within-host phylogeny for each individual who infected m > 0 individuals was chosen uniformly at random from all rooted , bifurcating phylogenies with m + 1 tips . Within-individual phylogenies were chosen independently and combined into a single phylogeny as in Ypma et al . [40] . Thus , the conditional probability Pr ( Φ|v , Epi ) for a phylogeny Φ given a transmission tree v in which each individual j infected mj ≥ 0 other individuals was proportional to ∏ j : m j > 0 2 m j - 1 ( m j - 1 ) ! ( 2 m j - 1 ) ! . ( 17 ) The set of transmission trees consistent with the phylogeny was determined using Algorithms 1 and 2 . We calculated the mean error , mean squared error , 95% confidence interval coverage probability , and relative efficiency of βinf , βsus , and ln λ0 estimates in all three analyses . The simulations were conducted in Python 2 . 7 ( www . python . org ) and analysis was conducted in R 3 . 2 ( cran . r-project . org ) via RPy2 2 . 7 ( rpy . sourceforge . net ) . The Python code is in S1 Text . Parameters , point estimates , and 95% confidence limits are in S1 Data . R code for the simulation data analysis is in S1 Text . To illustrate an application of these algorithms and likelihoods , we use them to analyze farm-to-farm transmission trees of foot and mouth disease virus ( FMDV ) in a cluster of 12 epidemiologically linked farms in Durham , UK in 2001 . The genetic and epidemiologic data are publicly available as Data S3 and Data S4 in Morelli et al . [29] . These data were previously analyzed by Cottam et al . [31 , 32] , Morelli et al . [29] , Ypma et al . [40] , and Lau et al . [43] . FMDV is a picornavirus that causes a highly contagious disease in cattle , pigs , sheep , and goats [51] . Upon infection , there is an incubation period of approximately 1–12 days in sheep , 2–14 days in cattle , and two or more days in pigs . The incubation period is followed by an acute febrile illness with painful blisters on the feet , the mouth , and the mammary glands . It is transmitted through secretions from infected animals , fomites , virus carried on skin or clothing , and aerosolized virus . Outbreaks of foot-and-mouth disease are difficult to control and can devastate livestock . During the FMDV outbreak , teams from the UK Department for Environment , Food , and Rural Affairs ( DEFRA ) visited each infected farm [30 , 31] . They recorded the number and types of susceptible and infected animals , examined infected animals to determine the age of the oldest lesions , and collected epithelial samples . Finally , they recorded the date of the cull . We assume that infectiousness begins on the day that the first lesions appeared and ends with the cull , and we assumed a latent period ( between infection and the onset of infectiousness ) of 2–16 days . Fig 2 shows the relative locations of the farms , and Fig 3 shows the timeline of the latent and infectious periods . Analysis was conducted in R 3 . 2 ( cran . r-project . org ) , and the code is available in S3 Text .
Table 1 shows the mean error , mean squared error , 95% confidence interval coverage probability , and relative efficiency of βinf , βsus , and ln λ0 estimators in the simulations . In all cases , the point estimates were nearly unbiased ( indicated by the mean error squared being much smaller than the mean squared error ) and the 95% confidence interval coverage probabilities were near 0 . 95 . Fig 4 shows that estimates of βinf using a phylogeny were more efficient than estimates using epidemiologic data only and less efficient than estimates using who-infected-whom . By mean squared error , the phylogenetic estimates had a relative efficiency of 1 . 39 compared to estimates using only epidemiologic data and 0 . 80 compared to estimates using who-infected-whom . Because knowledge of who-infected-whom does not add to our knowledge of who was infected , all three analyses were equally efficient for βsus ( similar results were obtained for estimates with and without who-infected-whom in Ref [46] ) . Fig 5 shows that estimates of ln λ0 using a phylogeny were more efficient than those using epidemiologic data only and less efficient than those using who-infected whom . By mean squared error , the phylogenetic estimates had a relative efficiency of 1 . 17 compared to estimates using only epidemiologic data and 0 . 90 compared to estimates using who-infected-whom . Table 2 shows the mean error , mean squared error , 95% confidence interval coverage probability , and relative efficiency of βinf , βsus , and ln λ0 estimators that excluded data on uninfected household members . The mean squared errors were much higher than the corresponding estimators in Table 1 , so their relative efficiency was very low . In all cases , the efficiency loss from excluding data on individuals who escape infection was much larger than the efficiency gain from incorporating a phylogeny or from knowing exactly who infected whom . For estimators of βinf and βsus , the square of the mean error was much smaller than the mean squared error , indicating little bias . Estimates of ln λ0 were biased upward , resulting in extremely low relative efficiencies and coverage probabilities . In [44] , similar results were seen for estimates of the basic reproduction number ( R0 ) when approximate likelihoods for mass-action models , which do not require data on uninfected individuals , were used to analyze data from network-based epidemics . Table 3 shows results the mean error , mean squared error , 95% confidence interval coverage probability , and relative efficiency of βinf , βsus , ln λ0 , and ln γ estimators from models with Weibull contact interval distributions with rate parameter λ0 = 2 and shape parameter γ = . 5 . All estimators are unbiased with 95% confidence interval coverage probabilities near 0 . 95 . The relative efficiencies are similar to those in Table 1 , showing that the gains in efficiency for estimates of infectiousness hazard ratios and baseline hazards occur under weak assumptions about the baseline hazard . With no phylogeny , there are 19 , 440 possible transmission trees linking the 12 farms in the Durham cluster . A phylogeny was constructed in SeaView [52] using consensus RNA sequences from 15 farms , including three farms not epidemiologically linked to the cluster [29] . We used a generalized time reversible ( GTR ) nucleotide substitution model with four rate classes on 8 , 196 sites . Fig 6 shows the rooted phylogeny for the 12 farms in the cluster with branch tips scaled to reflect the time of infectiousness onset at each farm ( interior branch lengths do not indicate branching times ) . The order of infectiousness onsets is known , so first ( x ) is the host with the earliest onset of infectiousness in clade Cx . Fig 7 shows the postorder host set Dx for each node x in the phylogeny , and Fig 8 shows the host sets . The host is uniquely determined by the phylogeny for all interior nodes except three . Fig 9 shows the six possible interior node host assignments and the corresponding transmission trees .
The simulations suggest that a phylogeny can recover much of the information that would be obtained by observing who-infected-whom . Incorporating a phylogeny generated more precise estimates of βinf and ln λ0 . This increase in efficiency remained when infectiousness varied over the course of the infectious period , as in the Weibull models . The simulations used only phylogenetic topologies and assumed that all within-host topologies were equally likely , limiting the ability of the phylogeny to constrain the set of possible transmission trees . Using branching times and more realistic models of within-host pathogen evolution would allow greater information about who-infected-whom to be extracted from a phylogeny . The simulations that excluded data on household members who escape infection showed that this information is critical to estimating βinf , βsus , and ln λ0 accurately . These individuals do not appear anywhere on the pathogen phylogeny , so this point has escaped the attention of many researchers working on incorporating phylogenetics into the analysis of infectious disease transmission data . Any analysis that excludes this data should have an explicit justification based on a complete-data model—for example , the initial spread of a mass-action epidemic can be analyzed without data on escapees [44] . In general , epidemiologic studies of emerging infections should be designed to capture information on individuals who were exposed to infection but not infected , which might justify greater emphasis on detailed studies of households , schools , or other settings with rapid transmission and a clearly defined population at risk . The data analysis showed that the increased precision found in the simulations can be obtained in practice . The incorporation of phylogenies allowed more precise estimates of the hazard of FMDV transmission from infected to susceptible farms . For simplicity , our analysis assumed that the times of infectiousness onset were accurately estimated . A data-augmented MCMC [53] could be used to account for uncertainty in the onset of infectiousness , showing the importance of extending our methods to account for missing data . A more important limitation of this analysis was the lack of data on uninfected farms . The hazard function estimates were highly sensitive to the number of uninfected farms in the area where the cluster occurred . These data often go uncollected in outbreaks because their importance is unrecognized . This insight has important implications for the theory and practice of molecular infectious disease epidemiology . | Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks . However , transmission trees from one outbreak do not generalize to future outbreaks . Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission . Accurate estimates of transmission parameters can help identify risk factors for transmission and aid the design and evaluation of public health interventions for emerging infections . Using statistical methods for time-to-event data ( survival analysis ) , estimation of transmission parameters is based on sums or averages over the possible transmission trees . By providing partial information about who infected whom , a pathogen phylogeny can reduce the set of possible transmission trees and increase the precision of transmission parameter estimates . We derive algorithms that enumerate the transmission trees consistent with a pathogen phylogeny and epidemiologic data , show how to calculate likelihoods for transmission data with a phylogeny , and apply these methods to a foot and mouth disease outbreak in the United Kingdom in 2001 . These methods will allow pathogen genetic sequences to be incorporated into the analysis of outbreak investigations , vaccine trials , and other studies of infectious disease transmission . | [
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"p... | 2016 | Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees |
Visceral leishmaniasis is associated with significant changes in hematological function but the mechanisms underlying these changes are largely unknown . In contrast to naïve mice , where most long-term hematopoietic stem cells ( LT-HSCs; LSK CD150+ CD34- CD48- cells ) in bone marrow ( BM ) are quiescent , we found that during Leishmania donovani infection most LT-HSCs had entered cell cycle . Loss of quiescence correlated with a reduced self-renewal capacity and functional exhaustion , as measured by serial transfer . Quiescent LT-HSCs were maintained in infected RAG2 KO mice , but lost following adoptive transfer of IFNγ-sufficient but not IFNγ-deficient CD4+ T cells . Using mixed BM chimeras , we established that IFNγ and TNF signalling pathways converge at the level of CD4+ T cells . Critically , intrinsic TNF signalling is required for the expansion and/or differentiation of pathogenic IFNγ+CD4+ T cells that promote the irreversible loss of BM function . These findings provide new insights into the pathogenic potential of CD4+ T cells that target hematopoietic function in leishmaniasis and perhaps other infectious diseases where TNF expression and BM dysfunction also occur simultaneously .
T cells reside in bone marrow ( BM ) and comprise 4–8% of total BM cells . Recent studies have indicated that the BM is a preferential site for homing and persistence of memory T cells that have a high proliferative potential following second encounter with a cognate antigen [1 , 2] . Furthermore , alterations in BM T cells have been reported in patients suffering from BM failure syndromes [3] and in experimental models for aplastic anaemia [4 , 5] . However , the function ( s ) of the BM T cell compartment are relatively poorly understood compared to their counterparts in lymphoid tissues , particularly so in the context of infectious diseases where pathogens themselves reside in the BM . An association between alterations in hematopoietic function and changes in BM T cells has been described in mice infected with Ehrlichia muris [6 , 7] , but mechanistic insight into these processes has been limited . Hematopoiesis is a strictly regulated process that depends on a small pool of Long-term hematopoietic cells ( LT-HSCs ) , which have self-renewal capacity and the potential to give rise to all mature blood cells during the lifespan of an individual . According to the classical pathway of hematopoiesis , LT-HSCs differentiate into short-term hematopoietic stem cells ( ST-HSCs ) that differentiate into a heterogeneous group of multipotent progenitors ( MPPs ) . LT-HSCs , ST-HSCs and MPPs are contained within the LSK population , so called for their lack of expression of mature blood cell-associated markers ( Lineage negative ) and their expression of Sca1 and cKit . MPPs give rise to intermediary progenitors , the common lymphoid progenitors ( CLPs ) and the common myeloid progenitors ( CMPs ) , the latter subsequently giving rise to both granulocyte/macrophage progenitors ( GMPs ) and megakaryocytic/erythrocyte progenitors ( MEPs ) [8] . Non-committed and lineage-committed progenitors are collectively defined as hematopoietic stem and progenitor cells ( HSPCs ) . The integration of systemic and local signals by HSPCs has been suggested to be one mechanism that allows these cells to respond to infection and subsequently help regulate immune effector function [9] . In contrast , prolonged activation and proliferation of HSCs has been associated with functional exhaustion in several infection models [7 , 10 , 11] , and may underlie the association between chronic infection and hematological dysfunction , as commonly described in humans [12] . The immune mechanisms associated with HSC exhaustion and whether these operate in an HSC-intrinsic manner or reflect alterations in the BM microenvironment remain important unanswered questions . Visceral leishmaniasis ( VL ) , caused by infection with the obligate intracellular parasites Leishmania donovani and L . infantum is characterized by parasite accumulation in systemic tissues , including BM , and clinical signs including hypergammaglobulinaemia , hepato-splenomegaly and disturbances in blood homeostasis , including anemia , thrombocytopenia , leucopenia and neutropenia [13–15] . The infection is fatal without drug treatment and even treated patients may die from bleeding or opportunistic bacterial infections [16 , 17] . In humans , splenic sequestration and ineffective haematopoiesis have been suggested as possible causes to explain peripheral cytopenia , and noted alterations in the BM include erythroid hyperplasia , increased plasma cells , increased frequency of granulocytic and megakaryocyte immature forms , and histiocytic hyperplasia [15] . Furthermore , several clinical reports have described pancytopenia in VL patients followed by a multilineage myelodysplasia reminiscent of true myelodysplastic syndrome ( MDS ) , suggesting the presence of ineffective haematopoiesis [18–20] . Experimental rodent models have been extensively used to study the immunopathology of VL , also reporting alterations in hematopoietic function . For example , L . donovani infection in BALB/c mice is associated with increased numbers of hematopoietic precursor cells , as assessed by colony-forming units in culture [21] , and both mice and hamsters show various degrees of cytopenia and changes in BM cellularity following infection [22 , 23] . Here , we demonstrate that L . donovani infection in mice drives LT-HSCs into active proliferation at the expense of cells in quiescence , leading to functional exhaustion . Importantly , this response was dependent upon increased numbers of IFNγ-producing CD4+ T cells with resident effector function in the BM of infected mice , but not on HSC-intrinsic IFNγ signalling . Unexpectedly , we found that expansion of BM effector T cells was regulated by T cell-intrinsic TNF receptor signalling , indicating a novel means by which TNF and IFNγ signalling pathways cooperate and converge at the level of CD4+ T cells to effect long-lasting impairment of hematopoietic function during infection .
We first characterised the impact of L . donovani infection ( Fig 1A and 1B ) on BM HSPCs using a panel of markers [8] ( S1 Table; Fig 1C ) . In this analysis , we allowed for the finding that Sca1 is up-regulated on all progenitors after infection [6 , 24] , leading to a deficiency in the cKit+ Sca1- and cKit- Sca1- cell populations compared to naïve mice ( Fig 1D ) . Infection resulted in a significant increase in the number of multipotent Lineage- cKit+ Sca1+ precursors in the BM that mirrored the course of infection and peaked on d28 post infection ( p . i . ) ( Fig 1E ) . CD48 has been associated with a loss of stemness amongst LT-HSC [25–28] . Hence , we further characterised LSK CD150+CD34- cells on the basis of CD48 expression . The number of LSK CD150+CD34-CD48- cells ( enriched for LT-HSCs ) was unaltered in d28-infected mice compared to uninfected mice . In contrast , the numbers of LSK CD150+CD34-CD48+ cells and LSK CD150+CD34+ cells were significantly increased ( Fig 1F ) . Notably , this increase in non-committed progenitors was not matched by increased numbers of lineage-committed precursors ( Fig 1G ) , suggesting the possibility that HSPC differentiation was inhibited . Collectively , these findings indicate that infection had induced changes in hematopoietic differentiation prior to lineage commitment . To evaluate whether L . donovani infection affected the function of HSPCs , we used a competitive adoptive transfer model . We selected this approach because it allows for the evaluation of the function of progenitor cells derived from infected and non-infected hosts in the same environment . Although long-term in vitro cultures are able to quantify more primitive progenitors , the growth factors required for LT-HSCs and their immediate progeny are not well established and may , therefore , impact the differentiation process [29] . BM lineage negative cells ( enriched for HSPCs ) from day 28 infected B6 . CD45 . 2 mice and from uninfected B6 . CD45 . 1 mice were mixed 50:50 and transferred into non-infected x-irradiated ( B6 . CD45 . 1 x B6 . CD45 . 2 ) F1 recipients and cellularity assessed seven weeks later ( Fig 2A; S1A Fig ) . HSPCs from infected mice contributed 21 . 81% ± 11 . 27 total donor cells in the BM of recipient mice and 24 . 76% ± 2 . 43 of total splenocytes ( Fig 2B ) . Hence , HSPCs from infected mice have reduced competitiveness compared to HSPCs derived from naïve mice . No significant differences were noted in the frequency of B cells , T cells and CD11b+ myeloid cells , however , indicating that lack of HSPCs competitiveness was not associated with any evident lineage bias ( Fig 2C ) . This was reflected in the similar frequencies of BM multipotent progenitors ( Fig 2D ) and lineage committed precursors ( Fig 2E ) derived from HSPCs from infected and naive mice . To determine whether LT-HSC might be infected with L . donovani , we infected mice for 28 days with Td-Tomato transgenic L . donovani and examined BM cells for the presence of amastigotes by flow cytometry . Although we observed that a very small percentage of BM lineage negative cells were infected with L . donovani , we did not observe infection of LT-HSCs ( S1E Fig ) , ruling out infection of LT-HSCs as a reason for their altered competitiveness . These data suggested that infection results in HSC-intrinsic functional impairment that occurs prior to lineage commitment . To test this hypothesis , we initially performed a long-term non-competitive adoptive transfer experiment , placing LSK CD150+ CD34- CD48- cells ( LT-HSCs ) from CD45 . 2 naive or infected mice into naive CD45 . 1 recipients ( Fig 2F ) . Donor LT-HSCs from infected mice showed a trend towards poorer reconstitution in BM ( 2 . 72 x 107 ± 1 . 41 x 107 cells vs . 6 . 96 x 106 ± 7 . 89 x 106 cells , naive vs . infected mice ) , and the same was observed in the spleen ( Fig 2G ) . We could not detect any significant alteration in the distribution of mature spleen cells derived from infected compared to naive donor cells , again suggesting no lineage bias arises from HSCs from infected mice ( S1B Fig ) . However , the number of LSK cells and LSK CD150+ FLT3- CD34- cells ( enriched in LT-HSCs ) derived from infected donors was significantly decreased compared to naïve donors ( Fig 2H ) , again indicating that HSCs from infected mice were less able to reconstitute the hematopoietic system and their self-renewal potential was compromised . To further investigate the long-term reconstituting potential and to more accurately evaluate the reduction in functional capacity of HSCs from infected mice , we transferred 50 LSK CD150+ CD34- CD48- cells that were isolated from the primary adoptive transfer recipients into secondary recipients ( Fig 2F ) . We identified BM and spleen cells in all ( 4/4 ) recipients of LT-HSCs originally obtained from naive mice . In contrast , only 2/3 BM and 2/3 spleens examined had detectable cells derived from HSCs originally taken from infected mice and these were very rare in number ( Fig 2I ) . In all secondary recipient mice transplanted with LT-HSCs isolated from non-infected mice , we could detect donor HSPCs for the three lineages in the BM of recipient mice and , following an overall period of 40 weeks of transfer into healthy recipients , LT-HSCs derived from naive donors were capable of self-renewal and of giving rise to all lineages . In contrast , we only found LSK CD150+ CD48- cells derived from infected donors in 1/3 recipients and we failed to find lineage-committed progenitors or mature progeny in 2/3 recipients ( Fig 2J , S1C and S1D Fig ) . Collectively , these data suggested that HSCs from infected mice show intrinsic long-term functional impairment . In naïve mice , LT-HSCs are commonly found in G0 ( Ki67- ) , and a strict regulation of LT-HSC proliferative status is required for the maintenance of long-term self-renewal and multi-lineage differentiation potential [30 , 31] . We found that approximately 40% of LT-HSCs in naïve mice expressed Ki67 ( Fig 3A and 3D ) . At day 28 p . i . , however , the frequency of LT-HSCs in cell-cycle was significantly increased ( 96 . 52% ± 3 . 19 ) ( Fig 3A and 3D ) . Onward populations of non-committed progenitors were highly proliferative both in naïve and infected mice , and following infection these resulted in an increased number of LSK CD150+ CD34- CD48+ Ki67+ cells and LSK CD150+ CD34+ Ki67+ cells in infected mice ( 90-fold and 4 . 8-fold , respectively , compared to naive mice ) ( Fig 3Ai and 3B ) . Furthermore , the loss of LSK CD150+ CD34- CD48- Ki67- cells during infection represented a 24-fold decrease in number ( from 831 ± 0 . 017 to only 34 . 62 ± 31 . 93 cells in total BM; Fig 3C ) . Hence , depletion of the reservoir of LT-HSCs due to proliferation may account for poor reconstitution efficiency shown by HSPCs from infected mice . To determine the mechanisms regulating proliferation , we assessed the expression of the transcription factor GATA-3 , as recent studies have suggested that GATA-3 is important in the regulation of HSC proliferative status and self-renewal potential during stress-induced hematopoiesis [32 , 33] . We observed an infection-associated increase in GATA-3 expression in most immature cells ( S2A Fig ) . We next determined whether the expression of GATA-3 could be related to the proliferative state of LSK CD150+ CD48- cells . Few cells in G0 expressed GATA-3 but concomitant with infection , we observed a significant increase in LSK CD150+ CD48- cells expressing Ki67 and GATA-3 ( 17 . 64 ± 11 . 23 vs . 46 . 68 ± 21 . 66 , in naive vs . infected mice , respectively ) ( S2B Fig ) , and in infected mice we determined a significant alteration in the distribution of LSK CD150+ CD48- cells segregated according to the expression of GATA-3 and Ki67 ( S2B Fig ) . As such , GATA-3 over expression due to inflammation may represent a mechanism for impaired maintenance of homeostatic numbers of quiescent LT-HSCs during L . donovani chronic infection . In contrast to wild type ( WT ) B6 mice , immunodeficient Recombination activating gene 2 ( Rag2 ) knockout mice showed no signs of infection-associated changes in HSPCs , despite a significantly higher parasite burden ( Fig 4A and 4C ) . Furthermore , infection of Rag2 KO mice did not deplete the reservoir of quiescent HSCs ( Fig 4B ) , suggesting a central role for adaptive immunity as a driver of these effects in immunocompetent mice . In WT mice , we found that BM CD4+ T cells increased in number 24-fold on infection , whereas CD8+ T cells increased ~2-fold ( Fig 5A ) . To determine if these BM CD4+ T cells were phenotypically similar to the recently described BM resident effector cells , we examined the expression of CD44 , CD127 and Ly6C [1 , 34] . In the BM , a majority of CD4+ T cells were CD44high cells , increasing from 72 . 22% ± 8 . 67 of total CD4+ T cells in naive mice to 93 . 56% ± 1 . 39 in infected mice . The most abundant population of CD4+ T cells was CD44high Ly6C-/low CD127-/low ( “effector T cells” ) ( Fig 5B ) , consistent with the accumulation of effector CD4+ T cell in BM during infection with L . donovani . To characterize their potential for cytokine production , we used in vitro stimulation with PMA and ionomycin followed by flow cytometry ( Fig 5C–5K ) . The frequency of BM CD4+ T cells capable of producing IFNγ within the total BM was very low ( 0 . 10% ± 0 . 05 ) in naive mice , but increased following infection ( 7 . 64% ± 3 . 57 of total BM cells; Fig 5C ) . The percentage of CD8+ T cells with the potential to produce IFNγ also increased in the BM of infected mice but to a lesser extent ( 0 . 24% ± 0 . 14 vs 1 . 16% ± 1 . 03 , naive vs . infected ) , and there was no indication of other relevant sources of IFNγ ( Fig 5C ) . Within the CD4+ T cell population of infected mice , 82 . 06% ± 14 . 23 of cells had the potential to produce IFNγ compared to 17 . 33% ± 8 . 95 in naive controls ( Fig 5E and 5K ) , and a significant increase in the frequency of cells expressing IFNγ driven by L . donovani infection was also determined directly ex vivo ( Fig 5F and 5K ) . Analysis of the MFI for INFγ+ within CD4+ T cells also demonstrated an increase in cytokine production on a per cell basis , compared to CD4+ T cells from naive mice ( Fig 5I ) . Similar data were also obtained for TNF production by BM CD4+ T cells ( Fig 5D , 5G , 5H and 5J ) . Overall these data show that L . donovani infection stimulated marked recruitment / expansion of highly active BM effector CD4+ T cells . We hypothesized that BM CD4+ T cells might drive the alterations observed in the hematopoietic compartment during infection . To assess whether this was the case , Rag2 KO mice were adoptively transferred with sorted CD4+ T cells from naive B6 mice , and then infected with L . donovani ( Fig 6A ) . The number of LT-HSCs was unchanged in all three groups ( Fig 6B ) , whereas the number of intermediary non-committed progenitors ( LSK CD150+CD34-CD48+ cells and LSK CD150+CD34+ cells ) increased in adoptively transferred Rag2 KO mice to a similar extent as observed in infected WT mice ( Fig 6C and 6D ) . More importantly , we found that whereas infected Rag2 KO mice preserved their reservoir of quiescent LT-HSCs , this was reversed following CD4+ T cell transfer ( Fig 6E ) . In contrast , adoptively transferred Rag2 KO mice that were not infected retained their full quiescent HSPC pool , indicating that infection rather than homeostatic expansion of CD4+ T cells is required to drive LT-HSCs out of quiescence ( Fig 6F and 6G ) . As IFNγ signalling has been associated with altered HSC function [6 , 7 , 10 , 11 , 35] , we assessed the competency of IFNγ-/- CD4+ T cells to regulate loss of quiescence in infected Rag2 KO recipients . All effects attributed to the transfer of CD4+ T cells to Rag2 KO recipients described above were lost when these cells were incapable of producing IFNγ ( Fig 6H and 6I ) , defining CD4+ T cell-derived IFNγ as a critical regulator of LT-HSC function during L . donovani infection . On the other hand , IFNγ was critical to control parasite burden ( Fig 6J ) , indicating that the mechanisms underlying host resistance to infection may also impact hematopoietic function when sustained over time . HSCs express receptors for IFNγ , which have been directly associated with LSK expansion and impaired engraftment in X-irradiated hosts [7 , 35] . To test whether LT-HSC intrinsic IFNγR signalling was required for loss of quiescence , we generated 50:50 mixed BM chimeras using cells derived from both WT and Ifnγr2-/-mice ( B6 . CD45 . 1 + B6 . CD45 . 2 . Ifnγr2-/-→B6 . CD45 . 1 ) ( Fig 7A ) . The percentage of BM cells derived from Ifnγr2-/- donor cells was comparable to WT donor cells ( Fig 7B ) . In infected recipient mice , the frequency of WT and Ifnγr2-/- LSK CD150+ CD48- cells in the BM was similar , as was the increase in frequency of LSK CD150+ CD48+ cells derived from WT donor cells and Ifnγr2-/- donor cells ( Fig 7C ) . Most importantly , loss of quiescent HSCs was observed equally for WT and Ifnγr2-/- derived cells ( Fig 7D ) . Thus , intrinsic IFNγ signalling was not mediating the expansion of multipotent progenitors or LT-HSC exhaustion in this model . Nonetheless , significant changes in the frequency of myeloid progenitors and mature myeloid cells derived from WT and Ifnγr2-/- were observed following infection , suggesting that IFNγ signalling may modulate the generation of myeloid cells in response to L . donovani infection ( S3A , S3C and S3E Fig ) . There was no indication that IFNγ signalling played a major role regulating the B cell compartment following infection ( S3B and S3D Fig ) . Lack of IFNγR2 led to an increase in the frequency of donor cells in the spleen compared to WT donor cells , suggesting that IFNγ signalling impacts on hematopoietic function both in steady-state and under inflammatory conditions ( Fig 7E ) . In BM and spleen of non-infected recipient mice , the frequency and absolute number of T cells was comparable between WT and Ifnγr2-/- donor cells , indicating that IFNγ signalling in T cells was not required for their development or homeostatic maintenance . In contrast , the lack of IFNγR2 intrinsic signalling prevented the expansion of the T cell compartment following infection that was seen with WT T cells , both in BM and in the spleen ( Fig 7F–7I ) . As such , intrinsic IFNγ signalling confers a proliferative or survival advantage for CD4+ T cells during L . donovani induced inflammation , but not under conditions of homeostatic expansion . TNF has also been proposed to play an important role in directly modulating HSC function and may cooperate with other mechanisms in driving stress-induced hematopoiesis and mediating hematopoietic dysfunction [36] . Non-committed progenitors upregulated the expression of receptors for TNF ( TNFR1a and TNFR1b ) during L . donovani infection ( S4 Fig ) . To formally test whether intrinsic TNF signalling plays a role in driving LT-HSCs into active cell-cycle and subsequent LT-HSC exhaustion , we transferred equal numbers of BM cells from WT and Tnfrsf1-dKO donors into lethally irradiated recipients ( Fig 8A ) . In naïve chimeras assayed at 13 weeks post BM transfer ( BMT ) , Tnfrsf1-dKO cells showed a clear competitive advantage ( representing 68 . 51% ± 7 . 58 of total cells in BM; Fig 8B ) . This was also observed in the spleen and suggested that TNF signalling may modulate hematopoietic function during homeostasis . Although minor changes in relative frequency occurred following infection , the bias towards Tnfrsf1-dKO cells was not enhanced ( Fig 8B ) . Loss of both TNFRs had no impact on the expansion of LSK cells ( Fig 8C ) or on the loss of quiescent LT-HSCs ( Fig 8D ) . Minor changes were observed in the lineage-committed progenitors , B cells and CD11b+ cells in the BM and spleen of infected recipients ( S5A , S5B , S5C and S5D Fig ) . These data argue against a role for LT-HSC intrinsic TNF signalling in the expansion of multipotent progenitors and in LT-HSC exhaustion . Remarkably , in non-infected chimeric mice , the frequencies and number of total BM T cells and BM CD4+ T cell derived from WT and Tnfrsf1-dKO donor cells were similar , whereas following infection differences in T cells derived from WT and Tnfrsf1-dKO donor cells became evident in the BM . Tnfrsf1-dKO CD4+ T cells did not increase in frequency or number following infection , whereas WT CD4+ T cells expanded approximately 15-fold ( Fig 8E and 8F ) . Likewise , in the spleen WT T cells but not Tnfrsf1-dKO T cells increased in frequency and number , although to a lesser extent than in the BM ( S5E and S5F Fig ) . Thus , TNF acts directly on BM CD4+ T cells to regulate their expansion following infection . As the expansion of BM CD4+ T cells following infection was prevented in cells lacking TNF signalling receptors , we used the mixed chimeras described above to assess whether T cells devoid for TNF signalling were impaired in their efficiency to produce IFNγ , a cytokine critical in regulating LT-HSC exhaustion ( Fig 6 ) . In the BM of these infected mice , 2 . 29x 106 ± 7 . 67x105 WT CD4+ T cells had the potential to express IFNγ compared to 7 . 87x104 ± 3 . 07x104 Tnfr1/Tnfr2 double KO donor cells ( Fig 8G ) . This disparity between WT and Tnfr1/Tnfr2 double KO donor CD4+ T cells was further amplified in terms of absolute number of IFNγ producing cells analysed directly ex vivo ( Fig 8H ) . TNFR deficiency also impacted on the ability of CD8+ T cells to produce IFNγ , although their contribution to the overall level of this cytokine in BM was minor ( Fig 8G and 8H ) . Collectively these data demonstrate that TNF acting directly on CD4+ T cells is required for the accumulation of a pathogenic population of CD4+ T cells expressing IFNγ in BM , which in turn mediates exhaustion of HSCs in mice infected with L . donovani .
Although it is well established that impairment of hematological function occurs during VL , the underlying mechanisms are poorly understood [15] . In the current study , we demonstrate that CD4+ IFNγ+ effector T cells expand in the BM of L . donovani infected mice and drive LT-HSCs into a state of functional exhaustion . Our data define a new pathogenic role of CD4+ T cells in this disease model , and describe a novel TNF signalling-dependent pathway for regulating effector T cells that may also have relevance for other diseases characterised by hematological dysfunction . In the current study , we have focused on early events in hematopoiesis examining the fate and self-renewal potential of LT-HSCs in mice . We have identified for the first time that following L . donovani infection early hematopoietic progenitors accumulate in BM due to an increase in active proliferation , but at the expense of the reservoir of quiescent LT-HSCs . We found that alteration in the proliferative status of LT-HSCs was associated with the upregulation of the expression of GATA-3 , a transcription factor previously associated with loss of reconstitution potential of HSCs [37] , and confirmed loss of self -renewal capacity through serial transfer . As residual HSC function was reflected by multilineage differentiation , within the constraints of our analysis , the impact of infection with L . donovani does not appear to extend to the epigenetic effects that have been reported in aging and some hematological malignancies [38] . Our findings are in agreement with and extend previous studies that have reported alterations in the proliferative status and self-renewal capacity of LT-HSCs under pro-inflammatory conditions [10 , 11 , 33 , 35 , 39] . Similar alterations in HSC behaviour have also been described in other models of infection although a consensus on whether this is host beneficial or detrimental has not been reached [7 , 24 , 40 , 41] . Unlike here , previous studies have not provided a causal link between changes in cytokine profile , T cell response and HSC exhaustion . A role for T cells has , however , been mooted . Using experimental models for aplastic anemia , T cells were proposed as key mediators of hematopoietic dysfunction and ultimately BM failure [4 , 5] , and in experimental ehrlichiosis , infection-induced expansion of LSK cells was shown to be dependent upon IFNγ production by CD4+ T cells [7] . We found clear alterations in the composition of T cells residing in the BM following infection , notably a dramatic increase in the frequency and number of CD4+ T cells displaying an “effector” phenotype and secreting high levels of IFNγ . Antigen-specific CD4+ T cells producing IFNγ are a well characterised feature of VL , both in murine models and in humans and play an important role in immune protection [42–44] . However , this is the first report addressing the profile of cytokine expression in BM CD4+ T cells following experimental L . donovani infection . In adoptive transfer settings , the production of IFNγ by CD4+ T cells was sufficient to drive LT-HSC exhaustion defining this pro-inflammatory cytokine as a key regulator of hematopoiesis during infection with L . donovani . Strikingly , the absence of CD4+ T cell-intrinsic TNF signalling prevented their expansion in the BM of infected mice , and limited their potential to produce IFNγ , indicating that TNF plays a central upstream role in regulating the BM T cell compartment during infection . Although evidence of T cell function in the BM of patients with VL is scare , available data also suggests that IFNγ is more abundant in BM aspirate fluid than in serum in active VL patients and that higher than baseline levels of IFNγ persist in BM post cure [45] . Alterations in the proliferative status of HSCs and their subsequent functional impairment have been linked to IFNγ in other situations of stress-induced hematopoiesis , and it has been suggested that this reflects the effects of direct HSC-intrinsic IFNγR signalling [10 , 11 , 46] . Thus , IFNγR-signalling on HSCs has been viewed as a pivotal mechanism to explain alterations in HSC function under inflammatory conditions [47–50] . Likewise , a similar role was more recently suggested for HSC-intrinsic TNFR signalling during stress-induced hematopoiesis [36] . To test the hypothesis that IFNγ signalling and/or TNFR signalling in LT-HSCs were driving increased proliferation during infection with L . donovani , we established BM mixed chimeras with equal number of total BM cells derived from WT and IFNγR2KO or TNFRdKO . This approach allowed the experimental evaluation of the impact of IFNγR and TNFR signalling in LT-HSCs , whilst excluded the confounding effect of global loss of IFNγ or TNF signalling evident in previous studies [10 , 11 , 46] . Our findings obtained using a mixed BM chimeric model do not support these conclusions . Rather , our data clearly demonstrate that LT-HSCs in a cytokine replete environment are driven into exhaustion in an identical manner , irrespective of whether they express IFNγR2 or TNFR . Our findings strongly suggest that , in contrast to directly affecting LT-HSCs , IFNγand TNF receptor signalling converge at the level of the CD4+ T cells to regulate expansion of a highly-activated effector population . Further work will be required to establish the cellular target of IFNγ produced by BM CD4+ T cells in L . donovani-infected mice . For example , changes in stromal cell function due to exposure to IFNγ have been proposed to explain the increased myelopoiesis in mice infected with Lymphocytic choriomeningitis virus ( LCMV ) strain WE [51] . Intrinsic IFNγR signalling has been suggested to regulate T cell differentiation in other settings [52 , 53] . For example , in mice immunized with LCMV , it was shown that antigen-specific CD4+ T cells expand at a much higher rate compared to CD4+ T cells lacking IFNγ signalling [53] . In contrast , in experimental Listeria monocytogenes infection , the expression of IFNγ by CD4+ T cells was negatively regulated by IFNγR [52] . Finally , in mixed BM chimeras infected with E . chaffeensis , IFNγR-deficient T cells were increased in frequency compared to IFNγR-sufficient T cells [6] , contrary to our observations with L . donovani in similar chimeric mice . Together , these studies argue for the existence of potentially diverse mechanisms of immune control that are exaggerated under different infection conditions . A key finding from our work is the identification of a role for intrinsic TNFR signalling in CD4+ T cells as a key step in their differentiation into cells able to drive LT-HSC exhaustion . Following L . donovani infection the lack of intrinsic TNF signalling led to a 30-fold reduction in the number of BM CD4+ cells expressing IFNγ . Therefore , TNF emerges as a crucial mediator in the development of this pathogenic population of CD4+ T cells in the BM , as well as in the spleen , of L . donovani-infected mice . As with studies on the role of IFNγR , the literature is divided over the role of intrinsic TNFR signalling in CD4+ T cells . For example , a lack of intrinsic TNF-R1b signalling has been reported to significantly curtail expansion of CD4+ T cells in response to low concentrations of specific antigen and compromise the ability of T cells to express IFNγ [54] , whereas lack of TNF or TNFR signalling lead to uncontrolled expansion of IFNγ+ T cells following BCG infection [55 , 56] . Compared with the spleen , T cells enriched for an “activated” phenotype reside in greater numbers in the BM of naive animals ( [2 , 57] ) and persist for much longer periods of time in BM compared to other lymphoid tissues following infection resolution [2 , 58] . These data , in combination with our findings suggest that the development of highly activated BM homing CD4+ T cells , induced not only by Leishmania infection , but by a variety of infectious challenges , may account for erosion of hematological function over time . Furthermore , the link established here between TNF and the development of BM CD4+ T cells with potential to irreversibly impact on LT-HSC function may be of importance in other non-infectious diseases where TNF production and hematological abnormalities co-exist . In summary , we propose a mechanism ( S6 Fig ) whereby following infection: ( i ) IFNγ and TNF produced as part of the ongoing immune response co-operate at the level of receptor signalling on CD4+ T cells to promote accumulation of highly activated effector CD4+ IFNγ+ cells in the BM; ( ii ) in these cells , TNF signalling ( possibly in association with other mediators ) drive the expression of IFNγ; ( iii ) IFNγ produced by CD4+IFNγ+ T cells operates indirectly to cause LT-HSCs to enter active cell cycle; and ( iv ) chronic stimulation of LT-HSCs via this pathway leads to their exhaustion through loss of quiescence . Our model also suggests that intermediary progenitors which we show accumulate in BM during chronic infection may be less efficient at producing mature progeny ( ineffective hematopoiesis ) , but we have not specifically addressed whether this reflects an intrinsic defect or a further consequence of residence in an environment chronically exposed to cytokines such as TNF and IFNγ . Recent studies by others have , for example , shown that stress-induced hematopoiesis promoted by chronic pro-inflammatory conditions results in DNA damage that impaired the differentiation of mature functional progeny [39] . Given the well-established role of CD4+ T cell-derived IFNγ in in the control of the parasite burden in VL [59 , 60] , that we confirmed in the present study , there is likely to be a trade-off between on the one hand the need for an effective anti-parasitic response to control primary infection versus on the other hand the potential for longer term and irreversible damage to hematopoietic fitness . The importance of this immunopathological sequela for the long-term health of patients faced with continued pathogen insult over their life course remains to be evaluated .
All animal experiments were carried out in accordance with the Animals and Scientific Procedures Act 1986 , under UK Home Office Licence ( project licence number PPL 60/4377 approved by the University of York Animal Welfare and Ethics Review Board ) , and conformed to ARRIVE guidelines . Animals were killed by CO2 asphyxia and cervical dislocation . B6 . CD45 . 1 , B6 . CD45 . 2 , ( B6 . CD45 . 1xCD45 . 2 ) F1 , B6 . EYFP . Rag1-/- and B6 . Rag2-/- . CD45 . 1Cg were used in this study , bred and maintained under specific-pathogen free ( SPF ) conditions at the Biological Services Facility , University of York . BM cells from mice lacking the Ifngr2 gene ( IFNγ-R2 KO ) on a B6 background were generously provided by Dr . Grainger ( University of Manchester , UK ) [61] . BM cells from TNF receptor double KO mice ( Tnfrsf1-dKO B6 . 129S mice ) [62] backcrossed >10 generations to C57BL/6 mice were provided by Dr . Lindbom ( Lund University , Sweden ) . IFNγ-KO ( B6 . 129S7-Ifngtm1Ts/J , stock no . 002287 ) mice were obtained from the Jackson Laboratory . All mice were between 5–8 weeks of age at the start of experimental work . Mice were infected via the lateral tail vein with 3x107 amastigotes of the Ethiopian strain of L . donovani ( LV9 ) or tandem Tomato fluorescent protein expressing L . donovani ( tdTom-L . donovani ) . Spleen parasite burden was expressed as Leishman-Donovan units ( LDU ) , where LDU was equal to the number of parasites/1000 host nuclei multiplied by the organ weight in milligrams . BM parasite burden was determined as the number of parasites/1000 host nuclei . Cell suspensions from spleen and BM ( tibias and fibulas ) were obtained as described previously [21] . For phenotypic analysis and FACS ( Fluorescence-activated cell sorting ) purification , BM cells and splenocytes were stained with a lineage marker cocktail ( CD3e ( 145-2C11 ) , Ly-6G and Ly-6C ( RB6-8C5 ) , TER-119 ( TER-119 ) , CD45R ( RA3-6B2 ) , and CD11b ( M1/70 ) ) , Sca1 ( D7 ) , cKit ( 2B8 ) , CD48 ( HM48-1 ) , CD34 ( RAM34 ) , FLT3 ( A2F10 ) , IL-7Rα ( A7R34 ) , CD45 ( 30-F11 ) , CD150 ( TC15-12F12 . 2 ) , CD45 . 1 ( A20 ) , CD45 . 2 ( 104 ) , CD3e ( 145-2C11 and UCHT1 ) , CD4 ( RM4-5 and 4SM95 ) , CD8β ( H35-17 . 2 ) , CD11c ( N418 ) , CD11b ( M1/70 ) , MHC-II ( M5/114 . 15 . 2 ) , F4/80 ( BM8 ) , CD16/32 ( 93 ) , TNFR1a ( TR75-89 ) , TNFR1b ( 55R-286 ) , TNF ( MP6-XT22 ) , IFNγ ( XMG1 . 2 ) , GATA3 ( 16E10A23 ) , Ki67 ( MOPC-21 ) , CD62L ( MEL-14 ) , CD44 ( IM7 ) , Ly6C ( AL-21 ) , TCRγδ ( GL3 ) and CD49b ( DX5 ) . HSPCs were assigned based on criteria shown in S1 Table . Negative controls were stained with matched-isotype controls and dead cells were excluded using LIVE/DEAD Fixable Dead Cell Stains ( Thermo Fisher Scientific ) . For intracellular staining with cytokines cell suspensions were stimulated with Phorbol-12-myristate-13-acetate ( PMA ) ( Sigma-Aldrich ) and ionomycin ( Sigma-Aldrich ) [63] . Analyses were performed either in the BD LSR Fortessa X-20 ( BD Biosciences ) or the CyAn ADP analyser ( Beckman Coulter ) . MoFlo Astrios ( Beckman Coulter ) was used to perform sorting ( to > 95% purity ) . Data was analysed with FlowJo software ( TreeStar ) . BM cells from primary donors or from previously chimeric mice were sort purified as CD45+ Lin- Sca1+ cKit+ CD150+ CD48- CD34- cells ( LT-HSCs ) or CD45+ Lin- cells ( enriched for HSPCs ) and transplanted into lethally irradiated recipient mice ( two doses of 550 rad , 24h apart ) . In non-competitive adoptive transfer experiments , radio-protective BM cells were transferred together with the sorted donor cells . For mixed BM chimeras , recipients received 1x106 BM cells from each donor strain post-irradiation . Mice were infected at 7–9 weeks of chimerism . For the transplant of CD4+ T cells , 6x105 of sort-purified splenic CD45+CD4+CD3+CD8-B220-TCRγδ-CD49b- cells were transplanted into B6 . Rag2-/- . CD45 . 1Cg recipient mice . Statistical analyses were performed by parametric or non-parametric tests , selected based on the distribution of the raw data . The comparisons between experimental groups were performed using student Unpaired t test , Mann-Whitney and one-way ANOVA . The analysis of population distribution was performed using Chi-square test . All analyses were conducted using GraphPad InStat ( version 6 ) software ( GraphPad ) . | Visceral leishmaniasis ( VL ) is a chronic often fatal disease caused by the protozoan parasites Leishmania donovani and L . infantum . Progressive disease in humans and in animal models is associated with parasite replication at systemic sites , including the bone marrow ( BM ) and results in significant changes in hematological profile . The mechanisms underlying hematologic dysregulation during infection are largely unknown . Using a panel of stem cell markers , we characterized murine haematopoietic stem and progenitor cells in the BM over the course of L . donovani-infection in C57BL/6 ( B6 ) mice . Most long-term hematopoietic stem cells ( LT-HSCs ) in naïve mice are found in a quiescent state , representing cells with the highest degree of reconstitution potential . In contrast , during L . donovani infection , most LT-HSCs had entered cell-cycle and this correlated with a reduced potential to engraft into syngeneic recipients . HSC exhaustion and other alterations in the hematopoietic compartment did not occur in infected immunodeficient mice , but adoptive transfer of IFNγ-sufficient CD4+ T cells restored this phenotype . Using mixed BM chimeras , we established that IFNγ signalling and TNF signalling pathways converge at the level of BM CD4+ T cells , with intrinsic TNF signalling being critical for the expansion / differentiation of CD4+ T cells that are responsible for HSC exhaustion . Contrary to commonly held views , in the setting of experimental visceral leishmaniasis neither IFNγ nor TNF signalling in HSCs was required for their functional exhaustion . Hence , pro-inflammatory cytokines commonly associated with host protection in leishmaniasis and many other infectious diseases can also drive the development of pathogenic CD4+ T cells that cause long term irreversible alterations in HSC function . | [
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"pr... | 2017 | TNF signalling drives expansion of bone marrow CD4+ T cells responsible for HSC exhaustion in experimental visceral leishmaniasis |
Bloom syndrome is a recessive human genetic disorder with features of genome instability , growth deficiency and predisposition to cancer . The only known causative gene is the BLM helicase that is a member of a protein complex along with topoisomerase III alpha , RMI1 and 2 , which maintains replication fork stability and dissolves double Holliday junctions to prevent genome instability . Here we report the identification of a second gene , RMI2 , that is deleted in affected siblings with Bloom-like features . Cells from homozygous individuals exhibit elevated rates of sister chromatid exchange , anaphase DNA bridges and micronuclei . Similar genome and chromosome instability phenotypes are observed in independently derived RMI2 knockout cells . In both patient and knockout cell lines reduced localisation of BLM to ultra fine DNA bridges and FANCD2 at foci linking bridges are observed . Overall , loss of RMI2 produces a partially active BLM complex with mild features of Bloom syndrome .
Bloom syndrome ( BS ) is a very rare genetic disorder with features of significant growth deficiency , hypo- and hyperpigmented skin , sun-sensitive facial skin lesions , cancer predisposition in early life and male infertility [1 , 2] . Early cytogenetic experiments revealed clues about the underlying mechanism with patient chromosomes exhibiting hyper-recombination and genome instability [3] . The only known gene , BLM , associated with BS was identified in 1995 [4] . The gene encodes for the BLM protein that is a member of the RecQ DNA helicase family of proteins . RecQ helicases are essential for genome maintenance and are conserved across evolution . Protein interaction studies have shown that the BLM protein is a member of a four-subunit complex that includes topoisomerase III alpha ( TOP3A ) [5 , 6] and RecQ-mediated genome instability proteins 1 [7–9] and 2 [10 , 11] ( RMI1 & 2 ) , collectively known as the BTR complex . The BTR promotes the dissolution of double Holliday junctions that can be formed during DNA replication into non-crossover products in a two-step process: 1 ) by pushing the Holliday junctions together by the helicase activity of BLM , and 2 ) the dissolution of hemi-catenated DNA by the cleavage and joining activities of TOP3A [12] . Crossover events between homologs in somatic cells can be detrimental to a cell’s survival as they lead to loss of heterozygosity ( LOH ) [13 , 14 , 15] . Notably LOH is elevated in BLM deficient cells [16] . Moreover , unresolved recombination intermediates that persist into mitosis lead to bridging and are a source of genomic instability [17] . Structure and function studies have shown that RMI1 and 2 form a heterodimer that is important in stabilising the BTR [18 , 19] . The BTR complex has been proposed to localise to stalled replication forks via interactions with Fanconi anaemia ( FANC ) subunits and Replication Protein A [20] . This super-complex is also known as BRAFT . Similar to BS , Fanconi anemia patients exhibit growth deficiencies , chromosomal breaks , heightened genomic instability and cancer predisposition [21] . Further evidence to support this connection is through structural analyses with a FANCM peptide and the RMI1-RMI2 heterodimer [22] . The FANC core complex consists of eight subunits that promote the monoubiquitination of FANCD2 and FANCI in response to sites of DNA damage where replication forks are obstructed [23 , 24] . FANCD2 acts at stalled replication forks to remove interstrand cross-links ( ICLs ) and additionally regulates homologous recombination proteins including BRCA2/FANCD1 [25–27] . BLM is known to cooperate with FANCD2 during S phase to restart stalled replication forks while also suppressing the firing of new replication origins; an activity that is independent of FANCI [28] . During mitosis , FANCD2 and FANCI subunits frequently appear at the sister chromatid anchor sites that link DAPI-negative chromatin threads also known as ultra fine bridges ( UFBs ) and also occasionally along the UFBs during anaphase [29 , 30] . FANCI/D2 sister foci in mitosis appear at chromosome arms and not centromeres and their localisation corresponds to fragile sites in the genome [29] . The foci that link UFBs during chromosome segregation imply a tethering or loading function for proteins that coat UFBs such as BLM and PICH [31 , 32] , but this is yet to shown . BLM , TOP3A and RMI1 are highly conserved in most eukaryotes but RMI2 is absent in some lineages including invertebrates and yeasts , suggesting that it is needed in organisms with higher genome complexity [11] . Further evidence to support RMI2’s functional role in higher eukaryotes was shown in chicken DT40 RMI2 null cells which display elevated levels of sister chromatid exchanges ( SCEs ) . At a cellular level , whether RMI2 is required during mitosis and at an organism level , its role during development and disease predisposition are all outstanding questions . Here , we show a homozygous deletion of RMI2 in two siblings with milder clinical features of Bloom syndrome . We have additionally mutated RMI2 using CRISPR-Cas9 gene-editing to further confirm the hyper-recombination and mitotic defect phenotypes observed in the patients’ cells .
The two affected siblings are the only children of first cousin parents of Pakistani descent . Their mother had one previous miscarriage but there was no other relevant family history . The clinical features of both siblings are summarised in Table 1 . Sibling 1 ( S1 Fig , Fig 1A ) , a male , was born by caesarean section at 36 weeks gestation . Birth weight was 2 . 7 kg ( 50th centile ) but other growth parameters were not recorded . He was noted to have large numbers of café-au-lait macules in the first year of life . The café-au-lait macules were mostly <1cm in diameter , but several were >5cm ( Fig 1A ) . There were no other features of neurofibromatosis type 1 . There were also several depigmented macules . He was otherwise healthy with normal growth and development , and was an average student at school . At age six years his height was 118 cm ( 50th-75th centile ) , weight 28 . 8 kg ( 75th-90th centile ) and head circumference 50 . 0 cm ( 50th centile ) . There was no cutaneous photosensitivity , reduction in subcutaneous fat , or feeding difficulties or recurrent infections . Neurological , cardiac , respiratory and abdominal examinations were normal and he did not have the characteristic facial appearance of Bloom syndrome . Full blood examination , electrolytes , blood glucose , liver function , and immune function were normal but alpha fetoprotein was mildly elevated . Sibling 2 ( S2 ) , a female , was born at 37 weeks gestation following a pregnancy that was complicated by slow growth in the 3rd trimester . She was delivered by caesarean section and birth weight was 2 . 2 kg ( <10th centile ) . Other growth parameters were not recorded . There were no neonatal complications . Gastro-esophageal reflux was diagnosed at age one month and was treated medically until age ten months . Multiple café-au-lait macules were noted in the first year of life with a similar pattern to her brother . Her growth was mildly delayed and she was microcephalic: at age four years her height was 95 . 0 cm ( 5th centile ) , weight 15 . 0 kg ( 25th centile ) and head circumference 45 . 5 cm ( 1 cm below 2nd centile ) . S2 was otherwise healthy and hearing , vision , voice and development were normal . There was no cutaneous photosensitivity or reduction in subcutaneous fat and there was no history of recurrent infections . Neurological , cardiac , respiratory and abdominal examinations were normal and she did not have the characteristic facial appearance of Bloom syndrome . Full blood examination , electrolytes , blood glucose , liver function , and immune function were normal but alpha fetoprotein was mildly elevated . The mild growth abnormalities of S2 and the presence of café-au-lait macules in both S1 and S2 suggested an underlying genetic defect and a chromosome microarray was requested for the affected family members . The microarray analysis in both siblings demonstrated long continuous stretches of homozygosity consistent with the parents being first cousins . A homozygous deletion was detected comprising 80 kb at chromosome band 16p13 . 13 , resulting in the deletion of the entire RMI2 gene and the micro RNA gene , MIR548H2 ( Fig 1B and S1 Fig ) . Both parents were heterozygous for the same deletion . Of note , BLM was not within a region of homozygosity . To identify the exact breakpoint region , oligonucleotides were designed adjacent to the closest positive array probe at each breakpoint . Long-range PCR produced a band of approximately 6 kb for both affected children , whereas an unrelated control displayed no fragment . Sequencing of the cloned PCR product revealed a non-allelic recombination event between two Alu repeat elements , without any loss or gain of Alu sequences ( S2 Fig ) . The deletion therefore covers a region of 84 , 871 bp located at chr16: 11 , 304 , 701–11 , 389 , 571 ( hg38 ) ( Fig 1C ) . Aside from RMI2 , the deleted region contained no other coding genes . The two Alu repeat elements share an overall sequence identity of 80% spanning 308 bp . Interestingly , a continuous stretch of 38 bp showing 100% identity between the repeats crossed the breakpoints . The deleted region does not span any copy number variable region and contains no known segmental duplication of >1000 bp as displayed on the UCSC Genome Browser . Routine G-banding analysis on lymphocytes showed no gross chromosomal rearrangements . S1 was 46 , XY in 15 metaphase cells , and S2 was 46 , XX in 15 metaphase cells . Solid staining for chromosomal breaks in 100 metaphase cells revealed a higher rate in the affected siblings . 15 and 5 chromosome or chromatid breaks were identified in S1 and S2 , respectively ( S3 Fig ) . Furthermore , the presence of quadriradial chromosome formations were not observed , which are present in around 2% of Bloom syndrome cells [33] . Control lymphocytes showed no detectable chromosome breaks . To confirm the cytological phenotype of elevated sister chromatid exchange events that is characteristic of Bloom-like syndrome , fresh peripheral blood lymphocytes were prepared for differential sister chromatid staining . Both affected siblings and two sex and age-matched controls were assayed microscopically for sister chromatid exchanges . 15 cells from each individual were examined and showed a mean of 40 and 36 chromatid crossovers for S1 and S2 , respectively , compared with a mean of five crossovers for controls ( Fig 2 ) . To examine the extent of chromosome entanglements in mitosis , fibroblast cell lines were established from the siblings and parents . These cell lines enabled a number of cytological analyses to be performed . Fibroblasts were grown on coverslips and then fixed and stained with DAPI . The presence of micronuclei are a useful biomarker for chromosomal breaks and missegregation events [34] . The number of cells containing at least one micronucleus was 4 . 8% and 7 . 4% , S1 and S2 , respectively ( Fig 3A ) . By contrast , the parents showed 1 . 5% and 0 . 89% , for P1 and P2 , respectively . This equates to a 5 to 8-fold higher frequency of micronuclei in the siblings fibroblast cells . Other features of mitotic errors were also measured . Chromatin threads or bridges connecting interphase nuclei were 0 . 10% and 0 . 28% for P1 and P2 , respectively , compared with 1 . 7% and 1 . 9% , S1 and S2 , respectively ( Fig 3B and 3C ) . Larger masses of chromatin in the form of bulky DNA bridges were 0 . 22% and 1 . 5% for P1 and P2 , respectively , compared with 7 . 5% and 9 . 3% for S1 and S2 , respectively ( Fig 4D , S6 Fig ) . Although both siblings share the exact same homozygous deletion spanning RMI2 , overall S2 was more affected than S1 across several mitotic assays . The differences between S1 and S2 however were not statistically significant . This is consistent with her ( S2 ) more severe clinical presentation and growth defects when compared against her brother ( S1 ) . In order to confirm the cytological results observed in the fibroblast cells lines , we chose to create independent isogenic knockout cell lines in the near-diploid human colorectal cell line , HCT-116 using CRISPR/Cas9 gene editing . To minimise off-target mutations we adopted a double-nicking strategy [35] . Two separate guide oligonucleotide pairs were used to generate several candidate RMI2 knockout cell lines . Two knockout cell lines ( 1–2 and 1–3 ) from guide pair 1AB and one cell line ( 4–6 ) from guide pair 4AB were used in subsequent functional characterisation . Details of the mutations are provided in ( S4 Fig ) . The three knockout cell lines were confirmed to be null for the RMI2 protein with immuno-blot ( Fig 4A ) . These cell lines provided an independent additional line to support and expand observations from fibroblast patient cells . The HCT-116 rate of SCEs per cell was 6 . 6 per cell compared with a combined average of 34 for the three KO clones ( Fig 4C and S5A–S5D Fig ) . This equates to a 5 . 2-fold increase in the RMI2 knockout cell lines . Anaphase bridges showed four-fold increase in frequency when compared to wild-type cells . Whereas , lagging chromosome frequency displayed a modest increase over wild-type cells ( Fig 4D and S6 Fig ) . Together , data from HCT-116 replicate findings from our patient fibroblast cell lines , with RMI2 null cells showing increased chromosome bridges compared to controls . In both experiments using patient fibroblast and HCT-116 cells there was no significant increase in cells showing lagging chromosomes , suggesting RMI2 and the BTR complex does not play a role in spindle-kinetochore attachment . DNA content analysis was performed on exponentially growing asynchronous cells from fibroblast and HCT-116 cell lines to determine if there was any polyploidy or aberrant cell cycling . No differences were observed between wild-type and RMI2 null cell lines ( S7 Fig ) . We were interested to ascertain whether RMI2 loss affected cell proliferation and UV sensitivity . A previous study in DT40 null cells had shown no effect on cell proliferation rates or sensitivity to DNA damaging chemicals [11] , whereas another study using RNAi knockdown in human cells had observed a lower survival rate in cells challenged with MMS [10] . To assess whether the loss of RMI2 had an impact on cell proliferation and colony forming ability , 300 cells were plated onto dishes and grown for six days before being fixed and analysed . The RMI2 null cells showed a 2 . 4-fold and 8 . 9-fold decrease over parental wild-type cells for number of colonies and the total area that the colonies occupied per well , respectively ( S5E and S5F Fig ) . To test whether the RMI2 null cells were sensitive to UV light , 300 cells were plated per well and allowed to recover for one day before being exposed to UV light . The average number of colonies in the RMI2 null cells dropped to 27% of untreated cells , compared with a similar drop of 27% for untreated wild-type cells ( Fig 4E ) . We also challenged the cells with the DNA replication inhibitor , hydroxyurea ( HU ) at varying doses ( Fig 4F ) . No consistent sensitivity was observed in the knockout cell lines . Our study analyzed bulky DNA bridges and found a significant increase due to the absence of RMI2 ( Fig 4D , S6 Fig ) . Another class of bridge that is associated with the BTR complex activity are ultra fine bridges ( UFBs ) that are finer , thread-like structures not detectable using DAPI . BLM is one of several proteins that co-localise with UFBs in the later stages of mitosis with members of the BTR appearing as a streak between separating chromosomes most commonly during early anaphase [32] . UFBs occur naturally in mitosis and although the precise function of BTR in cells undergoing chromosome segregation is still to be determined , it is thought the complex aids sister chromatid decantation during anaphase [36] . It is presumed that UFBs associate with loci that contain un-replicated DNA or unresolved recombination intermediates that persist into mitosis [17] . Relevant to this study , patients with Bloom syndrome show significantly elevated levels of UFBs as a result of defective BLM [32] . To test whether RMI2 null cells also showed significant increases in UFBs , HCT-116 wild type and null RMI2 cells were stained with Plk1-interacting checkpoint helicase ( PICH ) protein ( Fig 5 ) , which colocalises with BLM on UFBs during anaphase [31 , 32] . PICH is considered a useful marker of UFBs as its localisation is independent of the BTR . The results were striking and paralleled analogous scoring in BLM disrupted cells [32] . We found that there was only a slight increase in anaphase A cells displaying PICH fibers between wild-type and RMI2 null HCT-116 clones ( Fig 5A and 5C ) . However , by anaphase B approximately only 30% of wild type cells shows detectable PICH fibers , while the approximately 80% in null HCT-116 RMI2 null cells ( Fig 5B and 5D ) . The data clearly indicate UFBs persist into anaphase B as a result of RMI2 disruption . BTR complex members are a set of several proteins that co-localise with UFBs in the later stages of mitosis . We therefore analysed core components to determine if their localizations were altered in the absence of RMI2 . BLM appears as a streak on UFBs between separating chromosomes , with BLM fibers most evident during early anaphase [32] . Although the precise function of BLM in cells undergoing chromosome segregation is still to be determined , it is thought the complex aids sister chromatid decantation during anaphase . Furthermore , it is presumed that UFBs associate with loci that contain un-replicated DNA or unresolved recombination intermediates that persist into mitosis [17] , however the precise nature of the DNA is yet to be described . Examination of BLM localisation on anaphase fibroblast cells revealed little difference compared to controls in the prevalence of positively-staining fibers from both affected siblings and RMI2 HCT-116 null cells in anaphase B ( Fig 6B and 6E ) . What was apparent however was the intensity of BLM ( using pooled data from anaphase A and B ) on the fiber was significantly weaker in RMI2 null cells in both patient and HCT-116 systems compared to controls ( Fig 6A , 6C , 6D and 6F ) . Interestingly , in anaphase A there was small , but statistically insignificant drop in detection of BLM-positive fiber in HCT-116 RMI2 null cells relative to wild-type and an even larger decline in the analogous experiment in using patient fibroblast lines ( S8 Fig ) . We next examined TopoIIIα localisation onto UFBs in anaphase ( Fig 7 ) . We adopted a slightly different approach and measured the amount of anaphase B cells that showed PICH and TopoIIIα colocalisation . Our previous data ( Fig 5 ) showed approximately 30% of anaphase cells had PICH fibers , so we asked the question how many of these PICH fibers show colocalisation with TopoIIIα . The results were very clear . For wild type HCT-116 cells 94% of anaphase B cells with PICH overlapped with TopoIIIα compared 26% , 18% and 13% for RMI2 HCT-116 null clones 1–2 , 1–3 and 4–6 respectively . Together the results show RMI2 is necessary for the proper localization of the BTR complex members BLM and TopoIIIα , and provide a mechanistic link why UFBs persist during anaphase B in RMI2 null cells i . e . , due to disruption of BTR subunits in anaphase . The Fanconi anaemia ( FANC ) complex is needed for the repair of DNA ICLs generated during DNA replication [23] . Subunits of the FANC and BTR complexes interact together forming a super-complex known as BRAFT [22] . Furthermore , the FANCD2/FANCI subunits forms foci at regions of replication stress such as common fragile sites that anchor the BLM-staining fibers between segregating sister chromatids [17] . The FANCD2/FANCI foci on separating chromatids are visible from anaphase through to telophase [29] . We also noticed , FANCD2 can occasionally appears as a fiber across separating chromatids , reminiscent of the BLM and PICH ( S9 Fig ) . We have examined the localisation of FANCD2 on UFBs in the family’s fibroblasts and the HCT-116 RMI2 null cells . Both cell types show a decrease in the frequency of anaphase to telophase cells containing FANCD2 foci on sister chromatids ( Fig 8 and S9 Fig ) . Additionally , the intensity signals of the FANCD2 foci on the HCT-116 RMI2 null cells show a decrease in signal ranging from 1 . 9- to 2 . 4-fold . Taken together , these results suggest the stability of the BRAFT super-complex encompassing BLM and FANCD2 subunits is compromised through loss of RMI2 .
We have identified a homozygous deletion of the RMI2 gene that results in a Bloom-like phenotype from a consanguineous kindred . The two affected siblings exhibit a variable phenotype with some overlapping features of Bloom syndrome . Sibling , S2 , presented with growth deficiency and gastro-esophageal reflux , traits commonly found in Bloom syndrome children . Curiously , these indicators were absent in sibling S1 ( Table 1 ) . It is too early to tell whether homozygous deletion of RMI2 is associated with elevated risk of cancer in late childhood or adulthood . Consistent with the clinical presentation , our cell biology analyses also indicated sibling S2 was slightly more affected with mitotic assays for chromosome bridges in mitosis , bridges persisting between interphase nuclei and micronuclei all elevated compared to sibling S1 . The striking feature consistent with a Bloom syndrome phenotype , is both children display café-au-lait macules ( Fig 1A ) . These dermatological findings are often associated with childhood cancer syndromes [37] . Loss of RMI2 should therefore be added to the differential diagnosis of children presenting with multiple café-au-lait macules . Cytogenetic investigation into genome instability showed a higher rate of SCEs and chromatid breaks ( Fig 2 ) . BLM null individuals have a 10-fold elevation in the rate of SCEs when compared to wild-type cells [3] . By contrast , we have observed a slightly lower rate at seven- to eight-fold above wild-type . This is consistent with a similar decrease when SCE rates are compared between BLM and RMI2 knockout chicken DT40 cells [11] . These data suggest that the BLM helicase displays partial activity in dissolving catenated DNA in the absence of RMI2 . Indeed , in vitro addition of RMI2 to BLM-TopoIIIα-RMI1 caused a statistically significant increase in the rate and overall level of dissolution of radiolabelled double Holliday junction substrates [10] . The same study found the BLM-TopoIIIα-RMI1 complex alone still possesses significant dissolution activity; suggesting RMI2 enhances but is not essential to the enzymatic capability of the BTR complex . However , we note our in vivo analyses show removal of RMI2 has a profound affect on the stability of BLM on UFBs that likely represent a range of replication intermediates . Understanding the DNA structure of UFBs remains an important task that will provide insight into why and how UFB-associated proteins act . The notion that BLM-TopoIIIα-RMI1 alone can still dissolve double Holliday junctions is consistent with the observation that BLM patients show a more noticeable clinical presentation compared to RMI2 affected individuals . Additionally , BLM has activities that are independent of RMI2 . For instance , it is known BLM stimulates the resection activity of human exonuclease 1 [38] . It is therefore also likely that with the increase in DNA analysis capabilities and also clinical awareness that further RMI1 and also RMI2 affected individuals will be identified in the population . The failure to dissolve catenated DNA in the affected siblings is the main trigger for downstream mitotic errors such as DNA anaphase bridges and micronuclei ( Fig 3 and Fig 4 ) . These perturbations in mitosis are thought to have an impact on the cell proliferation rate . We have investigated whether there was any link between mitotic errors and growth rates in the affected siblings but no consistent association could be found . Homozygous knockout of the RMI2 gene in HCT-116 cells showed a noticeable slowing down in cell proliferation and the ability to form colonies from single cells ( Fig 4B and S5E and S5F Fig ) . This is in contrast to the chicken DT40 RMI2 knockout cell lines that did not display any reduction in cellular growth rate , although colony forming assays were not performed [11] . Correspondingly , one of the affected siblings showed prenatal and postnatal growth deficiency ( Table 1 ) . The variability in cell proliferation rates and impact on development is most likely to be dependent on genetic background . Furthermore , the growth deficiency phenotype observed in sibling S2 may be due to a co-existing disorder associated with the parents’ consanguinity . RMI2 mouse knockout studies will hopefully shed some light on these differences between model systems . DNA repair disorders are commonly associated with sensitivity to DNA-damaging agents such as chemical mutagens or short wave radiation such as UV light . Bloom syndrome affected individuals are mildly sensitive to sunlight where they display sun-sensitive lesions on exposed areas such as the face [1] . However , there are conflicting reports in the literature whether BLM null cells are sensitive to UV light in vitro [4 , 39 , 40] . The affected siblings did not show any signs of sun-sensitive skin lesions on exposed areas . HCT-116 RMI2 null cells also did not show any consistent reduction in the number or size of colonies after being exposed to short-wave UV light or hydroxyurea when compared to parental wild-type cells ( Fig 4 ) . Similar results were observed in chicken DT40 RMI2 null cells when challenged with DNA-damaging chemicals such as cisplatin or methyl methanesulfonate [11] . Taken together , these data support that the BLM helicase can perform some of its DNA repair functions without the participation of RMI2 . Earlier experiments on the knockdown of RMI2 from vertebrate cells had not examined its role during chromosome segregation . Examination of RMI2 null cells in fibroblasts and HCT-116 lines has shown hallmarks of mitotic errors in the form of DNA bridges and micronuclei ( Fig 3 and Fig 4 ) . Were these chromosome entanglements due to the lack of the BTR complex localising to UFBs ? We have shown both BLM ( Fig 6 ) and TopoIIIα ( Fig 7 ) localisation is disrupted when RMI2 is removed . Our data however show that BLM still can localise to UFBs , albeit at a significantly lower intensity . Together , this evidence supports our hypothesis that the BTR is functionally impaired during mitosis without RMI2 . Further evidence of this partial activity is illustrated with localisation experiments of FANCD2 in RMI2 null cells . Like BLM , FANCD2 sister chromatid foci are reduced in frequency and intensity , suggesting that BTR instability impacts upon important DNA repair complexes such as FANC ( Fig 8 ) . This is not without precedent as it is known that BLM co-immunoprecipitates with FANCD2 in human cells [41] , and mechanistically FANCD2 and the BTR complex cooperate to restart stalled replication forks [28 , 42] . These studies suggest a physical and mechanistic interplay between BTR and FANCD2 in S phase under replication stress and the dependencies seemingly persist through to M phase . Curiously ours ( Fig 8 and S9 Fig ) and another study [29] observed FANCD2 coated UFBs . The nature of FANCD2 UFBs has not been fully explored , but it is possible they exist as backup or additional activity in resolving catenated DNA structures during anaphase . The BLM Bloom syndrome gene was first identified over 20 years ago . Our report shows that this is the first clinical description of individuals with Bloomoid features of non-BLM subunit . Although producing a clinical presentation similar to Bloom syndrome , the hallmark features are not as severe . Independent studies using cell lines derived from homozygous affected siblings and also HCT-116 cells deleted of RMI2 both show overlapping defects with marked increase in DNA bridges during the later stages of cell division . Our data show removing RMI2 affects the stability of interacting partners in the BRAFT super complex with BLM and FANCD2 reduced on chromosomes during chromosome segregation . Significantly , those cells without RMI2 that displayed BLM fibers in anaphase showed a marked drop in signal intensity , suggesting RMI2 stabilises or activates the complex . Whether there is any residual activity of BLM and how the overall subunit composition and architecture of the BTR and BRAFT complexes is affected is not yet clear . These will be important questions for future studies . Our current study suggest both at the patient and cell biology level the effects are not as severe as lacking BLM altogether .
Family members were recruited to this study with the approval of the Hospital Research Ethics Committee at the Royal Children’s Hospital , Melbourne , Australia , ethics approval number , 28097 . Written consent for the affected individuals was provided by their parents . Genomic DNAs were isolated and purified from leukocytes using the NucleoSpin Tissue genomic DNA extraction kit ( Machery-Nagel , Germany ) . DNA samples were processed by the Illumina Infinium method using the HumanCytoSNP—12 v2 . 1 ( Illumina , San Diego , CA , USA ) microarray platform and analysed using KaryoStudio v1 . 4 software ( Illumina ) . Confirmation of the null deletions and cascade testing of the parents was performed using Affymetrix CytoScan 750K array using the manufacture’s protocols and analysed using Chromosome Analysis Suite vCytoB-N1 . 2 . 2 . 271 ( Affymetrix , Thermo Fisher Scientific ) . Fibroblast and HCT-116 cell lines were cultured in BME and RPMI , respectively . Media were supplemented with 10% FBS and penicillin/streptomycin . Primers were designed next to the closest positive microarray probe on either side of the breakpoints . The following oligonucleotides ( IDT ) , RM-delf ( 5’—CCTACTCCTCCTGCCCTTTTC—3’ ) and RM-delr ( 5’—CCTGCCTCTTTACCTGGAGTG—3’ ) were used in a long-range PCR amplification reaction using Phusion Hot Start II ( Thermo Fisher Scientific ) with the following conditions; 98°C 2 min ( 1 cycle ) , 98°C 30 sec , 61°C 30 sec , 72°C 3 min ( 40 cycles ) , 72°C 10 min ( 1 cycle ) . PCR products were A-tailed with AmpliTaq Gold DNA polymerase ( Thermo Fisher Scientific ) 72°C 10 min , and cloned into pGEM-T Easy ( Promega ) using standard methods . The plasmid insert was Sanger sequenced using primer walking at the Australian Genome Research Facility , Melbourne , Australia . Fresh blood cells were incubated for three to four days in RPMI 1640 media/10% FBS with 20 μg/ml phytohaemagglutin . BrdU ( Sigma-Aldrich ) was added to a final concentration of 10 μg/ml for 30 hours followed by 0 . 1 mg/ml colcemid ( Thermo Fischer Scientific ) treatment for 45 mins before standard metaphase chromosome harvest . HCT-116 cell lines were treated for 29 hours with 10 μg/ml BrdU , followed by 0 . 1 mg/ml colcemid for 1 . 5 hours . Phosphate buffer pH 6 . 8 was added to cover the dried slides to a depth of 2 mm . Slides were then placed in a biosafety cabinet and were exposed to UV light at a distance of 30 cm for 45 min . The slides were briefly rinsed in dH2O and added to prewarmed 2 x SSC at 65°C for 30 min , followed by another rinse in dH2O and stained in Leishman’s stain ( Sigma-Aldrich ) . RMI2-null HCT-116 clones were seeded onto 6-well dishes at 300 cells in three ml of media per well in triplicate for each cell line . The next day the cells were exposed to either 2 mJ UV ( 254 nm ) or mock treatment using a GS Gene Linker UV Chamber ( Bio-Rad ) . Cells were then grown for six days and then rinsed in PBS , fixed in ice-cold methanol and stained in crystal violet solution . The 6-well dishes were imaged and colonies of at least 0 . 032 mm2 were counted using ImageJ v2 . 0 . 0 . Cell extracts preparation for immunoblotting was performed as described before [43] . In brief , cells were collected and washed once with cold PBS . The pellets were resuspended in RIPA buffer with fresh prepared EDTA-free protease inhibitor ( Roche ) and incubated on ice for 15 min and then sonicated . Protein concentration were determined using the Quick Start Broadford Protein Assay ( Bio-Rad ) . 40 μg of total protein extract from each of the samples was run on 10% SDS PAGE gels ( Bio-Rad ) . The following antibodies were used for immunoblot detection , rabbit polyclonal anti-RMI2 ( 1:1000 ) ( Abcam ) , mouse monoclonal anti-α-tubulin antibodies ( 1:1000 ) ( Sigma-Aldrich ) , swine anti-rabbit IgG-HRP ( 1:10 , 000 ) ( Dako ) and rabbit anti-mouse IgG-HRP ( 1:10 , 000 ) ( Dako ) . ECL immuno-blotting substrate ( Pierce ) was used according to the manufacturer’s instructions . Fibroblasts or HCT-116 cells were seeded onto gelatinised 22 mm x 22 mm glass coverslips in 6-well trays . After at least 24 hours , media was removed and cells were rinsed in PBS . For immunofluorescence cells were fixed with 4% paraformaldehyde for 10 minutes , permeabilised with 0 . 3% Triton X-100 and blocked with 3% BSA in PBS . Cells were stained with rabbit polyclonal anti-BLM ( 1:500 ) ( Abcam ) , rabbit monoclonal anti-FANCD2 ( 1:500 ) ( Abcam ) , mouse-monoclonal anti-PICH ( Millipore , 1:200 ) , rabbit polyclonal anti-TopoIIIα ( kind gift from the Hickson laboratory , University of Copenhagen , 1:200 ) and mouse monoclonal anti-α-tubulin antibodies ( Sigma , 1:500 ) . Secondary antibodies were donkey anti-rabbit Alexa Fluor 488 ( 1:1000 ) ( Invitrogen ) and goat anti-mouse Alexa Fluor 594 ( 1:1000 ) ( Invitrogen ) . Cells were mounted with VectaShield containing DAPI ( Vector Laboratories ) . For sister chromatid exchange and breakage analyses , methanol-acetic acid fixed preparations were imaged using a Zeiss Axioplan 2 microscope with a 100× objective lens . Images were analysed using AxioVision 4 . 7 ( Zeiss ) . For FANCD2 images taken by DeltaVision , 36 sections ( 0 . 2 μm per section ) images were taken . Images were deconvolved , and projected in 2D using SoftWoRx 4 . 1 . Percentage of cells with symmetrical FANCD2 spots were scored and plotted . Obvious symmetrical FANCD2 spots intensity were further measured using the polygon function of SoftWoRx 4 . 1 . For BLM fibers scoring and intensity measurements , images were captured using Zeiss Axio Imager M1 microscope and processed by AxioVision 4 . 7 ( Zeiss ) . Percentage of cells with BLM fibers were scored and plotted . Line profiles across the fibers in the cells were analysed using ImageJ as described before [44] . Two independent nicking CRISPR/Cas9 guide pairs were designed using the CRISPR design tool at crispr . mit . edu . Both pairs targeted the coding sequence of exon 2 . The following target sites for nicking pair #1 , Guide A minus ( 5'—TCCCACATACTTTCATGGATGGG– 3' ) , Guide B plus ( 5'—TGGAGGTAGAAGATTTACACAGG—3' ) and #4 Guide A minus ( 5'—ATCTTCACAGCCTGCAGGCAGGG—3' ) , Guide B plus ( 5'—TCCCATCCATGAAAGTATGTGGG– 3' ) . Annealed oligonucleotides were cloned into the pSpCas9n ( BB ) -2A-GFP ( PX461 ) vector ( Addgene plasmid ID: 48140 ) [35] . HCT-116 cells were transfected in 6-well trays with Lipofectamine 3000 ( Thermo Fisher Scientific ) using the supplier's protocol . Two days after transfection , GFP-positive single cells were flow sorted into 96-well trays . Genomic DNA from clones was extracted using standard methods followed by PCR amplification screening across the CRISPR target site using the following oligonucleotides; RM-mf ( 5'—GATGGTGATGGGAGTGGTTC—3' ) RM-mr ( 5'–TCCTACATCCGGACTCCTTG—3' ) . PCR products were cloned into pGEM-T Easy ( Promega ) and Sanger sequenced at the Australian Genome Research Facility to confirm the presence of a knockout mutation . Three clones with knockout alleles at the DNA and protein levels were chosen for functional characterisation . DNA content analysis was performed as previously described [43] and analysed using FACSCalibur and Cell Quest ( Becton Dickinson ) . Box plots were generated using beeswarm R package ( https://cran . r-project . org/web/packages/beeswarm/index . html ) . Histograms were generated using Hmisc package ( http://cran . r-project . org/web/packages/Hmisc/index . html ) . Statistical analyses were conducted using Student’s t test ( unpaired ) . | Cells contain specific protein complexes that are needed to correct errors during the replication and segregation of DNA . Impairment in the activity of these proteins can be detrimental to the viability of the cell and organism development . Bloom syndrome is an example of a genome instability disorder where cells cannot efficiently untangle DNA after replication . The only gene that is known to cause Bloom syndrome is the BLM helicase . In this article , we describe two affected individuals with Bloom-like features with a homozygous deletion of the RMI2 gene . The RMI2 protein has previously been shown to form a complex with BLM , topoisomerase III alpha and RMI1 . Deletion of RMI2 in patient and unrelated cell lines show hyper-recombination and chromosome entanglements during cell division . Furthermore , we show that the BLM and FANCD2 proteins are diminished in the binding of DNA bridges that need to be dissolved during the late stages of cell division . Therefore , loss of RMI2 produces a milder Bloom phenotype and impairs the full activity of the BLM complex . | [
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"connective... | 2016 | Loss of RMI2 Increases Genome Instability and Causes a Bloom-Like Syndrome |
Visceral leishmaniasis ( VL ) , the most severe form of leishmaniasis , is endemic in Europe with Mediterranean countries reporting endemic status alongside a worrying northward spread . Serological diagnosis , including immunochromatographic test based on the recombinant antigen rK39 ( rK39-ICT ) and a direct agglutination test ( DAT ) based on the whole parasite antigen , have been validated in regions with high VL burden , such as eastern Africa and the Indian subcontinent . To date , no studies using a large set of patients have performed an assessment of both methods within Europe . We selected a range of clinical serum samples from patients with confirmed VL ( including HIV co-infection ) , Chagas disease , malaria , other parasitic infections and negative samples ( n = 743; years 2009–2015 ) to test the performance of rK39-ICT rapid test ( Kalazar Detect Rapid Test; InBios International , Inc . , USA ) and DAT ( ITM-DAT/VLG; Institute of Tropical Medicine Antwerp , Belgium ) . An in-house immunofluorescence antibody test ( IFAT ) , was included for comparison . Estimated sensitivities for rK39-ICT and DAT in HIV-negative VL patients were 83 . 1% [75 . 1–91 . 2] and 84 . 2% [76 . 3–92 . 1] , respectively . Sensitivity was reduced to 67 . 3% [52 . 7–82 . 0] for rK39 and increased to 91 . 3% [82 . 1–100 . 0] for DAT in HIV/VL co-infected patients . The in-house IFAT was more sensitive in HIV-negative VL patients , 84 . 2% [76 . 3–92 . 1] than in HIV/VL patients , 79 . 4% [73 . 3–96 . 2] . DAT gave 32 false positives in sera from HIV-negative VL suspects , compared to 0 and 2 for rK39 and IFAT , respectively , but correctly detected more HIV/VL patients ( 42/46 ) than rK39 ( 31/46 ) and IFAT ( 39/46 ) . Though rK39-ICT and DAT exhibited acceptable sensitivity and specificity a combination with other tests is required for highly sensitive diagnosis of VL cases in Spain . Important variation in the performance of the tests were seen in patients co-infected with HIV or with other parasitic infections . This study can help inform the choice of serological test to be used when screening or diagnosing VL in a European Mediterranean setting .
Visceral leishmaniasis ( VL ) is a life-threatening disease caused by protozoan parasites of the Leishmania donovani complex . It is widely endemic in South America , eastern Africa and Asia as well as in the Mediterranean basin [1] . More than 500 million people are at risk of acquiring leishmaniasis worldwide , with approximately 90% of the cases arising in rural areas of Bangladesh , Brazil , Ethiopia , India , Somalia , South Sudan and Sudan [2] . In Europe , nine countries report cases of VL annually accounting for less than 2% of the global burden [3] , where cases are mostly confined to the Mediterranean countries , but a spread towards northern Europe is being reported as a result of a range of factors , including vector and parasite migration , and changes to the environment and climate [4] . In Spain , a VL outbreak of unprecedented magnitude occurred in the southwest of the capital Madrid between 2009–2013 [5 , 6] , and the country was recently listed among the top 14 VL high-burden country [2] . Facing a possible ( re- ) emergence of leishmaniasis in Europe , it is important for national public health institutions to have established guidelines for clinical diagnosis of VL to support primary health care and epidemiological surveillance [3 , 7] . Parasitological confirmation through culturing and/or microscopy remains the gold standard for diagnosis , and gives the clearest indication of parasitic infection . The sensitivity of parasitological confirmation , however , depends on the sample used , where spleen and bone marrow aspirates yield the best results but these are obtained through invasive sampling procedures , with inherent complications , besides presenting variable sensitivity [8] . In addition , the absence of parasites in tissue sample does not necessarily indicate absence of infection . Nucleic acid amplification tools have shown to be more sensitive than microscopy or culture for VL diagnosis , even when using peripheral blood samples [9] . This technology is already available in many hospitals and reference centers in VL-endemic countries in Europe; unfortunately there is a consistent lack of standardization and a very high number of different protocols [9] . Serological tools provide a good diagnostic accuracy as long as they are used in combination with a standardized clinical case definition for VL [1] . Serological tests vary in the target antigen ( whole parasite or recombinant protein ) , ease-of-use ( rapid dipstick or necessity for some laboratory infrastructure ) , sensitivity , specificity , and cost . Underlying HIV infections , or other forms of immunosuppression , however , can affect their sensitivity [10] . The rK39 immunochromatographic test ( rK39-ICT ) and the direct agglutination test ( DAT ) have been widely validated in the VL endemic regions of eastern Africa and the Indian subcontinent , with rK39-ICT demonstrating varying sensitivity and specificity depending on the geographical setting [11–13] . To our knowledge , no studies using a large set of patients have performed an assessment of both methods on human samples within Europe . To establish evidence on serological VL diagnostic performance in this region , we assessed the sensitivity and specificity of rK39-ICT , DAT and IFAT using historical serum samples collected in Spain from 2009–2015 .
The serum samples used in this study are anonymized and are part of a registered collection , as described below . No ethical approval was required . The study was conducted at the WHO Collaborating Centre for Leishmaniasis , National Centre for Microbiology , Instituto de Salud Carlos III , Madrid , Spain ( WHOCCL-ISCIII ) , which is also the national reference laboratory for leishmaniasis . We used historical serum samples stored at -70°C at the WHOCCL-ISCIII . These samples are part of a collection registered at the National Biobank Register-Section Collections , Spain , with collection Reference ID: C . 0000898 . The serum samples in the collection are anonymized . Samples from suspected VL cases are derived from patients with clinical suspicion of VL as defined in the protocol of the Spanish national network for epidemiological surveillance [14] , and were referred from different hospitals to the WHOCCL-ISCIII for diagnosis from 2009–2015 . Briefly , a suspected VL case in Spain is defined as a patient who presents with irregular prolonged fever plus splenomegaly and/or weight loss , which may be accompanied by hepatomegaly , lymphadenopathy , leukopenia , anemia and thrombocytopenia . Each suspected cases had multiple samples ( whole blood , serum , bone marrow ) taken to facilitate diagnosis . While in this study only serum samples were tested , we used all laboratory and clinical results available from each patient to classify them as “case” or “non-case” , and therefore define the reference diagnostic result ( see parasitological confirmation below ) . Samples from VL suspects were further divided according to the HIV status of the patients . In addition , we chose samples from patients who were diagnosed with malaria , Chagas disease or other parasitic infections , as well as serum from healthy individuals ( blood donors ) from Spain , Belgium and Germany . All samples were anonymized and diagnostic test operators were blinded to the nature of the serum sample . The rK39-ICT ( Kalazar Detect Rapid Test , Inbios International Inc . , WA , USA ) , and the DAT with freeze-dried antigen ( ITM-DAT/VL; Institute of Tropical Medicine , Antwerp , Belgium ) were performed according to manufacturer’s instruction; with 20 and 1 μl serum respectively . DAT was performed by using the screening method , samples with a titer ≥ 1:3200 were considered positive [15] . An in house IFAT was performed by following a standard method [16]: the antigen was prepared from promastigotes of the L . infantum international reference strain MHOM/FR/78/LEM-75 . Antibody binding was detected using fluorescein isothiocyanate-conjugated sheep anti-human immunoglobulin G ( heavy and light chains ) . One μl serum was used . The threshold titer for positivity was ≥1/80 . Test results were interpreted and recorded on a standardized form by at least two observers at the minimum reading times , where each observer was blinded to the other’s reading . Any test returning an invalid result or lack of agreement between observers was repeated . As part of routine diagnosis VL suspect patients are tested at the WHOCCL-ISCIII by nested PCR of blood and bone marrow samples , bone marrow Giemsa microscopy and blood and bone marrow NNN culture , following procedures described elsewhere [17 , 18] . A serum sample from a VL suspect was defined as pertaining to a case when there was parasitological confirmation of Leishmania in blood and/or bone marrow aspirate in samples taken within 21 days before or after the serum sample was taken . The statistical software R was used with the ‘epiR’ package to determine sensitivity , specificity , positive and negative predictive values [19 , 20] . Exact binomial confidence limits were calculated for test sensitivity , specificity , and positive and negative predictive values . STARD checklist and workflow are provided as supplementary materials , S1 Table and S1 Fig respectively .
The estimated sensitivity of rK39 was 78 . 0% [70 . 8–85 . 2] for all 405 suspected VL patients . Of the 95 HIV-negative VL cases , 79 were correctly diagnosed by rK39 giving a sensitivity of 83 . 1% [75 . 1–91 . 2] . The sensitivity dropped to 67 . 3 [52 . 7–82 . 0] in individuals with underlying HIV infection . Of the 602 negative samples ( 338 control subjects and 264 non-confirmed VL suspects ) , rK39 gave 1 false positive in a malaria patient . The estimated sensitivity of DAT was 86 . 5% [80 . 5–92 . 5] for all 405 suspected VL patients . The sensitivity of DAT in HIV-negative VL suspects was 84 . 2% [76 . 3–92 . 1] , rising to 91 . 3% [82 . 1–100 . 0] in individuals with underlying HIV infection . Of the 602 negative samples ( 338 control subjects and 264 non-confirmed VL suspects ) , DAT gave false positive results for 41 serum samples , including 2 individuals with malaria and two individuals with other parasitic infections . The estimated sensitivity of IFAT was 79 . 4% [72 . 4–86 . 4] for all 405 suspected VL patients . The sensitivity of IFAT in HIV-negative VL suspects was 84 . 2% [76 . 3–92 . 1] dropping to 79 . 4% [73 . 3–96 . 2] in VL suspects with HIV . Of the 602 negative samples ( 338 control subjects and 264 non-confirmed VL suspects ) , IFAT gave false positives for 2 non-confirmed VL suspects and 15 patients with Chagas disease .
In this study , we assessed the sensitivity and specificity of rK39-ICT , DAT and IFAT on a varied set of historical serum samples collected in Spain from 2009–2015 . The diagnostic performance of rK39-ICT and DAT has been largely evaluated in highly endemic country settings [12] , with variable results in different geographic locations for rK39-ICT [21] , and the performance in a European setting remains largely unknown . A multicenter study compared different diagnostic tests using samples from 26 HIV-negative and 11 HIV-positive VL patients from southern France [22] . This study evaluated a different rK39-ICT ( IT-LEISH Bio Rad Laboratories , France ) and DAT ( the same as in our study ) , among other serological tests , and obtained a sensitivity of 88 . 5% for both in HIV-negative VL patients and 54 . 5% for DAT and 81 . 8% for rK39-ICT in HIV/VL patients . In a study from Italy with a sample size of 94 patients with suspected VL ( 21 patients were confirmed VL cases ) , the reported sensitivity of rK39-ICT was 52 . 4% , using a different manufacturer than in our study [23] . These results differed from our study , which are more in agreement with other large scale evaluations of rK39-ICT and DAT that show higher sensitivity for DAT in a setting with high HIV-co-infection rate [24] . The evaluation of diagnostic tools for visceral leishmaniasis in Europe is important as the burden of VL remains an issue for European public health officials [3] . We began addressing this issue using a large assembly of samples . Our results show lower sensitivity estimates when compared to published results on serological assay evaluation in South East Asia , the Americas and eastern Africa regions . In a meta-analysis of diagnostic performance , the combined sensitivity estimates of DAT and the rK39-ICT were 94 . 8% and 93 . 9% , respectively [25] , while in our study , the estimated DAT and rK39-ICT sensitivity was at 86 . 5% and 78 . 0% , respectively . In a WHO led evaluation of rK39-ICT , the sensitivity estimates varied greatly from region to region: 67 . 6% in eastern Africa , 84 . 7% in Brazil and 99 . 6% on the Indian subcontinent [21] . The different performance of serological tests between European samples and those tested elsewhere is most likely due to the epidemiological landscape . Patients residing outside of Europe will have different anti-VL immunoglobulin titers , different age patterns of infection , immune and/or nutritional background and/or are exposed to higher parasite diversity [21] . A study analyzing L . donovani strains from African and Asian origin revealed extensive genetic diversity in coding sequences of rK39 homologues , which may provide an explanation for the different performance of rK39-ICT across regions [26] . In the Mediterranean region VL is caused by different genetic variants of L . infantum [27 , 28] , whether this has an effect on the performance of rK39-ICT would be an interesting subject of study . Our study sample reflects a population living in a southern European member state where samples are routinely submitted for laboratory diagnosis after clinical suspicion of VL . Of the 405 samples submitted between 2009 and 2015 and analyzed in this study , 34% were classified as cases . To expand our sample population and assess the performance of serological tests with respect to cross-reactivity , we further selected confirmed VL negative serum samples with varying immunological exposures , including Chagas disease , caused by another member of the Trypansomatidae family , as well as German and Belgian blood donors who are less likely to have had previous parasite exposure . Although sensitivity and specificity varied between diagnostic tests , we found that the in house IFAT , rK39-ICT ( Kalazar Detect from InBios International , Inc . ) and DAT ( ITM-DAT/VL , Institute of Tropical Medicine , Antwerp ) are valid for VL diagnosis in Europe . The choice of test , however , is according to the epidemiological context and intended application . In the context of VL in Europe we find three main applications for serological tests: seroprevalence studies , clinical diagnosis and outbreak response tools . Seroprevalence studies involve large-scale screening of samples to determine the burden of disease in a given population . Previous prevalence studies in Europe on blood donors have used different immunological and/or molecular tests [29–31] . Based on our results , we find DAT performed best for seroprevalence studies , with the ability to batch process samples , the acceptable costs , and the specificity and sensitivity values at 86% and 85% , respectively . For clinical diagnosis , the choice of test depends partly on the immunological status of the patient . In our study , co-infection with HIV reduced the sensitivity of rK39-ICT , making it less applicable for a point-of-care test in HIV individuals in Spain . During the community outbreak of VL in Madrid , 16 out of 160 reported VL cases ( 10% ) had HIV [5] , and could therefore have been missed if rK39 was used as sole diagnostic . In a series of 73 VL patients ( 66% immunocompetent ) from that outbreak , another rK39-ICT ( SD BIOLINE Leishmania Ab , Standard Diagnostics , Inc . , South Korea ) showed 67% sensitivity and 100% positive predictive value [32] . To our surprise , in our study the DAT showed higher sensitivity in HIV-positive patients . Although a higher sensitivity in this group is somehow unexpected , it is important to highlight that DAT has returned acceptable sensitivity in the diagnosis of VL in HIV-positive patients , being superior to other serological tests [10 , 24 , 33–35] . It is difficult for us to find an explanation to this , and it could be suggested that the observed discrepancy may be due to the difference in the number of patients in each group; being only relevant for rK39-ICT ( the only test using a single antigen ) , for which the different performance according to the HIV status is especially marked . A study specifically designed to assess differences in the diagnostic performance of the tests according to the HIV status would be necessary to address this properly . We did not conduct a separate analysis in patients with other immunosuppressive conditions as previous studies have shown that the diagnostic sensitivity of serological tests is not decreased in patients receiving solid organ transplants , which were 11 out of 12 of our suspected VL cases with immunosuppressive conditions other than HIV [36–38] . During outbreak settings such as those seen in Madrid in 2009–2013 , point-of-care tests like rK39-RDT have the benefit of portability , simplicity and the speed of result , allowing quick identification and control of infection clusters . In our study we used all laboratory ( PCR , culture , serology ) and clinical results available from each patient to classify them as “case” or “non-case” . The reported sensitivities of the serological tests included in this study justify the algorithm proposed for VL diagnosis in the WHO European region , where rK39-ICT is first used in VL suspected cases and can be complemented with other serological or parasitological tests to ensure accurate diagnosis [3] . The rK39-ICT is a simple , fast , commercially available test that uses a less invasive sample . The application of this test for VL diagnosis and subsequent treatment of confirmed cases with liposomal amphotericin B , the reference treatment for VL in the WHO European Region [3] , has shown to be cost-effective for Mediterranean VL management in Morocco [39] . In terms of cross reactivity , we found that all true negative serum samples from blood donors from Belgium , Germany and Spain were diagnosed as negative for VL by DAT and rK39-ICT . Some false positive results were obtained with Chagas disease patients and those with other parasitic infections , this was particularly pronounced for the IFAT , a widely used serological test for VL diagnosis in Europe . This can be explained by serological cross reactivity between trypanosomatids [40] . In order to account for this , other infections such as Chagas disease , malaria or other parasites should be routinely discarded to increase diagnostic accuracy . This is particularly important in diagnosing a patient who has resided in or visited a country endemic for other parasitic disease , such as is common in the Spanish migrant populations [41] . To the best of our knowledge this is the first large-scale evaluation of rK39-ICT and DAT for VL diagnosis in Europe . These results can inform public health practitioners in the region on the strengths and limitations of serological diagnosis . In addition to serology , however , PCR diagnosis should always be considered for confirmation of infection , and for the added benefit that molecular characterization brings . Finding appropriate diagnostic solutions to VL is not only important to contain the burden of this Neglected Tropical Disease , but it will also help in the implementation of the United Nations Sustainable Development Goal of Universal Health Coverage [42] . | Visceral leishmaniasis is the most severe form of leishmaniasis , a disease transmitted through the bite of an infected sandfly . Although the biggest burden of leishmaniasis is in eastern Africa and the Indian subcontinent , the disease is also endemic in parts of Europe . Previous studies have looked at performance of diagnostic methods , but not in great detail on samples derived from a European setting . Using a large set of samples from a national reference laboratory in Madrid , Spain , we assessed a leishmaniasis rapid test and a direct agglutination test for serological diagnosis of visceral leishmaniasis in Europe . Both tests were effective at diagnosing VL , but important differences were seen when testing patients co-infected with HIV or with other parasitic infections . This study can help inform which diagnostic tests are suitable for use in a European Mediterranean setting . | [
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"immunodeficiency",... | 2018 | Validation of rK39 immunochromatographic test and direct agglutination test for the diagnosis of Mediterranean visceral leishmaniasis in Spain |
To understand how evolving systems bring forth novel and useful phenotypes , it is essential to understand the relationship between genotypic and phenotypic change . Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual , and it may help create evolvable artificial systems . Here we characterize the genotype-phenotype map of digital organisms in Avida , a platform for digital evolution . We consider digital organisms from a vast space of 10141 genotypes ( instruction sequences ) , which can form 512 different phenotypes . These phenotypes are distinguished by different Boolean logic functions they can compute , as well as by the complexity of these functions . We observe several properties with parallels in natural systems , such as connected genotype networks and asymmetric phenotypic transitions . The likely common cause is robustness to genotypic change . We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution . On the one hand , genotypic change is more likely to yield novel phenotypes in more complex organisms . On the other hand , the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes . Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems . They can also help identify properties , such as robustness , that are required for both human-designed artificial systems and synthetic biological systems to be evolvable .
In natural and artificial systems that undergo Darwinian evolution by random mutation and selection , a central distinction is that between a genotype ( the entire set of genetic material or a digital organism’s set of instructions , respectively ) and a phenotype ( the set of observable traits encoded by the genotype ) . This distinction is important for two main reasons . First , genotypic change causes heritable variation , whereas the phenotypic change it brings forth is the substrate of natural selection . Second , phenotypes form through complex processes such as protein folding and embryonic development . These processes influence how genotypic variation is translated into phenotypic variation . Specifically , they influence what kind of variation becomes available to natural selection . They thus also constrain the directions of evolutionary change . Most importantly , they affect the likelihood that new and beneficial phenotypes—evolutionary adaptations and innovations—originate in the first place [1–5] . To understand the biases and constraints in the production of novel phenotypes , it is necessary to understand how genotypic change translates into phenotypic change . The concept of genotype-phenotype mapping was introduced by Pere Alberch in 1991 as a framework for integrating genetics and developmental biology [6] . However , there is no universal definition of the genotype-phenotype map . We refer to the genotype-phenotype map of natural systems as a ( mathematical ) function from a space of genotypes to a space of phenotypes , which determines how genotypic information specifies phenotypes through processes such as protein folding and embryonic development . Genotype-phenotype maps have been studied in multiple biological systems , including proteins and RNA molecules [7–12] , genome-scale metabolism [13 , 14] , as well as biological circuits that regulate gene activity [15–17] . These studies have revealed a number of commonalities among otherwise very different systems . One of them is that such systems are to some extent robust to genotypic change [7–21] . Another is that this robustness leads to the existence of genotype networks [9 , 10 , 14 , 15 , 17 , 22 , 23] , i . e . , networks of genotypes that share the same phenotype , and that can be converted into one another by a series of phenotype-preserving small genetic changes ( point mutations ) . Such networks can facilitate the origins of novel phenotypes because they help populations explore many different regions in genotype space that may harbor such phenotypes [24–26] . A third commonality is pervasive epistasis—non-additive interactions among individual mutations—which makes the phenotypic effects of individual mutations highly dependent on the genetic background on which they occur [27–29] . In addition to naturally evolving systems , researchers are exploring an increasing number of synthetic or artificial evolving systems [30–36] that range from minor modifications of natural systems , such as proteins with non-natural amino acids [37–41] , to completely artificial systems such as digital organisms and computer viruses [31 , 33 , 34] . We know little about the genotype-phenotype maps of such artificial systems . Specifically , we know almost nothing about the organization of their genotype spaces , and how readily novel adaptive phenotypes can originate in such spaces . Such knowledge may help us compare and contrast natural and artificial evolving systems , including the extent to which natural systems are more evolvable . Any such comparison should take into account that the genotype-phenotype map of artifical systems has not evolved , but in contrast to that of natural systems , is designed . Here we address these issues with the Avida platform for digital evolution [30] . Digital evolution is a form of evolutionary computation in which self-replicating computer programs—digital organisms—evolve within a user-defined computational environment [31–33] . Avida is the most widely used software platform for research in digital evolution [33] . It satisfies the three essential requirements for evolution to occur: replication , heritable variation , and differential fitness . The latter arises through competition for the limited resources of memory space and central processing unit ( CPU ) time . A digital organism in Avida consists of a sequence of instructions—its genome or genotype—and a virtual CPU , which executes these instructions . Some of these instructions are involved in copying an organism’s genome , which is the only way the organism can pass on its genetic material to future generations . To reproduce , a digital organism must copy its genome instruction by instruction into a new region of memory through a process that may lead to errors ( i . e . , mutations ) . A mutation occurs when an instruction is copied incorrectly , and is instead replaced in the offspring genome by an instruction chosen at random ( with a uniform distribution ) from a set of possible instructions . Some instructions are required for replication ( i . e . , viability ) , whereas others are required to complete computational operations ( such as addition , multiplications , and bit-shifts ) , and are executed on binary numbers taken from the environment through input-output instructions . When the output of processing these numbers equals the result of a specific Boolean logic operation , the digital organism is said to have a functional trait represented by that logic operation ( Fig 1 ) . An organism can be rewarded for having a functional trait with virtual CPU-cycles , which speeds up its execution of instructions . These rewards create an additional selective pressure ( besides streamlining replication ) which favours those organisms with mutations that have produced sequences of instructions in their genomes that encode functional traits . Organisms that are more successful—those that replicate faster—are more likely to spread through a population . We use the Avida framework to characterize the genotype-phenotype map of its digital organisms , where this mapping is defined by a direct relationship between complex interactions among computer instructions and the ability for digital organisms to perform Boolean operations . On the one hand , we find that some properties of these maps resemble those found in natural systems , such as robustness , epistasis , and genotype networks . On the other hand , we also characterize a property that has not been found in natural systems . That is , a relationship between phenotypic complexity and the ability to bring forth novel phenotypes [42] . This property may be present but hidden in natural systems , whose overwhelming complexity hinders the analysis of their genotype-phenotype maps . Digital organisms have thus helped us identify a novel hypothesis about the evolvability of natural systems , potentially leading to new fundamental biological principles .
The genotype space for digital organisms with a genome length ( number of instructions ) L taken from an alphabet of available instructions A comprises AL different genotypes . We here consider genotypes with L = 100 instructions drawn from an alphabet of A = 26 instructions ( Methods ) , which yields a genotype space of G = 26 100 ≈ 3 . 14 × 10 141 ( 1 ) different genotypes . A genotype in this space encodes a viable organism if it is capable of self-replication . In addition to being viable , the instructions in an organism’s genome may enable it to compute one or more Boolean logic operations . We refer to this ability as a functional trait or as the organism’s phenotype ( Fig 1 ) . Specifically , we here focus on 9 logic operations such as the AND and OR Boolean functions , that organisms can perform on 32-bit one- and two-input numbers taken from the environment ( Methods ) . Because any organism could in principle be capable of computing any subset of these operations , the total number of possible phenotypes , i . e . , the size of phenotype space , equals 29 = 512 phenoypes . We note that this number includes organisms that are merely viable , i . e . , they do not have any functional trait because they cannot perform any of the operations consider in this study . In a first analysis , we wished to determine the fraction of viable genotypes . To this end , we uniformly sampled genotypes from genotype space until we had found 1000 viable organisms ( Methods ) . This required us to sample 1 . 5 × 109 genotypes , which implies that the fraction of viable genotypes is ≈1000/ ( 1000 + 1 . 5 × 109 ) = ≈ 6 . 6 × 10−7 , and its absolute number is ≈ 5 × 10135 . Because there can be only 512 phenotypes , this result implies that , on average , an astronomical number of genotypes must map onto any of these few possible phenotypes . Because not a single genotype in our sample of 1000 viable genotypes was able to compute any logic operation , we wanted to know next whether some of the immediate ( 1-mutant ) neighborhoods of genotypes in this sample have this ability . To this end , we created all L × ( A − 1 ) = 2500 1-mutant neighbors for each of the 1000 genotypes in our sample , and evaluated the phenotypes of the resulting 2 . 5 × 106 organisms . Even among this large number of organisms , we found only 13 distinct phenotypes . The proportion of the 1000 neighborhoods in which a phenotype appears at least once indicates a highly non-uniform distribution of phenotypes in genotype space ( S1 Fig ) . These observations suggest that some phenotypes—those we found—are frequent , whereas others must be very rare ( Fig 2A ) . In addition , rarer phenotypes are more complex ( ρ = −0 . 759 , n = 13 , p = 0 . 002 ) . We define overall phenotypic complexity as the sum of the complexity of the logic functions that an organism can compute . We approximate each function’s complexity as the minimum number of times that a nand instruction—the only instruction that is itself a logic operator—must be executed for computing the function [43 , 44] . To compute phenotypic complexity , we add the complexity value of the individual functions , and normalize the resulting sum by the complexity of the most complex phenotype . This measure of phenotypic complexity is not only simple but also sensible: when computed for all 511 functions , it is correlated with the minimum number of times that the nand instruction is executed ( ρ = 0 . 536 , n = 511 , p < 0 . 001 ) . Note that a complex phenotype results from executing a repeated combination of instructions that simpler phenotypes might already harbor in their genomes . Given the low number of phenotypes our random sampling had identified , we next undertook a two-step procedure to sample genotypes with all 512 phenotypes ( directional selection followed by purifying selection; see Methods ) . Briefly , the first step consisted of evolving 1000 populations of digital organisms subject to repeated cycles of mutations and selection for specific functional traits ( i . e . , favoring organisms with genomes where mutations had produced sequences of instructions that compute specific logic operations ) . We initialized each population from one of the 1000 randomly sampled viable genotypes . We allowed these 1000 populations to evolve for 106 updates , where an update is the amount of time during which an organism executes on average 30 instructions . After 106 updates , the total number of distinct phenotypes encountered in each evolving population did not increase further . At that point in time , we stopped the evolution process and kept only one genotype per phenotype , chosen at random from the genotypes previously encountered during the process . This procedure allowed us to find at least one genotype that mapped to each one of the 512 phenotypes comprising the whole phenotype space . We observed that 60% of phenotypes were discovered by only 10% of the populations , and only 12% of phenotypes were found by more than 90% of the populations ( S2 Fig ) . A few phenotypes—likely the rarest ones—were very difficult to find , and two of them were discovered by only one population . In the second step , we aimed to obtain a fixed number of 1000 independently sampled genotypes for each phenotype . To this end , we started from the previously discovered genotypes with a specific phenotype , and performed double-mutant random walks through genotype space that preserved viability and phenotype during 1000 mutational steps ( Methods ) . For each phenotype , we performed 1000 such random walks , thus creating 1000 randomly sampled genotypes with this phenotype ( data are provided as S1 File ) . With these samples in hand , we first asked how different two organisms can be in their genotypes if they share the same phenotype . For every phenotype , we found that the maximum genotype distance—measured as the Hamming distance of genotype instruction sets—among all genotypes with the same phenotype is as high as the genome length , D = 100 . That means that organisms whose genotypes differ in all positions along their genomes may indeed have the same phenotype ( Methods ) . This is possible because the effect of an instruction on a phenotype depends on other instructions contained in the genome , a phenomenon analogous to epistasis in genetic systems [43] . By applying a multiple local alignment algorithm to the genomes of organisms with the same phenotype ( Methods ) , we did not find any recurring subsequence pattern—sequence motif—revealing common ways of achieving the same phenotype nor any sequence motif containing the instructions required for viability . We only found a small motif in genotypes encoding the simplest phenotypes . It contains the flow-control operations involved in determining which intructions are going to be read and written ( S3 Fig ) . Overall , these observations show that genotypes with any one phenotype are not localized in a single small region of genotype space , but might rather occur throughout this space . We next asked whether genotypes with the same phenotype can be connected in sequence space through a series of point mutations ( single instruction changes ) that leave the phenotype intact . In other words , do genotypes with the same phenotype form a single connected network of genotypes ? To find out , we performed random walks involving multiple pairs of genotypes with the same phenotype , where each random walk aimed to reach one of the genotypes from the other without changing its phenotype . We found that this is not generally possible , and recorded the minimal distance between genotypes that we were able to obtain with this approach ( Methods ) . Since this is a computationally time-consuming process , we carried it out only for merely viable organisms and for organisms with single-trait phenotypes . We note that the random walks we performed can only provide upper bounds on the distance between different components of the same genotype network . Fig 2B shows the minimal distances between pairs of genotypes for single-trait phenotypes , arranged as a function of the complexity of the trait . We found that at least one pair of genotypes of every phenotype is connected and that the average minimum distance between genotype pairs increases with trait complexity ( ρ = 0 . 940 , n = 10 , p = 0 . 005 , for the median ) . Even though the preceding observations suggest that genotype network fragmentation rises with phenotypic complexity , an additional analysis shows that the gaps between different genotype networks might be easily bridged . In this analysis , we performed random walks analogous to those just described , where each step needed to preserve both viability and the phenotypic traits of the starting genotype . In addition , we also accepted steps that lead to genotypes with additional traits that had not been present in the starting genotype . Under these conditions , the average minimum distance between pairs of genotypes became significantly lower than in the preceding analysis ( from 4 . 5 to 4 for the simplest phenotype , and from 25 . 5 to 18 for the most complex one ) . In addition , the fraction of genotype pairs that were connected increased by 11% for the simplest single-trait phenotype , and by 63% for the most complex one . Because the additional traits we observe were not required by our selection criterion , they emerged spontaneously . In the language of evolutionary biology , they can thus be viewed as potential exaptations [44]—traits of organisms that are either not adaptive when they originate , or whose adaptive role changes [45] . Different phenotypes may not only differ in the number of genotypes that form them . They may also differ in their accessibility from genotypes with other phenotypes , that is , in the likelihood to reach them from such a genotype through a single point mutation . To estimate such differences in phenotypic accessibility , we used our samples of 1000 organisms with a given phenotype , and computed , for all phenotypes i and j , the probability of encountering an organism with phenotype j from an organism with phenotype i by a single point mutation . To this end , we first identified all genotypes that lie in the 1-mutant neighborhood of every organism having phenotype i . We then classified these genotypes according to phenotype , and computed the fraction of those genotypes that have phenotype j . We also refer to this fraction as the transition probability from phenotype i to phenotype j ( pi→j ) . The organisms we encountered in these neighborhoods fall into three classes . The first class holds inviable organisms . Fig 2C shows that the likelihood of encountering an inviable organism through a point mutation ( pi→0 ) increases with the complexity of the phenotype i ( Spearman’s ρ = 0 . 621 , n = 512 , p < 0 . 001; S4 Fig ) . The second class comprises viable organisms that have the same phenotype as i ( pi→i , see Fig 2C ) . We refer to the fraction of point mutations that preserve an organism’s phenotype , averaged over all organisms with this phenotype , as the mutational robustness of this phenotype . The higher the complexity of a phenotype , the lower is its robustness ( ρ = −0 . 689 , n = 512 , p < 0 . 001; S4 Fig ) . Since some instructions of an organism’s genome might not be executed during its self-replication process , we asked to what extent simple phenotypes correspond to organisms that execute fewer instructions . To answer this question , we computed the fraction of the genome as well as the number of instructions that organisms encoding the same phenotype executed during their replication . Neither one nor the other were correlated with phenotypic complexity ( ρ = 0 . 017 , n = 512 , p = 0 . 709; ρ = −0 . 049 , n = 512 , p = 0 . 271; respectively ) . Another factor that could be responsible for the association between phenotypic complexity and robustness is the fixed length of the genome . To rule this possibility out , we reduced the genome size for all organisms with single-trait phenotypes by deleting one randomly chosen instruction at a time , while preserving viability and phenotype , until no more instructions could be removed . We found that organisms having more complex functional traits required a larger minimal genome ( ρ = 0 . 902 , n = 9 , p < 0 . 001 , for the median , see S5 Fig ) . This observation implies that the higher the phenotypic complexity of an organism is , the smaller is the number of instructions in the genome that can be altered without perturbing the phenotype . In other words , phenotypic complexity comes at the price of lower phenotypic robustness . The third and most important class of organisms comprises the 1-mutant neighbors that have a different phenotype j . The greater the complexity of phenotype i is , the greater is the probability ( pi→j ) of finding a genotype with a novel phenotype ( ρ = 0 . 633 , n = 512 , p < 0 . 001; see Fig 2C and S4 Fig ) . We also found that the distribution of non-zero transition probabilities ( 68% ) is heavy-tailed ( Fig 3A ) . This means that only a few novel phenotypes are highly accessible through single point mutations , whereas most have a very low chance of being encountered . The probabilities of phenotypic change may be asymmetric [46 , 47]; that is , phenotype i may be easily accessible from phenotype j but not vice versa ( pi→j ≠ pj→i ) . We quantified this asymmetry by computing the quantity AS ( i , j ) = |pi→j − pj→i|/max ( pi→j , pj→i ) , where max refers to the maximum of two values [48] . We found that most reciprocal transition probabilities are highly asymmetric ( Fig 3B ) . These asymmetries in transition probabilities are just a consequence of the fact that different phenotypes have different numbers of genotypes that code for them ( see Methods for a simple mathematical explanation ) . This direct relationship between transition probabilities and the frequency of phenotypes has also been reported in models for protein folding and self-assembling protein quaternary structure [49] . Indeed , the ratios of the transition probabilities between pairs of phenotypes provide an estimate of the ratios of the frequencies of each phenotype in genotype space ( although this estimate might deviate from the exact value because of sampling errors ) . We also estimated the frequency of the single-trait phenotypes in genotype space relative to the number of merely viable organisms Nj . That is , N i = p j → i p i → j × N j , where Nj = 1 . It ranges between 10−3 and 10−11 for the simplest and most complex phenotypes , respectively . We found a negative relationship between the estimated frequency of each phenotype and its phenotypic complexity ( ρ = −0 . 889 , p = 0 . 001 , n = 9 ) . This result explains the association found between phenotypic complexity and phenotypic transition probabilities . Specifically , for 90% of phenotype pairs i and j , the probability of encountering phenotype i from phenotype j was higher if j was more complex than i . In other words , it is harder for a simple phenotype i to reach a more complex phenotype j than vice versa because genotypes with complex phenotypes are less common than genotypes with simple ones ( see Fig 3C ) . According to the predictions of models assuming a random distribution of genotypes in genotype space [50] , the robustness of single-trait phenotypes increases logarithmically with the frequency of the phenotypes estimated from the ratios of their transition probabilities ( R2 = 0 . 876 , n = 9 , p < 0 . 001 ) . Computational approaches have shown that epistasis is more common between mutations that fix under purifying selection than among randomly selected mutations [29 , 51 , 52] . Therefore , our non-uniform sampling procedure to find genotypes encoding the same phenotype ( directional selection followed by purifying selection ) might influence the topology of the genotype-phenotype map around evolved genotypes . To rule out this possibility , we calculated the correlation between the proportion of the 1000 neighborhoods of the merely viable organisms ( randomly sampled ) in which a phenotype appears at least once , and the frequencies of those phenotypes estimated from the ratio of the transition probabilities for our evolved genotypes . We found a positive and statistically significant relationship between the two estimates of the size of the genotype space occupied by a given phenotype ( ρ = 0 . 985 , n = 13 , p < 0 . 001 ) . This suggests that the topology of the genotype space around evolved genotypes might not be different from that around randomly sampled ones ( at least for the single-trait phenotypes ) . We next studied the evolvability of individual genotypes with phenotype i , which we define as the number of distinct phenotypes j ≠ i that can be reached by a single point mutation from genotypes with phenotype i . This genotypic evolvability increases with phenotypic complexity ( ρ = 0 . 833 , n = 511 , p < 0 . 001 ) . This association might be a simple consequence of the fact that it is easier to lose abilities ( functional traits ) than to gain them by random mutation . To exclude such degenerative mutations , we repeated this analysis with a constrained definition of evolvability including only those phenotypes j as novel that can compute at least one additional logic function compared to i . Because the number of phenotypes with novel traits j ≠ i decreases as the complexity of phenotype i increases , we divided the evolvability of phenotype i by the total number of phenotypes with novel traits j ≠ i . Even with this much more conservative notion of evolvability , genotypes with more complex phenotypes were more evolvable ( ρ = 0 . 832 , n = 510 , p < 0 . 001 ) . The preceding analysis did not take into account that different phenotypes differ in the size of their genotype network . That is , we analyzed the same number of genotypes for each phenotype , regardless of the fraction of genotype space occupied by each phenotype . This approach can be biased because genotype network size can affect the total number of novel phenotypes that are reachable by one mutation from any genotype with a given phenotype [53] . We refer to this number also as the evolvability of a phenotype , as opposed to that of a genotype . In other words , rare phenotypes were sampled more intensively than common ones . To estimate this phenotypic evolvability , we multiplied the genotypic evolvability from the preceding paragraph by the frequency of the corresponding phenotype in genotype space , which adjusts for genotype network size assuming that the number of phenotypes found scales linearly with the number of genotypes sampled . Fig 4 shows that evolvability increases with robustness for the 13 phenotypes for which we have frequency data ( ρ = 0 . 754 , n = 13 , p = 0 . 003 ) . In addition , more complex phenotypes ( larger circles ) are less evolvable ( ρ = −0 . 701 , n = 13 , p = 0 . 008 ) , most likely because they occupy a smaller subset of genotype space .
Our analysis of the genotype-phenotype map in the artificial life system Avida ( see Fig 5 ) revealed that the number of genotypes forming a phenotype differs greatly among phenotypes . The more complex the logic operations are that a phenotype performs , the fewer genotypes form this phenotype . Genotypes with any one phenotype tend to form one or more networks whose members are likely connected to one another by series of small genotypic changes that leave the phenotype unchanged , and thus help explore different regions of genotype space . The larger any one such network is , the greater is the number of novel phenotypes that can be reached through single point mutations from its members . We also find that the accessibility of novel phenotypes is highly asymmetric: it is much harder to evolve more complex phenotypes than simpler ones through single point mutations . One of the obvious parallels between biological systems and Avida is that our digital organisms are to some extent robust to genotypic changes , i . e . , to “point mutations” in their instruction sequence . It is this robustness that might give rise to large phenotype-preserving genotype networks [54 , 55] . In natural systems , most robustness to mutations is a consequence of the fact that organisms must persist in multiple different environments [55] . In an artificial system like Avida , robustness can be achieved in simple ways , by providing a genome with more instructions than needed , as we did . The resulting excessive genomic size allows more flexibility in tinkering with instructions while preserving a phenotype , which facilitates the origin of novel phenotypes near these genotypes . Observations like this provide guiding principles to design evolvable artificial systems . The genotype networks we examined are not all connected , and may consist of multiple different components . However , this fragmentation is most pronounced when we require the strict preservation of phenotypes in the random walks that aim to connect different organisms with the same phenotype . During some steps of these random walks , genotypes fortuitously acquire novel computational abilities that they do not require , and if we do not allow such “innovative” steps , some genotype networks are disconnected . If , however , we admit such steps , the chances for all phenotypes we examined to be connected in a single genotype network increases . We view such non-adaptive novel traits , which also exist in metabolic systems [56] , as analogous to potential exaptations [44] . They are not adaptive , but could become adaptive in the right environment . Moreover , they can help bridge gaps between disconnected genotype networks , and thus make more genotypes accessible by populations subject to phenotype-preserving point mutation . This additional connectivity , in turn , makes more novel phenotypes accessible that reside near these genotypes . The asymmetric phenotypic transitions we observe , where the likelihood of reaching phenotype i from phenotype j through a single point mutation is not equal to the converse probability , also have parallels in natural systems . For example , such asymmetries have been observed in phenotypic transitions between different RNA secondary structures [46 , 47 , 57] . They also occur in anisotropic morphospaces of paleobiology [58] , where a clade’s propensity to vary depends on the direction of phenotypic change . In our study system , asymmetric phenotypic transitions result from the vastly different number of genotypes that encode each phenotype . Some phenotypes , regardless of their adaptive value , are more likely to be “discovered” by evolving populations than others . Because such differences in phenotypic rarity are pervasive in other systems [8 , 42 , 53 , 54 , 59 , 60] , asymmetric transitions are likely to be a universal characteristic of phenotypic evolution . Regardless of their causes , they have practical consequences . For example , they can lead to spurious incidences of convergent evolution , and they can mislead reconstructions of ancestral phenotypes [61] . To our knowledge , the relationship we found between phenotypic complexity and evolvability has not been reported for any natural system . These relationships exist on two levels of organization . The first is that of individual genotypes with a specific phenotype . Mutations are more likely to create novel phenotypes in digital organisms with complex phenotypes . It is not difficult to see why , and we fully expect similar causes to be at work in natural systems . Phenotypes emerge from the coordinated execution of “genetic building blocks” , which are analogous to developmental processes guided by regulatory programs in biology . These building blocks can be modified to perform different logic operations . Evolving genomes can “discover” complex phenotypes only by combining the genetic building blocks of preexisting , simpler phenotypes [62] . The genome of organisms with complex phenotypes is expected to harbor more such building blocks , which can be altered and combined in more ways than in organisms with simpler phenotypes . The second level of organization is that of the entire genotype space . Here , we observe that complex phenotypes are more rare , that is , they are encoded by fewer genotypes . ( The larger minimal genomes required for such phenotypes are consistent with this observation , because they constrain genotypic evolution to a smaller region of genotype space ) . The main consequence of the rarity of complex phenotypes is that the total number of novel phenotypes from any of these genotypes—phenotypic evolvability—is lower . Both levels of organization can help explain the relationship of asymmetric transitions to phenotypic complexity , i . e . , single mutations from any one phenotype are more likely to yield a simple phenotype than a complex one , and the most complex phenotypes can be reached only through multiple steps . From the individual , mechanistic perspective , mutations are more likely to lead to a loss than a gain of a function in a genetic building block required for a phenotype . They are thus more likely to create a simpler phenotype ( or an inviable organism ) , than a complex phenotype . This asymmetry is also at the core of why the most complex phenotypes must be built in multiple small steps . From the collective , genotype space perspective , mutations are simply less likely to “hit” the smaller target of a complex phenotype with a small genotype network . We expect that natural systems , which display dramatic differences in the number of genotypes that form specific phenotypes [8 , 42 , 53 , 54 , 59 , 60] would show a similar relationship between complexity and evolvability . If so , two predictions follow . First , mutations in an evolving population whose members have a complex phenotype are more likely to create novel phenotypes . Second , on long evolutionary time scales , these phenotypes may be less diverse than for organisms with a simpler phenotype . Our results also have implications for the development of genetic languages in artificial life [63] . Not only can Avida organisms display robustness to mutations , Avida’s genetic language is itself robust to several modifications of the instruction set [64] . Only few modifications , such as the separation of the input and output instruction can alter an organism’s ability to perform logic operations . Future studies may systematically compare different genotype-phenotype maps , and identify those that are most evolvable . Their insights may also guide synthetic biologists in designing genetically engineered devices primed for evolutionary innovation [65] .
The genome of a digital organism is a circular sequence of instructions taken from a 26-instruction alphabet [33] . It comprises instructions for copying , as well as for completing computational operations ( such as additions , subtractions , and bit-shifts ) , which are executed on binary numbers taken from the environment . The default environment provides the organism with new , random input strings every time an input-output instruction is executed . The genome of a digital organism can harbor one or several input-output instructions that can be executed either only once or many times during the time it takes to generate an offspring . This means that the organism can take input numbers from the environment more than once before replicating and can compute the result of more than one logic operation ( see below ) . Only one instruction from the instruction set is itself a logic operator . This is the nand ( not-and ) instruction , which must be executed in coordination with input-output instructions to perform the NAND logic operation . The nand instruction reads in the contents of the BX and CX registers and performs a bitwise NAND operation on them ( i . e . , it returns 0 if and only if both inputs at the corresponding bit positions are 1 , otherwise it returns 1 ) . The result of this operation is placed in the BX register . The IO ( input-output ) instruction takes the contents of the BX register and outputs it , checking it for any logic operations that may have been performed . It will then place a new input into BX ( see S1 Appendix ) . All other logic operations must be performed using one or more nand instructions in combination with input-output instructions [33] . Since previous work has shown that organisms with a genome of 83 instructions are able to perform the most complex logic operation we consider here [66] , we decided to focus on genomes with L = 100 instructions—a genome size large enough to permit exploration of all phenotypes , but at the same time small enough to be computationally tractable . Phenotypes are defined by the combination of the following 9 Boolean logic operations that organisms can perform on 32-bit one- and two-input numbers: NOT , which returns 1 at a bit position if the input is 0 at that bit position , and 0 if the input is 1; NAND , which returns 0 if and only if both inputs at the corresponding bit positions are 1 ( otherwise it returns 1 ) ; AND , which returns 1 if and only if both inputs are 1 ( otherwise it returns 0 ) ; OR_N ( or-not ) , which returns 1 if for each input bit pair one input bit is 1 or the other is 0 ( otherwise it returns 0 ) ; OR , which returns 1 if either the first input , the second input , or both are 1 ( otherwise it returns 0 ) ; AND_N ( and-not ) , which only returns 1 if for each bit pair one input is 1 and the other input is 0 ( otherwise it returns 0 ) ; NOR ( not-or ) , which returns 1 only if both inputs are 0 ( otherwise it returns 0 ) ; XOR ( exclusive or ) , which returns 1 if one but not both of the inputs are 1 ( otherwise it returns 0 ) ; EQU ( equals ) , which returns 1 if both bits are identical , and 0 if they are different [33] . This logic operations are listed above in order , from least complex to most complex . Here , we define complexity as the minimum number of times that a nand instruction—the one required to compute all other logic operations—must be executed for completing a specific logic operation . Specifically , their complexities are 1 ( NOT ) , 1 ( NAND ) , 2 ( AND ) , 2 ( ORN ) , 3 ( OR ) , 3 ( ANDN ) , 4 ( NOR ) , 4 ( XOR ) , and 5 ( EQU ) [33] . We used a test environment provided by Avida to compute the phenotype of each digital organism’s genotype . In such a test environment each organism executes its instructions in isolation until it produces a viable offspring or until a timeout is reached , whichever comes first . We note that it is impossible to determine with certainty whether an organism is able to produce a viable offspring ( i . e . , its viability ) , because the number of instructions executed before replicating might be extremely large , for example because they might involve loops . We therefore limit how long an organism remains in the test environment before assuming that it is not going to replicate . Specifically , we set this limit to 20 × L because we found no additional viable organism when a sample of 107 randomly generated genomes was left in the test environment twice as long as our limit . That is , we kept each organism in the test environment until it had executed 2000 instructions . For the purpose of determining an organism’s phenotype , we allowed no mutations , such that the offspring is an exact copy of its parent . We recorded the logic operations performed by the organism in the test environment , thus assigning a unique phenotype to each genotype . Note that we have also explored to what extent a variable environment may elicit additional phenotypes for the same genotype ( S6 Fig ) . Instruction sequences representing the genomes of digital organisms might contain similar regions ( instruction sequence motifs ) that reflect similar ways of achieving specific phenotypes and/or self-reproduction . To find out whether such regions exist , we have applied the GLAM2 algorithm [67 , 68] for discovering both gapless and gapped motifs from the instruction sequences constituting the genomes of our sampled digital organisms . We searched for overrepresented gapped motifs because digital organisms may execute jump instructions that move the execution flow from one region of the genome to another . Although searching for gapped motifs might miss jumps , it would be less appropriate to search for gapless motifs in Avida . One of the advantages of GLAM2 is that it operates on sequences over arbitrary , user-defined alphabets . GLAM2 defines a scoring scheme for local alignments of multiple sequences and finds the alignment with the maximum score using simulating annealing . Since GLAM2 is a heuristic algorithm , we ran it 100 times to verify that it finds a reproducible , highly-scoring motif ( we used the default settings , except very large values for the following parameters to turn off deletions and insertions completely: -E 1e99 -J 1e99 ) . GLAM2 provides the statistical significance of an alignment by comparing its score with that obtained after a random reshuffling of the instructions along the sequences . To sample genotype space , we first aimed to generate 1000 viable organisms . To this end we first generated random genomes with 100 instructions , where we chose each instruction in a genome randomly and uniformly among the 26 possible instructions , and examined each genome for viability . After having generated 1 . 5 × 109 genomes in this way , we had found 1000 viable genomes . None of them were able to perform any logic operation . Next , we evolved 1000 populations of organisms in the standard mode of Avida , where we initialized each of the populations with one of the 1000 previously sampled organisms . We configured the standard mode of Avida to follow a Moran process , where every time an organism produces a viable offspring , the offspring replaces one organism randomly chosen from a population of 104 organisms . In our simulations , each offspring differed from its parent by a single point mutation , i . e . , one randomly chosen instruction in its genome was replaced with a instruction randomly chosen from the instruction set . In addition , we rewarded the ability of an organism to perform any of the 9 logic operations defining a phenotype with an extra amount of virtual CPU-cycles that sped up its replication process . This procedure introduced a selective pressure that favored organisms with genomes where mutations had produced sequences of instructions that compute one or more logic operations ( the more the better ) . We let each population evolve for 106 updates , where an update is the amount of time during which an organism executes on average 30 instructions . Every 1000 updates we recorded , for every distinct phenotype encountered in the population at that time , the genotype of one randomly chosen organism with that phenotype . After 106 updates , the number of distinct phenotypes encountered in each evolving population reached an asymptote . Then , we stopped the evolution process and kept only one genotype per phenotype and population , chosen at random from those previously recorded during the process . These 1000 evolving populations were enough to find at least one genotype that mapped to each one of the 512 phenotypes comprising the whole phenotype space . This number of 512 phenotypes includes the phenotype of the ancestors ( i . e . , merely viable organisms ) . In order to obtain a fixed number of 1000 independently sampled genotypes for each phenotype , we then performed 1000 random walks through the genotype space for each phenotype . These random walks started from the organisms ( genotypes ) with a given phenotype that had been found by our evolving populations . Some genotypes were used more than once because for some phenotypes fewer than 1000 genotypes with that phenotype had been found . We performed these random walks in the test environment . Each step in each random walk mutated two randomly chosen instructions in the random-walking genotype , and replaced them with two randomly chosen instructions from the 26-instruction alphabet . Whenever such mutations produced a non-viable organism or an organism whose phenotype had changed , we reverted the mutations and mutated two new , randomly chosen instructions , repeating this procedure until a viable organism with an unchanged phenotype appeared . We repeated this procedure for 1000 steps , that is , until a chain of 1000 viable organisms with the same phenotype as the starting genotype had been discovered , and kept the last genotype in the chain for further analysis . In sum , this procedure helped us create 1000 randomly sampled viable organisms for each phenotype ( data are provided as S1 File ) . We wished to estimate to what extent organisms with the same phenotype are connected in a single network of genotypes ( a graph whose nodes are genotypes with the same phenotype and where two nodes are connected if they differ by a single instruction ) . To this end , we started from 100 pairs of organisms with identical phenotypes produced through the random walks described above . For each such pair , we performed a random walk through genotype space , in which we changed one member of the pair through a series of single point mutations , where each mutation was required to preserve both viability and phenotype . In addition , no mutation was allowed to increase the genotype distance to the second member , which was measured as the number of positions at which the genomes of both organisms differed , i . e . , their Hamming distance . The goal of each random walk was to find a path through genotype space that would approach and eventually reach the other member of the pair of genotypes , while preserving the phenotype . Note that our algorithm does not take into account that finding connections among genotypes encoding the same phenotype might require reversals of mutations . After 104 steps , that is , until a chain of 104 viable organisms with the same phenotype as the initial genotype had been discovered , we counted the number of instruction matches in the genome of the random walker and the other member of the initial genotype pair . We repeated this procedure 10 times for each of the 100 pairs of organisms with a given phenotype . Finally , we recorded the smallest distance value from these 10 × 100 = 1000 replicates as the minimum genotype distance between the organisms with the same starting phenotype . This process is computationally time-consuming and we performed it only for the single-trait phenotypes ( i . e . , those corresponding to a single logic function ) . In addition , we repeated the entire process by relaxing the criterion of exact phenotype preservation during a random walk . Specifically , in this kind of random walk , the random walkers had to preserve viability and all the logic operations they were able to perform at the beginning of the random walk , but if they acquired the ability to perform additional logic operations during any one step ( but not any fewer ) , we considered that step acceptable . To estimate how likely it is that single point mutations cause transitions between two phenotypes i and j , we first computed , for each of the 1000 randomly sampled organisms with a given phenotype i , all of its L × ( A-1 ) = 2500 single point mutation neighbors . We then determined for all of the resulting 1000 × 2500 neighbors the fraction of neighbors that were viable and had phenotype j . We considered this fraction as an estimate of the likelihood that a single point mutation can produce a genotype with phenotype j from a genotype with phenotype i ( i . e . , the transition probability pi→j ) . We denote the fraction of non-viable neighbors of the 1000 genotypes with phenotype i as pi→0 . We note that transition probabilities smaller than 2 . 5 × 10−6 would be equal to zero . We repeated this procedure for all pairs of phenotypes i and j , and note that transition probabilities need not be symmetric , that is , it may be easier or harder to reach phenotype j from phenotype i than vice versa . The asymmetries in transition probabilities are just a consequence of the fact that different phenotypes have different numbers of genotypes that code for them . That is , if a forward mutation produces phenotype i from phenotype j , then the back mutation produces phenotype j from phenotype i . Denote as Ni and Nj the number of genotypes with phenotype i and j , respectively , as nij and nji the number of mutations from phenotype i to phenotype j and from j to i , respectively , and as A the size of the alphabet . Then for sequences of length L = 100 , p i → j = n i j ( 100 ( A - 1 ) N i and p j → i = n j i ( 100 ( A - 1 ) N j . Since nij = nji , p i → j p j → i = N j N i . This result requires no mathematical approximations and does not depend on any assumptions about the topology of the genotype-phenotype map . Since novel phenotypes arise in evolving populations , we computed the likelihood of reaching phenotype j from phenotype i in such populations , to test whether the corresponding entry of the transition probability matrix reflect this likelihood ( S7 Fig ) . | The phenotype of an organism comprises the set of morphological and functional traits encoded by its genome . In natural evolving systems , phenotypes are organized into mutationally connected networks of genotypes , which increase the likelihood for an evolving population to encounter novel adaptive phenotypes ( i . e . , its evolvability ) . We do not know whether artificial systems , such as self-replicating and evolving computer programs—digital organisms—are more or less evolvable than natural systems . By studying how genotypes map onto phenotypes in digital organisms , we characterize many commonalities between natural and artificial evolving systems . In addition , we show that phenotypic complexity can both facilitate and constrain evolution , which harbors lessons not only for designing evolvable artificial systems , but also for synthetic biology . | [
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"bioinformati... | 2017 | The genotype-phenotype map of an evolving digital organism |
The hepatitis C virus ( HCV ) envelope glycoproteins E1 and E2 form a non-covalently linked heterodimer on the viral surface that mediates viral entry . E1 , E2 and the heterodimer complex E1E2 are candidate vaccine antigens , but are technically challenging to study because of difficulties in producing natively folded proteins by standard protein expression and purification methods . To better comprehend the antigenicity of these proteins , a library of alanine scanning mutants comprising the entirety of E1E2 ( 555 residues ) was created for evaluating the role of each residue in the glycoproteins . The mutant library was probed , by a high-throughput flow cytometry-based assay , for binding with the co-receptor CD81 , and a panel of 13 human and mouse monoclonal antibodies ( mAbs ) that target continuous and discontinuous epitopes of E1 , E2 , and the E1E2 complex . Together with the recently determined crystal structure of E2 core domain ( E2c ) , we found that several residues in the E2 back layer region indirectly impact binding of CD81 and mAbs that target the conserved neutralizing face of E2 . These findings highlight an unexpected role for the E2 back layer in interacting with the E2 front layer for its biological function . We also identified regions of E1 and E2 that likely located at or near the interface of the E1E2 complex , and determined that the E2 back layer also plays an important role in E1E2 complex formation . The conformation-dependent reactivity of CD81 and the antibody panel to the E1E2 mutant library provides a global view of the influence of each amino acid ( aa ) on E1E2 expression and folding . This information is valuable for guiding protein engineering efforts to enhance the antigenic properties and stability of E1E2 for vaccine antigen development and structural studies .
Hepatitis C virus ( HCV ) is a major global health concern with over 170 million people currently infected and an additional 3 million being infected each year ( reviewed in [1 , 2] ) . While approximately 30% of infected individuals are capable of spontaneously clearing the virus , usually within the first 12 months of infection , the remainder generally develops life-long infection . Of those who progress to chronic infection , about 20% develop liver cirrhosis and 1–3% hepatocellular carcinoma , one of the leading causes of cancer mortality [2 , 3] . As a member of the Hepacivirus genus in the Flaviviridae family , HCV contains a positive-sense , single-stranded RNA genome coding for three structural proteins and seven non-structural proteins [4] ( Fig 1A ) . The RNA-dependent RNA polymerase , NS5B , which lacks proofreading activity , gives rise to the heterogeneous viral quasispecies and the diverse viral genotypes in circulation . The high rate of infection in endemic countries and the morbidity caused by subsequent liver damage[5] , as well as underdiagnosis , costly treatments and high rate of reinfection [6 , 7] , highlight the need for an effective HCV vaccine to limit virus infection and spread . E1 and E2 are heavily glycosylated envelope proteins and form a heterodimer complex on the viral surface that facilitates viral attachment and entry into host cells ( reviewed in [4] ) . E1 encompasses residues 192–383 of the HCV polyprotein ( prototypic strain H77 ) , while E2 is the larger of the two envelope proteins and spans amino acids 384–746 ( Fig 1B ) . In association with apolipoproteins , HCV forms lipoviroparticles that attach and infect hepatocytes using a number of host entry factors including CD81 , scavenger receptor class B member 1 ( SR-B1 ) , claudin-1 , occludin , low-density lipoprotein receptor ( LDLR ) , and others whose roles are still under investigation ( reviewed in [4 , 8] ) . CD81 was the first entry receptor identified and is the best-characterized entry factor to date . Many studies have demonstrated that CD81 is capable of binding to soluble E2 , and antibodies targeting the large extracellular loop of CD81 ( CD81-LEL ) prevent HCV infection both in vitro and in vivo ( reviewed in [9] ) . While E2 is known to interact with CD81 and SR-B1 ( reviewed in [10] ) , it appears that E1 may help modulate these interactions and could play a role in membrane fusion [11–13] . Our understanding of the E2 protein has been enhanced by the recent crystallization of the E2 core domain ( E2c ) , providing evidence that refutes the previous hypothesis of E2 as a class II fusion protein [14–16] . Canonical class II fusion proteins consist of three protein domains with an elongated structure in which domain 2 harbors the fusion peptide that is embedded within the dimer interfaces ( reviewed in [17] ) . In contrast , E2 is globular in shape with the E2c adopting a compact architecture surrounded by disordered variable loops . E2c consists of a central Ig-like β-sandwich scaffold flanked by front and back layers of protein consisting of loops , short helices , and β-sheets [15 , 16] . Cross-neutralizing antibodies that recognize E2c primarily map to the front layer , which is also a component of the CD81 binding site ( CD81bs ) [15] . Recent findings suggest that the CD81bs is exceptionally flexible in the soluble protein form and could present many non-optimal conformations during immunization [18] . Structural information for E1 is more limited , consisting of NMR studies of putative membrane proximal regions and a recent crystal structure of the N-terminal region , which displays an unusual disulfide-linked multimeric ( nE1 ) organization [19] . Since E1 and E2 are the targets of neutralizing antibody ( NAb ) responses , understanding how they interact with antibodies can offer valuable insights into the antigenic surface and folding of the vaccine immunogens . To date , a number of antibodies targeting E1 , E2 , or the E1E2 complex have been isolated , some exhibiting cross-neutralizing behavior when tested against multiple viral genotypes ( reviewed in [20–22] ) . The recent X-ray structures of E2c , E1 and E2 peptides complexed with several broadly neutralizing antibodies ( bNAbs ) have provided partial yet critical information on the architecture and functions of the glycoproteins [15 , 16 , 23–25] . To gain a greater understanding of the HCV envelope glycoprotein antigens , we exploited an alanine scanning mutant library spanning the entirety of E1 and E2 , which had been recently created using a high-throughput shotgun mutagenesis method [26] . The comprehensive alanine scanning mutagenesis , in combination with antibodies recognizing a wide range of discontinuous epitopes , can provide a snapshot of how the different regions in E1 and E2 may be brought together to form the epitopes . We tested binding by a panel of 13 monoclonal antibodies ( mAbs ) and CD81-LEL fused to the immunoglobulin Fc fragment to probe the diverse epitopes encompassing distinct continuous and discontinuous antigenic sites on E1 and E2 , thereby providing a global perspective of E1 and E2 antigenicity ( Table 1 , and Materials and Methods ) . Using high-throughput flow cytometry ( FC ) , the effect of each point mutation on the binding of the antibodies and CD81-LEL was determined . The results were compared with data in the literature to uncover new information about the HCV epitopes . Selected mutations were analyzed further in complementary experiments to evaluate the mutagenesis results . Our study revealed that several E2 back layer residues play a critical role in E1E2 folding and indirectly affect binding to CD81 and antigenic region ( AR ) 3 mAbs . The data also predict residues that are likely located at or near the interface for E1 and E2 complex formation .
High-throughput flow cytometry is a well-established system that has been used in the study of many viral antigens , and therefore was applied to probe the E1E2 mutant library with a panel of mAbs and CD81-LEL [26] . The results combined with data generated by other methods ( ELISA , immunoblots ) in the literature are summarized in S1 Table . The E1E2 mutant library used here is comprised of 545 individual alanine mutations spanning nearly the entire E1E2 protein sequence of a genotype 1a isolate ( H77 , GenBank accession NC_004102 ) . The remaining ten E1E2 mutations R237A , C272A , Q336A , D346A , T396A , C452A , K562A , Y613A , Y624A , and W712A were introduced into an equivalent E1E2 expression vector ( plasmid H77c , [27] ) . The expression of the mutants was monitored by comparing mAb binding to wild-type E1E2 via the C-terminal V5 epitope fusion tag or mAbs to E1 and E2 continuous epitopes ( A4 , HCV1 and AP33 ) . Only mutant T329A resulted in markedly reduced V5 expression compared to the other mutants in the library ( Fig 2A ) ; however , since a number of mAbs showed high levels of binding to the T329A mutant clone ( S1 Table ) , it is possible that the V5 tag is partially occluded in this clone . The expression of the remaining 10 mutant constructs lacking the V5 tag was assessed by flow cytometry or ELISA based on their reactivity to the control mAbs ( Fig 2B ) . Two mutations , Y613A and Y624A , resulted in reduced binding to control mAbs . To identify mutations that influence global protein folding , we sought residues that , when mutated , resulted in less than 50% binding to mAbs targeting conformational epitopes on AR1-5 described in Table 1 . About 7% of residues ( 40 of 555 ) are important for global folding using these criteria ( Fig 2C ) . All of these residues are present in E2 , between amino acids ( aa ) 490–650 , which form the central Ig scaffold and the back layer of E2c [15] . Mutations that resulted in greatly reduced E1E2 expression or improper global folding could not accurately be used for determining antibody epitopes and were excluded from subsequent analysis . To help validate findings from flow cytometry-based evaluation of the E1E2 mutant library , we compared the flow cytometry results to previously published data that utilized site-directed mutagenesis and ELISA for the bNAbs HCV1 and AP33 , both of which recognize the E2 antigenic site 412–423 ( AS412 ) and have been extensively characterized [24 , 25 , 30–32 , 36] . Residues critical for mAb binding were defined as those that retained >75% reactivity to one or more control mAbs , but also resulted in <25% binding to the mAb of interest upon mutation . The original mapping of mAb HCV1 resulted in the identification of a stretch of amino acids 412–423 following hypervariable region 1 ( HVR1 ) of E2 as the epitope with L413 and W420 being critical for HCV1 binding [30] . The mapping approach used in this study correctly identified the critical residues L413 , N415 , G418 , and W420 for both HCV1 and AP33 , in overall agreement with previous mapping studies ( Fig 3A and 3B [24 , 25 , 30 , 31 , 36] ) . Subsequent structural analysis revealed that N415 sidechain and G418 backbone amine form a hydrogen bond , stabilizing the β hairpin turn , required for both HCV1 and AP33 recognition [24 , 25] ( Fig 3C ) . These results show that the flow cytometry approach successfully identified critical residues for the well-characterized mAbs that target HCV linear epitopes . Previous studies used a small panel of alanine mutants and ELISA to define epitopes for seven mAbs , AR1A-B , AR2A , and AR3A-D , targeting three distinct antigenic regions on E2 [33] . Recently , co-crystallization of E2c with the Fab portion of the AR3C antibody offered a structural explanation for some of the mapping data [15] . AR1A and AR1B have previously been described as binding genotype-specific E2 but lack significant neutralizing capabilities , suggesting that their epitopes are exposed on isolated E1E2 but not on the viral surface [33] . As expected , in our screening , mAbs AR1A and AR1B required many of the same residues for binding , confirming that these two antibodies recognize distinct but overlapping epitopes . Eight residues were found to be important for binding of mAb AR1A to E2 ( Fig 4A ) while five of these residues were also required for AR1B binding ( Fig 4B ) . When visualized on the E2c structure ( Fig 4C ) , residues recognized by both AR1A and AR1B are localized to a pocket near the top of E2c that is formed from the outer layer of the β sandwich in the previously described non-neutralizing face of E2c [15] . The remaining residues that are important for AR1A binding—G495 , T519 , and Y632—are located on the periphery of the pocket and may play an indirect role in binding . While both mAbs target the same antigenic region , only AR1A is capable of blocking CD81 binding to E2 [33] . Indeed , our results confirm that mutations of the epitope residues shared between mAbs AR1A and AR1B did not alter binding of CD81-LEL , while mutations of the three residues specific for AR1A resulted in decreased CD81 binding ( S1 Table ) . The complete library scanning analysis also helped reassign the roles of several critical residues identified in previous mapping experiments ( S2A Table ) . For example , two residues , V538 and N540 , were originally thought to play a role in AR1A and AR1B binding [33] . However , flow cytometry results indicate that mutation of these two residues to alanine disturbed the global folding of E1E2 , thereby resulting in loss of binding of these mAbs . For AR2A , ELISA screening against a panel of back layer mutants showed that T625A and K628A reduced AR2A binding to less than 15% of wild-type reactivity , but with >75% binding for mAbs AR1A , AR1B , AR4A and AR5A ( Fig 5A , S2B Table and [18] ) . Flow cytometry analysis showed that only K628A exhibited less than 25% AR2A binding and greater than 75% binding for AR4A control mAb ( Fig 5B ) . We note that the T625A mutant was not identified as important by flow cytometry likely due to differences in antigen presentation between the assays ( see discussion section ) . AR3 mAbs were excluded as controls because of the effect of back layer mutations on AR3 mAb binding ( see below ) . Negative-stain EM reconstructions from Kong et al . [15] and the ELISA and flow cytometry mapping data that we report here strongly suggests that the back layer of E2 is involved in AR2A recognition ( Fig 5C ) . Similar to the AR1 antibodies , we confirmed that mAbs AR3A , AR3B , AR3C , and AR3D target overlapping but distinct epitopes on E2 ( Figs 6 and S2 ) . The four mAbs share 13 common critical amino acids with approximately half of these residues localized on the front layer of E2 ( amino acids 421–452 ) . The remaining critical residues are mostly spread throughout the Ig β sandwich and CD81 binding loop . Structurally , most of the surface-exposed residues localize towards the front of E2c on the neutralizing face of the protein ( Fig 6B ) , in agreement with the known binding site based on the E2c-AR3C Fab crystal structure [15] . With the exception of P525 , the critical residues that were identified in the original mAb characterization were also observed by flow cytometry ( S2C Table ) . Hydrogen-bond calculations on the E2c-AR3C structure indicated that front layer residues C429 , S432 , and Y443 have hydrogen-bonding partners on the heavy chain of the AR3C Fab ( Fig 6C ) . When these three residues were mutated , AR3C reactivity dropped to 1% , 55% , and 15% for these three residues , respectively , confirming their importance in the AR3C epitope . Bailey and colleagues reported that polymorphisms at four E2 amino acids ( L433I , L438I , F442I , and K446E ) resulted in resistance to mAb AR3C [34] . Yet in our study , we observed decreased AR3C reactivity for L438A and F442A , and enhanced AR3C binding for L433A and K446A mutants compared to wild-type ( S1 Table ) . While side chain loss by mutation to alanine can eliminate interactions energetically important for antibody binding , in this case the differences in side chains likely also modify the conformation of E2 front layer main chain for antibody recognition . The flow cytometry data indicated that 21 individual mutations in the back layer region led to <50% binding of all nine AR1-5 antibodies ( Fig 2C , S1 Table ) , revealing a critical role for this region in E1E2 folding . Hydrogen bond calculations suggest potential interactions between several residues in the back layer region with the central Ig scaffold and front layer , underscoring the role of E2 back layer in maintaining overall E1E2 folding ( S3 Table ) . However , in ELISA analysis , only two mutations caused impaired global folding based on the same criteria as above ( S4 Table ) . This observation highlights the subtle difference in antigen presentation in the different binding assays ( see discussion section ) . Recent structural data revealed that the cell surface receptor CD81 binds to the front layer and the CD81 binding loop of E2c ( residues 519–535 ) [15] . These data confirmed results from prior mapping experiments , which proposed that several residues between 420 and 535 are important for CD81 binding [27 , 37 , 38] . However , prior to the crystallization of E2c , alanine scanning mutagenesis indicated that several E2 back layer residues between 613–620 might also be involved in CD81 binding [38 , 39] . To evaluate these results , we screened a recombinant CD81-LEL Fc-fusion protein against the E1E2 mutant library . As expected , screening of CD81-LEL against the E1E2 mutant library confirmed that many residues within the front layer and the CD81 binding loop are required for CD81 recognition ( S1 Table , Fig 7A ) . We also noted several residues in the central Ig β sandwich scaffold that may play an indirect role possibly through stabilization of the front layer and CD81 binding loop structures . As noted above , many back layer mutants perturbed E1E2 global folding , thus preventing us from determining whether these residues bind CD81 directly . However , we found that six back layer mutants reduced CD81 reactivity to ≤25% of wild-type reactivity without disrupting global folding: P601A , T604A , Y613A , W616A , C620A , and V633A ( S1 Table ) . The back layer appears to affect front layer architecture indirectly with specific interactions between back layer α2 helix residues Y613 and W616 and front layer α1 helix residues W437 and L441 ( Fig 7B ) . Y613 and W616 are within close proximity to the front layer α helix and our analysis confirms that mutation of either residue abolished CD81-LEL binding . Conversely , I622A was found to enhance CD81-LEL binding by 56% and 49% , determined by ELISA and flow cytometry , respectively ( S1 Table ) , while F627A did not affect binding ( flow cytometry ) or mildly enhanced binding by 13% ( ELISA ) . These findings were confirmed by testing the ability of soluble E2c harboring I622A , F627A , or double mutations to bind CD81-Fc and a mutant that reduces CD81 dimerization , i . e . CD81-Fc ( K124T ) ( Fig 7C ) . Given that the E2 antigenic site 3 ( AR3 ) is known to overlap with the CD81 binding site [15 , 33] , we expected the same back layer mutants that inhibited CD81 binding to also abolish AR3 antibody binding . Indeed , of the six back layer residues that are critical for CD81 binding but not overall E1E2 folding ( P601 , T604 , Y613 , W616 , C620 , and V633 ) , individual mutations of four of them ( P601A , T604A , Y613A , and W616A ) also resulted in ≤50% reactivity to all four AR3 mAbs . Of the remaining two residues , C620A showed reduced reactivity to two AR3 mAbs ( <50% ) , and V633A reduced binding of AR3C to <50% . Furthermore , as with CD81 , I622A enhanced binding of mAbs AR3A , AR3C , and AR3D . To characterize the back layer region further , HCV pseudoparticles ( HCVpp ) with individual alanine substitutions in the back layer ( residues 600–645 ) were generated . The mutant viruses were found to be mostly poorly or non- infectious ( 0–27% wild-type infectivity ) ( S5 Table ) , confirming the crucial role of the back layer region . The incorporation of E1E2 onto HCVpp of 4 selected back layer mutants ( W616A , I622A , V629A and R639A ) was also examined ( S4 Fig ) . While the expression of glycoproteins in transfected cell lysates was similar for wild-type and mutants ( S4A Fig ) , E2 associated with purified HCVpp was reduced for 3 of the 4 back layer mutants ( I622A , V629A and R639A ) ( S4B Fig ) , possibly caused by improper protein folding . In addition , HCVpp produced in this study were found to contain covalently linked , oligomeric forms of E2 and noncovalent E1 ( S4C Fig ) . In a previous study of HCVpp by immunoprecipitation of transfected cell supernatant using anti-E2 mAbs , noncovalent E1E2 heterodimers , presumably E1E2 secreted from the transfected cells , were also detected [40] . In cell culture HCV ( HCVcc ) , the majority of E1 and E2 appeared to form covalent oligomers [41] . The mutant library combined with the antibody panel provided an opportunity to map the interface between E1 and E2 . The panel contained mAbs that recognize discontinuous epitopes on E2 ( anti-E2 , i . e . AR1 , AR2 and AR3 ) , and also mAbs that recognize discontinuous epitopes requiring both E1 and E2 ( anti-E1E2 , i . e . AR4 and AR5 ) . We used the two mAb types to identify potential E1E2 interface residues based on the effect of alanine substitution on their binding: ( 1 ) low binding by anti-E2 and anti-E1E2 , ( 2 ) low binding by anti-E1E2 only , or ( 3 ) low binding by anti-E2 only . Given that antibodies in both groups recognize non-overlapping discontinuous epitopes , residues that correlate with low binding by anti-E2 and anti-E1E2 ( Class 1 ) are likely to be critical to the global integrity of the E1E2 heterodimer complex . As expected from the fact that E2 can fold by itself whereas E1 folding requires E2 co-expression , all 34 Class 1 residues were mapped to E2 , to the β-sandwich and back layer ( Fig 8A ) . On average , Class 1 residues are 91% conserved and 6 are cysteines ( S1 Table ) . The 34 identified Class 2 residues could be considered as critical residues for binding by anti-E1E2 mAbs . However , since the two anti-E1E2 antibodies , AR4A and AR5A , recognize non-overlapping epitopes , mutation of residues that severely impact binding by one mAb should not affect binding by the other . Surprisingly , the majority ( 31 ) of Class 2 residue mutations affected binding of both mAbs in a similar manner regardless of their location on E1 or E2 . This implies that these Class 2 residues , outside the AR4A and AR5A binding epitopes , are important for formation of the E1E2 complex , either by influencing folding of E1 , or by being part of the complex interface . Class 2 residues are 97% conserved , which highlights their importance to overall structural integrity , with nine of them being cysteines . Among Class 2 residues , 20 of 34 are located on E2 within the flanking regions of VR2 ( 2 cysteines ) , β-sandwich scaffold , VR3 , post-VR3 , back layer and stalk regions ( Fig 8A ) . When visualized on the crystal structure of E2c , the Class 2 residues strikingly cluster on one surface of E2c that is opposite to the CD81 receptor-binding site ( Fig 8B ) . Similar to the CD81 binding site , this region is nearly glycan free , but contains flexible and disordered loops , which might require interaction with E1 to fold properly . We also tested binding of anti-E1E2 antibodies to a number of deletion mutants lacking regions harboring the Class 2 residues ( S3 Fig ) . Mutants lacking VR2 , VR3 , post-VR3 and stalk did not bind to AR4A and AR5A mAbs , in agreement with the results of the alanine scanning mutagenesis . Of note , E1E2 mutants lacking the stalk region did not bind to AR2A and AR3A mAbs suggesting that absence of stalk region can have a deleterious effect on overall E2 folding . Class 2 residues on E1 includes 6 of the 8 E1 cysteine residues that mainly map to the nE1 region that has been crystallized and whose structure has been determined ( Fig 8A , [19] ) . Residues that fall under Class 3 ( low binding by anti-E2 only ) are critical for E2 structural integrity but not for E1E2 complex formation . Eight such residues were mapped to either the β-sandwich ( 5 residues ) or the back layer of E2c ( 3 residue ) . Surprisingly , Class 3 residues are 93% conserved and none of them are cysteines . Together , the data suggest that specific residues in the E2 near VR2 , part of the glycan-free Ig β-sandwich , VR3 , post-VR3 , back layer and stalk appear to cluster around a region opposite to the CD81 binding site , which may interact with specific E1 residues to form the E1E2 interface . Although further investigation is required , these findings provide the basis for a general model that can describe at least some aspects of the E1E2 interface ( Fig 8C ) .
To advance our understanding of the structure and antigenicity of E1E2 , we selected a panel of 13 mAbs and CD81-LEL and tested them against a complete alanine-scanning mutagenesis library of E1E2 . The library was created by substituting alanine for every residue ( and serine for alanine residues ) of the H77 genotype 1a E1 and E2 proteins followed by high-throughput flow cytometry analysis to measure antibody reactivity to each mutant [26] . A number of mutations affected E1E2 global folding as determined by substantial reduction in reactivity to multiple conformation-dependent antibodies . Most of these residues are located in the central Ig scaffold and the back layer of E2 , indicating the importance of these regions for folding of E1E2 . In particular , the large number of back layer residues ( 42% ) that impact E1E2 global folding , and the indirect effects of this region on the neighboring central Ig scaffold and distal front layer , indicate the importance of the back layer on E2 structure and function . The high level of conservation among many back layer residues confirms the critical role of this region . Indeed , 36 of the 49 back layer residues are 90% conserved with 28 being >99% conserved ( S1 Table ) . Our flow cytometry analysis suggested that many of the N-terminal back layer mutants perturb global E1E2 folding ( Fig 2C ) . However , when tested by ELISA , only Y611A and R614A meet the criteria established for determining residues that impact overall protein folding ( S4 Table ) . Such differences based on assay method were also observed for AR2A reactivity to back layer mutants ( Fig 5A and 5B ) . AR2A is known to bind only to genotype 1a viruses unless the HVR1 region is deleted [33 , 43] , which raises the possibility that masking of epitopes under different conditions could affect reactivity . In ELISA , E1E2 antigens are often prepared in the form of cell lysates and the solubilized protein complex is captured by G . nivalis lectin ( GNL ) onto a solid support for detecting binding antibodies . In flow cytometry , E1E2 is presented as an intracellular membrane-associated antigen ( S1 Table , S1 Fig ) . Thus , subtle differences in epitope presentation may account for variations in reactivity , predominantly in the back layer but also at other antigenic sites . While the structure and function of E1 remains elusive , the N-terminal portion ( nE1 ) from residues 192–270 was recently crystallized and described as a disulfide-linked intertwined homodimer [19] . To our knowledge , purified E1E2 has no inter-molecular disulfide bonds [44 , 45] . Thus , it remains to be determined if full-length E1 retains the above-described structure in complex with E2 . Several studies have suggested that the viral fusion peptide is located within the E1 glycoprotein [46–49] . Peptide library experiments on membrane rupture , hemifusion , and fusion suggest that the putative fusion peptide is located between residue 265–296 [48] . However , there is evidence for and against this hypothesis . The hydrophobic region spanning residues 265–296 is relatively conserved among genotypes , especially the two cysteine and two glycine residues within this region , and displays similarities to other flavivirus fusion peptides [46] . Since viral fusion peptides and fusion loops are intrinsically metastable , they are usually hidden and protected until fusion is activated [50 , 51] . If this region is indeed the fusion peptide , mutations in this region should affect E1E2 assembly while maintaining CD81 receptor binding . Previous mutagenesis studies indicate that mutations within this E1 region did not affect E1E2 association or binding to CD81 receptor [46 , 52] . In contrast , flow cytometry analysis of this region here showed reduced CD81 receptor binding to several mutants including Y276A ( 50% ) , G278A ( 50% ) , and D279A ( 48% ) , while E1E2 complex formation was mostly unaffected . Since receptor binding occurs in a step preceding fusion , these residues are likely affecting entry and not fusion . These conflicting results demand stronger evidence to sustain the hypothesis that the E1 region 265–296 is the fusion peptide . Studies by various groups suggested that HVR1 , residues 412–447 , 528–535 , and 612–618 are involved in CD81 reactivity by either enhancing ( in the case of HVR1 ) or reducing CD81 binding when deleted or mutated [39 , 53 , 54] . Other groups have found that W420 , Y527 , W529 , G530 , and D535 were critical for CD81 binding , while H421 , I422 , S424 , G523 , T526 , and F550 reduced CD81 binding by at least 50% when mutated [27 , 55] . Analysis of the E1E2 library confirms that alanine mutations at the afore-mentioned 11 residues exhibit less than 25% CD81-LEL reactivity ( S1 Table ) . In a structural context , the E2c structure and the negative-stain EM reconstruction of the E2 ectodomain bound to CD81-LEL validate the roles of the residues located at the front layer ( aa 420–450 ) and CD81 binding loop ( aa 519–535 ) with some residues in the β-sandwich scaffold modulating this interaction [15] . The back layer Y613 and W616 were identified as also being crucial for CD81-LEL reactivity as they exhibited less than 3% binding activity when mutated to alanine . In the E2c structure , the side chains of both residues point towards the front layer α1 helix and potentially hydrogen bond with the main chain of L441 and W437 , respectively . These two residues may act as an anchor , stabilizing front layer folding and architecture of the CD81 binding site . Surprisingly , our study also found that mutation of back layer I622 enhanced CD81 binding . Recent mutagenesis studies have also corroborated the affinity enhancing effect of I622A [56] . Similarly , when several back layer residues were individually mutated , we observed reduced reactivity to AR3 mAbs , which target the front layer . Krey and colleagues suggested involvement of back layer residues 610–619 in binding of mAb HC-84 to the front layer [57] . Thus , the results presented here support the notion that the back layer can influence binding of CD81 and antibodies that target the front layer through indirect interactions . Additional interactions between back layer and β-sandwich residues were also observed , suggesting the back layer is highly involved in stabilizing the globular structure of E2 . Analysis of back layer residues using the HCVpp system shows that alanine substitution in this region is poorly tolerated , leading to severely reduced infectivity ( S5 Table ) . Further study of four representative back layer mutants ( W616A , I622A , V629A and R639A ) shows that the residues can affect different aspects of E1E2 critical to the infection cycle . Reduced glycoprotein incorporation was observed in 3 of the 4 back layer mutants examined ( I622A , V629A and R639A ) , which may partly explain the reduced viral infectivity . In contrast , W616A mutation did not affect E1E2 incorporation yet the virus was non-infectious ( S4B Fig and S5 Table ) . These differences suggest that changes in the back layer can affect E1E2 functions in different ways ( e . g . protein folding and incorporation onto virions , receptor binding , and other steps for productive infection ) . Although further studies are required to understand how the back layer is involved in these steps , our results underscore that this E2 region is an indispensable part of the E1E2 complex architecture . In addition to influencing AR3 antibody binding , the E2 back layer was found to interact with mAb AR2A . Using a combination of flow cytometry and ELISA , together with previous negative-stain EM data [15] , AR2A was found to recognize an antigenic site that is comprised , at least partially , of the highly conserved back layer of E2 . AR2A neutralizes only some strains of HCV [33 , 58] , but all HCV genotypes when HVR1 is deleted , suggesting that its target residues are conserved but likely shielded by HVR1 [43 , 59] . The E2 AR3 , also known as the neutralizing face of E2 , is devoid of N-linked glycans and overlaps with the CD81 binding site [15] . It is formed by two E2 regions , the front layer ( a . a . 421–452 ) and the CD81 binding loop ( a . a . 519–535 ) . Although the majority of the residues in the front layer and CD81 binding loop are highly conserved ( >90% conservation ) , natural variations are observed at a number of hot spots ( <75% conservation ) that include S424 ( 16% ) , E431 ( 11% ) , N434 ( 41% ) , W437 ( 46% ) , L438 ( 43% ) , G440 ( 56% ) , Q444 ( 0 . 2% ) , H445 ( 54% ) , K446 ( 65% ) , R521 ( 65% ) , S522 ( 33% ) , A524 ( 47% ) , S528 ( 25% ) , A531 ( 12% ) and D533 ( 33% ) ( S1 Table ) . Among the conserved residues , alanine substitution at T425 , L427 , N428 , C429 , G436 and L441 in the front layer region , T519 , D520 , G523 , W529 , G530 and D535 in the CD81 binding loop , greatly reduced the binding of the four AR3-specific mAbs . Obviously , viruses with natural variations at these conserved positions , provided not defective in replication , can potentially escape the AR3 mAbs . Nevertheless , the natural variations L442 in the genotype 5 isolates SA13 and UKN5 . 15 . 7 , N519 in the genotype 2 isolate UKN2a1 . 2 , and E535 in genotype 3 isolates S52 and UKN3a1 . 28 , have not rendered the viruses more sensitive or resistant to mAb AR3A [35 , 60 , 61] . On the other hand , natural variations at the conserved L433 ( 94% ) , and the less conserved L438 ( 43% ) , F442 ( 81% ) and K446 ( 65% ) , and E431 and a set of 3 residues outside AR3 ( V538 , L546 and V563 ) in some genotype 1 isolates , have been reported to confer resistance to some AR3 mAbs [34] . Interestingly , alanine substitution at L433 , L438 , F442 and K446 had variable effects on the binding of AR3 mAbs ( S1 Table ) . Given the differences in size , polarity , and charge , it is notable that alanine mutations at L433 and K466 have the opposite effect on AR3C binding compared to naturally occurring mutations to isoleucine , histidine , glutamic acid , or asparagine , which have been observed in E1E2 sequences [34] . Our analysis also shows that C429 is critical for binding of all four AR3 mAbs , suggesting that this amino acid is a crucial contact point for the mAbs . It is likely that the C429-C503 disulfide bond is critical for the integrity of the E2 CD81 binding site , since mutation of either specifically affects binding by AR3 antibodies and CD81-LEL , but not the other mAbs to discontinuous epitopes ( S1 Table , [56] ) . Overall , these data indicate that AR3 antibodies recognize overlapping but distinct epitopes targeting the front layer region and the CD81 binding loop of E2c , and that the surrounding region including the Ig central scaffold may play an indirect role in mAb binding . MAbs AR4A and AR5A recognize the quaternary structure of E1E2 complex and cross-neutralize multiple HCV genotypes [34 , 35] . Screening the mAbs against the E1E2 mutant library revealed that 15 residues in E1 and 21 residues in E2 are required for binding of mAbs AR4A or AR5A ( S1 Table ) . In fact , among the 34 mutants ( Class 2 residues , Fig 8A ) , flow cytometry analysis revealed that only 4 exhibited <25% binding for AR4A or AR5A alone ( two E1 and two E2 residues ) . Previous studies have revealed minimal overlap between the AR4A and AR5A epitopes suggesting that the remaining 30 residues , that reduce binding by both AR4A and AR5A , are required for E1E2 complex formation [35] . Among these 30 residues , 18 E2 residues cluster at the junction of the non-neutralizing and occluded faces of E2c , in an area with few glycosylation sites ( Fig 8B ) . The three E1 residues ( I308 , A330 , and M345 ) recently hypothesized by Douam and coworkers to be involved in functional E1E2 heterodimerization and viral fusion [13] did not affect AR4A or AR5A binding as determined by flow cytometry . The same group also proposed that the E2 region spanning amino acids 581–650 could be involved in a “crosstalk” with E1 and plays an important role for E1E2 function [13] . This region encompasses a flexible area and the back layer of E2c , and contains five residues found to be critical for AR4A and/or AR5A reactivity . Overall , results from AR4A and AR5A mapping support the hypothesis that some residues between E2 581–650 are involved in interactions with E1 . The variable regions of E2—HVR1 , VR2 , and VR3—have been shown to be unnecessary for folding of soluble , recombinant E2 since conformational mAbs retain their ability to bind even with one , two , or all three variable regions deleted [15 , 62] . Yet , in the context of virion-incorporated glycoproteins , VR2 and VR3 ( also known as IgVR , intergenotypic variable region ) appear to affect E2 folding , assembly of E1E2 complex , receptor binding , HCVpp entry , and HCVcc infectivity , highlighting differences in requirements for the variable regions between recombinant E2 and virion-associated E1E2 [63] . We found that mutations within the variable regions of E2 were generally well tolerated since binding of CD81-LEL , and mAbs with continuous and discontinuous epitopes , was maintained for most mutants . However , mutations in 9 residues , F465A , Q467A , I472A in VR2 , G573A , C581A , D584A , C585A in VR3 , F586A and Y594A in post-VR3 , reduced binding of E1E2-specific mAbs AR4A and AR5A by at least 50% compared to wild-type E1E2 without affecting binding of other mAbs . Thus , the variable regions seem to play a functional role in the formation of E1E2 complex . Six of the 14 Class 2 E1 residues are cysteines ( C207 , C226 , C229 , C238 , C304 and C306 ) , suggesting a role for disulfide bridges in E1 folding/stabilization and/or E1E2 complex formation . Due to limitation of the antibody reagents and the lack of properly folded E1 antigen , we cannot distinguish E1 residues important for E1 folding versus E1E2 complex formation . Of these 6 cysteines in E1 , four are located in the N-terminus and implicated in inter- and intra-molecular disulfide bonds [19] . Seven of the 20 important E2 residues that affect AR4A and AR5A binding are cysteines ( C459 , C486 , C569 , C585 , C597 , C652 and C677 ) . Mutation of these cysteines did not affect the binding of other conformation-dependent mAbs , suggesting that they may not affect the overall fold of E2 , but may instead play a role in maintaining the E1E2 complex . Alternatively , these cysteine residues could instead be unpaired and required for HCV infectivity . Fraser et al . proposed that both HCVpp and HCVcc systems require free thiol groups for entry and that E1E2 undergoes a shift from a reduced to an oxidized state during receptor attachment [64] . Moreover , McCaffrey and colleagues demonstrated that E2 can tolerate the presence of several free cysteines [65] . Thus , free cysteines may indirectly affect the binding of AR4A and AR5A while not impacting overall protein conformation . Altogether , our data suggest that a glycan-free face of E2 , distal from the front layer , interacts with E1 forming the complex interface . It also appears that residues within the post-VR3 region , back layer and the E2 stalk may play a role in complex formation , either directly or indirectly ( Fig 8C ) . The importance of variable regions and E2 stalk in E1E2 assembly was also confirmed independently using mutants that lacked these regions ( S3 Fig ) . Of note , it is yet to be proven that the Class 2 residues are physically in contact to form the E1E2 interface . Our attempts to study this by immunoprecipitation have not yielded conclusive results of a complete disruption of E1E2 complex formation . The transmembrane regions of E1 and E2 are crucial for E1E2 dimerization and specific mutations within either domain can reduce up to 75% of heterodimer formation [12] . It is possible that the interactions between E1 and E2 ectodomains are relatively feeble to allow for conformational rearrangement during viral entry and may not be easily detected in the presence of the transmembrane regions .
Overall , through the use of conventional ELISA and high-throughput flow cytometry analysis , we screened CD81-LEL and a panel of mAbs targeting five antigenic regions of E1E2 against a comprehensive alanine mutant library , encompassing the entire E1E2 protein sequence of the prototypic genotype 1a H77 strain . This approach offers a global perspective of folding and expression of the complex , and provides insight into E1E2 structure and antigenicity . The results reported here are in agreement with the previously mapped targets for HCV1 , AP33 , and mAbs specific for AR1 , AR2 , and AR3 . Residues in the E2 back layer appear to play a central role in maintaining not only E2 structure through interactions with the Ig scaffold and front layer , but also in overall folding of the E1E2 complex . Importantly , residues located on the back layer of E2 were also found to modulate AR3 and CD81-LEL binding , likely by stabilizing the structure of the front layer and CD81 binding loop . The E2 back layer region also appears to be central to E2 folding and function because individual alanine substitutions in this region universally reduced viral infectivity . It is evident that protein-engineering efforts should consider the contribution of several residues in the back layer region and the potential for global misfolding as a consequence of their mutation . This study also provides preliminary evidence of the location of the E1E2 interface at a glycan-deficient region opposite of the CD81 binding site . Several flexible or disordered loops present in this region have the potential to interact with E1 to form the functional E1E2 complex . Further studies to better define the E1E2 interface , particularly in the isolation and mapping of mAbs to E1 discontinuous epitopes and to study the mutations in the context of authentic virus , will greatly facilitate our understanding of the E1E2 complex . Intergenotypic incompatibilities between E1 and E2 suggesting coevolution of glycoproteins within a genotype should also be considered while studying complex formation [66] . In the absence of complete E1 and E2 structures , it is difficult to fully understanding how each alanine substitution influences E1 and E2 folding . The data here are useful for identifying key residues for antibody binding and distal protein regions required to form the epitopes . However , the data do not have the power to predict the effect of substituting E1E2 residues with amino acids of diverse chemical properties as seen in natural viral sequences . Other approaches , such as selection of antibody escape virus mutations or deep mutational scanning analysis , will complement alanine scanning in the study of E1E2 complex . This set of complete alanine scanning mutagenesis data will be valuable to inform design of new E1E2 constructs with improved biochemical properties ( e . g . folding , solubility and stability ) for structural studies and immunization . We believe that a folded , soluble complex of E1E2 ectodomain will be highly valuable to the field . Our study has established the importance of E2 VR2 flanking regions , VR3 , post-VR3 and back layer regions in E1E2 formation and these regions should be taken into account for future protein engineering and vaccine design efforts . Future research on E1E2 interface could lead to improved immunogen engineering for vaccine design . Our discussion of this large dataset has been restricted here to the antigenic regions , CD81 binding , and E1E2 complex formation because of publication length considerations . However , the entire dataset compiling results from the ELISA and flow cytometry analysis , amino-acid conservation , and previously published mutagenesis data are available online as an Excel spreadsheet ( S1 Table ) and we welcome further interpretation and discussion of the data .
Comprehensive high-throughput alanine scanning mutagenesis was carried out on an HCV E1E2 expression construct ( genotype 1a , strain H77; reference sequence NC_004102 ) encoding a C-terminal V5 epitope tag . Individual residues of E1 and E2 were mutated to alanine while existing alanine residues were mutated to serine to create a library of clones , each with a single point mutation . Overall , 545 mutants were generated by Integral Molecular , Inc . , covering 98 . 2% of the E1E2 target residues . The sequence of each clone was confirmed by DNA sequencing ( Macrogen ) and the library clones were arrayed in 384-well format with each well containing one mutation [26] . Remaining constructs were found to have additional mutations and to complete the library , these 10 alanine mutations ( R237A , C272A , Q336A , D346A , T396A , C452A , K562A , Y613A , Y624A , and W712A ) were introduced into the H77C E1E2 sequence [67] using the QuikChange Lightening Site-Directed Mutagenesis kit ( Stratagene ) and PCR primers for each mutation ( Integrated DNA Technologies ) . The sequence of these clones was confirmed by DNA sequencing ( Retrogen ) . Mouse mAb A4 and human mAb IGH526 target the N-terminal ( residues 197–207 ) and C-terminal portion ( residues 313–327 , linear component of IGH526 ) of E1 , respectively [23 , 28 , 29 , 40] . mAbs HCV1 and AP33 recognize E2 residues 412–423 , a region that is known for inducing potent , cross-reactive NAbs [30–32] ( reviewed in [20] ) . The antibodies A4 , IGH526 , HCV1 , and AP33 have been described previously and were produced in-house as recombinant antibodies [23 , 28–32 , 40] . The AR1-5 antibodies were isolated previously from an HCV antibody library by phage display [33 , 35] . They recognize three distinct E2 antigenic regions ( AR1-3 ) and two E1E2 antigenic regions ( AR4-5 ) . mAbs recognizing AR1 are strain-specific and mostly non- or weakly neutralizing , suggesting this region is occluded in native virions [33] . On the other hand , mAbs targeting AR2 and AR3 are capable of neutralizing several viral genotypes . AR3 mAbs recognize the neutralizing face of E2c , which overlaps with the CD81 binding site while mAb AR2A binds to the back layer of the E2 protein [15 , 33] . AR4 and AR5 mAbs are specific for the E1E2 complex and recognize non-overlapping epitopes . mAb AR4A has been shown to cross-neutralize the six major HCV genotypes and protected against the human liver chimeric mouse model from HCV challenge [35 , 60] . The flow cytometry method measures binding of antibodies to intracellular , membrane-associated E1E2 . In ELISA , antigens are typically presented as solubilized E1E2 in transfected cell lysate , enriched onto the microwell surface . Although extraction of E1E2 by non-denaturing detergents and enrichment of antigens by lectin capture could potentially alter epitope presentation , the antigenicity of the two different forms of E1E2 antigens are mostly equivalent with some exceptions , predominantly in the E2 back layer . We also confirmed that the antigenicity of cell lysate-derived E1E2 remains relatively stable over time , even after several freeze-thaw cycles . For the mutations introduced by Integral Molecular , Inc . , the HCV E1E2 mutant library , arrayed in 384-well microplates , was transfected into HEK-293T cells ( ATCC CRL-11268 ) and allowed to express for 22 hours . The cells were washed in PBS supplemented with calcium and magnesium , fixed in 4% paraformaldehyde ( Electron Microscopy Sciences ) , and permeabilized with 0 . 1% ( wt/vol ) saponin ( Sigma-Aldrich ) in PBS supplemented with calcium and magnesium . Cells were stained with mAbs ( 0 . 33 to 2 . 0 μg/ml ) diluted in 10% normal goat serum ( NGS ) ( Sigma ) , 0 . 1% w/v saponin , pH 9 . 0 . Optimal mAb concentrations and binding conditions were determined using an independent immunofluoresence curve against wild-type E1E2 for each mAb . A concentration within the linear range and with suitable signal to background ratio ( >5 ) was chosen for the library screening . The cells were incubated with anti-HCV antibody for 1 hour at 20°C , or overnight at 4°C , followed by washing three times with supplemented PBS and 0 . 1% saponin and a subsequent 30-minute incubation with Alexa Fluor 488-conjugated secondary antibody ( Jackson ImmunoResearch ) in 10% NGS , 0 . 1% saponin , and supplemented PBS . Stained cells were washed three times with supplemented PBS and 0 . 1% saponin , twice with PBS without calcium or magnesium , and were re-suspended in Cellstripper ( Cellgro ) plus 0 . 1% BSA ( Sigma-Aldrich ) . Cellular fluorescence was detected using the Intellicyt high throughput flow cytometer ( HTFC , Intellicyt ) . Background fluorescence was determined by fluorescence measurement of vector-transfected control cells . mAb reactivity against each mutant HCV E1E2 clone was calculated relative to wild-type E1E2 by subtracting the signal from mock-transfected controls and normalizing to the signal from wild-type HCV E1E2-transfected controls . The reactivity of the mAb panel to the Q336A , D346A , T396A , C452A , K562A , Y613A , Y624A , and W712A mutants was measured essentially as described above , but 0 . 5% saponin ( Sigma-Aldrich ) was used . As above , a titration curve for mAb binding to wild-type E1E2 was performed to determine the optimal mAb concentration ( linear range ) . Fluorescence was detected using a LSR II cytometer ( BD Biosciences ) . Reactivity was normalized to wild-type with background binding removed . Mutated residues within critical clones were identified as critical to the mAb epitope if they did not support reactivity of the test mAb but did support reactivity of other control anti-HCV mAbs . V5-tag expression was also measured to assess the effect of each mutation on overall E1E2 expression in transfected cells . The counter-screen strategy and V5 expression tests facilitates the exclusion of E1E2 mutants that are locally misfolded or that have an expression defect [68 , 69] . To be highlighted as an important residue , binding thresholds were established in which there was <25% binding of the mAb of interest but >75% binding of appropriate continuous and discontinuous control mAbs . Mutations resulting in <50% binding for all discontinuous mAbs were flagged as causing perturbations in global E1E2 folding . Lentiviral reporter viruses pseudotyped with HCV E1E2 ( HCVpp ) were produced essentially as described [72 , 73] but in 384-well plates , by co-transfecting the individual expression plasmids of wildtype and mutant E1E2 with a plasmid encoding HIV core ( gag-pol [74] ) and luciferase ( pNL4-3 . lucR−E− ) [75] . Cells were incubated at 37°C in 5% CO2 to allow for transfection and pseudovirus production . Supernatants were harvested 48 to 72 hours post-transfection and diluted 1:1 with 16 μg/ml Dextran/DMEM and stored at -80°C . Target Huh-7 cells were plated at 0 . 8 x 106 cells/well in DMEM containing additives and incubated at 37°C in 5% CO2 overnight . The following day , virus harvests were thawed , medium was removed from the cells and 40 μl virus was added , cells were then incubated at 37°C . At 24 hours post-infection , 100 μl of fresh media was added to each well . Infected target cells were lysed 72 hours post-infection and lysates were assayed for luciferase activity ( Promega ) . The raw luciferase activities for mutants were background-subtracted and then normalized to the average values obtained for wild-type E1E2 . HCVpp was generated by cotransfection of 293T cells with pNL4-3 . lucR−E− and the corresponding expression plasmid encoding wildtype or mutant E1E2 genes as described previously [35] . Cell lysates and culture supernatants were harvested 72 hours post-transfection for immunoblotting , infectivity assay and purification of virions . HCVpp was pelleted by centrifugation of culture supernatant at 16 , 000 rpm for 1 hour , resuspended , and purified over a 20% ( wt/vol ) sucrose cushion [35 , 76] . Envelope glycoproteins E1 and E2 were detected by immunoblotting using biotinylated mouse mAb A4 [28] and mAb HCV1 [24] and the IRDye680RD Streptavidin ( 1:2 , 000 ) and IRDye800CW goat anti-human IgG secondary antibodies ( 1:10 , 000 ) ( LI-COR Biosciences ) , respectively . HIV-1-p24 was detected using biotinylated mouse monoclonal antibody ( diluted to 1:1 , 000; Aalto Bio Reagents ) . The immunoblots were analyzed with the Odyssey Infrared Imaging System and Image Studio software ( LI-COR Biosciences ) . For amino-acid conservation , data were obtained from the NIAID Virus Pathogen Database and Analysis Resource ( ViPR ) online through the web site at http://www . viprbrc . org [77] . 2 , 345 complete genomes encompassing genotypes 1 through 7 as well as 52 unclassified genomes were preliminarily identified for HCV . Further inclusion criteria included: human host only , genders , all geographical regions , and no defined collection period or sample source ( e . g . serum , plasma , etc . ) . The criteria narrowed the results to 902 E1 and E2 sequences that were analyzed for sequence variation ( SNP ) at the amino-acid level . Percent conservation was calculated by dividing the total number of sequences analyzed by the number of sequences exhibiting the H77 reference strain ( NC_004102 ) amino acid and subsequently converting to a percentage . Hydrogen bond calculations were performed using LigPlot [78] . Complete calculation results for the E2c—AR3C PDB file 4MWF are available upon request . | The function and structure of the hepatitis C virus envelope glycoprotein complex E1E2 is poorly understood because of difficulties in producing pure and correctly folded proteins for biochemical and structural analysis . Here , we use monoclonal antibodies to non-overlapping epitopes on E1E2 , as well as the CD81 co-receptor , to probe a complete alanine-scanning library of the E1E2 protein . This comprehensive binding study delineates the antigenic regions of E1E2 . This information is valuable for understanding the folding of E1E2 and for vaccine antigen design efforts . | [
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"phy... | 2017 | Probing the antigenicity of hepatitis C virus envelope glycoprotein complex by high-throughput mutagenesis |
Recent data indicate that plasticity protocols have not only synapse-specific but also more widespread effects . In particular , in synaptic tagging and capture ( STC ) , tagged synapses can capture plasticity-related proteins , synthesized in response to strong stimulation of other synapses . This leads to long-lasting modification of only weakly stimulated synapses . Here we present a biophysical model of synaptic plasticity in the hippocampus that incorporates several key results from experiments on STC . The model specifies a set of physical states in which a synapse can exist , together with transition rates that are affected by high- and low-frequency stimulation protocols . In contrast to most standard plasticity models , the model exhibits both early- and late-phase LTP/D , de-potentiation , and STC . As such , it provides a useful starting point for further theoretical work on the role of STC in learning and memory .
It is widely believed that synaptic potentiation , as demonstrated by the physiological phenomenon of long-term potentiation ( LTP ) , plays an important rôle in memory formation in the brain [1] , [2] . This has triggered a vast number of experiments in which this phenomenon has been recorded , both in vivo and in vitro . Typically , LTP can be elicited in a population of CA1 neurons by placing an electrode into an input pathway in the stratum radiatum , and applying a burst of high-frequency stimulation . One major result that has emerged is that there are at least two distinct “phases” of LTP , see [3] for a review . Firstly , there is an “early” , transient phase ( e-LTP ) that can be induced by a single , brief ( ) , burst of high-frequency stimulation ( weak HFS ) . The lifetime of this phase is around three hours in slice experiments , and its expression does not require protein synthesis [4]–[6] . Secondly , there is late-phase LTP ( ) , which is stable for at least the eight hour time-span of a typical slice experiment , but which can last up to months in vivo [7]–[9] . can be induced by repeated ( typically three ) bursts of HFS , separated by 10 minute intervals ( strong HFS ) . Thus , notably , more stimulation does not increase the amount of synaptic weight change at individual synapses ( as often assumed in models ) , but rather increases the duration of weight enhancement . It has been shown that protein synthesis is triggered at the time of induction and is necessary for [4] , [5] , although a more complicated rôle for protein synthesis in LTP has been implied [10] , [11] . Interestingly , e-LTP at one synapse can be converted to if repeated bursts of HFS are given to other inputs of the same neuron during a short period before or after the induction of e-LTP at the first synapse [12]–[14] . This discovery led to the hypothesis that HFS initiates the creation of a “synaptic tag” at the stimulated synapse , which is thought to be able to capture plasticity-related proteins ( PRPs ) . The PRPs are believed to be synthesized in the cell body , although recent data suggest they may be manufactured more locally in dendrites [15] . The general framework for these hetero-synaptic effects is called “synaptic tagging and capture” ( STC ) . Which proteins are involved in each stage of STC has not been fully elucidated yet . Current data suggest that , at least in apical dendrites , calcium/calmodulin-dependent kinase II ( CaMKII ) is specifically involved in signaling the tag in LTP induction [15] and protein kinase ( ) is involved in the late maintenance of potentiated synapses [6] , [16] . The counterpart of LTP , long-term depression ( LTD ) , can be induced by stimulating CA1 hippocampal neurons with low-frequency stimulation ( LFS ) [17] , [18] . LTD states appear to have analogous properties to the LTP states discussed above . The early phase , which we call e-LTD , lasts around three hours , is not dependent on protein synthesis , and can be induced by weak LFS , consisting of , for example , 900 stimuli at 1 Hz . For induction of the late phase , , a stronger form of LFS is required , for example 900 bursts of three stimuli at 20 Hz , with an inter-burst interval of one second [19] . Like , is stable for the duration of most experiments and is protein synthesis dependent [20] . Moreover , e-LTD of one synapse can be converted to if strong LFS is given to a second synapse of the same neuron within an interval of around one hour [19] . The setting of LTD tags appears to be mediated by mitogen-activated protein kinases [15] , but no specific PRP is yet known . It turns out that LTP and LTD are not independent processes and that an interaction known as “cross-capture” can occur between synapses tagged for LTP and synapses tagged for LTD [19] . Thus 1 ) e-LTD of one synapse can be converted to by giving inducing strong HFS to a second synapse shortly before or after the induction of e-LTD at the first synapse; 2 ) e-LTP can be converted to in an analogous manner . Cross-capture suggests that strong HFS and strong LFS both trigger synthesis of both proteins and proteins . A separate strand of research has put forward the idea that plasticity protocols cause synapses to make discrete jumps between weak and strong states [21] , [22] . Discrete synapses have a number of interesting theoretical properties , for example: 1 ) old memories become at risk of being erased as new ones are stored , ( e . g . [23] ) ; 2 ) synaptic saturation , important in preventing run-away activity , is automatically included , while storage capacity can be high [24] . There have been several biochemical models that posit binary synapses [25]–[31] . Induction and maintenance of activity-dependent plasticity has been successfully incorporated into a recent study [31] , and the longevity of evoked synaptic changes has been investigated [28] , [29] . There is however great divergence between most network-level plasticity models and the experimental observations outlined above . Network models typically ignore interaction between synapses , use graded weights , and assume that the stimulus only determines the amount of weight change and not its longevity . Given the limited knowledge of the processes involved , a detailed model seems at present out of reach . Instead , the model we present in this paper aims to integrate the key results from experiments on induction , maintenance and STC together into a concise model , whilst remaining simple enough to be useful for neural network modeling . The model posits a set of possible physical states in which a synapse can exist , including , in particular , states with a tag present . The states are characterized by their synaptic strength , and also by their resistance to potentiation and depression . These characteristics are assumed to be determined by the number of AMPA receptors present in the membrane [32] , and by the configuration of proteins within the post-synaptic density ( PSD ) [33] . In our model , a synapse existing in one state will evolve by making stochastic transitions between the different states , the probability per unit time of any given transition being specified explicitly by the model . High- or low-frequency stimulation is assumed to change these transition probabilities . The model does not , at this stage , include the complete biochemical machinery involved in the induction , expression and maintenance of synaptic plasticity . Instead , for reasons of computational efficiency , we develop a high-level model that abstracts these processes and concentrates on the quantities important for network behavior , namely the induction protocols and the resulting weight changes . The model reproduces sufficient agreement with real data to render it useful in exploring further the functional consequences of STC in network modeling .
We have used our model to simulate several electrophysiology experiments with multiple populations of synapses . More specifically , we consider stimulation of multiple independent synaptic inputs to the same neuronal population in CA1 , such that a protein synthesis-triggering stimulus ( i . e . , strong HFS or strong LFS ) to one input affects all populations of synapses , and leads to STC interactions between populations . The stimulation protocol for the experiment sets the transition rates for synaptic state transitions within each population . In all cases we assume that at time there have been no recent stimulation protocols , and that the system is in equilibrium . Thus , initially , all transition rates are at their resting values , and all synapses occupy one of the basal states . Moreover , within each population , 80% of the synapses occupy the weak , as opposed to the strong , basal state ( see Results ) . Note though , that in a real experiment , not all synapses will be in basal states , because they might have experienced strong stimuli already , earlier in life . As a result , some synapses may already be in the or state before the experiment is started . These will however remain in these states throughout the experiment , and not interfere with other synapses , so they can be ignored . However , the presence of such synapses would reduce the observed amount of LTP/D , both in model and experiment . The actual number of synapses measured in experiments using extra-cellular recordings is not known and probably varies considerably between experiments . The results we obtain come from taking 1000 synapses in each population . Starting from the initial equilibrium condition , we update state occupancy numbers at each time-step by random sampling in accordance with the transition rates . Then , for each population , we can find the relative field excitatory post-synaptic potential by expressing the summed synaptic weight at time as a percentage of the initial summed synaptic weight: ( 1 ) where denotes , for population , the occupancy number of state at time , with the states numbered as in Figure 1 . Furthermore , we used that the weight of states 4 , 5 , 6 was , twice that of states 1 , 2 , 3 . In addition to stochastically simulating experiments , it is possible to calculate mean results as well as the inter-trial standard deviation for each experiment we simulate . Let us consider a single population of synapses within an experiment . Let denote the probability that a particular synapse is in state at time . Then the time evolution equation for the is given by ( 2 ) where the matrices are defined by ( 3 ) and denotes the transition rate from state to state at time ( with the convention that the ) . Using equations ( 2 ) and ( 3 ) , and the fact that at all times the occupancy numbers follow a multinomial distribution with parameters , it is straightforward to obtain the following equations for the moments of the : ( 4 ) ( 5 ) ( 6 ) From these equations , together with equation ( 1 ) , we obtain ( 7 ) ( 8 ) where is the weight associated with state i , , . Numerical integration of equations ( 4 ) , ( 7 ) and ( 8 ) , from appropriate initial conditions , enables us to plot the mean and the standard deviation of . Using the equilibrium multinomial distribution , the appropriate initial conditions are and .
Our model is designed to reproduce as much pre-existing electrophysiological data on long-term plasticity and STC as possible , whilst at the same time remaining as simple as possible for its purpose . In drawing up a list of states , a trade-off must be made between having few states and complicated transition rate dynamics or having lots of states and simple transition rate dynamics . Our convention is to say that states are distinct if they differ either in their synaptic strength or in the expected time it will take them to potentiate or depress in the absence of any plasticity protocols . This leads us to a six state model , containing three weak and three strong states: weak basal , strong basal , e-LTD , e-LTP , and . The reactions that are triggered by plasticity protocols are incorporated via time-variable transition rates between these six states . Figure 1 shows schematic drawings of the synaptic states of the model , together with the allowed transitions between states . The rate parameter associated with the transition from one state to another state gives the probability per unit time of a synapse in state making the transition . Equivalently , the inverse of the rate parameter is the average time it takes the synapse to make the transition ( assuming no other transition is available ) . In our simulations we model populations of synapses , with each individual synapse behaving independently with respect to making transitions between states . In mathematical terms , our model is a stochastic Markov process . Effects of stimulation protocols are modeled by transient changes to the transition rates . To model STC , certain stimulation protocols given to just one population of synapses can affect the transition rates of multiple populations . These hetero-synaptic effects reflect the capturing component of STC . In the absence of stimuli , synapses fluctuate between a weak and a strong basal state . The weak basal state is assigned an arbitrary synaptic weight , whilst the strong basal state is taken to have synaptic weight . These could correspond to the two states probed in the experiments of Ref . [22] , in which it was found that the pairing of a brief steady current injection with an appropriate depolarization led to switch-like approximate doubling or halving of synaptic efficacy . The difference in efficacy between the two states is assumed to come about from AMPA receptor insertion/deletion . The transition rate for changes from weak to strong efficacy is set to , whilst that for changes from strong to weak efficacy , , is set to . The values of these parameters are chosen ( a ) to fit the observation that 80% of synapses occupy the weak basal state when the population is in equilibrium [22]; ( b ) for the model to reproduce data on e-LTP/D decay to good agreement ( via decay from the e-LTP/D state followed by equilibration between the two basal states ) . These rates are comparable with AMPA receptor recycling times [34] . The other strong synaptic states are the e- and states . They have the same efficacy as the strong basal state , but are considered potentiated states due to their increased resistance to depression . Choosing all potentiated states to have the same weight is motivated by the data which shows that in experiments all LTP forms exhibit very similar amounts of weight change . This is actually surprising given the wide variety of mechanisms that underlie the different forms of LTP . Transitions into the potentiated states only occur during intervals following certain stimulation protocols , which we discuss below . Once a synapse enters the e-LTP state it will decay back into the strong basal state , with transition rate , unless it has the opportunity to move into the state . The motivation for this decay rate comes from experimental results on e-LTP decay . Furthermore , it is assumed there is a tag present in the e-LTP state since data suggest synapses in an e-LTP state convert to an state whenever PRPs become available for capture [12] , [13] . Although we do not model the biochemistry explicitly , we suggest that when a synapse is in the e-LTP state , the CaMKII in the synapse is in a phosphorilated state [15] . When a synapse enters the state , it becomes very stable , as the only transition is very slow decay to the strong basal state , with a rate of . Synapses in the state are assumed to have captured PRPs , such as [6] , [16] . Although there is some evidence that decay from the state is an active process rather than passive decay [8] , [35] , detailed knowledge of this is still lacking , so we did not attempt to include this . The given decay rate is not intended to be precise , but is intentionally of a smaller order of magnitude than the other time-constants of the model . Finally , the model is symmetric in potentiation and depression , and so the LTD states are analogous to the LTP states . The model has ten transitions in total , however setting some rates identical leaves a total of seven transition rate parameters , Figure 1 . We have so far mentioned and which are responsible for fluctuations between the basal states , as well as and which are the decay rates for e-LTP/D and respectively . In addition , there are three further parameters , , and , for transitions into e-LTP/D and states . These are only switched on following a plasticity-inducing protocol . Note that of these seven parameters , only and are constant; , , , and change transiently after stimulation . In this section and the next we discuss the effects of LTP-inducing protocols on the transition rates; the effect on synaptic weight dynamics is discussed in later sections . We model induction in a direct way , focusing on the effects of specific plasticity-inducing stimuli rather than introducing additional stimulus parameters ( such as strength , frequency or duration ) . Specifically , we consider 1 ) for e-LTP , a single one second burst of HFS ( weak HFS ) ; 2 ) for , three repeated bursts of HFS , separated by 10 minute time-intervals ( strong HFS ) . The time courses for the transition rates have been chosen so that the model matches the electrophysiological data that the model aims to reproduce . After any burst of HFS is applied , the following two changes occur . Firstly , the rate from the weak to strong basal state increases to some very large value for a short period , before returning to its original value . Mathematically , we use for a stimulus at time . This , in effect , moves all synapses occupying the weak basal state into the strong basal state . This rapid switching is motivated by the above-mentioned observations at the single synapse level [22] , and is assumed to come about from AMPA receptor insertion . Secondly , transitions from the strong basal state into the e-LTP state are transiently turned on . Following a stimulus at time , the rate of these transitions is given by an alpha-function . Thus the rate takes a few minutes to grow to a significant level , peaks at a value of , ten minutes after stimulation , and then decays back toward zero , Figure 2 . Alpha-functions arise naturally in chemical reaction dynamics . In general , a chain of first-order reactions will lead to a difference of exponentials , while two subsequent reactions with identical rates will yield an alpha-function . Here the alpha-function is assumed to arise from the biochemical induction process in the PSD . The time-course for is motivated from evidence that a synaptic tag takes a few minutes to form [36] . Biophysically , the transitions to the e-LTP state might correspond to the phosphorilation of serine-831 of the GluR1 AMPA receptor sub-units during LTP induction [33] , which is higher 30 minutes after LTP induction than immediately post-stimulus [2] . Serine-831 phosphorilation is driven by CaMKII phosphorilation which happens on a faster time-scale than that of tag stabilization [36] . A highly simplified model of this cascade would yield an alpha-function . Alternatively , the CaMKII phosphorilation itself might correspond to tag formation and the transition to e-LTP . In addition to evoking the rate changes described above , a synapse subject to strong HFS must incur additional changes resulting from the triggering of protein synthesis and diffusion [4] , [5] . This translates in our model to the triggering of the transition rate from the e-LTP state into the state . As discussed above this might correspond to the capture of . We assume that for the second burst of HFS crosses the threshold for protein synthesis and the rate begins to change . Simulations are not sensitive to the precise course takes , nor is this tightly constrained by experimental data . We assume the plausible form when the second burst of HFS comes at time . The maximum value of is reached at time . The precise conditions for protein synthesis are not known . The strong HFS protocol described here is not the only protocol that leads to ; sometimes a strong , single burst of HFS is used [11] . In that instance , we would need to assume that protein synthesis starts sooner . In general , this could be achieved by integrating the stimulation and thresholding it . The rate also governs transitions from the e-LTD to the state , which enables the model to describe “cross-capture” , whereby e-LTD of one synapse by weak LFS can be converted to by applying strong HFS to a different synapse [19] . We discuss this further in the section “Modeling synaptic tagging and capture” . Figure 2 summarizes the effects of weak and strong HFS on the transition rates in our model , including their courses . The effects of LFS are analogous to those of HFS . Both weak and strong LFS affect the rate from the strong basal to the weak basal state , and the rate from the weak basal to the e-LTD state in the same way that HFS affects the rates and , respectively . The only difference is that is held very high ( at ) for the extended period of four minutes , to reflect the longer duration of an LFS protocol . The rate follows the time-course , with being the time at stimulus onset . This transition could correspond to the de-phosphorilation of serine S-845 [33] . As mentioned above , the rate from the e-LTD state to the state is given by the same parameter as the rate from the e-LTP state to the state . Strong LFS triggers this parameter in the same way as strong HFS , i . e . , following strong LFS starting at time . ( This can be taken to start at stimulus onset since the strong LFS we consider consists of triple pulses separated by just one second intervals . This is in contrast to our strong HFS protocol , for which the bursts are separated by 10 minute time-intervals . ) In the above discussion , we have focused on stimulation of a single population of synapses . However STC relates to interactions between different populations of synapses . In our model , transitions from the weak basal state to the strong basal state ( ) , or from the strong basal state to the e-LTP state ( ) reflect synapse-specific changes; namely changes in the number of AMPA receptors , and configurational changes in the PSD [32] , [33] . The transition rates and are only modified in stimulated synapses , and hence weak HFS only affects synapses to which it is applied . However , transition from the e-LTP to the state results in cell-wide changes , i . e . , protein synthesis . Thus , after one population of synapses has received strong HFS , many populations of synapses will see a change in the rate of these transitions . Consistent with experiments , synapses in an unstimulated population have little chance of being in the e-LTP state , and will not be affected by the strong HFS; no tags are present . But if another population of synapses has received weak HFS and move into the tagged ( e-LTP ) state , then they have a chance to move into the state; proteins are captured by tags . The STC process for LTD is analogous to that for LTP . Note that there is evidence that the STC interaction has limited range , and can not occur between far away synapses , such as between a basal dendrite synapse and an apical dendrite synapse [15] , [37] . In this work we assume that when two different populations within the same neuron are stimulated , they are close enough to interact via STC . However , extension to compartmentalized STC is possible ( see Discussion ) . As we demonstrate below , the model also accounts for “cross-capture” in a straightforward way by using the same parameter for transitions from the e-LTP state to the state and from the e-LTD state to the state . Thus , for example , after one population of synapses has received strong HFS , synapses from a second population that find themselves in the tagged e-LTD state will have a chance to change into the state as a result of LTD tags capturing proteins . In addition to reproducing single-trial experiments , the model makes novel predictions about the theoretical mean and inter-trial standard deviation of the fEPSP . Figure 7A and 7B illustrate this for populations of 1000 synapses given weak HFS and weak LFS , whilst graphs C+D illustrate this for strong HFS and strong LFS . We see that when e-LTP is established the standard deviation is greater than at baseline , whilst when e-LTD is established the standard deviation is less , Figure 7B . In the former case , the increase is a result of variability in the number of synapses that make it into the e-LTP state . Although all synapses are initially moved into the strong basal state by the HFS , ( resulting briefly in zero fEPSP variability ) , while the tag-forming reaction in the PSD is still incomplete , a variable number of synapses drop into the weak basal state from where they can no longer access the e-LTP state , Figure 1 . Although an analogous process occurs during the onset of e-LTD , the standard deviation remains less in this case since the transition rate from the weak to strong basal state ( ) is much less than that from the strong to weak basal state ( ) . The standard deviation is also less when is established , Figure 7D . This is because strong HFS/LFS enables almost all the synapses to enter , first the e-LTP/D state , and then the state , in which the weight becomes stable . The theoretical predictions above can be used in a similar way to the noise analysis technique used to extract properties of voltage- and ligand-gated channels from measurements of their mean current and current fluctuations [39] . In all cases the transition matrix determines not only the evolution of the mean but also the fluctuations around the mean . In principle this means that a more accurate estimate of the transition matrix can be obtained by fitting both the mean and the fluctuations . In analogy with standard noise analysis , here the fluctuations in the basal state are inversely proportional to the number of synapses , the spectrum of the fluctuations can be used to determine the rate constants , and changes to the fluctuations as compared to baseline can be used to calculate how many synapses have made a transition . Although we have attempted to perform this type of analysis on data recorded by Roger Redondo , we found that too many additional noise sources , as well as non-stationarity , makes this analysis currently unsuitable .
We have presented a model of synaptic plasticity at hippocampal synapses which reproduces several slice experiments . It contains just six distinct states , yet gives rise to a rich set of electrophysiological properties . The model incorporates the two observed flavors of LTP and LTD , namely the early and late phases , and de-potentiation , as well as the interaction between these two phases , known commonly as synaptic tagging and capture . The model has a number of key features: Because all three LTP and all three LTD states have the same weight associated to them ( and , respectively ) , a given synapse has a binary weight . This is reminiscent of a number of models that have proposed bistable synapses to stabilize memories , often using CaMKII as a switch [25]–[31] . In the current model , synapses have three levels of stability ( basal , early-phase and late-phase ) , with the early- and late-phase being stable up to hours . It is likely that on a biochemical level , bistable switches underlie these more stable states and slow down the transition rates , consistent with those earlier models . Another key postulate of the model is the existence of a single state that corresponds both to the synapse exhibiting e-LTP and the presence of an tag , ( and similarly for LTD ) . They go hand in hand; under natural conditions there is no mechanism by which an tag can be removed , whilst still retaining e-LTP , or indeed vice-versa , Figure 6 . If tag formation is incomplete , de-potentiation ( from LFS ) can occur and tag formation halted , but if tag formation is complete , de-potentiation can not occur and the tag can not be destroyed , consistent with data in [36] . Pharmacological [15] and genetic manipulations ( reviewed in [40] ) can interfere with tag setting and capture . The reverse , tag setting without e-LTP , has not ( yet ) been observed . Finally , the model makes predictions about the noise level in the fEPSP during a period of potentiation ( or depression ) followed by a return to baseline value . In particular , it predicts that the noise level increases during a period of e-LTP , but decreases during a period of e-LTD , or , Figure 7 . The source of this noise is purely the random nature of the transitions between states . As experimental noise is not taken into account by the model , a test of these predictions would require systematic removal of experimental noise from a data set . The reason for the decreased variability during is that many synapses occupy a state that is immune to weight change . An alternative , more complicated model would allow for the possibility of a synapse in a “strong” state to become even stronger , say by insertion of even more AMPA receptors . If this were the case , then a greater level of noise could occur during as a result of synapses fluctuating between the state of our current model and an extra “even stronger” state . Note however that this would be inconsistent with experimental evidence that synapses have only two stable levels of efficacy , e . g . [2] , [22] . Next , we discuss shortcomings and potential extensions of the model . In general , it is likely that adding extra states and more complex dynamics would refine the agreement with experimental data . However , doing this incurs the cost of making the model more cumbersome to fit and computationally more expensive . Extra states could , for example , enable us to incorporate the biochemistry of the PSD , leading to a more realistic description of the flow from the basal states into the LTP and LTD states [41] . A recent model of LTP by Smolen [42] indeed incorporates continuous variables for the state of the tag and for protein expression , together with modeling of calcium dynamics . Protein synthesis probably plays a more subtle rôle in LTP than our model incorporates . For example , immunity to de-potentiation does not require protein synthesis in our model , even though some data suggest it does [43] . Other data suggest that , at high levels of synaptic activation , protein synthesis can be involved in e-LTP as well as in [10] . We have not considered such regimes of reduced protein synthesis in which there could be competition for the capture of proteins available [44] . To reduce the level of protein synthesis , one could simply decrease the post-strong stimulus growth and peak of the transition rate , ( the rate corresponding to the availability of PRPs ) . Competition could then be incorporated by reducing the value of further every time a synapse makes the transition into a state . Both these effects would reduce the number of synapses that enter the states and the long term change in the fEPSP would be reduced . Another extension would involve specifying the distances of the site of protein synthesis from the two stimulated populations . Our results are not sensitive to the precise time-course of the transition rate , and so our model does not make predictions about this . The time-course for could however be made to reflect the distance of the site of protein synthesis from the stimulated synapses . For very local protein synthesis , would grow faster and larger than for more distant protein synthesis . In particular , if different populations were at different distances from the site of protein synthesis , then the rate would differ between the two populations . For example , suppose protein synthesis took place near a population of synapses given strong HFS . Then a second population far from this site may only experience STC weakly upon receipt of weak HFS . Few PRPs would be available , so would only grow a little , and only a few synapses would move into the state , causing the stable level of the fEPSP to be lower than usual . Such an extension could perhaps account for recent data that suggest that STC interactions do not occur between basal and apical dendrites [15] . Finally , the model does not take into account pre-synaptic effects , which might play a rôle in plasticity on time scales shorter than those of e-LTP and [38] . Extending the model to take account of these could also enhance agreement with data . For instance , in experiments on immunity to de-potentiation one sees a large drop , followed by recovery , in the fEPSP following the application of LFS to an ( e-LTP ) potentiated population of synapses [36] . In simulations from the model , LTP is also immune , but without this large drop and subsequent recovery , Figure 6B . Nevertheless , we believe that the model will be useful for continuing theoretical work on the functional consequences of STC , as it captures most known phenomena and is efficient to simulate . In particular it provides a good starting point for neural network modeling . For example , information storage capacity , and the balance between learning and forgetting can be examined for a network of neurons obeying the biophysics of the model . In future theoretical work , this model could be incorporated into a higher-level model that incorporates reinforcement learning and dopamine neurons . It is known that dopamine must be present for to be established [19] , [45] . Moreover , Izhikevich [46] has hypothesized that e-LTP plus tag formation could have the function of maintaining a memory trace of some behavior until a reward signal arrives; upon reward is induced , whilst if there is no reward then the memory trace is lost . Work in these directions is underway . | It is thought that the main biological mechanism of memory corresponds to long-lasting changes in the strengths , or weights , of synapses between neurons . The phenomenon of long-term synaptic weight change has been particularly well documented in the hippocampus , a crucial brain region for the induction of episodic memory . One important result that has emerged is that the duration of synaptic weight change depends on the stimulus used to induce it . In particular , a certain weak stimulus induces a change that lasts for around three hours , whilst stronger stimuli induce changes that last longer , in some cases as long as several months . Interestingly , if separate weak and strong stimuli are given in reasonably quick succession to different synapses of the same neuron , both synapses exhibit long-lasting change . Here we construct a model of synapses in the hippocampus that reproduces various data associated with this phenomenon . The model specifies a set of abstract physical states in which a synapse can exist as well as probabilities for making transitions between these states . This paper provides a basis for further studies into the function of the described phenomena . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"computational",
"biology/computational",
"neuroscience",
"neuroscience/theoretical",
"neuroscience"
] | 2009 | State Based Model of Long-Term Potentiation and Synaptic Tagging and
Capture |
Use of tobacco products is injurious to health in men and women . However , tobacco use by pregnant women receives greater scrutiny because it can also compromise the health of future generations . More men smoke cigarettes than women . Yet the impact of nicotine use by men upon their descendants has not been as widely scrutinized . We exposed male C57BL/6 mice to nicotine ( 200 μg/mL in drinking water ) for 12 wk and bred the mice with drug-naïve females to produce the F1 generation . Male and female F1 mice were bred with drug-naïve partners to produce the F2 generation . We analyzed spontaneous locomotor activity , working memory , attention , and reversal learning in male and female F1 and F2 mice . Both male and female F1 mice derived from the nicotine-exposed males showed significant increases in spontaneous locomotor activity and significant deficits in reversal learning . The male F1 mice also showed significant deficits in attention , brain monoamine content , and dopamine receptor mRNA expression . Examination of the F2 generation showed that male F2 mice derived from paternally nicotine-exposed female F1 mice had significant deficits in reversal learning . Analysis of epigenetic changes in the spermatozoa of the nicotine-exposed male founders ( F0 ) showed significant changes in global DNA methylation and DNA methylation at promoter regions of the dopamine D2 receptor gene . Our findings show that nicotine exposure of male mice produces behavioral changes in multiple generations of descendants . Nicotine-induced changes in spermatozoal DNA methylation are a plausible mechanism for the transgenerational transmission of the phenotypes . These findings underscore the need to enlarge the current focus of research and public policy targeting nicotine exposure of pregnant mothers by a more equitable focus on nicotine exposure of the mother and the father .
Nicotine use by pregnant women is associated with increased risk of behavioral disorders , not only in their children but also in multiple generations of descendants [1–5] . Whereas maternal nicotine use is an undeniable concern , in reality more men smoke cigarettes than women [6 , 7] . Studies in human subjects suggest that paternal cigarette smoking adversely impacts attentional control [8] and increases the risk for attention deficit hyperactivity disorder ( ADHD ) in the offspring [9 , 10] . However , human studies cannot fully separate the effects of paternal smoking from those of genetic and environmental factors [8 , 9] . For example , ADHD and nicotine addiction are often comorbid , and ADHD tends to run in families , making it difficult to separate the role of paternal ADHD from paternal smoking on behavioral changes observed in the offspring [8 , 10] . Therefore , experimental animal models are valuable tools to address the specific role of paternal nicotine exposure [11] . We exposed male mice to nicotine and bred the mice with drug-naïve females to produce the F1 generation . We bred male and female F1 mice to produce the F2 generation . We found that male and female mice in the F1 and F2 generations showed significant impairment in multiple behavioral phenotypes . The F1 generation also showed significant changes in monoamine neurotransmitter signaling mechanisms in the brain . Analysis of spermatozoal DNA from the nicotine-exposed founder males suggested that nicotine-induced epigenetic modification of the DNA may be a plausible mechanism for the transgenerational transmission of the nicotine-induced behavioral and neurotransmitter phenotypes .
We exposed one group of male C57BL/6 mice to drinking water containing 200 μg/mL nicotine ( Sigma , N3876 ) and another group of control male mice to plain drinking water . Following 12 wk of such exposures , and while the exposures were ongoing , the males were bred with drug-naïve female mice to produce the F1 generation ( Fig 1A ) . The nicotine-exposed males had a serum cotinine ( primary metabolite of nicotine ) level of 77 . 18 ± 3 . 06 ng/mL , whereas cotinine was not detectable in the serum of water-exposed control mice . There was no significant difference in the daily water consumption between the nicotine-exposed and plain-drinking-water–exposed groups ( mean ± SEM; mL/d: water: 5 . 5 ± 0 . 4; nicotine: 5 . 4 ± 0 . 6 ) . When we examined body weights of mice in the 2 groups , there was no significant main effect of treatment ( F [1 , 40] = 0 . 01; p > 0 . 05 ) or treatment × time interaction ( F [4 , 40] = 18 . 0; p > 0 . 05 ) on body weight gain . The effect of time was significant ( F [4 , 40] = 0 . 79; p < 0 . 0001 ) , consistent with body weight gain over time expected in both groups of mice . We analyzed the length of gestation , litter size at birth , and weight gain of the F1 offspring . There was no significant difference in any of these measures between the offspring from the water or nicotine exposure groups ( Table 1 ) . Time of acquisition of developmental milestones such as ear detachment , fur appearance , and eye opening were also not significantly different ( Table 1 ) . Since nicotine exposure of pregnant dams produces hyperactivity in their offspring [4 , 5] , we analyzed spontaneous locomotor activity in postnatal day ( P ) 60 F1 mice over a 12-h period ( 19:00 to 07:00 h ) , which was the dark phase of the light-dark cycle when the mice are naturally more active . A two-way ANOVA revealed significant main effects of treatment ( F [1 , 50] = 13 . 68; p < 0 . 001 ) and sex ( F [1 , 450] = 12 . 63; p < 0 . 001 ) but no significant effect of treatment × sex interaction ( F [1 , 50] = 0 . 0006; p > 0 . 05 ) . Bonferroni multiple comparisons test revealed that the locomotor activity was significantly increased in male and female F1 mice in the paternal nicotine exposure group ( Fig 1B and S1 Data ) ( post hoc: male: t = 2 . 730; df = 50; p < 0 . 05; female: t = 2 . 516; df = 50; p < 0 . 05 ) . Preclinical studies [4 , 5 , 12–16] and clinical studies [3 , 17–20] show that prenatal nicotine exposure produces significant attention deficits . Therefore , we used an object-based attention test [13 , 21 , 22] to examine the effects of paternal nicotine exposure on attention in the F1 mice . Paternal treatment produced a significant main effect on attention ( two-way ANOVA; treatment: F [1 , 35] = 9 . 23; p < 0 . 01 ) . Neither the effect of sex nor the treatment × sex interaction was significant ( two-way ANOVA; sex: F [1 , 35] = 0 . 0451; p > 0 . 05; interaction: F [1 , 35] = 2 . 531; p > 0 . 05 ) . Bonferroni multiple comparisons test revealed significant attention deficit in the male ( t = 3 . 148; df = 35; p < 0 . 01 ) but not female offspring ( t = 1 . 069; df = 35; p > 0 . 05 ) derived from the nicotine-exposed fathers ( Fig 1C and S1 Data ) . Working memory and cognitive flexibility are components of executive function [23–27] impacted by drug exposure [28 , 29] . We have previously reported that nicotine or cocaine exposure of pregnant mice impairs working memory and cognitive flexibility in the offspring [22 , 30] . We analyzed spatial working memory in F1 mice using the Y-maze . There was no significant effect of paternal treatment , sex , or treatment × sex interaction ( two-way ANOVA; treatment: F [1 , 41] = 0 . 6889; p > 0 . 05; sex: F [1 , 41] = 0 . 265; p > 0 . 05; interaction F [1 , 41] = 0 . 001; p > 0 . 05; Fig 1D and S1 Data ) . Next , we examined cognitive flexibility using a Barnes Maze [31–33] . Latency to escape and number of nose-poke errors made in the process of escape were quantified . Over the 10-d period of acquisition learning , paternal treatment did not produce significant effects on either measure ( repeated-measures ANOVA; treatment: F [3 , 34] = 0 . 6614; p > 0 . 05 ( latency ) ; F ( 3 , 34 ) = 0 . 8189 , p > 0 . 05 ( nose-poke errors ) ; Fig 1E and 1F and S1 Data ) . Because all mice learned the task equally well , as expected , there was a significant main effect of time ( i . e . , day of learning ) in both analytical measures ( latency to escape: F [9 , 306] = 37 . 06; p < 0 . 001; number of nose poke errors: F [9 , 306] = 22 . 17; p < 0 . 001 ) . Treatment × day interaction was not significant ( latency: F [27 , 306] = 1 . 328; p > 0 . 05; errors: F [27 , 306] = 1 . 374 , p > 0 . 05 ) . However , upon reversal , there was a significant main effect of paternal treatment on latency to escape ( two-way ANOVA; F [1 , 34] = 12 . 26; p < 0 . 005 ) and the number of nose poke errors ( two-way ANOVA; F [1 , 34] = 16 . 78; p < 0 . 001 ) . There was a significant main effect of sex on latency ( two-way ANOVA; F [1 , 34] = 19 . 57; p < 0 . 001 ) but not on the number of nose poke errors ( two-way ANOVA; F ( 1 , 34 ) = 2 . 105; p > 0 . 05 ) . Bonferroni multiple comparisons test revealed that paternally nicotine-exposed male mice showed significant increases in the latency to escape ( t = 3 . 511; df = 34; p < 0 . 05; Fig 1G and S1 Data ) as well as nose-poke errors ( t = 2 . 625; df = 34; p < 0 . 05; Fig 1H and S1 Data ) . Paternally nicotine-exposed female mice had a significant increase in nose-poke errors ( t = 3 . 168; df = 34; p < 0 . 01; Fig 1H and S1 Data ) . Thus , both male and female mice derived from nicotine-exposed sires showed significant reversal learning deficits . Monoamine signaling in the basal ganglia and frontal cortex regulates motor and cognitive functions [23 , 28] . Because paternal nicotine exposure produced hyperactivity , attention deficit , and reversal learning deficit , we examined whether the paternally nicotine-exposed mice showed alterations in monoamine neurotransmitter signaling mechanisms . We analyzed tissue content of dopamine , noradrenaline , and their metabolites in the frontal cortex , orbitofrontal cortex , and the striatum in the F1 offspring . There was a significant main effect of paternal treatment on striatal tissue content of dopamine ( two-way ANOVA; dopamine: F [1 , 20] = 6 . 582; p < 0 . 05 ) and its metabolites 3 , 4-dihydroxyphenylacetic acid ( DOPAC: F [1 , 20] = 7 . 949; p < 0 . 05 ) , homovanillic acid ( HVA: F [1 , 20] = 8 . 522; p < 0 . 01 ) , and 3-methoxytyramine ( 3-MT: F [1 , 20] = 7 . 949; p < 0 . 05 ) . Bonferroni multiple comparisons test showed that paternally nicotine-exposed male F1 mice showed significant deficits in the tissue content of all four molecules ( dopamine: t = 3 . 542; df = 20; p < 0 . 01; DOPAC: t = 2 . 722; df = 20; p < 0 . 05; HVA: t = 3 . 06; df = 20; p < 0 . 05; 3-MT: t = 2 . 722; df = 20; p < 0 . 05; Fig 2A–2D and S2 Data ) , whereas the female F1 mice did not ( dopamine: t = 0 . 086; df = 20; p > 0 . 05; DOPAC: t = 1 . 265; df = 20; p > 0 . 05; HVA: t = 1 . 069; df = 20; p > 0 . 05; 3-MT: t = 1 . 265; df = 20; p > 0 . 05; Fig 2A–2D ) . Paternal treatment did not produce a significant main effect on dopamine or its metabolites in the frontal or orbitofrontal cortices ( S1 Table ) . Although neither paternal treatment nor sex produced significant effects on noradrenaline content in any brain region ( S1 Table ) , there was a significant paternal treatment × sex interaction for noradrenaline content in the frontal cortex ( two-way ANOVA; F [1 , 20] = 8 . 638; p < 0 . 01 ) . Bonferroni multiple comparisons test revealed a significant decrease in frontal cortical noradrenaline content in the paternally nicotine-exposed male F1 mice ( t = 3 . 257; df = 20; p < 0 . 01; Fig 2E and S2 Data ) and no significant change in the female F1 mice ( t = 0 . 8996; df = 20; p > 0 . 05; S1 Table ) . Next , we examined mRNA expression for dopamine receptor genes by quantitative PCR ( qPCR ) in the striatum and frontal cortex . Paternal treatment had a significant effect on D2 ( two-way ANOVA; F [1 , 12] = 5 . 364; p < 0 . 05 ) and D4 ( F [1 , 11] = 25 . 33; p < 0 . 001 ) receptor mRNA expression , and there was a significant main effect of sex on D1 ( two-way ANOVA; F [1 , 12] = 13 . 63; p < 0 . 01 ) and D5 ( F [1 , 11] = 17 . 37; p < 0 . 01 ) receptor mRNA expression . Bonferroni multiple comparisons test showed that D2 ( t = 3 . 519; df = 12; p < 0 . 01 ) and D4 ( t = 4 . 682; df = 11; p < 0 . 01 ) receptor mRNA levels and the D1 receptor mRNA level ( t = 3 . 994; df = 12; p < 0 . 01 ) were significantly decreased in the striatum of paternally nicotine-exposed male F1 mice ( Fig 2G and 2I and S2 Data ) . On the other hand , the D5 receptor mRNA expression was significantly higher ( t = 3 . 205; df = 11; p < 0 . 05 ) in the striatum of paternally nicotine exposed F1 female F1 mice ( Fig 2F and 2J and S2 Data ) . Dopamine D3 receptor mRNA expression was not significantly altered by the paternal treatment ( F [1 , 13] = 1 . 36; p > 0 . 05 ) or sex ( F [1 , 13] = 0 . 003; p > 0 . 05 ) ( Fig 2H and S2 Data ) . There was no significant effect of paternal treatment , sex , or treatment × sex interaction in dopamine receptor mRNA expression in the frontal cortex ( S2 Table ) . Because the F1 mice were not exposed to nicotine at any time before or after birth , the behavioral and neurotransmitter phenotypes in these mice are likely inherited from the founder generation . The F1 phenotypes were not consistent with Mendelian inheritance , suggesting nicotine-induced epigenetic modification of the father’s spermatozoal DNA or histones as a plausible mechanism of transgenerational transmission [34] . In germ cells , the majority of histones are replaced with protamines during development [35 , 36][37] . Therefore , epigenetic modification of the DNA , rather than histones , appeared more likely . Because nicotine is known to alter DNA methylation in somatic cells [38 , 39] , we focused on DNA methylation . We collected spermatozoa samples from the cauda epididymis using a double swim assay [40] . Analysis of total numbers of sperm showed that nicotine exposure did not produce significant changes in this parameter ( mean ± SEM , number/mL: water: 1 . 1 ± 0 . 2 × 106; nicotine: 1 . 2 ± 0 . 1 × 106 ) . We isolated spermatozoal DNA from nicotine-exposed and control males and analyzed global DNA methylation as well as methylation at promoter regions of the dopamine receptor genes . Global DNA methylation was significantly increased by the nicotine exposure ( t = 5 . 015; df = 6; p < 0 . 01; Fig 2K and S2 Data ) , and DNA methylation was significantly decreased at the dopamine D2 receptor promoter region ( t = 3 . 409; df = 6; p < 0 . 05; Fig 2L and S2 Data ) . There was no significant change in the methylation status of promoters of the other dopamine receptor genes ( Fig 2L ) . To examine whether behavioral phenotypes observed in the F1 generation persisted beyond the F1 generation , we produced F2 mice from male and female F1 mice ( Fig 3A ) . We analyzed spontaneous locomotor activity , attention , spatial working memory , and reversal learning in the F2 mice . There were no significant changes in spontaneous locomotor activity , object-based attention , or working memory in male or female F2 mice whether derived from male or female F1 founder ( S1 Fig and S3 Data ) . However , paternal treatment ( of the founder or F0 generation ) had a significant main effect on latency to escape during reversal learning in the F2 generation ( two-way ANOVA; treatment: F ( 2 , 42 ) = 4 . 354 , p < 0 . 05 ) . Because comparisons were made against a single group of F2 control mice ( derived from F1 male or female mice descending from F0 male mice that were exposed to plain drinking water ) , we used Dunnet’s multiple comparisons test for analysis of the differences among the groups [22] . Male F2 mice derived from female F1 founders showed significant deficits in reversal learning when latency to find the escape hole ( t = 3 . 838; df = 42; p < 0 . 001; Fig 3B and S4 Data ) was considered as the parameter . Male F2 mice derived from male F1 founders and female F2 mice derived from male or female F1 founders did not show significant changes in reversal learning ( Fig 3B and 3C and S4 Data ) .
We show that paternal nicotine exposure is associated with sex-dependent changes in behavioral phenotypes , monoamine content in the striatum and frontal cortex , and striatal dopamine receptor mRNA expression . The behavioral changes are apparent in F1 and F2 generations but not in the F0 generation . Nicotine-induced spermatozoal DNA methylation at dopamine receptor promoter regions may be a plausible epigenetic mechanism for transgenerational transmission of the effects of the paternal nicotine exposure . Recent evidence shows that the effects of a variety of environmental stimuli , including stress , hormones , drugs of abuse , nutritional deprivation , and psychological trauma , are heritable , in some instances across multiple generations [5 , 11 , 41–45] . A first step toward defining the molecular mechanisms of transgenerational transmission of environment-induced phenotypes is to identify changes in the germ cell DNA of the exposed generation [11 , 45 , 46] . Nicotine is known to produce epigenetic modification of DNA in germ cells and somatic cells [11 , 38 , 47] . Consistent with those findings , our present data also show that nicotine alters DNA methylation in the spermatozoa . The precise mechanisms mediating the nicotine-induced epigenetic changes are unclear and likely include the action of noncoding RNAs such as microRNAs ( miRNAs ) as well as histone modification besides the DNA methylation demonstrated here [36 , 37 , 48] . In fact , a recent study showed that miRNAs can mediate the effects of paternal nicotine exposure on behavioral and molecular phenotypes in the F1 generation [11] . Epigenetic marks on germ cell DNA are erased and reinstated on multiple occasions before and after fertilization , casting some doubt that such labile molecular signatures could be the basis for transgenerational transmission . However , developmentally relevant genes “escape” the global erasure of epigenetic marks during the embryonic period , and these “escapees” remain plausible candidates for transgenerational transmission [49] . The dopamine receptor genes are key components of developmental pathways such as neurogenesis , neuronal migration , and differentiation regulated by dopamine [50 , 51] . Therefore , nicotine-induced changes in DNA methylation at dopamine receptor promoter regions could remain stable and form the basis of transgenerational transmission of nicotine’s effects at least in this model of paternal nicotine exposure . Another example of nonlabile epigenetic modification is DNA methylation produced by psychoactive drugs during adolescence , which lasts into adulthood and can be inherited by the offspring [52] . A link between the reduced striatal dopamine D2 and D4 receptor mRNA expression produced by the paternal nicotine exposure and the phenotypes of attention deficit and hyperactivity in the F1 mice is consistent with reports of association between polymorphisms in the D4 receptor gene and ADHD , and the role of D2 receptor in hyperactivity in animal models of ADHD [53–55] . This raises the possibility that the paternal nicotine exposure mouse model may have significant face validity as an ADHD model . Although we did not find significant changes in dopamine receptor mRNA expression in the frontal cortex , a key brain region implicated in ADHD [24] , the changes in striatal D2 and D4 receptor mRNA expression demonstrated here are equally relevant to ADHD because striatal function and cortico-striatal communication critically regulate attentional mechanisms [56 , 57] . In the paternally nicotine-exposed females , only the D5 receptor mRNA showed a significant effect in the striatum . The significance of this increase remains uncertain . The present study directly addresses some of the drawbacks associated with human studies on the effects of paternal nicotine exposure on the offspring . For example , in our earlier study on this topic [10] , some of the fathers who smoked cigarettes also had ADHD , making it difficult to separate the independent effects of paternal smoking from paternal ADHD on the offspring . In another study on the effects of paternal nicotine exposure on behavioral phenotypes in the offspring [11] , the nicotine-exposed male mice ( founder generation ) displayed significant behavioral changes , some of which were also seen in their offspring . In the present study , which used nicotine exposure rather than cigarette smoke exposure , the nicotine-exposed founder males ( fathers ) did not display hyperactivity , attention deficit , or working memory deficit ( S2 Fig and S5 Data ) , suggesting that the behavioral phenotypes in the offspring occurred in the absence of similar phenotypes in their fathers . In other words , the present study offers evidence that the behavioral phenotypes in the offspring can emerge in the absence of similar phenotypes in the fathers , addressing a drawback associated with interpretation of data from the previous human [10] and mouse [11] studies . Moreover , the present study used relatively low levels of nicotine exposure compared to previous studies in mice [11 , 58] . Therefore , the present study highlights the potential risk of low levels of nicotine exposure for future generations . Hyperactivity and attention deficit , phenotypes that had arisen de novo in the F1 generation as a result of the F0 nicotine exposure , were not transmitted to the F2 generation . Only the reversal learning deficit was transmitted from the F1 to the F2 generation . Thus , we observed an “attenuation” of the phenotypes during F1 to F2 transmission . The “attenuation” suggests that at least some of the deleterious effects of the nicotine exposure may be transient . However , repeated exposure of each successive generation might render the phenotypes more permanent and perhaps even endemic to the population . Finally , the present study analyzed paternal nicotine-exposure–induced phenotypes in both male and female offspring in both F1 and F2 generations . In addition , male and female F1 founders derived from the nicotine-exposed and control groups were used to generate the F2 generation , permitting analysis of the role of sex not only in the F1 and F2 generations but also in the F1 to F2 transmission . We found that the F1 and F2 phenotypes as well as the transmission of the reversal learning deficit from the F1 to the F2 generation were sex dependent . In the F1 generation , hyperactivity and reversal learning deficits occurred in both male and female mice , whereas the attention deficit occurred only in the male mice . The only phenotype in the F2 generation was reversal learning deficit , and it was observed only in the male F2 mice . The F1 to F2 transmission of reversal learning deficit occurred via the maternal but not the paternal line of descent . The maternal transmission is reminiscent of similar observations in our earlier study of transgenerational transmission of hyperactivity following prenatal nicotine exposure [5] . These two studies together suggest potential differences in the vulnerability of male versus female germ cells to nicotine exposure . Earlier studies that examined preconception paternal [11] or preconception paternal and maternal nicotine exposure [58] reported a number of behavioral phenotypes in the F1 generation . Paternal nicotine exposure produced depression-like and anxiety-like phenotypes as well as reduced locomotor activity and impaired social interaction [11 , 58] . Maternal nicotine exposure , on the other hand , produced increased locomotor activity and increased mobility in the forced swim test . The latter is the opposite of the depression-like phenotype produced by the paternal exposure [58] . Following nicotine exposure of both parents , the depression-like phenotype and impaired social interaction were observed . These observations not only suggest that the paternal-only nicotine exposure produces robust changes in multiple behavioral domains , but it also suggests that the phenotypes produced when both parents are exposed to nicotine ( namely , depression-like phenotype and impaired social interaction ) more closely resemble the phenotypes produced by the paternal-only rather than maternal-only nicotine exposure . The mechanisms underlying the sex-specific nature of the behavioral and molecular phenotypes observed here remain unclear . Sex differences in nicotine’s effects on the brain and behavior have been described previously [59–62] . Sex differences in hypothalamic-pituitary axis signaling , estrogen receptor signaling , neurotransmitter receptor signaling , and especially dopamine receptor expression are among the candidate mechanisms proposed for sex differences in the effects of nicotine upon the brain and behavior [60 , 61 , 63 , 64] . Nicotine-induced epigenetic modification of the DNA or histones could also contribute to sex-dependent changes reported here . Genetic sex , organizational versus activational influences , imprinted genes , and mitochondrial DNA [65 , 66] play a role in the expression of the sex-specific phenotypes . In addition , there are imprint/parent-of-origin effects on transcription at over 1 , 300 loci and approximately 350 autosomal genes with sex-specific parent-of origin effects in the mouse brain [65 , 66] . Cigarette smoke contains over 1 , 000 chemical substances , many of which can produce changes in DNA methylation [47] . The effects of cigarettes on the brain and behavior are mediated via nicotine’s direct actions at the nicotinic acetylcholine receptor in the developing and mature brain [67 , 68] . Nicotine exposure via smokeless tobacco ( chewing , snuff or e-cigarettes ) is highly prevalent ( review in [69] ) . Equally importantly , the use of e-cigarettes ( vaporized nicotine ) is increasing , especially among young adults of reproductive age , due to false perceptions of increased safety . Between 2013 and 2014 , in just 1 year , the use of e-cigarettes tripled among high school students [70] . Therefore , the nicotine exposure ( as opposed to cigarette smoke exposure ) paradigm used here has significant ecological and public health validity . Our 12-wk nicotine exposure paradigm encompassed the entire mouse spermatogenesis cycle [71] . The 12-wk exposure did not produce adverse effects on water or food consumption , body weight gain , or sperm count in the fathers , or on litter size , weight , weight gain , or developmental milestones in the offspring . The mouse strain used here ( C56Bl/6 ) preferentially consumes nicotine when given free choice between plain drinking water and nicotine-containing water [72 , 73] . Therefore , stress due to forced exposure to nicotine-laced water is unlikely to be a confounding variable . Finally , our nicotine exposure paradigm did not involve nicotine withdrawal . Paradigms that employ drug self-administration involve withdrawal during breeding , which could be a confounding variable [43] . The behavioral phenotypes , the neurotransmitter and mRNA phenotypes , and the preponderance of the phenotypes in F1 and F2 males observed in the present study are consistent with the clinical presentation , putative neurobiological mechanisms , and rate of diagnosis of ADHD and autism [24 , 74 , 75] . In addition , 2 of the behavioral phenotypes , namely , attention deficit and cognitive inflexibility ( reversal learning deficit ) , observed in the present study occur in both ADHD and autism [76] [27 , 77] . In addition to autism , cognitive inflexibility is a core symptom of schizophrenia , obsessive-compulsive disorder , and anorexia nervosa [78–80] . The higher prevalence of smoking in the 1950s and 1960s compared to today , taken together with our present findings , raises the possibility that nicotine exposure in generations past could be contributing to the rise in the diagnosis of neurobehavioral disabilities such as ADHD and autism in the present generation . Finally , our findings underscore the need to shift the current selective focus of research and public policy on the consequences for future generations of nicotine exposure of the mother to a more equitable focus on nicotine exposure of the mother and the father .
The studies were approved by the Florida State University Animal Care and Use Committee ( protocol number 1714 ) . C57BL/6 mice were housed in the Florida State University Laboratory Animal Resource facility in a temperature- and humidity-controlled environment on a 12-h light-dark cycle with food and water available ad libitum . Male mice ( 8- to 10-wk-old ) were randomly assigned to one of 2 groups: plain drinking water or drinking water containing 200 μg/mL nicotine ( Sigma; N3876 ) . Following 12 wk of such exposure and while the exposure was ongoing , the male mice were bred with drug-naive female mice ( Fig 1A ) . The day of birth was designated P0 . Litter size was recorded , and pups were weighed on P0 , P7 , P14 , and P21 . All litters were standardized to contain 6 to 8 offspring with equal numbers of males and females per litter , and the offspring were weaned about P21 . All of the experimental procedures were in full compliance with Florida State University guidelines and the NIH Guide for the Care and Use of Laboratory Animals . From a given litter , no more than 2 to 3 male and female mice were used . At the time of weaning , the mice were housed 2 to 4 per cage and were handled by the experimenter for at least 3 min per day for at least 1 wk prior to the beginning of the behavioral analyses at P60 . Immediately prior to the commencement of the behavioral testing , mice were habituated to the testing room for at least 30 min . The handling , habituation , and behavioral testing occurred during the lights-off period , when mice are naturally more active . Dim red light was used for ambient illumination and for video recording , with the exception of the spontaneous locomotor activity test , for which video recording was not performed . The spontaneous locomotor activity test spanned a period of 16 h . This included a 2-h long “lights-on” session before the 12-h “lights-off” period . The initial 2-h permitted the mice to habituate to the testing environment . The lights-on sessions were not included in the data analysis . In all behavioral tests , mice from each of the 2 paternal treatment groups ( i . e . , nicotine or water ) were tested concurrently . Locomotor activity was measured in testing chambers equipped with photobeam motion sensors ( Photobeam Activity System; San Diego Instruments ) . The sensors create a 3-dimensional grid ( 5 . 4-cm spacing ) of infrared beams enveloping the entire cage . Mice were placed individually into testing chambers . As the mouse moves along the x- , y- , or z-axes , the number of breaks in the infrared beams are recorded . Each instance in which the movement of the mouse breaks consecutive beams was scored as an ambulatory event . The photobeam breaks were grouped into hourly activity measurements for statistical analysis . The analysis was conducted over a 12-h period from 19:00 h to 07:00 h ( daily lights-off period was between 19:00 h and 07:00 h ) . Spatial working memory was assayed using a custom-built clear Plexiglas Y-maze [22] . Each of the 3 arms of the maze was 35 cm long by 6 cm wide by 10 cm high; distinct visual cues were placed on the walls of each arm and on the walls of the testing room . The mouse was placed at the center of the Y and had free access for exploration of all 3 arms for a period of 6 min . An investigator blinded to the identity of the mouse calculated the number and sequence of arm entries over the 6-min period by analyzing video recordings of the maze exploration . The mouse was considered to have entered an arm only if all 4 limbs entered it . An “alternation” is a set of 3 nonrepeating consecutive arm choices ( e . g . , ABC , BCA , CBA but not ABB , CCB , BAA , etc . ) . An alternation index was calculated as follows: number of alternations ÷ ( number of entries − 2 ) × 100 . The rationale behind this test is that mice divide their attention between a familiar and novel object ( measured by time spent exploring an object ) such that they explore a novel object for a longer duration ( i . e . , pay more attention ) than a familiar object . A mouse with attention deficit is expected to either focus equal attention upon familiar and novel objects or focus less attention on the novel object . Details of the test methodology are described in our earlier publications [22 , 81] and are given here in brief . The apparatus consists of a training chamber and a test chamber separated by a sliding door . The test consists of 3 sessions: on day 1 , during the habituation session ( 10 min ) , mice are individually exposed to each of the 2 chambers ( 5 min each ) . On day 2 , during the training session ( 3 min ) , mice are allowed to explore 5 objects placed in the training chamber . All objects are made from the same wooden material , but each object has a distinct shape ( i . e . , rectangle , triangle , circle , oval , and octagon ) . Next , on the same day , the mice are allowed to explore 2 objects of different shapes ( e . g . , circle and triangle ) in the test chamber for 5 min . On day 3 , during the test session , the mouse is habituated to the empty training and test chambers for 6 min ( 3 min in each chamber ) and then allowed to explore the same 5 objects used on day 2 in the training chamber for 3 min . Following a 10-s interval , the sliding door is opened , and the mouse is allowed to enter the test chamber to explore 2 objects for 3 min . One of these two objects is randomly selected from the 5 objects that the mouse had explored in the training chamber and is placed in the test chamber in a position analogous to its original position in the training chamber . This object is therefore the familiar object . The second object is a novel object , to which the mouse had never been exposed . A recognition index for the test session is calculated using the formula: TN ÷ ( TF + TN ) × 100 , where TF and TN are time spent exploring the familiar and the novel objects , respectively . We included in the analysis only those mice that spent at least 20 s with both of the objects during the test session . A modified Barnes maze consisting of a circular arena ( 122-cm diameter and 140-cm height; Med Associates Inc . , St . Albans City , VT ) with 40 equally spaced holes along the periphery was used . An escape box was positioned under one of the holes to allow the mouse to escape . The position of the escape hole was assigned randomly for each mouse in advance of the test , and the position remained the same throughout the trials for that mouse . Visual cues were placed around the maze to serve as spatial cues . A bright light ( 150 W ) and a fan were positioned above the maze . A starting chamber ( metal bowl ) held the mice at the center of the maze at the start of each trial . Mice were tested in squads of 4 in the following 3 phases of testing: Mice were euthanized on P90 by anesthetic overdose , and the brains were removed . Frontal cortex and dorsal striatum were microdissected from both the hemispheres based on anatomical landmarks , and the samples from the 2 hemispheres were pooled . RNA was extracted using the RNeasy kit ( Qiagen , 74104; Valencia , CA ) . Reverse transcription reactions were performed using the SuperScript III cDNA synthesis kit ( Life Technologies , Grand Island , NY; 18080–044 ) . Primer sequences for 18s ( Life Technologies , Hs Hs99999901_s1 ) and dopamine receptors were chosen based on previously published data D1R ( Life Technologies , Mm01353211_m1 ) , D2R ( Mm00438541_m1 ) , D3R ( Mm00432887_m1 ) , D4R ( Mm00432893_m1 ) , and D5R ( Mm00658653_s1 ) . Real-time qPCR was performed in a StepOne Plus Thermocycler ( Life Technologies ) using Taqman PCR Master Mix ( Life Technologies; 4369016 ) through 50 PCR cycles ( 95°C for 30 s , 57°C for 60 s , 72°C for 90 s ) . Levels of mRNA were normalized to 18 s . Samples from 4 to 6 mice were used for each experiment . Tissue was collected as described above for mRNA analysis . The orbitofrontal cortex , medical prefrontal cortex , and dorsal striatum were microdissected based on anatomical landmarks , and samples of each region were collected as described earlier [82–84] . For each brain region , samples from the right and left hemispheres from the same subject were pooled into a single sample for that subject . Each pooled sample was weighed and immediately frozen with liquid nitrogen . The tissue samples were shipped to the Neurochemistry Core at Vanderbilt University , Nashville , Tennessee , where they were homogenized and the protein concentration in each sample evaluated . Tissue concentrations ( ng/mg protein ) of dopamine and norepinephrine ( NE ) and their metabolites—DOPAC , HVA , and 3-MT—were analyzed . Mice were euthanized by anesthetic overdose , and the testes were removed . Mature spermatozoa were collected using a double swim assay . Briefly , longitudinal cuts were made along the cauda epididymis in phosphate-buffered saline ( pH 7 . 2 ) containing 1% bovine serum albumin to release the spermatozoa . The saline solution containing the spermatozoa was incubated at 37°C for 30 min , during which time the sperm swam up and collected in the supernatant . The supernatant was removed and incubated for an additional 10 min ( “double swim assay” ) . A smear from each pellet was examined in a light microscope ( Axiovert 25 , Zeiss , Carl Zeiss , Thornwood , NY ) to confirm the presence of only mature spermatozoa . Following cell counts , the spermatozoa were pelleted and frozen on dry ice and stored at −80°C until further use . Genomic DNA was isolated from pelleted sperm using a ZR Genomic DNA-Tissue Microprep kit ( Zymo ZRGenomic; Zymo Research , Irvine , CA; D3041 ) . Following isolation , DNA was eluted with 20 μl DNA elution buffer and quantified using a Qubit 2 . 0 fluorometer ( Invitrogen , Carlsbad , CA ) . Next , 2 . 5 μl of normalized genomic DNA was amplified using a Qiagen REPLI-g Whole Genome Amplification kit ( Qiagen , Valencia , CA; 150023 ) according to the manufacturer’s protocol . Typical DNA yields were greater than 4 μg for each sample . Amplified DNA was then prepared for anti-5-methylcytosine immunoprecipitation as follows: 2 μg of the DNA was diluted up to 50 μl using Zymo MIP buffer , vortexed , incubated on ice for 10 min , then subjected to fragmentation using a Diagenode Bioruptor 300 ( 30 s on; 90 s off; 7 cycles ) to generate 200- to 500-bp fragments of genomic DNA . The amount of 1 μg of the disrupted DNA was stored as “input” DNA for later analysis . The remaining 1 μg of disrupted DNA was immunoprecipitated according to manufacturer’s protocol ( Methylated-DNA IP Kit; Zymo Research , Irvine , CA; D5101 ) using a 1:10 μg ratio of DNA/anti-5-methylcytosine antibody . Negative controls included no antibody mock and IgG . The recovered DNA was quantified using a Qubit 2 . 0 fluorometer . Random priming amplification of immunoprecipitated DNA was carried out using an Affymetrix 2 . 0 DNA polymerase following the method of Cheung and colleagues [85] ( Thermo Fisher Scientific; 70775X1000UN ) and quantified using a Qubit 2 . 0 fluorometer . Real-time thermal cycling was performed using methylated DNA immunoprecipitation ( MeDIP ) -derived DNA ( 2 . 5 ng/μL ) , 2X PerfeCTa SYBR Green SuperMix ( QuantaBio , Beverly , MA; 95056–500 ) , 0 . 5 μM of each primer , and StepOne Plus Thermocycler ( Life Technologies ) . All PCR reactions were performed in triplicate . Target DNA sequence quantities were estimated from threshold amplification cycle numbers ( Tc ) . For every gene sequence studied , a ΔTc value was calculated for each sample by subtracting the Tc value for Tc value for the input DNA ( 5 ng/μL ) from the Tc value for the corresponding immunoprecipitated sample to normalize for differences in MeDIP sample aliquots . DNA quantities were expressed as percentages of corresponding input using the following equation: ( antibody ChIP as a percentage of input ) = 2 − ( ΔTc ) × 100 . Dopamine receptor primers were designed using NCBI and Methylprimer software The sequences for each are as follows: DRD1 forward GGT GCT GAA GAT TGA AGA TCC A; DRD1 reverse CGT CCT GAC ACA TGC TGT TAT AG; DRD2 forward ACC TGT CCT GGT ACG ATG ATG; DRD2 reverse GCA TGG CAT AGT AGT TGT AGT GG; DRD3 forward CCT CTG AGC CAG ATA AGC AGC; reverse AGA CCG TTG CCA AAG ATG ATG; DRD4 forward GCC TGG AGA ACC GAG ACT ATG; DRD4 reverse CGG CTG TGA AGT TTG GTG TG; DRD5 forward CTC GGC AAC GTC CTA GTG TG; and DRD5 reverse AAT GCC ACG AAG AGG TCT GAG . Main effects of paternal treatment , sex , and paternal treatment × sex interaction were analyzed using a two-way ANOVA . Acquisition learning on the Barnes maze ( AL1-10; Fi . 1 E , F ) was analyzed using a repeated-measures ANOVA . Post hoc pair-wise comparisons were performed using Bonferroni multiple comparisons test ( Figs 1 and 2 ) when either of the 2 main factors ( paternal treatment and sex ) or the interaction between the factors was statistically significant by ANOVA . Each group was compared to every other group . For the F2 generation ( Fig 3 ) , following the ANOVA , a Dunnett test was used [22] , as multiple comparisons were made against a single control group ( F2 male or female mice from male or female F1 mice derived from plain drinking water–exposed F0 male founder; Fig 3 ) . A two-tailed Student t test was used when differences between only 2 groups were evaluated ( Fig 2K and 2L ) . GraphPad Prism 7 . 02 was used for all statistical analysis . The number of mice in each experimental group for each study is indicated in the legend to each Figure . | Use of tobacco products is a major public health concern throughout the world . Cigarette smoking by pregnant women receives significant attention by scientific , public health , and public policy experts because it poses health risks for the mother and her children . Although more men smoke cigarettes than women , the health consequences of paternal smoking for their descendants are much less explored . Using a mouse model , we show that the offspring of nicotine-exposed males have hyperactivity , attention deficit , and cognitive inflexibility . These behavioral phenotypes are associated with attention deficit hyperactivity disorder ( ADHD ) and autism spectrum disorder in humans . Cognitive inflexibility persists into the third ( F2 ) generation . The neurotransmitters dopamine and noradrenaline and their receptors , critically implicated in neurodevelopmental disorders , are also altered in the offspring’s brains . The nicotine-exposed males show significant alterations in spermatozoal DNA methylation patterns , especially at dopamine receptor gene promoter regions , implicating epigenetic modification of germ cell DNA as a mechanism for the transgenerational transmission of the behavioral and neurotransmitter phenotypes . The impact of nicotine on germ cells and the neurobehavioral impairments in multiple subsequent generations call for renewed focus of research and public policy on tobacco use by men and its consequences for their descendants . | [
"Abstract",
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"and... | 2018 | Nicotine exposure of male mice produces behavioral impairment in multiple generations of descendants |
Chloride-transporting membrane proteins of the CLC family appear in two distinct mechanistic flavors: H+-gated Cl− channels and Cl−/H+ antiporters . Transmembrane H+ movement is an essential feature of both types of CLC . X-ray crystal structures of CLC antiporters show the Cl− ion pathway through these proteins , but the H+ pathway is known only inferentially by two conserved glutamate residues that act as way-stations for H+ in its path through the protein . The extracellular-facing H+ transfer glutamate becomes directly exposed to aqueous solution during the transport cycle , but the intracellular glutamate E203 , Gluin , is buried within the protein . Two regions , denoted “polar” and “interfacial , ” at the intracellular surface of the bacterial antiporter CLC-ec1 are examined here as possible pathways by which intracellular aqueous protons gain access to Gluin . Mutations at multiple residues of the polar region have little effect on antiport rates . In contrast , mutation of E202 , a conserved glutamate at the protein–water boundary of the interfacial region , leads to severe slowing of the Cl−/H+ antiport rate . An X-ray crystal structure of E202Y , the most strongly inhibited of these substitutions , shows an aqueous portal leading to Gluin physically blocked by cross-subunit interactions; moreover , this mutation has only minimal effect on a monomeric CLC variant , which necessarily lacks such interactions . The several lines of experiments presented argue that E202 acts as a water-organizer that creates a proton conduit connecting intracellular solvent with Gluin .
Proton-coupled anion exchange-transporters of the CLC family carry out varied physiological tasks in virtually all eukaryotes and many prokaryotes , transporting Cl− , NO3− , or F− across membranes in strictly coupled exchange for H+ ions in the opposite direction [1] , [2] . These transporters can thus use a proton gradient to pump anions thermodynamically uphill or vice versa , depending on biological context . CLC proteins are membrane-embedded homodimers in which each subunit acts independently as a functional unit [3]–[8] . The pathways within each subunit taken by Cl− and H+ have been delineated mainly by studies of a homologue from Escherichia coli , CLC-ec1 , which is uniquely tractable at levels of biochemistry [9] , electrophysiology [10] , [11] , and X-ray crystallography [12] . The H+ and Cl− pathways ( Figure 1a ) run together on the extracellular side of the protein , diverging about halfway through , where a “central” Cl− ion resides in the ion-coupling chamber [13] . A critical “external glutamate , ” E148 ( Gluex ) , serves two mechanistically essential purposes . It forms an extracellular gate that closes or opens the ion pathways to the extracellular solution via side-chain rotation between buried and water-exposed positions and , coupled to this structural change , transfers H+ between protein and solution [12] . If this residue is substituted with a nonprotonatable group , H+ transport is completely abolished while Cl− movement , now uncoupled , persists [10] . A second key “internal glutamate” residue located towards the intracellular side of the protein , E203 ( Gluin ) , similarly mediates H+ transfer between protein and the internal solution; mutation of this residue to nonprotonatable moieties also abolishes H+ movement while preserving Cl− transport [13] . Unlike Gluex , Gluin need not physically move to hand off its proton [11] , but merely places a dissociable group at this location in a proton-transfer pathway . While the Cl− pathway is crystallographically visible , the H+ pathway has been glimpsed only indirectly through the two carboxylate way-stations visited during the transport cycle , Gluex and Gluin . These groups are located ∼15 Å apart , separated by a nonpolar—and in the crystal structure anhydrous—region containing only a single dissociable side chain: a tyrosine residue whose hydroxyl group may be removed without disruption of transport [14] , indicating its nonessential role . Moreover , while Gluex directly communicates with extracellular aqueous protons in its open position , Gluin is buried away from the internal solution by a protein layer ∼10 Å thick ( Figure 1a ) . Thus , two basic questions regarding the H+ pathway remain unresolved: ( 1 ) how do intracellular aqueous protons gain access to Gluin , and ( 2 ) how do protons negotiate the nonpolar desert interposed between Gluin and Gluex ? This study addresses the first of these questions by examining two potential access pathways through mutations that impair H+ transport . We find that intracellular H+ access to Gluin can be greatly slowed—indeed , made rate-limiting for Cl− antiport—by blocking one of these portals with mutation of E202 , a strongly conserved residue of hitherto unknown function that guides aqueous H+ to several water molecules positioned in proximity to the carboxylate moiety of Gluin .
The absence of polar groups between Gluin and Gluex suggests strongly that water wires catalyzing proton transfer somehow connect these essential carboxyl groups during the transport cycle . Previous computational studies [15]–[18] have arrived at pictures of such water-based H+ pathways , similar in theme but varied in detail . While it is widely thought from the physical character of CLC proteins that water-chains must be involved in H+ transfer between the two critical glutamates , such waters are neither experimentally manipulable by currently available tools nor crystallographically visible . However , the other unknown feature of the proton pathway—proton access to Gluin from intracellular solution—is susceptible to experimental inquiry . The unusually high Cl− transport rate of CLC-ec1 , 2000–3000 s−1 [14] , [19] , makes it natural to wonder , since Gluin is buried , whether cytoplasmic H+ access to Gluin is a rate-determining step in the transport cycle , and if not , why not . To obtain a sharper picture of this region in hopes of locating waters more prominent than in the original high-quality 2 . 5 Å crystal structure [12] , we engineered CLC-ec1 guided by its crystal-packing interfaces , deleting 15 N-terminal and four C-terminal residues . This construct , denoted ΔNC , removes residues either disordered or forming problematic crystal contacts in previous structures and produced high-quality electron density maps from crystals routinely diffracting to 2 . 2–2 . 7 Å Bragg spacing ( Table S1 ) . This trimmed construct is essentially identical to wild-type protein in structure , absolute turnover rate , and exchange stoichiometry ( Figure S1a , b , Table S2 ) . Henceforth , we use a ΔNC dataset refined to 2 . 5 Å for structural examination and full-length proteins for functional analysis . We confidently localized many fewer water molecules than were modeled in the original structure ( 52 versus 167 , exclusive of the Fab fragment ) , but the number and locations of waters near Gluin in both structures match well ( Figure S2a ) . Inspection of the transporter's intracellular surface reveals two watery clefts that might potentially allow protons access to Gluin ( Figure 1b ) . One of these—the “polar pathway”—contains five crystallographically visible waters embedded within a cluster of polar side chains , some of which form salt bridges ( Figure S2b , c ) . The other—the “interfacial pathway”—is a narrow , aqueous invagination , or fjord , between the two CLC subunits topped by a conserved glutamate , E202 . As described in the Supporting Information section ( Text S1 , Figure S3 , Table S2 ) , our attempts to disrupt the polar pathway by removing salt bridges , eliminating charges , or replacing polar side chains with hydrophobics caused only unimpressive inhibition of H+ transport and impaired Cl−/H+ coupling minimally or not at all . We therefore turned our attention to the interfacial pathway , focusing in particular upon E202 , for several reasons . First , this residue forms part of the protein surface that separates bulk water in the interfacial fjord from the protein interior where Gluin is buried . Second , a network of crystallographic water molecules near E202 and Gluin , including one bridging their carboxylates , invites closer examination of E202's role in the transport cycle ( Figure S2d ) . Third , E202 is arguably the most strictly conserved residue in the CLC superfamily , and yet its function is entirely unknown . Since our concern is with H+ access , and because we have no direct measurement of H+-binding kinetics to Gluin , we examined the rate of H+ pumping driven by a Cl− gradient . CLC-ec1-reconstituted liposomes loaded with 300 mM KCl were diluted into a lightly buffered solution containing 10 mM Cl− and 300 mM K+ , and the pH of the suspension was monitored continuously . Addition of the K+ ionophore valinomycin ( Vln ) initiates transport by electrically shunting Cl−/H+ exchange and setting the liposome membrane potential to zero . Two nonprotonatable mutants , E202Q and E202A , the former isosteric and polar and the latter small and nonpolar , were first tested ( Figure 2a ) and found to support H+ uptake at rates about 20% of WT CLC-ec1 . This result demonstrates that protonation of E202 is not required for antiport , an unsurprising conclusion since E202Q has been long known to maintain near-wild-type Cl−/H+ exchange stoichiometry [13] . Despite its strict conservation , E202 may be substituted with many other side chains without impairing protein expression , folding , or dimerization , and so we tested a series of nondissociable side chains at this position . A clear pattern emerges ( Figure 2b , c ) in which H+ transport slows as side-chain volume increases , with the large aromatics producing up to 200-fold inhibition of the H+ uptake rate . In these very slow mutants , the antiport mechanism remains intact , as shown by measurements of Cl− efflux , which slows down in parallel , although not to the same extent as H+ uptake ( Figure 2d , e ) . All rates are reported in Table S2 . It is tempting to imagine that the severe inhibition of H+ transport by these aromatic substitutions at E202 reflects blockage of the path from cytoplasmic water to Gluin , such that H+ access becomes rate-limiting for the transport cycle . But this conclusion would be invalid without evidence that these mutations act upon H+ movement itself , rather than on Cl− transport , the observed inhibition of H+ uptake being merely a secondary consequence of the coupled antiport mechanism . The question thus becomes: Which ion's pathway is the primary victim of the mutations ? We address this question by examining two diagnostics of the Cl− pathway that are independent of H+ involvement: Cl− binding and uncoupled Cl− transport . Equilibrium binding of Cl− indicates that the E202Y mutation does not act directly on the Cl− transport pathway , since Cl− affinity determined by isothermal calorimetry , known to reflect binding to the central anion site [19] , [20] , is weakened only 2-fold in E202Y ( Figure 3a , b , Table S3 ) . These mutations are further tested for Cl− transport in a CLC mutant ( E148A ) wherein all H+ transport is eliminated [10] . This mutant , lacking the external Gluex gate and devoid of acid activation and H+ coupling , provides a way of testing the effect of E202 mutants on the Cl− pathway alone . The question is straightforward: Do large nonpolar substitutions at E202 severely inhibit Cl− transport on a background of E148A , as they do on wild-type ? The answer is clear: they do not ( Figure 3c , d , Figure S4 ) . The Cl− efflux rate is altered by trivial factors of 0 . 5 , 1 . 7 , and 1 . 9 for the F , Y , and W substitutions , respectively . A similarly minimal effect of the F substitution is also seen on a different H+-uncoupled mutant background ( E148A/Y445S , Figure S4 ) with a 20-fold faster Cl− turnover rate than wild-type [21] . These several lines of evidence argue that large , nonpolar side chains at E202 slow the coupled transport cycle not by impairing the Cl− transport pathway but rather by rendering H+ diffusion between Gluin and intracellular solution rate-limiting . By what mechanism do these E202 substitutions so strongly inhibit H+ transport ? Though unable to grow crystals of any E202 mutant suitable for assessing water organization near this residue , we obtained a single dataset of E202Y at a resolution sufficient ( 3 . 2 Å ) to observe the disposition of this inhibitory side chain . The mutant is essentially identical in structure to wild-type protein and shows prominent density at the central Cl− binding site ( Figure S5 ) . The substituted side chain adopts the position that would otherwise be a polar portal at the top of the fjord , with the phenol ring bricking up that gateway with greasy mortar ( Figure 4a ) . Moreover , an unexpected consequence of this mutation is the movement of the I201 side chain from the neighboring subunit to pack closely against the Y202 aromatic ring , a subtle cross-subunit rearrangement that contributes additional nonpolar mass to sequester Gluin away from aqueous protons in the interfacial fjord ( Figure 4b , c ) . Thus , even at this rather low resolution , the E202Y structure neatly rationalizes the dramatic slowdown of H+ transport suffered by the large nonpolar E202 mutants . A strong prediction immediately arises from the E202Y structure: that inhibition by this mutation should be much less severe in a monomeric variant of the transporter , where the cross-subunit interaction between Y202 and I201 cannot exist . This prediction was tested by exploiting a monomeric CLC-ec1 construct known to support well-coupled Cl−/H+ antiport , albeit at lower absolute rates than the wild-type homodimer [8] . On this monomeric background , the E202Y substitution shows only a 4-fold reduction of H+ transport rate , while maintaining a Cl−/H+ exchange stoichiometry ( 3∶1 ) close to the normal value ( Figure 5 ) .
Ever since proton movements were understood to be coupled to anion transport in CLCs [10] , attention has focused on how H+ navigates its way through these channels and transporters [6] , [13] , [22]–[24] . Macromolecular H+ pathways , notoriously difficult to unravel solely by experimental approaches , can often be perceived only inferentially and indirectly . In CLC-ec1 , combined biochemical , electrophysiological , and structural experiments have deduced a rough trajectory for H+ transit through this Cl−/H+ antiporter , largely through recognition of Gluex and Gluin as key dissociable residues that H+ transiently occupies on its way across the membrane . But details remain vague on two key issues: ( 1 ) how H+ gains access to the buried Gluin residue from solution and ( 2 ) how it breeches the nonpolar gap between Gluin and Gluex . This study establishes a specific role for E202 in the first of these proton-transfer processes . E202 stands out structurally by virtue of its strict conservation and location close to the apex of the interfacial pathway , just as it stands out functionally as the unique governor of H+ access to Gluin . The results confirm that E202 is not itself a compulsory protonation-point in the pathway and instead imply that it acts as a “water-organizer” that promotes H+ transfer from bulk water in the interfacial fjord to the ordered water molecules inside the protein near E202 . We suppose that large nonpolar substitutions here disrupt this water conduit to Gluin , thereby slowing H+ access from intracellular solution so much that this step becomes rate-determining for overall transport . The E202Y crystal structure corroborates this idea by showing the aromatic side chain reaching across the subunit interface to interact with I201 of its homodimeric twin , thereby to plug the water-conduit . This mechanism is further validated by the minimal effect of this mutation in a monomeric variant of the transporter . Figure 6 summarizes in cartoon form the essential features of the proposal offered here for water/H+ access to Gluin . The E202 substitutions inhibit both Cl− and H+ in parallel , showing qualitatively that the basic coupling mechanism remains intact . However , we cannot ignore the higher Cl−/H+ stoichiometry in these slow mutants ( Figure S6 ) ; this is likely not a disruption of the basic coupling mechanism but rather reflects Cl− “slippage” through the inner gate in stalled transporters , which must wait for a proton to arrive before the antiport cycle can continue [14] , [25] . The preservation of Cl−/H+ exchange stoichiometry in monomeric E202Y supports the idea that Cl− slippage accounts for the higher Cl−/H+ stoichiometry . We emphasize that E202 is unique in controlling H+ access to Gluin; our many mutagenic maneuvers aimed at mutilating the hydrophilic character of the alternative polar pathway produced only minor effects on transport . It is worth recalling that all known CLC channels , which , like the antiporters , require transmembrane H+ movement for proper function [6] , [22] , [23] also carry the E202 equivalent; this conservation may underlie the observation that mutation of this glutamate in a mammalian CLC channel compromises its H+-dependent gating process [26] , [27] . The internal glutamate ( E203 ) is highly conserved among CLC antiporters , and CLC-ec1 is known to absolutely require protonation at this position for H+-coupled Cl− movement [11] , [13] , [24] . But a few violations of this pattern have recently come to light among CLC antiporter homologues with nondissociable residues here [2] , [28] , [29] . Moreover , H+ transport is linked to gating of CLC channels , all of which have valine instead of glutamate at this position . These exceptions to the proton-transfer function of Gluin make the strict conservation in all CLCs of the neighboring E202 position all the more notable . Thus , we imagine that while the details of H+ movement within the protein vary among CLC homologues , the water-organizing function of E202 proposed here for proton exchange with intracellular solvent is common to the entire superfamily .
We engineered a “ΔNC” CLC-ec1 construct by deleting 15 N-terminal ( residues 2–16 ) and four C-terminal ( residues 461–464 ) amino acids from the natural sequence . Mutations were introduced by standard PCR-mediated cassette mutagenesis , and full coding sequences were confirmed . Expression in E . coli and purification of CLC-ec1 in decylmaltoside ( DM ) were as described [30] , except that after removal of the His-tag , gel filtration ( Superdex 200 ) replaced ion exchange chromatography as the final purification step . For crystallization , a ΔNC-Fab complex ( 10–20 mg/mL ) [31] was mixed with an equal volume of 25%–35% PEG400 , 100 mM Ca-acetate or 300 mM KCl , 100 mM tris-HCl or 100 mM Glycine-NaOH , pH 8 . 5–9 . 5 . Typically , crystals were grown at 22°C in hanging or sitting drops for 2–3 wk , cryoprotected in 35% PEG400 , and flash frozen in liquid N2 . The E202Y-Fab complex was prepared as above , and crystals were formed in 38% PEG400 , 200 mM CaCl2 , 100 mM glycine-NaOH , pH 9 . 5 . However , only the E202Y protein was found in the structure , the Fab fragment having been kicked off during crystallization . X-ray diffraction data were collected remotely at beamline 8 . 2 . 1 or 5 . 0 . 2 , Advanced Light Source . Datasets were processed and structures solved by molecular replacement as described [11] . The refined models are deposited in the Protein Data Bank ( # 4ENE for ΔNC and 4FTP for E202Y ) . Formation of liposomes reconstituted with CLC-ec1 variants ( 1–5 µg protein/mg lipid ) and ion flux measurements have been described in detail [11] . E . coli phospholipids were used for all liposome experiments except with the monomeric variants , for which egg phosphotidylcholine/1-palmitoyl , 2-oleoyl phosphatidylglycerol ( 3/1 ) was used . Briefly , large multilamellar liposomes formed from several freeze-thaw cycles were extruded with a 0 . 4 µm filter . For H+ uptake , a 0 . 1 mL liposome sample loaded with 300 mM KCl , 40 mM citrate-NaOH , pH 4 . 8 was passed through a 1 . 5 mL Sephadex G-50 column swollen in 10 mM KCl , 290 mM K-isethionate , 2 mM glutamate-NaOH , pH 5 . 2 , and diluted into 1 . 8 mL of the same solution in a stirred cell , with pH monitored continuously with a glass electrode . Cl−-driven H+ uptake was initiated by addition of 1 µg/mL valinomycin ( Vln ) and terminated by 1 µg/mL H+ ionophore FCCP . Each experiment was calibrated by addition of 50 nmoles of HCl . Cl− efflux was performed similarly except that slightly different buffer systems were used . Liposomes loaded with 300 mM KCl , 25 mM citrate-NaOH , pH 4 . 5 were diluted as above into 1 mM KCl , 300 mM K+-isethionate , 25 mM citrate-NaOH , pH 4 . 5 , Cl− being monitored with an Ag/AgCl electrode . Efflux of Cl− was triggered by Vln/FCCP , and at the end of the run , 30 mM β-octylglucoside was added to determine total trapped Cl− . Cl−/H+ stoichiometry was measured by comparison of initial slopes of H+ uptake and Cl− efflux performed in 1–10 mM KCl , 290–300 mM K-isethionate , 2 mM citrate-NaOH , pH 5 . 2 using liposomes same as in H+ uptake experiments . Equilibrium binding of Cl− to CLC-ec1 was measured by isothermal titration calorimetry . In order to minimize Cl− contamination in the protein preparations , cobalt columns charged with CLC-ec1 were washed with Cl−-free buffer ( 100 mM Na/K tartrate , 20 mM tris-SO4 , 20 mM imidazole-H2SO4 , pH 7 . 5 , 5 mM DM ) and eluted with 400 mM imidazole . Size-exclusion chromatography was in 100 mM Na/K tartrate , 10 mM tris-SO4 , pH 7 . 5 , 5 mM DM . Protein ( 150–250 µM ) was titrated with 25 mM Cl− solution in a Nano ITC ( TA instruments ) at 25°C . Data were fitted to single-site isotherms using NanoAnalyze 2 . 1 . 9 software . | Chloride-proton antiport proteins of the “CLC” superfamily are transmembrane proteins that form homodimers and are used for myriad physiological purposes , all requiring the coordinated movements of Cl− anions and H+ cations in opposite directions across biological membranes . While the pathway for Cl− ions through CLC antiporters is known , we currently have only indirect glimpses of how protons navigate their way through these membrane-embedded proteins . By combining mechanistic and structural approaches , we identify a proton-access pathway in a bacterial Cl−/H+ antiporter that allows intracellular protons to enter the protein interior and engage in the coupled antiport mechanism . We conclude that E202 , a highly conserved glutamate residue , serves to organize water molecules and guide protons to the adjacent glutamate E203 ( known as “Gluin” ) , a critical residue for the antiport mechanism . | [
"Abstract",
"Introduction",
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"biochemistry",
"biology",
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] | 2012 | Intracellular Proton Access in a Cl−/H+ Antiporter |
Cross-feeding , a relationship wherein one organism consumes metabolites excreted by another , is a ubiquitous feature of natural and clinically-relevant microbial communities and could be a key factor promoting diversity in extreme and/or nutrient-poor environments . However , it remains unclear how readily cross-feeding interactions form , and therefore our ability to predict their emergence is limited . In this paper we developed a mathematical model parameterized using data from the biochemistry and ecology of an E . coli cross-feeding laboratory system . The model accurately captures short-term dynamics of the two competitors that have been observed empirically and we use it to systematically explore the stability of cross-feeding interactions for a range of environmental conditions . We find that our simple system can display complex dynamics including multi-stable behavior separated by a critical point . Therefore whether cross-feeding interactions form depends on the complex interplay between density and frequency of the competitors as well as on the concentration of resources in the environment . Moreover , we find that subtly different environmental conditions can lead to dramatically different results regarding the establishment of cross-feeding , which could explain the apparently unpredictable between-population differences in experimental outcomes . We argue that mathematical models are essential tools for disentangling the complexities of cross-feeding interactions .
Why are microbial communities so diverse , and how is this diversity maintained ? These questions have shaped research in microbial ecology for decades and mathematical models have been instrumental in providing answers . In particular , Gause’s theory of competitive exclusion [1] , popularized by Hardin [2] , has deeply influenced our understanding of the type of environments that can support organismal diversity . This theory states that simple environments containing a single resource niche can only support one competitor; therefore the search for mechanisms supporting diversity was , for years , focused around complex environments . This ecological principle was supported by an evolutionary principle articulated by Muller [3] , who postulated that a large asexual population evolving in a simple environment should evolve by periodic selection of successively fitter clones , each going to fixation and resulting in clonal replacement . However in the late 1980s , a seminal experimental work put the spotlight back onto simple constant environments by demonstrating , quite unexpectedly , that such environments could both generate and support genetic diversity [4] . A population of E . coli initiated from a single clone and cultured under constant glucose limitation for over 750 generations became stably polymorphic , with clones differing significantly in their glucose uptake kinetics as well as in their maximum specific growth rates and yield under non-limiting conditions . A subsequent study [5] demonstrated that the mechanism by which polymorphism was stably maintained in this population was cross-feeding , an independent relationship wherein one genotype consumes metabolites excreted by another . Specifically , Rosenzweig et al . found that the clone with the highest uptake kinetics of the primary limiting resource excreted metabolites that created alternative secondary resource niches on which other clones could specialize . While this finding did not contradict Gause and Muller , it showed that microorganisms could readily move outside the assumptions of the competitive exclusion principle . Interestingly , even though each clone specialized on a different niche , both retained the capacity to assimilate either resource , albeit at very different rates . The construction of multiple niches where initially there was only one , arose as the consequence of a metabolic trade-off whereby organisms can convert available limiting resources into energy either slowly but efficiently or rapidly but wastefully [5] . When the primary resource is utilized wastefully , the resulting waste product can serve as a secondary energy source . This rate-efficiency trade-off is considered to be a thermodynamic [6] and biophysical [7] necessity and has been observed in a wide range of microorganisms ( as discussed in [8] ) . It is becoming clear that cross-feeding , whereby one strain or species consumes metabolites produced by another , is a pervasive feature of microbial communities in nature [9 , 10] . Indeed , the advent of cross-feeding opens the door to other more complex interactions such as syntrophy , wherein a consumer strain releases metabolites that are useful to the producer . Syntrophic interactions , which demonstrably benefit both partners , are ubiquitous among free-living bacteria in pristine [11] and human-impacted environments [12 , 13] , and have now been studied systematically in synthetic communities [14] . These types of interactions may also be at work in clinically relevant settings where cells reproduce asexually . For example , chronic bacterial infections originating from a single clone become genetically heterogeneous [15 , 16] , and in some cases this heterogeneity appears to be supported by syntrophic interactions [17] . Extreme genetic heterogeneity is also a characteristic feature of evolving tumors [18] . As tumors are known to carry out aerobic glycolysis [19] leading to a release of overflow metabolites [20] , this may create opportunities for subpopulations to follow independent evolutionary trajectories reinforced by cross-feeding [21] . What is not clear is how readily cross-feeding interactions form . Even in environments known to favor cross-feeding , the emergence of such interactions is not consistently observed . For example , a study similar to [5] found that six out of twelve E . coli populations did not develop cross-feeding polymorphisms while the other six did [22] . An unrelated study [23] followed an initially clonal population of E . coli in glucose limited continuous culture over 100 generations . This population radiated into multiple phenotypic clusters each of which exhibited variations in global regulation , metabolic strategies , surface properties and nutrient permeability pathways . However , in this instance diversity resulted from a mixture of mechanisms including mutation-selection balance , frequency dependent selection , trade-offs and regulatory degeneracies [23 , 24] . A possible clue to the apparent instability of cross-feeding interactions may lie in the observed density-dependent dynamics between primary resource specialist and secondary resource specialist clones [5] . In particular , the equilibrium frequencies of coexisting clones were strongly dependent on the total population densities , with high population densities favouring primary resource specialists . To investigate this phenomenon we here develop a mathematical model that describes a cross-feeding interaction between two microbial strains growing and competing in a spatially homogeneous environment that contains a single limiting resource . This model is parameterized using data from the biochemistry and ecology of two E . coli isolates known to support cross-feeding interactions [5] . We show that the model can qualitatively capture the empirically observed short-term dynamics of the two competitors . We then consider the long-term population dynamics and ask under what environmental conditions is this cross-feeding maintained . We find that this seemingly simple system can display complex dynamic behaviors , which depend on the density and frequency of the competitors as well as on the concentration of available resources . At sufficiently low resource concentrations the strain that consumes the primary resource , outcompetes the strain that consumes the secondary resource , which is a byproduct of primary resource metabolism . At intermediate concentrations of the primary resource , either strain can outcompete the other depending on densities of the competitors and their initial frequencies . This can be explained by the fact that while each strain specializes on a different resource , they retain the capacity to utilize both . Finally , at sufficiently high concentrations of the primary resource three outcomes are possible: either strain outcompetes the other or they coexist through a cross-feeding interaction . Again , this tri-stable outcome is density and frequency dependent . Therefore whether cross-feeding emerges in such a system depends not only on the primary resource being sufficiently abundant but also on the size of the initial population and the frequency of competitors within that population . These findings illuminate the dynamic nature of cross-feeding interactions and demonstrate the utility of mathematical models in predicting conditions under which cross-feeding can become established and persist . Because cross-feeding leads to increased biocomplexity , knowledge of how cross-feeding arises in the lab deepens our understanding of the mechanisms by which asexual populations diversify in nature , whether in the context of soils and sediments , chronic infections or evolving tumors .
To investigate the stability of a cross-feeding interaction , we developed a chemostat model of competition for a simple sugar . For simplicity , we model the catabolism of sugar and its intermediates as a two-reaction process [25] corresponding to glycolysis and the tricarboxylic acid ( TCA ) cycle ( see Fig 1 for the pathway schematic and S1 Supporting Information for model details ) . In the first reaction , sugar is taken from the environment and partially oxidized to form an intracellular metabolic intermediate . In the second reaction , the intracellular metabolic intermediate is either completely oxidized to form CO2 , or excreted out of the cell as an extracellular metabolic intermediate . The extracellular metabolic intermediate can be subsequently taken up into the cell and the excretion/uptake of the metabolic intermediate shows saturating enzyme kinetics [5] . We also assume that both glycolysis and TCA reactions show saturating enzyme kinetics , that the rate of cell growth is proportional to the rate of ATP production [26] according to a proportionality constant G and that intracellular metabolic intermediate imposes an inhibitory cost to growth [27] denoted by a function c . Let S denote the concentration of a single limiting sugar in the environment while Xin and Xex denote the concentration of the intracellular and extracellular metabolic intermediates , respectively . The concentration of S , Xin and Xex is measured in μmol/L . In addition we consider two competitor strains whose densities are denoted by N1 and N2 measured as cells/L . Both strains can utilize S and Xex for growth but with the following important difference: N1 specialises on S , while N2 specialises on Xex . We use vg , i and vtca , i to denote the rate of glucose uptake and TCA cycle respectively while vrt , i denotes the rate of reversible transport of the metabolic intermediate in and out of the cell , where the subscript i = 1 , 2 refers to the two strains . The constants ng and nTCA denote the yield of ATP from glycolysis and the TCA cycle , respectively . In addition , by the very nature of cross-feeding , the strain specializing on the extracellular metabolic intermediate ( Xex ) possesses a high affinity enzyme for the uptake of Xex and the rate of this enzyme’s kinetics , which does not use ATP as cofactor , is denoted by vhat . Note that this term is not present in [25] as their model does not explicitly consider cross-feeding interactions . The competition dynamics of the two strains in the chemostat are described as dSdt=D ( S0−S ) −vg , 1 ( S ) N1−vg , 2 ( S ) N2dXexdt=vrt , 1 ( Xin , 1−Xex ) N1+ ( vrt , 2 ( Xin , 2−Xex ) −vhat ( Xex ) ) N2−DXexdN1dt=G ( vg , 1 ( S ) ng+vtca , 1 ( Xin , 1 ) ntca ) c1 ( Xin , 1 ) N1−DN1dN2dt=G ( vg , 2 ( S ) ng+vtca , 2 ( Xin , 2 ) ntca ) c2 ( Xin , 2 ) N2−DN2dXin , 1dt= ( 2vg , 1 ( S ) −vtca , 1 ( Xin , 1 ) −vrt , 1 ( Xin , 1−Xex ) ) N1−DXin , 1dXin , 2dt= ( 2vg , 2 ( S ) −vtca , 2 ( Xin , 2 ) −vrt , 2 ( Xin , 2−Xex ) +vhat ( Xex ) ) N2−DXin , 2 ( 1 ) where D represents the dilution rate and S0 is the concentration of resources in the input vessel . Note that in the last two equations a factor of 2 in front of the glucose uptake rate vg , i denotes a stoichiometric coefficient signifying that one hexose molecule is converted into two triose molecules . We parameterized this model using data on the biochemistry and ecology of E . coli using two cross-feeding strains: CV103 and CV101 grown in glucose-limited chemostats [5] . Strain CV103 best scavenges but incompletely metabolizes the primary ( limiting ) resource , glucose , while CV101 consumes a secondary resource , acetate , which is the overflow metabolite produced by CV103 ( see S1 Supporting Information for details of parameter values and the corresponding units of measurement ) .
Our parameterized system shows that CV103 is primarily a “fermenter” while CV101 is a “respirer” ( S1 Supporting Information ) . This is consistent with the findings of [28] and is further confirmed by measuring cell redox balance ( NADH/NAD+ ) in chemostat monocultures of the two cell types ( S1 Supporting Information ) . Experimental data on the growth of CV101 and CV103 in mixed cultures of the chemostat [5] showed that the two strains can be maintained over 30 generations . Our model successfully captures this outcome as it contains a stable cross-feeding steady state in which the two strains coexist in the long-term ( Fig 2 ) . Since the primary resource consumer ( CV103 ) specializes on glucose [4] while the secondary resource consumer ( CV101 ) specializes on acetate secreted by CV103 [5] , the outcome of their interaction depends on the levels of acetate in the culture . In particular , when the two strains were grown together in continuous mixed cultures the frequency of the competitors after 30 generations was strongly affected by the addition of acetate to the medium [5] . The addition of acetate into the culture leads to a decrease in the frequency of the primary resource specialist , CV103 and the model is able to qualitatively capture this result , as illustrated in Fig 3a . Finally , previous work showed that the frequency of competitors over 30 generations strongly depended on population density [5] with higher population densities achieved by increasing the concentration of the incoming limiting carbon source into the environment , providing growth advantage to the secondary resource specialist ( CV101 ) . Again , our model is capable of qualitatively capturing this outcome as shown in Fig 3b . The simplicity of the model assumptions enables a systematic exploration of the environmental conditions and their effects on the stability of cross-feeding interactions as described next . Having established that our metabolic population model is able to qualitatively capture the observed data , we ask a broader question: How stable are cross-feeding interactions for a wider variety of environmental conditions ? We address this question by carrying out a bifurcation analysis , which is classically used in mathematics to investigate the behavior of systems of differential equations like the one described in model ( 1 ) . Moreover , bifurcation theory is frequently used to study competitive interactions in biology [29–31] . The results of the analysis are presented as a bifurcation diagram ( Fig 2a ) , which shows steady states of our model as a function of a bifurcation parameter , namely the incoming primary resource concentration . Stable steady states are represented with a solid line and unstable states with a dashed line . The benefit of performing a bifurcation analysis is that we are able to systematically explore a large set of parameter values determining the steady-state outcomes of our model for all possible initial population frequencies and densities . Our analysis uncovers a complex picture ( Fig 2a ) arising from relatively simple assumptions regarding metabolic interactions of the competitors ( Fig 1 ) . In particular , for sufficiently high resource concentrations our system exhibits tri-stability which means that for a given resource concentration there are three possible competition outcomes depending on the initial population density and frequency of the two competitors . In that case , either of the two competitors can outcompete the other or both can coexist in a cross-feeding interaction ( Fig 2a , tri-stability region ) . By definition , a bifurcation diagram identifies all possible steady-states and their stability as a function of the bifurcation parameter , regardless of the initial population densities and frequencies . An illustration of the long-term model outcomes as a function of different initial population frequencies and densities for a fixed concentration of the incoming primary resource is shown in Fig 2b . To further illustrate the multi-stable dynamics of our system we plot time course solutions of our system ( 1 ) for different initial conditions . In particular we capture the hallmark of multi-stability whereby a small change in the initial conditions can cause a qualitative change in the behavior of the system . For example , the fitness of an initially rare secondary resource specialist ( N2 ) is frequency dependent ( Fig 4 ) , with small differences in initial frequencies giving rise to two outcomes: N2 decreases in frequency leading to its exclusion from the environment or N2 increases in frequency leading to coexistence between the competitors . For intermediate primary resource concentrations the system exhibits bi-stability where two outcomes are possible: either the primary resource specialist outcompetes the secondary resource specialist or vice versa . Which one is the case depends on the initial densities and frequencies ( Fig 2a , bi-stability region ) . For a certain range of low primary resource concentrations , the system again exhibits tri-stability where in addition to either strain outcompeting the other , cross-feeding is also possible ( Fig 2a , boxed plots ) . However , due to the narrow range of resource concentrations supporting cross-feeding in this case , it is unlikely that such outcome would be observed empirically . Decreasing further the primary resource concentration we found that only the primary resource specialist can persist ( Fig 2a , boxed plots ) . In addition , if the resource concentration in the input vessel is below a certain threshold , no competitor can survive; however this occurs at primary resource concentrations that are not clearly visible on the bifurcation diagram . Finally we note that the qualitative nature of competition outcomes presented in the bifurcation diagram in Fig 2a , including the existence of multi-stable steady states , is robust to changes in the model parameter values as discussed in S1 Supporting Information . Next we ask what are the key biological mechanisms that enable cross-feeding in our model ? It has been shown [32] that in simple environments cross-feeding is possible if the following two conditions are satisfied . First , there should exist a trade-off between uptake efficiencies on the primary and secondary resource niches and second that this trade-off takes a convex form . The trade-off convexity implies decreasingly costly investment [33] whereby initial improvement in uptake efficiency on a given resource leads to substantial decrease in uptake efficiency on the alternative resource , with subsequent improvements taking place at little or no additional costs . In general , it is known that the form of a trade-off determines the outcome of competition [34–36] . So is the existence of a convex trade-off between utilization of different resources enabling cross-feeding in our system ? While there is some experimental evidence for the existence of such a trade-off [5 , 37 , 38] its form has yet to be determined empirically . In fact , we argue that in our system this trade-off does not conform to a convex geometry for the following reasons . The secondary resource specialist abundantly expresses a high affinity enzyme ( acetyl CoA synthetase ) for the uptake of the secondary resource , acetate [5] . Doing so it substantially improves its ability to utilize this resource relative to the primary resource specialist which negligibly expresses this enzyme . However , the production of this high affinity enzyme by the secondary resource specialist comes at a relatively small cost in terms of its ability to utilize the primary resource; this can be observed by comparing the maximum glucose uptake rates of the two strains ( S1 Supporting Information ) and is a signature of a concave rather than a convex trade-off form [33] . Our model therefore shows that a convex trade-off between utilization of different resources is not a prerequisite for cross-feeding to be observed . Instead we highlight the importance of an additional mechanism for maintaining cross-feeding: a trade-off between rate and yield of primary resource utilization . Considered a thermodynamic [6] and biophysical ( Meyer et al . 2015 ) necessity , the rate-yield trade-off has been documented across microbial species [7 , 8] . In E . coli acetate overflow results not only in lost carbon , but also in production of a growth inhibitor [39] In our model the rate-yield trade-off is mediated by the glucose specialist secreting this toxic metabolite , acetate , which forms the secondary resource niche . For example , by being a “fermenter” ( S1 Supporting Information ) , the glucose specialist has a faster uptake rate of glucose than the acetate specialist who is a “respirer” ( S1 Supporting Information ) . On the other hand , the glucose specialist has a reduced yield of ATP production relative to the acetate specialist ( S1 Supporting Information ) . Further , the acetate specialist reduces the concentration of this toxic metabolic intermediate in the environment , thus benefiting the glucose specialist . However , removing the assumption that a secreted extracellular metabolite imposes a cost to growth does not destroy the existence of cross-feeding steady state . In this case higher concentration of the primary resource is required for cross-feeding to be observed . In the absence of a trade-off between utilization of different resources , the presence of the rate-yield trade-off does not guarantee coexistence of a respirer and a fermenter in simple environments , as shown in [25] . This allows us to conclude that the presence of both trade-offs is crucial for the maintenance of cross-feeding and for the complex density and frequency dynamics to be observed . Moreover , our model shows that convexity of the trade-off between utilization of different resources is not necessary to maintain cross-feeding , as originally thought [32] .
Simple environments , even those used in laboratory experimental evolution , have proven vastly richer than originally thought , capable of generating and supporting genetic and phenotypic diversity . This was not foreseen by the competitive exclusion principle [1] , which predicted that simple single niche environments cannot support diversity . A series of seminal laboratory studies [4 , 5] identified cross-feeding interactions between strains originating from a common ancestor as a diversity maintenance mechanism . Importantly , these studies showed that due to metabolic complexities within microorganisms , initially simple environments can quickly increase in complexity via the emergence of new resource niches , thus violating the simple assumptions of the competitive exclusion principle . We now know that such cross-feeding interactions are not just a feature of laboratory systems but are ubiquitous in natural microbial communities , microbial disease infections and even tumour cell populations . However , the precise conditions that promote or limit the emergence of diversity in simple environments are not well understood . To this end we developed a mathematical model that tracks , in time , the dynamics between two microbial strains: a strain that wastefully consumes the primary limiting resource , excreting a secondary metabolite on which another strain specializes . The complex metabolic processes were simplified and represented by a two-step metabolic model ( Fig 1 ) ; a similar framework has previously been successful in capturing a range of empirically observed microbial interactions [25] . Our model was parameterized using a well-established E . coli cross-feeding system ( Rosenzweig et al . [5] ) , and we showed it can qualitatively capture key experimental observations . In particular , cross-feeding interactions can be maintained in the system ( Fig 2 ) and are influenced by the level of secondary metabolite ( acetate ) excreted into the environment ( Fig 3a ) . As expected , the level of acetate in the environment is positively correlated with the availability of the primary resource ( glucose ) ; the higher the concentration of glucose , the higher the concentration of acetate that is excreted by the primary resource specialist ( S1 Supporting Information ) . Therefore high resource concentrations will favour the secondary resource specialist feeding on acetate ( Fig 3b ) . The relative simplicity of the mathematical model allowed us to systematically explore the stability of cross-feeding interactions across a range of parameters and initial conditions ( Fig 2 ) . We uncovered complex dynamics involving bi- and tri-stable steady states as well as the existence of critical points . This indicates that having a sufficiently high concentration of a primary resource , which gives rise to high concentrations of a secondary metabolite in the environment , does not guarantee that cross-feeding will be maintained . Instead , the emergence of cross-feeding in our model was frequency-and density-dependent , which is of particular relevance when one considers the type and order of specific mutants arising in an evolving system as we now discuss . In the empirical system motivating our theoretical model , cross-feeding emerged when a founder E . coli population was grown on a single limiting resource , glucose . Genetic changes that influence glucose assimilation arose early in this population [28] , and similar changes appear to have arisen in replicate populations [4] . Mutants with improved glucose assimilation , termed glucose specialists , were found to be the “engine” generating diversity , not because they produce new genetic variants , but because they create new niches via the wasteful consumption of the limiting primary resource , glucose [28] . The creation of new niches in the form of overflow metabolites opens the door to specialists that can profitably use these resources . How often do such interactions arise ? A small colony variant ( SCV ) phenotype typical of the glucose specialist arose in 11 of 15 independent chemostat populations studied by Helling et al . [4] . And in a majority of these ( 6 of 8 tested ) acetate scavengers evolved and rose to frequencies easily detected by colony screening [22] . Both phenotypes therefore commonly arise under continuous glucose limitation , and can do so within a few hundred generations . It is noteworthy that even ancestral E . coli incompletely metabolizes glucose , and leaves appreciable levels of residual acetate 194±20 nmol mL-1 [40] . Thus , there is an immediate selective advantage to any mutant that can access this secondary resource . In the case of experiments where SCV glucose specialists arise , this selective advantage is amplified , as mutations conferring this phenotype can result in higher levels of residual acetate . Also , beneficial mutations that enable increased glucose assimilation may arise in a lineage before it acquires the capacity to scavenge acetate . This clearly happened in the Kinnersley et al . experiments [28] . Phylogenetic analyses suggest that the glucose specialist and acetate specialist clades diverged early in evolutionary process: they share only one derived Single Nucleotide Polymorphism , while they differ by hundreds of SNPs , including different mutations that increase expression of LamB glycoporin . One , a MalK mutant ( 103 D297E ) , gave rise to the glucose specialist ( CV103 ) , while another , a MalT ( A53E ) mutant , gave rise to descendants able to scavenge acetate and other overflow metabolites . Recent work demonstrates that new beneficial alleles quickly proliferate under intense selection in nutrient-limited chemostats [41–43] . And in evolutionary experiments using the same ancestor described in [5] , population re-sequencing data reveal that new selectively favored mutations can increase in frequency from 0 to > 0 . 90 in only 100 generations ( Schwartz , Kinnersley , Sherlock and Rosenzweig Personal communication ) . While not an inevitable outcome of continuous glucose limited culture , acetate specialists may arise for a variety reasons , none mutually exclusive . For example , they may be favored at the onset , if sufficient residual acetate is present , or the causative mutations may hitchhike with novel alleles that enhance glucose transport . In the Helling et al . experiments the likelihood of these outcomes is increased by the facts that the ancestor is a mutator , and the key that unlocks the door to acetate scavenging by that ancestor is an unstable IS element in its acs operator [4] . But why don’t acetate specialists always emerge when overflow acetate creates a new niche [22] ? Predictions of our model provide possible answers . In particular the model possesses multi-stable steady states ( Fig 2a ) which , by definition , means that for a given glucose concentration , small changes in the initial frequency of the acetate specialist can lead to dramatically different competition outcomes as illustrated in Fig 4 . In general , density dependent multi-stable dynamics have been observed in a number of bacterial systems [29 , 44 , 45] where the authors reported dramatic differences in ecological and evolutionary outcomes between replicate populations . A direct consequence of multi-stability is the following observation: for a mutation that increases fitness via improved acetate assimilation to spread through a population , it has to emerge in a sufficiently large number of individuals . Given this , how can an acetate mutant ever become established ? Interestingly , we find that the higher the initial population density in an environment , the lower the minimal and maximal initial frequencies of the acetate specialist for which cross-feeding interactions form ( Figs 5a and 2b ) . After all , when the density of a glucose specialist , such as the genotype described in [28 , 40] , is increased by addition of glucose , there occurs a stoichiometric increase in concentration of the secondary resource , acetate . More amply supplied with this resource , a newly arisen acetate specialist can more rapidly increase to a higher frequency than it would otherwise , diminishing the likelihood that it fails to become established in the population . Consistent with this , our model also predicts that the higher the glucose concentration in the input vessel , the lower the initial frequency of acetate specialist has to be for the cross-feeding interactions to be observed ( Fig 5b ) . This means that for example , for a sufficiently high glucose concentration in the input vessel our model requires a frequency of 0 . 002 of acetate mutants in a population for the cross-feeding to become established . We now know that JA122 , the ancestral strain used as a founder by Helling et al . [4] , was a mutator , owing to nonsense mutation in the mismatch repair enzyme , MutY ( L299* ) [28] . The mutation rate of JA122 is nearly two orders of magnitude higher than that of wild-type strain K12 ( 1 . 00 x 10−7 vs . 3 . 6 x 10−9 /cell/generation ) , making it feasible that mutation required for cross-feeding could arise with a frequency of 0 . 002 in a sufficiently large population of JA122 . Interestingly , relative to K12 , JA122 acquired a regulatory mutation 93 nt upstream of acs , which encodes the acetate scavenging enzyme , acetyl CoA synthetase . In the system we have analyzed , abundant expression of acs has been traced to movement of a transposon , IS30 ( transposition rate ~5 x 10−6 /element/generation ) [46]; in independent evolution experiments constitutive activation of acs occurred via IS3 insertion mutations at -38 nt as well as via T➔ A transversions at -93 nt [22] Clearly , this system has the genetic potential to evolve cross-feeding; and indeed , where this potential has been realized secondary resource specialists arose early [28] and attained appreciable frequencies [22] . Our model also contains a critical point , predicting that small changes in environmental conditions can cause abrupt and irreversible shifts from cross-feeding interactions being established to not being possible ( Fig 3 ) . Critical points are a feature of many biological systems and recent studies showed that a combination of controlled laboratory experiments and mathematical models can be effective in determining the associated population dynamics [29 , 47] . At first glance , the basis for cross-feeding interactions seems intuitively obvious . Even in simple homogeneous environments where population growth is limited by a single primary resource , multiple competitors can coexist if additional niches open up . However , our mathematical model shows that complex density and frequency dependence dynamics govern both the establishment and maintenance of these interactions . Given that such complex multi-stable dynamics of cross-feeding exist in a simple chemostat model like ours , we hypothesize that they would also be found in systems with additional complexities such as temporal heterogeneities [48] . Unlike the chemostat where resource concentration and cell densities are maintained at a constant level , temporal heterogeneities are a feature of seasonal environments in which the cell population and the primary resource from which it draws sustenance profoundly change each day . Indeed , a hallmark of bi-stable dynamics has been observed in seasonal environments [49] as only 1 out of 6 replicate clonal populations evolved strong cross-feeding interactions which were maintained in the long-term [50] . Moreover , a theoretical model based on [32] showed that the observed cross-feeding interactions in seasonal environments give rise to linear negative-frequency dependence [51] . The lack of cross-feeding polymorphism in seasonal compared to chemostat environments was predicted in theory [32] , and it has been hypothesized that this could be driven by differences in cell densities and resource concentration between the two environments [48] . Namely , chemostat experimental studies where cross-feeding was observed [4 , 5] supported both higher concentration of the primary resource and higher cell densities than seasonal experiments [49] . In addition , we hypothesize that density and frequency dependent bi-stable dynamics could also be an important feature of seasonal environments whereby seemingly similar environmental conditions can lead to very different outcomes with respect to the evolution of cross-feeding , consistent with observations in [49] . Our model can readily be extended to make testable predictions regarding the stability of cross-feeding interactions over evolutionary time . Prior simpler theoretical models suggest that if cross-feeding emerges in a clonal population , it will lead to extreme specialization with one strain specializing on the primary resource and the other on the secondary [32] . However , the cross-feeding that evolved between E . coli strains described in both [5] and [48] is facultative; partners can grow independently on the primary resource but clearly exist over hundreds of generations as co-evolving lineages [28 , 52] . Experiments , informed by the modeling approach described here , can therefore be designed to address outstanding questions such as: how does this co-evolutionary process affect the boundary conditions for stable coexistence ? , what are the pre-requisites for cross-feeding to evolve further into syntrophy ( e . g . [53] ) ? , and under what conditions might we expect such interactions to increase the metabolic efficiency of a co-evolving system ( e . g . [54] ) ? The applications of our findings are broad . The origin and fate of genetic diversity are central organizing themes in biology , and trophic interactions may play a decisive role in promoting microbial diversity in extreme and/or nutrient-poor environments [55 , 56] . Furthermore , cross-feeding has relevance for human health: trophic interactions of this nature could help subpopulations in tumors and chronic infections become genetically heterogenous , providing thereby more ways for cells to evolve drug resistance and to escape immune system surveillance . Lastly , because extracellular metabolites contain information and can be co-opted as signaling factors , cross-feeding can set the stage for communication between pathogenic species in polymicrobial infections [57] . In light of the stochastic fluctuations that inevitably occur in both laboratory and natural settings , we argue that mathematical models are essential tools for disentangling the complexities of cross-feeding . | Simple environments , even those used in laboratory experimental evolution , have proven vastly richer than originally thought , capable of generating and supporting genetic and phenotypic diversity . This was not foreseen by Gause’s seminal competitive exclusion theory , which predicted that simple single niche environments cannot support diversity . We now know that cross-feeding interactions can be a major driver of diversity maintenance in simple environments . Cross-feeding , a relationship wherein one organism consumes metabolites excreted by another , is a ubiquitous feature of natural and clinically-relevant microbial communities and even tumour cell populations . However , it remains unclear how readily such relationships form , and therefore our ability to predict their emergence is limited . Here we developed a mathematical model of cross-feeding and find that this system can display complex dynamics including multi-stable behaviour separated by a critical point . Therefore , the emergence of cross-feeding depends on complex interplay between density and frequency of competitors . Moreover we predict that small changes in environmental conditions can cause abrupt and irreversible shifts from cross-feeding permissive to cross-feeding prohibitive states . We argue that mathematical models are essential tools for disentangling the complexities of cross-feeding interactions . | [
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"populat... | 2016 | Stability of Cross-Feeding Polymorphisms in Microbial Communities |
Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations . Yet , identification of causal variants within an established region of association remains a challenge . Counter-intuitively , certain factors that increase power to detect an associated region can decrease power to localize the causal variant . First , combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs . This tends to bias the relative evidence for association toward better genotyped SNPs . Second , re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions . However , using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag . Together these factors can reduce power to localize the causal SNP by more than half . Other strategies commonly employed to increase power to detect association , namely increasing sample size and using higher density genotyping arrays , can , in certain common scenarios , actually exacerbate these effects and further decrease power to localize causal variants . We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification , often doubling the probability that the causal SNP is top-ranked . Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked . This method is simple to implement using R scripts provided on the author's website .
The challenges of precise identification of disease-causing variants underlying GWAS signals have recently received much attention [1]–[3] . For post-GWAS statistical analysis that aims to accurately identify potentially causal variants , a major hurdle is the development of methods to distinguish disease-causing variants from their highly-correlated proxies . While GWAS-era statistical methods focused on identifying associated regions via tag SNPs at the coarse scale of GWAS arrays , next generation sequencing ( NGS ) technology offers the capability to not only detect associated regions , but to distinguish the causal SNPs within these associated regions . Here we make a distinction between ranking SNPs across the genome to identify an associated region , and ranking to pinpoint the potential causal variant within an associated region . Identifying an associated region requires that trait-associated SNPs be ranked above null SNPs , while identifying the causal variant requires that , among associated SNPs , associations due to causality are ranked above indirect associations due to other factors , e . g . linkage disequilibrium ( LD ) . GWAS and imputation studies typically report the top-ranked SNP for each associated locus , and follow-up studies typically attempt replication for these top-ranked SNPs ( for further discussion of ranking see Text S1 ) . Zaitlen et al [4] proposed a measure of performance for sequencing and fine mapping analysis , their localization success rate metric is the probability that the causal SNP has the top-ranked test statistic within an associated region . When multiple SNPs are in high LD , the localization success rate drops dramatically [5] . Udler et al ( 2010 ) investigated the difficulty in overcoming the stochastic effect of high LD among causal and non-causal SNPs [5] . The sample size required to distinguish the causal SNP can be 1 to 4 times the size required to detect the association at genome-wide significance . Zaitlen et al [4] showed that this problem could be overcome through joint analysis of samples from carefully selected populations with differing LD structure . Although candidate causal SNPs will require further bioinformatic or functional study to ultimately delineate potential causal mechanisms , optimized study design and analysis can point to the best possible candidate causal SNP ( s ) and help develop testable hypotheses about biological mechanisms . Studies of complex traits now underway are leveraging the cost efficiency of integrating GWAS , low- and high-coverage sequencing , and imputation to achieve sample sizes in the tens of thousands [6] , [7] . For example , the Genetics of Type 2 Diabetes ( GoT2D ) study is combining low and high-coverage sequencing with 2 . 5M-SNP GWAS genotyping and imputation to achieve a total sample size of over 28 , 000 [8] . Sequencing the GWAS sample exploits the GWAS findings to ensure that an association signal is present at the genome-wide level and eliminates the cost of recruiting new individuals . Analysis of sequenced and imputed SNPs ( post-GWAS data ) can thus be informed by previous GWAS results , allowing a prioritized use of post-GWAS data in fine-mapping regions surrounding significant GWAS tag SNPs [9]–[11] . Selection of associated regions for further studies can also be based on combined GWAS and post-GWAS criteria [12] , [13] . For example , the WTCCC [13] required a marginally significant ( p-value<10−4 ) GWAS SNP to support the evidence at a genome-wide significant imputed SNP . However , these strategies lead to two important issues that have received little attention in the context of causal SNP identification: ( 1 ) the effect of the re-use of successful GWAS data and ( 2 ) the effect of genotyping error rates that differ between sequenced or imputed SNPs . The re-use of GWAS data that had contributed to the identification of an associated region for post-GWAS analysis can adversely affect accurate causal SNP identification . For example , the simulation study of Wiltshire et al [14] showed that when a significant GWAS tag SNP is followed up by sequencing in the same sample , the tag SNP is in fact ranked higher than the true causal SNP 30% to 63% of the time , depending on the genetic model and effect size . When a GWAS tag SNP is selected based on small p-value , the magnitude of the association at the tag tends to be over-estimated; this form of selection bias is also known as the winner's curse [15]–[19] . To a variable extent , depending on the LD pattern , this selection bias is carried over from the GWAS tag to post-GWAS sequenced or imputed SNPs [20] . While this earlier work empirically demonstrated the effect of selection for a significant GWAS tag SNP on the causal SNP , no work to date explores whether it also affects the rank of the causal SNP among all neighboring SNPs within an associated region , and if so how to correct for the bias . High error rates and differences in error rates , due to differences in coverage , read length and depth , minor allele frequency ( MAF ) , GC content , local sequence structure , and other sequence-specific factors , are common to NGS SNPs and are well-recognized obstacles to analysis [21]–[29] . Error rates for low-read-depth sequencing studies are estimated to be 1%–3% [22] , [30] , [31] , and as little as 1% error can produce a large loss in power [27] . The strategy of low-coverage sequencing in a portion of GWAS samples has been used to discover sequencing variants and build a reference panel to drive imputation in the remaining samples , but the genotyping accuracy can be worse than if all individuals were sequenced [25] . The choice of lower-coverage design is also motivated by reports that low-coverage sequencing in a large sample , alone or combined with GWAS and imputation data , can achieve superior power to detect associations compared to high-coverage sequencing in a small sample with similar cost [25] , [29] , [32] , [33] . However , whether the localization success rate of the causal variants responsible for these associations is similarly high has not yet been examined . High error rates that differ among SNPs also occur in high-coverage sequencing; for example , within targeted high-coverage regions , highly repetitive elements can be difficult to capture resulting in low accuracy for some SNPs [34] . Differential genotyping accuracy between studies has been shown to reduce power of meta-analysis in the imputation setting [35] , and differential accuracy between cases and controls has been shown to cause confounding and elevated type I error [36] , [37] . Accounting for differential genotyping accuracy in the association test can recover some of the lost power and reduce type I error [35] , [36] . However , whether it affects our ability to distinguish causal SNPs from correlated SNPs , and how best to account for the effect of differential genotyping accuracy jointly for all SNPs ( GWAS tagged , imputed or sequenced ) is an open question . In this report , we first demonstrate that: We develop an analytic description of how these factors influence the probability of localization success and evaluate this probability for a range of plausible parameter values . We then show how to properly adjust for the adverse effects of these factors with a re-ranking procedure . We evaluate the performance of the method with extensive simulation studies under a wide range of realistic scenarios , and we demonstrate the practical use of re-ranking with an application to the NCBI BPC3 aggressive prostate cancer GWAS with imputation [38] .
Suppose that M sequenced ( or imputed ) SNPs , Si , i = 1 , … , M , in the region surrounding a significant GWAS tag SNP G are ranked by the magnitude of their association statistics in order to identify the causal SNP C . Table 1 provides the notation for the various parameters and statistics used throughout the report . Briefly , TSi is the Wald test statistic at a sequenced SNP Si; is the sample Pearson correlation coefficient between the GWAS/imputed/sequenced genotypes ( most likely or fractional allele dosage ) for SNPs G and Si ( r2 is the well-known pair-wise correlation measure of LD between two SNPs ) ; is the estimated correlation between the true genotype and the called genotype for a sequenced SNP Si ( we use correlation as a measure of genotyping accuracy because of its simple interpretation in terms of power and genotyping quality; this quantity is provided by both MACH [24] and BEAGLE [39] software ) ; and are proportions of samples with non-missing genotypes ( termed call rates ) at SNPs G and Si , respectively , and is the joint call rate , the proportion of samples with non-missing genotypes at both SNPs , and is the call rate at the causal SNP . Let be an estimate of the selection bias in genetic effect estimation at the tag SNP G ( described further below ) , that is the excess in the expected value of the test statistic at the tag SNP G induced by selection based on its small p-value ( or high rank ) . We call this phenomenon the selection effect ( ΔG is zero if the region was not selected via a tag SNP that achieved the given significance or ranking criterion in the same sample ) . Our proposed re-ranking statistic for a sequenced SNP Si is ( 1 ) Equation ( 1 ) depends on the selection effect , the tagging effect , the genotyping accuracy effect and scaling factors that depend on the call rates . Justification for Equation ( 1 ) now follows in the remainder of this section . ( Full details are provided in Text S2 . ) Without loss of generality , let >0 be the genetic effect ( e . g . the log odds ratio or the regression coefficient in the model relating the phenotype and genotype ) at the causal SNP C which could be: one of the sequenced or imputed SNPs Si , i = 1 , … , M; the GWAS tag SNP G although this is unlikely; or neither if the genomic coverage was incomplete . Let the tag SNP G be coded such that the coded allele is positively correlated with the causal allele . Let be the genetic effect estimate and be the estimated standard deviation ( SD ) of the estimate from n observations . We assume that the distribution of the Wald test statistic at the causal SNP , is approximately normal , , where . The following also applies to test statistics that are asymptotically equivalent to the Wald test statistic . Let be the difference between the observed test statistic and its expected value , ( 2 ) Here is the correlation between the genotypes of the causal C and the tag SNP G . ( We assume that the tag is coded so that it is positively correlated with the risk allele of the causal SNP . ) The value of is unobserved and needed only in the theoretical formation of the problem not in the practical implementation , which we discuss later . The selection effect is most pronounced when there is low power at the tag SNP . ( For discussion of this point see Text S3 ) . The conditional distribution of the test statistic TSi at the sequenced SNP Si , conditional on the value of the observed test statistic at the tag SNP G , is ( 3 ) Derivation of this distribution is detailed in Text S2 . The first term , , is the unconditional expected association signal at the sequencing SNP; the second term , , is the distortion due to the tag SNP selection propagated through correlation . Therefore , ΔG , the selection effect at the GWAS tag SNP G carries through to each sequenced SNP Si in proportion to the correlation between G and Si . The combination of attenuation due to LD and upward selection bias at the tag , ΔG , distorts the association evidence so that SNPs in high LD with the tag are more likely to be top-ranked . We call this phenomenon the tagging effect , and use an estimate to remove bias from the conditional expected value of in ( 3 ) . Third , differential call rates among SNPs ( , and ) and estimated genotyping accuracy ( is the estimated and is the actual correlation between the called genotype and true genotype ) of sequenced or imputed SNP Si appear in both the numerator and denominator of Equation ( 1 ) . In the numerator , the tagging bias , , is scaled by a factor of because correlation between the test statistics depends on the individual and joint call rates at the two SNPs ( see Text S2 for derivation ) . The bias-corrected statistic in the numerator is scaled by because ( 4 ) where is the correlation between the genotype of the causal SNP and the called or estimated genotype of the sequenced SNP ( in contrast to , for the true genotype of the sequenced SNP ) . Assuming the probability of genotyping error is independent of the actual genotype , then . It is clear that , without correction , smaller ρSi ( higher genotyping error ) and smaller ( higher missing data rate ) tend to lower the probability that SNP Si would be top-ranked . We call this phenomenon the genotyping accuracy effect .
To conceptually demonstrate the joint effects of selection , tagging and genotyping accuracy on the localization success rate ( the probability that the causal SNP is topped ranked within an associated region ) , we first consider the simplified case of 2 SNPs , one causal ( from sequencing or imputation ) and one tag ( from GWAS ) with correlation between the two SNPs ranging from r = 0 . 2 to 1 ( from almost no LD to perfect LD ) . The inclusion of low LD value is motivated by the fact that correlation between the causal SNP and the best tag is often lower than expected . The coverage of GWAS platforms tends to be overestimated for both sequenced and imputed SNPs ( see Text S4 for further discussion of this point ) . We assume that the MAFs of both SNPs are 0 . 12 , the causal SNP has an additive odds ratio ( OR ) of 1 . 25 , and selection at the tag SNP , if present , is based on its association test p-value<0 . 05 in a sample of 1000 cases and 1000 controls . Localization success rates ( before applying the proposed re-ranking procedure ) for all figures were computed based on Equations ( 2 ) – ( 3 ) and the equation in Text S3 and by numerically integrating over the following bivariate normal density function , ( 5 ) Analytical evaluations of Equation ( 5 ) were used to generate Figures 1–3 , which give insight into the relative influence of the tagging , selection , genotyping accuracy and sample size effects outlined in the Introduction and explicitly defined in Materials and Methods . We find similar patterns of influence for a rare SNP ( MAF = 0 . 02 , OR = 1 . 5; Figures S2 , S4 and S6 ) and a higher frequency SNP ( MAF = 0 . 25 , OR = 1 . 25; Figures S3 , S5 and S7 ) , and when the number of non-causal SNPs increases ( Figures S8 , S9 , S10 ) . The above analytical results demonstrate the need to correct for the joint effects of selection , tagging and genotyping accuracy on the localization success rate . The practical implementation of the proposed re-ranking statistic in Equation ( 1 ) is as follows . The estimated selection bias at the tag SNP G can be obtained using BR-squared that provides Bias-Reduced estimates via Bootstrap Resampling at the genome-wide level [40] , [41] . ( The original program , designed to provide estimates for the genetic effect β , has been modified slightly to provide estimates for the test statistic T; see software documentation on author's website for details . ) The bootstrap estimator can be applied whether the region of interest was selected by rank or by p-value threshold . Unlike the threshold-based likelihood and Bayesian methods [42]–[46] , the genome-wide bootstrap method incorporates information across the entire GWAS in order to account for the effects of LD and rank on the bias at each SNP . The values of the individual and joint call rates are available from the dataset , and genotype correlation can be estimated from the sample . Correlation between the actual and estimated genotypes at a sequenced SNP can be obtained from the mean posterior genotype ( e . g . MACH ratio of variances estimate , [24] ) or from the full genotype posterior probabilities ( e . g . BEAGLE allelic r2 estimate [39] ) . An R script that implements Equation ( 1 ) is available . The R script calls the BR2 software ( http://www . utstat . toronto . edu/sun/Software/BR2/ ) , which provides the essential quantity of if the original GWAS dataset was used for fine-mapping . We conducted extensive simulation studies to empirically evaluate the performance of the re-ranking method under five general scenarios ( Table 2 ) : The parameter values in Table 2 were chosen to best reflect realistic scenarios . For example , in order to address realistic tagging , we examined the Affymetrix 5 . 0 chip and identified the SNP that best captured each significant WTCCC T1D GWAS SNP . The correlation between the two SNPs ranges from r = 0 . 79 to 1 . For the range of genotyping accuracy , we note that in practice , the average sequencing ρ can vary substantially from study to study . For example , for low-coverage studies , it can vary from 0 . 63 to 0 . 99 depending on the coverage , MAF and sample size [25] . When low-coverage sequencing ( 4× ) and imputation are combined , the average ρ can range from 0 . 89 to 0 . 99 depending on the reference panel size [24] . Sequencing ρ also depends on MAF; the same error rate in a lower MAF SNP results in a smaller ρ . Even when the average ρ is high , SNP-level ρ can vary widely within a single study . Browning and Browning [39] found that imputation with a phased reference panel of 60 Hapmap CEU samples yielded a median ρ of 0 . 95 , however individual ρ was less than 0 . 77 for 20% of the SNPs . We show that coverage rates can also vary widely between SNPs ( Figure S1 ) by examining the 1000 Genomes low-coverage whole-genome pilot data from chromosome 1 in the CHB and JPT samples ( Figure S1; October 2010 release; 1000 Genomes Project , 2010 ) . We mimicked this variability in our simulations by randomly assigning each SNP in each dataset an error rate that ranged from zero to twice the overall average error rate . No random error however was introduced into the genotypes of the tag SNP ( ρG = 1 ) , because GWAS genotyping has been estimated to be over 99 . 8% accurate [13] , [47] . In order to ensure realistic correlation structure among post-GWAS sequencing/imputation SNPs , we examined all SNPs in the regions surrounding the WTCCC T1D significant SNPs using the HapMap3 dataset . The average correlation between adjacent SNPs in these regions was approximately 0 . 975 . One of the main findings of the simulation study is that GWAS-based region selection or moderate genotyping error can substantially reduce the probability of correctly identifying the causal SNP ( Tables 3–4 and Tables S1 , S2 ) , consistent with that of the analytical study . For example , results detailed in Table S1 demonstrate that the combined tagging and genotyping accuracy effect can reduce the localization success rate by over 30% . The simulation study also shows that the proposed re-ranking procedure can recover much of this lost power to identify the causal SNP , increasing the localization success rates by 1 . 5- to 3-fold in many cases ( Table 3 ) . When genotyping accuracy is high , the power lost due to tagging is small and so re-ranking tends to have little effect . For studies using GWAS-based selection ( scenario 1 ) , the adverse effects of tagging and genotyping accuracy on localization success rate are strongest when the causal SNP is well tagged ( larger r ) and less accurately sequenced/imputed ( smaller ρ ) ( Tables 3 , 4 and S1 ) . High-density GWAS followed up with low-coverage sequencing would fall into this category . Well-tagged causal SNPs tend to suffer from lower localization success rates because the perfectly genotyped tag often captures the association better than the imperfectly sequenced or imputed causal SNP . Re-ranking corrects this problem , so that the localization success rate does not depend on how well the causal SNP is tagged , except when the tag SNP is in fact the causal SNP . In this case , the tagging and genotyping accuracy effects actually increase the localization success rate . After re-ranking , the localization success rate is similar to levels seen when the tag is not causal . We consider this a minor tradeoff , because the causal SNP is unlikely to be found among the GWAS SNPs for a number of reasons: GWAS SNPs are typically selected independent of the phenotype of interest and post-GWAS SNPs tend to greatly outnumber GWAS SNPs . When the discovery sample is also used for fine-mapping , but significance is not required at the GWAS-tag SNP ( scenario 2 ) , the genotyping accuracy effect alone could still considerably reduce power to identify the causal variant ( Table 3 ) . When an independent sample is used for fine-mapping ( scenario 3 , Table 3 ) , localization success rates are very similar to those seen in scenario 2 . In both cases , the re-ranking method improves the probability of correctly identifying the causal SNP . The improvement is most pronounced ( 2- to 4-fold improvement ) when genotyping accuracy is low . When there is more than one causal variant ( scenario 4 , Table 3 ) , we find that re-ranking effectively increases localization success rates for both causal SNPs . Imperfect call rates affect localization success rate in a similar manner to imperfect genotyping accuracy ( scenario 5 , Table 4 ) . Equation ( 4 ) implies that a call rate of 0 . 80 should affect the distribution of the causal SNP test statistic in the same manner as a sequencing accuracy ρ of 0 . 89 , and this is borne out in our simulations . The re-ranking procedure corrects for both missing data and genotyping error to the same degree . In some cases , investigators are more interested in delimiting a set of best candidate causal SNPs instead of a single top SNP . In the supplementary material , we include additional simulation results for this scenario . We define an alternative localization success rate metric as the probability that the causal SNP is in the top 10% of SNPs by rank ( Table S2 ) . Briefly , we examine the probability that the causal SNP is among the top 5 SNPs when there are 50 total SNPs ( ranked by test statistic or re-ranking statistic ) . Without re-ranking , the probability that the causal SNP is in the top 10% of SNPs over the region is moderate . Re-ranking provides an improvement up to 1 . 8-fold . Machiela et al [28] used the August 2010 release of the 1000 Genomes Project European-ancestry ( EUR ) panel to impute 11 . 6 million variants in 2 , 782 aggressive prostate cancer cases and 4 , 458 controls . These subjects were genotyped as part of the NCI Breast and Prostate Cancer ( BPC3 ) Cohort Consortium aggressive prostate cancer GWAS [48] , [49]; genotyping platforms varied across the seven BPC3 studies , although all used versions of the Illumina HumanHap arrays and most used the Illumina HumanHap 610 Quad array . The correlation between imputed genotype dosage and genotypes thus varied across studies . Imputation and association analyses using imputed genotype dosages were conducted separately for each study , and the association results were combined via fixed-effect meta-analysis . For each imputed SNP , studies with imputation r2<0 . 8 were excluded from the meta-analysis test statistic , leaving a total of 5 . 8 million GWAS and imputed SNPs . Fine-mapping in the meta-analysis context ranks SNPs by the meta-analysis test statistic . Re-ranking requires that we compute the correlation between the meta-analysis test statistic on the Z-score scale ( i . e . normally distributed test statistic ) with and without accounting for genotyping error . Assume Zj is the normally distributed test statistic for study j , and wj is the weight for study j , the meta-analysis test statistic used for the standard naïve ranking isIf is an estimate of pair-wise correlation between the actual and imputed genotypes in study j ( e . g . the square root of allelic-r2 [39] , or ratio of variances r2 [24] ) , it follows that the estimated correlation between the meta-analysis test statistic computed with perfectly genotyped SNPs ( Zact ) and the meta-analysis test statistic computed with the observed imperfectly genotyped SNPs ( Zobs ) isThe re-ranking statistic in the meta-analysis case iswhere is the meta-analysis test statistic Z scaled for variance of 1 . Machiela et al [38] reported five statistically independent associated regions within the 8q24 . 21 locus and one for each of 11q13 . 3 and 17q24 . 3 . We selected all SNPs in LD ( r2>0 . 2 ) with the index SNP from each region for analyses ( Figures 4 and 5 , and Figures S11 , S12 , S13 ) . In the application , we first ranked SNPs using the naïve test statistics [38]; and excluded any SNP with MAF <0 . 01; but unlike Machiela et al [38] we did not exclude any studies . Machiela et al selected significant regions by examining all imputed and genotyped SNPs at once and so we corrected for the imputation accuracy effect only ( i . e . ) . Re-ranking identifies new top SNPs for 2 of the 3 associated loci: 8q24 . 21 and 17q24 . 3 ( Figures 4 and 5 respectively ) . In addition to the most significant region at 8q24 . 21 ( Figure 4 ) , re-ranking also identifies a new top SNP for the third most significant region ( Figure S11 ) . For both regions re-ranking also identifies SNPs that may have otherwise been missed due to imperfect imputation . After re-ranking , 2 SNPs in the most significant region at the 8q24 . 21 locus ( Figure 4 ) and 8 SNPs at the 17q24 . 3 locus ( Figure 5 ) move from the lower ranks into the top 10 percent . On the other hand , SNPs in the top 10% are moved down by only a few ranks . In this way , re-ranking keeps highly significant SNPs identified by the naïve ranking and adds a few SNPs that would have otherwise been missed . When the top test statistics are of similar size , re-ranking may identify a new top SNP . When most SNPs are well-genotyped , re-ranking makes only subtle changes ( Figure S11 , S12 and S13 ) . There is one poorly imputed SNP at 17q24 . 3 ( rs1014000 , r2 = 0 . 20 ) that moves from the naïve rank of 245 to the new rank of 16 after adjustment . This SNP's apparent association is largely driven by data from a single study: the naïve rank in the EPIC study is 10 . When we remove this study from the meta-analysis , the naïve rank is 306 and the adjusted rank is 119 . No other SNP in the top 10% is this drastically affected when the EPIC study is removed from the analysis . In the meta-analysis context , we recommend examining top SNPs for heterogeneity among studies when re-ranking produces dramatically different results .
Overall , we observed that the tagging and genotyping accuracy effects are non-trivial sources of bias that could obscure association evidence at the causal SNP . The proposed re-ranking procedure is simple to implement and can substantially increase the probability of identifying the causal SNP . For low-coverage sequencing , we recommend the re-ranking method to improve causal SNP identification . For imputation and high-coverage sequencing , we recommend that unfiltered SNPs in associated regions be examined to see if correlation varies across SNPs and if so , we recommend adjustment with the re-ranking method . Large changes in rank should be carefully examined for underlying issues such as heterogeneity among meta-analysis studies or differential accuracy between cases and controls , and procedures to correct for these issues should be incorporated . Re-ranking is most beneficial when genotyping accuracy is moderate to low , that is , the average correlation between the actual and estimated genotypes of post-GWAS ( sequenced or imputed ) SNPs is less than 0 . 97 . A large number of post-GWAS SNPs in a study may appear to be significant , but when not all were directly genotyped with high accuracy , re-ranking can help select the most probable causal SNPs for follow-up . High density genotyping followed by low-coverage sequencing in the same sample can produce misleading results , as demonstrated by our simulations , so we do not recommend this design for identifying causal variants . Our re-ranking method tends to down-rank the tag SNP . If the tag SNP is suspected to be causal ( e . g . based on prior study ) , we recommend examining the rank of the tag SNP using both the naïve and re-ranked methods when selecting SNPs for further study . Several imputation and sequencing software packages provide accurate estimates of ρ or quantities from which ρ can be computed [24] , [39] . Re-ranking depends on accurate estimates of ρ . Recalibration of sequencing quality scores can greatly improve accuracy and so we recommend this step prior to re-ranking [27] . Re-ranking is especially important when study-specific factors exacerbate the effects of GWAS-based selection and genotyping error . Such factors include: high genetic diversity which makes sequencing reads difficult to align [27]; low LD among SNPs or lack of population-specific reference panels which makes some populations particularly difficult to impute ( e . g . some African populations [50] ) ; and imputation error which can be as high as 10% for these populations . Low MAF SNPs tend to suffer from both low power ( which exacerbates the tagging effect ) and high genotyping error . Re-ranking can be applied to rare and low MAF SNPs with allele counts large enough for test statistics to reach asymptotic normality . Very low ( 1×−2× ) and extremely low ( 0 . 1×−0 . 5× ) read depth sequencing has received recent attention as a way to maximize cost efficiency and make use of off-target sequencing data [29] , [32] . Error rates for such regions would be both very high and highly variable among SNPs and so re-ranking to account for errors in the estimated genotypes would be crucial . When genotyping accuracy is extremely poor , the re-ranking method may not be able to sufficiently improve the localization success rate to ensure useful results . We recommend that investigators consider the accuracy thresholds recommended by the genotype calling or imputation algorithm they are using before re-ranking is applied . We emphasize that re-ranking improves the localization success rate when applied to SNPs under the alternative , i . e . SNPs that are themselves causal or in LD with a causal SNP . Including null SNPs in the re-ranking procedure increases the number of SNPs the causal must out-compete , and so we recommend that only SNPs suspected to be under the alternative be included . In our application we included all SNPs that had squared pairwise correlation ( r2 ) with the index SNP ( most significant SNP in the region ) greater than 0 . 2 . Existing methods that incorporate genotype uncertainty into tests for association to reduce power lost due to genotyping error or missing data [e . g . 51]–[54] do not completely recover lost power , and so the genotyping accuracy effect will remain . The simplest way to deal with genotype uncertainty in a test is to use the expected additive genotype ( i . e . the posterior mean or dosage ) in the standard linear or logistic regression . In this case , the re-ranking method can be applied using the allele dosages in place of called genotypes as described above . Guan and Stephens [55] compared several frequentist and Bayesian methods that incorporate genotype uncertainty into tests for association . The re-ranking procedure could be extended to any case where the correlation between test statistics or Bayes factors can be worked out . We expect that re-ranking will play an important role as sequencing costs fall and GWAS platform coverage increases . Ultra-high density GWAS platforms are more likely to include tag SNPs in very high correlation with the causal SNP , which increases power to detect indirect association at the tag SNP . However , without re-ranking , strong tagging also decreases power to correctly identify the causal SNP in subsequent low-coverage sequencing . Advances in GWAS and sequencing platforms will allow researchers to drill down into lower MAFs and smaller effect sizes . Both low MAF and small effect size yield lower power , which exacerbates upward bias at the tag [20] and , therefore , the adverse tagging effect . Low MAF SNPs tend to suffer from higher error rates , which exacerbates the genotyping accuracy effect . Association study sample sizes will therefore need to continue to increase , so even as sequencing costs fall , it is anticipated that low-coverage will continue to be the most cost-effective design for many studies , despite the high genotyping error rates [27] . In conclusion , we anticipate that re-ranking to correct for the adverse effects of selection , tagging and differential genotyping accuracy rates among SNPs will continue to be important in candidate causal SNP identification for some time . | As next-generation sequencing ( NGS ) costs continue to fall and genome-wide association study ( GWAS ) platform coverage improves , the human genetics community is positioned to identify potentially causal variants . However , current NGS or imputation-based studies of either the whole genome or regions previously identified by GWAS have not yet been very successful in identifying causal variants . A major hurdle is the development of methods to distinguish disease-causing variants from their highly-correlated proxies within an associated region . We show that various common factors , such as differential sequencing or imputation accuracy rates and linkage disequilibrium patterns , with or without GWAS-informed region selection , can substantially decrease the probability of identifying the correct causal SNP , often by more than half . We then describe a novel and easy-to-implement re-ranking procedure that can double the probability that the causal SNP is top-ranked in many settings . Application to the NCI Breast and Prostate Cancer ( BPC3 ) Cohort Consortium aggressive prostate cancer data identified new top SNPs within two associated loci previously established via GWAS , as well as several additional possible causal SNPs that had been previously overlooked . | [
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] | 2013 | Re-Ranking Sequencing Variants in the Post-GWAS Era for Accurate Causal Variant Identification |
MicroRNAs ( miRNAs ) suppress gene expression by forming a duplex with a target messenger RNA ( mRNA ) , blocking translation or initiating cleavage . Computational approaches have proven valuable for predicting which mRNAs can be targeted by a given miRNA , but currently available prediction methods do not address the extent of duplex formation under physiological conditions . Some miRNAs can at low concentrations bind to target mRNAs , whereas others are unlikely to bind within a physiologically relevant concentration range . Here we present a novel approach in which we find potential target sites on mRNA that minimize the calculated free energy of duplex formation , compute the free energy change involved in unfolding these sites , and use these energies to estimate the extent of duplex formation at specified initial concentrations of both species . We compare our predictions to experimentally confirmed miRNA-mRNA interactions ( and non-interactions ) in Drosophila melanogaster and in human . Although our method does not predict whether the targeted mRNA is degraded and/or its translation to protein inhibited , our quantitative estimates generally track experimentally supported results , indicating that this approach can be used to predict whether an interaction occurs at specified concentrations . Our approach offers a more-quantitative understanding of post-translational regulation in different cell types , tissues , and developmental conditions .
miRNAs are short ( 22 nt ) endogenous RNAs that exert regulatory control of many cellular processes by suppressing specific mRNAs via complementary base-pairing at a specific target site [1] . It has been suggested that a miRNA can use at least two distinctive mechanisms to regulate protein-coding genes: “switching-off” the entire function of the target gene , and “tuning” the expression level of multiple target genes within appropriate ranges [1] . In the former case , a miRNA reduces the expression of the target mRNA to a level at which the gene can no longer function , potentially leading to observable phenotypes including cell death or abnormal cell phenotypes [2] , [3] . In the latter case , a miRNA alters the expression of hundreds of genes to various degrees , maintaining cellular functionality [4] . Each miRNA-mRNA interaction is affected differently by the strength of miRNA-mRNA binding and by the concentration of each interacting species . For example , a specific miRNA might bind to a specific mRNA only if present in high concentration . In tumor cells , some miRNAs are expressed at unusually high or low concentrations [5] and thus may bind more or less extensively to specific mRNAs than in normal cells . The regulation a miRNA exerts on a specific target may also be altered if the concentration of the target mRNA changes during differentiation or development , or as the result of changes in the surrounding environment [6] . Current miRNA prediction methods can predict whether a specific miRNA binds to a specific mRNA , but do not predict whether and how these interactions vary under different concentrations . In this study , we aim not only to predict miRNA-mRNA interactions , but also to estimate their quantitative extent as a function of RNA concentration . Several distinct algorithmic approaches have been developed to predict miRNA targets . Most require more-or-less stringent base-pair complementarity across a “seed” region ( nucleotide positions 2–7 or 2–8 from the end of miRNA ) for miRNA-mRNA duplex formation to be predicted , as implemented in widely used prediction methods such as EMBL [7] , miRanda [8] , PicTar [9] , PITA [10] and TargetScan [11] . Suppression of a target mRNA by a miRNA is mediated by a protein complex referred to as the RNA-induced silencing complex ( RISC ) . A recent study of the crystal structure of this complex shows that the seed region is tightly bound to the complex , emphasizing the importance of seed-matching in recognizing the target site [12] . Other studies show that the efficiency of RNA-RNA ( including miRNA-mRNA ) interaction is positively correlated with physical accessibility of the target sites [13] , [14] . RISC by itself cannot unfold a structured region of mRNA to present a potential target site for interaction with miRNA , although it can promote RNA-RNA annealing [15] . Thus the specificity of miRNA-mRNA interaction involves ( at least ) two factors: base-pair complementarity between the two interacting RNA species ( especially at the seed region ) , and local folded structure of the potential target mRNA . Target-site accessibility can be assessed in reference to the change in structural energy of the ( folded ) mRNA before and after a potential target site is opened for interaction with a miRNA . This has led to a two-step hybridization reaction model: first the target site is opened ( unfolded ) for interaction , then an RNA-RNA duplex is formed at the site [16] . Computational methods to predict mRNAs targeted by miRNAs based on this two-step thermodynamic model have been developed [10] , [14] . Here we extend this two-step hybridization reaction model by incorporating another set of factors which critically affect the existence and extent of miRNA-mRNA interactions: concentrations of the interacting molecular species , miRNA and mRNA . On this basis we develop a new method that can estimate the quantitative extent of the interactions . We calculate the equilibrium concentrations of the unbound miRNA , unbound mRNA , and miRNA-mRNA duplex from the initial concentrations of the interacting species and free energies of the interactions . We apply our method to a set of Drosophila melanogaster miRNA-mRNA interactions that have been experimentally tested ( including interactions that were successfully confirmed , and those that failed to receive experimental support ) , and to a set of experimentally supported miRNA-mRNA interactions in human . First , we compare the ability of our method to predict target sites as assessed by sensitivity and specificity , to other methods under the same initial concentrations . Then we test the ability of our method to estimate the degree of interaction ( i . e . to predict functionally relevant target sites ) at the same initial miRNA concentrations used for experimental confirmation . We show that our method can predict target sites at specified concentrations with high accuracy , and that our quantitative estimates generally correlate with experimental results . We also show that some miRNAs can at low concentrations bind to target mRNAs , whereas others are unlikely to bind within a physiologically relevant concentration range .
Our method consists of three independent components . First we search for potential target sites by predicted free energy of the miRNA-mRNA duplex , rank these results by energy score , and filter this list requiring presence of a seed match . Second , for each identified potential target site , we compute the thermodynamic parameters described in the two-step model [16] . Then we compute the final concentrations of miRNA-mRNA , and the net free energy change ( ) of the interaction based on the initial concentrations of the RNAs . Instead of the free energy change ( ) used in the two-step model [16] , we use the net free energy change ( ) to evaluate the interaction at given initial concentrations ( see Materials and Methods ) . The net free energy change indicates whether a specific interaction occurs . If no interaction occurs between the two species ( miRNA and mRNA ) , the net free energy before and after the interaction does not change , i . e . the net free energy change is zero . If an interaction occurs between the miRNA and mRNA , the change will be always negative . We used FASTH [17] , which is computationally scalable for application to transcriptome-scale data , to search for potential target sites and to compute hybridization energies of the miRNA-mRNA duplexes , and UNAFold [18] , which adopts the same energy calculation model [19] used in FASTH , to compute mRNA folding energies . Here we introduce Ensemble_Calc to compute the final concentrations of miRNA and mRNA , and the net free energy change ( ) of interaction . The source code of Ensemble_Calc is available at http://mfold . rna . albany . edu/Ensemble . The number of copies of an individual mRNA species present in a single cell is considered to vary over four orders of magnitude ( 1 to >1000 copies ) , with most present in <100 copies but a few exceeding 1000 copies [20] . Individual miRNA species are likewise considered to vary widely in copy number per cell , with a few tissue-specific species present more than 10 , 000 copies per cell [21] . Although miRNA expression varies widely from one miRNA to another , miRNAs are more abundant ( average 500 copies per cell ) than mRNAs [21] , and this abundance can help explain the co-regulation of a target mRNA by several miRNAs , and the regulation of multiple mRNAs by a single miRNA . Thus mRNA concentrations in a typical animal cell ( 1000–25 , 000 volume ) can be as low as about 80 pM ( 1 copy in a 25 , 000 cell ) or can exceed 2 . 2 ( 1000 copies in a 1000 cell ) , while miRNA concentrations can exceed 22 ( 10 , 000 copies in a 1000 cell ) ( see Material and Methods ) . We applied our model to the set of 190 experimentally tested miRNA-mRNA interactions in Drosophila melanogaster reported by Kertesz et al . [10]; this set contains both interactions that were successfully confirmed , and those that failed to receieve experimental support . Reporter vectors are usually used to examine whether a miRNA directly represses the expression of a target mRNA by binding to a putative site . Most targets in Drosophila have been examined experimentally using reporter vectors , usually with the full-length UTR sequence inserted into the vectors [10] , [14]; thus their in vivo efficiency has been assessed against target structures that are , broadly , similar to those of the native mRNAs . We computed structural energies by folding entire mRNAs where possible; for longer mRNAs it was computationally feasible to fold only the UTR or part of the UTR region ( see Materials and Methods ) . Here we assume an initial concentration of 1 for each miRNA and each mRNA species ( see above ) , and follow common practice in requiring that the predicted mRNA concentration must be reduced by at least 30% for the interaction to be considered functionally relevant ( and thus for our prediction to be considered successful ) . In the following sections we use this criterion as a benchmark to compare with other methods . If we could not identify target sites during the initial search , we assume that no interaction occurs . Using this criterion , our approach recalls 74 ( 73% ) of 102 experimentally confirmed fly miRNAs ( Figure 1A and Table S1 ) . Of 88 miRNA-mRNA combinations in fly for which experimental assay failed to confirm an interaction , we were able to obtain the target mRNA sequence from NCBI RefSeq for 84 , and for these predict that 52 ( 62% ) do not have a site that is actively bound ( Figure 1B and Table S1 ) ; based on these data , the sensitivity and specificity of our method are 0 . 73 and 0 . 62 respectively . Since potential sites on an mRNA are usually predicted as either functional ( able to be bound by a small RNA , e . g . a miRNA ) or non-functional ( unable to be bound ) , Kertesz et al . [10] applied the standard area under the curve ( AUC ) to evaluate the sensitivity and specificity of selected existing prediction methods . They observed the highest true-positive rate , 0 . 79 , when the false-positive rate is 0 . 40; other methods yield true-positive rates of 0 . 64 ( MiRanda: [22] ) , 0 . 71 ( PicTar: [23] ) and 0 . 74 ( EMBL: [24] ) at the same 0 . 40 false-positive rate . We obtained a similar result , observing a 0 . 73 true-positive rate at 0 . 38 false-positive rate , using the above criteria . We also investigated whether experimentally supported miRNA binding sites on human mRNAs are predicted using our approach . Unlike the situation in fly ( above ) , human target sites have often been experimentally confirmed by inserting into the reporter vector only the site under investigation , together with short flanking sequences; therefore the energetics of the native mRNA structure has probably not been properly captured in these experiments . Hence , we selected for comparison 147 target sites for which further functional evidence is available . These sites were manually collected ( Table S2 ) based on the following two criteria: the experiment had to be conducted using a reporter gene , and additional validation , such as evidence of inverse correlation between miRNA and target protein expression levels , had to be provided . Of these 147 sites , we predict 106 ( 72% ) to bind to their targets using the criteria described above ( Figure 2A and Table S2 ) . The predicted interactions vary substantially , in extent and properties , among this set of miRNA-mRNA pairs . As shown in Figure 2B , some of these interactions are highly vulnerable to change of concentrations , whereas others are more robust . Furthermore we find that many interactions that can yield a low-energy ( strong ) duplex are not predicted to do so at typical physiological miRNA concentrations ( up to 2 ) . Until this point we have assumed equal concentrations for both miRNA and mRNA; however , as described earlier , their concentrations are unlikely to be equal . Therefore , we compared the effect of interactions of different initial concentrations of miRNA and mRNA , focusing on situations in which the concentration of miRNA is tenfold greater than that of the mRNA . As shown in Figures 2B and 2C , at 10∶1 we predict slightly greater duplex formation ( i . e . greater reduction of the level of unbound mRNA ) than at equal concentrations ( 1∶1 ) . The differences occur mostly at the highly efficient target sites , where mRNA concentrations are reduced by more than 50% . Target sites with more-moderate efficiency , where the estimated mRNA reduction is 30% , show similar reductions regardless of the ratio of initial concentrations of the two RNA species . Particularly for mRNAs with target sites that saturate quickly , there can be limited scope to reduce their concentration further by increasing the ratio of miRNA to mRNA; increasing the concentration of both species tenfold from 100∶100 to 1000∶1000 ( Figure 2B ) reduces the mRNA concentration proportionally more than does decreasing the mRNA relative to miRNA from 100∶100 to 100∶10 ( Figure 2C ) . The total concentration of the miRNA has a greater effect on extent of interaction than does the ratio of concentrations . For some of these experimentally confirmed miRNA-mRNA interactions , we predicted that a single miRNA binds to more than one target site on the UTRs of a single mRNA , and/or to different transcripts from the gene; in these cases , we use for Figures 1 and 2 the site that yields the greatest reduction in mRNA concentration . Details of predicted target sites with their free energy scores and equilibrium concentrations of unbound miRNA , unbound mRNA and duplex are presented in Tables S1 and S2 , and the references are presented in Text S1 . Among the experimentally supported targets described above in human , miRNA concentrations used for experimental confirmation were reported for 41; one target was confirmed using two different miRNA concentrations ( Table S3 ) . These concentrations ranged between 2 . 5 nM and 300 nM . We tested our model on these 42 interactions , using the reported miRNA concentration and setting the mRNA concentration to be the same . The 41 experimentally supported targets include six sites that we did not recover in our initial search , and four that were recovered but were not predicted to be bound at 1 miRNA ( above ) and are therefore not expected to bind miRNA at lower experimental concentrations . These ten are shown in dark blue in Figure 3A . If we require that the predicted mRNA concentration be reduced by at least 20% for the interaction to be considered functionally relevant ( the same minimum requirement used in the confirmation experiments: [25] ) , we predict 29 out of 42 interactions ( 69% ) successfully at the miRNA concentration used in each experiment ( Figure 3A ) . In addition to the 10 targets mentioned in the previous paragraph ( shown in blue ) , for three further experimentally supported interactions we predicted that the mRNA concentration is reduced by less than 20% ( shown in light blue , Figures 3A and 3B ) . For the remaining 35 sites ( 36 interactions with unique miRNA concentrations ) we recovered , the predicted degree of mRNA reduction generally tracks experimental results , with many successfully predicted target sites falling within ( or very close to ) 20% of the reported level of reduction ( Figure 3B ) . In cases where we predict that a single miRNA binds to more than one target site on the UTR of a single mRNA , and/or to different transcripts from the gene , in Figures 3A and 3B we show only the most energetically favorable interaction . Details of the target sites and associated information are available in Table S3 , and the references are available in Text S1 . In vitro confirmation experiments are normally carried out in triplicate , and the degree of mRNA reduction of each repeated experiment can vary 20% from the mean value reported within each study [26] . The degree of mRNA reduction can differ >20% for the same interaction in different type of cells [27] . For the experiments that reported the different mRNA reduction levels in different cell types , we compare our estimates against the mean value of these reported levels ( Figure 3B and Table S3 ) . Since total mRNA concentrations are rarely reported , we set all mRNA concentrations to equal the corresponding miRNA concentrations and examined miRNA-to-mRNA concentration ratios up to 10∶1 . As shown in the previous section , in this range our predictions are robust , as assessed by percent recall . As our approach is based on thermodynamic principles , we anticipate its continued applicability under a broader range of physiologically relevant conditions . Although mRNA destabilization is the major contributor to the repression of target-gene activity [28] , [29] , some miRNA-mRNA interactions repress translation without destabilizing the mRNAs . Therefore for some interactions , the level of unbound mRNA level may be lower than reported in these experiments . For three sites ( shown in orange in Figure 3B ) , we predicted a much greater degree of mRNA reduction than was reported in the in vitro confirmation experiments . Our method estimates the extent to which an mRNA is bound , but cannot predict the outcome of this binding , i . e . whether the bound mRNA may or may not be degraded or its translation inhibited . As shown above , using the benchmark criteria , the predictive power of our method is similar to those of other methods . We compared the target sites predicted by our method and by miRanda [8] , PicTar [9] , PITA , PITAtop [10] , and TargetScan [11] on UTRs of human RefSeq mRNAs , using 150 miRNAs for which the targets are predicted by all methods described above . In general , the proportion of overlap among sets of targets predicted by different methods reflects the selection criteria adopted by each method . All of the methods compared here , except PITA , use seed-matching and conservation of target sites across different species as selection criteria , although the definition of seed and the degree of conservation may vary among methods . Therefore the proportion of overlap among their predictions is relatively high ( 13–77% ) ( Table S4 ) . miRanda predicts the largest number of targets; 53% of PicTar , 77% of PITAtop and 51% of TargetScan targets are also predicted by miRanda . Maximum overlap is observed between predictions of PITAtop and TargetScan , where over 40% of their predictions overlap . Our method uses seed-matching but not site-conservation as a selection criterion . The overlap between our method and other methods ( 11–41% ) is lower than among other method ( 13–77% ) . PITA , like our method , incorporates site accessibility into its searching mechanism , but the predictions of PITAtop ( a list of top predictions produced by the PITA algorithm ) include only conserved sites . Overlap between our method and PITAtop is not different from that with other methods compared here . PITA assesses site accessibility; however , the set of PITA targets contains the target sites with positive free-energy changes ( ) described by the two-step model [16] . We discard those for which the predicted free energy change is 0 , then match to the remainder those targets predicted by our method and others . About 91% of our predicted targets are predicted by PITA ( overlapped by a PITA-predicted target ) , slightly higher than for the other methods investigated ( 85–89% ) . We summarize these comparisons as shown in Table S4 , and the list of our predicted targets is available in Table S5 . We also compared the miRNA targets predicted by five computational methods ( including our own ) with those identified by Hafner and colleagues [30] using PAR-CLIP . In this approach , cellular mRNAs are cross-linked with the AGO protein complex , and the protein complex is immunoprecipitated; sites of cross-linkage can be revealed by thymidine-to-cytidine transitions in the corresponding cDNAs , and nearby regions of reverse complementarity to miRNA seeds are interpreted as miRNA targets . Applying PAR-CLIP to HEK293 cells , Hafner et al . [30] found putative target sites for 98 of the 100 most-abundant miRNAs . Most ( 72% ) of the putative sites identified in this way are imperfectly complementary to miRNA seeds , i . e . contain a mismatch or bulge . From these 98 we selected the 68 for which target predictions are available from PicTar , miRanda , PITAtop , TargetScan and from our approach ( Table S6 ) , show a perfect WC match at nt 2–7 at the 5 end of miRNAs , and whose clusters can be mapped into UTRs of RefSeq mRNAs ( i . e . we compared unique miRNA-mRNA target combinations ) . PAR-CLIP predicts many fewer targets than does any of the computational methods discussed here , with overlap ranging from 1 . 71–1 . 87% ( PicTar , PITAtop and TargetScan ) to 1 . 31% ( our method ) to 0 . 87% ( miRanda ) of the computationally generated predictions . miRanda recovers the largest proportion of PAR-CLIP targets ( 50% ) from 974 predictions; PicTar , PITAtop and TargetScan 29–45% from 566–864 predictions; and our method 20% from 393 predictions . As miRNAs in the PAR-CLIP dataset are highly expressed in HEK293 cells , their concentrations may be greater than our default 1 . At higher initial miRNA concentrations and the same 30% mRNA reduction threshold we predict 24% ( at 2 ) and 27% ( 4 ) of the PAR-CLIP targets , with this improvement obviously accounted for by lower-affinity sites . Sites with perfect WC matches at nt 2–8 yield similar results as those with WC matches at nt 2–7 ( Table S6 ) . Although some of the PAR-CLIP putative target sites that contain WC matches at nt 2–7 may be non-functional , our quantitative results are consistent with the idea that miRNA control is transduced in part through imperfectly complementary sites on mRNAs .
We have developed a computational model that can provide quantitative estimates of RNA-RNA interaction as a function of the concentrations of the interacting species , and have applied our model to predict miRNA-mRNA interactions . Few target sites have been reported with the concentration of total miRNA used in the experiments , necessarily limiting the evaluation of our method . Except as otherwise indicated , we have based our predictions on 1 miRNA ( 500 copies/cell ) [21] , and require mRNA levels to be reduced by >30% for the prediction to be considered successful . First we applied our method to experimentally tested Drosophila miRNA targets , where positive as well as negative experimental results are available . We predicted these targets with 0 . 73 sensitivity and 0 . 62 specificity . By these measures the predictive power of our method is similar to that of these other , widely used methods . Next we applied our method to experimentally confirmed targets in human , and showed that we can achieve similar sensitivity ( 72% ) . Then we demonstrated how our method can predict targets at different miRNA concentrations . Using the subset of experimentally suported targets for which total miRNA concentrations are available , we predicted 69% of targets correctly at the specified concentrations and mRNA reduction ( requiring >20% reduction ) . We also showed that our quantitative estimates generally correlated with experimental results; most estimates fall within ( or very close to ) 20% of the experimentally corroborated level . Both known and unknown factors affect miRNA-mRNA interactions and make quantitative estimation difficult . One factor that directly affects the interactions is the cooperative effects of multiple target sites . A target mRNA bound simultaneously by more than one miRNA may show greater repression than one bound at a single site . However , reports suggest that cooperative effects occur only on target sites that are physically proximate , 40 nucleotides apart [31] , [32]; thus the majority of regulation may be transacted independently through single binding sites . The second factor that directly affects the interaction is the competition among the interactions . Co-expressed miRNAs ( and/or other ncRNAs ) can likewise compete for mRNA targets; while the binding of each may individually be weak , in a cell in which very many miRNAs are co-expressed there may be a cumulative off-target effect . The different degrees of mRNA reduction observed for the same identified interaction in different cell types , or resulting from transfection of artifical small interfering RNAs ( siRNAs ) [33] are good examples of these effects . In this study , we present a model of interaction in a simple system that contains two species ( one miRNA , one mRNA ) that is not able to capture the broad regulations in a systemic way; it will be useful to extend the model to predict the interactions of multiple species of miRNAs and mRNAs simultaneously . Another factor that may affect the interactions is the self-folded structure of mature miRNA . Since miRNAs are short , the secondary structures of most mature miRNAs are unstable . However a small number of miRNAs can fold into stable structures ( hairpins ) , or can form a homo-dimer duplex with each other [34] . There is also evidence that the secondary structure of ( mature ) guide siRNA ( not precursor structures ) also influences the efficiency of siRNA-mRNA interference , where unstructured guide siRNAs confer stronger silencing abilities than structured guide siRNAs [35] . Although our model can incorporate the structure of small RNAs such as miRNA into the free-energy calculation , in this study we did not take secondary structure of each miRNA into account ( the structural energy was set to zero ) , as miRNAs are accommodated into a RISC to interact with target mRNAs . It is nonetheless possible that these stable self- and duplex miRNA structures may affect the incorporation of mature miRNAs into a RISC , perhaps making some of them unavailable for interaction with mRNAs . Our results indicate that absolute concentration of miRNAs can be important for regulation . It has been reported that the concentration of a miRNA must exceed a threshold in order for a target mRNA to be suppressed [36] . The two species must furthermore be expressed at the same spatiotemporal location at the same time . Expression profiles of all RNA species should be described in absolute concentrations [6] , as ( for example ) , the same relative tenfold change from 1 nM to 10 nM may have significantly different biological outcomes than from 100 nM to 1 . Some interactions are robust and can regulate the target mRNAs at low concentrations; other interactions are predicted to be concentration-sensitive within the expected range of physiologically relevant concentrations , while yet others are predicted not to occur at all within physiologically likely concentrations . Computational approaches have proven valuable for predicting which mRNAs can be targeted by a given miRNA; however , although other methods predict which mRNAs can be targeted , they do not capture the sensitivity of the predicted interaction to concentrations of reactants . Incorporating concentration into thermodynamically based miRNA target prediction thus can provide finer-grained prediction while avoiding the artificiality of a priori thresholds .
miRNA sequences were obtained from miRBase release 14 . 0 [37] ( www . sanger . ac . uk/software/Rfam ) , and NCBI RefSeq mRNA sequences ( mrnaRefseq . txt ) were obtained from UCSC ( hgdownload . cse . ucsc . edu/downloads . html ) . mRNAs were mapped to gene annotations using the refFlat files also from UCSC , and the rna . gbff files downloaded from NCBI ( ftp . ncbi . nih . gov/refseq ) . The interaction between a short RNA ( e . g . miRNA or siRNA ) and an entire mRNA has been modelled as a competition between all folded states of the mRNA with or without hybridization of the short RNA to a particular location of the mRNA . The free energy of the folded states of the mRNA in the absence of hybridization is denoted by . If the short RNA binds to a particular location , then denotes the hybridization free energy of the short RNA binding to its target in the mRNA . Since the target site cannot interact with other bases of the mRNA , an additional computation yields , the free energy of the restricted folded states of the mRNA where the target site is single-stranded , or open . The change in free energy ( ) when the short RNA hybridizes to the mRNA is given by ( 1 ) The target sites for the above computations are chosen in two stages . In the first stage , we ignore folding of the mRNA , and consider only those target sites for which the hybridization free energy ( ) is “sufficiently negative”; i . e . for which the miRNA forms an energetically favorable duplex with the target mRNA , where “favorable” is assessed against a subjectively chosen energy threshold . The second computation finds target regions that are accessible for hybridization ( ) . In this way , suitable target sites are chosen by the change of the free energy [16] . The distributions of finite-length DNA or RNA molecules in a solution can be described as an ensemble of all possible polynucleotide sequences pairs of mixed species , such as single-folded strands and double-stranded hybridizations [38] . Here we assume that interactions between two or more mRNAs , and hybridization of short RNAs to each other , do not occur , since there is no reported evidence that such interactions directly afffect interactions between miRNA and mRNA . Then the distribution of a short RNA ( miRNA ) and an mRNA in a contained system can be described as a combination of the folded state of the mRNA , the hybridization of the short RNA to the mRNA ( if any ) , and the un-folded state of the short RNA . If S ( Short ) and T ( Target ) denote the short RNA ( miRNA ) and the target mRNA , respectively , then [S] and [T] denote their equilibrium ( final ) concentrations respectively , and [ST] the equilibrium concentration of the hybridized molecular species . Also , [] and [] denote the total ( initial ) strand concentrations of S and T respectively . Conservation of mass yields ( 2 ) and ( 3 ) At equilibrium , ( 4 ) where , R is the gas constant ( 1 . 987 Kcal/mol ) and T is the temperature in K . Solving equations ( 2 ) , ( 3 ) and ( 4 ) for [S] and [T] , a numerically stable formula for [S] is given by ( 5 ) similarly , ( 6 ) When [] = [] the solutions simplify to ( 7 ) Let be the potential energy of a short RNA in its initial state , and be the potential energy of a target RNA in its initial state . Then the total ( ensemble ) energy of the system in the initial state ( ) is ( 8 ) We also can compute the potential energy of a short RNA in its equilibrium state ( ) , and the potential energy of a target RNA in its equilibrium state ( ) , as ( 9 ) ( 10 ) Then the total ( ensemble ) free energy of the system at equilibrium state ( ) is ( 11 ) The net free energy change ( ) of the interaction is obtained as ( 12 ) If no interaction occurs , the net free energy change is zero , and if an interaction occurs the net free energy change is always negative , as the interaction is a spontaneous process . As described previously , our method consists of three independent components . We estimated the molar concentration of miRNA and mRNA species from the number of RNA copies expressed in a single cell as follows . Given that typical animal cells are 10–30 on an edge , assuming a cubical shape and assuming that the nucleus occupies 25% of the volume , the molar concentration ( C ) in a cell ( in a cytoplasm ) can be calculated as follows: ( 13 ) where N is the number of copies of an RNA species , V is the volume of cytoplasm in a cell , and NA is Avogadro's number . For example , the molar concentration of an RNA species present at 1 copy in a cell of 30 edge is ( 14 ) Similarly , the molar concentration of an RNA species present at 1000 copies in cell of 10 edge is ( 15 ) Considering that the cytoplasm in actual cells is replete with organelles , membranes and other structures that occupy volume , the actual concentrations of RNA species may be several-fold higher than the above numbers suggest . | MicroRNAs ( miRNAs ) are small RNA molecules that regulate post-transcriptional gene expression by binding messenger RNAs ( mRNAs ) , blocking their role in translation or marking them for degradation . To date , computational methods for predicting mRNA targets have assumed an all-or-nothing mode of miRNA-mRNA interaction . Here we introduce a computational approach that predicts the degree of interaction , taking into account initial miRNA and mRNA concentrations . Using this approach , we can predict whether specified interactions are likely to be functionally relevant within physiologically relevant concentration ranges . | [
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] | 2011 | Quantitative Prediction of miRNA-mRNA Interaction Based on Equilibrium Concentrations |
In C . elegans , removal of the germline extends lifespan significantly . We demonstrate that the nuclear hormone receptor , NHR-49 , enables the response to this physiological change by increasing the expression of genes involved in mitochondrial β-oxidation and fatty-acid desaturation . The coordinated augmentation of these processes is critical for germline-less animals to maintain their lipid stores and to sustain de novo fat synthesis during adulthood . Following germline ablation , NHR-49 is up-regulated in somatic cells by the conserved longevity determinants DAF-16/FOXO and TCER-1/TCERG1 . Accordingly , NHR-49 overexpression in fertile animals extends their lifespan modestly . In fertile adults , nhr-49 expression is DAF-16/FOXO and TCER-1/TCERG1 independent although its depletion causes age-related lipid abnormalities . Our data provide molecular insights into how reproductive stimuli are integrated into global metabolic changes to alter the lifespan of the animal . They suggest that NHR-49 may facilitate the adaptation to loss of reproductive potential through synchronized enhancement of fatty-acid oxidation and desaturation , thus breaking down some fats ordained for reproduction and orchestrating a lipid profile conducive for somatic maintenance and longevity .
Many studies have documented the apparent trade-off between aging and reproduction as reduced fertility is associated with increased lifespan in several species [1]–[3] . However , reproductive fitness also confers distinct physiological benefits [4] , [5] . A growing body of evidence underscores the complex interactions between aging and reproduction [6]–[9] but the mechanisms underlying this dynamic relationship remain obscure . Aging and reproduction are both inextricably connected to the energetics of fat metabolism . Reproduction is an energy-intensive process that relies heavily on lipid supplies and is influenced by lipid homeostasis . Epidemiological data indicate that obesity and low-body weight together account for ∼12% of female infertility [10] . Reproductive senescence in women and other female mammals is marked by re-organization of body fat and frequently associated with weight gain [11] . Similarly , obesity not only increases the susceptibility to a host of age-related diseases such as diabetes and CVD , it may also directly accelerate the aging clock by hastening telomere attrition [12] . Thus , it would appear that lipid metabolism influences both reproduction and the rate of aging and may provide the basis for the impact of these processes on each other . These molecular underpinnings are poorly understood and identifying them has relevance for multiple aspects of human health , procreation and longevity . In recent years , the nematode Caenorhabditis elegans has provided unique insights into the effect of reproductive status on the rate of organismal aging [7]–[9] . In C . elegans , sperm and oocytes are generated from a population of totipotent , proliferating germline-stem cells ( GSCs ) whose removal increases lifespan and enhances stress resistance [13] , [14] . This phenomenon is not just a peculiarity of a hermaphroditic worm , since similar lifespan extension is exhibited by Drosophila melanogaster and other insect and worm species following germline removal [15]–[17] . Moreover , ovarian transplantation experiments in mice [18] and studies in human populations [19] suggest that the reproductive control of lifespan may be widely prevalent in nature . The longevity of germline-ablated C . elegans is entirely dependent upon the presence of the conserved , pro-longevity FOXO-family transcription factor , DAF-16 [13] . DAF-16 is part of a transcriptional network that is activated in intestinal cells when the germline is eliminated [20] . DAF-16 is a shared longevity determinant that increases lifespan in response to multiple stimuli , including reduced insulin/IGF1 signaling ( IIS ) [21] . On the other hand , TCER-1 , the worm homolog of the conserved , human transcription elongation and splicing factor , TCERG1 [22] , specifically promotes longevity associated with germline loss [23] . Other components of the intestinal transcriptional network include regulators of cellular processes such as autophagy ( PHA-4 , HLH-30 ) [24] , [25] , heat-shock response ( HSF-1 ) [26] , oxidative stress ( SKN-1 ) [27] and transcriptional co-factors ( SMK-1 ) [28] . In addition to these proteins , a steroid signaling cascade that includes the nuclear hormone receptor ( NHR ) , DAF-12 , and components of a lipophilic-hormonal pathway that synthesize the DAF-12-ligand , dafachronic acid ( DA ) , enhance the lifespan of germline-ablated animals ( [29]; reviewed in [7] , [9] ) . DAF-12 mediates the up-regulation of another NHR , NHR-80 , that is in turn required for the increased expression of fatty-acid desaturases that catalyze the conversion of stearic acid ( SA , C18:0 ) to oleic acid ( OA , C18:1n9 ) [30] . DAF-12 also promotes DAF-16 nuclear localization in intestinal cells following germline ablation [31] . Several lines of evidence suggest that DAF-16-mediated lifespan extension relies on modulation of fat metabolism , at least in part , and involves lipophilic signaling [32] , [33] . However , the mechanism through which DAF-16 orchestrates these lipid-metabolic changes is not known . NHR-80 and DAF-16 function in parallel pathways and NHR-80-mediated SA-to-OA conversion is not sufficient to overcome the loss of DAF-16 [30] . Other lipid regulators , including NHRs , which may act in the DAF-16 pathway to alter fat metabolism following germline removal are yet to be identified . DAF-12 and NHR-80 are two members of a family of ∼284 NHRs represented in the worm genome , most of which have been derived from a hepatocyte nuclear factor 4 alpha ( HNF4α ) ancestor [34] . Many NHRs are lipid-sensing factors that respond to fatty acid and steroid ligands to alter gene expression . One such factor , NHR-49 , shows sequence similarity to HNF4α , but performs functions undertaken in vertebrates by peroxisome proliferator-activated receptor alpha ( PPARα ) . PPARα is a member of the PPAR family of proteins which plays essential roles in vertebrate energy metabolism and it operates at the hub of a regulatory complex that impacts fatty-acid uptake , lipoprotein transport and mitochondrial- and peroxisomal β-oxidation [35] . In worms , NHR-49 regulates of mitochondrial- and peroxisomal β-oxidation and fatty-acid desaturation during development and under conditions of food scarcity [36] , [37] . nhr-49 mutants exhibit metabolic abnormalities , shortened lifespan and reduced survival upon nutrient deprivation [36] , [37] . NHR-49 expression is also essential in a small group of GSCs that can survive long periods of starvation to re-populate the gonad and restore reproductive potential when the animal encounters food [38] . It is conceivable that this protein has a pervasive role in promoting organismal survival in diverse physiological contexts that induce metabolic flux and require the restoration of lipid homeostasis . Despite the identification of several genes that encode lipid-modifying enzymes , how lipid homeostasis is re-established following germline loss , and how this translates into enhanced survival of the animal remains recondite . In this study , we identify a group of NHRs required for the longevity of germline-less C . elegans . We describe a role for one of these , NHR-49 , in enhancing lifespan through modulation of specific lipid-metabolic pathways . We demonstrate that NHR-49 is transcriptionally up-regulated by DAF-16 and TCER-1 in the soma upon germline removal . NHR-49 causes the increased expression of multiple genes involved in fatty-acid β-oxidation and desaturation , triggering a metabolic shift towards lipid oxidation and an unsaturated fatty acid ( UFA ) -rich lipid profile . NHR-49 is critical for young germline-less adults to maintain their lipid reserves and de novo fat synthesis , and overexpression of the protein in fertile adults increases their lifespan modestly . nhr-49 single mutants display similar biochemical and age-related lipid deficits but not the widespread reduction in β-oxidation genes’ expression seen in germline-less mutants . NHR-49 expression during normal aging is DAF-16 and TCER-1 independent . It is also dispensable for the lifespan extension mediated by reduced insulin/IGF1 signaling ( IIS ) , a DAF-16-dependent longevity pathway , suggesting that the DAF-16- and TCER-1-directed elevation of NHR-49 is especially important for the metabolic and lifespan changes induced by germline loss . Our results suggest that through the concerted enhancement of fatty-acid oxidation and desaturation , NHR-49 may mediate the breakdown of fats designated for reproduction and restore lipid homeostasis . Together , they provide evidence for an important role for NHR-49 in adapting to loss of reproductive potential and augmenting longevity .
Lipid signaling and fat metabolism play important roles in the reproductive control of aging [7]–[9] . Hence , to identify components of the DAF-16/TCER-1 pathway that confer lifespan extension upon germline loss , we focused on NHRs . These transcription factors are activated by lipid ligands and many of them modulate lipid-metabolic pathways . From the two large-scale , feeding RNAi libraries that cover a majority of the worm genome [39] , [40] , we derived a focused ‘NHR-library’ to perform an RNAi screen . Our ‘NHR-library’ included RNAi clones targeting 259 of the 283 worm NHRs . We used temperature-sensitive glp-1 mutants , a widely used genetic model for the longevity resulting from germline removal [41] . Previously , we had identified a GFP reporter , Pstdh-1/dod-8::GFP that is jointly up-regulated by DAF-16 and TCER-1 in intestinal cells of long-lived glp-1 mutants [23] . We used this strain ( glp-1;Pstdh-1/dod-8::GFP ) to screen our ‘NHR library’ for clones that prevented the up-regulation of GFP in young adults at 25°C . We identified 22 RNAi clones , targeting 19 nhr genes , which prevented Pstdh-1::GFP up-regulation . 16 of these clones ( targeting 13 NHRs ) reproducibly reduced GFP expression ( S1 Table ) and also shortened the extended lifespan of glp-1 mutants , albeit with variable efficiency ( 11–48% suppression; S2 Table ) . We found that two independent RNAi clones targeting nhr-49 completely abrogated the longevity of glp-1 mutants ( Fig . 1A , S2 Table ) . We chose to focus on nhr-49 because of these strong phenotypes , and because it's functional similarity to PPARα provided an avenue for investigating the mechanisms that link fat metabolism and longevity . To substantiate the nhr-49 RNAi phenotype , we examined the effect of nhr-49 mutation on the extended lifespan of glp-1 mutants . We found that nhr-49 ( nr2041 ) , a mutant that carries an 893 bp deletion , caused a suppression of glp-1 longevity similar to that caused by nhr-49 RNAi ( Fig . 1B , S3 Table ) . The mutant also had a shorter lifespan compared to wild-type worms , as previously reported ( Fig . 1B , S3 Table ) . Surprisingly , nhr-49 was not essential for the longevity of daf-2 mutants that live long due to impaired IIS and represent another DAF-16-dependent longevity pathway [21] . nhr-49 mutation had no impact on the extended lifespan of daf-2 ( e1368 ) mutants in two of three independent trials and caused a small suppression in longevity in the third ( Fig . 1C and S4A Table ) . Similarly , results were obtained with nhr-49 mutants carrying another daf-2 allele , e1370 , ( Fig . 1C and S4A Table ) and upon RNAi-inactivation of daf-2 in nhr-49 mutants ( S4B Table ) . In C . elegans , lifespan is also enhanced by perturbations to mitochondrial electron transport chain activity through a distinct regulatory pathway that is daf-16 independent [42] . We found that RNAi treatment against cco-1 and cyc-1 , genes that encode components of mitochondrial electron transport chain , elicited a similar lifespan extension in nhr-49 mutants as in wild-type worms ( Fig . 1D , S4B Table ) . These observations suggest that nhr-49 has variable degrees of relevance for different physiological alterations that influence aging . It is critical for the longevity mediated by reproductive signals but is not central to the lifespan changes resulting from reduced IIS or deficient mitochondrial electron transport . To address the role of nhr-49 in the reproductive control of aging , we first examined NHR-49 localization in worms . We generated transgenic worms expressing GFP tagged to a full length NHR-49 transgene driven under control of its endogenous promoter from extra-chromosomal arrays ( Pnhr-49::nhr-49::gfp , henceforth referred to as NHR-49::GFP ) . Animals expressing NHR-49::GFP showed widespread fluorescence throughout embryonic and larval development ( S1A Figure ) . In adults , it was visible in all somatic tissues ( Fig . 1E–H ) , localized to both nuclei and cytoplasm , with highest expression in intestinal cells ( Fig . 1H ) . Expectedly , the transgene was silenced upon nhr-49 RNAi except in neuronal cells ( S1B Figure ) . To test if the NHR-49::GFP transgene was functional , we asked if it could rescue the shortened lifespan of nhr-49;glp-1 double mutants . In two independent trials , NHR-49::GFP completely rescued the longevity of nhr-49;glp-1 double mutants ( Fig . 1I; S3 Table ) , whereas the rescue was 77% in a third trial ( with strains generated by injecting the transgene at a lower concentration ) . This demonstrated that NHR-49::GFP is a functional protein that recapitulates the expression and function of the wild-type version . Intestinal DAF-16 nuclear localization and TCER-1 transcriptional up-regulation are important molecular hallmarks associated with germline loss-dependent longevity [20] , [23] . We asked if NHR-49 was similarly affected by germline removal . Germline depletion resulted in increased NHR-49::GFP , especially in intestinal cells ( Fig . 2A , B , E , F ) . Next , we used the NHR-49::GFP reporter to test if this increased expression was dependent upon DAF-16 and/or TCER-1 . In glp-1 mutants carrying the daf-16 null allele , mu86 , GFP expression was dramatically and uniformly reduced in all tissues ( Fig . 2C , E ) . daf-16 knockdown by RNAi caused a similar but less marked reduction in NHR-49::GFP in glp-1 mutants ( Fig . 2F , striped bars ) . In tcer-1;glp-1 double mutants , GFP expression pattern was unevenly affected . In most animals , some intestinal cells showed no GFP whereas others showed high GFP expression ( Fig . 2D , E ) . It is not clear if the mosaic expression observed in tcer-1;glp-1 mutants is a result of partial loss of function ( the tcer-1 allele , tm1452 , is a 392 bp deletion coupled to a 10 bp insertion that is predicted to disrupt three of five transcripts produced by the gene ) or reflects a spatial aspect of regulation by TCER-1 . glp-1 mutants subjected to tcer-1 RNAi had reduced GFP expression as well ( Fig . 2F , striped bars ) . In addition to these observations , an independent line of investigation supported the regulation of NHR-49 by DAF-16 and TCER-1 . In an RNA-Sequencing ( RNA-Seq ) analysis designed to map the transcriptomes dictated by DAF-16 and TCER-1 upon germline ablation , we identified nhr-49 as one of the genes jointly up-regulated by these two proteins ( Amrit et al . , manuscript in preparation ) . Using Q-PCR assays , we confirmed that germline removal produced a significant increase in nhr-49 mRNA , and this increase was repressed in daf-16;glp-1 and tcer-1;glp-1 mutants ( the tcer-1 mutant did not achieve statistical significance; Fig . 2G ) . Interestingly , DAF-16 and TCER-1 up-regulated nhr-49 expression only in germline-ablated worms . In daf-16 and tcer-1 mutants alone , we did not observe a reduction in nhr-49 mRNA during adulthood ( Fig . 2H and S2 Figure ) . RNAi knockdown of these genes also did not reduce NHR-49::GFP levels ( Fig . 2F , solid bars ) suggesting that NHR-49 is differentially regulated depending on the reproductive status of the animal . Together , our experiments show that both mRNA and protein levels of NHR-49 are elevated in somatic cells upon germline removal . These changes are strongly dependent on DAF-16 , at least partially dependent on TCER-1 , and indicate that DAF-16 and TCER-1 mediate the transcriptional up-regulation of NHR-49 when the germline is eliminated . Since NHR-49 expression is increased in glp-1 mutants , we asked if elevating levels of the protein in normal animals could circumvent the need for germline removal and directly enhance longevity . We used the NHR-49::GFP animals that overexpressed the protein due to the presence of multiple extra-chromosomal arrays of the transgene ( Fig . 3A ) . Indeed , we found that wild type , fertile worms overexpressing NHR-49 lived ∼15% longer than their non-transgenic siblings and wild-type controls ( Fig . 3B , S3 Table ) . Intriguingly , the lifespan enhancement was greater when NHR-49 was overexpressed in an nhr-49 mutant background . Not only was the lifespan of nhr-49 mutants rescued to wild-type levels , it was augmented even further ( Fig . 3C ) . These long-lived worms did not display any obvious fertility defects ( S3 Figure ) . NHR-49 overexpression in glp-1 mutants caused a small additional increment in their longevity as well ( Fig . 3D ) . This lifespan increment was dependent on both daf-16 and tcer-1 ( Fig . 3E , F and S5A Table ) . These data show that elevating NHR-49 levels can increase lifespan modestly without compromising fertility . During development and in response to food deprivation , NHR-49 regulates the expression of multiple genes predicted to function in mitochondrial- and peroxisomal- β-oxidation ( Fig . 4A ) as well as fatty-acid desaturation pathways ( Fig . 4B ) [36] , [37] . Strikingly , along with nhr-49 , many of these genes , were also identified as DAF-16 and TCER-1 targets in the RNA-Seq analysis mentioned above ( S4 Figure; Amrit et al . , manuscript in preparation ) . This led us to ask ( a ) if the expression of these genes was enhanced in glp-1 mutants , and ( b ) whether their up-regulation was dependent upon nhr-49 . We focused on the mitochondrial β-oxidation genes . In Q-PCR assays , the mRNA levels of 12 genes we tested were all elevated in long-lived , glp-1 mutants as compared to wild-type worms , although to variable degrees ( Fig . 4C–N ) . Of these , the up-regulation of seven genes was significantly reduced or abolished in nhr-49;glp-1 mutants ( Fig . 4C-N ) . These genes encode enzymes that participate in different steps of mitochondrial β-oxidation including: i ) acyl CoA synthetases ( ACS; acs-2 and acs-22 ) that catalyze the conversion of fatty-acids to acyl CoA ii ) carnitine palmitoyl transferases ( CPT; cpt-2 , cpt-5 ) that transport activated acyl groups from the cytoplasm into the mitochondrial matrix and iii ) acyl CoA dehydrogenases ( ACDH; acdh-9 , acdh-11 ) , enoyl CoA hydratases ( ech-1 . 1 , ech-7 ) and thiolase ( acaa-2 ) whose combined activities result in the shortening of fatty-acid moieties and generation of acetyl CoA ( Fig . 4A ) [43] . Thus , NHR-49 mediates the increased expression of genes involved in different steps of mitochondrial β-oxidation following germline loss . We did not observe a similar , conspicuous difference in the expression of these genes on comparing wild-type worms with nhr-49 single mutants . The expression of two genes , acs-2 and ech-1 . 1 , was reduced in nhr-49 mutants ( Fig . 4C , E ) and acdh-9 was elevated ( Fig . 4G ) , but the others were not significantly altered ( Fig . 4 and S5 Figure ) . Surprisingly , worms overexpressing NHR-49 also did not exhibit a consistent change in the mRNA levels of these genes , although they are longer lived and it was conceivable that they may have elevated β-oxidation gene expression ( S6 Figure ) . These observations suggest that germline removal may provide the impetus for a perspicuous up-regulation of β-oxidation genes by NHR-49 . To test if these gene expression changes had any relevance on the lifespan extension observed in germline-less animals , we examined the effect of RNAi knockdown of each of these genes on the longevity of glp-1 mutants . RNAi was initiated with the onset of adulthood to circumvent developmental requirements . We found that RNAi knockdown of seven of nine genes shortened glp-1 longevity to variable degrees ( 7–48%; Table 1 and S6 Table ) . On the other hand , RNAi knockdown of the same genes in a control strain with wild-type lifespan either had no statistically significant effect ( 7/9 genes tested ) or an inconsistent lifespan reduction ( 2/9 genes ) ( Table 1 and S6 Table ) . These results underscore the singular importance of the β-oxidation genes we tested to the longevity of germline-less animals . Together , our data defined a functional role for the NHR-49-mediated up-regulation of mitochondrial β-oxidation genes in response to germline removal . Moreover , they suggested that in germline-less animals , NHR-49 triggers an increase in fatty-acid β-oxidation and this metabolic shift is critical for the consequent lifespan extension . Germline loss results in increased triglyceride ( TAG ) storage in C . elegans [44] . Based on the observations described above , we asked if inhibiting mitochondrial β-oxidation affected the elevated fat stores of glp-1 mutants . As a preliminary test , we compared the lipid levels of glp-1 and nhr-49;glp-1 mutants by staining the animals with the dye Oil Red O ( ORO ) that labels TAGs and whose estimation closely matches biochemically detected TAG levels [44] . Since β-oxidation is a lipolytic pathway , it was conceivable that nhr-49;glp-1 mutants would exhibit a further increase in TAGs due to their impaired mitochondrial β-oxidation gene expression profile . However , we found that ORO levels were indistinguishable between glp-1 and nhr-49;glp-1 day 1 adults ( Fig . 5A , B and I ) . Surprisingly , by day 2 , nhr-49;glp-1 adults showed a small but significant reduction in ORO staining as compared to glp-1 mutants ( Figs . 5C , D , I and S6A Figure ) . As the animals aged , this difference became more pronounced . By days 6–8 of adulthood , nhr-49;glp-1 mutants underwent a striking loss of ORO staining ( Fig . 5E–I ) . By comparison , glp-1 mutants continued to show high ORO staining from day 2 till at least day 18 of adulthood ( S6B Figure ) . To obtain a direct measure of lipid levels , we used gas chromatography/mass spectrometry ( GC/MS ) and found that TAG levels are indeed significantly reduced in nhr-49;glp-1 mutants as compared to glp-1 adults ( Fig . 5J ) . To understand why nhr-49;glp-1 mutants exhibited reduced TAG levels despite diminished expression of β-oxidation genes , we explored the possibilities that ( a ) they consumed less food than glp-1 mutants , and/or ( b ) they also experienced a simultaneous reduction in fatty-acid synthesis . We observed no difference in the pharyngeal pumping rates of the two strains , indicating that they ate similar quantities of food ( S6C Figure ) . Next , we compared the dietary fat absorption and de novo fat synthesis between glp-1 and nhr-49;glp-1 mutants using a previously described 13C isotope fatty-acid labeling assay [45] . de novo fat synthesis was substantially reduced in nhr-49;glp-1 mutants ( Fig . 5K ) . We confirmed that this reduction was not due to repressed transcription of genes involved in initiation of fat synthesis or those mediating conversion to stored fats . mRNA levels of pod-2 {that encodes acyl CoA carboxylase ( ACC ) , the rate-limiting enzyme required for initiation of fat synthesis} and fasn-1 {that encodes fatty-acid synthase ( FASN-1 ) , another key regulator of fat synthesis} were not reduced by nhr-49 reduction of function ( S7A and B Figure ) . Similarly , nhr-49 did not affect the expression of dgat-2 , a gene that encodes a rate-limiting enzyme diacylglycerol acyl transferase ( DGAT ) needed for conversion of diglycerides ( DAGs ) into TAGs ( S7C Figure ) . Thus , our experiments showed that germline-less animals require nhr-49 for de novo lipid synthesis and to retain their high TAG levels during adulthood . They suggest that impairing NHR-49 may impact other important metabolic processes besides β-oxidation such as lipid synthesis , storage and maintenance . Since nhr-49 mutants are short-lived compared to wild-type controls [36] , [46] , we asked if they also exhibited age-related fat phenotypes , and if NHR-49 played an analogous role during normal aging . nhr-49 mutants have been reported to have higher fat . These studies predominantly relied on staining live larvae with the dye Nile Red [36] , [47]–[49] , an inaccurate technique for labeling fats as the dye is trafficked to the lysosome-related organelle in live animals [44] , and in some cases these observation have not been corroborated by other methods [50] . Using ORO labeling , we did not observe a significant difference between wild-type , day 2 animals and age-matched nhr-49 mutants ( Fig . 5L and S6A Figure ) . However , while wild-type worms underwent increased fat accumulation with age , nhr-49 mutants , similar to nhr-49;glp-1 , exhibited a progressive loss of fat ( Fig . 5L ) . Intriguingly , a similar age-related loss of ORO staining was also observed in worms overexpressing NHR-49 ( Fig . 5L ) . These phenotypes could not be explored biochemically in the reproductively active day 2 adults of these strains due to the confounding effects of eggs and progeny ( see Materials and Methods ) . Hence , we used late L4 larvae/early day 1 adults to compare the lipid profiles of wild-type worms and nhr-49 mutants . GC/MS data showed that , at least in late L4/early day 1 adults , lipid levels were the same between the two strains ( Fig . 5M ) . Since the biochemical analyses could not be extended to adults , it is formally possible that nhr-49 mutants have elevated fat . But , our experiments strongly indicate that nhr-49 loss of function does not increase fat accumulation . Instead , in both germline-less and normal adults , it causes an age-related loss of stored lipids . On comparing the dietary fat absorption and de novo lipid synthesis profiles between late L4/early day 1 nhr-49 mutants and wild-type worms , we noticed fatty-acid specific differences . Some fatty acids were synthesized at a higher level in nhr-49 mutants as compared to wild-type ( eg . , OA ) whereas the synthesis of others ( eg . , Vaccenic Acid , C18:1n7 ) was reduced ( Fig . 5N and S8A Figure ) . Together , these experiments showed that NHR-49 is required for the maintenance of TAG stores during normal aging and its absence causes de novo lipid synthesis abnormalities . The similarities between the phenotypes of nhr-49 and nhr-49;glp-1 mutants suggest a shared role for the gene in the two contexts . However , the gene expression and lifespan studies described in the previous section also point towards mechanistic and possibly functional differences in NHR-49's modulation of these processes in fertile vs . germline-less adults ( see Discussion ) . NHR-49 regulates both fatty-acid β-oxidation and desaturation during development and nutrient deprivation [36] , [37] so we asked if it impacted desaturation in the glp-1 mutant context and/or during normal aging . The genes fat-5 , fat-6 and fat-7 encode desaturase enzymes that catalyze the conversion of SFAs to MUFAs . FAT-5 converts palmitic acid ( PA , C16:0 ) to palmitoleic acid ( POA , C16:1n7 ) while FAT-6 and -7 function redundantly to convert stearic acid ( SA , C18:0 ) to oleic acid ( OA , C18:1n9 ) ( Fig . 4B ) [51] , [52] . Under control of NHR-80 , FAT-6/7 mediated conversion of SA to OA is necessary for the longevity of glp-1 mutants; OA supplementation completely rescues the short lifespan of glp-1;fat-6;fat-7 mutants to glp-1 level [30] . We found that , similar to NHR-80 , NHR-49 was also required for the changes in levels of fat-5 , fat-6 and fat-7 observed in glp-1 mutants ( Fig . 6A–C ) . However , OA supplementation did not rescue the shortened lifespan of nhr-49;glp-1 mutants ( S7 Table ) indicating other critical functions for NHR-49 . On comparing the lipid profiles of nhr-49;glp-1 with glp-1 mutants through GC/MS , we observed an increased SA:OA ratio in the former , as expected ( Fig . 6D and S9A Figure ) . In addition , the ratio of PA:POA was enhanced as well ( Fig . 6E and S9B Figure ) . Overall , nhr-49;glp-1 mutants exhibited a widespread decline in MUFAs and increased SFAs in both the neutral and phospholipid pools ( Figs . 6F , G and S9C , D Figure ) . Desaturation is coupled to the elongation of fatty-acid chains that is mediated by elongase enzymes ( encoded by the ‘elo’ genes in C . elegans ) . Our Q-PCR assays showed that nhr-49 was not required for the up-regulation of elo-1 and elo-2 in glp-1 mutants ( S7D , E Figure ) implying a selective role for the gene in desaturation . Overall , these data showed that NHR-49 , similar to NHR-80 , is required for SA-to-OA conversion in glp-1 mutants . In addition , it also promotes the desaturation of other SFAs to MUFAs and PUFAs to ensure an UFA-rich lipid profile . Hence , while NHR-80 influences desaturation alone , NHR-49 modulates both desaturation and β-oxidation and has a broader effect on lipid composition . This may also explain the more severe phenotypes associated with nhr-49 reduction-of-function . In nhr-49 single mutants , the levels of fat-5 and fat-7 mRNAs were reduced , whereas the effect on fat-6 was inconsistent and statistically insignificant ( Figs . 6A-C and S5 Figure ) . Despite these weak gene-expression effects , the fatty-acid profile of late L4/early day 1 nhr-49 mutants showed increased SA:OA ratio ( PA:POA ratio was increased only in neutral lipids; Figs . 6H , I and S10A , B Figure ) and an increased accumulation of SFAs with a concomitant reduction in MUFAs ( Figs . 6J , K and S10C , D Figure ) indicating a role for NHR-49 in establishing a MUFA-rich lipid profile in normal animals too . Overall , the multiple fat phenotypes of nhr-49;glp-1 mutants , the role of nhr-49 in enhancing fatty-acid β-oxidation as well as desaturation and our biochemical and functional data together suggest that through the coordinated enhancement of β-oxidation and desaturation , NHR-49 helps establish lipid homeostasis that is critical for the survival of germline-less animals , and may also impact normal aging .
A key finding of our study is the identification of multiple genes predicted to function in fatty-acid β-oxidation whose expression is up-regulated following germline loss , and the strong dependence on NHR-49 for this up-regulation . These genes encode enzymes that together cover all the catalytic reactions of β-oxidation including some that are specific to the process ( e . g . , CPTs ) [43] . While we cannot rule out the possibility that they function together in a different pathway , the simplest interpretation of our data is that these genes enhance mitochondrial β-oxidation . These data imply that germline removal causes a shift towards fatty-acid metabolism . Lipid oxidation confers several advantages over glucose metabolism such as more efficient energy production and reduced reactive oxygen species ( ROS ) generation [57] . In C . elegans , fatty-acid oxidation provides energy in other situations where stored lipids are used for long-term survival such as dauer diapause and caloric restriction [47] , [58] . However , in these contexts , the animal is food deprived and not faced with the hazard of large-scale lipid accumulation due to thwarted procreation . Following germline loss , a metabolic shift towards increased β-oxidation coupled to lipid repartitioning may allow the animal to eliminate fats normally delegated for reproduction and restore lipid homeostasis , thus averting the negative consequences of loss of fertility . Such a metabolic shift can also explain the extraordinary dependence of germline-ablated animals on the presence of NHR-49 , a key mediator of both oxidation and desaturation . Fatty-acid oxidation and desaturation , although independent processes , are intimately interlinked and inter-dependent . Deficiency of the mouse desaturase , SCD1 , inhibits β-oxidation in cardiac cells [59] . Alternatively , impaired β-oxidation impacts lipid composition and is implicated in human dyslipidemias [60] . A coordinated up-regulation of these processes would be especially relevant for germline-less animals , since they face the dual challenges of eliminating superfluous fat and transforming their lipid profile in adaptation to an altered physiological status . We were intrigued by the progressive depletion of stored fats , despite impaired expression of β-oxidation genes , in nhr-49;glp1 mutants . While the precise reason for this is unknown , we postulate that it may be due to the simultaneous inhibition of β-oxidation and desaturation that causes accumulation of free fatty acids ( FFAs ) [61] . FFAs stimulate insulin release and serve as key signaling molecules . But their chronic accrual causes deregulated insulin secretion and apoptosis in pancreatic β cells [62] and insulin resistance in muscle and liver cells [63] . Impaired fatty-acid oxidation and non-metabolized SFAs are implicated as the primary agents underlying lipotoxicity [64] . We observed a significant increase in such SFAs in nhr-49;glp-1 mutants . Hence , it is conceivable that in nhr-49;glp-1 mutants inadequate mobilization of fat stores and impaired desaturation together cause FFA accretion and an energy imbalance that may lead to early death . Further studies will be needed to test this hypothesis and unravel the molecular basis of this intriguing phenotype . The requirement of NHR-49 for enhancement of both β-oxidation and desaturation following germline removal distinguishes the protein from other regulators such as NHR-80 which influences desaturation , especially SA to OA conversion . In our experiments , NHR-49 also had a wider impact on the fatty-acid composition of glp-1 mutants . Besides SA , nhr-49;glp-1 mutants exhibited reduced desaturation of multiple fatty acids including PA conversion to POA . They displayed overall reduction in MUFAs and PUFAs and a concomitant increase in SFAs ( Fig . 6 ) . These data suggest a broader role for NHR-49 in the increased fatty-acid desaturation associated with germline-loss . Two independent approaches led us to the identification of DAF-16 and TCER-1 as regulators of nhr-49: the RNAi screen described here and an RNA-Seq study designed to identify DAF-16 and TCER-1 targets ( Amrit et al . , manuscript in preparation ) . NHR-49::GFP confirmed the RNA-Seq and Q-PCR data . Loss of daf-16 almost completely abolished NHR-49::GFP in glp-1 mutants but had no impact in fertile adults ( Fig . 2 ) . TCER-1 also specifically enhanced NHR-49 in a glp-1 background . These observations provide clues as to how reproductive stimuli may modulate somatic metabolism . Since germline loss triggers intestinal nuclear relocation of DAF-16 and elevated TCER-1 expression [20] , [23] , it is possible that these two events stimulate increased nhr-49 transcription . But , it is not clear at present if nhr-49 is a direct DAF-16 target because we did not find a canonical DAF-16-Binding Element ( DBE ) [65] in the promoter used in our study . The strong DAF-16-dependence in glp-1 mutants also distinguished NHR-49 from NHR-80 whose up-regulation in germline-ablated animals is largely DAF-16 independent [30] . While the lifespan of daf-16;glp-1 mutants is increased by NHR-80 [30] , NHR-49 overexpression in these animals did not rescue longevity significantly ( S5B Table ) . The short lifespan of nhr-49 mutants led us to explore its role during normal aging . nhr-49 loss causes similar age-related fat loss and biochemical deficits in both germline-less and wild-type adults , but we also noticed mechanistic and regulatory differences between the two paradigms . For instance , nhr-49;glp-1 mutants exhibited a consistent reduction in the de novo synthesis of OA , an important determinant of glp-1 longevity [30] , whereas , nhr-49 mutants appeared to synthesize more of it , at least at late L4/early day 1 stage . Similarly , DAF-16 and TCER-1 mediated increased NHR-49 expression in glp-1 mutants but were not needed for the basal expression in wild-type adults . Further , NHR-49 was required for the up-regulation of multiple mitochondrial β-oxidation genes in glp-1 mutants , whereas , their levels were largely unchanged by its depletion in fertile adults . RNAi knockdown of these genes also impacted glp-1 longevity selectively ( Table 1 ) . These data suggest that germline-less animals experience enhanced β-oxidation and are more dependent upon it for survival , whereas basal levels are maintained in young , fertile adults . In the light of these differences , the similarities in age-related fat loss and fatty-acid composition defects between nhr-49 and nhr-49;glp-1 mutants are intriguing . One possible explanation for these contradictory observations is that nhr-49 controls the same pathway in the two situations but through the regulation of different targets , a premise supported by the considerable redundancy observed in C . elegans mitochondrial β-oxidation genes . It is also plausible that NHR-49 influences a different process in fertile adults whose inactivation also leads to progressive fat loss . Interestingly , other longevity-promoting genes exhibit similar phenotypes . For instance , HSF-1 is needed for the longevity of daf-2 mutants and their enhanced stress resistance . But , its depletion also shortens lifespan and increases stress-susceptibility in wild-type worms [26] . Similarly , DAF-16 and SKN-1 are both essential for daf-2 longevity and stress-resistance and they are also critical for normal worms’ ability to mount a response against oxidative stress , pathogen attack and other noxious stimuli [66]–[68] . Mutations in both genes shorten lifespan in wild-type worms [66] , [67] , though not to the extent seen in hsf-1 and nhr-49 mutants . These similarities may reflect a common mechanism by which normal cellular and metabolic pathways are leveraged and enhanced by an organism to cope with major physiological changes , and how this may in turn lead to a change in the length of life . Our results suggest that increased mitochondrial β-oxidation and transformation of the lipid profile into one enriched in UFAs may not only allow adaptation to germline loss but also be beneficial to normal aging animals .
All strains were maintained by standard techniques at 20°C . Lifespan experiments were conducted as described previously and have been discussed in detail elsewhere [69] . For all lifespan assays that involved the glp-1 genetic background , eggs were incubated at 20°C for 2–6 h , transferred to 25°C to eliminate germ cells , then shifted back to 20°C on day 1 of adulthood ( ∼72 h later ) for the rest of their lifespan . For fer-15;fem-1 lifespans , eggs were similarly transferred to 25°C to induce sterility and left at the same temperature for life . For lifespans with daf-2 mutants , worms were grown at 15°C till L4 stage and then transferred to 20°C for life . All other lifespan assays were performed at 20°C . In all cases , the L4 stage was counted as day 0 of adulthood . Fertile strains were transferred every other day to fresh plates until progeny production ceased . For lifespans performed with transgenic strains , eggs were transferred to fresh plates and after 48 h scored under a Leica M165FC microscope with a fluorescence attachment ( Leica Microsystems , Wetzlar , Germany ) . Transgene-carrying , fluorescent L4 larvae ( day 0 ) were separated from their age-matched , non-transgenic siblings . The latter were used as internal controls in the same experiment . For whole-life RNAi experiments , worms were exposed to RNAi clones from hatching by transferring eggs to RNAi plates . For adult-only RNAi lifespans , the worms were grown on E . coli OP50 till day 0 and then transferred to freshly-seeded RNAi plates for the rest of adulthood . pAD12 , an empty vector plasmid without an RNAi insert [42] was used as the control in all RNAi lifespans along with pAD43 [42] and tcer-1 RNAi constructs to knock-down daf-16 and tcer-1 , respectively . Data from animals that crawled off the plate , exploded , bagged , or became contaminated were censored on the day the abnormality was observed . Stata 10 . 0 , 8 . 2 ( Stata Corporation , Texas , USA ) and OASIS ( Online Application of Survival Analysis , http://sbi . postech . ac . kr/oasis ) were used for statistical analysis . P-values were calculated using the log-rank ( Mantel–Cox method ) test . The complete genotypes and pertinent details of all the strains used in this study are given in S8 Table . According to Wormbase WS239 , 283 genes are annotated as nuclear hormone receptors ( www . wormbase . org ) . Of these , we could isolate 429 clones targeting 259 nhr genes from the feeding RNAi feeding libraries created by the laboratories of Julie Ahringer and Marc Vidal [39] , [40] . This ‘sub-library’ was screened to identify RNAi clones that suppressed the up-regulation of Pstdh-1/dod-8::gfp in a long-lived glp-1 mutant using the strain CF2573 [23] . Briefly , RNAi clones were inoculated overnight at 37°C in LB medium containing 100 µg/ml ampicillin . 100 µl culture of each clone was seeded onto NGM plates with ampicillin ( 100 µg/ml ) and supplemented with 1 mM IPTG . Synchronized eggs of CF2573 were isolated by hypochlorite treatment and seeded onto freshly-seeded RNAi plates . After 4–6 h at 20°C the plates were moved to 25°C for ∼70–72 h and then screened under a Leica M165FC microscope with a fluorescence attachment ( Leica Microsystems , Wetzlar , Germany ) . In addition to pAD12 , multiple random clones were also used as baseline negative controls ( since pAD12 causes a modest , non-specific reduction in fluorescence in all GFP-expressing strains ) . All screen plates were independently examined by two observers . Clones identified by both observers were tested in three subsequent trials ( S1 Table ) . All confirmed suppressor ( and some enhancer ) RNAi clones were confirmed by sequencing ( M13-forward primer ) and before any experiment , RNAi clones were tested by PCR ( T7 primers ) . To generate the Pnhr-49::nhr-49::gfp construct , 6 . 6 kb region of nhr-49 gene ( 4 . 4 kb comprising the coding region covering all nhr-49 transcripts and 2 . 2 kb sequence upstream of the first nhr-49 exon ) was amplified with primers modified to introduce PstI and SalI restriction sites ( forward 5′ gctagCTGCAGgaccagaaagagcaagagccaatattct 3′; reverse 5′ taagcaCCCGGGtcgagcatatgattattctgctcactg 3″ ) . The amplified product was cloned into the GFP expression vector pPD95 . 77 ( Addgene plasmid 1495 ) . The full-length nhr-49 fragment was inserted upstream of , and in frame with , GFP at the PstI and SalI sites ( pAG4 ) . To generate the NHR-49::GFP expressing worms , Pnhr-49::nhr-49::gfp ( pAG4 ) was injected at a concentration of 25 ng/µL or 100 ng/µL with 3 . 75 ng/uL or 15 ng/µL of Pmyo-2::mCherry co-injection marker , respectively . Three to six independent stable transgenic lines were generated for each of the genetic backgrounds in which the transgene was injected . Transgenic strains were maintained by picking fluorescent worms in each generation . The strains generated for this study are listed in S8 Table . For GFP assays involving NHR-49::GFP , eggs were transferred to freshly-seeded E . coli OP50 or RNAi plates , incubated at 20°C for 2–6 h , transferred to 25°C ( to eliminate germ cells in strains containing glp-1 mutation ) , then shifted back to 20°C on day 1 of adulthood . GFP assays were conducted on day 2 of adulthood , using the Leica MZ16F stereomicroscope . All assays were performed blind after initial familiarization with GFP levels in control plates by the experimenter . For imaging purposes , 6 to 10 worms were immobilized in 35 mm optical glass bottomed dishes ( MatTek Corporation , Ashland , MA ) with 6 µl of 0 . 1 mM sodium azide in PBS . Confocal images were taken using a Leica TCS SP8 microscope . GFP fluorescence was illuminated using a 488 nm argon laser line with a 63×1 . 4NA oil Apochromat CS2 objective . Fluorescence was captured using a spectral HyD detector over ∼100 Z-planes . Confocal images were visualized , rendered and analyzed using Volocity Visualization Software ( v 5 . 4 , PerkinElmer ) . ORO staining was done as described in earlier [44] . Briefly , 0 . 5 g ORO ( Sigma-Aldrich St . Louis , MO ) was dissolved in 100 mL isopropanol and the solution was equilibrated for four days . One day before staining , the stock solution was diluted to 60% with water and filtered twice on the day of the experiment through a 0 . 22 µm filter . 30–40 adults were picked into a 1 . 5 mL tube containing 1×PBS , washed twice with 1×PBS pH 7 . 4 and settled by spinning at 2000 rpm for 1 min . The worms were then re-suspended in 120 µL PBS to which an equal volume of 2×MRWB buffer was added . Samples were rocked gently for 1 h at room temperature and centrifuged at 2000 rpm for 1 min . The buffer was aspirated , worms washed with PBS , re-suspended in 60% isopropanol and incubated for 15 minutes at room temperature . After 15 minutes , the 60% isopropanol solution was removed and worms were then incubated overnight with rocking in 1 ml filtered 60% ORO stain . Next day the dye was removed after allowing worms to settle , and 200 µL of 1×PBS 0 . 01% Triton X-100 was added . Animals were mounted and imaged with using a Leica M165FC microscope mounted with a Retiga 2000R camera ( Q Imaging , Burnaby , British Columbia , Canada ) . Images were captured with the QCapture Pro7 software ( Q Imaging , Burnaby , British Columbia , Canada ) and quantified using ImageJ software ( NIH ) . To perform Q-PCRs , total RNA was isolated using mirVana miRNA Isolation Kit ( Ambion , Austin , TX ) from approximately 5 , 000 day 2 worms of each strain grown under identical conditions . RNA was treated with DNase I , ( Sigma-Aldrich St . Louis , MO ) and cDNA was prepared from 1 µg of total RNA in a 20 µL reaction using a ProtoScript first strand cDNA synthesis kit ( New England Biolabs , Beverly , MA , USA ) . For comparing mRNA levels from strains carrying extra-chromosomal transgenes , fluorescent worms were picked on to a 10 cm NGM plate seeded with E . coli OP50 and allowed to lay eggs that were maintained at 20°C . On day 1 of adulthood , worms were washed with M9 and transferred to a NGM plates seeded with E . coli OP50 to prevent starvation . Transgenic worms were isolated on day 2 using a Leica MZ16F stereomicroscope ( Leica Microsystems , Wetzlar , Germany ) with standard fluorescence filters . For each strain , approximately 200 worms were used for RNA isolation . Total RNA was extracted with TRIzol ( Ambion , Austin , TX ) and converted to cDNA as described above . Q-PCRs were performed using an ABI 7000 machine ( Applied Biosystems ) . PCR reactions were undertaken in 96-well optical reaction plates ( ABI PRISM N8010560 ) . A 25 µL PCR reaction was set up in each well with 12 . 5 µL SensiMix SYBR Hi-ROX Kit ( Bioline , USA ) , 1/20th of the converted cDNA and 0 . 25 M primers . For every gene at least three independent biological samples were tested , each with three technical replicates . Primers used in this study are listed in S9 Table . The pharyngeal pumping assay was done as previously described [70] . Briefly , age matched glp-1 and nhr-49;glp-1 worms were obtained by picking eggs on to E . coli OP50 seeded plates . On the day of counting one worm was transferred to a freshly seeded E . coli OP50 plate and allowed to recover for 2–5 minutes . Pumping rate was determined by counting the contraction of the terminal bulb of the pharynx for 30 seconds under a dissecting microscope . The counting was repeated 4 more times to get the average . After the fifth replicate , the worm was moved to a freshly seeded E . coli OP50 plate . Pumping rate was measured on day 2 , 4 and 6 of adulthood . To assess reproductive health , brood size , percentage of hatching and oocyte ratio were calculated , using at least 10 worms per strain , per biological replicate , as described previously [70] . For each strain , gravid adults were bleached to obtain approximately 15 , 000 eggs that were transferred to NGM plates seeded with E . coli OP50 for growth . The plates were incubated for 2 hours at 20°C and then transferred to 25°C for growth to desired stage . For experiments performed with day 2 sterile adults , the plates were transferred back to 20°C after 72 hours for another 18 hours of additional growth and then transferred to prepared stable isotope plates for 6 hours . The same protocol was followed harvesting worms at late L4/early day 1 adults , except for harvesting after 52 hours ( N2 and glp-1 ) or 64 hours ( nhr-49 and nhr-49;glp-1 ) . The additional growth time was provided for nhr-49 mutant strains to compensate for their developmental delay under large-scale growth conditions . By day 2 of adulthood , the altered de novo synthesis and fatty-acid composition profiles of nhr-49;glp-1 mutants were similarly changed when compared to nhr-49;glp-1 mutants harvested together with glp-1 ( simultaneous 96 hour harvest ) or after a 12 hour delay ( 108 hour harvest ) ( S12 Figure ) . Larvae and adult animals utilize their fatty acids differently . In growing larvae , large quantities of lipids are used to build membranes and establish lipid stores , whereas in fully-grown adults , they are utilized to fulfill the demands of reproduction . Using wild-type N2 and other fertile strains in lipid assays confounds the results as we begin to see the metabolic profile of the progeny skew the data as early as day 2 ( Shaw Wen-Chen and Carissa Olsen , unpublished data ) . To circumvent this , we used late L4 larvae/early day 1 adults for our lipidomic studies with fertile strains . The stable isotope plates were prepared as previously described; in short , each plate was seeded with a 1∶1 ratio of 12C-bacteria and 13C-bacteria grown respectively in LB or Isogro media ( 98 . 5% 13C-enriched media , Sigma-Aldrich ) . The animals were harvested , washed in M9 three times , and frozen in a dry ice/ethanol bath before being stored at −80°C until processed . Total lipids were extracted as previously described with the following modifications [45] . Briefly , standards were added to each sample ( 1 , 2-diundecanoyl-sn-glycero-3-phosphocholine , Avanti Polar Lipids , for PLs and tritridecanoin , Nu-Chek Prep , for TAGs ) before the start of the extraction procedure . The lipids were extracted with 2∶1 chloroform:methanol for 90 minutes at room temperature while shaking continuously . Residual carcasses were pelleted by centrifugation and the extracted lipids were transferred to fresh tubes and dried under a constant nitrogen stream . Dried lipids were re-suspended in 1 mL chloroform and loaded onto a pre-equilibrated solid phase exchange ( SPE ) columns ( 100 mg capacity , Fisher Scientific ) . Lipid classes were eluted from the column in the following order: neutral lipids in 3 ml of chloroform , glycosphingolipids in 5 ml of acetone:methanol ( 9∶1 ) and then phospholipids in 3 ml of methanol . Purified lipids were dried under nitrogen , re-suspended in methanol/2 . 5% H2SO4 and incubated for 1 h at 80°C to create fatty acid methyl esters ( FAMEs ) that were analyzed by gas chromatography/mass spectrometry ( GC/MS ) ( Agilent 5975GC , 6920MS ) . The relative abundance of fatty acids in each class was determined for all the major fatty acid species in the nematode as previously described [45] . To quantify TAG and PL yields , total PL and TAG abundance were normalized using the added standards , and data were presented as a TAG:PL ratio . de novo synthesis was calculated through a series of described equations which allow for the quantification of the amount of each fatty acid species generated from synthesis by determining the abundance of each isotopomer [45] . The synthesis numbers reported here represent the amount of 13C-labeled fatty acids derived from synthesis when compared to the total amount of fatty acids newly incorporated into the animal . | Much is known about how increasing age impairs fertility but we know little about how reproduction influences rate of aging in animals . Studies in model organisms such as worms and flies have begun to shed light on this relationship . In worms , removing germ cells that give rise to sperm and oocytes extends lifespan , increases endurance and elevates fat . Fat metabolism and hormonal signals play major roles in this lifespan augmentation but the genetic mechanisms involved are poorly understood . We show that a gene , nhr-49 , enhances worm lifespan following germ-cell removal . NHR-49 is increased in animals that lack germ cells by conserved longevity proteins , DAF-16 and TCER-1 . NHR-49 , in turn , increases levels of genes that help burn fat and convert saturated fats into unsaturated forms . Through synchronized enhancement of these processes , NHR-49 helps eliminate excess fat delegated for reproduction and converts lipids into forms that favor a long life . NHR-49 impacts these processes during aging in normal animals too , but using different regulatory mechanisms . Our data helps understand how normal lipid metabolic processes can be harnessed to adapt to physiological fluctuations brought on by changes in the reproductive status of animals . | [
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"metabolis... | 2014 | Germline Signals Deploy NHR-49 to Modulate Fatty-Acid β-Oxidation and Desaturation in Somatic Tissues of C. elegans |
The arsenal in anthelminthic treatment against schistosomiasis is limited and relies almost exclusively on a single drug , praziquantel ( PZQ ) . Thus , resistance to PZQ could constitute a major threat . Even though PZQ is potent in killing adult worms , its activity against earlier stages is limited . Current in vitro drug screening strategies depend on newly transformed schistosomula ( NTS ) for initial hit identification , thereby limiting sensitivity to new compounds predominantly active in later developmental stages . Therefore , the aim of this study was to establish a highly standardized , straightforward and reliable culture method to generate and maintain advanced larval stages in vitro . We present here how this method can be a valuable tool to test drug efficacy at each intermediate larval stage , reducing the reliance on animal use ( 3Rs ) . Cercariae were mechanically transformed into skin-stage ( SkS ) schistosomula and successfully cultured for up to four weeks with no loss in viability in a commercially available medium . Under these serum- and cell-free conditions , development halted at the lung-stage ( LuS ) . However , the addition of human serum ( HSe ) propelled further development into liver stage ( LiS ) worms within eight weeks . Skin and lung stages , as well as LiS , were submitted to 96-well drug screening assays using known anti-schistosomal compounds such as PZQ , oxamniquine ( OXM ) , mefloquine ( MFQ ) and artemether ( ART ) . Our findings showed stage-dependent differences in larval susceptibility to these compounds . With this robust and highly standardized in vitro assay , important developmental stages of S . mansoni up to LiS worms can be generated and maintained over prolonged periods of time . The phenotype of LiS worms , when exposed to reference drugs , was comparable to most previously published works for ex vivo harvested adult worms . Therefore , this in vitro assay can help reduce reliance on animal experiments in search for new anti-schistosomal drugs .
Schistosomiasis , a chronic and debilitating helminthic disease , is one of the most important neglected tropical diseases ( NTD ) . The WHO estimates that more than 206 million people are currently infected and in need of chemotherapy world-wide [1] . Moreover , over 200 , 000 people die each year due to the sequelae of the disease [2 , 3] . Among the parasitic diseases , schistosomiasis is often considered only second in importance to malaria [2] and thus a major public health menace . Therefore , the WHO aims to eliminate schistosomiasis as a public health problem globally by 2025 [4 , 5] . Implementation of safe water , sanitation and hygiene ( WASH ) strategies [6] , intensified case management , veterinary public health , vector control and mass drug administrations ( MDAs ) are all crucial in reducing the disease burden [5] . Of all these approaches , MDA dominates national control programs thanks to the excellent safety and efficacy profile of praziquantel ( PZQ ) , the only currently available drug [7 , 8] , as well as its low cost per treated individual [9] . However , the reliance on PZQ also raises concerns about emerging resistance should the drug pressure increase . Resistance against PZQ has already been observed in experimental models [10] while first instances of decreased drug efficacy have been observed in the field [7 , 11 , 12] . To be prepared for the emergence of resistant strains of schistosomes and to support the elimination of schistosomiasis , new drugs and complementary strategies , such as vaccines , are of imminent importance . To identify new drugs and new vaccine targets , in vitro assays of larval and adult worm stages are paramount for high throughput testing and simultaneously for reducing reliance on in vivo or ex vivo experiments in accordance with the 3Rs ( replacement , reduction and refinement ) of animal testing [13] . The current cultivation protocols for in vitro generated larval stages such as the schistosomula rely on the supplementation of fetal calf serum ( FCS ) for short-term culture [14 , 15] and on the supplementation with FCS , human serum ( HSe ) , erythrocytes and peripheral blood mononuclear cells ( PBMCs ) for long-term culture and in vitro juvenile worm development [16] . Such non-standardized culture conditions are prone to variability of serum batches , rely on the continuous supply of fresh human blood , and make it difficult to isolate pure schistosomula-derived soluble antigens during in vitro culture or to investigate the role of specific serum proteins in the development of the parasite . In addition to these hindrances and to avoid inter-assay or inter-laboratory fluctuations , well-defined and standardized culture conditions independent of serum and cell supplementations are needed . Up to now , long-term culture has been reported to rely on costly , non-commercially available and highly complex culture media such as Basch medium 169 which are difficult to implement in routine use and prone to batch variability [17] . Thus , simplifying culture conditions as well as the generation and handling of advanced-stage parasites opens new possibilities in the search for new drugs and facilitates the upscaling of existing drug screening strategies . To establish a robust in vitro assay , it is important to continuously imitate the parasite’s in vivo development . This development within the final host is quite complex and occurs over a period of six to seven weeks . After penetration of the skin , cercariae transform to schistosomula which remain in the skin for about three days before migrating through the host’s vasculature . These skin stage schistosomules ( SkS ) traverse the capillaries of the lung where the majority of the parasites can be found on day 7 after infection [18] . To facilitate their migration , these SkS become longer , more slender and active and are hence called lung stage schistosomula ( LuS ) . These continue their journey to the portal and mesenteric veins following the bloodstream [19 , 20] where they continue to undergo morphological changes . The bifurcated gut is fused , the parasites initiate feeding , and continue to grow . This early liver stage ( early LiS ) is followed by the late LiS characterized by a drastic increase in length and prominent oral and ventral suckers . These juvenile worms ( late LiS ) then start to pair up and become fertile upon which the oviposition starts approximately 35 days after infection [21] . Treatment with PZQ , although initially with good efficacy , does not diminish the high reinfection rates encountered in the field [22] . This is partly due to the inability of PZQ to efficiently target early larval stages of the parasite [23 , 24] . In the current search for new anti-schistosomal drugs , a two-step strategy is used . Firstly , SkS are tested immediately after their transformation from cercariae and then , once an active compound has been identified , it is tested mainly on ex vivo cultured adult worms that have been isolated from infected hamsters or mice [14 , 25–27] . Other larval stages like the LuS , early and late LiS are omitted as potential drug targets . Therefore , future compounds with an activity predominantly directed against juvenile and adult stages might be overlooked . Thus , a highly standardized and robust way to generate advanced larval stages of Schistosoma mansoni would provide an opportunity to incorporate initial advanced-stage schistosomula testing into the current drug screening strategies to meet the desired target candidate profiles ( TCP ) . We disclose here a new serum- and cell-free cultivation method of newly transformed schistosomula ( NTS ) up to the LuS ( 7 days p . t . ) and a cell-free cultivation method up to late LiS worms ( starting to show 28 days after transformation ) of S . mansoni . Our culture system allows the detection of stage-dependent differences in the activity of drugs with known anti-schistosomal properties , such as PZQ , Oxamniquine ( OXM ) , the standard drug to treat schistosomiasis caused by S . mansoni prior to the advent of PZQ , and two antimalarial drugs that have recently been described to have anti-schistosomal properties , Mefloquine ( MFQ ) and Artemether ( ART ) [16 , 28] . Therefore , this highly standardized , straightforward and reliable culture method is a basis for integrating drug screenings of advanced larval stages into initial hit identification in the search of novel schistosomicidal drugs .
Cercariae of an in-house Brazilian or imported NMRI strain of S . mansoni were harvested from infected Biomphalaria glabrata snails and used for mechanical transformation into NTS as described before [29] . Biomphalaria glabrata snails infected with the NMRI strain were provided by the NIAID Schistosomiasis Resource Center of the Biomedical Research Institute ( Rockville , MD ) through NIH-NIAID Contract HHSN272201700014I for distribution through BEI Resources and used for experiments as indicated due to temporary limited availability of the Brazilian strain . Briefly , cercariae were incubated 30 min on ice then centrifuged at 1932 x g for 3 min at 4°C . The pellet was resuspended in Hank’s balanced salt solution ( HBSS ) ( Cat . No . H6648 , Sigma-Aldrich , Germany ) supplemented with 200 U/ml Penicillin and 200 μg/ml Streptomycin ( Cat . No . P4333 , Sigma-Aldrich ) and transformed by mechanical stress applied by pipetting and vortexing , which was confirmed by microscopy ( 10x magnification ) . Separation of tails and cercarial bodies was accomplished by repeated sedimentation in ice-cold HBSS . The NTS were then transferred to the various culture media and kept overnight to complete transformation . NTS ( ~100 NTS in 150 μl ) were maintained in HybridoMed Diff 1000 ( HM ) ( Cat . No . F 8055/1 , Biochrom GmbH , Germany ) , Medium 199 ( M199 ) ( Cat . No . M4530 ) , Dulbecco’s Modified Eagle Medium ( DMEM ) ( Cat . No D5796 ) or RPMI 1640 ( Cat . No . R8758 , Sigma-Aldrich , Germany ) supplemented with 200 U/ml penicillin , and 200 μg/ml streptomycin ( Sigma-Aldrich ) in a 96-well flat bottom tissue culture plate ( Cat . No . 353075 , Corning Incorporated , USA ) and incubated at 37°C in 5% CO2 and humidified air for up to 4 weeks . For each condition , experiments were performed in triplicates . The medium was replaced every 7 days . Scoring was performed on day 1 , 3 and 7 post-transformation ( p . t . ) and then again weekly until week 4 p . t . The viability of NTS was scored using an Axiovert10 microscope ( Zeiss , Germany ) . The scoring system was adapted from the Swiss TPH [29] and the WHO-TDR [27] . For the scoring , three main criteria were assessed: motility , morphology and granularity . The score was applied ranging from 0 ( dead parasites , no movement , heavy granulation , blurred outline , rough outer tegument and blebs ) to 1 ( very reduced motility , rough outer tegument with some blebs ) to 2 ( reduced motility or increased uncoordinated activity , slight granularity , intact tegument with slight deformations ) , and finally 3 ( regular smooth contractions , no blebs and a smooth outer surface , no granulation with clear view of internal structures which are visible under bright field microscope ) . Single NTS die during the mechanical transformation and are , thus , present from the beginning . The amount of dead parasites is taken into consideration when applying the viability score , thereby lowering the overall score . The score represents an overall impression of the visual appearance of all NTS in a well . In order to capture any subtle changes in the appearance of all schistosomula per well , viability scores 0–3 were further subdivided into 0 . 25 steps ( e . g . 0 , 0 . 25 , 0 . 50 , 0 . 75 ) . For determination of larval development stage , morphological characteristics were used and based on previously published works [17 , 21] . The skin stage was characterized by resembling the cercarial head in shape and undirected regular movements , the lung stage by an increase in length and decrease in diameter . The early LiS was characterized by growth of the parasite initially in diameter and then further in length and the clear visibility of the gut . The late LiS showed clearly identifiable oral and ventral suckers and a further increase in length , especially of the body past the ventral sucker . Blood sampling of HSe was prepared from blood of consenting healthy volunteers with no previous history of schistosomiasis upon written consent . Fresh blood was left at room temperature for 30 min to clot , then centrifuged at 1845 x g for 20 min and serum was collected and pooled from 6 individuals and stored at -20°C until further use . NTS were incubated ( 100 in 150 μl ) in Basch-Medium 169 [30] kindly provided by Prof . C . Grevelding and Dr . T . Quack ( Universität Giessen , Germany ) , DMEM and HM supplemented with 200 U/ml Penicillin and 200 μg/ml Streptomycin and with HSe in different concentrations ( 1–50% ) or 20% heat-inactivated FCS ( Cat . No . F7524 , Sigma-Aldrich , Germany ) at 37°C for 8 weeks in a 96-well plate . Medium without HSe supplementation served as controls . The medium was changed weekly , and viability was scored on day 1 , 3 and 7 p . t . and then again , every 7 days . Developmental stages were determined by bright field microscopy using an inverted Axiovert 10 microscope ( Zeiss ) . For each condition experiments were performed in triplicates . PZQ ( kindly provided by Merck , Germany ) , OXM ( Cat . No . PH002704 ) , MFQ ( Cat . No . M2319 ) and ART ( Cat . No . A9361 , Sigma-Aldrich , Germany ) were dissolved in DMSO depending on drug solubility ( PZQ 10 mg/ml , OXM 5 mg/ml , MFQ 33 . 3 mg/ml , ART 10 mg/ml ) and stored at 4°C until use . NTS were cultured in HM supplemented with ( SkS , LuS and LiS ) or without ( SkS and LuS ) 20% HSe . To test the drug sensitivity of the distinct developmental stages , we incubated SkS ( 24-hour-old NTS ) , LuS ( 7-day-old NTS ) and LiS ( 6-week-old NTS ) parasites with PZQ , OXM , MFQ and ART at different concentrations ( 1 , 10 , 100 μg/ml ) . Before the addition of the drugs , a medium exchange was performed . For each condition experiments were performed in triplicates . Scoring was performed before ( 0 h ) and 3 , 24 , 48 , 72 and 168 h after treatment ( a . t . ) . For statistical analysis of the experiments to determine the optimal culture medium as well as the HSe concentration , a Kruskal-Wallis test was performed on day 1 and 3 , week 1 , 4 and 8 p . t . and if significant ( p ≤ 0 . 05 ) , the data was further analyzed by employing Mann-Whitney U tests comparing pure medium with serum-supplemented medium followed by a Bonferroni correction . For the statistical analysis of the experiment comparing HM to Basch medium 169 , testing was performed in the same manner except that Mann-Whitney U testing was used to compare HM to Basch medium 169 for each of the shown conditions . For statistical analysis of the drug treatment experiments , Mann-Whitney U testing was performed to determine statistically significant differences between the DMSO control and the treated groups . Statistical testing was done with IBM SPSS Statistics 24 ( IBM ) . Graphs were made with PRISM 5 ( GraphPad ) .
To generate and maintain NTS under serum- and cell-free conditions , we cultivated NTS immediately following transformation in different highly standardized and commercially available culture media such as HM , DMEM , RPMI and M199 ( Fig 1A and 1B ) supplemented only with 200 U/ml penicillin and 200 μg/ml streptomycin . Over a period of four weeks , regular visual viability scoring of the parasites was performed following defined criteria adapted from previously published works [27] ( S1 Fig ) . In DMEM , NTS survived well for at least four weeks . Their viability peaked on day 3 p . t . , ( 2 . 6 ± 0 . 1 ) , characterized by an increased motility and hardly any granularity . Then viability declined slightly ( 2 . 0 ± 0 . 0 ) due to the death of single NTS and slight internal granulation on day 7 after which the viability stabilized throughout the remainder of the experiment ( Fig 1A and 1B ) . In HM , NTS initially scored ( 2 . 0 ± 0 . 3 ) on day 3 p . t . , slightly lower compared to those in DMEM . However , from one week p . t . onwards , NTS in HM had already surpassed those kept in DMEM in the viability scoring ( 2 . 7 ± 0 . 3 ) due to the absence of granularity and increase of regular , steady movement . NTS in HM stayed viable throughout the experiment ( four weeks ) ( Fig 1A and 1B ) . Compared to these two media ( HM and DMEM ) , M199 and RPMI supported viability of NTS rather poorly . In M199 , viability already started to drop ( 1 . 8 ± 0 . 3 ) on the third day p . t . , characterized by the majority of the NTS being heavily damaged or already dead . By week 3 p . t . , all NTS had died ( Fig 1A and 1B ) . In RPMI , the viability declined even faster with the majority of the NTS already heavily damaged or dead on day 3 p . t . and the death of all NTS by week 2 p . t . ( Fig 1A and 1B ) . In all conditions , NTS were initially oval shaped on day 1 to day 3 , resembling the SkS . Skin-stage schistosomula were characterized by a stumpy looking oval outline with regular elongations and contractions either straight or to the side and were 105 . 7 ± 7 . 8 μm in length ( in HM ) ( S2 Fig ) . By one week p . t . , the surviving NTS had developed LuS characteristics . LuS NTS were characterized by a more elongated and slender form with an increased activity compared with the SkS . The parasites’ movements were still characterized by regular elongation and contraction in changing directions with an average length of 184 . 4 ± 45 . 5 μm ( Fig 1B and S2 Fig ) [31] . In all media , however , development of surviving NTS halted after the first week of culture and , therefore , remained at the LuS stage for the remainder of the experiment . Dead NTS , in M199 or RPMI medium , displayed massive granulation internally and an irregular outer tegument . In contrast , healthy NTS , in HM and DMEM , were elongated and slender with a clear interior and a well-contrasted outline ( Fig 1B ) . Taking all of this into consideration , HM is the most suitable medium for long-term culture of LuS NTS without any serum or cell supplementation; however , development under these conditions is halted in the LuS . To test whether , in the absence of any cell supplementation , LuS NTS could develop further into the LiS by adding serum of the most important definite host , namely the human [2 , 32] , we supplemented HM and DMEM with 20% HSe . In both serum-free controls , we observed a decline in viability after the fourth week of observation as before ( Fig 2A ) . More specifically , in HM and DMEM , all SkS that survived , developed to the LuS by the first week p . t . Until the fourth week p . t . , around 49% ( 48 . 7 ± 4 . 2 dead out of 99 . 7 ± 10 . 6 total parasite count/well ) of the NTS died in DMEM compared to only around 21% ( 39 . 0 ± 9 . 0 dead out of 185 . 7 ± 14 . 0 total ) in HM . Following the fourth week p . t . , a steady increase in dead NTS was present in both conditions ( Fig 2B and 2D ) . However , the addition of 20% HSe to the culture halted the loss of viability and greatly reduced NTS-death within the first 4 weeks of transformation . Interestingly , the next developmental stage , the early LiS started to develop from 2 weeks p . t . , as evident by the further increase in size ( growing wide and stumpy rather than in length ) and the gut becoming clearly visible under the light microscope . By week 4 p . t . , the parasite had developed further into the late LiS , characterized by clearly visible ventral and oral suckers and elongation of the aboral part of the parasite’s body . Its activity could be either restricted to the oral or aboral part of the body or could encompass the entire worm . This stage was characterized by a steady increase in length ( 1289 . 6 ± 247 . 0 μm ) after 6 weeks of culture ( S2 Fig ) . The time point of this development was the same for HM and DMEM . Even though the percentage of the early LiS in DMEM ( 29 . 4% or 34 . 0 ± 2 . 8 out of 115 . 5 ± 6 . 4 total ) was higher than that in HM ( 6 . 1% or 9 . 3 ± 1 . 2 out of 153 . 0 ± 20 . 0 ) , the percentage of the late LiS was comparable between DMEM ( 11 . 5 ± 5 . 0 out of 115 . 5 ± 6 . 4 total ) and HM ( 15 . 3 ± 3 . 2 out of 153 . 0 ± 20 . 0 ) with around 10% having developed after 8 weeks of incubation ( Fig 2C and 2E ) . Importantly even though the initial time point for the occurrence of the late LiS in HM and DMEM was the same , the overall growth rate of the parasites in HM supplemented with HSe was increased compared with that in DMEM since the late LiS in HM ( week 4 and after ) grew to be remarkably bigger ( Fig 2F , arrows ) . Strikingly , despite the addition of HSe , a larger number ( ~ 40% ) of NTS had died in DMEM by 8 weeks ( Fig 2F ) as compared to HM , where only ~10% of NTS were dead . Since one of the media currently considered to be the “gold standard” to raise advanced developmental stages is Basch medium 169 , we cultured NTS ( derived from the S . mansoni NMRI strain ) with Basch medium 169 and HM medium without further serum supplementation . Interestingly , NTS raised in HM again did not develop past the LuS , whereas in Basch medium 169 , the development to the early LiS stage was observed starting from week two onwards . Furthermore , in Basch medium 169 the viability score ( 2 . 3 ± 0 . 1 at week 3 p . t . ) was around 0 . 5 score points higher than in HM ( 1 . 9 ± 0 . 4 at week 3 p . t . ) for the first 3 weeks of the experiment . This was followed by a slight drop in viability in Basch medium 169 in the fourth week with a final score of 1 . 9 ± 0 . 1 for Basch medium 169 and 1 . 8 ± 0 . 3 for HM . Upon addition of FCS , the currently most commonly used serum supplement in in vitro culture of S . mansoni [33–35] , we could observe a drastic decline in viability for both media within the first week after transformation starting after the first three days of culture ( 0 . 8 ± 0 . 1 in HM and 0 . 7 ± 0 . 1 in Basch medium 169 ) ( Fig 3A ) . In Basch medium 169 supplemented with 20% FCS the rate of development was reduced and delayed with only 8 . 7% early LiS by week 4 compared to 58 . 9% in unsupplemented medium . ( Fig 3B and 3C ) . FCS supplementation to HM lead to an increase in dead NTS ( from 38 . 7% to 79 . 4% dead parasites on week 4 p . t . ) and was unable to promote development past the LuS . ( Fig 3E and 3F ) . The addition of HSe to Basch medium did not further increase the viability score in contrast to the addition of HSe to HM which resulted in an overall increase of ~0 . 5 viability score points ( 2 . 3 ± 0 . 1 score points on week 4 p . t . ) ( Fig 3A ) . HSe was able to promote development to the late LiS in both media , and the first late LiS were observed 3 weeks p . t . in Basch medium 169 and 4 weeks p . t . in HM . However , the percentage of developing late LiS was much higher in HM ( 46 . 7% ) compared to Basch-medium 169 ( 23 . 9% ) ( Fig 3D and 3G ) indicating that addition of HSe to HM had a more pronounced effect on promoting larval development towards juvenile worms . Next , we investigated whether HSe induced development of advanced larval stages of S . mansoni increases in a concentration-dependent manner . Therefore , we cultured NTS in HM supplemented with 1 , 5 , 10 , 20 or 50% of HSe . Even at the lowest concentration ( 1% ) , HSe successfully prevented the death of NTS which otherwise started to occur in the 5th week p . t . ( Fig 4A ) and survival rates remained steadily above 67% ( 49 . 0 ± 7 . 4 dead in 151 . 7 ± 15 . 3 total ) throughout the experiments ( 8 weeks ) in all HSe-supplemented conditions ( Fig 4B–4D ) . Surprisingly , however , at a concentration of 50% HSe , the survival rate ( 69 . 1% or 44 . 7 ± 10 . 1 dead out of 144 . 7 ± 21 . 2 total ) as well as viability ( 2 . 2 ± 0 . 6 ) started to decline around week 8 ( Fig 4D and 4E ) . Nevertheless , the final viability score and survival rate was still higher than in the control ( viability score of 0 . 3 ± 0 . 0 and survival rate of 1 . 8% or 160 . 3 ± 7 . 8 dead out of 163 . 3 ± 8 . 5 total ) ( Fig 4A and 4E ) . However , development of NTS up to early and late LiS was only observed at higher concentrations ( single early LiS starting in 5% HSe and late in 20% HSe ) of HSe ( Fig 4C , 4D and 4F ) . Early LiS development could be observed starting 2 weeks p . t . in all concentrations but their number LiS increased concentration-dependently . The first parasites in the late LiS were detected at 4 weeks p . t . in 20% HSe and single worms even one week earlier in 50% HSe . The overall percentage of late LiS parasites increased with rising serum concentrations , and , finally , around 10% LiS had developed ( 15 . 3 ± 3 . 2 in 153 . 0 ± 20 . 0 total parasite count/well ) in 20% HSe compared to approx . 18% ( 25 . 3 ± 4 . 2 in 144 . 7 ± 21 . 2 total ) in 50% HSe-supplemented HM ( Fig 4C and 4D ) . Since viability decreased in 50% HSe-supplemented medium after the 7th week p . t . and sufficient numbers ( 13–19 late LiS/well ) of late LiS worms were generated at 20% HSe [26 , 28] , we decided to use 20% HSe supplementation for the generation of parasites for drug screening experiments . To test the different stages ( SkS , LuS and late LiS ) generated with the new method in HM and 20% HSe supplementation for the in vitro drug testing , we chose drugs with known anti-schistosomal properties [28 , 36] . Late LiS ( 6-week-old schistosomula ) ( Fig 5A , 5C , 5E and 5G ) , LuS ( 7-day-old NTS ) ( Fig 5B , 5D , 5F and 5H ) and SkS ( 24-hour-old NTS ) ( S3A–S3D Fig ) NTS were cultured in the presence or absence of different concentrations ( 100 , 10 and 1 μg/ml ) of PZQ ( Fig 5A and 5B and S3A Fig ) , OXM ( Fig 5C and 5D and S3B Fig ) , MFQ ( Fig 5E and 5F and S3C Fig ) or ART ( Fig 5G and 5H and S3D Fig ) and assessed by regular viability scoring . PZQ treatment was detrimental to LiS and LuS at all concentrations , which was clearly evident from the drop in viability as soon as 3h a . t . and the death of almost all parasites on day 1 a . t . ( Fig 5A and 5B ) , characterized by contracted parasites , damaged tegument and no detectable motility ( S4 Fig ) . No such pronounced effect of PZQ was observed on SkS , however , only high concentrations of PZQ slightly reduced the observed viability starting at 3h a . t . , but NTS viability then stabilized for the remaining part of the experiment ( S3A Fig ) . In serum-free conditions , PZQ activity was comparable to serum-supplemented HM as observed on day 3 a . t . At that time point , the susceptibility of the LiS and LuS to PZQ compared with that of the SkS . Serum-free conditions did not alter the activity profile of PZQ on the SkS or LuS ( Fig 6A ) . OXM and MFQ both had similar effects on the LiS at high concentrations . In particular , MFQ had a clear and strong anti-schistosomal activity at 100 μg/ml . In contrast to MFQ , OXM was also potent at 1 μg/ml . even more so than at 10 μg/ml ( Fig 5C and 5E ) . MFQ showed similar activity on the SkS and LuS compared to the LiS ( Fig 5E and 5F and S3C Fig ) . Overall , OXM was potent in reducing the viability of the parasites in the SkS at all tested concentrations . Even though the LuS was the least susceptible stage to OXM , a reduction in the viability could still be observed which was most prominent at 1 μg/ml ( Fig 5D ) . The stage-dependent vulnerability of the schistosomula was observed quite well on day 3 a . t . It is worth mentioning that LuS and SkS NTS were slightly more susceptible to OXM in serum-free culture in the concentration of 100 μg/ml . Also , MFQ exerted an increased activity against both SkS and LuS NTS in serum-free compared to HSe-supplemented culture ( Fig 6B and 6C ) . Closer and careful observations revealed that the highest concentration of OXM induced a hyperactive state directly following the addition of the drug at all stages , which was followed by heavy granulation and tegumental damage in the SkS and LiS already starting on day 1 a . t . and , in the LuS , slightly delayed starting one week a . t . Morphologically , MFQ caused almost instantaneous contraction as well as heavy granulation with a blurred , disintegrated outline of the parasite at 100 μg/ml ( S4 Fig ) . ART did not show any effect on viability or a morphological alteration in LiS or LuS ( Fig 5G and 5H and S4 Fig ) , but a slight reduction in viability of the SkS at 100 μg/ml ( S3D Fig ) . On day 3 a . t . , we could not detect a drop in viability or any morphological changes in any of the tested stages in HSe-supplemented medium ( S4 Fig ) or in serum-free medium . However , in the serum-free medium we could detect a clear schistosomicidal effect at 100 μg/ml , with death following an initial paralysis of the parasites . However , upon addition of the drug to serum free-culture , the previously dissolved drug precipitated to a small extent ( Fig 6D ) . Taken together , we could show the varying stage-dependent activity profiles of selected compounds already known for their anti-schistosomal properties and no schistosomicidal activity for artemether .
In the London Declaration of 2012 , the world , represented by partners in governments , academia , NGOs , pharmaceutical companies and more , committed itself to accelerate the control , elimination and eradication of 10 NTDs by 2020 [37] . Despite successes in several areas , progress towards the elimination of schistosomiasis has remained rather limited [38] . Reasons for this lack of progress range from challenges in schistosome vector control to limited developments in drug discovery programs . Rather than developing new compounds , the scarce resources are focused towards drug repurposing . The limited interest of pharmaceutical companies in researching entirely new compounds is due to the resource- and time-consuming process to get an approval by the health authorities reviewed in 2014 by Panic et al . [39] . Another limitation in in vitro screening used for testing compound libraries is the availability of in vitro-generated advanced stage parasites . Therefore , current in vitro compound screenings of S . mansoni still mostly focus on SkS schistosomula observed for up to 72 h after treatment for initial hit identification or on adult worms retrieved from infected mice for hit confirmation [14 , 40 , 41] . This focus leaves a “blind spot” for drug efficacy toward intermediate developmental stages of the parasite . The value of monitoring drug efficacy for initial hit identification at all consecutive stages of parasite development in parallel is clearly illustrated by the stage-specific efficacy of PZQ , which shows decreased efficiency between 1 day and 5 weeks of development with minimal worm burden reduction at the age of 4 weeks in vivo [42 , 43] , but still represents the most effective and widely used schistosomicidal drug available [44] . Studies on ex vivo harvested LuS schistosomula sometimes manage to bridge this gap , but are challenging and require a living host ( invoking practical as well as ethical aspects ) [45] . In vitro S . mansoni culture systems obviously can circumvent some of these challenges and allow for the continual observation of consecutive larval stages . In vitro transformation and culture techniques have first been established in the 1970s and 1980s [17 , 46] and since then different culture media have rarely been evaluated [47] . Until today , those techniques rely on the presence of serum ( mainly FCS ) for early larval stages [48 , 49] and the addition of human blood cells to generate advanced larval and juvenile worm stages [16 , 50] . Viability scoring relies on visual assessment of the larvae in culture by bright field microscopy , which still is the gold standard for drug efficacy tests [27 , 28] . While the method has its merits and can be highly effective for dedicated drug efficacy testing , it is very labor intensive . At the same time , microphotographic-based automated analysis [51] using algorithms is complicated by the presence of large numbers of RBCs and/or PBMCs that overlay or co-localize with larvae , making reliable assessment of tegument damage , for example , something for the “trained eye” only . Moreover , the repeated addition of fresh cells from human donors is a factor that is difficult or impossible to fully standardize . In this study , we generated a novel cell-free in vitro assay that allows the development and long-term observation of S . mansoni larvae . Mechanically transformed NTS [25 , 28] were successfully cultured in a serum- and cell-free culture medium for up to four weeks , allowing for their development to LuS schistosomula but not further . Supplementation with 20% FCS decreased the viability after the third day of cultivation and the developmental block persisted throughout the period of cultivation . Most recently published in vitro NTS assays that have reported the use of FCS as supplement in either Medium 169 or Medium 199 do not score viability after 3 days of culture [33–35] . Thus , the relative loss in viability in FCS supplemented HM after three days needs to be explored further since batch differences in FCS are a well-known phenomenom in cell culture assays and could explain our observations as well . However , the addition of HSe to the culture broke the developmental block at the LuS and propelled development to LiS worms , and survival for at least eight weeks . Thus , this cell-free culture for advanced stage S . mansoni represents important progress in existing tools that rely on the addition of “non-standardized” human blood cells [16] . Instead , the commercially available HM , normally used to cultivate hybridomas and other cell lines , is highly standardized , supplemented with transferrin , insulin , bovine serum albumin ( BSA ) /oleic acid complex , absorbable amino acids , D-glucose , vitamins and minerals . The presence of albumin and insulin may be critical for the prolonged survival ( ≥ 4 weeks ) of parasites under serum-free conditions , and indeed , earlier findings showed that schistosomula can ingest and digest albumin and IgG in vitro , utilizing them as nutrient sources [52] . In addition , it was shown that S . mansoni has two insulin receptors ( SmIR1 and SmIR2 ) , essential for the survival of the pathogen in vitro as well as in vivo [53] . Nevertheless , and in contrast to the natural development in vivo , NTS development halted in serum-free insulin-containing media at a stage that , in terms of size and morphology , closely resembles that found in the lung vasculature [21] . This indicates that other factors than insulin or albumin contribute to the further development beyond the lung-stage . This is possibly not so surprising considering that the parasite is blood-dwelling and therefore permanently surrounded by the blood components . Nevertheless , FCS which is widely used in in vitro culture [33–35] could not see development past the LuS . Cattle , in contrast to humans , is an atypical definitive host for S . mansoni , and nevertheless an important alternative definitive host [2 , 32 , 54] . HSe did not only improve overall viability but also induced development of NTS to LiS parasites in a concentration-dependent manner . When compared to ‘gold standard’ Basch medium 169 , which is a non-commercial mixture of 15 components which needs to be freshly prepared before use [17] , HM supplemented with HSe yielded comparable viability scores but increased the development of LiS schistosomes in vitro . Furthermore , we observed a difference in the rate of development between two strains of S . mansoni , whereby late liver stage parasites were detected in a higher proportion in the NMRI compared to the Brazilian strain starting from the 4th week p . t . ( Fig 3A and 3B ) . Differences in the dynamics of development of several S . mansoni strains within a host have been previously demonstrated using an in vivo mouse model [55 , 56] . Juvenile worms could be maintained for extended periods of time ( more than a year although no pairing or egg production could be observed ) through the addition of HSe to HM alone in contrast to non-supplemented controls . However , concentration-dependent advances in stage development ( in 50% HSe ) came at the price of increased larval mortality at seven weeks and onwards . This may be explained by the increased numbers of parasites reaching the LiS , their increased size and corresponding increased rate in medium nutrient depletion . This would be in line with the fact that the parasite’s feeding and cell divisions are limited , both indicators of low metabolism rates until it reaches the LiS about 15 days after infection in vivo [21] . We found supplementation with 20% HSe to provide an optimum between HSe consumption and frequency of medium changes , generating a reasonable number of late LiS worms for further studies . This allows the incorporation of all developmental stages in initial hit identification strategies for future drug screenings , making it possible to identify compounds with an activity profile predominantly targeting lung to liver stage parasites . Such drugs would probably otherwise be missed . Taken together , we show that in contrast to cercariae that are susceptible to the complement system present in serum [57] , schistosomula not only tolerate the presence of HSe but require it to progress to late larval stages within their definitive host , the human . Another aim of this study was to investigate the suitability of this method to screen compound libraries for schistosomicidal activity against , in particular , advanced stages of S . mansoni in addition to the mostly used early stages [28 , 51] . NTS showed comparable stage-specific susceptibility to four drugs known to possess anti-schistosomal properties as seen previously with other in vitro assays [16 , 28] , confirming the validity of our novel culture method . For example , PZQ , undoubtedly the most important tool to control schistosomiasis in the field , [16 , 58] was described to have reduced efficacy starting 24h after infection and lasting until day 35 after infection when susceptibility is increasing again [42 , 43 , 59] . We could confirm this in our novel culture with the SkS ( day 1 ) and LuS parasites ( day 7 ) being less vulnerable compared to late LiS worms ( day 42 ) which is in accordance with previous publications although to a slightly lesser extent . This could be attributed on the one hand to the difference in the media that were employed ( e . g . M199 , RPMI 1640 or DMEM as compared to HM ) [33–35] and on the other hand to the difference in S . mansoni strains used to generate NTS for in vitro drug testing [28 , 60 , 61] . The substantial tegumental damage and anti-schistosomal activity in our assay induced by OXM , the drug used against S . mansoni before the discovery of PZQ [62 , 63] , was also comparable to that observed by others [16 , 28] . Specifically , morphological changes of the tegument as well as schistosomicidal activity , which was more pronounced against LiS and SkS schistosomula at high concentrations , could be observed . The increased activity in 1 μg/ml compared with 10 μg/ml of OXM was surprising . A similar observation has been made in a previous study [28] , but the reason for this is still unknown . MFQ , a well-known anti-malarial drug , that was shown to also be active against S . mansoni in vivo and in vitro [64] , is thought to impact the heme detoxification as well as glycolysis in the parasite [65] . Interestingly , the stage-dependent activity profile of MFQ is different to that of PZQ and more potently targets NTS than mature worms [29] , something we were able to confirm in our novel assay as well . ART is another anti-malarial drug that was previously shown to act against S . mansoni [36] and is thought to act via toxication by the hemin byproduct of the parasite’s digestion of hemoglobin . Indeed , in the absence of a cellular source of hemoglobin and comparable to our cell-free system , ART lost activity , supporting the notion that hemin is required for the efficacy of the drug [66] . In the absence of serum during early developmental stages , we found strongly enhanced schistosomicidal activity of MFQ , slightly increased potency of ART , but mostly unchanged activity of PZQ and OXM . Bioavailability of MFQ is known to be strongly dependent on binding to plasma proteins ( which can be as high as 98% [67] ) , and for ART plasma-binding lies between 92–98% , in line with our observed increase in activity in serum-free cultures [68] . On the other hand , PZQ binds to a lesser extent to plasma proteins ( ~80% ) in concentrations of 10–100 μg/ml and in vitro as low as 50% [69] , something that is also reflected in the limited increase in toxicity we observed in non-serum supplemented cultures . Importantly , our assay allows researchers to continually observe toxicity effects and inhibition of maturation from the SkS ( day 1–3 p . t . ) parasite over LuS ( from day 7 p . t . ) and early LiS ( from day 14 p . t . ) to the late LiS ( from day 28 p . t . ) in settings ranging from compound or drug testing , but also to screen for natural factors from non-permissive hosts or , reversely , to identify growth-promoting compounds from the host-adapted , parasite friendly environment . Taken together , since late LiS worms generated in our cell-free assay showed drug-specific phenotypes for all drugs except artemether and responded similarly to ex vivo harvested worms from infected laboratory animals [28] , this assay has the potential to reduce the reliance on in vivo generated worms by replacing ex vivo harvested worms in the initial hit identification and to thereby reduce costs and labor for large-scale drug screening assays . We are , however , aware that hit confirmation in ex vivo harvested worms might remain a necessity and that further studies such as comparative gene-expression of in vivo and in vitro generated parasite stages are ultimately necessary to clarify the extent of similarity . Eventually , the independence from host blood cells facilitates the automated assessment of larval viability in large-scale assays , due to a lack of visual interference by host cells . In addition , the high level of standardization will allow researchers to investigate and identify components within HSe that are exploited by the parasite for its development in the dominant definite human host and thus define mechanisms that underlie the host-specificity of this parasite . Ultimately , such understanding will pave the way for the identification of new drug and vaccine targets . | Schistosomiasis remains a major health threat , predominantly in developing countries . Even though there has been some progress in search of new drugs , praziquantel remains the only available drug . Probably the most important advance in the search for new drugs was in vitro transformation of cercariae and their subsequent culture . However , hit identification in compound screenings is exclusively tested in skin stage parasites and is only confirmed for more mature worms in a subsequent step . This is in part due to the lack of an easy culture system for advance-stage parasites . We present here a reliable and highly standardized way to generate LiS worms in vitro in a cell-free culture system . The inclusion of in vitro drug tests on advanced-stage parasites in initial hit identification will help to identify compounds that might otherwise be overlooked . Furthermore , the ability to continuously observe the parasite’s development in vitro will provide an important platform for a better understanding of its maturation in the human host . Taken together , this opens up new avenues to investigate the influence of specific cell types or host proteins on the development of Schistosoma mansoni and provides an additional tool to reduce animal use in future drug discovery efforts ( 3Rs ) . | [
"Abstract",
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"antimicrobials",
"schistosoma",
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"phar... | 2019 | A novel cell-free method to culture Schistosoma mansoni from cercariae to juvenile worm stages for in vitro drug testing |
Trachoma is targeted for elimination by 2020 . World Health Organization advises districts to undertake surveillance when follicular trachoma ( TF ) <5% in children 1–9 years and mass antibiotic administration has ceased . There is a question if other tools could be used for surveillance as well . We report data from a test for antibodies to C . trachomatis antigen pgp3 as a possible tool . We randomly sampled 30 hamlets in Kilosa district , Tanzania , and randomly selected 50 children ages 1–9 per hamlet . The tarsal conjunctivae were graded for trachoma ( TF ) , tested for C . trachomatis infection ( Aptima Combo2 assay: Hologic , San Diego , CA ) , and a dried blood spot processed for antibodies to C . trachomatis pgp3 using a multiplex bead assay on a Luminex 100 platform . The prevalence of trachoma ( TF ) was 0 . 4% , well below the <5% indicator for re-starting a program . Infection was also low , 1 . 1% . Of the 30 hamlets , 22 had neither infection nor TF . Antibody positivity overall was low , 7 . 5% and increased with age from 5 . 2% in 1–3 year olds , to 9 . 3% in 7–9 year olds ( p = 0 . 015 ) . In 16 of the 30 hamlets , no children ages 1–3 years had antibodies to pgp3 . The antibody status of the 1–3 year olds indicates low cumulative exposure to infection during the surveillance period . Four years post MDA , there is no evidence for re-emergence of follicular trachoma .
Trachoma , the leading infectious cause of blindness world-wide , is caused by repeated episodes of ocular infection with the bacterium Chlamydia trachomatis [1] . Trachoma is the target of a massive global control program , from global mapping to country programs working to eliminate blinding trachoma district by district [2–4] . The World Health Organization ( WHO ) has established Ultimate Intervention Goals as guidance for countries , and included two metrics: ( 1 ) reduction in the prevalence of follicular trachoma ( TF ) in children ages 1–9 to less than 5% at district level , and ( 2 ) reduction in the number of cases of trachomatous trichiasis , the late-stage complication where the eyelashes rub the globe , to less than 1/1 , 000 total population at district level . A population-based impact survey to check the progress of program activities is the recommended monitoring tool [5] . Once an impact survey has documented that a district has achieved a TF prevalence of <5% in children ages 1–9 years , the program can cease antibiotic interventions while is still encouraged to continue with facial hygiene and environmental change activities . The district now enters into a surveillance phase to monitor for re-emergence of the disease . In September 2014 , WHO convened a working group which released surveillance guidelines: a single population-based surveillance ( “pre-validation” ) survey will be carried out at district level , at least two years after the last round of mass drug administration . The guidelines anticipate that re-emergence , if it is to happen , will be evident by two years although re-emergence to what level of TF ( >5% or >10% TF for example ) has not been defined . The surveillance survey still relies on clinical assessment of trachoma ( TF ) to determine if the program has succeeded in sustained reduction of trachoma . There is no other accepted measure to use to guide surveillance . A test of infection with C . trachomatis is highly susceptible to antibiotic pressure , and studies have shown that where the prevalence of trachoma ( TF ) is high but infection is near zero , there is a risk of re-emergence of infection [6] . Other studies suggest that when infection is re-introduced into low TF prevalence settings , transmission is not sustainable and infection dies out [7 , 8] . There is no accepted prevalence level of infection that could guide programs on when to cease antibiotics or to indicate a risk of re-emergence of either infection or trachoma ( TF ) . Recent work on a test for antibodies to C . trachomatis antigens suggests serology is a promising tool that indicates cumulative risk of exposure to C . trachomatis[9 , 10] . If serology can be used in the youngest age groups to monitor evidence of exposure since the cessation of mass antibiotic provision , it may prove to be a useful tool for confirming interruption of transmission . We report data using the new WHO guidelines on trachoma surveillance , supplemented with a test of infection and a serologic test of antibody positivity , from a district-wide , population-based surveillance survey carried out in Kilosa district , Tanzania , four years after program activities were stopped . The goals were two fold: first , to determine the overall prevalence of TF and infection at district level , and second , to determine the relationship of antibody status to prevalence of TF and Infection by age and by community prevalence of infection and/or TF .
Kilosa district , Tanzania , was formerly trachoma-endemic , with village surveys in 2004 averaging 25% trachoma prevalence . Mass drug administration ( MDA ) using azithromycin was undertaken sporadically in all hamlets , then in earnest throughout the district from 2007–2010 . The national program conducted an impact survey at that time and estimated the rate of follicular trachoma ( TF ) in children ages 1 to 9 years at 4·17% . For the current surveillance survey , all rural hamlets ( n = 522 ) were eligible . The hamlets ranged in population size from 225 to 876 . In 2014 , we took a simple random sample of 30 hamlets . The hamlets did not have maps or a list of households from which to randomly sample sentinel children , so we used a random walk method to obtain a sample of 50 children ages 1 to 9 years . We went to the center of the hamlet , as identified by the local hamlet leader . From there , the closest house was designated as the direction to start , and a random number between 0 and 9 was picked from a sealed envelope for that hamlet . The survey team proceeded in the chosen direction , going one by one to the house that corresponded to that number . If there were no children ages 1 to 9 years , the team proceeded in the same direction to the next house , going one by one until 50 children were surveyed per hamlet . In the event the team reached the physical end of the hamlet , they turned to the right and proceeded to the next house , then back in the same direction to the center of the hamlet . Clinical signs of trachoma were assessed by a grader trained by a senior grader ( SW ) using the WHO simplified grading scheme [11] . Because trachoma was rare in this setting , the grader was trained using an extensive set of images , and then completed an assessment using the Global Trachoma mapping project set of images which required agreement of kappa = 0 . 6 . For the first five hamlets , any positive case was also reviewed by the senior grader . Since all were confirmed , the grader was left to assess the remaining hamlets . Active trachoma was classified as either follicular trachoma ( TF ) or intense inflammatory trachoma ( TI ) . In order to detect C . trachomatis infection , conjunctival swabs were obtained from the right eye of each child . Strict adherence to protocol was observed to avoid field contamination , and control swabs were taken in the field to monitor possible contamination . These were labeled and analyzed in an identical fashion to true specimens . The swabs were placed in a dry tube , kept cold in the field and in Kilosa and sent to the International STD Laboratory at Johns Hopkins University in Baltimore , MD within 30 days of collection . Blood was collected by finger prick from each child onto filter papers with six circular extensions , each calibrated to collect 10 μl of whole blood ( TropBio Pty Ltd , Townsville , Queensland , Australia ) . These were dried and also shipped to the Johns Hopkins University . The ocular swabs were analyzed by nucleic acid amplification test ( NAAT ) for presence of ocular C . trachomatis rRNA usingAptima Combo2 ( AC2 ) ( Hologic , San Diego , CA ) , following manufacturer’s specifications . The lab personnel were masked to ocular and control swabs . A positive swab is considered infection for purposes of this analysis . The blood spots were analyzed for antibody to chlamydial antigen pgp3 as previously reported [9] using a multiplex bead assay on a Luminex 100 platform . Two different couplings of pgp3 to beads were used , necessitating the generation of unique cutoffs for each coupling . The results are reported as median fluorescence intensity minus background ( MFI-BG , where background is the signal from beads with buffer only ) and the positivity cut-off for the first set was 606 , and for the second set was 869 as determined by receiver operator characteristics ( ROC ) analyses [9] . ROC analyses were done for both bead sets . The prevalences of TF , C . trachomatis infection ( defined as a positive ocular swab ) , and antibodies against the chlamydial antigen pgp3 in children 1 to 9 were calculated for each community as the number of children positive divided by number of children examined . The overall district prevalences of the three outcomes are estimated as the mean of the individual community prevalences . In order to derive the confidence intervals for the overall prevalence , because the distribution of prevalence of TF and infection with C . trachomatis were skewed with most communities having zero prevalence we recalculated the estimate using 1000 bootstrap samples to derive the 2·5% and 97·5% percentiles . The proportion of children positive for antibodies against pgp3 is presented for 3 age categories: 1 to <4 years ( born after last MDA ) , 4 to <7 ( born during MDA period ) , and 7 years or older ( born before initiation of MDA ) . The Mantel-Haenzel Chi-Square statistic was used to test for the presence of a linear trend with increasing age . The study protocol and forms were reviewed and approved by the Johns Hopkins Institutional Review Board and the National Institute for Medical Research in Tanzania . All guardians gave written , informed consent to participation of the children . Children , ages seven and older provided assent .
A total of 1474 children in 30 communities were enrolled in the study , from the 1501 that were invited ( 98% ) . The district prevalence of TF was 0·4% ( 95% CI = 0·01–0·80 ) , and the prevalence of infection with C . trachomatis was 1·1% ( 95% CI = 0 . 3–2·4 ) . None of the field “air” swabs were positive . The overall prevalence of antibody positivity to pgp3 was 7·5% ( 95% CI = 5·1–10·1 ) ; the overall response to antibody suggests the majority of children had very low levels with only a few having MFI-BG greater than the cut off value ( Fig 1 ) . The antibody positivity increased by age group ( Table 1 ) . Because we expected that antibody levels in children ages 1–3 years would reflect the period post-program intervention , we further evaluated the characteristics of antibody positivity in that age group in addition to the 1–9 year-old age group . The level of antibody positivity according to the TF presence , or presence of infection , in the children is shown in Figs 2 and 3 . While most of the TF cases had antibody below the cut offs , all but one of the cases of infection had antibody levels well above cut off . Eight hamlets had infection or trachoma ( TF ) , or both infection and trachoma ( TF ) , while 22 communities had neither infection nor trachoma ( TF ) . Of the eight hamlets with TF and/or infection , two had both TF and infection , three had infection alone , and three had TF alone ( Table 2 ) . TF was low in the five hamlets with TF , between 2% and 3% prevalence ( Table 2 ) . The prevalence of Infection was greater than 5% in the few hamlets with infection and TF or infection alone , with 6% and 7% infection rates , respectively , in 1–9 year olds . Overall , antibody positivity among 1–3 year olds—the children born after cessation of MDA—was higher in the hamlets with infection compared to those without ( Table 2 ) . A range of antibody-positive rates for children ages 1–3 years was observed within the 22 hamlets that had neither infection nor TF , although 13 of these hamlets ( 59% ) had no individuals with detectable antibody responses . Six hamlets ( 27% ) had seropositivity rates between 1 and<10% , one was between 10 and 20% seropositive , and two ( 9% ) were >20% seropositive for children ages 1–3 years ( Fig 4 ) . For children ages 1–9 years , 17 hamlets ( 77% ) had overall antibody-positivity rates <10% ( Fig 2 ) .
This is the first “surveillance” survey to be reported for trachoma ( TF ) using the new World Health Organization interim guidelines . Kilosa district appears to have sustained reduction in TF four years after stopping all trachoma program activities , now at 0·4% TF , consistent with interruption of transmission for most communities . Interestingly , the prevalence of infection , measured using a very sensitive nucleic acid amplification test , was 1·1% , and higher than disease rates , but was concentrated in just a few villages . This was also observed in a previous survey in this district [12] . The antibody-positivity prevalence in two of the villages with the highest rates of infection was greater than 10% in children ages 1–3 years , further supporting ongoing transmission in those villages . However , in three hamlets with neither infection nor TF , the antibody positivity was also above 10% . This was clearly not indicating ongoing transmission of infection , but was likely indicative of past exposure , as we see in other trachoma endemic communities where groups of children who do not have infection or TF have high rates of antibody positivity [10] . We do not expect antibody positivity to be indicative of current infection or transmission , but rather cumulative exposure to C . trachomatis . In these hamlets , children in the age group 1–3 . 9 years may have had exposure after the program stopped activity , but do not currently have either infection or disease . Other possible reasons are described below . A characteristic increase by age in prevalence of antibody positivity against C . trachomatis antigens was observed , even in this district with virtual absence of trachoma . Previous reports in trachoma-endemic communities have found a similar trend but with a sharper rise in the age prevalence of antibody positivity [9 , 10] . In this district , the youngest age group , ages 1–3 years , who were born post- MDA and during surveillance , had the lowest prevalence at 5 . 2% . The 4–7 year olds , born during the last three cycles of yearly rounds of MDA , had the next lowest prevalence , 8·1% . The children who were born before the last MDA cycle began , ages 7–9 years at this survey , had a prevalence of antibody positivity of 9·3% . Pgp3-specific antibody responses were generally quite low in this district . This is in contrast to high seroprevalence seen in previous studies in trachoma endemic villages with ongoing transmission [10] . These responses were low even in comparison to 4 villages in neighboring Kongwa district , where TF prevalence averaged 2 . 9% and antibody prevalence was 22% in 1–9 year olds ( S1 File ) . Kilosa had already had intermittent mass drug administration over a seven year period prior to 2008 which may have reduced exposure to ocular C . trachomatis infection even in the oldest children . The low antibody levels seen in this survey likely reflect the low transmission in Kilosa district , but the heterogeneity across hamlets , and especially the >20% seroprevalence in two hamlets with no infection or trachoma , suggests that other causes of antibodies recognizing pgp3 should also be considered . For example , pgp3-specific antibodies may be generated from ocular or respiratory infection arising from transmission from a mother with genital infection to her newborn during delivery [13 , 14] . We have no data on genital infection in this district , but it is notable that the two high antibody prevalence villages were close to the main truck road in Kilosa . It is also possible that families that travel for cultural reasons to other , still-endemic , districts in Tanzania may acquire trachoma there and return to Kilosa . A similar finding was observed in The Gambia , where—despite the fact that families returned with infection—transmission was not sustainable and trachoma died out [7] . That phenomenon appears to also be the case in Kilosa where district rates of infection are still low . There are no data on the level of infection required to cause seroconversion , but it may be possible that infection was introduced then died out , but left cases of seroconversion . There are limitations to the study . Ideally , the determination of antibody status at the time of the impact survey immediately post-MDA would have enabled us to follow the trajectory of antibody responses over the time of surveillance and provided stronger evidence for the assertion that <6% in children ages 1–3 years represents evidence of low transmission . The fact that close to half the sample hamlets had young children with no antibodies to pgp3 is encouraging and suggests that this district has not experienced re-emergence of transmission as determined by antibody testing . Also , the antibody positivity rate is subject to the choice of cut-off for each run . In this study , two different bead couplings necessitated the generation of two separate cut-offs . An international standard , such as a humanized monoclonal antibody against pgp3 , would allow direct comparisons between different couplings and analyses done in different settings . We also recognize that the use of a Luminex platform is expensive , not widely available , and technically complicated . If it is to be used in a program context , further work on development of an ELISA or a field test strip would be more practical . We are working on this aspect , but are encouraged to do so by the data found here at district level . Finally , the surveillance survey was powered to detect low prevalences at the district level , and there are more uncertainties around the estimates of prevalence within the sub groups . However , the data on prevalences within age groups and within the hamlets organized by prevalences of TF and infection were informative and we felt reasonable to show . The heterogeneity observed in the different hamlets also points to the importance of interpreting data at the district , not individual village , level . Districts represent a spectrum of their communities’ prevalence of disease , infection , and antibody status . Focusing undue attention on a few seemingly anomalous hamlets with infection would have obscured the general finding that our surveillance survey indicated trachoma ( TF ) is no longer a public health problem in this district , regardless of the tool we might have used to measure it . Our surveillance survey found , four years after cessation of MDA , that TF had not returned and that a prevalence of less than 6% pgp3 antibody in children ages 1–3 years was associated with absence of re-emergence of trachoma in this district . In fact , these data suggest that any of the assessments used provided a good marker of trachoma elimination . | Trachoma , the leading infectious cause of blindness world-wide , is targeted for elimination by 2020 . The World Health Organization advises districts to undertake surveillance for trachoma when follicular trachoma ( TF ) is less than 5% in children 1–9 years . In a trachoma-endemic district that stopped its program four years ago , we undertook a surveillance survey , adding to the assessment of TF a test for C . trachomatis infection , and a dried blood spot which was processed for antibodies to C . trachomatis antigen pgp3; antibody status may indicate cumulative past exposure to infection . The prevalence of TF was 0 . 4% , below the 5% cut-off indicating that trachoma elimination had been achieved with no re-emergence . The antibody positivity overall was low , 7 . 5% , and increased with age from 5 . 2% in 1–3 year olds , to 9 . 3% in 7–9 year olds ( p = 0 . 015 ) . In 16 of the 30 hamlets , no children ages 1–3 years had antibodies to pgp3 . The antibody status of the 1–3 year olds indicated low cumulative exposure to infection during the surveillance period . In summary , four years post -program , there is no evidence for re-emergence of trachoma using any indicator sufficient to cause re-emergence . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2016 | Can We Use Antibodies to Chlamydia trachomatis as a Surveillance Tool for National Trachoma Control Programs? Results from a District Survey |
Understanding how genomes encode complex cellular and organismal behaviors has become the outstanding challenge of modern genetics . Unlike classical screening methods , analysis of genetic variation that occurs naturally in wild populations can enable rapid , genome-scale mapping of genotype to phenotype with a medium-throughput experimental design . Here we describe the results of the first genome-wide association study ( GWAS ) used to identify novel loci underlying trait variation in a microbial eukaryote , harnessing wild isolates of the filamentous fungus Neurospora crassa . We genotyped each of a population of wild Louisiana strains at 1 million genetic loci genome-wide , and we used these genotypes to map genetic determinants of microbial communication . In N . crassa , germinated asexual spores ( germlings ) sense the presence of other germlings , grow toward them in a coordinated fashion , and fuse . We evaluated germlings of each strain for their ability to chemically sense , chemotropically seek , and undergo cell fusion , and we subjected these trait measurements to GWAS . This analysis identified one gene , NCU04379 ( cse-1 , encoding a homolog of a neuronal calcium sensor ) , at which inheritance was strongly associated with the efficiency of germling communication . Deletion of cse-1 significantly impaired germling communication and fusion , and two genes encoding predicted interaction partners of CSE1 were also required for the communication trait . Additionally , mining our association results for signaling and secretion genes with a potential role in germling communication , we validated six more previously unknown molecular players , including a secreted protease and two other genes whose deletion conferred a novel phenotype of increased communication and multi-germling fusion . Our results establish protein secretion as a linchpin of germling communication in N . crassa and shed light on the regulation of communication molecules in this fungus . Our study demonstrates the power of population-genetic analyses for the rapid identification of genes contributing to complex traits in microbial species .
In most filamentous ascomycete species , hyphae form an interconnected network or syncytium of multi-nucleate cells known as a mycelium [1] . In nature , the formation of a mycelium often occurs via the germination of wind-dispersed asexual spores ( conidia ) [2] . Upon landing on a suitable substrate , conidia germinate to form germlings that are capable of fusion via specialized structures called conidial anastomosis tubes ( CATs ) to form the interconnected mycelial network common in this group of organisms [3] , [4] . The formation of mycelial networks by germling fusion increases cytoplasmic flow and is important for the distribution of nutrients , signals and organelles within the colony [5] , [6] . Similar to cell fusion in other organisms , the process of germling fusion in the filamentous ascomycete fungus Neurospora crassa requires cell recognition and attraction , adhesion , cell wall remodeling and membrane merger [7] . Genetically identical germlings of N . crassa exhibit remarkable chemotropism to each other , which enhances the formation of the inter-connected hyphal network [8] , [9] . A number of mutants have been identified in N . crassa that fail to undergo germling and hyphal fusion , including nrc-1 , mek-2 and mak-2 , which are components of a conserved MAP kinase pathway [3] , [10] , [11] , [12] . Other mutants of unknown biochemical function , such as soft ( so ) , also show defects in chemosensing and cell fusion [13] , [14] . The components of the MAP kinase pathway ( NRC1 , MEK2 and MAK2 ) and SO are recruited in a rapid and oscillatory manner to the plasma membranes of germling pairs undergoing chemotropic interactions [12] , [14] . The oscillation of MAK2 and SO to CAT tips has been proposed to allow genetically identical cells to alternate between two different physiological states associated with signal delivery or response [14] , [15] , [16] . Given the complex physiology of cell communication and fusion , many other genes and proteins likely play a role in this process . N . crassa is a heterothallic , obligate outbreeding species that has been a model for the study of population structure and genetic variability of fungi in the wild [17] , [18] , [19] , [20] , [21] . Recent advances in nucleic acid sequencing technologies have allowed for large-scale sampling of wild populations in this model microbe , and we recently harnessed this strategy in a population structure analysis of N . crassa by RNA-seq [21] . Data from such a sequencing survey provides a dense map of genetic variants across the genome and raises the possibility of genome-wide association studies in N . crassa . Association mapping is a powerful tool to identify candidate cases in which genetic variation at the DNA level underlies differences between wild individuals in a trait of interest . This approach is in common use in human genetics but has had little application to date in model organism systems , although recent work has established the power of association studies in mapping the genetic basis of trait variation across wild individuals in Drosophila [22] , [23] , [24] , Arabidopsis [25] , [26] , [27] , [28] and sunflower [29] . In fungi [30] , [31] and in most other organisms beside humans , studies seeking to use natural variation as a screening tool to map genotype to phenotype have been largely limited to experimental cross designs , which survey polymorphisms in only a few wild individuals . Here we describe the results of the first genome-wide association analysis used to identify novel loci underlying trait variation in a microbial eukaryote . We applied an association strategy using wild isolates of N . crassa to identify the genetic basis of the complex trait of germling communication . Developing a detailed , quantitative assay well-suited to the medium-throughput association-mapping paradigm , we surveyed germling communication across wild N . crassa strains and mapped differences in this trait to DNA sequence variants . We subsequently tested the function of genes mapped in our association study by assessing the germling communication phenotype of deletion strains , revealing mutants that showed both decreased and increased germling fusion frequency . We also tested the effect of some gene deletions on MAK2 and SO oscillation during chemotropic interactions . And we localized within hyphae the protein product of the gene that showed the most significant association with germling communication phenotype , a homolog of mammalian neuronal calcium sensor-1 ( NCS-1 ) .
Our previous study of the relatedness of wild N . crassa isolates from the Western hemisphere by RNA-seq revealed a well-defined population of 20 individuals from Louisiana [21] . To establish a larger set of genotyped Louisiana strains suitable for use in association mapping , we transcriptionally profiled an additional 92 Louisiana strains ( Table S1 ) . Analysis of the regulatory variation across the Louisiana population detected in these data will be reported elsewhere; here we used the RNA-seq reads to identify 1 . 09 million single-nucleotide polymorphisms ( SNPs ) in coding regions of the seven N . crassa chromosomes ( Dataset S1 ) . Phylogenetic analysis of these SNPs ( Figure S1 ) indicated a set of 100 strains with little population substructure , including the smaller sample of Louisiana isolates that we had previously characterized [21] . We identified 81 , 614 SNPs at which the minor allele was present in >25% of strains , and which were equally distributed throughout the euchromatic regions of all seven chromosomes of N . crassa ( Figure S2 and Dataset S2 ) . Across the 9 , 730 protein-coding genes of the N . crassa genome ( http://www . broadinstitute . org/annotation/genome/neurospora/MultiHome . html ) , the average gene harbored ∼10 high-frequency SNPs . To use our genotyped Louisiana strains to dissect the genetics of germling communication , we first developed a communication assay as follows . When genetically identical macroconidia of the N . crassa laboratory strain FGSC 2489 germinate near each other , ∼89% of the germlings within 15 µm of other germlings sense their neighbors , reorient their growth , and engage in cell fusion via CATs [3] ( Figure 1A ) . The remaining germlings ignore each other , do not show chemotropism , do not form CATs and do not fuse ( Figure 1B ) . We thus quantified communication by isolating macroconidia from each given wild strain , plating them on agarose minimal medium , and tabulating the percent of germling pairs exhibiting redirected CAT growth ( communication ) or fusion after 3–4 hours of incubation . Applying this procedure to 24 Louisiana strains showed that the germling communication trait varied among the wild isolates , from a high of 90% communication/cell fusion efficiency to a low of less than 40% communication ( Table S2 and Figure 2 ) . To map loci underlying the variation in communication efficiency/cell fusion across our wild population , we first scored patterns of germling interactions as a qualitative , binary trait , such that the phenotype of a given individual was classified as either avidly or poorly communicating . We then used our set of genotypes at high-frequency SNPs to test each locus in turn for co-inheritance with the communication trait across the strains of the population , using a permutation strategy , described in Methods , to correct for multiple testing . This mapping calculation yielded 3 SNPs showing significant association with germling communication at a threshold at which we expected ∼0 . 01 SNP by chance ( Figure 3 and Dataset S3 ) . All three SNPs lay in the 3′ UTR of the gene NCU04379 with linkage disequilibrium decaying sharply around this peak ( Figure 4 ) ; we detected no differential expression of NCU04379 between strains with avid germling communication and those whose germlings communicated poorly ( data not shown ) . NCU04379 encodes CSE1 , a homolog of the vertebrate neuronal calcium sensor-1 ( NCS-1 ) and of Frq1p in Saccharomyces cerevisiae [32] . Deletion of cse-1 in N . crassa results in a mutant that is viable , but sensitive to calcium stress and ultraviolet light , and which shows slightly impaired growth [33] . Similar to NCS-1 and Frq1p , CSE1 harbors a consensus signal for N-terminal myristoylation and four EF-hand domains ( PF00036 ) predicted to be involved in calcium binding [32] , [34] , [35] . We hypothesized that CSE1 played a role in germling communication and that mutations in this gene would impact cell fusion behavior . Germling CAT fusion experiments validated this prediction , revealing a striking 3 . 6-fold reduction in the frequency of communication and cell fusion between Δcse-1 germlings relative to communication between germlings of the wild-type , isogenic strain from which the Δcse-1 strain was derived ( Figure 5A ) . The defect was rescued by integration of a wild-type copy of cse-1 at the his-3 locus in the Δcse-1 strain , confirming the specificity of the phenotype to the cse-1 lesion ( Figure S3 ) . To evaluate the ability of Δcse-1 germlings to respond to communication with wild-type isolates , we assayed Δcse-1 germlings positioned alongside those of the isogenic fusion-competent strain , and observed a defect similar to that of Δcse-1 germlings interacting with one another ( Figure 5A ) . Thus , CSE1 is essential for chemotropic interactions , including the sensing of and response to the presence of a fusion-competent partner . We next sought to learn if CSE1 acts before or after a required , chemotropic interaction event in germling fusion , the observable oscillations of MAK2 and SO to the tips of communicating CATs [14] . To address this question , we obtained a wild-type strain expressing either MAK2-GFP or SO-GFP , and we visualized the subcellular localization of the latter proteins during interactions between wild-type germlings and those of the Δcse-1 mutant background . In the few cases in which a Δcse-1 germling showed chemotropic interactions toward a wild-type germling , we observed normal recruitment and oscillation of both MAK2 and SO to wild-type germling tips ( every ∼4 minutes ) ( Figure 6 ) . In the ∼75% of cases in which a Δcse-1 germling and a wild-type germling showed no evidence of chemotropic interactions , MAK2 and SO did not localize or oscillate to CAT tips , but remained in the cytoplasm . We conclude that CSE1 acts upstream of the signaling that underlies chemotropic interactions , because in the rare instances where Δcse-1 germlings commit to chemotropic interactions and cell fusion , they successfully drove MAK2 and SO oscillation . The mammalian homolog of CSE1 , NCS-1 , functions during regulated exocytosis in response to calcium signaling [36] , [37] , and the yeast homolog Frq1p localizes to the Golgi membrane [38] . We reasoned that these attributes would likely be conserved in N . crassa . We first focused on the role of calcium; the Δcse-1 mutant shows growth sensitivity to excess calcium , as well as to calcium depletion [33] . We therefore hypothesized that calcium could be required for chemotropic interactions between N . crassa germlings , and to test this , we assayed fusion of wild-type germlings on growth medium depleted of Ca2+ . The results ( Figure 7B ) bore out our prediction , with no detectable chemotropic interactions or CAT fusion in the absence of Ca2+ . We next investigated the localization of CSE1 in N . crassa . For this purpose , we used a Δcse-1 strain in which the cse-1 allele with a C-terminal GFP tag had been integrated at the his-3 locus . The introduction of the GFP-tagged cse-1 allele restored wild-type growth and germling communication phenotype to the Δcse-1 strain ( Figure S3 ) . We compared the localization of CSE1-GFP to that of the late Golgi marker VSP52 tagged with RFP [39] , [40] . The results , shown in Figure 8 , revealed colocalization of the CSE1 and VPS52 , with CSE1-GFP also present in the cytoplasm . Mammalian NCS-1 and S . cerevisiae Frq1p interact with phosphatidylinositol 4-kinase ( Pik1p ) [32] , [37] , a protein involved in secretion from the Golgi to the plasma membrane . As Frq1p is required for regulated exocytosis through Pik1p [38] , we hypothesized that N . crassa homologs of components of this secretion pathway would play a role in germling communication . To test this hypothesis , we first assayed germlings carrying a deletion of the Pik1p homolog in N . crassa , NCU10397 ( pik1 ) , and observed a 1 . 5-fold reduction of germling communication ( Figure 5A ) . A communication defect of similar magnitude was apparent when Δpik1 mutant germlings were assayed for interactions with wild-type fusion partners ( Figure 5A ) . We next investigated 14-3-3 proteins , regulatory molecules that bind diverse signaling proteins [41] and in S . cerevisiae transport Pik1p from the nucleus to the cytoplasm [42] . Two members of this family have been identified in N . crassa , NCU03300 ( nfh-1 , encoding the DNA damage checkpoint component RAD24 ) and NCU02806 ( nfh-2 , encoding a 14-3-3 protein ) ; we assayed germling communication in strains harboring deletions in each of these genes in turn . The results revealed no effect of the Δnfh-1 mutation ( data not shown ) , but Δnfh-2 germlings communicated with one another at a frequency 1 . 5-fold less than that of isogenic wild-type germlings ( Figure 5A ) , and Δnfh-2 conidia mixed with those of a wild-type strain exhibited a similar defect ( Figure 5A ) . Echoing our findings from the Δcse-1 mutant , we observed normal oscillation of MAK2-GFP and SO-GFP to the CATs of wild-type germlings when they participated in chemotropic interactions with Δnfh-2 germlings , while wild-type germlings that did not communicate with those of the Δnfh-2 background showed uniquely cytoplasmic localization of MAK2-GFP and SO-GFP ( Figure 6 ) . Taken together , these data indicate that CSE1 , PIK1 , and NFH2 are each required for the calcium-dependent initiation of germling communication and chemotropic interactions , strongly suggesting their joint function in a Golgi secretion pathway involved in signaling to initiate germling fusion . Given the robust genetic association between cse-1 genotype and germling communcation in wild strains ( Figure 3 ) , we reasoned that additional determinants of germling communication could be revealed by mining our genome-wide association data at lower significance levels . For this purpose , we re-examined our association results using a permissive threshold of p<0 . 015 . Permutation testing estimated that 22% of loci reaching this level would be true positives ( see methods ) ; as such , independent gene-by-gene validation could uncover bona fide communication genes among this set , potentially both activators and repressors of the communication trait . We focused on genes annotated in secretion , kinase signalling pathways , or peptide hydrolysis in which SNPs showed association reaching our permissive significance cutoff . Of the 18 genes that fit this description and for which deletion strains were available and viable ( Table 1 ) , deletion of six genes had significant impact on communication frequencies as compared to a wild-type strain ( Figure 5B ) . The most extreme phenotype , a complete failure of chemotropic interactions and CAT fusion , was observed in the deletion strain for the exocyst complex component sec15 ( NCU00117 ) ( Table 1; Figure 5B ) . The latter mutant also exhibited slower growth , reduced conidiation , and slower conidial germination . Deletion of two additional genes , the protein transporter sec22 ( NCU06708 ) and the acetylornithine-glutamate transacetylase arg-15 ( NCU05622 ) [43] , also compromised fusion frequency ( 68%±2 and 53%±4 , respectively ) ( Figure 5B ) . Remarkably , deletion of each of three genes heightened germling communication and fusion frequencies ( Figure 5B ) : a GTPase activating protein ( NCU06362; 96%±2 ) , the nonidentical kinase-2 nik-2 ( NCU01833; 97%±0 . 7 ) , and the secreted subtilisin-like serine protease spr-7 ( NCU07159; 97±1 . 3 ) . The elevated fusion frequency in each of these strains contrasts with any known germling fusion mutant , all of which reduce or eliminate chemotropic interactions or cell fusion , and highlights the ability of association mapping to pinpoint negative regulators as well as genes with a positive role in cell communication . In each mutant with heightened fusion frequency , germlings were also often involved in fusion events with more than one germling ( multiple fusion events ) ( 26 . 33%±5 . 24 in ΔNCU06362 , 21 . 33%±1 . 8 in Δnik-2 , and 20 . 66%±4 . 07 in Δspr-7; Figure 7C ) . By contrast , multiple germling fusion events was a phenotype only observed at a low level in a wild-type strain ( 2%±2 ) . To investigate further the novel gain-of-fusion phenotype , we focused on the putative secreted serine protease spr-7 . We first confirmed that the introduction of an ectopic copy of spr-7 at the his-3 locus restored hyphal communication of the spr-7 deletion strain to wild-type levels , establishing the deletion as the sole cause of the increased communication phenotype ( Figure S3 ) . We next asked whether the presence of wild-type germlings would be sufficient to complement the Δspr-7 phenotype during communication . Assays of Δspr-7 germlings mixed with those of a wild-type strain confirmed this hypothesis , revealing a fully wild-type communication phenotype ( fusion frequency 82%±3 ) , a striking contrast to the failure to communicate with wild-type germlings we had noted in Δcse-1 , Δpik1 and Δnfh-2 mutants ( Figure 5 and see above ) . These results support a model in which secreted SPR-7 from wild-type germlings acts in a cell-non-autonomous fashion to restrict communication and CAT fusion between wild type germlings .
In N . crassa , genetically identical germlings chemotropically sense partner cells and undergo mutual recognition-directed growth and cell fusion [14] , [15] , [16] . The molecular basis of this phenotype is only partly understood , and tools to identify candidate genes involved in fusion are at a premium in the field . In this work , we genotyped more than 100 wild N . crassa isolates , advanced our understanding of germling communication and fusion , and established this population as a powerful resource for high-resolution association mapping that can be used with any variable phenotype . Our study is the first to illustrate the utility of genome-wide association mapping to identify novel loci underlying trait variation in a microbe . We anticipate that this methodology will be a powerful and generally applicable tool in future genetic study of many eukaryotic microbes , owing to the small genome sizes and deeply-sampled populations of a number of species , particularly filamentous fungi . The top hit from our association analysis was cse-1 , which is homologous to a neuronal calcium sensor gene in animals that shows nervous-system-specific expression and neuron-specific phenotypes; neurons , like hyphae in filamentous fungi , are a highly polarized tissue . Neuronal calcium sensor-1 ( Frequenin ) is a myristolylated protein with four EF hands that functions as a calcium ion sensor for modulation of syntaptic activity and secretion [34] , [44] , [45] , [46] . Our analysis revealed a near-complete loss of cellular communication during germling fusion in a N . crassa Δcse-1 mutant . In animals and in S . cerevisiae , NCS-1/Frq1p and Bmh1p-Bmh2p regulate phosphatidylinositol 4-kinase/Pik1p , with Bmh1p-Bmh2p mediating the nucleocytoplasmic shuttling of Pik1p [42] . NCS-1/Frq1p promotes association of Pik1p with the Golgi membrane , which is required for its role in regulated exocytosis [37] , [38] . Our results established that in N . crassa , CSE1 localized to the Golgi and that deletion of pik1 or nfh-2 phenocopied a cse-1 deletion strain . These observations together support a model in which , in N . crassa , CSE1 , PIK1 and NFH2 regulate exocytosis of an unidentified ligand and/or receptor , perhaps initiated via calcium signaling , which is important for establishing communication between cells and subsequent chemotropic interactions ( Figure 9 ) . Recently , an essential kinase ( MSS-4 ) involved in the generation of phosphatidylinositol 4 , 5-bisphosphate ( PtdIns ( 4 , 5 ) P ( 2 ) ) was found to localize to contact points between germlings during cell fusion [47] , indicating that the generation of different phosphatidylinositol phosphate moieties may regulate different aspects of germling fusion . A role for phosphorylation is suggested by our finding that the defect in germling communication observed in the Δcse-1 , Δpik1 and Δnfh-2 mutants correlates with an absence of oscillation of MAK2 and SO to CAT tips , because MAK2 kinase activity has been shown to be required for chemotropic interactions and MAK2 and SO oscillation [14] . In S . cerevisiae , Pik1p is required for full activation of the MAP kinases Fus3p and Hog1p and repression of Kss1p [48] , and the Fus3p ortholog in N . crassa is MAK2 [10] . It is therefore tempting to speculate that the activation of PIK1 by CSE1 may play an important role in germling communication by affecting activation of MAK2 , thus modulating MAK2 phosphorylation targets as well as downstream transcriptional targets required for germling fusion ( Figure 9 ) . In addition to our mapping of cse-1 as a determinant of variation in germling communication across wild N . crassa , further mining of our association results led to the identification and validation of six other genes associated with CAT fusion . Of these , one gene , sec15 , is a homolog of a component of the exocyst complex in S . cerevisiae , a multiprotein complex that localizes at the bud tip and is associated with exocytosis [49] . Our results indicated that sec15 is essential for CAT fusion in N . crassa . Likewise , our results revealed a defect in germling communication and fusion frequency in a strain bearing a deletion in a homolog of SEC22 in N . crassa , NCU06708; in S . cerevisiae , Sec22p assembles into a SNARE complex and plays a role in ER-Golgi protein trafficking [50] . Our demonstration that cse-1 , pik1 , nfh-2 , sec15 , and sec22 are all required for germling communication establishes the importance of protein secretion and trafficking for chemotropic interactions and cell fusion in N . crassa . Our results also established that mutation of the acetylornithine-glutamate transacetylase arg-15 [43] confers a defect in germling communication . The homolog of arg-15 in S . cerevisiae , Dug2p , is involved in degradation of the antioxidant glutathione and other peptides containing a gamma-glu-X . dug2 mutants show deficient utilization of glutathione [51] , which reacts non-enzymatically with reactive oxygen species and detoxifies oxidatively stressed cells [52] . A role for redox reactions in germling communication through arg-15 would dovetail with reports that mutants in components of the NADPH oxidase complex , which is involved in redox signaling , are defective in CAT fusion [9] . Our work has uncovered a new category of fusion mutants that exhibited germling fusion frequencies higher than those of wild-type , and which displayed multiple fusion events . Of the genes whose deletions gave rise to this striking phenotype , one encoded an uncharacterized predicted GTPase activating protein ( GAP ) ( NCU06362 ) . NCU06362 contains a TBC domain ( PF00566 ) and is a paralog of GYP5 in S . cerevisiae; Gyp5p is involved in the recruitment to sites of polarized growth of the BAR domain protein Rvs167p , which has been implicated in exocytosis at the bud tip [53] . Rvs167p interacts with a second BAR domain protein , Rvs161p , and together this complex plays a role in receptor-mediated endocytosis [54] . Gyp5p also has in vitro GAP activity towards Ypt1p , which is involved in ER-to-Golgi trafficking , and towards Sec4p , which regulates exocytosis [55] . Thus , the increase in germling fusion frequencies observed in the ΔNCU06362 mutant could be due to alterations in secretion or in the reduction of endocytosis of a receptor involved in germling communication . A second gene whose deletion enhanced hyphal communication , spr-7 , encodes a secreted subtilisin-related serine protease , part of a family whose members carry out a wide range of peptidase activities [56] . The increase in fusion frequency and germlings involved in mutiple fusion events in the Δspr-7 mutant suggests that SPR-7 may be responsible for the degradation of a peptide required for extracellular communication ( Figure 9 ) . The nature of the extracellular ligand and receptor ( s ) that guide chemotropic interactions during cell fusion in N . crassa is currently unknown . In fungi , secreted peptides involved in extracellular communication have not been reported , apart from peptide pheromones involved in mating [57] , [58] or small secreted proteins with antifungal properties [59] , [60] . The genes we have uncovered here will serve as targets for future genetic and biochemical efforts to identify extracellular ligands and receptors involved in germling communication and cell fusion in N . crassa . Our results also revealed an increase in germling communication in a nik-2 deletion strain . This gene encodes a histidine kinase , a member of a canonical two-component signal transduction pathway and part of an 11-member family in N . crassa . No phenotype for the Δnik-2 mutant has been previously reported [61] . However , other histidine kinases affect MAPK signal transduction pathways in fungi , including nik-1 , a member of the osmoregulatory OS-2 pathway in N . crassa [62] , and the histidine kinase Sln1p , which regulates the Hog1p MAPK pathway in S . cerevisiae [63] . We hypothesize that the increase in fusion frequencies in the absence of nik-2 may stem from a defect in the regulation of the MAK2 phosphorylation pathway , leading to a hyper-activated state during chemotropic interaction ( Figure 9 ) . Further research will be necessary to elucidate the specific role of nik-2 in this process . By identifying multiple novel determinants of germling communication , our results underscore the power of association studies for the mapping of genes to phenotypes in wild populations . Importantly , our N . crassa population is particularly amenable to GWAS , with little discernable population structure and low linkage disequilibrium , allowing the detection of strong association to finely resolved loci . These attributes of N . crassa stand in contrast to S . cerevisiae , where GWA studies are hampered by a mosaic and heterogenous population structure [64] . Our relatively modest , medium-throughput phenotyping of a quantitative phenotype in wild individuals compares favorably with the high-throughput approach that would be required to survey the >9000 strains of the N . crassa deletion collection [65] , not only by saving 98% of the labor , but in enabling analysis of all genes , including those that are essential . However , our molecular follow-up of GWAS hits was aided by the availability of a near-full genome deletion strain collection for N . crassa . When the central question , as in our work , is to infer novel function for poorly annotated genes , comparing a given gene's deletion strain and the isogenic wild-type strain is a straightforward and precise approach that obviates potential complications from epistasis in allele-swapping experiments . Our GWAS method also compares favorably to two-parent crossing schemes for the dissection of natural variation [66]: first , because linkage blocks in our outbreeding population often contain a single gene , whereas more than 50 can be contained in those resulting from just one cross [67] , and second , because we sample phenotypes that vary among multiple individuals and not just those that differ between two parents . With the availability of our collection of 112 genotyped individuals to the fungal genetic community , future studies will require only phenotyping to map the molecular basis of trait variation using the strategy we have pioneered here . And as population-genomic resources are developed in many taxa , we anticipate that association mapping will be successfully applied in other species , within and outside the fungal kingdom .
All 112 strains used in this study were isolated from Louisiana , USA ( Table S1 ) and are available from the Fungal Genetics Stock Center ( FGSC ) [68] . The deletion mutants used in these study were generated by the Neurospora Genome Project [65] , [69] and are administered by the FGSC [70] . The rfp-vps-52 transformant was generously provided by Barry Bowman [40] . All strains were grown on Vogel's medium [71] and all crosses were performed on Westergaard's synthetic cross medium [72] . The his-3 A mutant ( FGSC# 6103 ) and a his-3 a strain ( FGSC #9716 ) were used as females in crosses with deletion mutants . Progeny bearing the deletion mutations and the his-3 mutation were isolated and used in complementation experiments . Total RNA was isolated for each of the 112 strains listed in Table S1 . Strains were grown for 16 hrs on cellophane on Bird medium [73] . Mycelia were harvested and immediately added to 1 mL of TRIzol reagent ( Invitrogen Life Technologies ) [74] and zirconia/silica beads ( 0 . 2 g , 0 . 5-mm diameter; Biospec Products ) . Cells were disrupted using a MiniBeadBeater instrument ( Biospec Products ) at maximum speed for 30 seconds twice in succession . Total RNA was extracted according to the manufacturer's protocol for TRIzol ( Invitrogen ) and quantified on a Bioanalyzer ( Agilent ) . For polyA RNA purification , 10 µg of total RNA was bound to dynal oligo ( dT ) magnetic beads ( Invitrogen 610 . 02 ) two times , using the manufacturer's instructions . Purified polyA RNA was fragmented by metal-ion catalysis [75] using fragmentation reagents from Ambion ( AM12450 ) . For first strand cDNA synthesis 1 µg fragmented polyA RNA was incubated with 3 µg random hexamers ( Invitrogen 48190-011 ) , and incubated at 65°C for 5 minutes and then transferred to ice . 1st strand buffer ( Invitrogen 18064-014 ) was added to 1× final concentration ( 4 µL ) . Dithiothreitol ( DTT ) , dNTPs and RNAseOUT ( Invitrogen 10777-019 ) were added to 100 mM , 10 mM , and 20 U/20 µL respectively , and the sample was incubated at 25°C for 2 minutes . 200 U of Superscript II ( Invitrogen 18064-014 ) were added and the sample was incubated at 25°C for 10 minutes , 42°C for 50 minutes and 70°C for 15 minutes . For second strand synthesis , 51 µL of H2O , 20 µL of 5× second strand buffer ( Invitrogen 10812-014 ) , and dNTPs ( 10 mM ) were added to the first strand cDNA synthesis mix and incubated on ice for 5 minutes . RNaseH ( 2 U ) ( Invitrogen 18021-014 ) , DNA pol I ( 50 U ) ( Invitrogen 18010-017 ) were then added and the mixture was incubated at 16°C for 2 . 5 hours . End-repair was performed by adding 45 µL of H2O , T4 DNA ligase buffer with 10 mM ATP ( NEB B0202S ) ( 10 µL ) , dNTP mix ( 10 mM ) , T4 DNA polymerase ( 15 U ) ( NEB M0203L ) , Klenow DNA polymerase ( 5 U ) ( NEB M0210S ) , and T4 PNK ( 50 U ) ( NEB M0201L ) to the sample and incubating at 20°C for 30 minutes . A single base was added each to cDNA fragment by adding Klenow buffer ( NEB M0212L ) , dATP ( 1 mM ) , and Klenow 3′ to 5′ exo- ( 15 U ) ( NEB M0212L ) . The mixture was then incubated at 37°C in for 30 minutes . Standard Illumina adapters ( FC-102-1003 ) were ligated to the cDNA fragments using 2× DNA ligase buffer ( Enzymatics L603-HC-L ) , 1 µL of adapters , and DNA ligase ( 5 U ) ( Enzymatics L603-HC-L ) . The sample was incubated at 25°C for 15 minutes . The sample was purified in a 2% low-melting point agarose gel , and a slice of gel containing 200-bp fragments was removed and the DNA purified . The polymerase chain reaction ( PCR ) was used to enrich the sequencing library . A 10-µL aliquot of purified cDNA library was amplified by PCR . PCR cycling conditions were: a denaturing step at 98°C for 30 seconds , 14 cycles of 98°C for 10 seconds , 65°C for 30 seconds , 68°C for 30 seconds , and a final extension at 68°C for 5 minutes . All libraries were sequenced using an Illumina Genome Analyzer-II using standard Illumina operating procedures . RNAseq data for all strains used in these analyses has been deposited in Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo/; accession no . GSE45406; GSM1103708-GSM1103819 ) . Mapping of RNA-seq reads to the genome sequence of N . crassa strain FGSC 2489 [76] and calling of single nucleotide polymorphisms ( SNPs ) was carried out with Maq [77] . All RNA-seq reads that mapped to multiple locations were eliminated from analysis , as were SNPs located in regions of low consensus read quality . These variants were further filtered to retain only those that were bi-allelic , yielding a complete data set of 1 . 09×106 SNPs ( Dataset S1 ) which were used as input into phylogenetic inference with FastTree; because patterns of inheritance in one strain , JW168 , were suggestive of misclassification ( data not shown ) we did not include this strain in the tree shown in Figure S1 . For markers used as input into calculations of genetic association with the germling communication phenotype ( see below ) , we filtered the complete SNP set to retain only sites at which the minor allele was present at >25% frequency ( Dataset S2 ) . For germling communication assays , each strain was grown on Vogel's minimal media [71] in slant tubes for 4–6 days or until significant conidiation occurred . Conidial suspensions were prepared by collecting conidia with wood sticks and suspending in 600 µl of sterile distilled water . The conidial suspension was filtered by pouring over cheesecloth to remove hyphal fragments . Conidia were diluted to a concentration of 3×107 conidia/ml and 300 µl of this final mixture were spread either on an agar or agarose minimal-medium plates . The plates were incubated for 3–4 hours at 30° . At each of 2–3 timepoints for each strain , agar squares of 1 cm were excised and observed with a Zeiss Axioskop 2 using a 403 Plan-Neofluor oil immersion objective . For image acquisition DIC images were taken with a Hamamatsu Orca 03 camera ( Hamamatsu , Japan ) using the iVision Mac4 . 5 software and a Zeiss Axioimager microscope . Fusion events were counted for 50 germling pairs in each of 2–3 biological replicates . Complementation experiments were done using the pMF272 plasmid system [78] to insert a wild type copy of the deleted gene into the intergenic region 3′ of the his-3 locus; transformants were subsequently analyzed for germling fusion frequencies . Wild type copies of genes were amplified using Taq polymerase from New England Biolabs ( Ipswich , CA , USA ) . Primers were designed to amplify the coding regions and also contained an added restriction enzyme site . The amplified DNA fragments were TOPO ( Invitrogen ) cloned , cut with restriction enzymes and ligated into restriction enzyme-digested pMF272 plasmid . The ligated DNA was used to transform Escherichia coli ( DH5a ) , and the plasmid isolated from individual transformants . The DNA sequence of each plasmid was determined; plasmids containing wild type copies of the genes were used for complementation experiments . Some mutants showing reduced fusion frequencies were further characterized by studying the ability of the mutant germlings to induce recruitment of MAK2-GFP or SO-GFP to the plasma membrane of opposing germlings as described by Fleißner et al [13] . Conidia from MAK2-GFP and SO-GFP strains were mixed with equal amounts of conidia from the respective deletion mutants and samples were prepared for microscopy as described above . Images were taken at two-minute intervals using a Leica SD6000 microscope with a 100×1 . 4 NA oil-immersion objective equipped with a Yokogawa CSU-X1 spinning disk head and a 488-nm laser controlled by Metamorph software ( Molecular Devices , Sunnyvale , CA ) . To visualize CSE1-GFP and RFP-VPS-52 localization , the strains were grown on Vogel's MM plates overnight and squares of 1 cm were excised and examined in the same confocal microscope explained above using the 488-nm laser for GFP and 563 nm laser for RFP . To study co-localization of both proteins , heterokaryons were made by mixing conidia from both strains in the center of a plate and incubating them overnight to allow cell fusion and cytoplasmic mixing from both strains . The samples were prepared and imaged as explained above . We used germling communication phenotype measurements in biological triplicate from 24 Louisiana strains in a genome-wide association analysis as follows . For each strain , we first calculated the average communication frequency across all replicates and timepoints to yield a final quantitative communication measurement . We then converted the latter value to a qualitative score: we calculated the grand mean and standard deviation of communication frequency across all strains , and we classified a given strain as low-communicating if its communication measurement was more than one standard deviation below the grand mean , and high-communicating otherwise . We then tested each marker in turn , from our set of SNPs with >25% minor allele frequency ( see above ) , for co-inheritance with this qualitative communication score using Fisher's exact test [79] . To evaluate the experiment-wise false discovery rate at a given Fisher's p-value threshold pthresh , we shuffled the vector of phenotype category values among strains , repeated the association test , and tabulated the number of SNPs with Fisher's p-value<pthresh , in this null data set . Averaging over 1000 such permutations yielded an expectation of 0 . 011 SNPs called at pthresh = 5 . 6×10−6 and 652 SNPs at pthresh = 0 . 015 , under a null model of no true association . Given the 3 and 837 SNPs , respectively , reaching these levels in the real data ( Dataset S3 ) , false discovery rates at these thresholds were 0 . 4% and 78% , respectively . Linkage disequilibrium in Figure 4 was calculated between all high-frequency SNPs in the region of cse-1 using the LDcorSV package in R . | Many phenotypes of interest are controlled by multiple loci , and in biological systems identifying determinants of such complex traits is challenging . Here , we genotyped 112 wild isolates of Neurospora crassa and used this resource to identify genes that mediate a fundamental but poorly-understood attribute of this filamentous fungus: the ability of germinating spores to sense each other at a distance , extend projections toward one another , and fuse . Inheritance at a secretion gene , cse-1 , was associated strongly with germling communication across wild strains; this association was validated in experiments showing reduced communication in a cse-1 deletion strain . By testing interacting partners of CSE1 , and by assessing additional secretion and signaling factors whose inheritance associated more modestly with germling communication in wild strains , we identified eight other novel determinants of this phenotype . Our population of genotyped wild isolates provides a flexible and powerful community resource for the rapid identification of any varying , complex phenotype in N . crassa . The success of our approach , which used a phenotyping scheme far more tractable than would be required in a screen of the entire N . crassa gene deletion collection , serves as a proof of concept for association studies of wild populations for any organism . | [
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"g... | 2013 | Genome Wide Association Identifies Novel Loci Involved in Fungal Communication |
Oncogenes , which are essential for tumor initiation , development , and maintenance , are valuable targets for cancer therapy . However , it remains a challenge to effectively inhibit oncogene activity by targeting their downstream pathways without causing significant toxicity to normal tissues . Here we show that deletion of mir-181a-1/b-1 expression inhibits the development of Notch1 oncogene-induced T cell acute lymphoblastic leukemia ( T-ALL ) . mir-181a-1/b-1 controls the strength and threshold of Notch activity in tumorigenesis in part by dampening multiple negative feedback regulators downstream of NOTCH and pre-T cell receptor ( TCR ) signaling pathways . Importantly , although Notch oncogenes utilize normal thymic progenitor cell genetic programs for tumor transformation , comparative analyses of mir-181a-1/b-1 function in normal thymocyte and tumor development demonstrate that mir-181a-1/b-1 can be specifically targeted to inhibit tumor development with little toxicity to normal development . Finally , we demonstrate that mir-181a-1/b-1 , but not mir-181a-2b-2 and mir-181-c/d , controls the development of normal thymic T cells and leukemia cells . Together , these results illustrate that NOTCH oncogene activity in tumor development can be selectively inhibited by targeting the molecular networks controlled by mir-181a-1/b-1 .
Oncogenes elicit driving signals required for tumor initiation , development and maintenance and are valuable targets for cancer therapy . However , oncogenes often have essential functions in normal cellular physiology and produce intracellular proteins that are difficult to inhibit with small molecule drugs without causing significant toxicity to normal tissues . Therefore , it is imperative to identify downstream networks that can be targeted to dampen the oncogenic signals in tumor cells with limited toxicity to normal cells . Despite intense efforts , it remains a challenge to identify the downstream pathways controlled by oncogenes that are essential and specific for tumor development but not for normal development . MicroRNAs ( miRNAs ) are an abundant class of small regulatory RNAs that control gene expression at the post-transcriptional levels . Some miRNAs are capable of potentiating tumorigenic activity of oncogenes , such as Myc and Notch1 , possibly by repressing known tumor suppressors [1] , [2] . In some cases , miRNAs can function as oncogenes and aberrant expression of such an miRNA is sufficient to induce cancer [3] , [4] . Finally , some miRNAs can inhibit activities of oncogenes when delivered into tumor cells through viral transduction [5] , [6] . Clearly , miRNAs are integral components of oncogenic and tumor suppressing networks; however , the quantitative nature of miRNA effects on gene expression and cellular functions raises the issue as to whether loss of a miRNA would be sufficient to inhibit oncogene-induced tumorigenesis [7] , [8] , [9] . More importantly , few studies have demonstrated that miRNAs have essential roles in tumor development caused by human oncogenes using rigorous loss-of-function analyses . We used a NOTCH-induced T-cell acute lymphoblastic leukemia ( T-ALL ) model [10] to gain insight into how activity of human oncogenes is modulated by miRNAs . Activating mutations in Notch1 , which cause ligand-independent activation of the receptor and/or inhibit proteasome-mediated receptor turnover , are observed in about 60% of human T-ALL cases [11] . The requirement of γ-secretase for NOTCH1 activation led to the clinical evaluation of γ-secretase inhibitors ( GSIs ) for the treatment of T-ALL . Treatment of patients with these inhibitors was unsuccessful because of limited anti-leukemic activity and severe gastrointestinal toxicity [12] . In humans and in animal models , transformation by Notch1 oncogenes blocks T cell development at the immature double-positive ( DP ) cell stage but not at the mature T cell stages [13] , indicating that Notch1 oncogenes may utilize the genetic programs that operate in normal thymic progenitor cells for tumor transformation . Intriguingly , miR-181a family miRNAs are highly expressed in T-ALL leukemia cells and down-regulated during remission [14] , suggesting that miR-181 miRNAs play a role in the pathogenesis of human T-ALL . Here we carried out comparative analyses to examine the roles of mir-181 alleles in normal development and NOTCH-induced T-ALL using a loss of function approach . We found that mir-181ab1 controls the strength and threshold of Notch1 oncogenes by repressing the negative feedback regulators downstream of NOTCH and pre-TCR signaling . Deletion of mir-181ab1 effectively inhibits NOTCH-induced T-ALL without significant impact on normal development . These findings illustrate a general approach in uncovering pathways that are essential for oncogene activity in tumor development .
The members of the miR-181 family of genes , which will be referred to as mir-181ab1 , mir-181ab2 and mir-181cd , produce four nearly identical mature miRNAs ( miR-181a , miR-181b , miR-181c and miR-181d , respectively ) from three polycistronic transcripts ( Figure 1A–C ) . Given the dynamic expression of miR-181 miRNAs during normal lymphocyte development and during T-ALL progression [14]–[18] and that Notch1 oncogenes utilize genetic programs in early thymic progenitor cells for T-ALL development [13] , we used loss-of-function analyses to identify the mir-181 allele with critical roles in the development of normal thymic progenitor cells and in development of NOTCH-induced T-ALL . We obtained conditional mouse strains for all mir-181 alleles ( Figure 1D–F and Figure S1A–G ) . Germline deletion of each individual mir-181 allele completely abolished expression of pri-miRNA transcripts from the corresponding allele ( Figure 1G ) . No protein coding genes were found within 10 kb up- or downstream of the mir-181ab1 and mir-181ab2 alleles ( Figure S1G ) , and it is unlikely that loss of mir-181ab1 and mir-181ab2 would affect expression of protein-coding genes further from the alleles . Interestingly , Nanos3 , a germline specific gene that plays an essential role in germ cell development , is about 2 kb upstream of mir-181cd ( Figure S1G ) . However , mir-181cd null mice are viable and have no apparent defects in fertility . Thus , loss of mir-181cd did not affect Nanos3 expression to a degree that would compromise the fertility of mir-181cd null mice . Since Notch1 oncogenes may utilize the genetic programs that operate in normal thymic progenitor cells for tumor transformation [13] , we further characterized mature miR-181 miRNA expression in these germline knockout mice to determine whether one or more alleles contribute to miR-181 expression in thymocytes . T cell differentiation in the thymus can be divided into discrete stages based on CD4 and CD8 expression: CD4 and CD8 double-negative ( DN ) early thymic progenitors , more differentiated CD4 and CD8 double-positive ( DP ) thymocytes and differentiated CD4 or CD8 single-positive ( SP ) thymocytes . DN cells are further subdivided into DN1 ( CD44+ CD25− ) , DN2 ( CD44+ CD25+ ) , DN3 ( CD44− CD25+ ) and DN4 ( CD44− CD25− ) cell populations , listed in the order of their appearance during development . The DN1 subset also encompasses the earliest thymic T-cell progenitors , ETPs [19] . Previous sequencing analyses of small RNAs in CD4 and CD8 DP cells showed that mature miR-181a is expressed at levels about 100-fold higher than those of mature miR-181c [17] . Interestingly , loss of the mir-181ab1 allele reduced mature miR-181a expression to near background levels in the thymus as indicated by the northern blot analyses ( Figure 1H ) . Furthermore , as shown by qPCR analyses , loss of the mir-181ab1 allele reduced mature miR-181a and mature miR-181b expression to ∼22 and 8 copies per cell in DP cells ( Figure 1I ) , respectively . Deletion of mir-181ab2 and mir-181cd alleles did not affect mature miR-181a and miR-181b expression levels ( Figure 1I ) . Together , these results demonstrate that miR-181a and miR-181b are predominantly expressed from mir-181ab1 in thymocytes , suggesting that mir-181ab1 , but not mir-181ab2 and mir-181cd , may play a specific role in early thymocyte development . We then systematically examined the consequences of loss of individual mir-181 alleles on the development of T and B lymphocyte populations in various lymphoid and hematopoietic organs with a focus on the thymocyte populations ( see Table S1 for cell populations and FACS definitions of ∼40 individual T and B lymphocyte populations ) . Consistent with predominant expression of mature miR-181a and miR-181b from mir-181ab1 , the deletion of mir-181ab1 caused more apparent defects in early thymocyte development than deletion of the other alleles . We noted statistically significant but modest decreases in ETP , DN3 and DP cell populations and an increase in CD4 SP thymocytes upon mir-181ab1 deletion ( Figure 2A and Figure S2A , B ) . mir-181ab1 deletion also resulted in significant changes in germinal center , marginal zone and peripheral B cell development ( Figure 2B ) . Germline deletion of mir-181cd caused more than a 2-fold increase in the percent of CD8 T cells in the thymus but did not cause notable changes in other T and B lymphocyte populations examined including DN1–4 , DP and CD4 cells ( Figure 2C ) . Germline deletion of mir-181ab2 had no apparent effects on any of the cell populations examined . Cell populations without significant changes upon the loss of individual mir-181 alleles are not shown in Figure 2 . Moreover , loss of individual mir-181 alleles did not significantly change the cellularity of hematopoietic organs , such as bone marrow , spleen , thymus and peripheral blood , in the germline-knockout mice . Thus , changes in proportions shown in Figures 2A–C should correlate with corresponding changes in cell numbers . miR-181 miRNAs are also expressed in the brain and in muscle [15] , but further analyses must be carried out to dissect the function of mir-181 alleles in these tissues . Overall , germline mir-181 knockout mice are viable and have no noticeable defects for up to twelve months . The effects of mir-181ab1 on early T cell development were further confirmed with in vitro and in vivo analyses . Using an OP9-DL1 stromal ( expressing NOTCH ligand , delta-like 1 ) co-culture assay [20] that recapitulates early thymic T cell development in culture ( Figure S2C ) , we showed that ectopic mir-181a-1 expression in thymic progenitor cells potentiates DP cell development ( Figure 2D , and Figure S2D , E ) , whereas conditional ( Figure 2E and Figure S2F–I ) or germline ( Figure 2F ) deletion of mir-181ab1 inhibits it . The ectopic expression of mir-181ab1 , but not mir-181cd , in thymic progenitors rescued the defects caused by mir-181ab1 germline deletion ( Figure 2F ) , demonstrating that these two alleles have different functions in early T cell development [21] . Most importantly , conditional deletion of the allele using tamoxifen-induced CreER resulted in 50–75% decrease in cellularity in the thymus ( Figure 2G , p<0 . 05 ) – a decrease from an average of ∼53 million cells/thymus in wild-type mice to ∼23 million cells/thymus in mice with mir-181ab1 alleles floxed – and a significant reduction in the percentage of DP cells ( Figure 2H , p<0 . 05 ) . Finally , miR-181a expression decreased during the DN3a to DN3b transition during β-selection ( Figure S2J ) , and loss of mir-181ab1 resulted in a significant reduction in the percentage of DN3 and DN4 cells that expressed intracellular TCR-β ( Figure S2K ) , but preTα expression in DN3 subsets was normal ( data not shown ) . These results suggest that mir-181ab1 contributes to the DN3 to DP transition , possibly by regulating β-selection and post β-selection expansion , and its effects are intrinsic to thymic progenitor cells . Importantly , the fact that strong effects on thymic T cell development as a result of mir-181ab1 deletion in vitro ( Figure 2E–F ) and acute deletion of mir-181ab1 in vivo were observed ( Figure 2G , H ) , suggests that it is likely that such effects might be compensated in germline knockout mice through homeostatic mechanisms that maintain the stability and robustness of immune systems ( Figure 2A–C ) . In summary , mir-181ab1 , but not mir-181ab2 or mir-181cd , has specific roles in early T cell development in the thymus . The fact that Notch1 oncogenes block T cell development at the immature DP cell stage but not at the mature T cell stages [13] suggests that Notch1 oncogenes utilize the genetic programs of normal thymic progenitor cells for T-ALL induction . Indeed , NOTCH and pre-TCR signals , which act synergistically to promote thymic T cell development during β-selection and post β-selection T cell expansion , are also critical for NOTCH-induced T-ALL development [22] , [23] . Given the role of mir-181ab1 in early thymocyte development and during the DN3 to DP transition ( Figure 2D–H and S2J , K ) , mir-181ab1 may also be important for NOTCH-induced T-ALL . We next examined the effects of loss of mir-181ab1 on T-ALL induced by the intracellular domain of NOTCH1 ( ICN1 ) ( Figure 3A ) . Loss of mir-181ab1 caused a 32% increase in the median survival time of T-ALL mice from 41 days to 54 days ( Figure 3B , p<0 . 0001 , 20 mice/group , a representative plot of 4 independent experiments is shown ) . The delayed mortality correlated strongly with a drastic decrease of ICN1-infected blood cells ( as measured by the number of GFP+ cells ) and DP leukemia cells ( GFP+DP ) in the peripheral blood ( PB ) and in hematopoietic and non-hematopoietic organs of recipient mice at 4 weeks after transplantation ( Figure 3C–F and Figure S3A–D ) . It is important to note the distinct developmental kinetics of GFP+ and GFP+DP cells in the wild-type and knockout groups . In mice reconstituted with ICN1:181ab1+/+ bone marrow ( BM ) cells , the percentage of GFP+ and GFP+DP cells in peripheral blood increased during the 6 weeks after transplantation ( Figure 3C and Figure S3A ) , whereas in mice reconstituted with ICN1:181ab1−/− BM cells the percentage of GFP+ and GFP+DP cells first decreased from 2 to 4 weeks and then increased from 4 to 8 weeks post-transplantation ( Figure 3C and Figure S3A ) . Loss of mir-181ab1 also compromised other steps that are required for the development of ICN1-induced T-ALL . For example , ICN1:mir-181ab1−/− DP cells can develop in the thymus ( Figure 3F , bottom panel , 2-week time point ) , whereas ICN1:mir-181ab1+/+ DP cells cannot . Thus , loss of mir-181ab1 altered the tissue distribution of ICN1-infected T-ALL cells and enabled the development ( or migration ) of ICN1-infected DP cells in the thymus at 2 weeks post-transplantation ( Figure 3F , lower panel ) . This is significant because ICN1 expression in hematopoietic progenitor cells is known to block T cell development in the thymus while promoting ectopic T-cell development in BM and other organs [13] , [24] . Interestingly , loss of mir-181ab2 did not inhibit development of ICN1-induced T-ALL , and loss of mir-181cd actually exacerbated ICN1-induced T-ALL ( Figure S3F–G ) . Finally , loss of mir-181ab1 did not have detrimental effects on the reconstitution potentials of hematopoietic stem/progenitor cells ( Figure S3E and see Text S1 for additional details and Figure S3H for histology ) . Since multiple independent infections and large cohorts of recipients were used in each of these analyses , it is unlikely that the effects observed here are due to variations in clonal outgrowth . Together , these results demonstrate that mir-181ab1 , but not mir-181ab2 or mir-181cd , specifically potentiates ICN1-induced T-ALL . Intriguingly , we noted that mir-181ab1 deletion appeared to have stronger inhibitory effects on T-ALL cells with lower levels of ICN1 expression and presumably weaker NOTCH oncogenic signals ( Figure 3G–K ) . Two distinct GFP cell populations were found in the BM of T-ALL mice at 2 , 4 and 6 weeks after transplantation: One cell population expressed lower levels of GFP ( GFPlow ) and the other cell population expressed higher levels of GFP ( GFPhigh ) ( Figure 3G ) . As shown by FACS analyses of intracellular ICN1 ( Figure 3H ) , GFPhigh DP cells have higher levels of ICN1 protein than do the GFPlow DP cells . To determine the leukemogenic potential of DP cells with different levels of ICN1 expression , we carried out secondary transplantation analyses . We sorted GFPhigh and GFPlow DP cells from primary ICN1:181ab1+/+ and ICN1:181ab1−/− T-ALL mice at 2 , 4 and 6 weeks post-transplantation and transplanted sorted DP cells into new recipients to generate secondary T-ALL mice . We then monitored the leukemogenic potential of these DP cells . Consistent with previous observations on the correlation between the signaling strength of various Notch1 mutants and T-ALL activity [25] , we noted that GFPhigh DP cells , but not the GFPlow DP cells , isolated from ICN1:181ab1+/+ T-ALL mice 2 weeks after transplantation caused T-ALL in secondary recipients ( Figure 3I ) . We also observed that the GFPhigh DP cells caused earlier onset and more aggressive T-ALL than the GFPlow DP cells isolated 4 weeks after transplantation ( Figure 3J ) . More importantly , DP cells isolated from ICN1:181ab1−/− primary recipients with either high or low GFP expression levels at 2 or 4 weeks post-transplantation did not induce leukemia in the secondary recipients ( Figure 3I , J ) , whereas the equivalent cell populations from ICN1:181ab1+/+ primary recipients did . Finally , although the GFPhigh DP cells from ICN1:181ab1+/+ and ICN1:181ab1−/− primary T-ALL mice collected at 6 weeks after transplantation were strongly leukemogenic , mir-181ab1 deletion significantly reduced the oncogenic activity of ICN1 ( Figure 3K ) . There were essentially no GFPlow DP cells in T-ALL mice at 6 weeks after transplantation . Thus , loss of mir-181ab1 effectively inhibited T-ALL development in recipients of cells with lower levels of ICN1 expression ( GFPlow DP cells ) ( Figure 3J ) and delayed T-ALL development in recipients of cells with high levels of ICN1 expression ( GFPhigh DP cells ) ( Figure 3K ) . These results also demonstrate that mir-181ab1 deletion has strong intrinsic effects on the development of DP leukemia cells , although we cannot rule out that this miRNA may affect non-T cell types that can dampen NOTCH-induced T-ALL . Most importantly , these findings show that loss of mir-181ab1 may be more effective in suppressing T-ALL development induced by Notch1 mutations with lower levels of ICN1 and weaker signaling strength than that induced by mutations with higher levels of ICN1 and stronger signaling strength . These results demonstrate that mir-181ab1 controls the strength and threshold of ICN1 oncogenic signals . Thus , mir-181ab1 deletion can delay T-ALL development induced by strong Notch oncogenes ( Figure 3B ) and blocks T-ALL development induced by weaker oncogenic signals ( Figure 3I–K ) . Among the known human Notch1 mutants , the P12ΔP mutant is one of the strongest [25] and is found in 15–20% of pediatric T-ALL patients . Loss of mir-181ab1 strongly inhibited T-ALL development induced by P12ΔP , causing a decrease in mortality from 60% in P12ΔP:181ab1+/+ T-ALL mice to 10% , a striking 80% reduction in mortality ( Figure 4A , 20 mice/group , a representative plot of 2 independent experiments is shown . ) . Importantly , percentages of P12ΔP-infected PB cells ( GFP+ ) and pre-leukemia cells ( GFP+ DP cells ) decreased in both P12ΔP:181ab1+/+ and P12ΔP:181ab1−/− T-ALL mice at 5 weeks after transplantation ( Figure 4B ) . Infected cells reappeared in 12 out of 20 P12ΔP:181ab1+/+ T-ALL mice ( Figure 4B ) and caused mortality as early as 10 weeks after transplantation ( Figure 4A ) . In contrast , GFP+ DP cells only reappeared in 2 out of 20 P12ΔP:181ab1−/− T-ALL mice after 20 weeks post-transplantation and caused mortality no earlier than 24 weeks after transplantation . Thus , loss of mir-181ab1 nearly completely blocked leukemia development induced by P12ΔP . Viral integration effects may have caused higher P12ΔP expression and stronger oncogenic signaling in the two P12ΔP:181ab1−/− T-ALL mice that died of T-ALL ( Figure 4B and Figure S3I ) . Comparable percentages of GFP+ cells were found in the majority of the hematopoietic/lymphoid organs of P12ΔP:181ab1+/+ and P12ΔP:181ab1−/− T-ALL mice at 6 to 7 weeks ( Figure 4C , upper panel ) and at ∼48 weeks after transplantation ( Figure 4D , upper panel ) . Therefore , loss of mir-181ab1 did not have observable detrimental effects on the reconstitution potential of P12ΔP-infected bone marrow cells . Of note , at ∼48 weeks after transplantation ( Figure 4D , lower panel ) , the P12ΔP:181ab1−/− T-ALL mice that did not develop leukemia had a significant percentage of P12ΔP-infected ( GFP+ ) DP cells in the thymus but not in the PB , BM or spleen ( * , p<0 . 05 ) . These results demonstrate that loss of mir-181ab1 inhibits extrathymic development of P12ΔP-infected ( GFP+ ) DP cells and rectifies extrathymic tissue-distribution of oncogenic T-ALL cells previously observed in BM , spleen and other organs [13] . Thus , the loss of mir-181ab1 effectively reduced the tumorigenic activity of the P12ΔP oncogene below a functional threshold . These findings further confirm that mir-181ab1 deletion effectively inhibits T-ALL development induced by weaker NOTCH oncogenic signals as a result of low ICN1 oncogene expression ( Figure 3 ) or P12ΔP mutations ( Figure 4 ) . Since P12ΔP is one of the strongest Notch1 oncogenes identified in human T-ALL cells , targeting mir-181ab1 may effectively inhibit T-ALL development induced by other human Notch1 mutants . The fact that strong NOTCH oncogenic signals can overcome the inhibitory effects of mir-181ab1 deletion raised the question of whether mir-181ab2 or mir-181cd might compensate for the loss of mir-181ab1 in T-ALL development . Levels of miR-181a , miR-181b and miR-181c remained at a few copies per cell in ICN1:181ab1+/+ T-ALL mice at 2 , 4 and 6 weeks after transplantation ( Figure 5A ) , but the levels of miR-181a and miR-181c in ICN1-infected DP cells increased from less than 10 copies/cell at 2 and 4 weeks to 80 and 35 copies/cell , respectively , at 6 weeks post-transplantation in ICN1:181ab1−/− T-ALL mice ( Figure 5B ) . This suggests that ICN1 may up-regulate the expression of miR-181a and miR-181c from mir-181ab2 and mir-181cd alleles , respectively , to compensate for the loss of mir-181ab1 . Deletion of both mir-181ab1 and mir-181ab2 did not further potentiate the effects of loss of mir-181ab1 on ICN1-induced T-ALL development ( Figure 5C , E ) . In fact , loss of both mir-181ab1 and mir-181cd actually diminished the inhibitory effects of mir-181ab1 deletion on T-ALL development ( Figure 5D , F ) . Together , these results indicate that increased expression of miR-181a and miR-181c from the corresponding mir-181ab2 and mir-181cd alleles at the late stage of ICN1-induced T-ALL does not compensate for the loss of mir-181ab1 . To determine whether mir-181ab1 affects T-ALL development by directly controlling NOTCH signaling , we carried out transcriptional profiling analyses . To this end we generated triplicate microarray data sets from normal DP thymocytes , primary ICN1:181ab1+/+ GFPlow DP cells and primary ICN1:181ab1−/− GFPlow and GFPhigh DP cells from T-ALL mice at 4 weeks post-transplantation ( Table S2 ) . It is important to note that at 4 weeks post-transplantation , although ICN1:181ab1−/− and ICN1:181ab1+/+ DP cells express similar levels of ICN1 ( Figure S3J ) , these cells have different leukemogenic potential . Primary ICN1:181ab1+/+ GFPlow DP cells induce leukemia in secondary recipient mice , whereas ICN1:181ab1−/− GFPlow or GFPhigh cells do not ( Figure 3J ) . Thus , comparing the changes in gene expression between the primary ICN1:181ab1+/+ GFPlow DP cells and ICN1:181ab1−/− GFPlow or GFPhigh cells should reveal the effects of mir-181ab1 deletion on ICN1-controlled oncogenic programs on a global level . ICN1 expression in DP cells resulted in aberrant expression of over 500 genes ( >2-fold , p<0 . 01 , Table S3 ) . Unsupervised hierarchical clustering analyses classified the ICN1-controlled gene set into four clusters , I–IV ( Figure 6A , B ) . The cluster I genes , which were up-regulated by ICN1 and reverted in the mir-181ab1 null T-ALL DP cells , include numerous genes that are known to be critical for NOTCH ( e . g . , Dtx1 , Notch1 , Hes1 , Hey1 and Nrarp ) , pre-TCR ( e . g . , Ptcra ) , cytokine and apoptosis pathways ( Figure 6C , Table S3 ) . Down-regulation of some direct targets of the ICN1 oncogene ( Dtx1 , Hes1 and Hey1 ) as a result of mir-181ab1 deletion was confirmed by quantitative PCR analyses ( Figure S3K ) . Expression of genes in the other clusters ( II–IV ) was less impacted by mir-181ab1 deletion , and these clusters are not enriched for known NOTCH pathway genes . Gene set enrichment analyses also confirmed that ICN1-controlled gene sets were effectively reversed to basal or near basal levels in the absence of mir-181ab1 ( Figure 6D ) . Overall , loss of mir-181ab1 had drastic effects on the expression of the ICN1-controlled gene set and reverted a significant portion of them back to the levels of normal DP cells ( Figure 6A , B ) . However , Sylamer analyses did not reveal significant enrichment of 7-mer or 8-mer miR-181a seed sequences among the up-regulated genes ( Figure 6E ) . Given that over 25% of ICN1-regulated genes ( ∼550 , >2-fold , p<0 . 01 , Table S3 ) were predicted to be targets of miR-181a by TargetScan , PicTar or miRanda ( Figure 6A , indicated by arrow ) , it is likely that mir-181ab1 deletion affects the expression of many downstream NOTCH targets . Together , these results suggest that mir-181ab1 plays a critical role in potentiating NOTCH oncogenic signals . mir-181ab1 deletion did not completely block NOTCH oncogenic signaling as it did not completely revert the expression of many cluster I genes to the level observed in normal DP cells ( Figure 6C ) . This observation suggests that mir-181ab1 deletion may have dampened NOTCH oncogenic signals by permitting higher expression of negative feedback molecules , which results in a reduction in NOTCH signaling as indicated by lower induction of NOTCH target genes . Thus , mir-181ab1 may mediate its effects on the ICN1 oncogenic program through dampening the negative feedback loops controlled by Notch1 oncogenes . We found that many known negative regulators of the NOTCH signaling pathway , such as Nrarp , Numb , Numb-like , Hes6 and Lunatic Fringe ( Lfng ) mRNAs , contain multiple putative miR-181a binding sites in their 3′ UTR regions ( Figure S4A ) . We devised a biological screen to identify the functionally relevant targets using the OP9-DL1 co-culture assay ( Figure S2C ) . If candidate targets are functionally relevant , ectopic expression of the miR-181a-insensitive version of the targets ( binding sites absent or mutated ) in thymic progenitor cells should inhibit or dampen the effects of miR-181a on normal DP cell development , resulting in a phenotype that opposes that of ectopic expression miR-181a . In contrast , if candidate targets are functionally irrelevant , ectopic expression of the miR-181a-insensitive version of the targets should have no such effects . We found that ectopic expression of only the open reading frame ( ORF ) of Nrarp caused strong inhibition of DP thymocyte development; ectopic expression of the Numb-like ORF had only a slight effect ( ∼25% reduction ) ; and ectopic expression of Numb-like , Numb , Hes6 or Lfng ORFs had no significant effects on DP cell development ( Figure 7A and see Figure S4B for representative FACS plots ) . Thus , consistent with the observed function of Nrarp during early thymocyte development [26] , these results demonstrate that Nrarp mRNA is likely a functional target of miR-181a in early thymocyte development . Further epistatic analyses showed that expression of Nrarp-FLwt , which contains the full-length Nrarp 3′UTR and intact miR-181a binding sites , had limited suppressive activity ( Figure 7B , C , and see Figure S4C for representative FACS plots ) . However , the suppressive activity of Nrarp-FL on T cell development significantly increased when the predicted pairings to the miR-181a seeds were abrogated . Moreover , the Nrarp ORF had much stronger suppressive activity than did Nrarp-FLSM , suggesting that there may be cryptic miR-181a binding sites in the 3′ UTR of Nrarp . All three Nrarp expression constructs produced similar levels of Nrarp transcripts in a miR-181a-negative cell line as demonstrated by qPCR analyses ( Figure S4D ) . Thus , the differential functional activities observed are not due to inherent differences in Nrarp mRNA levels . Since endogenous Nrarp mRNAs are present in these assays ( Figure S4E ) , these results demonstrate that Nrarp transcripts are suppressed by endogenous miR-181a in early thymocytes . Together , our data show that the predicted miR-181a binding sites in the Nrarp 3′ UTR ( Figure 7B ) are targeted by endogenous miR-181a during early T cell development . We previously showed that miR-181a potentiates TCR signaling by suppressing the expression of multiple phosphatases , including Dusp5 , Dusp6 , Shp2 and Ptpn22 , in DP and mature T cells [16] . Since pre-TCR and TCR pathways share common signaling molecules and , more importantly , NOTCH and pre-TCR signaling act synergistically to promote early T cell and T-ALL development [23] , [27] , miR-181a may also regulate pre-TCR signaling during these developmental processes . Indeed , we found that ectopic expression of the coding regions ( miR-181a-insensitive forms ) of Dusp5 , Dusp6 , Shp2 and Ptpn22 efficiently inhibited the development of DP thymocytes , whereas expression of the full-length cDNA versions ( miR-181a-sensitive forms ) of these phosphatases had little or no effect ( Figure 7D and see Figure S4F for representative FACS plots ) . Since the mRNA transcripts of these phosphatases are readily detectable in various thymic progenitor populations ( Figure S4E ) , and they are validated miR-181a targets in DP and mature T cells [16] , these results demonstrate that these phosphatase transcripts are also suppressed by the endogenous miR-181a during early T cell development . Importantly , expression of shRNAs targeting Nrarp , Shp2 , Dusp5 or Dusp6 genes did not recapitulate the phenotype of miR-181a ectopic expression in early T cell development ( Figure 7E–G ) despite the fact that these shRNAs can suppress the expression of corresponding proteins more effectively than miR-181a [16] . Together , these results demonstrate that the effects of miR-181a on early T cell development are mediated through the regulation of multiple negative feedback regulators in both NOTCH and pre-TCR signaling pathways ( Figure 7H ) . Since both NOTCH and pre-TCR pathways are important for T-ALL development [22] , [23] and can each be targeted for T-ALL treatment [28] , [29] , the above findings ( Figure 7A–D ) suggest that mir-181ab1 controls similar pathways in T-ALL cells and in normal DP cells . We went on to examine whether miR-181a contributes to the maintenance of NOTCH oncogenic activity in T-ALL cells by altering expression of similar targets in mouse and human T-ALL cells . First , we stably expressed wild-type miR-181a ( miR-181aWT ) in T6E cells , a murine T-ALL cell line [30] . As shown by western blot analyses , expression of miR-181aWT resulted in ∼40% less HA-tagged Nrarp from a full-length Nrarp cDNA than observed in cells that expressed a seed mutant miR-181a ( Figure 8A ) . Second , transient inhibition of miR-181a expression in T6E cells with antagomirs resulted in up-regulation of Nrarp ( by 61% ) , Dusp5 ( by 50% ) , Dusp6 ( by 100% ) and Shp2 ( by 84% ) mRNAs compared to cells treated with the mismatched control ( Figure 8B ) . Thus , miR-181a dampens expression of at least some of the same targets in T6E cells as it does during normal thymocyte development ( Figure 7 ) . Finally , in the T6E cells treated with antagomir-181a , we observed increased Nrarp and DUSP6 protein expression ( Figure 8C ) , down-regulation of expression from Notch1 controlled targets including c-Myc , Dtx1 , Hes1 and Hey1 ( Figure 8D ) , a decrease in proliferation ( Figure 8E ) , and an increase apoptosis ( Figure 8F ) . Further supporting the observations made in T6E cells , we noted that expression of Nrarp-FLmut ( an miR-181a-insensitive mRNA ) but not Nrarp-FLwt ( miR-181a-sensitive ) in T-ALL DP cells from primary recipients suppressed the development of T-ALL DP cells and tumorigenic potential in secondary recipients ( Figure 8G ) . Moreover , induced deletion of mir-181ab1 caused a significant and persistent decrease in the DP leukemia cell population with low levels of ICN1 expression ( GFPlow DP cells ) in T-ALL mice ( Figure 8H ) . Finally , we examined whether miR-181a contributes to the maintenance of NOTCH oncogenic activity in human T-ALL cells . We found that antagomir inhibition of miR-181a in the human T-ALL cell line DND41 [31] had effects similar to those we observed in murine T6E T-ALL cells ( Figure 6C , 8B–D and S3K ) . Antagomir miR-181a treatment of DND-41 cells caused a reduction in cell proliferation ( Figure 8I ) , an increase of apoptotic cells in the culture ( Figure 8J ) , up-regulation of miR-181a targets ( Figure 8K ) , and down-regulation of Notch targets ( Figure 8L ) . Together , these findings demonstrate that miR-181a contributes to the maintenance of human and mouse T-ALL cells by dampening the negative feedbacks and potentiating NOTCH and pre-TCR signals ( Figure S5 ) , suggesting that miR-181a may be a therapeutic target in human T-ALL .
In this study , we examined the roles of three mir-181 genes , mir-181ab1 , mir-181ab2 and mir-181cd , in normal thymocyte development and in T-ALL development . We found that deletion of mir-181ab1 , but not mir-181ab2 and mir-181cd , effectively inhibited NOTCH1-induced T-ALL . Moreover , the effects of mir-181ab1 deletion on Notch1 oncogenic potential depend on the expression levels and the signaling strength of the oncogenes . In particular , we showed that mir-181ab1 deletion inhibits the oncogenic activity of P12ΔP — one of the strongest NOTCH1 mutants identified in human T-ALL patients [25] — indicating that targeting mir-181ab1 may effectively inhibit the tumorigenic potential of other human Notch1 mutants . Our results demonstrate that mir-181ab1 can regulate the strength and threshold of Notch1 oncogenic activity . It is important to note that deletion of mir-181ab1 had a quantitative effect on normal development that was sufficient to dampen Notch1 oncogenic potential and dramatically improve mortality in T-ALL mice ( Figure 4 ) . Together with our previous study showing that miR-181a functions as a rheostat in regulating the strength and threshold of TCR signaling and T cell sensitivity to antigen [16] , these results illustrate a general model of miRNA function in controlling the strength and threshold of receptor signaling by regulating multiple targets during normal and oncogenic developmental processes . These findings support the notion that quantitative regulation of oncogenic signal strength can be sufficient to block cancer development [8] and demonstrate that miRNAs may be effective therapeutic targets [9] . Our comparative analyses revealed that the pathways controlled by mir-181ab1 are not of equal importance in normal thymic progenitor cells and T-ALL DP cells ( Figure 2–4 ) . The fact that co-expression of mir-181a-1 together with ICN1 did not significantly potentiate the oncogenic activity of ICN1 ( Figure S6 ) implies that endogenous mir-181ab1 and the pathways it controls may be sufficient to potentiate Notch1 oncogene signaling . Clearly the effects of mir-181ab1 deletion were compensated for during normal thymic progenitor development but not during T-ALL development ( Figure 2–4 ) . It is possible that normal vertebrate immune systems , which have central roles in controlling host immunity and homeostasis , may have many intrinsic mechanisms to withstand many forms of perturbations and can compensate for the mir-181ab1 deletion . In contrast , T-ALL cells may be more reliant on the pathways controlled by mir-181ab1 and homeostatic mechanisms most likely do not exist in T-ALL cells . Our findings illustrate that comparative studies on the pathways utilized by normal cells and tumor cells can reveal how tumorigenic pathways may be selectively inhibited with limited damage to normal tissues . Since germline mir-181ab1 knockout mice are viable and have no noticeable defects for up to twelve months , inhibition of mir-181ab1 activity should block NOTCH1-induced tumorigenesis without significant side effects . However , since miR-181 miRNAs are highly expressed in brain and muscle tissue [15] , further studies should be carried out to examine the function of mir-181 alleles in the development and function of non-hematopoietic tissues and organs . The ability to regulate multiple targets enables a miRNA to mediate its biological function by controlling varied gene sets in different cell types . This results in the expansion of regulatory complexities conferred by the same set of protein-coding genes during normal lineage development and tumorigenesis . In this study , dissecting the multi-target networks controlled by miRNAs during normal thymic T cell development and NOTCH-induced T-ALL allowed us to unravel the downstream molecular networks that contribute to normal thymic T cell development and NOTCH-induced tumorigenesis . Early studies have elegantly shown that NOTCH and pre-TCR signals play critical roles during normal thymic progenitor cell development and T-ALL development [22] , [23] . However , neither NOTCH nor pre-TCR signals can be targeted effectively for treatment of T-ALL with inhibitors like GSI or cyclosporine , respectively , due to weak therapeutic effects and severe toxicity [28] , [29] . Our finding that mir-181ab1 modulates both normal and leukemogenic DP cell development in part by repressing the negative feedback regulators of NOTCH and pre-TCR signaling pathways ( Figure 2–4 , 6 , 7 ) demonstrates that it is possible to target mir-181ab1 to inhibit both NOTCH and pre-TCR signals simultaneously and effectively block T-ALL development . Given the extensive list of predicted mir-181ab1 targets ( Figure 6A and Table S3 ) , we could not exhaustively identify all functional targets for mir-181ab1 in normal and T-ALL DP cells and therefore , loss of mir-181ab1 may also compromise other pathways that are required for NOTCH-induced T-ALL . A recent study by Cichocki et al . suggested that miR-181 might influence human natural killer cell development by targeting NLK , a negative regulator of WNT and NOTCH signaling [32] . We found that NLK mRNA is expressed in DP T-ALL cells . However , ICN1 expression did not change the NLK mRNA levels in DP cells and loss of mir-181ab1 did not alter NLK mRNA levels in DP cells ( Table S2 ) . Although these observations suggest that NLK might not be regulated by miR-181a in T-ALL cells at the mRNA level , it will be interesting to explore whether NLK is regulated by miR-181a at a translational level . Of note , according to various target prediction program [33] , [34] and functional analyses , miR-181a may regulate similar targets in human T-ALL cells ( Figure 8 ) , including Nrarp ( Figure S7 ) and various phosphatase mRNAs . Many validated miR-181a targets in mouse cells Together , these findings illustrate that study of miRNA function in cancer will help to elucidate the molecular networks required for oncogenic transformation and shed insights into the downstream networks that can be targeted to inhibit tumor development . Interestingly , our results showed that the miR-181 family miRNAs are not functionally equivalent during normal and T-ALL development even though all have the same seed nucleotides . Deletion of mir-181ab2 and mir-181cd has different effects on T-ALL development , and neither allele could compensate for the loss of mir-181ab1 ( Figure 5 , and Figure S3F , G ) . There are several likely causes of the differential effects of loss of mir-181ab1 , mir-181ab2 and mir-181cd on normal DP cell development and ICN1-induced T-ALL development . First , differences in expression levels in DP leukemia cells may contribute to their various effects on T-ALL development ( Figure 5A , 5B ) . Second , their differential expression patterns in various thymic T cell populations may underlie their varied effects on normal thymocyte and T-ALL development [14]–[18] . Lastly , the extended nucleotide differences between miR-181a , miR-181b , miR-181c , and miR-181d and their coding genes may contribute to their varied effects on T-ALL development [21] . Further in-depth analyses will be needed to examine these possibilities and to elucidate the mechanisms through which various mir-181 alleles mediate differential activities during normal thymocytes and T-ALL development . Of interest , mice with all three mir-181 alleles deleted could not be generated from crossing the single knockouts presumably due to early lethality ( data not shown ) , suggesting that there might be toxicity if all three mir-181 alleles are targeted simultaneously for T-ALL treatment . Thus , the fact that mir-181ab1 , but not mir-181ab2 and mir-181cd , controls the development of normal DP cells and T-ALL DP cells suggests that specific targeting mir-181ab1 may be an effective approach to inhibition of NOTCH-induced T-ALL development .
C57BL/6J or 129/SvJ mice were obtained from Jackson Laboratory and maintained at the Stanford University Department of Comparative Medicine Animal Facility in accordance with National Institutes of Health guidelines . Knockout strains are maintained on either a 129 background or mixed B6 . Hematopoietic stem/progenitor BM cells were isolated from mice treated with 5-fluorouracil ( 5′ FU ) and infected with MigR1 , ICN1 , P12ΔP or ICN1:mir-181a-1 retroviruses . Secondary transplantation was carried out by sorting GFP+DP+ BM cells from primary T-ALL recipient mice . A mixture of 1×105 infected cells and 1×105 total BM cells ( supporting cells ) were intravenously injected into lethally irradiated ( 9 . 5 Gy ) 129-strain recipient mice ( ∼6 weeks old ) . To assess T-ALL development , peripheral blood samples were acquired from the recipient mice at various time points after transplantation and analyzed by FACS to determine the percentage of DP cells . The Kaplan-Meier estimator was used to determine the median rate of survival . The p values were determined using the Mantel-Cox test . Sorted or total thymocytes were cultured and differentiated on OP9-DL1 cells as described [20] , [21] . FACS analyses were carried out to determine the effects of miRNAs on DP thymocyte development . Anti-CD45 antibody staining and/or FSC/SSC gating were used to differentiate infected thymocytes and GFP+ stromal cells . The results are summarized in box-plots to describe the % DP cells from more than 12 replicate cultures . The ends of the boxes define the 25th and 75th percentiles; a line indicates the median and bars define the 5th and 95th percentiles . In some cases , the results were normalized so that the negative control had a median activity of 0 and the wild-type mir-181a-1 expressing vector had a median activity of 1 . Due to the heterogeneous nature of the thymic progenitor cells and intrinsic variation between the batches of mice used , normalization allowed for comparison among the independent repeats . Mann-Whitney rank sum tests were performed to determine statistical significance . Total RNAs were labeled using Illumina's Total Prep RNA Amplification Kit and hybridized to Illumina MouseRef-8_V2 BeadChips according to the manufacturer's instructions . Data were normalized using the quantile method ( the Bioconductor lumi package ) . SAM analyses were performed to select differentially expressed genes ( >2-fold , p<0 . 01 ) . Gene expression patterns were determined by hierarchical clustering with Pearson correlation as similarity metric . Heatmaps were generated with the Gplots R package . Ingenuity Pathways Analyses were carried out to determine the functional pathways within gene clusters . Gene set enrichment analyses ( GSEA ) were used to determine the effects of mir-181ab1 deletion on the gene sets up- or down-regulated by ICN1 in T-ALL DP cells ( permutation = 1000 ) . miR-181a targets with perfect “seed” matches were identified using TargetScans 5 . 1 ( http://www . targetscan . org/ ) , PicTar ( http://pictar . mdc-berlin . de/ ) or miRanda ( http://www . microrna . org/microrna/home . do ) . Alternatively , the m-fold program was used to identify putative miR-181a binding sites on selected target mRNAs . Sylamer analyses were carried out to determine the enrichment of seed matches among the genes up- or down-regulated in the absence of mir-181ab1 [35] . Control seeds were from other miRNAs in the miRBase ( release 12 ) . | Oncogenes elicit driving signals required for tumor initiation , development , and maintenance and are valuable targets for cancer therapy . However , oncogenes often have essential functions in normal cellular physiology and produce intracellular proteins that are difficult to inhibit with small molecule drugs without causing significant toxicity to normal tissues . Thus , one of the challenges in cancer therapy is to identify downstream networks that can be targeted to specifically dampen the oncogenic signals in tumor cells without harming normal tissues . In this study we demonstrate that deletion of a microRNA ( miRNA ) gene , mir-181a-1/b-1 , specifically inhibits the activity of the Notch oncogene in tumorigenesis without causing significant defects in normal development . Although earlier studies have elegantly shown the essential role of NOTCH and pre-TCR signals in NOTCH-induced tumorigenesis , neither NOTCH nor pre-TCR signals can be targeted effectively for treatment of T-ALL with available drugs due to their weak therapeutic effects and severe toxicities . Our findings illustrate that dissecting the downstream targets of miRNAs can reveal the molecular networks that can be targeted to control tumor transformation caused by oncogenes . More importantly , our results illustrate that comparative studies on the pathways utilized by normal cells and tumor cells may reveal novel insights into how tumorigenic pathways may be selectively inhibited with limited damage to normal tissues . | [
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"growth",... | 2012 | Modulating the Strength and Threshold of NOTCH Oncogenic Signals by mir-181a-1/b-1 |
The Elongator complex promotes formation of 5-methoxycarbonylmethyl ( mcm5 ) and 5-carbamoylmethyl ( ncm5 ) side-chains on uridines at the wobble position of cytosolic eukaryotic tRNAs . In all eukaryotic organisms tested to date , the inactivation of Elongator not only leads to the lack of mcm5/ncm5 groups in tRNAs , but also a wide variety of additional phenotypes . Although the phenotypes are most likely caused by a translational defect induced by reduced functionality of the hypomodified tRNAs , the mechanism ( s ) underlying individual phenotypes are poorly understood . In this study , we show that the genetic background modulates the phenotypes induced by the lack of mcm5/ncm5 groups in Saccharomyces cerevisiae . We show that the stress-induced growth defects of Elongator mutants are stronger in the W303 than in the closely related S288C genetic background and that the phenotypic differences are caused by the known polymorphism at the locus for the mRNA binding protein Ssd1 . Moreover , the mutant ssd1 allele found in W303 cells is required for the reported histone H3 acetylation and telomeric gene silencing defects of Elongator mutants . The difference at the SSD1 locus also partially explains why the simultaneous lack of mcm5 and 2-thio groups at wobble uridines is lethal in the W303 but not in the S288C background . Collectively , our results demonstrate that the SSD1 locus modulates phenotypes induced by the lack of Elongator-dependent tRNA modifications .
A general feature of tRNA molecules is that a subset of their nucleosides harbors post-transcriptional modifications . Modified nucleosides are frequently found in the anticodon region of tRNAs , especially at position 34 ( the wobble nucleoside ) and 37 . Modifications at these positions typically influence the decoding properties of tRNAs by improving or restricting anticodon-codon interactions [1 , 2] . Uridines present at the wobble position in eukaryotic cytoplasmic tRNAs often harbor a 5-methoxycarbonylmethyl ( mcm5 ) or 5-carbamoylmethyl ( ncm5 ) side-chain and sometimes also a 2-thio ( s2 ) or 2ʹ-O-methyl group [3 , 4] . The first step in the synthesis of the mcm5 and ncm5 side-chains requires the Elongator complex , which is composed of six Elp proteins ( Elp1-Elp6 ) [5–9] . Elongator is thought to catalyze the addition of a carboxymethyl ( cm ) group to the 5-position of the uridine which is then converted to mcm5 by the Trm9/Trm112 complex or to ncm5 by a yet unidentified mechanism [5 , 8–12] . In the budding yeast Saccharomyces cerevisiae , the inactivation of any of the six ELP genes ( ELP1-ELP6 ) not only leads to the lack of the mcm5/ncm5 groups but also a slower growth rate and numerous additional phenotypes [5 , 13] . These phenotypes include increased sensitivity to elevated temperatures and various chemical stresses as well as defects in transcription , exocytosis , telomeric gene silencing , and protein homeostasis [14–18] . Even though Elongator mutants lack mcm5/ncm5 groups in 11 tRNA species [5 , 19] , the pleiotropic phenotypes are suppressed by increased expression of various combinations of the hypomodified forms of the three tRNA species that normally harbor a mcm5s2U34 residue , tRNA UUU Lys , tRNA UUG Gln and tRNA UUC Glu [18 , 20 , 21] . These findings suggest the pleiotropic phenotypes of Elongator mutants are caused by a reduced functionality of the hypomodified tRNA UUU Lys , tRNA UUG Gln and tRNA UUC Glu in translation [20 , 21] . The importance of the modified wobble residue in these tRNAs was supported by the finding that strains lacking the s2 group show the same but slightly weaker phenotypes that are also suppressed by increased expression of the three tRNAs [20 , 21] . Moreover , ribosome profiling experiments have shown that the inactivation of Elongator causes an accumulation of ribosomes with AAA , CAA or GAA codons in the ribosomal A-site [18 , 22 , 23] . However , the pausing at the codons appears to be relatively small [18 , 22] and the mechanism ( s ) underlying the pleiotropic phenotypes of Elongator mutants are poorly understood . In yeast , the cell wall stress that arises during normal growth or through environmental challenges is sensed and responded to by the cell wall integrity ( CWI ) pathway [24 , 25] . The CWI pathway is induced by several different types of stresses , including growth at elevated temperatures , hypo-osmotic shock , and exposure to various cell wall stressing agents [25] . A family of cell surface sensors ( Wsc1-Wsc3 , Mid2 and Mtl1 ) detects the cell wall stress and recruits the guanine nucleotide exchange factors Rom1 and Rom2 which activate the small GTPase Rho1 . Rho1-GTP binds and activates several effectors , including the kinase Pkc1 . Pkc1 activates a downstream MAP kinase cascade comprised of the MAPKKK Bck1 , the two redundant MAPKK Mkk1 and Mkk2 , and the MAPK Mpk1 ( Slt2 ) . The phosphorylated Mpk1 then activates factors that promote transcription of genes important for cell wall biosynthesis and remodeling . In addition to the CWI pathway , several other factors and pathways are known to influence the cell wall remodeling that occurs upon stress , e . g . the mRNA-binding protein Ssd1 . Ssd1 has been reported to bind and influence the translation , stability and/or localization of a subset of cellular mRNAs of which many encode proteins important for cell wall biosynthesis and remodeling [26–31] . The wild-type SSD1 gene was originally identified as a suppressor of the lethality induced by a deletion of the SIT4 gene , which encodes a phosphatase involved in a wide range of cellular processes [32] . The study also led to the finding that some wild-type S . cerevisiae laboratory strains harbor a mutation at the SSD1 locus that is synthetic lethal with the sit4Δ allele [32] . The SSD1 locus has since been genetically implicated in many cellular processes , including cell wall integrity , various signal transduction pathways , cell morphogenesis , cellular aging , virulence , and transcription by RNA polymerase I , II and III [33–38] . Although the mechanisms by which Ssd1 influences these processes are poorly understood , they possibly involve both direct and indirect effects of Ssd1´s influence on messenger ribonucleoprotein ( mRNP ) complexes [28 , 29 , 39] . With respect to the transcripts that encode cell wall remodeling factors , Ssd1 seems to act as a translational repressor and this function is controlled by the protein kinase Cbk1 , which is a component in the RAM ( Regulation of Ace2 and cellular morphogenesis ) network [28] . In addition to relieving the translational repression , the phosphorylation of Ssd1 appears to promote polarized localization of some Ssd1-associated mRNAs [28 , 31] . In this study , we show that increased activation of the CWI signaling pathway counteracts the temperature sensitive ( Ts ) growth defect of elp3Δ mutants in the W303 but not in the S288C genetic background . Further , the stress-induced growth phenotypes caused by the tRNA modification defect are generally stronger in W303- than in S288C-derived strains . We show that the phenotypic differences are due to the allelic variation at the SSD1 locus , i . e . the phenotypes are aggravated by the nonsense ssd1-d2 allele found in the W303 background . We also show that the phenotypes linking the tRNA modification defect to histone acetylation and telomeric gene silencing are caused by a synergistic interaction with the ssd1-d2 allele . The difference at the SSD1 locus also provides a partial explanation to the finding [18 , 40 , 41] that cells lacking both the mcm5 and s2 group are viable in the S288C but not in the W303 background .
The observation that Elongator mutants are sensitive to cell wall stressing agents , e . g . calcofluor white and caffeine , implies a defect in cell wall integrity [14] . This notion is further supported by the finding that the Ts growth defect of Elongator-deficient cells is partially suppressed by osmotic support ( 1 M sorbitol ) [42] . As the caffeine sensitivity and Ts growth defect are suppressed by increased levels of the hypomodified tRNA UUU Lys and tRNA UUG Gln [20] , the phenotypes are likely caused by reduced functionality of these tRNAs in translation . To further define the wall integrity defect in Elongator mutants , we investigated , in the W303 genetic background , if the Ts phenotype of elp3Δ cells is suppressed by increased expression of factors in the CWI signaling pathway . The analyses revealed that the introduction of a high-copy MID2 , WSC2 , ROM1 , or PKC1 plasmid into the elp3Δ strain partially suppressed the growth defect at 37°C ( Fig 1A ) . No suppression of the phenotype was observed when the cells carried a high-copy RHO1 , BCK1 or MPK1 plasmid ( Fig 1A ) . As the overexpression of neither the upstream GTPase ( Rho1 ) nor the downstream kinases ( Bck1 and Mpk1 ) suppressed the Ts phenotype , we considered the possibility that the levels of these factors may be too high when expressed from a high-copy plasmid . Accordingly , low-copy RHO1 , BCK1 and MPK1 plasmids suppress the Ts phenotype of elp3Δ cells to a level similar to that observed for the high-copy MID2 , WSC2 , ROM1 , and PKC1 plasmids ( Fig 1A ) . The level of suppression is , however , smaller than that observed for increased tK ( UUU ) and tQ ( UUG ) dosage , encoding tRNA UUU Lys and tRNA UUG Gln , respectively ( Fig 1A ) . Since the Ts phenotype of elp3Δ cells is counteracted by elevated tRNA UUU Lys and tRNA UUG Gln levels [20] , it was possible that the activation of the CWI pathway leads to an increase in their relative abundance . To investigate this possibility , we used northern blotting to analyze the effect of increased PKC1 dosage on the levels of tRNA UUU Lys and tRNA UUG Gln in elp3Δ cells grown at either 30°C or 37°C . The blots were also probed for tRNA i Met and 5 . 8S rRNA , which served as the loading control . These RNAs were selected as tRNA i Met does not contain an Elongator-dependent tRNA modification and 5 . 8S rRNA is transcribed by a different RNA polymerase ( RNA polymerase I ) . In contrast to the ≈2-fold increase in tRNA UUU Lys and tRNA UUG Gln levels induced by increased tK ( UUU ) and tQ ( UUG ) dosage ( Fig 1B and S1 Table ) , the abundance of the tRNAs was largely unaffected by increased PKC1 dosage at 30°C ( Fig 1B and S1 Table ) . At 37°C , the increased PKC1 dosage correlated with a slight increase in the levels of tRNA UUU Lys ( Fig 1B and S1 Table ) . However , a similar effect was observed in the elp3Δ strain complemented with the ELP3 gene , making it difficult to assess if the increase is the cause or the consequence of the improved growth at 37°C . Nevertheless , our results suggest that the Ts phenotype of elp3Δ cells is , at least in the W303 genetic background , partially suppressed by increased activation of the CWI signaling pathway . Phenotypes caused by a mutation in an individual gene can be modulated by the genetic background of the cell . In fact , the Ts phenotype induced by an elp3Δ allele is more pronounced in strains derived from W303 than in those from S288C ( Fig 2A ) . As the inactivation of Elongator causes a lack of wobble mcm5/ncm5 groups in both strain backgrounds [5 , 43] , the Ts phenotype is likely modulated by genetic variation between W303 and S288C . The difference in phenotype ( Fig 2A ) prompted us to investigate if increased MID2 , WSC2 , ROM1 , RHO1 , PKC1 , BCK1 or MPK1 dosage counteracts the Ts phenotype of elp3Δ cells in the S288C background . The analyses showed that none of the plasmids counteracted the phenotype ( S1A Fig ) . Moreover , the growth defect of elp3Δ cells at 37°C is counteracted by osmotic support ( 1 M sorbitol ) in the W303 , but not in the S288C background ( S1B Fig ) . Importantly , the Ts phenotype of elp3Δ cells is suppressed by increased tK ( UUU ) and tQ ( UUG ) dosage in both genetic backgrounds ( Fig 1A and S1A Fig ) , showing that the underlying cause is the hypomodified tRNA UUU Lys and tRNA UUG Gln . We conclude that the genetic background influences the phenotypes linking the tRNA modification defect to cell wall integrity . Although W303 is closely related to S288C , comparisons of the genomes identified polymorphisms in ≈800 genes that lead to variations in the amino acid sequence [44 , 45] . To identify the cause of the phenotypic differences between the elp3Δ strains , we examined polymorphisms that have been shown to be physiologically relevant . The polymorphism at the SSD1 locus was a good candidate as SSD1 has been genetically implicated in many cellular processes , including the maintenance of cellular integrity [32 , 33 , 46–50] . Ssd1 is a RNA binding protein that associates with a subset of mRNAs of which many encode proteins important for cell wall biosynthesis and remodeling [26–28] . The SSD1 allele in the S288C background encodes the full-length Ssd1 protein ( 1250 amino acids ) whereas the allele in W303 , designated ssd1-d2 , contains a nonsense mutation that introduces a premature stop codon at the 698th codon of the open reading frame [32 , 35] . To investigate if the allele at the SSD1 locus contributes to the phenotypic differences between the elp3Δ mutants , we analyzed congenic ssd1-d2 , SSD1 , ssd1-d2 elp3Δ , and SSD1 elp3Δ strains in both genetic backgrounds . By analyzing the growth of the strains , we found that the ssd1-d2 allele augments the Ts phenotype of elp3Δ cells in both backgrounds ( Fig 2B and S2 Table ) . The ssd1-d2 allele also appears to cause a slightly reduced growth rate of strains with a wild-type ELP3 gene at the elevated temperature ( S2 Table ) . Even though the elp3Δ mutants grow slower than the ELP3 strains at 30°C , the effect of the ssd1-d2 allele is less pronounced at that temperature ( S2 Table ) . In both genetic backgrounds , the increased activation of the CWI signaling pathway , through increased PKC1 dosage , suppresses the Ts phenotype of the ssd1-d2 elp3Δ , but not the SSD1 elp3Δ strains ( Fig 2C ) . Although the elp3Δ strains show increased sensitivity to caffeine , irrespective of the allele at the SSD1 locus , the ssd1-d2 elp3Δ cells are in both backgrounds more caffeine-sensitive than the SSD1 elp3Δ cells ( Fig 2D ) . This observation is consistent with the previous finding that the ssd1-d2 allele enhances the growth inhibitory effects of caffeine [32] . The inactivation of Elongator not only leads to increased sensitivity to caffeine , but also to other stress-inducing agents [14 , 17 , 18] . To investigate if the ssd1-d2 allele influences these phenotypes , we analyzed the growth of the ssd1-d2 , SSD1 , ssd1-d2 elp3Δ , and SSD1 elp3Δ strains on medium containing rapamycin , hydroxyurea , or diamide . The analyses revealed that the ssd1-d2 allele , irrespective of background , also increases the sensitivity of elp3Δ cells to these compounds ( Fig 2D ) . To ensure that the effect of the ssd1-d2 allele is not restricted to one set of congenic strains , we repeated the growth assays , in both genetic backgrounds , using a second set of ssd1-d2 , SSD1 , ssd1-d2 elp3Δ , and SSD1 elp3Δ isolates . The stress-induced growth defects of elp3Δ cells were also in these strains augmented by the ssd1-d2 allele ( S2 Fig ) . As translational readthrough of the premature stop codon in the ssd1-d2 mRNA could generate low levels of full-length functional Ssd1 protein , we also investigated the effect of an ssd1Δ allele on the growth phenotypes of elp3Δ mutants . In both genetic backgrounds , the effect of the ssd1Δ allele is comparable to the ssd1-d2 mutation ( S3 Fig ) . Collectively , these results show that the allele at the SSD1 locus influences stress-induced growth defects of elp3Δ cells . As the phenotypes of Elongator mutants are largely caused by the reduced functionality of the hypomodified tRNA UUU Lys and tRNA UUG Gln [20] , it seemed possible that the ssd1-d2 allele may influence tRNA abundance or function . To investigate if the allele at the SSD1 locus influence tRNA abundance , we used northern blotting to determine the levels of tRNA UUU Lys , tRNA UUG Gln , and tRNA i Met in the ssd1-d2 , SSD1 , ssd1-d2 elp3Δ , and SSD1 elp3Δ strains . The analyses revealed that the relative levels of the tRNAs are , in both genetic backgrounds , largely unaffected by the allelic variant at the SSD1 locus ( S4 Fig and S3 Table ) . Moreover , HPLC analyses of the nucleoside composition of total tRNA isolated from the strains showed that the levels of ncm5U , mcm5U , and mcm5s2U are comparable in the ssd1-d2 and SSD1 strains and not detectable in the ssd1-d2 elp3Δ and SSD1 elp3Δ mutants ( S4 Table ) . The HPLC analyses also revealed that the allele at the SSD1 locus has no apparent effect on the abundance of other modified nucleosides present in tRNAs ( S5 Fig and S4 Table ) . To investigate if the allele at the SSD1 locus influences tRNA function , we utilized a +1 frameshifting reporter system [51 , 52] . By using this system , it was previously shown that elp3Δ cells show elevated levels of +1 frameshifting on a CUU AAA C frameshifting site [52] . Further , this increase is caused by slow entry of the hypomodified tRNA UUU Lys into the ribosomal A-site and the consequent slippage of the P-site tRNA [52] . Although analyses of the ssd1-d2 , SSD1 , ssd1-d2 elp3Δ , and SSD1 elp3Δ strains confirmed that the elp3Δ mutants show elevated levels of +1 frameshifting on the CUU AAA C sequence , we detected no influence of the allele at the SSD1 locus ( Table 1 ) . Thus , Ssd1 does not appear to influence the functionality of the hypomodified tRNA UUU Lys . In the W303 background , elp3Δ mutants show reduced acetylation of histone H3 [15] . Although the phenotype was originally thought to reflect a function of Elongator in RNA polymerase II transcription [15] , the reduced acetylation of lysine-14 ( K14 ) in histone H3 was subsequently shown to be an indirect consequence of the tRNA modification defect [20] . To investigate if the ssd1-d2 allele contributes to the phenotype , we analyzed , in the W303 background , the histone H3 K14 acetylation levels in the ssd1-d2 , ssd1-d2 elp3Δ , SSD1 , and SSD1 elp3Δ strains . As previously shown [15 , 20] , the level of K14 acetylation is lower in the ssd1-d2 elp3Δ mutant than in the ssd1-d2 strain ( Fig 3A ) . However , the SSD1 and SSD1 elp3Δ strains show comparable levels of K14 acetylated histone H3 , indicating that it is the combination of the elp3Δ and ssd1-d2 alleles that induces the histone H3 acetylation defect . W303-derived Elongator mutants have also been reported to show delayed transcriptional activation of the GAL1 and GAL10 genes upon a shift from raffinose- to galactose-containing medium [20 , 53] . To determine if the ssd1-d2 allele influences this phenotype , we analyzed the induction of the GAL1 mRNA in the W303-derived ssd1-d2 , ssd1-d2 elp3Δ , SSD1 , and SSD1 elp3Δ strains . Unexpectedly , we observed no obvious delay in the induction of GAL1 transcripts in the elp3Δ strains regardless of the nature of the allele at the SSD1 locus ( S6 Fig ) . As the phenotype is thought to reflect a reduced ability of Elongator mutants to adapt to new growth conditions [53] , it is possible that differences in media or the handling of the cultures can explain why elp3Δ cells show rapid GAL1 induction in our experiments . Another phenotype reported for Elongator mutants in the W303 background is a defect in telomeric gene silencing [17 , 21] . The telomere silencing defect of Elongator mutants was inferred from experiments where the expression of a URA3 gene integrated close to the left telomere of chromosome VII was assayed [17 , 21] . The defect in telomeric gene silencing leads to increased expression of the URA3 gene , which is scored as reduced growth on plates containing 5-fluoroorotic acid ( 5-FOA ) [17 , 21] . Even though the integration of an SSD1 allele into ssd1-d2 cells does not influence the level of telomeric gene silencing [36] , the inactivation of SSD1 does increase the expression of a reporter gene at the silent mating type locus HMR [54] . The latter finding implies that the ssd1-d2 allele may influence the assembly of silent chromatin and consequently contribute to the silencing defect in Elongator mutants . Accordingly , the introduction of a low-copy SSD1 plasmid into the ssd1-d2 elp3Δ TELVIIL::URA3 strain [21] complemented the 5-FOA sensitivity to a level similar to that observed with a plasmid carrying the wild-type ELP3 gene ( Fig 3B ) . Thus , the telomeric gene silencing defect in Elongator mutants is caused by a synergistic interaction between the ssd1-d2 and elp3Δ alleles . In the formation of mcm5s2U34 , Elongator promotes synthesis of the mcm5 side-chain whereas the thiolation of the 2-position is catalyzed by the Ncs2/Ncs6 complex [40 , 43 , 55–57] . The simultaneous lack of mcm5 and s2 groups was originally reported to be lethal [40] . However , those experiments were performed in the W303 background and more recent studies have shown that strains lacking both groups are viable in the S288C background [18 , 41] . To investigate if the allele at the SSD1 locus accounts for the difference in viability , we constructed ssd1-d2 elp3Δ ncs2Δ and SSD1 elp3Δ ncs2Δ strains in both backgrounds all carrying a wild-type ELP3 gene on a low-copy URA3 plasmid . Analyses of the strains revealed that the elp3Δ ncs2Δ double mutant is viable in the W303 background if it encompasses an SSD1 allele ( Fig 4A; S7 Fig shows the same plates after a 2-day incubation ) . In the S288C background , the ssd1-d2 elp3Δ ncs2Δ strain is viable but it grows slower than the SSD1 elp3Δ ncs2Δ strain ( Fig 4A and 4B ) . These observations not only show that allele at the SSD1 locus influences the viability of elp3Δ ncs2Δ cells , but they also indicate that the growth phenotype is modulated by additional genetic factors . The lack of the mcm5 and/or s2 groups has , in the S288C background , been shown to correlate with an increased accumulation of protein aggregates [18] . This effect was most pronounced in a strain lacking both groups and the phenotype was suggested to be a consequence of co-translational misfolding due to slower decoding of AAA and CAA codons by the hypomodified tRNA UUU Lys and tRNA UUG Gln [18] . Moreover , the increased load of aggregates during normal growth was proposed to account for observation that the double mutant is impaired in clearing diamide-induced protein aggregates [18] . As strains deleted for SSD1 show a defect in the disaggregation of heat-shock-induced protein aggregates [58] , it seemed possible that the ssd1-d2 allele would augment the protein homeostasis defect in elp3Δ ncs2Δ cells . To investigate this possibility , we isolated aggregates [18 , 59] from the ssd1-d2 , SSD1 , ssd1-d2 elp3Δ ncs2Δ , and SSD1 elp3Δ ncs2Δ strains . Analyses of the insoluble fractions revealed that the levels of aggregated proteins are comparable in the ssd1-d2 and SSD1 strains ( Fig 4C ) . However , the ssd1-d2 elp3Δ ncs2Δ mutant shows increased accumulation of aggregates compared to the SSD1 elp3Δ ncs2Δ strain ( Fig 4C ) . Thus , the allele at the SSD1 locus modulates the protein homeostasis defect induced by the simultaneous lack of the wobble mcm5 and s2 groups .
The phenotypic penetrance of a mutation is often impacted by the genetic background , a phenomenon frequently observed in monogenic diseases [60 , 61] . In this study , we investigate the effect of genetic background on the phenotypes of S . cerevisiae mutants defective in the formation of modified wobble uridines in tRNAs . We show that the phenotypes of Elongator mutants are augmented by the ssd1-d2 allele found in some wild-type laboratory strains . Moreover , the histone H3 acetylation and telomeric gene silencing defects reported for Elongator mutants are only observed in cells harboring the ssd1-d2 allele . Thus , the ssd1-d2 allele sensitizes yeast cells to the effects induced by the lack of mcm5/ncm5 groups in U34-containing tRNAs . Although the pleiotropic phenotypes of Elongator mutants are largely caused by the reduced functionality of the hypomodified tRNA UUU Lys , tRNA UUG Gln and tRNA UUC Glu , the basis for individual phenotypes is poorly understood . Several not necessarily mutually exclusive models have been proposed to explain how the lack of the mcm5/ncm5 groups can lead to a particular phenotype . One model postulates that phenotypes can be induced by inefficient translation of mRNAs enriched for AAA , CAA and/or GAA codons and the consequent effects on the abundance of the encoded factors [21 , 62–64] . In this model , the inefficient decoding of the mRNAs leads to reduced protein output without affecting transcript abundance . The mechanism by which the slower decoding of the codons leads to reduced protein levels is unclear , but it may involve the inhibition of translation initiation by the queuing of ribosomes . Alternative models suggest that the phenotypes can be caused by defects in protein homeostasis and/or by indirect effects on transcription [18 , 22] . The inactivation of SSD1 not only influences translation and stability of the transcripts normally bound by Ssd1 , but it also leads to altered abundance of many transcripts that do not appear to be Ssd1-associated [28 , 39] . Thus , the effects of the ssd1-d2 allele on the phenotypes of elp3Δ cells could be due to either direct or indirect effects on gene expression . Moreover , the ribosome profiling experiments of Elongator mutants have been performed in the S288C background [18 , 22 , 23] and it remains possible that the lack of Ssd1 influences decoding of the AAA , CAA , and GAA codons . However , we observed no apparent effect of the ssd1-d2 allele on the +1 frameshifting induced by the lack of Elongator ( Table 1 ) , indicating that Ssd1 does not influence the A-site selection rate . While the difference at the SSD1 locus partially explains the nonviability of elp3Δ ncs2Δ cells in the W303 background , the W303-derived SSD1 elp3Δ ncs2Δ strain grows slower than the corresponding strain in the S288C background ( S7 Fig ) . Moreover , the ssd1-d2 elp3Δ ncs2Δ strain is viable , although with a growth defect , in the S288C background . These findings indicate that the growth phenotypes of elp3Δ ncs2Δ cells strains are modulated by additional genetic factors . Consistent with the finding that ssd1Δ cells show a defect in Hsp104-mediated protein disaggregation [58] , the ssd1-d2 elp3Δ ncs2Δ strain shows increased accumulation of protein aggregates compared to the SSD1 elp3Δ ncs2Δ strain in the S288C background ( Fig 4C ) . It is , however , unclear if this increase is the cause or the consequence of the reduced growth of the ssd1-d2 elp3Δ ncs2Δ strain .
Strains and plasmids used in this study are listed in S5 and S6 Tables . Yeast media were prepared as described [65 , 66] . The medium was where appropriate supplemented with 2 . 5 ng/ml rapamycin ( R0395 , Sigma-Aldrich ) , 7 mM caffeine ( C0750 , Sigma-Aldrich ) , 100 mM hydroxyurea ( H8627 , Sigma-Aldrich ) , 0 . 25 mg/ml diamide ( D3648 , Sigma-Aldrich ) , or 1 mg/ml 5-fluoroorotic acid ( R0812 , Thermo Fisher ) . To generate ssd1-d2 derivatives of BY4741 and BY4742 ( S288C background ) [67] , we first replaced the sequence between position 2907 and 3315 of the SSD1 ORF with a URA3 gene PCR-amplified from pRS316 [68] . The oligonucleotides used for strain constructions are described in S7 Table . The generated strains were transformed with an ssd1-d2 DNA fragment PCR-amplified from W303-1A [69] . Following selection on 5-fluoroorotic acid ( 5-FOA ) -containing plates and subsequent single cell streaks , individual clones were screened for the integration of ssd1-d2 allele by PCR and DNA sequencing . The generated strains ( UMY4432 and UMY4433 ) were allowed to mate producing the homozygous ssd1-d2/ssd1-d2 strain ( UMY4434 ) . Strains deleted for SSD1 , ELP3 , or NCS2 were constructed by transforming the appropriate diploid ( UMY3387 , UMY2836 or UMY4434 ) with an ssd1::KanMX4 , elp3::KanMX4 , or ncs2::KanMX4 DNA fragment with appropriate homologies . The DNA fragments were PCR-amplified from ssd1::KanMX4 ( Open Biosystems deletion collection ) , elp3::KanMX4 ( UMY3269 ) , or ncs2::KanMX4 ( UMY3442 ) strains . Following PCR confirmation of the deletion , the generated heterozygous diploids were allowed to sporulate and the W303 ssd1Δ ( UMY4558 ) , S288C ssd1Δ ( UMY4559 ) , W303 elp3Δ SSD1 ( UMY4456 and UMY4457 ) , W303 ncs2Δ SSD1 ( MJY1019 ) , S288C elp3Δ ssd1-d2 ( UMY4438 and UMY4439 ) , S288C ncs2Δ ssd1-d2 ( UMY4442 ) , S288C elp3Δ SSD1 ( MJY1036 ) , and S288C ncs2Δ SSD1 ( MJY1021 strains were obtained from tetrads . The ssd1Δ elp3Δ mutants ( MJY1227 and UMY4574 ) were obtained from crosses between the relevant ssd1Δ and elp3Δ strains . The elp3Δ ncs2Δ SSD1 mutants ( MJY1058 and UMY4467 ) were obtained from crosses between the relevant strains . The diploids used to generate the elp3Δ ncs2Δ ssd1-d2 strains ( MJY1159 and UMY4454 ) were transformed with pRS316-ELP3 [40] before sporulation . MJY1159 was able to lose the plasmid generating strain UMY4449 . To construct plasmids carrying individual genes for factors in the CWI signaling pathway , we PCR-amplified the gene of interest using oligonucleotides that introduce appropriate restriction sites ( S7 Table ) . The DNA fragment was then cloned into the corresponding sites of pRS425 [70] or pRS315 [68] . The abundance of individual tRNA species was determined in total RNA isolated from exponentially growing cultures at an optical density at 600 nm ( OD600 ) of ≈0 . 5 [66] . Samples containing 10 μg of total RNA were separated on 8M urea-containing 8% polyacrylamide gels followed by electroblotting to Zeta-probe membranes ( Bio-Rad ) . The blots were sequentially probed for tRNA UUU Lys , tRNA UUG Gln , tRNA i Met , and 5 . 8S rRNA using 32P-labeled oligonucleotides ( S7 Table ) . Signals were detected and analyzed by phosphorimaging using a Typhoon FLA 9500 biomolecular imager and Quantity One software . To analyze the induction of GAL1 transcripts , cells were grown at 30°C in 50 ml synthetic complete ( SC ) medium containing 2% raffinose ( SC/Raf ) to OD600≈0 . 45 . The culture was harvested by centrifugation at 1 , 500 x g for 5 min at room temperature , and the cell pellet resuspended in 15 ml pre-warmed ( 30°C ) SC/Raf medium . Following reincubation in the shaking water bath for 10 min , transcription of GAL1 was induced by the addition of 1 . 5 ml pre-warmed 20% galactose . Aliquots were harvested [66] at various time points after the addition of galactose and the cell pellets frozen on dry ice . The procedures for determining mRNA levels have been described [66] . To analyze the nucleoside composition of total tRNA , the tRNA was isolated from exponentially growing cultures at OD600≈0 . 8 [21] . The tRNA was digested to nucleosides using nuclease P1 ( Sigma-Aldrich , N8630 ) and bacterial alkaline phosphatase ( Sigma-Aldrich , P4252 ) and the hydrolysate analyzed by HPLC [71 , 72] . The compositions of the elution buffers were as described [72] with the difference that methanol concentration in buffer A was changed to 5% ( v/v ) . Cells transformed with pABY2139 or pABY2144 were grown in synthetic complete medium lacking uracil ( SC-ura ) to OD600≈0 . 5 . Cells representing 10 OD units were harvested and the β-galactosidase activity determined in protein extracts as described previously [65] . The +1 frameshifting levels were determined by dividing the β-galactosidase activity in extracts of cells containing the frameshift construct ( pABY2139 ) with that of cells containing the in-frame control ( pABY2144 ) . Histones were isolated from cells grown in SC medium at 30°C to OD600≈0 . 8 . Cells representing 100 OD600 units were harvested , washed once with water , resuspended in 30 ml of buffer A ( 0 . 1 mM Tris-HCl at pH 9 . 4 , 10 mM DTT ) , and incubated on a rotator at 30°C for 15 min . Cells were collected , washed with 30 ml buffer B ( 1 M Sorbitol , 20 mM HEPES at pH7 . 4 ) and resuspended in 25 ml of buffer B containing 600 U yeast lytic enzyme . After 1 hour incubation on a rotator at 30°C , the sample was mixed with 25 ml of ice-cold buffer C ( 1 M Sorbitol , 20 mM PIPES at pH 6 . 8 , 1 mM MgCl2 ) followed by centrifugation at 1 , 500 x g for 5 min . The pellet was resuspended in 40 ml nuclei isolation buffer [73] and the suspension incubated with gentle mixing at 4°C for 30 min . Cell debris were removed by centrifugation at 1 , 500 x g for 5 min . The supernatant was homogenized with 5 strokes in a Dounce homogenizer followed by centrifugation at 20 , 000 x g for 10 min . Histones in the pelleted nuclei were extracted by re-suspension in 5 ml of cold 0 . 2 M H2SO4 and overnight incubation on a rotator at 4°C . After centrifugation at 10 , 000 x g for 10 min , proteins in the supernatant were precipitated by adding 0 . 5 volumes of 100% trichloroacetic and 30 min of incubation on ice . Following centrifugation , the pellet was washed twice with acetone and then dissolved in 200 μl 10 mM Tris-HCl at pH 8 . 0 . Fractions ( 10 μl ) were resolved by 15% SDS-PAGE and transferred to Immobilon-P ( Millipore ) membranes . The blots were incubated with rabbit anti-acetyl-histone H3 ( Lys14 ) antibodies ( 1:1 , 000 dilution , Millipore , 07–353 ) and then with horseradish peroxidase-linked donkey anti-rabbit IgG ( NA934 , GE Healthcare ) . Blots were stripped and reprobed with rabbit anti-histone H3 antibodes ( 1:5 , 000 dilution , Millipore , 07–690 ) . Proteins were detected using ECL Western blotting detection reagents ( GE Healthcare , RPN2209 ) and Amersham Hyperfilm ECL ( GE Healthcare , 28906836 ) . Protein aggregates were analyzed in exponentially growing cultures in SC medium at 30°C . Cells representing 50 OD600 units were harvested at OD600≈0 . 5 and protein aggregates were isolated [18 , 59] from samples containing 5 mg of total protein . 1/10 of the aggregates and 5μg of total protein were resolved on a 4–12% NuPAGE Bis-Tris gel ( Thermo Fisher , NP0321BOX ) followed by staining with the Colloidal Blue Staining Kit ( Thermo Fisher , LC6025 ) . | Modified nucleosides in the anticodon region of tRNAs are important for the efficiency and fidelity of translation . The Elongator complex promotes formation of several related modified uridine residues at the wobble position of eukaryotic tRNAs . In yeast , plants , worms , mice and humans , mutations in genes for Elongator subunits lead to a wide variety of different phenotypes . Here , we show that the genetic background modulates the phenotypic consequences of the inactivation of budding yeast Elongator . This background effect is largely a consequence of a polymorphism at the SSD1 locus , encoding a RNA binding protein that influences translation , stability and/or localization of mRNAs . We show that several phenotypes reported for yeast Elongator mutants are either significantly stronger or only detectable in strains harboring a mutant ssd1 allele . Thus , SSD1 is a suppressor of the phenotypes induced by the hypomodification of tRNAs . | [
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"biology... | 2019 | SSD1 suppresses phenotypes induced by the lack of Elongator-dependent tRNA modifications |
We developed a whole-circulation computational model by integrating a transmission line ( TL ) model describing vascular wave transmission into the established CircAdapt platform of whole-heart mechanics . In the present paper , we verify the numerical framework of our TL model by benchmark comparison to a previously validated pulse wave propagation ( PWP ) model . Additionally , we showcase the integrated CircAdapt–TL model , which now includes the heart as well as extensive arterial and venous trees with terminal impedances . We present CircAdapt–TL haemodynamics simulations of: 1 ) a systemic normotensive situation and 2 ) a systemic hypertensive situation . In the TL–PWP benchmark comparison we found good agreement regarding pressure and flow waveforms ( relative errors ≤ 2 . 9% for pressure , and ≤ 5 . 6% for flow ) . CircAdapt–TL simulations reproduced the typically observed haemodynamic changes with hypertension , expressed by increases in mean and pulsatile blood pressures , and increased arterial pulse wave velocity . We observed a change in the timing of pressure augmentation ( defined as a late-systolic boost in aortic pressure ) from occurring after time of peak systolic pressure in the normotensive situation , to occurring prior to time of peak pressure in the hypertensive situation . The pressure augmentation could not be observed when the systemic circulation was lumped into a ( non-linear ) three-element windkessel model , instead of using our TL model . Wave intensity analysis at the carotid artery indicated earlier arrival of reflected waves with hypertension as compared to normotension , in good qualitative agreement with findings in patients . In conclusion , we successfully embedded a TL model as a vascular module into the CircAdapt platform . The integrated CircAdapt–TL model allows detailed studies on mechanistic studies on heart-vessel interaction .
The CircAdapt platform , a zero-dimensional whole-heart model developed in our lab , historically focussed on cardiac mechanics . It has been successfully used for simulating haemodynamics during cardiac conductance disorders , valve pathologies , and changes in afterload [1 , 2 , 3 , 4] . Lacking a distributed model of the vascular system , the current CircAdapt model is yet unable to simulate heart-vessel interaction at the level of arterial wave dynamics . Arterial pulse waves , constituting a component of ventricular afterload , appear to have implications in age-related changes in left ventricular mass , and left ventricular hypertrophy [5 , 6] . So-called wave intensity analysis ( WIA ) allows characterisation of both pulse wave magnitude and propagation direction , thereby requiring synchronous and co-localised measurements of blood pressure and blood flow velocity signals [7] . WIA applied to patient measurement data is sensitive to synchronisation errors and the signal processing characteristics of the measurement devices [8] , which hampers or limits detailed studies on heart-vessel interaction , especially concerning causal relationships . Computational models of whole-circulation mechanics , such as CircAdapt , allow for well-controlled simulations , facilitating comprehensive study of single- and multi-factorial relationships between arterial system properties and cardiac structure and function . In the present study we introduce and demonstrate the CircAdapt-TL model ( Fig 1 ) : a whole-circulation model with an integrated segmental transmission line ( TL ) module , describing vascular wave propagation , reflection and transmission . We verify the numerical implementation of the TL model by a benchmark comparison of the model to the established pulse wave propagation ( PWP ) model of Kroon et al . [11] . Additionally , we demonstrate operation and output of the integrated CircAdapt-TL model , by simulating systemic normotensive- and hypertensive conditions . We evaluate the implications of modelling vascular wave transmission on aortic haemodynamics by comparing simulated left ventricular- and aortic pressure tracings of the integrated CircAdapt-TL model with the tracings obtained with the systemic circulation lumped into the existing CircAdapt non-linear three-element windkessel ( 3WK ) model ( i . e . , neglecting wave transmission effects ) . Further evaluation includes WIA applied to simulated carotid arterial pressure and flow waveforms in semi-quantitative comparison with WIA applied to patient measurements .
Our vascular module describing wave transmission in vascular networks will be integrated into the existing CircAdapt platform ( www . circadapt . org ) . This model platform has a modular setup , currently consisting of a 0D whole-heart mechanics model , valve haemodynamics model , and non-linear three-element windkessel models of the pulmonary and peripheral circulations ( Fig 1 ) . In the next section , we introduce the governing equations , modelling assumptions and implementation of our new vascular module in detail . To model pressure-flow waves within segments of blood vessels , we assume 1 ) blood vessels to be thick-walled , longitudinally constrained non-linear elastic tubes , 2 ) blood to be incompressible and Newtonian and 3 ) that gravity forces can be neglected . Furthermore , we assume 4 ) no leakage of blood to small side-branches that are not explicitly modelled . Application of the laws of balance of mass and momentum , and subsequent integration over the tube’s cross-sectional area yield the governing equations [12]: C∂ p ∂ t + ∂ q ∂ z = 0 , ( 1 ) L ( ∂ q ∂ t + ∂ ∂ z ∫ A v z 2 d A ) + ∂ p ∂ z = f , ( 2 ) where p = p ( z , t ) is the pressure at the axial vessel coordinate z , and q = q ( z , t ) the flow rate at that coordinate . Furthermore , A denotes cross-sectional lumen area , and L and C are the tube inertance and compliance per unit length , respectively . Term L ∂ ∂ z ∫ A v z 2 d A represents the convective acceleration term , with vz the axial blood velocity . Term f represents friction force per unit volume caused by viscous properties of the blood , defined f = 2πr0τw/A0 [13] . Here , symbol τw denotes wall shear stress , r0 reference radius , and A0 reference cross-sectional lumen area , respectively . After neglecting the convective acceleration term and assuming an approximate velocity profile to estimate τw [13] , the governing equations can be rewritten to the telegrapher’s equations: - ∂ q ∂ z = C ∂ p ∂ t , ( 3 ) - ∂ p ∂ z = L ( α 0 ) ∂ q ∂ t + R ( α 0 ) q , ( 4 ) with L ( α0 ) and R ( α0 ) a characteristic Womersley number-dependent inertance and resistance term , defined by L ( α 0 ) = g ( α 0 ) ρ A 0 , ( 5 ) R ( α 0 ) = h ( α 0 ) 8 π η A 0 2 , with ( 6 ) α 0 = r 0 ρ ω 0 / η . ( 7 ) The functions g ( α0 ) and h ( α0 ) were derived by Bessems et al . [13] and are detailed in S1 Text , Section ‘Derivation of the attenuation constant , wave speed and wave impedance’ . The characteristic Womersley number ( α0 ) describes the ratio of instationary inertia forces and viscous forces , governed by vessel radius ( r0 ) , characteristic angular frequency ( ω0 = 2π/T , with T the cardiac cycle duration ) , blood dynamic viscosity ( η ) and blood density ( ρ ) , respectively ( Table 1 ) . To solve the governing equations , we also need a constitutive law to relate ( changes in ) transmural pressure ( ptrans ) to ( changes in ) current cross-sectional area ( A ) . The rationale of this method is to calculate R and L based on an approximated velocity profile for which the viscous boundary layer thickness is approximated for the characteristic frequency [13] . We formulated a non-linear power-law to phenomenologically capture the experimentally observed non-linear pressure-area relation of arteries and veins [16 , 20]: p trans ( A ) = - p ext + p 0 ( ( 1 + b ) ( A A 0 ) 1 + k / 3 - 2 1 + b - b A 0 A ) , and C = d A d p trans , ( 8 ) with p0 a reference pressure , A0 a reference cross-sectional area , and k the vessel stiffness coefficient . Furthermore , b is a small fraction to simulate collapse of the tube with negative transmural pressure ( Table 1 ) and pext represents a prescribed external pressure ( if present ) . Now we can solve the resulting governing equations in either the time domain or frequency domain [21] . We explicitly chose a time-domain approach , since this permits using non-linear boundary conditions as already present in the CircAdapt platform [2] . Our solving method uses a TL model . A detailed overview of our solving method is provided in S1 Text , Section ‘Solving strategy’ . The terminal end of a tube was coupled to a non-linear three-element windkessel ( 3WK ) element [10] . We assumed the windkessel compliance to be pressure-dependent , and scaled by an estimate of the tissues’ vessel bed length [22] . As a consequence , wave impedance also becomes pressure-dependent ( Fig 1 ) : C AV= l AV d A d p AV and Z wave , AV = ρ A d p AV d A , ( 9 ) with AV , the subscript for the arterial and venous contributions , i . e . AV = [art , ven] . Such approach enables simulating large changes in haemodynamic load ( e . g . exercise or hypertension ) without requiring to manually adjust the 3WK parameter values . The derivatives in the aforementioned equations were calculated at the connection point ( i . e . a node ) of a tube with a 3WK , using the constitutive relation as given in Eq 8 . The parameter lAV represents the characteristic length of a peripheral bed . We estimated the vessel bed length using the relation given by l AV = 6 q AV 1 / 3 , with qAV the mean peripheral flow through any terminal tube . To obtain first-order approximations of lAV among all peripheral beds , we utilised this relation in conjunction with flow distribution estimates as reported in Table B in S1 Text . Furthermore , using a physiology textbook [18] , we estimated that in rest 21% of the cardiac output is directed to the head , 47% to the abdomen , 18% to the pelvis and lower extremities , and 14% to the upper extremities , respectively . The peripheral resistance ( Rp ) was defined via a flow source , controlled by the instantaneous arterio-venous pressure difference ( Fig 1 ) [10]: R p = p art - p ven q AV . ( 10 ) The atria and ventricles of the heart were modelled as contractile chambers . The ventricles are surrounded by three cardiac walls: the left ventricle free wall , interventricular septum and right ventricle free wall ( Fig 1 ) . Ventricles are mechanically coupled , based on force equilibrium in the junction of the ventricular walls [9] . The atria are surrounded by the left atrial wall and right atrial wall ( Fig 1 ) . The cardiac chambers are considered as contractile cavities formed by the one-fibre model , relating myofibre stress to cavity pressure using the assumption that myofibre stress is homogeneously distributed within the myocardial wall [23] . The phenomenological model of myofibre mechanics was previously described [2] . The one-fibre model is used to calculate myofibre stress from myofibre strain . Total Cauchy myofibre stress experienced by cardiac tissue comprises of a summation of active stress , present in the actin filaments and separate microstructural contributions ( i . e . titin and the extracellular matrix , assumed to act in parallel ) . Transmural pressure is calculated from wall tension , derived from total Cauchy stress and wall curvature using Laplace’s law [2] . Cavity pressures are calculated by adding the transmural pressures to the pericardial pressure surrounding the myocardial walls . As commonly used in other cardiac models , the pericardium was assumed a compliant bag , modelled using a non-linear relation relating pericardial pressure and volume [24] . The pulmonary circulation was modelled as 3WK ( see Section ‘Arterio-venous impedance module’ ) , connecting the pulmonary artery with the pulmonary veins [25] . Full details of the cardiac model can be retrieved from Walmsley et al . [2] and Lumens et al . [9] . Valve flow ( qvalve ) was generated using the unsteady Bernoulli equation , assuming incompressibility and inviscid , irrotational flow: ρ l valve A valve ∂ q valve ∂ t = Δ p - 1 2 ρ q valve | q valve | ( 1 A valve 2 - 1 A p 2 ) , ( 11 ) with the term on the left hand side the unsteady inertia , governed by blood density , effective valve length ( lvalve ) and valve cross-sectional area ( Avalve ) [1] . The first term on the right hand side denotes the pressure difference ( Δp ) and the second term is the change in kinetic energy , with Avalve and Ap cross-sectional areas of the valve and proximal to the valve , respectively . For Avalve , a phenomenological valve opening and closing function depending on the pressure gradient was used [1] . In case Δp > 0 , Avalve instantaneously increases towards an effective valve area representing a completely opened valve . In case Δp < 0 , on the other hand , flow gradually decreases due to inertia . Furthermore , Avalve gradually decreases towards a quasi-closed state , with small leakage to avoid division by zero [1] .
Agreement between pressure and flow waves of the TL model and PWP model for five tubes in the model domain is graphically depicted in Fig 4 . Root mean square errors ( Eq 13 ) for pressures and flows for all tubes are given in Table 2 . Between models , we found good agreement in terms of pressure and flow waveforms for proximal arteries ( e . g . aorta , carotid , subclavian and vertebral arteries ) , expressed by relative errors δp ≤ 1 . 5% and δq ≤ 5 . 6% At the distally located interosseous artery , the difference between both models slightly increased , expressed by δp , 25 equal to 2 . 9% and δq , 25 equal to 5 . 3% . Nevertheless , the shape of the pressure and flow waveforms as well as absolute systolic and diastolic pressure and flow values were highly similar ( Fig 4 ) . In Figs 2 and 3 , pressure and flow waveforms in normotension are displayed for arteries and veins at three regions ( i . e . the central- , arm- and leg region ) . Arterial pressure waveforms at distal locations are characterised by an increase in pressure amplitude , as well as a reduction in peak width . The arterial pressure waveforms at distal locations show a more prominent dicrotic notch compared to the pressure waves at proximal locations . For veins , a biphasic pressure waveform can be distinguished , with venous flow and pressure in anti-phase ( Fig 3 ) . In the REF–TL simulation , pulse wave velocity ( PWV ) was 5 . 5 m s–1 , representing a PWV value commonly found in subjects aged < 30 years [29] . For the HYP simulation , pulse wave velocity ( PWV ) was 8 . 0 m s–1 , representing a PWV value clinically associated with early aortic stiffening , and commonly found in subjects aged > 50 years [29] . The blood pressure values in the REF–TL simulation were within the normal range ( Table 3 ) . As shown in Fig 5 , simulating systemic hypertension ( HYP ) caused arterial pressure to increase . This resulted in an increase in left ventricular pressure and left atrial pressure , whereas pulmonary artery pressure and pulmonary venous pressure slightly increased ( Fig 5 ) . The HYP–TL simulation showed an increase in systolic blood pressure ( psys ) from 128 to 193 mmHg and an increased diastolic blood pressure ( pdia ) from 75 to 92 mmHg ( Table 3 ) . Fig 6 shows LV and ascending aortic pressure tracings obtained using the CircAdapt–3WK model and the CircAdapt–TL model , respectively . The aortic pressure tracings of the REF–TL and HYP–TL simulation showed pressure augmentation ( i . e . a systolic pressure boost ) as well as an dicrotic notch , whereas for CircAdapt–3WK model simulations , these waveform characteristics were absent . In the REF–TL simulation , systolic pressure augmentation occurred after time of peak systolic pressure , whereas in the HYP–TL simulation this occurred prior to the time of peak systolic pressure . Wave intensity tracings ( dI+ , dI− , and dI , respectively ) of the REF–TL and HYP–TL simulation were calculated for the left common carotid artery ( Fig 7A ) . The carotid arterial wave intensity tracings of the REF–TL simulation indicate a forward compression wave ( FCW ) followed by a backward compression wave ( BCW ) . At end-systole , there is a forward expansion wave ( FEW ) associated with the deceleration of the rate of myocardial contraction [30] . In the REF–TL simulation , the onset of the BCW occurred 38 ms after onset of left ventricular ejection , whereas for the HYP–TL simulation the delay was 28 ms ( Fig 7A ) . Peak wave intensity of the BCW was approximately equal for the HYP–TL simulation ( 4 . 19 ⋅ 105 W m–2 s–2 ) as compared to the REF–TL simulation ( 4 . 23 ⋅ 105 W m–2 s–2 ) ( Fig 7A ) . Overall , the pattern of the simulated wave intensity tracings was similar to measured carotid arterial wave intensity tracings as reported by Hughes et al . [31] ( Fig 7B ) .
A simplification in the TL model is that convective acceleration is neglected . However , the influence of convective acceleration on arterial pressures and flows is believed to be small [41] . Moreover , it was found that inclusion of convective acceleration in an arterial model domain , similar to the one used in the present study , changed pressure and flow waveforms in the various arteries only slightly ( i . e . a root mean square error of 1 . 3 mmHg for thoracic aortic pressure waveform and 11 . 3 ml s–1 for thoracic aortic flow waveform , respectively [15] ) . We expect , however , that the effect of convective acceleration will be more important when simulating exercise conditions . Therefore , in such studies the modelling error related to convective acceleration needs to be properly considered . By excluding cerebral and coronary vessels from our model domain , we neglect the presumed influence that wave reflections and re-reflections from head and neck vessels or vessels in the myocardium may have on observed ascending aortic and carotid waveforms [15 , 42] . Our model neither contains a skeletal-muscle pump model nor does it incorporate venous valves . Hence , the present vascular model will not account for these functional aspects with postural changes . For studies with emphasis on venous haemodynamics , the CircAdapt platform allows for a straightforward implementation of venous valves , using for instance the existing valve module as a starting point . Like all distributed models of 1D wave transmission , our model cannot capture the complex pressure losses or local wall shear stresses when applied to disease conditions ( e . g . stenosis or aneurysm ) . This requires either use of calibrated loss models or coupling of detailed 3D models of stenoses and aneurysms to 1D models , respectively [43 , 44] . Reymond et al . [45] reported for the case of an apparently healthy aorta , that pressure and flow waveforms from a 3D CFD model and from a 1D PWP model are highly similar . The latter finding supports our notion that , in general , distributed models of wave transmission are well suited to examine and quantify heart-vessel interaction at the level of pressure and flow waveform characteristics . The utility of the CircAdapt–TL model should be further tested by direct comparisons against detailed haemodynamic data from humans . We consider the concurrent use of in vivo as well as simulated data as most valuable , because both arms bring complementary assets . The model allows error free assessment of phase relationships between signals and in vivo data enables characterisation of biological and pathological variability . In the future , we aim to further extend the CircAdapt–TL model with the cardiac adaptation module of Arts et al . [46] . This module contains a homeostatic control loop that senses offsets in mechanical load ( e . g . as present in chronic hypertension ) , and in response , imposes geometrical ( i . e . cavity volume and wall volume ) adaptation of the heart . We believe that modelling cardiac adaptation is vital in assessing candidate haemodynamic indices . Key clinical studies in this field include that of Hashimoto et al . [6] . They found a positive association between left ventricular mass , and wave reflection magnitude derived from pressure and flow velocity measurements , following antihypertensive treatment in left ventricular hypertrophy patients . However , a limitation of such clinical studies is that for non-invasive acquisition , pressure signals are obtained at distal measurement sites ( e . g . at the radial artery ) and therefore require a transfer function to obtain an estimate of the aortic pressure signal . Moreover , for clinically-gathered data , correct synchronisation of pressure and flow velocity signals is crucial , because only a small ( e . g . 5 ms ) misalignment can cause substantial changes in derived wave ( intensity ) quantities [7] . We validated and incorporated a one-dimensional vascular module into the CircAdapt platform . The resulting CircAdapt–TL model enables fast simulation of whole-heart mechanics , pressure and flow waveforms at various locations along the arterial and venous systems , and allows detailed haemodynamics studies . The CircAdapt–TL model provides a valuable tool for testing hypotheses concerning heart-vessel interaction and evaluating existing haemodynamics indices . | Arterial pulse wave characteristics show associations with left ventricular hypertrophy in clinical studies . However , in such studies , measurement and signal processing errors limit assessment of causality between wave characteristics and left ventricular hypertrophy . When validated , a computational model would allow comprehensive causality studies on heart-vessel interaction without such errors . In the present study , we integrated a novel vascular module describing wave transmission in vascular networks into the CircAdapt model of whole-heart mechanics . A benchmark comparison between our vascular module and a previously validated but more complex method , showed good agreement in terms of pressure and flow waveforms . The extended CircAdapt model is now also able to quantitatively describe vascular haemodynamics , including wave dynamics . | [
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"pressure... | 2019 | Large vessels as a tree of transmission lines incorporated in the CircAdapt whole-heart model: A computational tool to examine heart-vessel interaction |
The spontaneous dissociation of six small ligands from the active site of FKBP ( the FK506 binding protein ) is investigated by explicit water molecular dynamics simulations and network analysis . The ligands have between four ( dimethylsulphoxide ) and eleven ( 5-diethylamino-2-pentanone ) non-hydrogen atoms , and an affinity for FKBP ranging from 20 to 0 . 2 mM . The conformations of the FKBP/ligand complex saved along multiple trajectories ( 50 runs at 310 K for each ligand ) are grouped according to a set of intermolecular distances into nodes of a network , and the direct transitions between them are the links . The network analysis reveals that the bound state consists of several subbasins , i . e . , binding modes characterized by distinct intermolecular hydrogen bonds and hydrophobic contacts . The dissociation kinetics show a simple ( i . e . , single-exponential ) time dependence because the unbinding barrier is much higher than the barriers between subbasins in the bound state . The unbinding transition state is made up of heterogeneous positions and orientations of the ligand in the FKBP active site , which correspond to multiple pathways of dissociation . For the six small ligands of FKBP , the weaker the binding affinity the closer to the bound state ( along the intermolecular distance ) are the transition state structures , which is a new manifestation of Hammond behavior . Experimental approaches to the study of fragment binding to proteins have limitations in temporal and spatial resolution . Our network analysis of the unbinding simulations of small inhibitors from an enzyme paints a clear picture of the free energy landscape ( both thermodynamics and kinetics ) of ligand unbinding .
A wide variety of physiological processes and biochemical reactions are regulated by the binding of natural ligands to proteins . Furthermore , most known drugs are small molecules that , upon specific binding , modulate the activity of enzymes or receptors . Several experimental techniques for fragment-based drug design have been developed in the past 15 years and successful applications have been reported ( see for a review [1] , [2] ) . At the same time , a plethora of computer-based approaches to small-molecule docking have been developed and applied to a wide variety of protein targets . These in silico methods make use of simple and efficient scoring functions and are based mainly on stochastic algorithms , e . g . , genetic algorithm optimization of the ligand in the ( rigid ) substrate-binding site of an enzyme [3] , [4] . Only recently , explicit solvent molecular dynamics ( MD ) simulations have been used to investigate the binding of small fragments to proteins at atomistic level of detail , which is very helpful for the design of small-molecule inhibitors [5] , [6] , [7] , [8] . Out of equilibrium simulations of pulling have been carried out for an hapten/antibody complex [9] and small molecule inhibitors/enzyme complexes [10] , but it is not clear how much the external pulling force alters the free energy surface . In the past five years , new methods based on complex networks have been proposed to analyze the free energy surface of folding [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , which governs the process by which globular proteins assume their well-defined three-dimensional structure . These methods have been used successfully to analyze MD simulations thereby revealing multiple pathways and unmasking the complexity of the folding free energy surface of -sheet [11] , [13] , [20] , [21] , [22] and -helical [23] , [24] , [25] peptides , as well as small and fast-folding proteins [26] , [27] , [28] , [29] . Yet , no network analysis of the free energy surface of ligand ( un ) binding has been reported as of today . There are two main reasons for investigating the ( un ) binding free energy landscape . First , a wide variety of biochemical processes are regulated by the non-covalent binding of small molecules to enzymes , receptors , and transport proteins , and the binding/unbinding events are governed by the underlying free energy surface . Second , the characterization of metastable states within the bound state is expected to help in the identification of molecular fragments that bind to protein targets of pharmacological relevance , which could have a strong impact on experimental [2] and computational [4] approaches to fragment-based drug design . Here we use complex network analysis [11] and the minimum cut-based free energy profile ( cut-based FEP ) method [13] to study the free energy landscape of the bound state and the unbinding pathways of six small ligands of FKBP sampled by explicit water MD at physiological temperature . These compounds were chosen not only because of the knowledge of their binding mode ( X-ray structures of three of them ) but also because their experimentally measured dissociation constants are in the mM range [30] . Therefore , we expected that several events of spontaneous ligand unbinding from FKBP could be sampled by running independent MD simulations starting from the bound state without any external bias and within a 20-ns simulation time ( which requires about four days on a commodity processor ) .
The coordinates of FKBP in the complex with dimethylsulfoxide ( DMSO ) , methyl sulphinyl-methyl sulphoxide ( DSS ) , and 4-hydroxy-2-butanone ( BUT ) were downloaded from the PDB database ( entries 1D7H , 1DHI , and 1D7J , respectively ) . The starting conformation of tetrahydrothiophene 1-oxide ( THI ) , 5-hydroxy-2-pentanone ( PENT ) , and 5-diethylamino-2-pentanone ( DAP ) were prepared manually by overlapping the ( CHSO group of THI to the DMSO atoms in the DMSO/FKBP structure , while the ( CH ) CO group of PENT and DAP was overlapped to the corresponding atoms in BUT . To reproduce neutral pH conditions the side chains of aspartates and glutamates were negatively charged , those of lysines and arginines were positively charged , and histidines were considered neutral . The protein was immersed in an orthorhombic box of preequilibrated water molecules . The size of the box was chosen to have a minimal distance of 13 Å between the boundary and any atom of the protein . Solvent molecules within 2 . 4 Å of any heavy atom of the protein were removed except for six water molecules present in the crystal structure . The simulation system contained 8 sodium and 9 ( 10 for the DAP ) chloride ion to compensate for the total charge of FKBP which is +1 electron units . The MD simulations were carried out with NAMD [31] using the CHARMM22 force field [32] and the TIP3P model of water [33] . The parameters of the six ligands were determined according to the general CHARMM force field [34] . Periodic boundary conditions were applied and electrostatic interactions were evaluated using the particle-mesh Ewald summation method [35] . The van der Waals interactions were truncated at a cutoff of 12 Åand a switch function was applied starting at 10 Å . The MD simulations were performed at constant temperature ( 310 K or 350 K ) using the Langevin thermostat and constant pressure ( 1 atm ) [36] with a time step of 2 fs . The SHAKE algorithm was used to fix the covalent bonds involving hydrogen atoms . For each ligand and temperature value , 50 independent MD runs were carried out with different initial velocities . The runs were stopped after 20 ns or before if the intermolecular distance exceeded 30 Å . The Cartesian coordinates were saved every 4 ps along the trajectories . Thus , the number of snapshots used for analysis is different for different ligands , and ranges from 109569 for DMSO to 169511 for DSS . The analysis of the MD trajectories was carried out with CHARMM [37] and the MD-analysis tool WORDOM [38] . The leader algorithm as implemented in the latter program was employed for clustering according to the distance root mean square between two MD snapshots a and b , DRMS , which was calculated using the intermolecular distances between pairs of non-hydrogen atoms in the ligand and eight residues in the FKBP active site ( Tyr26 , Asp37 , Phe46 , Val55 , Ile56 , Trp59 , Tyr82 , and Phe99 ) . A DRMS threshold of 1 Å was used for clustering by the leader algorithm . The complex network analysis ( see below ) and cut-based FEP ( see Fig . S22 in Text S1 ) are robust with respect to the choice of the DRMS threshold in the range 0 . 8 to 1 . 0 Å . The DRMS calculation does not require structural overlap . In other words , rigid-body fitting is not necessary , which is an advantage with respect to the root mean square deviation . The clustering of about 150000 MD snapshots of BUT ( 35 runs of 10 ns , and 15 runs of 15–20 ns ) yielded 18021 clusters with two or more snapshots and 11425 one-snapshot clusters . The 29446 clusters are the nodes of the network and the transitions between them are edges . Note that the terms node and cluster are used as synonyms in this work . Totally there were 73473 edges within nodes and 74801 edges between different nodes . The networks were plotted using a spring-embedder algorithm [39] as implemented in the program igraph ( cneurocvs . rmki . kfki . hu ) . The overall features of the network are robust with respect to the choice of the thresholds on link and node size . Moreover , it is important to note that the clustering was not used for the analysis of unbinding kinetics but only for plotting the network and the cut-based FEP . The unbinding times were extracted directly from the MD trajectories without using the clustering . Projected free-energy surfaces are most useful if they preserve the barriers and minima in the order that they are met during the sequence of events . Krivov and Karplus have exploited an analogy between the kinetics of a complex process and equilibrium flow through a network to develop the cut-based FEP , a projection of the free energy surface that preserves the barriers [13] and can be used for extracting folding pathways and mechanisms from MD simulations [21] . The input for the cut-based FEP calculation is the transition network , which is derived by clustering , e . g . , as described above . For each node in the transition network , the partition function is , i . e . , the number of times the node is visited , where is the number of direct transitions from node to node observed along the time series . The transition probabilities can then be calculated as . If the nodes of the transition network are partitioned into two groups A and B , where group A contains the reference node , then ( the number of times a node in is visited ) , , and ( the number of transitions between nodes in and nodes in ) . The free energy of the barrier between the two groups is , where is the partition function of the full transition network ( Fig . 1 ) . The progress coordinate then is the normalized partition function of the reactant region containing the reference node , but other progress coordinates can be used , because the cut-based FEP is invariant with respect to arbitrary continuously invertible transformations of the reaction coordinate [40] . In practice , the procedure to calculate the cut-based FEP consists of three steps ( Fig . 1 ) : ( 1 ) The mean first passage time ( mfpt ) of node to the reference-node is the solution of the system of equations mfpt with initial boundary condition mfpt [41] . The timestep corresponds to the saving frequency of 4 ps; i . e . , the mfpt of a node is defined as one timestep plus the weighted average of the mfpt values of its adjacent nodes . ( 2 ) Nodes are sorted according to increasing values of mfpt ( or decreasing values of the probability of binding ) ; for each value of the progress variable the relative partition function and the cut are calculated . ( 3 ) The individual points on the profile are evaluated as ( , y = ) . The cut-based FEP method has been applied to characterize the free energy surface and folding pathways of the -hairpin of protein G [13] , a three-stranded antiparallel -sheet peptide [21] , [22] , and a cross-linked -helical peptide [25] . Recently , the cut-based FEP analysis of a simplified model of an amphipathic aggregation-prone peptide has provided strong evidence that amyloid fibril formation is under kinetic control [42] . Detailed balance was imposed to the network , i . e . , the number of transitions from node to node ( and vice versa ) was set equal to the arithmetic mean of the transitions from to and from to . Such symmetrization of the transition network improves the statistics and introduces a negligible error in the bound state since the trajectories are much longer than the slowest relaxation time within the bound state . Moreover , for each fragment several rebinding events were observed along the MD runs , so that the sampling of the dissociation barrier is at local equilibrium . The mfpt and the cut-based FEPs were calculated by the program WORDOM [38] using , as mentioned above , a time interval of 4 ps . The cut-based FEPs were also evaluated using the same DRMS clustering but taking into account MD snapshots saved with a time interval of 8 ps ( see Fig . S23 in Text S1 ) to check that the clustering procedure preserves the diffusive behavior of the dynamics [40] . This test is a necessary ( though not sufficient ) condition for the appropriateness of the clustering because the dynamics of spontaneous ligand unbinding is expected to be in the diffusive regime . The probability of unbinding can be evaluated for each MD snapshot very efficiently by considering that all snapshots in a node have the same probability of unbinding as described originally for the probability of folding [43] . The basic assumption is that conformations that are structurally similar have the same kinetic behavior , hence they have similar unbinding probability [22] , [43] . The MD trajectory following a given snapshot is analyzed to check if the unbinding condition is satisfied within a commitment time that has to be chosen much shorter than the unbinding time . An unbinding event is defined by a separation between the centers of mass of the FKBP active site and the ligand larger than 15 Å . For each node , the unbinding probability is the ratio between its members that unbind and the total number of snapshots in the node . The node with unbinding probability between 0 . 45 and 0 . 55 are defined as the transition state ensemble ( TSE ) . Among these , only those with at least 20 MD snapshots were taken into account .
In the majority of the runs the ligand separates completely from the surface of FKBP ( Fig . 2 , top , see also Figs . S1 and S2 in Text S1 ) . The ligand with the lowest affinity , DMSO , shows the highest number of unbinding events ( 49 in the 50 MD runs ) , while the two ligands with highest affinity , THI and DSS the smallest number ( 32 and 29 , respectively , Table 1 ) . The number of rebinding events ranges from 5 for DMSO to 12 for DAP ( Table 1 and see Fig . S2 in Text S1 ) . Since there are many more unbinding events than rebinding events the analysis focusses on unbinding kinetics and the relative probabilities of the binding modes . The dissociation rates , extracted for each ligand by fitting the cumulative distribution of the unbinding events observed in the 50 MD runs ( , see subsection Multiple unbinding pathways and single-exponential kinetics of unbinding ) , show a Pearson correlation coefficient of −0 . 84 with the equilibrium dissociation constants measured by a fluorescence assay [30] ( see Fig . S3 in Text S1 ) . Since the dissociation constant is the ratio between the off-rate and the on-rate the correlation indicates that the on-rate might be similar for the six ligands considered in this study . The residence time of a ligand on a protein surface or cavity can be measured by NMR spectroscopy or surface plasmon resonance . Experimentally , the residence time varies from picoseconds for very small ligands , e . g . , water and urea [44] , [45] , [46] , [47] , to milliseconds and seconds for potent binders , like high affinity inhibitors and antibodies [48] , [49] . The six small ligands of FKBP considered in the present study have intermediate size and affinity so that their unbinding times in the nanosecond time scale are consistent with the residence times measured experimentally for smaller and larger molecules . It is not possible to calculate the free energy of binding directly from the populations of bound and unbound as the MD runs where stopped upon ligand dissociation so that the relative populations are not correct . Therefore , the linear interaction energy ( LIE ) model [50] is used to approximate the binding energy as ( 1 ) where and are the electrostatic and van der Waals interaction energies between the ligand and its surroundings , respectively . The denotes an ensemble average sampled over a MD [51] or Monte Carlo [52] trajectory . Here , each of the two non-bonding terms is averaged independently over the trajectory segments during which the ligand is bound ( ligand/protein plus ligand/water interactions ) and the segments when the ligand is fully dissociated ( ligand/water interactions ) . The coefficient is determined empirically [51] by linear fitting using the five neutral compounds . The multiplicative factor 1/2 for the electrostatic term has a physical justification which can be explained by the fact that the electrostatic contribution to the hydration energy of a single ion is equal to half the corresponding ion-water interaction energy [53] , [54] . One advantage of the LIE model is that the two non-bonding energy terms can be analyzed individually . For the five neutral ligands the values of the binding affinity ( in the LIE approximation ) span a relatively small range , from kcal/mol for DMSO to kcal/mol for THI , and the van der Waals term has a more favorable contribution than the electrostatic term ( Table 1 ) . In contrast , the LIE binding affinity of DAP is much more favorable ( kcal/mol ) and is dominated by the electrostatic energy because of the salt bridge between the Asp37 side chain and the tertiary amino group of DAP which is positively charged . Therefore , the binding affinity in the LIE model is not a good approximation of the free energy of binding particularly for charged compounds for which polarization effects [55] are neglected in force fields with fixed partial charges . In addition , the electrostatic desolvation penalty depends strongly on the water model used in the simulations , which has a much stronger influence on charged species than neutral . Analysis of the MD trajectories reveals that multiple binding modes in the active site of FKBP are sampled for all six ligands ( Fig . 2 and see Figs . S4–S15 in Text S1 ) . Interestingly , the electron density maps indicate that PENT and DAP are present in the soaked FKBP crystals , but the quality of the maps was poor so that the crystallographers stated that “it is likely that these rather flexible ligands bind in a number of different conformations” [30] . Other computational and experimental studies have also reported and analyzed multiple binding modes [56] , [57] , [58] . It is useful to focus on BUT because it is one of the three ligands ( the other two are DMSO and DSS ) for which the X-ray structure in the complex with FKBP has been solved [30] . The ligand BUT has two hydrogen bond acceptors and one donor , the carbonyl and hydroxyl groups , separated by two methylene groups . It either accepts a hydrogen bond from the amide nitrogen of Ile56 or donates a hydrogen bond to the side chain of Asp37 as the distance between the two polar groups of BUT is not long enough to allow for the simultaneous formation of both intermolecular hydrogen bonds . The network analysis [11] and FEP [13] consistently reveal multiple subbasins in the bound state of BUT ( Fig . 2 ) as well as for the other ligands ( See Figs . S4 and S5 in Text S1 ) . The red and green subbasins make up about 50% of the number of snapshots of the bound conformation of BUT , and the binding mode of BUT with its carbonyl group acting as acceptor for the NH of Ile56 ( red subbasin ) is identical to the one in the X-ray conformation ( Fig . 3 ) . There is also an end-to-end flipped orientation of BUT in which its hydroxyl group ( instead of the carbonyl ) accepts from the NH of Ile56 . This pose makes up the subbasin of yellow nodes , which include about 25% of total bound conformations . The energy barriers between poses in different subbasins are small , which allows fast interconversions as observed in the time series of DRMS deviation from the X-ray structure ( Fig . 2 ) . There are more jumps between green and red subbasins than between green/red and yellow as the latter transitions require an end-to-end flip of BUT . The time series of DRMS show that in most trajectories of BUT there are several interconversions between different binding modes , which take place before the event of total dissociation ( Fig . 2 ) . In addition , the network analysis illustrates that there are different unbinding pathways without a single predominant route ( Fig . 4 ) . The unbinding pathways are spread over a large section of the active site and/or its rim ( see also subsection Unbinding transition state and Hammond effect ) . Despite the multiple pathways of unbinding , the cumulative distribution of the unbinding time shows single-exponential behavior ( Fig . 5 ) . Given that equilibration within the bound state is much faster than unbinding ( the time series in Fig . 2 top , left shows that multiple interconversions between bound state subbasins take place before unbinding ) , the single-exponential kinetics suggests that different pathways of dissociation have similar barrier height . Note that the multiple interconversions within the bound state , multiple pathways of dissociation , and single-exponential time dependence of the unbinding kinetics are observed for all six ligands ( see Figs . S1 , S16–S19 in Text S1 ) . The observation that the unbinding barrier is much higher than the barriers between subbasins suggests that , at least for small and low-affinity ligands , the starting pose does not influence the unbinding simulation results . To provide additional evidence to this observation , 10 conformations in the bound state of DMSO were randomly chosen from the 50 MD simulations , and 10 runs at 310 K with different initial velocities were started for each of them . In another test , 50 runs with different initial velocities were started for each of five randomly oriented poses of DMSO in the active site of FKBP . The 250 simulations of the second test were carried out at 350 K to speed up the sampling . The unbinding times ( values ) derived from simulations using different starting conformations of DMSO are very similar among each other ( see Figs . S20 and S21 in Text S1 ) . The unbinding network and cut-based FEP at 350 K are qualitatively similar to those extracted from simulations at 310 K and reveal multiple binding modes . The main difference is that the dissociation kinetics are faster as the unbinding barriers are lower at 350 K than 310 K ( See Fig . S3 in Text S1 ) , which is consistent with the mainly enthalpic nature of the dissociation barrier . The probability to unbind can be defined analogously to the probability of folding [59] , [60] . For each ligand , the TSE is determined along the 50 MD trajectories by a procedure based on the probability to unbind within a certain commitment time [22] , [43] . Values of 0 . 45 to 0 . 55 for the probability to unbind and commitment time of 0 . 8 ns are used , and the robustness of the TSE on these choices is documented in Table S1 in Text S1 . The unbinding TSE consists of a broad variety of positions and orientations of the ligand in the FKBP active site and/or at its rim ( Fig . 6 , top ) . The heterogeneity of the TSE , and in particular the broad distribution of TSE structures over the whole surface of the active site , is consistent with the multiple unbinding pathways detected by the network analysis . For ligands with different values of the dissociation rate ( and affinity ) it is interesting to compare the position of the TSE along the reaction coordinate of unbinding . The distance between the centers of mass of ligand and FKBP active site can be used for this analysis as it is an intuitive geometric coordinate and a good predictor of the mfpt to the most populated node ( Pearson correlation coefficient higher than 0 . 90 up to distances of 30 Å ) . Despite the relatively small difference in affinity for FKBP of only a factor of about 100 , the TSE of DMSO is shifted with respect to the one of THI along the center of mass distance towards the state that is destabilized , i . e . , the bound state ( Fig . 6 ) . The TSE conformations of THI is located mainly at the rim of the active site which might be due in part to its additional van der Waals interactions with FKBP as THI has two more carbon atoms than DMSO . An intermediate shift is observed for BUT ( Fig . 6 , bottom ) and the other four ligands ( Table S1 in Text S1 ) which is consistent with their values of the dissociation constant being between those of THI and DMSO . Note that the shift is not due to the different sizes and number of atoms of the ligands because there is no correlation between TSE shift and size ( Table S1 in Text S1 ) . The TSE shift is a manifestation of the Hammond effect , which was described 55 years ago for chemical reactions: As the substrate ( here the ligand-bound state ) becomes more unstable , the transition state approaches it in structure [61] . A shift of the protein folding TSE in the direction of the destabilized state has been observed previously upon single-point mutations in small , single-domain proteins [62] . On the other hand , Hammond behavior has not been reported for ligand ( un ) binding .
Five main results emerge from the network and cut-based FEP analyses of the MD simulations of unbinding of six small ligands from the active site of FKBP . First , fully atomistic simulations of spontaneous ligand unbinding from the active site of an enzyme are computationally feasible . The MD trajectories can be used to characterize the free energy surface of the bound state and the unbinding kinetics . Second , both the network analysis and cut-based FEP method reveal that each ligand has multiple poses ( characterized by distinct intermolecular hydrogen bonds ) in the bound state . Moreover , unbinding proceeds through multiple pathways . A similar free energy landscape with multiple pathways was previously observed in equilibrium simulations of the reversible folding of structured peptides [21] , [23] and small proteins [27] , [63] , [64] . Third , the kinetics of small ligand dissociation from FKBP are simple and their time dependence can be fitted by a single-exponential function despite the presence of multiple binding modes and multiple exit pathways . The rate-limiting step of unbinding is characterized by a free energy barrier that is much higher than the barriers between subbasins ( i . e . , binding modes ) in the bound state . Fourth , the unbinding TSE consists of a broad variety of ligand poses which lead to multiple dissociation pathways . Finally , a comparative analysis of the TSE of the six ligands shows that the smaller the stability of the bound state the closer are the TSE poses to the bound structure which is a new example of Hammond behavior , i . e . , shift of the TSE towards the destabilized state . It is likely that some of the conclusions of this work are valid also for drug-like compounds , which are larger ( 20 to 50 non-hydrogen atoms ) and more potent ( M to nM affinity ) than the six ligands investigated here . In particular , multiple ( un ) binding pathways are likely to exist also for high-affinity ligands , even if they usually have a single binding mode . Using network analysis and the cut-based FEP method it might become possible in the future to investigate ligands of nM affinity , which will require about one to two orders of magnitude longer simulations . This estimation is based on the aforementioned linear fitting of natural logarithm of unbinding times of the six ligands of FKBP to their experimentally measured binding energy values ( See Fig . S3 in Text S1 ) , which yields an extrapolated unbinding time of about 200 ns for a 200 nM ligand . In this context , it is important to note that small fragments used in the early phase of drug discovery bind usually in the mM to M range . Another interesting application could be the analysis of the free energy landscape of binding of small molecules with very similar chemical structure but significantly different binding affinity , e . g . , a series of protein kinase inhibitors that differ by only one to two heavy atoms and whose affinity ranges from micromolar to single-digit nanomolar [65] . | Most known drugs used to fight human diseases are small molecules that bind strongly to proteins , particularly to enzymes or receptors involved in essential biochemical or physiological processes . The binding process is very complex because of the many degrees of freedom and multiple interactions between pairs of atoms . Here we show that network analysis , a mathematical tool used to study a plethora of complex systems ranging from social interactions ( e . g , friendship links in Facebook ) to metabolic networks , provides a detailed description of the free energy landscape and pathways involved in the binding of small molecules to an enzyme . Using molecular dynamics simulations to sample the free energy landscape , we provide strong evidence at atomistic detail that small ligands can have multiple favorable positions and orientations in the active site . We also observe a broad heterogeneity of ( un ) binding pathways . Experimental approaches to the study of fragment binding to proteins have limitations in spatial and temporal resolution . Our network analysis of the molecular dynamics simulations does not suffer from these limitations . It provides a thorough description of the thermodynamics and kinetics of the binding process . | [
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] | 2011 | The Free Energy Landscape of Small Molecule Unbinding |
Herceptin ( trastuzumab ) is used in patients with breast cancer who have HER2 ( ErbB2 ) –positive tumours . However , its mechanisms of action and how acquired resistance to Herceptin occurs are still poorly understood . It was previously thought that the anti-HER2 monoclonal antibody Herceptin inhibits HER2 signalling , but recent studies have shown that Herceptin does not decrease HER2 phosphorylation . Its failure to abolish HER2 phosphorylation may be a key to why acquired resistance inevitably occurs for all responders if Herceptin is given as monotherapy . To date , no studies have explained why Herceptin does not abolish HER2 phosphorylation . The objective of this study was to investigate why Herceptin did not decrease HER2 phosphorylation despite being an anti-HER2 monoclonal antibody . We also investigated the effects of acute and chronic Herceptin treatment on HER3 and PKB phosphorylation in HER2-positive breast cancer cells . Using both Förster resonance energy transfer ( FRET ) methodology and conventional Western blot , we have found the molecular mechanisms whereby Herceptin fails to abolish HER2 phosphorylation . HER2 phosphorylation is maintained by ligand-mediated activation of EGFR , HER3 , and HER4 receptors , resulting in their dimerisation with HER2 . The release of HER ligands was mediated by ADAM17 through a PKB negative feedback loop . The feedback loop was activated because of the inhibition of PKB by Herceptin treatment since up-regulation of HER ligands and ADAM17 also occurred when PKB phosphorylation was inhibited by a PKB inhibitor ( Akt inhibitor VIII , Akti-1/2 ) . The combination of Herceptin with ADAM17 inhibitors or the panHER inhibitor JNJ-26483327 was able to abrogate the feedback loop and decrease HER2 phosphorylation . Furthermore , the combination of Herceptin with JNJ-26483327 was synergistic in tumour inhibition in a BT474 xenograft model . We have determined that a PKB negative feedback loop links ADAM17 and HER ligands in maintaining HER2 phosphorylation during Herceptin treatment . The activation of other HER receptors via ADAM17 may mediate acquired resistance to Herceptin in HER2-overexpressing breast cancer . This finding offers treatment opportunities for overcoming resistance in these patients . We propose that Herceptin should be combined with a panHER inhibitor or an ADAM inhibitor to overcome the acquired drug resistance for patients with HER2-positive breast cancer . Our results may also have implications for resistance to other therapies targeting HER receptors .
Dysregulation of human epidermal growth factor ( HER/ErbB ) receptors is implicated in various epithelial cancers [1] . The four HER receptors are capable of dimerising with each other ( homodimerisation ) or with different HER receptors ( heterodimerisation ) upon ligand binding [2] . The homo- or heterodimerisation of the receptors results in the activation of the intrinsic tyrosine kinase domain and autophosphorylation of specific tyrosine residues in the C-terminal tail [2] . The ligand-induced HER receptor dimerisation follows a strict hierarchy , and HER2 has been shown to be the preferred dimerisation partner [3] . The crystal structure explains why HER2 is ligandless , since its extracellular domain is always in the “open” conformation , with the projection of domain II ready for dimerisation even when monomeric [4] . This fixed “open” conformation of HER2 in the absence of ligand binding ( mimicking the ligand-bound form in the EGFR structure ) may account for why it is the preferred dimerisation partner [3] . Herceptin ( trastuzumab ) is a humanised mouse monoclonal antibody 4D5 and binds to the juxtamembrane region of HER2 of domain IV [4] . However , the precise mechanisms of its action and its acquired resistance are still poorly understood . Around 15%–20% of patients with breast cancer have HER2-positive tumours , and the amplification or overexpression of HER2 has been shown to be a significant predictor for both overall survival and time to relapse in these patients [5] . Herceptin has been shown to induce tumour regression in about a third of patients with metastatic HER2-positive breast cancer , but the response is rarely sustained if Herceptin is given as a single agent [6] . Therefore , understanding the mechanisms of its acquired resistance is of paramount importance . The current proposed primary mechanisms of action for Herceptin include HER2 receptor down-regulation and inhibition of aberrant receptor tyrosine kinase activity [7] , [8] . There is strong evidence of an immune-mediated mechanism in which the interaction of Herceptin's human Fc region with immune effector cells results in the stimulation of natural killer cells and activation of antibody-dependent cellular cytotoxicity [9] , [10] . Other proposed mechanisms of Herceptin's action include inhibition of basal and activated HER2 ectodomain cleavage in breast cancer cells [11] , the induction of G1 arrest and cyclin-dependent kinase inhibitor p27Kip1 levels [12] , or activation of PTEN [13] . Although Herceptin was developed to target the HER2 receptor , recent studies have shown that Herceptin does not decrease HER2 phosphorylation [14] , [15] . Its failure to abolish HER2 phosphorylation may be a key to why acquired resistance inevitably occurs for all responders if Herceptin is given as monotherapy . To date , no studies have explained why Herceptin does not abolish HER2 phosphorylation . The objective of our study was to investigate why Herceptin did not decrease HER2 phosphorylation despite being an anti-HER2 monoclonal antibody . We also investigated the effects of acute and chronic Herceptin treatment on HER3 and PKB phosphorylation in HER2-positive breast cancer cells . We showed that HER2 phosphorylation was maintained by the activation of other HER receptors during Herceptin treatment through an ADAM17-mediated ligand release . Although Herceptin initially decreased HER3 phosphorylation , reactivation of HER3 occurred in prolonged Herceptin treatment through a PKB negative feedback loop . The reactivation of HER3 and failure of Herceptin to abolish HER2 phosphorylation may be responsible for acquired resistance to Herceptin in HER2-overexpressing breast cancer .
We investigated how binding of HER2 receptors by the anti-HER2 monoclonal antibody Herceptin affects HER2 receptors . Although Herceptin was initially thought to inhibit aberrant HER2 receptor tyrosine kinase activity , recent studies have shown that Herceptin does not decrease HER2 phosphorylation [14] , [15] . However , the mechanisms of why Herceptin does not inhibit HER2 phosphorylation have not been elucidated . Furthermore , studies that have investigated the effect of Herceptin on HER2 phosphorylation have typically been based on classical Western blot analysis , which cannot detect phosphorylation status in individual cells . We proceeded to monitor the effect of Herceptin on HER2 phosphorylation in HER2-overexpressing cells using classical biochemical methods in combination with an established Förster resonance energy transfer ( FRET ) methodology that can assess HER2 phosphorylation in individual cells [16] . Using the classical biochemical methods , we confirmed that Herceptin down-regulated HER2 receptors in sensitive SKBR3 cells after 10 d of treatment ( Figure 1A ) . We then assessed the effect of Herceptin on HER2 phosphorylation . Herceptin did not decrease nor abolish HER2 phosphorylation ( Figure 1A ) . Paradoxically , it increased HER2 phosphorylation in SKBR3 cells . However , despite an increase in HER2 phosphorylation , there was a decrease in cell viability in SKBR3 cells after 10 d of Herceptin treatment compared to untreated cells ( p = 0 . 02 ) ( Figure 1B ) . Since a Western blot is unable to assess HER2 phosphorylation in individual cells or assess heterogeneity between cells , we proceeded to use FRET to assess HER2 phosphorylation in individual cells . Using an established method to assess HER2 phosphorylation by FRET [16] , [17] , we conjugated an anti-HER2 antibody to a Cy3b fluorophore ( HER2-Cy3b ) and an anti-phospho-HER2 antibody to Cy5 ( pHER2-Cy5 ) to assess HER2 phosphorylation in fixed SKBR3 cells with or without 40 µg/ml Herceptin ( see Materials and Methods ) . The median donor lifetime of Cy3b was 2 . 15 ns ( Figure 1C ) . HER2 phosphorylation would bring the donor and acceptor fluorophores into close proximity , resulting in a decrease of donor lifetime . We first monitored the basal phosphorylation in SKBR3 cells ( without Herceptin treatment ) and found a decrease in the average lifetime of HER2-Cy3b when pHER2-Cy5 was present ( from 2 . 15 ns to 1 . 4 ns ) ( Figure 1C ) . Following Herceptin treatment , there was considerable heterogeneity between the cells , with suppression of HER2 phosphorylation in a few cells , although the phosphorylation of HER2 was maintained in the majority of cells ( Figure 1C ) . After 10 d of Herceptin treatment , the remaining treated cells still had persistent HER2 phosphorylation ( Figure 1C ) , and this represented approximately 50% of the cell number compared to untreated cells ( p = 0 . 02 ) ( Figure 1B ) . Herceptin has been shown to target ALDH-positive stem cells in HER2-overexpressing breast cancer cells [18] , [19] . We proceeded to show that after 6 d of Herceptin treatment , there was a decreased proportion of cells that were ALDH positive compared to untreated cells , correlated with a decrease in HER2 receptors ( Figure S1 ) . As control , the effect of Herceptin on HER2 phosphorylation in the normal breast epithelial cell line MCF12F was also assessed . Even though acute Herceptin treatment could not inhibit HER2 phosphorylation in SKBR3 cells , it was able to decrease HER2 phosphorylation ( shown by increase of lifetime ) in MCF12F cells ( Figure S2A ) . The inability of Herceptin to inhibit HER2 phosphorylation in SKBR3 cells was not due to the degradation of Herceptin ( Figure S2B ) . We also observed similar results in another HER2-overexpressing cell line , BT474 ( Figure 1D ) . These cells were also sensitive to Herceptin treatment after several days of treatment , with decreased cell viability compared to control ( Figure S3A , upper panel ) . As in SKBR3 cells , acute Herceptin exposure did not decrease HER2 phosphorylation in these cells ( Figure 1D ) . HER2 phosphorylation increased in BT474 cells after 1 h of Herceptin treatment ( Figure 1D ) . After treating these cells with Herceptin for 8 mo ( with replacement of Herceptin every week ) , the cells became resistant to 40 µg/ml Herceptin ( Figure S3A , lower panel ) . Herceptin was able to decrease but not eliminate ALDH-positive stem cells in long-term Herceptin-treated BT474 cells compared to untreated cells ( Figure S3B ) . The decrease in ALDH-positive cells correlated with down-regulation of HER2 receptors . However , HER2 phosphorylation and cell viability remained in these resistant cells treated for a prolonged period with Herceptin ( Figure 1D ) . We found that the down-regulation of HER2 receptors was detectable after 1 h of Herceptin treatment in BT474 cells , and was associated with an increase in HER2 phosphorylation ( Figure 1E ) . Lee-Hoeflich et al . [20] showed that knockdown of HER2 receptors but not EGFR caused a significant decrease of HER3 phosphorylation in HER2-positive breast cell lines . We investigated whether this occurred in our experiments . After 1 h of Herceptin treatment in SKBR3 and BT474 cells , there was a decrease in HER3 phosphorylation correlating with a down-regulation of HER2 receptors ( Figure 1F ) . HER2-overexpressing cells have been shown to constitutively suppress PTEN activity with increased PKB activity , and it has been shown that acute Herceptin exposure decreased PKB phosphorylation through PTEN activation [13] . We found that after 1 h of Herceptin treatment , there was a decrease in PKB phosphorylation , and this correlated with a decrease in HER3 phosphorylation in both SKBR3 and BT474 cells ( Figure 1F ) . Thus , acute Herceptin treatment down-regulated HER2 receptors , resulting in a decrease of HER3 phosphorylation and PKB phosphorylation . The amplification of HER2 results in constitutive activation of HER2 in a human mammary epithelial cell system [21] . We found that although Herceptin down-regulated HER2 receptors , the remaining cells had persistent and increased HER2 phosphorylation in both SKBR3 and BT474 cells ( Figure 1A and 1D ) . Since HER2 is the preferred dimerisation partner , we postulated that HER2 phosphorylation was maintained by the other HER receptors via their dimerisation with HER2 . We proceeded to show , using the streptavidine-biotin immunoprecipitation method ( see Materials and Methods ) , that acute Herceptin treatment increased EGFR/HER2 dimerisation in BT474 cells ( Figure 2A , left two panels ) . This effect was specifically induced by Herceptin , since 1 h of IgG treatment did not increase EGFR/HER2 dimerisation ( data not shown ) . There was also an increase in HER2/HER3 dimerisation in both SKBR3 and BT474 cells after 1 h of Herceptin treatment ( Figure 2B , left upper and lower panels ) . Furthermore , there was increased HER2 dimerisation with the phosphorylated EGFR and HER3 receptors in BT474 cells treated with Herceptin ( which was demonstrated using two immunoprecipitation methods; see Materials and Methods ) ( Figure 2B , middle two upper and lower panels ) . There was also increased HER2 dimerisation with the phosphorylated HER4 receptor ( Figure 2B , right upper and lower panels ) . Because of a low level of HER4 expression in these cells , the quality of the Western blot was not optimal using the streptavidin-biotin immunoprecipitation method , despite repeated attempts ( Figure 2B , right upper panels ) . However , the quality of the blot was better using the immunoprecipitation method with Herceptin ( Figure 2D , right lower panels ) . We postulated that the increased dimerisation of EGFR , HER3 , and HER4 with HER2 was due to activation by their respective ligands . We proceeded to assess the levels of endogenous ligands , using heregulin ( ligand for HER3 and HER4 ) and betacellulin ( ligand for EGFR and HER4 ) as examples . Herceptin-treated cells were lysed , and endogenous ligand levels were detected using ELISA . We found that Herceptin induced a statistically significant up-regulation of heregulin and betacellulin ( p = 0 . 0152 and p = 0 . 0286 , respectively ) after 1 h of Herceptin treatment compared to untreated cells in both SKBR3 and BT474 cells ( data on BT474 cells are shown in Figure 2C ) . There was also increased secretion of these ligands in the conditioned medium of these cells ( Figure 2D ) . Thus , Herceptin increased the dimerisation of EGFR , HER3 , and HER4 with HER2 as a result of activation by their ligands . We showed that Herceptin induced an up-regulation of HER ligands , including betacellulin and heregulin ( Figure 2C and 2D ) . This resulted in an increased phosphorylation of EGFR and HER4 ( Figure 2E ) and an increase in their dimerisation with HER2 ( Figure 2A and 2B ) . Herceptin , however , decreased HER3 phosphorylation initially after 1 h of Herceptin treatment ( Figure 1F ) . We postulated that the increased heregulin release ( Figure 2C and 2D ) with Herceptin treatment would have an effect on HER3 phosphorylation . We showed that with prolonged Herceptin treatment , HER3 phosphorylation was reactivated in SKBR3 cells ( Figure 2E ) . Reactivation of HER3 phosphorylation also occurred in BT474 cells that became resistant to Herceptin ( Figure 2F ) . The total expression of HER3 and HER4 increased in SKBR3 cells treated with Herceptin , but the total EGFR expression decreased ( Figure 2E ) . Thus , Herceptin induced ligand activation of EGFR and HER4 as well as reactivation of HER3 phosphorylation during prolonged Herceptin treatment . We analysed the effects of Herceptin on the downstream signalling pathways in HER2-positive breast cancer cells . It was found that the effects of acute Herceptin treatment on phosphorylation of PKB and ERK1/2 were not concordant ( Figures 1F , 2A , and 2E ) , in contrast to acute tyrosine kinase inhibitor ( TKI ) treatment , which decreased both PKB and ERK phosphorylation [17] . Acute Herceptin exposure increased ERK phosphorylation ( Figure 2A and 2E ) but decreased PKB phosphorylation in BT474 and SKBR3 cells ( Figure 1F ) . Acute Herceptin exposure increased EGFR/HER2 and HER2/HER4 dimerisation , correlating with an increase in ERK phosphorylation ( Figure 2A and 2E ) . In contrast , acute Herceptin treatment decreased PKB phosphorylation ( Figures 1F and 2E ) ; this decrease has been shown to be due to activation of PTEN [13] , correlating with a decrease in HER3 phosphorylation ( Figure 1F ) . With prolonged Herceptin treatment , reactivation of PKB and HER3 occurred ( Figure 2E ) . The increased ERK phosphorylation was transient ( Figure 2A and 2E ) , mimicking the effect of exogenous ligand stimulation . Therefore , Herceptin treatment decreased PKB phosphorylation because of a decrease in HER3 phosphorylation induced by HER2 down-regulation . However , 1 h of Herceptin treatment increased ERK phosphorylation as a result of ligand-dependent EGFR and HER4 activation . To further show that HER ligands play a role in the acquired resistance to Herceptin , we stimulated BT474 cells with 100 ng/ml EGF , heregulin , or betacellulin while they were treated with 40 µg/ml Herceptin . After 5 d , we assessed their cell viability . For Herceptin treatment without exogenous ligands , there was a decreased cell viability of BT474 cells , which was statistically significant ( p<0 . 001 ) ( Figure 2G ) . However , when Herceptin treatment was given in BT474 cells with concurrent stimulation of exogenous HER ligands , the decrease in cell viability was reversed . The reverse in cell viability in these conditions was statistically significant compared to Herceptin treatment alone ( p = 0 . 0001 for EGF , p = 0 . 002 for heregulin , p = 0 . 003 for betacellulin , respectively , compared to Herceptin alone ) ( Figure 2G ) . We investigated the role of ADAM proteases since they mediate shedding of pro-HER ligands including HB-EGF , epiregulin , heregulin , and betacellulin [22] . As ADAM17 is one of the most important ADAM proteases for HER ligands , we studied the role of this ADAM protease in Herceptin treatment . SKBR3 cells were transfected with small interfering RNA ( siRNA ) against ADAM17 , and the knockdown was validated by Western blot . There was a decrease in both pro and active forms of ADAM17 in transfected cells compared to the control ( Figure 3A , left panels ) . We also showed that heregulin production increased in response to acute Herceptin exposure in cells transiently transfected with control siRNA , but this production was inhibited by siRNA against ADAM17 ( Figure 3A , right panel ) . We also assessed the effect of Herceptin on the expression of ADAM17 protease . We demonstrated that after treating the cells with Herceptin for 1 h , ADAM17 protease mRNA was increased by 2 . 2-fold ( n = 4 , p = 0 . 0008 ) ( Figure 3B ) . To assess whether an increase in mRNA production of ADAM17 was translated to protein , we proceeded to assess ADAM17 protein level in response to Herceptin treatment . We showed that Herceptin increased the protein levels of ADAM17 in a dose-dependent manner after 1 h of treatment in both BT474 ( Figure 3C , left two panels ) and SKBR3 ( Figure 3C , right two panels ) cells . Furthermore , the increase of ADAM17 protease was shown to correlate with the suppression of PKB phosphorylation by Herceptin ( Figure 3C ) . As a control , we treated MCF12F cells with Herceptin for comparison . Herceptin did not cause significant pPKB inhibition nor affect ERK phosphorylation in these cells after 1 h of treatment ( Figure S2C , right panels ) . In addition , Herceptin did not induce a significant increase in ADAM17 level nor HER ligand levels ( heregulin and betacellulin ) in the conditioned medium of the normal epithelial breast cell line MCF12F treated with Herceptin ( Figure S2C ) . In summary , we showed that ADAM17 is involved in the up-regulation of heregulin in response to Herceptin treatment . We found increased levels of mRNA and protein levels of ADAM17 protease in response to acute Herceptin exposure in HER2-positive cells but not in normal epithelial MCF12F cells . The up-regulation of ADAM17 was shown to correlate with the suppression of PKB phosphorylation in HER2-overexpressing cells . We observed earlier that the up-regulation of ADAM17 correlated with the suppression of PKB phosphorylation by Herceptin treatment , suggesting the existence of a negative PKB feedback loop involving ADAM17 in acute Herceptin treatment . We hypothesized that if there was a negative PKB feedback loop , a PKB inhibitor should initiate the same response as Herceptin treatment , inducing an up-regulation of HER ligands and ADAM17 levels . To assess the role of a PKB feedback loop induced by Herceptin treatment , we treated BT474 cells with a PKB/Akt inhibitor ( Akt inhibitor VIII , Akti-1/2 ) , which can decrease PKB phosphorylation via a mechanism different from that of Herceptin . Using the quantitative Meso Scale Discovery ( MSD ) method ( see Materials and Methods ) , we showed that the PKB inhibitor decreased PKB phosphorylation after 1 h of treatment , which was statistically significant in comparison to DMSO control treatment ( p<0 . 0001 ) ( Figure 4A , left panel ) . Herceptin also decreased PKB phosphorylation in comparison with untreated cells ( p = 0 . 03 compared to untreated ) ( Figure 4A , left panel ) . Neither PKB inhibitor nor Herceptin decreased total PKB levels in comparison to control cells treated with IgG or DMSO ( Figure 4A , right panel ) . We also assessed the effects of the PKB inhibitor on PKB and ERK phosphorylation in BT474 cells using Western blot . As expected , 1 h of treatment with 2 . 5 µM PKB inhibitor , but not DMSO , decreased PKB phosphorylation . However , it also increased ERK phosphorylation ( Figure 4B ) , just like acute Herceptin treatment ( Figure 2A and 2E ) . More importantly , the decrease in PKB phosphorylation by the PKB inhibitor was also associated with an increase in heregulin production ( p = 0 . 012 ) ( Figure 4C ) and an up-regulation of ADAM17 mRNA levels ( p = 0 . 016 ) ( Figure 4D ) . Thus , the decrease in PKB phosphorylation by a PKB inhibitor initiated the same feedback loop as that seen in Herceptin treatment , which reduces PKB phosphorylation via a different mechanism . In order to further prove the existence of a PKB negative feedback loop involving ADAM17 during Herceptin treatment , we needed to demonstrate that we can abrogate the loop and suppress HER2 phosphorylation by inhibiting ADAM17 . HER2-overexpressing cells express autocrine ligands , including heregulin , resulting in HER2 activation and basal phosphorylation . We showed that 1 h of treatment with TAPI-1 ( an ADAM17 and metalloprotease inhibitor ) or with specific ADAM17 inhibitor and ADAM10/17 inhibitor was able to inhibit basal HER2 phosphorylation in SKBR3 cells ( Figure 5A ) . Whereas acute Herceptin exposure increased HER2 phosphorylation in SKBR3 cells ( Figure 1A ) , combination treatment with Herceptin and TAPI-1 decreased HER2 phosphorylation ( Figure 5A ) . We also investigated the effect of the combination of Herceptin and TAPI-1 in individual SKBR3 cells using FRET . There was a basal phosphorylation of HER2 in SKBR3 cells , as shown by a decrease in the average lifetime of HER2-Cy3b with pHER2-Cy5 from about 2 . 05 ns to 1 . 6 ns ( Figure 5B ) . Acute Herceptin treatment did not decrease HER2 phosphorylation ( Figure 1A and 1C ) , but with concurrent TAPI-1 inhibitor treatment there was suppression of HER2 phosphorylation ( an increase in the average lifetime ) ( p = 0 . 008 ) ( Figure 5B ) . To further prove the role of ADAM17 in the negative feedback loop , we transiently transfected SKBR3 cells with siRNA against ADAM17 . We showed that Herceptin was unable to decrease HER2 phoshorylation in control cells . However , in cells that were transfected with specific siRNA against ADAM17 , HER2 phosphorylation was decreased after Herceptin treatment ( Figure 5C ) . We proceeded to assess the effect of various ADAM17 inhibitors on cell viability with or without concurrent Herceptin treatment . We hypothesized that the combination of Herceptin with ADAM17 inhibitors would exert greater inhibition than Herceptin alone . We showed that combination of Herceptin with either TAPI-1 or specific ADAM17 inhibitor exerted greater inhibition of cell viability in BT474 cells after 2 , 3 , or 6 d of treatment ( Figure 5D ) . Thus , our data prove that inhibition of ADAM17 is able to abrogate the feedback loop that maintains HER2 phospshorylation during Herceptin treatment . We demonstrated earlier that ADAM17 inhibitors were able to abrogate the PKB negative feedback loop and inhibit HER2 phosphorylation during Herceptin treatment . Since an up-regulation of ADAM17 and HER ligands resulted in activation of all HER receptors , we hypothesized that a panHER inhibitor should also be able to reverse the effect of the PKB negative feedback loop induced by Herceptin treatment . We investigated whether a panHER inhibitor , which inhibits the activation of all HER receptors , could decrease HER2 phosphorylation and be synergistic in tumour growth inhibition with Herceptin treatment . JNJ-26483327 is a potent multi-kinase inhibitor against EGFR ( half-maximal inhibitory concentration [IC50] = 9 . 6 nM ) , HER2 ( IC50 = 18 nM ) , and HER4 ( IC50 = 40 . 3 nM ) [23] ( Figure S4A ) . It is also known as a panHER inhibitor since its IC50 against these receptors is comparable with that of other panHER inhibitors [24] . We found that 1 h of treatment with either 40 µg/ml Herceptin or 10 µM JNJ-26483327 was not able to decrease HER2 phosphorylation in SKBR3 cells ( Figure 6A ) . However , the combination of Herceptin treatment with either 5 µM or 10 µM JNJ-26483327 for 1 h was able to decrease HER2 phosphorylation in these cells ( Figure 6A ) . Furthermore , whereas Herceptin treatment alone increased ADAM17 ( p = 0 . 011 ) and heregulin ( p = 0 . 008 ) mRNA levels , neither JNJ-26483327 treatment nor the combined treatment of Herceptin with JNJ-26483327 increased their levels compared to the untreated cells ( Figure 6B ) . Therefore , the combined treatment of a panHER inhibitor with Herceptin could abrogate the PKB feedback loop involving ADAM17 and heregulin . We also showed that the combination of Herceptin and panHER inhibitor JNJ-26483327 exerted greater inhibition of cell viability after 3 , 6 , or 8 d of treatment compared to Herceptin or JNJ-26483327 alone ( Figure 6C ) . We investigated whether a panHER inhibitor in combination with an ADAM17 inhibitor without Herceptin treatment could exert similar synergistic effect . However , JNJ-26483327 with TAPI-1 exerted less cell viability inhibition than Herceptin alone in both SKBR3 and BT474 cells ( Figure S4C ) . This may be because neither JNJ-26483327 alone nor JNJ-26483327 with TAPI-1 could decrease pHER3 and pAKT after 1 h of treatment , in contrast to Herceptin or Herceptin with JNJ-26483327 ( Figure S4B ) . To test the in vivo relevance of the interaction between a panHER inhibitor and Herceptin , BT474 xenografts were treated with an empty vehicle , Herceptin alone , the panHER inhibitor JNJ-26483327 alone , or the combination of drugs for 21 d ( Figure 6D ) . As seen in Figure 6D , Herceptin alone or the panHER inhibitor alone could only delay xenograft tumour growth compared to vehicle treatment , but the combination of the two drugs caused an almost complete inhibition of tumour growth in these HER2-positive xenografts . In summary , Herceptin in combination with a panHER inhibitor was able to decrease HER2 phosphorylation and abrogated the up-regulation of ADAM17 and heregulin in response to Herceptin treatment . Whereas either a panHER inhibitor or Herceptin treatment alone delayed BT474 xenograft tumour growth , the combination treatment was synergistic in tumour inhibition .
It was previously thought that Herceptin inhibits HER2 receptor tyrosine kinase activity , but recent studies have shown that this is not the case [14] , [15] . The mechanisms whereby Herceptin fails to decrease HER2 phosphorylation remain unclear . Our results confirmed that Herceptin did not decrease HER2 phosphorylation although it down-regulated HER2 receptors in HER2-positive SKBR3 and BT474 breast cell lines . We showed that HER2 phosphorylation was maintained and increased by the ligand-induced activation of EGFR , HER3 , and HER4 receptors , which preferentially dimerise with HER2 . This is consistent with reports that Herceptin does not prevent the dimerisation of HER2 with other receptors [25] . It was previously demonstrated that primitive mammary stem cells are enriched in vitro in non-adhering spherical colonies called mammospheres [26] . These cells have stem-cell-like properties , with the ability to undergo multi-lineage differentiation [26] . The proportion of these stem cells in normal mammary epithelial cells is increased by HER2 overexpression , as demonstrated by in vitro mammosphere assays and the expression of the stem cell marker ALDH [18] . One of the clinical benefits of Herceptin is thought to be its ability to target the cancer stem cell population in HER2-amplified tumours [19] . We confirmed in our study that Herceptin decreased ALDH-positive stem cells after a prolonged treatment , in correlation with a decrease in HER2 receptors . However , there was significant heterogeneity in the inhibition of HER2 phosphorylation by Herceptin between different cells , especially during the first week of treatment . Thus , Herceptin was able to decrease HER2-mediated signalling in some of these cells , resulting in decreased HER2 receptors and phosphorylation , but Herceptin monotherapy could not eliminate all the stem cells . The surviving cells had decreased HER2 receptors and fewer ALDH-positive cells compared to untreated cells but had maintained HER2 phosphorylation via activation of other HER receptors as a result of ADAM17-mediated ligand release . HER3 is kinase-defective , and its phosphorylation depends on other HER receptors . The effects of Herceptin on HER3 phosphorylation have been controversial . Yakes et al . [27] showed that 1 h of Herceptin treatment induced a transient increase of pHER3 in BT474 cells , whereas Junttila et al . [15] reported a decrease of pHER3 with acute Herceptin treatment . We showed that acute Herceptin treatment initially decreased HER3 phosphorylation . This decrease is thought to be due to HER2 down-regulation , since loss of HER2 induced by siRNA decreased pHER3 levels in HER2-positive breast cancer cells [20] . It is possible that the difference in observed pHER3 is due to several factors that are competing to affect pHER3 levels . The dominant effect of acute Herceptin treatment is a decrease in HER2 levels , but this is in competition with the increased HER3 phosphorylation as a result of increased ligand production induced by Herceptin treatment . This would account for the subsequent reactivation of HER3 with prolonged Herceptin treatment . Furthermore , variable cell lines and experimental conditions , as well as different treatment durations and doses of Herceptin used by different investigators , may account for differences in pHER3 levels seen [15] , [27] . We found that Herceptin had a discordant effect on PKB and ERK signalling . HER2-overexpressing cells have been shown to activate Src with constitutively suppressed PTEN activity and increased PKB activity [13] , [28] . Herceptin increased PTEN membrane localisation and phosphatase activity by reducing PTEN tyrosine phosphorylation via Src inhibition , leading to a decreased PKB phosphorylation [13] . The decrease in PKB activity is not due to PI3K inhibition , since there was no decreased PI3K activity after Herceptin treatment [13] . However , acute Herceptin activates ERK1/2 pathways , correlating with an increase in EGFR and HER4 dimerisation with HER2 . Since EGFR , HER3 , and HER4 have binding sites for Shc and Grb2 [29] , their ligand-dependent activation would account for increased activation of ERK by acute Herceptin treatment . The sudden increase in ERK phosphorylation induced by Herceptin treatment is very much similar to MAPK/ERK activation in cells stimulated with exogenous HER ligands [30] . This observation supports our data that Herceptin induces the up-regulation of HER ligands through ADAM proteases . Sergina et al . [31] showed that TKI treatment failed to suppress HER3 phosphorylation for a sustained duration because of a PKB-mediated feedback loop . However , they did not link the PKB-mediated feedback loop with the HER ligands and ADAM proteases . We have previously shown that reactivation of HER3 and PKB in response to TKI is due to HER ligand release [16] . We have now shown that acute Herceptin treatment decreases the phosphorylation of HER3 and PKB , which in turn induces the activation of a feedback loop involving HER ligands and ADAM proteases . We postulated that Herceptin treatment initiated a PKB-mediated negative feedback loop . If such a negative loop exists , we predicted that an inhibitor that decreases PKB phosphorylation should also induce the up-regulation of HER ligands and ADAM17 protease . Indeed , we demonstrated that a PKB inhibitor , which decreases PKB phosphorylation via a mechanism different from that of Herceptin , could also initiate the same feedback loop induced by Herceptin treatment . Thus , it is a Herceptin-induced decrease in PKB phosphorylation that results in the activation of a feedback loop involving ADAM proteases and HER ligands . This PKB feedback loop activates the other HER receptors and maintains HER2 phosphorylation , which is a key to acquired resistance to Herceptin ( Figure 7 ) . There have been several reports of positive and negative feedback loops linking the complex cross talk of MAPK and PI3K signalling pathways with scaffolding protein Grb2-associated binders 1 and 2 ( Gab1 and Gab2 ) [30] , [32] . mTOR inhibition has also been shown to lead to MAPK activation through a PI3K-dependent feedback loop in human cancer [33] . The exact mechanism of how these feedback loops link to each other is likely to be very complex . It is likely that FoxO proteins , which are downstream targets of PKB , are central players in this PKB feedback loop [34] . The phosphorylation of FoxO transcription factors by PKB promotes the cytoplasmic sequestration of these transcription factors , including FoxO3a [34] . It is likely that FoxO transcriptional factors can modify Akt/PKB phosphorylation indirectly by modifying the expression of kinases or phosphatases [35] . It is also possible that FoxO proteins regulate the transcription of ADAM17 , since PKB inhibition increases mRNA production of ADAM17 . The interactions of FoxO transcriptional factors with ADAM proteases and phosphatase , as well as how they affect the phosphorylation and dephosphorylation of PKB and HER receptors , are likely to be very complicated . The mechanisms are currently being investigated in our lab . Our xenograft experiment showed that neither panHER inhibitor nor Herceptin treatment alone was adequate to control tumour growth in a HER2-oncogene-driven tumour . In HER2-positive breast cancer , the underlying problem is HER2 overexpression , which results in increased HER2-related signalling . Herceptin is able to down-regulate HER2 receptors , whereas TKI-like lapatinib induces HER2 accumulation at the cell surface [14] . We have shown that a panHER inhibitor with TAPI-1 ( ADAM17 inhibitor ) resulted in less inhibition of cell viability than Herceptin alone in both SKBR3 and BT474 cells . This confirms that TKI treatment without Herceptin is not as effective as treatment with either Herceptin or Herceptin with TKI in HER2-positive breast cancer cells . However , the combination of Herceptin with a panHER inhibitor , which inhibits the activation of all HER receptors , was able to abrogate the feedback loop during Herceptin treatment and was synergistic in tumour inhibition in a HER2-positive BT474 xenograft model . Our data would support the rationale of combining a panHER inhibitor with Herceptin treatment in patients with HER2-positive breast cancer . Our results would also explain why pertuzumab and Herceptin are synergistic in tumour inhibition in breast cancer cells and xenograft models [20] . Furthermore , it would account for the effectiveness of a combination treatment of pertuzumab with Herceptin in some patients whose disease progressed while on Herceptin alone [36] . Our results demonstrate why it is inadequate to consider individual HER receptors alone for anti-HER therapies as these receptors are intrinsically linked together with a close network of feedback loop ( s ) . We have demonstrated that a PKB feedback loop is activated upon Herceptin treatment , resulting in the activation of all HER receptors and maintenance of HER2 phosphorylation . We have not attempted to look at all the positive and negative feedback loops linking MAPK and PI3K [30] , [32] . It is likely that more feedback loops are involved in the acquired resistance to Herceptin . Future research should identify the exact candidates , other than ADAM17 and HER ligands , involved in the feedback loops . Such candidates are best identified through a systems biology approach , which may help to further dissect the mechanisms of acquired resistance to Herceptin in HER2-overexpressing tumours and to improve the survival for patients with this type of tumour .
SKBR3 and BT474 cells were obtained from cell services at Cancer Research UK ( Lincoln's Inn Fields laboratory ) . The human cell lines BT474 and SKBR3 are HER2-overexpressing breast cancer cell lines . SKBR3 cells were cultured in Dulbecco's Modified Eagle Medium ( DMEM ) supplemented with 10% fetal bovine serum and the antibiotic penicillin-streptomycin . BT474 cells were cultured in RMPI supplemented with 10% fetal bovine serum and the antibiotics penicillin-streptomycin . For these cells , 10 µg/ml insulin was added to the medium when cells were split or medium was refreshed . Normal breast epithelial MCF12F cells were purchased from the ATCC and cultured in a 1∶1 mixture of DMEM and Ham's F12 medium supplemented with 20 ng/ml EGF , 100 ng/ml cholera toxin , 0 . 001 mg/ml insulin , 500 ng/ml hydrocortisone , and 5% horse serum . Anti-HER2 ( recognising the intracellular residues ) , anti-phospho-HER2 ( Tyr1221/1222 ) , anti-phospho-HER3 ( Tyr1289 ) , anti-HER4 ( recognising the intracellular residues near the carboxyl-terminus of human HER4 ) , and anti-phospho HER4 ( Tyr1284 ) antibodies were obtained from Cell Signalling Technology . The polyclonal anti-phospho-EGFR ( Thr992 ) was obtained from Invitrogen . F4-IgG1 mouse monoclonal antibody ( against the EGFR cytoplasmic domain ) was obtained from the monoclonal antibody laboratory of Cancer Research UK ( Lincoln's Inn Fields laboratory ) . Antibodies recognising PKB , phospho-PKB ( Ser473 ) , p44/42 MAP kinase ( Erk1/Erk2 ) , and phospho-Erk1/Erk2 ( Thr202/Tyr204 ) were from Cell Signalling Technology . Anti-ADAM17 was purchased from Abcam . The secondary antibodies , goat anti-mouse IgG and goat anti-rabbit IgG , were purchased from GE Healthcare . The mono-conjugated fluorophores Cy3B and Cy5 were from GE Healthcare . PKB inhibitor ( Akt inhibitor VIII , isozyme-selective , Akti-1/2 ) was obtained from Calbiochem . Herceptin was initially a gift from Roche , but subsequent supply was obtained from the pharmacy department of Oxford Radcliffe Hospitals , National Health Services Trust . Human IgG control was purchased from R&D Systems . EGF , heregulin , and betacellulin were purchased from Sigma Aldrich . ADAM and metalloprotease inhibitor ( TAPI-1 ) was purchased from Calbiochem . Incyte kindly provided ADAM17 inhibitor INCB4298 and ADAM10/17 inhibitor INCB3619 . Janssen ( Johnson & Johnson ) kindly provided panHER inhibitor JNJ-26483327 . For Western blotting confluent six-well plates of cells were placed on ice and washed with PBS . Cells were scraped off the plates and incubated for 10 min in lysis buffer ( 10 mM EDTA , 20 mM Tris [pH 7 . 5] , 150 mM NaCl , 10 mM Na2P2O7 , and 100 mM NaF with 1% Triton and protease inhibitor cocktail [Roche] ) . Samples were centrifuged at 4°C to remove the insoluble cell pellets , and a protein assay was performed to check protein quantity . Equal amounts of protein sample were prepared in 4× SDS with 10% beta-mercaptoethanol and boiled for 10 min at 95°C . Then samples were loaded on a NuPage 4%–12% gel ( Invitrogen ) and run at 130 V . The proteins were semi-dry-transferred to a membrane for 2 h at 12 V . The membrane was blocked in 3% BSA in PBS-Tween ( 0 . 2% ) for a minimum of 1 h . Then the blot was incubated with primary antibody in the same solution for 3 h at room temperature . The membrane was washed four times with 1% milk in PBS-Tween ( 0 . 2% ) before secondary antibody was added in 5% milk in PBS-Tween ( 0 . 2% ) . The membrane was incubated at room temperature for 1 h before it was washed four times again with 1% milk in PBS-Tween ( 0 . 2% ) . Antibodies were visualised with an enhanced chemiluminescent ( ECL ) system ( GE Healthcare ) . BT474 and SKBR3 cells were grown to near confluency before they were lysed as described above . The cell lysate was centrifuged for 10 min at maximum speed before transferring the supernatant to a new reaction vial . A protein assay was performed to check protein quantity , and equal amounts of protein were used for immunoprecipitation . In order to look at the interaction between HER2 and other HER receptors after Herceptin treatment , we needed a technique that would specifically pull down our protein of interest . We could not use magnetic protein G beads because they would bind Herceptin as well as our receptor-specific antibody ( data not shown ) . Therefore , streptavidin-coated magnetic beads ( Bio-Nobile ) were absorbed with biotin-conjugated HER antibodies ( 1∶100 ) ( conjugated using kit from Innova Biosciences ) for 1 h , as illustrated in the figures . After the bead-antibody complex was washed with PBS-Tween ( 0 . 2% ) , it was incubated with the supernatant for at least 1 h . After that , the complex was thoroughly washed with PBS-Tween and transferred to a new reaction vial . Wash buffer was taken off and 50 µl of 4× SDS with 10% beta-mercaptoethanol was added to the beads . Samples were boiled for 10 min at 95°C to elude protein off the beads . Twenty microlitres was loaded per lane in a SDS gel for Western blot analysis , as described above . For further confirmation , we also used protein G beads labelled with Herceptin ( anti-HER2 ) to pull down HER2 , and looked at the levels of phosphorylated HER receptors . SKBR3 and BT474 cells were grown in 24-well plates after seeding approximately 20 , 000 cells per well . The cells were grown for at least 24 h before treatment with 40 µg/ml Herceptin , 5 µM JNJ-26483327 , 10 µM TAPI-1 , 10 µM ADAM17 inhibitor , or a combination of these drugs for different durations , as illustrated in the figures . For the exogenous ligand experiments , 100 ng/ml EGF , heregulin , or betacellulin was added to the cells in addition to Herceptin ( 40 µg/ml ) for a total of 5 d in BT474 cells . On the day of the experiment , the cells were trypsinized and diluted with PBS . The viable cells were counted using a cell counter . For all transient transfections , the cells were plated in 10-cm2 dishes ( 50% confluent ) in medium and given the opportunity to settle overnight . The next day , medium was replaced by 7 ml of fresh normal medium . Transfection mix containing 10 nM siRNA in 300 µl of OptiMEM ( Invitrogen ) was incubated with 15 µl of NeoFX ( Ambion ) in 300 µl of OptiMEM for 15 min at room temperature . This mix was added drop-wise to the cells , and cells were placed back into the incubator . After 24 h , transfected cells were re-plated into six-well plates for further experiments . Validated siRNAs were obtained from Ambion . Cells treated with Herceptin ( 40 µg/ml ) , PKB inhibitor ( 2 . 5 µM ) , or panHER inhibitor JNJ-26483327 ( 5 µM ) were analysed for mRNA levels of ADAM17 and heregulin . RNA was purified from cells using the Aurum Total RNA mini kit ( Bio-Rad ) according to manufacturer's instructions . RNA purity and quantity were determined using Nanodrop ( Nanodrop Technologies ) . For synthesis of the cDNA , a high-capacity cDNA reverse transcription kit was used ( Qiagen ) . Quantitative PCR ( QPCR ) reactions were performed using the following primers , together with FAM-labelled probes from the Universal ProbeLibrary ( Roche ) or Sybergeen: Beta-actin primers , 5′-ATTGGCAATGAGCGGTTC-3′ and 5′-GGATGCCACAGGACTCCAT-3′ , and universal probe 11; heregulin-β1 primers , 5′- CTTGTGGTCGGCATCATGT-3′ and 5′-CAGCTTTTTCCGCTGTTTCT-′3 , and probe 49; ADAM17 primers , 5′-CCTTTCTGCGAGAGGGAAC-3′ and 5′- CACCTTGCAGGAGTTGTCAG-3′ and probe 78 . cDNA samples were assayed in triplicate using a detection system ( Chromo4; GRI ) , and gene expression levels for each individual sample were normalised relative to Beta-actin . Levels of mRNA in untreated samples were set to 1 . We used immunoprecipitation to look at the levels of ligands ( betacellulin and heregulin ) in the media of untreated or Herceptin-treated cells . Cells were treated with Herceptin in serum-free medium for 1 h . The medium was then collected and incubated with magnetic protein G beads ( Invitrogen ) linked to antibodies for heregulin or betacellulin for at least 1 h . The samples were prepared for Western blot analysis as described above . Cells treated with Herceptin ( 40 µg/ml ) or PKB inhibitor ( 2 . 5 µM ) were analysed for HER ligands . Levels of the HER ligands betacellulin and heregulin were measured in cell lysate using ELISA ( R&D Systems ) . Cells were lysed in 20 mM Hepes buffer containing 1 . 5 mM EDTA and protease inhibitor ( Roche ) . Cells were then scraped off and homogenised by putting the lysate through a syringe with needle . After spinning the lysate down , the supernatant was used for ELISA analysis . To detect heregulin or betacellulin , the R&D Systems ELISA kits were used as the protocol prescribes . Briefly , the ELISA plate was coated overnight with coating buffer . After blocking the plate for 2 h , 100-µl samples and standards were loaded and the plate was kept at room temperature for 2 h . Samples were washed and incubated with detection antibody for 2 h , then washed and incubated with streptavidin labelled with horseradish peroxidase for 20 min . With the use of a substrate solution that reacts with horseradish peroxidase , the levels of ligands could be detected . After 20 min , the reaction was stopped using stop solution , and absorption was measured using an ELISA reader . BT474 cells were plated on a six-well plate and left to settle overnight . The next day , the cells were treated for 1 h with 40 µg/ml Herceptin or 2 . 5 µM PKB inhibitor at 37°C . After 1 h , the cells were lysed in a lysis buffer provided by MSD . The assay was then performed following manufacturer's protocol . In brief , MSD provided the plates pre-coated with pPKB ( Ser473 ) and PKB spots . Multiple spots could be placed in one well of a 96-well plate , making it possible to look at several proteins in one sample . The plates provided were blocked for 1 h with 3% BSA at room temperature . The plates were then washed three times with wash buffer before 25 µl ( 20 µg ) of sample was loaded onto the plates ( in duplicate ) . After incubation for 2 h at room temperature , the plates were washed again , and 25 µl of detection antibody in 1% BSA was added . After 1 h of incubation , 150 µl of read buffer was added , and the plate was analyzed using a SECTOR imager ( MSD ) . FRET experiments were performed as described previously [16] , [17] . In short , 30 , 000 cells were seeded onto cover slips and left to attach overnight . The next day , the cells were treated with 40 µg/ml Herceptin or the ADAM inhibitor TAPI-1 or the combination of these drugs ( for different durations as illustrated ) in the medium at 37°C . The medium was washed off with PBS , and the cells were fixed with 4% paraformaldehyde ( Pierce ) in PBS for 10 min at room temperature . The cells were then permeabilized with 0 . 2% Triton X-100 ( T8532 , Sigma-Aldrich ) in PBS for 5 min at room temperature before being treated for 10 min with 1 mg/mg sodium borohydrate ( Sigma-Aldrich ) in PBS to quench the background fluorescence . The cells were washed twice with PBS between steps . Following the above steps , the cells were treated for 1 h with in 1% BSA ( Sigma-Aldrich ) in PBS at room temperature to block unspecific binding of the antibodies . After that , antibodies conjugated to the fluorescent dyes Cy3b or Cy5 were added to the samples sequentially starting with the Cy3b-conjugated antibody . Cells were washed twice with PBS and twice with sterile water , after which they were mounted onto a microscope slide using Fluoromount-G ( Southern Biotech ) . All images were taken using a Zeiss Plan-APOCHROMAT 6100/1 . 4 NA phase three-oil objective . Images were recorded at a modulation frequency of 80 MHz . The donor ( Cy3b ) was excited using the 514-nm line of an argon/krypton laser , and the resultant fluorescence was separated using a combination of dichroic beam splitter ( Q565 LP , Chroma Technology ) and narrow band emitter filter ( BP 610/75 , Lys and Optik ) . To detect ALDH activity in cells , we used the ALDEFLUOR kit from Aldagen . The manufacturer's protocol was followed carefully . In brief , cells were trypsinized and , per treatment protocol , 1×106 cells were taken up into 1 ml of assay buffer provided in the kit . This tube was the sample tube . To a separate control tube , 5 µl of DEAB solution was added . ALDEFLUOR substrate ( 5 µl ) was added to the sample tube , and immediately after mixing , 500 µl of the sample solution was transferred to the control tube with DEAB . The procedure was repeated for all samples . Both control and sample tubes were then placed at 37°C for 45 min . After the cells were spun down for 5 min at 250 g , they were taken up in new assay buffer and analyzed using a FACS Cyan . Control samples were used to set a gate for ALDH positivity , after which the test sample was analysed . The Mann-Whitney test was used to compare the medians of the protein levels of heregulin and betacellulin as well as mRNA levels of heregulin and ADAM17 between the untreated samples and those treated with drugs . For each figure , at least three experiments were done , and both technical and biological replicates were used in the calculation , using a confidence interval of 95% . Data were analysed against the untreated samples unless otherwise stated . | HER2 ( ErbB2 ) is a surface protein and member of the epidermal growth factor receptor ( EGFR ) family that is overexpressed in approximately one-fifth of breast cancers . HER2-positive breast tumours tend to be very aggressive , and patients with this type of tumour have a poor prognosis . A therapeutic monoclonal antibody called trastuzumab ( Herceptin ) has been designed to block HER2 signalling and is used as a treatment for patients with HER2-positive breast cancer . However , recent studies have shown that Herceptin does not decrease HER2 activation . This may be why patients invariably develop resistance if treated with Herceptin monotherapy . To date , no study has explained why Herceptin cannot abolish HER2 signalling despite being an anti-HER2 monoclonal antibody . We have found that Herceptin switches on a feedback loop that increases the production of the ADAM17 protein , a protease that in turn releases the growth factors that activate HER ( ErbB ) receptors . These growth factors activate HER2 and also the other members of the HER receptor family—EGFR , HER3 and HER4—in such a way as to maintain HER2 activation and cell survival in HER2-positive breast cancer cells . We have found that when Herceptin is provided in combination with ADAM17 inhibitors , the feedback loop is abrogated in cells . Furthermore , a pan-HER inhibitor that decreases the activation of other HER receptors can also inhibit the feedback loop and decrease HER2 activation when used in combination with Herceptin . We further demonstrated that the combination therapy of Herceptin with a pan-HER inhibitor is more effective than Herceptin alone in an animal model of breast cancer . We believe our results offer treatment strategies that may help overcome acquired Herceptin resistance in patients with HER2-positive breast cancer . | [
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"Introduction",
"Results",
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] | [
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"resistance",
"oncology/breast",
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] | 2010 | HER2 Phosphorylation Is Maintained by a PKB Negative Feedback Loop in Response to Anti-HER2 Herceptin in Breast Cancer |
Following infection of the central nervous system ( CNS ) , the immune system is faced with the challenge of eliminating the pathogen without causing significant damage to neurons , which have limited capacities of renewal . In particular , it was thought that neurons were protected from direct attack by cytotoxic T lymphocytes ( CTL ) because they do not express major histocompatibility class I ( MHC I ) molecules , at least at steady state . To date , most of our current knowledge on the specifics of neuron-CTL interaction is based on studies artificially inducing MHC I expression on neurons , loading them with exogenous peptide and applying CTL clones or lines often differentiated in culture . Thus , much remains to be uncovered regarding the modalities of the interaction between infected neurons and antiviral CD8 T cells in the course of a natural disease . Here , we used the model of neuroinflammation caused by neurotropic Borna disease virus ( BDV ) , in which virus-specific CTL have been demonstrated as the main immune effectors triggering disease . We tested the pathogenic properties of brain-isolated CD8 T cells against pure neuronal cultures infected with BDV . We observed that BDV infection of cortical neurons triggered a significant up regulation of MHC I molecules , rendering them susceptible to recognition by antiviral CTL , freshly isolated from the brains of acutely infected rats . Using real-time imaging , we analyzed the spatio-temporal relationships between neurons and CTL . Brain-isolated CTL exhibited a reduced mobility and established stable contacts with BDV-infected neurons , in an antigen- and MHC-dependent manner . This interaction induced rapid morphological changes of the neurons , without immediate killing or impairment of electrical activity . Early signs of neuronal apoptosis were detected only hours after this initial contact . Thus , our results show that infected neurons can be recognized efficiently by brain-isolated antiviral CD8 T cells and uncover the unusual modalities of CTL-induced neuronal damage .
A better understanding of the interactions between viruses and the central nervous system ( CNS ) represents a major issue in viral pathogenesis . Indeed , viral persistence in the CNS represents a challenge both for the host and the pathogen . On the virus side , it is essential to adapt a strategy of replication that will minimize virus-induced cell damage and limit its recognition by the immune response . On the host side , it is essential to quickly halt virus multiplication , while causing minimal damage to CNS resident cells and in particular to neurons which have limited capacities of renewal [1] , [2] . These issues are complicated by the unique immunologic properties of the CNS , originally referred to as an immune privileged site . It is now clear that this “privilege” is very relative and that despite the blood-brain barrier and the absence of dedicated lymphoid drainage [3] , the immune response can generally control invasion of the CNS by pathogens , although often at the expense of irremediable tissue damage due to excessive inflammation . Among the different immune effectors involved in viral elimination , CD8 T cells have received much attention , owing to their essential roles in the primary protection of the host against infectious diseases . CD8 cytotoxic T lymphocytes ( CTL ) mediate their antiviral effects by recognizing viral peptides presented by class I major histocompatibility complex ( MHC I ) molecules . Upon engagement of the T cell receptor ( TCR ) with the peptide-MHC I complex , CTL mediate cell killing essentially through two independent pathways: perforin-dependent delivery of granzymes and interaction of Fas-ligand ( FasL ) with the Fas-receptor on the target cell surface . These lytic mechanisms could , however , have devastating consequences in the CNS and it has been shown that alternative non-cytolytic mechanisms can also be engaged by CTL [4] , [5] . In particular , antiviral cytokines produced by CD8 T cells , such as interferon gamma ( IFN-γ ) or tumor necrosis factor alpha ( TNF-α ) , can stimulate intracellular pathways that interfere with viral replication , resulting in complete or partial clearance from the cell without destroying it . Furthermore , both mechanisms can be combined to limit viral multiplication in the brain . One example is infection with Herpes simplex virus type 1 ( HSV-1 ) , for which latency is controlled by IFN-γ produced by CD8 T and by granzyme B-mediated degradation of the HSV-1 immediate early protein ICP4 [6] , [7] . As recognition of peptide-MHC I complexes is essential to elicit CTL effector functions , it has long been considered that neurons were spared from CD8 T cell attack because they did not express MHC I molecules [8] , [9] . Recent studies , however , have challenged this view . First , accumulating evidence has revealed that MHC I expression in the developing and adult CNS can have additional non-immune functions . Indeed , neuronal MHC I molecules appear to be crucial for normal brain development , neuronal differentiation and plasticity [10] , [11] . In addition to their role in CNS function , it was also shown that neurons could express MHC I molecules under inflammatory conditions . For example , in Rasmussen's encephalitis , histopathological examination of autopsy material revealed the presence of granzyme B-containing CD8 T cells in direct apposition to MHC I positive neurons [12] . Further evidence of MHC I inducibility in neurons was provided in vitro . Indeed , it has been shown that primary cultures of neurons treated with IFN-γ and electrically silenced using Tetrodotoxin ( TTX ) can be induced to express MHC I molecules on their surface [13] . This expression is functionally relevant , as CTL can attack peptide-pulsed neurons in an antigen-specific manner in vitro [14] . One caveat of such studies , however , is that they were performed using neurons loaded with non-limiting amounts of exogenous peptides . Moreover , CD8 T cells used in these assays were either CTL clones or lines with pre-defined epitope specificity , often derived for practical reasons from TCR-transgenic animals . CTL were also often further differentiated in vitro or restimulated in culture . Despite the above-mentioned limitations , several studies using variations of this experimental paradigm have provided considerable insight on MHC I-restricted T cell interactions with neurons [2] , [15] . One still unsatisfactorily resolved issue in neurovirology , however , is whether antiviral CD8 T cells can indeed recognize and directly attack virus-infected neurons in the course of a natural disease . Infection with Borna disease virus ( BDV ) appears as a very suitable model system to address these questions . BDV is an enveloped virus with a non-segmented , negative strand RNA genome [16] , [17] , which is characterized by a remarkable non-cytolytic strategy of replication . BDV infects the CNS of a wide variety of mammals [18] , [19] and induces a large spectrum of neurological disorders [18] , [20] , [21] . One of the best-investigated animal models for the pathogenesis of BDV infection is the Lewis rat . After intracerebral infection , the rats develop an acute meningo-encephalitis in which the infiltrating immune cells are mainly comprised of CD8 T cells , together with less numerous CD4+ T cells and macrophages [22] , [23] . Several studies showing the preferential presence of CD8 T cells in direct proximity to neuronal cell lesions and adoptive transfer experiments have clearly established the central role of CD8 T cells in BDV-induced encephalitis and neuronal destruction [24]–[26] . In this study , our goal was to provide further insight on the specifics on neuron / CD8 T cell interactions in the course of a natural disease and to determine the ensuing functional consequences . We used BDV-infected primary cultures of neurons , which were incubated , without any further manipulation , with CD8 T cells directly extracted from the brains of BDV-infected Lewis rats . Thereafter , we analyzed the outcome of the cognate interactions between CD8 T cells and BDV-infected neurons .
We used cortical neurons prepared from Lewis rat embryos that had been infected with cell-free BDV one day after plating and further cultured for 14 days . At this time point , neurons have established mature synapses [27] and the virus has spread to all of them , in agreement with our previous findings [28]–[30] . Detection of the BDV nucleoprotein using immunofluorescence analysis ( Figure 1A and B ) confirmed that the large majority ( >95% ) of neurons was positive for BDV antigens and that infection proceeded without detectable effect on the morphology or viability of the cultures , consistent with the non-cytolytic replication strategy of BDV . We then assessed expression of MHC I molecules on the neuronal surface , since this is a key prerequisite for antigen presentation to CD8 T lymphocytes . We used infected and non-infected neurons , as well as non-infected neurons that had been treated for 72 h with IFN-γ ( 100 U/ml ) and TTX ( 1 µM ) , to induce MHC I expression [13] . We also used non-infected neurons treated for 72 h with IFN-ß ( 100 U/ml ) . Living neurons still attached to the plastic were stained using a monoclonal antibody recognizing the rat MHC I RT1-A molecule . After staining , neurons were rapidly harvested , fixed and further stained for the intracellular neuronal protein Tau . Flow cytometry analysis ( Figure 1C and D ) revealed that cultures contained >98% neurons and that non-infected neurons expressed little or no MHC I molecules ( 0 . 5±0 . 3% positive neurons ) . As previously published [13] , treatment with IFN-γ and TTX strongly induced MHC I expression ( 70±5% positive neurons ) . Interestingly , BDV infection triggered a significant expression of neuronal MHC I ( 30±3 . 5% positive neurons ) . Analysis of fluorescence intensities ( Figure 1E ) revealed that levels of surface expression of MHC I induced by BDV infection were nevertheless lower than those obtained by neuronal exposure to IFN-γ and TTX . Of note , treatment of non-infected neurons with IFN-ß induced comparable proportion ( 30±3% ) and levels of MHC I expression ( Figure 1E ) than infection with BDV . Thus , BDV infects neuronal cultures efficiently and triggers significant neuronal MHC I expression . For all subsequent studies , we used neurons that had been infected for the same length of time ( 14 days ) and verified for each experiment that infection was indeed complete prior to any subsequent analysis . Lewis rats were sacrificed 14 days after intracerebral infection with BDV . In previous experiments , we determined that , similar to others [24] , [31] , this time point corresponded to the peak of the neurological disease caused by BDV infection and that inflammatory infiltrates were most abundant in the brain at this stage [32] . Consistent with previous reports [24] , [31] , phenotypic characterization of brain-infiltrating lymphocytes using flow cytometry revealed that CD8 T cells accounted for 51%±1 . 5% of all T cells present in the brain ( i . e . , positive for the TCR ) , whereas CD4 T cells represented 30%±3% of TCR+ cells ( Figure 2A ) . This 1 . 7 to 1 ratio of CD8 to CD4 cells in the brain contrasted with the usual 1 to 3 ratio found in cervical lymph nodes of the same animals , reflecting the high proportion of CD8 T cells recruited to the CNS upon BDV infection . Moreover , this ratio was probably under-estimated since we detected a high proportion of TCR+ cells ( ≈20% ) that were negative for both CD4 and CD8 expression , presumably due to down-modulation of CD8 expression upon activation [33] . To further analyze the phenotype of CD8 T cells , we purified them using cell sorting ( >99% pure ) , within a few hours after harvesting . We prepared total RNA from sorted CD8 T cells and analyzed expression of several genes by real-time quantitative RT-PCR . When compared to CD8 T cells purified from lymph nodes of the same animals ( Figure 2B ) , brain-infiltrating lymphocytes expressed high levels of effector molecules such as IFN-γ ( 300-fold induction on average ) , Granzyme B ( 180-fold induction ) or FasL ( 50-fold induction ) . Expression of Perforin mRNA was also significantly elevated , albeit at lower levels . To determine the proportion of brain-derived CD8 T cells specific for BDV , highly purified CD8 T cells were stimulated with irradiated syngeneic Lewis fibroblasts ( infected or not with BDV ) and assayed by FACS for IFN-γ production . On average , 25%±3% of CD8 T cells were positive for IFN-γ following a 48 h-stimulation period , whereas this percentage was of 11%±2 . 5% upon culture with non-infected cells ( Figure 2C ) . Finally , we assessed whether brain- or lymph node-purified CD8 T cells secreted cytokines upon incubation with primary cultures of neurons that were infected with BDV or not . Cytokine levels in the supernatants were assayed at 48 h with a Luminex assay specific for 9 rat cytokines . We did not detect any cytokine above the threshold level of detection of the assay ( 4 . 88 pg/ml ) in the supernatants of CD8 T cells purified from lymph nodes . In contrast , brain-purified CD8 T cells produced high levels of IFN-γ ( up to 9 ng/ml ) upon culture with infected neurons ( Figure 2D ) . These cells also produced IL-10 , although at levels below those of IFN-γ . Remarkably , levels were also much lower when brain-purified CD8 T cells were incubated with non-infected neurons , suggesting that cytokine secretion resulted mainly from the recognition of viral antigens presented by neurons . There were no significant differences regarding levels of IL-6 , IL-17 or TNF-α ( Figure S1 ) , while results were below threshold for IL-4 , IL-5 , IL-9 and IL-13 . Together , these data show that BDV infection triggers a prominent recruitment of CD8 T cells in the brain . These cells are strongly activated , express high levels of effector molecules and produce large quantities of IFN-γ upon interaction with BDV-infected neurons . Brain-purified CD8 T cells are arrested upon contact with infected neurons , which they recognize in an antigen- and MHC I-dependent manner . To get further insight on the dynamics of interactions between CD8 T cells and neurons , we stained purified CD8 T cells with the lipophilic dye PKH-26 , added them to neuronal cultures previously labeled with Calcein-AM and performed confocal microscopy imaging using a temperature-controlled imaging setup ( Figure 3 , video S1 and video S2 ) . Strikingly , the mobility of CD8 T cells isolated from the brain was extremely reduced upon incubation with BDV-infected neurons compared to non-infected ones ( Figure 3A and 3B ) . Indeed , the measured mean velocity was 2 . 83 µm/min ( 95% confidence interval ( CI ) of mean 2 . 59 - 3 . 03 ) in the presence of infected neurons compared to 15 . 27 µm/min , ( 95% CI 13 . 00–17 . 53 ) with non-infected ones ( Figure 3C and 3D ) . Furthermore , once arrested , CD8 T cells established stable interactions with BDV-infected neurons throughout the whole imaging period ( up to 45 min ) . CD8 T cells were found in apposition to neurites and neuronal somas . In contrast , the same brain CD8 T cells exhibited a much more dynamic behavior upon incubation with non-infected neurons . Induction of MHC I expression at the surface of non-infected neurons with IFN-γ and TTX was not sufficient to arrest CD8 T cells ( mean velocity 14 . 84 µm/min , 95% CI 11 . 60–18 . 07 ) . Conversely , masking MHC I molecules on BDV-infected neurons using OX-18 antibody restored a high mobility of CD8 T cells ( 16 . 62 µm/min , 95% CI 14 . 24–19 . 00 ) . In addition , relative frequencies of CD8 T cell moving at various velocities were similar when comparing non-infected neurons , non-infected neurons treated with IFN-γ and TTX or BDV-infected neurons treated with anti-MHC I antibody ( Figure 3D ) . Finally , CD8 T cells purified from cervical lymph nodes of the same infected animals were highly mobile , regardless of whether they were assayed on infected or non-infected neurons ( video S3 and data not shown ) . Collectively , these data show that CD8 T cells freshly purified from BDV-infected rat brains establish stable conjugates with infected neurons and that this interaction depends upon both neuronal infection and MHC I expression . Intriguingly , a fraction of brain-purified CD8 T cells formed long projections once arrested on BDV-infected neurons , reminiscent of the T cell-extended processes ( TCEP ) recently described by McDole et al . [34] . These TCEP were 0 . 5 µm thick and 50 µm long on average , although we observed projections of up to 270 µm . Several TCEP were sometimes observed from a single CD8 T cells and displayed a very dynamic behavior ( video S4 ) . This dynamic behavior was also evidenced by their occasional rapid contraction ( video S5 ) . Such projections were never observed in CD8 T cells purified from lymph nodes . Brain purified CD8 T cells induce early changes in neuronal permeability , associated with increased neuronal electrical activity . We then sought to analyze the impact of this stable interaction with CD8 T cells on BDV-infected neurons . When calcein-loaded infected neurons were incubated with brain-purified CD8 T cells , we observed the formation of “axonal beading” figures in BDV-infected neurons , revealed by the formation of calcein dots lining the neurites , while there were no visible changes in the neuronal network of control non-infected neurons incubated with brain CD8 T cells ( compare Figure 4A with 4B and video S6 with video S7 and S8 ) . Upon incubation with CD8 T cells , calcein beading figures were observed throughout the culture , affecting both neurites and neuronal somas , without any noticeable proximity with the sites of CD8 T cell interaction with neurons ( video S8 ) . In order to quantify this phenomenon , neurons were imaged for 45 minutes after addition of brain CD8 T cells and levels of calcein fluorescence measured at the beginning of the experiment were subtracted from levels obtained at the end of the incubation . The resulting differences in fluorescent signals therefore provided a good estimate of calcein beading ( Figure 4C and 4D ) . While there were only minimal changes when brain CD8 T cells were applied to non-infected neurons , we detected significant residual fluorescence when using BDV-infected neurons . Consistent with a role of CD8-neuron cognate interaction in this process , incubation of BDV-infected neurons with anti-MHC I antibody markedly decreased calcein beading ( Figure 4D ) . Given the changes in neuronal morphology consecutive to incubation with brain CD8 T cells , we next studied its impact on the electrophysiological properties of neuronal networks . We used a system based on microelectrode arrays ( MEA ) , which allows to monitor the firing pattern of a neuronal network grown on a grid of sixty electrodes embedded in a culture dish ( Figure 5A ) [35] . We assessed the impact of incubation with CD8 T cells by measuring the frequency of grouped action potentials , or bursts , over the neuronal network ( Figure 5B ) . Before adding CD8 T cells , spontaneous firing frequencies were similar whether neurons were infected or not with BDV ( 0 . 037±0 . 0008 Hz vs . 0 . 038±0 . 0011 Hz ) , in agreement with our previous studies [28] , [30] . Upon addition of brain-purified CD8 T cells ( at a ratio of 1 to 1 ) , the electrophysiological properties of non-infected neurons remained remarkably stable throughout the whole experiment ( 0 . 038±0 . 0038 Hz after 6 h of incubation ) . In sharp contrast , the addition of CD8 T cells to BDV-infected neurons triggered a significant increase of the mean burst frequency , which doubled to attain 0 . 074±0 . 0038 Hz . Remarkably , the neuronal network remained electrically active up to 3 h after addition of CD8 T cells . Thereafter , we witnessed a decline of the firing pattern , with neurons becoming nearly completely silent by 6 h of incubation ( Figure 5B ) . To explore the basis for this electrical silencing occurring after longer incubation times with CD8 T cells , we analyzed whether interaction with CD8 T cells eventually triggered neuronal apoptosis . The fluorescent probe FLICA , which involves covalent binding of fluorescent Z-VAD to activated caspases , was used to detect activation of caspases 3 and 7 in live neurons . The intensity of fluorescent signals therefore provided a direct quantification of apoptosis ( Figure 6 ) . Consistent with the maintenance of their electrical activity , we did not detect significant apoptosis in non-infected neurons , even 4 h after addition of brain CD8 T cells . BDV-infected neurons did not display any caspase induction up to 2 h after incubation with CD8 T cells , but after 4 h we detected a strong increase in fluorescence , indicating prominent neuronal apoptosis . This observation was consistent with the delayed loss of electrical activity at longer time points of incubation ( Figure 5B ) . At longer time points , we noted a progressive disaggregation of the neuronal network , which was however apparent only 6 to 8 hours after incubation with CD8 T cells ( data not shown ) . Pre-incubation of BDV-infected neurons with anti-MHC I antibody significantly reduced neuronal apoptosis ( Figure 6 ) , further indicating that neuronal attack by CD8 T cells is a process which depends on the recognition of MHC I/viral antigen complexes at the surface of neurons .
Besides their central role in the response to neurotropic viral infections , CD8 T cells are increasingly being recognized as key players in the pathogenesis of many neuroinflammatory diseases , including multiple sclerosis [36]–[38] . It is thought that CTL act as effector cells and contribute to tissue damage , based on their preferential accumulation in parenchymal infiltrates . The modalities of action of CD8 T cells in the CNS and notably the interplay between immune regulation and pathogen control are , however , complex and variable . Here , we provide novel information concerning the modalities of interaction between antiviral CTL and infected neurons . Our findings that BDV infection triggers MHC I expression by neurons were surprising . In general , viruses are better known for their capacity to down regulate or prevent the expression of MHC I molecules on the cell surface , through the expression of immunomodulatory viral proteins . Relevant examples of such proteins include HIV Nef , KSHV MIR1 and MIR2 , myxoma virus MV-Lap or several CMV proteins [39]–[41] . These mechanisms of control should theoretically be even tighter in neurons , which are less prone to express MHC I molecules . Nevertheless , in the case of Flaviviruses such as West-Nile virus , it has been previously shown that CD8 T cells could recognize and destroy infected neurons , although expression of MHC I molecules was not formally assessed [42] , [43] . There may be several non-exclusive mechanisms to explain our findings . First , as impairment of neuronal electrical activity has been shown to induce MHC I expression , one hypothesis is that BDV infection may trigger MHC I expression through its effects on neuronal activity . Indeed , we have previously shown that BDV can impair synaptic plasticity [28] , [30] and these electrophysiological alterations could play a role in MHC I induction . Alternatively , BDV infection of neurons could trigger the production of type-1 interferons , which in turn , could induce MHC I expression . Recent evidence has clearly demonstrated that neurons can take part to the antiviral defense by being both IFN-α/ß producers and responders [44] . In addition , it is now well established that IFN-α/ß can induce detectable levels of MHC I expression by neurons , although not as efficiently as IFN-γ [1] . The fact that levels of MHC I expression induced by BDV were similar to those obtained upon exogenous application of IFN-ß ( Figure 1D and 1E ) provides indirect evidence in support of this hypothesis . Also consistent with these findings , a microarray analysis provided experimental evidence of a type-1 interferon signature in BDV-infected neurons ( Table S1 ) . We also confirmed our microarray data by demonstrating the up regulation of interferon-stimulated genes in BDV-infected neurons , by using both real-time quantitative RT-PCR and western blot analyses ( Figure S2 ) . Given that our neuronal cultures were prepared from embryonic ( E18 ) cortical tissue and further cultured for a few weeks , we cannot exclude the possibility that our neurons may behave differently than fully mature neurons , in particular regarding their MHC I inducibility . Even more surprising were our findings that neurons could not only express MHC I but also present viral antigens CD8 T cells , triggering the secretion of cytokines such as IFN-γ and IL-10 . These findings uncover a novel aspect of neuro-immune interactions and reveal that neurons can process and present antigenic peptides to CTL , an aspect which could not be assessed in experimental systems using peptide-pulsed neurons . This is also consistent with previous reports showing that neurons express different components of the molecular machinery required for epitope processing and MHC I presentation , such as TAP1/TAP2 and LMP2/LMP7 [45] , [46] . Based on our cytokine secretion data and imaging studies , antigen processing and MHC I presentation by neurons are clearly functionally relevant . Indeed , CTL arrest and the ensuing changes in neuronal permeability and apoptosis were all significantly prevented or delayed by the addition of an antibody masking MHC I molecules . Interestingly , brain-infiltrating CD8 T cells produced the cytokine IL-10 in addition to IFN-γ at the peak of BDV-induced encephalitis . Similar observations have been made recently in a Coronavirus-induced encephalitis model [47] . This secretion of IL-10 has been interpreted as a mechanism protecting critical organs from bystander tissue injury , while permitting viral clearance [47] , [48] . Upon CTL arrest , we observed the formation of highly dynamic structures , designated as TCEP in a recent report studying CD8 T cells specific for Theiler's virus [34] . It was suggested that these dynamic cellular projections could confer a higher motility to lymphocytes , allowing them to navigate through brain tissue or sample the microenvironment surrounding the cell . Since such TCEP have now been described in two separate models of neurotropic virus infections , it would be important to assess in future studies whether these modifications of lymphocyte morphology are organ-specific and characteristic of lymphocytes infiltrating the CNS , or if they are dependent on their engagement of infected cells within this tissue . Another interesting aspect concerned the modalities of attack of CTL against BDV-infected neurons studied by real-time imaging . Within 45 minutes after CTL addition , we observed changes in neuronal membrane permeability , revealed by the formation of calcein dots lining the neurons . These were accompanied by morphological changes , variably designated as axonal beading or blebbing by several authors , which appear to represent a general response of neurons to various stressing insults . In particular , they have been visualized in neurons following axonal transection and Wallerian degeneration [49] , oxidative stress [50] , ischemia [51] , trauma [52] or even allogeneic CTL [53] . Although the underlying mechanism is not well understood , it seems to be accompanied by mitochondrial dysfunction and delayed cell death . Recently , it was suggested that calcein leakage from neurons following oxygen-glucose deprivation could result from opening of neuronal hemichannels , a type of half-gap junctions that form large-conductance channels and allow flux of ions and molecules [51] . It will be interesting to test whether this is also the case following CD8 T cell engagement . Remarkably , despite these early changes in neuronal permeability , the electrical properties of infected neurons were preserved for a significant duration after addition of brain-purified CTL . This contrasted with the immediate shutdown of electrical signaling , both in single neurons and networks , which follows incubation of OVA peptide-pulsed neurons with primed OT-I cells , as recently described by Meuth et al . [15] . Since neurons were killed rapidly upon recognition by highly differentiated CD8 OT-I cells , our observations suggest that impairment of electrical activity upon CTL encounter may also be dependent on the intensity of the “lethal hit” delivered by the CTL . Indeed , the different kinetics of CTL engagement with neurons in our system is probably related to the fact that CD8 T cells purified for the brains of BDV-infected rats were not subjected to further in vitro stimulation and that the neurons were undergoing a natural viral infection , with the density of MHC I / cognate peptides being closer to physiological levels . At present , we can only speculate about the mechanisms of neuronal death triggered by CTL . CD8 T cells purified from brains of BDV-infected rats expressed all cytolytic effectors , including IFN-γ , perforin , granzyme-B or FasL . Both Fas/FasL and perforin/granzyme pathways have been demonstrated to be effective against neurons upon engagement with CTL [14] , [54] , [55] . Since cell death appears to develop with slower kinetics when triggered through the Fas/FasL pathway [56] , [57] , given our observations that BDV-infected neurons are still electrically active after three hours , we would favor the hypothesis that killing of BDV-infected neurons preferentially occurs by this route . However , previous studies in the rat model of BDV infection have also shown expression of perforin mRNA in the brain [24] . Alternatively , the relatively low density of MHC I expression triggered by infection , together with a restricted expression of perforin could lead to a limited release of cytolytic granules towards the infected neurons . Finally , protection against excessive apoptosis may also be conferred by BDV infection per se . Recently , it was shown by Poenisch et al . that the BDV X protein could protect cells against Fas-mediated apoptosis in vitro [58] . In our system , however , the kinetics or characteristics of CTL-induced apoptosis were unchanged when we used neurons infected with the BDV-X ( A6A7 ) mutant [58] , which has lost its capacity to block Fas-induced apoptosis in vitro , resulting from loss of the mitochondrial localization of the X protein ( Figure S3 ) . Our findings have several important implications . First , we confirmed the capacity of neurons to process and viral antigens during the course of a natural infection , leading to simulation of CD8 T cells and production of inflammatory cytokines . Therefore , beyond being the mere targets of CD8-mediated cytotoxicity , neurons may play an active part in the development of CNS inflammation through T cell activation and cytokine release . Second , the slow kinetics of neuronal death suggests that this progressive neuronal loss of function may leave open a window of opportunity for future treatments . Very recently , it was shown that the immune-mediated axonal damage that develops during autoimmune encephalomyelitis ( in mice ) or multiple sclerosis ( in humans ) was a slow and likely reversible process [59] . These authors described this process as “focal axonal degeneration” , beginning with axonal swelling and mitochondrial dysfunction , very reminiscent of the axonal beading phenomenon described herein . A better understanding of the mechanisms involved in our system may provide novel putative targets that could then be tested in other systems triggering axonal death , be they virally-mediated or not .
This study was carried out in strict accordance with EU regulations and with the recommendations of the French national chart for ethics of animal experiments ( articles R 214- 87 to 90 of the “Code rural” ) . The protocol was approved by the committee on the ethics of animal experiments of the région Midi Pyrénées and by IFR 150 ( permit numbers: 04-U563-DG-06 and MP/18/26/04/04 ) . All procedures were performed under deep anesthesia as described below and all efforts were made to minimize suffering . Primary cortical neurons were prepared from Lewis rat embryos at gestational day 18 using a previously described procedure [28] , [29] with the following modifications: after dissection , the cortex tissue was dissociated by incubation for 15 min at 37°C in phosphate buffer saline ( PBS ) containing 10 U/ml Papain ( Worthington ) , followed by gentle dissociation in PBS containing 1 . 5 mg/ml BSA and 1 . 5 mg/ml Trypsin inhibitor ( from chicken egg , Sigma ) . Cultures were maintained in serum-free Neurobasal medium ( Invitrogen ) supplemented with 0 . 5 mM glutamine , 1% penicillin/streptomycin and 2% B-27 supplement ( Invitrogen ) . Plating was performed on variable supports depending of the use ( Lab-Tek chambered coverglass , Nunc , culture dishes or glass coverslips ) , all previously coated with poly-ornithine ( Sigma ) and Laminin ( Roche ) . One day after plating , half of the cultures were infected with cell-free BDV ( Giessen strain He/80 ) . Cell-released virus stocks were prepared as described [60] , [61] , using persistently infected Vero cells . By 14 days post-infection , BDV infection of neurons was verified by immunofluorescence for each experiment using an anti-BDV nucleoprotein serum . Female Lewis rats ( 4-week-old ) were obtainedfrom Janvier SAS ( Le Genest St Isle , France ) and maintained in our animal facility . The day of infection , rats were anesthetized with a mixture of ketamine ( 50 mg/kg of body weight ) and xylazine ( 15 mg/kg of body weight ) . They were inoculated intracranially with 50 µl of a 20% ( w/v ) stock of BDV-infected rat brain homogenate , corresponding to 1000 focus-forming units of BDV . We used the fifth brain passage in newborn rats of the Giessen strain He/80 . To minimize reflux along the injection tract , the needle was left in place for 20 seconds before being slowly withdrawn . BMCs were isolated by a method adapted from previously described procedures [24] , [32] , [62] . Briefly , rats were deeply anesthetized and perfused with 50 ml of PBS through the left ventricle to remove blood from the organs . Brains were collected in Hank's buffered salt solution ( HBSS ) containing 20 mM Hepes ( HH ) and dissociated using a glass Potter . Brain suspensions were enzymatically digested for 1 hour at 37°C in HH containing collagenase D ( 1 mg/ml ) , trypsin inhibitor ( TLCK , 0 . 5 µg/ml ) and DNase I ( 10 µg/ml ) . The digested suspensions were filtered ( 70 µm cell strainer , Falcon ) , pelleted and resuspended in 30 ml of 30% Percoll ( Pharmacia ) . This solution was carefully placed on top of 10 ml of 70% Percoll and centrifuged for 30 min at 2000 rpm at 20°C . BMCs were collected at the interface between the 30% and 70% Percoll layers , extensively washed with RPMI 1640 medium ( Invitrogen ) and directly used for FACS staining and cell sorting . Cell suspensions were also prepared from the cervival lymph nodes of the same animals , filtered as above , washed with PBS containing 5% fetal calf serum ( FCS ) and kept on ice until FACS staining and cell sorting . The monoclonal antibodies ( mAbs ) used were as follows: PE-conjugated anti-rat TCRαβ ( clone R73 ) ( PharMingen , San Diego , CA ) ; anti-rat CD8β ( clone 3 . 4 . 1 ) either FITC-conjugated ( PharMingen , San Diego , CA ) or Alexa 647-conjugated ( Biolegend ) . For staining , BMCs or lymph node cells ( LNC ) were suspended in FACS buffer ( consisting of PBS with 5% FCS ) containing the different mAbs and incubated for 20 min at 4°C . After extensive washes with FACS buffer , CD8 T cells were sorted based on PE+ and ( FITChigh or Alexa-647high ) expression using a FACS Aria II-Sorp ( BD Biosciences ) . The purity of the cells was always higher than 99% . Flow cytometry data were collected on FACSCalibur or LSRII cytometers ( BD Biosciences ) and analyzed using FlowJo software ( TreeStar , version 8 . 8 . 6 ) . Neurons grown on 35 mm dishes were washed with Tyrode's solution ( 119 mM NaCl , 5 mM KCl , 2 mM CaCl2 , 2 mM MgCl2 , 25 mM Hepes , 30 mM glucose ) prior to incubation at 4°C for 45 min with 5 µg/ml of purified anti-rat MHC I antibody ( RT1-A , clone OX18 ) , under gentle agitation . After several washes with Tyrode's solution , neurons were rapidly detached from the dish using 0 . 5% Trypsin , washed with PBS and blocked with PBS containing 2 . 5% FCS and 5 mM EDTA . The collected neurons were fixed and permeabilized using Cytofix/Cytoperm and Perm/Wash buffers ( Becton Dickinson ) , according to the manufacturer's instructions and processed for staining at room temperature for 1 hour with anti-Tau antibody ( Sigma-Aldrich ) . Neurons were washed twice in Perm/Wash buffer before FACS analysis . To induce MHC I expression , non-infected neurons were treated with either 100 U/ml mouse IFN-γ ( Biosource ) plus 1 µM Tetrodotoxin ( TTX , Sigma ) , or 100 U/ml rat IFN-ß ( PBL interferon source ) . Reagents were added to the culture medium 48 hrs before analysis of MHC I expression as described above . IFN-γ levels were assessed by intra-cytoplasmic staining , essentially as described [63] . Briefly , BMCs or LNC were stimulated by coculture with irradiated Lewis fibroblasts ( a gift from Pr . L . Stitz , Tübingen , Germany ) , either infected or not with BDV , in complete RPMI 1640 medium containing 10% FCS , 1% sodium pyruvate , 1% non essential amino acids , 1% L-glutamine , 1% penicillin-streptomycin , 2×10−5 M 2-mercaptoethanol , and 50 U/ml recombinant human IL-2 ( AbCys ) . After 24 hours , GolgiPlug ( 1 µl/ml , BD Biosciences ) was added in the medium and cultures were further incubated overnight . Thereafter , cells were labeled for membrane expression of TCR and CD8ß and for intra-cytoplasmic production of IFN-γ using DB1-FITC mAb ( a gift from Dr . A . Saoudi , Toulouse , France ) and analyzed by flow cytometry . Cytokine levels were also measured in the supernatants as follows: directly after cell sorting , CD8 T cells purified from the brain or lymph nodes ( 1 . 5×105 cells/well ) were incubated with BDV-infected or non-infected neurons grown in 96-well culture plates ( Falcon , Becton Dickinson ) . Forty-eight hours after coculture , cytokine production ( IL-4 , IL-5 , IL-6 , IL-9 , IL-10 , IL-13 , IL-17 , IFN-γ and TNF-α ) was examined in the cell supernatants by Luminex multiplex immunoassay ( Millipore ) . Standard immunofluorescence was performed as described previously [29] . Briefly , neurons grown on glass coverslips were fixed for 20 min at room temperature with 4% paraformaldehyde , permeabilized using PBS + 0 . 1% Triton-X100 during 4 min , rinsed with PBS , and blocked overnight at 4°C with PBS + 2% normal goat serum . Incubation for 1 h at room temperature or overnight at 4°C with primary antibodies was followed , after several washes in PBS , by a 1-h incubation at room temperature with secondary antibodies . After extensive washing , coverslips were mounted using ProLong Gold ( Molecular Probes ) . Total RNA was isolated from highly purified CD8 T cells using the RNeasy Minikit ( Qiagen , Courtaboeuf , France ) . Reverse transcription was performed with 10 µL of total RNA ( 5 to 10 ng ) , random hexamer primers ( 0 . 1 µg ) ( Gibco BRL ) , and SuperScript Reverse Transcriptase ( 200 U; Gibco BRL ) . cDNAs were stored at −20°C until use . Quantitative cDNA amplification was performed according to manufacturer's instructions . The products of polymerase chain reaction ( PCR ) LightCycler amplification were detected with SYBR green I dye ( Roche Diagnostics ) . PCR cycling conditions were 50°C for 2 minutes and 95°C for 10 minutes , followed by 40 cycles of 95°C for 15 seconds , 60°C for 1 minute , followed by the final melting curve program . The melting curve analysis of PCR products together with their analysis by electrophoresis revealed the presence of a single amplicon at the expected size . Each sample was run in duplicate and mean values were used for quantitation . Relative quantification of mRNA concentrations was performed with the standard curve method , with amplification of target mRNA and actin mRNA for normalization . The relative amount of mRNA in each sample was calculated as the ratio between the target mRNA and the corresponding endogenous control actin mRNA . The primers used were as follows . IFN-γ: 5′-GCCAAGTTCGAGGTGAACAAC-3′ , 5′-TTCATTGACAGCTTTGTGCTGG-3′; FasL: 5′-AAAAGCAAATAGCCAACCCCAG-3′ , 5′-AGCCTCATTGATCACAAGGCC-3′; GranzymeB: 5′-GACAGATGGCAGCAACTGAA-3′ , 5′-GGCAGAAGCATTCCATTCAT-3′; Perforin: 5′-GGAAGCAAACGTGCATGTGT-3′ , 5′-GCGAAAACTGTACATGCGACA-3′; β-actin 5′-TGGAATCCTGTGGCATCCATGAAA-C-3′ , 5′-TAAAACGCAGCTCAGTAACAGTCCG-3′ . After cell sorting to high purity ( >99% ) , CD8 T cells were labeled with PKH-26 red fluorescent cell linker kit ( Sigma ) , according to the manufacturer's instructions . Briefly , cells were stained with 4 µM PKH-26 at room temperature for 5 min , the reaction was stopped by adding FCS and cells were washed once in complete RPMI medium containing 10% FCS . In parallel , neurons grown on Labtek chambered coverglass were labeled with 1 µM calcein-AM ( Molecular Probes ) in neuronal medium for 30 min at 37°C . Neurons were then washed twice with Tyrode's solution . Fluorescence measurements and imaging were performed on a Zeiss LSM-510 inverted confocal microscope with 10X , 20X or 40X objectives , whilst maintaining the cells at 37°C and 5% CO2 . To minimize photobleaching , one frame was captured every 20 s on average . Single-cell-tracking analysis was performed automatically ( intensity >130±20; size ≈10 µm ) with Imaris sotware and only tracks with durations of >120 s were included in the analysis . Image sequences of the time-lapse recordings were processed using Metamorph and Imaris softwares and 3-D images were generated with Imaris . For each movie , the first picture in the green channel ( corresponding to neuronal staining ) was subtracted from the last one ( 45 min later ) in order to obtain the difference in green fluorescence due to the formation of beads between these two time points . After coculture with CD8 T cells , neurons were washed with neuronal culture medium and labeled with Image-iT LIVE Green Caspase Detection Kit ( Molecular Probes ) which provides FLICA reagent specific for caspase-3 and -7 . Staining was performed according to the manufacturer's recommandations . At different times after incubation , levels of fluorescence intensities were measured on microsopic fields chosen at random . Neuronal cortical cultures were prepared from Lewis rats as described above and seeded at a density of 105 cells on multi-electrode arrays ( MEA , Multi Channel Systems GmbH , Reutlingen , Germany ) . Half of the MEA dishes were infected with BDV on day 1 . After addition of highly purified CD8 T cells ( at a ratio of one CD8 T cell per neuron ) , signals corresponding to the electrical activity from the 60 electrodes of the MEA were recorded using the MC Rack Software ( Multi Channel Systems GmbH , Reutlingen , Germany ) , which allows both online visualization and raw data storage . The signal corresponding to the firing of a single action potential by a neuron in the vicinity of an electrode was identified as a spike . We also detected high frequency grouped spikes trains , known as bursts , which represent an important parameter of the analysis of neuronal network activity [64] . Bursts were defined as a series of more than 3 spikes occurring in less than 100 ms . Spikes and bursts were detected by a dedicated analysis software developed at INSERM U862 ( Bordeaux , France ) [35] , which computes the signal obtained from the electrodes , calculates a threshold and detects a spike every time the signal crosses this threshold with a negative slope . The threshold was set to a minimum of three standard deviations of the average noise amplitude computed over the whole recording and applied from the signal averaged value as a baseline [65] . For each time point , recordings were performed over a 3 min period , and the mean burst frequency was calculated by averaging the results obtained for all electrodes . Comparisons between groups were performed with different statistical tests ( Student's t-test , Mann-Whitney U-test or Kruskal-Wallis ) using the GraphPad Prism software . | When a virus infects the brain , it is important to quickly block viral replication without causing excessive damage to neurons , which are not easily renewed . Cytotoxic T lymphocytes ( CTL ) are one of the main actors for virus elimination . However , the question of whether CTL are indeed capable of destroying infected neurons remains controversial . For this work , we analyzed the characteristics of interactions between infected neurons and CTL using neurotropic Borna disease virus ( BDV ) . This virus infects neurons and triggers severe inflammation in the brain . We isolated CTL directly from the brains of rats infected with BDV and analyzed their interaction with primary cultures of neurons . Using live-cell fluorescence microscopy , we observed that CTL were arrested upon encounter with infected neurons and that they established stable contacts with them . Thereafter , infected neurons exhibited rapid changes in permeability but remained alive and electrically active for several hours , before ultimately being destroyed . Our study shows that neurons can indeed be recognized by CTL , an important observation for a better understanding of the physiopathology of virus-induced brain inflammation . In addition , it reveals that neurons are relatively resistant to CTL-induced killing , which may open a window of opportunity for new treatments . | [
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"m... | 2011 | Neurons are MHC Class I-Dependent Targets for CD8 T Cells upon Neurotropic Viral Infection |
Plasmacytoid dendritic cells ( pDCs ) are “natural” interferon α ( IFNα ) -producing cells . Despite their importance to antiviral defense , autoimmunity , and ischemic liver graft injury , because DC subsets are rare and heterogeneous , basic questions about liver pDC function and capacity to make cytokines remain unanswered . Previous investigations failed to consistently detect IFNα mRNA in HCV-infected livers , suggesting that pDCs may be incapable of producing IFNα . We used a combination of molecular , biochemical , cytometric , and high-dimensional techniques to analyze DC frequencies/functions in liver and peripheral blood mononuclear cells ( PBMCs ) of hepatitis C virus ( HCV ) -infected patients , to examine correlations between DC function and gene expression of matched whole liver tissue and liver mononuclear cells ( LMCs ) , and to determine if pDCs can produce multiple cytokines . T cells often produce multiple cytokines/chemokines but until recently technical limitations have precluded tests of polyfunctionality in individual pDCs . Mass cytometry ( CyTOF ) revealed that liver pDCs are the only LMC that produces detectable amounts of IFNα in response TLR-7/8 stimulation . Liver pDCs secreted large quantities of IFNα ( ~2 million molecules of IFNα/cell/hour ) and produced more IFNα than PBMCs after stimulation , p = 0 . 0001 . LMCs secreted >14-fold more IFNα than IFNλ in 4 hours . Liver pDC frequency positively correlated with whole liver expression of “IFNα-response” pathway ( R2 = 0 . 58 , p = 0 . 007 ) and “monocyte surface” signature ( R2 = 0 . 54 , p = 0 . 01 ) . Mass cytometry revealed that IFNα-producing pDCs were highly polyfunctional; >90% also made 2–4 additional cytokines/chemokines of our test set of 10 . Liver BDCA1 DCs , but not BDCA3 DCs , were similarly polyfunctional . pDCs from a healthy liver were also polyfunctional . Our data show that liver pDCs retain the ability to make abundant IFNα during chronic HCV infection and produce many other immune modulators . Polyfunctional liver pDCs are likely to be key drivers of inflammation and immune activation during chronic HCV infection .
Plasmacytoid dendritic cells ( pDCs ) are rare innate immune cells that comprise about 0 . 5% of peripheral blood mononuclear cells ( PBMCs ) . They migrate into tissues and are known as “natural” producers of interferon alpha ( IFNα ) . pDCs constitutively express toll-like receptor ( TLR ) -7 and TLR-9 , as well as interferon regulatory factor ( IRF ) -7 , enabling them to detect viral nucleic acids and to quickly secrete type I IFNs ( IFNα and IFNβ ) , which bind neighboring cells and induce hundreds of IFN stimulated genes ( ISGs ) , initiating antiviral defenses . The activity of pDCs during HCV infection remains obscure . Several groups examined pDC frequency and function during chronic infection . Nearly all found a reduced frequency of pDCs in blood [1–7] . Some reported that circulating pDCs are functionally intact [6 , 7] , but the majority reported impairment after stimulation with various TLR ligands [1–5] , which was attributed to toxic effects of tumor necrosis factor α ( TNFα ) [5] and direct inhibitory effects of HCV proteins [8] [9] . In contrast to these inhibitory effects , HCV RNA stimulates pDCs by activating TLR-7 and RIG-I [10–13] . Past studies of patients and chimpanzees provide circumstantial evidence that liver pDCs do not produce IFNα during HCV infection . IFNA mRNA levels are not consistently elevated during acute [14] or chronic [15 , 16] infection , and liver IFNA2 mRNA levels rose to detectable levels only after HCV was cured [17] , suggesting that HCV may shut down IFNα production . The absence of detectable IFNA mRNA was initially puzzling because ISGs are highly induced in HCV-infected liver [18] , but the discovery of type III IFNs ( IFNλs ) provided a possible explanation for the seeming paradox because these cytokines up-regulate many of the same genes as IFNα [19] . These investigations left the question of pDC functionality during HCV infection unanswered . We explored an alternative explanation: the possibility that intrahepatic pDCs remain functional during chronic HCV infection but generate an IFNA mRNA signal that is too low to be detected in extracts of whole liver tissue . To improve the signal-to-noise ratio , liver mononuclear cells ( LMCs ) were purified and examined in parallel with whole liver tissue and PBMCs . We found that liver pDCs retain the ability to produce large quantities of IFNα and made more IFNα per cell than blood pDCs . Liver pDC frequency had strong positive correlations with whole liver expression of the “IFNα-response ( I ) ” pathway of blood transcriptomic ( BT ) modules and with three monocyte-specific modules , indicating that pDCs are active in vivo and have effects on other liver immune cells . Single-cell mass cytometry ( CyTOF ) revealed that liver pDCs are the only LMCs that make IFNα and demonstrated that most IFNα-producing pDCs are polyfunctional and a single IFNα+ pDC makes several additional cytokines/chemokines . Individual liver BDCA1 DCs were similarly polyfunctional . These findings demonstrate that intrahepatic pDCs and BDCA1 DCs can be intense point sources of a constellation of immune activators and they establish that liver pDCs remain competent for IFNα production despite chronic exposure to viral products .
Medical record data , liver , and blood were obtained from 19 patients with chronic HCV infection who were undergoing liver transplantation . The median age was 62 years [interquartile range ( IQR ) , 59–65]; 79% were male ( S1 Table ) . The median natural model for end stage liver disease ( MELD ) , which assesses the amount of liver damage , was 18 ( IQR , 13–32 ) . LMCs and PBMCs were prepared by density gradient centrifugation and either examined immediately , “ex vivo” , by flow cytometry , microarray , and RT/PCR or they were incubated for four hours with TLR ligands ( or media alone ) prior to analysis ( Fig 1A ) . Total ex vivo liver mRNA of 11 of the 19 patients was analyzed by microarray and RT/qPCR ( Fig 1B ) . LMCs of three additional anonymous HCV+ patients were analyzed by CyTOF ( Fig 1C ) . pDC frequencies in CD45+ PBMCs and LMCs were determined by flow cytometry using the gating strategy in S1 Fig , although this gating strategy does not rule out the possibility of including preDCs within the pDC population [20] . The pDC frequency was the same in liver and blood , suggesting that pDCs do not concentrate in the liver ( Fig 2A ) , but the median fluorescent intensity ( MFI ) of HLA-DR on the liver pDCs was higher ( Fig 2B ) , indicating greater activation . The impact of pDCs on surrounding liver cells was analyzed by examining correlations between pDC frequency and transcriptomic data of 11 whole livers . Four modules had a strong correlation ( R2≥0 . 5 ) with liver pDC frequency ( Fig 2C–2E ) : “IFNα response ( I ) ” ( Fig 2D ) , “Monocyte surface signature” ( Fig 2E ) , “Enriched in activated dendritic cells/monocytes , ” and “Enriched in monocytes ( surface ) . ” These results indicate that liver pDCs are active in vivo . Fifty-four percent of the genes in these four blood transcription ( BT ) modules are part of the Interferome [21] . The genes in these and other BT modules are listed in S4 Table . Liver pDC frequency also strongly correlated with the percentage of liver HCV RNA in double-stranded form ( Fig 2F ) , consistent with published data showing that IFNα increases HCV RNA duplexes [22] . Analysis of clinical data revealed a significant inverse relationship between liver pDC frequency and blood platelet counts ( p = 0 . 03 , Fig 2G ) . We also analyzed two additional DC subsets , BDCA1 and BDCA3 DCs . As determined by flow cytometry , BDCA1+ ( classical ) DCs were enriched in blood compared to liver ( Fig 3A , left ) , while BDCA3+ ( cross-presenting ) DCs were enriched in liver ( Fig 3B , left ) . The MFI of HLA-DR was higher on liver BDCA1+ DCs than on their counterparts in blood ( Fig 3A , right ) , but HLA-DR did not differ between liver and blood BDCA3+ DCs ( Fig 3B , right ) . Single sample gene set enrichment analysis ( ssGSEA ) revealed a strong correlation between the frequency of intrahepatic BDCA1+ DCs and expression of “Hox cluster III” , R2 = 0 . 6 ( Fig 3C and 3D ) and “Cell movement , Adhesion & Platelet activation” , R2 = 0 . 52 ( Fig 3C and 3E ) . Hox genes are critical for proliferation and differentiation of hematopoietic cells , especially T cells [23] . Liver BDCA3+ frequency strongly correlated with three pathways ( Fig 3F ) : “Formyl peptide mediated neutrophil response” ( Fig 3G ) , “Cell division stimulated CD4+ T cells” ( Fig 3H ) , and “Enriched in B cells ( IV ) . ” Taken together , these data suggest that BDCA1+ and BDCA3+ DCs increase immune infiltration , migration , and induction of adaptive and innate immune responses . CyTOF was used to definitively identify the IFNα-producing liver cells ( Fig 1C ) ; the CyTOF antibody panel is presented in S5 Table . LMCs were analyzed after incubation for 4 hours with media or R848 , a TLR7/8 agonist , in the presence of brefeldin A ( BFA ) to block cytokine secretion . viSNE was employed to project the high-dimensional data onto two-dimensional space . Nine major subsets of CD45+ cells were identified based on canonical markers ( Fig 4A; S1 Fig ) . pDCs comprised a distinct cluster in all three patients ( Fig 4A , orange ) . The normalized mean signal intensity ( nMSI ) for IFNα ( Fig 4B ) was determined for all subsets and IFNα positivity was plotted for each population ( Fig 4C ) . pDCs comprised the only population of IFNα+ cells ( Fig 4B and 4C ) ; on average 26% ( 13–40% ) of the pDCs expressed IFNα following R848 stimulation ( Fig 4C and 4D ) . The quantity of IFNs secreted by liver pDCs and other LMCs was investigated ( Fig 5A–5D ) after incubation in media alone , with R848 or with Poly I:C , a TLR-3 agonist that is important for IFNλ production [24] . LMCs secreted 3-fold more IFNα than PBMCs in response to R848 stimulation , 345±207 pg/mL vs . 115±111 pg/mL , p = 0 . 0001 ( Fig 5A ) . The amount of IFNα secreted per liver pDC per hour was calculated by combining data from flow cytometry , Luminex , and CyTOF . Secretion assays contained a mean of 6000 pDCs , with ~26% ( 1560 pDCs ) producing IFNα . Mean secretion was 86 pg of IFNα2a/2b/hour , which indicates that each IFNα-producing pDC was secreting over 1 . 7x106 molecules per hour . Actual secretion may exceed this number because the Luminex assay targets only IFNα2a/2b and there are 11 additional forms of human IFNα . Wimmers et al . showed that over the course of 12 hours , the small percentage of pDCs that initially produce IFNα later induce IFNα production in neighboring pDCs , in a local amplification loop [25] . Our calculation does not consider this amplification process because our incubations were only for 4 hours . The amount of IFNα secreted by LMCs correlated strongly with the frequency of liver pDCs , p = 0 . 0002 ( Fig 5E ) , consistent with CyTOF data indicating that pDCs are the only IFNα producers ( Fig 4B ) . It also correlated with expression of the “Immune activation-generic cluster” in whole liver , p = 0 . 007 ( Fig 5F ) , as well as expression of the KEGG pathway “Class I MHC Mediated Antigen Processing Presentation” ( p = 0 . 007 , Fig 5G ) and “Enriched in B cells ( IV ) ” ( p = 0 . 026 , Fig 5H ) . Minimal IFNα was secreted by LMCs or PBMCs incubated in media alone or with Poly I:C . Compared to IFNα , LMCs secreted far less IFNλ1 or 2/3 . The greatest amount was 24±21 pg/mL of IFNλ1 ( Fig 5B–5D ) , which is more than 14-fold lower than the greatest amount of IFNα . The quantities of IFNλ secreted by LMCs in response to TLR stimulation did not correlate with the frequency of any of the three DC subsets we examined . RT/qPCR and microarrays were used to investigate gene expression in LMCs , PBMCs , and whole liver . Notably , IFNA1 mRNA was readily detected in ex vivo LMCs by RT/qPCR ( Fig 6A ) , but neither IFNA1 mRNA , nor any of the other IFN mRNAs could be detected by RT/qPCR in whole liver: all 11 whole liver extracts had Ct values above 35 . Ex vivo LMCs expressed higher levels of IFNA1 , IFNB , and type III IFN mRNA ( IL29 and IL28A/B ) than ex vivo PBMCs ( Fig 6A–6D ) . Consistent with the RT/qPCR results , microarray data showed that ex vivo LMCs had higher expression of the “Immune Activation–Generic Cluster” and higher “TLR and Inflammatory Signaling” than PBMCs ( Fig 6E and S2B Fig , respectively ) . IFN gene expression was also examined following four hours of incubation in media with and without TLR ligands . RT/qPCR analysis showed that the TLR-7/8 agonist , R848 , increased expression of IFNA1 and IFNB in LMCs compared to ex vivo LMCs and compared to LMCs incubated in media ( Fig 6A and 6B ) . R848 treatment also increased IL29 expression in LMCs relative to ex vivo LMCs , but it did not increase IL28A/B expression ( Fig 6C and 6D ) . GSEA of microarray data of LMCs showed that R848 treatment up-regulated many IFNα genes ( Fig 6F ) and enhanced the “antiviral IFN signature” ( S2A Fig ) . The TLR-3 agonist , Poly I:C , did not induce IFNA1 in LMCs , but it did induce IFNB , IL29 , and IL28A/B ( Fig 6A–6D ) . Poly I:C enhanced the “antiviral IFN signature” relative to ex vivo LMCs ( S2C Fig ) , but not as intensely as R848 ( S2D Fig ) . Compared to ex vivo or media , Poly I:C did not increase expression of any of the type I or type III IFN genes in PBMCs . To explore the cytokine milieu more fully , TNFα , CXCL10 , IL-6 , and IL-10 secretion were examined by Luminex . LMCs made an average of 10-fold more TNFα ( 66±79 vs . 6±5 pg/mL ) , 3-fold more CXCL10 ( 278±236 vs . 98±110 pg/mL ) , 8-fold more IL-10 ( 16±12 vs . 2±2 pg/mL ) , and 10-fold more IL-6 ( 269±472 vs . 7±10 pg/mL ) than PBMCs after incubation in media ( without TLR stimulation ) , p≤0 . 05 for all comparisons ( S3A–S3D Fig ) . We used mass cytometry to measure the ability of individual pDCs to produce multiple factors , a capacity known as “polyfunctionality” . To test for polyfunctionality , we used a CyTOF panel with antibodies against 10 cytokines/chemokines , IFNα , TNFα , IL-8 , IFNλ1 ( IL-29 ) , IL-6 , IL-1β , IL-10 , CCL3 , CCL4 , and CXCL10 . Polyfunctionality was initially explored by selecting pDCs , BDCA1 DCs , or BDCA3 DCs of each patient by manual gating and then further gating on each combination of cytokines/chemokines ( Fig 7A and 7B ) . IFNα+ pDCs were highly polyfunctional: >90% produced two or more additional factors . Remarkably , 5% of the IFNα-producing pDCs made five or more additional cytokines/chemokines ( Fig 7A ) . IFNα- pDCs were less polyfunctional: 33% did not make any of the factors in our panel and most ( 58% ) made only 2 or 3 . Approximately 74% of the pDCs expressed TNFα and 73% expressed IL-8 , more than the 26% that make the signature cytokine , IFNα ( Fig 7D ) . BDCA1 DCs had a comparable level of polyfunctionality as pDCs , while BDCA3 DCs were mostly negative for the factors we analyzed ( Fig 7B ) . We used two additional analysis methods to characterize pDCs and to ensure that our findings were consistent regardless of which analytical method was applied . In the first approach , the pDCs of each patient were selected by manual gating and then Phenograph was used to identify subpopulations across all three patients ( Fig 7C and Fig 7E , Fig 7F ) [26] . Four metaclusters of R848-stimulated pDCs were identified ( Fig 7F , blue box ) . Three ( metaclusters 8 , 17 , and 7 ) had a high IFNα normalized mean signal intensity ( nMSI ) and also highly expressed TNFα and IL-8; they had variable expression of IL-6 , CCL3 , CCL4 , and/or IFNλ1 ( IL-29 ) . In a final analysis , pDCs were first clustered using viSNE [27] followed by manually gating ( Fig 7C–7G , and 7H ) . This process delineated eight clusters ( Fig 7G ) . Similar to the metaclusters ( Fig 7F ) , the viSNE clusters with high expression of IFNα ( viSNE clusters 5 , 6 , 8 ) also had high levels of IL-8 , TNFα , and they had variable expression of IL-6 and IL-29 ( Fig 7H , left ) ; nearly all the cells in cytokine-producing clusters came from cells stimulated by R848 ( Fig 7H , right ) , consistent with manual gating in Fig 7I . To obtain cells from a liver of a patient with no underlying liver disease , we turned to the buffer solution that is used to transport donor livers . This “perfusate” is a validated source of liver immune cells and has been used in previous studies [28] . We obtained two perfusates , one from a healthy liver donor and the other from a HCV-infected liver donor . Both livers were deemed healthy enough for organ donation . We assayed the pDCs for polyfunctionality . After a four hour stimulation with R848 , 16% of pDCs from the healthy perfusate made IFNα ( Fig 8A , top ) , while only 8% of the HCV+ perfusate’s pDCs made IFNα ( Fig 8B , top ) . We used the same CyTOF panel with antibodies against 10 cytokines/chemokines to explore polyfunctionality ( Fig 8A and 8B , bottom ) . IFNα+ pDCs were more polyfunctional than their IFNα- counterparts for both the healthy and HCV+ perfusates . Nonetheless , pDCs from a patient with no underlying liver disease were polyfunctional .
This is a detailed characterization of human liver pDCs from patients with a chronic hepatitis virus infection . It revealed that these rare innate immune cells are point sources of multiple immune activators and pro-inflammatory mediators , adding important new details about the fundamental capabilities of tissue-specific pDCs . Single cell mass cytometry ( CyTOF ) revealed that liver pDCs are the only IFNα producers among LMCs and revealed that only 15–40% synthesize it when stimulated with a TLR7/8 agonist ( Figs 4D and 7D ) . Activated pDCs secreted large amounts of IFNα protein: ~2 million molecules per pDC per hour . While pDCs are traditionally known as “natural interferon producing cells , ” our data revealed that they produce an array of bioactive molecules . Approximately 20% of pDCs make IFNα plus 4 of the other nine cytokines/chemokines we analyzed [TNFα , IL-8 , IFNλ1 ( IL-29 ) , IL-6 , IL-1β , IL-10 , CCL3 , CCL4 , and CXCL10] and 13% make IFNα plus 5 or more ( Fig 7A ) . Most IFNα+ pDCs expressed TNFα and IL-8 , with variable amounts of IL-6 , CCL3 , CCL4 , and IFNλ1 ( IL-29 ) . We did not determine the percentage of intrahepatic TNFα that is made by liver pDCs , but blood pDCs are major producers [29] , suggesting that liver pDCs may be a significant source of this proinflammatory cytokine . The perfusates from organ donors with livers healthy enough to transplant show that end-stage-liver disease is not a leading factor in whether or not pDCs are polyfunctional ( Fig 8 ) , nor does it seem to depend on HCV infection . The healthy perfusate pDCs were slightly more polyfunctional than the HCV-infected perfusate pDCs ( Fig 8 , bottom ) , suggesting that polyfunctionality is an innate characteristic of pDCs after stimulation through TLR7 . It is worth noting that pDCs purified from liver tissue are more polyfunctional than the pDCs that did not pass through our lengthy extraction process ( compare Fig 7A and Fig 8A , bottom ) . In future studies we hope to investigate pDCs from additional donors . Four previous studies used flow cytometry to investigate pDC polyfunctionality; they demonstrated that individual pDCs can produce IFNα , TNFα , and IL-6 [30–33] . By using CyTOF , we were able to interrogate a larger number of factors than flow cytometry allows . We found that individual cells could express six or more cytokines and chemokines . It is likely that a broader CyTOF panel would reveal an even greater number of immune factors . One flow cytometric study demonstrated that gut pDCs of simian immunodeficiency virus-infected macaques secrete IFNα , TNFα , and MIP-1β [30] . Two showed that most activated human blood pDCs express two of the three factors analyzed [31 , 32] . The fourth revealed that more than 95% of blood pDCs make two or fewer cytokines out of the four that were tested ( IFNα , TNFα , IL-6 , and IFNγ ) after stimulation with R848 [33] . In addition , using a combination of single-cell RNA sequencing and single-cell cytokine analysis , Wimmers et al . recently reported that only a fraction of pDCs make IFNα , while most make TNFα , consistent with our data [25] . With our more comprehensive panel ( 10 cytokines and chemokines were analyzed ) using CyTOF , we were able to show that liver pDCs , and similarly liver BDCA1 DCs , are highly polyfunctional for cytokine/chemokine production . Traditionally , polyfunctionality has been attributed to T cells and interpreted as an indicator of high functional capacity . Further studies are needed to understand the biological significance of having a single dendritic cell acting as a beacon of secreting multiple immune modulators . We postulate that polyfunctionality is important because it allows a single pDC to activate multiple signaling pathways on target cells , potentially raising the response to higher levels than could be achieved through the maximal activation of a single pathway . The exact composition of the cytokine/chemokine mix may also be important for eliciting appropriate responses . Many recent studies provide novel information about the heterogeneity of pDCs . This heterogeneity may influence which pDCs acquire polyfunctionality . Alculumbre et al . demonstrated that when blood pDCs were stimulated with either influenza or CpG for 24 hours , the population matured into three distinct functional groups [34] . One subset produced IFNα , another stimulated T cells and a third had elements of both . At their 4 hour time point , the three subsets were not apparent . In addition , single cell RNA sequencing revealed that there are cells within the typical pDC gate that are not pDCs [20 , 35] . Villani et al . showed that these pDC-like cells express AXL and SIGLEC1/6 but in fact function like conventional DCs by activating T cells [35] . Michea et al . showed that the microenvironment can increase pDC heterogeneity [36] . MacParland et al . demonstrated that the liver microenvironment changes the phenotype of resident macrophages [37] suggesting that the liver microenvironment may impact the phenotype of liver pDCs . Our study sheds new light on the paradoxical absence of detectable type I IFN mRNA in the HCV-infected liver despite the central role IFNα plays in host viral defenses . RT/qPCR data revealed that while IFNα/β mRNAs were not detectable in whole liver RNA extracts , confirming published findings [15 , 16] , they were readily detected in extracts of isolated liver leukocytes , demonstrating that purifying LMCs prior to mRNA analysis improved the signal-to-noise ratio in the RT/qPCR assay . Importantly , type I IFN mRNAs were detected in whole liver using microarrays , indicating that mRNA expression occurred in the whole liver and did not require cell isolation . Consistent with this , the frequency of liver pDCs strongly correlated with expression of BT module of the IFNα response and three monocyte-specific modules , indicating that pDCs activate surrounding immune cells . The liver pDC frequency also strongly correlated with the percentage of HCV RNA in double-stranded form , which provides additional evidence that IFNαs were produced in vivo; published data establish that IFNα increases the percentage of double-stranded HCV RNA [12] . R848-stimulation strongly induced IFNα genes in LMCs in vitro , as demonstrated by RT/qPCR and transcriptomic analysis . The amount of IFNα produced in response to R848 strongly correlated with the frequency of liver pDCs and with expression of the “Immune activation–generic cluster” in whole liver , suggesting that pDCs activate multiple antimicrobial , inflammatory , and immune response pathways in liver immune cells , as depicted in S4 Fig . pDCs retain the ability to respond to TLR ligands even in the face of HCV . Our study revealed interesting differences between pDCs in the liver and blood during chronic HCV infection . Liver pDCs were more highly activated , as indicated by higher expression of HLA-DR and ex vivo LMCs had higher expression of the “Immune Activation–Generic Cluster” and “TLR and Inflammatory Signaling” genes than ex vivo PBMCs ( Fig 6E and S2B Fig ) . LMCs secreted more IFNα than PBMCs . Some of the observed differences may reflect the different procedures used to prepare PBMCs and LMCs , the latter were exposed to collagenase/DNase and mechanical disruption , which may have activated the liver pDCs . However , single cell RNA sequencing studies have shown that the microenvironment plays an important role in shaping the phenotype and function of immune cells [36 , 37] . Importantly , LMCs remained responsive to TLR agonists during ex vivo culture , indicating that whatever effect the extraction process might have had it did not render the cells refectory to further activation . Additionally , despite on-going exposure to viral proteins like pathogen-associated molecular patterns ( PAMPs ) and cellular debris ( danger-associated molecular patterns ) , pDCs remained functional . When stimulated for four hours , LMCs secreted minimal IFNλ , whereas liver BDCA3+ DCs produced abundant IFNλ3 in response to 24 hours of stimulation [38] indicating that our experimental conditions were not optimal for IFNλ production . This suggests that a four hour time course is not appropriate for analyzing type III IFN responses . While acknowledging the importance of type III IFNs , we consider it likely that pDCs play an important role in liver immune responses during chronic HCV infection because the antiviral signature in LMCs was more strongly induced by the TLR-7/8 ligand than by the TLR-3 ligand . Because pDCs express TLR-7 and not TLR-3 [24] , this finding suggests that pDCs , and by extension IFNα , stimulate antiviral defenses in the HCV-infected liver ( S4 Fig ) . Our results are consistent with evidence that blood pDCs make IFNα in response to cell culture-derived HCV [10] , a TLR-7 agonist . In addition to LMCs , hepatocytes , sinusoidal endothelial cells , and other liver cells can produce IFNs in response to stress , including HCV infection . Hepatocytes secrete greater amounts of IFNλs than IFNαs [19] . Liver endothelial cells make primarily IFNλs after HCV exposure [39] . Type I and type III IFNs have distinctive effects . IFNα is more effective at inhibiting HCV replication in vitro [39] , but IFNλ induces a more prolonged ISG induction [40] . Moreover , HCV infection of primary human hepatocytes causes a down-modulation of IFNAR1[41] . This down-modulation , if it occurs during chronic HCV infection , could protect HCV from antiviral defenses and foster chronic inflammation . After successful HCV treatment , expression of some IFNA genes may increase [17] . If expression continues into the post-cure period , it could drive persistent liver inflammation , while also helping to suppress HCV recrudescence . Liver injury and inflammation continues in up to 66% of patients cured of HCV [42] and the immunopathology pre- and post-cure may involve some of the same molecular pathways . Pathologists were recently warned that the histopathology of liver transplant patients cured of HCV so closely resembles that of chronic infection that the conditions can be easily mistaken for each other [43] . Inflammation increases cancer risk; the HCC risk in cured cirrhotic patients remains elevated , up to 5% annually [44] . A limitation of the study is that most of the samples came from HCV-infected patients with end-stage liver disease and/or hepatocellular carcinoma . Future experiments need to be done on liver pDCs from additional sources . In summary: Our study provides important new details about primary human liver pDCs and their activity during chronic HCV infection . The investigation used a novel combination of CyTOF , molecular techniques , cytokine quantitation , and cell purification methods and provided evidence that activated liver pDCs produce large quantities of IFNα . The liver pDC response to stimulation was heterogeneous , as also reported by Wimmers et al . for blood pDCs [25] . A minority of liver pDCs produced IFNα and most IFNα+ pDCs also expressed 2 or more of the other nine cytokines/chemokines we examined . Liver BDCA1+ DCs were also highly polyfunctional . The circuits regulating gene expression in polyfunctional liver DC subsets merit investigation as the orchestrators of complex immune responses and as potential therapeutic targets .
This is a prospective study of specimens and medical records of 19 HCV-positive adults who received a liver transplant at the Mount Sinai Medical Center between 11/2013 and 8/2014 and who gave written informed consent . Blood for research and clinical testing was collected before surgery . Explants of three additional anonymous HCV-infected patients were also analyzed . Perfusates of two anonymous liver donors , one healthy and one HCV-infected , were collected and analyzed . The study was approved by Mount Sinai’s IRB in accordance with Helsinki guidelines . No explants were obtained from prisoners or other institutionalized persons . Specimens were brought to the laboratory at a median of 45 min post-explantation . The liver capsule was removed . Tissue was minced , washed in Hank’s balanced salt solution ( HBSS ) /1% fetal calf serum ( FCS ) , incubated in RPMI/5% FCS/0 . 1 mg/mL collagenase/50 μg/mL DNase at 37°C for 30 min , shaking every 5 min . Tissue was pressed through stainless steel mesh while washing with HBSS/1% FCS . Cells were washed and resuspended in HBSS/1% FCS and filtered through 100μm nylon mesh . Percoll gradients were used to purify LMCs from the filtrates [45 , 46] and from PBMCs . Perfusates were kept on ice during transportation and brought to the laboratory after anhepatic phase of liver transplantation was complete . Perfusates were spun down and resuspended in HBSS/1% FCS . Percoll gradients were used to purify PMCs from the perfusates . The flow cytometry panel for DC subsets appears in S3 Table . Cells were stained with the surface stain panel , then fixed with 2% paraformaldehyde solution ( Thermo Scientific ) in PBS . Samples were run on an LSR Fortessa ( BD ) and analyzed using Flojo . Without BFA: One million PBMCs or LMCs per 0 . 5mL media were incubated in RPMI with 10% FBS for four hours alone or with 1 μg/mL R848 or with 50 μg/mL Poly I:C at 37°C . Supernatants were collected for proteomic analysis . Cells were collected in Trizol ( Life Technologies ) for RT/qPCR and microarray analysis . With 1:1000 BFA ( eBioscience ) : Up to ten million PBMCs , LMCs , or PMCs in 0 . 5mL media were incubated in RPMI with 10% FBS for four hours alone or with 1 μg/mL R848 at 37°C . Cells were collected for CyTOF antibody staining and acquisition . RNAs were purified as before [47] . cDNA was made using SuperScriptIII First-Strand Synthesis ( Invitrogen ) and amplicons were quantified using the LightCycler480 SYBR Green II Master kit ( Roche ) . Expression of genes was calculated using the ΔΔCt method normalized to RPS11 and to expression of the PBMC ex vivo sample . Primers for IFNA1 , IFNB , IFNL1 , IFNl2/3 , RPS11 , and TNFA were described previously [48] . Double stranded HCV RNA was quantified as described previously [47] . Luminex multiplex cytokine assays ( Millipore ) quantified IFNα2a/b , IFNλ1 ( IL-29 ) , IFNλ2 ( IL-28A ) , IFNλ3 ( IL-28B ) , interferon gamma-induced protein 10 ( IP10 aka CXCL10 ) , interleukin 6 ( IL-6 ) , IL-10 , and TNFα . Profiling data from Illumina Human-HT-Expression Beadchips were normalized using GenomeStudio’s quantile method . GenePattern was used for gene set enrichment analysis ( GSEA ) and single sample GSEA ( ssGSEA ) of immune pathways [49] using BT modules [50] and KEGG pathways . A false discovery rate ( FDR ) below 0 . 25 was considered statistically significant . LMCs/PBMCs: To obtain sufficient RNA , LMC samples of matched pairs of patients were pooled . Matching was based on age , gender , HCV genotype , baseline HCV RNA , natural MELD score , and HCC ( yes/no ) . PBMCs were pooled similarly . Whole liver: Whole liver microarray data of 11 of the 19 patients consented for this study was used as before [47] . Panel presented in S5 Table . Samples were washed , fixed , and permeabilized ( eBiosciences ) then stained with intracellular antibodies . Samples were stored at 4°C in Ir intercalator ( Fluidigm ) in 2% formaldehyde until acquisition . Before acquisition , samples were mixed with EQ4 Element Beads ( Fluidigm ) and were acquired on a CyTOF2 ( Fluidigm ) . Data were normalized using bead-based normalization in the CyTOF software and gated to exclude beads , dead cells , and doublets . Method 1: Gated pDCs were analyzed using an automated CyTOF data analysis pipeline at the Mt . Sinai HIMC , which uses an R-based implementation of Phenograph [26] , an agonistic clustering method that utilizes the graph-based Louvain algorithm for community detection and identifies a hierarchical structure of distinct phenotypic communities . We utilized dynamic activation markers and intracellular cytokines as clustering parameters to resolve functional heterogeneity within the pDC population . Phenotypic clusters from 3 donors were meta-clustered identify consistent populations that could be reproducibly detected across individuals , thereby generating a consistent cluster structure across all samples in the dataset , while preserving the diversity and heterogeneity of all subpopulations . Method 2: viSNE was used to cluster the single-cell pDC data , creating t-distributed stochastic neighbor embedding ( tSNE ) plots in Cytobank [27] . viSNE uses a dimensionality-reducing algorithm to express multi-dimensional data in two dimensions . Canonical cell surface markers were then analyzed to identify cell populations overlaid on the viSNE map or manually identified clusters were gated on and overlaid on the viSNE map . The Luminex-measured mean quantity of secreted IFNα was divided by the incubation time to determine production per hour . This quantity was divided by the molecular mass of IFNα and multiplied by Avogadro’s constant . The result ( the molecules of IFNα secreted per hour ) was divided by the mean number of IFNα-producing pDCs per reaction , which was determined by multiplying the number of LMCs per reaction by the frequency of IFNα-producing pDCs as determined by CyTOF . GraphPad Prism was used . Paired and unpaired t-tests were performed . Pearson’s correlation coefficient was used for correlations . | This is a detailed characterization of human liver plasmacytoid dendritic cells from patients with a chronic viral infection . It revealed that these rare innate immune cells can become point sources of multiple immune activators and pro-inflammatory mediators . This study adds new information about the fundamental properties of pDCs , which are traditionally known as “natural interferon producing cells . ” In fact , these cells produce an array of bioactive molecules and may play an important role in organizing the liver’s immune response . | [
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"m... | 2019 | Individual liver plasmacytoid dendritic cells are capable of producing IFNα and multiple additional cytokines during chronic HCV infection |
Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain . There are many different tractography algorithms and each requires the user to set several parameters , such as curvature threshold . Choosing a single algorithm with specific parameters poses two challenges . First , different algorithms and parameter values produce different results . Second , the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles , subjects , and acquisition parameters . We propose using ensemble methods to reduce algorithm and parameter dependencies . To do so we separate the processes of fascicle generation and evaluation . Specifically , we analyze the value of creating optimized connectomes by systematically combining candidate streamlines from an ensemble of algorithms ( deterministic and probabilistic ) and systematically varying parameters ( curvature and stopping criterion ) . The ensemble approach leads to optimized connectomes that provide better cross-validated prediction error of the diffusion MRI data than optimized connectomes generated using a single-algorithm or parameter set . Furthermore , the ensemble approach produces connectomes that contain both short- and long-range fascicles , whereas single-parameter connectomes are biased towards one or the other . In summary , a systematic ensemble tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles .
Tractography uses diffusion-weighted magnetic resonance imaging ( diffusion MRI ) data to identify specific white matter fascicles as well as the connections these fascicles make between cortical regions [1–6] . Specifying the pattern of connections between brain regions ( “connectome” ) is a fundamental goal of neuroscience [7–9] . One of the major goals of tractography is to establish a model of the complete collections of white matter tracts and connections ( “structural connectome” , also referred as “tractogram” ) in the human brain . Hereafter , we refer to structural connectomes estimated using tractography as “connectomes” or “connectome models” . A variety of tractography algorithms are in wide use [10–18] ( see “Related literature” in Discussion ) . These algorithms calculate streamlines ( also called “estimated fascicles” ) through the white matter using somewhat different principles . Some tractography methods ( local tractography ) calculate streamlines by tracking the orientation of diffusion signal locally and step-wise based on deterministic [10 , 19–21] or probabilistic selection methods [11 , 12] . Other tractography methods ( global tractography ) reconstruct the trajectory of streamlines based on goodness-of-fit to diffusion signals [16 , 22–31] . Each algorithm offers some advantages and disadvantages . For any tractography method , investigators must set parameter values . Key tractography parameters include maximum and minimum streamline length , seed selection , and stopping criteria for terminating a streamline , and the minimum radius of curvature allowed for building each streamline . Differences in parameter values yield differences in streamlines [32–39] . The parameter dependency of tractography has been observed in both local and global tractography algorithms [34] . In common practice , investigators choose an algorithm and set fixed parameter values in the hope of optimizing streamlines for general use . However , recent studies [40 , 41] demonstrated that no algorithm or parameter values are optimal across all conditions . Specifically , Chamberland and colleagues [41] show that the best choice depends on a variety of factors such as the specific region of white matter or the specific tract being studied . For example , Fig 1 compares two tracts and shows how the best parameter value differs . Tracts between nearby regions on the cortical surface have short association fibers with relatively high curvature ( U-fiber; left panels in Fig 1 ) . To identify U-fibers investigators must set parameters that allow tracts with high curvature ( top panels in Fig 1 ) . In contrast , the major fascicles of the brain , such as the Inferior Longitudinal Fasciculus ( ILF ) or the Superior Longitudinal Fasciculus ( SLF ) , have relatively long and straight cores . Better estimates of the core of these tracts are obtained by sampling streamlines with relatively low curvature ( middle panels in Fig 1 ) . Additional factors affecting the optimal parameter choice for streamline generation may include diffusion MRI acquisition parameters ( e . g . , b-value , voxel size and angular resolution ) . In general , no single parameter value may capture the full range of streamlines globally in every brain . In the machine learning and statistical classification literature , it has been shown that for large and heterogeneous data sets combining multiple types of classifiers improves performance over single classifier methods ( Ensemble methods [42–44] , see [45] for a review ) . The human white matter provides similar challenges , because it contains large sets of heterogeneous fascicles different in length , volume and curvature . Given the complexity of human white matter , ensemble methods incorporating a range of tractography algorithms and parameters may be a valuable approach for improving tractography performance . The idea of incorporating tracts from multiple sources in the initial construction of a connectome has been suggested in earlier publications [27 , 31] . We describe an ensemble method , which we call Ensemble Tractography ( ET ) , to reduce problems arising from single algorithm and parameter selection . We illustrate the method with an example that addresses the parameter selection problem . First , we create a set of connectomes , each generated using a different parameter setting . These are called single parameter connectomes ( SPCs ) . We then combine streamlines from multiple SPCs into a new candidate connectome , and we use Linear Fascicle Evaluation ( LiFE [46] ) to optimize this connectome and eliminate redundant streamlines . We call the result the Ensemble Tractography Connectome ( ETC ) . Fig 2 shows a flow diagram of the ET algorithm . We report two key findings . ETCs ( 1 ) include streamlines that span a wider range of curvatures as compared to any of the SPCs , including both short- and long-range fibers ( bottom panel in Fig 1 ) , and ( 2 ) ETCs predict the diffusion signal more accurately than any SPC . To support reproducible research , the algorithm implementation and example data sets are made available at an open website ( http://purl . stanford . edu/qw092zb0881 ) .
We now return to the example in Fig 1 . All connectomes in Fig 1 were optimized using LiFE . The left-panels show U-fibers connecting two adjacent cortical regions , V3A/B and V3d ( see Materials and Methods and S1 Fig ) . The SPCs with high ( 1/0 . 25 mm ) and low ( 1/2 mm ) curvature parameters return very different results . The high curvature parameter SPC includes many streamlines , and the low curvature SPC has very few streamlines . The right-panels show estimates of the relatively long-range projections that make up the inferior longitudinal fasciculus ( ILF ) . In this case the situation is reversed: the high curvature SPC has many fewer streamlines than the low curvature SPC . Moreover , the terminations of these streamlines do not show the same branching pattern and do not extend into the occipital lobe . The images in the bottom panels of Fig 1 show the streamlines in the optimized ETC . The ETC model includes many U-fiber streamlines , similar to the 0 . 25 mm SPC . The estimated ILF contains the same branching pattern that extends into the occipital lobe as the 2 mm SPC . The color of the individual ETC streamlines indicates its SPC origin . The ETC estimates of the U-fibers include streamlines mainly from SPC that permit high curvature ( 0 . 25 mm ) . The optimized ILF includes streamlines mainly from SPCs with lower curvature ( 1 to 4 mm ) . The ETC includes streamlines from all of the SPCs . Nominally , the curvature parameter is a bound—one should not have higher curvature than the specified level [18] . In practice , however , we find that the bound impacts many properties of the candidate connectome . We illustrate the effect of the curvature threshold on each SPC in the occipital white matter of the 10 hemispheres in STN96 dataset ( Fig 3; see Materials and Methods; S2 Fig depicts white matter regions used for the analysis ) . For each of the bounds we tested , the candidate and optimized connectome curvatures form compact , single-peaked distributions; the peak increases monotonically as the minimum radius of curvature increases ( see S3 Fig for distribution in candidate connectomes ) . When the curvature bound is high ( small radius of curvature ) , the candidate connectome streamlines tend to have a relatively high mean curvature . When the curvature bound is low ( high radius of curvature ) , the candidate connectome tends to have a relatively low mean curvature . Thus , the curvature parameter is not simply a threshold; it influences the distribution of streamline curvatures in the optimized and candidate connectomes . For this reason , setting a lenient bound on the curvature ( i . e . , a low value of the minimum radius of curvature ) does not yield a good representation of long-straight fascicles ( Fig 1 ) . Conversely , setting a strict bound on the curvature ( i . e . , a high value of minimum radius of curvature ) eliminates U-fibers from the candidate connectome . We confirmed that the lenient bound on the curvature does not produce many straight streamlines using other tractography algorithm implemented in a different software package ( PICo [11]; S4 Fig , S1 Text , Section 1 ) . To reduce the curvature bias present in each SPC , the candidate connectome for the ETC combines samples from multiple SPCs whose parameters span a significant curvature range ( thick orange line; Fig 3 ) . Hence , the ETC strategy is effective in the sense that ETCs include streamlines with a broader range of curvatures . The optimized ETC includes more streamlines than any of the optimized SPCs ( Fig 4a ) . Importantly , nearly twice as many streamlines from the candidate ETC survive the LiFE process and contribute to the diffusion signal predictions . Typically streamlines generated using whole brain tractography do not pass through all of the voxels in the white matter . For very simple algorithms , such as deterministic tracking based on diffusion tensors [10] , as many as 17% of the white matter voxels contain no streamlines ( see S8c Fig ) . We show that ETC streamlines pass through a larger percentage of white matter voxels than any of the individual SPCs ( Fig 4b ) . The streamlines in SPCs ( based on CSD and probabilistic tractography methods [18] ) cover up to 95% of the white matter , whereas streamlines in the ETC cover up to 98% of the white matter . Because in reality the entire white matter volume contains streamlines , this result suggests that ET recovers more information from the diffusion data . The failure to find streamlines in about 2% of the voxels shows that we continue to miss some fascicles . While the number of ETC streamlines is nearly twice the number in any SPC , the white matter coverage is only about 3 percent greater . It follows that the number of streamlines per white matter voxel in the ETC is larger than the number in any of the SPCs . Whereas the mean number of streamlines per voxel in the SPCs is around 13 , the mean in the ETC is nearly 18 . Fig 4c shows a histogram that counts the number of streamlines in each voxel , comparing the 2 mm SPC and the ETC . Notice that many of the voxels ( 77 . 9% voxels on average ) have more streamlines in the ETC . The larger number of streamlines within each voxel implies that the ETC streamlines can predict more complex diffusion orientation distribution functions . S5 Fig describes the example crossing fascicle voxel in which ETC predicts diffusion signal significantly better than SPC . This is because each streamline can point in a slightly different direction and thus potentially predict diffusion in more directions . Coupled with the greater coverage across white matter voxels , the ETC should be able to provide a better prediction of the diffusion signal . Next , we compare SPC and ETC connectome accuracy ( Fig 5 ) . Accuracy is evaluated as the ratio of root mean square error between model and data to the test-retest reliability ( Rrmse [46–48]; see Eq 3 in Materials and Methods ) . Fig 5a shows a two-dimensional histogram comparing the accuracy of the ETC and the 2 mm SPC in a single , typical subject . For large portions of the white matter ( 62 . 4% voxels in Fig 5a ) , accuracy is higher ( Rrmse lower ) for the ETC than the SPC . Fig 5b describes the median Rrmse of the 6 connectome models ( SPC; 0 . 25 , 0 . 5 , 1 , 2 and 4 mm and ETC ) across all ten occipital lobes . The median ETC accuracy is significantly higher than any of the SPCs . S6 Fig compares the prediction accuracy of ETC and SPC ( 2 mm ) and tests whether increasing the size of the candidate SPC reduces the primacy of the ETC over the SPC ( see S1 Text , Section 2 ) . In this comparison , we matched the size of candidate SPC to that of ETC ( 800 , 000 streamlines; BigSPC model; see S1 Text , Section 2 ) . The optimized BigSPC supports as many streamlines as the ETC ( S6b Fig ) but the ETC covers a larger portion of the total white matter volume ( S6c Fig ) . Importantly , the prediction accuracy of ETC is consistently higher than BigSPC ( S6d Fig; see S6e Fig for comparison in individual hemispheres ) . Fig 6 compares connectome model accuracy between different white matter pathways ( U-fiber and the ILF , as shown in Fig 1 ) . We compared the accuracy of six connectome models in the voxels defined by the best U-fiber ( Fig 6a , left , ETC U-fiber ) and ILF ( Fig 6b , left , ETC ILF ) within the same hemisphere of the same subject . In all SPC models , 0 . 25 mm curvature threshold produces the best performance as compared with other thresholds in the U-fiber voxels , whereas the 4 mm SPC performs better than others in the ILF voxels ( Fig 6b ) . This shows that the best SPC differs between white matter pathways and brain volumes . In both U-fiber and the ILF , ETC model performs similarly or better than the best SPC model ( Fig 6 ) . Testing the ETC performance in the total white matter volume is computationally demanding , because of the increase of the matrix size in LiFE with ET ( see the recent paper [49] for computational load of LiFE ) . For example , if we combine five whole-brain SPCs including 2 million streamlines , the candidate ETC size is 10 million streamlines . In order to generate whole-brain ETC model , we used the ETC-preselection method ( see S1 Text , Section 5 ) . Briefly , we selected streamlines from each SPC with highest weight ( best contributing to predicting the diffusion signal ) to build the candidate ETC . This ETC-preselection method reduces the size of the candidate ETC , but produces better prediction accuracy as compared with any SPC ( S10 Fig ) . Using ETC-preselection method , we optimized the whole-brain ETCs in five brains ( Fig 7 ) . We compared properties of preselected ETC with those of the SPCs . Consistent with results in occipital white matter ( Figs 4 and 5 ) , the whole-brain ETC supports a larger number of streamlines ( Fig 7a ) , covers larger portion of white matter ( Fig 7b ) and predicts the diffusion signal better than any of the SPCs ( Fig 7c ) . Fig 7d shows maps of measured ( Data 1 and 2 ) and predicted diffusion signal for a single diffusion direction using two connectome models ( SPC 0 . 25 mm and ETC with preselection ) . The result suggests that the ET approach is also effective for whole-brain connectome analysis . We evaluated ET also using data from the Human Connectome Project ( HCP90 [50]; see Materials and Methods ) . Consistent with results obtained on the STN96 data set , ET included a wider range of curvatures ( S7b Fig ) , increased streamline count and white matter coverage ( S7a and S7c Fig ) , and higher accuracy for predicting diffusion signal ( S7d Fig ) . ETC on HCP90 dataset also supports example short- and long-range fascicles , U-fiber and the ILF ( S7e Fig ) , as identified on the STN96 data . Thus , the properties of ET are consistent between these different datasets . In addition to the ET method described above , we also used the ET method to create candidate connectomes that include streamlines from different algorithms ( Tensor deterministic , CSD deterministic and CSD probabilistic in MRtrix [18]; see S1 Text , Section 3 ) . The optimized connectomes from the ensemble of these algorithms had better prediction accuracy , and both increased streamline count and white matter coverage ( S8 Fig ) . We also observed that the ETC generated using an ensemble of Fiber Orientation Distribution ( FOD ) amplitude cutoff parameters had better prediction accuracy as compared with SPCs ( S9 Fig; S1 Text , Section 4 ) . Hence , we find substantial evidence across different diffusion datasets , tractography methods and parameters sets that ET improves the connectome model .
There is an enormous space of possible methods for creating candidate ETCs . The method for creating ensembles will need to evolve over many experiments from different laboratories . This paper presents one simple ET architecture that we found to be effective and efficient; just adding all streamlines from each parameter setting and optimize the ETC . One of the disadvantages of the ETC method presented in this paper is the computational demand required in building large candidate sets . In the following we discuss alternative architectures that we considered . S1 Text ( Section 5 ) proposes one alternative ET method; ETC-preselection . In this method , we chose 20% of streamlines contributing diffusion signal prediction from each of the individually optimized SPCs to build a new candidate ETC . The advantage of this method is that the resulting size of new candidate ETC becomes equal to that of original candidate SPCs . The disadvantage of this method is that we must evaluate ( using LiFE ) individually each SPC and also the ETC . Our results show that ETC-preselection performs significantly better than SPCs , and only slightly worse than ETC without preselections ( S10 Fig ) . Preselection is particularly useful for whole-brain models including large streamline sets ( Fig 7 ) , but not necessarily the best for connectome models with smaller size . As it is impossible to evaluate all possible ET algorithms in an initial paper , we describe the method and provide an open-source implementation ( francopestilli . github . io/life/; github . com/brain-life/life/ ) to the community for exploration of the many possible options . Several groups have analyzed tractography limitations , including parameter and algorithm dependence [32–40] . Bastiani and colleagues [34] analyzed how parameter and tractography algorithms influence connectomes and network properties . Their paper and others motivates the need for a means of deciding which solutions are best supported by the data [46 , 51–55] ( see also [56] ) . Several other groups also noted that the best parameter differs between different white matter pathways [40 , 41] . BlueMatter [27] used streamlines generated by three different algorithms ( STT [20] , TEND [21] , ConTrack [16] ) to create a candidate connectome . An important difference is that the BlueMatter algorithm could only be run on a supercomputer ( BlueGene/L , a 2048-processor supercomputer with 0 . 5 TB of memory ) , while the current ET algorithm using LiFE runs on a personal computer [49] . This advance enables investigators to systematically combine streamlines from many different parameters and algorithms and adopt ensemble tractography into their daily work flow . This paper is the first systematic exploration to sweep out several key parameters ( curvature , stopping criterion ) in tractography and demonstrate the advantage of ensemble methods in terms of anatomy ( Fig 1 ) and prediction accuracy for diffusion signal ( Figs 5 and 7 ) . A number of groups compared tractography with an independent measurement , such as invasive tract tracing or manganese enhanced MRI in macaques or mice [39 , 40 , 57–60] . For example , Thomas et al . [22] collected a diffusion data set in one macaque and compared the results of several single parameter connectomes with tracer measurements from a different macaque . This comparison has several limitations . First , the tracer measurements depend upon factors including the tracer type ( e . g . , anterograde or retrograde ) and the selection of planes and injection sites; hence , they can differ substantially ( e . g . [61 , 62] ) . When the methods disagree , it is often best to assemble a conclusion from multiple studies . Second , comparisons in a particular data set do not guarantee validation in a different experiment . For example , we cannot use high-resolution human adult brain fMRI data acquired in 7T scanner to support conclusions made from lower resolution fMRI data in children acquired using a 1 . 5 T scanner . Each methodology requires means for stating both the conclusions and the strength of the support for those conclusions . It is best to integrate fully justified findings derived by a variety of methods rather than discarding one method or another . Others have proposed to evaluate tractography by defining ground truth using synthetic phantoms [31 , 63–66] . Some investigators have pointed out the logical limitations of this approach [5] . We agree that there are limitations to using phantoms for testing tractography but that in some cases synthetic phantoms can be valuable for analyzing computational methods . Unfortunately , for our current work none of the currently available phantoms can be used . This is because most phantoms have been generated using either single tractography parameters [67] or simple fiber configurations [63] . Close and colleagues [68] provide software for generating numerical phantoms that can simulate complex fiber organization . However , their method was not proposed to evaluate tractography performance by comparison with ground truth . This fact makes it impossible for us to use the current phantoms to test the superiority of multiple tractography approaches such as ET to resolving multiple types of fiber configurations simultaneously . The potential value of creating connectomes from a collection of tractography methods was mentioned by both Sherbondy et al . [27] and Lemkaddem et al . [31] . Here , we provide a specific , open-source , implementation , and we begin a systematic analysis of this methodology . The analyses show that ET based on sweeping out the curvature parameter has the specific benefit of creating connectomes with both short- and long-range fascicles . In addition , the ET method produces more fascicles , larger coverage , and a better cross-validated prediction error . In this paper , we described the advantage of combining multiple tractography parameters and algorithms in order to improve the accuracy of connectome models . We use several example parameters and algorithms as a target for ET applications , and there are likely to be other beneficial combinations of algorithms and parameters which will be tested in future work . For example , we could combine connectomes by sweeping out two different parameters , or combine connectomes generated by different software packages that implement different algorithms , or combine connectome generated by using different seeding strategy tested in the literature [38 , 65 , 69] . Although it is impossible to test every pattern of combinations in this paper , we made LiFE software open ( http://francopestilli . github . io/life/; https://github . com/brain-life/life/ ) to help other researchers test different ET architectures . Future studies by multiple research groups will clarify the optimal ET architecture in both model accuracy and computational efficiency . ET will be generally applicable for many different proposed tractography algorithms . Several groups proposed generating streamlines based on the goodness-of-fit on the local diffusion signals ( global tractography; [16 , 22–31 , 53] ) . While global tractography has advantages compared with local tractography [70] , it too requires the user to set the parameters and this produces a parameter-selection dependency [34] . The ET approach will be effective in supporting both local and global tractography . Current tractography uses a fixed set of parameters to generate each streamline . However , several fascicles , such as many within the optic radiation , include both curving and straight sections [71–74] . When this is known a priori , it may be more accurate to change the tractography parameter along one fascicles , allowing high and low curvature in the relevant portions of the tract . LiFE and ET will provide the opportunity to evaluate the model accuracy of new tractography tools in terms of the prediction accuracy on diffusion signal . It is widely agreed that diffusion MRI contributes useful information about the large and long-range fasciculi in the human brain [75–78] . Meanwhile , the existence of U-fiber system has been supported [79 , 80] , but not extensively studied in the literature presumably because of the limitations in tractography parameter selections . The optimized ETCs extend tractography to include both long- and short-range fascicles in a single connectome , improving on the optimized SPCs which include one or the other . The higher model-accuracy and the inclusion of both short- and long-range fibers is a validation that the optimized ETC improves on any SPC . The preliminary ET results are encouraging , but they will surely benefit from further optimization . Tracer studies are not well-suited to identifying long-range pathways in the human brain . Even in animal models , with more than a century of history , recent tracer measurements challenge conventional thinking about long-range pathways . Reports describing many new found projections demonstrate that the field is active and evolving [62 , 81 , 82] . The progress in human tractography complements the strengths of tracer studies in animal models . Ultimately , combining insights from these technologies will provide a more complete view of human brain anatomy and function .
We used two magnetic resonance diffusion imaging datasets . The STN96 dataset was acquired at the Stanford Center for Neurobiological Imaging ( CNI ) ; the HCP90 dataset was acquired by the Human Connectome Consortium [50] . We identified several tracts within each optimized connectome to compare how different connectome represents anatomical features of the white-matter fascicles . All figures of brain anatomy and fascicles were made using Matlab Brain Anatomy ( www . github . com/francopestilli/mba ) . We evaluated model accuracy for whole-brain connectomes . To do so , we generated five 2-million streamlines candidate SPCs by using different curvature thresholds ( from 0 . 25 mm to 4 mm ) . We then used LiFE to assign a weight to each streamline . Next , we selected the top 400 , 000 streamlines with highest weight from each SPC ( preselection method; see S1 Text , Section 5 ) . This resulted in an ETC connectome containing 2 million streamlines . Finally , we optimized this ETC using LiFE . The processing of one whole-brain connectome model with 2 million streamlines requires 28 . 4 hours on a computer with 16 processing cores and 32GB Random Access Memory . The ILF extends outside the occipital white matter region used for the main analysis ( S2 Fig ) . In order to evaluate the connectome model along these fascicles , we selectively fitted LiFE to white matter voxels containing these tracts . To do so , we ( 1 ) identified the ILF from candidate connectome in all connectome models using the identification method described above , ( 2 ) concatenated all streamlines identified as ILF across multiple connectome models , ( 3 ) extracted the voxels in which any of streamlines are passing through . Finally we obtained a white matter region covering the ILF . LiFE analysis on the ILF is limited to these portions of white matter in all connectome models tested . | Diffusion MRI and tractography opened a new avenue for studying white matter fascicles and their tissue properties in the living human brain . There are many different tractography methods , and each requires the user to set several parameters . A limitation of tractography is that the results depend on the selection of algorithms and parameters . Here , we analyze an ensemble method , Ensemble Tractography ( ET ) , that reduces the effect of algorithm and parameter selection . ET creates a large set of candidate streamlines using an ensemble of algorithms and parameter values and then selects the streamlines with strong support from the data using a global fascicle evaluation method . Compared to single parameter connectomes , ET connectomes predict diffusion MRI signals better and cover a wider range of white matter volume . Importantly , ET connectomes include both short- and long-association fascicles , which are not typically found together in single-parameter connectomes . | [
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"and... | 2016 | Ensemble Tractography |
The importance of the genus Thrichomys in the retention of infection and transmission of Leishmania species is supported by previous studies that describe an ancient interaction between caviomorphs and trypanosomatids and report the natural infection of Thrichomys spp . Moreover , these rodents are widely dispersed in Brazil and recognized as important hosts of other tripanosomatids . Our main purpose was to evaluate the putative role of Thrichomys laurentius in the retention of infection and amplification of the transmission cycle of Leishmania infantum and L . braziliensis . Male and female T . laurentius ( n = 24 ) born in captivity were evaluated for the retention of infection with these Leishmania species and followed up by parasitological , serological , hematological , biochemical , histological , and molecular assays for 3 , 6 , 9 , or 12 months post infection ( mpi ) . T . laurentius showed its competence as maintenance host for the two inoculated Leishmania species . Four aspects should be highlighted: ( i ) re-isolation of parasites 12 mpi; ( ii ) the low parasitic burden displayed by T . laurentius tissues; ( iii ) the early onset and maintenance of humoral response , and ( iv ) the similar pattern of infection by the two Leishmania species . Both Leishmania species demonstrated the ability to invade and maintain itself in viscera and skin of T . laurentius , and no rodent displayed any lesion , histological changes , or clinical evidence of infection . We also wish to point out the irrelevance of the adjective dermotropic or viscerotropic to qualify L . braziliensis and L . infantum , respectively , when these species are hosted by nonhuman hosts . Our data suggest that T . laurentius may act at least as a maintenance host of both tested Leishmania species since it maintained long-lasting infections . Moreover , it cannot be discarded that Leishmania spp . infection in free-ranging T . laurentius could result in higher parasite burden due the more stressing conditions in the wild . Therefore the tissular parasitism of the skin , infectiveness to the vector , and amplification of the transmission cycle of both Leishmania species could be expected .
Although recognized as one of the most important and widespread parasitic diseases in the world , leishmaniasis prevention and control remains a challenge for health authorities in some countries [1] . In Brazil , human cutaneous leishmaniasis occurs in association with different Leishmania species , but Leishmania ( Viannia ) braziliensis is the most frequent and widespread species in the country . The visceral form is exclusively associated with Leishmania ( Leishmania ) infantum ( syn . L . ( L . ) chagasi ) . The Leishmania genus comprises more than 20 vector-borne species , primarily enzootic parasites , which includes species capable to infect a broad range of mammalian hosts and to be transmitted a variety of phlebotomine vectors . The transmission cycles of Leishmania spp . involves a variety of phlebotomine vectors and mammalian hosts . Failure to interrupt human transmission and prevent new epidemics are related , among others , to the involvement of wild and synanthropic hosts , mainly rodents and marsupials , that can colonize peri-urban areas [2]–[4] . Till now , the majority of studies that point out Leishmania spp . wild reservoirs are based on punctual observations of infection , most of them by molecular methods ( PCR ) rather than by parasite isolation and characterization . This can conduct to misinterpretation of concepts since the mere finding of Leishmania DNA in a given mammal species is not sufficient to consider this species a reservoir host [5] , [6] . Reservoir is better defined not as a single species , but as an assemblage of species responsible for the long lasting maintenance of a parasite in a given environment [7] , [8] . This concept does not include target species ( human or domestic mammals ) neither does it consider the eventual symptoms displayed by the reservoir hosts . Natural Leishmania sp . infection in wild rodents was already reported in different parts of the world [2] , [9] , [10] , and some of them were also successful in demonstrating the persistence of infection up to two years [11]–[13] . Laboratory studies using natural hosts as experimental models provide a suitable indication of the importance of these hosts as reservoirs , since it allows a better understanding of the dynamics of infection , especially concerning the ability to retain the infection and amplify the parasite populations in a given environment , due to a feature that favors the parasite transmission ( e . g . , presence of parasites in the skin ) . There are only rare studies that follow up experimentally infected wild hosts by Leishmania species , mostly due to the difficulties of managing wild mammals in captivity . Thrichomys laurentius is a South American caviomorph rodent formerly included in a monospecific genus . The formerly recognized species , Thrichomys apereoides , was recently split into five species: T . apereoides , T . laurentius , T . pachyurus , T . inermis and T . sp [14] , [15] . The recently described species within this genus comprise crepuscular and scansorial rodents that inhabit open vegetation in various Brazilian biomes: savannah ( “Cerrado” ) , white shrub ( “Caatinga” ) and marshland ( “Pantanal” ) , widespread from western to northern Brazil [16] . Some reasons point to the importance of the Thrichomys genus as a putative reservoir for Leishmania species: 1 ) the probable ancient association between caviomorph rodents and the trypanosomatids . It was proposed that the entry of new species of Leishmania ( Leishmania ) subgenus was the consequence of the arrival of infected caviomorph rodents during the Oligocene [17]; 2 ) the detection of Leishmania sp . DNA in free ranging Thrichomys sp . These rodents were found infected by Leishmania species from different complexes – L . mexicana and L . donovani – in an endemic area for both visceral and tegumentar leishmaniasis in Minas Gerais state , Brazil [3]; 3 ) the importance of these rodents as reservoirs of other trypanosomatids – Trypanosoma cruzi and T . evansi . This feature is confirmed by both experimental [18] , [19] and field work studies [20] , [21]; and 4 ) Thrichomys spp are widely dispersed throughout Brazil , comprising one of the most abundant species in the three Brazilian biomes where they occur [16] . Moreover , they are habitat generalists , found even in degraded areas , and can also frequent human dwellings [20] , [22] . In the present work , we investigated the experimental infection of Thrichomys laurentius with Leishmania infantum and L . braziliensis . Our main purpose was to evaluate the putative role of T . laurentius for the retention of infection and amplification of the transmission cycle of these Leishmania species . To achieve this aim , we: ( i ) studied the differences on the course of infection on L . infantum and L . braziliensis experimentally infected T . laurentius; ( ii ) followed up the health status of experimentally infected rodents by hematological and biochemical parameters , in order to evaluate the consequence on rodents' health of the experimental infection; and ( iii ) analyzed the parasitism distribution in the host .
Twenty-four Thrichomys laurentius of both sexes born in captivity were kindly supplied by Dr . Paulo D'Andrea . The colony of T . laurentius was derived from 9 males and 38 females captured in Piauí state ( northeast region of Brazil ) in 2000 . The animals are free from other parasites , provided from food and water ad libitum and kept under conventional conditions ( temperature 24±2°C , natural daylight ) at animal facilities of the Laboratory of Biology and Parasitology of Small Reservoir Mammals , Oswaldo Cruz Institute . Animals were individually housed in 41-34-17 cm polycarbonate cages with sawdust as bedding and fed with NUVILAB CR1 mouse pellets ( Nuvital nutrients S . A . , Colombo/PR , Brazil ) [23] . The rodents were divided into two groups and intradermically inoculated into the right ear pinna ( 0 . 05mL maximum volume ) by either Leishmania infantum – MHOM/BR/2001/HP-EMO = IOC-L2504 ( n = 12 ) or L . braziliensis – MHOM/BR/2000/LTCP13396 = IOC-L2483 ( n = 12 ) obtained from the Oswaldo Cruz Institute Leishmania collection ( Coleção de Leishmania do Instituto Oswaldo Cruz , CLIOC ) . At 60 day-old , animals were infected with 106 promastigotes derived from stationary phase culture starting from freshly amastigotes and followed up for 3 , 6 , 9 or 12 months post infection ( mpi ) . The age of the animals at the time of inoculum was based on calculations from the weightless T . laurentius caught in nature , i . e . , when young rodents starts to be exposed to infections outside their nest ( personnal observations ) . The parasites ( isolated no more than 2 weeks before Thrichomys infection ) were maintained by in vivo passage in golden hamsters ( Mesocricetus auratus ) derived from the animal facilities of Oswaldo Cruz Foundation . In this case , promastigotes were intradermically inoculated in hamsters footpads and re-isolated from inoculation site ( L . braziliensis ) and spleen ( L . infantum ) 4–5 months after infection . Hamsters were also used for control of the infectivity of the inocula . The study design was carried out according to the protocol approved by the Institutional Committee for Experimentation and Care of Research Animals ( CEUA-Fiocruz: P0076/01 and P0269/05 ) and animal facilities are supported by the Brazilian Institute of Environment and Renewable Natural Resources ( IBAMA license 02022 . 002062/01-04 ) . The present study was conducted from November 2005 to December 2008 . Blood samples were collected in heparinized and nonheparinized tubes from the retro-orbital plexus of animals previously intramuscularly ( IM ) anesthetized with 100 mg/kg ketamine hydrochloride and 2 drops of local anesthesia with colirium ( 0 . 5% solution of proximetacaine chloridrate ) every 3 weeks . The following parameters were determined: ( i ) red ( RBC ) and white ( WBC ) blood cell count , using a Neubauer hemocytometer; ( ii ) hematocrit , by the centrifugation of microcapilar tubes; ( iii ) hemoglobin levels , using a commercial test kit ( Labtest , Lagoa Santa/MG , Brazil ) ; ( iv ) percentage of leukocyte cells , by microscopical observation of thin blood smears stained with Panótipo Rápido ( derived from the Romanowski stain ) . Heparinized blood was also collected onto filter paper ( Whatman 5 , Maidstone , UK ) for the molecular assay , while the serum obtained from non-heparinized blood samples was used for the biochemical and serological follow-up . Medium corpuscular volume ( MCV ) , medium corpuscular hemoglobin ( MCH ) and medium corpuscular hemoglobin volume ( MCHV ) were also calculated . Values of all parameters obtained for each group one-day before the inoculum were considered normal and used to calculate the reference values . The ability to produce nitric oxide was evaluated by the nitrite level in rodent sera , using the Griess Reagent System ( Promega , Madison , USA ) . Only for rodents infected by L . infantum , albumin and total protein levels were determined using commercial test kits ( Labtest , Lagoa Santa/MG , Brazil ) . All of these assays were done according to the manufacturers recommendations . The kinetics of the humoral immune response was evaluated by indirect immunofluorescence test ( IFAT ) and enzyme-linked immunosorbent assay ( ELISA ) using Leishmania antigen deriving from axenic promastigotes of the same strain used for the experimental infection ( homologous ) and/or deriving from a mixture of L . infantum and L . braziliensis formalin-treated promastigotes ( mix ) , the latter only for the rodents followed for more than 6 months . IFAT was performed assaying two-fold sera dilutions ( 1∶10–1∶1 , 280 ) against Leishmania promastigotes and the reactions conducted using a specific in-house intermediary antibody anti-Thrichomys sera produced in rabbits . The reaction was visualized using a commercial anti-rabbit IgG-FITC ( Sigma-Aldrich , St . Louis , USA ) , according to Camargo [24] . Standard micro-ELISA was conducted according to Rosario et al . [25] , using a commercial anti-rat IgG-peroxidase ( Sigma-Aldrich , St . Louis , USA ) . We established 1∶20 and 1∶30 , 000 for the sera and conjugate dilutions , respectively , after the analysis of different serum dilutions derived from experimentally infected and non infected captivity Thrichomys using the ROC Curve ( BioStat 5 . 0 software ) . For each rodent , the cut-off value was determined using sera collected before the experimental infections and the absorbance at 492 nm was measured in an EMax Microplate Reader ( Molecular Devices , Ramsey , USA ) . The DNA extraction from filter paper was conducted by boiled water , according to Marques et al . [26] . The DNA product amplifications were conducted using pureTaq Ready-To-Go PCR beads ( Amersham Biosciences , Buckinghamshire , UK ) and primers directed to the conserved region of the Leishmania kDNA minicircle , as follows: forward: 5′-GGGGAGGGGCGTTCTGCGAA-3′ and reverse: 5′-GGCCCACTAT ATTACACCAACCCC-3′ . The PCR conditions were as follows: initial denaturation at 94°C for 5 min , followed by 30 cycles at 94°C for 1 min , 60°C for 1 min , 72°C for 30 s , and a final extension at 72°C for 5 min [27] . Blood from uninfected rodents and uninfected blood samples to which promastigotes axenically cultured were added , were used as control for both extraction and amplification processes . The amplified polymerase chain reaction ( PCR ) products were analyzed in polyacrylamide gel electrophoresis ( 4% ) and the negative samples were re-analyzed by electrophoresis in 12 . 5% polyacrylamide gels using the Genephor electrophoresis system apparatus ( Pharmacia Biotech ) . All of the gels were stained using the DNA Silver Staining Kit ( GEHealthcare , Chalfont St . Giles , UK ) . Euthanasia was performed by CO2 inhalation on months 3 , 6 , 9 and 12 post inoculation ( n = 3 for each batch ) . Procedures were undertaken in a Class II biosafety cabinet: ( i ) inoculation of fragments of spleen , liver , inoculation site ( right pinna skin ) and bone marrow in biphasic culture mediums ( NNN/Schneider's ) supplemented with 10% fetal bovine serum ( v/v ) and antibiotics ( 350 IU penicillin and 150 µg/mL streptomycin ) , which was examined every 3–4 days for 1 month; ( ii ) slide imprints of spleen , liver and inoculation site , which were Giemsa-stained and microscopically observed at ×400 magnification; ( iii ) collection of tissue fragments – spleen , liver , inoculation site , skin and bone marrow – in 1 . 5 mL tubes containing ethanol and stored at −20°C , which were used for the molecular analyses; ( iv ) fixing of tissue fragments – spleen , liver , skin , lymph nodes and both ears separately – in 10% neutral buffered formalin for histological studies . After dehydration and paraffin-embedding , 4 µm sections in thickness were made , routinely stained with hematoxylin and eosin ( H&E ) , and the sections examined by light microscopy . For the slide imprints , histological and molecular tests , liver tissue samples were performed considering two fragments from different lobes . For the molecular diagnosis , tissue fragments were washed three times with Milli-Q water and DNA extraction realized using the Wizard Genomic DNA Purification Kit ( Promega , Madison , USA ) according to the manufacturer's recommendations . The PCR was conducted as described above for the blood collected on filter paper . Normal ranges for the hematological and biochemical values were determined in relation to medium values and two-fold standard errors obtained for each group one-day before the inoculum . The differences on the hematological and biochemical kinetics between rodents infected by either L . braziliensis or L . infantum were evaluated by the non-parametric Mann-Whitney test . The differences between the normal values and each point of the hematological and biochemical follow-up were evaluated by the Kruskal-Wallis and Student-Newman-Keuls tests . All of the data were analyzed using the BioStat 5 . 0 software ( Instituto Mamirauá , Tefé , Brazil ) and considering p<0 . 05 significant .
The Leishmania sp . inoculated were shown to be infective as demonstrated by the parasite recovery from liver , spleen and inoculation site of hamsters . However , only L . braziliensis could be isolated from the inoculation site . As expected for outbred animals , a great individual variability among infected T . laurentius was noted . Despite that , all Thrichomys rodents were able to efficiently control the infection without presenting lesion or clinical evidence of disease . Growth development , determined by weekly body mass measure , was not affected by the Leishmania infection and no expressive alterations of health markers were observed . Amastigotes of Leishmania spp . were absent in the thin blood smears and Leishmania DNA could not be detect in blood samples collected in filter paper in 3 weeks interval . Leishmania infection did not result in anemia and all of the rodents displayed values that were inside the normal range during the complete follow-up . Nevertheless , L . infantum infected T . laurentius tended to display lower red blood cell counts and hemoglobin levels when compared to those infected by L . braziliensis . During the follow-up , most of the values obtained for RBC counts and hemoglobin levels showed significant differences ( p<0 . 05 ) between T . laurentius infected by L . braziliensis and L . infantum ( Figure 1 ) . This same feature was also observed for the hematocrit values ( data not shown ) . Significant leucopenia from the 120 dpi day on ( p<0 . 05 ) , was observed in rodents inoculated with L . braziliensis . A similar , but not significant , picture was also observed in the rodents infected by L . infantum ( Figure 2 ) . No differences before and after the inocula were observed for MCV , MCH , MCHV and differential counts of WBC ( data not shown ) . Albumin and total protein levels were not affected by L . infantum infection , excepting for the rodent ( 7548 ) where re-isolation of parasites was possible . This rodent displayed a marked decrease in albumin levels and increase in total protein levels after 200 dpi , resulting in declined in albumin/total protein level ( Figure 3 ) . The nitrite level in rodent sera displayed a great individual variability and could not be correlated to any other hematological , serological or parasitological parameter . All infected T . laurentius were able to produce a humoral response that could be detected during the three initial weeks by both IFAT and ELISA assays . The IFAT showed no differences on serological titers among assays performed with homologous or a mixture of antigens . The response onset and magnitude of titers were very homogeneous and similar among the infected rodents . Rodents infected by L . braziliensis displayed serological titers that were always slight higher than those observed for the rodents infected by L . infantum ( Figure 4 ) . The ELISA assay revealed an individual variability on the response onset and magnitude of titers that varied from negligible to strong responses . In common , infected T . laurentius displayed a peak of absorbance values on 100 dpi that were in medium four times higher than the day 0 and kept constant until the end of follow-up ( data not shown ) . Both Leishmania species demonstrated the ability to invade and maintain itself on viscera and skin of the infected T . laurentius , although this parasitism was not expressive since isolation of parasites was rare: from liver , spleen and inoculation site 3 mpi from one rodent infected by L . braziliensis; and from liver and spleen 12 mpi from one rodent infected by L . infantum . Leishmania DNA was detected in all experimental batches , independent of the Leishmania species ( Table 1 ) . Nevertheless , the low parasitic burden was evidenced by the large amount of positive PCR ( 73% ) observed only when electrophoresis was conducted on the GenePhor electrophoresis system apparatus . Up to 77% and 50% of the L . braziliensis and L . infantum infected T . laurentius , respectively , displayed Leishmania DNA in at least one of the tissues collected on the necropsy . The individual variability , peculiarities of the host , parasite and host-parasite interaction , and the time of the infection seems to be the major factors that influenced the different percentage of positive reactions . Parasite distribution in viscera was not homogeneous and 30 . 4% of the tissues ( spleen , liver and inoculation site ) that had two different fragments examined , displayed a positive and a negative result for the presence of Leishmania DNA . No sign of parasitism was observed in tissue imprints or histological sections . Comparative histologic analysis did not detect any inflammatory or degenerative changes in T . laurentius infected with L . infantum or L . braziliensis . Our study on the pathology of Leishmania sp . in golden hamster ( Mesocricetus auratus ) demonstrated that this rodent developed definite evidence of infection , characterized by extensive spleen necrosis and inflammation associated to high number of amastigotes ( Figure 5A ) . No histological abnormalities or other histological differences were observed between positive and negative culture tissues obtained from infected T . laurentius ( Figure 5B–F ) . Discrete differences in the cellularity of primary splenic follicles and periarterial lymphoid sheath seem not related to infection and were seen in seven T . laurentius from both groups .
The genus Thrichomys comprises recently described cryptic species that are undergoing a process of allopatric and/or parapatric differentiation [14] . Within this widespread rodent genus , T . laurentius is distributed in northeast Brazil , from Ceará to Bahia state , a region that reports numerous cases of both visceral and tegumentar human leishmaniasis [28] , [29] . For demonstrating the potential to act as a maintenance host , a given mammal species must be able to control and retain the parasite infection . In our experimental conditions this rodent species showed to be able to retain long term infections by the main etiological agents of human leishmaniasis in Brazil , Leishmania infatum and L . braziliensis . This ability was undoubtedly demonstrated by the parasite re-isolation in liver and spleen of rodents experimentally infected by both Leishmania species . Asymptomatic infection is usually considered as an essential attribute to be considered a reservoir host . This is currently not considered as a rule; in fact , it is the transmission strategy of the parasite that is positively selected in a successful host-parasite system , independent of the damage caused by the parasite or health status displayed by the host . Even ancient host-parasite interaction may not necessarily evolve in the direction of less damage or lower virulence , but instead of that , to the maximum transmissibility of the parasite [30] , [31] . According to the concept proposed by McMichael [32] and Roque et al . [19] , maintenance host is the one who retain the infection ( where a given parasite persists ) while an amplifier host displays an infection course that favors the transmissibility of the parasite . Taken together our data suggest that T . laurentius may act at least as a maintenance host of both tested Leishmania species since it maintained long-lasting infections . Moreover , it cannot be discarded that in nature , infected rodents display higher parasite burden and tissular parasitism on skin , acting then as an amplifier hosts of Leishmania species . Anemia , a characteristic trait in L . infantum infected humans , dogs and laboratory rodents [33]–[35] , was not observed in the L . infantum infected T . laurentius . Although animals infected by L . infantum displayed a significant decrease of the hematological parameters in comparison to those infected with L . braziliensis , this decrease still did not characterize anemia . Leucopenia , another common trait observed on L . infantum , but not on L . braziliensis infections [36] , was only observed in the L . braziliensis infected T . laurentius from the 120 dpi onwards . This finding was not surprising in the light of the parasite disseminated to liver and spleen . Surprising was ( i ) the absence of leucopenia in L . infantum infected T . laurentius; and ( ii ) the later presence of that leucopenia , only after 120 dpi , while the L . braziliensis isolation occurred before that . Hypoalbuminemia and hypergamaglobulinemia , the most common biochemical alterations in L . infantum symptomatic infection in humans and dogs [34] , [37] , were only observed on the rodent from which the re-isolation of parasites was possible , probably due to a higher parasite burden in this animal . Considering that biochemical alterations are not described as being associated to L . braziliensis infections , these parameters were not tested in the animals infected by this Leishmania species . Moreover , given the similar pattern observed in the rodents infected with both Leishmania species we question whether analyses of further parameters could display alterations in the animal from which L . braziliensis was isolated in the necropsy . Experimental T . laurentius infection by two different Leishmania species did not result in important damage for rodents , but this can be quite different for naturally infected T . laurentius . Captivity rodents are free from other pathogens , are provided food and water ad libitum and maintained in controlled environmental conditions . In nature , rodents are constantly facing out stress ( search for food and escape from predators ) , competitions ( intra and inter-specific ) , and infection by other parasites and Leishmania re-infections . All of these factors will directly influence the course of any parasitic infection and be reflected by higher virulence and/or host damage . The effectiveness of the serological assays was demonstrated even for the rodents infected by L . braziliensis , an infection usually not associated to an important humoral response [36] , [38] . This is probably due to the visceralization of this parasite species in T . laurentius . This data emphasizes that the search for Leishmania reservoir should consider all possibilities of the infection course , which includes a broad range of diagnostic methods independent of the current knowledge in other mammal hosts . The efficacy of the IFAT assay for serological screening of Thrichomys sp . was already demonstrated for Trypanosoma cruzi and T . evansi infections [19] , [39] . In the present study , ELISA showed to be a promising tool , since it was able to detect a humoral response production in all of the infected rodents . The use of an intermediate anti-Thrichomys antibody and the determination of cut-off values based on a great number of positive and negative serum samples might result in a standardized and efficient assay to diagnose Leishmania infection in wild Thrichomys sp . In this study , we were not able to detect Leishmania DNA in any of the blood samples examined; even considering that 24 infected T . laurentius were analyzed and blood samples were collected every 21 days post infection , totalizing 236 samples evaluated . These data show that whole blood is not a reliable sample to detect Leishmania infection , at least in this mammal host species . The persistence of both Leishmania species with an extremely low burden in T . laurentius could only be demonstrated by the use of a more sensitive technique: PCR targeting a high copy number DNA sequence coupled to a high resolution electrophoresis ( in this study , the Genephor electrophoresis system ) . Unfortunately , the elevated cost of some commercial kits makes the routine use still unfeasible . In T . laurentius , L . braziliensis can invade and maintain itself in other tissues in addition to the skin . The parasite's ability to invade and maintain itself in internal organs , such as spleen and liver , in non-human hosts was described several times since the 1950ths [40] , [41] . Despite that , the description of L . braziliensis as a dermotropic parasite is widespread throughout the scientific community . Our results demonstrated that the definition of dermotropic or viscerotropic based on the clinical feature observed in humans should not be applied to the natural hosts of that Leishmania species . Studies based only on molecular probes are successful to determine parasite hosts , but lack the capacity to determine the transmissibility of that parasite , and thus the importance of that putative reservoir host on the transmission cycle . Moreover , contamination in isolation attempts in field conditions seriously hampers the successful isolations . For that reasons only few studies were capable to isolate Leishmania parasites in naturally infected wild rodents [2] , [9] , [42] . The polymerase chain reaction ( PCR ) methodology is undoubtedly a great advance for the diagnosis of Leishmania infection , but it cannot be associated to parasite transmissibility . The scarce studies on the L . infantum experimental infection of wild rodents report the failure to re-isolate the parasite [43]–[45] , and only in one of them , parasite DNA could be detected [45] . We were able to re-isolate L . braziliensis and L . infantum from experimentally infected T . laurentius . Moreover , we detected L . infantum DNA in bone marrow samples of another species of Thrichomys , T . pachyurus , one year after the experimental infection ( unpublished data ) . The ability to maintain and disseminate to different organs ( which include bone marrow , spleen , liver and skin ) during long term infections by Leishmania species and their wide and abundant distribution in Brazilian endemic leishmaniasis areas point to the importance of Thrichomys spp . at least as maintenance host for Leishmania species . Future studies concerning the natural infection of Thrichomys spp . becomes crucial to understand the role of these caviomorph species on the wild transmission cycles of Leishmania species . | For Leishmania , one genus among several genera belonging to the parasitic Trypanosomatidae family , many nonhuman mammals are known to be hosts in addition to humans . Most studies that describe Leishmania wild reservoirs are based on isolated descriptions of infection that can lead to misinterpretation of information . The definition of the epidemiological importance of a putative reservoir host depends on adequate data on the dynamics and peculiarities inherent to the host-parasite interactions and their involvement in the transmission cycle of these parasites . Our objectives were to sort out the features displayed by nonhuman mammal populations ( the caviomorph rodent Thrichomys laurentius ) which , with an insect host , perpetuate Leishmania transmission cycles . This rodent species had the ability to act as maintenance and/or amplifier host of both tested Leishmania species . The similar pattern of infection displayed by T . laurentius infected by these two Leishmania species shows that the definition of dermotropic or viscerotropic based on the clinical features observed in humans should not be applied to natural hosts , and it emphasizes that the search for Leishmania reservoirs should consider all possibilities of the infection course , independent of current knowledge in other mammal hosts . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/protozoal",
"infections",
"infectious",
"diseases/neglected",
"tropical",
"diseases"
] | 2010 | Thrichomys laurentius (Rodentia; Echimyidae) as a Putative Reservoir of Leishmania infantum and L. braziliensis: Patterns of Experimental Infection |
In order to get a comprehensive repertoire of foldable domains within whole proteomes , including orphan domains , we developed a novel procedure , called SEG-HCA . From only the information of a single amino acid sequence , SEG-HCA automatically delineates segments possessing high densities in hydrophobic clusters , as defined by Hydrophobic Cluster Analysis ( HCA ) . These hydrophobic clusters mainly correspond to regular secondary structures , which together form structured or foldable regions . Genome-wide analyses revealed that SEG-HCA is opposite of disorder predictors , both addressing distinct structural states . Interestingly , there is however an overlap between the two predictions , including small segments of disordered sequences , which undergo coupled folding and binding . SEG-HCA thus gives access to these specific domains , which are generally poorly represented in domain databases . Comparison of the whole set of SEG-HCA predictions with the Conserved Domain Database ( CDD ) also highlighted a wide proportion of predicted large ( length >50 amino acids ) segments , which are CDD orphan . These orphan sequences may either correspond to highly divergent members of already known families or belong to new families of domains . Their comprehensive description thus opens new avenues to investigate new functional and/or structural features , which remained so far uncovered . Altogether , the data described here provide new insights into the protein architecture and organization throughout the three kingdoms of life .
Domains are the modular building blocks of proteins and correspond to recurring , fundamental units of both protein structure and evolution . Protein domains may exist alone , but frequently are part of larger , multi-domain proteins [1] . The advent of complete genomes sequences has led to the estimation that 40% of prokaryotic proteins are multidomain , whereas this number increases to about two thirds in eukaryotes [2] . Protein domains are classified into families; several domain families are common to most species , indicating that there is a limited repertoire , which is used to create the large functional space of proteins [3] . Some domain families , considered as “promiscuous” , occur in diverse protein domain architectures ( which are defined as the linear orders of the individual domains in multi-domain proteins ) and are especially involved in interaction networks [4] . The recognition of domain family membership for uncharacterized proteins is often a first step towards the understanding of their biological roles . Information about protein domains is stored in dedicated databases , in the form of profiles or hidden Markov models ( HMMs ) , which are constructed through sequence similarity searches . These profiles and HMMs can be searched for detecting the domain composition of proteins , starting from their amino acid sequences [5] . By this way , approximately half of the residues of proteomes can be assigned to well-classified domains , such as those stored in the PfamA classification [2] . The percentage of assigned residues increases when less well-characterized domain databases , such as PfamB , are searched . The remaining residues , representing 10–20% of the proteomes and referred to as “orphan” domains , do not match any known domains [2] . These sequences include disordered structures , among which are found linkers between structured domains , but also folded units , which are difficult to characterize , principally due to their small size or their fast evolution relative to an ancestral protein . These can thus not be easily predicted by these sequence similarity-based methods . The prediction of domain boundaries can also be approached through ab-initio methods , which don't have such restrictions as they consider solely the protein sequence . These focus on either globular domains or disordered regions and are based on learning models , using a series of proteins for which information on residue properties is known and algorithms such as artificial neural networks and support vector machines ( e . g . [6]–[11] ) . However , the accuracy of domain boundary prediction is often too low for general , practical use . Improvement of the quality of ab-initio predictions has been obtained by hybrid methods , adding evolutionary information ( e . g . [12] , [13] ) . Here , in order to get insight into orphan regions corresponding to foldable regions , without consideration of any evolutionary information , we have developed a strategy inspired from our experience in Hydrophobic Cluster Analysis ( HCA ) . This two-dimensional method is used for ( i ) delineating the position of globular-like domains and ( ii ) comparing fold signatures at low levels of sequence identity ( Fig . 1 ) . HCA is based on the physico-chemical and topological principles underlying the fold of globular domains ( dichotomy between hydrophobic/non-hydrophobic amino acids , overall compactness ) [14] , [15] . It allows a direct statistical access to regular secondary structures gravity centers for a single amino acid sequence through the hydrophobic clusters defined in this way [16] , [17] . The immediate information available from this lexical analysis of the protein sequence text is a direct , comprehensive analysis of the protein texture , revealing in particular structured and non-structured regions . Structured regions contain typical hydrophobic clusters , the length of which is similar to those of regular secondary structures , whereas non-structured regions lack or have less and smaller hydrophobic clusters . This property has led to the manual identification of a lot of domain boundaries , constituting crucial starting points for experimental and computational investigations ( see examples at the following url http://www . impmc . upmc . fr/~callebau/HCA . html ) . Besides this property , the HCA hydrophobic clusters constitute efficient signatures for comparing remote sequences , allowing to link orphan sequences to known families of domains ( e . g . [18] ) or identify new families of domains ( e . g . [19]–[21] ) . On this basis , we developed a fast and automated procedure , called segmentation-HCA ( SEG-HCA ) in order to delineate the foldable domains within proteins from the knowledge of their sequences alone and applied it to the characterization of whole proteomes . This approach is distinct from Scooby-Domain [22] , [23] , which uses the distribution of observed lengths and hydrophobicity in domains with known 3D structures . SEG-HCA also uses a simple binary hydrophobic scale , but enriched from the two-dimensional information highlighting regular secondary structures through the hydrophobic clusters defined by this way . Moreover , SEG-HCA is not limited to the observed lengths in domains with known 3D structures , but cover any foldable region of any length . The information provided by SEG-HCA can then be compared with that included into structural databases , in order to support the structural meaning of the predictions . It can also be compared with that provided by the NCBI's conserved domain database ( CDD ) [24] in order to highlight «orphan» domains , i . e . predicted globular-like domains that don't match any conserved domain ( CD ) . Finally , this information also merits consideration with regard to protein disorder . Intrinsically disordered proteins ( IDPs ) or Intrinsically unstructured proteins ( IUPs ) do not , by themselves , assume any stable 3D structures , under physiological conditions [25]–[29] . IUPs however cover different forms of disorder , from totally unfolded chains ( “coil”-like or natively unfolded ) , to coupled folding and binding ( i . e . disorder-to-order transition ) , and to pre-molten or molten globules having well-developed secondary structures [29] . We thus also investigated here how eventually a distinction between these different disordered states can be made using the SEG-HCA predictions .
So far , analysis of HCA plots was manual and thus limited to small sets of protein sequences . The SEG-HCA procedure now allows the automation of one aspect of the HCA plot analysis by delineating , from the consideration of a single protein sequence , the positions of segments having a high density in hydrophobic clusters ( H2CD segments , Fig . 2 ) . The methodology is fully described in the Material and Methods section . Briefly ( Fig . 2 ) , SEG-HCA first defines hydrophobic clusters into a protein sequence , using the current HCA rules , and calculates the percentage of positions included in these hydrophobic clusters ( HCP , after Hydrophobic Cluster Positions , shaded green and blue on Fig . 2B ) , within a sliding window of 17 amino acids ( 1-amino acid increment ) . Note that this is not equivalent to a simple calculation of mean hydrophobicity , because non-hydrophobic amino acids within clusters ( blue on Fig . 2B ) are also taken into account . Hence , the HCP percentage approximates , from the consideration of a single sequence , the density in regular secondary structures . Then , a HCP threshold value of 10% is chosen to define potential hinge regions between segments having a high density in hydrophobic clusters ( H2CD ) , whose positions are then refined through the consideration of a sequence based-hydrophobic cluster distance tree ( see Material and Methods for the details ) . SEG-HCA now gives access to the analysis of whole proteomes . We collected here the H2CD segments ( as predicted by SEG-HCA ) for the entire , archetypal proteomes of Homo sapiens , Saccharomyces cerevisiae , Plasmodium falciparum , Escherichia coli and Archeoglobus fulgidus . The number of predicted H2CD , as well as the total number of amino acids predicted in H2CD , are given in Table 1 . The proportion of amino acids in H2CD is higher in Bacteria and Archaea than in Eukarya . In the following text , results will be illustrated for the human proteome , except in some special cases where a different behavior is observed for specific species . The prediction of structured regions might be considered as the simple converse of disorder predictions . We thus compared the SEG-HCA predictions to the disorder predictions performed by IUPRED [30] , [31] . IUPRED is an available well-recognized method , which considers interaction energies for predicting stretches of amino acids that should not contribute to stable structures . According to the D2P2 database [32] , IUPRED provides among the lowest estimation of global percentage disorder . We achieved this comparison according two different , but parallel routes . First , we compared the total number of amino acids predicted in H2CD segments to those predicted as disordered by IUPRED ( IUPREDdis ) . We used for this prediction the long ( L ) variant of IUPRED , which has been trained on long forms of disorder . These IUPRED-L predictions are in agreement with those reported in the D2P2 database [32] . According to the current view [32] , [33] , Eukarya have a higher content in disorder than the two species chosen here for illustrating Bacteria and Archaea ( Table 2 ) . However , Bacteria and Archaea show wide disorder distribution , with very low level of predicted disorder for some species , such here observed with the A . fulgidus proteome . We show ( Fig . 3A and Table 2 ) that to a large extent , there is a clear relationship between the two predictions , which are opposite . However , SEG-HCA H2CD predictions are not the simple converse of disorder predictions , as the overlap between the two sets constitutes 13 . 8% of the total number of amino acids in the human proteome ( Fig . 3A ) . Similar overlap percentages are observed for other eukarya ( 10 . 9% ( S . cerevisiae ) , 13 . 8% ( P . falciparum ) , Table 2 ) . For E . coli and especially for A . fuldigus , the overlaps are much lower ( 3% and 0 . 9% , respectively ) , but represent similar ratios of the total number of amino acids predicted as disordered by IUPRED . We made several additional statistics , especially in order to clarify the structural meaning of these three distinct datasets ( H2CD , IUPREDdis and H2CD ∩ IUPREDdis ) . We first evaluated the overall match of protein sequences included in the three datasets with PDB information . Most of the regions ( 96 . 1% ) covered by PDB ( 14 . 4% of the total number of amino acids ) are included in the H2CD sets ( H2CD and H2CD ∩ IUPREDdis ) , indicating that most of the 3D structures included in PDB well cover H2CD predictions ( Fig . 3B and Table 2 ) . However , PDB files may include some disordered regions . Then , in order to select only regions with defined 3D structures , we filtered the PDB assignments for disorder using MobiDB , a recent comprehensive centralized database on different flavors of disorder [34] , including the well-known DisProt database [35] . We also considered classes A to F of the SCOP database [36] , covering globular as well as transmembrane domains . Although these procedures are likely not sufficient to completely remove all disordered regions , the results clearly show that amino acids from PDB-filtered files and SCOP A to F classes are well covered by amino acids in H2CD ( 97 . 8% and 97 . 5% , respectively , Fig . 3B ) . This is confirmed on the different proteomes analyzed here ( Table 2 ) . We further examined examples of PDB sequences included into each set . Pure H2CD are found associated with 3D structures of globular or membrane domains , whereas pure IUPREDdis mainly correspond to small segments without regular secondary structures ( e . g . linker between two domains , pdb 3qp5 ( A ) ( aa 312–321 ) , or unstructured interacting peptide , pdb 1e4g ( P ) ( aa881–895 ) ) . In contrast , many examples were found In the H2CD ∩ IUPREDdis set , where the binding of a partner is coupled to folding ( e . g . the alpha helix of the p53 TAD , which folds upon Tfb1 binding ( pdb 2gs0 ( B ) [37] , Fig . 4D ) , the CREB KID domain interacting with the KIX domain of CBP ( pdb 1kdx ( B ) [38] ) and the BH3 domain from PUMA interacting with Mcl-1 ( pdb 2roc ( B ) [39] , Fig . 4F ) ) . The two complementary interaction domains of mouse CBP and human ACTR , which undergo synergistic folding , were also detected in the H2CD ∩ IUPREDdis set ( pdb: 1kbh [40] ) . Some rare examples were also found of small stable globular domains , as illustrated in Fig . 4A . Worth noting is that small sequence segments of the same ET domain structural family [41] , also falling in this H2CD ∩ IUPREDdis category , behave either as stable domains ( BRD4 ET domain , pdb 2jns ( A ) [42] ) or as IDP undergoing coupled folding and binding ( AF9 ET-like domain 2lm0 ( A ) [43] ) . This indicates that for small domains , the distinction between stable and unstable 3D structures may be tenuous . Of note is that the H2CD ∩ IUPREDdis set principally contains small H2CD ( mean length 28 amino acids , versus 159 amino acids for H2CD not covered by IUPREDdis predictions ( 70% coverage ) , also see below ) . As shown above , known 3D structures in the H2CD ∩ IUPREDdis set include several segments that undergo coupled folding and binding . We thus wondered if the overlap between H2CD and IUPREDdis may actually correspond to regions predominantly predicted by ANCHOR , a predictor of disordered regions that undergo binding transitions during protein-protein interaction , using the same energy estimation than IUPRED [44] , [45] . ANCHOR predictions fall in the three sets , but represent the highest ratio in the H2CD-IUPREDdis set ( 46 . 1% , versus 32 . 6% and 6 . 4% in the IUPREDdis and H2CD sets , respectively , Fig . 3C ) . This strongly supports that the overlap between H2CD and disordered predictions is enriched in segments that fold on binding and that the comparison between H2CD and IUPREDdis may allow the definition of distinct categories within disorder . Second , we also estimated the coverage of order predicted by different approaches: a ) H2CD prediction , b ) IUPRED prediction of order ( ordIUPRED ) , c ) converse of IUPRED prediction of disorder ( convdisIUPRED ) and d ) ANCHOR prediction . H2CD predictions well cover the ordIUPRED and convdisIUPRED predictions ( 95% and 95% coverage , respectively ) . However , the ordIUPRED and convdisIUPRED predictions cover only partially the H2CD predictions ( 59% and 63% coverage , respectively ) , highlighting a larger coverage of order by SEG-HCA predictions , consistently with results presented in Fig . 3A ( H2CD-IUPREDdis overlap ) . 80% of the amino acids highlighted by ANCHOR are covered by H2CD , indicating that ANCHOR predictions include a large proportion of segments with high density in hydrophobic clusters . ANCHOR predictions not covered by H2CD are generally small ( mean length 13 amino acids ) and have few hydrophobic amino acids ( mean 18% ) . We also calculated the proportion of small H2CD ( length ≤50 amino acids ) which are covered by ANCHOR predictions and observed that these cover only 60% of H2CD . On average , small H2CD not covered by ANCHOR predictions are 19 amino acids long and possess 29% hydrophobic residues . A propensity for folding might thus be predicted for H2CD with a strong IUPREDdis signal , as it is the case for the four examples of coupled folding and binding mentioned above or that shown in Fig . 4E . Using this rule , small stable 3D structures , such as the UBA domain of Rad23 ( Fig . 4A ) may also be picked out . In contrast , regions without any H2CD signal might be then classified as “non foldable” segments . This represents on average 23% of the protein residues in the human proteome , in agreement with previous estimations ( 21 . 6% in Ward 2004 ) . Similar trends were observed for S . cerevisiae and P . falciparum , whereas in E . coli and A . fulgidus , this percentage is lower ( 10% and 6% , respectively ) ( Table 1 ) . We collected for each protein the CDD assignments ( as found using RPS-BLAST [24] ) and discarded multi-domains , as these are already counted with domains . 69348 , 7251 , 5161 , 5497 and 2679 CD were identified in the proteomes of H . sapiens , S . cerevisiae , P . falciparum E . coli and A . fulgidus , respectively ( Table 1 ) . The number of predicted H2CD is approximately twice higher than the number of CD: 137665 and 18555 for H . sapiens and S . cerevisiae , respectively , whereas it is nearly similar ( 6745 and 3075 ) for E . coli and A . fulgidus , respectively ( Table 1 ) . Interestingly , the number of H2CD ( 22939 ) is more than 4 four times higher than the number of CD ( 5161 ) for the P . falciparum proteome . Most ( 97% ) of the CD amino acids ( 40% of the total number of amino acids ) are included in the H2CD set ( human proteome H2CD and H2CD ∩ IUPREDdis , Fig . 3D and Table 2 ) . As regards to the highlighted relationship between H2CD and foldable regions ( see above ) , this indicates that CD mainly include foldable domains . The similarity in hydrophobic cluster composition between the CD and H2CD databases , as well as with the SCOP database ( classes A to F ) further supports their relationship ( Fig . S1 ) . We wondered whether CD , as assigned from CDD , are well detected and covered by H2CD , as predicted by SEG-HCA ( Table 1 , Fig . 5 ) . First , we calculated the percentages of CD positions , which are predicted as H2CD ( Fig . 5A and Table 1 ) . 75% of the CD have up to 95% of their length covered by H2CD in all the proteomes , except for P . falciparum and A . fulgidus , for which the coverage is even higher ( 82 and 86% , respectively ) . These high percentages thus demonstrated that the vast majority of CD are well identified by SEG-HCA , as already observed in Fig . 3D . Only a few number of CD ( 435 ( 1 , 2% ) in the H . sapiens proteome ( star in Fig . 5A and Fig . 3D ) , and between 0 and 9 in other ones ( Table 1 ) ) are not covered by H2CD . These are logically less hydrophobic , with in average 28% of hydrophobic amino acids . Metal ions or disulfide bridges often stabilize some of these domains . Hence , 194 of the H . sapiens CD that are not covered by H2CD ( 45% ) correspond to zinc fingers , as deduced from the CDD annotations . We looked more precisely at positions of CD that are not covered by H2CD , either in upstream , downstream or in the middle of CD ( Fig . 5B ) , and observed high peaks for a value of 0 , meaning that the limits of CD are well covered by H2CD . The extent of non-coverage appears more pronounced for internal segments of limited length , probably highlighting large loops and/or poorly hydrophobic segments within domains , which are not predicted by SEG-HCA . CD , which don't match any H2CD or match multiple H2CD , can also be highlighted on Fig . 5E and Fig . S2 ( white bars ) . We also looked at the converse information , i . e . the extent to which H2CD are covered by CD . We calculated the percentages of H2CD positions , which are assigned as CD ( Fig . 5C ) . Only 22 . 4% of the H2CD have up to 95% of their length covered by CD , indicating that CD only partially cover H2CD , as already observed in Fig . 3D . A large number of H2CD have no CD assignment , meaning that they are orphans . They constitute 34% of the H2CD in the human proteome , and up to 72 . 8% in the P . falciparum proteome ( 72 . 8% , Table 2 ) . These percentages slightly decreased when considering only large H2CD ( >50 amino acids , 19 . 8% of the total number of H2CD , white circle in Fig . 5C and Fig . 5E ( black bar at 0 ) ; Table 1 ) . These large H2CD have 31% hydrophobic amino acids and an average length of 139 amino acids . These features are very close to those of CD domains ( 31% hydrophobic amino acids and 111 amino acids long ) . Only a weak decrease is observed when considering multi-domains , for the definition of CD domains from CDD . The percentage of CD-orphan H2CD range between 8% ( E . coli ) and 22% ( S . cerevisiae ) , with the outstanding exception of P . falciparum , for which this percentage rises to 54% ( Table 1 ) . For this proteome , we further investigated whether amino acid compositions are different within and outside H2CD . As shown in Fig . S3 , no clear difference can be highlighted between the P . falciparum and S . cerevisiae sequences , indicating that the codon usage bias and associated biased amino acid composition affect foldable segments in a similar way than the rest of the protein sequences . Consequently , this biased amino acid composition may explain the resistance of Plasmodium H2CD sequences to the detection by CD profiles . As before , we determined the positions of H2CD , which do not correspond to CD assignments ( Fig . 5D ) , and also observed main peaks for a value of 0 , meaning that the limits of H2CD are well covered by CD . Moreover , a higher peak is observed for no mismatch in intermediate positions , likely indicating the preference of 1 CD for 1 H2CD , as also observed in Fig . 5E and Fig . S2 ( black bars ) . However , mismatch occurrences decrease more slowly with the length of the mismatch , likely revealing the importance of partial CD orphans in H2CD . From Fig . 5E and Fig . S2 , it is obvious that most of CD are covered by a single H2CD and vice versa . A small proportion of CD are covered by two distinct H2CD and vice-versa . This can be explained by the fact that SEG-HCA considers either two distinct segments , when large loops or regular secondary structures with few strong hydrophobic amino acids are present within CD ( Fig . S4A ) , or a unique domain when too short or too hydrophobic segments separate two distinct CD ( Fig . S4B ) . Examination of particular cases of non-detected linkers suggests that some improvement of the SEG-HCA prediction tool might be yet expected , by considering for the definition of hydrophobic clusters within potential linkers , alanine residues , which have the highest preference for αlpha-helices . We calculated the distributions of CD and H2CD lengths ( Fig . 6 and Fig . S5 ) . Above 50 amino acids , the distributions are quite similar . However , SEG-HCA predicts a lot of small H2CD ( length ≤50 amino acids ) , which are not observed in the CD distribution . Indeed , 64804 ( 47% ) , 7823 ( 42% ) and 7591 ( 33% ) of the H2CD from the H . sapiens , S . cerevisiae and P . falciparum proteomes , respectively , have less than 50 amino acids . By comparison , only 27% , 9% and 31% of CD are small-sized . Our results thus also showed that , in three distinct eukaryotic proteomes , it exists a considerable number of short segments , rich in hydrophobic clusters . An in-depth examination of PDB entries matching these segments revealed that they constitute small stable domains ( e . g . UBA and Zinc-finger domains ( Fig . 4A and 4B ) ) , linear interaction motif likely embedded in a small stable domain ( Fig . 4C ) , segments that fold upon contact with partners ( Fig . 4D to 4F ) or that they are included in larger domains , surrounded by large loops ( Fig . 4F ) , in C-terminal tails ( Fig . 4G ) and in structured linkers ( Fig . 4H ) . A distinction of segments that may undergo coupled folding and binding could be made by considering overlaps with disorder predictions ( see before ) . The large proportion of small H2CD seems to be specific for eukaryotic genomes . Indeed , in the E . coli genome these small H2CD represent 19% of all H2CD and this number decreases to 11% in the A . fulgidus genome ( Table 1 and Fig . S5 ) . Interestingly , SEG-HCA thus provides a way to access to this information , which is generally missed by standard methods of profile construction ( due to their limited size and/or high sequence divergence ) , as those contributing to CDD . The percentages of amino acids included in H2CD or CD segments are clearly different ( Table 1 , Fig . 7A and Fig . S6 ) . On average , 44% of the amino acids of a human protein are included in CD segments , against 77% in H2CD segments . These amino acid coverage values are similar to those found by Ekman and colleagues [2] , when considering only the Pfam-A/SCOP assignments or the whole set of predicted domains . The CD amino acid coverage values vary from 29% ( Plasmodium ) to 74% ( E . coli ) , whereas elevated H2CD amino acid coverage values are observed in any cases ( 86% ( Plasmodium ) and 90% ( E . coli ) ) . The distributions of CD and H2CD segments within proteins ( protein coverage ) are also different . Indeed , whereas 17% of the human proteins do not have any CD , only are found 2 . 6% with no H2CD longer than 50 amino acids ( Table 1 and Fig . 7A ) . This indicates that the number of wholly disordered proteins is very low and that 14% of human proteins contain large foldable domains , which remain uncharacterized ( CD-orphan domains ) . The number of orphan proteins is lower than that reported by Ekman and colleagues , which described that 93% of the eukaryotic proteins could be assigned with a ( known or unknown ) domain . More generally , the very low percentage of H2CD orphan proteins contrast with previous estimations of fully disordered proteins [46] , which are much higher , but these ones include all types of disorder , including regions that are predicted as foldable , and not only natively unfolded segments . One can also consider the coverage of proteins by only one domain or multiple domains , being advised that one H2CD may cover multiple domains , as discussed from Fig . 5E . Hence , we estimated that the human proteome has 45% and 52% single- and multi-H2CD domain proteins , respectively , whereas bacteria/archaea proteomes have 71%/84% and 26%/14% single- and multi-H2CD domain proteins , respectively ( Table 1 , Fig . 7A and Fig . S6 ) . This is consistent with the study of Ekman and colleagues [2] , which however tipped the balance in favor of multi-domains proteins ( 65% versus 35% of mono-domain proteins in the human proteome ) . This last study however fixed the cut-off at 100 residues , and thus did not consider small domains , which are well documented here . The proportions of proteins with multiple , unique or no H2CD are similar in Plasmodium falciparum ( 56% , 42% and 1% ) , although the percentages of proteins with multiple and no CD are different ( 20% and 38% versus 40% and 17% in human proteins ) ( Table 1 and Fig . S6 ) . This suggests that the higher number of CD orphan sequences in Plasmodium sequences may be distantly related to domains existing in other species .
The study presented here provides access to a whole repertoire of foldable H2CD segments in proteomes from the three kingdoms ( the eukaryotic genomes of H . sapiens , S . cerevisiae , and P . falciparum , as well as those of the eubacteria E . coli and of the archaea A . fulgidus ) . By a mirror effect , this also gives new insight into disordered regions located outside foldable segments , which are devoid of fold-promoting hydrophobic clusters . Consistently with numerous reports [47] , [48] , the content in disorder , or precisely non-foldable segments if we consider the converse of SEG-HCA predictions , increases with evolutionary complexity . The major point that our study now highlights is that in eukaryotes , there are many small H2CD segments , whose limited length makes them generally difficult to characterize . This information is in particular absent from or poorly represented in domain databases . Some of these small H2CD segments correspond to isolated stable domains ( as exemplified in Fig . 4A and 4B ) . However , as observed from several case studies ( Fig . 4D to 4F ) , small H2CD segments may also be intrinsically disordered and undergo coupled folding and binding . These segments are generally predicted as IUPs or IDPs by current disorder predictors , and the overlap between disorder and H2CD predictions may thus provide a new interesting way to highlight disorder-to-order transitions , which play key roles for molecular recognition . These segments , which are characterized by structural plasticity and make weak and transient binding , play important roles in regulatory and signaling process [29] . Tools , such as ANCHOR [45] and MorFPred [49] , have been developed for detecting such segments , but are based on different principles . MoRFPred is based on a machine learning classifier , based on a comprehensive datasets of MoRFs ( Molecular Recognition Features , [49] ) , whereas ANCHOR relies on pairwise energy estimation , which is also the basis of the disorder predictor IUPred [45] . ANCHOR segments are likely to gain stabilizing energy by interacting with a globular partner . As shown here , ANCHOR predictions are especially found in the overlap between IUPRED ( a current disorder predictor ) and SEG-HCA predictions . SEG-HCA appears thus well adapted to detect such foldable segments , which have been also named protean segments ( ProS , [50] ) , as it highlights specific features of their interface , enriched in hydrophobic amino acids [45] , [51]–[53] . The binding of some foldable segments , such as a beta-strand of MCAF1 ( Fig . 4E ) and an alpha-helix of BH3 ( Fig . 4F ) , is similar to the folding process and the interface between protein and ligand , richer in hydrophobic residues than the surrounding surface , is similar to the hydrophobic core . In both cases , hydrophobic amino acids participate in the binding interfaces and the hydrophobic cluster has a shape typical of the formed secondary structures . In other situations , such as those presented in Fig . 4C and 4D , the foldable segments are included in small globular-like regions . Hydrophobic amino acids are here likely to participate in both the binding interface and the hydrophobic core of the small globular-like domain in which the peptide is embedded , and the shape of the corresponding hydrophobic clusters deviates from the observed secondary structures [54] . A “folding propensity” may thus be deduced from the consideration of small H2CD , especially included in the H2CD ∩ IUPRED set , provided that these can be distinguished from artifacts ( partial domains , as illustrated in Fig . 4G ) . However , such cases could be solved using evolutionary information . The residues undergoing coupled folding and binding and participating in the interaction with the partner can usually be mapped in a single continuous segment , and hence , have been connected through common examples to linear motifs [55] . These are collected in the ELM database [56] , which captures sequence features shared by common interacting partners . An example is shown here with a peptide of the Apollo ( SNM1B ) protein ( Fig . 4C ) , a member of the beta-CASP family [57] , which form small alpha helices upon binding to the telomere repeat binding factor TRF2 [58] , [59] . This peptide is included in a small , 32 amino acid long H2CD . The simplicity of linear motifs offers a good tool for identifying possible partners but generally results in a large amount of false positives . The complementary nature of the two concepts has been recently explored through the comparison of the generic ELM ligand binding motifs ( LIG ) and ANCHOR predictions [60] , indicating that ANCHOR can be used as a structural filter to improve the predictive power of linear motifs . A similar effect or an alternative analysis can be expected from the consideration of the SEG-HCA predictions , which also give information about the structural context of linear motifs . A first calculation made on the ELM LIG motifs showed a mean coverage by H2CD predictions of 67% ( with more than a half being predicted as disordered by IUPRED , data not shown ) . SEG-HCA is however limited to linear motifs including hydrophobic amino acids and thus does not address hydrophilic linear motifs . Another major observation of our work is that in eukaryotic proteomes , the number of H2CD is approximately higher than the number of domains assigned from the conserved domain database , revealing a lot of CD-orphan domains , which are otherwise not considered by other predictive methods based on homology searches . Orphans domains have either evolved too far from the nearest neighbors to be assigned to a domain or they have been created by some de novo mechanisms . Studies have however indicated that most of the solved structures of orphan proteins show structural similarity to already known proteins domains , suggesting that the fraction of orphan domains that have distant homologs is high [61] . A preliminary study on a small set of human CD-orphan H2CD segments reveals that approximately one fifth of them can be assigned by direct inference ( from the PSI-BLAST significant results ) to already known families of domains ( Faure and Callebaut , unpublished data ) . This study has been performed using the TREMOLO-HCA tool , which combines sequence similarity searches with information on domain architecture and amino acids likely participating in the hydrophobic core [41] . This first emphasizes the sequence divergence of some domain families and the necessity to improve the specificity of associated CD profiles . This also highlights the large amount of putative domains without any known characterized function . These orphan domains are particularly abundant in proteomes from genomes with extreme compositional bias , such as that of the apicomplexan P . falciparum ( 12459 CD-orphan H2CD ( 81% ) , Table 1 ) . Previous analyses have already shown the interest of HCA for revealing functional features of such segments [62] , which can be now analyzed in a systematic and comprehensive manner . A recent study [63] , [64] has also addressed the problem of regions with no structural domain ( SD ) assignment ( named cryptic domains ) , through an approach , called DICHOT , for determining structured domains and intrinsically disordered ( ID ) regions in proteomes . This approach uses sequence conservation in order to distinguish between cryptic structured domains , with no known 3D structures , and disorder . This is fundamentally different from the SEG-HCA approach , which does not use at all sequence conservation for the definition of H2CD segments . Consideration of sequence conservation does not take into account that i ) CD domains can have diverged so far that the sequence similarity between family members can be difficult to detect , ii ) the sequence of some IDs may share significant similarities with other sequences ( this is particularly true for ID sequences undergoing coupled folding and binding ) . As a consequence , the two approaches are difficult to compare and led to different results , especially for the estimation of the frequency of disordered amino acids , which is much higher in the DICHOT approach ( 35% , for the human genome ) . The comprehensive repertoire described here thus opens new perspectives for the genome-wise characterization of structured domains or potentially foldable regions , as well as for the identification of new domains or motifs , which may play critical functional roles .
SEG-HCA ( after SEGmentation through HCA ) first identifies the strong hydrophobic amino acids of the sequence , considering for their definition the HCA alphabet ( V , I , L , M , F , Y , W ) and including cysteine ( C ) ( green in Fig . 2A and B ) . This alphabet has proven to be optimal , providing the best correspondence between hydrophobic clusters and regular secondary structures [17] . Then , the definition of HCA hydrophobic clusters relies on the consideration of a minimal distance between two hydrophobic amino acids , which is necessary to assign them to separate clusters . This minimal distance , called connectivity distance , is 4 sequential amino acids when the alpha-helix is used as a 2D support for the 2D HCA transposition of the sequence and allows to delineate cluster breakers . These breakers are thus composed of at least four consecutive non-hydrophobic amino acids or a proline ( red in Fig . 2A and B ) . The groups of amino acids between the breakers define hydrophobic clusters , which contain hydrophobic residues ( green in Fig . 2A and B ) , but also may include non-hydrophobic residues ( blue-green in Fig . 2A and B ) , provided the connectivity distance between hydrophobic residues is not reached . As small hydrophobic clusters , containing only one or two hydrophobic amino acids , are not frequently associated with regular secondary structures , these were not considered in our counting if there is no other hydrophobic cluster within their first close neighborhood ( 7 amino acids ) ( star in Fig . 2A and 2B ) . An artificial , simplified binary sequence is then built , where the amino acids composing hydrophobic clusters are represented by 1 and those composing the breakers ( which essentially contain non-hydrophobic amino acids ) are represented by 0 . From the binary sequence , the percentage of positions included in hydrophobic clusters ( HCP , after Hydrophobic Cluster Positions ) is computed using an overlapping window of length 17 and assigned to the central position of this window ( hexagon in Fig . 2B ) . This window size was chosen as it corresponds to the segment length encompassing the close neighbors of a central residue on the 2D HCA plot ( two rings encircling one central amino acid ) . This value is similar to the window of 15 amino acids currently used by secondary structure predictors [65] . For the N- and C-terminal eight residues , values were set to those assigned to the ninth and n-8th positions of the sequence , respectively ( n being the total length of the considered sequence ) . The HCP percentages can be plotted , giving a view of the hydrophobic cluster profile of the protein sequence ( Fig . 2B ) . High and low values are associated with high and low densities of hydrophobic clusters , respectively . SEG-HCA next identifies areas of high hydrophobic cluster density ( H2CD ) , typical of structured or folded regions ( Fig . 2B ) . To that aim , HCP minimal values are identified for a threshold level of 10% , defining thereby potential hinge regions separating these areas of high hydrophobic cluster density ( first rough limits , labeled 1 in Fig . 2B ) . This level is considered as the minimal value , for which and below which a linker is predicted to separate two distinct H2CD . This value was first fixed according to numerous case studies . Optimization of this threshold was considered using a non-redundant ( 40% sequence identity ) SCOP database , downloaded from ASTRAL ( http://scop . berkeley . edu/ ) . SEG-HCA was used at different HCP threshold and for each 3D structure , we looked at the values that yield only one H2CD . The distribution of the HCP threshold values was then analyzed , showing an optimum at 22% , thus slightly above 10% . On average , 1 . 1 H2CD are observed , with 78% coverage . However , it should be noted that the optimization is made on well-stable , already large globular domains . The used threshold is intended to detect such canonical domains , but also small segments , which may be structured and are not ( or poorly ) represented in the SCOP structural database . Therefore , as supported by several case studies , using the lower threshold of 10% allows the detection of these small segments , in addition to larger ones . At this level , 1 . 04 H2CD are observed , with 86% coverage ( thus values close to those observed for the optimized threshold ) . Increasing this threshold value may allow the split of multi-domains into domains , but lead to loose small segments , as these last ones generally have low HCP values . Then , SEG-HCA builds a tree , starting from the observed distances between hydrophobic clusters ( leafs ) . The closest hydrophobic clusters are grouped , constituting nodes , the root ( labeled 2 in Fig . 2B ) , gathering the whole set of hydrophobic clusters contained within the analyzed sequence . SEG-HCA then compares each of the regions identified as the first rough limits ( labeled 1 in Fig . 2B ) to those defined by the different nodes of the tree . The best overlap between the two is chosen to define the refined limits of the H2CD segment ( labeled 3 in Fig . 2B ) . SEG-HCA is fully implemented in python v2 . 7 . Scripts are available in Supporting Information ( Software S1 ) . Proteome sequences were downloaded from the National Center for Biological Information ( NCBI ) ( ftp://ftp . ncbi . nlm . nih . gov/genomes/ ) : Hsapiens , Scerevisiae_uid128 , Ecoli_042_uid161985 , Archeoglobus_fulgidus_DSM_4304_uid57717 and Pfalciparum . Each sequence from each proteome was searched for similarities either with the Protein Data Bank ( PDB ) , using the BLASTPGP program version 2 . 26 . Only one iteration was done from each entire protein sequence . Hits sharing more than 95% of sequence identity were selected . The disorder was then filtered by using Mobi-DB ( [34] , http://mobidb . bio . unipd . it/ ) , which extends the experimental disorder observations found Disprot database to the whole PDB , or by only considering the classes a , b , c , d , e , f from the SCOP structural databases ( [36] , http://scop . mrc-lmb . cam . ac . uk/scop/ ) . These classes correspond to all alpha protein , all beta protein , all alpha and beta protein ( mainly parallel beta sheet ) , all alpha and beta protein ( mainly anti-parallel beta sheet ) , multi-domain proteins , membrane and cell surface proteins and peptides , respectively . We obtained information on Conserved Domain ( s ) ( CD ) for each protein sequence through the NCBI server ( http://www . ncbi . nlm . nih . gov/Structure/cdd/cdd . shtml ) , which has pre-computed domain architectures . These pre-computed architectures were fixed using the RPBLASTprogram ( blast tools version 2 . 26 ) with the Conserved Domain Database ( version 3 . 1 ) [24] . IUPRED was used as a disorder predictor ( [30] , [31] , http://iupred . enzim . hu/ ) . We used the “long” mode ( IUPRED-L ) to predict disordered segments . A probability to be disordered up to 0 . 5 was used to consider a position to be disordered . The “glob” mode ( IUPRED-G ) was used to predict globular domain boundaries from the disordered positions . The “short” mode was not used as it focuses on small disordered regions such as both loops and termini tails . We used ANCHOR to predict disorder to order transitions ( [44] , [45] http://anchor . enzim . hu/ ) . ANCHOR is developed from the IUPRED program , and was used with default parameters . | Spontaneous or induced folding into a specific 3D structure is a key property of proteins to perform their biological functions . Folded 3D structures of proteins perform specific functions , including interactions with other proteins . Intrinsically disordered regions also mediate interaction , gaining structure only when bound to a target protein . In both cases , hydrophobicity generally plays a major role in the protein segment “foldability” . Here , we developed an original procedure to identify foldable segments from only the information of a single amino acid sequence and to explore protein structures at a proteomic scale . Our approach goes beyond the simple consideration of mean hydrophobicity , by including the secondary structure information through the use of a two-dimensional transposition of the sequence . The developed procedure , combined with disorder predictors , may facilitate the specific identification of small segments that undergo coupled folding and binding . Combined with the analysis of specific domain databases , it also highlights orphan foldable segments , which remain yet uncharacterized . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Comprehensive Repertoire of Foldable Regions within Whole Genomes |
A large proportion of functional sequence within mammalian genomes falls outside protein-coding exons and can be transcribed into long RNAs . However , the roles in mammalian biology of long noncoding RNA ( lncRNA ) are not well understood . Few lncRNAs have experimentally determined roles , with some of these being lineage-specific . Determining the extent by which transcription of lncRNA loci is retained or lost across multiple evolutionary lineages is essential if we are to understand their contribution to mammalian biology and to lineage-specific traits . Here , we experimentally investigated the conservation of lncRNA expression among closely related rodent species , allowing the evolution of DNA sequence to be uncoupled from evolution of transcript expression . We generated total RNA ( RNAseq ) and H3K4me3-bound ( ChIPseq ) DNA data , and combined both to construct catalogues of transcripts expressed in the adult liver of Mus musculus domesticus ( C57BL/6J ) , Mus musculus castaneus , and Rattus norvegicus . We estimated the rate of transcriptional turnover of lncRNAs and investigated the effects of their lineage-specific birth or death . LncRNA transcription showed considerably greater gain and loss during rodent evolution , compared with protein-coding genes . Nucleotide substitution rates were found to mirror the in vivo transcriptional conservation of intergenic lncRNAs between rodents: only the sequences of noncoding loci with conserved transcription were constrained . Finally , we found that lineage-specific intergenic lncRNAs appear to be associated with modestly elevated expression of genomically neighbouring protein-coding genes . Our findings show that nearly half of intergenic lncRNA loci have been gained or lost since the last common ancestor of mouse and rat , and they predict that such rapid transcriptional turnover contributes to the evolution of tissue- and lineage-specific gene expression .
The mammalian transcriptome has recently been shown to be surprisingly diverse in its extent and encoded functions [1]–[3] , much of which are noncoding RNAs ( ncRNAs ) as they are not translated into proteins . The ability to sequence the entire transcriptome in an unbiased manner has not only allowed more complete characterization of well described and highly abundant noncoding RNAs with known function , such as transfer RNAs , small nuclear RNAs , small nucleolar RNAs and ribosomal RNAs , but have also revealed additional ncRNA species . For example , a number of long ncRNAs ( lncRNAs ) larger than 200 nucleotides ( nt ) have been discovered [2] , [4] , [5] . Many lncRNA loci are intergenic , when transcription occurs wholly within the genomic intervals between two adjacent protein-coding genes [6] . Some lncRNAs can be transcribed divergently from a neighbouring protein-coding transcript using identical or almost identical transcriptional initiation complexes [6] . In addition , lncRNAs overlapping with protein-coding genes can be transcribed from either strand [6]–[8] . Although the precise roles of many lncRNAs remain unknown , in general they are thought to act in transcriptional regulation [6] , [9] , [10] . LncRNAs can regulate gene expression programs through a variety of mechanisms , including interactions with chromatin remodelling complexes or transcription factors [11] . Consistent with a cis-regulatory role , co-expression of intergenic lncRNA loci with their neighbouring protein-coding genes has been observed [12] , [13] and a number of intergenic lncRNAs have demonstrated roles in regulating the expression of genes in their genomic vicinity [9] . Some intergenic lncRNAs appear to regulate the expression of both neighbouring and distal genes [14] , [15] . Indeed , many intergenic lncRNAs have been experimentally demonstrated to have roles in regulating transcription of distally located targets , in trans [16] . Nevertheless , the exact proportion and the distinguishing features of cis- and trans-acting intergenic lncRNAs remain unknown . If lncRNAs' functional roles are conserved it is expected that their loci should be evolutionarily preserved . Indeed , the transcripts and promoters of mammalian intergenic lncRNAs exhibit signatures of selective constraint: their promoters are highly conserved across vertebrates [2] and they have accumulated fewer substitutions than neighbouring putative neutral sequence [17] , [18] . However little is yet known of the evolutionary persistence of lncRNA transcription . Generally the loss and gain of functional noncoding sequence can occur rapidly , with approximately half of all functional ancestral nucleotides predicted to have been gained or lost in mouse or rat since their common ancestor [19] . Other noncoding RNAs , in particular tRNAs , have been shown to exhibit rapid turnover of their transcribed loci , despite conservation of their function [20] . Turnover of regulatory elements underlies species-specific transcriptional evolution and may be associated with phenotypic changes [21] . Only a small minority of intergenic lncRNAs in mouse or human were found to have transcribed orthologous sequences in the other species [22] , [23] . This might reflect turnover of transcribed loci , or it might imply that intergenic lncRNAs , which are often lowly expressed and tissue specific [6] , [9] , [18] , [23] , have transcribed orthologous sequences that remain undetected . Indeed , analysis of the transcription of three intergenic lncRNA loci across homologous regions of the mammalian and avian brain revealed that some intergenic lncRNAs can have conserved expression patterns [24] . To resolve the extent of lncRNA transcriptional turnover it is important to undertake a careful comparison of lncRNA transcription in homogeneous and homologous tissues . Achieving this in closely related species also allows the distinction of transcriptional turnover from DNA sequence turnover and furthermore might reveal otherwise unexpected mechanisms of regulatory divergence . Here we experimentally and computationally explored the genetic structure and function of lncRNA loci in matched tissues from three closely related rodent species , Mus musculus domesticus ( C57BL/6J ) , Mus musculus castaneus and Rattus norvegicus .
We identified transcripts expressed in the liver of three young adult male Mus musculus domesticus ( inbred strain C57BL/6J termed hereafter Mmus ) individuals by directional , stranded ribosomal RNA ( rRNA ) -depleted transcriptome sequencing ( total RNAseq ) ( Figure 1A ) ( see Materials and Methods ) . Data from three independent biological replicates were pooled . About 80% of sequencing reads were mapped [25] to the reference Mmus ( mm9 ) genome and liver gene expression was detectable for 61% of all UTRs and coding exons annotated in the mouse genome ( coverage: 66% ) . We found that a substantial fraction of sequencing reads map to unannotated , likely noncoding , loci consistent with previous results [26] . Using our total transcriptome sequencing data we assembled de novo 56917 transcripts [27] expressed in the Mmus liver ( Figure 1A ) . As a consequence of the short-read single end nature of our data , our transcripts can be fragmented due to incomplete coverage of the full-length cDNA . To identify independent transcripts , we performed genome-wide chromatin immunoprecipitation followed by sequencing ( ChIPseq ) against trimethylation of lysine 4 of histone H3 ( H3K4me3 ) , which marks the beginning of actively transcribed genes [28] and identified enriched regions [29] ( Figure 1A ) ( see Materials and Methods ) . We intersected the genomic locations of 18303 H3K4me3 enriched regions with the predicted 5′ end of our RNAseq-defined Mmus transcripts longer than 200 bases in length , thereby predicting 8915 distinct transcription start sites ( TSSs ) ( Figure 1A ) . As found in previous studies , we identified a limited number of protein-coding genes that exhibited evidence of bidirectional transcription at their TSS ( Figure S1 , Table S10 ) [30] . Most of these transcribed regions are likely noncoding and are not further addressed in our study except when supported by a de novo assembled noncoding transcript [31] . Similarly , we identified transcripts that were either intergenic ( n = 388 ) or intragenic ( n = 8527 ) based on their overlap with Mmus protein-coding gene annotations ( Figure 1A ) ( see Materials and Methods ) . Intergenic transcripts lacking protein-coding potential [32] were annotated as long intergenic ncRNAs ( intergenic lncRNAs ) ( n = 316 , Table S3 ) . Next we defined transcribed loci as clusters of one or more transcripts with overlapping exonic or intronic nucleotides . From 293 of these loci only intergenic lncRNA transcripts were expressed ( Table S3 and S4 ) . The vast majority ( n = 233 ) of these intergenic lncRNA loci have no overlap with intergenic lncRNAs annotated in the mouse genome by Ensembl ( build 64 ) , demonstrating that current mouse intergenic lncRNA catalogues are largely incomplete [18] . Mmus liver intergenic lncRNAs transcripts were significantly ( two-tailed Mann-Whitney test , typically p<1×10−4 ) found to be: ( i ) more lowly expressed , ( ii ) shorter and ( iii ) to have fewer exons than their protein-coding transcript counterparts ( Table S2 ) consistent with previous reports [23] , [33] . The second group of 7289 intragenic loci comprises 8527 transcripts overlapping protein-coding genes ( Ensembl build 60 , Table S3 and S4 ) . Forty-nine loci have overlapping antisense RNAs transcribed from the opposite strand and marked by separate H3K4me3 enriched regions indicating independent transcriptional initiation ( Table S9 ) . Examples in this category include the constitutively expressed noncoding RNA Kcnq1ot1 [34] . Most protein-coding genes are expressed in multiple tissues [35] . In contrast , lncRNA expression tends to be spatially and temporally restricted [6] , [18] , [23] , [36] . We validated the expression of 15 randomly selected liver expressed intergenic lncRNA transcripts by quantitative PCR ( RT-qPCR ) in seven Mmus adult tissues ( Figure 1B ) and nine intragenic antisense lncRNA transcripts by strand specific RT-qPCR [8] in four adult tissues ( Figure S2D ) . These tissues were chosen because they show different degrees of cell type complexity and biological functionality . We found that the large majority of the tested intergenic and intragenic antisense lncRNA transcripts are predominately expressed in liver . Large changes in gene expression are observed during tissue development [37] . In order to identify whether the intergenic lncRNAs we identified are developmentally regulated during hepatocyte differentiation , we measured the abundance of representative lncRNAs by RT-qPCR at embryonic stages E10 , E12 , E14 and E18 and adult stage P62 . Our data showed that lncRNAs are also extremely specific to the adult developmental stage of liver . In summary , the intergenic lncRNAs we identify are specifically expressed in nutritionally unstressed adult liver ( Figure 1C ) . Sequence comparison of mouse intergenic lncRNAs and their human and rat orthologous sequence have shown that these transcripts tend to be constrained , an evolutionary hallmark of functionality , albeit at much lower levels than protein-coding genes [17] , [18] . However little is yet known about transcriptional turnover of lncRNA during evolution . To address the transcriptional turnover of lncRNAs , we explored their transcription across three rodents . In addition to Mmus , we studied transcript expression in the adult liver of a closely related mouse Mus musculus castaneus ( CAST/EiJ termed Mcas ) and in the rat ( Rattus norvegicus , termed Rnor ) ( Figure 2 ) . The two mouse subspecies , Mmus and Mcas , diverged about one million years ago ( MYA ) and last shared a common ancestor with Rnor about 13 to 19 MYA [38] ( Figure 2A ) . These differences in species separation across evolutionary time allowed us to take two snapshots of transcriptional turnover during rodent evolution , using the closest wild-derived mouse species ( Mcas ) to Mmus that is commercially available and Rnor as the evolutionary nearest rodent species with a well-annotated genome . Similar to the characterization of transcripts in Mmus liver , we performed RNAseq and H3K4me3 ChIPseq experiments in Mcas and Rnor , and identified 158 and 605 intergenic lncRNAs respectively ( Tables S1 , S5 , S6 , S7 , S8 ) . The observed difference between the numbers of annotated intergenic lncRNA loci across the three rodents ( 293 , 158 and 605 for Mmus , Mcas and Rnor , respectively ) can be either due to experimental bias or underlying biology . To test the contribution of the difference in read number of each species RNAseq library ( Table S1 ) , we reassembled transcripts in Mmus and Rnor after randomly selecting from Mmus and Rnor libraries the same number of reads as Mcas , our smallest library ( Table S1 ) . For each species we repeated this procedure 10 times . By comparing the numbers of intergenic lncRNAs in Mmus or Rnor that overlapped a transcript from these recreated libraries , we found that the differences in numbers of lncRNAs between mice ( Mmus and Mcas ) species are mostly due to the depth of sequencing . After adjusting the read number of the Mmus RNAseq library to the Mcas RNAseq library , we identified a mean of 154 intergenic lncRNA loci ( standard deviation = 3 . 4 ) for Mmus , a similar number to the one assembled in Mcas ( n = 158 ) , suggesting that the difference in the number of lncRNA loci is due to an experimental bias . In contrast , in Rnor , using the same number of sequencing reads the reduction approach afforded a mean of 284 intergenic lncRNA loci ( standard deviation = 5 . 9 ) . This number corresponds to a 80% rise over the 158 Mcas intergenic lncRNA loci and indicates that there is an increase of liver lncRNA loci in the rat lineage . We next considered if during rodent evolution lncRNA loci were conserved in their transcription in a similar manner to protein-coding genes . We defined transcriptional turnover as instances of genomic loci for which syntenic sequence is conserved between two or more species yet transcription of this conserved sequence is not . To determine conservation of transcribed loci , we combined H3K4me3 peaks with RNA sequencing reads overlapping ( by more than 1 bp ) the syntenic regions to create a stringent set of conserved loci ( see Materials and Methods ) . These loci show evidence of both transcriptional initiation and transcript formation . Owing to the availability of its larger number of publicly available genome wide resources , such as spatial and temporal expression patterns [39] , we anchored our analysis on Mmus . To allow differentiation between sequence and transcriptional turnover we only considered Mmus loci that have aligned orthologous sequence in the rat genome [intergenic lncRNA loci n = 268 ( 91 . 5% ) , protein-coding loci n = 6723 ( 92 . 2% ) ] . We then classified mouse loci according to their transcriptional conservation into three classes: those specific to Mmus , if evidence of expression was found only in Mmus; those conserved in Mus genus , when evidence of transcription was found in Mmus and Mcas but not in Rnor; and , those conserved across these rodents , when expression evidence was found in Mmus , Mcas and Rnor ( Figure 2A , Table S4 ) . Our definition does not explicitly take into account conservation of exon-intron structure . Globally , H3K4me3 and RNAseq signals were grouped according to our classification ( Figure 2B–2C ) . In order to confirm that the observed differences were not solely a consequence of biases introduced by sequencing depth , we validated our interspecies comparisons by semi-quantitative RT-PCR in independent biological replicates from adult livers of Mmus , Mcas and Rnor for 24 intergenic lncRNA transcripts from four categories ( rodent conserved , Mus genus conserved , Mmus-specific , and Rnor-specific , Figure S3 ) . These RT-PCR results confirmed that our global approach accurately identifies species- and lineage-specific intergenic lncRNAs . Turnover of transcription is considerably more frequent for intergenic lncRNA loci than for protein-coding genes in the rodent liver ( Figure 2D ) . A significantly smaller fraction of intergenic lncRNA than protein-coding loci exhibit conserved transcription across rodents [intergenic lncRNA loci n = 160 ( 59 . 7% ) , protein-coding loci n = 6169 ( 91 . 7% ) , two-tailed Fisher's exact test , p<10−3] . Conversely , a significantly higher proportion of intergenic lncRNA than protein-coding loci are specific to the Mmus lineage [intergenic lncRNA loci n = 30 ( 11 . 2% ) , protein-coding loci n = 75 ( 1 . 1% ) , two-tailed Fisher's exact test , p<10−3] . The difference in sequencing depth between the three species influenced the number of annotated intergenic lncRNAs . To account for this effect and provide a more conservative estimate of transcriptional conservation we considered the set of intragenic and lncRNA loci that were assembled after adjusting the Mmus and Rnor RNAseq library sizes to that of Mcas ( see Materials and Methods ) . Intragenic and intergenic lncRNA loci were annotated as previously . We considered a Mmus locus to have conserved expression if it had an overlapping H3K4me3 peak and an overlapping transcript ( >1 bp ) . As previously , we found protein-coding gene loci to be more often conserved in rodents ( 1326/2415 , 55% ) than intergenic lncRNA loci ( 31/110 , 28% , two-tailed Fisher's exact test , p<10−3 ) . Next we aimed to gain initial insights into the conservation of exon-intron structures of Mmus intergenic lncRNAs . For mouse intergenic lncRNAs and protein-coding loci whose transcription was conserved in rat ( 160 and 6169 loci , respectively ) we compared the coverage by RNAseq reads of mouse exonic nucleotides in the rat orthologous regions . We found that rodent conserved protein-coding transcripts have a significantly higher coverage ( median 78% ) than intergenic lncRNA ( median 47% , two-tailed Mann-Whitney test , p<2×10−16 , Figure S4 ) . This observation can be a consequence of lower coverage of low abundance transcripts and/or lower conservation of exon-intron structure for intergenic lncRNAs . Similarly , we observed that the transcriptional conservation of noncoding transcripts that overlap protein-coding genes in antisense orientation also showed a rapid decay across rodent evolution . Only 36% of the Mus conserved intragenic antisense transcripts are expressed in Rnor ( Figure S2 ) . These results indicate that the large majority of ncRNAs are conserved in the Mus genus but not in the evolutionarily further distant species Rnor . The apparent low conservation of intragenic antisense transcription is consistent with previous conservation analysis [33] . To investigate transcriptional turnover of intergenic lncRNAs beyond the rodent lineage , we used publicly available polyA+ transcriptome sequencing data for the adult human liver ( Human BodyMap 2 . 0 RNAseq data ) . Rodents and human shared a common ancestor over 90 MYA [40] . We considered in this analysis only Mmus transcripts whose expression was supported by at least one overlapping polyA+ sequencing read [41] . We found that the majority of mouse intergenic lncRNA loci overlap polyA+ reads ( 273/293 loci ) , suggesting that few intergenic lncRNA loci assembled here transcribe only non-polyadenylated transcripts . We discarded 1368 ( 18 . 8% ) protein-coding and 159 ( 58 . 2% ) intergenic lncRNA loci in Mmus that lack an apparent orthologous sequence in the human or rat genome [42] . As observed for the rodent lineage , a significantly smaller fraction of Mmus intergenic lncRNA than protein-coding genes orthologous in humans are expressed in the liver [intergenic lncRNA loci ( n = 76 , 56 . 7% ) , protein-coding loci ( n = 5689 , 96 . 1% ) , two-tailed Fisher's exact test , p<10−3 ) ( Figure S5 ) . Our data indicate that the fraction of liver transcribed mouse intergenic lncRNAs expressed in the orthologous region of the human genome is two-fold higher ( two-tailed Fisher's exact test , p<10−3 ) than prior estimates [22] , which supports the use of homologous tissue types to investigate levels of transcriptional conservation of tissue specific transcripts , such as intergenic lncRNAs . We conclude that rapid turnover of intergenic lncRNAs is not restricted to the rodent lineage , but is widespread among eutherian mammals . Next we examined how sequence constraint reflects transcriptional conservation of intergenic lncRNA and protein-coding loci . For each transcript we considered its most 5′ nucleotide to correspond to the transcriptional start site and defined its promoter as the 400 nucleotides upstream of this site . We compared the mouse-rat nucleotide substitution rate for intergenic lncRNA loci ( dloci ) and promoters ( dpromoter ) , to rates for genomically neighbouring and non-overlapping ancestral repeats [ARs ( dAR ) ] with matched G+C content [18] , [43] . ARs are transposable element-derived sequences that were present in the last common ancestor of human and mouse; most of these sequences have been observed to evolve neutrally and hence provide reliable proxies for local neutral mutation rates [44] . We first confirmed that Mmus liver-expressed intergenic lncRNA loci accumulated mutations at a significantly slower rate than adjacent neutral sequence ( Figure S6A ) ( dloci = 0 . 148 , dAR = 0 . 164 , two-tailed Mann-Whitney test , p<3×10−7 ) . In line with this observation , long sequence segments that have preferentially purged insertions or deletions in Mmus and Rnor lineages were 1 . 6-fold enriched in intergenic lncRNA transcription over expected levels ( permutation test , p<10−3 ) [44] . As previously reported [12] , [18] the sequences of intergenic lncRNA loci evolve more rapidly than those of full-length protein-coding loci ( Figure S6B ) ( dloci/dAR = 0 . 902; protein-coding dloci/dAR = 0 . 857; two-tailed Mann-Whitney test , p<2×10−3 ) . Additionally , the putative core promoters of intergenic lncRNAs accumulated significantly more substitutions than those of protein-coding genes ( Figure S6C ) ( intergenic lncRNA dpromoter/dAR = 0 . 843; protein-coding dpromoter/dAR = 0 . 746 , two-tailed Mann-Whitney test , p<2×10−5 ) . The discrepancy between this result and published findings [2] is likely due to the incompleteness of lncRNA transcripts' 5′ ends and thus to incomplete delineation of lncRNA promoter sequences . To determine whether loss of transcription is associated with loss of sequence constraint , we compared Mmus to Rnor nucleotide substitution rates between two groups of intergenic lncRNAs: those specific to the Mus genus ( Mmus and Mcas ) and those conserved among these rodents ( Mmus , Mcas and Rnor ) . Rodent conserved intergenic lncRNA loci show evidence for purifying selection on both transcribed ( two-tailed Mann-Whitney test , p<4×10−10 ) ( Figure 3A ) and putative promoter sequences ( two-tailed Mann-Whitney test , p<3×10−12 ) ( Figure 3B ) . Intergenic lncRNA loci transcribed in the Mus genus but not in Rnor , exhibit no constraint in transcribed regions ( two-tailed Mann-Whitney test , p>0 . 2 ) ( Figure 3A ) . Mus genus-conserved putative core promoters accumulated significantly fewer substitutions than neighbouring putatively neutral sequence ( median dprom = 0 . 151 and dAR = 0 . 165 , two-tailed Mann-Whitney test , p<5×10−3 ) suggesting they evolved under purifying selection ( Figure 3B ) . Negative selective pressure was significantly higher on the promoters of loci with rodent conserved transcription than on promoter sequence with Mus genus-specific transcription ( rodent conserved median dprom/dAR = 0 . 783 , Mus genus-specific median dprom/dAR = 0 . 901 , two-tailed Mann-Whitney test , p<7×10−3 ) . We asked whether the observed low degree of sequence constraint on intergenic lncRNA loci , relative to protein-coding genes , was due to rapid transcriptional turnover of a subset of intergenic lncRNAs . To test this , we compared Mmus to Rnor nucleotide substitution rates for the transcribed sequences ( including exons and introns ) between the subset of intergenic lncRNA loci exhibiting conserved expression in the rodent liver ( n = 160 ) with the corresponding set of protein-coding genes ( n = 6641 ) and found no significant difference ( intergenic lncRNA dloci/dAR = 0 . 827 , protein-coding dloci/dAR = 0 . 842 two-tailed Mann-Whitney test , p>0 . 58 ) ( Figure S7A ) . For loci conserved in rodents , nucleotide substitution rates of intronic and exonic sequence were compared between Mmus and Rnor . Introns ( dintron ) of protein-coding genes and intergenic lncRNAs evolved at comparable rates ( intergenic lncRNA dintron/dAR = 0 . 959 , protein-coding dintron/dAR = 0 . 986 , two-tailed Mann-Whitney test , p>0 . 28 ) ( Figure S7C ) . In contrast , protein-coding gene exons evolve under strong purifying selection ( intergenic lncRNA dexon/dAR = 0 . 805 , protein-coding dexon/dAR = 0 . 484 , two-tailed Mann-Whitney test , p<10−15 ) ( Figure S7B ) likely to ensure the maintenance of their coding potential during evolution . Our results therefore indicate that intergenic lncRNA loci that were gained or lost in recent Mus evolution evolved neutrally between mouse and rat . Conversely , rodent conserved intergenic lncRNAs have accumulated fewer substitutions than neighbouring neutral sequence indicating that conservation of transcription is reflected in sequence constraint . Mammalian intergenic lncRNA loci and their genomically adjacent protein-coding genes show a significant tendency to exhibit similar spatiotemporal expression profiles [12] , [13] , [15] , [23] , [45] . We found intergenic lncRNA transcription in liver occurs significantly more frequently near to protein-coding genes that are expressed in the liver [39] than expected by chance ( see Materials and Methods; 1 . 6-fold; permutation test , p<5×10−3 ) . Complementary results were obtained using Database for Annotation , Visualization , and Integrated Discovery ( DAVID ) tissue annotation categories ( Figure S8 ) [46] . About 30% of the protein-coding genes closer to intergenic lncRNA loci were classified as liver expressed ( p<3×10−5 ) . We considered whether lineage-specific transcription of intergenic lncRNAs might associate with the expression level of genomically adjacent protein-coding genes ( see Materials and Methods ) . If intergenic lncRNAs have no effect on nearby protein-coding gene expression , then lineage-specific differences in gene expression of genes should be unaffected by whether a neighbouring intergenic lncRNA locus is transcribed . The existence of relatively large numbers of lineage-specific intergenic lncRNAs in mouse and rat permitted this hypothesis to be tested using Mmus and Rnor . Two additional reasons that we specifically analysed the intergenic lncRNAs identified in these two species were ( i ) the high quality of the genome annotations , relative to Mcas , and ( ii ) the existence of other published datasets that permitted further validation of our results [20] . First , we normalised gene expression for Mmus and Rnor RNAseq data ( see Materials and Methods , Figure S9A ) and validated the fold-difference on 17 selected protein-coding mRNA by RT-qPCR ( Figure S9C and S9D ) . In order to obtain a baseline for transcriptional variation between species from this normalised set , we first estimated the fold difference in liver expression between 230 Mmus housekeeping protein-coding genes [47] and their one-to-one orthologous genes in Rnor ( median fold-difference in expression = 0 . 020 , see Materials and Methods ) . Next , we identified the closest protein-coding gene for each conserved or lineage-specific Mmus or Rnor intergenic lncRNA . We selected the intergenic lncRNA loci whose neighbouring protein-coding genes had annotated [48] one-to-one orthologs in the second species ( Table S9 ) . We found that the expression levels of the genes whose nearest intergenic lncRNA locus showed conserved expression between rodents ( n = 148 ) were similar to housekeeping gene levels ( median fold-difference = −0 . 035 , two-tailed Mann-Whitney test , p>0 . 36 ) ( Figure 4 , Table S12 ) . We then asked whether gene expression levels alter when a nearby intergenic lncRNA is gained or lost in one species . In contrast to the conserved situation above , we found that those protein-coding genes A nearest to lineage-specific intergenic lncRNA loci ( n = 137 ) tended to be expressed at a higher level , with a median increase in gene expression of approximately 25% ( median fold-difference = 0 . 212 , two-tailed Mann-Whitney test , p<0 . 005 ) ( Figure 4 , Table S12 ) . We repeated this analysis and confirmed this result using an independent dataset [20] . We found that the median expression levels of protein-coding gene loci adjacent to lineage-specific intergenic lncRNA loci were significantly higher than those of protein-coding gene loci near conserved intergenic lncRNA loci ( two-tailed Mann-Whitney test , p<7×10−5 ) ( Figure S9B , Figure S10 ) . Transcription increased for half ( 50% ) of those protein-coding genes lying adjacent to lineage-specific intergenic lncRNA loci , when assessed using either total RNA or mRNA expression; in contrast , less than a third ( 29% ) of protein-coding genes near conserved intergenic lncRNA loci show consistent increased expression in both datasets ( two tailed Fisher's exact test , p<0 . 05 , Figure S11 ) , suggesting that in some cases gain or loss of intergenic lncRNAs may influence the expression levels of neighbouring genes . We next investigated if some relative orientations of lineage-specific lncRNA transcription were more frequently associated with increased expression of the most proximal protein-coding gene . We divided lineage-specific intergenic lncRNA and protein-coding gene pairs into three classes ( Figure S12A ) : tandem ( 48 gene pairs ) if transcription occurred in the same orientation , divergent ( 71 gene pairs ) , or convergent ( 17 gene pairs ) if transcription occurred in opposite directions either diverging or converging , respectively . All three relative genomic arrangements are associated with increased expression of the closest protein-coding genes . Both tandem and convergent orientations are associated with significantly increased expression at the 5% level while divergent orientation is significant at the 10% level ( p<0 . 08 , Figure S12B ) . We considered a number of possible interpretations for this apparent association of lineage-specific intergenic lncRNAs with increased transcription of nearby protein-coding genes . The increased gene expression could be either ( i ) due to regional modifications to the genome that co-ordinately influence all coding and noncoding loci [49] or ( ii ) correlated with the transcription of the proximal intergenic lncRNA locus [13] , [15] . A key distinguishing feature between these two mechanisms is whether lineage-specific expression of intergenic lncRNAs is associated with regional increases in transcription . To test this , we identified the next most proximal protein-coding gene B , beyond its closest protein-coding gene A ( Figure 4A ) . Genes duplicated in tandem often share regulatory elements and , as a consequence , exhibit similar expression patterns [50] . To account for this evolutionary bias , we excluded 17 protein-coding genes B that were annotated [48] as protein-coding gene A paralogs ( see Materials and Methods ) . In contrast to the observed lineage-specific effects on protein-coding genes A , the expression levels of protein-coding genes B were not significantly affected ( two-tailed Mann-Whitney test , p>0 . 7 ) by either conserved ( median fold-difference = 0 . 078 ) or lineage-specific ( median fold-difference = −0 . 088 ) intergenic lncRNA transcription ( Figure 4 , Table S13 ) . We next tested whether similar results might be obtained for lineage-specific protein-coding genes . We used the previously identified set of Mus-genus lineage-specific expressed protein-coding genes . We identified genes A′ as the closest protein-coding genes to these loci , protein-coding A′ ( Figure S13 ) . We excluded paralogous protein-coding gene pairs and considered only protein-coding genes A′ with a one-to-one ortholog in rat ( 89 genes ) . Transcription levels of nearby genes appear unaffected by the presence of lineage-specific protein-coding gene transcription in the genomic vicinity ( median fold-difference = 0 . 052 , two-tailed Mann-Whitney test , p>0 . 4 ) ( Figure S13 ) . As an additional control , we compared the densities of chromatin boundary elements ( CCCTC-binding factor [CTCF]-bound sites ) and DNase I hypersensitivity sites in the intergenic regions between ( i ) the lineage-specifically expressed intergenic lncRNA locus and its neighboring protein-coding gene A and ( ii ) protein-coding genes B , using data from previous studies [51] , [52] . We found no significant differences between these densities ( permutation test p>0 . 2 ) . The association between lineage-specific lncRNA transcription and increased expression levels of neighbouring protein-coding genes might depend on the distance between their transcriptional start sites ( TSSs ) . The median distance of the TSS of a lineage-specifically expressed intergenic lncRNA with its closest protein-coding gene is 22 kb . However , no significant correlation was observed between this distance and the median fold difference in expression for protein-coding genes measured between mouse and rat ( Pearson correlation , R = −0 . 03 , p = 0 . 76 , Figure S14 ) . Our comparison of matched tissues in two species thus revealed that birth or death of intergenic lncRNAs is associated with changes in transcription of proximal protein-coding genes .
Previous studies have indicated that 12 to 15% of lncRNAs are conserved between human and mouse , based on comparison of EST and cDNA datasets from disparate experimental designs [22] , [23] . Our matched interspecies data are perhaps better suited to establish experimentally the rate of lncRNA turnover . The use of mouse and rat , being closely related species , minimises the effects of genomic sequence divergence , thus better uncoupling sequence and transcriptional changes . Transcription of noncoding loci is more frequently gained or lost than transcription of protein-coding genes; between 28% and 61% of intergenic and antisense lncRNAs , respectively are specific to the Mus genus . We expect similar turnover will be found in most cell types of various developmental stages given that liver is a typical somatic tissue [53] . The transience of intergenic lncRNA transcription is mirrored by changes to selective pressures acting on their sequences . Our results are consistent with purifying selection acting on transcribed intergenic lncRNA loci , and with no selection acting on untranscribed orthologous sequence in other species . This coupling of transcriptional conservation with sequence constraint suggests that conserved intergenic lncRNA loci are biologically significant in rodents . The expression levels of intergenic lncRNAs and their genomically neighbouring protein-coding genes have previously been shown to be positively correlated [12] , [13] . We find that species-specific transcription of intergenic lncRNAs correlates with elevated expression of neighbouring protein-coding genes . The increased transcription observed among neighbouring genes is unique to intergenic lncRNAs , and seems unlikely to be due to local changes in chromatin environment . If the intergenic lncRNAs in other tissues and species behave similarly , intergenic lncRNAs could contribute substantially to lineage-specific and tissue-specific evolution of gene expression . The rapid turnover we observed in lncRNA transcription strongly resembles what was recently reported for transcription factor binding events [54]–[56] , tRNA transcription [20] and functional regulatory sequences in general [19] . For instance , between 10 to 20% of transcription factor binding events overlap between human and mouse liver [56] , which is similar in scale to what we now find for intergenic lncRNAs . These parallels suggest that rapid evolution is a general feature of noncoding regulatory mechanisms . It was recently proposed that intergenic lncRNAs have minimal impact on the transcriptional regulation of their neighbouring protein-coding genes [16] , [23] . By exploiting the rapid birth and death of noncoding RNAs , we revealed that intergenic lncRNAs could contribute to lineage-specific changes in the expression levels of neighbouring protein-coding genes . Our data do not preclude distal regulatory roles , which might be lineage-specific , for some or all intergenic lncRNAs we investigate . It will now be crucial to understand how intergenic lncRNAs evolve and to unravel the molecular mechanisms underlying lineage-specific gene expression changes associated with intergenic lncRNAs .
ChIPseq , RNAseq , and RT-PCR experiments were performed on liver material isolated from three rodents: Mus musculus domesticus ( Mmus ) , Mus musculus castaneus ( Mcas ) , and Rattus norvegicus ( Rnor ) . Each ChIPseq and RNAseq experiments were performed on at least two independent biological replicates from different animals . Mmus and Mcas ( male adults , 10 weeks old ) were obtained from the Cambridge Research Institute . Rnor ( male adults , 9 weeks old ) were obtained from Charles River . All tissues were either treated post-mortem with 1% formaldehyde for ChIP experiments or flash-frozen in liquid N2 for RNA experiments . The investigation was approved by the ethics committee and followed the Cambridge Research Institute guidelines for the use of animals in experimental studies under Home Office license PPL 80/2197 . ChIP sequencing experiments were performed as described previously [57] using H3K4me3 antibody ( CMA304 ) [58] . In brief , the immunoprecipitated DNA was end-repaired , A-tailed , ligated to the sequencing adapters , amplified by 18 cycles of PCR and size selected ( 200–300 bp ) . For RNA-sequencing library preparation , total RNA was extracted using Qiazol reagents ( Qiagen ) and DNase-treated ( Turbo DNase , Ambion ) . Ribosomal RNA was depleted from total RNA using RiboMinus ( Invitrogen ) . RNA was reversed transcribed and converted into double-stranded cDNA ( SuperScript cDNA synthesis kit , Invitrogen ) , sheared by sonication followed by paired end adapter ( Illumina ) ligation and prior to PCR amplification cDNA was UNG-treated to maintain strand-specificity [59] . After passing quality control on a Bioanalyzer 1000 DNA chip ( Agilent ) libraries were sequenced on the Illumina Genome Analyzer II ( single-ended ) and post-processed using the standard GA pipeline software v1 . 4 ( Illumina ) . H3K4me3 ChIPseq and associated input DNA control ChIPseq reads were aligned to the corresponding reference genomes ( mm9 for Mus musculus domesticus and Mus musculus castaneus; Rn4 for Rattus norvegicus ) using MAQ version 0 . 7 . 1 ( default parameters ) [60] . Reads mapping to multiple genomic locations were discarded . Genomic regions enriched over matching input DNA control were defined using MACS version 1 . 3 . 7 . 1 using the default parameters [61] . Comparative analysis was carried out using the Galaxy web tool [62] . Total RNA sequencing reads were mapped with Tophat ( version 1 . 3 . 0 ) [25] , using default parameters . A file containing the mapped coordinates of mouse and rat ESTs and mRNA mapped coordinates ( downloaded from UCSC on the 11th March 2011 ) was provided to facilitate total RNA read mapping across splice junction for Mmus and Mcas , and Rnor respectively . Reads mapping to rRNA , tRNA and mtRNA were masked and the remainder were used to assemble transcripts de novo using Cufflinks ( version 1 . 3 . 0 ) [27] . We filtered out transcripts smaller than 200 nucleotides ( nt ) and without an H3K4me3 peak overlapping their predicted transcriptional start site ( TSS ) . Transcripts overlapping protein-coding gene annotations ( by one or more base pair ) from RefSeq , Ensembl ( build 60 ) [48] and UCSC were annotated as intragenic . To discriminate between unannotated protein-coding and putatively noncoding transcripts we estimated the coding potential of all intergenic transcripts using the coding potential calculator ( CPC ) [32] . We annotated all transcripts with a coding potential less than 0 as intergenic long noncoding RNAs ( intergenic lncRNAs ) . The 400 nt region upstream of the 5′ end ( TSS ) of each intergenic lncRNA or protein-coding transcript was annotated as a putative promoter . Transcribed loci were defined as non-overlapping regions with one or more transcripts that can contain overlapping exonic or intronic nucleotides . Loci containing only transcripts predicted to be intergenic lncRNAs were annotated as intergenic lncRNA loci . The remainder were annotated as protein-coding loci . For the identification of antisense transcripts from the Cufflinks output file ( n = 56917 ) , we first identified 2383 transcripts overlapping protein-coding genes in antisense orientation in Mmus . This number included four types of ambiguous cases that were systematically removed: ( i ) annotated protein-coding transcripts ( removing 1816 transcripts ) , ( ii ) antisense transcripts lacking an H3K4me3 peak independent from the TSS of overlapping protein-coding gene ( removing 324 transcripts ) , ( iii ) transcripts lacking H3K4me3 marks at their 5′ end , and ( iv ) mapping assembly artefacts , revealed by visual inspection ( collectively removing 90 transcripts ) . Taking all of these cases into consideration , 49 loci ( or 153 antisense transcripts ) were annotated in Mmus . A similar procedure was conducted in Mcas and Rnor , revealing 66 loci in total . To identify lncRNAs deriving from bidirectional transcription at TSSs of protein-coding genes , we subtracted divergently transcribed protein-coding genes from our list of actively transcribed protein-coding genes . The TSSs of gene loci are spanned by one H3K4me3 peak and the evidence of divergent transcription is represented by RNAseq reads mapping in opposite directions . We identified divergent reads within an 1 kb window of a protein-coding gene's annotated TSS ( Ensembl , build 60 ) [30] . Heatmaps and transcription start site aggregation plots were constructed using seqMINER [63] . To account for the difference in RNAseq library size between the three rodent species ( Table S1 ) Mmus and Rnor transcripts were assembled using the same number of reads in Mcas library , the smallest RNAseq library . Reads were randomly selected without replacement and transcripts reassembles using Cufflinks and annotated as described above . RT-PCR analysis of lncRNAs was performed by reverse transcription of 10 µg of DNase-treated total RNA according to the manufacturer's protocols using 200 U SuperScript-II Reverse Transcriptase ( Invitrogen Corporation ) , 0 . 5 µg oligo ( dT ) and 0 . 5 µg random primers or 1 µg gene-specific primers ( see Table S11 ) . Negative controls were included in RT reactions . The cDNAs were then treated with RNase H at 37°C for 1 hour . Each PCR reaction typically contained 25 ng of cDNA , 5 pmol of the gene-specific primers ( Table S11 ) , 10 µL PCR Master Mix ( Bioline ) , and 2 µL of the diluted cDNAs in a total volume of 20 µL . Reactions were carried out in triplicate in ABI 7900HT Fast Real-Time PCR system at the optimal temperature , as defined by provider instructions . The significance of genome-wide associations between intergenic lncRNAs and their neighbouring protein-coding genes was assessed using Genome Association Tool ( GAT ) ( Heger et al . , in preparation ) . GAT compares the observed number of overlapping nucleotides between a set of segments with particular annotations to what would be expected from random placement of these segments . Expected densities are obtained using a randomisation procedure that accounts for G+C content and chromosome specific biases . A previous version of GAT was used in [9] , [18] . This tool infers associations between intergenic lncRNA loci ( segments ) across the following annotation sets: ( I ) mouse-to-rat indel purified segments [44] and ( II ) liver-expressed protein-coding gene territories ( Average Difference values >200 ) [39] . A protein-coding gene territory is defined as the genomic region containing all nucleotides that are closer to the gene than they are to its most proximal up- and downstream protein-coding genes , as described elsewhere [9] , [18] . As a second tool , we used the gene functional classification tool Database for Annotation , Visualization , and Integrated Discovery ( DAVID ) ( default parameters: count = 2 and ease = 0 . 1 ) [46] to explore the enrichment of tissue gene expression . Regions of the mouse and rat genome that are enriched in CTFC binding were obtained from [51] . DNase hypersensity sites ( DHS ) in the mouse adult liver were obtained from [52] . Only male and sex independent DHS peaks that were either annotated as being robust and standard were considered in this analysis . GAT ( Heger et al . , in preparation ) was used to test the observed density of these two class of regulatory elements in the intergenic region between lineage-specific intergenic lncRNAs and protein coding gene A ( Figure 4 ) to what would be expected based on their distribution across the intergenic regions between lineage-specific intergenic lncRNA and protein-coding gene B ( Figure 4 ) . Orthologous regions between Mmus and Rnor were identified using whole genome pairwise alignments [42] . An intergenic lncRNA locus was considered to be expressed in another species when its orthologous ( between Mus species and Rnor ) or equivalent ( between Mmus and Mcas ) position had an overlapping ( >1 bp ) H3K4me3 peak and one or more overlapping RNAseq reads . Due to the lack of H3K4me3 data for human , overlap ( >1 bp ) by one or more RNAseq reads in the orthologous human location was considered sufficient evidence for transcriptional conservation of an Mmus locus in human sequence . Only Mmus loci whose transcription was supported by one or more polyA+ selected sequencing read [41] were considered in this analysis . Identical criteria were used to determine the conservation of antisense lncRNA loci . An antisense lncRNA locus was judged to be expressed in another species when its orthologous position had an overlapping ( >1 bp ) H3K4me3 peak and one or more overlapping RNAseq reads in opposite orientations . We visually inspected these calls on 66 loci across the three rodent species . Nucleotide constraint between Mmus and Rnor locus , exon , intron or putative promoter was estimated as described previously [18] . Pairwise substitution rates between Mmus and Rnor genomic regions were estimated using BASEML from the PAML package with the REV substitution model [64] . The substitution rate of the region of interest was compared to the rate observed for non-overlapping adjacent ( <500 kb ) ancestral repeats ( inserted before the primate and rodent split ) with similar G+C content [18] . Mmus and Rnor protein-coding transcript annotations were downloaded from Ensembl ( build 60 , http://www . ensembl . org/index . html ) and used to define a set of constitutive exons for each gene . To account for differences in size of constitutively expressed portions of Mmus and Rnor genes , the total number of overlapping reads per nucleotide in Rnor was adjusted to what would be expected if the sequence in Rnor had the same length as that observed in Mmus . The expression of a gene in Rnor or Mmus is proportional to the sum of reads mapped to their exons divided by their combined length . To allow comparison of gene expression between species , read counts were normalized using TMM ( edgeR package ) [65] . Briefly , to estimate the normalised library size for each species , it was assumed that 60% of expressed genes were transcribed at similar levels in the two species . Other cut-offs ( 50% and 70% ) yielded similar results . The normalised Mmus and Rnor library size was used to calculate the expression level ( as total number of fragments per kb of sequence per million reads mapped , FPKM ) of each gene in each species . Each intergenic lncRNA locus was paired with its genomically closest protein-coding gene . Only pairs whose protein-coding genes had one-to-one orthologs between Mmus and Rnor were considered . The fold difference in expression levels of protein-coding genes associated with lineage-specific ( Mus-genus or Rnor-specific ) or rodent conserved expression was estimated between [6] the same direction . To calculate the fold difference in expression for each housekeeping gene between Mmus and Rnor species X and Y were randomly assigned . Fold expression differences for protein-coding genes B or A′ ( Figure 4 , Figure S12 ) were calculated in a similar manner . Apart from permutation tests all other statistical analysis were performed using the R package [66] . RNAseq and H3K4me3 ChIPseq sequencing data are available from ArrayExpress under accession number E-MTAB-867 . Additional mRNAseq data used was E-MTAB-424 . | The best-understood portion of mammalian genomes contains genes transcribed into RNAs , which are subsequently translated into proteins . These genes are generally under high selective pressure and deeply conserved between species . Recent publications have revealed novel classes of genes , which are also transcribed into RNA but are not subsequently translated into proteins . One such novel class are long noncoding RNA ( lncRNA ) . LncRNA loci are controlled in a similar manner to protein-coding genes , yet are more often expressed tissue-specifically , and their conservation and function ( s ) are mostly unknown . Previous reports suggest that lncRNAs can affect the expression of nearby protein-coding genes or act at a distance to control broader biological processes . Also , lncRNA sequence is poorly conserved between mammals compared with protein-coding genes , but how rapidly their transcription evolves , particularly between closely related species , remains unknown . By comparing lncRNA expression between homologous tissues in two species of mouse and in rat , we discovered that lncRNA genes are “born” or “die” more rapidly than protein-coding genes and that this rapid evolution impacts the expression levels of nearby coding genes . This local regulation of gene expression reveals a functional role for the rapid evolution of lncRNAs , which may contribute to biological differences between species . | [
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] | 2012 | Rapid Turnover of Long Noncoding RNAs and the Evolution of Gene Expression |
In Saccharomyces cerevisiae , the essential mismatch repair ( MMR ) endonuclease Mlh1-Pms1 forms foci promoted by Msh2-Msh6 or Msh2-Msh3 in response to mispaired bases . Here we analyzed the Mlh1-Mlh2 complex , whose role in MMR has been unclear . Mlh1-Mlh2 formed foci that often colocalized with and had a longer lifetime than Mlh1-Pms1 foci . Mlh1-Mlh2 foci were similar to Mlh1-Pms1 foci: they required mispair recognition by Msh2-Msh6 , increased in response to increased mispairs or downstream defects in MMR , and formed after induction of DNA damage by phleomycin but not double-stranded breaks by I-SceI . Mlh1-Mlh2 could be recruited to mispair-containing DNA in vitro by either Msh2-Msh6 or Msh2-Msh3 . Deletion of MLH2 caused a synergistic increase in mutation rate in combination with deletion of MSH6 or reduced expression of Pms1 . Phylogenetic analysis demonstrated that the S . cerevisiae Mlh2 protein and the mammalian PMS1 protein are homologs . These results support a hypothesis that Mlh1-Mlh2 is a non-essential accessory factor that acts to enhance the activity of Mlh1-Pms1 .
DNA mismatch repair ( MMR ) recognizes single base and insertion/deletion mispairs generated by errors in DNA replication and some forms of chemically damaged bases [1]–[5] . Both types of errors can lead to mutations if uncorrected . Arguably , the best understood MMR system is the Escherichia coli methyl-directed MMR system where mispair excision is targeted to the transiently unmethylated newly synthesized DNA strand before Dam methylase acts on the newly synthesized strand . MMR is initiated by the MutS homodimer , which directly recognizes mispaired bases in DNA . After mispair recognition , MutS recruits the MutL homodimer , which promotes the MutH-mediated cleavage of the unmethylated strand at hemi-methylated GATC sites . The MutH-generated strand discontinuity ( nick ) functions as the initiation site for an excision reaction that results in the degradation of a stretch of the newly synthesized strand followed by its resynthesis . However , there are other bacteria that lack MutH and do not use DNA methylation for strand discrimination [6]–[8] . In eukaryotes , the early steps of MMR are conserved with those in E . coli [1] , [3]–[5] with the partially redundant MutS-related complexes , the Msh2-Msh6 and Msh2-Msh3 heterodimers , recognizing mispairs followed by recruitment of MutL-related complexes . Genetic evidence in the yeast Saccharomyces cerevisiae indicates that the Mlh1-Pms1 heterodimer ( called MLH1-PMS2 in humans ) is the primary MutL homolog complex that functions in promoting post-replication MMR [5] . In contrast to E . coli , the steps during MMR following the recruitment of the MutL homologs have remained poorly understood in eukaryotes and other organisms that lack methyl-directed MMR . Recent experiments in S . cerevisiae have indicated that MMR proteins are coupled to DNA replication and have demonstrated the existence of a short window of time after DNA is replicated during which MMR has to initiate [2] . These and other results suggest that some aspect of the DNA replication intermediates themselves may play a role in mediating strand discrimination in organisms that lack methyl-directed MMR [2] , [9] . Most eukaryotes encode multiple MutL homologs that function as heterodimers . Mlh1-Pms1 ( called MLH1-PMS2 in humans ) possesses an endonuclease activity that can introduce single-stranded nicks into double-stranded DNA , suggesting that Mlh1-Pms1 functions as the equivalent of a combination of both the bacterial MutL and MutH proteins [10]–[12] . This endonuclease activity is required for MMR in vivo as well as for suppression of homeologous recombination and responses to DNA methylating agents [10] , [13]–[15] . This endonuclease activity is also present in MutL homologs from bacteria lacking methyl-directed MMR [16]–[20] . S . cerevisiae encodes two additional MutL complexes , Mlh1-Mlh3 and Mlh1-Mlh2 . Mlh1-Mlh3 plays only a minor role during MMR [21]–[23] but plays a major role in the resolution of recombination intermediates during meiosis [24]–[26] . Mlh3 contains the conserved metal-binding motif that is required for the endonuclease activity of Pms1 , and mutations affecting this motif in MLH3 cause defects in MLH3-dependent MMR and meiotic crossing over [27]; consistent with this , the Mlh1-Mlh3 complex has recently been directly shown to have endonuclease activity [28] , [29] . Mlh1-Mlh2 is more poorly understood than either Mlh1-Pms1 or Mlh1-Mlh3 [22] , [25] , [30]–[32] . Mlh2 lacks the metal binding motif present in Pms1 and Mlh3 , and in most studies reported , deletion of MLH2 causes a weak or no mutator phenotype , and the results of double mutant analysis have been taken to suggest a partial redundancy between MLH2 and MLH3 in MSH3-dependent MMR [22] . It has also been reported that deletion of MLH2 increases the frequency of reversion of the lys2ΔA746 frameshift mutation reporter due to the formation of large deletions [22] . Deletion of MLH2 does not affect meiotic crossing over or meiotic MMR , unlike deletion of MLH3 or PMS1 [25] , [31] . The mlh2Δ mutation does increase the frequency of gene conversion events , suggesting a partial role for Mlh2 in preventing heteroduplex formation , but not in subsequent mismatch correction; this property is unique among the S . cerevisiae MMR genes [25] , [31] . Consistent with the idea that Mlh2 plays a role in recombination , simultaneous deletion of PMS1 , MLH2 and MLH3 was required to cause defects in a mitotic heteroduplex rejection assay equivalent to that caused by an mlh1Δ mutation [32] . An mlh2Δ mutation as well as deletions of MSH2 , MSH3 , MSH6 and MLH1 but not PMS1 have also been reported to cause a modest increase in resistance to some DNA damaging agents like cisplatin , reminiscent of that seen in human and mouse cells [30] . However , it is unclear how MLH2 contributes to either recombination or MMR and why loss of Mlh2 only results in weak phenotypes . Here , we employ live cell imaging in S . cerevisiae , complemented by genetic and biochemical assays , to analyze Mlh2 function in MMR . A similar approach applied to Pms1 previously revealed that the accumulation of Pms1 foci can be used to distinguish between genetic defects that affect events prior to Mlh1-Pms1 loading and those affecting downstream steps [33] . We found that Mlh2 formed nuclear foci similar to Pms1 , whereas Mlh3 did not . Mlh2 foci partially colocalized with Pms1 foci and were dependent on MSH2 , MSH6 , and MLH1 but not MSH3 . Mlh2 foci increased in abundance in strains with increased mispair formation and in strains containing mutations that disrupt downstream steps in MMR similarly to what was previously observed for Pms1 foci [33] . In vitro , Mlh1-Mlh2 was recruited to mispair-containing DNA by both Msh2-Msh6 and Msh2-Msh3 . Deletion of MLH2 caused a synergistic increase in mutation rates when combined with a deletion of MSH6 or a promoter substitution that reduced the expression of Pms1; by contrast , no synergy was observed when deletion of MLH2 was combined with deletions of MSH3 or EXO1 . Together , these data support the hypothesis that MLH2 encodes a homolog of MutL that functions in conjunction with Mlh1 as a MMR accessory factor whose roles become increasingly important under conditions when other MMR components are limiting .
We recently visualized Pms1 in live S . cerevisiae cells and demonstrated that Mlh1-Pms1 foci are intermediates in MMR [33] . To gain insight into the roles of Mlh1-Mlh2 and Mlh1-Mlh3 , we integrated a cassette encoding a 4×GFP tag at the 3′ end of the chromosomal MLH2 or MLH3 genes . Live cell imaging of these strains revealed that Mlh2-4×GFP formed nuclear foci similar to those observed for Pms1-4×GFP in ∼8% of unsynchronized cells ( Figure 1A and B ) . Mlh2-4×GFP foci were almost exclusively observed in small budded cells , characteristic of cells in S-phase ( Figure 1C ) . The observation that the Mlh2 foci visualized using different tags ( 4×GFP or a monomer tdTomato tag ) were similar in number and appearance ( Figure 1; see below ) indicate that the fluorescent tags do not contribute to focus formation . In contrast to Mlh2-4×GFP , few if any cells had Mlh3-4×GFP foci ( Figure 1B ) . The reason for the lack of Mlh3 foci is unclear , but this could indicate a limited role of Mlh3 in MMR or that the levels of Mlh3 at repair sites were too low to visualize . To test if Mlh2 localizes to the same MMR intermediates as Pms1 , we examined colocalization of Mlh2-tdTomato with Pms1-4×GFP by live cell imaging . Interestingly , the Pms1 and Mlh2 foci partially colocalized , with ∼50% of Pms1 foci showing colocalization with Mlh2 foci at the limit of resolution of deconvolution microscopy ( Figure 1D and E ) . To further examine the relationship between the Pms1 and Mlh2 foci , image stacks were taken at one-minute intervals to observe the localization of Pms1 and Mlh2 foci over time . We analyzed 50 cases where we could follow the formation , retention for at least two images , and disappearance of an Mlh2 or Pms1 focus . In agreement with the single images , approximately half ( 21/50 , 42% ) of the foci displayed colocalization between Pms1 and Mlh2 at some point during their lifetimes ( Figure 1F ) , 36% ( 18/50 ) were Pms1 foci with no colocalization of Mlh2 , and 22% ( 11/50 ) were Mlh2 foci with no colocalization of Pms1 . For the 21 cases of colocalization observed by time-lapse imaging , Pms1 and Mlh2 first appeared within the same frame for the majority of events ( 13/21 ) , indicating that both proteins were recruited to the same site within the one minute temporal resolution of the imaging ( Figure 1F , bottom ) . In 3/21 events , a Pms1 focus preceded the colocalized Mlh2 focus , and in the remaining 5/21 events , the Mlh2 focus preceded the colocalized Pms1 focus . Interestingly , Mlh2 foci frequently persisted after the colocalized Pms1 was no longer detectable ( 11/21 cases ) ; the Pms1 foci were usually visible for 1 to 4 min , whereas the Mlh2 foci were visible for up to 7 min ( average = 4 min ) after the associated Pms1 focus was no longer detected ( 11/21 cases , Figure 1F ) . In the remaining 10 cases , the colocalized Pms1 and Mlh2 foci disappeared at the same time . Strikingly , Pms1 foci were never present after the disappearance of Mlh2 , suggesting that Mlh2 frequently marks the site of Pms1 foci and likely the site of MMR after Pms1 has been removed . In our previous study [33] , we identified Pms1 foci as an MMR repair intermediate based on the following observations: ( i ) the formation of the Pms1 foci depended on mispair recognition by Msh2-Msh6 or Msh2-Msh3; ( ii ) the abundance of Pms1 foci increased with increasing levels of mispaired bases; and ( iii ) Pms1 foci increased in abundance in cells defective in MMR at steps that were downstream of recruitment of Mlh1-Pms1 [33] . Given the partial colocalization of Pms1 and Mlh2 , we investigated Mlh2 foci by performing the same set of perturbations used to analyze Pms1 foci . Deletion of MSH2 , which eliminates the Msh2-Msh3 and Msh2-Msh6 mispair recognition complexes , completely abolished Mlh2 foci ( Figure 2A ) . Similarly , other mutations that disrupted mispair recognition also eliminated Mlh2 foci ( Figure 2A ) , including the msh3Δ msh6Δ double mutation that eliminates both the Msh2-Msh3 and Msh2-Msh6 complexes and the msh3Δ msh6-F337A double mutation that eliminates Msh2-Msh3 and eliminates mispair binding by Msh2-Msh6 [34] . Deletion of MSH6 alone also greatly reduced the number of Mlh2 foci , whereas deletion of MSH3 had no effect , suggesting that the majority of Mlh2 foci were dependent upon Msh2-Msh6 but not Msh2-Msh3 ( Figure 2A ) . The DNA polymerase epsilon and delta active site mutations ( pol2-M644G and pol3-L612M , respectively ) [35] , [36] or a mutation causing a defect in the 3′-5′-exonuclease activity of DNA polymerase delta ( pol3-01 ) [37] , all of which increase the level of misincorporated bases , greatly increased the abundance of Mlh2 foci ( Figure 2B ) . Deletion of EXO1 , which encodes the 5′-3′ exonuclease that participates in the mispair excision reaction , increased the percentage of nuclei with Mlh2 foci to ∼50% ( Figure 2A ) . Together , these results mirror what was previously observed for Pms1 foci , with the one notable exception that Pms1 foci were substantially increased and not decreased in an msh6Δ mutant suggesting that Pms1 and Mlh2 differ in their ability to be recruited by Msh2-Msh6 and Msh2-Msh3 in vivo [33] . We next examined the interdependencies of Pms1 and Mlh2 on their ability to form foci . Mlh2 has been shown to interact with Mlh1 , but not with Pms1 or Mlh3 , by yeast two-hybrid and affinity-capture mass spectrometry [25] , [38]–[40] , indicating the existence of an Mlh1-Mlh2 heterodimer that is distinct from Mlh1-Pms1 and Mlh1-Mlh3 heterodimers . Consistent with this , deletion of MLH1 eliminated the vast majority of Mlh2 foci ( Figure 2A ) . Deletion of MLH2 had no effect on the number of Pms1 foci ( Figure 2C ) . In contrast , deletion of PMS1 drastically increased the percentage of cells containing Mlh2 foci to ∼95% ( Figure 2D ) . This increase in Mlh2 foci could either be due to increased formation of the Mlh1-Mlh2 complex because of loss of competition for the Mlh1 partner protein by Pms1 or due to loss of Mlh1-Pms1 endonuclease activity and the consequent inhibition of downstream steps in MMR . To differentiate between these two possibilities , we measured the frequency of Mlh2 foci in cells containing the endonuclease active site pms1-E707K mutation that results in expression of an endonuclease-defective Mlh1-Pms1 complex . We observed a similar increase in the number of Mlh2 foci in pms1Δ and pms1-E707K mutant cells ( Figure 2D ) , suggesting that the increase in Mlh2 foci in pms1Δ cells is likely due to the inhibition of downstream steps in MMR . The high levels of Mlh2 foci seen in the pms1Δ mutant were completely abolished by an msh2Δ mutation ( Figure 2D ) , confirming that the increased recruitment of Mlh1-Mlh2 into foci in cells lacking Pms1 was dependent on Msh2 . In human cells , Mlh1 and other MMR components are recruited to sites where DNA damage has been induced by UV-laser micro-irradiation [41]–[43] . This has been interpreted as the recruitment of MutL homolog complexes to double-strand breaks ( DSBs ) . Consistent with these observations , treatment of S . cerevisiae cells with the radiomimetic drug phleomycin greatly increased the percentage of cells containing Pms1 and Mlh2 foci ( ∼5-fold and ∼3-fold , respectively ) ( Figure 3A ) . This was unlikely to be the result of simply activating the DNA damage response ( DDR ) because treatment with hydroxyurea , which also activates the DDR by causing replication fork stalling by depleting dNTP pools , did not cause an increase in the abundance of Pms1 or Mlh2 foci ( Figure 3A ) . As with foci formed in untreated cells , foci induced by phleomycin treatment were not observed in msh2Δ strains . To determine if the Msh2-dependent Pms1 and Mlh2 foci were formed at DSBs and not other types of DNA lesions generated during phleomycin treatment , we investigated the recruitment of Pms1 and Mlh2 to a site-specific DSB generated by a galactose-inducible I-SceI endonuclease . The DSB was generated adjacent to a tandem array of tetO sequences on chromosome V that was marked by expression of TetR-mRFP1 [44] . Cells expressing Pms1-4×GFP , Mlh2-4×GFP , or Mre11-GFP ( as a positive control ) were monitored before and after the addition of galactose to induce the DSB . Consistent with published results [45] , Mre11 rapidly formed foci that colocalized with the tetO array ( Figure 3B and C ) . In contrast , neither Pms1 nor Mlh2 formed foci that colocalized with the tetO array . These results suggest that the recruitment of Pms1 and Mlh2 to lesions induced by phleomycin ( and the similar recruitment of mammalian MMR proteins to the sites of laser micro-irradiation ) may not be due to recognition of DSBs but rather due to recognition of the other types of DNA damage induced by these treatments that might mimic mispaired bases . We next investigated the effects of deleting MLH2 on MMR using the hom3-10 and lys2-10A frameshift reversion and CAN1 forward mutation rate assays ( Table 1A ) . Deletion of MLH2 alone did not cause a significant increase in mutation rate in either the frameshift reversion or forward mutation assays , in agreement with previous work [22] . We next tested if the mlh2Δ mutation exacerbated the defects caused by mutations in other MMR genes . The mlh2Δ msh3Δ or mlh2Δ exo1Δ double mutant strains did not exhibit mutation rates that were higher than the mutation rates of the single mutants . In contrast , the mlh2Δ msh6Δ double mutant strain exhibited a synergistic increase in the hom3-10 and lys2-10A frameshift reversion assays but not in the CAN1 forward mutation assay that , in addition to frameshift mutations , detects base substitution mutations and other kinds of mutations [46] . Although the frameshift reversion rates of the mlh2Δ msh6Δ double mutant were higher than that of the respective single mutants , the rates were still substantially lower than caused by the msh3Δ msh6Δ double mutation that eliminates mispair recognition and causes a complete MMR defect . The combination of the specificity of MLH2 for suppressing frameshift mutations and synergy of the mlh2Δ mutation with the msh6Δ mutation but not with the msh3Δ mutation suggests that MLH2 contributes preferentially to MSH3-dependent MMR . Given the results of the Mlh2 localization studies , we hypothesized that Mlh2 becomes more important for MMR under conditions where the major MutL-related complex Mlh1-Pms1 is limiting . To test this idea , we took advantage of the previously reported tetracycline repressible system [47] to regulate Pms1 expression ( tetO2 promoter ) in a doxycycline-dependent manner . After titrating doxycycline , we found that 10 µg/ml of doxycycline resulted in partial downregulation of Pms1 protein expression ( Figure S1 ) and a weak MMR defect in the frameshift reversion assays but not in the CAN1 forward mutation assay ( Table 1B ) . Consistent with the hypothesis , we observed a synergistic increase in the mutation rate in the frameshift reversion assays when the mlh2Δ mutation was combined with reduced expression of Pms1 . Thus , Mlh2 becomes more important for MMR when the level of Pms1 is reduced , suggesting that Mlh2 normally plays an accessory role in MMR . The genetics of Mlh2 foci formation suggested that Mlh1-Mlh2 is primarily recruited to mispair-containing DNA by Msh2-Msh6 ( Figure 2A ) , whereas the genetics of frameshift mutation reversion suggested that MLH2 functions primarily in an MSH3-dependent pathway ( Table 1A ) , suggesting that Mlh2 can function in conjunction with either Msh2-Msh6 or Msh2-Msh3 depending on the assay tested . To address this possibility , we purified the S . cerevisiae Mlh1-Mlh2 complex and tested its ability to be recruited to mispair-bound Msh2-Msh6 or Msh2-Msh3 using a previously developed surface plasmon resonance assay [48] . As previously demonstrated [48] , Msh2-Msh6 has low affinity for a substrate lacking a mispair ( the ‘GC’ substrate ) and a higher affinity for substrates with a central T:G mispair ( the ‘TG’ substrate ) or a +T insertion ( the ‘+1’ substrate ) ( Figure 4A–C ) . As expected , Mlh1-Pms1 readily bound Msh2-Msh6 on all of these substrates and the increase in resonance units correlated with the amount of pre-bound Msh2-Msh6 . Msh2-Msh6 also recruited Mlh1-Mlh2 and , similar to Mlh1-Pms1 , the increase in resonance units correlated with the amount of pre-bound Msh2-Msh6 ( Figure 4A–C ) . However , the kinetics of Mlh1-Mlh2 recruitment differed from Mlh1-Pms1 in that initial binding was slower and the binding failed to saturate . Msh2-Msh3 had a low affinity for both the GC and TG substrates , but bound well to the +1 substrate ( Figure 4D–F ) , consistent with the function of MSH3 in the repair of insertion/deletion mispairs and an inability to function in the repair of many kinds of base-base mispairs [46] , [49] , [50] . As seen with Msh2-Msh6 , both Mlh1-Pms1 and Mlh1-Mlh2 were recruited to substrates bound by Msh2-Msh3 with the level of recruitment correlating with the amount of Msh2-Msh3 bound ( Figure 4D–F ) . The ability of Msh2-Msh3 to recruit Mlh1-Pms1 was consistent with the fact that Pms1 foci form in msh6Δ and msh3Δ strains but not in msh2Δ and msh3Δ msh6Δ strains and with our previous study demonstrating the recruitment of Mlh1-Pms1 to mispair-containing DNA by Msh2-Msh3 in vitro [33] , [51] . Overall , these results support the idea that Mlh1-Mlh2 can function in conjunction with both Msh2-Msh3 and Msh2-Msh6 , although the extent of involvement of Mlh2 may depend on the assay used and hence the exact MMR substrate being acted on . Because MLH2 lacks conserved endonuclease motifs and mutations abolishing pms1 endonuclease function cause a weakly dominant MMR defect that is enhanced by overexpression [13] , we tested if overexpression of MLH2 would cause an MMR defect . We therefore engineered S . cerevisiae strains in which the endogenous promoters of the MLH2 , MLH3 and PMS1 genes were replaced by the strong promoter of the glyceraldehyde 3-phosphate dehydrogenase gene ( pGPD ) and monitored these strains for mutator phenotypes using the hom3-10 and lys2-10A frameshift reversion assays and the CAN1 forward mutation assay . Overexpression of PMS1 did not cause increased mutation rates; however , overexpression of MLH2 or MLH3 drastically increased the mutation rates up to levels that were almost indistinguishable from an MMR defective strain ( msh3Δ msh6Δ ) ( Table 2 ) . Similar results were obtained upon the expression of these genes driven by their native promoters on high copy number plasmids in wild-type cells ( data not shown ) . The endogenous expression level of Pms1 was roughly 5–10-fold higher than that of either Mlh2 or Mlh3 ( Figure 5A ) , and the pGPD promoter increased the expression of each MutL homolog by >50-fold relative to the endogenous level of Pms1 ( Figure 5B ) . The mismatch repair defect caused by the overexpression of MLH2 was largely suppressed by the simultaneous overexpression of PMS1 ( Table 2 ) . These data suggest that increasing the level of Mlh2 or Mlh3 by overexpression allows Mlh2 or Mlh3 to outcompete Pms1 for binding to the Mlh1 present in the cell , thereby preventing the formation of sufficient levels of Mlh1-Pms1 complex to support MMR , and that neither Mlh2 nor Mlh3 is sufficient to replace Pms1 function in MMR . In the case of Mlh1-Mlh2 , this is most likely because Mlh1-Mlh2 lacks endonuclease activity . In the case of Mlh1-Mlh3 , it is possible that Mlh1-Mlh3 lacks sufficient endonuclease activity to substitute for Mlh1-Pms1 or it does not function sufficiently in the Msh2-Msh6 pathway to promote MMR [21] . It is also possible that overexpression leads to much higher levels of Mlh1-Mlh2 or Mlh1-Mlh3 complexes , which then outcompete the Mlh1-Pms1 complex for a key substrate . Because S . cerevisiae MLH2 plays only an accessory role in MMR , we examined the conservation of MLH2 . We first identified MutL homologs using BLAST [52] in the Saccharomycotina subphylum of Ascomycota , which includes S . cerevisiae . Obvious homologs of MLH1 , PMS1 , MLH3 , and MLH2 were identified ( Figure 6 and S2; Table S1 ) by reciprocal BLAST , by analysis of conserved synteny [53] , and/or by the characteristic C-terminal sequence motifs of MLH1 , PMS1 , and MLH3 involved in endonuclease activity . The origin of MLH2 predated the whole-genome duplication that occurred ∼100 million years ago and led to S . cerevisiae and related yeasts [54] , because MLH2 homologs were present in species that diverged from S . cerevisiae prior to the genome duplication and two MLH2 ohnologs ( paralogs produced by the whole-genome duplication [55] , [56] ) were present in Vanderwaltozyma polyspora . A few clades in Saccharomycotina have lost MLH2 , including the ‘CTG’ yeast that encode serine instead of leucine with the codon CTG [57] and species in the Lachancea genus ( Figure S2 , Table S1 ) . We also identified MutL homologs in other sequenced fungi ( Figure 6 , Table S1 ) . MLH1 , PMS1 , MLH3 , and MLH2 genes were found in the Pezizomycotina subphylum of Ascomycota , but the early diverging Taphrinomycotina subphylum , which includes Schizosaccharomyces pombe , only contained MLH1 and PMS1 . In a small number of species , the MLH2 homologs contained stop codons and frameshifts , which could reflect errors in the genome sequences , loss of non-essential portions of MLH2 or inactivation of the MLH2 homologs ( Table S2 ) . MLH2 homologs were also not identified in Basidiomycetes but were observed in the basal fungi Mucor circinelloides ( Mucoromycotina ) and Allomyces macrogynus ( Blastocladiomycota ) , which was consistent with the loss of MLH2 in Basidiomycetes rather than the gain of MLH2 in Ascomycetes . Phylogenetic analysis of the full-length Mlh2 protein sequences was consistent with the major divisions within fungi ( Figure S3 ) . S . cerevisiae MLH2 has a number of similarities to metazoan PMS1 ( note that PMS2 in metazoans is the name for the homolog of S . cerevisiae PMS1 ) . Like S . cerevisiae MLH2 , metazoan PMS1 lacks endonuclease motifs and does not support MMR reactions in vitro [58] , [59] , and deletion of metazoan PMS1 causes an extremely weak mutator phenotype [60] . To examine the relationship between metazoan Pms1 and fungal Mlh2 , we performed phylogenetic analysis on the sequences of the N-terminal domains of MutL homologs from select unikont species ( Figure S4 ) . This analysis identified distinct clades with strong support ( clade credibility scores of 100% ) for the homologous human PMS2 and fungal Pms1 proteins , human MLH3 and fungal Mlh3 proteins , as well as the human PMS1 and fungal Mlh2 proteins . Thus , this analysis suggests that human PMS1 is evolutionarily related to fungal Mlh2 and that the accessory MMR function is conserved across diverse eukaryotes ( Figure 6 ) .
In this study , we demonstrated that both Pms1 and Mlh2 form foci that often colocalize and that the formation of these foci depends upon Mlh1 , Msh2-Msh6 and , in the case of Pms1 , can also be promoted by Msh2-Msh3 [33] . The frequency of both types of foci increase in strains with increased mispair formation or defects in the downstream steps of MMR . In contrast , no Mlh3 foci were detected despite the fact that Mlh3 was expressed at levels similar to Mlh2 . Deletion of MLH2 did not cause a significant increase in the mutation rate in frameshift reversion assays unless Pms1 levels were reduced , and an MLH2 deletion synergized with a deletion of MSH6 , but not with a deletion of MSH3 . In vitro , both Msh2-Msh6 and Msh2-Msh3 could recruit Mlh1-Mlh2 to mispair-containing DNA . These results are consistent with a role for Mlh2 in MMR . However , the lack of endonuclease motifs in Mlh2 suggests that its ability to promote MMR must involve mechanisms other than Mlh1-Mlh2-mediated cleavage of DNA . Together , these data suggest that Mlh1-Mlh2 acts as an MMR accessory or stimulatory factor that functions in conjunction with Mlh1-Pms1 . The studies performed here could be taken to present an apparent discrepancy in the placement of MLH2 in known MMR sub-pathways . The genetics of foci formation suggest that Mlh2 recruitment is primarily mediated by Msh2-Msh6 but not Msh2-Msh3 , whereas the frameshift reversion assays indicate the involvement of MLH2 in MSH3-dependent but not MSH6-dependent MMR . In contrast , the ability of Mlh1-Mlh2 to be recruited to mispair-containing DNA in vitro by both Msh2-Msh6 and Msh2-Msh3 is consistent with a role for Mlh2 in both pathways . One potential explanation for this apparent discrepancy is that Mlh2 plays a role in both pathways , but the recruitment and function of Mlh1-Mlh2 in MMR are highly dependent on the type of mispair that is recognized . The relative contribution of Mlh1-Mlh2 to repair may be influenced by the sequence context of the mispair and whether repair occurs by substitution , deletion or insertion . Thus , the different assay-dependent activities of Mlh1-Mlh2 observed could reflect the fact that the three different assays used in our studies all by necessity use different mispaired substrates . Additionally , the in vitro mispair-dependent Mlh1-Mlh2 recruitment assays use different ratios of Msh2-Msh6 and Msh2-Msh3 than present in cells , resulting in different apparent efficiencies of recruitment of Mlh1-Mlh2 by Msh2-Msh6 and Msh2-Msh3 in vitro and in vivo . These types of differential activities have been clearly documented in the case of the Mlh1-Mlh3 complex [21]–[23] , [25] , [28] . We propose the following hypothesis for the role of Mlh2 in MMR . The Mlh1-Mlh2 complex may have some functional overlap with the non-endonuclease functions of the Mlh1-Pms1 complex in MMR such as recruitment of downstream MMR components and discrimination of the newly synthesized DNA strand , allowing MMR to occur at lower levels of Mlh1-Pms1 at the sites of repair . This role of Mlh1-Mlh2 in reducing the requirement for Mlh1-Pms1 while not being able to replace the activity of Mlh1-Pms1 suggests that Mlh1-Mlh2 acts as a non-essential accessory or stimulatory factor in MMR . It is also possible that Mlh1-Mlh2 in some way regulates the availability or activity of Mlh1-Pms1 . Because the Mlh1-Pms1 endonuclease activity is essential for MMR in vivo , this hypothesis suggests that it might be possible to reveal these Mlh2 functions through the isolation of separation-of-function mutations in Pms1 that eliminate MMR in the absence of Mlh2 but leave the endonuclease activity of Mlh1-Pms1 intact . In addition , this hypothesis predicts that Mlh1-Pms1 and Mlh1-Mlh2 might be loaded onto the same DNA substrate , as suggested by the colocalization observed between Pms1 and Mlh2 foci as well as the accumulation of Mlh2 foci in the absence of Mlh1-Pms1 endonuclease activity; future studies will be required to determine if both complexes are recruited to the same mispaired substrate and if this is functionally significant . Our results are reminiscent of bacteriophage Lambda site-specific recombination where the biochemical requirement for the FIS protein during excision in vitro , which acts as an accessory factor , could only be revealed at sub-optimal levels of XIS protein [61] . Our studies have provided strong evidence that S . cerevisiae Mlh2 is the homolog of mammalian PMS1 . Mammalian PMS1 is known to form a complex with MLH1 , although the possible role of mammalian PMS1 in MMR is unclear [58] , [60] . Consequently , the results described here may also provide new insights into a possible role of the mammalian MLH1-PMS1 complex in MMR . In addition , the ability of S . cerevisiae Mlh1-Mlh2 to be recruited by mispairs and the mutator phenotype caused by overexpression of MLH2 ( and MLH3 ) suggests that increased expression of human PMS1 ( or human MLH3 ) might represent a mechanism that could lead to MMR inactivation and promote tumorigenesis , analogous to MMR defects in Lynch Syndrome and other types of sporadic cancer characterized by microsatellite instability [1] , [3]–[5] .
S . cerevisiae strains were grown at 30°C in yeast extract-peptone-dextrose media ( YPD ) or appropriate dextrose-containing synthetic dropout media for selection of Lys+ or Thr+ revertants or canavanine-resistant ( CanR ) mutants . All strains used for mutation analyses in this study ( Table S3 ) were derivatives of the S288c strain RDKY3686 ( MATα ura3-52 leu2Δ1 trp1Δ63 his3Δ200 hom3-10 lys2-10A ) [62] . Strains used for the colocalization experiments with the inducible I-SceI-generated double strand break contained the pGAL-I-SceI construct , the I-SceIcs restriction site adjacent to the 3xURA3-tetOx112 array , and TetR-mRFP derived from W9561-17A [63] ( generously provided by R . Rothstein , Columbia Medical School ) . Gene deletion , tagging and promoter replacements for gene overexpression ( pGPD ) were performed using standard PCR-based recombination-mediated gene targeting methods [64] followed by confirmation with PCR . The correct insertion of tags/promoters and the absence of additional mutations were confirmed by sequencing . Strains expressing Pms1 under the tetracycline repressible promoter ( tetO2 ) were generated as previously described [47] . The parental strain ( RDKY8158 ) containing the chimeric repressor ( tetR′-SSN6 ) , the transactivator ( tTA ) and the MMR reporters ( lys2-10A and hom3-10 ) was obtained after mating RDKY3686 with the CML476 strain ( MATa ura3-52 leu2Δ1 his3Δ200 GAL2 CMVp ( tetR′-SSN6 ) ::LEU2 trp1::tTA ( Euroscarf ) . Plasmid pCM324 ( Euroscarf ) was used to introduce the tetO2 promoter immediately upstream of the Pms1 start codon . Specific point mutations were introduced in chromosomal genes using standard integration/excision methods and the following integrating plasmids: msh6-F337A was introduced with pRDK1602 [33]; pms1-E707K was introduced with pRDK1583 [33]; and the DNA polymerase alleles pol2-M644G , pol3-01 , and pol3-L612M were integrated as previously described [35] , [37] , [65] , [66] . The presence of the desired mutation and absence of additional mutations was confirmed by DNA sequencing . For visualization of the low abundance proteins Pms1 and Mlh2 , we tagged them at the C-terminus at the endogenous gene locus with four tandem copies of GFP ( 4×GFP ) using the pSM1023 plasmid ( gift of E . Schiebel , University of Heidelberg ) . Testing of the tagged strain RDKY7893 ( MLH2-4GFP . KanMX6 ) for sensitivity to cisplatin as described previously [30] showed that it had the same sensitivity as the wild-type parental strain RDKY5964 and was more sensitive than the mlh2Δ control strain RDKY7926 ( mlh2::KanMX4 ) , indicating that the tag on the C-terminus of Mlh2 was unlikely to have an effect on Mlh2 function [30] . The nuclear pore protein Nic96 was tagged at the C-terminus with mCherry using the plasmid pBS35 as a template ( Yeast Resource Center YRC ) . The C-terminus of Mlh2 was tagged with tdTomato using a PCR-based strategy and the plasmid pRDK1663 . This plasmid was derived from pYM25 yeGFP . hphNT1 ( Ref . [64]; obtained from Euroscarf ) by excising the HindIII-BglII fragment containing the yeGFP coding sequence , replacing it with a HindIII-BglII fragment ( generated by gene synthesis at Integrated DNA Technologies , IDT ) encoding an S . cerevisiae codon optimized tdTomato gene and a linker ( Gly Ala ) 5 immediately upstream of the Met start codon of tdTomato . S . cerevisiae whole-cell extracts were prepared using the Yeast Extract Buffer ( 1 . 85 M NaOH , 7 . 5% beta-mercaptoethanol ) , and the soluble proteins were precipitated by addition of an equal volume of 50% trichloroacetic acid . The protein extracts were analyzed by SDS-PAGE using 4%–15% gradient gels ( BioRad ) and immunoblotting . Detection of GFP-tagged proteins was performed using the anti-GFP antibody , clone B34 ( Covance ) . Using the anti-Pgk1 antibody ( clone 22C5D8; Invitrogen ) , Pgk1 expression was monitored as a loading control . MYC-tagged proteins were detected with the monoclonal anti-MYC antibody ( clone 4A6; Millipore ) . Mutation rates were determined using the hom3-10 and lys2-10A frameshift reversion assays and CAN1 inactivation assay by fluctuation analysis [67] as previously described [46] , [62] . The mutation rates presented in Table 1B were determined in the absence or presence of 10 µg/ml of doxycycline ( present in liquid cultures as well as in agar plates ) . All the proteins were expressed from plasmid expression vectors in either E . coli or S . cerevisiae as indicated below . Typical yields ranged from 100 µg to 500 µg per liter of expressing cells . All the protein preparations were confirmed to be greater that 95% pure as judged by SDS-PAGE followed by staining of the resulting gels with Coomassie Blue . The recruitment of Mlh1-Pms1 or Mlh1-Mlh2 by Msh2-Msh6 or Msh2-Msh3 bound to DNA was analyzed using a Biacore T100 instrument essentially as described previously [13] , [68] . Biotinylated 236 bp-long double-stranded DNAs containing the terminal lacO sequence and a central TG mispair , +T insertion or GC base pair were conjugated to 3 flow cells of a streptavidin-coated Biacore SA chip ( GE Healthcare ) to obtain a signal of ∼100 Resonance Units ( RU ) . The signal from the unmodified flow cell was used for reference subtraction in all experiments . A constant flow rate of 20 µl/min was maintained and experiments were performed at 25°C . First , 30 nM purified LacI tetramer ( a gift from Kathleen Matthews , Rice University ) in reaction buffer ( 25 mM Tris-Cl ( pH 8 . 0 ) , 4 mM MgCl2 , 110 mM NaCl , 0 . 01% Igepal , 2 mM DTT and 2% glycerol ) was injected over the chip for 60 s . Next , at time t = 0 a sample containing 30 nM LacI , 20 nM Msh2-Msh6 or 20 nM Msh2-Msh3 and 250 µM ATP in reaction buffer was injected for 100 s , followed by the immediate injection of a sample containing the same mixture and in addition 40 nM Mlh1-Pms1 or 40 nM Mlh1-Mlh2 , or no MutL homolog for 150 s . The chip surface was regenerated using a 60 s pulse of 2 M NaCl . Control experiments were performed in which the first injection consisted of 30 nM LacI , the sond injection consisted of 30 nM LacI and 250 µM ATP , and the third injection consisted of 30 nM LacI , 250 µM ATP and either 40 nM Mlh1-Pms1 or 40 nM Mlh1-Mlh2 . These data were subtracted from the data obtained using Msh2-Msh6/Msh2-Msh3 in the second and third injections , and the subtracted curves are presented . Data were analyzed using the BiaEvaluation 2 . 0 . 3 ( GE Healthcare ) and Prism 6 . 0 software ( GraphPad ) . Exponentially growing cultures were washed and resuspended in water and placed on minimal media agar pads , covered with a coverslip , and sealed with valap ( a 1∶1∶1 mixture of Vaseline , lanolin , and paraffin by weight ) . Cells were imaged on a Deltavision ( Applied Precision ) microscope with an Olympus 100×1 . 35NA objective . Fourteen 0 . 5 µm z sections were acquired and deconvolved with softWoRx software . For time-lapse imaging of Pms1 foci , images were collected every min with fewer z sections to minimize photobleaching . Further image processing , including maximum intensity projections and intensity measurements , was performed using ImageJ . For drug treatments , cells that were growing logarithmically in YPD medium were treated with either 200 mM hydroxyurea or 5 µM phleomycin for 3 hr and prepared for microscopy as described above . Cell cycle arrest was confirmed by examining cell morphology using a microscope . For DSB induction by I-SceI , strains RDKY7906 , RDKY7907 , and RDKY7908 were grown overnight in medium containing 2% raffinose . The cultures were diluted into the same medium and after 3 hr , pelleted and resuspended in medium containing either 2% raffinose or 2% galactose , incubated at 30°C for 2 . 5 hr , and prepared for microscopy . Foci were considered to be colocalized if over half of their diameters overlapped . Colocalization was scored if at least one focus per nucleus displayed colocalization in the same z section . Images with the same fluorescent fusion protein in the same figure have identical contrast adjustment . The data presented here contain representative images and quantitative data from at least two independent experiments , each performed using two independent strain isolates , which gave similar results . The total number of cells/nuclei ( n ) analyzed for each strain is indicated . MutL homologs were identified using Protein BLAST [52] against the non-redundant protein sequences ( nr ) database . For some MutL homologs , alignments of the protein sequences revealed that strongly conserved regions were missing . We analyzed the genomic sequences encoding these genes and discovered that these were often due to incorrect assignment of exons , as the missing regions tended to either be at exon boundaries , suggesting the inappropriate identification of a predicted splice site , or in introns , suggesting the failure to identify an exon . For these genes , we re-annotated the exons , typically merging in-frame introns with the surrounding exons or identifying missing exon sequences , and retranslated the protein sequences ( Table S2 ) . The criteria for re-annotation of the exons were to improve homology to the conserved portions of the protein sequence alignment and maintain conservation of the intron/exon structure with closely related species . In some cases , a frameshift or stop codon was present in an exon . In these cases , the surrounding protein sequence was translated to prevent truncations from having an inordinately large effect on the phylogenetic analysis; however , this analysis could not distinguish between sequencing errors or species having mutations that inactivated the gene . The cases in which a protein was translated in spite of frameshifts or stop codons in the reference genomic sequence are explicitly labeled in Table S2 . To decipher relationships between MutL homologs , amino acid sequences of the N-terminal domains were aligned with MAFFT [73] to avoid misalignments due to effects of the rapidly evolving and likely unstructured linker between the MutL N- and C-terminal domains . Phylogenetic analyses were performed with MrBayes [74] using the mixed amino acid rate matrices model and 1 , 000 , 000 generations . Clade credibility values above 75 were considered significant . | Lynch syndrome ( hereditary nonpolyposis colorectal cancer or HNPCC ) is a common cancer predisposition syndrome . In this syndrome , predisposition to cancer results from increased accumulation of mutations due to defective mismatch repair ( MMR ) caused by a mutation in one of the human mismatch repair genes MLH1 , MSH2 , MSH6 or PMS2 . In addition to these genes , various DNA replication factors and the excision factor EXO1 function in the repair of damaged DNA by the MMR pathway . In Saccharomyces cerevisiae , the MLH2 gene encodes a MutL homolog protein whose role in DNA mismatch repair has been unclear . Here , we used phylogenetic analysis to demonstrate that the S . cerevisiae Mlh2 protein and the mammalian Pms1 protein are homologs . A combination of genetics , biochemistry and imaging studies were used to demonstrate that the Mlh1-Mlh2 complex is recruited to mispair-containing DNA by the Msh2-Msh6 and Msh2-Msh3 mispair recognition complexes where it forms foci that colocalize with Mlh1-Pms1 foci ( note that scPms1 is the homolog of hPms2 ) and augments the function of the Mlh1-Pms1 complex . Thus , this work establishes the Mlh1-Mlh2 complex as a non-essential accessory factor that functions in MMR . | [
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] | 2014 | Mlh2 Is an Accessory Factor for DNA Mismatch Repair in Saccharomyces cerevisiae |
CD8+ lymphocytes play an important role in suppressing in vivo viral replication in HIV infection . However , both the extent to which and the mechanisms by which CD8+ lymphocytes contribute to viral control are not completely understood . A recent experiment depleted CD8+ lymphocytes in simian immunodeficiency virus ( SIV ) -infected rhesus macaques ( RMs ) on antiretroviral treatment ( ART ) to study the role of CD8+ lymphocytes . CD8+ lymphocytes depletion resulted in temporary plasma viremia in all studied RMs . Viral control was restored when CD8+ lymphocytes repopulated . We developed a viral dynamic model to fit the viral load ( VL ) data from the CD8 depletion experiment . We explicitly modeled the dynamics of the latent reservoir and the SIV-specific effector cell population including their exhaustion and their potential cytolytic and noncytolytic functions . We found that the latent reservoir significantly contributes to the size of the peak VL after CD8 depletion , while drug efficacy plays a lesser role . Our model suggests that the overall CD8+ lymphocyte cytolytic killing rate is dynamically changing depending on the levels of antigen-induced effector cell activation and exhaustion . Based on estimated parameters , our model suggests that before ART or without ART the overall CD8 cytolytic killing rate is small due to exhaustion . However , after the start of ART , the overall CD8 cytolytic killing rate increases due to an expansion of SIV-specific CD8 effector cells . Further , we estimate that the cytolytic killing rate can be significantly larger than the cytopathic death rate in some animals during the second phase of ART-induced viral decay . Lastly , our model provides a new explanation for the puzzling findings by Klatt et al . and Wong et al . that CD8 depletion done immediately before ART has no noticeable effect on the first phase viral decay slope seen after ART initiation Overall , by incorporating effector cells and their exhaustion , our model can explain the effects of CD8 depletion on VL during ART , reveals a detailed dynamic role of CD8+ lymphocytes in controlling viral infection , and provides a unified explanation for CD8 depletion experimental data .
Current antiretroviral treatment ( ART ) can suppress viral replication in HIV infected patients , but it cannot eliminate the infection due to the persistent latent reservoir . Once ART is interrupted , the majority of patients exhibit viral rebound and lose viral control . To eliminate HIV infection or control HIV replication to low levels leading to functional cure , HIV infected cells need to be eliminated . These infected cells can be killed by viral cytopathic effects or through cell-mediated killing by cytotoxic CD8+ T cells , CD4+ T cells , or NK cells . CD8+ lymphocytes can also reduce viral production through non-cytolytic effects . CD8+ lymphocytes play an important role in suppressing in vivo viral replication in HIV infection . However , the detailed mechanisms of action and relative contributions of CD8+ lymphocytes in controlling viral infection are not fully understood[1] . CD8 depletion experiments have shown that in untreated chronically infected rhesus macaques ( RMs ) , viral load ( VL ) increases after CD8 depletion , and the VL returns to baseline after CD8+ lymphocytes are repopulated[2–4] , suggesting a strong role of CD8+ lymphocytes in controlling chronic SIV infection . Klatt et al . [5] and Wong et al . [6] showed that the first phase slope of VL decay after ART was initiated was not changed in CD8 depleted animals suggesting that CD8+ lymphocytes may not be killing infected cells . This explanation has been controversial and other explanations have been proposed [5–9] . Klatt et al . [5] and Wong et al . [6] suggested that CD8+ lymphocytes might be killing infected cells during the eclipse phase . Because cells in the eclipse phase are not yet producing virus such killing would not influence the first phase viral decay rate . Gadhamsetty et al . [8] developed a model including an eclipse phase that suggested that under certain circumstances the slope of VL decline after ART initiation is not determined by the death rate of productively infected cells . In this paper we will provide yet another explanation . In a recent CD8 depletion study by Cartwright et al . [10] , CD8+ lymphocytes in ART-treated SIV-infected rhesus macaques were depleted after the VL was driven to near or below the detection limit by ART to study the role of CD8+ lymphocytes in viral control . CD8 depletion during ART resulted in a temporary increase of plasma viremia in all studied RMs , and viral control was regained when the CD8+ lymphocytes population recovered . In this study , we model the data from this CD8 depletion experiment using different mathematical models to explore the mechanisms of action of CD8+ lymphocytes in viral control . We characterize the relative roles of viral cytopathic cell death and CD8 cell cytolytic killing as well as non-cytolytic effects in controlling viral replication . Interestingly , our model suggests a dynamic role of CD8 lymphocyte effects , which are small before or without ART due to CD8 lymphocyte exhaustion , but which can significantly increase after ART initiation and VL decease . Specifically , our model analysis of the data presented in Cartwright et al . [10] predicts that in the absence of ART , the CD8 cytolytic killing rate is considerably less than the viral cytopathic death rate in all but one macaque . This result can explain the findings in Klatt et al . [5] and Wong et al . [6] of similar VL decline rates after ART initiation with or without prior CD8 depletion .
We built mathematical models to explain the VL data and infer mechanisms . We used the L-BFGS-B algorithm [11] in R to fit the models to the data by maximizing the log-likelihood function: argmaxθ , σ∑t[−ln ( σ2π ) −0 . 5 ( log10v ( t ) −log10f ( θ , t ) σ ) 2+lnΦ ( log10LOQ−log10f ( θ , t ) σ ) −lnΦ ( log103 . 1−log10f ( θ , t ) σ ) ] . We assume v ( t ) = f ( θ , t ) + e , where v ( t ) is the measured viral load at time t , θ is a vector of parameters to estimate , f ( θ , t ) is the model simulated VL with parameters θ at time t , and e is the error between the theoretical model and the data , which we assume is normally distributed with variance σ2:e~N ( 0 , σ2 ) [12 , 13] . LOQ is the limit of quantification for censored VL data equal to 30 copies/mL [10] . We use Φ ( x ) , the cumulative density function ( CDF ) of the Gaussian distribution to compute the probability that the model simulated VL f ( θ , t ) is below the LOQ . We assume that the residual plasma VL under therapy is not smaller than 3 . 1 copies/mL [14 , 15] , and incorporate this constraint in the log-likelihood function [16] . We run 100 data fittings for each RM with uniformly sampled initial parameter values from the log10-scaled parameter space . We select the parameter values from the best fits that maximize the log-likelihood . We compare models and parameter choices by calculating the Bayesian Information Criteria ( BIC ) . In general , we prefer the parameter choices that give the smallest overall BIC . The typical guideline to accept one model over another is to have a BIC at least 2 points smaller . However , in our fitting , we preferentially accept the parameter choices that give the smallest BIC , regardless of whether the difference is larger or smaller than 2 points .
To understand the mechanism by which VL is controlled by CD8+ lymphocytes , we built a mathematical model to describe the dynamics of target cells ( T ) , latently infected cells ( L ) , productively infected cells ( I ) , virus ( V ) , effector cells ( E ) and allowed for the possibility that effector cells can become exhausted cells ( X ) . As the anti-CD8α depleting antibody , MT-807R1 , used in this study will deplete both CD8+ T cells and NK cells the effector cell population could comprise both cell types . As only CD8+ T cell levels were measured during the experiment , we focus on the CD8 T cell response and use the term cytotoxic T lymphocyte ( CTL ) interchangeably with the term effector cell with the understanding that the effector cell population could also contain NK cells . The model is a generalization of the model in Conway and Perelson [17] , which was developed to explain the phenomenon of post-treatment control ( PTC ) observed in the VISCONTI study [18 , 19] . The CTL-VC model is given by Eq 1 and illustrated in Fig 1 . Target cells ( T ) are constantly produced at rate λ and die at per capita rate dT . Virus ( V ) can infect target cells ( T ) at rate β . A fraction , αL , of the infection events generate latently infected cells ( L ) and the remaining fraction generate productively infected cells ( I ) . Viral infection can be suppressed by ART with a drug efficacy , ϵ , where 0 < ϵ < 1 . Latently infected cells ( L ) can be activated into productively infected cells ( I ) at a small rate a . Latently infected cells ( L ) proliferate at per capita rate ρ and die at per capita rate dL . Productively infected cells ( I ) die at per capita rate δ due to a combination of viral cytopathic effects , activation induced cell death and natural death , and are killed by SIV-specific effector cells ( E ) at per capita rate mE . Virus is produced from productively infected cells at a maximum rate p per cell , and are rapidly cleared at rate c per virion . CTL effector cells ( E ) can reduce the viral production rate p by the factor ( 1 + ηE ) due to non-cytolytic effects [3 , 20–23] . CTL effector cells ( E ) are produced at rate λE and die at per capita rate μ . Exhausted CD8 effector cells die at per capita rate dX . Also , the effector cells ( E ) are activated into clonal expansion by interacting with productively infected cells ( I ) according to a saturating term with a maximum growth rate bE and 50% saturation coefficient KB . Simultaneously , the effector cells ( E ) become exhausted ( X ) according to a saturating term with maximum rate dE and 50% saturation coefficient KD ≫ KB . This model for effector cell expansion and exhaustion has been introduced previously by Conway and Perelson [17] and by Bonhoeffer et al . [24] and Adams et al . [25] to model effector cell expansion at low viral loads and “immune impairment” at high viral loads . To study how depleting the CD8+ lymphocytes affects the post-CD8 depletion viral rebound , we explicitly incorporate a CD8 depletion term in the model . Both effector and exhausted cells are depleted by the anti-CD8 antibody at per capita rate kdAb ( t ) EC50+Ab ( t ) , where kd is the maximum depletion rate and EC50 is the 50% effective antibody concentration . All parameters and their descriptions and fixed values are listed in Table 1 . The pharmacokinetics of the CD8 depleting antibody MT-807R1 have not been characterized . Here we assume that after infusion the Ab decays according to a biphasic model: Ab ( t ) =c1e−k1t+c2e−k2t , where t is the time after antibody administration . We estimated the antibody decay parameters for each RM by fitting a phenomenological model to the dynamics of the total CD8 T cell data during depletion . We assume the dynamics of the total CD8 T cells follow the equation: dTotdt=λT−μTTot+kpTot ( 1−TotTotss ) −kdAb ( t ) EC50+Ab ( t ) Tot , and assume the total CD8 T cell population is at steady state before depletion . Note that the total CD8 population is not the same as X+E , which is the total antigen-specific CD8 population . We use the total CD8 T cell count data before depletion as its steady state value Totss and we use a logistic term to model the homeostatic control of total CD8 T cells . We assume the maximum proliferation rate of CD8 T cells under lymphopenic conditions is kp = 6 d−1 [31] . We fix μT = 0 . 011 d−1 [27] ( Table 1 ) , then we choose λT = μTTotss for each RM to maintain the steady state CD8 T cell level . The dose of anti-CD8 antibody MT-807R1 in the experiment was 50 mg/kg [10] . Assuming an average blood volume of 55mL/kg for RMs [32] , we calculate the maximum antibody concentration Cmax = 50/55 mg/mL≈900 μg/mL . We let c1 = 200 μg/ml and c2 = 700 μg/ml to have c1 + c2 = Cmax . We estimate the values of kd , k1 , and k2 that lead to the best fit of the model for the total CD8 T cell dynamics to the CD8 data for each animal , while scanning EC50 through the values 0 . 0001 μg/mL , 0 . 0005 μg/mL , 0 . 001 μg/mL , 0 . 002 μg/mL , and 0 . 003 μg/mL . We fixed EC50 = 0 . 001 μg/mL as it gave the smallest overall BIC . The fits to the total CD8 T cell dynamics with EC50 = 0 . 001 μg/mL in all RMs are shown in S4 Fig , and the estimated parameters for anti-CD8 antibody are listed in S3 Table . Note that due to lack of pharmacokinetic data , the antibody decay parameters estimated from this fitting are only for simulating the phenomenological dynamics of CD8 T cell depletion . They do not necessarily reflect the realistic in vivo parameter values . We estimate 5 model parameters by fitting the CTL-VC model to the VL data ( Table 1 ) , namely the probability of latent infection αL , the viral production rate p , the antigen induced maximum effector cell exhaustion rate dE , the 50% saturation coefficients for effector cell production KB , and the strength of the CD8 non-cytolytic effect η that modulates the viral production rate , while scanning δ through different values between 0 . 15 d−1 and 0 . 90 d−1 with an interval of 0 . 05 d−1 and scanning β through values between 1 . 5 × 10−8 mL d−1 and 4 . 5 × 10−8 mL d−1 with an interval of 5 × 10−9 mL d−1 with the understanding that if the best-fit values of these parameters occur at the ends on these intervals we would extend the searched parameter interval . The upper end of search interval for δ was chosen to be less than 1 . 5 d−1 , a value estimated by Brandin et al . [33] using a model without effector cells , so as to allow effector cell killing to play a role . We fit the value of αL so as to allow the possibility that each RM has a different latent reservoir size . Because CD8 T cell exhaustion occurs later in infection than activation , we assume the 50% saturation coefficient for exhaustion KD is larger than KB . Here we let KD = f × KB , and vary f through different values between 30 and 70 with an interval of 5 . The value of f = 55 gives the smallest overall BIC . We therefore fix KD = 55 KB in all our subsequent analysis . We fit the CTL-VC model to the data and compute the overall BIC from the best-fits for each different value of δ and β in each animal ( S4 Table and S5 Table ) . The values of δ = 0 . 40 d−1 and β = 3 . 0 × 10−8 mL d−1 gives the smallest total BIC . Although the best-fit values for δ and β are different among animals in terms of BIC , the best BICs of most animals are within 2 of the BIC obtained using δ = 0 . 40 d−1 and β = 3 . 0 × 10−8 mL d−1 , and thus for simplification we fix δ = 0 . 40 d−1 and β = 3 . 0 × 10−8 mL d−1 . All other fixed parameter values are listed in Table 1 . Estimated parameters are listed in Table 2 . The estimated values for αL and p are within ranges estimated in other studies [28 , 34] . The best-fit of the CTL-VC model to the data accurately captured the VL dynamics in all three phases , pre-ART , post-ART , and post-CD8 depletion in most RMs ( red lines in Fig 2 ) . The model correctly captured the first VL peak during primary infection in all RMs , and the slow second phase decay in VL dynamics during ART . The model also correctly captured the VL dynamics after ART and before CD8 depletion . Importantly , the model correctly captured the post-CD8 depletion VL rebound in all RMs , including the small post-depletion VL peak in RGb13 , which has only one supporting data point above the detection limit ( Fig 2 ) . Based on simulations using the estimated parameters , the model predicted that the VL will not rebound if CD8 T cells are not depleted ( S5 Fig ) . Furthermore , the model predicted that if these RMs are CD8 depleted in the absence of treatment , a transient increase in viremia will occur with different amplitudes in different animals ( S6 Fig ) , as observed in previous experiments [4] . Interestingly , the model also predicted the total CD4+ T cell dynamics well without fitting to the CD4+ T cell data ( S7 Fig ) . The size of the post-CD8-depletion VL peak is different among RMs ( S2 Fig ) . We hypothesize that the size of latent reservoir and drug efficacy might be contributing to the size of the post-CD8 depletion VL peak . A strong correlation ( R = 0 . 6 ) was demonstrated between post-depletion peak VL and the latent reservoir size measured by SIVgag DNA copy number in Cartwright et al . [10] . Further , longitudinal single genome sequencing of plasma virus showed that the sequences of the post-CD8 depletion viruses were more similar to sequences derived at peak viremia than to those derived immediately prior to ART suggesting that the rebound virus may have been archived in long-lived or latently infected cells [10] . To test the hypothesis that latent reservoir size influences the post-CD8-depletion VL peak , we first calculated the correlation between the peak VL in the data and the pre-CD8-depletion latent reservoir size predicted by the CTL-VC model using the estimated parameter values ( Table 2 ) . The peak VL after CD8 depletion has a strong positive correlation with the predicted pre-depletion latent reservoir size ( R = 0 . 77 with p = 0 . 002 ) ( Fig 3A ) . These results suggest that the latent reservoir significantly contributes to the viral rebound after CD8 depletion . To compare the relative contributions of drug efficacy and latent reservoir size to the peak VL after CD8 depletion , rather than fixing the drug efficacy ( Table 1 ) , we estimate the drug efficacy for each RM by fitting the CTL-VC model to the VL data . To maintain the number of parameters we fit at 5 , rather than fitting the probability of infection yielding a latently infected cell , αL , we now allow αL to take a fixed value 10−6 , 10−5 , 10−4 , 10−3 , 10−2 , or 10−1 that yields the best-fit each different animal . The estimated parameters along with the value of αL for each animal are listed in S6 Table . Based on the estimated drug efficacy , the post-depletion peak VL has a weak and not statistically significant negative correlation with drug efficacy ϵ ( R = −0 . 023 with p = 0 . 94 ) ( Fig 3B ) . This result confirms that contribution of the latent reservoir to the size of the peak VL after CD8 depletion is more significant than the contribution from drug efficacy and is consistent with the sequencing data in Cartwright et al . [10] indicating that the virus emerging after CD8 depletion is similar to “peak” virus and not the virus circulating immediately prior to ART initiation . To understand the role of SIV-specific effector CD8 T cells in controlling viral infection , we simulated the trajectory of the effector cell ( E ) population and the exhausted cell ( X ) population based on the estimated parameters . In most RMs , CD8 T cells show significant exhaustion during primary infection before ART resulting in an exhausted population that is larger than the effector cell population ( Fig 4 ) . Early exhaustion of CD8 memory T cells has also been seen during acute viral infections of mice with lymphocytic choriomeningitis virus [35] . Interestingly , after initiation of ART , our model predicted an increase in the effector cell population ( red lines in Fig 4 ) in most RMs . As a result , the effector cell population become dominant in most animals after initiation of ART ( Fig 4 ) . We also predicted that the total SIV-specific CD8 T cells ( S8 Fig ) including both effector and exhausted cells hardly changes in most animals , with the exception of two intermediate controller animals in which there is a predicted increase ( see S2 Fig ) . Why these animals show a predicted increase is not clear , although increases in antigen specific CD8 T cells after ART initiation have been previously observed but could be influenced by factors such as redistribution , which are not part of our model . HIV/SIV infected cells can die by viral cytopathic effects , activation induced cell death , natural death or by CD8 T cell cytolytic killing . Based on the parameters estimated from our modeling , we compared the predicted dynamics of the CD8 cytolytic killing rate , mE , with the rate of cell death induced by viral cytopathic effects δ ( Fig 5 ) . In phase I , before ART , the cytolytic killing rate tends to be small ( Fig 5 ) . However , after ART is started , the cytolytic killing rate increases in some RMs due to the expansion of the CD8 effector cell population ( Fig 4 ) . The peak cytolytic killing rate during ART can be as high as about 2 . 0 d−1 ( e . g . RWj14 in Fig 5 ) , which is significantly higher than the cytopathic cell death rate ( δ = 0 . 40 d−1 ) . According to the model , when VL control is achieved , the CD8 cytolytic killing ( mE ) starts to decline ( S9 Fig ) . In most RMs , the cytolytic killing rate declines back to a level smaller than the cytopathic death rate . Overall , these results suggest an important role of CD8 T cell cytolytic killing in control of the viral load during ART . Our modeling suggests that the cytolytic killing rate of CD8 T cells is not constant , rather it is dynamically changing as the effector cell population size is changing , and can be dramatically affected by the residual VL . In comparison with the death rate of productively infected cells due to cytopathic effects ( black horizontal lines in Fig 5 ) , the cytolytic killing rate in some animals can be significantly larger during ART . To compare the role of CD8 cytolytic killing and non-cytolytic effects on suppressing viral replication , we further fit the CTL-VC model to the VL data with either no non-cytolytic effect ( η = 0 ) or with no cytolytic killing ( m = 0 ) . We then compared the qualities of the fits with the original CTL-VC model , which has both cytolytic and non-cytolytic effects . Estimated parameters for both mechanisms are listed in S7 Table and S8 Table , respectively . The fitting qualities of the CTL-VC model with either only cytolytic effect or only non-cytolytic effect are worse than that of original CTL-VC model with both effects , while the fitting quality of CTL-VC model with only cytolytic effect is the worst . To further study the role of non-cytolytic effect in viral suppression , we plotted the predicted viral production rate p/ ( 1 + ηE ) based on the estimated parameters of CTL-VC model ( Table 2 ) for all 13 RMs ( red lines in S10 Fig ) . In some animals , the maximum suppression of viral production can be more than 50% ( S10 Fig ) , while the effect can be minor in other animals . In the model with no CD8 cytolytic killing ( m = 0 ) , the viral production rates are predicted to be suppressed to close to 0 d−1 in some animals through the non-cytolytic effect ( S11 Fig ) . This overly strong suppression of viral production ( S11 Fig ) does not seem biologically realistic and suggests that CD8 non-cytolytic effects might not be the only mechanism to suppress viral replication . Overall , the comparisons of fitting qualities between two effects suggest that CD8 cytolytic and non-cytolytic effects likely are both important for viral suppression , but quantitative comparisons are difficult . One interesting feature of the CTL-VC model is that it captures the slow kinetics of the second phase of viral decay without introducing a population of long-lived infected cells . The viral load data in most intermediate controllers and slow controllers exhibits a fast first phase decay and a slow second phase decay ( S12 Fig ) , while in the fast controllers there is not enough data to characterize the second phase . The half-life of second phase decay in slow controllers ( ~36 . 3 days on average ) is longer than in intermediate controllers ( ~22 days on average ) ( S12 Fig ) . Interestingly , according to our model the slow controllers have smaller CD8 cytolytic killing rates than those intermediate controllers ( Fig 5 and S9 Fig ) . This suggests that the dynamically changing contribution of effector cell killing could be responsible for the second phase of VL decay , an idea previously suggested in a modeling study by Arnaout et al [36] . To examine the effect of CD8 T cells on the half-life of second phase decay , we first compared the contributions of the two infected cell populations , i . e . latently infected cells and productively infected cells to the overall VL dynamics . To do this , we simulated the contributions to the VL of the two cell populations ( S13 Fig ) based on the estimated parameters from the CTL-VC model . Results showed that productively infected cells dominantly contributed to the VL during the initial phase of VL decay after initiation of ART ( S13 Fig red lines ) . The second phase VL decay was mostly contributed by activation from latently infected cells ( S13 Fig green lines ) . The calculated half-lives of second phase decay from simulations are consistent with those seen in the data . We further simulated VL dynamics using the estimated parameters but varying the CD8 effector cell killing rate , m , and found that strong CD8 T cell responses can reduce the half-life of second phase decay , while weak CD8 responses prolong it ( S14 Fig ) . To study whether these conclusions hold in a model with long-lived infected cells , we developed a new model , called the CTL-VC long-lived infected cell model , by explicitly incorporating the dynamics of infected long-lived cells ( M* ) and their contributions to viral production into the original CTL-VC model . We fit the CTL-VC long-lived infected cell model to the VL data and estimated the same 5 parameters as in the original CTL-VC model , while varying the density of uninfected long-lived cells M0 , the infection rate βM , the cytopathic death rate δM and cytolytic killing rate mM for long-lived cells over a large range . Estimated parameters are listed in S9 Table and the data fits are shown in S15 Fig . Details for the CTL-VC long-lived infected cell model are included in SI . The overall quality of fits with the CTL-VC long-lived infected cell model is slightly improved ( total BIC = 689 , S15 Fig ) over that of the original CTL-VC model ( total BIC = 703 ) . Simulations of the CTL-VC long-lived infected cell model using best-fit parameters suggest that the long-lived cell population does not significantly contribute to the second phase decay dynamics , which is mostly contributed by activation of latently infected cells ( S16 Fig green lines ) . The long-lived infected cells contributed mostly to the VL during the transition from the first to the second phase decay ( S16 Fig ) . The positive correlation between the pre-depletion latent reservoir size and post-depletion peak VL remains strong in the CTL-VC long-lived infected cell model ( R = 0 . 74 and p = 0 . 004 ) ( S17A Fig ) . While the model also predicted a positive correlation between the pre-depletion long-lived cell population size and the post-depletion peak VL ( R = 0 . 64 and p = 0 . 018 ) ( S17B Fig ) , the magnitude of VL contribution from long-lived cell population is about 2–3 logs smaller than the contribution from latency activation . Furthermore , the predicted CD8 cytolytic killing rates for both productively infected ( mE ) and long-lived ( mME ) infected cells of the CTL-VC long-lived infected cell model appears to have similar patterns as those of the original CTL-VC model ( S18 Fig ) , i . e . mE increases after the start of ART and CD8 cytolytic killing plays an important role in some animals during ART . Overall , results from the CTL-VC long-lived infected cell model are compatible with the conclusions based on the original CTL-VC model . Two groups studied the effect of CD8 T cells on VL decay by depleting CD8 T cells before ART in SIV-infected RMs [5 , 6] . Both Klatt et al . [5] and Wong et al . [6] found that when CD8 T cells were depleted about one week before the initiation of ART there was no significant difference between the VL decline slope in these animals and in a control group with no CD8 depletion after ART was initiated . Based on these experiments , it was argued that the contribution of CD8 killing to suppressing viral replication in productively infected cells might be negligible in the setting of high virus production . However , the experiments were compatible with possible CD8 killing of cells before viral production began or with an effect of the eclipse phase in determining the viral decay slope [5 , 6 , 8] . To test if our model can also explain the phenomena of similar post-ART first phase VL decline slopes with or without CD8 depletion , we simulated the VL dynamics of all 13 RMs when their CD8 T cells are depleted one week before ART initiation based on the estimated parameters from the CTL-VC model . After CD8 T cells were depleted but before ART was started , most RMs had an average 1 log of VL increase ( blue lines in Fig 6A ) . After the start of ART , the VL decline slope in most simulations with CD8 depletion ( mean slope −0 . 33 d−1 , blue lines in Fig 6A ) are close to the decline slopes in simulations with no CD8 depletion ( mean slope −0 . 35 d−1 , red lines in Fig 6A ) , is consistent with the experimental observations in Klatt et al . [5] and Wong et al . [6] . According to our model , this is explained by the small contribution of CD8 cytolytic killing before the initiation of ART ( Fig 6C ) when VLs are high and most CD8+ T cells are exhausted ( Fig 4 ) . Thus , depleting CD8 T cells has little predicted effect . However , based on our modeling , the SIV-specific CD8 response reaches peak cytolytic killing rate around the time the VL becomes undetectable during ART . Therefore , depletion of CD8 T cells after VL control can have a more significant effect on VL rebound , as shown in Cartwright et al . [10] . We also tested the effect of infected cell killing during the eclipse phase on the first phase decline rate . To do this , we expanded the CTL-VC model by explicitly incorporating the eclipse phase of SIV infection ( see the eclipse-CTL-VC model in SI for more details ) . Results show that the eclipse-CTL-VC model can also fit the data well ( S19 Fig , estimated parameters in S10 Table ) with a slightly worse BIC ( total BIC = 727 ) than that of the original CTL-VC model ( total BIC = 703 ) . The results from the eclipse-CTL-VC model are consistent with the results from the CTL-VC model ( Fig 6B ) , in that both models showed a VL increase and similar first phase VL decline slope after ART initiation when CD8 T cells are depleted about one week before ART ( Fig 6 ) . The eclipse-CTL-VC model also predicted the small contribution of CD8 cytolytic killing before the initiation of ART ( Fig 6D ) , consistent with the CTL-VC model .
Cartwright et al . showed that CD8 T cells play important roles in controlling SIV infection even when animals are treated with ART [10] by observing that depleting CD8 T cells in ART-treated SIV-infected rhesus macaques ( RM ) was followed by viral resurgence with viral control being re-established after CD8 T cell repopulation . To study the detailed role of CD8 T cells in viral control , we built mathematical models and fit the models to the viral load data from this study . To understand the detailed mechanism of viral control driven by CD8 T cells , we first built a viral kinetic model that explicitly incorporated latently infected cells as well as the dynamics of antigen-specific CD8 effector cells and CD8 exhausted cells , namely the Cytotoxic T Lymphocyte-Viral Control model ( CTL-VC model ) . The CTL-VC model incorporated both CD8 cytolytic killing and non-cytolytic viral suppression . One important limitation of our work is that no killing marker or exhaustion marker was measured in the experiment and thus we have no direct experimental evidence of CTL activity or CD8 cell exhaustion , although as we showed in Results and discuss below the model assuming CTL activity and CD8 exhaustion is consistent with the observed viral dynamics . We further assumed the CD8 depleting antibody depleted both effector and exhausted cells . We constructed a simple antibody pharmacokinetics model and fit the model to the total CD8 T cell count data so that we could accurately represent the effects of the anti-CD8 antibody . We incorporated this into the CTL-VC model and then fit the VL data and estimated 5 model parameters . The CTL-VC model accurately captured the viral dynamics in all RMs ( Fig 2 ) . Based on the model fits and the estimated parameters , we examined the contributions of the latent reservoir and drug efficacy to the peak value of the VL rebound after CD8 depletion . We found a strong correlation ( R = 0 . 77 with p = 0 . 002 ) between the predicted latent reservoir size before CD8 depletion and the size of the peak VL after CD8 depletion . The estimated drug efficacies in different RMs showed a weak and not statistically significant negative correlation with the post-depletion peak VL ( R = −0 . 023 with p = 0 . 94 ) . This result suggests that the size of the latent reservoir or the factors that control the activation of latently infected cells are major contributors to the post CD8 depletion VL resurgence , consistent with the experimental data in Cartwright et al . [10] showing a correlation between SIV DNA+ cells and the post CD8 depletion VL peak and that the viral sequences that emerged post CD8-depletion were more similar to those derived at peak viremia than to those present immediately before ART was initiated . We further examined the detailed roles of CD8 T cells during viral control in terms of effector activation and exhaustion . The predicted dynamics of the effector cell population is different among RMs , but most RMs were predicted to exhibit an increase in the SIV-specific CD8 T effector cell population during first 2–20 weeks after the initiation of ART , followed by a long-term decay ( Fig 4 ) . This result agrees with previous experimental observations that HIV-specific CD8+ T cells decay in the long-term ( >20 weeks ) during ART [37–39] , but increase in the short-term after initiation of ART [38–41] . The model predicted that after initiation of ART , the effector cell population expands faster than the exhausted cell population in most RMs . As a result , the effector cell population becomes dominant in most RMs after a certain length of ART , which further suggests a significant role of SIV-specific CD8 T cells in viral control . The relative contributions of CD8 cytolytic killing , non-cytolytic effects , and viral cytopathic cell death in viral control remain important undetermined quantities . Based on estimated parameters , our model suggests that the CD8 cytolytic killing rate dynamically changes with the VL . As CD8 exhaustion increases when the VL is high , the cytolytic killing rate becomes low . On the other hand , when the VL is low , loss of effector cells due to exhaustion occurs at a very low rate , and the CD8 effector cell population increases , as a result the cytolytic killing rate becomes elevated . Before the initiation of ART or without ART , most RMs have a high VL , which we predict results in a smaller cytolytic killing rate than the viral cytopathic-induced cell death in most RMs . At this stage , viral cytopathic effects are the major mechanism that causes infected cell death . Specifically , the model predicted that in the absence of ART , the CD8+ cytolytic killing rate is considerably less than the viral cytopathic death rate in all but one macaque . However , after initiation of ART , the CD8 cytolytic killing rate starts to increase in most RMs due to expansion of the SIV-specific CD8 effector cell population with little exhaustion . The CD8 cytolytic killing rate reaches its peak rate ( as high as 2 . 0 d−1 in some RMs ) when the VL is very low under ART , which is somewhat surprising since the stimulus for CD8 expansion is also low . During the second phase of VL decay under ART , when the VL is low , cell death is mainly due to CD8 cytolytic killing rather than cytopathic death in some animals . Overall , our model suggests a dynamic role of CD8 cytolytic killing . In comparison with the cytopathic effect , CD8 cytolytic killing might be playing a minor role before ART or with no ART if viral loads are high which leads to CD8 exhaustion . Conversely , cytolytic killing can play an important role during VL suppression under ART if CD8 exhaustion is low . In addition , we studied the role of the CD8 non-cytolytic effect in control of viral infection by comparing data fitting qualities of different models . Our modeling suggests both cytolytic and non-cytolytic effects are important during viral control . However , given the data we have it is difficult to quantitatively compare the relative contributions of the CD8 non-cytolytic effect and cytolytic effect on viral control . RMs in the experiment by Cartwright et al . can be grouped into 3 groups: fast , intermediate and slow controllers , by their different rates to reach VL control after start of ART [10] ( S12 Fig ) . Interestingly , the mean half-life of second phase VL decay in slow controllers is longer than that of intermediate controllers ( S12 Fig ) . According to our model the slow controllers have smaller CD8 cytolytic killing rates than those intermediate controllers ( Fig 5 and S9 Fig ) , which suggests that the dynamically changing contribution of CD8 effector cell killing could change the half-life of the second phase VL decay . By simulating VL dynamics based on estimated parameter with different CD8 effector cell killing rates , m , we found that strong CD8 T cell responses can reduce the half-life of second phase decay , while weak CD8 responses can prolong it ( S14 Fig ) . As previous models that did not include an effector cell response attributed the second phase VL decay after start of ART to the decay of long-lived infected cells [42] , we built a model ( see the CTL-VC long-lived infected cell model in SI for more details ) to explicitly incorporate the long-lived infected cell population to fit the VL data . The CTL-VC long-lived infected cell model yielded slightly improved fits to the data over the original CTL-VC model ( S15 Fig ) , but included four extra parameters whose values were approximated by scanning over a set of values . Predicted contributions to the VL dynamics based on estimated parameters from the CTL-VC long-lived infected cell model suggested the second phase of VL decay , which continues over a long period , mostly reflected the loss of cells in the latent reservoir ( S16 Fig ) , while the loss of long-lived infected cells partly contributed to the VL during the transition from the first to the second phase . This is different than in HIV infection , where the loss of long-lived infected cells can explain second phase decay . We computed the decay slope and half-life for the SIV latent reservoir based on the average frequencies of circulating resting CD4 T cells harboring replication-competent virus , which were measured as infectious cells per million cells ( IUPM ) at 4 different time points in 5 SIV-infected HAART suppressed monkeys as described on page 9251 and in Fig 5 in Dinoso et al . [30] . In humans on long-term suppressive ART , the latent reservoir decays with a half-life of 44 months [43 , 44] , which is too slow to explain the second phase decay . However , in SIV-infected macaques treated with short-term ART as in the Cartwright et al . study the latent reservoir seems to decay much faster , with an average half-life of about 38 days in the Dinoso et al . study [30] , which is sufficiently fast to contribute substantially to second phase decay . However , we need to be cautious about this conclusion as the Dinoso et al . study used pigtail macaques , while the data analyzed here was obtained from rhesus macaques using a completely different ART regime than in the Dinoso et al . study . Simulation results from the CTL-VC long-lived infected cell model also show that the pre-CD8 depletion size of the latent reservoir , rather than the size of long-lived cell population , is responsible for the post-depletion peal viral load . Results from the CTL-VC long-lived infected cell model are consistent with the original CTL-VC model , and do not change the conclusions based on the CTL-VC model . The Cartwright et al . study also included a set of data regarding virus sequencing at the time of peak viremia ( “early virus” ) , immediately before ART initiation ( “late virus” ) , and at the time of viremia rebound after CD8 depletion ( “rebound virus” ) . In all study animals it was observed that the virus emerging after CD8 depletion is closely related to the virus circulating at the time of peak viremia and did not include the immune-escape mutations that accumulated in the SIV-Env gene during the interval between peak viremia ( ~day 10 post infection ) and initiation of ART ( day 56 post infection ) . These results of this longitudinal sequence analysis are not included in the current model of the mechanisms underlying virus control by CD8+ T cells under ART . However , it should be noted that the most parsimonious explanation for this pattern of sequence data is that the rebound of SIV production occurring after CD8+ lymphocyte depletion is due to latency reversal in cells that harbor “early virus” ( as opposed to “late virus” that continued to replicate under ART ) . Further modeling studies in which the sequence data are taken into consideration will help assess the impact of latency reversal as a mechanism responsible for increased virus production after CD8 depletion . Two experimental studies , in which CD8 T cells were depleted before ART , showed the VL decline slopes after initiation of ART were not changed by CD8 depletion when compared with undepleted controls [5 , 6] . Based on these experiments , it was argued that the contribution of CD8 killing to suppressing viral replication in productively infected cells might be negligible . However , Klatt et al . [5] and Wong et al . [6] suggested that CD8 killing might still be occurring during the eclipse phase and Gadhamsetty et al . [8] suggested that the slope of VL decline is mainly determined by the rate of transit through the eclipse phase , rather than the rate of CD8 cytolytic killing . The CTL-VC model suggested a different mechanism . Specifically , due to the small contribution of CD8 cytolytic killing to cell death before ART , our model predicted that depletion of CD8 T cells before ART does not have a significant impact on the VL decay rate . However , although there were no significant changes in the decline slope , the VL was predicted to increase before ART in RMs with CD8 depleted one week before ART ( Fig 6 ) , consistent with the mean dynamics in Klatt et al . [5] . With explicit modeling of the CD8 effector and exhausted cell populations , our model provides a novel unified mechanism to explain the contribution of CD8 T cells in viral control . Overall , our modeling suggests that CD8 T cell cytolytic killing plays an important role in control of viral infection and that the magnitude of cytolytic killing is inversely dependent on the VL . Our models suggest a more important role of CD8 T cell cytolytic killing during ART and when VL is low . Therefore , future CD8 depletion experiments during the second phase VL decay might be useful to test these hypotheses , and also be useful to further study the role of CD8 T cells in viral control . | CD8+ lymphocytes play an important role in suppressing in vivo viral replication in HIV infection . However , both the extent to which and the mechanisms by which CD8+ lymphocytes contribute to viral control are not completely understood . By mathematically modeling data from a recent CD8 depletion experiment done in antiretroviral ( ART ) treated animals , our results suggest that the overall CD8+ lymphocyte cytolytic killing rate is dynamically changing depending on the levels of antigen-induced effector cell activation and exhaustion , i . e . before ART or without ART the overall CD8 cytolytic killing rate is small due to exhaustion . However , after the start of ART , the overall CD8 cytolytic killing rate increases due to an expansion of SIV-specific CD8 effector cells . By incorporating effector cells and their exhaustion , our model explains the effects on viral load of CD8 depletion done before ART or during ART , reveals a detailed dynamic role of CD8+ lymphocytes in controlling viral infection , and provides a unified explanation for CD8 depletion experimental data . | [
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"pre... | 2018 | CD8+ lymphocyte control of SIV infection during antiretroviral therapy |
The transcription factor ATF2 has been shown to attenuate melanoma susceptibility to apoptosis and to promote its ability to form tumors in xenograft models . To directly assess ATF2's role in melanoma development , we crossed a mouse melanoma model ( NrasQ61K::Ink4a−/− ) with mice expressing a transcriptionally inactive form of ATF2 in melanocytes . In contrast to 7/21 of the NrasQ61K::Ink4a−/− mice , only 1/21 mice expressing mutant ATF2 in melanocytes developed melanoma . Gene expression profiling identified higher MITF expression in primary melanocytes expressing transcriptionally inactive ATF2 . MITF downregulation by ATF2 was confirmed in the skin of Atf2−/− mice , in primary human melanocytes , and in 50% of human melanoma cell lines . Inhibition of MITF transcription by MITF was shown to be mediated by ATF2-JunB–dependent suppression of SOX10 transcription . Remarkably , oncogenic BRAF ( V600E ) –dependent focus formation of melanocytes on soft agar was inhibited by ATF2 knockdown and partially rescued upon shMITF co-expression . On melanoma tissue microarrays , a high nuclear ATF2 to MITF ratio in primary specimens was associated with metastatic disease and poor prognosis . Our findings establish the importance of transcriptionally active ATF2 in melanoma development through fine-tuning of MITF expression .
Malignant melanoma is one of the most highly invasive and metastatic tumors [1] , and its incidence has been increasing at a higher rate than other cancers in recent years [2] . Significant advances in understanding melanoma biology have been made over the past few years , thanks to identification of genetic changes along the MAPK signaling pathway . Those include mutations in BRAF , NRAS , KIT and GNAQ , all of which result in a constitutively active MAPK pathway [3]–[5] . Consequently , corresponding transcription factor targets such as microphthalmia-associated transcription factor ( MITF ) [6] , AP2 [7] , and C-JUN [8] and its heterodimeric partner ATF2 [9] are activated and induce changes in cellular growth , motility and resistance to external stress [10] , [11] . In addition , constitutively active MAPK/ERK causes rewiring of other signaling pathways [4] . Among examples of rewired signaling is upregulation of C-JUN expression and activity [8] , which potentiates other pathways , including PDK1 , AKT and PKC , and plays a critical role in melanoma development [12] . Activating transcription factor 2 ( ATF2 ) , a member of the bZIP family , is activated by stress kinases including JNK and p38 and is implicated in transcriptional regulation of immediate early genes regulating stress and DNA damage responses [13]–[15] and expression of cell cycle control proteins [16] . To activate transcription , ATF2 heterodimerizes with bZIP proteins , including C-JUN and CREB [17] , [18] , both of which are constitutively upregulated in melanomas [8] . ATF2 is also implicated in the DNA damage response through phosphorylation by ATM/ATR [19] . Knock-in mice expressing a form of ATF2 that cannot be phosphorylated by ATM are more susceptible to tumor development [20] . Nuclear localization of ATF2 in melanoma tumor cells is associated with poor prognosis [21] , likely due to transcriptional activity of constitutively active ATF2 . Indeed , expression of transcriptionally inactive ATF2 or peptides that attenuate endogenous ATF2 activity inhibits melanoma development and progression in xenograft models [22]–[26] . These studies suggest that ATF2 is required for melanoma development and progression . The transcription factor MITF has been shown to play a central role in melanocyte biology and in melanoma progression [27] , [28] . Yet , the role of MITF in early stages of melanoma development remains largely unexplored . Factors controlling MITF transcription have been well documented and include transcriptional activators , such as SOX10 , CREB , PAX3 , lymphoid enhancer-binding factor 1 ( LEF1 , also known as TCF ) , onecut domain 2 ( ONECUT-2 ) and MITF itself [29]–[33] , as well as factors that repress MITF transcription , including BRN2 and FOXD3 [34] , [35] . In addition , MITF is subject to several post translational modifications which affect its availability and activity , including acetylation , sumoylation and ubiquitination [27] , [28] . To directly assess the importance of ATF2 in melanoma development , we employed a mouse melanoma model in which ATF2 is selectively inactivated in melanocytes . We demonstrate that melanoma development is markedly attenuated in mice expressing a transcriptionally inactive form of ATF2 in melanocytes . Surprisingly , ATF2 control of melanoma development was mediated , in part , through its negative regulation of SOX10 and consequently of MITF transcription . Inhibition of ATF2 abolished mutant BRAF-expressing melanocytes' ability to form foci on soft agar , which was partially rescued when expression of MITF was attenuated . The significance of these findings is underscored by our observation of human melanoma tumors , in which high ratio of nuclear ATF2 to MITF expression was associated with poor prognosis . These findings identify a novel mechanism underlying melanocyte transformation and melanoma development .
Global Atf2 knockout in mice leads to early post-natal death [36] . Therefore , the Cre-loxP system was utilized to disrupt Atf2 in melanocytes . Deletion of its DNA binding domain and a portion of the leucine zipper motif results in a transcriptionally inactive form of ATF2 ( Figure 1a; [36] ) . To generate loss-of-function mutants , we established mice that would allow CRE-dependent deletion of these domains . Mice homozygous for the loxP-flanked ( floxed ) Atf2 gene ( Atf2f/f ) were born at the expected Mendelian ratios and presented no apparent abnormalities . In addition , in several tissues analyzed , ATF2 expression levels were comparable between WT and Atf2f/f mice ( data not shown ) . To elucidate the role of ATF2 in melanoma , Atf2f/f mice were crossed with mice harboring a 4-hydroxytamoxifen ( OHT ) -inducible Cre recombinase-estrogen receptor fusion transgene under the control of the melanocyte-specific tyrosinase promoter , designated Tyr::CreER ( T2 ) . Upon administration of OHT , we predicted that CRE-mediated recombination would be induced in a spatially and temporally controlled manner in embryonic melanoblasts , melanocytes , and in putative melanocyte stem cells [37] . The resulting Atf2f/f/Tyr-CreER ( T2 ) mice , designated melanocyte-deleted ( md ) Atf2md ) , indeed expressed the gene encoding the ATF2 transcriptional mutant in melanocytes . Immunoblot analysis of ATF2 protein confirmed that melanocytes prepared from wild-type TyrCre+::Atf2+/+::Nras+::Ink4a−/− ( WT ) mice express a 70 kDa band corresponding to full length ATF2 , whereas melanocytes of TyrCre+::Atf2md::Nras+::Ink4a−/− mice express only a 55 kDa band , corresponding to the size of ATF2 lacking the DNA binding and leucine zipper domains ( Figure 1b ) . To address the role of ATF2 in de novo melanoma formation Tyr::CreER::NrasQ61K::Ink4a−/− ( KO of exon 2–3 of Cdkn2a locus , which encodes for both p16Ink4a and p19Arf; [38] ) mice , which develop spontaneous melanoma ( Lynda Chin , unpublished observations ) , were crossed with Atf2md mice . Similar to findings reported by Ackermann et al . [39] , mutant N-Ras/Ink4a−/− mice developed melanoma within 8–12 weeks with metastatic lesions often seen in the lymph nodes . However , the incidence of melanoma was lower in Tyr::CreER::NrasQ61K::Ink4a−/− mice used in the present study ( 50% penetrance , of which 50% of the tumors were confirmed to be melanoma ) , probably because expression of mutant NRAS was induced only after birth , as opposed to activation of NRAS during embryogenesis , as reported in [39] ) . Thus , Atf2md::N-RasQ61K::Ink4a−/− mice were used to assess changes in melanoma incidence in the absence of functional ATF2 over a period of up to 8 months . In all cases , mouse skin was treated with Tamoxifen within 3–5 days after birth to inactivate ATF2 ( Figure 1b ) and with doxycycline in their drinking water to induce expression of the NRAS mutant transgene ( See Materials and Methods for details; Figure 1c ) . In the control group ( Tyr::CreER::Atf2+/+::NrasQ61K::Ink4a−/− ) , 11/21 mice ( 52% ) developed tumors within 8–16 weeks ( Table 1 ) . In ATF2 heterozygotes ( Tyr::CreER::Atf2−/+::NrasQ61K::Ink4a−/− ) , 18/44 mice ( 41% ) developed tumors within 8–16 weeks , and in the Tyr::CreER::Atf2md::NrasQ61K::Ink4a−/− group only 3 of 21 animals ( 15% ) developed tumors within 24–36 weeks ( Figure 1d and Table 1 ) . To evaluate tumor type , we examined melanoma markers including DCT and S100 in all tumors ( Figure 1e , Figure S1 ) . This analysis identified 55–63% of tumors as melanomas in both the Atf2+/+ ( 7/11 ) and Atf2+/− ( 10/18 ) groups ( Table 2 ) . Only one of the three tumors observed in the Atf2md group was identified as a melanoma . Kaplan Meier curve did not reveal significant differences in survival among the different genotypes , probably since this study was primarily designed to follow tumor incidence . Common to all genotypes , most tumors that were not identified as melanomas were fibrosarcomas and lymphomas , consistent with previous reports [38] . These data suggest that transcriptionally active ATF2 is required for melanoma development in the NrasQ61K::Ink4a−/− mouse melanoma model . To assess the mechanism underlying ATF2's contribution to melanoma development , we conducted gene profiling array analysis of primary melanocytes prepared from Tyr::Cre+::Atf2+/+::NrasQ61K::Ink4a−/− and Tyr::Cre+::Atf2md::NrasQ61K::Ink4a−/− mice . Analysis was limited to melanocytes , since , as reported above , only one melanoma formed in the ATF2 mutant group . In all cases , ATF2 was inactivated and NRAS was induced in culture within 48h of plating cells , as monitored by western blots ( Figure 1b , 1c and data not shown ) . Melanocytes were enriched , and immunostaining with appropriate markers confirmed that samples were free of keratinocytes and fibroblasts ( data not shown; see Materials and Methods for details ) . RNA was prepared from cultures and two biological and technical replicates were used for data analysis . As shown in Table 3 , among transcripts differentially expressed in ATF2 WT and mutant cultures were several factors that play an important role in melanocyte pigmentation , including Mitf , Silver , Tyrp1 and Dct . qPCR analysis , performed on independently prepared RNA samples from melanocytes expressing WT ( Tyr::Cre+::Atf2+/+::NrasQ61K::Ink4a−/− ) or mutant ATF2 ( Tyr::Cre+::Atf2md::NrasQ61K::Ink4a−/− ) , confirmed altered expression of pigmentation genes ( Table 3 ) . These data provide the initial indication that ATF2 negatively regulates Mitf and several other important pigmentation genes . As the pigmentation genes identified in this array are known to be regulated by MITF [27] , we focused on regulation of MITF by ATF2 . To confirm that ATF2 negatively regulates Mitf expression , we assessed MITF transcription in primary mouse melanocytes harboring WT ( Tyr::Cre−::Atf2+/+ ) or mutant ( Tyr::Cre+::Atf2md ) forms of ATF2 . RNA prepared from whole skin of these mice ( 3 mice per group ) was subjected qPCR analysis . Significantly , Mitf expression was inversely correlated to the presence of functional ATF2; samples obtained from ATF2 mutant skin exhibited a greater than 2-fold increase in MITF expression compared with those obtained from WT ATF2 mice ( Figure 2a ) . Likewise , we found that genes transcriptionally regulated by MITF , such as Dct , Silver and Tyrp1 , were upregulated in the skin of mutant ATF2 mice ( Figure 2a ) . The degree of altered expression of pigmentation genes was less pronounced in whole skin samples than in cultured melanocytes ( Table 3 ) , probably due to confounding effects of in vitro cell culture . To confirm the qPCR data , we performed immunostaining of skin tissue samples obtained from 4 days old WT or ATF2 mutant mice and observed increased MITF expression in melanocytes from Atf2md mice relative to their WT counterparts ( Figure 2b ) . Quantification of MITF staining revealed an approximate 2-fold increase in nuclear MITF expression in Atf2md compared to WT mice ( Figure 2c ) . Of note , the level of S100 staining in the hair matrix was markedly reduced in the skin of Atf2md mice . At a later time point ( 2 weeks ) representing an advanced stage of melanocyte development , S100 staining was similar in both genotypes , while MITF expression remained upregulated in Atf2md mice ( not shown ) . In all , these data confirm our initial observations in primary mouse melanocytes that MITF levels are elevated in ATF2 mutant-expressing cells . Additional assessment was performed in melan-Ink4a-Arf1 melanocytes , a line derived from black Ink4a-Arf null mice [40] , and in primary human melanocytes . In both , ATF2 expression was inhibited by viral infection with the corresponding mouse or human shRNA ( shATF2 ) . Infection of either primary human ( Figure 3a ) or melan-Ink4a-Arf1 melanocytes ( Figure 3b ) with shATF2 markedly increased MITF transcription and protein expression ( Figure 3a , 3b ) . These findings show that loss of transcriptionally active ATF2 allows higher expression of MITF and strongly suggest that ATF2 negatively regulates MITF expression in melanocytes . Given that ATF2 negatively regulates MITF in melanocytes of mouse and human tissues and in related melanocyte cell lines , we asked whether ATF2 also regulates MITF in human melanoma cells . Initially , we assessed changes in MITF expression in six human melanoma lines harboring oncogenic mutations in BRAF or NRAS , and in which ATF2 expression was effectively inhibited by corresponding shRNA ( shATF2 ) . In all cases , shRNA specificity was confirmed using three independent sequences ( data not shown ) . Surprisingly , the six melanoma lines fell into two classes based on distinct patterns of regulation of MITF by ATF2 ( Table 4 ) . The first class comprised four of the six melanoma cultures ( 1205Lu , WM35 , WM793 and WM1361 ) , in which MITF expression was elevated 3–6-fold following inhibition of ATF2 expression ( Figure 3c , S2a ) . Conversely , a second class of cells , including MeWo and 501Mel cells , exhibited decreased MITF expression after ATF2 knockdown ( KD ) , suggesting positive regulation of MITF by ATF2 ( Figure 3d , S2b ) . Notably , this latter group showed high levels of basal MITF expression [41] , [42] , suggesting that regulation of MITF expression in these cells differs mechanistically from that of the first group . Further , in response to stress ( UV or hypoxia ) the MeWo and 501Mel lines further reduced MITF expression ( Figure 3d and data not shown ) , providing further evidence for differential regulation of MITF in these cells both prior to and in response to stress stimuli . Additional analyses were performed , employing 12 more melanoma cell lines . Inhibition of ATF2 expression revealed that 4/12 exhibited increase in MITF expression , while 6/12 decreased MITF expression . Two of the 12 lines did not exhibit change in MITF expression following ATF2 KD ( Table 4 , S5 ) . Collectively , out of 18 melanoma lines we found that 8 ( 44% ) retained similar negative regulation of MITF by ATF2 as observed in the melanocytes . However , another 8 ( 44% ) exhibited positive regulation of MITF by ATF2 , pointing to a transcriptional switch that occurred in the course of melanocyte transformation . MITF was not affected by altered ATF2 expression in 2/18 cell lines ( Table 4 , Figure S5 ) . In all , in about 50% of the melanoma cell lines ATF2 elicits negative regulation of MITF , similar to what was seen in human and mouse melanocytes . MITF transcription is regulated by complex positive and negative cues [27] . For instance , while CREB and SOX10 positively regulate MITF , BRN2 and FOXD3 have been shown to downregulate MITF expression [29] , [30] , [34] , [35] . Hence we used melanocytes and representative melanoma lines to assess mechanisms underlying positive or negative regulation of MITF . Infection of the human melanocyte line Hermes 3A with shATF2 effectively inhibited ATF2 expression , upregulated Mitf transcription and increased transcription of SOX10 and FOXD3 ( from 7- to 10-fold ) and to a lesser extent of Pax3 and Brn2 ( from 1 . 5- to 2-fold ) ( Figure 4a , S3a ) . Similarly , inhibition of ATF2 transcription in human melanoma 1361 cells increased SOX10 and FOXD3 transcription , albeit , to a lesser degree ( 3- and 1 . 5-fold , respectively ) compared with human melanocytes ( Figure S3b ) . Neither BRN2 nor PAX3 transcription was elevated in melanoma cells in which ATF2 expression was inhibited ( Figure S3a ) . These observations suggest a role for ATF2 in FOXD3- and SOX10-mediated regulation of MITF transcription in melanocytes and melanoma cells . To assess the possible role of FOXD3 in regulation of MITF we inhibited FOXD3 expression in melanocytes expressing control shRNA and shATF2 . Inhibition of FOXD3 expression increased SOX10 transcription and protein expression , albeit to lower levels compared with inhibition of ATF2 expression ( Figure S4 ) . Concomitant increase of MITF RNA and protein levels was also lower , compared with that seen upon inhibition of ATF2 expression . Notably , inhibition of both ATF2 and FOXD3 resulted in additive increase of SOX10 and MITF ( Figure S4 ) . These data suggest that FOXD3 may also contribute to negative regulation of MITF in melanocytes , independent of ATF2 . Since inhibition of FOXD3 elicited a less pronounced effect compared with ATF2 , and since the effect appeared ATF2-independent and furthermore did not appear to mediate similar changes in human melanoma cells ( Figure S3b and data not shown ) , we focused on assessment of direct mechanisms underlying ATF2 effect on MITF transcription . To this end we first analyzed MITF promoter sequences for ATF2/CRE elements ( Cyclic AMP response element ) , which can be targeted by ATF2 , as well as sequences recognized by BRN2 and SOX10 using a luciferase reporter construct ( MITF-Luc ) [43] . Using either a wild-type ( WT ) construct or one in which the BRN2 site was mutated , we observed increased luciferase activity following inhibition of ATF2 transcription in WM1361 melanoma ( Figure 4b ) , as well as in LU1205 and WM35 melanoma cells ( data not shown ) . The relative increase in luciferase activity following ATF2 inhibition was equivalent in both constructs , suggesting that an ATF2 effect is not mediated by BRN2 ( Figure 4b , left panel ) . Similarly , MITF transcriptional activities were altered to a similar degree following inactivation of the CRE element ( Figure 4b , right panel ) , suggesting that ATF2 down-regulation of the MITF promoter is indirect . We therefore assessed whether SOX10 , which positively regulates MITF and whose transcription markedly increases in melanocytes and melanoma cells in which ATF2 expression is inhibited ( Figure 4a , S3b ) , may mediate ATF2 effect on MITF transcription . Analysis of a MITF-Luc construct harboring a mutant SOX10 binding site revealed that ATF2 inhibition no longer elicited increased MITF transcription in human melanocytes or in melanoma cells ( Figure 4c ) . In agreement , inhibition of SOX10 expression by corresponding siRNA attenuated the increase in MITF transcription seen in shATF2-expressing human melanocytes ( Figure 4d ) or melanoma cells ( Figure 4e ) . These results suggest that ATF2 regulation of MITF transcription is mediated by SOX10 . In agreement , chromatin immunoprecipitation ( ChIP ) assays confirmed increased binding of SOX10 to the MITF promoter in melanoma cells expressing shATF2 ( Figure 5a ) . A putative response element for AP1 ( which can serve as an ATF2 response element through ATF2 heterodimerization with JUN family members; [9] ) has been identified in upstream regions of the Sox10 promoter [44] . We examined potential ATF2 binding to this element by ChIP and found that endogenous ATF2 , but not ATFa , binds to that AP1 sequence ( −4797–4791 ) in both human melanocytes and melanoma cells ( Figure 5b ) . We next set to identify ATF2 heterodimeric partner , which could mediate negative regulation of SOX10 transcription . Among members of the JUN family implicated in transcriptional silencing is JunB . Thus , further assessment was performed to determine if JunB functions as an ATF2 heterodimerization partner to regulate SOX10 transcription through the AP1 site . ChIP analysis confirmed that JunB binds to the AP1 site found in SOX10 promoter sequences ( Figure 5c ) . To confirm a possible role for JunB in regulating MITF transcription we asked whether expression of TAM67 , a negative regulator of Jun family members , could attenuate the binding and transcriptional activities elicited by JunB . Expression of TAM67 indeed reduced the degree of ATF2 and JunB binding to the AP1 site on SOX10 promoter . Further , KD of ATF2 expression abolished binding of both ATF2 and JunB to the AP1 site on the SOX10 promoter ( Figure 5c ) . These data confirm the presence of ATF2-JunB complex on Sox10 promoter and suggest that ATF2 recruits JunB for binding to the AP1 site on SOX10 promoter . To assess the role of JunB on SOX10 transcription we have monitored changes in Sox10 expression at the protein and RNA levels . Expression of TAM67 caused increased expression of SOX10 in both human melanoma ( ∼2 folds; Figure 5d ) and melanocytes ( ∼3 folds; Figure 5e ) , indicating some relief of JunB inhibition . Co-expression of TAM67 with Jun B attenuated this increase , reducing the level of Sox10 expression to basal levels ( Figure 5d , 5e ) . Over-expression of JunB , but not Jun D , effectively inhibited Sox10 expression in both the melanoma and melanocytes cells ( Figure 5d , 5e ) . These data suggest that JunB mediates inhibition of Sox10 expression . To further reveal the role of ATF2 in this inhibition , we assessed the effect of JunB on Sox10 expression in cells expressing control shRNA or shATF2 . While ectopic expression of JunB reduced the expression of Sox10 in control shRNA-expressing cells , such decrease was no longer seen in cells expressing shATF2 ( Figure 5f ) . Collectively , these findings suggest that ATF2 , in concert with JunB , is responsible for inhibition of Sox10 expression . We next assessed the effect of ATF2 on SOX10 and MITF expression in 12 additional human melanoma cell lines . In all cases cells were infected with shATF2 and changes in SOX10 and MITF were monitored at the level of RNA . Notably , about 4/12 melanoma lines revealed increase in both SOX10 and MITF expression upon KD of ATF2 ( Figure S5 , Table 4 ) . In contrast , 6/12 melanoma lines revealed decrease in MITF expression , of which 5 also shown decrease in SOX10 expression , pointing to positive regulation of SOX10 and MITF in these melanoma cells . In two out of the 12 melanoma lines ATF2 affected SOX10 but not MITF transcription ( Figure S5 ) . Overall , our cohort of 18 melanoma lines revealed that about 50% of the melanomas retained negative regulation of MITF by ATF2 , as seen in the melanocytes ( primary and cell lines ) ( Table 4 ) . To further assess whether ATF2 regulation of MITF is SOX10-dependent in melanocytes and melanoma cells , we coexpressed SOX10 in shATF2-expressing cells . As seen in earlier analysis , inhibition of ATF2 expression caused increase in MITF transcription in the human melanocytes and 4 melanoma cell lines , ( WM1361 , WM793 , LU1205 , WM35; Figure S6 ) . Notably , the melanocytes and 2/4 melanoma cell lines revealed ATF2 effect on MITF expression is SOX10-dependent ( WM1361 , WM793; Figure S6 ) . Two of the four melanoma cell lines did not reveal increased SOX10 expression , although they retained increased MITF expression , upon inhibition of ATF2 ( Lu1205 , WM35; Figure S6 ) . These findings confirm that while in melanocytes , expression of SOX10 and MITF is negatively regulated by ATF2 , this mechanism is conserved in approximately half of melanomas surveyed . Along these lines , the two melanoma lines ( MeWo and 501 Mel ) that exhibit positive regulation of MITF by ATF2 also exhibited positive regulation of SOX10 by ATF2 ( Figure S7 ) . Inhibition of ATF2 expression reduced SOX10 and MITF RNA and protein levels ( Figure S7a–c ) . In order to determine whether JunB lost its ability to elicit negative regulation of SOX10 and MITF in melanoma cells where ATF2 no longer inhibited SOX10 or MITF expression , we transfected those cell lines with TAM67 and JunB alone and in combination . In these cells , whereas TAM67 effectively attenuated Sox10 and MITF expression , JunB did not alter expression of these genes , suggesting that positive regulation of MITF and SOX10 by ATF2 depends on other members of the Jun family of transcription factors ( Figure S7d ) . Conversely , TAM67 or JunB had no effect on melanoma cells in which ATF2 inhibits MITF independently of SOX10 , suggesting that in these cases , ATF2 likely cooperates with transcription factors other than JunB to elicit negative regulation of SOX10 and MITF ( Figure S7d ) . Consistent with this observation , ChIP assay confirmed ATF2 and CREB , but not JunB , binding to the Sox10 promoter in these cells ( Figure S7e ) . These findings suggest that changes in ATF2 heterodimeric partner ( from JunB to CREB ) are likely to cause the switch from negative to positive regulation of SOX10 , and in turn , MITF ( see below ) . The possibility that altered expression of JunB may account for ATF2 positive or negative regulation of Sox10 and MITF were excluded , as no clear correlation between JunB expression and the ability of ATF2 to elicit negative regulation of Sox10/MITF were seen ( Figure S7f ) . Among response elements potentially required to upregulate MITF transcription is the CRE element , which is implicated in CREB-mediated upregulation of MITF transcription [45] . Although transcriptional activity from a CRE mutant MITF promoter was lower compared to the WT promoter ( 30% ) , it was no longer responsive to inhibition of ATF2 expression in the MeWo cells ( Figure S8a ) . Pull-down assays using biotin-tagged MITF promoter sequences harboring the CRE identified ATF2 and CREB as CRE-bound proteins in MeWo melanoma cells ( Figure S8b ) . In agreement , ChIP analysis confirmed occupancy of the CRE site on MITF promoter by ATF2 ( Figure S8c ) . These findings are consistent with the fact that ATF2 heterodimerizes with CREB [9] and with a report that p38/MAPK14 ( which phosphorylates ATF2 ) plays an important role in MITF transcription dependent on the CRE site [46] . These results establish that ATF2-dependent activation of MITF transcription in these melanoma cells is mediated through the CRE site , likely in cooperation with CREB . Notably , MeWo and 501Mel lines are known to express high MITF levels compared to other melanoma lines [41] , [42] , suggesting these cells harbor distinct mechanisms that preclude negative regulation of MITF by ATF2 . To determine whether the contribution of ATF2 to melanocyte transformation and development is MITF-dependent , we assessed melanocytes' ability to grow and form colonies in soft agar , which is indicative of their transformed potential . Expression of mutant BRAFV600E in immortal melanocytes is reportedly sufficient for growth on soft agar [47] . Thus we infected melan-Ink4a-Arf1 melanocytes with mutant BRAF ( Figure 6a ) and confirmed their ability to form colonies in soft agar . Mutant BRAF expression effectively caused formation of about 1000 colonies per 5000 cells ( Figure 6b , 6c ) . In contrast , melanocytes infected with BRAF600E and with shATF2 formed on average about 20 colonies , indicative of loss of tumorigenicity ( Figure 6b , 6c , 6d ) and consistent with our initial observation that the number of melanoma tumors significantly decreases in the absence of transcriptionally functional ATF2 ( Tables 1–2 ) . To determine the importance of MITF at this early stage of melanocyte transformation we inhibited MITF expression ( using shRNA ) in melanocytes expressing mutant BRAF alone or mutant BRAF+shATF2 . Significantly , inhibition of MITF expression decreased the number of BRAF-induced foci ( from 1000 to about 100 per well ) . Over-expression of MITF in BRAF-expressing melanocytes also inhibited focus formation , to a degree similar to that seen following inhibition of MITF expression ( Figure 6b , 6c , 6d ) . This observation implies that effective inhibition or overexpression of MITF attenuates melanocyte transformation , consistent with previous reports ( 52 ) . Remarkably , inhibition of MITF expression in melanocytes expressing both mutant BRAF and shATF2 rescued , at least partially , melanocytes' ability to form foci on soft agar ( 400 compared with 20 seen in shATF2 cells; Figure 6b , 6c , 6d ) . These findings suggest that inhibition of MITF expression in melanocytes lacking ATF2 expression can promote transformation . That MITF inhibition in melanocytes expressing ATF2 WT can attenuate their ability to form foci on soft agar is attributable to the relative expression of MITF RNA and protein in each condition ( Figure 6d ) . MITF expression levels in ATF2 KD cells increased 7 . 5-fold compared with control BRAF-expressing melanocytes . Inhibition of MITF expression in ATF2 KD melanocytes reduced MITF expression 2 . 5-fold relative to controls , whereas MITF KD alone resulted in lower MITF expression ( 5-fold; Figure 6d ) . Thus , complete abrogation of MITF expression attenuates melanocyte transformation , whereas low to moderate levels of MITF expression are sufficient to promote growth on soft agar . Higher MITF expression levels , as seen in ATF2 KD cells , result in a total loss of melanocytes' ability to form foci on soft agar . These findings are in line with the proposed rheostat model in which medium levels of MITF are optimal for growth and melanoma development [48] and in agreement with our observations in a mouse melanoma model . We next assessed whether inhibition of melanocyte growth on soft agar by altered ATF2 and/or MITF expression can be attributed to decreased proliferation or increased apoptosis . Inhibition of ATF2 expression caused notable accumulation of cells in G2 ( 60% ) , with significant cell death induction ( 22% ) compared to controls ( 4% ) , ( Figure 6e , 6f ) . Interestingly , such altered cell cycle distribution and cell death rate were associated with a significant increase in MITF protein levels ( Figure 6d ) . In contrast , inhibition of MITF expression did not significantly induce cell death ( 6 . 5% ) but resulted in fewer cells in G2/M-phase and more cells in G1 , compared with inhibition of ATF2 alone . These observations suggest that MITF inhibition is sufficient to reduce the rate of cell cycle progression through G2/M phase and that inhibited growth of BRAF600E-expressing melanocytes on soft agar may be attributed to abrogation of distinct cell cycle-regulatory mechanisms . Combined inhibition of ATF2 and MITF restored cell cycle distribution to that seen in control melanocytes , and reduced cell death from 22 . 4% to 12 . 9% . Of interest , MITF overexpression promoted a similar degree of cell death ( 11 . 4% ) without altering cell cycle distribution , similar to combined inhibition of ATF2 and MITF ( Figure 6e , 6f ) . Together , these observations suggest that simultaneous inhibition of ATF2 and MITF averts cell cycle abrogation induced when expression of either of these factors is perturbed individually , further substantiating regulation of MITF by ATF2 . The availability of a melanoma TMA , consisting of over 500 melanoma samples and in which expression of both ATF2 and MITF in the same tumors had been measured enabled us to assess possible associations between ATF2 and MITF and their correlation with survival and other clinical and pathological factors . Our earlier studies revealed that ATF2 subcellular localization in tumors is significantly correlated with prognosis: nuclear localization , reflecting constitutively active ATF2 , was associated with metastasizing tumors and poor outcome [7] . Here we quantitated immunofluorescent staining of TMAs for MITF and ATF2 by employing our automated , quantitative ( AQUA ) method . To normalize ATF2 and MITF levels , expression of each of the two proteins in individual patients was divided by the median expression level of the respective protein in all patients , and the nuclear ATF2/MITF ratio was calculated and log-transformed . By ANOVA analysis , the ratio was higher in metastatic than in primary specimens ( t value = 2 . 823 , P = 0 . 0051 ) , as shown in Figure 7a . No association was found between nuclear ATF2/MITF ratio and disease-specific survival among patients with metastatic melanoma ( not shown ) . Significantly , a high nuclear ATF2/MITF ratio in primary melanoma specimens was associated with decreased 10-year disease-specific survival ( P = 0 . 0014; Figure 7b ) . On Cox multivariable analysis , this association with survival was independent of patient age , Breslow thickness or the presence or absence of ulceration ( data not shown ) . Nuclear ATF2 alone in primary specimens was associated with poor survival , but to a lesser degree than the ratio of nuclear ATF2/MITF ( P = 0 . 0118 for ATF2 as a single discriminator versus P = 0 . 0014 for the ratio of nuclear ATF2/MITF ) . Nuclear MITF as a single discriminator was not a significant predictor of survival ( P = 0 . 185 ) , as was reported previously using immunohistochemistry [49] . These observations suggest that active ( nuclear ) ATF2 in melanoma can suppress MITF expression , and that this phenomenon is associated with poor prognosis .
Identifying mechanisms underlying early phases of melanocyte transformation and melanoma development is central to understanding the etiology of this devastating tumor , as well as for developing novel treatment approaches . Previous studies indicate the presence of mutant BRAF in melanocytic lesions , as well as its effect on pigment gene expression [6] , [50] , [51] . The present study enhances our understanding of early events contributing to melanoma development . We demonstrate that loss of a transcriptionally active form of ATF2 in melanocytes inhibits melanoma development in an Nras/Ink4a model . Our quest to understand mechanisms underlying ATF2 activity in this process led us to identify an important role for ATF2 regulation of MITF , an important regulator of melanocyte biogenesis and a factor implicated in melanoma progression [49] . Surprisingly , ATF2 negatively regulated MITF expression in mouse and human melanocytes , suggesting that ATF2 transcriptional activities limit MITF expression . We demonstrate that such negative regulation is elicited through downregulation of SOX10 by ATF2 , in cooperation with JunB . A putative AP1 response element has been identified in SOX10 promoter sequences and ChIP analysis of this domain showed ATF2 and JunB binding . Overexpression of JunB efficiently suppressed SOX10 expression in an ATF2-dependent manner and inhibition of Jun transcriptional activities phenocopied the effect of shATF2 , suggesting that negative regulation of SOX10 by ATF2 is direct , and is mediated in cooperation with JunB . Importantly , ATF2-dependent negative regulation of Sox10 and consequently of MITF seen in melanocytes , but only in about 50% of the 18 melanoma cell lines studied here . Correspondingly , JunB , which is required for ATF2-dependent inhibition of Sox10 transcription , is no longer found on the promoter of SOX10 in melanoma cells ( i . e . 501Mel ) that exhibit positive regulation by ATF2 . Rather , CREB and ATF2 are found on SOX10 and MITF promoters , pointing to a switch in ATF2 heterodimeric partners to enable positive regulation of these genes . Notably , melanoma cell lines that exhibit positive regulation of SOX10 and MITF by ATF2 , also show high basal levels of MITF expression [41] , [42] , suggesting that additional genetic or epigenetic changes distinguish these lines from melanocytes and the other melanoma lines in which ATF2 elicits negative regulation of MITF . Notably , ATF2 control of MITF expression affected the ability of BRAF600E-expressing melanocytes to exhibit transformed phenotype in culture , monitored by their ability to grow on soft agar . Inhibition of ATF2 abolished soft agar growth of BRAF600E-expressing melanocytes , which was partially rescued upon KD of MITF . Interestingly , both the over expression or the KD of MITF resulted in inhibition of melanocytes ability to grow on soft agar , substantiating the notion that a fine balance of MITF expression must be maintained in order to ensure its contribution to cellular proliferation and transformation . We propose that excessively low or high MITF levels block melanocyte transformation , whereas intermediate levels allow transformation to occur . Overall , our observations demonstrate that ATF2 plays an important role in fine-tuning those levels and support the rheostat model proposed for MITF's role in melanoma development and progression [48] . Of importance , ATF2 and MITF affect the ability of BRAF600E-expressing melanocytes to grow on soft agar via distinct mechanisms . Whereas specific inhibition of ATF2 causes both accumulation of cells in G2 and induction of cell death , specific alteration of MITF protein levels—particularly depletion—significantly affects cell proliferation and inhibit growth on soft agar by non-lethally slowing cell cycle progression at G2/M . These observations are consistent with a report from Wellbrock and Marais [52] , who showed that altered MITF expression inhibits melanocyte proliferation . Importantly , inhibiting MITF expression in ATF2 KD melanocytes was sufficient to partially rescue melanocyte growth on soft agar . While supportive of our finding in the Nras::Ink4a mouse melanoma model , where expression of transcriptionally inactive ATF2 inhibits melanoma formation , these observations provide the foundation for a model in which ATF2 inhibition causes increased MITF levels and concomitant inhibition of melanocyte growth , possible induction of cell death and delayed development . The latter is suggested by IHC analysis of mouse skin from ATF2md mice , which shows notably reduced S100 staining indicative of delayed melanocyte development: ATF2 KO melanocytes appear to represent anagen stage IV , whereas WT represent anagen stage VI . This delay was seen at the 4- but not the 14-day time point , suggesting that an ATF2 effect might be limited to a specific subpopulation or phase of melanocyte development . The early ( 4 day ) time point is within the time frame that allows induction of melanoma development by UV-irradiation of c-Met or H-Ras mutant mice [53] . It is therefore plausible that timely control of MITF expression by ATF2 determines melanocyte susceptibility to transformation . Our analysis of genes whose expression is altered by ATF2 KD in melanocytes identified a cluster of pigmentation genes , many reportedly regulated by MITF [6] , [54] . Therefore , changes in TYRP1 , DCT and SILVER expression could be attributed to altered MITF expression . However , initial analysis points to a more complex mechanism since ( i ) the degree of changes in expression of these genes was often greater than that seen for MITF and ( ii ) expression of some pigmentation genes was found to be independent of MITF in some melanoma and melanocyte cultures . Hence , further studies are required to address mechanisms underlying ATF2 regulation of these pigmentation genes and the significance of such regulation to melanocyte transformation and melanoma development . While our present studies focused on the ATF2-MITF axis , it is expected that additional ATF2-regulated genes contribute to melanoma development [12] . In agreement , our earlier studies using both human and mouse melanoma lines demonstrate that inhibition of ATF2 effectively inhibits tumorigenesis and blocks metastasis [22]–[26] . Important for ATF2 function is its subcellular localization . While findings presented here position ATF2 as an oncogene functioning in melanocyte transformation and melanoma development , earlier studies from our laboratory and others suggest that in keratinocytes and mammary glands , ATF2 elicits a tumor suppressor function [55] , [56] . Of interest , assessing the localization of ATF2 in the melanoma cell lines studied here revealed that all express nuclear ATF2 . Interestingly , in most cases the nuclear staining revealed a punctate staining , resembling the localization of ATF2 to DNA repair foci following DNA damage ( Figure S9 ) . A possible link between the presence of ATF2 in repair foci in most melanoma cells points to the possible presence of activated DNA damage response which may be associated with genomic instability [19] , [20]—aspects that will be explored in future studies . Significantly , the appearance of nuclear ATF2 is correlated with poor prognosis in melanoma , whereas melanomas that exhibit cytosolic ATF2 exhibit a better survival . Notably , cytosolic ATF2 is primarily seen in non-malignant skin tumors [55] . Here we demonstrate that high nuclear ATF2/MITF ratios are associated with poor prognosis in primary melanomas , but not with metastatic melanomas . The latter finding attests for the important role ATF2 plays to control MITF expression in the early phase of melanocyte transformation and melanoma development . Overall , using the mutant Nras/Ink4a melanoma model we provide genetic evidence for a central role for ATF2 in melanoma development . We demonstrate that in the absence of transcriptionally active ATF2 , melanoma formation is largely inhibited . Furthermore , our data point to an unexpected role of ATF2 in fine-tuning of MITF transcription through regulation of its positive regulator SOX10 . Mouse melanoma models and in vitro transformation studies indicate that this newly identified regulatory pathway is required for early phases of melanocyte transformation . Given that ATF2 affects activity of the oncogenes N-Ras ( mouse model ) and BRAF ( melanocyte growth on soft agar ) ; we expect that ATF2 play significant roles in melanomas that carry either of these mutations .
Research involving human participants has been approved by the institutional review board at Yale University ( where the TMA was prepared and analyzed ) . All animal work has been conducted according to relevant national and international guidelines in accordance with recommendations of the Weatherall report and approved by the IACUC committee at SBMRI . Mice bearing a conditional allele for mutant ATF2 in which the DNA binding domain and part of the leucine zipper domain were deleted , were generated as previously described [36] , [55] . To study the function of ATF2 in melanocytes , we utilized the Cre-loxP system for disruption of the ATF2 gene in melanocytes [37] . The Tyr::CreER::Atf2md mice and their littermate controls ( WT ) were of FVB/129P2/OlaHsd ( TyrCreERT mice were FVB , ATF2fl/fl were 129P2/OlaHsd ) and N-Ras/Ink4a−/− mice were C57Bl/6/129SvJ . For melanoma studies we have used Tyr::CreER::NrasQ61K::Ink4a−/− mice ( developed at HMS by LC ) following their cross with the Tyr::CreER::Atf2md mice . Skin specimens were fixed in neutral buffered formalin solution and processed for paraffin embedding . Skin sections ( 5 µm in thickness ) were prepared and deparaffinized using xylene . For MITF , DCT and S100 immunostaining , tissue sections were incubated in DAKO antigen retrieval solution , for 20 min in a boiling bath , followed by treatment with 3% hydrogen peroxide for 20 min . Antibodies against MITF ( 1∶100 from Sigma ) , DCT ( 1∶500 , kind gift from Dr . Vincent Hearing ) and S100 ( 1∶100 , DAKOCytomation; Carpinteria , CA ) were allowed to react with tissue sections at 4°C overnight . Biotinylated anti-rabbit IgG was allowed to react for 30 min at room temperature and diaminobenzidineor Nova Red were used for the color reaction . Hematoxylin was used for counterstaining . The control sections were treated with normal mouse serum or normal rabbit serum instead of each antibody . Immortalized human melanocytes Hermes 3A which has hTERT ( puro ) and CDK4 ( neo ) expression [57] were grown in RPMI 1640 medium containing Fetal Bovine Serum ( FBS , 10% ) , 12-O-tetradecanoyl-phorbol-13-acetate ( TPA , 200 nM , Sigma , St . Louis , MO ) , Cholera toxin ( 200 pM , Sigma ) , human stem cell factor ( 10 ng/ml , R&D systems , Minneapolis , MA ) , and endothelin 1 ( 10 nM , Bachem Bioscience Inc . , Torrance , CA ) . Primary human melanocytes ( NEM-LP; Invitrogen ) were grown in medium 254 and HMGS ( Cascade Biologics ) . Mouse melanocytes ( melan-Ink4a-Arf1 ) were grown as for immortalized human melanocytes excluding human stem cell factor and endothelin . Melanoma cell lines were grown in DMEM medium supplemented with 10% FBS and penicillin/streptomycin ( P/S; Cellgro ) . Melanoma cell lines used in this study LU1205 , WM793 , 501MEL , WM35 , WM1361 , MeWO ( kind gift from Meenhard Herlyn ) , UACC903 were maintained in DMEM medium supplemented with 10% FBS and Penicillin/Streptomycin . Melanoma cell lines SbCl2 , WM9 , WM4 , WM1650 , A2068 , WM1366 , WM3629 , WM1552 , SKMEL2 , SKMEL5 , and SKMEL8 were maintained in RPMI medium supplemented with 10% FBS and Penicillin/Streptomycin . Primary melanocytes cultures were prepared from mice carrying the Atf2 WT or mutant genotypes and N-Ras/Ink4a−/− as follows . Dorsal-lateral skin was removed from one day-old pups , disinfected with 70% ethanol for 1 min and then washed at least twice with sterile PBS . The skin was submerged in 1× Trypsin/EDTA overnight at 4°C and next day , the skin was placed in a Petri dish with mouse melanocyte culture medium ( described below ) . The epidermis and sheared tissue was removed and discarded with forceps . The tissue was transferred to 15 ml centrifuge tubes and vortexed vigorously until solution becomes cloudy ( 1–2 min ) . The cell suspension was transferred to tissue culture flasks . After 3 days , melanocyte growth medium containing 0 . 8 µg/ml geneticin ( Sigma-Aldrich ) was added to eliminate contaminating fibroblasts ( melanocytes are resistant to such treatment ) . Geneticin-containing medium was removed and replaced with fresh media after 1 day . Media was changed twice a week . Primary mouse melanocytes were grown in F-12 media ( Invitrogen ) containing 20% L-15 media ( Invitrogen ) , 4% of FBS and Horse serum ( Invitrogen ) , Penicillin ( 100 units ) and streptomycin ( 50 µg ) antibiotics , db-cAMP ( 40 µM , Sigma-Aldrich ) , 12-O-tetradecanoyl-phorbol-13-acetate ( TPA , 50 ng/ml , Sigma-Aldrich ) , alpha-Melanocyte stimulating hormone ( α-MSH , 80 nM , Sigma-Aldrich ) , Fungizone ( 2 . 5 µg/ml , Sigma-Aldrich ) and melanocyte growth supplement ( Invitrogen ) . Primary melanocytes were treated with 4-OHT ( 10 µM ) for 8h followed by addition of doxycycline ( 2 µg/ml ) for 24h to inactivate ATF2 and induce expression of N-Ras . ATF2-specific shRNA clones were obtained from Open Biosystems ( catalog no . RHS4533 ) . Five different shRNA were obtained and tested for their efficiency of KD . Clone TRCN0000013714 was more efficient in inhibiting ATF2 in human cell lines while clone TRCN0000013713 was more efficient for knocking down mouse ATF2 . For subsequent experiments we used the respective shATF2 clone depending on human or mouse cell lines . We also tested 3 different clones for KD of ATF2 to rule out any off target effect ( Data not shown ) . siRNA control ( cat # 4611 ) and three SOX10-specific siRNA oligonucleotides were obtained from Ambion ( cat # 4392420 ) . Four FOXD3 specific siRNA were obtained from Dharmacon ( Cat # J-009152-06 -07 , -08 , -09 ) . These siRNAs were pooled together in equimolar ratio for transient transfection . An MITF specific shRNA , and MITF promoter luciferase constructs ( WT and mutant CRE-Luc constructs ) were obtained from Dr . David Fisher [58] . pGL3 vectors containing wild-type and BRN2-site-mutated MITF promoters were obtained from Dr . Colin Goding [34] . pGL3 vectors containing wild-type and SOX10-site-mutated MITF promoters were obtained from Dr . Michel Goossens [59] . Retroviral vectors encoding a fusion protein consisting of full length human BRAF and BRAFV600E linked to the T1 form of the human estrogen receptor hormone-binding domain were generously provided by Dr . Martin McMahon [60] . SOX10 expression vector obtained from Dr . Alexey Terskikh , RSV-JunB , RSV-JunD were obtained from Dr . Michael Karin and pBabe-Flag-TAM67 from Dr . Michael Birrer . Antibodies against SOX10 and CREB ( sc-1734 and sc-186 respectively ) were from Santa Cruz Biotechnologies; antibodies against ATF2 , pERK and ERK ( catalogue # 9226 , 4337 and 4695 respectively ) were obtained from Cell Signaling; antibodies against MITF ( C5 ) were purchased from Cell Lab vision . Protein extract ( 40–60 µg ) preparation and western blot analysis were done as described previously [8] . Specific bands were detected using fluorescent-labeled secondary antibodies ( Invitrogen , Carlsbad , CA ) and analyzed using an Odyssey Infrared Scanner ( Li-COR Biosciences ) . β-Actin antibody was used for monitoring loading . Human melanoma and melanocytes were grown in coverslips , fixed ( 4% paraformaldehyde and 2% sucrose in 1×PBS ) , and then permeabilized and blocked ( 0 . 4% Triton X-100 and 2% BSA in 1×PBS ) at room temperature . The cells were then washed ( 0 . 2% Triton X-100 and 0 . 2% BSA in 1×PBS ) and incubated overnight at 4°C with monoclonal anti-rabbit antibody against ATF2 ( 20F1 , 1∶100 ) , followed by five washes and then subsequent incubation at room temperature for 2 h with anti-rabbit IgG ( Invitrogen , 1∶300 ) and Phalloidin ( Molecular Probes , 1∶1000 ) . DNA was counterstained with 4 , 6-diamidino-2-phenylindole ( DAPI; Vector Laboratories ) containing mounting medium . Skin samples were collected from the backs of mice and immediately fixed with Z-fix , processed , and embedded in paraffin . Paraffin sections were routinely stained by H&E . Dewaxed tissue sections ( 4 . 0–5 . 0 µm ) were immunostained using rabbit polyclonal antibodies to MITF ( Sigma-Aldrich ) , S100 ( S100B; DAKOCytomation; Carpinteria , CA ) , and DCT ( αPEP8 , kindly provided by Dr . Vincent Hearing ) . Application of the primary antibody was followed by incubation with goat anti-rabbit polymer-based EnVision-HRP-enzyme conjugate ( DakoCytomation ) . DAB ( DakoCytomation ) or SG-Vector ( Vector Lab , Inc . ; Burlingame , CA ) chromogens were applied , yielding brown ( DAB ) and black ( SG ) colors , respectively . Quantitative analysis was performed as described previously [61] . Briefly , all slides were scanned at an absolute magnification of 400× [resolution of 0 . 25 µm/pixel ( 100 , 000 pix/in . ) ] using the Aperio ScanScope CS system ( Aperio Technologies; Vista , CA ) . The acquired digital images representing whole tissue sections were analyzed applying the Spectrum Analysis algorithm package and ImageScope analysis software ( version 9; Aperio Technologies , Inc . ) to quantify IHC and histochemical stainings . These algorithms make use of a color deconvolution method [62] to separate stains . Algorithm parameters were set to achieve concordance with manual scoring on a number of high-power fields , including intensity thresholds for positivity and parameters that control cell segmentation using the nuclear algorithm . Primary melanocytes were treated with 4-OHT and Doxycycline before isolation of total RNA . 500 ng of total RNA was used for synthesis of biotin-labeled cRNA using an RNA amplification kit ( Ambion ) . The biotinylated cRNA is labeled by incubation with streptavidin-Cy3 to generate probe for hybridization with the Mouse-6 Expression BeadChip ( Illumina MOUSE-6_V1_1_11234304_A ) that represents 46 . 6K mouse gene transcripts . We analyzed the BeadChips using the manufacturers BeadArray Reader and collected primary data using the supplied Scanner software . Data analysis was done as follows . First , expression intensities were calculated for each gene probed on the array for all hybridizations using illumina's BeadStudio 3 . 0 software . Second , intensity values were quality controlled and normalized: quality control was carried out by using the BeadStudio detection P-value set to <0 . 01 as a cutoff . This removed genes which were never detected in the arrays . All the arrays were then normalized using the cubic spline routine from the BeadStudio 3 . 0 software . This procedure accounted for any variation in hybridization intensity between the individual arrays . Finally , these normalized data were analyzed for differentially expressed genes . The groups of 2 biological and 2 technical replicates were described to the BeadStudio 3 . 0 software and significantly differentially expressed genes were determined on the basis of the difference changes in expression level ( Illumina DiffScor>60 or DiffScore<−60 ) and expression difference p-value<0 . 01 . Microarray data are available under accession number GSE23860 . Human embryonic kidney 293T cells were transfected with corresponding retro- or lentiviral shRNA constructs ( 10 µg ) , Gag-pol ( 5 µg ) and ENV expression vectors ( 10 µg ) by calcium phosphate transfection into 10 cm plates and supernatant was collected after 48 hours to obtain viral particles . 2 million melanocytes and melanoma cells in 10 cm plates were infected with 5 ml of viral supernatant along with 5 ml of medium in the presence of 8 µg/ml polybrene . The virus was replaced with fresh media after 8 hours of infection . After two days , puromycin ( 1 . 5 µg/ml ) was used to select cells for 3 days . For human and mouse melanocytes the media was changed to DMEM containing 10% FBS 24 h prior to harvesting cells . 50 nM duplexes of scrambled and SOX10- or FOXD3- specific siRNA were transfected into human melanocytes and WM1361 melanoma cells ( 2 million cells per transfection ) by Nucleofection using Amaxa reagents ( NHEM-Neo Nucleofector and Solution R respectively ) for SOX10 or FOXD3 knock down . Over 90% of the cells transduced were able to resist drug selection , indicating efficient infection of the respective genes . GFP was also used to monitor efficiency of infection , confirming >90% GFP expression by fluorescence microscopy . Quantitative PCR was performed as described earlier [8] . Total RNA was isolated using an RNeasy mini kit ( Sigma , St . Louis , MO ) and reverse transcribed using a high cDNA capacity reverse transcription kit ( Applied Biosystems , Foster City , CA ) following the manufacturer's instructions . Specific primers ( Valuegene , San Diego , CA ) used for PCR were as follows: Human ATF2 , forward: tgtggccagcgttttaccaa , reverse: tgatgtgggctgtgcagttt . , human MITF , forward: aaaccccaccaagtaccaca , reverse: acatggcaagctcaggac . , human SOX10 , forward: caa gtaccagcccaggcggc , reverse: gggtgccggtggtccaagtg . , human FOXD3 , forward: gcgacgggctggaagag , reverse: gctgtccgtgatggggtgcc . , human PAX3 , forward: ggaactggagcgtgcttttg , reverse: ggcggttgctaaaccagac . , human BRN2 , forward: gaaagagcgagcgaggaga , reverse: caggctgtagtggttagacg . , mouse MITF , forward: agatttgagatgctcatcccc , reverse: gatgcgtgatgtcatactgga , mouse TYRP1 , forward: ccctagcctatatctccctttt , reverse: taccatcgtggggataatggc . , mouse DCT , forward: gtcctccactcttttacagacg , reverse: attcggttgtgaccaatgggt , mouse Silver , forward: tgacggtggaccctgcccat , reverse: agctttgcgtggcccgtagc . The reaction mixture was denatured at 95°C for 10 min , followed by 40 cycles of 95°C for 15s , annealing at 60°C for 30s and extension at 72°C for 30s . Reactions were performed using the SYBR Green qPCR reagent ( Invitrogen ) and run on an MX3000P qPCR machine ( Stratagene , La Jolla , CA ) . The specificity of the products was verified by melting curve analysis and agarose gels . The amount of the target transcript was related to that of a reference gene ( Cyclophilin A for both human and mouse ) by the Ct method . Each sample was assayed at least in triplicate and was reproduced at least three times . Chromatin immunoprecipitation was performed using the Magna-Chip ( Upstate ) according to the manufacturer's instructions . Control shRNA and ATF2 knocked down WM1361 cells ( one 10 cm plate for each , 80% confluent ) were fixed in 37% formaldehyde and sheared chromatin was immunoprecipitated and subjected to PCR for 32 cycles . The following primers corresponding to the MITF promoter , spanning the SOX10 binding site were used , forward: gcagtcggaagtggcag , reverse: caactcactgtcagatcaa . Antibodies against Sox10 and CREB ( sc-1734 and sc-186 respectively ) were from Santa Cruz Biotechnologies . IgG control , and glyceraldehyde-3-phosphate dehydrogenase oligonucleotides were provided by the kit . Antibody against ATF2 ( sc-6233 ) , JunB ( sc-8051 ) , JunD ( sc-74 ) were obtained from Santa Cruz . Antibodies against ATFa were provided generated by Nic Jones . For Sox10 promoter , the following primers spanning AP-1 binding site were used; forward: cccagtgctggcctaatagc , reverse: cacccttgatatccccaagtga . MeWo , WM35 , WM1361 , Lu1205 cells in six-well plates were transiently transfected with 0 . 5 µg of reporter plasmid containing WT or CRE mutant , BRN2 mutant or SOX10 mutant MITF promoter and 0 . 1 µg of pSV-β-Galactosidase ( Promega , San Luis Obispo , CA ) using Lipofectamine 2000 reagent ( Invitrogen ) . Human melanocytes ( 2 million ) were transfected with 2 µg of reporter plasmid containing WT or SOX10 mutant MITF promoter and 0 . 3 µg of pSV-β-Galactosidase using Amaxa reagent ( NHEM-Neo nucleofector kit , Lonza ) according to the manufacturer's protocol . Cell lysates were prepared from cells after 48 h . Luciferase activity was measured using the Luciferase assay system ( Promega ) in a luminometer and normalized to β-galactosidase activity . The data were normalized to β-galactosidase and represent the mean and SD of assays performed in triplicate . All experiments were performed a minimum of 3 times . Melan-Ink4a-Arf1 cells were transduced with a retroviral vector expressing BRAFV600E:ERT1 and selected with puromycin for 3 days . These cells were treated with 200 nM of estrogen receptor antagonist ICI 182780 ( ICI , Tocris Bioscience ) to induce expression of BRAFV600E . After one day , these cells were transduced with a lentiviral vector expressing either shATF2 or shMITF separately , or in combination . Colony formation was carried out as described by Franken et al . [63] . Briefly , 5 , 000 cells were plated into each well of a 6-well plate , and cells were grown in mouse melanocyte media containing ICI and puromycin ( 1 . 5 µg/ml ) for 3 weeks until colonies became visible . The colonies were stained with P-Iodonitrotetrazolium Violet ( 1 mg/ml Sigma , St . Louis , MO ) . This experiment was performed in triplicate and reproduced 2 times . Genomic DNA was isolated from tail tissue was subjected to PCR resulting in amplification of a 549 bp DNA fragment for Atf2 floxed and a 485 bp DNA fragment for wild type mice . PCR conditions included one cycle at 95°C for 3 min; and 30 cycles of 94°C/30 sec , 55°C/30 sec and 72°C/1 min and one cycle at 72°C for 5 min . Primers used for PCR reactions were forward: caatccactgccatggcctt , reverse: tcagataaagccaagtcgaatctgg . MeWo cells were left untreated or treated with 20 mJ/cm2 of UV-B for 1 h . The cells were lysed using lysis buffer containing 1% Triton-100 and incubated with 4 µg of biotin-labeled MITF promoter spanning the CRE site oligo ( 5′-gaaaaaaaagcatgacgtcaagccaggggg-3′ ) in the presence of poly- ( dI-dC ) ( 20 µg/ml ) for 2h at 4°c . The oligo-bound proteins were captured using streptavidin-agarose ( Invitrogen ) for 1 h incubation , followed by extensive washes with washing buffer ( 20 mm HEPES , 150 mm NaCl , 20% glycerol , 0 . 5 mm EDTA , and 1% Triton-100 ) and analyzed using SDS-PAGE and western blots . To evaluate the cell cycle index of Melan-Ink4a-Arf1 cells stably overexpressing BRAFV600E:ERT1 alone or in combination with shRNA to the genes indicated in Results , cells were plated in media containing ICI and puromycin ( 1 . 5 µg/ml ) at 2×106 cells per 10 cm plate O/N . Cells were labeled with 10 µM of 5-bromo-2-deoxyuridine ( BrdU; Sigma Chemical Co . ) , for an hour . Cells were then washed , fixed , and stained with anti-BrdU mAbs and propidium Iodide ( BD Biosciences , San Jose , CA ) according to the manufacturer's protocol , and analyzed on a BD FACSCanto machine . Cell cycle phase was analyzed using the Mod Fit LT v . 2 program ( Verity Software , Topsham , ME ) . In a separate experiment the cells were stained with Annexin V-APC and 7-AAD ( BD Pharmingen , San Diego , CA ) according to manufacturer's protocol , to enable analysis of early apoptosis and cell death . Cells were treated under hypoxia ( 1% O2 ) for indicated time points using a hypoxia chamber ( In Vivo 400; Ruskin Technologies Ltd , Bridgend , UK ) . Mice were treated with 4-Hydroxytamoxifen ( 25 mg/ml in DMSO ) by swabbing the entire body ( excluding the head ) on days 1–3 after birth . On day 4 the pups were placed under UVB light source ( FL-15E; 320 nm ) and exposed to 20 µW/cm2 for 22 seconds . Ninety minutes after UVB treatment mice were sacrificed and entire skin was removed and processed . Tissue microarrays were constructed as previously described [21] . The arrays included a series of 192 sequentially collected primary melanomas and 299 metastatic melanomas . Slides were stained for automated , quantitative analysis ( AQUA ) for ATF2 and MITF as previously published [49] , [64] . The AQUA scores for the two markers were obtained from the AQUAmine database ( www . tissuearray . org ) . | Understanding mechanisms underlying early stages in melanoma development is of major interest and importance . Recent studies indicate a role for MITF , a master regulator of melanocyte development and biogenesis , in melanoma progression . Here we demonstrate that the transcription factor ATF2 negatively regulates MITF transcription in melanocytes and in about 50% of melanoma cell lines . Increased MITF expression , seen upon inhibition of ATF2 , effectively attenuated the ability of BRAFV600E-expressing melanocytes to exhibit a transformed phenotype , an effect partially rescued when MITF expression was also blocked . Significantly , the development of melanoma in mice carrying genetic changes seen in human tumors was inhibited upon inactivation of ATF2 in melanocytes . Melanocytes from mice lacking active ATF2 expressed increased levels of MITF , confirming that ATF2 negatively regulates MITF and implicating this newly discovered regulatory link in melanoma development . Primary melanoma specimens that exhibit a high nuclear ATF2-to-MITF ratio were found to be associated with metastatic disease and poor prognosis , further substantiating the significance of MITF control by ATF2 . In all , these findings provide genetic evidence for the role of ATF2 in melanoma development and indicate an ATF2 function in fine-tuning MITF expression , which is central to understanding MITF control at the early phases of melanocyte transformation . | [
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] | 2010 | A Role for ATF2 in Regulating MITF and Melanoma Development |
The cell envelope of mycobacteria , a group of Gram positive bacteria , is composed of a plasma membrane and a Gram-negative-like outer membrane containing mycolic acids . In addition , the surface of the mycobacteria is coated with an ill-characterized layer of extractable , non-covalently linked glycans , lipids and proteins , collectively known as the capsule , whose occurrence is a matter of debate . By using plunge freezing cryo-electron microscopy technique , we were able to show that pathogenic mycobacteria produce a thick capsule , only present when the cells were grown under unperturbed conditions and easily removed by mild detergents . This detergent-labile capsule layer contains arabinomannan , α-glucan and oligomannosyl-capped glycolipids . Further immunogenic and proteomic analyses revealed that Mycobacterium marinum capsule contains high amounts of proteins that are secreted via the ESX-1 pathway . Finally , cell infection experiments demonstrated the importance of the capsule for binding to cells and dampening of pro-inflammatory cytokine response . Together , these results show a direct visualization of the mycobacterial capsular layer as a labile structure that contains ESX-1-secreted proteins .
Mycobacteria are the causative agent of tuberculosis and other chronic diseases such as leprosy and causes about 1 . 7 million deaths annually . The interaction of Mycobacterium tuberculosis with its macrophage host cell is largely dictated by its unique cell envelope components that are able to elicit immuno-modulatory responses similar to that of the pathogen [1] . The mycobacterial cell envelope has a complex structure composed of a typical phospholipid bilayer plasma membrane ( PM ) , an outer membrane and an outermost layer known as the capsule in the case of pathogenic species [2] . The mycobacterial outer membrane henceforth referred to as ‘mycomembrane’ is mainly composed of long chain ( C60-C90 ) mycolic fatty acids with free intercalating glycolipids and is covalently linked to the arabinogalactan-peptidoglycan layer [3] , [4] . The chemical nature of mycomembrane has been examined in detail [4] and recent cryo-transmission electron microscopy ( EM ) data shows that it organizes into a structure analogous to the outer membrane of Gram-negative bacteria [5] , [6] . The structural organization of this highly insoluble matrix is invariable among different mycobacterial species and is responsible for the low permeability of the mycobacterial cell envelope [4] . The capsule is visible as an electron transparent zone ( ETZ ) surrounding the mycobacterial cell envelope [2] , [7] in conventional EM preparations [8] , [9] . This layer was not , however , observed by cryo-EM [5] , [6] , questioning somehow its existence . This layer , mainly composed of polysaccharides , proteins and small amounts of lipids , is considered to have a different molecular composition in pathogenic and non-pathogenic species [2] , [10] , [11] . The presence of the capsular layer is influenced by the culturing conditions used; laboratory cultures commonly grown in the presence of detergent with agitation to prevent clumping [12] usually shed the capsule into the medium [13] , [14] . In the present study , we adopt a close to native-state approach to study the mycobacterial cell envelope and demonstrate that mycobacteria express a distinctive outer layer . We have characterized the mycobacterial capsule layer by immunological and proteomic analyses and showed that some mycobacteria have ESX-1 secreted proteins in their capsule layer . ESX-1 encodes a type VII secretion system that mediates the secretion of potent T cell antigens such as ESAT-6 ( EsxA ) and CFP-10 . Furthermore , the presence of a capsule not only enhanced the Mycobacterium-macrophage interaction but also dampened the pro-inflammatory cytokine response .
The ultra-structure of the mycobacterial cell envelope was investigated using 30 nm vitreous sections , as previously described [15] . As a control for structural preservation of the envelope morphology , we also examine vitreous sections of the Gram-negative bacterium Shigella flexneri and the Gram-positive bacterium Staphylococcus epidermidis . The width of the PM and outer membrane or cell wall in both species are consistent with previous results [16] , [17] ( Figure S1 , Table S1 ) . The vitreous sections of four mycobacterial species [M . tuberculosis , M . marinum , Mycobacterium bovis Bacille Calmette-Guérin ( BCG ) and Mycobacterium smegmatis] examined appeared similar and show a well preserved cell envelope composed of a PM and a mycomembrane with a bilayer profile ( Figure S1C , S2 and S3 ) . Although the intensity of the mycobacterial PM is similar to that of S . flexneri and S . epidermidis , the width ( ≈7 nm ) is at least 16% wider ( Table S1 ) . The mycomembrane , with an apparent thickness of ≈8 . 3 nm is also thicker than the 6 . 8 nm thick outer membrane of S . flexneri . This slight increase in thickness is similar to recently published data [5] , [6] . Cryo-electron tomography analysis was performed to determine the 3D architecture of the envelope ( Figure S1D-F ) . In the reconstructed slices of M . smegmatis cell envelope , two additional layers are present in the periplasmic space . These layers are similar to those observed as L1 and L2 in [5] and as the granular layer and the medial wall zone referred to in [6] . The L1 layer appears immediately after the plasma membrane while the L2 layer appears closely apposed to the mycomembrane and may possibly be the peptidoglycan/arabinogalactan matrix . Taken together , these findings show that the cell envelope morphology of the mycobacteria is structurally related to , but more complex than that of the Gram-negative bacteria . The capsule , being a substantial component of the cell envelope , should be visible in vitreous sections as an extra layer extending from the mycomembrane . Yet , in our analyses and previous studies [5] , [6] , such a layer is not visible ( Figure S1C , S3 ) . We reasoned that the detection of this layer may have been obscured by the presence of the dextran cryoprotectant , which may have an electron density similar to that of the capsule . Alternatively , the capsule might have been removed by growing the cells in the presence of detergent and agitation in order to prevent clumping [2] , [13] , [14] . To investigate this , mycobacterial cells were cultured with or without Tween-80 and agitation and subsequently frozen in a close to native-state by the plunge freezing method [18] for direct visualization by EM ( Figure 1 ) . This method does not rely on the use of cryoprotectants and allows intact cells to be frozen in their medium of culture . The Gram-negative bacterium S . flexneri was used as a control ( Figure 1A ) . When grown without any perturbation , all mycobacterial species examined , showed a thick outermost capsule-like layer ( Figure 1C to 1F ) . To our knowledge , this is the first time this layer is visualized in a close to native state surrounding both pathogenic and nonpathogenic mycobacterial species . In comparison , cells grown with perturbation show this layer to be partially or completely removed ( Figure 1B ) , meaning that growing mycobacteria under routine culturing conditions involving perturbation promotes shedding of this layer . To demonstrate that this outermost layer is distinct from the mycomembrane , we sought to localize OmpATb porin known to be present in the mycomembrane [19] by labeling fixed cryo-sections of M . smegmatis cells over-expressing this protein [20] with anti-OmpATb serum and probed with protein-A attached to 10 nm gold . When grown in the presence of perturbation , the mycomembrane was efficiently labeled with the antibody ( Figure 2A ) . In comparison , cells grown in the absence of the detergent maintain the outermost layer , though the OmpATb antibody only specifically labeled the underlying mycomembrane ( Figure 2B ) . Parental M . smegmatis strain , naturally devoid of OmpATb , cultured under unperturbed state lacks any labeling , demonstrating the specificity of this antibody ( Figure 2C ) . These results indicate that the outermost layer removed by perturbation is distinct and not part of the mycomembrane . To confirm that the observed outermost layer is indeed the capsule , immuno gold-EM analysis of whole bacteria using antisera against known capsular components was performed . Whole bacteria were immobilized on EM grids and incubated with specific antibodies and gold probes . This method also allowed us to visualize and quantify the different components . Since α-glucan is described to be the major capsular polysaccharide [2] , [11] , we first investigated this using an α-glucan-specific monoclonal antibody [21] . The bacterial surface of cells grown in unperturbed state was distinctly labeled homogenously with this antibody ( Figure 3 ) , whereas cells grown in perturbed state showed weak or no labeling . Fixation of the cells was important for efficient labeling , since the same unperturbed culture without any fixation showed reduced or weak labeling . Non-acylated arabinomannan ( AM ) is also a described component of the capsule [11] , [22] . Using antibodies directed against arabinomannan structural motifs , our experiments confirmed the presence of AM in the capsule ( Figure S4 ) Subsequently , we tried to localize the glycolipid phosphatidylinositol mannosides ( PIMs ) , which is possibly associated with the capsule [23] . For this we used an antibody that on M . smegmatis specifically recognizes the PIM6 epitope , as it does not interact with M . smegmatis pimE mutant ( Figure 3F ) [24] , [25] . The antibody labeled the surface of intact cells very efficiently , supporting the presence of specific PIM epitopes on M . smegmatis and possibly other mannoconjugates and PIM on the pathogenic mycobacteria except for M . marinum which is poorly labeled with this antibody . Importantly , all of the antibodies significantly labeled less when the bacteria were grown agitated in the presence of detergent ( Figure 3A , 3D and S4 ) . Agitation in the absence of detergent contributes to the erosion of the capsule albeit lesser than when detergent is also included , conversely , the addition of detergent alone also shows less capsular disruption , thus the effect is additive . In addition to glycans and glycolipids , the capsule also contains proteins [2] , [10] , although these are generally not well described . One putative capsular protein recently described by Carlsson et al [26] is the ESX-1-associated protein EspE [formerly known as Mh3864 [27]] , which together with EspB is also secreted by ESX-1 encoded type VII secretion system [26] , [28] . To investigate EspE localization , whole cells were labeled with anti-EspE antibody which homogenously labeled the surface of M . marinum and M . tuberculosis . A preference of EspE for the polar region as previously reported [26] was not observed ( Figure 3G-I ) . The localization of EspE in a M . marinum strain ( Fas 3 . 1 ) with a transposon insertion in eccCb1 [formerly known as Mh1784 [27]] which blocks ESX-1-dependent secretion ( van der Sar et al , in preparation ) , showed no specific labeling , demonstrating both the specificity of the antibody used and the ESX-1 dependence of EspE export . Finally , EspE labeling was not strongly affected by Tween-80 , suggesting that this protein is more tightly associated with the cell envelope . To screen for specific ( ESX-secreted ) proteins within the capsule for which no antibodies are available , we analyzed capsular extracts by liquid chromatography mass spectrometry ( LC-MS ) . For this , capsular material was isolated from cells grown under unperturbed conditions by treating the cells with the mild detergents Tween-80 ( 1% ) or Genapol-X080 ( 0 . 25% ) ( Figure 4 ) . First , we analyzed the isolated fractions and the cell pellet for the presence of capsular α-glucan by a spot-blot assay . Consistent with our EM data , both M . marinum and M . smegmatis cells contained more α-glucan when they were grown in the absence of Tween-80 ( Figure 4A and S5 , respectively ) . Furthermore , spot-blot analysis showed that α-glucan was extracted by these mild detergents . These fractions were subsequently analyzed by SDS-PAGE . Extracts from bacteria grown in the absence of detergent repeatedly contained higher amounts of proteins , which indicate that their presence depends on an intact capsule ( Figure 4B ) . In addition , more proteins were extracted by Genapol as compared to Tween . The Genapol-extracted fractions from bacteria grown in the absence of Tween-80 were analyzed by LC-MS ( Table 1 ) . The highest number of peptides found in the M . marinum extract belonged to EspE , whose predicted molecular weight of 40 kDa corresponds to the size of the most abundant protein in the extract ( Figure 4B ) . Interestingly , other ESX-1-associated proteins such as EspB , EspF ( Mh3865 ) , EspK ( Mh3879c ) , ESAT-6 ( EsxA ) , CFP-10 and PPE68_1 were also present in high amounts . In addition , several other PE and PPE proteins were found in the Genapol extracts . OmpA was not detected in the Genapol-extracted material , confirming that this treatment did not extract proteins from the mycomembrane . These data show that M . marinum has a specific set of proteins within its capsule . The situation was different for M . smegmatis and M . tuberculosis . The capsular extracts of these two species did not show any major protein bands ( Figure 4B ) . This observation was confirmed by LC-MS analysis , which showed that the major proteins were cytosolic proteins such as GroEL2 , DnaK and translation elongation factor Tu ( Table S2 and S3 ) . The presence of some of these proteins in the capsule was also recently described by Hickey et al . [29] . In addition to these predominantly cytosolic proteins only very small amounts of putative capsular proteins , such as the ESX-1-associated proteins , were identified by LC-MS ( data not shown ) . This could indicate that the capsular proteins of M . smegmatis and M . tuberculosis might be more tightly associated with the cell envelope than in M . marinum and are therefore missed during analysis . The capsule of M . marinum contains a high amount of ESX-1 associated protein . To investigate whether these proteins depend on ESX-1 for their localization , Genapol extracted proteins of the M . marinum Fas 3 . 1 eccCb1 mutant were analyzed ( Figure 4C ) . The protein pattern of the ESX-1 mutant differed considerably from the wild-type protein pattern . Importantly , the 40 kDa product corresponding to EspE was not detectable in the capsular extract of the ESX-1 mutant ( Figure 4C and D ) , suggesting that the capsular localization of EspE depends on an active ESX-1 secretion system in M . marinum as previously reported [26] . This result was also confirmed by LC-MS analysis ( Table 1 ) and shows that EspF , EspK and the PPE proteins associated with the ESX-1 pathway are dependent on an active ESX-1 system for capsular localization and are therefore new putative ESX-1 substrates . The presence of other PE and PPE proteins in the capsule was not affected by the ESX-1 mutation , which is in agreement with the recent observation that many of these proteins , including the capsule PPE protein MMAR_1402 , depend on another secretion system , ESX-5 for their export [30] . As demonstrated above , growing mycobacteria in the presence of detergent with agitation , promotes capsular shedding and thus may influence the biological characteristics of the bacteria . To investigate this issue and to potentially gain insight into the biological relevance of the capsule , M . bovis BCG was grown under unperturbed conditions after which the bacteria were treated with 1% Tween-80 to remove the capsule . First , we investigated the consequence of capsule removal on the ability of the bacteria to bind to human monocyte-derived macrophages . As shown in Figure 5A , removal of the capsule significantly reduced bacterial binding at all tested multiplicities of infection ( MOIs ) , suggesting that the presence of the capsule promoted association to the macrophages . Next , we investigated whether the presence of a capsule may also differentially modulate the macrophage pro-inflammatory cytokine responses . As shown in Figure 5B , non-detergent-treated M . bovis BCG induced significantly lower amounts of IL-12p40 , IL-6 , and TNFα ( ∼30% ) as compared to the detergent-treated bacteria at both MOIs tested . The only exception was IL-6 , for which the difference at MOI 8 was non-significant . Reduced cytokine induction by untreated ( encapsulated ) M . bovis BCG was observed in both resting macrophages ( Figure 5B ) and in macrophages primed with LPS ( data not shown ) . Interestingly , treatment with detergent did not differentially affect the macrophage response for non-pathogenic M . smegmatis ( Figure 5C ) . Overall , these findings demonstrate that at least for M . bovis BCG the capsule is involved in facilitating macrophage infection and , simultaneously , plays a role in dampening the pro-inflammatory macrophage response . Furthermore , these data imply that growing mycobacteria in the presence of detergent , as is done under many standard laboratory circumstances , may have important consequences for the biological characteristics of these bacteria . In this study we used a plunge freezing method [18] that avoids artifacts introduced by chemical fixation and thus gives a close to native estimation of the thickness for these in vitro grown bacteria . We demonstrated the presence of the capsular layer in the cell envelope of both pathogenic and nonpathogenic mycobacterial species and determined its thickness to be around 30 nm ( Table S1 ) , taking into account the instability of the capsule in vitro; both groups have been previously shown to differ in the amount of extracellular material found in static and non-detergent-treated medium , which is interpreted as representing part of the capsule shed from growing cells [10] , [11] . One possibility of circumventing this instability is to measure this layer inside phagocytic cells in the vitreous state . It is however currently very difficult to freeze large mammalian cells under physiological conditions in a vitreous state [31] . The detection of capsular components α-glucan , arabinomannan , and oligo- and poly-mannosylated compounds on the surface of whole cells by immuno gold-EM is in agreement with chemical data about the nature of the capsule [4] , [10] , [11] . The presence of PIMs exposed at the surface of mycobacteria was already demonstrated [32] , [33] , [34] , however here we showed that PIM , α-glucan , and ESX secreted proteins are accessible to antibodies on intact bacteria . Mycobacterial α-glucan is implicated in the regulation of phagocytosis [35] , [36] , modulation of immune response [1] , [21] , and persistence of infection [37] . In this study we have shown that binding to macrophages and a dampening of the pro-inflammatory cytokine response is observed for encapsulated M . bovis BCG , which suggests that components of the capsule , such as glycans , play a role in immune response . The identification of many ESX-5 and ESX-1 secreted proteins ( ESAT-6 and CFP-10 , EspB , EspE , EspF , EspK and an ESX-1 associated PPE ) in the capsule , a number of which have not been identified previously as being secreted via ESX-1 ( EspF , EspK and PPE68_1 ) was surprising . These proteins were only detectable in the detergent-labile capsule fraction of M . marinum . Some ESX-1-secreted proteins have an essential role in the interaction with the host [38] , [39] , [40] , and more specifically in bacterial translocation from the phagosome into the cytosol [41] . The fact that EspE can only be partly removed by Tween-80 , suggests a distinction between proteins that are freely associated with the capsule and those more tightly associated with the rest of the cell envelope . In this context , these ESX-1 associated proteins may form a surface-exposed macromolecular structure through ( and/or within ) the capsule , specialized for the interaction with the host ( Figure 6 ) . Together , we showed that the mycobacterial capsule contains various components that manipulate the host , making this layer an attractive target for vaccine and drug development .
M . tuberculosis 6020 strain [42] , M . bovis BCG strain Copenhagen , M . smegmatis mc2155 , M . smegmatis-OmpATb [20] , M . marinum E11 strain and the E11 fas 3 . 1 eccCb1 mutant ( van der Sar et al , in preparation ) were grown at 37°C ( or 30°C for M . marinum ) in Middlebrook 7H9 media ( Difco ) supplemented with 10% oleic acid-albumin-dextrose catalase ( OADC ) ( BBL ) with or without 0 . 05% Tween 80 where applicable . For the growth of M . smegmatis-OmpATb and M . bovis BCG expressing dsRed , 25 µg and 50 µg mL−1 kanamycin and 50 µg mL−1 hygromycin were added , respectively . S . flexneri ipaC strain SF621 [43] was grown in tryptic casein soy broth ( Sigma ) at 37°C . S . epidermidis was grown in Luria-Bertani broth ( Difco ) at 37°C . Vitrification , vitreous sectioning and tomography is as described in Supplementary information ( SI ) Text and [44] . For plunge freezing , bacteria were taken directly from culture medium for processing without further centrifugation . A 4 µl droplet of various mycobacteria sample were applied to a glow-discharge quantifoil copper grids ( Quantifoil Micro Tools , Jena , Germany ) mounted in an environmentally controlled chamber at 100% humidity , blotted and frozen in vitreous ice by plunging into liquid ethane using the vitrobot ( FEI ) . Grids were transferred to a Gatan model 626 cryoholder ( Gatan , Pleasanton , CA ) under liquid nitrogen and inserted into a Tecnai 12 ( FEI , Eindhoven , Netherlands ) operating at 120 kV . The vitreous state of the preparation was confirmed by electron diffraction . Low-dose images , with exposures between 10 and 20 electrons per Å2 and under-focus values of 2 to 4 µm were recorded with a 4096×4096 pixel CCD camera ( Gatan ) at ×18 , 000 −×23 , 000 magnification . Bacteria cells were fixed by addition of an equal volume of 0 . 4M PHEM buffer containing 4% paraformaldehyde and 0 . 4% glutaraldehyde to the culture and incubating for 2 hours at room temperature [41] . For whole mount cells , samples were taken directly from the fixed culture without pelleting and incubated for 5 minutes on carbon coated formvar grids . The rabbit antibody used is OmpATb [19] . The following murine IgM monoclonal antibodies ( Mabs ) were used: anti-Ara6 ( F30-5 ) recognizes Ara6-motive in the arabinan domain of ( L ) AMs [45] , [46]; anti-ManLAM ( 55 . 92 . 1A1 ) binds the mannosyl caps on mannosyl-capped ( L ) AMs [46]; anti-PIM6 ( F183-24 ) recognizes terminal α ( 1 , 2 ) -linked mannosyl residues as present in PIM6 and the mannosyl caps on ManLAM [25] , [45] . In M . smegmatis [which lacks the mannosyl-caps on ( L ) AM] F183-24 specifically recognizes PIM6 with no reactivity to other components [25] . The Mab against capsular α-glucans was generated by O . Baba [47] . Immuno-labeling is essentially as described [see Text S1 and [41]] . Labeled whole mount cells were observed without further staining with a Tecnai 12 or CM 10 electron microscope ( FEI , Eindhoven , Netherlands ) . Cryo-sectioning and immuno gold labeling of fixed cells is essentially as described in Text S1 and [41] . Mycobacteria were grown under conditions mentioned above to an OD600 of ∼1 , diluted 100 times in 7H9 without supplement or Tween-80 , but with 0 . 2% dextrose and grown to exponential phase . Bacteria were harvested by centrifugation and washed 3 times in PBS . Bacteria were resuspended in PBS and incubated with 1% Tween-80 or 0 . 25% Genapol X-080 for 30 min . at room temperature before centrifugation to separate extracted fractions from bacteria cells . To determine the presence of α-glucan the extracted fractions were spotted on a nitrocellulose membrane , the membrane was dried for 1 hour at 80°C , blocked by 5% skimmed milk in PBS-T overnight and immuno-labeled using an anti-α-glucan monoclonal antibody . For analysis of the protein content , the extracted fraction was concentrated by TCA precipitation and analyzed via SDS-PAGE and Coomassie staining or Western blotting using anti-EspE [26] . Protein lanes from Coomassie stained SDS-PAGE gels were excised and prepared for LC-MS analysis as described in Text S1 . M . bovis BCG , M . bovis BCG expressing dsRed , or M . smegmatis were grown in Middlebrook 7H9 broth ( Difco ) supplemented with 10% ADC enrichment in the absence of Tween-80 . Exponentially growing bacteria were collected and washed once with 50 ml PBS . The bacteria were resuspended in 20 ml PBS , split into two 10 ml fractions , and incubated with/without 1% Tween-80 for 30 min with rotation at room temperature . The treated and untreated bacteria were washed twice with 25 mL Tris-buffered saline supplemented with 1 mM MgCl2 and 2 mM CaCl2 ( TSM ) containing 0 . 5% human serum albumin ( HSA ) , passed through a 5 µm filter ( Millipore ) for declumping ( bacterial suspensions were first centrifuged for 10 min at 200×g to prevent filter-clogging ) , and diluted with TSM/0 . 5% HSA to the appropriate optical density . Colony forming units were determined by plating the suspensions on 7H10 agar supplemented with 10% Middlebrook OADC . M . bovis BCG expressing dsRed was added to 5×105 macrophages [generated as described in Text S1 and ref . [21]] in 100 µL TSM/0 . 5% HSA at a MOI of 8 , 2 , or 0 . 5 . After 45 min at 4°C , cells were washed twice with TSM/0 . 5% HSA and the percentage of fluorescent cells was determined using a FACScan Analytic Flow Cytometer ( Becton Dickinson ) . Data was analyzed using manufacturer's software ( CellQuest version 3 . 1f ) . Macrophages were released by trypsiniation and resuspended in culture medium ( containing 50 U mL−1 GM-CSF ) at a concentration of 1 . 25×106 cells mL−1 . Eighty µl cell suspension ( 1×105 cells ) was transferred to a sterile 96-well U-bottom plate ( Greiner ) and left for 16 h ( 37°C , 5% CO2 ) to allow cells to adhere . Macrophages were stimulated with M . bovis BCG or M . smegmatis ( treated with or without 1% Tween-80 ) in the absence or presence of 20 ng mL−1 lipopolysaccharide ( LPS ) ( from Salmonella enterica serotype abortus equi ( Sigma-Aldrich L5886 ) . Unstimulated cells served as controls in all experiments . Culture supernatants were harvested after 24 h of incubation ( 37°C , 5% CO2 ) by centrifugation and stored at −80°C for cytokine measurements using an enzyme-linked immunosorbent assay ( ELISA ) according to the manufacturer's instructions ( Invitrogen ) . Data were statistically analyzed using a Student's t test ( two-tailed , two-sample equal variance ) . Differences were considered to be significant when p<0 . 05 . | The genus Mycobacterium contains a number of important pathogens , such as Mycobacterium tuberculosis . The highly characteristic cell envelope of these bacteria plays a crucial role in the infection process . The most apparent difference with other bacteria is the recently described outer membrane composed of unique ( glyco ) lipids . However , on top of this membrane mycobacteria also have an ill-defined capsular layer . In this paper , we studied this capsular layer using different electron microscopy techniques and mass spectrometry . Using close to native state preparation method , we show that both pathogenic and non-pathogenic mycobacteria have a labile capsular layer that covers the outer membrane . This capsular layer , in addition to containing arabinogalactan , glycan and mannose-containing glyco-lipids , also surprisingly contains a large amount of ESX-1-secreted proteins in Mycobacterium marinum . Furthermore , we also show that the capsule plays a role in the binding of macrophages and the induction of cytokines . Collectively , these results show for the first time that the capsule can be visualized on both pathogenic and non-pathogenic mycobacteria . In addition , growing mycobacteria under standard laboratory conditions in the presence of detergent with agitation promotes capsular shedding and influences the biological characteristics of the bacteria . | [
"Abstract",
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] | [
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] | 2010 | Direct Visualization by Cryo-EM of the Mycobacterial Capsular Layer: A Labile Structure Containing ESX-1-Secreted Proteins |
Adenoviruses are common pathogens , mostly targeting ocular , gastrointestinal and respiratory cells , but in some cases infection disseminates , presenting in severe clinical outcomes . Upon dissemination and contact with blood , coagulation factor X ( FX ) interacts directly with the adenovirus type 5 ( Ad5 ) hexon . FX can act as a bridge to bind heparan sulphate proteoglycans , leading to substantial Ad5 hepatocyte uptake . FX “coating” also protects the virus from host IgM and complement-mediated neutralisation . However , the contribution of FX in determining Ad liver transduction whilst simultaneously shielding the virus from immune attack remains unclear . In this study , we demonstrate that the FX protection mechanism is not conserved amongst Ad types , and identify the hexon hypervariable regions ( HVR ) of Ad5 as the capsid proteins targeted by this host defense pathway . Using genetic and pharmacological approaches , we manipulate Ad5 HVR interactions to interrogate the interplay between viral cell transduction and immune neutralisation . We show that FX and inhibitory serum components can co-compete and virus neutralisation is influenced by both the location and extent of modifications to the Ad5 HVRs . We engineered Ad5-derived HVRs into the rare , native non FX-binding Ad26 to create Ad26 . HVR5C . This enabled the virus to interact with FX at high affinity , as quantified by surface plasmon resonance , FX-mediated cell binding and transduction assays . Concomitantly , Ad26 . HVR5C was also sensitised to immune attack in the absence of FX , a direct consequence of the engineered HVRs from Ad5 . In both immune competent and deficient animals , Ad26 . HVR5C hepatic gene transfer was mediated by FX following intravenous delivery . This study gives mechanistic insight into the pivotal role of the Ad5 HVRs in conferring sensitivity to virus neutralisation by IgM and classical complement-mediated attack . Furthermore , through this gain-of-function approach we demonstrate the dual functionality of FX in protecting Ad26 . HVR5C against innate immune factors whilst determining liver targeting .
For the immunocompromised host , human adenoviruses ( Ad ) have emerged as a significant pathogen capable of exploiting the impaired immunological response and becoming invasive , manifesting in prolonged , severe and life threatening conditions [1–5] . There are seven human species ( A-G ) of this common non-enveloped , double-stranded DNA virus . Whilst in healthy individuals infections are self-limiting , targeting defined tissues such as the lung , eye and gastrointestinal system over a short time frame , disseminated Ad infections occur when immunity is low ( e . g . sufferers of hereditary immune deficiencies , patients undergoing immunosuppressive treatment [1–5] ) . Systemic infections can culminate in serious and diverse clinical syndromes , ranging from fulminant hepatic failure , coagulopathy , hemorrhagic cystitis , myocarditis , encephalopathy , nephritis to multi-organ failure [2 , 6] . The immunocompromised patient population is expanding due to increased use of immunosuppressive therapies ( e . g . cytotoxic drugs ) and consequently Ads are gaining increased recognition as a clinical problem . The presence of a high viral load in the blood is often strongly indicative of a severe outcome [5 , 6] . Incidence of infection is approximately 2 . 5–47% in stem cell transplant recipients , whilst paediatric transplantation patients are more prone to the disseminated disease , with mortality rates reaching up to 70% [5 , 7 , 8] . Despite the high risk , there is no FDA approved drug specific to treating Ad infection and therapeutic options can be limited . Advancing our knowledge of the complex mechanisms underlying Ad5 infection in vivo is of great importance . Using species C Ad2/5 as the prototype , the in vitro infection pathway has been very well documented . Studies have finely detailed the individual steps from virus binding via the fiber knob protein to the primary cell surface coxsackie and adenovirus receptor ( CAR ) [9] , engagement of the Ad penton base with αvβ3/5 integrins leading to internalization [10] and subsequent trafficking from endosomes to nuclear import [11 , 12] . However , the lack of suitable animal models , which allow viral replication and closely mimic the human immune system , has challenged the study of Ad infection in vivo . Nevertheless an abundance of valuable information has been gained about viral spread , immune responses and methods to combat such , especially from its popularity as a viral vector for gene therapy , vaccination and virotherapy protocols . Of the 60+ human Ads identified [13] , Ad5 is the virus-based gene transfer vector most frequently employed . Concomitantly , Ad5 is also one of the most seroprevalent of the family . The use of Ad5 as a viral vector has deepened our understanding of virus:host binding events , involvement of innate and adaptive immunity and of the factors leading to the substantial accumulation of Ad5 particles in the liver following bolus injection into the bloodstream . When administered intravenously ( I . V . ) the virus rapidly encounters a multitude of interactions with circulating blood components . These include virion neutralisation by pre-existing antibodies [14] , sequestration by Kupffer cells [15] , MARCO+-expressing splenic macrophages [16] , polymorphonuclear leukocytes [17] , natural IgM and complement opsonisation [18 , 19] . Binding to platelets [20 , 21] , erythrocytes [22 , 23] , and blood coagulation factors [24–26] all contribute to the substantial interplay between the virus and host . Dissecting the precise interactions which occur in vivo is key to our understanding of the virus infection pathways partnered with an individual’s defence mechanisms . Previous work suggests that Ads belonging to species C ( Ad1 , Ad2 and Ad5 ) are more commonly associated with disseminated disease than other types and have been implicated in severe hepatic failure [5 , 8] . Hepatitis is a frequent and serious consequence of systemic Ad infections [27–29] . Coagulation factor X ( FX ) plays a fundamental role in determining the characteristic hepatic tropism of Ad5 [24–26] . Selectively blocking FX prevents Ad liver transduction in rodent and non-human primates following I . V delivery of virus [30–32] . FX binds with nanomolar affinity to the Ad5 hexon hypervariable regions ( HVR ) , and acts as a bridge to attach the virus to N and O-linked heparan sulphate proteoglycans ( HSPG ) on the surface of hepatocytes [24 , 33 , 34] . Crystallographic and cryogenic electron microscopy identified contact points within and around Ad5 HVR5 and HVR7 which are responsible for interacting with the FX Gla ( γ-carboxylated glutamic acid ) domain [32] . Genetically swapping regions or specific amino acids within the Ad5 HVR5 and HVR7 for those of a non-FX-binding Ad ( e . g . species D Ad48 or Ad26 ) has proven an effective strategy to abrogate the FX interaction and diminish liver transduction [32 , 35] . In addition to binding the FX Gla domain at the HVR7 amino acid motif T423-E424-T425 , Ad5 is also capable of interacting with coagulation factor VII ( FVII ) at these points [36] . However , FVII does not support Ad5 transduction as it binds to the virus in an alternate orientation to FX , with dimerization of the FVII serine protease domains disguising potential HSPG receptor binding sites [36] . Further to its influence on liver transduction , Doronin et al . showed that FX coating the virus triggers recognition by the innate immune system via nuclear factor-κB ( NFκB ) activation and subsequent TLR4/TRAF6/NFκB-mediated inflammation , an effect absent when Ad5 was genetically manipulated to be devoid of FX binding [37] . Recent work by Xu et al . has produced additional insight into the function of FX in adenovirus biology [19] . They indicated a role for FX in protecting Ad5 from attack by natural IgM antibodies and the classical complement system upon exposure to murine blood [19] . In co-operation with IgM , complement activation acts as an innate host defense mechanism and has previously been shown to result in neutralisation of invading pathogens including Ads [18 , 19 , 38] . In vitro data demonstrated that FX can prevent Ad5 from this neutralisation when incubated with murine serum [19] . In contrast to studies in wild-type mice , the Ad5:FX interaction was not essential for liver transduction in mice deficient in natural antibodies or the complement components C1q and C4 [19] . Instead FX binding to Ad5 acted as a protective "shield" , decorating the viral capsid and preventing natural IgM and classical complement mediated inhibition of Ad gene transfer [19] . This study has led to some speculation surrounding the impact of FX in determining Ad liver tropism . It is evident that blood components influence Ad tropism , whilst other interactions ( e . g . Kupffer cell uptake ) remain dominant barriers to widespread Ad dissemination . Here , we studied the binding events and mechanisms deciding the fate of the virus in circulation . We attempted to dissect the importance of these interactions in determining viral cellular uptake and tropism . In this study we used genetically mutated Ad vectors to identify key hexon regions responsible for IgM and complement-mediated attack . The Ad5 HVRs were identified as the critical viral capsid components . We then incorporated these regions and FX binding capability onto a non-FX-binding Ad26 background . We utilised this novel vector to investigate the significance of FX and the role of the Ad5 HVRs in the interplay between viral immune recognition and tropism in vivo .
FX coating Ad5 shields the virus against IgM and classical complement-mediated immune attack [19] . Many other Ad types also bind to human FX ( hFX ) [24] . However it is unknown whether they are sensitive to neutralisation via the same pathway . Therefore , we compared the sensitivity of a selected panel of Ad vectors based on different species/types; the FX-binding Ad5 , Ad35 and Ad50 , and non-binding Ad48 and Ad26 [24] to neutralisation by murine serum in vitro . When Ad5 was incubated with C57BL/6 serum there was a significant increase in transduction compared to the media control , likely due to the presence of native FX in the murine serum ( Fig . 1A ) . However when serum was pretreated with X-bp ( binds the FX Gla domain blocking Ad:FX interaction [24] ) Ad5 gene transfer was dramatically reduced , to levels significantly below control conditions , consistent with previous results [19] ( Fig . 1A ) . This demonstrates that in the absence of FX Ad5 is sensitive to neutralisation by murine serum components [19] . In contrast , Ad35 , Ad50 , Ad48 and Ad26-mediated cell transduction was not affected by serum regardless of the presence of X-bp ( Fig . 1A ) , indicating that these vectors are not sensitive to the same mechanism that mediates Ad5 neutralisation . It is noteworthy that the overall charge in the region of the Ad5 hexon hypervariable loops is more negative than that of Ad26/Ad35/Ad48/Ad50 ( S1 Table ) . Protection from neutralisation by FX is also evident for Ad5 in rat serum ( S1 Fig . ) . Next we investigated the role of different capsid proteins in mediating Ad5 neutralisation . For this we employed a range of Ad5-based chimeric vectors ( S2 Table ) . We found no role for the fiber or penton base , as swapping those of Ad5 for those of Ad35 ( i . e . the Ad5 . F35 and Ad5 . F35P35 vectors ) , still resulted in vector neutralisation in the absence of FX ( Fig . 1B ) . Next we assessed the contribution of the Ad5 hexon in enabling neutralisation . Here we utilised Ad5 . HVR48 ( 1–7 ) , which we previously showed to lack FX binding [24] ( S2 Table ) . Ad5 . HVR48 ( 1–7 ) was not inhibited by serum components regardless of FX , thus illustrating an essential requirement for the Ad5 hexon HVRs in mediating neutralisation via this mechanism ( Fig . 1C ) . In addition , unlike the parental Ad5 , the Ad5 . HVR48 ( 1–7 ) chimeric vector caused no induction of C3 activation in serum preincubated with X-bp ( Fig . 1D ) . Hence , Ad5 . HVR48 ( 1–7 ) cannot bind to FX [24] and is not sensitive to neutralisation . Therefore these data indicate a pivotal role for the Ad5 HVRs in enabling immune attack via the IgM/classical complement pathway . To investigate the contribution of different Ad5 HVRs in mediating neutralisation by mouse serum , we evaluated the sensitivity of Ad5 vectors containing a range of Ad26 HVR modifications . Ad26 was chosen for these studies as it does not bind FX [24] and is not susceptible to serum neutralisation in vitro ( Fig . 1A ) . This series of Ad5/26 chimeric vectors ( Ad5 . HVR5 ( Ad26 ) , Ad5 . HVR7 ( Ad26 ) and Ad5 . HVR5+7 ( Ad26 ) ) ( S2 Table ) were previously shown to lack FX binding [32] . Unlike the Ad5 control , the Ad5 vectors with HVR ( Ad26 ) swaps were all sensitive to inhibition by serum ( Fig . 2A ) , suggesting the involvement of multiple HVRs in enabling neutralisation . Next , we employed a series of Ad5 vectors engineered with individual point mutations to alter specific amino acids to those found in Ad26 ( S2 Table ) . These vectors were previously demonstrated to have reduced but not eliminated ( e . g . Ad5 . HVR5* ( HVR5 mutations T270P and E271G ) or abolished FX binding ( e . g . Ad5 . HVR7* ( HVR7 mutations I421G , T423N , E424S , L426Y ) and Ad5 . HVR5* . HVR7* . E451Q ( hereafter referred to as Ad5T* ) ) [32] . We evaluated vector sensitivity to neutralisation over a range of concentrations of C57BL/6 mouse serum ( 1–90% final volume ) in vitro ( Fig . 2B , S2 Fig . ) . In contrast to the media control , transduction of all FX-binding deficient Ad5 vectors was dramatically reduced in the presence of serum ( Fig . 2B , S2 Fig . ) . This inhibitory effect was significantly lessened at lower serum concentrations ( <25% ) for the vectors engineered with point mutations , Ad5 . E451Q , Ad5 . HVR5* , Ad5 . HVR7* and Ad5T* compared to vectors with entire HVR exchanges Ad5 . HVR5+7 ( Ad26 ) ( Fig . 2B ) . The latter vector remained highly sensitive to neutralisation even in the presence of 5% serum . This suggests that the sensitivity to serum is influenced by the location and extent of modifications to the Ad5 HVRs , and is , at least , partially independent of the complete loss of FX-binding capacity . It has previously been shown that Ad5 . HVR5* is capable of direct interaction with FX , albeit at lower levels than Ad5 , however detailed affinity kinetics are not available [32] . We hypothesized that immune components may compete with FX for binding sites within related exposed Ad5 HVR loops . Notably we found the concentration of hFX in human serum to be ∼50% lower than that of normal plasma ( S3 Table ) . To test whether increasing concentrations of FX could protect Ad5 . HVR5* from immune attack , we spiked C57BL/6 mouse serum with 10 μg/mL hFX and examined the susceptibility of the non-FX-binding Ad5T* control vector and Ad5 . HVR5* to neutralisation ( Fig . 2C ) . As expected the presence of hFX did not affect the neutralisation of Ad5T* by serum and mediated no increase in gene transfer ( Fig . 2C ) . However , hFX prevented neutralisation of Ad5 . HVR5* , particularly in lower concentrations of serum ( Fig . 2C ) . The presence of hFX also increased gene transfer of Ad5 . HVR5* indicating its ability to both protect and act as a bridge to HSPGs despite sub-optimal FX:hexon binding conditions [24] . These data indicate that both hFX and the murine serum components can compete with one another , likely through binding to similar HVRs , and this is dependent on their relative concentrations and/or affinities . To further dissect the role of the Ad5 HVRs and FX in protecting Ad from serum neutralisation , we engineered FX binding capacity into Ad26 by substituting the Ad5 HVRs into the Ad26 hexon . We attempted to generate a number of Ad26-based mutants however only three Ad26 chimeras were successfully packaged into mature viral vectors at high titer ( Fig . 3A , S3 Fig . , S4 Table ) . Specific hexon sequences were chosen for mutagenesis ( S3 Fig . ) . These included the point mutant Ad26 . Q461E ( the corresponding Glu residue is conserved in all FX binding Ads ) and the HVR exchanges Ad26 . HVR5 ( Ad5 ) . Q461E and Ad26 . HVR5C [in which Ad26 . HVR ( 1–3 and 5–7 ) were replaced by those of Ad5] , respectively ( Fig . 3A , S4 Table , S3 Fig . ) . To note , due to the genetic capsid modifications the vp:PFU ratio was ∼10 fold higher for both Ad26 . HVR5C and Ad26 . HVR5 ( Ad5 ) . Q461E compared to the parental Ad26 vector ( Fig . 3A ) . We next measured the ability of each virus to bind hFX by SPR . Ad26 . HVR5C showed efficient binding when injected over a hFX biosensor chip , as did the positive control Ad5 , while Ad26 , Ad26 . Q461E and Ad26 . HVR5 ( Ad5 ) . Q461E did not bind hFX ( Fig . 3B ) . We then quantified affinity kinetics ( Fig . 3C ) . The calculated association rate constant ( ka ) and dissociation rate constant ( kd ) values of the hFX for immobilized Ad26 . HVR5C were 3 . 085x106 ( 1/Ms ) and 1 . 068x10–2 ( 1/s ) , giving an overall equilibrium dissociation constant ( KD ) of 3 . 462x10–9 M . When Ad5 was immobilized and hFX was injected across the biosensor , ka and kd values were 1 . 308x106 ( 1/Ms ) and 3 . 053x10–3 ( 1/s ) , giving a KD of 2 . 334x10–9 M ( Fig . 3C ) , consistent with previously reported kinetics [24] . Therefore , incorporation of Ad5 HVR ( 1–3 and 5–7 ) into Ad26 generates de novo binding to FX by Ad26 at an affinity similar to Ad5 . Ad26 . HVR5C provides a novel virus through which to ascertain whether inclusion of Ad5 HVR ( 1–3 , 5–7 ) are sufficient for exposure to neutralisation and whether FX binding influenced the sensitivity of the parental virus to mouse serum . Therefore we assessed cellular transduction by the Ad26 mutants in the absence and presence of C57BL/6 or RAG2-/- murine sera +/- X-bp . RAG2-/- mice are immune-deficient , lacking mature T and B lymphocytes , closely related to the RAG1-/- strain in which Ad5 liver tropism was previously demonstrated to be FX independent [19] . Parental Ad26 was resistant to neutralization by both strains of serum regardless of FX , again demonstrating Ad26 is not sensitive to the same mechanism that mediates Ad5 neutralisation ( Fig . 4A ) . Interestingly , Ad26 . HVR5C was resistant to neutralization in the presence of FX but sensitive to neutralization when immune-competent C57BL/6 serum was pre-incubated with X-bp , a similar profile to that seen with Ad5 ( Fig . 4A ) . RAG2-/- serum did not neutralize Ad26 . HVR5C or Ad5 ( Fig . 4A ) , indicating a requirement for efficient T and/or B cell antibody function [19] . Ad26 . HVR5 ( Ad5 ) . Q461E and Ad26 . Q461E were unaffected by either sera regardless of the presence of FX . Furthermore , whilst there were differences amongst the basal levels of C3a between vectors , both Ad5 and Ad26 . HVR5C , but not Ad26 enhanced C3a in a FX-dependent manner . Notably , the FX protection mechanism is not dependent on the heparin binding exosite in the FX serine protease domain , as blocking these HSPG interacting sites did not alter Ad induced C3a levels compared to the matched serum only control ( Fig . 4C ) . Hence , inclusion of the Ad5 HVRs in Ad26 . HVR5C not only leads to FX binding , but also sensitises the virus to attack in immune-competent mouse serum indicating the importance of these hexon regions in both FX binding and immune recognition . We next investigated whether creating Ads engineered to bind FX influenced their ability to interact with cells in vitro and in vivo . We performed cell binding and transduction assays with Ad26 . HVR5C to assess vector:cell interaction profiles ( Fig . 5 ) . SKOV3 cells were employed for these assays as they express low levels of CAR [39] and allow focus on the FX-mediated pathway . Both cell binding and transduction for Ad26 . HVR5C were significantly increased in the presence of FX compared to the parental Ad26 which was unaffected by FX ( Fig . 5A-B ) . This suggests that Ad26 . HVR5C functionally binds FX leading to Ad:FX engagement with cellular HSPGs and subsequent gene transfer . Next , we examined whether the Ad26 . HVR5C hexon:FX interaction generates hepatic tropism via FX bridging to hepatocytes [24 , 33 , 34] . Immune competent MF1 control mice were first treated with warfarin to deplete vitamin K-dependent coagulation factors , and then administered I . V . with 1011 vp of Ad5 , Ad26 or Ad26 . HVR5C . luc+ . Luciferase expression was visualised by whole-body bioluminescence imaging and quantified at 72 h . Ad26 produced low level , widely biodistributed gene transfer and this was unaltered by warfarin ( Fig . 6A ) . However , both Ad5 and Ad26 . HVR5C produced selective transduction of the liver in non-warfarin treated mice ( Fig . 6A-C ) albeit the levels mediated by Ad26 . HVR5C were significantly lower than those of Ad5 . Despite the vp:PFU ratio of Ad26 . HVR5C being ∼10 fold lower than Ad26 ( Fig . 3A ) , there was a significant increase in liver luciferase levels for the Ad26 . HVR5C vector compared to Ad26 , in non-warfarin treated animals ( Fig . 6C ) . For both Ad5 and Ad26 . HVR5C , liver transduction was reduced to less than 0 . 5% of control following warfarin treatment . Quantification of viral genome accumulation in the livers of Ad5 and Ad26 . HVR5C injected animals revealed a similar pattern of virus-mediated gene transfer ( Fig . 6D ) . Thus , inclusion of FX binding into Ad26 . HVR5C leads to profound retargeting effects following I . V . injection into MF1 mice . We then performed the same experiment in immune-deficient RAG2-/- mice . Ad5 mediated FX-independent liver transduction in RAG2-/- mice , similar to that previously reported in RAG1-/- mice [19] ( Fig . 7A-D ) . Ad26 demonstrated widespread gene expression in vivo which was equivalent in both non-warfarin and warfarin-treated mice ( Fig . 7A ) . However , Ad26 . HVR5C transduction was focused in the liver in non-warfarin treated RAG2-/- mice , whilst hepatic gene expression was dramatically decreased in the absence of FX ( Fig . 7A-C ) . The transduction profile of Ad26 . HVR5C in warfarin-treated mice was completely altered , no longer targeting the liver but instead exhibiting a more widespread biodistribution similar to the parental Ad26 ( Fig . 7A ) . This demonstrates the ability of the hexon:FX interaction to determine Ad26 . HVR5C hepatocyte uptake . Through this gain-of-function approach , incorporation of FX binding into Ad26 , FX was shown to effectively alter vector tropism in vivo and dictate liver targeting of Ad26 . HVR5C in control and immune-deficient mice . The high levels of gene expression by Ad5 in warfarin treated RAG2-/- mice ( Fig . 7A ) is in contrast to the diminished luciferase expression ( Fig . 6A ) observed in warfarin treated MF1 mice . This is due to IgM and classical complement-mediated vector neutralisation , similar to that demonstrated previously [19] . Unlike Ad26 but in parallel to Ad5 , gene expression by Ad26 . HVR5C was also inhibited in warfarin treated MF1 mice but not warfarin treated RAG2-/- mice ( Fig . 6A , 7A ) . This suggests Ad26 . HVR5C was neutralised in immune competent mice as a direct consequence of the engineered Ad5 HVRs . This indicates that in the immune competent setting , in the absence of a FX protective mechanism , engineering the Ad5 HVRs into Ad26 confers sensitivity to immune attack and vector neutralisation in vivo .
Here , the role of the Ad5 HVRs and the significance of FX in defending the virus from host attack whilst determining liver targeting are described . We first identified the Ad5 HVRs as responsible for conferring sensitivity to serum neutralisation . We then genetically engineered the Ad5 hexon loops , onto a non-FX-binding Ad26 background to generate the novel vector Ad26 . HVR5C . Employing this as an efficient tool , we deciphered the importance of Ad5 HVRs and FX in immune recognition , protection and biodistribution following systemic Ad administration . Previous work reported that coagulation FX coats Ad5 to protect from IgM and classical complement-mediated immune neutralisation and that Ad5 liver uptake was not solely dependent on FX under immune-deficient conditions [19] . However the capsid proteins to which IgM and the classical complement components bind and initiate this cascade eventually leading to virus inactivation have not been identified . In addition to this work , Doronin et al . , indicated that displacement of FX from the coagulation system through binding to Ad5 is sensed by the host and an inflammatory response is initiated [37] . Therefore , with both of these recent studies in mind , we attempted to identify the Ad capsid regions responsible for mediating immune neutralisation and further decipher the functional consequences of the Ad:FX interaction . We began by showing the FX protection mechanism was not conserved amongst human Ads . Ad5 was the only type tested susceptible to serum neutralisation in the absence of FX and using a series of genetic and pharmacological approaches this was shown to be reliant on the presence of the Ad5 HVRs . We then investigated the sensitivity of Ad5-based HVR mutants and FX-deficient vectors to neutralisation by serum from immune-competent mice and found that all viruses were inhibited , but interestingly there were differences amongst these inactivation levels . These variations indicate that it is more than simply the loss of FX binding or these contact points which likely dictates immune recognition . For our experiments we used fresh mouse serum . Unlike citrated or EDTA treated plasma there is no interference with calcium , which has previously been found to influence results [18] . During the coagulation process , a proportion of the coagulation factors will be activated and levels of intrinsic FX reduced . When we investigated the effects of spiking mouse serum with hFX and compared the null-binder Ad5T* to the FX-defective binder Ad5 . HVR5* , we found hFX was capable of rescuing Ad5 . HVR5* from neutralisation and further increasing gene transfer to levels above the media control . These data thereby suggests that hFX and the inhibitory murine serum components compete for binding within similar regions within the Ad5 hexon . Interestingly previous data has demonstrated using targeted PEGylation that similar Ad5 HVRs ( 1 , 2 , 5 and 7 ) are also responsible for interacting with scavenger receptors and subsequent Kupffer cell uptake , but in that case it has been suggested that FX and the scavenger receptors do not have overlapping binding sites [40] . Our work demonstrates a key role for the Ad5 HVRs in determining virus sensitivity to serum . We successfully engineered an Ad26 vector with Ad5-derived HVRs , Ad26 . HVR5C , capable of FX-mediated cell binding and transduction in vitro . The species D Ad26 is a less seroprevalent virus than Ad5 [14] . It has been shown to utilise both CD46 [41] and CAR [42] as primary cellular receptors and is currently gaining a lot of attention as a vector-based vaccine against HIV [43] . Interestingly , the Ad5 HVR ( 1–3 , 5–7 ) exchange in this Ad26-based vector suddenly made the virus susceptible to neutralisation by mouse serum , indicating the driving influence of the HVRs and the likelihood they possess a critical IgM recognising epitope . The smaller regions of exchange in Ad26 . HVR5 ( Ad5 ) . Q461E or the single point mutation in Ad26 . Q461E were not sufficient to induce sensitivity to neutralisation . Conversely , incorporating the Ad26 HVR5+7 into Ad5 resulted in a vector ( Ad5 . HVR5+7 ( Ad26 ) ) that remained susceptible to the inhibitory effects of serum . This indicates it is perhaps likely that multiple HVR loops working in conjunction which are responsible for mediating the crucial serum binding event . It will require further study to fine map the essential contact sites . IgM antibodies are broadly reactive , capable of binding to unrelated structures with low affinity [44] and mostly exist as a pentameric structure , containing ten potential antigen binding sites [45] . Therefore it is possible one IgM molecule may be capable of interacting across a wide hexon region . These natural antibodies are often directed against highly repetitive and charged motifs [46 , 47] . A recent study suggested the involvement of the electronegative potential of antigens in IgM recognition [47] . To note , the panel of recombinant Ad5-based FX-binding deficient viruses , with the point mutations were all modulated to have lower net negative charge , whilst the more sensitive entire HVR exchanges in Ad5 . HVR5+7 ( Ad26 ) had more negative charge compared to the parental vector ( S1 Table ) . In addition it is perhaps interesting that the net charge of the Ad5 hexon protein is more negative than the other vectors tested in this study , Ad26/Ad35/Ad48/Ad50 . The HVRs of Ad types tested here differ significantly and charge-specific mutation analysis is required to further address this potential issue . Engineering FX-binding into the Ad26 vector had profound effects on viral biodistribution in in vivo models of viral dissemination . In complete contrast to its parental virus , Ad26 . HVR5C exhibited selective liver gene transfer in the presence of FX in immune-competent and deficient mice . In the absence of FX and immunity , Ad26 . HVR5C reverted to its native tropism , with the same biodistribution pattern as Ad26 . This demonstrates the tropism defining force instilled by the inclusion of FX binding . The interaction with FX instils new tropism in addition to its native Ad26 receptor usage ( e . g . via CAR/CD46 ) [41 , 42] . It is evident from the work presented here , and from others [19] , that Ad5 remains hepatotropic in warfarin treated immune-deficient mice . Therefore this suggests , that an alternative mechanism is utilised by Ad5 to transduce hepatocytes of immune deficient animals when FX binding is diminished . Further investigation is required to answer the important question of what is the precise mechanism determining Ad5 liver targeting in the absence of FX . The relevance of the Ad:FX interactions and protection pathways in humans suffering from the disseminated disease is yet to be examined . Further study is therefore required to investigate whether human natural IgM can bind to Ads and if this initiates similar pathways as those in the mouse i . e . classical complement mediated neutralisation in the absence of FX [19] . It may also be beneficial to directly compare and interrogate the influences and interplay of complement , natural and acquired human immunoglobulins [48] . This may help to better predict the human immune defense to widespread Ad infection or when using Ads as gene therapy vectors . In the general population the majority of specific neutralising antibodies ( e . g . IgG-based ) are directed against the Ad5 hexon protein and particularly focused on the Ad5 HVRs [49 , 50] . Previous work has demonstrated that such neutralising human sera can prevent FX-mediated cellular binding and transduction in vitro [51] . In addition , in MF-1 mice preinjected with human sera , higher levels of pre-existing neutralising antibodies , correlated with decreased hepatocyte transduction in the presence of FX [51] . It would be interesting to determine whether different families of human immunoglobulins ( IgG/IgM/IgA ) can compete for binding sites within the Ad hexon HVRs , the relative affinities of these interactions and , further , whether FX coating of the hexon affects these processes . This would also be of clinical relevance in terms of gene therapy and for determining patient suitability or the type of Ad vector employed ( FX-binding or non-FX-binding vectors ) for intravascular Ad administration . Investigating the relevance of the FX protection mechanism from rodent to human models is an important next step for the field . Evidently , the complexity of Ad interactions with host factors is extensive and small animal models may only provide limited information . For instance , CAR-mediated Ad5 binding to human erythrocytes is an important clinical consideration however mouse erythrocytes are devoid of CAR expression , again highlighting the necessity for using human samples [22 , 52 , 53] . Despite the high morbidity and mortality rate associated with disseminated Ad infection there are currently no licensed specific anti-adenoviral drugs available for the management of the condition . Those that have been used in clinical settings , such as ribavirin , cidofovir , and vidarabiner , have yielded varied results and treatment options remain largely unsatisfactory [54 , 55] . A previous study identified small molecule pharmacological agents to block FX-mediated Ad5 infection following intravenous administration in a mouse model , and found it to be an effective strategy by which to prevent hepatic targeting [56] . However , in that case the pharmacological inhibitor was acting post the Ad5:FX binding event [56] . Doronin et al . used cryoEM and molecular dynamics flexible fitting simulations techniques to reveal an interaction between the FX Gla K10 residue and the Ad5 HVR7 residues E424 and T425 [37] . From our work it is evident that the host natural IgM and classical complement mediated defence mechanism is effectively governed via the Ad5 HVR loops , immune recognition is dependent on more than solely the loss of FX contact points , and FX functions in mediating liver uptake in both immune competent and deficient models . Identification of a small molecule inhibitor with favourable pharmacological properties capable of specifically blocking the Ad5:FX interaction at these contact points may enable a more robust host anti-viral immune response whilst limiting liver infection and thus be very valuable in treating systemic disease . In summary , here we have identified the Ad5 HVRs as the key regions conferring sensitivity to the IgM and complement mediated host defence pathway . FX has both a pivotal role in protecting the virus from attack and has profound effects on Ad biodistribution . Through this work we gain a greater insight into the mechanism and significance of FX in protecting Ads against innate immunity and determining virus tropism .
All animal procedures were approved by the University of Glasgow Animal Procedures and Ethics Committee and performed under UK Home Office license PPL 60/4429 in strict accordance with UK Home Office guidelines . All efforts were made to minimize suffering . HEK293 ( human embryonic kidney: ATCC CRL-1573 ) and HeLa ( human cervical adenocarcinoma: ATCC CCL-2 ) cells were cultured in Dulbecco’s modified Eagle’s medium ( DMEM; Invitrogen , Paisley , UK ) and SKOV3 ( human ovarian carcinoma: ATCC HTB-77 ) and A549 ( human lung carcinoma: ATCC CCL-185 ) cells in RPMI-1640 medium ( Invitrogen ) , with 2 mM L-glutamine ( Invitrogen ) , 10% fetal calf serum ( FCS; PAA Laboratories ) and 1 mM sodium pyruvate ( Sigma-Aldrich , UK ) at 37°C 5% CO2 . PER . C6/55K cells [57] were cultured in DMEM with 2 mM L-glutamine , 10% FCS , 1 mM sodium pyruvate and 10 mM MgCl2 , at 37°C 10% CO2 . E1/E3 deleted Ad5 , Ad35 , Ad5/35 chimeras ( Ad5 . F35 , Ad5 . F35P35 ) , Ad26 , Ad50 , Ad48 and Ad5/48 chimeric ( Ad5 . HVR48 ( 1–7 ) ) vectors encoding CMV-luciferase reporter genes were generated as described previously [14 , 24 , 58] . S2 and S4 Tables provide detail on each of the mutated vectors used in this study . E1/E3 deleted Ad5 and FX-binding deficient Ad5/Ad26 chimeric vectors ( Ad5 . HVR5+7 ( Ad26 ) , Ad5 . HVR5* , Ad5HVR7* , Ad5E451Q and Ad5T* ) encoding CMV-lacz reporter genes were generated as described previously [32] . Hexon gene-modified Ad26 vector genomes were constructed in the context of pAd26 . luc , a plasmid that contains a PacI site-flanked , full-length Ad26 vector genome with E1 , E3 , and E4 deletions/modifications [14] . The vector genome is further equipped , at the site of E1 deletion , with a CMV promoter-driven expression cassette for firefly luciferase . To generate the desired hexon gene-modified pAd26 . luc plasmids , the concerning Ad26/Ad5 hexon modifications ( S4 Table ) were first made in the context of a smaller ‘hexon shuttle’ plasmid ( gene synthesis and subcloning by GeneArt/LifeTechnologies ) , and then shuttled into a hexon gene-deleted derivative of pAd26 . luc by homologous recombination in E . coli BJ5183 ( Stratagene/Agilent Technologies ) , as described previously [32] . S3 and S4 Figs . further describe construction and Ad26/Ad5 sequence alignment , highlighting the regions targeted for mutagenesis . Linearised Ad plasmids were transfected in PER . C6/55K using Lipofectamine 2000 ( Invitrogen ) . Cells were harvested 10–14 days post-transfection . Viral particles ( vp ) were propagated and purified by CsCl gradients . Titers were determining by protein concentrations using micro-bicinchoninic acid ( BCA ) Protein Assay ( Thermo Scientific ) . Titer calculations used the formula 1 μg protein = 4x109 vp and end-point dilution assays using PER . C6/55K for quantification of plaque forming units ( pfu ) /mL [59] . Purified Ads were analysed by SDS-PAGE followed by silver staining ( Sigma-Aldrich ) according to manufacturer’s instructions in order to verify the capsid composition and confirm the vector modifications did not interfere with the structural integrity of the particles . Immobilized hFX: Performed using Biacore 2000 ( GE Healthcare ) as described [31] . Purified hFX was purchased from Cambridge Biosciences ( Cambridge , UK ) . hFX was covalently immobilized onto the flowcell of a CM5 biosensor chip by amine coupling . Immobilized Ad: Performed using T200 ( GE Healthcare ) . Virus was biotinylated using the EZ-link sulfo-NHS-LC biotinylation kit ( ThermoFisher ) . The biotinylated products were coupled to streptavidin-coated sensorchips ( SA; Biacore ) ; Ad5 ( 482RU ) , Ad26 . HVR5C ( 484RU ) . SPR was performed in 10 mM HEPES ( pH7 . 4 ) 150 mM NaCl , 5 mM CaCl2 , 0 . 005% Tween20 at a flow rate of 30 μL/min and sensorchips were regenerated by injection of 10 mM HEPES ( pH 7 . 4 ) 150 mM NaCL , 3 mM EDTA , 0 . 005% Tween20 . Kinetic analysis was performed using 2-fold serial dilutions ( in duplicate , starting with 30 μg/mL ) of hFX and fitted using a 1:1 binding model ( Biacore Evaluation software , Biacore ) . Cells were plated in 24-well formats ( 2x105 cells/well ) and incubated overnight at 37˚C . Cells were cooled ( 4°C ) for 30 min , washed with phosphate buffered saline ( PBS ) before adding 1000 or 5000 vp/cell Ad -/+ hFX . hFX was used at 10 μg/ml . Cells were incubated for 1 h at 4°C , washed with PBS , and harvested . DNA was extracted from cells using the QIAamp DNA mini kit ( Qiagen ) and quantified using Nanodrop ( ThermoScientific ) . Viral genomes ( 200 ng DNA ) were quantified by quantitative polymerase chain reaction ( PCR ) analysis ( 7900HT Sequence Detection System; Applied Biosystems ) using Power SYBR Green PCR mastermix and CMV primers ( Applied Biosystems ) . Cells were plated in 96-well formats ( 1x104 cells/well ) and incubated overnight at 37˚C . Cells were infected with 1000 or 5000 vp/cell Ad -/+ hFX . Cells were incubated for 3 h at 37°C , washed with PBS , maintained with medium and harvested 48 h post-transfection . Luciferase activity was measured using the luciferase assay ( Promega , Southhampton , UK ) . Protein concentrations were calculated by BCA . Values expressed as relative light units ( RLU ) /mg of protein . 8–9 week old male MF1 outbred mice ( Harlan , UK ) and Rag2 knockout mice ( on a C57BL/6 genetic background ) ( kind gift from Dr Alison Michie , University of Glasgow ) were used . Mice were warfarin-treated ( 133 μg/mouse ) prior to virus administration as previously described [24] . 1011 vp of Ad in 100 μL PBS were administered via tail vein injection . 72 h post-injection IVIS imaging was performed and animals were sacrificed . Livers were harvested and luciferase activity measured . DNA was extracted from livers using the QIAamp DNA mini kit . Viral genomes in 200 ng DNA were quantified by qPCR as above . Cells were plated in 96-well formats ( 1x104 cells/well ) and incubated overnight at 37˚C . Fresh serum from C57BL/6 mice , Rag2-/- mice or Wistar rats was separated from whole blood , diluted in RPMI-1640 and incubated with 2x1010 vp/mL Ad in a final volume of 50 μL . 40 μg/mL X-bp was added to samples to block FX . Where specified serum was spiked with 10 μg/mL hFX prior to Ad addition . Controls were vectors alone in serum-free medium . Vectors were incubated with serum or medium for 30 min at 37°C . Mixtures were diluted 200-fold in serum-free medium . 1000 vp/well was added for 2 h at 37°C , then replaced with medium containing 2% FBS . After ∼16 h , cells were harvested for determination of transgene expression and protein content . Serum was separated from fresh murine blood . Virus ( 5x1010 vp/mL ) was incubated with 50 μL of serum-/+ 40 μg/mL Xbp or 20 μg/mL NapC2 ( a kind gift from Dr G . Vlasuk ( Corvas International , San Diego , CA , USA ) ) for 90 min at 37°C , then 10 mM EDTA was added . Samples were frozen at -80°C until evaluation in a mouse C3a ELISA as previously described [18] . The capture antibody for ELISA was rat anti-mouse C3a ( BD Pharmingen #558250 ) and the detection antibody used was biotin rat anti-mouse C3a ( BD Pharmingen #558251 ) . The levels of hFX in human serum and plasma samples were measured using a Matched-pair antibody for ELISA of human Factor X FX-EIA ( Quadratech Diagnostics , Surrey , UK ) according to the manufacturer’s instructions . Purified hFX ( Cambridge Bioscience ) was used as the control . Statistical significance was calculated using one-way ANOVA followed by Bonferroni post-hoc test with GraphPad Prism . In vitro results presented are representative data from three separate experiments with at least 3 experimental replicates per group . Each in vivo experiment was performed with a minimum of 3 animals per group ( n = 4–6 for Ad treated groups , n = 3 for PBS groups ) . All error bars represent SEM . | Adenoviruses are mostly considered self-limiting pathogens associated with respiratory , gastrointestinal and ocular infections; however , in immunocompromised subjects disseminated Ad infection can occur with life-threatening consequences . Many human Ads are capable of binding to coagulation factor X ( FX ) . Following intravenous administration in animal models , FX binds directly to the major Ad capsid protein , the hexon , which subsequently results in virus accumulation in the liver . FX coating Ad5 also acts to shield against immune neutralisation via natural IgM antibodies and the classical complement system . Here we show that FX protection is not a conserved mechanism amongst Ads and identify the Ad5 hexon hypervariable regions ( HVR ) as the capsid proteins targeted by this host defense pathway . Furthermore , we show that genetic inclusion of Ad5 HVRs onto a native non-FX binder Ad26 to be sufficient to confer sensitivity to immune attack in vitro and in vivo . Using intravenous administration , we determine the significance of FX binding to the Ad5-derived HVRs with respect to defending the virus from neutralisation whilst mediating virus tropism . Our study gives new insight into the role of the viral HVRs and of FX at the interface between virus and host defense mechanisms . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Manipulating Adenovirus Hexon Hypervariable Loops Dictates Immune Neutralisation and Coagulation Factor X-dependent Cell Interaction In Vitro and In Vivo |
The DNA damage checkpoint and the spindle assembly checkpoint ( SAC ) are two important regulatory mechanisms that respond to different lesions . The DNA damage checkpoint detects DNA damage , initiates protein kinase cascades , and inhibits the cell cycle . The SAC relies on kinetochore-dependent assembly of protein complexes to inhibit mitosis when chromosomes are detached from the spindle . The two checkpoints are thought to function independently . Here we show that yeast cells lacking the DNA damage checkpoint arrest prior to anaphase in response to low doses of the DNA damaging agent methyl methane sulfonate ( MMS ) . The arrest requires the SAC proteins Mad1 , Mad2 , Mad3 , Bub1 , and Bub3 and works through Cdc20 and Pds1 but unlike the normal SAC , does not require a functional kinetochore . Mec1 ( ATR ) and Tel1 ( ATM ) are also required , independently of Chk1 and Rad53 , suggesting that Mec1 and Tel1 inhibit anaphase in response to DNA damage by utilizing SAC proteins . Our results demonstrate cross-talk between the two checkpoints and suggest that assembling inhibitory complexes of SAC proteins at unattached kinetochores is not obligatory for their inhibitory activity . Furthermore , our results suggest that there are novel , important targets of ATM and ATR for cell cycle regulation .
Two evolutionarily conserved checkpoints , the DNA damage checkpoint and the spindle assembly checkpoint ( SAC ) , control the fidelity of chromosome segregation . The DNA damage checkpoint responds to a variety of DNA lesions and controls entry into S phase , completion of S phase and entry into mitosis [1] , [2] . The DNA damage checkpoint is a signal transduction network consisting of sensors , signal transducers and downstream effectors . Central to the signal transduction network in budding yeast are two phosphotidylinositol 3’ kinase-like kinases ( PIKKs ) , Mec1 ( the yeast homolog of ATM and Rad3-related protein , abbreviated ATR ) and Tel1 ( the yeast homolog of the ataxia-telangiectasia-mutated protein abbreviated ATM ) [1] , [3] , [4] . Mec1 and Tel1 activate the protein kinase transducers Rad53 , Chk1 and Dun1 leading to cell cycle arrest and induction of DNA repair genes [5]–[9] . The SAC responds to chromosomes that are either unattached from the spindle or are not under tension and delays the metaphase to anaphase transition [10] . The kinetochore has an integral role in the SAC and a popular model is that the kinetochore initiates checkpoint signaling by being the site of assembly of inhibitory complexes of SAC proteins that inhibit mitosis [10] , [11] . The inhibitory complexes are made up of combinations of the evolutionarily conserved proteins Bub1 Bub3 , Mad1 , Mad2 , and Mad3 ( BubR1 in higher cells ) but the exact details of their assembly and inhibitory activities are unknown [12]–[15] . The two checkpoints share a common target to regulate mitosis . Pds1 ( securin in higher organisms ) is an anaphase inhibitor that is stabilized by two different mechanisms when the two checkpoints are activated . Pds1 is phosphorylated and thereby stabilized by the DNA damage checkpoint [16] . The SAC stabilizes Pds1 by inhibiting Cdc20 , the specificity factor for an E3-ubiquitin ligase called the anaphase-promoting complex ( APC ) that is responsible for the proteolysis of Pds1 [17] , [18] . There are indications , from yeast to humans , that the DNA damage checkpoint and the SAC have overlapping functions . Laser microbeam-induced DNA damage during late prophase in some human cell lines delays progress through metaphase in a P53-independent manner and the delay is abrogated by inhibiting Mad2 [19] . Cells derived from a mouse mutant , heterozygous for a deletion of BubR1 , are defective in the response to genotoxic agents suggesting that BubR1 is limiting in the DNA damage response [20] . Drosophila grapes mutants ( grp ) , lacking the homolog of Chk1 , delay anaphase after X-irradiation and the delay is dependent on BubR1 [21] . Camptothecin induces a mitotic delay in fission yeast cells lacking the DNA damage checkpoint and the delay requires Mad2 [22] . In addition , fission yeast Mad2 plays a minor role in the mitotic delay imposed by growing cells in the presence of the ribonucleotide reductase inhibitor hydroxyurea ( HU ) but Mad1 , Bub1 and Mad3 do not play a role [23] . Budding yeast cells lacking the DNA damage checkpoint ( rad9 rad24 double mutants ) and compromised for DNA replication by mutations in cdc2-1 , pol1-17 , mcm2-1 , or mcm3-1 delay in mitosis in a Mad2-dependent fashion [24] . Compromising both DNA replication and the DNA damage checkpoint in orc1-161 rad53-11 cells causes a delay in mitosis in a Mad2 and Bub1-dependent manner [25] . The DNA alkylating agent , methyl methane sulfonate ( MMS ) , HU , and ultraviolet light also induces a mitotic delays in cells lacking the DNA damage checkpoint and the delays require Mad1 and Mad2 [24] , [26] . Models to explain why such diverse mutants and treatments cause a SAC-dependent mitotic delay propose that kinetochores may be damaged or poorly assembled due to aberrant centromere DNA replication or defects in sister chromatid cohesion may result in a loss of tension across sister kinetochores [23]–[27] . These models are in accord with the proposition that the SAC signal is generated at kinetochores that are either detached from the mitotic spindle or from kinetochores that are on chromatids lacking tension , as would be caused by defective cohesion [10] , [11] , [28]–[31] . However , explanations invoking a role for the kinetochore in a DNA damage response are harder to reconcile with observations that double strand DNA breaks near telomeres in yKu70Δ cells or a single double strand break induced by HO at URA3 induces a mitotic delay in cells lacking the DNA damage checkpoint [32] , [33] . It was proposed that telomere proximal double strand breaks in cells lacking Yku70 results in dicentric chromosomes that are known to activate the SAC , presumably by altering tension at kinetochores [32] . The single double strand break introduced at URA3 causes a delay in the second cell cycle after HO induction which may also reflect the formation of dicentric chromosomes as the source of the SAC signal [33] . In this study we test the model that the kinetochore is required to activate the SAC proteins in response to DNA damage . We show that cells arrest prior to anaphase when grown in the presence of MMS and that the arrest requires the SAC proteins Mad1 , Mad2 , Mad3 , Bub1 and Bub3 . Surprisingly , temperature-sensitive ndc10-1 cells that are devoid of kinetochores also arrest in response to MMS suggesting that the kinetochore is not required to convert the SAC proteins into inhibitors under these conditions . We show that the downstream effectors of the SAC ( Cdc20 and Pds1 ) are required for the arrest suggesting that the inhibition by the checkpoint proteins works through the canonical SAC . Furthermore , we show that the SAC is capable of restraining anaphase in response to MMS in cells lacking the DNA damage checkpoint and that the yeast homologs of ATM ( Tel1 ) and ATR ( Mec1 ) are required for the SAC-dependent arrest suggesting that the PIKKs are required to activate both the DNA damage checkpoint and the SAC . These studies reveal an intimate relationship between the DNA damage and SAC pathways and highlight the importance of preventing anaphase in cells with damaged chromosomes .
We applied several different assays to measure the mitotic delay in cells treated with MMS . Cells were arrested in G1 by growth in the presence of α-factor and then released to the cell cycle in the presence and absence of 0 . 01% MMS [24] . We monitored cell cycle progression by a combination of flow cytometry , cell morphology and Pds1 ( securin ) stability . Cells from four isogenic strains cycled normally in the absence of MMS as judged by DNA flow cytometry ( Figure 1A , upper panels ) , cellular morphology ( Figure 1B ) and Pds1 stability ( Figure 1C ) . MMS treated wild type and mad2 cells delayed progress though S phase , as determined by flow cytometry and arrested with a G2/M content of DNA ( Figure 1A , lower panels ) , prior to anaphase ( Figure 1B ) with high levels of Pds1 ( Figure 1C ) due to activation of the DNA damage checkpoint . rad9 rad24 cells , lacking the DNA damage checkpoint , also delayed with a G2/M content of DNA when grown in the presence of MMS ( Figure 1A , lower panel ) , failed to complete anaphase and accumulated as large budded cells with a single undivided nucleus ( Figure 1B and Figure S2 ) and stabilized Pds1 ( Figure 1C ) . The MMS-dependent mitotic delay was abrogated in rad9 rad24 mad2 cells that failed to accumulate with a G2/M content of DNA ( Figure 1A , lower panel ) , progressed into anaphase ( Figure 1B and Figure S2 ) and failed to stabilize Pds1 ( Figure 1C ) . We measured reproducibility of the response by analysis of multiple flow cytometry profiles ( Figure S1A–S1D ) . Each experiment was performed between 2–6 times and duplicates for each of the flow cytometry experiments are shown including the mean percentage of cells with the G2/M content of DNA determined from the flow cytometry profiles along with the variance in those data . The range of measurements is shown for experiments performed twice and the standard deviation was calculated and is indicated as error bars at each time point for experiments done more than twice . These data confirm that MMS treatment of rad9 rad24 cells lacking the DNA damage checkpoint cause a pre-anaphase delay that is dependent on Mad2 [24] . Haploid rad9 rad24 cells delayed with a G2/M content of DNA suggesting that they had arrested after S phase . We used Clamped Homogeneous Electric Field ( CHEF ) gels to analyze whole chromosomes in cells treated with MMS to determine if the synchronized cells completed DNA replication in response to MMS treatment . CHEF gels are used to separate large ( yeast chromosome-sized ) fragments of DNA by electrophoresis and are useful for karyotyping yeast cells [34] . In addition , they can be used to determine if DNA replication is complete as chromosomes from cells with unreplicated DNA either do not enter the gel and therefore bands are not present or the DNA appears as faintly staining bands with smeared appearances [35]–[37] . Untreated wild type , rad9 rad24 and rad9 rad24 mad2 cells had normal CHEF karyotypes with clearly identified chromosomes ( Figure 1D ) . Wild type cells treated with the ribonucleotide reductase inhibitor hydroxyurea ( HU ) do not complete DNA replication and chromosomes do not enter the gel and were not detected ( Figure 1D ) . Chromosome staining in cells grown in the presence of MMS was weak in both rad9 rad24 cells and rad9 rad24 mad2 cells and was similar to wild type cells grown in the presence of HU ( Figure 1D ) . We detected some chromosomal staining with a smeared appearance in wild type cells grown in the presence of MMS ( Figure 1D ) . We conclude that cells grown under our conditions of 0 . 01% MMS and that delayed with a G2/M content of DNA had completed the bulk of DNA replication but accumulated with lesions , most likely stalled or collapsed replication forks . We assayed cell cycle progression in other SAC mutants to determine if all SAC proteins were required for the delay in response to MMS . Cells lacking the DNA damage checkpoint and either mad1 or mad3 proceeded normally through the cell cycle in the absence of MMS ( Figure 2A , upper panels ) . The same cells did not accumulate with a G2/M content of DNA when grown in the presence of MMS ( Figure 2A , lower panels ) and reproducibility of the flow cytometry , as per Figure 1 , is shown in Figure S3A and S3B . The rad9 rad24 mad1 and rad9 rad24 mad3 cells did not delay anaphase and completed nuclear division in the presence of MMS ( Figure 2B and Figure S4 ) . bub1 cells delayed with a G2/M content of DNA in the presence and absence of MMS ( Figure 2A ) . However , bub1 cells failed to restrain anaphase and completed nuclear division slowly perhaps suggesting that they partially retain the delay ( Figure 2A , 2B , and Figure S3C ) . Reproducibility for the flow cytometry of the bub1 cells is shown in Figure S3C . It was difficult to determine the response of rad9 rad24 bub3 cells by the same assay because of a high degree of inviability in the strain which made flow cytometry difficult to interpret . We assayed cell cycle progression by arresting cells in G1 with α-factor and allowed sufficient time for the viable cells to form mating projections . We released the cells and monitored the progression of only the cells with mating projections that subsequently budded and determined whether they completed nuclear division . Both treated and untreated cells completed nuclear division although MMS treated bub3 cells slowly entered into anaphase ( Figure 2C ) . We conclude that bub3 , like bub1 , abrogates the delay . The kinetochore is required for the SAC and is thought to act as a platform that recruits checkpoint proteins when microtubules are unattached and assembles them into novel complexes that inhibit mitosis [10] , [11] . Temperature sensitive ndc10-1 cells are unable to assemble kinetochores and are unable to arrest in mitosis in response to nocodazole , a benzimidazole drug that depolymerizes microtubules [11] , [38] , [39] . Therefore ndc10-1 cells lack the SAC at the restrictive temperature . We synchronized haploid rad9 rad24 ndc10-1 cells with α-factor at 23°C , incubated the cells at 35°C for 1 hour to inactivate Ndc10 and then released the cells to allow them to progress through the cell cycle at the restrictive temperature . Chromosomes lacking kinetochores are unable to be segregated at mitosis and remain in the mother cell . DNA replication in the next cell cycle causes an increase in ploidy . ndc10-1 cells , untreated with MMS , completed S phase and had a 2C content of DNA and then proceeded to the next cell cycle and increased the ploidy producing cells with a 4C content of DNA ( Figure 2A , upper panel , reproducibility shown in Figure S3D ) . Wild type cells cycled normally in the absence of MMS at 35°C and did not produce cells with a 4C content of DNA ( not shown ) . Therefore , the ndc10-1 cells with a 4C content of DNA are the result of inactivating the kinetochore during the 1 hour incubation at 35°C . The same ndc10-1 cells delayed in the first mitosis when grown in the presence of MMS ( Figure 2A , lower panel and Figure S3D ) . Therefore kinetochores are not required for SAC-dependent inhibition of anaphase in response to MMS . The SAC prevents the metaphase-to-anaphase transition by inhibiting the ubiquitylation and degradation of Pds1 by the APC . The target of the SAC is the APC regulatory subunit Cdc20 [18] , . We determined if MMS inhibits anaphase through APCCdc20 inhibition using CDC20-127; a dominant checkpoint-defective allele that produces a protein unable to bind Mad2 [40] . We generated CDC20-127 ( CDC20Y205N ) by site directed mutagenesis , confirmed it by DNA sequencing and replaced the endogenous allele by a one-step gene replacement . CDC20-127 and CDC20-127 rad9 rad24 cells were delayed with a G2/M content of DNA in the absence of MMS ( Figures S5A and S5B , upper panels ) and cells completed nuclear division ( Figure 2D ) . Reproducibility is shown in Supplementary Figure S5 . CDC20-127 cells delayed with a G2/M content of DNA when grown in the presence of MMS and delayed entry into anaphase ( Figure S5A and Figure 2D , upper panel ) . In contrast , CDC20-127 rad9 rad24 cells , grown in the presence of MMS , did not delay with a G2/M content of DNA , failed to restrain anaphase ( Figure S5B and Figure 2D , lower panel ) and did not stabilize Pds1 ( Figure S5C ) . We conclude that CDC20-127 abrogated the delay in response to MMS in rad9 rad24 cells . Therefore , MMS induces a delay in rad9 rad24 cells by promoting Mad2 binding to Cdc20 and inhibiting APCCdc20 . A hypomorphic top2-B44 mutant , with reduced activity of type 2 topisomerase , delays the onset of anaphase using SAC proteins independently of Pds1 suggesting a novel mitotic topoisomerase II checkpoint [42] . We assayed pds1 cells using the assay described above for bub3 cells to determine if rad9 rad24 cells treated with MMS utilize this novel pathway . Growth in the presence of MMS delayed anaphase of rad9 rad24 cells but not rad9 rad24 mad2 and rad9 rad24 pds1 cells ( Figure 2E ) . Therefore the delay in response to MMS works through Cdc20 and Pds1 and is different from the one reported for partial topoisomerase inhibition . The lack of a kinetochore requirement for Mad1 , Mad2 and Mad3-dependent APCCdc20 inhibition was surprising because kinetochores are believed to be the source of the signal that activates the SAC [43]–[46] . One possibility for how the SAC proteins respond to DNA damage , independently of the kinetochore , is that they become activated in a DNA damage-dependent manner . We analyzed mec1 and tel1 mutants to determine if there was a role of either protein in transducing the signal from DNA damage to the SAC proteins . MEC1 encodes a PIKK that is homologous to the human ATR and is a central transducer of the checkpoint response in yeast [1] , [3] . TEL1 encodes the related PIKK homologue ATM and plays a lesser role in the DNA damage checkpoint in yeast . mec1-1 cells , grown in the presence of MMS , arrested with a G2/M content of DNA ( Figure 3A ) . Similarly , rad9 rad24 tel1 cells delayed with a G2/M content of DNA in response to MMS ( Figure 3A ) suggesting that the delay is independent of Mec1 and Tel1 . We constructed a mec1 tel1 double mutant to determine if the kinases contributed redundantly in activating the SAC . Only 60% of the mec1 tel1 cells were viable which precluded analysis by flow cytometry . We used the same assay as described above for bub3 and pds1 cells to determine the effect of MMS in mec1 tel1 cells . Wild type and mec1 cells arrested prior to anaphase when grown in the presence of MMS but mec1 mad2 cells completed nuclear division ( data not shown ) . Therefore mec1 cells , like rad9 rad24 cells , arrest in mitosis in a Mad2-dependent fashion in response to MMS . Interestingly , mec1 tel1 cells were unable to arrest and completed nuclear division when grown in the presence of MMS ( Figure 3B ) . Together , these data suggest that Mec1 and Tel1 act redundantly to activate the SAC proteins and inhibit APCCdc20 in response to MMS . It is possible that the effects of Mec1 and Tel1 on the SAC were indirect . The single mutants lacking either Mec1 or Tel1 may retain sufficient PIKK activity to activate the downstream effector kinases Rad53 and Chk1 and contribute to the pre-anaphase G2/M delay . Perhaps cells lacking both Mec1 and Tel1 do not activate Rad53 and Chk1 and in their absence the SAC is unable to restrain anaphase . This is an important distinction because it would affect the interpretation that the SAC is activated in a Mec1 and Tel1-dependent fashion . The MEC1 gene is essential and mec1-1 cells are viable in the presence of a second mutation , sml1 , that suppresses the mec1-1 lethality but does not suppress the DNA damage checkpoint phenotype . We used the same assay as described above for bub3 , pds1 and mec1 tel1 cells to determine if there was a an effect of MMS on mitotic progression in a set of isogenic strains lacking Sml1 and proteins of the DNA damage checkpoint and the SAC . The sml1 cells , treated with MMS , behaved like wild type cells ( Figure 1A ) and arrested in mitosis prior to anaphase in contrast to the mec1 tel1 sml1 cells described above ( Figure 3B ) . rad9 mrc1 sml1 cells that lack the S-phase checkpoint delayed prior to anaphase when grown in the presence of MMS ( Figure 3B ) . rad53 chk1sml1 cells also delayed prior to anaphase when grown in the presence of MMS although a small percentage of cells entered into anaphase . However , the delay in rad53 chk1sml1 cells was abrogated by deleting MAD2 ( rad53 chk1 mad2 sml1 ) as shown in Figure 3B . Therefore a partially activated DNA damage checkpoint is not sufficient to explain the entire pre-anaphase delay in MMS treated rad9 rad24 cells . We conclude that the SAC is sufficient to delay anaphase in the absence of the DNA damage checkpoint and that the SAC is activated in a Mec1 and Tel1 dependent fashion . An important study has recently shown that there is PIKK-dependent phosphorylation of SAC proteins in response to DNA damage in human cells suggesting that SAC proteins are substrates of ATM and ATR in response to DNA damage [47] . Together the data suggest that there may be an evolutionarily conserved response of cells to DNA damage that involves ATM and ATR-dependent phosphorylation of SAC proteins that helps to enforce a mitotic arrest in response to DNA damage . Our data extends the previous observation that the SAC mediates a mitotic delay in response to multiple lesions affecting DNA replication [22]–[25] , [48] . Two previous studies have shown that the SAC contributes to survival of cells lacking the DNA damage checkpoint when cells are treated with MMS or when compromised for DNA replication [24] , [25] . Our data extend these previous studies in two important ways . We have shown that the SAC inhibits APCCdc20 when cells are grown in the presence of MMS and SAC-dependent inhibition does not require a functional kinetochore . In addition , we have shown that the SAC depends on the PIKKs Mec1 and Tel1 . Our data are summarized in a model in Figure 4 . Tel1 and Mec1 , in response to MMS ( and other mutations and treatments ) , activate both the DNA damage checkpoint and the SAC . The DNA damage checkpoint and the SAC converge on Pds1 , by independent mechanisms , to restrain anaphase . One possible reason is that the DNA damage checkpoint recruits the SAC as a backup to assure that cells do not enter anaphase . MMS treatment causes stalled replication forks [49] . Cells will activate the DNA damage checkpoint only after they surpass a threshold of stalled replication forks , presumably because stalled and active forks are similar in structure [50] , [51] . This threshold assures that the DNA damage checkpoint does not interfere with normal replication . A cell that enters into mitosis with stalled replication forks , below the threshold , could initiate a catastrophic mitosis . If cells arrest because of some threshold of stalled replication forks , then this would constitute a new checkpoint for the completion of DNA replication . Such a checkpoint is controversial [52] but the exciting possibility that Mec1 and Tel1 activates the SAC to achieve a cell cycle arrest warrants further investigation .
All strains were derivatives of W303 ( MATa or MATα ade2-1 trp1-1 can1-100 leu2-3 , 112 his3-11 , 15 ura3-1 ) and are listed in Table S1 . Cells were arrested using the mating pheromone , α-factor at 5 µM for BAR1 strains and 0 . 1 µM for bar1 strains . Cells were released from α-factor by washing in water for three times and released into fresh pre-warmed medium . The temperature sensitive strain , rad9 rad24 ndc10-1 , was grown at 23°C ( permissive ) and tested at 35°C ( restrictive ) . Standard yeast genetics techniques and media were used [53] . Cells were grown in YPD medium ( 1% yeast extract , 2% Bacto Peptone , 2% Glucose , 40 mg of adenine per liter ) . Yeast transformations were by the lithium acetate method [54] . Epitope-tagged alleles PDS1-13MYC-HIS were constructed by PCR-mediated one-step gene replacements [55] . The ndc10-1 mutant was obtained as by double fusion PCR [53] . Deletion of MAD1 , MAD3 , BUB1 , and BUB3 genes were generated by PCR and transformation for each coding region was replaced by the KanMx4 or ClonNAT ( NAT ) genes by one-step gene replacement . The CDC20-127 dominant allele was made from PCR and transformed to wild type and rad9 rad24 strains [40] . Other mutants were made by standard tetrad genetics . Cells were grown to O . D . of 2 . 0 overnight in YPD medium . For synchrony , cells were diluted to O . D . of 0 . 2 in YPD medium for bar1 deletion strains or acidic YPD ( pH 3 . 5 ) medium ( BAR1 strains ) with α-factor . Cells were monitored under microscope to arrest 85–100% as unbudded cells typically after 2 . 5–3 hours . Cells were washed with water and resuspended in fresh medium under experimental conditions . Methylmethane sulfonate ( MMS , Sigma M-4016 ) concentration was 0 . 01% V/V . For experiments with the temperature sensitive strain rad9 rad24 ndc10-1 , wild type and mutant cells were grown and arrested with α-factor at 23°C . They were shifted to 35°C for 1 hour to inactivate Ndc10 and then released in fresh medium at 35°C with or without MMS . At each time point and for each strain , cells were taken for DAPI staining or FACScan ( flow cytometry ) using Sytox Green ( Molecular Probes , Inc . ) and western blot analysis . Western blots were with mouse monoclonal anti-Myc antibody 9E10 , or rabbit anti-Tub2 antibody FY124 , a generous gift from Frank Solomon ( MIT ) , for tubulin loading controls . Flow cytometry was as previously described [56] and performed at the University of Virginia core fluorescence-activated cell sorting facility . Every strain was tested independently at least twice and up to six times by flow cytometry . Nuclear division for cells stained with Sytox green or DAPI was determined using a Nikon E600 microscope equipped with epifluorescence . At least 100 cells were counted for each time point . Cells were arrested with α-factor and after 3 hrs at 23°C they were washed and released in fresh media with or without 0 . 01% MMS . The cells arrested in S phase were treated with 0 . 1 M Hydroxyurea ( HU , Sigma H-862 ) . Samples were taken in every hour . Plugs for CHEF gels were prepared as soon as the cells were sampled according to manufacturer’s instructions ( BioRad ) . Samples were subjected to CHEF; 120° field angle , 6 V/cm , initial switch time of 60 s , final switch time of 120 s for 21 h at 11°C . | Genome integrity is assured , in part , by regulatory systems called “checkpoints” that assure that cells do not improperly progress through the cell cycle . The DNA damage checkpoint assesses the status of DNA replication and inhibits cell cycle progression when the cell makes mistakes in DNA replication or when the cell has been assaulted by a DNA damaging agent from the environment . The checkpoint allows the cell time to repair the DNA and then permits the cell cycle to resume . There is a separate “spindle checkpoint” that monitors whether chromosomes are properly attached to the spindle and if so , allows cells to proceed through mitosis . The DNA damage checkpoint and the spindle checkpoint assure that daughter cells receive the correct number of chromosomes that are identical in DNA sequence . Here we show that the two checkpoints are not independent but that they cooperate to restrict mitotic progression in the face of DNA damage . We show that the spindle checkpoint can be induced by DNA damage and that there is a novel kinetochore independent mechanism to activate the spindle checkpoint proteins . In addition , we implicate the ATM and ATR kinases as kinetochore-independent activators of the spindle checkpoint . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/nuclear",
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"and",
"function",
"genetics",
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"genomics/cancer",
"genetics",
"genetics",
"and",
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"biology"
] | 2008 | DNA Damage Activates the SAC in an ATM/ATR-Dependent Manner, Independently of the Kinetochore |
The division of cellular space into functionally distinct membrane-defined compartments has been one of the major transitions in the history of life . Such compartmentalization has been claimed to occur in members of the Planctomycetes , Verrucomicrobiae , and Chlamydiae bacterial superphylum . Here we have investigated the three-dimensional organization of the complex endomembrane system in the planctomycete bacteria Gemmata obscuriglobus . We reveal that the G . obscuriglobus cells are neither compartmentalized nor nucleated as none of the spaces created by the membrane invaginations are closed; instead , they are all interconnected . Thus , the membrane organization of G . obscuriglobus , and most likely all PVC members , is not different from , but an extension of , the “classical” Gram-negative bacterial membrane system . Our results have implications for our definition and understanding of bacterial cell organization , the genesis of complex structure , and the origin of the eukaryotic endomembrane system .
The compartmentalization of cellular space has been an important evolutionary innovation , allowing for the functional specialization of the membrane-bound organelles . This compartmentalization is extensively developed in eukaryotes , and although not as complex and developed , compartments with specialized function are known to occur in bacteria [1] . Some examples include protein-bound organelles , like carboxysomes , which increase the concentration of metabolite in a closed space [2] and gas vesicles , which are gas-filled protein-bound organelles that function to modulate the buoyancy of cells [3] . Other examples include the magnetosomes in magnetotactic bacteria , which are invaginations of the cytoplasmic membrane that enclose a magnetic mineral without achieving separation into individual vesicles [4] . Individual magnetosomes are arranged into one or more chains within the cell , where they act to orient the cell within a magnetic field . Photosynthetic prokaryotes including the purple bacteria , the cyanobacteria , and the green bacteria have photosynthetic membranes extending from their inner membrane ( IM ) , also called cytoplasmic membrane , maximizing the size of the membrane surface exposed to light . These membranes can adopt diverse shapes , including the formation of membrane stacks continuous with the cell membrane , spherical invaginations of the inner membrane so that multiple membrane spheres are connected to one another or are folded in an accordion-like structure and adjacent to the cell membrane [5] . Lastly , the anammoxosome is a membrane-bound compartment found in the anammox bacteria , which are divergent planctomycetes . It houses the anaerobic ammonium oxidation reaction . Its membrane is enriched in unusual concatenated lipids , the ladderane lipids , which form an impermeable barrier preventing the diffusion of the toxic intermediates produced during the anammox reaction [6] . Bacterial cell organization can be surprisingly complex . Nevertheless , members of the Planctomycetes , Verrucomicrobiae , and Chlamydiae ( PVC ) bacterial superphylum are exceptional in displaying diverse and extensive intracellular membranous organization . For this reason they have been labeled the “compartmentalized bacteria” [7] , [8] . The planctomycete Gemmata obscuriglobus is particularly interesting because a double membrane , formed from a folded single membrane , has been suggested to surround its genetic material . This double membrane is reminiscent of the eukaryotic nuclear envelope , leading to the name “nucleated bacterium” [7] , [9] . Early ultrastructural analysis based on thin sections of cryo-substituted cells , freeze-fracture replicas , and computer-aided 3-D reconstructions has been used to argue that the DNA in G . obscuriglobus is enclosed within a compartment separated from the rest of the cytoplasm [8] , [10] . However , the data are not entirely convincing . A three-dimensional ( 3D ) reconstruction from serial sections and fluorescence microscopy of living cells was presented to support the claim of “the continuous nature of the membranous envelope surrounding the nuclear body and completely enclosing the nucleoid , apart from where gaps appear in the envelope” [8] . As stated by the authors , the “outer region of the nuclear body has a similar appearance to the cytoplasm , ” and ribosomes are located in the same compartment as the DNA , arguing against the specific nature of this compartment . In addition , ribosomes line the walls of the internal membrane of the “nuclear compartment” [8] , as observed along the inner membrane ( IM ) of classical bacteria . This and other analyses have led to the suggestion that the PVC cell plan is different from “classical” Gram-negative bacteria , such as E . coli , because of the absence of a typical outer membrane ( OM ) [7] , [8] . The outermost membrane closely juxtaposed to the cell wall was interpreted as the cytoplasmic membrane , while the remaining membrane was called the intracytoplasmic membrane ( ICM ) , mainly based on the distinctive organization of the ICM supposedly surrounding the DNA . The claimed absence of an OM implied the absence of a periplasm , the volume located between IM and OM in Gram-negative bacteria . More recent evidence based on genomic information argue against this conclusion , including the presence of genes associated with the OM and the periplasm in Gram-negative bacteria [11] , [12] , and the presence of remnants of the division cluster and the peptidoglycan synthesis pathway ( typically anchored in the OM ) [13] . A more recent analysis of vitrified sections by cryo-electron tomography implied that the “internal membrane” system might be continuous with the ICM , but formed by membrane invaginations and that “the bacterial nucleoid is not completely sealed by the double-membrane system” [14] . It was observed that “the double-membrane network of G . obscuriglobus cells emanates from the intracytoplasmic membrane to form unsealed compartments . ” In that study , the bacteria were preserved close to native state , sectioned , and imaged under cryogenic conditions to reduce preparation-induced artifacts . However , because of the difficulties involved in sectioning cells under liquid nitrogen temperatures and the technical challenges presented by the use of vitrified sections in obtaining serial sections of a whole cell , the analysis was based on tomographic reconstruction of only a fraction , up to 150 nm thick sections , of G . obscuriglobus cells , which are usually ∼2 µm in diameter . We have recently contributed to this series of analyses and have described the cell organization in two types of G . obscuriglobus cells [15] . In the first type , the dividing form , the inner membrane protrudes deeply into the cytoplasm to form thin membrane sheet invaginations extending towards the inside of the cell . The second cell type is not budding , and has increased periplasmic volume populated by vesicle-like structures . Till present , how the membranes are organized in 3D is not known for any of the PVC bacteria . We have thus investigated the 3D membrane organization in multiple cells of the species G . obscuriglobus . In order to capture the membrane organization of entire cells , we chose to use plastic embedding for this study . Here we present the reconstructed volume of one complete cell of the first , dividing type , where we followed the entire organization of internal membranes within the cell . We report for the first time the 3D reconstruction of a bacterium with a complex endomembrane system . Our 3D reconstruction reveals that G . obscuriglobus cells are neither compartmentalized nor nucleated . We show that the spaces created by the membrane invaginations are all interconnected and not closed . The organization of cellular space is similar to that of a classical Gram-negative bacterium modified by the presence of large invaginations of the IM inside the cytoplasm .
Three-dimensional reconstruction reveals that G . obscuriglobus cells have a cell plan that is not radically different from that of a typical Gram-negative bacterium ( Figure 1; Figure S1 ) . The organization is topologically compatible with an extension of the periplasmic space by invagination of the bacterial IM towards the cell's interior . This is supported by the fact that ribosomes line the IM and its invaginations in G . obscuriglobus cells , as they do along the IM of other Gram-negative bacteria . This similarity of topological organization is supported by genomic information [11]–[13] . The main difference is that the G . obscuriglobus IM invaginates extensively towards the interior of the cell to form a network of sheets within the cytoplasm ( Figure 1; see Supplementary Movies 1 and 2 , available at http://www . bork . embl . de/~devos/project/apache/htdocs/plancto/g3d/ [Text S1] ) . The space inside the invaginations is continuous with the periplasm and devoid of ribosomes , as in other bacteria ( Figure 1; Figure S1 ) . We have observed ribosome-covered extended membrane sheets , as in the eukaryotic rough endoplasmic reticulum ( ER ) or the nuclear envelope , which have associated ribosomes , as opposed to membrane tubules associated to the eukaryotic smooth ER . The mean lumenal width of the internal membrane sheets is ∼20 nm ( mean of 18 . 8 ) . This is slightly smaller than the ∼30 nm and ∼50 nm reported , respectively , for yeast and mammalian ER sheets [16] . These membrane extensions have a significant impact on the cell organization , in particular on the ratio of OM versus IM . E . coli cells are about 1 . 5 µm long and 0 . 5–0 . 6 µm in diameter , with a cell volume of ∼0 . 65 µm3 [17] . Their periplasm comprises between 20% and 40% of the total cell volume [18] . With a diameter of ∼2 µm , the complete volume of the reconstructed G . obscuriglobus cell is 3 . 4 µm3 , while the cytoplasm is 2 . 6 µm3 . The periplasm , including the space created by the invaginations of the IM , has a volume of . 82 µm3 ( ∼one third , 31 . 7% , of the cell's volume , similar to E . coli ) . The important difference is observed at the membrane surface . In E . coli , the IM/OM ratio is slightly below 1 . In this particular G . obscuriglobus cell , the OM has a surface of 13 . 7×106 nm2 , while the IM is almost exactly three times bigger , with a surface of 42 . 7×106 nm2 ( Table S2 ) . Based on our observations , this ratio likely varies from cell to cell . Although extensively developed , the membrane does not create individualized compartments within the cytoplasm . All membranes are connected and isolated compartments defined by membranes within the cell volume do not exist . The only cellular volumes are the cytoplasm and the periplasm ( Figure 1; see Supplementary Movies 1 and 2 , available at http://www . bork . embl . de/~devos/project/apache/htdocs/plancto/g3d/ [Text S1] ) . G . obscuriglobus membrane invaginations and derived membrane morphologies appear to be dynamic and possibly cell-cycle-dependent [9] , [15] . We have acquired partial volumes for six cells and complete volumes for four cells with various morphologies and believe it is highly unlikely that the membrane completely encloses or forms isolated compartments during any stage of the cell cycle . We have always observed connected pseudo-compartments that we could follow in 3D . The changes in membrane organization and connection of the pseudo-compartments , as well as the variation of periplasm organization , can be followed in consecutive slices from the tomograms ( Figures S2 , S3 , S4 , S5 , S6 , S7 , S8 ) . We observed five isolated clusters of DNA in one completely reconstructed cell and similar results in other cells ( Figure 1; Figures S2 , S3 , S4 , S5 , S6 , S7 , S8 ) . Some regions appeared more condensed than others , possibly due to differences in the replication or transcriptional status of the genetic material , which is unknown since the cell is in a dividing state . Importantly , the genetic material is not restricted to a closed compartment with communicating pores—that is , in a “nucleus-like” organization as previously concluded [8] , [10] . Membrane invaginations are sometimes found close to the DNA , but never enclose it completely . It is , however , easy to see why this can lead to false interpretations when looking at 2D images of single sections ( Figure 1 ) . 3D reconstruction rules these out . We have obtained similar tomograms for nine additional G . obscuriglobus cells with distinct overall membrane organization , and reached the same conclusion in each case ( Figures S2 , S3 , S4 , S5 , S6 , S7 , S8 ) . This conclusion is consistent with the presence of ribosomes in the cytoplasm surrounding the nucleoid . The DNA appears to be floating freely within the cytoplasm and does not obviously interact with the membranes , as in other bacteria . Almost all planctomycetes reproduce by budding [7] , instead of fission , the most common form of bacterial division . During the early phases of the budding process , the bud is mostly devoid of membranes and DNA [9] . Consistently , we imaged the bud where only one membrane sheet is present and we do not detect any DNA . This membrane sheet is ∼20 nm thick , similar to those observed in the mother cell . Furthermore , the IM of the bud is continuous with the IM of the mother cell , as can be observed at the neck of the bud ( Figure 2; see Supplementary Movie 3 , available at http://www . bork . embl . de/~devos/project/apache/htdocs/plancto/g3d/ [Text S1] ) , implying continuity for all membranes between the mother and daughter cell . The cytoplasm of the mother and daughter cells are connected by a narrow channel through the neck of the bud . At its narrowest point , the channel is roughly 30 nm wide , explaining why it has been missed in previous studies . Moreover , electron dense material is observed inside the periplasm around the neck , possibly suggesting the periplasm as an alternative route for the transfer of material between the mother and the daughter cells . However , this dense material requires further study and confirmation . As the bud enlarges , the neck of the bud opens up , with dimensions ranging from 80 to 375 nm ( Figure S9 ) . The genetic material , being cytoplasmic , can pass freely into the bud without interference from a “nuclear membrane . ” This structure must somehow close during completion of cell division . The membrane organization in the mother cell appears to become more complex in the proximity of the budding neck ( Figure 3; see Supplementary Movie 3 , , available at http://www . bork . embl . de/~devos/project/apache/htdocs/plancto/g3d/ [Text S1] ) , possibly due to the process of membrane transfer to the bud . However , also here , there are no defined compartments and all membranes are in continuity with the IM . These results have important implications for our understanding of planctomycete division . Crateriform structures have previously been reported as homogeneously distributed in G . obscuriglobus as opposed to other planctomycetes [19] . These structures are associated with depressions of the OM as can be seen from the side view perpendicular to the membrane ( Figure 4 ) . They have an opening of ∼35 nm and are uniformly distributed around the cell periphery , except in the mother cell within ∼1 µm diameter around the neck of the bud , with a density of between 50 and 100 crateriform structures per µm2 ( Figure 1 ) . We have observed the presence of intracellular electron dense granules that are not enclosed by a membrane ( Figure 1 , depicted in dark blue ) . These are visible in roughly 50% of the cells that have been observed , and there is generally one per cell . X-ray micro-analysis confirmed that those granules are mainly composed of poly-phosphate ( PolyP; Figure S10 ) . PolyP can perform different biological functions , such as serving as an energy source for ATP synthesis [20] .
Eukaryogenesis has long been a question of major interest to biologists . Although it is increasingly accepted that eukaryotes and archaea share a common ancestor , the nature of this ancestor ( if it was already an archaea per se or an intermediate organism ) is still debated [30] . The eukaryotic cell is differentiated from bacterial and archaeal cells by many features whose origins are for the most part still unknown . These features include the actin- and tubulin-based cytoskeleton , the mitochondria , the nuclear pore , the spliceosome , the proteasome , and the ubiquitin signaling system [31] . Features reminiscent of these are increasingly detected in prokaryotes , including the members of the PVC bacterial superphylum [23] , [32] . Because PVCs display some features related to eukaryotes or archaea , including sterol production [26] and ether-linked lipids [6] , it has been proposed that the PVC ancestor might have shared a sisterhood relationship with the ancestor of the eukaryotes and archaea [23] , [32] . Other scenarios involving a relationship between PVC and eukaryotes have also been proposed [21] , [33] . However , whether the PVC features are homologous or analogous to their eukaryotic or archaeal counterparts is still under discussion [34] . If there is no evolutionary relationship between PVC and eukaryotes , the complex endomembrane system of those bacteria highlights that endomembrane systems have evolved more than once . The complex endomembrane system of G . obscuriglobus is in direct contact with proteins displaying structural similarities to eukaryotic membrane coat proteins like clathrin or sec31 that sustain the eukaryotic endomembrane system [15] , [35] . In addition , G . obscuriglobus endomembranes are involved in the otherwise strictly eukaryotic process of endocytosis [36] . These data reinforce the possibility of an evolutionary relationship between the eukaryotic and PVC endomembrane systems and suggest that the latter could represent intermediary steps in the development of the former from a “classical” Gram-negative bacterium [23] , . Deeper characterization of the PVC endomembrane system is therefore of great interest . In conclusion , our analysis reveals that the membrane organization in G . obscuriglobus is not fundamentally different from that of “classical” bacteria , but a complex variant of it . The next step is to link those observations with the development of this endomembrane system in cells using live imaging methods .
G . obscuriglobus cells were grown as previously described [15] . The cells were frozen in an HPM010 ( Abra Fluid , Switzerland ) high-pressure freezing machine and freeze substituted with either 1% Osmium tetroxide , 0 . 1% uranyl acetate , and 5% H2O and embedded in Epon or with 0 . 5% uranyl acetate and embedded in Lowicryl HM20 . Thin ( 60 nm ) and thick sections ( 250 nm ) were placed on formvar-coated grids and post-stained with uranyl acetate and lead citrate . Thin sections were imaged on a CM120 Phillips electron microscope . For tomography , acquisition was done on a Technai F30 300 kv ( FEI Company ) microscope with dual axis tilt series ( first axis from −60° to +60° with 1° tilt increment , second axis from −60° to +60° with 1 . 5° increment ) . We acquired nine serial sections and reconstructed them using fiducial gold particles with the weighted back projection algorithm . We joined consecutive serial sections using the etomo graphical user interface from IMOD ( Boulder Laboratory for 3-D Electron Microscopy of Cells ) . We fully acquired four cells ( eight to nine sections ) , two cells that are ∼75% complete ( six sections ) , two cells that are ∼50% complete ( four sections ) , and two cells that are ∼40% complete ( three sections ) ( Table S1 ) . The budding cell was modeled with IMOD and we traced the contours on at least every fifth slice over a range of 1 , 130 slices . Tomograms have been deposited in the EMDB ( http://www . ebi . ac . uk/pdbe/emdb/ ) under the accession numbers EMDB-2362 and EMDB-2363 . | The compartmentalization of cellular space has been an important evolutionary innovation , allowing for the functional specialization of cellular space . This compartmentalization is extensively developed in eukaryotes and although not as complex and developed , compartments with specialized function are known to occur in bacteria and can be surprisingly sophisticated . Nevertheless , members of the Planctomycetes , Verrucomicrobiae , and Chlamydiae ( PVC ) bacterial superphylum are exceptional in displaying diverse and extensive intracellular membranous organization . We investigated the three-dimensional organization of the complex endomembrane system in the planctomycete bacterium Gemmata obscuriglobus . We reveal that the G . obscuriglobus cells are neither compartmentalized nor nucleated , contrary to previous claims , as none of the spaces created by the membrane invaginations is topologically closed; instead , they are all interconnected . The organization of cellular space is similar to that of a classical Gram-negative bacterium modified by the presence of large invaginations of the inner membrane inside the cytoplasm . Thus , the membrane organization of G . obscuriglobus , and most likely all PVC members , is not fundamentally different from , but is rather an extension of , the “classical” Gram-negative bacterial membrane system . | [
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The rostromedial tegmental nucleus ( RMTg ) , also called the GABAergic tail of the ventral tegmental area , projects to the midbrain dopaminergic system , dorsal raphe nucleus , locus coeruleus , and other regions . Whether the RMTg is involved in sleep–wake regulation is unknown . In the present study , pharmacogenetic activation of rat RMTg neurons promoted non-rapid eye movement ( NREM ) sleep with increased slow-wave activity ( SWA ) . Conversely , rats after neurotoxic lesions of 8 or 16 days showed decreased NREM sleep with reduced SWA at lights on . The reduced SWA persisted at least 25 days after lesions . Similarly , pharmacological and pharmacogenetic inactivation of rat RMTg neurons decreased NREM sleep . Electrophysiological experiments combined with optogenetics showed a direct inhibitory connection between the terminals of RMTg neurons and midbrain dopaminergic neurons . The bidirectional effects of the RMTg on the sleep–wake cycle were mimicked by the modulation of ventral tegmental area ( VTA ) /substantia nigra compacta ( SNc ) dopaminergic neuronal activity using a pharmacogenetic approach . Furthermore , during the 2-hour recovery period following 6-hour sleep deprivation , the amount of NREM sleep in both the lesion and control rats was significantly increased compared with baseline levels; however , only the control rats showed a significant increase in SWA compared with baseline levels . Collectively , our findings reveal an essential role of the RMTg in the promotion of NREM sleep and homeostatic regulation .
Dopamine ( DA ) produced by neurons in the midbrain plays a key role in processing reward , aversive , and cognitive signals [1] . Abnormal DA is closely associated with neuropsychiatric disorders such as Parkinson disease , schizophrenia , and substance abuse . Severe sleep disturbances have been observed in nearly all of these types of patients [2–5] . Growing evidence suggests that DA-containing neurons are important for arousal maintenance in both humans [6 , 7] and animals [8–11] . Moreover , it has been found that activation of ventral tegmental area ( VTA ) γ-amino-butyric acid ( GABA ) neurons , which indirectly inhibits the firing of VTA DAergic neurons , is sufficient to elicit a place aversion [12 , 13] . This suggests that the ability of midbrain DAergic neurons to regulate sleep–wake behavior and sleep problems in DA-associated mental illnesses may be affected by upstream inhibitory neuronal systems . The rostromedial tegmental nucleus ( RMTg ) is a newly identified structure in the brainstem that is rich in μ-opioid receptors . It primarily comprises GABAergic neurons that are distributed dorsolateral to the interpeduncular nucleus ( IPN ) . The RMTg is strikingly innervated by the afferent input from the lateral habenula and additional inputs from the extended amygdala and other closely connected regions , such as the lateral septum and periaqueductal gray matter . The GABAergic axons from the RMTg densely project to midbrain DAergic neurons [14–18] . The RMTg acts as a hub converging and integrating widespread signals toward DAergic systems [19] . Neuroanatomical and electron microscopic studies have found that most RMTg axons form symmetric synapses with tyrosine hydroxylase ( TH ) -containing dendrites in the substantia nigra compacta ( SNc ) and VTA [20 , 21] . An in vivo electrophysiological study showed that the RMTg exerted greater inhibition of the SNc DAergic neurons than the inhibitory afferents arising from the striatum , globus pallidus , or substantia nigra parsreticulata [20 , 22 , 23] . Likewise , GABAergic RMTg neurons inhibited the activity of VTA DAergic cells by inhibiting synaptic transmission more effectively than intermediate GABAergic neurons in the VTA [21 , 24 , 25] . DAergic cells are controlled by excitatory and inhibitory inputs whose balance finely tunes cell activity [26] . The RMTg is now recognized as a GABA brake for midbrain DAergic systems [19] . Aside from the heavy output to the midbrain DAergic neurons , the RMTg sends projections to the dorsal raphe nucleus ( DRN ) , pedunculopontine tegmental and laterodorsal tegmental nuclei ( PPT , LDT ) , and locus coeruleus ( LC ) and has relatively meager output to the forebrain , including the lateral hypothalamus and lateral preoptic area [14 , 15] . Generally , neurons in these cell groups fire most actively during wakefulness [7] . Although the RMTg has been confirmed to inhibit the electrical activity of midbrain DAergic neurons associated with wakefulness activation , whether it is implicated in sleep–wake behavior is unknown . Considering that RMTg neurons are prominently GABAergic and are thus speculated to inhibit rather than facilitate the activity of targeted neurons , we propose that the RMTg is involved in promoting sleep . To test this hypothesis , we employed pharmacogenetics using designer receptors exclusively activated by designer drugs ( DREADDs ) [27] and pharmacological approaches to manipulate neuronal activity , neurochemistry , electrophysiology , and immunohistochemistry along with optogenetics and transgenic mice to investigate whether the RMTg plays a role in the regulation of sleep and homeostasis . We then explored whether the RMTg nucleus controls sleep through the modulation of DAergic neuron activity .
To test the effects of RMTg neuron activation on sleep and waking regulation , adeno-associated virus ( AAV ) vectors containing excitatory modified muscarinic G protein-coupled receptors ( hM3Dq ) ( Fig 1A ) were bilaterally microinjected into the rat RMTg ( Fig 1B ) . Cell-surface expression of hM3Dq receptors was observed via red fluorescent mCherry protein . Confocal images of double labeling with mCherry and GABA immunofluorescence showed that 87% of mCherry-positive neurons in the RMTg region coexpressed GABA ( 245 of 280 ) , which indicated GABAergic neurons in the RMTg were targeted . Moreover , we found 56% of GABA-positive neurons colabeled hM3Dq/mCherry ( 245 of 437 ) ( Fig 1C and 1D ) , indicating that the virus was efficient for RMTg neurons . Immunohistochemistry showed that clozapine-N-oxide ( CNO , 0 . 3 mg/kg ) , a specific hM3Dq agonist ( Fig 1E ) , but not saline ( Fig 1F ) , could drive c-Fos expression in hM3Dq-expressing neurons in the RMTg . Confocal image of double labeling with mCherry and c-Fos immunofluorescence showed that the 56% of mCherry-positive neurons in the RMTg region expressed c-Fos in the CNO group compared to 2% in the saline group ( Fig 1G ) . In addition , bath application of CNO ( 500 nM ) depolarized the RMTg hM3Dq-expressing neurons and significantly increased the firing of action potentials in hM3Dq/mCherry-positive neurons , as indicated by whole-cell current clamp recordings ( Fig 1H–1K ) . Thus , the DREADD system used in this study stimulates the activity of rat RMTg neurons both in vivo and in vitro . On average , the CNO-injected rats showed a 32 . 2% increase in non-rapid eye movement ( NREM ) sleep and reductions of 84 . 6% and 34 . 8% in rapid eye movement ( REM ) sleep and wakefulness , respectively , during the 7-hour post-CNO injection period ( Fig 2A and 2B ) . The number of stage conversions from wakefulness to NREM sleep ( Fig 2C ) and the total NREM sleep episodes ( Fig 2D ) did not change , even though CNO induced fewer NREM sleep bouts of 1–2 minutes . However , the number of prolonged NREM sleep episodes with durations between 4–16 minutes was significantly increased ( Fig 2E ) , resulting in a longer mean duration of NREM sleep ( Fig 2F ) . CNO administration promoted NREM sleep at the expense of REM sleep , significantly reducing REM sleep bouts of all durations ( Fig 2G ) . Electroencephalogram ( EEG ) power spectrum analysis revealed that during the 7 hours after CNO injection , the average slow-wave activity ( SWA ) —a commonly used quantitative measure of sleep intensity , indicated by an EEG power between 0 . 5 and 4 Hz within NREM sleep [28–30]—in CNO-injected rats increased by 11 . 0% compared with the saline-injected controls . In contrast , the CNO-injected rats displayed a 9 . 6% decrease in the power density of REM sleep during the theta band range of 6–10 Hz ( Fig 2H and 2I ) . The above results indicate that activation of RMTg neurons played an important role in maintaining NREM sleep in rats . In the saline group , a total of 8 c-Fos+ neurons were found in the RMTg of rats infected with viral vectors encoding hM3Dq , indicating that RMTg neurons were not remarkably activated during the spontaneous sleep–wake cycle . When we analyzed distribution of the c-Fos+ cells in the CNO group , we found that 78% of them expressed hM3Dq/mCherry ( 151 of 194 ) and that the other 22% were mCherry− ( 43 of 194 ) . These c-Fos+/mCherry− neurons may possibly be activated by CNO-induced sleep . Since the regulation of cell activity is very complicated , the increased RMTg c-Fos expression induced by sleep promotion must be a comprehensive result , in which many neural circuits are involved . To explore the functional nature of the RMTg-to-midbrain DAergic connections , we employed an optogenetic-assisted circuit mapping approach . Channelrhodopsin-2 ( ChR2 ) , a blue light-gated cation channel , was expressed in RMTg neurons by injecting AAV-ChR2-mCherry into the RMTg of Sprague–Dawley rats . After 3 weeks , acute coronal brain slices containing the RMTg or VTA/SNc were prepared for in vitro patch-clamp recording ( Fig 3A ) . The expression of ChR2 allowed the activation of cell bodies within the RMTg and the selective stimulation of terminals from the RMTg that projected to the VTA and SNc . First , we found that there were dense mCherry+ terminals of RMTg neurons in VTA and SNc , which showed anatomical connections between RMTg neurons and midbrain DAergic neurons ( Fig 3B ) . We then tested the responses of ChR2-expressing neurons within the RMTg to optogenetic stimulation . Blue light pulses at 20 Hz evoked action potentials with high fidelity and elicited robust photocurrents under voltage mode ( Fig 3C ) . Next , cells in the VTA and the SNc were patch clamped while blue light flashes were used to stimulate the axon terminals of RMTg neurons . To identify the cell types of recorded midbrain neurons , we added biocytin to the pipette solution and performed immunostaining using TH as a marker for DAergic neurons after recording . We found that light-evoked inhibition could be recorded in TH-positive neurons within the VTA and the SNc ( Fig 3D and 3E ) , and the connected neurons were distributed throughout the rostrocaudal extent of the VTA/SNc ( Fig 3F ) . In the cell-attached patch mode , photostimulation ( 5-millisecond pulses , 20 Hz ) of RMTg terminals in the VTA or the SNc was sufficient to decrease the firing rate; in some cases , light application totally inhibited the spikes of midbrain neurons , and the firing rate recovered immediately upon the termination of photostimulation ( Fig 3G and 3J ) . In the whole-cell voltage-clamp mode , light evoked fast inhibitory postsynaptic currents ( IPSCs ) in VTA and SNc TH-positive neurons with latencies less than 5 milliseconds in both cases ( Fig 3H and 3K ) , indicating a direct inhibitory connection between the terminals of RMTg neurons and midbrain TH-positive neurons . Moreover , the light-evoked IPSCs were completely abolished by 100 μM picrotoxin ( PTX , a GABAAR antagonist; Fig 3I and 3L ) , indicating that these responses were mediated by GABA released from axon terminals of RMTg neurons . The present study confirmed that midbrain DAergic neurons receive direct inhibitory innervation from RMTg neurons , which is now known to be the predominant GABAergic control for midbrain DAergic neuron activity . Furthermore , functional studies have revealed that the roles of the RMTg are mediated by the modulation of DAergic neurons . Thus , we wondered whether sleep control by the RMTg also occurs through the modulation of DAergic neuron activity . To specifically manipulate DAergic neuron activity , Cre-dependent AAVs ( Fig 4A ) were microinjected into the VTA or SNc areas ( Fig 4E ) of TH-Cre mice to express the sequence of hM3Dq or hM4Di , which was activated by CNO treatment; as a result , the DAergic neurons were reversibly excited or inhibited , respectively . To detect whether AAVs could be expressed specifically in DAergic neurons rather than in other kinds of neurons , hM4Di-expressing AAVs combined with red fluorescent protein mCherry were microinjected into the VTA of TH-Cre mice . Immunofluorescent staining of colocalization ( yellow ) of mCherry ( red ) , DAPI ( blue ) , and TH ( green ) showed that 56% of TH-expressing neurons in the VTA coexpressed hM4Di/mCherry ( 170 of 302 ) and that 79% of hM4Di/mCherry-positive neurons were colabeled with TH ( 170 of 214 ) ( Fig 4B–4D ) , which validated the efficiency and specificity of this targeting strategy for midbrain DAergic neurons . To test the brain states in which the DAergic neurons were inhibited , we microinjected Cre-inducible AAVs expressing hM4Di fused with red fluorescent protein into the target of TH-Cre mice . The microinjection sites and AAV-hM4Di-infected areas were confirmed by mCherry expression in the VTA ( Fig 4F ) and SNc ( Fig 4H ) . Intraperitoneal injection of both CNO and saline did not induce c-Fos expression in hM4Di-expressing mice ( Fig 4G and 4I ) . Next , we performed in vitro electrophysiological experiments to confirm the inhibitory effects of CNO on hM4Di-expressing neuron activity . The recorded mCherry-expressing neuron displayed firing properties with hyperpolarization-activated cation current ( Ih ) ( Fig 4J , top ) that were similar to the properties previously reported for DAergic neurons [31] . Whole-cell current clamp recordings demonstrated that bath application of CNO ( 500 nM ) inhibited the firing rate of a VTA hM4Di-expressing DAergic neuron ( Fig 4J , bottom ) . These results indicated that after AAV-hM4Di microinjection , DAergic neurons were inhibited by CNO in vivo and in vitro . When CNO was administered at 09:00 hours , the TH-Cre mice microinjected with AAV-hM4Di into the VTA or SNc showed an increase in NREM sleep , a decrease in wakefulness , and no change in REM sleep . Compared with the saline control , when VTA DAergic neurons were inhibited , total NREM sleep at 4 hours following CNO treatment was increased by 25 . 6% , while wakefulness decreased by 36 . 9% ( Fig 5A and 5B ) . Similarly , the inhibition of SNc DAergic neurons by CNO administration induced a 3-hour increase of 45 . 5% in NREM sleep , with a 37 . 8% decrease in wakefulness ( Fig 5C and 5D ) . The number of prolonged NREM sleep episodes with durations between 8 and 64 minutes showed a tendency of increase , which may have caused the increase of NREM sleep when VTA DAergic neurons were inhibited using the pharmacogenetic approach ( Fig 5E ) . Similarly , inhibition of SNc DAergic neurons produced an increase in the number of NREM sleep episodes lasting between 4 and 16 minutes ( Fig 5G ) . Although the amount of NREM sleep induced by pharmacogenetic activation of the RMTg was mimicked by inhibiting midbrain DAergic neurons , the enhanced quality of NREM sleep with higher SWA levels induced by activation of the RMTg was not demonstrated ( Fig 5F and 5H ) . In order to investigate whether the RMTg plays a role in physiological sleep promotion , bilateral lesions were formed in the RMTg neurons by microinjection of ibotenic acid , a cell-specific neurochemical toxin , using a glass microelectrode technique . After a recovery period , continuous EEG was performed for 48 hours . We compared lesioned and saline-injected control animals for sleep–wake parameters , including time spent in each sleep–wake state , bout numbers , average bout durations , and SWA . On completion of EEG recordings , the lesion position and extent was immunohistologically confirmed by neuron-specific nuclear-binding protein ( NeuN ) staining . Compared with the intact neurons in the control rats , the animals with ibotenic acid-induced lesions showed extensive cell loss within the RMTg . The lesion extent of each rat was outlined by drawing the boundary of the NeuN-positive neuron population when the staining images had been scaled by the same proportion as the referenced atlas in the same canvas ( Fig 6A and 6B ) . At 8 days after lesions , rats showed a 25 . 2% decrease in NREM sleep and a 47 . 8% decrease in REM sleep along with a 35 . 9% increase in wakefulness during the 12-hour period from 04:00 to 16:00 hours ( Fig 6C and 6D ) . Analysis of sleep architecture showed that the average duration of NREM sleep was not shortened , and the REM sleep duration was prolonged in the lesioned rats from 04:00 to 16:00 hours . However , the total number of episodes of NREM and REM sleep was markedly decreased in the lesioned rats ( Fig 6E and 6F ) . The rats with RMTg lesions tended to have fewer NREM sleep bouts in the range of 10–50 seconds than controls , although the difference was not statistically significant , and had significantly fewer NREM sleep bouts between 1–2 minutes and REM sleep bouts between 10–50 seconds than controls ( Fig 6G and 6H ) . The lesioned rats also had fewer conversions from wakefulness to NREM sleep , NREM sleep to REM sleep , and REM sleep to wakefulness ( Fig 6I ) . Therefore , the decreased NREM and REM sleep amount was mainly the result of a reduction in the number of short fragments of NREM sleep ( <2 minutes ) and REM sleep ( <1 minute ) in the lesioned rats . Across the 24-hour period , SWA was at the highest level—48 . 0% ± 2 . 5% and 38 . 6% ± 1 . 6% in control and lesioned animals , respectively—during 07:00–08:00 , immediately after light onset . The highest level of SWA in the NREM sleep was significantly lower in the lesioned rats than in the control rats ( p < 0 . 01 ) . Then , SWA gradually decreased until immediately before and after lights off , when it was at the lowest level ( Fig 6J ) . Similarly , at 16 days after RMTg lesions , the amount of sleep and wakefulness and sleep–wake architecture differed significantly between the lesion and control groups . NREM sleep decreased by 9 . 4% , and REM sleep decreased by 14 . 3% , with corresponding increases in wakefulness by 10 . 7% was observed in the lesioned rats in comparison with the controls over a 24-hour period ( see S1A and S1B Fig ) . The lesioned rats had fewer episodes of NREM and REM sleep and fewer conversions from wakefulness to NREM sleep , NREM sleep to REM sleep , and REM sleep to wakefulness than the control rats ( see S1C–S1E Fig ) . SWA in NREM sleep during the hour immediately after lights turned on was also significantly lower in the lesioned rats ( 42 . 1% ± 2 . 3% ) than in the control rats ( 50 . 6% ± 2 . 9% ) ( see S1F Fig ) . The loss of NREM and REM sleep in the RMTg lesion rats was not observed at 25 days after neuron damage ( see S2A and S2B Fig ) . There were no differences in episode bouts , durations , or conversions among NREM and REM sleep and wakefulness between the lesioned and control rats ( see S2C–S2E Fig ) . However , at 25 days after RMTg lesions , the rats still had lower SWA levels in NREM sleep , similar to those at 8 and 16 days . SWA in NREM sleep during the first hour of the light phase ( 34 . 9% ± 6 . 4% ) was continually and markedly lower than that of the control rats ( 48 . 0% ± 2 . 0% ) ( see S2F Fig ) . The results suggest that the RMTg is involved in the initiation of NREM sleep , which is compensated by some brain structures at 25 days after damage to RMTg neurons . Furthermore , the RMTg maintains NREM sleep depth , and this effect lasts longer than that on sleep initiation . Sleep homeostasis is primarily studied through sleep deprivation ( SD ) experiments [32] . Thus , we performed 6-hour SD from 13:00 to 19:00 hours in control and lesioned rats and compared their rebound sleep to determine the role of the RMTg in homeostatic regulation of sleep . The control rats and rats with 8 days of RMTg lesions exhibited similar responses in their NREM sleep during the subsequent recovery period following SD ( Fig 7A and 7B ) . We calculated the total time spent in NREM sleep for 2 hours after SD because the increase in NREM sleep was maintained for 2 hours during the recovery period after 6-hour SD . From 19:00 to 21:00 hours , there was no difference between the control and lesioned rats in the baseline level of NREM sleep , which increased by 57 . 6% and 128 . 2% , respectively , after 6-hour SD ( Fig 7C ) . In both groups , the increase in NREM sleep was mainly due to prolonged bout duration ( Fig 7D ) . To compare the rebound of SWA within NREM sleep after 6-hour SD in the control and lesioned groups , the SWA data were analyzed using 2-way ANOVA followed by paired t tests . The analysis revealed a significant increase in SWA in the control rats during the first 7 hours ( from 19:00 to 02:00 hours ) following 6-hour SD ( p = 0 . 041 ) compared with the baseline level . In contrast , 6-hour SD induced no increase in SWA in the lesioned rats even during the first 2 hours ( from 19:00 to 21:00 hours ) immediately after SD ( p = 0 . 394 ) compared with the baseline level . There was no change from baseline in the mean SWA in control ( 33 . 1% ± 2 . 2% ) or lesioned ( 27 . 5% ± 2 . 7% ) rats during the first 2-hour interval from 19:00 to 21:00 hours . After 6-hour SD , the average SWA increased by 38 . 2% ± 4 . 3% and 21 . 5% ± 4 . 2% from the baseline levels to 46 . 1% ± 4 . 0% and 33 . 5% ± 3 . 7% in the control and lesioned rats , respectively ( Fig 7E and 7F ) . The magnitude of the increase in SWA after 6-hour SD was significantly lower in lesioned rats than in control rats ( p < 0 . 05 , unpaired t test ) . The above results showed that in the recovery period following 6-hour SD in RMTg lesioned rats , the amount of NREM sleep rebounded normally , but the rebound of NREM sleep depth was impaired . Therefore , the RMTg plays an important role in the homeostatic regulation of NREM sleep . The RMTg is rich in μ receptors , and in vivo electrophysiological experiments have found that morphine , a μ-receptor agonist , inactivates RMTg neurons [25 , 33] . Therefore , to rapidly and reversibly inhibit RMTg neurons , we bilaterally microinjected morphine through guide cannulas . After EEG recording , Nissl staining was performed to confirm the microinjection site . Only samples in which the needle tips were located in the RMTg were included in data analysis ( Fig 8A ) . Under current-clamp conditions , morphine perfusion at 10 μmol/L ( μM ) was found to decrease the firing rate and induce significant hyperpolarization of RMTg neurons from −49 . 3 ± 1 . 3 to −56 . 3 ± 2 . 2 mV ( p < 0 . 05 ) . In some cases , morphine completely inhibited the firing of RMTg neurons within 1–2 minutes ( Fig 8B and 8C ) . These results were consistent with previous reports that opioids could inactivate RMTg neurons [33] . During the 3-hour post-microinjection of morphine at 2 nmol/side , the rats showed an immediate 48 . 5% decrease in NREM sleep and a 93 . 1% decrease in REM sleep , with an increase in wakefulness by 107 . 6% compared with the artificial cerebrospinal fluid ( ACSF ) controls ( Fig 8D and 8E ) . EEG architecture analysis showed that during the 3 hours after microinjection of morphine at 2 nmol/side , although the episode numbers of NREM and REM sleep did not change ( Fig 8F ) , the longer fragments of NREM sleep with durations between 1 and 4 minutes and longer fragments of REM sleep between 1 and 2 minutes were significantly decreased ( Fig 8G and 8H ) . As a result , the mean duration of NREM and REM sleep was shortened compared with ACSF microinjection controls ( Fig 8I ) . In addition , the morphine-induced inhibition of RMTg neurons did not affect the number of stage shifts between wakefulness and NREM sleep ( Fig 8J ) . EEG power spectrum analysis revealed that the power density of NREM sleep was significantly decreased in the rats microinjected in the RMTg with 2 nmol/side morphine compared with the ACSF controls over the frequency range of delta activity from 0 . 5 to 4 Hz during the 3 hours from 10:00 to 13:00 hours following microinjection ( p < 0 . 05 ) ( Fig 8K ) . The above results suggest that the RMTg plays roles in the maintenance of the duration and quality of NREM sleep . Similar results were observed after pharmacogenetic inhibition of cells in the RMTg with Gi-coupled DREADDs ( see S3 Fig ) . When RMTg neurons were inactivated ( see S4A and S4B Fig ) , the rats showed a decrease in time spent in NREM sleep and a corresponding increase in time awake , but no decrease in REM sleep ( see S4C Fig ) . The decreased NREM sleep resulted mainly from the reduction of NREM bout occurrence , particularly the number of shorter episodes with durations between 10–110 seconds ( see S4D and S4E Fig ) and fewer conversions from wakefulness to NREM sleep ( see S4F Fig ) following CNO injections . These results suggest that the RMTg plays a role in the initiation of NREM sleep . However , the changes in the mean duration and SWA level of NREM sleep did not differ significantly between saline-treated rats and CNO-treated rats ( see S4G and S4H Fig ) . The decrease in NREM sleep without any change in SWA induced by pharmacogenetic inhibition of the RMTg using hM4Di was mimicked by selective pharmacogenetic activation of VTA/SNc DAergic neurons using hM3Dq in TH-Cre mice ( see S5 Fig ) .
We find that the RMTg is essential for NREM sleep and homeostatic regulation . Since the RMTg exerts major inhibitory control over the midbrain DAergic system , we hypothesize that the RMTg regulates sleep–wake behavior through the modulation of DAergic neuron activity . However , we cannot exclude the possibility that other RMTg GABAergic projected targets also contribute to the roles of the RMTg in sleep–wake behavior . For further study , we will use transgenic VGAT ( vesicular GABA transporter ) -Cre mice to specifically target the GABAergic neurons in order to expand on our findings in rats that RMTg neurons are necessary for sleep and use pharmacogenetic and optogenetic manipulations together with polysomnographic recordings to elucidate the role of the downstream elements of GABAergic RMTg neurons in the control of sleep . There is evidence that sleep , especially NREM sleep with high levels of EEG delta power , is important not only for neurobehavioral functions such as memory consolidation [54] but also for peripheral physiological functions such as the maintenance of normal glucose homeostasis [55 , 56] . Thus , the current results suggest that the RMTg may be considered as a potential intervention for prolonging NREM sleep duration and improving sleep quality to maintain human health . Moreover , the data obtained in the present study enrich our understanding of how the brain regulates sleep–wake behavior and provide a potential target for understanding the roles of DA in the physiological regulation of sleep–wake states as well as in the pathologic process of sleep disturbances . Our results will inform the development of potential therapeutic targets against sleep disorders in DA-implicated mental illness .
All experimental procedures involving animals were approved by the Committee on the Ethics of Animal Experiments of School of Basic Medical Sciences , Fudan University , with license identification number 20150119–067 . The animals were anesthetized with an IP injection of chloral hydrate before surgery or killing . The animals were put on the heating pad until they woke up from anesthesia after surgery . During the postoperative recovery period , the animals were observed every day , and the sawdust was kept clean . Every effort was made to minimize animal suffering or discomfort and to reduce the number of animals used . Male Sprague–Dawley rats ( 280–370 g ) were obtained from the Laboratory Animal Center , Chinese Academy of Sciences ( Shanghai , China ) . Adult male TH-Cre mice and non-Cre-expressing littermate mice ( 8–16 weeks old , 25–30 g ) were also used . The animals were housed in individual cages at an ambient temperature ( 22 ± 0 . 5°C ) with relative humidity of 60% ± 2% in an automatically controlled 12:12-hour light/dark cycle ( lights on at 07:00 hours , illumination intensity approximately 100 lux ) , with free access to food and water . The AAVs of serotype rh10 for AAV-hSyn-DIO-hM3Dq-mCherry , AAV-hSyn-DIO-hM4Di-mCherry , and AAV-hSyn-DIO-ChR2-mCherry were generated by tripartite transfection ( AAV-rep2/caprh10 expression plasmid , adenovirus helper plasmid , and pAAV plasmid ) into 293A cells . After 3 days , the 293A cells were resuspended in ACSF , freeze-thawed 4 times , and treated with benzonase nuclease ( Millipore ) to degrade all forms of DNA and RNA . Subsequently , the cell debris was removed by centrifugation , and the virus titer in the supernatant was determined using an AAVpro Titration Kit for Real Time PCR ( Takara ) . The final viral concentrations of the transgenes were 1 × 1012–2 × 1012 genome copies/mL . Aliquots of viral vectors were stored at −80°C before stereotaxic injection . To induce lesions in the RMTg , rats were anesthetized with chloral hydrate ( 10% in saline , 360 mg/kg ) and immobilized in a Stereotaxic Alignment System ( RWD Life Science , Shenzhen , China ) . Ibotenic acid ( 1% in saline , 200 nL/side; Sigma , St . Louis , Missouri , United States ) was bilaterally injected through a glass pipette ( glass stock: 1 mm in diameter; tip: 10–20 μm ) with nitrogen gas pulses of 20–40 psi using an air compression system [57] into the RMTg according to the atlas [58] ( coordinate relative to bregma: AP −6 . 8 mm; ML ± 0 . 3 mm; DV −8 . 4 mm ) for 5–10 minutes . After leaving the pipette in the brain for an additional 5 minutes , the pipette was slowly retracted . Control rats received 200 nL/side saline . For microinjection of morphine in the RMTg , rats were implanted with 2 guide cannula ( 30 gauge ) . The cannulas were inserted at stereotaxic coordinates based on the rat brain atlas [58] ( coordinate relative to bregma: AP −6 . 8 mm; ML ± 0 . 3 mm; DV −7 . 4 mm ) . The 2 cannulas were fixed to the skull with dental cement and 3 stainless steel screws for anchorage [59] . In order to manipulate neuronal activity , we used hM3Dq or hM4Di that was selectively activated or inhibited by the pharmacologically inert agent CNO [60] . Under anesthesia with chloral hydrate ( 10% in saline , 360 mg/kg ) , a burr hole was made , and a fine glass pipette containing AAVs carrying Cre-independent hM3Dq , hM4Di , or ChR2 was lowered bilaterally into the rat RMTg ( coordinate relative to bregma: AP −6 . 8 mm; ML ± 0 . 3 mm; DV −8 . 4 mm ) . The AAV vectors were delivered with 300 nL/side . Mice were anesthetized with chloral hydrate ( 5% in saline , 720 mg/kg ) and then placed in a stereotaxic frame so that the head was fixed . After opening a burr hole , a fine glass pipette containing AAV carrying Cre-dependent hM3Dq/hM4Di ( Taiting , Shanghai , China ) was bilaterally lowered into the VTA ( coordinate relative to bregma: AP −3 . 4 mm; ML ± 0 . 3 mm; DV −4 . 0 mm ) or SNc ( coordinate relative to bregma: AP −3 . 4 mm; ML ± 1 . 2 mm; DV −4 . 0 mm ) according to the atlas[5] . The AAV vectors ( 50 nL/side ) were delivered over a 5-minute period per hemisphere . After an additional 10 minutes , the pipette was slowly withdrawn . Following ibotenic acid or saline injection ( or guide cannula implantation ) or after 2 weeks of recovery from virus injection , the rats were implanted with EEG and electromyography ( EMG ) electrodes for polysomnographic recordings . The implant consisted of 2 stainless steel screws ( 1-mm diameter ) inserted through the frontal ( AP +2 mm; ML +3 mm ) and parietal ( AP −4 mm; ML +3 mm ) bones , and a stainless steel screw ( 1 . 5-mm diameter ) inserted in the left frontal bone ( AP +3 mm; ML −3 mm ) as a reference electrode . All of the above positions were coordinately relative to the bregma [58] . The mice were recovered for 2 weeks after virus injection before electrodes were implanted . Two stainless steel screws ( 1-mm diameter ) were inserted through the skull into the cortex ( AP +1 mm to the bregma; AP +1 mm to the lambda; ML +1 . 5 mm to the midline ) [61] and served as EEG electrodes . Two Teflon-coated , stainless steel wires were bilaterally placed into both trapezius muscles for EMG recordings in rats or mice . All electrodes for the rats or mice were attached to a connector and fixed to the skull with dental cement . The animals were then allowed to recover on a heating pad until awakening from anesthesia [62] . After a 1-week recovery period from EEG electrode implantation , the animals were transferred to the recording room and habituated to the recording cables and conditions for 2–3 days . Following this habituation period , 48 hours of EEG/EMG recordings were performed on all the animals . The data collected during the first 24 hours served as baseline , and the second 24 hours served as experimental data . Morphine hydrochloride , a nonselective μ-opioid receptor agonist , diluted in ACSF , was injected at 09:00 hours through a syringe connected with a lengthened flexible pipe under red illumination; thus , the rats could freely move or have rest without irritant body touch , disturbing noises , and natural light . A syringe that was designed with an oblique tip for easy aligning was gently and smoothly inserted into the hollow and straight guide cannula ( O . D . = 0 . 6 mm ) . A volume of 0 . 5 μL morphine was injected in each side by microinjector at a slow and constant speed for 0 . 5 minutes . Injection was made unilaterally in sequence . After the end of each injection , the syringe was left for an additional 3 minutes for complete local absorption . The control group was injected with ACSF . In the DREADD experiment , saline was administered IP at 09:00 hours on day 1 of the EEG recording . On the next day , CNO ( LKT Laboratories , Minneapolis , Minnesota , US ) was dissolved in saline before use and injected at 09:00 hours at 1 mg/kg for mice ( 0 . 1 mL/10 g ) or 0 . 3 mg/kg for rats ( 1 mL/100 g ) . Cortical EEG and EMG signals were amplified , filtered ( EEG , 0 . 5–30 Hz; EMG , 20–200 Hz ) , digitized at a sampling rate of 128 Hz , and recorded using VitalRecorder ( Kissei Comtec , Nagano , Japan ) . When complete , polygraphic recordings were automatically scored offline by 10-second epochs as waking , NREM sleep , and REM sleep using SleepSign according to standard criteria . Defined sleep–wake stages were examined visually and corrected if necessary [57] . SD was achieved by gentle handling that included tapping the cage , introducing novel objects into the cage , or removing the rat from the cage when behavioral signs of sleep were observed . Rats were deprived of sleep during the light phase for 6 hours from 13:00 to 19:00 hours . Undisturbed rats that served as a control group were never disturbed when they were spontaneously awake , feeding , or drinking in the same time period as the corresponding 6-hour SD group [30 , 42] . On completion of EEG recordings of the rats with lesions or morphine microinjection , the rats were anesthetized with chloral hydrate ( 10% in saline , 360 mg/kg ) , with 150–200 mL saline followed by 500 mL 4% paraformaldehyde ( PFA ) in PBS through the heart . The brains were removed , post-fixed for 4–5 hours at 4°C in 4% PFA , and then equilibrated in phosphate buffer containing 10% , 20% , and 30% sucrose solution at 4°C . The brain sections were serially cut in the coronal plane at 30 μm on a freezing microtome ( CM1950 , Leica , Wetzlar , Germany ) , protected in cryoprotectant solution , and stored at −20°C until further processing for immunostaining . For verification of ibotenic-acid-induced brain lesions , one series of tissues was processed for NeuN staining . Brain sections were incubated in primary antibody in PBS containing Tween-20 ( PBST ) ( mouse anti-NeuN , 1:50 , 000; Millipore , Bedford , Maryland , US ) overnight , and then staining was revealed using the avidin–biotin complex method ( ABC kit SC-2017; Santa Cruz Biotechnology , Santa Cruz , California , US ) . The sections were incubated for 2 hours in biotinylated secondary antibody in PBST ( 1:500 ) , followed by incubation with avidin–biotin–horseradish peroxidase ( HRP ) conjugate and staining with 3 , 3-diaminobenzidine tetrahydrochloride ( DAB ) . Sections were then mounted , dried , dehydrated , and cover slipped . Only samples in which the lesion sites were confined to the RMTg were included in data analysis . For confirmation of the microinjection site of morphine , one series of sections was subjected to Nissl staining . Brain sections were mounted on adhesive slides , dried naturally for 2 consecutive days , and stained by cresyl violet method . Sections were washed in water and PBS successively , incubated in 0 . 1% cresyl violet for 15 minutes , differentiated in graded ethanol , and cleared in xylene before being cover slipped . All sections containing a cannula-insertion site were compared with a rat brain atlas to confirm the 3-dimensional coordinates of the site relative to bregma . Only experiments in which the tip of the microinjection cannula was located above the RMTg were included in data analysis [59] . The AAV vectors were linked with a red fluorescent protein mCherry; therefore , the viral injection site was determined by mCherry expression . Induction of c-Fos , the protein of an immediate early gene , is supposed to be an indicator of neuronal activity [63] . Thus , whether the virus-infected neurons were activated or inhibited could be determined through c-Fos expression . After EEG/EMG recording , the animals were injected with saline or CNO ( 0 . 3 mg/kg for rats; 1 mg/kg for mice ) . Ninety minutes later , the animals were anesthetized and perfused , and the brain coronal sections were prepared . The brain tissue sections were rinsed in 0 . 1 M PBS ( 3 times , 5 minutes/each wash ) and then incubated in rabbit anti-c-Fos primary antibody ( 1:5 , 000; Millipore ) diluted in PBST for 48 hours . The tissues were washed 3 times in PBS and incubated with Alexa Fluor 488-conjugated donkey anti-rabbit secondary antibody in PBST ( 1:1 , 000; Invitrogen , Carlsbad , California , US ) for 2 hours in the dark . After being washed in PBS , the sections were mounted on glass slides , cover slipped using FluoroGuard Mounting Medium , and kept at 4°C before imaging [40] . Colocalization of mCherry and c-Fos expression was observed by confocal microscopy . Localization of the rat RMTg and mouse VTA and SNc were confirmed by staining and reference to the brain atlas . For double immunofluorescence staining of TH/mCherry or GABA/mCherry , brain sections were incubated with a rabbit antibody against TH ( 1:3 , 000; Millipore ) or GABA ( 1:1 , 000; Invitrogen ) in PBST over night at 4°C . The sections were then rinsed and incubated in a donkey anti-rabbit Alexa Fluor 488-conjugated secondary antibody ( 1:1 , 000; Invitrogen ) at room temperature for 2 hours in the dark . After 3 washes in PBS , sections were incubated in 4 , 6-diamidino-2-phenylindole ( DAPI; 1:3 , 000; Invitrogen ) for 10 min at room temperature . Finally , sections were washed in PBS and mounted on glass slides using FluoroGuard Mounting Medium . For quantification of the colocalization of mCherry-expressing RMTg neurons in rats and VTA/SNc DAergic neurons in mice microinjected with hM3Dq/hM4Di-containing vectors and other histological markers ( GABA , c-Fos , TH ) , the area used for counting was demarcated by cells that were labeled by mCherry . All cell counting was conducted blindly on 3 × 2 tiled confocal images of the target area . For each rat or mouse , brain sections were analyzed bilaterally [64] . To investigate whether CNO manipulates the activity of AAV-infected cells or RMTg has an inhibitory projection to midbrain DAergic neurons , male Sprague–Dawley rats , 20–30 days old weighing 50–60 g , were microinjected with AAV vectors carrying Cre-independent hsyn–hM3Dq ( hM4Di , ChR2 ) -mCherry under anesthesia with chloral hydrate into the RMTg , and TH-Cre mice were microinjected with AAV vectors carrying Cre-dependent hsyn–hM3Dq/ hM4Di–mCherry into the VTA or SNc . Slices containing the RMTg of the rats or VTA/SNc of TH-Cre mice were prepared from the animals 3 weeks after AAV microinjection or from male Sprague–Dawley rats , 20–30 days old weighing 50–60 g , without AAV injection to investigate whether morphine inhibited RMTg neurons in vitro . The RMTg or VTA/SNc was identified according to stereotaxic coordinates [58 , 61] . Coronal slices of the rats ( 280-μm thick ) were cut using a vibratome ( VT-1200S; Leica ) in ice-cold sucrose-based ACSF , bubbled with 95% O2 and 5% CO2 , containing 230 mM sucrose , 2 . 5 mM KCl , 3 mM MgSO4 , 1 . 25 mM NaH2PO4 , 26 mM NaHCO3 , 0 . 5 mM CaCl2 , and 10 mM d-glucose . The slices were allowed to recover for at least 1 hour in a holding chamber with ACSF without sucrose in a water bath ( 32°C ) before recording . For preparation of the mouse brain slices , the coronal slices were cut at 300-μm thickness in ice-cold glycerol-based ACSF , which was different from the rats , containing 260 mM glycerol , 5 mM KCl , 1 . 25 mM KH2PO4 , 1 . 3 mM MgSO4 , 0 . 5 mM CaCl2 , 20 mM NaHCO3 , and 10 mM glucose . Coronal slices were transferred to the recording chamber , where they were held down with a platinum ring . Carbonated ACSF with 95% O2 and 5% CO2 flowed through the bath ( 2 mL/minute ) . Patch pipettes were pulled from thick-walled borosilicate glass capillaries ( 1 . 5-mm outer diameter , 0 . 84-mm internal diameter , Sutter Instruments , San Rafael , California , US ) using a 2-step vertical puller ( PC-10; Narishige , Japan ) . Pipette resistance was typically 4–7 MΩ when filled with internal solution containing 120 mM potassium gluconate , 20 mM KCl , 1 mM MgCl2 , 0 . 16 mM CaCl2 , 10 mM HEPES , 0 . 5 mM EGTA , 2 mM Mg-ATP , and 0 . 5 mM NaGTP . RMTg or VTA/SNc neurons were identified under visual guidance using a fixed-stage upright microscope ( BX-51; Olympus , Tokyo , Japan ) fitted with a 40 × water immersion objective lens . The image was detected with an infrared sensitive charge coupled device camera ( U-TV1X-2; Olympus ) and displayed on a screen in real time . The output signals were amplified ( Molecular Devices , Eugene , Oregon , US ) , filtered at 5 kHz , and digitized at 20 kHz using a National Instruments digitization board ( NI-DAQmx , PCI-6052E; National Instruments , Austin , Texas , US ) . Neurons were current clamped to record spontaneous action potentials and/or membrane potentials . The series resistance and input resistance were monitored throughout the cell recording , and data were discarded when either of the 2 resistances changed by >20% [57 , 65] . To visualize the recorded cells in the RMTg , biocytin ( 0 . 2% ) was included in the pipette solution to confirm the position of patched cells . Slices were fixed immediately after recording in 4% formaldehyde for 2 hours and then immersed in 0 . 3% PBST . Slices were incubated in Fluor-488-conjugated streptavidin ( Invitrogen , 1:2 , 000 , 12 hours at 4°C ) . Sections were mounted on slides using FluoroGuard Antifade Reagent ( Bio-Rad , Hercules , California , US ) and visualized under an Olympus microscope . For the optogenetic experiment , whole-cell and cell-attached recordings were made from RMTg or VTA/SNc DAergic neurons . ChR2 was stimulated by 473-nm light delivered via an optical fiber coupled to a laser source ( Guang Teng , Shanghai , China ) . For recording light evoked inhibitory synaptic currents , the internal solution contained 105 mM potassium gluconate , 30 mM KCl , 4 mM ATP-Mg , 10 mM phosphocreatine , 0 . 3 mM EGTA , 0 . 3 mM GTP-Na , and 10 mM HEPES . In the voltage-clamp mode , cells were held at −70 mV . When needed , 25 μM d- ( - ) -2-amino-5-phosphonopentanoic acid ( d-APV ) , 5 μM NBQX , and 100 μM PTX were added to block NMDA , AMPA , and GABAA receptors , respectively . The internal solution also contained 0 . 2% biocytin . To test for expression of TH , brain slices were incubated in rabbit anti-TH antibody ( 1:2 , 000 , Millipore ) containing 3% normal donkey serum ( v/v ) , 0 . 5% Triton X-100 ( v/v ) for 24 hours at 4°C . This was followed by incubation with Alexa Fluor 488-conjugated donkey anti-rabbit ( 1:800; Invitrogen ) and Alexa Fluor 405 streptavidin ( 1:1 , 000 , Invitrogen ) for 12 hours at RT . The sum of sleep and wakefulness and other sleep architecture parameters in the RMTg-lesioned and control rats were compared using unpaired t tests . The sleep–wake profiles among groups with different doses of morphine microinjection and vehicles were assessed using 1-way ANOVA followed by least significant difference tests . The hourly durations of each stage and SWA analyses were compared using 2-way ANOVA ( repeated measures ) followed by unpaired t tests . Two-way ANOVA ( repeated measures ) followed by paired t tests were conducted to analyze the sleep rebound induced by 6-hour SD in rats . For viral microinjection , the sum of sleep and wakefulness and other sleep architecture parameters after CNO or saline injection as well as membrane potentials of rat RMTg neurons before and after bath application of CNO were compared using paired t tests . The hourly duration of each stage of sleep and wake profiles was compared using 2-way ANOVA ( repeated measures ) followed by paired t tests . For GABA , c-Fos , or TH immunohistochemistry analysis , each group consisted of data obtained from 3 rats or TH-Cre mice; as a result , a total of 3 bilateral sections containing target area were analyzed for each animal . The quantification results were compared using unpaired t tests between CNO and saline control groups . All results were expressed as the mean ± SEM . We analyzed the data using Prism 5 . 0 ( GraphPad software , San Diego , California , US ) . In all cases , p < 0 . 05 was taken as the level of significance . | Sleep–wake behavior is controlled by networks of neurons and neurotransmitters in the brain . There are multiple populations of wake-promoting neurons , but few sleep-promoting neurons have been identified . In this study , we revealed that the rostromedial tegmental nucleus , the GABAergic tail of the ventral tegmental area , regulates non-rapid eye movement sleep . We show that neurons in the rat rostromedial tegmental nucleus , when activated by pharmacogenetics , increase and deepen non-rapid eye movement sleep . Inhibition of these neurons exhibits the opposite effects . Furthermore , rats with lesion in the rostromedial tegmental nucleus have a reduced response of sleep homeostasis following sleep deprivation . We show that stimulation of the terminals of the neurons in the rostromedial tegmental nucleus inhibits dopaminergic neurons in the midbrain . Interestingly , inhibition of these dopaminergic neurons also has sleep-promoting effects . The current results provide a potential target for prolonging non-rapid eye movement sleep , improving sleep quality , and treating sleep disorders in dopamine-implicated mental illness . | [
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... | 2018 | The rostromedial tegmental nucleus is essential for non-rapid eye movement sleep |
Neuronal calcium acts as a charge carrier during information processing and as a ubiquitous intracellular messenger . Calcium signals are fundamental to numerous aspects of neuronal development and plasticity . Specific and independent regulation of these vital cellular processes is achieved by a rich bouquet of different calcium signaling mechanisms within the neuron , which either can operate independently or may act in concert . This study demonstrates the existence of a novel calcium signaling mechanism by simultaneous patch clamping and calcium imaging from acutely isolated central neurons . These neurons possess a membrane voltage sensor that , independent of calcium influx , causes G-protein activation , which subsequently leads to calcium release from intracellular stores via phospholipase C and inositol 1 , 4 , 5-trisphosphate receptor activation . This allows neurons to monitor activity by intracellular calcium release without relying on calcium as the input signal and opens up new insights into intracellular signaling , developmental regulation , and information processing in neuronal compartments lacking calcium channels .
Neuronal calcium plays a dual role as a charge carrier and as an intracellular messenger . Calcium signals regulate various developmental processes , such as migration in the central nervous system ( CNS ) [1] , growth-cone behavior [2] , dendritic development [3 , 4] , and synaptogenesis [5] , but calcium is also involved in apoptosis [6] , and it regulates neurotransmitter release and membrane excitability [7] . How can one ubiquitous intracellular messenger regulate so many different vital processes in parallel , but independently ? The answer lies in the versatility of the calcium signaling mechanisms in terms of amplitude and spatiotemporal patterning within a neuron [8] , and the present study adds a novel mechanism to the bouquet of neuronal calcium signals . The neuronal plasma membrane contains numerous voltage-operated ( VOCs ) , receptor-operated ( ROCs ) and store-operated ( SOCs ) calcium channels carrying out different functions in different parts of the cell . For example , in vertebrate neurons , N- and P/Q-type VOCs trigger vesicle fusion at synaptic terminals , whereas L-type VOCs are located proximally to provide calcium signals regulating gene transcription . Structural plasticity of dendrites and filopodia is mediated via calcium influx through VOCs and ROCs and also by local release from internal stores [5 , 9] . Internal calcium stores are held within the membrane system of the endoplasmic reticulum ( ER ) . Release from the ER is mediated by the inositol 1 , 4 , 5-trisphosphate receptor ( IP3R ) and the ryanodine receptor ( RYR ) families , both of which depend on the concentration of free intracellular calcium ( [Ca2+]i [10] ) . The patterns of calcium signals depend on the distributions of the calcium entry mechanisms and calcium-binding proteins within a specific neuronal compartment [8] . Calcium buffers , such as parvalbumin and calbindin , can locally confine calcium signals even to individual synapses [11] . Calcium-sensing proteins translate elevation or even temporal patterns in [Ca2+]i into diverse cellular processes [7 , 12] . In summary , different spatiotemporal distributions of calcium signals , buffers , and sensors in various neuronal compartments allow distinct calcium signals to be assigned separately to specific cellular processes . Conversely , multiple calcium signals can act as coincidence detectors [13] . Nevertheless , to date , no mechanism is known by which a neuron can monitor its own activity by ER calcium release in the absence of both metabotropic receptor activation and calcium influx from the extracellular space . Consequently , influx and store activation are thought to be irrevocably linked , such that they neither operate independently from each other , nor work in concert for coincidence detection . In the present study , we use well-characterized and identified insect central neurons , the dorsal unpaired median ( DUM ) neurons , as a model to analyze a novel calcium-release mechanism in neurons . DUM neurons are among the best-studied insect neurons that can be individually identified in culture [14 , 15] . Therefore , they are well suited for pharmacological analysis of cellular signaling mechanisms . We demonstrate the existence of a novel voltage-dependent , but not calcium-dependent , mechanism of IP3 production , by simultaneous patch clamping and calcium imaging . This mechanism requires voltage-dependent , but not receptor-dependent , G-protein activation , which in turn , leads to phopholipase C activation , IP3 production , and calcium release from internal stores .
Simultaneous patch clamp and optical recordings of freshly isolated DUM neurons were conducted under pharmacological block of voltage-activated potassium and sodium currents . Consequently , voltage steps of 2 . 5-s duration from a holding potential of −90 mV to 0 mV induced a large transient voltage-activated calcium inward current followed by a calcium-activated potassium current ( Figure 1A , middle trace ) , both of which have previously been described in detail [15] . Simultaneous optical recordings of the changes in fluorescence of the calcium indicator Oregon Green Bapta ( percent change as ΔF/F ) revealed a long-lasting increase in ( [Ca2+]i which outlasted the calcium inward current; Figure 1A , top trace ) . The kinetics and the amplitude of the calcium signal suggest calcium release from intracellular stores in addition to calcium influx through VOCs , as is typical for most neurons . Both the calcium current and the calcium-activated potassium current were completely abolished after 3 min in zero-calcium ( calcium replaced by magnesium , 1 μM EGTA ) saline ( Figure 1B , middle trace ) . Strikingly , the neurons still showed an increase in calcium indicator fluorescence as a response to the same voltage step from −90 mV holding to 0 mV ( Figure 1B , top trace ) in the absence of any calcium inward current . This voltage-induced calcium signal also remained after bath application of calcium-free saline with 500 μM cadmium to block all voltage-activated calcium channels ( unpublished data ) . The time course was considerably slower and the amplitude was significantly smaller as compared to the intracellular calcium signal in the presence of calcium inward current ( compare Figure 1A and 1B , top traces ) . Washing with calcium-containing saline led to a complete recovery of the initial calcium signal within 3 min ( compare Figure 1A and 1C ) . After depletion of intracellular calcium stores by bath application of cyclopiazonic acid ( CPA; 20μM ) , which has been demonstrated to block the SERCA pump in insect neurons [16] , no increase in fluorescence was evoked by a depolarizing voltage step in calcium-free saline ( Figure 1D ) . These data demonstrate calcium release from intracellular stores as a response to membrane depolarization without calcium influx from the extracellular space . Quantitatively , in the presence of extracellular calcium , a voltage step from −90 mV to 0 mV induced an increase in fluorescence of 36 ± 3 . 4% ( mean and standard deviation ) that peaked after 1 . 67 ± 0 . 05 s . Without calcium influx through VOCs , the neurons responded with fluorescence increases of 6 . 3 ± 3 . 0% and the time to peak was considerably longer ( 2 . 53 ± 0 . 05 s; Figure 1E ) . To make sure that the calcium signals observed were caused by membrane depolarizations of the recorded neurons , and not by interactions between neighboring cells , recordings were performed from completely isolated neurons not in contact with any other cell in the dish . Living neurons were imaged in bright field mode just prior to the physiological tests to demonstrate that no cell–cell contacts existed ( Figure 1G ) . This was further supported by immunocytochemical labeling of the same neurons after the physiological experiments ( Figure 1H ) . No nuclei of glia cells or neurons were detected with confocal microscopy in proximity to the recorded neuron , nor did a general neuronal marker detect any small cells nearby ( resolution is 200 × 200 × 300 nm ) . And finally , scanning electron micrograph ( SEM ) pictures clearly demonstrated that neither the somata nor the primary neurites of the recorded neurons were in contact with any other neighboring cell . Therefore , the calcium signals occurring in the absence of extracellular calcium were due to membrane depolarizations of the recorded neurons and not to the release of signaling substances from nearby cells . To test whether spiking patterns of DUM neurons , as occurring during normal behavior [17 , 18] , were sufficient to produce [Ca2+]i elevations in the absence of calcium influx , a similar protocol as depicted in Figure 1 was repeated under current clamp conditions ( Figure 2 ) . First , in calcium-containing saline , bursts of action potentials were induced by current injection of 500-ms duration just above firing threshold . Individual bursts caused an increase in fluorescence of up to 15% ( Figure2A , top trace ) . Second , after 3 min in calcium-free saline , action potential amplitude was reduced due to the lack of calcium influx , and action potential duration was increased due to the lack of calcium-activated potassium current . Consequently , spike frequency within the burst was also reduced ( Figure 2B , bottom trace ) . However , under calcium-free conditions , bursts of only six action potentials were sufficient to produce intracellular calcium signals of up to 5% amplitude , which followed a slower time course as compared to control conditions ( compare Figure 2A and 2B ) . Washing in normal calcium-containing saline led to a complete recovery of action potential shape and frequency and of the initial calcium signal ( Figure 2C ) . This showed that the spiking activity that occurs during normal behavior without blocking sodium or potassium channels induced calcium release from internal stores without calcium influx through VOCs . In principle , in neurons , calcium release from the ER is mediated either by IP3R or by RYR activation [10] . In insect DUM neurons , RYRs can be reliably blocked by intracellular application of 100 nM dantrolene [19] . To further prove the effectiveness of intracellular dantrolene in our experiments , we activated RYRs by bath application of caffeine , imaged the resulting calcium signal caused be RYR activation , and then demonstrated that dantrolene completely blocked calcium release in responses to caffeine ( see Figure S1 ) . However , dantrolene had no effect on voltage-induced increases in [Ca2+]i under calcium-free conditions ( Figure 3 ) , demonstrating that this effect was not mediated by RYRs . Under calcium-free extracellular conditions , calcium inward current was zero , but an increase in intracellular calcium indicator fluorescence of 32 ± 6% amplitude still occurred in response to the depolarizing current step ( Figure 3B ) . In contrast , intracellular application of the IP3R-blocker heparin completely abolished voltage-dependent calcium release from internal stores in the absence of extracellular calcium ( Figure 4A ) . Heparin has been shown to block insect DUM neuron IP3Rs at micromolar concentrations [19] . We have further demonstrated the effectiveness of heparin in our experiments by demonstrating that it blocks intracellular calcium release induced by bath application of the PLC agonist m-3M3FBS ( 25 μM; Calbiochem , San Diego , California , United States; see Figure S2 ) . Loading the neurons intracellularly with heparin ( 0 . 5 μM ) via the patch pipette had no effect on calcium currents recorded in voltage clamp ( Figure 4Ai ) . Under zero extracellular calcium conditions , heparin-loaded cells showed no increase in [Ca2+]i as a response to membrane depolarizations . After 3 min in calcium-free saline , no voltage-activated calcium inward current and no increases in fluorescence were observed ( Figure 4Aii ) . Washing for 3 min in calcium-containing saline completely restored the initial voltage-activated calcium current and the resulting increases in fluorescence ( Figure 4Aiii ) . These data showed that membrane depolarization–induced intracellular calcium release without calcium influx depended on IP3R activation . These pharmacological data were further substantiated by the finding that voltage-induced intracellular calcium signals in the absence of calcium influx could be blocked by bath application of the membrane-permeable IP3R blocker 2-aminoethoxydiphenyl borate ( 2-APB ) . However , the effect of 2-APB was only partially reversible in locust DUM neurons ( see Figure S3 ) . IP3 is produced by phospholipase C ( PLC ) activity . In insect DUM neurons , PLC activity can be specifically blocked by U73122 [20] . Intracellular application of U73122 ( Figure 4B ) led to the same results as IP3R block . In calcium-containing saline PLC block had no effect on calcium inward currents ( Figure 4Bi ) . Under zero extracellular calcium conditions , U73122-loaded cells showed no intracellular calcium signal in response to membrane depolarizations ( Figure 4Bii ) . Washing for 3 min in calcium-containing saline completely restored the initial voltage-activated calcium current and the resulting increases in fluorescence ( Figure 4Biii ) . This demonstrated that voltage-induced calcium release from intracellular stores in the absence of calcium influx was mediated via the classical PLC/IP3R pathway [10] . In neurons , activation of many different G-protein–coupled receptors may activate PLC . In our experiments , activation of any of these receptors was impossible , because the neurons were freshly isolated , they were patched and imaged prior to neurite outgrowth , they had no contact with other neurons , no transmitters or signaling substances that could possibly activate any receptor located on the plasma membrane were added to the saline , and a constant flow of fresh saline was washed over the isolated somata throughout the experiments . Furthermore , calcium signals that occurred in the absence of calcium influx were time locked to imposed membrane depolarizations , and spontaneous depolarizations or baseline fluctuations of comparable amplitude never occur in freshly isolated DUM neurons . Due to the lack of synaptic specializations and the constant saline flow , it seemed unlikely that depolarizations to 0 mV might have caused release of the natural transmitter octopamine from freshly isolated DUM somata , which in turn , might have activated autoreceptors . Nevertheless , we repeated the experiments shown in Figure 1 under pharmacological block of octopamine receptors ( epinastine [21] ) . This had no effect on the calcium responses , showing that activation of octopamine autoreceptors was not the cause for PLC/IP3R–mediated intracellular calcium release ( unpublished data ) . Therefore , PLC was activated by the depolarization of the plasma membrane , and not through ligand activation of a G-protein–coupled receptor . To test whether PLC was activated by a G-protein , the neurons were loaded with a non-hydrolysable GTP analog ( GTPγS , 100 μM ) . Consequently , activated G-proteins coupled to GTPγS retained separated subunits and could not be made available for future activations . To saturate native intracellular G-proteins with GTPγS , neurons were loaded with GTPγS via the patch pipette ( 100 μM ) and depolarized 30 times for 2 . 5 s each in calcium-containing saline ( Figure 5A ) . In calcium-containing saline , the neurons could be depolarized several hundred times without depleting intracellular calcium stores . Switching to zero-calcium saline completely abolished the calcium inward current as described above . However , in GTPγS-loaded neurons , no voltage-induced increase in calcium indicator fluorescence was observed in the absence of calcium inward current ( Figure 5B ) . Washing in calcium-containing saline restored both the calcium current and also the resulting calcium signals ( Figure 5C ) . The saturation of native intracellular G-protein could also be fully accomplished by 25 depolarizations of GTPγS-loaded cells in calcium-containing saline , so that no calcium signal was observed in subsequent test depolarizations in zero-calcium saline . Using only 20 depolarizations of GTPγS-loaded neurons in calcium-containing saline prior to the test depolarization in calcium-free saline yielded approximately 75% reduction in the signal amplitude of voltage-induced calcium release in the absence of calcium inward current ( unpublished data ) . In summary , the data showed that step membrane depolarizations from −90 to 0 mV induced calcium release from internal stores which occurred in the absence of calcium influx and relied on G-protein ( Figure 5 ) , PLC , and IP3R ( Figure 4 ) activation . Moreover , calcium elevations without any calcium influx also occurred during spike trains as observed during normal behavior ( Figure 2 ) . In a final set of experiments , we determined the voltage dependency of this novel calcium-release pathway . In order to avoid depletion of internal calcium stores during the course of the experiments , in a first set of measurements , the activation voltage was roughly narrowed down , and in a second set of measurements , the voltage dependency was determined more accurately within the pre-determined voltage range . First , in calcium-free saline , neurons were clamped to −90 mV followed by test steps to −60 mV , −30mV , and 0 mV . Calcium signals were not caused by depolarizations to −60 mV , but by those to −30 mV and to 0 mV ( unpublished data ) . Second , in calcium-free saline neurons were clamped to −60 mV , followed by test pulses to −50 , −40 , −30 , and 0 mV ( Figure 6A ) , and the percentage increase in fluorescence was averaged over three neurons and plotted as a function of the command voltage ( Figure 6B ) . The threshold for G-protein–PLC-IP3–mediated calcium release without calcium influx was between −60 mV and −50 mV . This was followed by a nearly linear increase of the calcium signal amplitude for command potentials between −50 mV and 0 mV . Command potentials more positive than −30 mV did not further increase the calcium signal amplitude ( Figure 6B ) .
Our data clearly demonstrate G-protein activation upon plasma membrane depolarization , which in turn activates PLC and IP3R to cause calcium release from the ER . The mechanism by which membrane voltage is translated into G-protein activation remains to be unraveled . One possibility to consider might be intracellular signaling by ions entering the neurons via voltage-operated channels . In addition to calcium [10] , chloride [22] and sodium [23] also act as intracellular messengers . They seem unlikely , however , as no membrane currents were recorded in calcium-free saline also containing pharmacological agents to block voltage-operated sodium and potassium currents . Furthermore , influx of small amounts of “rest calcium” in the absence of calcium current can be excluded , because the calcium-activated potassium current also disappeared , and additional blockade of VOCs by cadmium did not affect the intracellular calcium signal . Chloride influx is unlikely because voltage-dependent chloride channels activating at depolarizing current steps are not present in DUM neurons [15 , 24] . Sodium channels were blocked with TTX , but in the absence of calcium , sodium may also pass VOCs [25] . Sodium-induced dissociation of G-protein subunits has been demonstrated in neurons [26] . However , neither pharmacological block of VOCs by cadmium or by magnesium , nor activation of voltage-dependent sodium channels ( leaving out TTX ) affected the amplitude of the intracellular calcium signal . Therefore , we conclude that G-protein activation was not mediated by ion influx that was hidden to our patch clamp recordings . Alternatively , G-protein activation might be mediated by interactions with the voltage-dependent subunit of a membrane protein complex . During recent years , it has become evident that most ion channel pore-forming subunits interact with several modulatory binding partners to form a dynamic signaling protein complex [27] . For example , the G-protein β-γ heterodimer binds directly to VOCs ( and also to potassium channels ) , and the channel sites have been mapped in detail [28] . In Caenorhabditis elegans , the potassium channel β-subunit , MPS-1 , is also a serine/threonine kinase [29] , and multiple regulatory proteins bind to Drosophila BK channels [30 , 31] . Research in this field has been channel-centric , assuming that these interactions exist only to regulate the properties of the membrane channels , but the possibility of information flow from the channel protein to the associated protein complex has also to be considered [27] . This is further supported by findings in non-excitable megakaryocytes , in which IP3R-dependent calcium release induced by purinergic receptors ( P2Y1 ) can be potentiated by membrane depolarizations [32] . Additional evidence for voltage-dependent G-protein activation comes from cultured arterial myocytes [33] , and a recent study on transfected fibroblasts suggests MAP kinase activation depending on the conformational state of the voltage sensor of ether-a-go-go potassium channels [34] . The current study demonstrates the existence of voltage-dependent G-protein activation for the first time in neurons . Since neurons process information by membrane voltage changes , the consequences of such a signaling pathway for brain development and function may be multi-fold and hard to foresee at this point . The voltage dependency of the intracellular calcium signal observed in this study follows a Boltzmann kinetic that closely resembles the activation characteristics of VOCs in insect DUM neurons [15] . Although we have no further evidence for direct interactions between the VOCs and a G-protein , G-protein activation via a highly sensitive voltage-sensing protein located in the plasma membrane remains the most likely explanation for our data . What are the functions of voltage-dependent calcium release from the ER that does not rely on calcium influx ? Neurons possess a rich blend of different types of VOCs located in different cellular compartments [10] . This ensures voltage-dependent calcium influx at highly specialized locations to regulate vital processes such as vesicle fusion at synaptic terminals [7] , filopodia morphology and motility [5] , or local spine stability at postsynaptic sites . However , neurons most certainly also possess dendritic or axonal sites that are devoid of VOCs , but become depolarized either passively or actively by sodium influx during normal neural function . The novel mechanism described here might enable neurons to locally monitor membrane depolarizations by intracellular calcium release at such sites . The ER , in turn , is organized in modular signaling units capable of performing independent functions ranging from the local activation of kinases , phosphatases , or SOCs ( via local protein–protein interactions of the ER and the plasma membrane ) to the activation of stress signaling pathways and transcription [35] . The fundamental new aspect resulting from this study is that the ER can acknowledge neuronal activity by local calcium release without relying on calcium as an input signal . Such a voltage-dependent intracellular calcium signal is not a unique peculiarity of locust DUM neurons , but strong evidence for its existence has recently also been observed in other neurons of the invertebrate CNS where it triggers BK channel activation ( P . Kloppenburg , personal communication ) . Conversely , instead of a spatial separation of different intracellular calcium signals , calcium influx through VOCs or through ROCs may act in concert with voltage-induced activation of PLC . In hippocampal neurons , for instance , IP3R activation is evoked by synaptic activation of metabotropic glutamate receptors paired with back-propagating action potentials [13] . The IP3R is well suited to operate as a coincidence detector , because its sensitivity for calcium is altered by IP3 . In general , IP3Rs show a bell-shaped calcium dependence , but in the presence of high IP3 , their calcium dependence becomes sigmoidal [36 , 37] . Consequently , a calcium-independent voltage sensor activating PLC to produce IP3 should tune IP3Rs towards responding to higher calcium concentrations . Although the physiological function of this novel calcium-release mechanism for normal neural function remains to be investigated , it clearly expands the calcium signaling tool kit of neurons to facilitate either calcium release from the ER without calcium as input signal or coincidence detection of multiple activity-dependent signals on the level of the IP3 receptor .
Adult desert locusts Schistocerca gregaria were obtained from the laboratory culture of the Department of Neurobiology at the Free University of Berlin . Animals were dissected and DUM neurons were isolated as described earlier [15] . Only acutely isolated cells were used for measurements ( neurons older than 4 h were discarded ) . To exclude cell–cell interactions , all neurons recorded from had no contact with other neurons or glia cells , nor was any other cell located within a 100-μm circumference ( see Figure 1G–1I ) . Locust thoracic DUM neurons can be subdivided into different subclasses [15 , 18] . All types of thoracic DUM neurons show a slow , non-inactivating calcium current and a fast , inactivating calcium current , but different subtypes may show different relative amounts of both calcium currents . However , the novel calcium signaling mechanism described in this manuscript is present in all subclasses of locust thoracic DUM neurons . Acutely isolated neurons were fixed in 4% paraformaldehyde , washed in PBS buffer ( 0 . 1 M; 3 × 10 min ) , incubated for 15 min at 37 °C in RNase ( 0 . 1 mg/ml buffer ) to diminish RNA , and washed 3 × 10 min in PBS buffer . This was followed by blocking with NGS ( 10% in PBS ) for 60 min . Then anti-HRP antibody ( AffiniPure Rabbit Anti-Horseradish Peroxidase; Dianova , Hamburg , Germany ) was applied in PBS ( 1:5 . 000 ) overnight . This was followed by six washes in PBS ( 10 min each ) , 20-min incubation with propidium iodide ( 50 μl of a 500 μM stock solution/l PBS ) , six washes in PBS ( 5 min each ) , 60-min incubation of Cy2-goat anti rabbit secondary antibody ( 1:1 , 000 in 0 . 1 M PBS , Dianova ) , an additional six washes in PBS ( 5 min each ) , and mounting in glycerol . Confocal images were obtained with a 40× oil immersion lens on a Leica SP2 confocal microscope ( Leica , Wetzlar , Germany ) . Cy2 was excited with an argon laser at 488 nm , and the emission was detected between 495 and 520 nm . PI was excited with a green helium neon laser at 543 nm , and emission was detected between 560 and 590 nm . The standard external saline contained ( in mM ) : 150 NaCl , 5 KCl , 5 CaCl2 , 2 MgCl2 , 10 Hepes , 25 sucrose , adjusted to pH 7 . 40 with NaOH . In all voltage clamp experiments , 300 nM tetrodotoxin ( TTX; Sigma , St . Louis , Missouri , United States ) was added to block Na+ currents . To reduce K+ current amplitudes at the test potential of 0 mV , high-K+ saline and tetraethylammonium chloride ( TEA-Cl ) were used for most experiments ( in mM ) : 50 KCl , 35 NaCl , 5 CaCl2 , 2 MgCl2 , 70 TEA-Cl , 10 Hepes , 25 sucrose . For Ca2+-free saline , CaCl2 was replaced by an equal concentration of MgCl2 and 1 μM EGTA was added . Calcium-free extracellular solution was achieved by switching the saline flow from calcium-containing to calcium-free saline . At a flow rate of 1 ml per minute , the bath volume of approximately 500 μl was exchanged ten times within 5 min . The standard pipette solution contained ( in mM ) : 180 K gluconate , 10 NaCl , 0 . 1 CaCl2 , 1 MgCl2 , 10 Hepes , 2 ATP-Mg , 1 EGTA . The pH was adjusted to 7 . 40 with KOH . Dye filling and recording were conducted with different electrodes to exclude changes in intracellular dye concentration during the course of the experiments . For cell staining , the tips of thick-walled micropipettes ( resistance , 20–25 MΩ ) were filled with 350 μM Oregon Green Bapta-2 octapotassium salt ( Invitrogen , Carlsbad , California , United States ) in standard pipette solution . The shafts were filled with 2 M potassium acetate with an air bubble left between the dye and the potassium acetate . Neurons were dye loaded iontophoretically by applying hyperpolarizing current of 1–1 . 5 nA amplitude for 5 min . After dye loading , the electrode was removed and the cells were patched . Whole-cell patch clamp recordings were carried out using an EPC-9 patch clamp amplifier ( HEKA Elektronik , Lambrecht , Germany ) in the voltage-clamp and current-clamp mode . PULSE 8 . 30 software ( HEKA Elektronik ) was used to generate voltage steps or to inject current . Patch pipettes were pulled from filamented borosilicate glass capillaries ( Harvard Apparatus , Edenbridge , United Kingdom ) with an outer diameter of 1 . 5 mm ( resistance , 1–2 MΩ ) . Series resistance compensation of 75%–80% was achieved . Liquid junction potential was corrected before conducting a giga seal . Recordings were analyzed using PULSEfit 8 . 30 software and Igor pro 5 . 2 software . Leak correction was conduced off-line . Oregon Green Bapta-2 octapotassium salt was excited at 480-nm wavelength . The fluorescent images were captured through a 510- to 530-nm band-pass filter with a cooled CCD camera ( Hamamatsu 4742–95; Hamamatsu Photonics , Hamamatsu City , Japan ) mounted on a fluorescence microscope ( Zeiss Axioskop 2FS; Carl Zeiss , Oberkochen , Germany ) . Data acquisition and analysis were conducted with Simple PCI software ( Compix , Sewickley , Pennsylvania , United States ) . Excel 4 . 0 ( Microsoft , Redmond , Washington , United States ) and Microcal Origin 7 . 0 ( Microcal Software , Northampton , Massachusetts , United States ) were used for statistical analysis . Currents that resulted from voltage steps from −90 mV to 0 mV are shown together with the underlying K+ currents , Na+ currents were potently blocked by application of 300 nM TTX . Ca2+ currents resulting from the performed voltage steps were isolated by an off-line subtraction protocol using PULSEfit software ( HEKA Elektronik ) . Currents were leak corrected by off-line subtraction routines . Calcium signals were always depicted as percentage change in the fluorescence ( ΔF/F ) . Background fluorescence was subtracted routinely . Background fluorescence was measured as the average fluorescence of three randomly chosen regions of interest located between 50 and 70 μm outside the DUM neuron soma of interest . Average background fluorescence was subtracted in each frame of every time series from the fluorescence signal of the calcium indicator Oregon Green Bapta measured inside the DUM neuron somata . Baseline correction for bleaching was not necessary . | In neurons , calcium ions play a dual role as charge carriers and intracellular messengers , thereby linking brain activity to cellular changes . Alterations in the electrical potential across the cell's outer membrane ( as happens , for example , when a neuron fires an action potential ) , can induce an influx of calcium ions through voltage-dependent membrane channels , which in turn regulate multiple cellular processes , such as gene transciption , cytoskeletal rearrangements , or even cell death . Stores of calcium ions also exist within neurons , which release their contents in response to multiple intracellular signals , including calcium itself . Here , we demonstrate that the neuronal cell membrane also possesses a voltage sensor that activates an intracellular calcium-release mechanism . This sensor enables neurons to recruit intracellular calcium signaling pathways in response to electrical activity without relying on calcium channels in their membrane . As large parts of a neuron's membrane may not contain calcium channels , this novel mechanism adds previously unanticipated calcium signaling possibilities to the neuron's intracellular communication machinery . | [
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] | 2007 | Expanding the Neuron's Calcium Signaling Repertoire: Intracellular Calcium Release via Voltage-Induced PLC and IP3R Activation |
How combinations of gene-environment interactions collectively give rise to genotype-environment interactions is not fully understood . To shed light on this problem , we genetically dissected an environment-specific poor growth phenotype in a cross of two budding yeast strains . This phenotype is detectable when certain segregants are grown on ethanol at 37°C ( ‘E37’ ) , a condition that differs from the standard culturing environment in both its carbon source ( ethanol as opposed to glucose ) and temperature ( 37°C as opposed to 30°C ) . Using recurrent backcrossing with phenotypic selection , we identified 16 contributing loci . To examine how these loci interact with each other and the environment , we focused on a subset of four loci that together can lead to poor growth in E37 . We measured the growth of all 16 haploid combinations of alleles at these loci in all four possible combinations of carbon source ( ethanol or glucose ) and temperature ( 30 or 37°C ) in a nearly isogenic population . This revealed that the four loci act in an almost entirely additive manner in E37 . However , we also found that these loci have weaker effects when only carbon source or temperature is altered , suggesting that their effect magnitudes depend on the severity of environmental perturbation . Consistent with such a possibility , cloning of three causal genes identified factors that have unrelated functions in stress response . Thus , our results indicate that polymorphisms in stress response can show effects that are intensified by environmental stress , thereby resulting in major genotype-environment interactions when multiple of these variants co-occur .
Genotype-environment interaction ( ‘GxE’ ) occurs when genetically distinct individuals show different phenotypic responses to the environment [1 , 2] . Although GxE is known to influence many agriculturally , evolutionarily , and medically relevant traits ( e . g . , [3–6] ) , our basic knowledge of the genetic and molecular mechanisms that underlie GxE remains incomplete . Recent work on this topic in Saccharomyces cerevisiae suggests GxE can arise due to not only individual loci that show gene-environment interactions , but also sets of loci that show environment-dependent epistatic interactions [7–9] . However , because the underlying genetic basis of GxE has only been comprehensively dissected in a small number of cases ( e . g . , [7–9] ) , the relative contributions of these different types of genetic effects to GxE is unclear . Here , we generate an additional , detailed example of the genetic basis of GxE in the budding yeast Saccharomyces cerevisiae . We focus on characterizing the genetic basis of a poor growth phenotype that occurs specifically when certain segregants from a cross of the BY4716 ( ‘BY’ ) lab strain and the YJM789 ( ‘YJM’ ) clinical isolate [10] are cultured on ethanol at 37°C ( ‘E37’; Fig 1 ) . Although yeast is typically grown on glucose as the carbon source and at 30°C as the temperature ( ‘G30’ ) , it can tolerate a broad range of environmental conditions , including other carbon sources and temperatures [10 , 11] . Among the different carbon sources that yeast can utilize , ethanol can be particularly stressful because it is metabolized via respiration instead of fermentation , which results in increased oxidative stress [12] . Furthermore , high temperature is known to be a stressor for budding yeast [13] , with some isolates incapable of growing at 37°C or above [11 , 14–20] . To determine the genetic basis of poor growth in E37 , we use a genetic mapping strategy involving recurrent backcrossing with phenotypic selection ( Fig 2 ) . Through this approach , we identify 16 loci that contribute to poor growth in E37 . We then conduct a more detailed study of four of these loci , which collectively result in poor growth in E37 when they co-occur in the YJM background . By analyzing the growth of all 16 haploid multi-locus genotypes involving the loci on all four combinations of two carbon sources ( glucose and ethanol ) and two temperatures ( 30 and 37°C ) , we find that the four loci contribute to poor growth in E37 in a primarily additive manner . Furthermore , we also show that these loci exhibit weaker , negative effects on growth when only carbon source or temperature is altered relative to standard conditions . These results indicate that GxE in our system reflects the composite effect of multiple additive loci that show condition-dependent effect magnitudes . Additionally , by resolving three of these loci to a component of the vacuolar protein sorting machinery ( VPS70 ) , a stress granule-associated RNA binding protein ( YGR250C ) , and a stress responsive kinase ( IKS1 ) , we implicate genetic variation in stress response as the source of the identified gene- and genotype-environment interactions .
We screened 112 haploid BYxYJM F2s for growth on both glucose and ethanol at both 30 and 37°C . We found that five of these individuals exhibited noticeably poor growth specifically in E37 ( Fig 1 ) . To determine the genetic basis of this phenotype , we used a recurrent backcrossing with phenotypic selection strategy ( Fig 2 ) . In brief , we mated one of the five poor growing F2s to both BY and YJM , and generated and phenotyped at least 576 haploid F2B recombinants from each backcross ( Methods ) . 14 F2Bs ( seven per backcross ) were then used to breed haploid Nearly Isogenic Lines ( NILs ) that carry alleles that collectively cause poor growth in E37 ( Fig 2; Methods ) . To identify these alleles , we sequenced the genomes of the NILs to an average per site coverage of 21X and identified genomic regions that had been introgressed ( Fig 3; Methods ) . Based on these data , we determined that three of the YJM NILs harbored aneuploidies or appeared to be replicates of other NILs ( S1 and S2 Figs ) . We excluded these individuals from all subsequent analyses . Among the remaining 11 NILs , we detected 41 introgressed genomic regions ( Fig 3 ) . To verify that the introgressed regions contribute to poor growth in E37 , we generated a population of haploid F2B7s by backcrossing YJM NIL 3 to YJM an additional time . Ignoring a control marker at CAN1 on Chromosome V , five genomic regions ( Chromosome I , VII , X_1 , X_2 , and XVI ) , were polymorphic in the F2B7 population ( Fig 3B and 3C ) . Four of these loci were detected in other YJM NILs ( Chromosome I , VII , X_1 , and X_2 ) , while the genomic region on Chromosome XVI was unique to this NIL ( Fig 3C ) . By screening 864 F2B7s , we obtained 45 individuals that grow poorly in E37 ( Methods ) . These individuals , as well as a distinct population of 192 random F2B7s , were then genotyped by low coverage whole genome sequencing or restriction enzyme typing ( Methods ) . We tested for allelic enrichment among the poor growing individuals relative to the random controls ( Methods ) . Fisher’s exact tests indicate that the Chromosome I , VII , X_1 , and X_2 loci contribute to YJM NIL 3’s poor growth in E37 ( I: p ≤ 3 . 8 x 10−8 , VII: p ≤ 4 x 10−20 , X_1: p ≤ 8 . 4 x 10−7 , X_2: p ≤ 1 . 6 x 10−20; S3 Fig ) , while the Chromosome XVI locus does not ( XVI: p = 0 . 34; S3 Fig ) . Given that the former loci were detected in two or more NILs and the latter locus was only identified in a single NIL , these results suggest that loci that were detected independently at least twice among the NILs have biological effects . Extension of this finding to the entire set of introgressed genomic regions conservatively implicates at least 16 loci as contributors to poor growth in E37 ( Fig 3C; S1 Table ) . We analyzed the phenotypic effects of the Chromosome I , VII , X_1 , and X_2 loci using the population of 192 random F2B7s ( Methods ) . These strains were quantitatively phenotyped for growth in E37 , and the additive and epistatic effects of the four loci were assessed ( Methods ) . In a full factorial ANOVA that included all possible additive effects and pairwise or higher-order epistatic interactions ( Methods ) , genetic factors explained 79 . 9% of the phenotypic variance ( Table 1 ) . 94 and 6% of this genetic contribution to growth was due to additive and epistatic effects , respectively . Furthermore , 7 , 11 . 1 , 24 . 7 , and 32 . 4% of the phenotypic variance was explained by the Chromosome X_1 , I , X_2 , and VII loci , respectively ( Table 1 ) . Each of these additive effects were highly significant ( F statistic > 60 , d . f . numerator , = 1 , d . f . residuals = 175 , p < 6 x 10−13; Fig 4; Table 1 ) . In contrast , only four epistatic interactions showed significant effects ( F statistic > 5 . 2 , d . f . numerator , = 1 , d . f . residuals = 175 , p < 0 . 024 ) . These were each pairwise interactions that explained only between 0 . 6 and 2% of the phenotypic variance ( Table 1 ) . Thus , our results indicate that extremely poor growth in E37 has a genetic basis that is mostly additive . We also examined the effects of the Chromosome I , VII , X_1 , and X_2 loci in G30 , ethanol at 30°C ( ‘E30’ ) , and glucose at 37°C ( ‘G37’ ) . As a first step , full factorial ANOVA models were implemented in each of these conditions . In G30 , the only nominally significant effect was a higher-order epistatic interaction involving all four loci , which explained 3 . 3% of the phenotypic variance ( F statistic = 6 . 4 , d . f . numerator , = 1 , d . f . residuals = 175 , p < 0 . 013; S2 Table ) . In comparison , full factorial models for E30 and G37 revealed that all four loci showed significant additive effects in both conditions ( F statistic > 7 . 3 , d . f . numerator , = 1 , d . f . residuals = 175 , p < 0 . 004; Fig 4; S3 and S4 Tables ) . The only other significant genetic effect in E30 or G37 occurred in the former condition , with a pairwise epistatic interaction detected between the Chromosome VII and X_1 loci ( F statistic = 15 . 5 , d . f . numerator , = 1 , d . f . residuals = 175 , p = 0 . 0001; S3 Table ) . These results show that the Chromosome I , VII , X_1 , and X_2 loci are influenced by both carbon source and temperature , and act in a largely additive manner within a given non-standard growth condition ( Fig 4; Table 1; S2 through S4 Tables ) . We next assessed the relationship between the effects of the Chromosome I , VII , X_1 , and X_2 loci and the different conditions . Based on the aforementioned full factorial models , we found that the average percent phenotypic variance explained by the additive effects of the four loci was 0 . 48 , 5 . 4 , 9 . 2 , and 18 . 8% in G30 , G37 , E30 , and E37 , respectively . These changes in average effect size across conditions show a negative association with the average growth levels seen among F2B7s in the respective conditions , which exhibit the relationship G30 > G37 > E30 > E37 ( Fig 5A ) . These reductions in average growth levels across conditions may reflect increases in environmental stress , suggesting that lower absolute growth , higher stress , or a combination of the two intensifies the effect magnitudes of the loci ( Fig 4 ) . This finding helps explain how gene- and genotype-environment interactions of varying magnitudes can occur across conditions , while variability in growth can remain predominantly additive in its genetic basis within a condition ( Fig 5B ) . To help determine the mechanism that relates average growth level within a condition to the effect sizes of the four loci , we attempted to clone the causal genes underlying the loci . The F2B7 data allowed us to resolve the Chromosome I , VII , X_1 , and X_2 loci to small intervals containing on average 5 , 943 bp ( S5 Table; S1 Note; Methods ) . For each candidate gene in each locus , we performed allele replacements that included the promoter and coding region ( Methods ) . Specifically , the existing BY allele of each candidate gene was replaced with the YJM allele in YJM NIL 3 ( Methods ) . Through these experiments , we were able to resolve the Chromosome VII , X-1 , and X-2 loci to YGR250C , IKS1 , and VPS70 , respectively ( Fig 6 ) . YGR250C encodes a RNA binding protein that localizes to stress granules [21–23] . Stress granules are cytoplasmic messenger ribonucleoprotein ( mRNPs ) complexes that form in response to stress and are thought to aid in the translation of mRNAs by increasing the local concentration of translation initiation factors [24–26] . We were able to further resolve the YGR250C locus to a derived , YJM-specific amino change in a predicted RNA binding motif ( S4 Fig; Methods ) . As for IKS1 , this gene encodes an uncharacterized protein kinase that has been shown to be induced during mild heat stress and to alter the sensitivity of yeast to a number of different small molecules [22] . Lastly , VPS70 encodes an uncharacterized protein involved in vacuolar protein sorting , which is known to mediate cellular response to a wide range of environmental stresses [27–29] . These findings implicate polymorphisms in different components of stress response as major contributors to the heritable growth variation in our study . We have determined the genetic basis of an example of GxE in which certain yeast segregants exhibit extremely poor growth in a specific environmental condition . Our results indicate that this poor growth is caused by a number of environmentally responsive loci that individually show additive effects that increase with the severity of environmental stress and collectively result in very poor growth under stressful conditions . This finding provides support for the concept of decanalization , which has been hypothesized to occur when environmental perturbation uncovers sets of deleterious cryptic genetic variants that result in conditional disease phenotypes or other genotype-environment interactions [30] . However , our results are also compatible with recent work illustrating the largely additive genetic basis of quantitative trait variation in yeast [31–33] . Indeed , our work suggests that when many loci show similar gene-environment interactions with environmental stress , decanalization can occur across conditions while trait variation retains an additive genetic architecture within conditions . The current study also provides a valuable contrast to previous work from our group and others showing a substantial epistatic contribution to GxE [7–9] . Here , we find that epistasis does not meaningfully contribute to GxE in growth variation under our assay conditions . Although we have could have underestimated the contribution of epistasis to our study by focusing on a particular set of four loci , our results might also reflect a major difference in the molecular mechanisms that give rise to the focal phenotypes in the present and past studies . In particular , in previous work on colony morphology [9] and sporulation [8] , the examined phenotypes were controlled by specific gene regulatory networks involving multiple polymorphic transcription factors . Genetic variability in such networks is known to be an important source of pairwise and higher-order epistatic interactions [34–39] . In contrast , our current effort is focused on growth , which unlike colony morphology or sporulation , is not a phenotype that arises due to a single predominant gene regulatory network . Thus , our past [9] and current findings suggest that GxE can show a range of genetic architectures from largely additive to largely epistatic . Where the genetic architecture of GxE in a particular trait lies along this spectrum likely depends on the molecular mechanisms that give rise to the phenotype .
Using the synthetic genetic array marker system [40] , 112 recombinant BYxYJM MATa segregants were generated . The BY parent of our cross was MATα can1Δ::STE2pr-SpHIS5 lyp1Δ his3Δ , while the YJM parent was MATa his3Δ::NatMX ho::HphMX . The BY and YJM haploids were mated to produce a diploid , which was then sporulated using standard techniques [41] . MATa segregants were obtained using random spore plating on minimal media containing canavanine , as previously described [36 , 42] . Strains were phenotyped on 2% agar plates containing yeast extract and peptone ( YP ) with either 2% glucose ( dextrose ) or 2% ethanol as the carbon source ( YPD and YPE , respectively ) at 30°C or 37°C . Prior to pinning onto the agar plates , strains were grown overnight to stationary phase in liquid YPD . After this culturing step , strains were then pinned onto agar plates and allowed to grow in the appropriate condition for five days . Individuals were considered poor growing in E37 based on three replicate phenotyping experiments that were performed using randomized designs . Qualitatively poor growth was never observed in G30 , G37 , or E30 . Similar to our past work [36 , 43] , F2B backcross segregants that grow poorly in E37 were obtained by screening haploid progeny from backcrosses of a relevant BYxYJM F2 segregant to MATα ho his3Δ versions of BY and YJM . Seven BY and seven YJM F2Bs were then subjected to five additional rounds of backcrossing with selection for maintenance of poor growth in E37 . Each round of backcrossing was performed using MATα his3Δ versions of BY and YJM . Sporulation and selection for MATa segregants was performed as described for the initial F2 population . The NILs were genotyped by Illumina sequencing . Whole genome libraries were constructed using the Illumina Nextera kit , with each library tagged with a unique barcode for multiplexing . Each library was sequenced to an average per site genomic coverage of at least 21X on a NextSeq with 100 base pair ( bp ) x 100 bp reads . The BY and YJM parent strains were also sequenced to an average per site genomic coverage of ~100X , and these data were used to identify 57 , 402 high confidence SNPs . Reads for the NILs were mapped to the S288c genome ( version S288C_reference_sequence_R64-2-1_20150113 . fsa from SGD [http://downloads . yeastgenome . org] ) using Burrows-Wheeler Aligner ( BWA ) version 0 . 7 . 7-r441 [44] and mpileup files were generated with SAMTOOLS [45] version 0 . 1 . 19-44428cd . The default parameters for BWA and SAMTOOLS were used for mapping Illumina reads to the genome . Genotypes for each individual were called by taking the fraction of BY allele calls at each of the SNPs and employing a Hidden Markov Model by chromosome , using the HMM ( ) package version 1 . 0 in R , as described in [36] . The parameters used for transition and emission probabilities were transProbs = matrix ( c ( . 9999 , . 0001 , . 0001 , . 9999 ) , 2 ) and emissionProbs = matrix ( c ( . 0 . 5 , 0 . 5 , 0 . 5 , 0 . 5 ) , 2 ) , respectively . We also used the sequencing data to screen the NILs for aneuploidies . If the average sequence coverage for any individual chromosome was 1 . 5 times higher or lower than the average genome-wide sequencing coverage for a given individual , that strain was classified as aneuploid . Two YJM NILs were found to be aneuploid and thus were excluded from all analyses described in the paper ( S1 Fig ) . Additionally , we found that two YJM NILs possessed nearly identical sets of introgressed regions , suggesting a technical error on our part during the recurrent backcrossing process . Only one of these NILs was included in our analyses ( S2 Fig ) . YJM F2B7 segregants were created by backcrossing YJM NIL 3 ( Fig 3B ) to a MATα hoΔ his3Δ version of YJM . Sporulation and selection for MATa segregants was performed as described for the initial F2 population . 96 YJM F2B7 random segregants and 45 additional F2B7s that grew poorly on E37 were genotyped by sequencing to an average per site coverage of at least 5X using the same method described for the BY and YJM NILs . An additional 96 YJM F2B7 random segregants were genotyped at the five loci that had been introgressed into YJM NIL 3 using PCR and restriction enzyme typing . All reactions are provided in S6 Table . Fisher’s exact tests were then performed in R , using two-by-two matrices in which the first row contained the counts of BY and YJM alleles among the 45 F2B7s showing poor growth in E37 , and the second row contained the counts of BY and YJM alleles among the 192 YJM F2B7 random population . Allele counts were measured at a single site for each locus that showed maximal allelic enrichment among the 45 F2B7s that grow poorly in E37 . To further analyze growth in the F2B7 population , we grew each of these individuals on all possible combinations of carbon sources—glucose and ethanol—and temperatures—30 and 37°C . Individuals were pinned onto agar plates and then grown in the appropriate condition for three days . The plates were then imaged using the BioRAD Gel Doc XR+ Molecular Imager . The dimensions of all the images were set at 13 . 4x10 cm ( WxL ) and imaged under white Epi illumination with an exposure time of 0 . 5 seconds . The images were then exported as tiff files with a publishing resolution of 300dpi . To measure the pixel intensity of each colony , ImageJ [46] was used . The total pixel intensity within a circle ( spot radius = 50 pixels ) surrounding each colony in the image was measured using the Plate Analysis JRU v1 plugin for ImageJ , which was downloaded from the Stowers Institute ImageJ Plugins page ( http://research . stowers . org/imagejplugins/index . html; S5 Fig ) . The Circ Background option was used to control for background noise . The average pixel intensity was determined by dividing the total pixel intensity by the area of the circle examined ( 7845 pixels2 ) . Five biological replicate measurements using different randomized designs were taken for each F2B7 in each condition ( S7 Table ) . The median pixel intensity among these five replicates was then used in downstream analyses ( S8 Table ) . To measure the additive and epistatic effects of the Chromosome I , VII , X_1 , and X_2 loci among the F2B7s in a particular condition , we implemented full factorial ANOVAs in R . Specifically , we modeled the median pixel intensity of the F2B7 segregants in each condition as a function of all possible additive and epistatic effects involving the four loci . The model was specified using the statement: lm ( median_pixel_intensity_for_each_condition ~ genotype_at_locus_I * genotype_at_locus_VII * genotype_at_locus_X_1 * genotype_at_locus_X_2 ) . ANOVA tables were then obtained using the anova ( ) function . In addition to the terms provided by R , we computed the percent of phenotypic variance explained for each locus by dividing the sum of squares associated with a particular term by the sum of squares total ( Table 1 and S2 , S3 and S4 Tables ) . Respectively , the fractions of phenotypic variance explained by all genetic effects ( R2G ) or only additive genetic effects ( R2A ) were computed by summing the fractions of phenotypic variance explained by all genetic terms or only additive genetic terms in a given model . Within each condition , we modeled the median pixel intensities of the F2B7s as a function of how many YJM alleles they carried . This model assumes complete additivity with loci showing equal effect sizes . These linear models were fit in R using the lm ( ) function in R with the statement lm ( median_pixel_intensity_for_each_condition ~ number_of_YJM_alleles_at_four_loci ) . All transformations were conducted using standard PCR-based techniques [47] . Allele replacement strains were constructed using the co-transformation of two partially overlapping PCR products as described in [43] . One product contained the promoter and coding region of the gene to be replaced , while the other included ( in order ) 60 bp of overlap with the 3’ end of the gene PCR product , kanMX , and 60 bp of the genomic region downstream of the transcribed portion of the gene , such that the entire coding and the promoter region of a given gene was replaced ( S9 Table ) . All engineerings were performed in YJM NIL 3 and involved replacement of the BY allele of a given gene with the YJM allele . Each putative allele replacement was verified by Sanger sequencing . Controls were also generated to ensure that inserting kanMX near each gene was not responsible for our findings . DNA sequences for other S . cerevisiae strains were downloaded from the Saccharomyces Genome Database ( http://www . yeastgenome . org; S10 Table ) , as well as from different S . cerevisiae resequencing projects [10 , 48] . DNA sequence alignments were then generated using Geneious v7 . 0 . 6 and the amino acid sequences of these other isolates was determined by translating the DNA sequence alignment . The amino acid sequences of other closely related fungal species were obtained using WU-BLAST2 with default settings ( http://www . yeastgenome . org/blast-fungal ) . The putative RNA binding motifs of YGR250C were then identified from domain predictions available through InterPro ( http://www . ebi . ac . uk/interpro/protein/P53316 ) [49] . | Determining the genetic and molecular mechanisms that give rise to genotype-environment interaction ( ‘GxE’ ) is important for many areas of biology , including agriculture , evolution , and medicine . To help advance knowledge regarding this topic , we dissect the genetic basis of an example of GxE in which certain Saccharomyces cerevisiae cross progeny show extremely poor growth specifically on ethanol at 37°C . This environment differs from the standard condition used for culturing budding yeast in both its carbon source ( ethanol as opposed to glucose ) and temperature ( 37°C as opposed to 30°C ) . We provide evidence that poor growth on ethanol at 37°C is caused by a number of predominantly additive loci that individually exhibit gene-environment interactions with both carbon source and temperature . These loci show their largest effects when carbon source and temperature are simultaneously modified , indicating their effect magnitudes may be influenced by the severity of environmental stress . Consistent with this possibility , we clone three causal genes and find they encode functionally unrelated components of stress response . Our work suggests that polymorphisms in stress response can contribute additively to genotype-environment interactions that vary in intensity across conditions in a stress level-dependent manner . | [
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"chr... | 2016 | Gene-Environment Interactions in Stress Response Contribute Additively to a Genotype-Environment Interaction |
Complex genetic networks consist of structural modules that determine the levels and timing of a cellular response . While the functional properties of the regulatory architectures that make up these modules have been extensively studied , the evolutionary history of regulatory architectures has remained largely unexplored . Here , we investigate the transition between direct and indirect regulatory pathways governing inducible resistance to the antibiotic polymyxin B in enteric bacteria . We identify a novel regulatory architecture—designated feedforward connector loop—that relies on a regulatory protein that connects signal transduction systems post-translationally , allowing one system to respond to a signal activating another system . The feedforward connector loop is characterized by rapid activation , slow deactivation , and elevated mRNA expression levels in comparison with the direct regulation circuit . Our results suggest that , both functionally and evolutionarily , the feedforward connector loop is the transitional stage between direct transcriptional control and indirect regulation .
Related organisms often express orthologous genes in response to a particular cellular or environmental cue . However , the regulatory mechanisms promoting expression of these genes can be drastically different , ranging from direct transcriptional control to multi-stage architectures involving feedback loops , feedforward loops and regulatory cascades [1]–[5] . Extensive studies of the functional properties of recurrent regulatory architectures–termed network motifs–have revealed that they exhibit quantitative differences in the levels and timing of gene expression [1] . Whereas the dynamical properties of distinct network motifs are relatively well understood , there is still limited knowledge about the general principles underlying the quantitative features and evolutionary relationships of genetic regulatory architectures . A prevalent form of bacterial signal transduction is the two-component system and its more complex version , the phosphorelay [6]–[9] . The activity of two-component systems and phosphorelays can be modulated at the post-translational level by members of the recently emerged class of proteins designated connectors ( reviewed in [10] ) , which modulate the output of a two-component system in response to signals other than the ones directly sensed by the system . In addition to facilitating signal integration , connectors confer specific quantitative properties on the regulated systems , which could result in survival advantages for the bacterium [2] . The best characterized connector-dependent pathway is mediated by the PmrD protein ( NCBI protein database accession number AAL21205 ) in the bacterium Salmonella enterica serovar Typhimurium , where it enables expression of genes controlled by the PmrA/PmrB two-component regulatory system in response to the low Mg2+ signal that activates the PhoP/PhoQ system [2] , [11]–[13] ( Figure 1A ) . PmrD is a PhoP-activated protein that binds to the phosphorylated form of the DNA binding regulatory protein PmrA ( PmrA-P ) , thereby protecting it from dephosphorylation by PmrA's cognate sensor PmrB [11] . This results in binding of PmrA-P to its target promoters and in changes in transcription of the corresponding genes such as pbgP ( also referred to as pmrH [14] and arnB [15] ) , which mediates a chemical modification in the lipopolysaccharide that confers resistance to the antibiotic polymyxin B [16]–[18] . This architecture allows S . enterica to express PmrA-activated genes and to display polymyxin B resistance in response to the signals activating the PhoP/PhoQ system [19] as well as in the presence of Fe3+ , Al3+ or acid pH , which are specific activating signals sensed by PmrB [20] , [21] . Expression of PmrA-dependent genes is slightly reduced in a pmrD mutant when both inducing signals , low Mg2+ and Fe3+ , are present [2] , [11]–[13] . The related enteric species Yersinia pestis also promotes pbgP expression and demonstrates polymyxin B resistance in response to Fe3+ and/or low Mg2+ , even though it lacks pmrD [22] . This is because the Y . pestis pbgP promoter harbors binding sites for both the PhoP and the PmrA proteins [22] ( referred to as PhoP and PmrA boxes , respectively ) ( Figure 1B ) . A comparison of the Yersinia-like direct transcription regulation circuit , which was reconstructed in an engineered S . enterica strain , to the connector-mediated pathway of wild-type S . enterica demonstrated that the latter pathway exhibits heightened induction ratios , which results in increased levels of polymyxin B resistance [2] . The connector-mediated pathway also displayed slower expression induction and increased persistence of expression after a shift from inducing to repressing conditions in comparison with the direct activation pathway [2] . Persistence of expression may facilitate the continuous synthesis of the PmrA-dependent cell envelope modifying determinants in fluctuating environments [2] . In this paper , we identify a novel regulatory architecture that combines structural and functional features of the direct regulation circuit and the connector-mediated pathway . The novel architecture , termed feedforward connector loop , possesses a direct regulatory branch , like that in Y . pestis , and an indirect branch that is analogous to the connector-mediated pathway of S . enterica . Even though the simultaneous presence of direct and indirect branches of regulation also characterizes one of the most abundant network motifs ( i . e . , the feedforward loop ) [1] , [3] , the identified architecture demonstrates substantial differences in dynamical behavior . Analysis of several enteric species suggests that the feedforward connector loop is the evolutionary link between direct transcriptional control and the connector-mediated regulatory circuit .
To explore the potential evolutionary scenario responsible for the PmrD-mediated architecture , we analyzed the distribution of the pmrD gene , and of PhoP and PmrA boxes in the pmrD and pbgP promoters among enteric bacteria ( Figure 2 ) . We looked for a bacterial lineage displaying evidence for both connector-mediated ( Figure 1A ) and direct ( Figure 1B ) regulation of the pbgP operon . K . pneumoniae appeared to fit these criteria because its genome harbors a pmrD ortholog ( Figure S1 ) that is preceded by a PhoP box ( Figure 3A ) , and because sequences resembling PhoP and PmrA boxes were present upstream of the pbgP operon ( Figure 2 ) . We tested the genomic prediction that the K . pneumoniae pmrD gene is PhoP-activated by investigating pmrD transcription in wild-type , phoP and pmrA strains grown under different conditions . The pmrD gene was expressed during growth in low Mg2+ in a PhoP-dependent manner but not in high Mg2+ ( Figure 3B ) , like the S . enterica [13] and E . coli [23] orthologs . In contrast to what happens in S . enterica , pmrD transcription was not repressed by the PmrA protein in K . pneumoniae ( Figure S2 ) , consistent with the absence of sequences resembling a PmrA box in the pmrD promoter region ( Figure 3A ) . To define the regulatory circuit governing pbgP transcription in K . pneumoniae , we investigated pbgP transcription in isogenic wild-type , pmrA , phoP and pmrD strains grown under different conditions promoting activation of the PhoP/PhoQ and PmrA/PmrB systems . S1 mapping experiments revealed two transcription start sites for the pbgP gene in wild-type K . pneumoniae: an ORF-proximal site that was active upon growth in low Mg2+ or in low Mg2++Fe3+ , but not in high Mg2+; and an ORF-distal site that displayed higher activity in low Mg2++Fe3+ than in low Mg2+ ( Figure 4A ) . The ORF-proximal promoter was PhoP-dependent but PmrA- and PmrD-independent , whereas the ORF-distal promoter was induced in low Mg2+ in a PhoP- , PmrD- and PmrA-dependent fashion , and in the presence of Fe3+ in a PmrA-dependent but PhoP- and PmrD-independent manner . DNase footprinting experiments with the conserved PhoP and PmrA proteins from S . enterica demonstrated specific binding to the K . pneumoniae pbgP promoter at the predicted PhoP and PmrA boxes ( Figure 4B and Figure S3 ) , indicating that the PhoP and PmrA proteins exert their regulatory effects directly . This regulatory architecture , in which PhoP activates pbgP expression directly by binding to the pbgP promoter , and indirectly via PmrD-dependent activation of the PmrA protein also binding to the pbgP promoter , was designated feedforward connector loop ( or FCL ) ( Figure 1C ) because it resembles the feedforward loop [3] network motif [1] . The feedforward loop ( FFL ) is one of the most abundant network motifs in prokaryotic regulatory circuits [1] , [3] , [24] . In a FFL , a transcriptional regulator X controls expression of gene z both directly , by binding to its promoter region , and indirectly , by promoting expression of gene y encoding a transcriptional regulator Y that also binds to the promoter of gene z ( Figure 1D ) . FFLs exhibit special functional features [1] , [3] , [25] , including the ability to act as sign-sensitive delay elements: they can increase the time it takes to activate gene expression while keeping the deactivation time unaffected , or the other way around [3] , [26] , [27] . For example , the coherent , activation-type FFL with an OR-gate can promote deactivation delays when compared to a circuit with direct regulation , though activation times for both designs are similar [3] , [26] . Regulation by the FCL architecture identified in K . pneumoniae ( Figure 1C ) is qualitatively equivalent to regulation by the latter type of the FFL , because the FCL follows the OR type of logic ( Figure 4A ) . Yet , the FCL differs from the FFL in that , instead of a two-stage transcriptional activation cascade , it relies on one transcription factor ( i . e . , PhoP ) to promote expression of a connector protein ( i . e . , PmrD ) that activates another transcription factor ( i . e . , PmrA ) at the post-translational level ( Figure 1C , D ) [3] , [26] . To define the salient characteristics of the FCL architecture , we analyzed activation and deactivation times , and contrasted these properties to those of the direct regulation circuit , the connector-mediated pathway , and the FFL . We utilized a variety of parameter values with a mathematical modeling methodology that was successfully used in the comparative analysis of the connector-mediated and direct regulation pathways [2] ( see Materials and Methods ) . In our computations , the PhoP-P level ( determined by the abundance of Mg2+ in the extracellular environment ) was the main input for the regulatory circuits . An additional input was the level of PmrA-P , which reflects the activity of the PmrA/PmrB system ( stimulated by Fe3+ ) ; in the FFL case , the second input was the level of activated ( phosphorylated ) protein Y ( Figure 1D ) . For this second input , we considered the cases of mild and strong activation . The case of mild activation of the second input for the transcriptional cascade was not considered because when the second input is inactive , two-component systems connected by a transcriptional cascade cannot be activated [28] ( Figure 5C , D: no green solid lines ) . The FCL and the FFL displayed an equivalent ability to promote small activation delays with respect to the direct regulation circuit ( Figure 5A ) . Whereas the FFL promoted large deactivation delays only with a small probability , large deactivation delays in the FCL could be observed in a substantial fraction of the cases ( Figure 5B ) . The FCL acted as a true sign-sensitive delay element for most of the simulated parameter values , but the FFL did not ( Figure 5 and Figures S4 , S5 , and S6 ) . Therefore , the FCL architecture generally provides much stronger sign-sensitive delay elements than the FFL design . Models for the connector-mediated pathway and a two-stage transcriptional cascade ( corresponding to the FCL and FFL with the direct regulation branches removed , respectively ) possessed a high ability to promote both activation and deactivation delays ( Figure 5C , D; Figures S4 , S5 , and S6 ) , in agreement with experimental data [2] , [5] , [29] . Notably , deactivation delay distributions for the FCL and the connector-mediated pathway in the case of strong activation of the second input are nearly identical ( Figure 5B , D; Figures S4B , D , S5 , and S6B , D ) . This allows us to conclude that , when the second input is strong ( which leads to elevated PmrA-P level and , therefore , heightened induction of the connector-mediated branch of regulation ) , the deactivation delays are determined almost entirely by the connector-mediated branch . A mathematical comparison of model outputs suggested that the FFL and FCL give higher output levels than their counterparts lacking direct activation branches ( Equation 16 in Text S1 ) . This can be ascribed to the presence of an additional branch of pbgP regulation which would increase the proportion of active pbgP promoters , leading to a higher production rate for the pbgP mRNA . To test the modeling predictions regarding the timing and output levels of pbgP expression in the different architectures ( Figure 5A , B , C , D; Equation 16 in Text S1 ) , we measured the pbgP mRNA levels in isogenic S . enterica serovar Typhimurium strains harboring the connector-mediated pathway ( Figure 1A ) , or engineered to express pbgP utilizing the direct regulation circuit ( Figure 1B ) or the FCL ( Figure 1C ) . This allowed us to focus on the quantitative features determined by the circuit architecture ( as opposed to its specific implementation in a particular species ) , and to avoid comparison biases arising from the inherently distinct biology of different bacterial species [1] . This is consistent with the previously established genetic circuit comparison methodology [2] . Our computational analysis showed that the connector-mediated pathway typically displays activation delays ( when compared to the direct regulation circuit ) whereas the FCL does not ( Figure 5A , C ) , suggesting that pbgP expression would be activated sooner in the strain with the FCL than in the one with the connector-mediated pathway . Indeed , when cells were grown under non-inducing conditions ( i . e . , 10 mM Mg2+ ) for 4 h and then switched to inducing conditions ( i . e . , 20 µM Mg2+ ) at time 0 , the pbgP mRNA level rose much faster in the FCL than in the connector-mediated pathway ( Figure 5E ) . ( Activation and deactivation affected only the PhoP-dependent input of the circuits through changes in the Mg2+ concentration , because there was no direct PmrA activation input due to the absence of Fe3+ in the medium . ) This rapid activation was ascribed to the direct regulation branch because the connector-mediated pathway , which lacks a direct regulation branch ( Figure 1A ) , displayed delayed activation ( Figure 5E ) [2] . Furthermore , the direct regulation circuit ( in a similar way to the FCL ) demonstrated rapid activation ( Figure 5E ) . For the case of deactivation , our computations predicted that the FCL and the connector-mediated pathway typically generate a delayed deactivation response compared to the direct regulation circuit ( Figure 5B , D ) . When cells were grown for 2 h in a medium containing 20 µM Mg2+ and then switched to non-inducing conditions at time 0 , deactivation was notably slower in the FCL than in the direct regulation circuit and was correlated with the expression persistence displayed by the connector-mediated pathway ( Figure 5F ) . These results are in agreement with the previously obtained experimental data on the connector-mediated pathway dynamics [2] . Finally , the output levels promoted by the FCL were generally higher than those for the connector-mediated pathway ( Figure 5E , F ) , consistent with our theoretical prediction regarding the contribution of two positive regulation branches ( Equation 16 in Text S1 ) .
The level at which a gene is transcribed depends on the cis features of the gene promoter , which govern its interactions with RNA polymerase and regulatory proteins , as well as on the architecture that determines the levels and activity of these proteins . We have identified a novel regulatory architecture–termed FCL–that mediates activation of the polymyxin B resistance gene pbgP by the PhoP protein in K . pneumoniae . The FCL is characterized by two branches of regulation: a direct branch where the PhoP protein directly promotes pbgP transcription by binding to the pbgP promoter , and an indirect branch in which the PhoP-dependent PmrD protein activates the PmrA protein , which , in turn , binds to the pbgP promoter . The FCL structure was inferred from the following findings . First , expression of the connector protein PmrD is activated in low Mg2+ in a PhoP-dependent fashion . Second , the PhoP-mediated activation of pmrD transcription appears to be direct because the pmrD promoter harbors a PhoP box ( Figure 3A ) . Third , growth in low Mg2+ activates two pbgP promoters: one that is PhoP-dependent , but PmrA- and PmrD-independent , and another one that is PhoP- , PmrA- , and PmrD-dependent ( Figure 4A ) . And fourth , the PhoP and PmrA proteins bind to the pbgP promoter region ( Figure 4B and Figure S3 ) . The FCL may represent an intermediate stage between direct control ( Figure 1B ) and the connector-mediated pathway ( Figure 1A ) . From the point of view of regulatory logic , the FCL would appear to be a redundant circuit because any one of the two activation branches is sufficient to promote pbgP expression ( Figure 4A ) . Such a “redundancy” also characterizes the FFL ( Figure 1D ) , one of the most abundant network motifs identified in bacteria [1] , [3] , [24] . However , the presence of an extra branch of regulation confers special dynamic properties on these two designs . The FCL acts as a sign-sensitive delay element , promoting large deactivation delays but no ( or very small ) activation delays ( Figure 5A , B , E , F ) . The ability of the FCL to promote sign-sensitive delays can be explained by its architecture ( Figure 1C ) . Fast activation is due to the presence of a direct activation branch ( as in a direct regulation circuit ( Figure 1B ) ) , which distinguishes the FCL from the connector-mediated pathway exhibiting longer activation delays associated with the necessity to synthesize the PmrD protein ( Figure 5C , E ) [2] . At the same time , the indirect branch of the FCL guarantees pbgP expression persistence upon deactivation ( Figures 5 , S4 , S5 , and S6 ) , which , as with the connector-mediated pathway [2] , is likely due to the PmrD protein made before the cells were switched to non-activating conditions . In addition , our results indicate that the FFL promotes only relatively small deactivation delays , which is in contrast to the large delays that are typical of the FCL ( Figure 5B ) . The presence of two branches of activation in the FCL results in higher pbgP expression levels compared with the connector-mediated pathway ( Equation 16 in Text S1; Figure 5E , F ) . Additional insights into the functionality of the FCL might be provided by dynamics studies in the stochastic ( single-cell ) setting [30] as demonstrated for the FFL [25] . The discovery of the novel PmrD-mediated architecture–the FCL–suggests a plausible parsimonious scenario for the evolution of Mg2+-dependent polymyxin B resistance in enteric bacteria . First , the Klebsiella and Salmonella lineages diverged after their common ancestor had split from the Yersinia lineage ( Figure 2 ) . Second , PmrD homologs are present in all species derived from this common ancestor , but in none of the remaining species ( Figure 2 ) . And third , the pbgP promoter of Serratia marcescens , which is a close relative of the immediate ancestor of Klebsiella , harbors both PhoP and PmrA boxes ( Figure 2 ) . It is thus conceivable that the pmrD gene was “invented” or horizontally acquired by the common ancestor of Salmonella , Klebsiella , Shigella , and Escherichia [31]–[33] . After diverging from the Klebsiella lineage , the ancestral lineage of Salmonella , E . coli and Shigella would have lost the direct branch of pbgP activation by the PhoP protein , as none of these species harbor a PhoP box in the pbgP promoter . The hypothesized transition from the FCL design utilized by K . pneumoniae to the connector-mediated pathway operating in S . enterica might have obeyed the need to avoid overproduction of PmrA-activated gene products . Indeed , hyperactivation of the PmrA/PmrB system can have detrimental effects , such as increased susceptibility to the detergent deoxycholate [34] and to the antimicrobial peptide protamine ( E . A . Groisman , unpublished results ) . Apparently , this need had a substantial influence on the connector-mediated pathway as S . enterica evolved a negative feedback loop to repress PmrD production [12] , thereby preventing excessive expression of PmrA-activated genes . The activation delays , which result from elimination of the direct regulation branch , are in the case of S . enterica relatively small [2] . Thus , the circuit's responsiveness , while somewhat decreased , appears to be sufficient for survival in the specific niche occupied by this bacterium . The evolution of connector-mediated pathways is driven by changes both in the connector protein genes and in the transcriptional regulatory interactions . Genes encoding connectors can undergo rapid sequence and functional divergence , resulting in novel regulatory architectures . For example , diversifying selection on the PmrD protein has resulted in the majority of E . coli natural isolates lacking the ability to express PmrA-activated genes in response to the signals activating the PhoP/PhoQ system [23] . Likewise , the divergence of the iraP promoter sequence between S . enterica and E . coli results in the inability of the E . coli connector IraP to inhibit the degradation of the alternative sigma-factor RpoS in low Mg2+ , whereas the S . enterica IraP performs this function because it is produced under such conditions [35] . Bacterial genetic regulatory circuits are shaped by the properties of the specific environments that bacterial species occupy [36] . It is plausible that emergence of connector-mediated regulation , which leads to persistence of expression of the polymyxin B resistance operon pbgP under the conditions of low Mg2+ ( Figure 5B , D , F ) , contributed to the ability of K . pneumoniae and S . enterica to survive in soil environments [37] , [38] . ( Notably , Y . pestis , which lacks the connector protein PmrD , is reported to survive in soil only for short periods of time [39] . ) Indeed , polymyxin B is present in soil as a result of natural activity of some bacteria [40] . Additionally , the lipopolysaccharide ( LPS ) modifications brought about by the pbgP operon products confer resistance to metal ions such as Fe3+ and Al3+ , which are abundant in soil [41] . This could explain the advantage of activating pbgP under high Fe3+ conditions sensed by the PmrA/PmrB system [20] . The benefit of pbgP induction by low Mg2+ ( sensed by the PhoP/PhoQ system ) may come from the fact that Mg2+ normally neutralizes the negative charges in the LPS [42]; thus , when the levels of Mg2+ are low , the LPS is chemically modified by PmrA-activated gene products that neutralize these negative charges [2] . It is likely that the rapid activation and delayed deactivation of pbgP , as well as the heightened pbgP expression level promoted by the FCL architecture ( Figure 5E , F ) , contribute to the lifestyle of K . pneumoniae , including its ability to survive in soil for extended times [37] . Environmental selection of genetic regulatory circuits can be analyzed within the framework of cost–benefit theory [43] , [44] . For example , it has been shown that the FFL with AND logic has a selective advantage over the direct regulation circuit ( with an AND-gate ) in environments where the duration distribution for an input pulse is sufficiently broad ( both long and short pulses are probable ) [43] . Because the FCL is expected to act as a strong sign-sensitive delay element ( stronger than the FFL ) ( Figure 5A , B ) , it is conceivable that the FCL is the preferred design in environments where delayed activation and rapid deactivation result in a selective disadvantage .
To isolate the RNA used in the S1 nuclease assay ( Figure 4A ) , overnight cultures of K . pneumoniae grown in N-minimal medium containing 10 mM Mg2+ were washed and diluted 1∶50 into 50 ml of N-minimal medium containing either 10 µM MgCl2 , 10 mM MgCl2 or 10 µM MgCl2 and 100 µM FeSO4 . Total RNA was extracted from early-logarithmic phase cultures ( OD600 , 0 . 250 ) with the MasterPure RNA purification kit ( Epicentre Technologies ) according to the manufacturer's recommended protocol . Double stranded DNA probes to the pbgP promoter regions of K . pneumoniae were generated by PCR using the primers 3249 ( 5′-TTCGTGACAGGAACGCATCT′-3′ ) and 3250 ( 5′-GGGCGCGAAAAAGGCAAAAA-3′ ) . S1 nuclease reactions were performed as described previously [12] . Hybridization products were analyzed by electrophoresis on a 6% polyacrylamide , 7 . 5 M urea gel and compared with Maxam-Gilbert A+G DNA ladders generated from the appropriate DNA probe . Assays were performed in triplicate . DNase I footprinting was performed as described previously [12] . The K . pneumoniae pbgP promoter region probe was generated as described in Materials and Methods . The S . enterica PhoP and PmrA proteins were purified as described previously [45] . Samples were analyzed by electrophoresis on a 6% polyacrylamide , 7 . 5 M urea gel and compared with a Maxam-Gilbert A+G DNA ladder generated from the same DNA probe . K . pneumoniae strains harboring the pAG , pAG-rpsM , pAG-pmrDKlebsiella plasmids were grown in N-minimal media , pH 7 . 7 or 5 . 8 , containing 38 mM glycerol with either 10 µM MgCl2 , 10 mM MgCl2 or 10 µM MgCl2 and 100 µM FeSO4 and supplemented with 10 µg/ml tetracycline . GFP expression was analyzed following 4 hours of growth at 37°C using a Becton Dickinson fluorescent-activated cell sorter . Assays were performed in triplicate . Error bars ( Figure 3B ) indicate standard deviation . Identification of protein orthologs and putative transcription factor binding sites is described in Text S1 . For phylogenetic reconstruction , the amino acid sequences encoded by three housekeeping genes ( gapA , groEL and gyrA ) were concatenated to infer the molecular phylogeny for the eight enteric species [46] ( Figure 2 ) . Sequences were aligned using ClustalX and subjected to maximum parsimony and nonparametric bootstrap resampling analysis as implemented in PAUP* ( version 4 . 0b10 ) . The tree was rooted with Pseudomonas aeruginosa as the outgroup . To test pmrD transcription ( Figure S2 ) , RNA was isolated from K . pneumoniae strains EG13127 , EG13129 and EG15289 , and the quantification of pmrD mRNA levels were performed as described [47] with the following modifications: aliquot of cells was taken at 1 hour post-induction , and the PCR analysis was performed using Fast SYBR Green Master Mix and a 7500 Fast Real-Time PCR System ( Applied Biosystems , Foster City , CA ) . The following primers were used in the real-time PCR analysis ( 5′ to 3′ ) : 7873 ( TCTGCCGCGTCGTGC , pmrD forward ) , 7874 ( CAATCTCTGCGATCATCTCCAG , pmrD reverse ) , 8813 ( TTGACGTTACCCGCAGAAGAA , rrs , forward ) , 8816 ( GCGCTTTACGCCCAGTAATT , rrs , reverse ) . Data were normalized with the values corresponding to 16S RNA , and represent five independent experiments with the highest and lowest outliers omitted . Error bars ( Figure S2 ) correspond to standard deviation . The activation and deactivation experiments ( Figure 5E , F ) with the S . enterica strains 14028s , EG17353 and EG17354 , including pbgP mRNA isolation and quantification using real-time-PCR , were performed as described [2] with the following modifications: the reverse transcription reaction was run with ∼6 . 5 ng total RNA , and the PCR analysis was performed using a 7500 Fast Real-Time PCR System ( Applied Biosystems , Foster City , CA ) . Activation time-course measurements done over larger time intervals have produced results similar to those shown in Figure 5E . In the PCR reaction , the following primers were used ( 5′ to 3′ ) : 6522 ( TGATGTCGGACTTTTTGCCTT , pbgP , forward ) , 6523 ( GCTCTTCCGCGCCCAT , pbgP , reverse ) , 3023 ( CCAGCAGCCGCGGTAAT , rrs , forward ) , 3024 ( TTTACGCCCAGTAATTCCGATT , rrs , reverse ) . Data were normalized with the values corresponding to 16S RNA . Measurements were done in duplicate; error bars ( Figure 5E , F ) correspond to standard deviation . The mathematical models of the FFL , FCL and direct regulation circuit are systems of ordinary differential equations ( ODEs ) that describe concentration dynamics for the main chemical components of the three regulatory circuits . The FCL model comprises five ODEs for the PmrD , PmrA , PmrA-P , the PmrD/PmrA-P complex , and pbgP mRNA concentrations ( Equations 1–5 in Text S1 ) . The FFL and direct regulation models consist of three equations each; the equations describe changes in the concentrations of PmrA , PmrA-P , and pbgP mRNA ( Equations 6–11 in Text S1 ) . In all models , the concentration of PhoP-P is an external variable representing the main input; the chemical reactions are modeled by using mass action kinetics , and transcriptional control is described with sigmoidal functions [2] , [48] . The activation dynamics of PhoP-P was modeled using piecewise Hermite interpolating polynomials fitted to the experimental data for PhoP-P activation dynamics [47]; deactivation dynamics was modeled with an exponential decay function ( see Text S1 ) . The balance of phosphorylation and dephosphorylation rates for PmrA ( and for protein Y of the FFL , Figure 1D ) represented the second input of the circuits; we consider the situations when this input is strongly activated ( high phosphorylation rate ) or mildly activated ( low phosphorylation rate ) . For all computational experiments , the initial concentrations ( at time 0 ) were the steady-state concentrations corresponding to the PhoP-P level at time 0 . All computations were performed in MATLAB R2007a ( MathWorks , Natick , MA ) . In delay distribution computations , the delays were defined as the differences between the activation and deactivation times for the FCL ( or FFL ) and those for the direct regulation circuit . Activation time was defined as the time required to reach an activation level equal to inactive level+ ( activatedlevel−inactive level ) /10; deactivation time was defined in an analogous way . Activation and deactivation delays correspond to situations when the PhoP-P input of the circuits was activated and deactivated , respectively . The delay distributions for the FCL ( Figure 5A , B , C , D ) were simulated as follows: parameter values for the models in the simulations were sampled independently from uniform distributions over intervals provided in Table S2 . While the real-life parameter value distributions for the genetic regulatory systems are unknown , in our choice of uniform distributions we followed the established methodology of statistical analysis for biochemical pathways [49] . A pair of randomly generated parameter sets , one for the FCL and the other one for the direct regulation circuit , was accepted or rejected depending on whether the model outputs for these models satisfied certain filtering criteria ( Text S1 ) . The purpose of filtering was to retain only the parameter sets that rendered functional regulatory circuits [3] . The pairs of parameter sets were generated randomly until the number of accepted pairs was equal to 1000 . These parameter sets were used to calculate model trajectories necessary for the estimation of activation and deactivation delays of the FCL with respect to the direct regulation circuit . The delay distributions for the FFL ( Figure 5A , B , C , D ) were simulated in an analogous fashion . To test the robustness of the simulation results , we applied alternative sampling strategies ( used to produce Figures S4–S6 ) , which , along with the details of our simulation procedures , are described in Text S1 . | A regulatory protein can activate the expression of a target gene either directly , i . e . , by binding to the gene's promoter , or indirectly , i . e . , by altering the expression of regulators , which , in turn , bind to the target gene's promoter and induce or inhibit its transcription . Indirect regulatory circuits can contain multiple components and functional elements , such as feedforward and feedback loops . The complex structure of indirect regulation raises the question of its evolutionary origins . Here , we study the dynamic and evolutionary properties of regulatory architectures that involve members of the recently emerged class of bacterial proteins termed connectors . Such proteins post-translationally modulate the activity of two-component systems and phosphorelays , which constitute the prevalent form of bacterial signal transduction . We describe a novel connector-mediated regulatory circuit that combines the structural and functional properties of direct and indirect regulation . Our results indicate that this architecture is the evolutionary link between direct and connector-dependent regulatory designs . | [
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"biol... | 2008 | Evolution and Dynamics of Regulatory Architectures Controlling Polymyxin B Resistance in Enteric Bacteria |
Cutaneous leishmaniasis ( CL ) is a vector-borne parasitic disease characterized by the presence of one or more lesions on the skin that usually heal spontaneously after a few months . Most cases of CL worldwide occur in Southwest Asia , Africa and South America , and a number of cases have been reported among troops deployed to Afghanistan . No vaccines are available against this disease , and its treatment relies on chemotherapy . The aim of this study was to characterize parasites isolated from Canadian soldiers at the molecular level and to determine their susceptibility profile against a panel of antileishmanials to identify appropriate therapies . Parasites were isolated from skin lesions and characterized as Leishmania tropica based on their pulsed field gel electrophoresis profiles and pteridine reductase 1 ( PTR1 ) sequences . Unusually high allelic polymorphisms were observed at several genetic loci for the L . tropica isolates that were characterized . The drug susceptibility profile of intracellular amastigote parasites was determined using an established macrophage assay . All isolates were sensitive to miltefosine , amphotericin B , sodium stibogluconate ( Pentostam ) and paromomycin , but were not susceptible to fluconazole . Variable levels of susceptibility were observed for the antimalarial agent atovaquone/proguanil ( Malarone ) . Three Canadian soldiers from this study were successfully treated with miltefosine . This study shows high heterogeneity between the two L . tropica allelic versions of a gene but despite this , L . tropica isolated from Afghanistan are susceptible to several of the antileishmanial drugs available .
Cutaneous leishmaniasis ( CL ) is a vector-borne parasitic disease characterized by one or more sores or nodules on the skin that often heal spontaneously after a few months , resulting in scar formation . This disease has been frequently diagnosed in military personnel who were returning from duty in Southwest Asia [1] , [2] , with several outbreaks observed in troops deployed to Iraq [3] and Afghanistan [2] , [4] . Currently , Kabul is believed to be the largest focus of CL worldwide , having an estimated incidence of 67 , 500 new cases per annum [5] . Whereas CL in Iraq has been mostly caused by Leishmania major , CL in Afghanistan can either be due to Leishmania tropica or Leishmania major [6] , and differences in clinical features have been observed between the two species . Notably , L . tropica tends to cause more chronic infections and may rarely progress to a systemic form of the disease termed viscerotropic leishmaniasis , a situation requiring special attention [7] . There is a lack of consensus about the best therapeutic options for the treatment of CL , mainly due to the lack of properly controlled clinical trials [8] . Because of the self-healing nature of the illness , the treatment of CL depends on several factors such as the site and number of lesions , the aetiology of the disease , and personal preferences . One of the main therapeutic options that has been used for the treatment of CL for many years relied on the local or systemic administration of pentavalent antimony [9] . Because Leishmania species are susceptible to heat , the local application of radio frequency to generate heat at the site of the lesions was also shown to yield cure rates equivalent to systemic pentavalent antimony [10] , [11] . Nonetheless , the availability of effective oral treatments would constitute attractive therapeutic options against CL , and there is evidence of benefit for the use of oral triazoles like itraconazole and fluconazole against L . tropica and L . major , respectively [8] . Miltefosine , another orally administered drug , was shown to be an effective treatment against visceral leishmaniasis in India [12] and cutaneous leishmaniasis in South America [13] , but there is only limited data about its efficacy against CL in Southwest Asia [14]–[16] . In this report , we describe the molecular characterization and in vitro drug susceptibility profiles of Leishmania parasites isolated from four Canadian soldiers suffering from CL after returning from Afghanistan . Primary treatment based on oral fluconazole failed to improve the appearance of lesions in three of them . We show that L . tropica was responsible for the lesions in every patient and that the parasites are highly heterogeneous but nonetheless remained sensitive to most known antileishmanials .
The skin biopsies were taken after appropriate informed consent was obtained , and as part of the routine patient care . Leishmania parasite isolates were submitted for susceptibility testing in order to assist in the clinical management of individuals with suboptimal response to fluconazole . No additional samples or procedures were done . Fresh tissue samples obtained through biopsy of the skin lesions were collected from three Canadian soldiers who returned from duty in southern Afghanistan with suspected CL lesions at the Department of Medical Microbiology and Infectious Diseases of the University of Manitoba in Winnipeg . Samples were submitted to culture , pathological examination , and PCR analyses . The histology revealed the presence of granulotomatous inflammation . The isolates identified as 017102 , 431462 , and 072218 underwent routine clinical laboratory studies at the National Reference Center for Parasitology in Montreal , QC . An additional skin lesion sample ( identified as 18693 ) was collected from a Canadian soldier also returning from Afghanistan and suspected of suffering from CL at the CHUQ in Quebec , QC . Parasites were isolated from the biopsy in SDM-79 medium supplemented with 20% heat-inactivated fetal calf serum , 5 µg/ml hemin and 10 µM biopterin at pH 7 . 0 and 25°C . The molecular characterization of parasites was done at the Centre de Recherche en Infectiologie du Centre de Recherche du CHUL , Quebec , QC . The L . tropica strains 175 and 482 , isolated from Iranian patients [17] , and L . tropica MHOM/SU/74/K27 , obtained from the ATCC , were used as reference isolates . Agarose blocks containing Leishmania cells were prepared as described [18] . Briefly , cells were resuspended in HEPES buffer at a density of 5×108 cells/ml and mixed with low-melting-point agarose . Cells were lysed in the presence of 0 . 5 M EDTA ( pH 9 . 5 ) , 1% SLS , and proteinase K ( 500 µg/ml ) . Their chromosomes were electrophoresed by a BioRad ( Hercules , California , United States ) contour-clamped homogeneous electric field ( CHEF ) mapper for separating 0 . 1–1 . 0 Mbp DNAs over a period of 27 h . Chromosomes were visualized after ethidium bromide staining . Species identification and heterogeneity were studied by sequencing the pteridine reductase 1 ( PTR1 ) , glucose-6-phosphate isomerase ( GPI ) , nucleoside hydrolase 1 ( NH1 ) , dihydrofolate reductase-thymidylate synthase ( DHFR-TS ) , stearic acid desaturase ( SAD ) , mannose phosphate isomerase ( MPI ) , aspartate aminotransferase ( ASAT ) , 6-phosphogluconate dehydrogenase ( PGD ) , glucose-6-phosphate dehydrogenase ( G6PDH ) and cytochrome B ( CYTB ) genes . Genomic DNA was extracted from mid-log phase parasites using the DNAzol reagent ( Invitrogen ) as described by the manufacturer . PCR reactions were performed in 50 µl using the primers listed in Table S1 and contained 100 ng of total gDNA , 50 pmol of each primer , 0 . 2 mM of dNTPs , 1 . 5 mM of MgCl2 and 5 U of Taq polymerase . Amplification was performed in 30 cycles , each cycle using the following conditions: denaturation at 94°C for 1 min , annealing at 58°C for 1 min and extension at 72°C for 1–2 min ( depending on the size of PCR products ) . A final extension was performed at 72°C for 5 min . PCR products were migrated on agarose gel , purified with the QIAquick Gel Extraction Kit ( Qiagen ) and sequenced with an ABI Prism 3100 DNA sequencer . The PTR1 sequences obtained were compared to those of eight Leishmania reference isolates using the Lasergene Software ( DNASTAR , Inc . ) . Multiple sequence alignments were performed on the amino acid sequence of the PTR1 coding region using ClustalW [19] with the default settings . The resulting multiple alignments were subjected to phylogenetic analysis using the neighbor-joining method [20] with the Poisson correction distance method as implemented in the MEGA3 . 1 software [21] . The reliabilities of each branch point were assessed by the analysis of 1000 bootstrap replicates . The 50% inhibitory concentrations ( IC50 ) of drugs on macrophages were established by using the 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ( MTT ) assay . Briefly , THP-1 cells were differentiated in 96-well flat-bottom microtiter plates in a volume of 100 µl of complete RPMI 1640 medium supplemented with 10% heat-inactivated fetal calf serum and 20 ng/ml phorbol myristate acetate . Plates were incubated at 37°C in the presence of 5% CO2 for 3 days . Drugs were added at 1/10 of the final concentration in a volume of 10 µl in duplicate . After 96 h of incubation , 10 µl of MTT ( 10 mg/ml ) was added to each well and plates were further incubated for 4 h . The enzymatic reaction was stopped by the addition of 100 µl of 50% ethanol-10% sodium dodecyl sulfate . The plates were incubated for an additional 30 minutes under agitation at room temperature before reading the optical density at 570 nm with a 96-well scanner . The viability assays were performed in duplicates . As a control , the cytotoxicity of reagents used to solubilize the drugs was determined and no substantial toxicity was found . L . tropica promastigote parasites were transfected with the firefly luciferase-containing vector pSP1 . 2 LUC αHYGα as previously described [22] . THP-1 cells were differentiated by incubation at 37°C in the presence of 5% CO2 for 3 days in complete RPMI 1640 medium supplemented with 10% heat-inactivated fetal calf serum and 20 ng/ml phorbol myristate acetate . The cells were washed with pre-warmed medium and subsequently infected with L . tropica promastigotes at a parasite/macrophage ratio of 15∶1 for 3 h . Non-internalized parasites were removed by several washes . Luciferase activity was measured after 4 days of exposure to fluconazole , Pentostam , amphotericin B , miltefosine , paromomycin or Malarone as described elsewhere [23] .
Parasites recovered from biopsy samples of four Canadian military personnel who returned from deployment in Kandahar , Afghanistan , with clinical manifestations of CL were characterized by pulsed field gel electrophoresis ( PFGE ) . PFGE conditions optimized for the analysis of larger chromosomes did not show any major differences in the chromosome numbers and sizes between our isolates ( Fig . 1A ) and revealed that they were genetically closely related to the ATCC L . tropica strain MHOM/SU/74/K27 and to a L . tropica isolate recovered from a patient suffering from CL in Iran ( L . tropica 175 ) [17] , [24] . The analysis of smaller chromosomes revealed considerable karyotype differences , however ( Fig . 1B ) . The isolates were further characterized on the basis of the pteridine reductase 1 ( PTR1 ) sequence [17] . PCR fragments of the coding region of PTR1 were amplified from genomic DNA extracted from the clinical isolates and sequenced . The sequences generated were compared to those of eight Leishmania reference strains and were shown to be closely related to L . tropica sequences ( data not shown ) . A neighbor-joining phylogenetic analysis generated from the translated PTR1 sequences further confirmed that the four CL strains derived from Canadian soldiers were L . tropica parasites ( Fig . 2 ) . The PTR1 nucleotide sequences of the four L . tropica isolated from Canadian soldiers revealed the presence of single nucleotide polymorphisms ( SNPs ) at five different positions ( Table 1 ) . The changes in nucleotide were conservative ( Table 1 ) , and the same polymorphisms were also observed in two other strains of L . tropica ( strains 482 and MHOM/SU/74/K27 ) that we have analyzed ( Table 2 ) . The heterozygous gene sequences were detected as split peaks in the chromatogram generated by the sequencing of the PTR1 locus in both directions , using DNA extracted from populations of parasites . This type of polymorphism was not observed when sequencing the PTR1 gene of L . infantum and L . major ( Table 2 ) . To assess whether these polymorphisms corresponded to population heterogeneity or to parasite heterozygocity , the PTR1 sequence of cloned parasites from three distinct L . tropica strains ( MHOM/SU/74/K27 , 072218 and 482 ) was determined ( 3 clones for each strain ) . Again , the same PTR1 polymorphisms were detected in every clone tested . Each allele was detected at a frequency of 50% , which suggests that parasites were harbouring two distinct alleles . The same two alleles were detected in every L . tropica strain studied ( Table 1 ) , which is reflected by the homogenous clustering of the L . tropica isolates in the neighbour-joining phylogenetic analysis ( Fig . 2 ) . To assess the extent of the genetic polymorphism in our panel of L . tropica strains , nine additional genes located on distinct chromosomes , i . e . glucose-6-phosphate isomerase ( GPI ) , nucleoside hydrolase 1 ( NH1 ) , dihydrofolate reductase-thymidylate synthase ( DHFR-TS ) , stearic acid desaturase ( SAD ) , mannose phosphate isomerase ( MPI ) , aspartate aminotransferase ( ASAT ) , 6-phosphogluconate dehydrogenase ( PGD ) , glucose-6-phosphate dehydrogenase ( G6PDH ) , and cytochrome B ( CYTB ) , were also sequenced in clones of strains 482 , 072218 and MHOM/SU/74/K27 . Heterozygous sites were observed at every locus ( Table 1 ) except for MPI and CYTB ( Table 2 ) . In addition , the same alleles were detected in every L . tropica strains studied , except for the ASAT gene , which had three additional polymorphic sites common to L . tropica 482 and 072218 that were absent from L . tropica MHOM/SU/74/K27 ( Table 2 ) , and the PGD , G6PDH , and SAD genes , which were not polymorphic in L . tropica MHOM/SU/74/K27 ( Table 2 ) . In contrast to the PTR1 locus , however , the polymorphisms observed at the NH1 , DHFR-TS , SAD , PGD , G6PDH , ASAT , and GPI loci were non-conservative for at least one position , with some genes having several non-conservative heterozygous sites ( Table 1 ) . These polymorphisms were not observed in other species outside L . tropica ( Table 2 ) . To rule out polymerase errors , the sequencing of at least three independent PCR products was done for each particular nucleotide position , as it would be quite rare that the presence of the same SNPs in different PCR reaction comes from polymerase errors . Moreover , for some genes ( for example PTR1 ) , PCR products were TA cloned and independent clones were sequenced for the confirmation of the SNPs using primers within the pGEM-T easy vector , hence ruling out the possibility of polymerase errors . Three of the four Canadian soldiers were initially treated with oral fluconazole without any clinical improvements . To establish whether the therapeutic failure was due to parasites unresponsive to fluconazole and to test whether these parasites were sensitive to classical antileishmanials , in vitro susceptibility testing was performed with the four L . tropica isolates using the human monocyte cell line THP-1 and recombinant parasites transfected with the firefly luciferase gene . The latter system is a convenient and rapid quantitative method to monitor the growth of intracellular parasites [23] . Growth was compared between mock-treated parasites and parasites exposed to fluconazole , Pentostam , amphotericin B , miltefosine , and paromomycin . We observed that L . tropica intracellular amastigotes were insensitive to fluconazole at concentrations that were achievable in vitro ( Table 3 ) . Fluconazole was highly active when we tested it against C . albicans ( results not shown ) . In contrast , when compared to our L . tropica reference strain 175 , all L . tropica isolates tested were sensitive to amphotericin B , miltefosine , Pentostam , and paromomycin as intracellular parasites ( Table 3 and Figure 3 ) . The only exception was L . tropica 18693 , for which a slightly higher miltefosine EC50 was observed in comparison to the other L . tropica strains ( Table 3 ) . One soldier infected by CL had to travel to Central Africa and was on Malarone anti-malaria prophylaxis . Intriguingly , his cutaneous lesions cicatrized while being on Malarone prophylaxis , so we tested whether Malarone had any activity against L . tropica isolates using the intracellular amastigote assay . The toxicity of Malarone to the THP-1 cells was first established by MTT viability assay , and this cell line was found to display a Malarone IC50 of 32 ug/ml . No THP-1 cytotoxicity was observed for Malarone concentrations up to 10 ug/ml . Using drug concentrations below 10 ug/ml , we found that three L . tropica strains were sensitive to Malarone as intracellular amastigotes ( Table 3 ) , including the strain that was isolated from the patient whose CL regressed during Malarone prophylaxis .
We describe here the drug susceptibility and molecular characterization of L . tropica isolates derived from Canadian soldiers returning from Afghanistan . The isolates were identified as L . tropica by phylogenetic studies based on the PTR1 sequence , an approach proven to be useful for the molecular identification of Leishmania species [17] . Moreover , the PFGE karyotypes of the recovered Leishmania parasites were similar to those of L . tropica reference strains . This is consistent with epidemiological data that showed the majority of CL cases in Afghanistan being due to this species [5] , [25] . Interestingly , the sequence of PTR1 revealed several SNPs in distinct L . tropica isolates . This phenomenon appeared to be widespread across the L . tropica genome , as it was also observed at other genetic loci on different chromosomes . Most of the loci analyzed code for proteins that are part of the panel of enzymes used for the characterization of Leishmania species by multilocus enzyme electrophoresis [26] . Among these , six ( GPI , NH1 , ASAT , G6PDH , PGD , and MPI ) were further shown to be useful markers for the molecular characterization of Leishmania strains and species [27]–[29] . CYTB was chosen as a mitochondrial gene representative , since it has also been reported to be phylogenetically informative [30] , [31] . The SAD and DHFR-TS loci were randomly chosen . DNA sequencing of cloned parasites revealed a number of heterozygous sites at these loci , some of which led to non-conservative changes . Although the prevailing mode of reproduction of Leishmania appears to be clonal [32] , heterozygosity at several sites within genes or at distinct loci is suggestive of genetic exchange between strains [27] , and this phenomenon has previously been observed in other Leishmania species [27] , [28] , [32]–[35] . Most of these studies used microsatellite markers with high mutation rates as indicators of heterozygocity , however , and this is the first report about extensive heterozygocity within coding regions in L . tropica . Nonetheless , the heterozygosity of the L . tropica isolates appears to be fixed , the same alleles being found among strains for most of the loci studied except for the reference L . tropica MHOM/SU/74/K27 . This is suggestive of clonal propagation within foci of endemicity and is consistent with the anthroponotic mode of transmission of L . tropica in urban and peri-urban environments of Afghanistan [5] . L . tropica parasites were known to display genetic heterogeneity at the population level [34]–[37] and to be responsible for a spectrum of clinical manifestations including cutaneous , chronic , or viscerotropic leishmaniasis [7] . Unfortunately , the small number of isolates available for analysis prevented correlating heterozygocity with clinical data or drug susceptibility . However , this seems to be unique to L . tropica since other species did not show this level of allelic polymorphism ( Table 2 ) . Although CL is generally self-limiting , the complexity of the clinical spectrum associated with L . tropica infections emphasizes the need for treatment . Evidence suggested that the disruption of ergosterol biosynthesis by oral azoles is an effective treatment against CL [38] , [39] . However , a species-specific effect was found to be important to the clinical outcome conferred by azole molecules , with itraconazole and fluconazole being more active against L . tropica and L . major , respectively [8] . Here , the failure of oral fluconazole to improve the appearance of cutaneous lesions was indeed explained by the intrinsic resistance of our L . tropica isolates , the amastigote parasites being insensitive to the highest fluconazole concentration achievable in vitro using an established intracellular assay ( Table 3 ) . All isolates were sensitive to the other drugs tested , however , with the exception of Malarone , for which variable levels of susceptibility were observed . While anecdotal , CL regressed in one soldier during Malarone prophylaxis . Although we cannot exclude spontaneous healing , it might be worthwhile to evaluate the usefulness of this drug against CL in properly controlled experiments . Miltefosine is an orally administered antileishmanial approved for the treatment of visceral leishmaniasis in India [12] , with demonstrated efficacy against CL in some regions of South America [13] . In contrast , mostly sporadic data have been reported regarding the efficacy of miltefosine against CL in Southwest Asia [14]–[16] . Based on the results of our drug susceptibility screening , soldiers were treated with miltefosine and healing of their CL lesions was observed [40] . The patient treated with Malarone received miltefosine but elected to discontinue therapy due to abdominal pain and in the face of a contracting lesion . The other soldiers tolerated medication well and lesions resolved at follow up . | Cutaneous leishmaniasis ( CL ) is a vector-borne parasitic disease transmitted by the bite of sandflies , resulting in sores on the skin . No vaccines are available , and treatment relies on chemotherapy . CL has been frequently diagnosed in military personnel deployed to Afghanistan and returning from duty . The parasites isolated from Canadian soldiers were characterized by pulsed field gels and by sequencing conserved genes and were identified as Leishmania tropica . In contrast to other Leishmania species , high allelic polymorphisms were observed at several genetic loci for the L . tropica isolates that were characterized . In vitro susceptibility testing in macrophages showed that all isolates , despite their genetic heterogeneity , were sensitive to most antileishmanial drugs ( antimonials , miltefosine , amphotericin B , paromomycin ) but were insensitive to fluconazole . This study suggests a number of therapeutic regimens for treating cutaneous leishmaniasis caused by L . tropica among patients and soldiers returning from Afghanistan . Canadian soldiers from this study were successfully treated with miltefosine . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"biology"
] | 2012 | Genetic Polymorphisms and Drug Susceptibility in Four Isolates of Leishmania tropica Obtained from Canadian Soldiers Returning from Afghanistan |
In most cell types , mitosis and cytokinesis are tightly coupled such that cytokinesis occurs only once per cell cycle . The fission yeast Schizosaccharomyces pombe divides using an actomyosin-based contractile ring and is an attractive model for the study of the links between mitosis and cytokinesis . In fission yeast , the anaphase-promoting complex/cyclosome ( APC/C ) and the septation initiation network ( SIN ) , a spindle pole body ( SPB ) –associated GTPase-driven signaling cascade , function sequentially to ensure proper coordination of mitosis and cytokinesis . Here , we find a novel interplay between the tetratricopeptide repeat ( TPR ) domain–containing subunit of the APC/C , Nuc2p , and the SIN , that appears to not involve other subunits of the APC/C . Overproduction of Nuc2p led to an increase in the presence of multinucleated cells , which correlated with a defect in actomyosin ring maintenance and localization of the SIN component protein kinases Cdc7p and Sid1p to the SPBs , indicative of defective SIN signaling . Conversely , loss of Nuc2p function led to increased SIN signaling , characterized by the persistent localization of Cdc7p and Sid1p on SPBs and assembly of multiple actomyosin rings and division septa . Nuc2p appears to function independently of the checkpoint with FHA and ring finger ( CHFR ) –related protein Dma1p , a known inhibitor of the SIN in fission yeast . Genetic and biochemical analyses established that Nuc2p might influence the nucleotide state of Spg1p GTPase , a key regulator of the SIN . We propose that Nuc2p , by inhibiting the SIN after cell division , prevents further deleterious cytokinetic events , thereby contributing to genome stability .
The eukaryotic cell cycle is composed of an invariant sequence of events , in which DNA replication precedes mitosis and mitosis in turn precedes cytokinesis [1] . Cells also possess mechanisms to ensure that DNA replication , chromosome segregation and cytokinesis occur only once per cell cycle [2 , 3] . While much progress has been achieved in understanding the temporal regulation of DNA synthesis and chromosome segregation , the mechanisms by which cytokinesis is restricted to once per cell cycle has not been fully explored . In recent years , the fission yeast Schizosaccharomyces pombe has emerged as an attractive organism for the study of cytokinesis and its relation to the rest of the cell cycle [4] . S . pombe cells , like animal cells , divide utilizing an actomyosin based contractile ring [5–15] . The actomyosin ring is assembled upon entry into mitosis and prior to chromosome segregation and it contricts after chromosome segregation and mitotic spindle disassembly [16 , 17] . In fission yeast , a signaling pathway known as Septation Initiation Network ( SIN ) is a key determinant of cytokinesis [4 , 7 , 18] . While loss of SIN function leads to an inability of cells to undergo cytokinesis , ectopic activation of SIN allows cytokinesis to proceed even prior to entry into mitosis [3 , 7 , 19] . SIN is a GTPase-driven signaling cascade that comprises a small GTPase , Spg1p , three protein kinases: Cdc7p , Sid1p , Sid2p , and their associated factors: Cdc14p , Mob1p [20–25] . This pathway is negatively regulated by a two-component GTPase Activating Protein ( GAP ) , Cdc16p and Byr4p [26–29] . Components of the SIN localize to the spindle pole body ( SPB ) by association with the scaffold proteins Cdc11p and Sid4p [30–33] . In addition , two of the SIN components , Sid2p and Mob1p , also localize to the cell division site during cytokinesis [24 , 25] . Although most components of the SIN are detected at the SPB throughout the cell cycle , Cdc7p and Sid1p are detected at the SPB only during mitosis and cytokinesis [18 , 22 , 34 , 35] . In particular , since the localization of Sid1p to the SPB depends on cyclin B proteolysis and cyclin dependent kinase ( CDK ) inactivation , it has been proposed that the SIN might link cytokinesis to mitotic exit [22] . Proteolysis of cyclin B ( and thereby the inactivation of CDK activity ) is triggered by a multisubunit E3 ubiquitin ligase , termed the anaphase-promoting complex/cyclosome ( APC/C ) [36 , 37] . After completion of cytokinesis , the SIN pathway is inactivated , as characterized by the presence of Cdc16p and Byr4p on the SPB and the concomitant loss of Cdc7p and Sid1p from the SPB [18 , 38 , 39] . How the SIN pathway is inactivated following cytokinesis and how its precocious activation in interphase is prevented , while the CDK activity is low , remain poorly understood . In this study , we uncover a function of Nuc2p , a Tetratricopeptide repeat ( TPR ) -domain containing subunit of APC/C , in preventing inappropriate cytokinetic events following cell division . While loss of Nuc2p function leads to uncontrolled septation , the current study and a previous study have shown that overproduction of Nuc2p leads to inhibition of cytokinesis [40] . Nuc2p appears to exert its effects on cytokinesis by modulating the nucleotide state of the Spg1p-GTPase and thereby down regulating the SIN .
Previous studies have shown that cells overproducing Nuc2p are not defective for mitotic exit , but die as elongated multinucleate cells [40] . Based on this observation it has been suggested that Nuc2p , a TPR-domain containing subunit of APC/C , is an inhibitor of septation [40] . To understand how Nuc2p functions in cytokinesis , we investigated the organization and function of the cytokinetic machinery in the nuc2-663 mutant . As previously reported , nuc2-663 cells , upon shift to the restrictive temperature , undergo cytokinesis in the absence of chromosome segregation [41] . Interestingly , we observed that approximately 28% of nuc2-663 cells ( n = 112/404 ) displayed multiple septa , indicating that the cytokinetic machinery might be constitutively active in these cells ( Figure 1A and 1B ) . Since hyperactivation of SIN signaling also results in multiseptated cells , we tested the possibility that SIN signaling is up-regulated in the nuc2-663 mutant . Previous studies have shown that Cdc7p and Sid1p are localized to the SPBs in mitotic cells and are lost from the SPBs upon completion of cytokinesis in fully septated cells [18] . Accordingly , in wild-type cells , Cdc7p and Sid1p were detected at the SPBs in cells undergoing cytokinesis and were not detected at the SPBs in fully septated cells ( Figure 1C , wt panel ) . Interestingly , in nuc2-663 cells shifted to the restrictive temperature , Cdc7p and Sid1p persisted at the SPBs even after completion of septation ( Figure 1C , nuc2-663 panel ) . In addition , Sid1p was routinely detected on both SPBs in nuc2-663 cells shifted to the restrictive temperature ( Figure 1C , indicated by arrowhead ) . This effect of persistent localization of Cdc7p and Sid1p in septated cells was similar to that observed in cells defective for Cdc16p function [22 , 35] . The protein kinase Sid2p and its binding partner Mob1p appear to constitute the most downstream elements of the SIN . In wild type cells , Sid2p and Mob1p localize to the SPBs throughout the cell cycle and are also detected at the cell division site during cytokinesis [24 , 25] . Interestingly , in the nuc2-663 mutant , Sid2p was detected at the division site in fully septated cells ( Figure 1C , nuc2-663 panel; indicated by arrow ) . Furthermore , additional ring like structures containing Sid2p were also detected in these cells ( Figure 1C ) . Similar localization of Sid2p was not seen in wild type cells , in which the medial localization of Sid2p was lost upon completion of septation ( Figure 1C , wt panel ) . The persistent localization of Sid1p and Cdc7p to the SPBs and Sid2p at the division site in nuc2-663 cells suggested that Nuc2p might be involved in down regulation of SIN function following cytokinesis . The nuc2-663 mutant has been shown to be capable of partially proteolyzing the mitotic B-type cyclin , Cdc13p [42] . Since cyclin proteolysis as well as CDK inactivation is sufficient for completion of cytokinesis , it was possible that the multiseptated phenotype we observed was purely due to partial proteolysis of cyclin B and/or re-entry into a subsequent round of mitosis . To address if Nuc2p played a role in the regulation of cytokinesis after execution of its function in mitotic exit , we inactivated Nuc2p function after passage through anaphase . To this end , we generated a nuc2-663 nda3-KM311 strain so as to allow for the inactivation of Nuc2p function following anaphase . The nda3+ gene encodes the β-subunit of the tubulin heterodimer and the nda3-KM311 mutant results in cold-sensitivity and lethality [43] . The nda3-KM311 allows the synchronization of cells at metaphase due to the activation of the spindle checkpoint , caused by the loss of β-tubulin function . The product of the cold-sensitive allele nda3-KM311 resumes its ability to polymerize into microtubules within 6 minutes of return to the permissive temperature [43] and allows progression through chromosome segregation and mitotic exit . The nuc2-663 nda3-KM311 and nda3-KM311 ( as a control ) cells were first cultured at 19 °C to inactivate Nda3p function . Under these conditions , at least 50% of cells arrested at metaphase due to the activation of the spindle assembly checkpoint . Subsequently , these cells were shifted to 32 °C to inactivate Nuc2p function and to reactivate Nda3p function ( Figure 2A ) . Since reactivation of Nda3p function ( based on the immediate assembly of the mitotic spindle and 40% of total cells formed an elongated anaphase spindle after 45 min at 32 °C ) occurred more rapidly than the inactivation of Nuc2p , nuc2-663 nda3-KM311 ( and the control nda3-KM311 ) underwent anaphase and septation . Interestingly , maintenance of nuc2-663 nda3-KM311 at 32 °C led to the accumulation of cells that either formed multiple septa , or deposited excessive septum material in the vicinity of the first septum ( Figure 2B and 2C , cells i–iii ) . Cells with mis-oriented ectopic septa were also frequently observed ( Figure 2C , cell iv ) . These effects were observed less frequently in the control nda3-KM311 cells ( Figure 2B ) . In addition to division septa , additional actomyosin rings ( as visualized with antibodies against the myosin light chain Cdc4p ) were also detected in nuc2-663 nda3-KM311 cells ( Figure 2D ) . These rings were either orthogonally placed or mis-oriented ( Figure 2D , Cdc4p panel ) . The fact that cells with actomyosin rings contained interphase microtubule arrays ( Figure 2D , tubulin panel ) , suggested that the assembly of additional actomyosin rings was not linked to re-entry into mitosis . The formation of excessive septum after one round of cytokinesis raised the possibility that SIN activation in nuc2-663 mutant might be able to trigger cytokinesis in interphase cells as has been observed in cdc16-116 mutants [3 , 39] . To test whether this is the case , nuc2-663 mutant and cdc16-116 mutant were treated with hydroxyurea to block cells in S phase and shifted to restrictive temperature to heat inactivate Nuc2p and Cdc16p functions . As previously reported , heat inactivation of Cdc16p function led to formation of septated cells during interphase as indicated by the interphase microtubule organization and presence of septum in these cells ( Figure 2E , cdc16-116 panel , and 2F ) . The nuc2-663 mutant at restrictive temperature , however , was similar to wild-type cells and did not assemble division septa ( Figure 2E , wt and nuc2-663 panels , and 2F ) . The observation that interphase arrested nuc2-663 cells did not assemble division septa suggested that Nuc2p might play an important role in preventing additional rounds of cytokinetic events following septation . Since Nuc2p is a subunit of the APC/C , it was possible that the entire APC/C might function to inhibit inappropriate cytokinesis . Alternatively , it was possible that Nuc2p regulated cytokinesis in a manner independent of other subunits of the APC/C . To distinguish between these possibilities , we assayed the ability of cut9-665 and lid1-6 ( two essential components of the APC/C [44 , 45] ) mutants to accumulate multiple septa . As before , we tested for the presence of multiple and excessive septa upon shift of synchronous nda3-KM311 cut9-665 and nda3-KM311 lid1-6 to conditions that inactivated the APC/C components . In these experiments , nda3-KM311 cut9-665 and nda3-KM311 lid1-6 behaved similar to nda3-KM311 cells and did not accumulate multiple and excessive septa ( Figure S1 ) . Furthermore , activation of APC/C by overexpression of Slp1p ( Figure S1B ) or Ste9p [46 , 47] did not lead to septation defects , indicating that APC/C activation per se does not lead to septation defects . Collectively , these experiments suggested that the inhibition of inappropriate cytokinesis by Nuc2p might not require the other subunits of the APC/C . We have found that loss of Nuc2p function leads to persistent localization of SIN components , such as Cdc7p , Sid1p , and Sid2p , even after completion of septation . Previous studies have shown that overproduction of Nuc2p leads to defective cytokinesis , although the basis of this effect remained unknown [40] . To gain insights into the mechanism by which Nuc2p overproduction inhibits cytokinesis , a strain of yeast was generated in which the thiamine-repressible promoter , nmt1 , was used to replace the endogenous nuc2 promoter ( hereafter referred to as nmt1-nuc2 ) . The nmt1-nuc2 strain was used in all overexpression experiments . The nmt1-nuc2 strain resembled wild type cells in morphology upon growth in medium supplemented with thiamine . To analyze the overexpression phenotype more thoroughly , we generated an nmt1-nuc2 strain that expresses the nuclear marker Uch2p-GFP ( Ubiquitin C-terminal hydrolase fused to GFP ) . When this strain was grown in medium containing thiamine , cells were found to contain either a single nucleus or two nuclei ( Figure 3A ) , depending on the cell cycle stage . Upon removal of thiamine from the medium ( leading to overexpression of Nuc2p ) , a high proportion of cells accumulated two or more nuclei ( Figure 3A ) . In particular , after 18 h of derepression of the nmt1 promoter , more than 40% of the cells contained 4 or more nuclei , while another 40% of the cells contained two nuclei , most of which were of a post-mitotic configuration . We also attempted to overproduce Nuc2p in synchronous cultures , but these experiments did not lead to significant synchrony at the point of maximal induction of nmt1 promoter , due to the long amount of time required to fully derepress the nmt1 promoter ( unpublished data ) . It is known that cytokinesis in fission yeast requires the function of an actomyosin ring [16] . We considered the possibility that overexpression of Nuc2p affects the assembly of actin and/or myosin components at the cell division site . To address this question , we grew the nmt1-nuc2 cells in medium lacking thiamine to overexpress Nuc2p and stained these cells with phalloidin . In cells overexpressing Nuc2p , F-actin assembled into ring structures in mitotic cells , as in control cells , suggesting that the recruitment and assembly of F-actin into the actomyosin ring was not affected ( Figure 3B ) . In addition , we tested the effect of Nuc2p overexpression on the localization of the FCH domain protein , Cdc15p , which is essential for actomyosin ring maintenance and septum assembly [10 , 48 , 49] . As in the case of F-actin , cells were able to assemble Cdc15p rings upon Nuc2p overexpression ( Figure 3C ) . To observe the dynamics of ring assembly and constriction process , we performed time-lapse microscopy in nmt1-nuc2 cells expressing Rlc1p-GFP ( regulatory light chain of myosin fused to green fluorescent protein ) and Uch2p-GFP as the markers for actomyosin ring and nucleus , respectively . Under conditions of Nuc2p overexpression , cells assembled Rlc1p rings overlying the position of the interphase nuclei ( Figure 3D , −T panel ) . However , these rings were not stable and collapsed in late anaphase and failed to constrict , leading to failure of septation ( Figure 3D , −T panel ) . In contrast , the Rlc1p rings in control cells constricted normally at the end of mitosis leading to the formation of a division septum ( Figure 3D , +T panel ) . Collectively , these studies established that the presence of excess Nuc2p led to defects in maintenance of actomyosin rings in late anaphase , leading to failure of division septum assembly . Previous studies have shown that the mutants defective in the septation initiation network ( SIN ) assemble actomyosin rings , but these rings collapse late in anaphase , leading to a defect in division septum assembly [16 , 34] . Since we observed a phenotype similar to that seen in SIN mutants in cells overexpressing Nuc2p and a complementary phenotype in nuc2-663 cells , it was possible that overexpression of Nuc2p might affect SIN signaling . To determine whether this was the case , we examined the localization of SIN components Sid4p , Cdc11p , Spglp , Cdc7p , Sid1p , and Sid2p upon Nuc2p overexpression . Of these proteins , Sid4p , Cdc11p , Spg1p , and Sid2p are known to localize to the SPBs throughout the cell cycle , while Cdc7p and Sid1p localize during cytokinesis to one of the two SPBs ( which has been shown to contain GTP-bound Spg1p ) [18] . The localization of Sid4p , Cdc11p , and Spg1p to the SPBs was unaltered upon overexpression of Nuc2p ( Figure 4A and our unpublished observation ) . Interestingly , the localization of Cdc7p and Sid1p was severely affected in mitotic cells overexpressing Nuc2p . Cdc7p was detected at the SPBs in 12/61 cells with actomyosin rings , while Sid1p was detected in 6/71 cells with rings ( Figure 4A and 4D , −T and Induced panels ) , compared to uninduced cells in which at least 80% of cells with actomyosin rings contained Cdc7p-GFP and GFP-Sid1p at the SPBs ( Figure 4D ) . Furthermore , in Nuc2p overproducing cells that did display Cdc7p and Sid1p at the SPBs , the signal of Cdc7p and Sid1p was significantly reduced compared to that observed in control cells ( Figure 4A ) . Consistent with the loss of Cdc7p and Sid1p from the SPBs , the fluorescence signal of the most downstream kinase of the SIN , Sid2p , was dramatically reduced upon overexpression of Nuc2p ( Figure 4B and quantification of Sid2p-GFP fluorescence in relation to Sid4p-mRFP is shown in Figure 4C ) . Next , we determined if overexpression of Nuc2p affected the recruitment or the maintenance of SIN components at the SPBs . To this end , we performed time lapse microscopy to observe the localization of Cdc7p during cytokinesis . We found that Cdc7p was recruited to one of the SPBs during actomyosin ring constriction in cells grown in the presence of thiamine ( Figure 4E , top panel; indicated by arrowhead ) . In contrast , in cells overexpressing Nuc2p , Cdc7p was not detected at the SPBs throughout the cell division cycle and the actomyosin ring disassembled at the end of mitosis ( Figure 4E , bottom panel ) . Taken together , these studies suggested that overexpression of Nuc2p affects the recruitment of the SIN components , Cdc7p and Sid1p to SPB . Since Nuc2p is a component of the APC/C , and participated in degradation of molecules regulating mitosis , we considered the possibility that overproduction of Nuc2p might lead to instability and degradation of one or more of the SIN components . We therefore tested the steady state levels of the SIN components Cdc7p , Sid1p , and Sid2p in cells overexpressing Nuc2p . We found that the steady state levels of Cdc7p , Sid1p , and Sid2p were not significantly altered upon overexpression of Nuc2p ( Figure 5A; and unpublished observations on Sid1p ) . Thus , Nuc2p appears to affect the SPB localization of the SIN kinases but not their stability , consistent with our findings that other subunits of APC/C were not required for the inhibition of septation by Nuc2p . It has been shown that the small GTPase Spg1p binds to Cdc7p and recruits this protein kinase to SPBs during mitosis . Cdc7p binds preferentially to GTP-bound Spg1p , which is thought to be the activated form of this GTPase [35 , 50] . The fact that the recruitment of Cdc7p to the SPBs was affected in Nuc2p overexpressing cells suggested that the binding between Spg1p and Cdc7p might be affected upon Nuc2p overproduction . To test if this was the case , we overproduced Nuc2p in cells expressing Spg1p-GFP and Cdc7p-3HA and performed co-immnunoprecipitation experiments to look for a physical interaction between Spg1p and Cdc7p . Control immunoprecipitation experiments were carried out from lysates prepared from cells expressing Cdc7p-3HA ( but not Spg1p-GFP ) . Immunoblotting with HA antibodies showed that the level of Cdc7p was comparable in both strains used as well as under both conditions ( + or – thiamine ) used ( Figure 5B ) . Immunecomplexes generated with GFP antibodies contained Cdc7p when cells were grown in medium containing thiamine ( Figure 5B , +T of IP panel ) . Interestingly , immunecomplexes generated with GFP-antibodies from cells overproducing Nuc2p contained very little or no Cdc7p-HA ( Figure 5B , −T of IP panel ) . Immune complexes generated with GFP antibodies from cells expressing Cdc7p-HA , but not Spg1p-GFP , did not contain Cdc7p-HA , establishing the specificity of the immunoprecipitation procedure . These experiments established the binding between Cdc7p-3HA and Spg1p-GFP was interrupted in cells overproducing Nuc2p . We have shown that cells overproducing Nuc2p are able to localize Sid4p , Cdc11p , and Spg1p to the SPBs . In contrast , the localization of Cdc7p , Sid1p and Sid2p-Mob1p was significantly reduced/altered upon overproduction of Nuc2p . Cdc7p has been shown to preferentially bind to GTP-bound Spg1p [35] , and we have shown that overproduction of Nuc2p leads to a reduction/failure of physical interaction between Spg1p and Cdc7p . Thus it was possible that overproduction of Nuc2p led either to the inactivation of putative guanine nucleotide exchange factors ( GEF ) for Spg1p or to the persistent activation of the two-component GAP ( Byr4p-Cdc16p ) , leading to the maintenance of Spg1p in its inactive GDP bound form . Since proteins related to the budding yeast Lte1p ( functions as a GEF for the Tem1p-GTPase , which is related to fission yeast Spg1p ) [51] , have not been identified in fission yeast , we considered the possibility that overproduction of Nuc2p might cause activation of Byr4p-Cdc16p . We tested this idea by overproduction of Nuc2p in cdc16-116 mutants and assayed the ability of these cells to localize Cdc7p to the SPB and to assemble division septa ( Figure 6A ) . Inactivation of Cdc16p function in cells overexpressing Nuc2p resulted in the recruitment/maintenance of Cdc7p on SPBs ( Figure 6B , bottom panel ) , whereas this was not detected in the presence of Cdc16p function ( Figure 6B ) . More than 90% of the tetranucleate cells were able to undergo cytokinesis and septation ( n = 190/203 , Figure 6C and 6D ) . In contrast , less than 25% of tetranucleate cells in which Cdc16p was functional underwent cytokinesis and septation ( n = 44/204 , Figure 6C and 6D ) . Although other interpretations are possible , we favour the idea that overproduction of Nuc2p might lead to activation of Byr4p-Cdc16p , thereby to the inability to maintain SIN function and septation . Previous studies have shown that overproduction of Dma1p , a fission yeast protein related to human CHFR , leads to inhibition of SIN function and defective cytokinesis [52 , 53] . Since overproduction of Nuc2p also led to defects in SIN signaling , we tested if the cytokinesis-inhibitory effect upon Nuc2p overproduction depended on Dma1p . To this end , Nuc2p was overproduced in cells lacking Dma1p . Cytokinesis defects were observed in control ( dma1+ ) cells as well as dma1Δ cells overproducing Nuc2p ( Figure 7A ) . This experiment suggested that the SIN-inhibitory effect caused by overproduction of Nuc2p was independent of Dma1p . To firmly establish if this was the case , double mutants defective in nuc2 and dma1 were constructed . Interestingly , whereas nuc2-663 and dma1Δ cells were able to form colonies at 26 °C , the double mutants were unable to do so ( Figure 7B ) . Staining of DNA and septum material revealed that the nuc2-663 dma1Δ , but not nuc2-663 , cells displayed an aberrantly septated phenotype , similar to that observed in nuc2-663 cells at higher temperatures ( Figure 7C ) . Immunostaining of nuc2-663 dma1Δ cells with antibodies against Cdc4p and tubulin revealed that 65 . 3% of septated cells contained segregated DNA , interphase microtubules and actomyosin cables/rings ( Figure 7D , type I ) . Furthermore , 34 . 7% of the septated cells contained unsegregated DNA , interphase microtubules , and actomyosin rings/cables ( Figure 7D , type II ) . Since the multi-septate phenotype is largely detected in cells in which chromosome segregation is not affected , we conclude that the effect of septation in the nuc2-663 dma1Δ double mutants is not purely due to synthetic effects on APC/C function . Collectively , these studies established that Nuc2p and Dma1p inhibited the SIN by different mechanisms .
Previous studies have characterized the role of Nuc2p , a TPR-containing subunit of APC/C , in the regulation of progression through events of mitosis [44 , 54 , 55] . Previous studies have also proposed that Nuc2p might function as an inhibitor of septation , although the mechanism of this inhibition was not investigated [40] . In the current study we show that Nuc2p prevents septum assembly by inhibiting the septation initiation network ( SIN ) . Cells defective for nuc2 display a “cut” phenotype , characterized by septum assembly in the absence of proper segregation of chromosomes [41] . Interestingly , we have found that prolonged incubation of nuc2-663 mutants at the restrictive temperature leads to the accumulation of cells with multiple septa . The specific effects of Nuc2p on septation are particularly clear in nuc2-663 mutants shifted to the restrictive temperature after execution of Nuc2p function in mitosis . The multiseptate phenotype of nuc2-663 is reminiscent of that displayed by cells in which the SIN is constitutively activated , such as in cells defective in Cdc16p , which together with Byr4p functions as a GTPase activating protein for the Spg1p-GTPase [28 , 29] . As in the case of cdc16-116 cells , the SIN components Cdc7p and Sid1p are retained at the SPBs even after septum assembly in nuc2-663 cells . In addition , Sid2p , considered to be the most downstream element of the SIN , is retained at the cell cortex after septum assembly in cdc16-116 and nuc2-663 mutants . Interestingly , overproduction of Nuc2p has been shown to cause defects in cytokinesis ( [40] and this study ) . Furthermore , overproduction of Nuc2p leads to defects in actomyosin ring maintenance and localization of Cdc7p , Sid1p , and Sid2p to the SPB , suggestive of defective SIN signaling . Thus , while nuc2 mutants phenocopy cdc16 mutants ( in which SIN is constitutively active ) , overproduction of Nuc2p leads to a phenotype indistinguishable from that displayed by SIN-defective mutants . These results imply that Nuc2p might be a bona fide inhibitor of the SIN . Interestingly , whereas activation of SIN , by loss of Cdc16p function , promotes events of cytokinesis ( actomyosin ring and division septum assembly ) in premitotic interphase arrested cells , the loss of Nuc2p function in premitotic interphase does not lead to actomyosin ring and septum assembly . Thus , although Nuc2p appears to be an inhibitor of the SIN , Nuc2p might inhibit SIN specifically after cell division . How does Nuc2p inhibit the SIN ? Cells overexpressing Nuc2p phenocopy mutants defective in SIN function , which is a Spg1p GTPase driven signaling cascade . Interestingly , the localization of the upstream components of SIN , Cdc11p , Sid4p and Spg1p are not altered upon overproduction of Nuc2p . The increased level of Nuc2p , however , specifically affects the localization of downstream kinases: Cdc7p , Sid1p and Sid2p . Since Spg1p , but not its effectors Cdc7p and Sid1p , remains at the SPB , after Nuc2p overexpression , it can be speculated that the Spg1p in cells overexpressing Nuc2p is GDP-bound . Consistent with this , we have found that the physical interaction between Cdc7p and Spg1p is dramatically reduced in cells overproducing Nuc2p . Two possibilities can be envisaged for the mechanisms that maintain Spg1p in GDP-bound form . First , it is possible that overexpression of Nuc2p inhibits a putative guanine nucleotide exchange factor ( GEF ) for Spg1p , thereby preventing the loading of GTP onto Spg1p and the activation of SIN signaling . The second possibility is that Nuc2p promotes the activation of the two-component GTPase activating protein ( GAP ) , Cdc16p-Byr4p , leading to the conversion of GTP-Spg1p into GDP-Spg1p . We do not favour the first possibility since no guanine nucleotide exchange factor for Spg1p has been identified to date . Our genetic analysis ( restoration of Cdc7p localization and septum assembly in cdc16-116 cells overproducing Nuc2p ) points to the second possibility that Nuc2p might function as an activator of Cdc16p-Byr4p . However , it also remains possible that Nuc2p might prevent the conversion of GDP-Spg1p to GTP-Spg1p by acting as a GDP dissociation inhibitor ( GDI ) . Future studies should test these possibilities . What is the physiological function of the inhibition of SIN by Nuc2p ? Both cell division and cell growth utilize actin cytoskeleton and its modulators . Persistent SIN signaling might sequester actin cytoskeleton at the division site and thereby block actin remodeling at the growth sites . Thus , prevention of cytokinesis after cell division might ensure proper growth polarity establishment . Several mechanisms have been uncovered in fission yeast that prevent cytokinesis by inhibiting SIN [4] . During metaphase , the human CHFR protein homolog in fission yeast , Dma1p , inhibits SIN signaling to prevent precocious cytokinesis [52 , 53] . Prior to mitotic exit ( in metaphase and anaphase A ) , the inhibition of SIN function by high CDK activity ensures the coordination of mitosis and cytokinesis [22] . Our studies show that Nuc2p inhibits SIN function after completion of cytokinesis . Genetic analysis suggests that Nuc2p and Dma1p , both inhibitors of SIN , might function independently of each other . Our study also points to an additional function for Dma1p after cell division in the prevention of SIN activation . Thus , it appears that high CDK activity and Dma1p inhibit SIN during metaphase , while Nuc2p and Dma1p are required to prevent SIN activation and inappropriate cytokinesis after completion of cell division ( Figure 8 ) . The current study shows that Nuc2p inhibits SIN signaling . However , it remains unclear whether Nuc2p acts independently or requires a specific set of APC/C components in order to inhibit septation . We have not been able to detect a multiseptate phenotype upon inactivation of function of two additional subunits of the APC/C , namely Cut9p and Lid1p , after passage through anaphase . These observations might suggest that Nuc2p , rather than the entire APC/C , is required for the inhibition of SIN following cell division . In addition , hyperactivation of APC/C by overexpression of Slp1p ( this study ) and Ste9p [46 , 47] does not lead to septation defects , suggesting that activation of APC/C does not lead directly to abnormal septation . In contrast , we have found that overproduction of Cut23p gives weak cytokinesis defects ( [56] and our unpublished observation ) , suggesting a role for the entire APC/C in prevention of inappropriate cytokinesis . Collectively , our present studies lean toward a specific role for Nuc2p ( rather than the entire APC/C ) in the inhibition of cytokinesis . However , additional studies with stronger alleles of other APC/C components , which inactivate faster than the currently available alleles , might be required to firmly conclude if Nuc2p inhibition of the SIN requires the entire APC/C .
S . pombe strains used in this study are listed in Table 1 . YES medium or Minimal medium with appropriate supplements were used to culture fission yeast cells . Strains were constructed by either random spore germination method or by tetrad dissection . The nmt1-nuc2 strain was created by transforming a DNA fragment generated by Polymerase Chain Reaction ( PCR ) using the forward primer: 5′CAATAACAACCACCTGTTTGTACCCACATGTTTTTGTTGACATTAACTCCCATCGTTTCCAAAACTTTAATAGATTTGTCGAATTCGAGCTCGTTTAAAC3′ and the reverse primer: 5′CGTTCTGAATAAAAAATTGAATTATCATAATTCTGATTATCAATGCAATACCATATTAAACATTTCAATCGATCTGTCATCATGATTTAACAAAGCGACTATA3′ . The positive clone was selected using Geneticin ( Sigma ) and confirmed by PCR . To overexpress Nuc2p in the nmt1-nuc2 strain , cells were first grown in minimal medium containing 15 μM thiamine . The culture was then washed three times and re-inoculated into medium without thiamine , to induce Nuc2p expression . To arrest cells in S phase , cells were treated with 12 mM hydroxyurea ( HU; Sigma ) for 6 h prior to any experimental manipulation . To synchronize nuc2-663 mutant at metaphase , nda3-KM311 ( control ) and nuc2-663 nda3-KM311 mutants were first grown at 27 . 5 °C in YES medium supplemented with 1 . 2 M sorbitol . Cells were washed three times with YES before shifting to 19 °C in YES medium to achieve metaphase-arrest . To fuse Rlc1p and Sid4p with monomeric red fluorescence protein ( mRFP ) , DNA fragments containing either the rlc1 or sid4 were first amplified by PCR and were then cloned into pJK210-mRFP . The products were linearized using restriction enzymes and transformed into wild type S . pombe by the lithium acetate method [57] . DNA fragments containing C-terminal sequences of rlc1 and sid4 were amplified by PCR with primer pairs MOH461 5′GAGAGCTGGTACCTGAATGTTCTCTTCGAAGGAA3′ and MOH462 5′GAGAGTGCCCGGGATTGCTATCTTTTGACCC3′; MOH2460 5′CGGGGTACCTAAGGAGATGAATGCCACAATACAATC3′ and MOH2461 5′TCCCCCGGGCAAACTACGTTTTTTAAGCTCCC3′ , respectively , for cloning . Immunoprecipitation and Western blotting was performed as described [58] . Briefly , cell extracts were prepared by glass bead disruption and solubilised in buffer containing 1% Triton X-100 , 150 mM NaCl , 2 mM EDTA , 6 mM Na2HPO4 , 4 mM NaH2PO4 , and complete protease inhibitors ( Roche Diagnostics ) . Cell extracts were then clarified by centrifugation at 14 , 000 rpm for 10 min at 4 °C . To immunoprecipitate protein complex , 500 μl of soluble protein was incubated with 5 μl of -GFP antibodies for 1–2 h at 4 °C . Protein A-Sepharose beads ( 100 μl , Amersham Biosciences ) were then added to the antigen-antibody immunecomplex and incubated for 45 min at 4‘°C . After six washes with buffer containing 1% Triton X-100 , the beads were resuspended in SDS-PAGE loading buffer and heated at 95 °C for 5 min . The Protein A-Sepharose beads were spun down at 14 , 000 rpm for 5 min and the supernatants were subjected to SDS-PAGE . To detect GFP , Myc , or HA-tagged proteins , antibodies recognizing GFP , Myc ( Sigma ) , or HA ( Sigma ) were used to probe the PVDF membranes containing separated proteins . To visualize the F-actin cytoskeleton , cells were fixed with 7% formaldehyde and stained with Alexa Fluor-488 phalloidin ( Molecular Probes ) . Septum/cell wall and DNA were stained with aniline blue ( Sigma ) and 4′ , 6-diamidino-2-phenylindole ( DAPI ) , respectively . For immunofluorescence studies , cells were fixed either with formaldehyde or with methanol . Cells were then processed as described [59] . Antibodies against Cdc4p and β-tubulin were used to stain the actomyosin ring and microtubules , respectively . Images were captured using an Olympus IX71 microscope equipped with a Photometrics CoolSNAP ES camera . All images were processed with MetaMorph 6 . 1 . For confocal imaging , Zeiss LSM 510 confocal microscope equipped with a 63×/1 . 4NA PlanApo objective lens was used . | Cytokinesis is the process by which a mother cell is physically partitioned into two daughter cells . Cytokinesis is well coordinated with segregation of the genetic material to ensure that the genome is not damaged by the cell division apparatus . How untimely cytokinesis is prevented is not fully understood , and is a topic of current interest . Studies of the mechanisms of segregation of the genetic material and cytokinesis have benefited extensively from the use of the fission yeast Schizosaccharomyces pombe . In this study , we make the discovery that fission yeast Nuc2p , a protein previously known to form part of a multi-protein machine that regulates genome segregation , has a second function in regulating cytokinesis . Nuc2p appears to dampen the septation initiation network , which is an important signaling pathway that is essential for cytokinesis . Thus , Nuc2p prevents the occurrence of cytokinetic events prior to segregation of the genetic material and thereby contributes to genome stability . Since the multi-component machinery that Nuc2p forms part of , as well as Nuc2p itself , has relatives in essentially all eukaryotic cells , a similar mechanism might operate in other cells as well . | [
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] | 2008 | Nuc2p, a Subunit of the Anaphase-Promoting Complex, Inhibits Septation Initiation Network Following Cytokinesis in Fission Yeast |
Internalization of the pathogenic bacterium Pseudomonas aeruginosa by non-phagocytic cells is promoted by rearrangements of the actin cytoskeleton , but the host pathways usurped by this bacterium are not clearly understood . We used RNAi-mediated gene inactivation of ∼80 genes known to regulate the actin cytoskeleton in Drosophila S2 cells to identify host molecules essential for entry of P . aeruginosa . This work revealed Abl tyrosine kinase , the adaptor protein Crk , the small GTPases Rac1 and Cdc42 , and p21-activated kinase as components of a host signaling pathway that leads to internalization of P . aeruginosa . Using a variety of complementary approaches , we validated the role of this pathway in mammalian cells . Remarkably , ExoS and ExoT , type III secreted toxins of P . aeruginosa , target this pathway by interfering with GTPase function and , in the case of ExoT , by abrogating P . aeruginosa–induced Abl-dependent Crk phosphorylation . Altogether , this work reveals that P . aeruginosa utilizes the Abl pathway for entering host cells and reveals unexpected complexity by which the P . aeruginosa type III secretion system modulates this internalization pathway . Our results furthermore demonstrate the applicability of using RNAi screens to identify host signaling cascades usurped by microbial pathogens that may be potential targets for novel therapies directed against treatment of antibiotic-resistant infections .
Pseudomonas aeruginosa is one of the leading causes of nosocomial infections in humans . In the setting of pre-existing epithelial tissue damage and/or host immunocompromise , P . aeruginosa is able to cause severe infections of the respiratory and urinary tract , skin , and eye [1] . In addition , P . aeruginosa has a unique ability to cause chronic infections in the lungs of patients with cystic fibrosis , leading to end stage lung disease and death [2] . Like many gram-negative pathogens , P . aeruginosa possesses a type III secretion system ( T3SS ) that is critical to virulence in vitro and in vivo [1] . Through this apparatus P . aeruginosa secretes and translocates into the host cell bacterial effectors that subvert host cell functions . Four T3SS effectors have been identified in P . aeruginosa: ExoU is a potent phospholipase that causes rapid host cell death [3] , [4]; ExoY is an adenylate cyclase that induces cell rounding [5]; ExoS and ExoT are highly homologous bifunctional proteins , with N-terminal GTPase activating protein ( GAP ) domains and C-terminal ADP ribosyltransferase ( ADPRT ) domains . For both ExoS and ExoT , the GAP domain targets Rho family GTPases , including Rho , Rac1 , and Cdc42 [6]–[9] . In contrast , the substrate specificity of the ADPRT domains is distinct and non-overlapping [10] . While the ExoS ADPRT domain ADP ribosylates diverse proteins , such as Ras , Ral , Rabs , Rac1 , and Ezrin [11]–[14] , the ADPRT domain of ExoT primarily targets the SH2 domain of Crk family proteins [15] , [16] . Together , the activities of these T3SS effectors are critical for initial colonization and subsequent acute damage to the mucosal barrier , in part by causing disruption of the host cell cytoskeleton , breakdown of cell-cell junctions , and inhibition of wound healing [17]–[20] . The presence and/or production of T3SS effectors are variable amongst P . aeruginosa strains and may account for some of the phenotypic differences observed in different isolates . Indeed , almost no strain encodes all four effectors [21] . Approximately 25% of P . aeruginosa strains examined thus far encode only ExoU and ExoT [21] . These strains are cytotoxic and poorly internalized by epithelial cells , however isogenic mutants lacking these two effectors more efficiently enter host cells [22] . The remaining 75% of P . aeruginosa strains produce only ExoS and ExoT , actin-disrupting toxins that have been shown to cause cell death and inhibit bacterial internalization [18] , [23] . Despite the presence of ExoS and ExoT , these strains are efficiently internalized into epithelial cells [24] . Taken together , these observations demonstrate that all strains of P . aeruginosa are capable of entering host cells , suggesting a fundamental role of invasion in the pathogenesis of P . aeruginosa infections . The molecular mechanisms underlying P . aeruginosa invasion into non-phagocytic cells , such as those that line the mucosal barrier , are incompletely understood . P . aeruginosa entry is an actin-dependent process that involves Rho family GTPases [25] . Recent studies suggest that Phosphatidylinositol 3-kinase ( PI3K ) and its effector Protein kinase B/Akt , which act both upstream and downstream of Ras and Rho family GTPases [26] , are necessary for and activated upon internalization of P . aeruginosa into Madin Darby Canine Kidney ( MDCK ) cells [27] . P . aeruginosa entry also leads to activation of tyrosine kinases , such as Src [28] , [29] , and subsequent tyrosine phosphorylation of several host proteins , including Caveolin [30] . Some strains of P . aeruginosa are internalized through activation of acid sphingomyelinase and the release of ceramides in sphingolipid-rich rafts [31] . Reorganization of these rafts into larger signaling platforms is required for internalization of bacteria , induction of apoptosis , and the regulation of the cytokine response in infected cells [31] . While these studies are informative , a comprehensive understanding of P . aeruginosa internalization requires more extensive and far ranging approaches . The use of RNA interference ( RNAi ) to rapidly and efficiently inhibit the expression of proteins [32] affords the possibility of carrying out unbiased forward genetic screens to identify host proteins critical to P . aeruginosa invasion . Drosophila melanogaster with its relatively small , non-redundant but evolutionarily conserved genome , provides an ideal “genetic” host in which to study host-pathogen interactions . Drosophila readily takes up double stranded RNA ( dsRNA ) , allowing efficient inactivation of gene expression in whole flies as well as in Drosophila tissue culture cell lines . RNAi-based forward genetic screens in Drosophila S2 cells , a cell line derived from phagocytic hematopoietic cells [33] , have been used successfully to identify new genes involved in cell division , phagocytosis , and recognition of bacteria [34]–[38] . In this study , we establish that P . aeruginosa infection of S2 cells mimics key aspects of mammalian cell infection including type III secreted effector-mediated modulation of bacterial entry , suggesting a conserved mode of entry . We used a library of dsRNAs representing conserved genes involved in the regulation of the actin cytoskeleton to systematically identify host genes required for P . aeruginosa uptake in Drosophila S2 cells . Our forward genetic screen revealed an invasion pathway for P . aeruginosa that involves Abl tyrosine kinase , its target Crk , the small GTPases Rac1 , Cdc42 , and p21-activated kinase ( Pak1 ) . We further validated the role of this signaling cascade in mammalian cells employing chemical , genetic , and siRNA-based approaches . This Abl-dependent pathway has not previously been associated with P . aeruginosa internalization and our studies reveal new complexities in the modulation of this pathway by the T3SS proteins ExoS and ExoT . Together our results demonstrate the potential of using RNAi-based screens to identify host molecules that are important in the pathogenesis of P . aeruginosa and that may serve as novel drug targets for treating infections resistant to conventional antibiotics .
To conduct a functional genomic screen to identify host cell factors required for internalization of P . aeruginosa , we exploited the susceptibility of Drosophila S2 cells , a macrophage-like cell line , to RNAi-mediated gene inactivation . Using a standard aminoglycoside protection assay to quantify bacterial internalization , we initially established that P . aeruginosa invasion of Drosophila S2 cells mimics entry into mammalian cells by assaying two important characteristics . First , as with mammalian cells , Cytochalasin D , an inhibitor of actin polymerization , diminished entry of P . aeruginosa strain K ( PAK ) into S2 cells ( Figure 1A ) . PAK encodes the effector proteins ExoS , ExoT and ExoY , but lacks ExoU . Second , entry of PAKΔSΔT , an isogenic strain , in which the ExoS and ExoT genes have been deleted , into S2 cells was 2–8 fold more efficient than wild type PAK ( Figure 1B ) ; entry of PAKΔSΔT was also sensitive to cytochalasin D ( data not shown ) . This finding is consistent with the known anti-internalization activity of ExoS and ExoT in mammalian cells [18] , [24] . These results demonstrate that Drosophila S2 cells recapitulate important aspects of P . aeruginosa entry , including involvement of the actin cytoskeleton and translocation and functionality of the two T3SS effectors . To identify host gene products crucial for the internalization of P . aeruginosa , we screened a library of dsRNAs representing phylogenetically conserved genes of Drosophila melanogaster that are known regulators of the actin cytoskeleton [37] . Given the known requirement for the actin cytoskeleton in P . aeruginosa invasion , we reasoned that this approach would yield a high likelihood of identifying host genes essential to P . aeruginosa invasion . Drosophila S2 cells were treated with dsRNAs for 4 days and bacterial invasion assays with PAK were performed in triplicate three times . Invasion rates were normalized to S2 cell number for each dsRNA treatment to eliminate any apparent changes in invasion efficiency secondary to siRNA-mediated changes in cell number ( Table S1 & S2 ) . We chose to further study host proteins whose depletion reduced invasion by at least 33% , representing 36% of the dsRNAs tested ( Table S1 ) . For comparison , we also tested 23 random dsRNAs from a larger library and found that only 2 of 23 RNAs ( 9% ) reduced entry ( data not shown ) . These findings are consistent with our thesis that the subset of genes involved in regulating the actin cytoskeleton would be enriched for candidates involved in P . aeruginosa entry . We predicted that depleting proteins known to directly affect actin assembly would inhibit P . aeruginosa invasion . Indeed , RNAi-mediated inactivation of Capping protein beta ( Cpb ) , Kette , WASP , Sra-1 , Abi , SCAR , and the p20 subunit of the Arp2/3 complex reduced invasion ( Table S1 ) . We also identified PI3K and Protein kinase B/Akt , kinases that we have previously shown to be required for PAK entry into mammalian cells [27] . The identification of host genes whose depletion is predicted or has already been shown to modulate internalization confirmed the validity of this methodology . Interestingly , our screen identified several components of a signaling pathway that has not previously been implicated in P . aeruginosa invasion , including Abelson tyrosine kinase ( Abl ) , its target CT10 regulator of Kinase ( Crk ) , and p21-activated kinase ( Pak ) . The identification of multiple components within a pathway indicated that this pathway might be relevant to uptake of P . aeruginosa in host cells . The identification of Abl kinase and its target protein Crk was even more intriguing as Crk had previously been shown to be targeted by the P . aeruginosa anti-internalization factor ExoT [15] , although direct demonstration of the role of Crk in P . aeruginosa entry is lacking . We therefore further investigated the role of the Abl kinase pathway in the internalization of PAK into cultured mammalian epithelial cells using pharmacological , genetic , and biochemical approaches . The Abl family of non-receptor tyrosine kinases consists of two widely expressed members , Abl and Arg ( Abl2 ) [39] , [40] . Besides catalytic and protein-protein-interaction domains , Abl kinases contain a C-terminal actin-binding domain , a characteristic that is unique among all known tyrosine kinases . Abl kinases have been shown to regulate Rac1-dependent cytoskeletal dynamics that underlie protrusion formation in mammalian cells and have been implicated in the regulation of a number of cellular processes , including cell survival , proliferation , adhesion and motility [40] . Using Gleevec ( STI571 , imatinib ) , a well-characterized inhibitor of Abl tyrosine kinase activity [41] , [42] , we preliminarily assessed the role of Abl kinase in P . aeruginosa invasion into mammalian cells . Treatment with Gleevec inhibited PAK and PAKΔSΔT invasion into mammalian cells to the same extent in a dose-dependent manner without affecting bacterial adhesion , or host or bacterial viability ( Figure 2A and data not shown ) . Gleevec did not inhibit Salmonella typhimurium invasion or adhesion in HeLa cells ( Figure S1A ) . As Gleevec is not entirely specific for Abl/Arg kinases [43] , [44] , we confirmed these results by quantifying the entry of PAK and PAKΔSΔT into 3T3 fibroblasts derived from mice lacking both Abl and Abl-related kinase Arg [39] . Consistent with the known effects of ExoS and ExoT on bacterial entry into mammalian cells [18] , [24] , internalization of PAK into 3T3 fibroblasts was about 3 . 5-fold less efficient than internalization of PAKΔSΔT . Furthermore , entry of either strain in the Abl/Arg deficient cells was decreased to 60–70% compared to entry into parental cells ( Figure 2B ) . The absence of Abl kinases did not have an effect on bacterial binding ( data not shown ) . Entry of S . typhimurium into Abl/Arg depleted cells was unaffected ( Figure S1B ) . Finally , we demonstrated that siRNA-mediated depletion of Abl ( Figure 2C ) decreased invasion of PAK ( data not shown ) and PAKΔSΔT approximately 2-fold compared to untreated and control siRNA-exposed cells ( Figure 2D ) , but did not affect adhesion ( data not shown ) . Collectively , these results indicate that efficient invasion of P . aeruginosa into mammalian cells requires Abl tyrosine kinase activity . Crk is an SH2- and SH3-domain containing adaptor protein which has been shown to be involved in multiple cellular processes including phagocytosis , cell adhesion , cell migration , and immune responses [45] . CrkI and CrkII are splicing variants that differ by the presence of an additional C-terminal SH3 domain in CrkII and a tyrosine residue between the two SH3 domains . CrkII is phosphorylated by Abl kinase at tyrosine 221 , resulting in a conformational change that affects its subcellular localization and alters its ability to interact with other signaling effectors [46] . CrkI and II have also been shown to be the major targets of the ADPRT domain of the effector protein ExoT . ADP-ribosylation of arginine 20 of the SH2 domain of Crk by ExoT disrupts the interaction of this domain with binding partners [47] . However , a direct role for Crk in P . aeruginosa entry has not previously been demonstrated . Using RNAi , we tested whether Crk plays a role in P . aeruginosa entry into mammalian cells . Following depletion of CrkI and CrkII by dsRNA directed against both isoforms ( Figure 3A ) , invasion of PAK and PAKΔSΔT was reduced to 70±11% and 58±9% , respectively , compared to bacterial uptake in control RNAi-treated cells ( Figure 3B ) . CrkI/II depletion had no effect on adhesion of PAK to host cells ( data not shown ) or on internalization of S . typhimurium ( Figure S2 ) . Since Crk is a known target of ExoT , it might have been expected that depletion of Crk would affect invasion of PAKΔSΔT to a greater extent than invasion of the ExoT-expressing wild type strain . Our finding that invasion of both strains was decreased to similar extents may be explained by the observation that the effects of the translocated effector proteins are only apparent after a delay ( see below ) . The implication of these results will be discussed later in more detail . Altogether , our finding that depletion of Crk decreased PAK invasion into Drosophila S2 cells as well as into mammalian epithelial cells indicates that Crk is required for P . aeruginosa invasion and is consistent with the notion that ExoT inhibits internalization at least in part by disrupting Crk function . Having shown that Abl kinases and Crk are required for internalization , we tested Abl kinase activation by assaying CrkII phosphorylation in response to bacterial infection . Lysates of HeLa cells infected with PAK and PAKΔSΔT were immunoblotted with antibodies that specifically recognize total Crk or CrkII phosphorylated on tyrosine 221 ( Figures 3C and D ) . For both PAK and PAKΔSΔT , increased CrkII phosphorylation could be detected as early as 15 minutes post infection . In the case of PAKΔSΔT , CrkII phosphorylation increased over time and remained readily detectable up to 90 minutes post infection ( Figure 3C ) . In contrast , the fraction of CrkII phosphorylation in PAK-infected cells did not further increase after 15 minutes and was undetectable by 60 minutes post infection ( Figure 3D ) . These results suggest that upon binding , P . aeruginosa activates Abl , which leads to CrkII phosphorylation . Subsequent T3SS-dependent translocation of ExoS and/or ExoT by the wild type strain PAK inhibits further CrkII phosphorylation . While both effector proteins exhibit GAP activity towards Rac1 and Cdc42 [7] , [8] , [48] , signaling molecules that are likely downstream of Crk , ExoT is known to directly interfere with Crk function . Indeed , infection with isogenic PAK mutants lacking either ExoT or ExoS revealed that ExoT was responsible for the inhibition of PAKΔSΔT-induced CrkII phosphorylation ( Figure 3E ) . To further test whether Abl is responsible for the phosphorylation of CrkII upon infection with P . aeruginosa , HeLa cells were either treated with the Abl kinase inhibitor Gleevec ( Figure 3F ) or depleted of Abl by siRNA ( Figure 3G ) . Either treatment abrogated PAKΔSΔT-induced phosphorylation of CrkII , indicating that Abl is required for the PAKΔSΔT induced phosphorylation of CrkII . We determined whether phosphorylation of CrkII at tyrosine 221 is required for internalization of PAK by examining the effect of over-expression of either wild type CrkII or a non-phosphorylatable CrkII mutant ( CrkII-Y221F; [49] ) in HeLa cells on bacterial internalization . Each protein was over-expressed to similar levels ( Figure 3H ) . As shown in Figure 3I , over-expression of CrkII-Y221F resulted in a 34% reduction of the invasion rate of PAKΔSΔT compared to invasion in HeLa cells over-expressing wild type CrkII . Expression of CrkII-Y221F in HeLa cells would not be expected to completely abolish P . aeruginosa internalization as these cells still express endogenous CrkII . Nonetheless , these data demonstrate that phosphorylation of CrkII by Abl kinase is important for efficient P . aeruginosa internalization . The Rho family GTPases have previously been linked to Abl through genetic studies in Drosophila and loss-of-function studies in mammalian cells [40] , [50] . In addition , Rac1- dependent signaling has been shown to be regulated by CrkII , whose ability to interact with other signaling molecules is modulated upon phosphorylation [49] . Previous studies demonstrated that Rho-family GTPase activity is required for internalization of a different strain of P . aeruginosa , PA103 [25] . Although our initial RNAi screen suggested only minor effects of Rac1 and Cdc42 on the entry of PAK into S2 cells ( Table S1 ) , RNAi mediated depletion of either GTPases ( Figure 4A ) inhibited internalization of PAK into mammalian cells ( Figure 4B ) . We further tested the effect of Rac1 and Cdc42 depletion on PAKΔSΔT , PAΔS and PAKΔT . Entry of PAK , PAKΔSΔT and PAKΔT in Rac1-depleted HeLa cells was diminished to 61±8% , 67±5% and 55±1% ( Figure 4B ) , respectively , compared to bacterial entry in control-siRNA treated cells , while entry of PAKΔS was unaffected ( 90±18% ) . Depletion of Cdc42 in HeLa cells decreased the entry of PAK , PAKΔSΔT , PAKΔS and PAKΔT to 60±13% , 62±2% , 71±12% and 59±1% , respectively ( Figure 4B ) . Bacterial binding was unaffected ( data not shown ) . We further examined in detail the kinetics of PAK internalization into epithelial cells . Figure 5 reveals that all four strains ( PAK , PAKΔSΔT , PAKΔT , PAKΔS ) are equally invasive at early times of infection , providing a potential explanation for why depletion of host cell targets of ExoS and ExoT similarly reduced invasion of PAK and PAKΔSΔT . The effects of the effector proteins ExoS and ExoT are only apparent at 30 minutes post infection . This delay correlates with the kinetics of translocation of the effector proteins into the host cell cytosol ( P . Balachandran , personal communication ) . After 30 minutes of infection , only a limited further increase in the entry of PAK or PAKΔS was observed , suggesting that the anti-internalization activities of ExoT prevailed . By 1 h post infection , there were 4-fold more intracellular PAKΔSΔT than the wild type strain ( Figure 5 ) . Interestingly , PAKΔT , the strain that expresses only ExoS , was even slightly more invasive than PAKΔSΔT . We next looked for a correlation between the invasion time course and activation of Rac1 and Cdc42 . ExoS and ExoT are predicted to have complicated and even opposing effects on Rac1 and Cdc42 activation: both ExoS and ExoT harbor GAP activity towards Rho , Rac1 , and Cdc42 [16] , but ADP ribosylation of Rac1 by ExoS has also been shown to lead to Rac1 activation in some cell types [51] . Direct correlation of invasion and activation of these proteins is further complicated by virtue of the fact that we are examining activation of total cellular Rac1 or Cdc42 whereas the relevant effect in the host cell could be due to local changes in concentration or activation . Moreover , as described in the preceding section , ExoT affects other targets ( e . g . CrkII ) , which will also impact the overall invasion rate . Nevertheless , we measured the fraction of activated Rac1 and Cdc42 at various times after infection with PAK , PAKΔSΔT , PAKΔS or PAKΔT . Between 30 minutes and 1 hour , the time at which the strains began to show divergent invasion profiles , a slight activation of Cdc42 is apparent in PAKΔS , PAKΔT and PAKΔSΔT relative to PAK ( Figures 4C and D ) . This finding would be consistent with both ExoS and ExoT contributing to Cdc42 inhibition through their respective GAP activities . During this 30 minutes to 1 hour time frame ExoS apparently promotes activation of Rac1 , as PAK and PAKΔT exhibit Rac1 activation , while PAKΔS and PAKΔSΔT do not ( Figures 4E and F ) . The following model may account for the observed requirement for Cdc42 and Rac1 along with the complex changes in total cellular Cdc42 and Rac1 activation observed over the first hour of invasion . We propose that PAKΔSΔT causes local activation and/or recruitment of Rac1 and Cdc42 , resulting in entry into non-phagocytic cells . PAKΔT is reproducibly slightly more invasive than PAKΔSΔT at later time points , likely due to the ExoS-mediated Rac1 activation . Both strains lead to phosphorylation of CrkII , which , as we demonstrated above , also contributes to invasion of these strains . PAK , though it shows similar ( and also ExoS-dependent ) Rac1 activation relative to PAKΔT , is less invasive , presumably due to ExoT-mediated inhibition of Cdc42 and the ExoT-mediated inhibition of CrkII phosphorylation . Our observation that PAK activates Rac1 further suggests that the ADPRT activity of ExoS prevails over the GAP activity of ExoT . PAKΔS does not express ExoS and can therefore not activate Rac1 . In addition , PAKΔS is subject to ExoT-mediated inhibition of Rac1 and Cdc42 ( compared to PAKΔSΔT ) as well as ExoT-mediated inhibition of CrkII phosphorylation . Consequently this strain shows even less invasion than PAK . Pak1 belongs to a family of serine/threonine kinases and becomes strongly activated upon binding of activated Rac1 and Cdc42 to their GTPase binding domain ( PBD ) . Pak1 also plays a role in growth arrest upon wound closure . Interestingly , this function is dependent upon the ability of Pak1 and its guanine exchange factor ( GEF ) Pix to localize to focal contacts and is disrupted in both dominant negative and constitutively active mutants [52] . As shown in Figure 6B , siRNA-mediated depletion of Pak1 in HeLa cells ( Figure 6A ) decreased PAKΔSΔT invasion approximately 2-fold . Bacterial adhesion was not affected ( data not shown ) . In addition , simultaneous depletion of Pak1 and Abl ( Figure 6C ) did not additively inhibit P . aeruginosa invasion ( Figure 6D ) , suggesting that Abl kinase and Pak1 function in the same pathway in P . aeruginosa invasion . We confirmed these results using MDCK cells that can be induced to express human Pak1 , a kinase-dead mutant of human Pak1 ( Pak1KD; K299R ) or a constitutively active Pak1 allele ( Pak1CA; T423E ) . Figure 6E demonstrates that over-expression of either the kinase-dead or the constitutively active form of Pak1 inhibits PAKΔSΔT invasion . The results are consistent with published reports showing that cycling of Pak1 between its active and inactive form is critical for its function [52] .
Understanding how pathogens subvert the host cell cytoskeleton to induce their own internalization is of great interest , opening new avenues to develop treatments to control antibiotic-resistant infections as well as furthering our understanding of fundamental aspects of cell biology . In the experiments reported here , we used RNAi-mediated gene inactivation in Drosophila S2 cells to carry out an unbiased forward genetic screen to identify host molecules crucial to entry of P . aeruginosa . As S2 cells are phagocytic in origin , our screen had the potential to identify genes involved in phagocytosis or in pathogen-directed uptake into non-phagocytic cells . We identified the tyrosine kinase Abl , the adaptor protein Crk , the Rho family GTPases Rac1 and Cdc42 , and Pak as components of a host signaling pathway which has not previously been demonstrated to be required for P . aeruginosa entry . Using comprehensive and complementary approaches , we validated the role of the Abl kinase pathway in P . aeruginosa entry into mammalian epithelial cells . Remarkably , three of its components , Crk , Rac1 and Cdc42 , are known targets of ExoS and/or ExoT , T3SS effector proteins of P . aeruginosa that have been shown to modulate P . aeruginosa internalization into mammalian cells [18] , [24] . Our results further reveal new complexities in the regulation of bacterial entry by ExoS and ExoT . Through the use of a chemical inhibitor of Abl kinase , an Abl/Arg deficient cell line , and RNAi-mediated depletion of Abl , we demonstrate that this cytoplasmic tyrosine kinase is essential for efficient internalization of P . aeruginosa by mammalian cells ( Figure 2 ) . Abl kinase has been shown to be a key component of various steps in the infection of several pathogens , including actin motility in poxvirus infection , pedestal formation in enterophathogenic E . coli , and the entry of Coxsackievirus , and Shigella flexneri into non-phagocytic cells [50] , [53]–[55] . However , the requirement for Abl in P . aeruginosa internalization does not simply reflect utilization of a general phagocytic pathway , as Abl is not required for the phagocytosis of dead bacteria [36] . Likewise , Abl depletion apparently does not affect the uptake of several other pathogens , including S . typhimurium ( Figure S1 ) , Listeria monocytogenes , Mycobacterium fortuitum , and Candida albicans [34] , [35] , [38] . Taken together , these results suggest that a subset of microbial pathogens subvert Abl-dependent pathways during pathogenesis . Our data also provide new evidence that Crk plays a role in P . aeruginosa internalization ( Figure 3B ) , that CrkII is phosphorylated by Abl upon P . aeruginosa infection ( Figures 3C-G ) , and that the phosphorylation of CrkII contributes to the internalization of P . aeruginosa ( Figure 3I ) . The phosphorylation of CrkII at tyrosine 221 , which is required for its membrane localization , has been shown to modulate the ability of this adaptor protein to interact with other signaling molecules and to regulate the localization of Rac1 and Rac1-dependent signaling [49] . Phosphorylation of CrkII has also been demonstrated to be essential for Rac1 and Cdc42 activation upon Shigella infection [50] . Based on these findings , we postulate that infection with P . aeruginosa leads to phosphorylation of CrkII , facilitating its transport to the plasma membrane , where it interacts with other signaling molecules such as the small GTPases , eventually leading to bacterial internalization . The role of Crk in P . aeruginosa internalization is even more intriguing as this adaptor protein has been identified as the substrate for the T3SS effector ExoT [15] . ExoT has been shown to ADP ribosylate Crk on arginine 20 of its SH2 domain , disrupting its ability to interact with Paxillin and p130Cas [47] . Our data also suggest that ExoT inhibits CrkII phosphorylation ( Figures 3C–E ) . Thus , upon translocation of its effector protein ExoT , P . aeruginosa can downregulate its internalization , at least in part by disruption of CrkII phosphorylation and function . This study further reveals that invasion of PAK into epithelial cells is at least in part a Cdc42 and Rac1 dependent process ( Figure 4B ) that is subject to complex regulation . Using isogenic mutants in ExoS and/or ExoT , we examined the state of Rac1 and Cdc42 activation , the effect of depletion of Rac1 or Cdc42 , and the kinetics of entry to formulate the following model . The effector deficient strain , PAKΔSΔT , likely locally activates Rac1 and Cdc42 to enhance entry into non-phagocytic cells , possibly through the insertion of the T3SS complex . PAKΔT , which translocates ExoS , is even more invasive than PAKΔSΔT , likely because of enhanced activation of Rac1 by the ADPRT activity of ExoS . Our finding that depletion of Rac1 or Cdc42 diminished entry suggests that there may be local activation of Cdc42 in addition to the observed ExoS-dependent activation of Rac1 . PAKΔS is the least invasive of the four strains , and is least affected by depletion of Rac1 or Cdc42 . This finding suggests that ExoT is able to effective abrogate Rac1 and Cdc42 activation . Finally , the phenotype of PAK may be explained as a complex combination of the synergistic and antagonistic effects of ExoS and ExoT . It is less invasive than PAKΔSΔT and PAKΔT , likely because the GAP activity of ExoT partially counteracts the activation of Rac1 by ExoS . Previously , our lab reported that ectopic expression of ExoS in PA103ΔUΔT , a P . aeruginosa strain that does not normally express ExoS , inhibited internalization into macrophages but variably inhibited internalization into MDCK cells [18] . These disparate results are readily explained by reports showing that the ability of the ADPRT domain of ExoS to activate Rac1 are cell type specific; Rac1 activation was observed in fibroblasts and epithelial cells , but not macrophages [51] , [56] . Our finding that ExoS has a slight stimulating effect on bacterial internalization into epithelial cells is particularly remarkable as it might represent a mechanism that explains why ExoS-expressing strains of P . aeruginosa are more invasive than strains that do not express ExoS . Furthermore , it implies that the effect of ExoS on invasion is context ( i . e . cell type ) specific . The exact physiological consequence of this remains to be determined , but it is striking that the vast majority of P . aeruginosa strains produce both ExoS and ExoT [57] . It is interesting to speculate that this imparts a flexibility that allows PAK to enter epithelial cells , such as those that line the mucosal barrier , while avoiding uptake by macrophages . Alternatively or in addition , ExoS/ExoT producing strains may exhibit enhanced fitness in the environment . Our work further demonstrates a role for Pak1 in P . aeruginosa invasion . Pak1 belongs to a family of highly conserved serine/threonine kinases that are implicated in cytoskeletal rearrangements induced by GTP-bound forms of Rac1 and Cdc42 [58] ( Figure 6 ) . Interestingly , expression of a constitutively active mutant as well as a kinase-dead mutant of Pak1 inhibited bacterial internalization ( Figure 6E ) . These findings corroborate that cycling of this kinase between an active and inactive state is required for its function [52] . Pak1 may facilitate P . aeruginosa invasion by regulating Arp2/3-dependent actin polymerization . Indeed , the Arp2/3 complex is also required in P . aeruginosa invasion ( Table S1 ) . Pak1 has been shown to interact both in vivo and in vitro with p41-Arc , a putative regulatory component of the human Arp2/3 complex [59] . Pak1 phosphorylation of p41-Arc regulates its localization with the Arp2/3 complex in the cortical nucleation regions of cells [59] . This interaction may represent a mechanism by which the signaling cascade triggered by P . aeruginosa influences the function of the Arp2/3-complex , leading to the formation of new actin filaments and lamellipodia , and eventually to bacterial uptake . The activation of the Arp2/3 complex is also mediated by the Wiscott-Aldrich syndrome proteins WASP and WAVE , which are known effectors of Cdc42 and Rac1 , respectively [60] , [61] . As RNAi mediated depletion of WASP and WAVE decreased internalization of P . aeruginosa into S2 cells ( Table S1 ) , these proteins may also be involved in processes leading to bacterial uptake . This is furthermore supported by the finding that depletion of Abi , Sra-1 and Kette , which form a complex that regulates the function of WASP and WAVE in coordinating the formation of F-actin [61] , also affected bacterial internalization ( Table S1 ) . Whether these observations are relevant to non-phagocytic cells remains to be determined . Our RNAi screen also identified Phosphatidylinositol 3-kinase ( PI3K ) and Protein kinase B/Akt as host molecules that contribute to efficient P . aeruginosa internalization . Indeed , recent work in our laboratory demonstrated that PI3K and its downstream effector Protein kinase B/Akt are required for internalization of PAK in MDCK cells [27] . It will be important to determine if the PI3K/Akt pathway intersects with the Abl kinase internalization pathway . Preliminary results using the pharmacological Abl inhibitor Gleevec and the PI3K inhibitor LY294002 suggest that these signaling pathways may be separate ( Pielage and Engel , unpublished data ) . It is also possible that the interaction between these two pathways occurs further downstream , such as at the level of the Rho family GTPases . Alternatively , they may share a mutual receptor , though further work will be required to elucidate the details . As clinically important antibiotic resistance of P . aeruginosa continues to increase , the identification of host genes essential for the pathogenesis of P . aeruginosa infections may lead to new drug targets . The Abl inhibitor Gleevec , a well tolerated drug which has become a mainstay for the treatment for chronic myelogenous leukemia and stromal tumors with few side effects [41] , has been shown to protect against vaccinia virus infection in mice [53] and may prove to be effective against P . aeruginosa and other pathogens that subvert Abl kinase-dependent pathways . As drugs such as Gleevec affect host instead of bacterial proteins , they are much less likely to engender resistance compared to conventional antimicrobial treatments , and may be applicable to a wide range of pathogens . Future studies will be directed towards assessing these host cell targets as candidates for new therapies .
P . aeruginosa strain K ( PAK ) , PAKΔS ( ExoS::omega ) , PAKΔT ( ExoT::gent ) and PAKΔSΔT ( ExoS::omega , ΔT::gent ) [23] were routinely grown with vigorous aeration overnight in low salt ( 90 mM NaCl ) Luria-Bertani ( LB ) broth at 37°C . Overnight cultures were diluted 1∶30 in LB , grown to a mid-log OD600 and adjusted to OD600 of 0 . 1 in cell culture medium . Salmonella typhimurium SL1344 ( obtained from Dr . S . Falkow , Stanford ) was grown overnight without shaking in high salt ( 180 mM NaCl ) LB broth . Overnight cultures were diluted 1∶20 in cell culture medium and grown to an OD600 of 0 . 1 . S2 cells ( obtained from Dr . R . Vale , UCSF ) were cultured in Schneider's Drosophila medium ( Invitrogen ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS; HyClone ) at 28°C . HeLa cells ( ATCC CCL-2 ) were routinely grown in minimal essential medium ( MEM , UCSF Cell Culture Facility ) supplemented with 10% heat-inactivated FBS . 3T3 cells or 3T3 cells derived from Abl−/−Arg−/− mice [39] were grown in Dulbecco's minimal essential medium ( DMEM; UCSF Cell Culture Facility ) supplemented with 20% heat-inactivated FBS . MDCK cells expressing human wild type Pak1 , a constitutively active allele ( Pak1CA; T423E ) or a kinase-dead allele ( Pak1KD; K299R ) under control of a controllable transactivator using the tet-off system [52] were cultured in DMEM containing 5% FBS and 20 ng/ml doxycycline ( Sigma ) . To induce expression of the transgene , cells were grown in the absence of doxycycline . All mammalian cells were maintained at 37°C in a humidified atmosphere containing 5% CO2 . Invasion and adhesion assays were performed as described previously [62] with minor modifications . 1×106 Drosophila S2 cells were seeded into 24-well plates and infected with PAK growing in exponential phase ( multiplicity of infection ( MOI ) of 30 ) for 2 h at 28°C . Alternatively , 1×105 HeLa cells were seeded in 24-well plates and incubated overnight . The next day , cells were infected with exponentially growing bacteria ( MOI of 30 ) for 1 h ( except where noted ) at 37°C . For assays performed in the presence of inhibitors , cells were pre-incubated with medium containing Cytochalasin D from Zygosporium mansonii ( inhibitor of actin polymerization; 10 µM final concentration; Sigma ) or Gleevec ( STI571; selective inhibitor of Abl tyrosine kinase ) [41] at 37°C and 5% CO2 for 1 h prior to the infection . All invasion and adhesion assays were done in triplicate and error bars indicate standard errors of the mean ( SEM ) . p values were calculated using the two-tailed student's t test . 4×104 HeLa cells were seeded per well of a 24-well-plate . 24 h later , cells were transfected with pCAGGS-CrkII ( wild type Crk II ) , pCAGGS-CrkII-Y221F ( non-phosphorylatable CrkII mutant ) [49] and pCAGGS ( vector only ) using Effectene ( Qiagen , Valencia , CA ) following the manufacturer's instructions . After an incubation period of 16 h , invasion assays were performed and cells were lysed to check for efficacy of transfection . dsRNAs were generated from a library of DNA templates for 77 genes encoding actin-binding proteins [37] by in vitro transcription reactions for 6 h at 37°C using RiboMAX™ Large Scale RNA production system T7 ( Promega ) . 5×104 S2 cells were seeded into 96-well-plates , incubated with a final concentration of 10 µg/ml dsRNA for 4 days and infected with P . aeruginosa ( MOI of 30 ) for 2 h at 28°C following the protocol described above . siRNAs were purchased from Santa Cruz Biotechnology: Abl ( sc-29843 ) , CrkII ( sc-37072 ) , Cdc42 ( sc-29256 ) , Rac1 ( sc-36351 ) , Pak1 ( sc-29700 ) and control siRNA ( sc-37007 ) . HeLa cells were transfected with siRNAs according to the manufacturer's instructions . After 42 h , standard adhesion and invasion assays were performed . In parallel , lysates were immunoblotted with appropriate antibodies to evaluate the efficiency of protein depletion . 2×106 HeLa cells were seeded onto 10 cm plates , serum-starved over night , and infected with P . aeruginosa ( MOI of 100 ) for the indicated times . Cells were washed with PBS and lysed in 1% Triton X-100 in PBS supplemented with proteinase inhibitors ( Complete; Roche Diagnostics ) for 20 minutes at 4°C . Cell lysates were clarified by centrifugation and separated by SDS-PAGE . After transfer to PVDF membranes ( Immobilon , Millipore ) , membranes were blocked in 5% milk in PBS-T ( PBS supplemented with 0 . 1% Tween 20 ) , incubated with primary antibodies overnight at 4°C , washed in TBS-T buffer ( 20 mM Tris-HCl , pH8 , 137 mM NaCl , 0 . 7% Tween 20 ) , incubated with appropriate horseradish peroxidase-conjugated secondary antibodies for 1 h at RT , washed again and developed using a chemiluminescence kit ( ECL , Amersham Pharmacia ) . 2×106 HeLa cells were seeded onto 10 cm plates and serum-starved overnight . The next day , cells were infected with P . aeruginosa ( MOI of 100 ) for the indicated times . To precipitate GTP-bound Rac1 and Cdc42 , cell lysates were incubated with Pak1-PBD agarose ( Rac1/Cdc42 activation assay , Upstate Biotechnology ) following the manufacturer's instructions . Samples were run on 12% Bis-Tris gels and immunoblotted as described above . For quantification GTP-bound Rac1 or Cdc42 was compared to total Rac1 or Cdc42 and normalized to uninfected cells . Antibodies include mouse-anti-c-Abl ( sc-23 , Santa Cruz Biotechnology; 1∶400 ) , mouse-anti-Crk ( BD Transduction Laboratories; 1∶2 , 500 ) , rabbit-anti-phospho-CrkII ( Tyr221; Cell Signaling; 1∶500 ) , rabbit-anti-Pak1 ( N-20; sc-882 , Santa Cruz Biotechnology; 1∶500 ) , mouse-anti-Rac1 ( Upstate Biotechnology; 1∶500 ) , rabbit-anti-Cdc42 ( sc-87 , Santa Cruz Biotechnology; 1∶200 ) , mouse-anti-GAPDH ( Glyceraldehyde-3-phosphate dehydrogenase; MAB374 , Chemicon; 1∶20 , 000 ) , peroxidase-conjugated goat-anti-mouse ( Jackson Immunoresearch; 1∶5 , 000 ) , and peroxidase-conjugated goat-anti-rabbit ( Jackson Immunoresearch; 1∶5 , 000 ) . p values were calculated using the two-tailed student's t test . P<0 . 05 was considered significant . | Mortality from Pseudomonas aeruginosa infections , one of the leading causes of hospital acquired infections , approaches 40% , and multiple drug resistant infections are common and increasing . Internalization of P . aeruginosa by the host cell appears to play a fundamental role in the pathogenesis of this opportunistic bacterium , but the host cell factors involved in this process are incompletely understood . We used a targeted RNAi screen in Drosophila S2 cells to identify a subset of regulators of the host actin cytoskeleton that contribute to bacterial entry and confirmed their involvement in infection of mammalian cells . We found that P . aeruginosa can modulate this internalization pathway in a complex manner by injecting the bacterial toxins ExoS and ExoT into the host cell via its type III secretion system . The identified host cell molecules may serve as targets for novel drugs to treat infections resistant to conventional antibiotics and may be applicable to a wide range of pathogens . | [
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] | 2008 | RNAi Screen Reveals an Abl Kinase-Dependent Host Cell Pathway Involved in Pseudomonas aeruginosa Internalization |
In several species , including rodents and fish , it has been shown that the Major Histocompatibility Complex ( MHC ) influences mating preferences and , in some cases , that this may be mediated by preferences based on body odour . In humans , the picture has been less clear . Several studies have reported a tendency for humans to prefer MHC-dissimilar mates , a sexual selection that would favour the production of MHC-heterozygous offspring , who would be more resistant to pathogens , but these results are unsupported by other studies . Here , we report analyses of genome-wide genotype data ( from the HapMap II dataset ) and HLA types in African and European American couples to test whether humans tend to choose MHC-dissimilar mates . In order to distinguish MHC-specific effects from genome-wide effects , the pattern of similarity in the MHC region is compared to the pattern in the rest of the genome . African spouses show no significant pattern of similarity/dissimilarity across the MHC region ( relatedness coefficient , R = 0 . 015 , p = 0 . 23 ) , whereas across the genome , they are more similar than random pairs of individuals ( genome-wide R = 0 . 00185 , p<10−3 ) . We discuss several explanations for these observations , including demographic effects . On the other hand , the sampled European American couples are significantly more MHC-dissimilar than random pairs of individuals ( R = −0 . 043 , p = 0 . 015 ) , and this pattern of dissimilarity is extreme when compared to the rest of the genome , both globally ( genome-wide R = −0 . 00016 , p = 0 . 739 ) and when broken into windows having the same length and recombination rate as the MHC ( only nine genomic regions exhibit a higher level of genetic dissimilarity between spouses than does the MHC ) . This study thus supports the hypothesis that the MHC influences mate choice in some human populations .
In vertebrates , several studies have revealed that highly polymorphic genes within the Major Histocompatibility Complex ( MHC ) may have a role in mate choice . In particular , it has been shown that MHC genes influence individual body odor in mice and rats [1]–[7] and that mice prefer MHC-dissimilar mates e . g . [8]–[11] , and [12] for a review . Evidence for MHC-disassortative mating was also found in sand lizards [13] . Studies in fish ( and in particular Arctic charr ) have shown their ability to discriminate the odors of similar and dissimilar MHC siblings [14] , and shown that salmon prefer MHC dissimilar mates [15] while female sticklebacks choose a mate in order to complement their own set of MHC genes and to optimize the number of different alleles in their offspring [16] . Complex MHC-based mate choice was also observed in birds [17] , [18] . The MHC is the most important part of the genome with respect to immunity [19] and such MHC-based mate choice could increase or optimize the number of non-self antigens that future offspring can recognize and thus increase their resistance to pathogens [12] , [20] , [21] . It could also have contributed to the extraordinary polymorphism observed at MHC loci [20] . On the other hand , in humans , the role of the MHC in mate choice is very controversial . Ober et al studied classical HLA types for 400 couples from the Hutterite community and found significantly fewer HLA matches between husbands and wives than expected when taking into account the social structure of Hutterites [22] . On the other hand , no evidence of MHC-based mate choice was found in a study of 200 couples from South Amerindian tribes [23] . In a less direct way , other studies have focused on odor preferences: in “sweaty T-shirts experiments” , in which females were asked to smell T-shirts worn by different males , it was shown that females significantly prefer the odor of T-shirts worn by MHC-dissimilar males , although such preference was not found among females taking the contraceptive pill [24] , [25] . However , in another sweaty T-shirts experiment , in which males where chosen from a different ethnicity from the females and females were not aware of the nature of the smell ( contrary to the two previous studies ) , females significantly preferred the odor of males having a small number of HLA alleles matching their paternal inherited alleles than the odor of males having fewer matches [26] . Although it has not been established that odor preference is a key factor in mate choice , such studies support the hypothesis that humans are able to discriminate MHC types of potential mates through odor cues and that humans may use such information when choosing a mate . However , the lack of congruence between these studies means that there is still uncertainty as to whether MHC variation influences mate choice in humans , and to what extent . The availability of genetic variation data at genomic scales now allows careful assessment of this question . Crucially , it allows us to distinguish MHC-specific effects from genome-wide effects . In this study , we tested the existence of MHC-disassortative mating in humans by directly measuring the genetic similarity at the MHC level between spouses . These data were extracted from the HapMap II dataset , which includes 30 European American couples from Utah and 30 African couples from the Yoruba population in Nigeria [27] . Our analyses are based on HLA types and on 9 , 010 Single Nucleotide Polymorphisms ( SNPs ) densely distributed across the MHC . Moreover , in order to control for genome-wide effects , we compared the pattern observed in the MHC region to patterns assessed in the rest of the genome , using 3 , 214 , 339 SNPs .
The genetic similarity at a given genetic variant for a given couple c was measured using a relatedness coefficient R , defined as a ratio of probabilities of identity in state R = ( Qc−Qm ) / ( 1−Qm ) , where Qc is the proportion of identical variants between the two spouses and Qm is the mean proportion of identical variants in the sample ( that is , averaged over all possible pairs of individuals ) . This coefficient , combined over the genetic variants in a region or across the genome , allows an assessment of whether spouses are more genetically similar or dissimilar than random pairs of individuals . Significance was assessed by permuting individuals between couples . All p-values below are two-sided . Positive values of R indicate genetic similarity between spouses and negative values indicate genetic dissimilarity between spouses , relative to random mating in the sample . Using molecular markers ( average relatedness coefficients across 9 , 010 SNPs ) , we observed that on average European American spouses were significantly more MHC-dissimilar from each other than random pairs of individuals ( R = −0 . 043 , p = 0 . 015 ) . Moreover , the distribution of genetic relatedness coefficients across couples shows no outliers ( Figure 1 ) , thus excluding the possibility that this significantly negative coefficient could result from only a few couples having extremely low genetic relatedness . On the other hand , the MHC relatedness coefficient was positive but not significantly so in African couples ( R = 0 . 015 , p = 0 . 23 ) . In addition , our analyses based on HLA types for 6 genes confirmed this broad pattern: the multilocus relatedness coefficient was marginally significantly negative in European American couples ( R = −0 . 062 , p = 0 . 084 ) and not significantly positive in Yoruba couples ( R = 0 . 023 , p = 0 . 412 ) . ( These analyses refer to the 4 digit classification . Similar patterns were seen with 2 digit classification; data not shown . ) Using SNP data , we observed a higher mean SNP diversity in the MHC region in the African sample ( 0 . 366 ) than in the European American sample ( 0 . 349 ) . To control for genome-wide effects , we compared these observations to the pattern of genetic similarity across the genome ( 3 , 214 , 339 markers ) . Genome-wide , European American spouses were not significantly more or less similar than random pairs of individuals ( genome-wide R = −0 . 00016 , p = 0 . 739 ) . On the other hand , African spouses were more similar genome-wide than random pairs of individuals ( genome-wide R = 0 . 00185 , p<10−3 ) . To further control for genome-wide effects , we asked whether the MHC region was unusual relative to similar regions across the genome with regard to its similarity/dissimilarity between spouses , by comparing the similarity between spouses at the MHC to that of all genomic windows having the same length as the MHC ( 3 . 6 Mb ) . Strikingly , in the European American couples , only 0 . 4% of the windows , concentrated in 9 genomic regions ( listed in Table 1 ) , exhibited a higher level of genetic dissimilarity between spouses than the MHC ( Figure 2 ) . To account for the particular linkage disequilibrium structure of the MHC and its low recombination rate [28] , we compared the MHC region to a sub-set of windows having the same or lower recombination rate and still found that only 0 . 1% of these windows had less genetic similarity between spouses than did the MHC . In the African sample , 9% of the windows ( and 17% when matching for the recombination rate ) concentrated in 116 regions exhibited more genetic similarity between spouses than the MHC ( Figure 2 ) .
At the molecular level , we found that the European American couples we studied are significantly more MHC-dissimilar than random pairs of individuals , and that this pattern of dissimilarity is extreme when compared to the rest of the genome , both globally and when broken into windows having the same length and recombination rate as the MHC . Our analyses based on HLA types also show a signature of dissimilarity between spouses . Such dissimilarity , observed from both molecular and serological data , cannot be explained by demographic processes , since such effects would affect the whole genome . On the other hand , this MHC dissimilarity could result from pressure for disassortative mating at the MHC level . Such a mechanism could be triggered by our olfactory capacity for discriminating MHC-mediated odour types [21] , [29] . Alternatively , this genetic dissimilarity could result from selection of the spermatozoa by the female oocyte ( post-copulatory sexual selection ) , a further safeguard favouring the production of MHC-heterozygous offspring more resistant to pathogens see [29]–[31] for reviews . Indeed , all studied couples were selected for having offspring , and the excess of dissimilarity observed could be restricted to fertile couples , rather than couples in general . However , further analysis showed that the offspring of these couples were not more MHC-diverse than expected by random selection of parental gametes ( results not shown ) . Moreover , our results in European American couples reinforce previous evidence of MHC-disassortative mating among Hutterite couples [22] , in which all couples were included , regardless of whether they had a child ( C . Ober , personal communication ) . Like the Ober study , the sampled couples in our study are from a cultural isolate ( in our case sampled from the Mormon community ) , so one might speculate that MHC-based mate choice is stronger or easier to detect in settings where there is less heterogeneity in other factors which influence mate choice , but the current absence of detailed molecular studies of mate choice in other human populations makes this impossible to assess . The two studies in Swiss males and females showing a significant preference of females for the odor of MHC-dissimilar ( over MHC-similar ) males [24] , [25] implicate one possible mechanism by which couples may implement MHC-dependent mate choice . Taken together , these results strengthen the hypothesis that MHC genetic variation influences mate choice in some human populations . Our analyses of the European American sample also show that the results based on molecular data were more significant than those based on HLA types . Although we cannot rule out power effects in explaining such a difference , it seems plausible , and consistent with our data , that the biological mechanisms involved in disassortative mating would depend on the MHC in ways that are not simply captured by HLA types . Such biological mechanisms could possibly result from a summation of effects over multiple genes , and not only from the six HLA genes studied here . On the other hand , Yoruba couples exhibited a significant genome-wide signature of assortative mating , which is likely to result from socio-demographic processes specific to this population . The Yoruba are still organized in paternal lineages , which are exogamous units [32] and C . Adebamowo , personal communication . Although we do not have specific ethnological data collected with the Yoruba samples to explain our observations , a process in which matrimonial exchanges between genealogically related lineages are more frequent than matrimonial exchanges between genealogically unrelated lineages could have left such a genome-wide signature . On the contrary , for the MHC region , no significant pattern of similarity/dissimilarity was observed , at either the molecular level or the serological level . Several hypotheses can be proposed to explain this observation: firstly , it is possible either that the MHC is not involved in mate choice in this population , or that social factors are relatively more important than the MHC and that the sample size here does not allow detection of MHC effect on mate choice . Secondly , it is possible that MHC-based mate choice is aiming for an optimal , rather than maximal , number of MHC alleles previous theoretical and experimental evidence for this hypothesis are reviewed in [21] . Such a mechanism would explain why evidence of disassortative mating was found in the European Americans , all sampled in the Mormon community exhibiting a relatively low SNP diversity in the MHC ( 0 . 349 ) , as well as in the genetically isolated Hutterite community [22] , but not in Yoruba . Indeed , the Yoruba exhibit a relatively higher SNP diversity in the MHC ( 0 . 366 ) than the European American , and the optimization of the number of HLA alleles in Yoruba may involve mating with a not-so-MHC-dissimilar individual . This hypothesis is also consistent with the “sweaty T-shirts” experiment performed between females and males from different ethnicities ( thus having a higher range of MHC dissimilarity than males and females coming from the same community ) and showing that females prefer the odor of males with little MHC-dissimilarity than the odor of males with more extreme MHC-dissimilarity [26] . Finally , it is possible that in African populations , individuals carrying pathogen-resistant alleles are easier to identify than elsewhere , because of the higher pathogen pressure . In such conditions , it is possible that mating preferences for particular pathogen-resistant MHC alleles are stronger than mating preferences for MHC-dissimilarity per se [21] . In conclusion , our study , based on a large number of molecular markers which allow us to control for genome wide effects , indicates a clear-cut signature of MHC-disassortative mating in a sample of European American couples . This supports the existence of MHC-related biological factors contributing to mate choice in at least some human populations . On the other hand , the Yoruba exhibit a genome-wide tendency for enhanced similarity among couples but no significant pattern at the MHC level . This suggests that socio-demographic factors may be more important than biological factors for mate choice in this population , although the existence of MHC-dependent mate choice in Yoruba , aimed at optimizing ( rather than maximizing ) the number of HLA alleles in the offspring , cannot be excluded . Our study indicates that the relative importance of biological and social factors varies from one population to another . It also highlights the need for the exploration of further genome-wide data in larger sample sizes , including “just married” childless couples , sampled in several ethnically differentiated groups , in order to build a more robust view of the biological determinants acting on mate choice in humans .
Two datasets were analysed in this study: We estimated the genetic relatedness between spouses using SNP data and HLA type data . In all cases , the relatedness coefficient for a given pair of spouses R was defined as R = ( Qc−Qm ) / ( 1−Qm ) , where Qc is the proportion of identical variants between the two spouses and Qm is the mean proportion of identical variants in the sample ( that is , averaged over all possible pairs of individuals ) [33] . The proportion of identical variants at a given SNP for a given pair of individuals was 0 if both individuals were homozygous and carrying a different allele ( eg 00 and 11 ) , 1 if both individuals were homozygous and carrying the same allele ( eg 00 and 00 ) , and 0 . 5 in all others cases . We also considered a variation of this definition , with pairs of heterozygous individuals ( 01 and 01 ) being attributed a proportion of identical variants of 1 ( instead of 0 . 5 ) . Both definitions gave similar results ( we present here coefficients based on the first definition ) . We estimated the average genetic relatedness coefficient between spouses across the MHC and across the whole genome . We checked that our estimates were not affected by the heterogeneity of SNP density and linkage patterns across the genome , by redoing our analyses on reduced sets of approximately independent SNPs prepared using two different procedures implemented in the software PLINK ( one based on pairwise SNP r2 values and the other on the variance inflation factor ) [34] . We also computed the average genetic relatedness coefficient between spouses for sliding windows of 3 . 6 Mb across the genome ( in increments of 300 Kb ) having at least 1 , 000 SNPs and not overlapping a centromere . In the case of the HLA type data , we defined the proportion of identical variants as 0 if the two individuals carried different types , 0 . 5 if one of their two types was similar , and 1 in all other cases and we computed a multi-locus relatedness coefficient between spouses based on types for 6 HLA genes . R was summarized across the MHC region , the genome or the six HLA genes by averaging Qc and Qm over all SNPs or over all HLA loci ( and then computing the ratio ( Qc−Qm ) / ( 1−Qm ) ) . Using molecular data , we also computed the mean SNP diversity ( probability that two randomly chosen chromosomes are different at a given SNP ) in the MHC region for both samples . We removed from both datasets two European American and three African couples , in which one of the spouses had previously been found to be closely related ( relatedness coefficient equal or higher to 1/32 , see supplementary table 15 from [35] ) to another sample ( in each case , we chose at random the couple to be excluded ) . The relatedness coefficients before and after these exclusions were very similar ( we present in the paper the estimates without these couples ) . In the case of HLA type data , we considered both the 4 digit and the 2 digit classification , and found congruent relatedness coefficients ( coefficients based on the 4 digit classification only are reported in this paper ) . The significance of the relatedness coefficient was assessed using a permutation approach: the two-sided p-value is the proportion of permutations ( attributing a new wife randomly to each husband ) in which the permuted couples had the same or more extreme mean relatedness coefficient than the real couples . 1000 permutations were performed . | There has been a longstanding hypothesis that selection may have led to mating patterns that encourage heterozygosity at Major Histocompatibility Complex ( MHC ) loci because of improved immune response to pathogens in the offspring of such matings , and , indeed , this has been observed in several model systems . However , in humans , previous studies regarding the role of the MHC in mate choice or preference , both directly in couples and also indirectly in “sweaty T-shirts” experiments , have reported conflicting results . Here , by using genome-wide genotype data and HLA types in African and European American couples , we test whether humans tend to choose MHC-dissimilar mates . This approach allows us to distinguish MHC-specific effects from genome-wide effects . In the African sample , the patterns at MHC loci is confounded by genome-wide effects , possibly resulting from demographic processes relating to the social organization of this population , and no tendency to choose MHC-dissimilar mates is detected . On the other hand , the sampled European Americans appear to have favoured MHC-dissimilar mates , supporting the hypothesis that MHC influences mate choice in some human populations . Thus , this study suggests that , in some cases , humans may rely on biological factors , in addition to social factors , when choosing a mate . | [
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] | 2008 | Is Mate Choice in Humans MHC-Dependent? |
The constant bombardment of mammalian genomes by transposable elements ( TEs ) has resulted in TEs comprising at least 45% of the human genome . Because of their great age and abundance , TEs are important in comparative phylogenomics . However , estimates of TE age were previously based on divergence from derived consensus sequences or phylogenetic analysis , which can be unreliable , especially for older more diverged elements . Therefore , a novel genome-wide analysis of TE organization and fragmentation was performed to estimate TE age independently of sequence composition and divergence or the assumption of a constant molecular clock . Analysis of TEs in the human genome revealed ∼600 , 000 examples where TEs have transposed into and fragmented other TEs , covering >40% of all TEs or ∼542 Mbp of genomic sequence . The relative age of these TEs over evolutionary time is implicit in their organization , because newer TEs have necessarily transposed into older TEs that were already present . A matrix of the number of times that each TE has transposed into every other TE was constructed , and a novel objective function was developed that derived the chronological order and relative ages of human TEs spanning >100 million years . This method has been used to infer the relative ages across all four major TE classes , including the oldest , most diverged elements . Analysis of DNA transposons over the history of the human genome has revealed the early activity of some MER2 transposons , and the relatively recent activity of MER1 transposons during primate lineages . The TEs from six additional mammalian genomes were defragmented and analyzed . Pairwise comparison of the independent chronological orders of TEs in these mammalian genomes revealed species phylogeny , the fact that transposons shared between genomes are older than species-specific transposons , and a subset of TEs that were potentially active during periods of speciation .
The most abundant type of DNA in the human genome consists of the four major classes of interspersed transposable elements ( TEs ) , comprising ∼45% of our total DNA [1] . Short interspersed repeat elements ( SINEs ) , long interspersed repeat elements ( LINEs ) , and retrovirus-like long terminal repeat ( LTR ) retrotransposons propagate by reverse transcription of an RNA intermediate . DNA transposons move by a direct “cut and paste” mechanism [2] . TEs have been active in mammalian genomes for hundreds of millions of years , and have had a huge impact on our genomic structure [3 , 4] . Each TE has had a distinct period of transpositional activity in which it has spread through the genome , followed by inactivation and accumulation of mutations . Both SINE and LINE transpositions have been associated with insertional mutations causing human disease and pseudogene formation [1] . TEs may actively influence the expression of nearby genes , usually due to the regulatory promoter and terminator sequences found in LTRs [5] . TEs in the human and other genomes have been classified into a comprehensive database , called Repbase [6] . A program called Repeat Masker [7] was developed in order to identify all known repeat elements based on homology to the derived consensus sequences curated in Repbase . Repeat Masker has proven to be extremely valuable in gene identification and genome annotation , primarily by “masking” transposable elements in query sequences during homology searches so that the presence of a common transposon does not lead to many spurious , biologically uninteresting matches . Repeat Masker also provides a wealth of information regarding the classification , genome position , length , fragmentation , and divergence of each repeat element . Each copy of a particular TE in a genome is derived from an active sequence that , once transposed , has accumulated mutations randomly and separately from other copies [3] . Consensus sequences of the original active copies , found in Repbase [8] , have been derived from multiple sequence alignments of the present-day diverged copies . The age of these elements can be inferred from the average sequence divergence of the copies from the consensus sequence , and such classification has been applied to both Alu [9 , 10] and L1 [11] elements , permitting assignment of approximate ages [3] . However , these divergence-based classifications are limited by the assumption that the mutation rate , or molecular clock , has been constant both over time and between the different classes of transposable elements [12 , 13] . Substitution rates will depend on the original sequence of the element , especially the CpG frequency , because of its higher mutation rate . Substitution rates are known to change significantly during evolution and to differ between species , chromosomes of the same species , and even regions of the same chromosome [14–16] . Furthermore , the variance in percent divergence within a TE family will be dependent on both the length and age of the element . Hence , while estimates of the age of younger TE subfamilies have been presented [9–11] , this has not been possible with older , more diverged elements . Nevertheless , the apparent age of TEs is increasingly being used to obtain reference points in phylogenomic analysis [17] . Schueler et al . relied on the relative ages of LINE elements to date different parts of the human X chromosome centromeric alpha satellite arrays [18 , 19] . Specific insertions of MLT1A0 and L1MA9 elements were used as evidence for the sister–taxon relationship of primates and rodents [20 , 21] . Recently , evidence has been presented that some individual TEs have been exapted for use as conserved , functional , noncoding elements in mammalian genomes , which places these particular elements under selective pressure [22–25] . This study presents a novel genomic analysis of TE evolution and its impact on genomic organization , which will greatly facilitate the analysis of TEs for use in phylogenomics . A genome-wide defragmentation of TEs in the human and other mammalian genomes was performed , and the number of times that each TE has inserted into each other TE was compiled in a matrix . A novel computational method was developed that uses the age information implicit in the patterns of TE insertions to determine the relative chronological age of TEs in the human and other genomes spanning over 100 million years , independent of sequence divergence and the molecular clock . This method confirms the relative ages of TEs within classes , and was used to determine the relative ages of TEs between different classes and for older elements for which sequence divergence is particularly unreliable . This study also provides the methodological framework for the analysis of the patterns of interruptions of TEs by TEs on a genome-wide level , which represents a large , essentially untapped genomic dataset that is of fundamental importance regarding TE classification and organization . The data and analysis tools supplied here will provide a rich source of genomic information for data mining to further explore transposon biology and genome evolution .
The constant bombardment of the human genome by different TEs over millions of years has resulted in the high density of TEs in the human genome . During this time , many TEs have directly inserted into the sequence of other TEs that were already present , splitting the original TE into two noncontiguous TE fragments . We define the occurrence of TEs that interrupt other TEs as “transposon clusters . ” Large transposon clusters can reveal the evolutionary history of regions of the human genome ( Figure 1 ) resulting from the succession of transposition events over time . The relative age of the TEs in transposon clusters is implicit in their organization , where newer TEs have interrupted older TEs that were already present . We have developed a software package called Transposon Cluster Finder ( TCF; available at http://www . mssm . edu/labs/warbup01/paper/files . html ) that identifies transposon clusters in the human genome by defragmentation of TEs and identification of TEs that have inserted into them . TCF starts with the collection of TE fragments provided by Repeat Masker [6 , 7] . Potential transposon clusters were initially identified by collecting sets of transposon fragments that ( 1 ) had the same name , ( 2 ) were on the same strand , and ( 3 ) were separated by ≤500 bp of nontransposon ( not Repeat Masked ) DNA sequence . Within these potential clusters , TE pairs were defragmented based on the difference in repeat indicies ( ΔRI; see Materials and Methods ) . TCF provides a custom track to visualize all TE clusters in the human genome on the University of California Santa Cruz ( UCSC ) genome browser ( http://genome . ucsc . edu/cgi-bin/hgGateway ) , as shown for the examples in Figures 1 and S1 . TCF identifies common family-specific variations in patterns of TE occurrence that do not represent independent transposition events [26] . TCF identified 3 , 101 examples of intact LTR transposons in which two LTRs with the same name precisely flank a full-length internal LTR element in the same orientation , and the second LTR was not counted as an independent transposition event ( Dataset S1; example in Figure S1 ) . TCF also identified 2 , 273 examples of L1 LINE elements that contain a 5′ inversion , proposed to be due to the twin priming mechanism [27] , and were counted as a single interruption ( Dataset S2; example in Figure S1 ) . Both the intact LTR and the 5′ L1 inversions are detected regardless of whether they have undergone subsequent fragmentation . Human endogenous retroviruses ( HERVs ) such as HERV-H show recurrent patterns of specific deletions due to transposition complementation in trans , where transpositionally inactive elements can nevertheless be packaged together with active elements in viral particles and be propagated in the genome [28] . These elements were properly defragmented by TCF and counted as single interruptions ( Figure S1 ) . The genome also contains many types of tandem repeats that have been generated from TE fragments that have spread by processes , including replication slippage or unequal crossing-over , that appear as spurious clusters or interruptions . Clusters that contained many interruptions of the same TE were screened for possible tandem repeats . A total of 40 clusters were found that contained tandemly repeated TE fragments , often amplified internal portions of a larger more complete TE ( Dataset S3; example in Figure S1 ) . In addition , several larger arrays of tandem repeats contained TE clusters that were duplicated in each repeat unit , or contained spurious clusters because of defragmentation of TEs in adjacent tandem repeats ( Dataset S4; example in Figure S1 ) . Spurious interruptions seen in tandem repeats were removed from the dataset by exclusion of the genomic regions . Finally , regions of segmental duplications in the human genome ( hg18 ) were searched to identify clusters that had been duplicated one or more times , and only a single copy of each was included in the dataset ( Dataset S5 ) . The custom track provided by TCF ( available for upload from the Datasets ) shows all clusters , but indicates which were excluded from the final dataset as not representing independent transposition events . Running TCF on hg18 after removing the interruptions described above yielded 307 , 412 clusters , which contain 636 , 125 interruptions and cover 542Mbp ( or ∼19% of the genome and >40% of all transposon base pairs ) . The largest cluster observed covers 91 kb on chromosome Xq13 . 3 ( Figure S1 ) , found in a region of the X chromosome previously noted for a high density of LINEs [29] . A 21-kb cluster in chromosome band Xq11 . 22 contained 86 interruptions , the most observed in any single cluster ( Figure S1 ) . A Web-based Cluster Browser ( http://sungene-bk . genetics . mssm . edu/cluster/index . html ) is available that permits the user to query for any TEs interrupting any other TEs , with wild cards available , across the human genomes , and provides cluster tables ( as in Figure S1 ) and links to custom tracks on the UCSC genome browser . TE dispersal in mammalian genomes can be characterized by a period of transpositional activity during which copies of the TE are spread throughout the genome , followed by gradual inactivation by loss of transpositional ability due to accumulated damage . The remnant copies of the TE remain behind and become further degraded over time by mutational events , including fragmentation by the insertion of newer TEs . The result , over eons , is that older TEs will be heavily interrupted by newer elements , but will not have inserted into newer elements . In contrast , newer elements , with a relatively recent period of activity , will have inserted into older elements that were present in the genome , but will not be interrupted by older elements . Elements of intermediate age will have both inserted into older elements and been themselves fragmented by newer elements . The TCF analysis presented above provided an accurate count of the number of times every TE interrupts every other TE , and takes into account the most common family-specific variation that does not represent independent transposition events [26] . Therefore , computational methods were developed to take this unique dataset provided by TCF and determine the relative age of TEs in the genome based on interruptions of TEs into each other . Many of the 908 types of TEs in the human genome are found in very few copies , and therefore interact with none or very few other TEs ( interactions defined as either getting interrupted by or interrupting another TE; Table S1 ) . Therefore , a method was developed to identify a subset of TEs that interacted with a certain percentage of other TEs , which was defined as percent connectedness ( see Materials and Methods ) . For our initial analysis , the percent connectedness was set at 29% ( each TE interacts with at least 29% of all other TEs ) . This provided a set of 360 TEs for further analysis , which nonetheless represented >95% of all TEs and >92% of all interruptions found in clusters by TCF . The number of times that each of these 360 TEs interacts with every other TE was displayed as an n × n ( 360 × 360 ) adjacency matrix ( Figure 2A ) . Each point in the matrix shows the number of times that the TE on the vertical axis ( the interrupTER ) has transposed into the TE on the horizontal axis ( the interrupTEE ) ( Figure 2A ) . We realized that a hypothetical matrix where the TEs are arranged in the correct chronological order of decreasing in age on both the horizontal ( left to right ) and vertical ( top to bottom ) axes ( Figure 2B ) would have certain properties as follows . The top left corner of the matrix represents old TEs interrupting old TEs; the bottom left corner represents new TEs interrupting old TEs; and the bottom right corner represents new TEs interrupting new TEs . The top right corner represents old TEs interrupting new TEs , which should not be observed . Thus , in theory , the region of the matrix above the diagonal , the upper triangle submatrix , should be mainly populated by zeros ( no interruptions ) . Nonzeros will , however , be found above the diagonal when pairs of TEs have both interrupted each other , which indicates that these TEs had overlapping periods of activity ( were contemporaneous ) . Additional nonzeros above the diagonal might also represent defragmentation errors , cluster misidentification , or other mutational events that give the appearance of TE insertion . Notably , interruptions of the same type of TEs into themselves ( which would be recorded directly on the matrix diagonal ) are not scored due to the fact that they are difficult to confidently identify and do not affect the ordering analysis . Therefore , we developed a computational method called interruptional matrix analysis ( IMA ) that performs systematic repositioning of all elements on the axes of the n × n matrix , and searches for an ordering that minimizes the summation of nonzero entries ( hereafter called the penalty score ) in the upper triangle matrix , selecting a new order when the penalty score decreases . Instead of direct summation of nonzero entries , the penalty score uses a continuous log function for values greater than 3 to prevent TEs with large numbers of interruptions from dominating the results ( see Materials and Methods ) . Starting from a random order ( with an initial penalty score of ∼45 , 000; e . g . , Figure 2A ) , approximately seven rounds of repositioning each element were required to reach a minimum penalty score ( of ∼7 , 800 ) , where changing the position of any element either does not change or increases the penalty score ( e . g . , Figure 2C ) . Note that in the final ordering , the oldest , newest , and intermediate age elements follow the expected patterns of fragmentation and insertion described at the beginning of this section ( Figure 3D ) . The IMA algorithm is a version of hill climbing . A single run of IMA will find a penalty score that represents a local minima from that random starting order , but this is not guaranteed to be the ordering with the overall lowest possible minimum penalty score for the entire matrix . Furthermore , inevitable errors in the defragmentation data preclude using any single result of IMA as a final solution . Therefore , in order to optimize the objective function over the very large ( 360 ! or ≈10500 ) number of possible orders , we chose to estimate the correct ordering from many independent runs of the method . IMA was run 100 , 000 times , starting each time from a different randomized order of TEs , which resulted in a distribution of possible positions for each of the 360 TEs in chronological order ( see below ) . A chronological order was obtained by ordering the TEs by their median positions , resolving ties using their mean positions ( Figure 3 and Table S2 ) . A subset of this final matrix is shown for the L1PA family of primate specific LINEs ( including L1Hs ) in Table 1 . The L1PA elements are shown in the final chronological order derived by IMA ( Figure 3 ) , with decreasing age running from top to bottom and left to right . The ordering of these elements is in remarkable agreement with published chronologies , in that numerical order ( e . g . , L1PA15 , L1PA14 , etc ) reflects relative age ( Figure 3B ) [11 , 30] . The number of times each L1PA element has inserted into each other L1PA element is shown ( Table 1 ) . As expected , the older elements are heavily interrupted by younger elements , indicated by the relatively large positive values below the diagonal of the matrix . Conversely , the newer elements are not interrupted by the older elements , indicted by the abundance of zero values above the diagonal of the matrix . Several larger values appear above but near the diagonal , which represent bona fide interruptions of contemporary elements into each other ( e . g . , 12 interruptions of L1PA15 into L1PA16 , and six interruptions of L1PA16 into L1PA15 ) . A notable discrepancy is the placement of L1Hs , the newest and only remaining active L1 element in the human genome , slightly before the inactive L1PA2 in the chronological order ( Figure 3 ) . The chronological order derived from this IMA method agreed very well not only with the L1PA elements , but also with the other families of TEs for which limited phylogenetic analyses has been performed ( Figure 3A ) . For example , the oldest TEs found by this method include LINE L3 and LINE L2 , and the MIR elements that were dependent on them for transposition [26 , 31] . The different subfamilies of LINE1 are in remarkable agreement with published chronologies based on sequence divergence [11] , including an overlap between the L1M ( mammalian ) and L1PA and L1PB ( primate ) elements [32] . The radiations of the Charlie and later Tigger families of DNA transposons are also observed ( however , see below for further analysis ) [3] . The relative age of the Alu element families is also consistent with published reports [9] . Note that the chronological order in general agrees with the median percent divergence . Thus , we conclude that our novel method of determining transposon chronology is accurate and robust , and can be used to infer the relative age of the human TEs both within and between different classes . Running IMA from 100 , 000 random starting orders resulted in a distribution of possible positions for each of the 360 TEs in chronological order ( Figure 3 and Table S2 ) . The width of this distribution represents an estimate of the confidence of the position of each TE in the chronological order . Since TEs had a distinct period of activity and did not occur at a single point in time , these positional distributions may represent useful estimates of the relative timespan of transpositional activity of each individual TE . The positional distributions for the LINE 1 subfamilies L1ME , L1MB , and L1PA are shown in Figure 3C . The fact that these positional distributions overlap within each subfamily support the continuous evolution of these elements over time , suggested previously by derived phylogenetic trees [30 , 32] . We suggest that overlapping distributions represent TEs that were contemporaneous with each other in time ( Figure 3C ) , even though there may not be any examples where they actually transposed into each other . The width and overlaps of the positional distributions of older elements may be somewhat extended , because TEs with a high percent divergence from the consensus may be more prone to Repeat Masker misidentifcation of specific elements within subfamiles . Examination of the results obtained above for the 360 human TEs ( Figure 3 ) showed agreement with the two major radiations of Charlie and Tigger DNA transposons in the human genome [3] . However , we observed that the Tigger6a element was placed significantly earlier in the chronological order ( position 111 ) than the eleven other Tigger elements , which clustered tightly together from positions 185 to 213 ( Figure 3A ) , suggesting that Tigger6a was active at an earlier time than the other Tigger elements . We used TCF and IMA to further investigate the evolutionary history of DNA transposons in the human genome . The Charlie and Tigger DNA transposons belong to the hAT medium reiterated sequence 1 ( MER1 ) and the Tc1-like MER2 families , respectively , which are distinguished by the structure of the target site duplication and the terminal inverted repeat [33] . IMA was run using an interruption matrix that included the 45 additional human DNA transposons from the MER1 and MER2 families , as classified by Repeat Masker , for a total of 405 TEs ( Table S2 ) . Figure 4 shows the chronological order and positional distribution of the MER1 ( red ) and MER2 ( green ) DNA transposons from this run of IMA , which again shows the two major radiations of MER1 followed by MER2 . However , several additional MER2 elements were apparently active quite early , especially Tigger8 ( position 8 ) and MER46c ( position 58 ) . Tigger6 ( position 87 ) is also positioned early , suggesting that the Tigger6 subfamily ( Tigger6 and Tigger6a ) occurred earlier than most of the other Tigger elements . The majority of MER2 activity occurred following the MER1 activity , with the remaining Tigger elements active during this period . However , another period of MER1 activity apparently occurred following the MER2 activity . Importantly , the median percent divergence for these elements is consistent with the periods of activity found by IMA . Many DNA transposons are found as internal deletion products of larger intact transposons . These nonautonomous elements retain intact terminal inverted repeat sequences but are dependent on transposases from autonomous “parental” transposons for their transposition [33] . The second period of activity of MER1 elements consist of two distinct subfamilies of Charlie elements and their nonautonomous deletion products , the Charlie12 element and deletion products MER30 , MER30b , and MER107 , and the Charlie3 and deletion products MER1a and MER1b . The nonautonomous members of transposon subfamilies would be expected to be “active” only when the parental autonomous transposon is active , and IMA has independently grouped these elements together in time with no a priori consideration of their sequence structure or subfamily classification . Additional subfamilies are also grouped together , including Tigger7 ( position 219 ) and its nonautonomous elements MER44a , MER44b , MER44c , and MER44d ( positions 218 , 217 , 213 , and 220 , respectively ) , and others ( Figure 4 ) . However , not every subfamily appeared to group correctly , such as MER46c ( position 58 ) , which did not group with Tigger4 ( position 224 ) and its other nonautonomous family members MER46a and MER46b ( positions 243 and 233 , respectively ) . However , the relatively high median divergence ( 24 . 9% ) of MER46c compared with the 14%–15% divergence of the other Tigger4 elements supports the finding of IMA , and suggests that MER46c may be derived from another Tigger element that was active earlier than Tigger4 . These overall results are consistent with a recent analysis of DNA transposons in human and primate lineages [34] . To provide further insight into transposon history across multiple mammalian species , TCF was run on six additional mammalian genomes for which Repeat Masker data were available ( from the UCSC genome browser; Figure 5 and Table S3 ) . Each species contained a distinct set of TEs , including elements that were either species-specific or shared between two or more genomes ( see Materials and Methods ) . An independent chronological order for the set of TEs found in each genome was derived using IMA . These chronological orders from different species were compared in a pairwise manner to examine the extent of agreement and the regions of overlap and divergence ( Figure 5 and Tables S4 and S5 ) . The position of each element in the chronological order is represented by the positional distributions calculated by IMA ( e . g . , Figure 3 ) , so that the order of elements between two species need not be exactly the same to represent significant agreement . Elements were considered to be in matching positions if the positional distributions were significantly overlapping ( see Materials and Methods ) . These pairwise comparisons are shown in Figure 5 , where each genome is represented by a different color . See Figure S2 to access the original Excel file with full details . Matching TEs are shown by a solid-color bar , nonmatching TEs are shown by a lighter stippled bar , and TEs not present are shown by a gray bar ( see Figure 5 legend for details ) . Older TEs ( starting at the top of the chronological orders ) are shared between the different species . The mouse and rat share fewer old TEs with the other species , consistent with a higher mutation rate in the rodent lineages , making the older TEs less recognizable by Repeat Masker [35 , 36] . The point of divergence of rat and mouse from the other species is visible as the position where the majority of TEs are no longer shared , after which rat and mouse share many additional TEs ( Figure 5 , blue and brown bars ) and form a rodent clade . Most species-specific TEs in the cow , dog , rat , and mouse are found in the newer positions at the bottom of the chronological order . Human , chimp , and rhesus show the best agreement between the chronological orders ( Figure 5 and Table S4 ) , forming a primate clade with few species-specific elements . The cow and dog genomes also show a similar period of overlap with the primates and rodents , followed by a series of species-specific elements . These results further confirmed the ability of TCF and IMA to accurately age TEs across multiple mammalian species . The pairwise comparison of the chronological orders of TEs provides a novel method for constructing a phylogenetic tree containing these seven mammalian species by computing a distance matrix based on the degree of matching between species ( see Materials and Methods ) . The oldest elements ( older than position 49 in the human ) were not included in this analysis because many of them are no longer recognizable in the rodent species and thus would overestimate the distance between rodents and other species . A neighbor-joining tree constructed using this distance matrix was in good agreement with the current view of mammalian evolution [17 , 20 , 37] . TEs in those parts of the chronological orders where species diverge are informative and may be useful for phylogenomic analysis . For example , the L1MA family of elements is the youngest of the LINE1 elements shared by mammals , and are found near the points of divergence of the different species ( Figure 5 ) . MLT1A0 also appears in this region . Both L1MA9 and MLT1A0 have been observed in clade-specific insertions and used to support phylogenies ( see Discussion ) [20 , 21] . However , we suggest that many of the other TEs found in the region of divergence , such as MER2 , and Tigger1 , Tigger2 , and Tigger5 , will also prove useful for further phylogenomic studies . Furthermore , several TEs in the most recent region of the rat chronological order that do not match in the other genomes ( Figure 5 , light-colored bars ) were the MIRb , L3 , and MIR3 elements , which are among the oldest TEs in the mammalian genomes , suggesting that these may represent relatively new rat-specific elements that have been misidentified by Repeat Masker in the rat genome .
Although mammalian TEs represent almost half the DNA sequences in mammalian genomes , they are disproportionately understudied . We have described in this report a unique genome-wide evolutionary analysis of TEs that takes advantage of the completed human and other genome sequences and consider all TEs in the genomes on a comprehensive basis . A software package called TCF has been developed that performs a genome-wide defragmentation of all TEs in the human and other genomes . This defragmentation is based on a simple parsimonious tenet that fragments from the same TE in the same orientation , relatively close together , and with successive repeat indices are most likely from the same original transposon ( Figures 1 and S1 ) . Importantly , the defragmentation events that TCF identifies includes all the more sophisticated defragmentations performed by Repeat Masker itself , which assesses by homology to derived consensus sequences whether fragments initially identified as different elements could be from the same element which has been fragmented . However , TCF finds many additional defragmentation events . After attempting to consider other genomic parameters in the defragmentation , such as genomic distance between fragments and difference in percent divergence from the Repbase consensus sequence , extensive analysis of resulting clusters showed that simply using repeat indices provided the most reasonable TE defragmentation with recognizable insertions of TEs into other TEs . When more stringent conditions for defragmentation were used , many clusters contained TE fragments that appeared to originate from the same TE but were not defragmented . However , keeping the amount of distance of non–Repeat Masked sequences between fragments considered for possible defragmentation to ≤500 bp prevented very large inaccurately defragmented clusters from being identified . TCF is completely dependent on Repeat Masker data , and obviously as Repeat Masker data improves , our defragmentation data will improve . One could in theory improve the data presented here by a manual evaluation of each defragmentation event , especially those found in the upper triangle matrix after IMA , which may not be consistent with the derived chronological order , and by rejection of those that do not appear accurate ( Figure 2C ) . Such an analysis would be greatly facilitated by the Web-based Cluster Browser made available in this study that allows the user to perform specific queries of clusters where a particular TE interrupts another TE . The analysis presented here will also improve the Repeat Masker output by refining TE subfamily classifications , such as removing MER46c from the Tigger4 subfamily ( Figure 4 ) , removal of outliers such as the ancient insertion of MLT1F1 into L1MC3 ( see Materials and Methods ) , and identification of TEs that show wide disagreement between species , such as the “MIR” and “L3” elements , which in the rat fall within the most recent elements ( Figure 5 ) . Uniquely , TCF also records the number of times that each TE interrupts each other TE and provides these data in an adjacency matrix , or an interruptional matrix . TCF identified and excluded from this matrix certain types of transposon organization seen in the human genome that do not represent independent transposition events [26] ( Datasets S1–S5 ) . The relative age of TEs in individual transposon clusters is implicit in their organization ( Figure 1 ) . Nevertheless , TCF does not provide the means to arrange the TEs in the interruptional matrix in an overall chronological order . Therefore , we developed a computational method called IMA that approximates the ideal matrix of elements arranged in chronological order ( Figure 2B ) by searching for an order that minimizes an objective function ( the penalty score ) ( Figure 2C ) . The robustness of the chronological order derived by this method was confirmed in several ways . ( 1 ) The position of different subfamilies of human LINEs , DNA transposons , and SINE elements ( Figures 3A and 4 ) were consistent with approximate ages based on limited phylogenetic analysis [3 , 9–11] . ( 2 ) Analysis of human DNA transposons showed that the transpositional activity of nonautonomous elements coincided in the chronological order with the autonomous elements on which they depended for transposition ( Figure 4 ) [33 , 34] . ( 3 ) Analysis of six additional mammalian genomes showed that clade- and species-specific TEs were found in the most recent positions of the chronological orders ( Figure 5 ) . Because the rate of sequence divergence ( the molecular clock ) may not be constant over time or between lineages , the age estimates of TEs based on percent divergence may not be entirely reliable , especially for the older , more diverged elements . Our method to determine relative ages of TEs is not dependent on the percent divergence from derived consensus sequences or on an assumption of a constant molecular clock , and hence can be applied to all TEs in a given genome that have interacted with ( inserted into or been interrupted by ) enough TEs . Furthermore , this analysis is independent of the actual DNA sequence of the elements . Hence , the relative ages are determined across different classes and subfamilies of TEs . This method is as applicable to the older elements as it is to the younger elements . This to our knowledge is the first method to derive age and chronological information that does not rely on divergence of DNA sequence . Nevertheless , our relative age estimates are consistent for the most part with average percent divergence ( Figures 3 and 4 ) . One could specifically examine elements that show a disagreement between the derived chronological order and the percent divergence to find elements that may be undergoing positive or negative selection at the sequence level . A total of seven mammalian genomes were analyzed using our method , and the chronological orders were aligned and compared , which showed older elements shared between species and newer elements , primarily species- or clade-specific . Phylogenetic trees derived from this type of TE data may be suitable to help resolve phylogenetic issues concerning the evolution of mammals [17 , 20 , 37] and other species with sufficient numbers of TEs . Analysis of elements found within regions of divergence of these chronological orders provided a set of TEs that may be phylogenomically informative , including MLT1A0 and L1MA9 . Thomas et al . [20] observed three insertions of MLT1A0 elements that were shared between rodents and primates , but not between carnivores ( dog ) and artiodactyls ( cow ) , and one MLT1A0 and two L1MA9 insertions that were shared between carnivores and artiodactyls , but not between rodents and primates . These clade-specific TE insertions were used as evidence for placing rodents and primates in one sister group and the carnivore and artiodactyl in another sister group , and supported the idea that these TEs were active around the time of divergence of these sister groups . The analysis presented in Figure 5 provides many additional TEs for use in intergenomic examination of TE insertion and phylogenetic relationships , such as several of the more recent Tigger elements ( e . g . , Tigger1 , Tigger2 , and Tigger5 ) as well as MER2 and MLT1A1 . Thus , we have performed the first genome-wide transposon defragmentation analysis of the human genome , and used the overall age information implicit in these fragmentation events to derive relative ages of TEs . This interruptional analysis of TEs represents an essentially untapped genomic dataset that represents as much as 45% of the genome . The rich and complex nature of the data presented in this report will provide a great potential for genomic data mining to further understand the evolutionary history and impact of TEs in mammalian genomes .
TCF scans Repeat Masker data collected from the UCSC genome browser , and only considers TEs , not low complexity , satellite , or simple repeats from the Repeat Masker input . TCF scans the Repeat Masker data and looks for transposon fragments that could be combined into a unit . To be considered for defragmentation , two fragments X and Y must be the same transposon ( have the same TE name ) , on the same strand , and separated in the genome by no more than 500 bp of nonrepeat masked sequence . Note that additional TE fragments may lie between X and Y , but the lengths of those fragments ( which would be masked by Repeat Masker ) are not counted toward the 500 bp . TCF then checks any TE fragments found between fragments X and Y , and looks for transposon fragments that could be combined with them using the same criteria as for fragments X and Y . In this way , TCF collects a list of TE fragments that contain possible pairs for defragmentation and additional fragments between them . TCF closes this list when no more TE fragments on the list have possible pairs for defragmentation . Once the list is closed , TCF determines which TE pairs to defragment into units based on the difference between the repeat indices ( ΔRI ) . TCF joins TE fragments together from the whole list in order of increasing ΔRI ( fragments with the most closely matching consecutive repeat indices get defragmented first ) . TE pairs that overlap ( e . g . , appear to have a duplication of a portion of the transposon ) are allowed to be defragmented only when they overlapped by ≤50% of the size of the smaller of the two fragments , in which case the ΔRI is the amount of overlap . This overlap rule was important because many TE fragment pairs showed an overlap of one or very few base pairs , due to Repeat Masker often extending the homology match of both fragments to the consensus by several base pairs . Any fragment that does not pair up becomes its own unit . Additional TE fragments can be added onto defragmented pairs , but only on their free ends . Note that for any fragment order X W Y T , once X and Y are combined , W and T are not allowed to combine , because the two units XY and WT would imply that each fragmented the other ( see Figure S1I ) . Once all the units in a list are defragmented , TCF checks for units that fall between fragments in another unit ( interruptions ) . Units that are interrupted by another unit are clusters , which always consist of two or more units and at least three fragments . Note that several different clusters can result from the initial list of fragments . After the units are constructed , TCF examines them to detect L1 5′ inversions and intact LTRs . If a unit is an L1 , TCF examines the next unit in the cluster . If it has the same name or is from the same L1 subfamily , is within 6 bp , is on the opposite strand , and has repeat indices within 25 bp , it will be tagged as a 5′ L1 inversion , not be considered a separate unit , and not counted as an interruption . If a unit is an LTR , TCF examines the next two units in the cluster . If the next unit is an LTR–internal sequence , and the next unit is an LTR with the same name as the first LTR unit , and all three are on the same strand , than it might constitute an intact LTR . The first LTR must have ≤10 bp missing from its end , and the last LTR must have ≤10 bp missing from its start . This is tagged an intact LTR , and the second LTR element is not counted as an interruption . TCF writes the custom track file in the 12-column BED format used by the UCSC genome browser , which is stored as a compressed gzip file on our server ( http://www . mssm . edu/labs/warbup01/paper/files . html ) for downloading . TCF generates text files containing descriptions of all the clusters . TCF generates tab-delimited files to populate a MySQL database , which is used by Cluster Browser . Queries of TE interruptions run with Cluster Browser are processed by a Java servlet that accesses the MySQL database and returns the relevant clusters in an html format with links to the UCSC Genome Browser . TCF produces an n × n interruption matrix , where n is the number of types of TEs under consideration . The cell for row i and column j stores the number of times that TE i was found to interrupt TE j ( Figure 2 ) . For each TE , the percent connectedness in the matrix is defined as the fraction of other TEs that have either interrupted or been interrupted by the TE . For TE i , it is the number of other TEs j ( j ≠ i ) such that cell ( i , j ) or cell ( j , i ) is nonzero , divided by n − 1 . To determine the set of TEs with a minimum connectedness ( e . g . , 29% for the human TEs in Figure 3 ) , the connectedness of each TE is initially calculated for the entire matrix ( as shown in Table S1 for all human TEs ) . If any TEs had a connectedness less than the minimum cutoff , then the TE with the lowest connectedness is removed , and the connectedness of each remaining TE is recalculated . This process was iterated until every remaining TE was at or above the minimum cutoff ( e . g . , Table S2 , 306 elements ) . TCF then generates an interruption matrix for those features , which is submitted to IMA as described below . When additional elements are added back to an existing set ( e . g . , the additional 45 DNA transposons; Figure 4 ) or the overlapping sets of elements between different species ( Figure 5 ) , the percent connectedness is recalculated for each element in the final set used , and a corresponding table is included in Tables S2 and S5 . IMA seeks to determine a chronological ordering of the TEs that minimizes the interruption of newer TEs by older TEs ( Figure 2B ) . It defines an ordering penalty score as the summation of nonzero entries in the upper triangle of the interruption matrix ( Figure 2 ) ; i . e . , in all cells ( i , j ) with j ≥ i . Before the summation , the nonzero values are transformed by a continuous function τ ( x ) = x for x ≤ 3 and τ ( x ) = 3 + log ( x + 1 ) / 4 for x > 3 . The median of nonzero entries is three in the upper triangle matrix , and the log part of function τ ( x ) moderates the effects of the large nonzero entries on the penalty score . This transformation results in a penalty score in a randomly ordered matrix of about 45 , 000 , even though there are ∼650 , 000 interruptions . IMA searches for an ordering of the TEs that minimizes the penalty score by repositioning TEs in the interruption matrix . IMA starts at the first TE ( top of the matrix ) , and moves it to the position that results in the greatest decrease in the penalty score . A new interruption matrix is generated by moving the rows and columns of the matrix appropriately . Since in the adjusted matrix the first TE is now different , IMA checks the first TE again . When repositioning of the first TE no longer results in a decrease in the penalty score , IMA checks the second TE in the matrix , and when it can no longer be repositioned to decrease the penalty score , it checks the third TE , and so on until it reaches the last TE . This constitutes one round of processing . IMA then repeats the process from the first TE until it reaches a minimum penalty score , where repositioning of any element does not result in a decrease in the penalty score . Approximately seven to ten rounds of repositioning were required from each random starting order to reach the local minima from that random starting order . IMA iterates this procedure multiple times ( 100 , 000 times ) and records the ordering of TEs after each local minimum is produced . For each TE , the distribution of its positions across all iterations is recorded and displayed as an interval ( e . g . , positional distribution in Figure 3 ) , with the interval divided into lowest 5% and highest 5% of positions , next lowest 20% and next highest 20% of positions , the middle 50% of positions , and the median position . The individual graphs showing the numbers of interrupTEEs and interupTERs for each TE ( Figure 2D ) were generated using Excel Visual Basic ( Microsoft , http://www . microsoft . com ) , and have been normalized as follows . InterrupTER values ( pink ) are normalized for the target size of the fragmented TE ( interruptions per Mbp of the fragmented TE ) . InterrupTEE values ( pink ) are normalized for the total number of each inserting element ( interruptions per 10 , 000 elements of the inserting TE; a factor of 10 , 000 is used to put the numbers on an integral scale ) . Some additional TE clusters contained interruptions that did not represent independent transposition events . These were identified by analysis of the individual graphs showing the numbers of interrupTEEs and interupTERs for each TE ( Figure 2D ) . For the graph of each TE , any element that was greater than three standard deviations from the mean of either the interrupTERs or interrupTEEs values was identified , and that pair of TEs was examined for unusual or spurious transposition events using Cluster Browser and by consulting Repbase . For example , the LTR MLT1F1 was seen to interrupt both LINE L1MC3 and L1MD3 114 and 93 times , respectively , which was much more frequently than it interrupted other elements , and indeed Repbase [6] described this as an ancient insertion that has subsequently been propagated by transposition of these LINEs . Several similar putative ancient insertions were identified in this manner , including LTR8 into MER4A1-int ( 107 times ) . Additional outliers identified were LTR37A into MER31-int ( 22 times ) , LTR49 into MER4A1-int ( 23 times ) , MER112 into L1ME3b ( 34 times ) , and MER77 into MER21c ( 75 times ) . These pairs of elements were removed from the adjacency matrix , and the chronological order was recalculated ( the final order after removing these outliers is included in Figures 2 and 3 ) . These outliers still appear in the custom tracks and in Cluster Browser queries so that they may be examined . For each additional genome—chimp ( panTro2 ) , rhesus ( rheMac2 ) , cow ( bosTau2 ) , dog ( canFam2 ) , rat ( rn4 ) , and mouse ( mm8 ) —TE defragmentation was performed by TCF . The same conditions were used for excluding intact LTR elements and 5′ L1 inversions as for the human genome; these datasets are available by request . To determine the set of elements for consideration in this analysis , the percent connectedness was set at a value that accounted for approximately 95% or greater of the total TEs in the genome ( 29% for human , 30% for chimp and rhesus , and 10% for cow , dog , rat , and mouse ) . The overlap of these sets was further maximized by subsequently adding back to each set any elements that were present in two or more of the genomes analyzed ( but not within the original percent connectedness threshold ) . After initially running 40 , 000 iterations of IMA on these sets of TEs , elements were excluded whose positions within the chronological order were not-well supported because they showed a very low connectedness and a very large positional distribution . IMA was rerun for 40 , 000 iterations , which generated a chronological order and positional distributions for this set of TEs for each of the genomes ( Table S5 ) . The chronological orders from each genome were compared pairwise . Elements that were found in only one of the two genomes under comparison were identified , and all elements below it and up were shifted up in the chronological order ( by subtracting 1 from the position of all TEs below it ) , which maintained the alignment of the remainder of the elements that were in common between the two species . Subsequently , the degree of agreement between the chronological orders of each genome in a pairwise manner was determined . A TE was considered to be matching in position in the two chronological orders if the mean of the positional distribution of the TE in both genomes fell between the central 90% of positions calculated for that TE in the other genome ( yielding a solid-color bar in Figure 5 ) . Each genome , assigned a different color ( Figure 5 ) , was compared pairwise to each other genome , and a set of matrices where each genome in turn is the reference genome was produced . These matrices contained the set of TEs used in the IMA analysis for each reference genome ( in rows ) , with the alignment of the TEs from each of the other genomes ( the aligned genomes ) in the columns . A value and color was assigned to each position to indicate whether the TE in the reference genome was: ( 1 ) gray , not present in the aligned genome ( as included in the set of TEs used for IMA analysis; Table S4 ) ; ( 2 ) solid color , matching in the aligned genome; ( 3 ) lighter stippled color , not matching in the aligned genome; and ( 4 ) black , present in the aligned genome but not found in theset used for IMA analysis . An Excel Visual Basic macro was used to give each cell its color depending on its value . See Figure S2 for full details . Figure 5 also included a secondary alignment of the matrices of reference and aligned genomes ( all genomes were aligned to the human , and a portion of the rat genome was aligned to the mouse; see Figure 5 ) . This used an Excel Visual Basic script to maintain the alignment of TEs that match between the two species by inserting additional spaces ( white ) . This was especially important for comparisons between the human and rodent species , where many TEs found in the human genome were not in the rodent genomes . This served to keep the genomes aligned so that they could be more easily compared across Figure 5 . The phylogenetic tree ( Figure 5 , bottom ) was constructed for the seven mammalian genomes by calculating pairwise distances with the following formula: mismatches / ( matches + mismatches ) . The oldest elements were excluded above position 49 in the human order ( Figure 5 ) because this is the position where the transposon alignments are continuous in all species examined . The resulting distance matrix was used to build a neighbor-joining phylogenetic tree , using the program T-Rex ( http://www . labunix . uqam . ca/~makarenv/trex . html ) . Although this method is suitable to determine the correct topology of the tree , the branch lengths may not be accurate because of differences in transposon activity over time and in different species . The Warburton lab Website ( http://www . mssm . edu/labs/warbup01/paper/files . html ) contains this manuscript , with its figures , tables , and supporting information . It also contains a link to automatically upload the TCF custom track onto the UCSC genome browser , and a link to the Cluster Browser . To manually upload the custom track , upload the following URL onto the UCSC genome browser ( http://www . mssm . edu/labs/warbup01/tracks/tcftrack . gz ) . Cluster Browser is available at http://sungene-bk . genetics . mssm . edu/cluster/index . html .
Datasets S1–S5 provide lists of clusters with interruptions that were not considered independent transposition events , with links to the UCSC genome browser for each cluster . Each dataset contains a different category of transposon organization that was not considered an independent tranposition event . To view TCF custom tracks for each cluster , upload the custom track file using the link included . | Transposable elements ( TEs ) are interspersed repetitive DNA families that are capable of copying themselves from place to place; they have literally infested our genome over evolutionary time , and now comprise as much as 45% of our total DNA . Because of their great age and abundance , TEs are important in evolutionary genomics . However , estimates of their age based on DNA sequence composition have been unreliable , especially for older more diverged elements . Therefore , a novel method to estimate the age of TEs was developed based on the fact that as TEs spread throughout the genome , they inserted into and fragmented older TEs that were already present . Therefore , the age of TEs can be revealed by how often they have been fragmented over evolutionary time . We performed a genome-wide defragmention of TEs , and developed a novel objective function to derive the chronological order of TEs spanning >100 million years . This method has been used to infer the relative ages of TEs from seven sequenced mammalian genomes across all four major TE classes , including the oldest , most diverged elements . This age estimate is independent of TE sequence composition or divergence and does not rely on the assumption of a constant molecular clock . This study provides a novel analysis of the evolutionary history of some of the most abundant and ancient repetitive DNA elements in mammalian genomes , which is important for understanding the dynamic forces that shape our genomes during evolution . | [
"Abstract",
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] | 2007 | Evolutionary History of Mammalian Transposons Determined by Genome-Wide Defragmentation |
The formation of multinucleated muscle cells through cell-cell fusion is a conserved process from fruit flies to humans . Numerous studies have shown the importance of Arp2/3 , its regulators , and branched actin for the formation of an actin structure , the F-actin focus , at the fusion site . This F-actin focus forms the core of an invasive podosome-like structure that is required for myoblast fusion . In this study , we find that the formin Diaphanous ( Dia ) , which nucleates and facilitates the elongation of actin filaments , is essential for Drosophila myoblast fusion . Following cell recognition and adhesion , Dia is enriched at the myoblast fusion site , concomitant with , and having the same dynamics as , the F-actin focus . Through analysis of Dia loss-of-function conditions using mutant alleles but particularly a dominant negative Dia transgene , we demonstrate that reduction in Dia activity in myoblasts leads to a fusion block . Significantly , no actin focus is detected , and neither branched actin regulators , SCAR or WASp , accumulate at the fusion site when Dia levels are reduced . Expression of constitutively active Dia also causes a fusion block that is associated with an increase in highly dynamic filopodia , altered actin turnover rates and F-actin distribution , and mislocalization of SCAR and WASp at the fusion site . Together our data indicate that Dia plays two roles during invasive podosome formation at the fusion site: it dictates the level of linear F-actin polymerization , and it is required for appropriate branched actin polymerization via localization of SCAR and WASp . These studies provide new insight to the mechanisms of cell-cell fusion , the relationship between different regulators of actin polymerization , and invasive podosome formation that occurs in normal development and in disease .
Actin filaments are major components of a cell’s dynamic cytoskeleton . The remodeling of actin networks controls cell autonomous behaviors , such as cell shape changes and intracellular trafficking [1] . Highly regulated actin remodeling is also required in intercellular processes , such cell-cell adhesion and cell-cell fusion . Cell-cell fusion of myoblasts gives rise to the functional unit of muscle , the multinucleated myofiber ( [2] , reviewed in [3] ) . A series of conserved steps , including cell-cell recognition , adhesion , membrane alignment , membrane pore formation and cytoplasmic mixing , have been identified during myogenic cell fusion across species . Given its powerful genetic approaches , its optical tractability , and its simplicity , the Drosophila embryonic body wall musculature is an ideal system to study the mechanisms underlying these steps in myoblast fusion in vivo . In Drosophila , a multinucleated muscle fiber arises through the fusion of two types of myoblasts: a single Founder Cell ( FC ) , which determines muscle identity by expressing a unique combination of transcription factors ( [4 , 5] , reviewed in [6 , 7] ) , and multiple Fusion Competent Myoblasts ( FCMs ) ( reviewed in [8 , 9 , 10] ) . Upon fusion , the nucleus of the FCM adopts the identity and transcriptional profile of the FC/Myotube ( reviewed in [11 , 12] ) . As in vertebrates , fusion in Drosophila is an iterative process and in the fly embryo , the different individual muscles result from as few as 2 events to as many as 24 events [4] . Recognition and adhesion between the FCs/Myotubes and FCMs is mediated by four transmembrane molecules belonging to the immunoglobulin superfamily: the FC/Myotube-specific proteins , Dumbfounded ( Duf; also known as Kirre ) and Roughest , and their binding partners on the FCMs , Sticks and Stones ( Sns ) and Hibris [13 , 14 , 15 , 16] . After bidirectional signaling via these transmembrane receptors , a fusogenic synapse is established between the FC/Myotube and FCM , and accumulations of filamentous actin ( F-actin ) are observed on the opposing sides of the fusion site [17 , 18 , 19 , 20] . On the FC/Myotube side , a thin sheath of F-actin is present . On the FCM side , the F-actin focus , which makes up the podosome-like , invasive structure ( PLS ) , forms [21] . These enrichments of F-actin are highly dynamic and resolve prior to cytoplasmic mixing between the two cells [19] . F-actin accumulation and resolution at the fusion site suggest a functional role for actin during fusion . Supporting this role , genetic screens have identified a number of fusion mutants that map to genes involved in Arp2/3-based actin remodeling [8 , 9 , 10 , 22] . Arp2/3 is regulated by two nucleation-promoting factors ( NPFs ) , SCAR/WAVE ( WASp family verprolin-homologous protein ) and WASp ( Wiskott-Aldrich syndrome protein ) [23 , 24] . Both SCAR and WASp activate Arp2/3 through simultaneous binding of actin and Arp2/3 [24 , 25] . During myoblast fusion , the stability , localization , and activity of SCAR are regulated by the WAVE complex member , Kette ( Nap1 ) , and by the small GTPase Rac [19 , 26 , 27] , which is activated by the bipartite GEF , Myoblast city ( Mbc; Dock180 ) and Elmo [28 , 29] . WASp is recruited to the fusion site via the WASp-interacting protein Solitary ( Sltr ) ( also known as DWIP and Verprolin ) and Blown fuse ( Blow ) [30] . The coordinated activities of SCAR and WASp lead to Arp2/3 activation and subsequently the formation of the F-actin focus , the invasive podosome , a fusion pore [21] and finally , cytoplasmic continuity [17 , 31] . Arp2/3 has been shown to bind to an existing F-actin filament and nucleate a new branch . While Arp2/3 can nucleate F-actin filaments de novo , it does this slowly [32] . The presence of pre-existing filaments accelerates Arp2/3’s ability to form branched F-actin [33] . Formins , another group of actin regulators , complement the activity of Arp2/3 by generating linear actin filaments . Studies have revealed both collaborative and antagonistic relationships between members of the WAVE regulatory complex , Arp2/3 , and formins . As examples , Abi , a member of the WAVE complex , has been shown to interact with the formin mDia1 to positively regulate cell-cell adhesion in tissue culture cells [34] . In contrast , mDia2 , WAVE , and Arp2/3 have been shown to form a multimeric complex , which inhibits mDia2-dependent filopodium formation in cultured cells [35] . Arp2/3 and formins often act together in different in vivo contexts , including pseudocleavage furrow formation , cytokinesis , and filopodia formation in Drosophila primary neurons [36 , 37] . Particularly relevant for our studies in myoblast fusion are findings that , in cancer cells and macrophages , Arp2/3 and formins are required for the formation of podosomes , which resemble the invasive structure at the myoblast fusion site [21 , 38 , 39] . How Arp2/3 and formins interact to regulate actin dynamics in different in vivo contexts , particularly myoblast fusion , remains to be investigated . The best characterized formin in Drosophila is Diaphanous ( Dia ) , where it is critical for cellularization [40 , 41] , wound healing [42 , 43] , segmental groove formation [44] , dorsal closure [45] , and synapse growth [46] . Dia nucleates and elongates actin filaments through its FH1 and FH2 domains . The FH1 domain interacts with Profilin , which is an actin monomer-binding protein , to increase the local actin monomer concentration [47 , 48] . The FH2 domain binds to actin barbed ends , stabilizes newly formed actin dimers , and promotes the elongation of actin filaments [36 , 37 , 49 , 50] . The regulation of Dia activity involves autoinhibition and Rho GTPase-mediated activation . Dia autoinhibition relies on the interaction between its C-terminal DAD ( Diaphanous Autoinhibitory Domain ) region and the N-terminal DID ( Diaphanous Inhibitory Domain ) region [51 , 52] . The autoinhibited state of Dia is relieved when Rho-GTP binds to the N-terminal GBD ( GTPase binding domain ) region , thereby disrupting the DID-DAD interaction . Deletion of the Dia DAD domain inhibits the folding of Dia into the autoinhibitory conformation and results in constitutively active Dia [45] . Given the well-established role that Dia plays in actin regulation during development and the central position that actin plays in myoblast fusion , it is likely that Dia would play a role in myoblast fusion . However , no such role has been established . Here we show that Dia-mediated F-actin polymerization is required for the formation of the podosome-like structure at the myoblast fusion site and is essential for the invasion of the FC/myotubes by the FCMs . We show that Dia is localized to the fusion site and there regulates F-actin polymerization: loss of Dia activity blocks fusion , and no actin focus forms at the fusion site . Failure in focus formation arises from a block in F-actin polymerization as well as an inability to accumulate the Arp2/3 NPFs , SCAR and WASp , at the fusion site . Gain of Dia activity also blocks fusion and significantly changes the organization of the F-actin focus through increased actin turnover , leading to an excess of non-invasive filopodia at the fusion site . We further demonstrate that Dia-mediated SCAR and WASp localization is disrupted at the fusion site under these conditions . Based on our findings , we propose that Dia is necessary for two activities at the fusion site: Dia initiates invasive podosome formation through formation of linear actin filaments . Dia activity is also required for the accumulation of the Arp2/3 NPFs , SCAR and WASp , whose activity subsequently leads to Arp2/3 activation at the fusion site . The concerted F-actin elongation and branching processes likely provide the structural integrity and the necessary force generation for the invasive podosome , which ultimately leads to cell-cell fusion .
To investigate the role of Dia during myoblast fusion , we first examined its subcellular localization in fusing myoblasts ( S1 Fig ) . The fusion site is identified by the presence of the F-actin focus [19 , 21] . Immunostaining revealed that Dia is present in the cytoplasm and cell cortex of myoblasts ( S1 Fig ) and accumulates at the fusion site between adhered myoblasts ( Fig 1A and 1B ) . The specific accumulation of Dia at the fusion site was verified by quantification of Dia fluorescence intensity and comparison to phalloidin and Actin::GFP intensities ( Fig 1B and 1C , n = 10; S2 Fig ) . Together , our analysis of fixed embryos indicates that Dia is enriched at the fusion site . The F-actin focus is a dynamic structure that forms and subsequently resolves upon myoblast fusion . To determine whether Dia displays a similar profile , we used time-lapse analysis to compare the spatial and temporal dynamics of Dia and F-actin during fusion . Moesin::mCherry [53] and Dia::GFP [45] were expressed in myoblasts to label F-actin and Dia , respectively . Dia::GFP is reported to retain all Dia activity [45] . In myoblasts co-expressing both constructs , the lifetime of the F-actin focus was , on average , 16 . 8±6 . 9 minutes ( n = 5 ) , with a range from 9 to 25 min . This is comparable to expression of F-actin reporters alone [19 , 54] . We also confirmed that expression of these constructs under these conditions had no observable effects on muscle differentiation . Subsequent analysis of Dia::GFP and Moesin::mCherry revealed that Dia is present at the fusion site during F-actin focus formation and resolution ( Fig 1D and 1Di ) . While Dia localizes to the cell cortex before and after a fusion event , it clearly accumulates at the fusion site coincident with the F-actin focus . Together , these data indicate that Dia becomes enriched at the fusion site with the same spatial and temporal dynamics as the F-actin focus . Myoblast fusion is an asymmetric process in which the FCM produces a podosome-like structure that invades and promotes fusion with the FC/myotube . On the subcellular level , this asymmetry manifests in an uneven distribution of F-actin [21] . We therefore examined if Dia was also asymmetrically localized . To do this , we expressed Dia::GFP specifically in either FC/Myotubes or in FCMs and co-stained with the Dia antibody . By examining the overlap of exogenous and endogenous Dia signal , we can determine whether Dia is localized at the fusion site in one or both cell types . We first expressed Dia::GFP specifically in FCs/Myotubes and examined Dia::GFP and Dia distribution . Before fusion pore formation and cytoplasm mixing , Dia::GFP could readily be detected only in the FC/Myotube . Dia antibody , in contrast , detected endogenous Dia in both the FC/Myotubes and FCMs . With this labeling approach , the Dia and Dia::GFP signals partially overlapped ( Fig 1E ) . The partial colocalization between the FC/Myotube derived Dia::GFP and Dia antibody staining was confirmed by the separated peaks of the fluorescence intensity curves ( Fig 1F ) . Thus , Dia is present in the FC/Myotube side during fusion , but this expression only constitutes a small portion of the total Dia enrichment at the fusion site . Next , we examined the Dia accumulation on the FCM side . We expressed Dia::GFP specifically in FCMs , and assessed the localization of Dia::GFP and Dia . To prevent cytoplasmic mixing and the introduction of Dia::GFP into the FC/Myotube after fusion , Dia::GFP was expressed in FCMs of mbc mutant embryos , in which myoblast fusion is blocked prior to fusion pore formation [55 , 56] . Hence , no cytoplasmic exchange occurs between FCM and FC in mbc mutants . In FCMs , Dia::GFP accumulated at the fusion site ( Fig 1G ) and colocalized with endogenous Dia and the F-actin focus . The colocalization of Dia::GFP , Dia , and F-actin was confirmed by the overlapping fluorescence intensity curves ( Fig 1H ) . These findings demonstrate that Dia enrichment at the fusion site , like F-actin , occurs primarily in the FCM , whereas in FC , only a thin layer of Dia is detected along the fusion interface . Dia enrichment at the fusion site suggested a role for Dia in myoblast fusion . To determine where in the fusion pathway Dia could function , we examined Dia localization in mutants in which myoblast fusion is blocked ( Fig 2; S1 Table; S2 Fig ) . In sns mutants , where FCM-FC recognition is disrupted and no F-actin focus forms , Dia did not accumulate at the fusion site , but showed diffuse localization in the cytoplasm ( Fig 2B , S1 Table ) , suggesting that Dia functions downstream of cell recognition and adhesion during fusion . Embryos carrying mutations in genes that regulate SCAR activity—rac , mbc and kette—display an enlarged actin focus that does not resolve . In these mutants , Dia accumulation is largely unaffected in the examined actin foci ( Fig 2C–2E; S1 Table ) . We next examined embryos mutant for genes that regulate WASp activity: mutants in blow and wsp show enlarged F-actin foci , whereas mutants in sltr/Dwip/vrp , show normal sized foci . Dia localization appeared unchanged in all these mutants ( Fig 2G–2I; S1 Table ) . Mutants in loner , which encodes an ARF-GEF family member [57] display normal-sized actin foci . In loner mutants , Dia accumulated at the F-actin focus at the stalled fusion site ( Fig 2F; S1 Table ) . Dia’s enrichment at the fusion site in all these mutant conditions was quantified by fluorescence intensity curves ( Fig 2Aiv–2Iiv , n = 5/genotype; S2 Fig ) . Together these data indicate that Dia localization at the fusion site is dependent on FC/FCM recognition and adhesion , but appears to be independent of Arp2/3 actin regulation . The localization of Dia in fusion mutant embryos suggested a role for Dia downstream of FC/FCM recognition and adhesion . Hence , we examined muscle formation , and myoblast fusion in particular , in dia mutant embryos , using well-established dia alleles [40 , 58] . During Drosophila embryogenesis , Dia is required in numerous processes , including metaphase furrow organization during division , cellularization , pole cell formation [40] , segmental groove formation [44] and dorsal closure [59] . In zygotic dia5/dia5 and dia2/dia2 mutants , abnormalities in the muscle pattern were found , including insufficient fusion ( detected by free myoblasts ) , missing muscles , muscle morphology changes , and muscle detachment from its tendon cell ( S2 Table; S3A Fig ) . We quantified the level of myoblast fusion by counting the total number of nuclei in the four Lateral Transverse ( LT ) muscles/hemisegments in both dia2 and dia5 homozygous mutants . Using this approach , we found a reduction in fusion ( fusion index: dia2: 14 . 1±1 . 3 , n = 21; dia5: 21 . 5±1 . 3 n = 20; control: 27 . 8±0 . 3 n = 12; p<0 . 001; S3B Fig ) . These defects in the musculature could contribute to reduced viability of the dia2 and dia5 homozygous mutants , as less than 10% of dia2 homozygous mutants and only 20% of dia5 homozygous mutants hatched into larvae . While these data suggested a role for dia in myoblast fusion , we sought out genetic conditions that would enable us to study myoblast fusion in more detail . In particular , we wanted to: 1- eliminate the effects of loss of Dia’s function in the ectoderm . The ectoderm is known to impact muscle development [6 , 60]; 2- increase the number of embryos which have Dia’s function abrogated; and 3- increase , if possible , the level of fusion block when Dia’s function is reduced . The Gal4/UAS system [61] allows generation of embryos in which 100% of the embryos express the transgene and can have a phenotype rather than 25% that results from traditional genetic alleles . Pairing the mesoderm/muscle specific Dmef2-GAL4 with an appropriate UAS-line would allow manipulation of Dia in the cell type and during the time period in which fusion occurs . Available UAS-DiaRNAi lines , however , did not prove effective in knocking down Dia function during embryonic muscle development ( S2 Table ) . We thus generated dominant negative Dia ( DiaDN::GFP ) transgenic flies to reduce Dia activity specifically in the developing mesoderm/muscle during myoblast fusion . The FH2 domain of mDia1 , the mammalian homologue of Drosophila Dia , is required for its function in stress fiber generation in cultured cells [62 , 63] . A deletion of the first 21 amino acids of this domain was reported to act as a dominant negative protein , either by competing with endogenous mDia for F-actin binding or by binding to endogenous mDia to form non-functional dimers [62 , 63] . Since the key amino acids in this domain are identical between mDia1 and Drosophila Dia , we designed a Drosophila dominant negative Dia ( DiaDN ) modeled after this mouse construct ( Fig 3A ) . To confirm that the DiaDN construct affects Dia-based actin regulation , we examined filopodia in cultured cells and in the Drosophila epidermis . Previous work indicated that reduction of Dia leads to reduction of actin-based structures , such as filopodia , in cell culture and in vivo [45 , 64] . In S2R+ cells that express DiaDN the number of filopodia was greatly reduced compared to the neighboring control cells ( 5 . 7± 3 . 0 vs 17 . 5±5 . 8 , n = 20 , p<0 . 001 ) ( Fig 3B ) , consistent with a reduction of endogenous Dia activity . This reduction in filopodia was rescued by overexpression of a full length Dia with DiaDN , revealing the specificity of the DN construct ( S3C and S3D Fig ) . We also tested the efficiency of DiaDN in vivo , specifically by examining filopodia in leading edge ( LE ) cells during Drosophila dorsal closure . Similar to dia5 maternal and zygotic mutant embryos [45] , filopodium number was reduced in embryos expressing DiaDN ( Fig 3C–3Cii ) . Both these data sets are consistent with a reduction of Dia activity via our DiaDN construct . As additional test of our DiaDN construct , we examined another context in which Dia is known to play a role . dia1 homozygous mutants are sterile due to defects in cytokinesis in the germline [58] . Expression of DiaDN in the male germline leads to reduced fertility due to fewer sperm ( S3F and S3G Fig ) , consistent with a reduction in Dia activity in this context . Collectively , these data indicate the DiaDN construct reduces Dia activity . We next examined the effects of DiaDN when expressed specifically in myoblasts . When one copy of DiaDN was expressed , we observed defects in muscle development , including myoblast fusion , in 50% of embryos ( n = 20; Fig 3D ) . Other muscle differentiation processes also were disrupted , including muscle attachment and morphology ( S3E Fig ) . These phenotypes were similar to those observed in dia mutant embryos ( S2 Table; S3A and S3B Fig ) , reinforcing that the defects using DiaDN were due to Dia loss of function . To increase the penetrance and expressivity of the fusion phenotype in embryos expressing DiaDN , we increased DiaDN expression levels in two ways: increasing genetic copy numbers of the mesoderm/muscle driver DMef2-Gal4 driver and UAS-DiaDN and increasing the temperature at which we raised the embryos , since higher temperatures correlated with increased Gal4/UAS activity [61 , 65] . We generated a fusion index as described for dia mutants ( Fig 3E and 3F; S3 Fig ) . Expression of one copy of DiaDN with one copy of the Gal4 driver ( 1X ) resulted in a significant decrease in myoblast fusion ( 22 . 5±2 . 2 vs 27 . 1±2 . 3 LT nuclei/ hemisegment in control , p<0 . 001 ) . Free myoblasts also were detected , in addition to detached muscles . Expression of two copies of Dia DN with two copies of the Gal4 driver ( 2X ) resulted in a more severe fusion block ( 15 . 5±2 . 7 p<0 . 001 ) and more free myoblasts were observed . Under these conditions , some myotubes also failed to properly attach to tendon cells , and , as a result , formed myospheres ( Fig 3D , arrowheads; S3E Fig ) . Specification and differentiation of both FCs and FCMs occurred normally under these conditions , as revealed by expression of MHC and apRed positive nuclei ( Fig 3D and 3E ) [19 , 54] . In combination with Dia’s localization at the fusion site , these data indicate that Dia activity is necessary for myoblast fusion . To determine which cellular step of fusion requires Dia activity , we next examined F-actin foci and myoblast morphology in embryos where DiaDN::GFP was expressed in myoblasts . We found that FCMs oriented towards the FC/Myotube , showing the characteristic teardrop shape [8 , 19 , 56] . FCMs also attached to FC/Myotubes . Localized expression of the recognition and adhesion receptors , Duf and Sns , at the fusion site confirmed that these FCMs were adhered to the FC/Myotube ( Fig 4A ) . However , in 40% of the attached FCMs , an actin focus failed to form , consistent with the extent of fusion block found under these conditions ( Fig 4B and 4C ) . Further confirmation of these data was obtained using time-lapse imaging of myoblast fusion ( Fig 4D , S1 Movie , S3H Fig ) . Embryos expressing DiaDN::GFP in myoblasts showed movement of FCMs towards and attachment to FC/myotubes . A subset of these adhering FCMs showed no significant accumulation of actin that resembled the F-actin focus . The FCMs that failed to form a F-actin focus also failed to fuse to the FC/Myotube during the time-lapse sequence , consistent with reduction of Dia activity causing a fusion block . In agreement with Dia regulating actin at the fusion site , expression of DiaDN also significantly reduces actin focus size in the blow1 mutant , which normally has an enlarged actin focus ( S3I Fig ) . Together these data support the critical role of Dia for the formation of the F-actin focus as well as the importance of the F-actin focus during the fusion process . Previous data [21 , 54] indicated that simultaneous loss of both Arp2/3 NPFs , SCAR and WASp , leads to a fusion block with no F-actin foci , due to the loss of Arp2/3 activity . To address whether the lack of the actin focus in the DiaDN expressing myoblasts was due just to reduced F-actin polymerization by Dia or whether Dia loss could also influence Arp2/3 activity , we examined the localization of the Arp2/3 regulators , SCAR and WASp , in myoblasts expressing the DiaDN construct ( Fig 4E and 4F ) . We found that neither Arp2/3 regulator was present at the fusion site in those FCMs in which a focus failed to form ( WASp: enrichment at 0% fusion site versus 100% in control . SCAR: enrichment at 20% fusion site vs 70% in control ) . These data suggest that Dia activity is required , through localization of SCAR and WASp , for Arp2/3 activity at the fusion site . To further investigate whether Dia functions upstream of the Arp2/3 pathway , we examined Dia localization in sltrs1946; ketteJ4-48 double mutants . In this double mutant , Arp2/3 is inactivated , due to the lack of activated SCAR and WASp , and no actin focus is observed at the fusion site [54] . Immunostaining of these mutants revealed that Dia accumulated at the fusion site ( Fig 4G ) , suggesting that Dia accumulates at the fusion site prior to actin focus formation , and Dia’s localization is independent to Arp2/3 activity . Moreover , these data imply , that Dia expression , in the absence of Scar and Wasp activity ( and by extension , absence of Arp2/3 activity ) , is not sufficient to build the F-actin focus . As another test of our model , we examined Dia expression in embryos where PI ( 4 , 5 ) P2 signaling is abrogated . In other contexts [66] , PI ( 4 , 5 ) P2 signaling provides a localization cue for Dia . However , under conditions in which reduction of PI ( 4 , 5 ) P2 signaling leads to a myoblast fusion block [54] , Dia was still localized to the fusion site ( Fig 4H ) . Hence , PI ( 4 , 5 ) P2 signaling appears not to be required for Dia localization during fusion . In addition , reduction in PI ( 4 , 5 ) P2 signaling at the fusion site leads to a reduction in actin focus size . PI ( 4 , 5 ) P2 signaling functions upstream of Arp2/3 activity and the reduced focus size correlated with reduced recruitment/maintenance/activity of Arp2/3 NPFs at the fusion site [54] . Localization of Dia at the fusion site in this background now provides , in part , a possible explanation for this smaller actin focus . Dia localization in this PI ( 4 , 5 ) P2 signaling mutant background would lead to low level recruitment/activity of Arp2/3 NPFs , subsequent Arp2/3 activity , and actin focus formation ( albeit smaller ) . Nevertheless , Dia requires PI ( 4 , 5 ) P2 signaling to build an effective actin focus , capable of mediating myoblast fusion . Taken together , we conclude that Dia is essential for myoblast fusion , and this function occurs after recognition and adhesion between FC and FCMs , but prior to Arp2/3-based actin polymerization . Importantly , Dia activity appears to be required for actin focus formation , both through its regulation of F-actin polymerization and the accumulation of Arp2/3 regulators at the fusion site . To gain further insight to Dia’s role in actin polymerization and in the localization of the Arp2/3 NPFs at the fusion site , we examined the effects of constitutively active Dia on myoblast fusion ( Fig 5 , S4 Fig , S5 Fig ) . Several well-characterized constitutively active Dia constructs ( DiaCA ) were employed , including DiaΔDAD , which has a deletion of the DAD domain , and FH1FH2 , which consists of only Dia’s FH1 and FH2 domains ( Fig 5A , S4A Fig ) [59] . Expression of any of these DiaCA constructs in myoblasts blocked myoblast fusion in 100% of the embryos , as witnessed by the presence of unfused , free myoblasts ( Fig 5B; S4B and S4C Fig ) . FCs were properly specified and myoblast recognition and adhesion , as measured by Sns and Duf localization , were unaffected ( S5 Fig , S2 Movie ) ; however , fusion did not occur ( LT muscle fusion index: 5 . 2±1 . 0 vs 26 . 6±1 . 5 , p<0 . 001/ hemisegment; Fig 5C; S4C Fig ) . Examination of the localization of constitutively active Dia during myoblast fusion revealed that , as with endogenous Dia , all DiaCA constructs showed enrichment at the fusion site ( Fig 5D , S4D Fig ) . Time lapse imaging showed , however , that DiaCA was associated with highly dynamic filopodia; for example , DiaΔDAD::GFP was found concentrated at the tip of each of the multiple filopodia at the fusion site ( Fig 5D , arrows; S3 Movie ) . Under wild-type conditions or when Dia::GFP is overexpressed , such increased numbers of dynamic filopodia were not detected . We next examined actin organization in myoblasts expressing DiaCA . GFP-tagged actin , which labels both G- and F-actin , was used to visualize actin ( Fig 5E ) . We observed Actin::GFP accumulation at fusion sites , which were similar in size to that in control embryos ( 1 . 8±0 . 37μm vs 1 . 9±0 . 43μm , respectively; n = 50 , p = 0 . 25 ) . Multiple filopodia were also detected with Actin::GFP , extending from both the FCM and the FC/Myotube . The actin accumulation did not resolve during the 1h observation time , as fusion failed to occur . Together , these data indicate that DiaCA is recruited appropriately to the fusion site . There , DiaCA enhances actin polymerization , visualized as increased filopodia; however , this increase in actin polymerization appears not to be productive , as fusion progression is blocked . The highly dynamic filopodia at the fusion site suggested that actin undergoes rapid remodeling in myoblasts expressing DiaCA . We employed fluorescence recovery after photobleaching ( FRAP ) to quantify the consequence of expressing DiaCA on actin dynamics at the fusion site . In control experiments , photobleaching of individual actin foci in wild-type embryos expressing Actin::GFP resulted in a rapid recovery of the fluorescent signal to pre-bleaching levels ( Fig 6 , S3 Table ) . Parallel experiments in myoblasts expressing both Actin::GFP and DiaCA revealed that DiaCA significantly enhanced the actin recovery rate relative to control ( Fig 6A–6C ) : the half time of fluorescence recovery in embryos expressing DiaCA was significantly less than that in control ( 16 . 3±6 . 7s vs 53 . 3±17 . 7s , respectively , p<0 . 001 ) . The percentage recovery for embryos expressing DiaCA , however , was similar to controls ( Fig 6D ) . The rapid turnover rate of Actin::GFP upon DiaCA expression indicated that actin filaments undergo faster polymerization and depolymerization cycles than in control myoblasts . These data are consistent with the increase of rapidly extending filopodia observed in time lapse of myoblasts expressing constitutively active Dia . The previous experiments with Actin::GFP measured both G- and F-actin at the fusion site . To examine the organization and distribution of F-actin alone at the fusion site , we used phalloidin staining in fixed preparations . In myoblasts expressing DiaCA , the F-actin focus displayed a diffuse distribution rather than a compact spherical organization seen in wild-type FCMs ( Fig 7A and 7B ) . This altered distribution was reflected in the fluorescence intensity curve: the peak of F-actin intensity curve in DiaCA myoblasts is broader in comparison to controls ( Fig 7C ) . Together with the FRAP experiments , these data imply that DiaCA blocks myoblast fusion by altering actin dynamics and the actin focus organization at the fusion site . Our loss-of-function experiments indicate that Dia is required , not only for filamentous actin polymerization , but also for the localization of the Arp 2/3 NPFs , SCAR and WASp , at the fusion site . To better understand the link between Dia and the two Arp2/3 NPFs during fusion , we employed epistasis experiments , using the DiaCA and mutants in the NPF regulators ( Fig 7 ) . We first examined F-actin focus morphology in ketteJ4-48 mutant embryos in which DiaCA is expressed in myoblasts . Kette regulates the stability and localization of SCAR during myoblast fusion . In kette mutants , myoblast fusion is blocked and F-actin foci are enlarged [19] . When expressing DiaCA in the ketteJ4-48 background , F-actin did not form a dense focus , but rather , displayed diffuse localization , reminiscent of the F-actin distribution in DiaCA expressing myoblasts ( Fig 7D ) . We also examined F-actin focus morphology sltrS1946 mutant myoblasts; Sltr directly binds to and activates WASp . In sltrS1946 mutants , myoblast fusion is blocked but the size of the F-actin focus is not changed [19] . When expressing DiaCA in the sltrS1946 background , F-actin was diffuse at the fusion site and did not form a restricted focus , which again resembled F-actin distribution when DiaCA was expressed in myoblasts alone ( Fig 7E ) . The distribution of F-actin in kette-/- and sltr-/- backgrounds when DiaCA was expressed suggested that Dia functions upstream of Arp2/3 in regulating F-actin assembly at the fusion site . To confirm and build upon these data , we examined the localization of SCAR and WASp , the targets of Kette and Sltr activity in embryos expressing DiaCA . In control embryos , SCAR accumulated at the fusion site ( Fig 7F ) . In embryos expressing DiaCA in myoblasts , SCAR was still present , but it was no longer enriched at , or restricted to , the fusion site: SCAR was found mislocalized throughout the FCM ( Fig 7F ) . The mislocalization of SCAR was verified by fluorescence intensity curves: SCAR displayed several peaks in myoblasts expressing DiaCA , and only one overlapped with the F-actin . WASp also localized at the fusion site in control embryos ( Fig 7G ) . In myoblasts expressing DiaCA , WASp , like SCAR , displayed a diffuse localization in the cytosol . Interestingly , when we evaluated the localization of WASp using fluorescence intensity curves , we found that WASp colocalized with the diffuse F-actin foci at the fusion site ( Fig 7G ) . The mislocalization of SCAR and WASp suggests that , in myoblasts expressing DiaCA , Arp2/3 was activated over a larger area compared to control , therefore , could contribute to the diffuse localization of F-actin at the fusion site . The mislocalization of SCAR and WASp in both DiaDN and DiaCA expressing myoblasts , as well as the lack of an F-actin focus in myoblasts expressing DiaDN , indicates that Dia functions upstream of Arp2/3 and its regulators and that a particular level of Dia actin polymerization activity at the fusion site is required for optimal Arp2/3 activity and focus formation at the fusion site .
Actin remodeling is critical for myoblast fusion , but Arp2/3 was the only known actin polymerization factor that was shown to be necessary for myoblast fusion [17 , 19] . We now show that the formin Dia is also required during myoblast fusion . Whereas Arp2/3 preferably binds to pre-existing actin filaments and generates uncapped F-actin , formins nucleate F-actin both de novo and from the barbed ends of pre-existing actin filaments . Thus , Dia can generate actin filaments de novo , which Arp2/3 can bind or elongate [67 , 68] . We also show that the level of Dia activity is critical for myoblast fusion . Too much actin polymerization leads to too many filopodia and absence of an invasive podosome with its characteristic F-actin core . Too little polymerization leads no actin focus and no podosome formation . Our FRAP data with DiaCA also hint at whether a limited pool of actin is available for the actin polymerization factors during myoblast fusion . Despite the high rates of actin turnover with expression of DiaCA , the final fluorescence levels of actin returns to the same value as in controls . Additional actin monomers are not recruited to the site , even with high levels of polymerization activity . Interestingly , the rate of actin turnover has also been measured in mutants that affect Arp2/3 activity: specifically , mutations in blow , which regulates the Arp2/3 NPF WASp , show lower rates of actin exchange than in controls , due to a reduced exchange rate for WASp on the barbed ends of actin at the fusion site [30] . Together these data suggest future experiments aimed at examination of whether rates of actin polymerization regulated by both Dia and Arp2/3 are optimized for the available actin pool and tightly controlled for myoblast fusion to properly occur . Both cooperative and antagonistic functions between Dia and Arp2/3 have been reported [69 , 70] . Here we demonstrate that the coordinated and cooperative activities of these two actin polymerization factors leads to the formation of the F-actin focus . With the exception of sltr/Dwip/vrp mutants that form a focus of wild-type size , single mutants in the Arp2/3 NPF pathways , WASp and SCAR , lead to enlarged foci; however , double mutants in WASp and SCAR pathways do not form foci [54] . This is the same phenotype that we have seen in myoblasts expressing the DiaDN . Our data support Dia activity being upstream of WASp and SCAR activation of Arp2/3 at the fusion site . This suggests that , at the fusion site , Dia initially provides the necessary context upon which Arp2/3 can act and not vice versa , as has been suggested in other contexts in which linear actin filaments emerge from Arp2/3 based structures [30] . Nevertheless , both sets of actin regulators are necessary for F-actin focus formation that provides the core of the invasive podosome . Neither Dia nor Arp2/3 alone are sufficient . The interplay between Dia and Arp2/3 at the fusion site is also reflected by our localization studies . Too little or too much Dia activity resulted in improper localization and , by extension , improper activity of Arp2/3 NPFs . How could Dia regulate this localization ? One possibility is that Dia indirectly regulates Arp2/3 localization . Dia could nucleate linear actin filaments , which then would provide the necessary substrate for recruitment , maintenance and /or activation of Arp2/3 and its regulators , such as the WASp-WIP complex [30] . Another possibility is that Dia , through its interactions with members of the SCAR/WAVE complex such as Abi , may directly localize and/or maintain the localization of Arp2/3 regulators , which are then activated at the fusion site . Abi has been reported to bind directly with Dia in vitro , and this interaction is required for the formation and stabilization of cell-cell junctions [34] . Dia likely changes the localization and integrity of the SCAR/WAVE complex by competitively binding to the N-terminal part of Abi , dissociating Kette/Nap1 from the complex , and thus changing the stability and localization of SCAR/WAVE . It has also been established that the recognition and adhesion receptor , Sns , is capable of recruiting the Arp2/3 NPFs , such as WASp , to the fusion site [30] . While Sns is still clustered at the fusion site in DiaDN and DiaCA , its recruitment activity appears not sufficient for focus formation capable of supporting an invasive podosome . We have shown that localization of Arp2/3 NPFs is affected in Dia loss and gain of function . In addition to this spatial control , another important way of controlling Arp2/3 activity is through activation of the NPFs via small GTPases . SCAR is activated through Rac-dependent dissociation from SCAR inhibitory complex [19 , 26 , 27] . WASp is activated by binding to Cdc42 , which releases it from auto-inhibited state [17 , 18 , 19 , 20] . In this study , we did not examine the localization of these activated GTPases . However , previous work has shown that PI ( 4 , 5 ) P2 signaling is required for proper localization of activated Rac at the fusion site [54] . How the localization and activity of small GTPases at the fusion site contribute to the spatial and temporal interplay between Dia and Arp2/3 regulation of actin polymerization requires further investigation . It remains unresolved how Dia itself is recruited to the fusion site . Our data suggest that the recognition and adhesion receptors Duf and Sns would be involved either directly or indirectly in recruiting Dia to the fusion site , as embryos that fail to express either of these adhesion receptors fail to recruit Dia to the fusion site . In addition , recent data from Drosophila epithelial tubes [66] indicate that PI ( 4 , 5 ) P2 serves as a localization cue for Dia . Previous work in our lab has shown that PI ( 4 , 5 ) P2 accumulates at the fusion site after FC-FCM recognition and adhesion; sequestering of PI ( 4 , 5 ) P2 results in a significant fusion block [54] . We thus tested whether PI ( 4 , 5 ) P2 regulates Dia localization at the fusion site . We find that Dia is recruited to the fusion site in the PI ( 4 , 5 ) P2 sequestered myoblasts , suggesting that , in this context , PI ( 4 , 5 ) P2 signaling is not required for Dia localization . These data provide possible explanations for why in PI ( 4 , 5 ) P2 sequestering embryos , smaller actin foci are detected: the localized Dia may be sufficient to recruit low levels of Arp2/3 and its NPFs , which , upon activation , lead to the formation of small F-actin foci . Nevertheless , in the absence of PI ( 4 , 5 ) P2 signaling , Dia that is recruited to the fusion site is not sufficient to produce functional actin focus , capable of directing a fusion event . Recent work [71] also indicates that charged residues in the N- and C-termini of mDia1 are sufficient both for mDia’s clustering of PI ( 4 , 5 ) P2 and its own membrane anchorage . This interaction between mDia1 and PI ( 4 , 5 ) P2 , in turn , regulates mDia1 activity . Whether such a mechanism is in play at the myoblast fusion site needs to be further investigated . We propose a working model for the interplay between the actin regulators during myoblast fusion ( Fig 8 ) . Dia is recruited to the fusion site upon engagement of the recognition and adhesion receptors by a yet-to-be determined mechanism . We propose that PI ( 4 , 5 ) P2 signaling at the fusion site regulates the localization and activation of downstream targets such as Rho-family of small GTPases . These small GTPases lead to the activation of Dia . Activated Dia , in turn , polymerizes linear actin filaments and , in combination with the recognition and adhesion receptors and PI ( 4 , 5 ) P2 , recruits the Arp2/3 NPFs , SCAR and WASp . Activation of these Arp2/3 NPFs at the fusion site would be accomplished by small GTPases such as Rac . These , in turn , would activate Arp2/3 , leading to branched actin and formation of the F-actin focus and the invasive podosome . Whether the Arp2/3 NPFs such as SCAR/WAVE would negatively regulate Dia to downregulate linear actin polymerization , as suggested for mDia2 in cell culture [35] , or whether Dia competes with WASp for barbed end binding remains to be investigated[72] . However , these mechanisms would underscore a switch from linear F-actin filopodium formation to the linear and branched F-actin invasive podosome–like structure that is necessary for fusion . The actin focus formed at the fusion site is an F-actin rich , invasive podosome-like structure that has been suggested to provide a mechanical force for FCMs to invade the FC/Myotube [30] . Similar invasive actin structures named invadosomes have been seen in different cell types , such as podosomes in macrophages and invadopodia in cancer cells [73] . Arp2/3 is known to play a key role in invadosome formation , and recent studies have revealed the involvement of formins in developing invadosomes [38 , 39] . Our data indicate that specific temporal and spatial interactions between the formin Dia and Arp2/3 are required for the actin focus and invasive podosome formation . Our data thus provide new mechanistic insights for the interplay of Arp2/3 and Formins during invadosome formation in these contexts .
The following stocks were used: Oregon R ( control , wildtype ) , UAS-diaFH1FH2 ( Bloomington #27616 ) , twist-actin::GFP [19 , 74] , apME-NLS::dsRed [19] , UAS-dia::GFP , UAS-DiaΔDAD::GFP; UAS-diaFH1FH2::GFP , UAS- DiaΔDAD::HA , UAS-DiaDDFH1FH2::GFP ( M . Peifer ) [45] , duf5 . 1-Gal4 ( 5 . 1kb enhancer region of duf fused with Gal4 , sequence information based on [14 , 75] ) , blow[1] [56] , sns[XB3] [13] , mbc[c1] [55] , kette[J4-48] [76] , loner[T1032] [57] , sltr[S1946] [20] , Rac1[J11] , Rac2[Δ] , Mtl[Δ] [77] , wsp[3D3-35] [78] . Stocks were balanced over CyO , dfd-GMR-YFP or TM3 , dfd-GMR-YFP [79] , and identified by GFP staining . UAS-moesin::mCherry [53] , DMef2-Gal4 [80] , dia5 [40] , dia2 [58 , 59] . UAS-DiaDN::GFP ( this study ) , UAS-PHplcγ::GFP [81] , net-Gal4;nos-Gal4 [82 , 83] . The GAL4-UAS system [61] was used for expression studies . Embryos were staged according to [84] . Embryos were collected , fixed in 4% PFA , and stained according to standard lab protocols [19] . Antibodies were used at the following concentrations: α-Dia ( 1:500 ) ( S . Wasserman ) , α-Duf ( 1:200 ) ( K . -F . Fischbach ) , α-Sns ( 1:250 ) ( S . Abmayr ) , α-WASp ( 1:500 ) ( E . Schejter ) , α-SCAR ( 1:100 ) ( J Zallen ) [85] , α-Blow ( 1:100 ) ( E . Chen ) , α-GFP ( 1:500 ) ( Invitrogen A11120 ) , α-dsRed ( 1: 500 ) ( Clontech 32392 ) , Alexa Fluor 647-phalloidin ( 1:100 ) ( Invitrogen A22287 ) . For secondary antibodies , Alexa Fluor 488- , Alexa Fluor 555- , and Alexa Fluor 647-conjugated fluorescent secondary antibodies at 1:200 dilution ( Invitrogen ) were used . Fluorescent images were acquired on a Leica SP5 laser scanning confocal microscope equipped with a 63X 1 . 4 NA HCX PL Apochromat oil objective and LAS AF 2 . 2 software . Maximum intensity projections of confocal Z-stacks were rendered using Volocity visualization software ( Improvision ) . Cell outlines of somatic myoblasts and myotubes were determined in saturated images , and cells were false colored using Adobe Photoshop CS4 ( S1 Fig ) . Nuclei in the four LT muscles were specifically labeled by expressing dsRed fused to a nuclear localization signal under the control of the apterous mesodermal enhancer ( apRed ) [19 , 86] . Fusion index was quantified by counting the number of dsRed positive nuclei in each hemisegment in stage 17 embryos . 10–40 hemisegments were analyzed . Line scans were done in ImageJ [87] . The focal plane was selected when the cross sectional area of F-actin focus was its largest [19] . A line of predetermined length was drawn across the F-actin focus ( Fig 1 ) , or along the FCM cell outline that was adhered to the myotube ( all other Figures ) . Grey values were measured along the line . The relative intensity was calculated at each point by yrelative = ( y-ymin ) / ( ymax-ymin ) . Five to ten F-actin foci were measured for each genotype . The average relative intensity and standard deviation of mean at each point were calculated from those samples . Fluorescence intensity along the line was similarly measured for each of the different channels . The different curves were aligned according to the location of the line . For control , a line of the same length was dropped outside the cell . Signal intensity was measured , normalized , and averaged in the same way as described above . The pipeline for our line scan analysis is shown in S2 Fig . Male adult flies carrying net-Gal4;nos-Gal4>UAS-diaDN::GFP or net-Gal4;nos-Gal4>w ( ctrl ) [82 , 83] were crossed to w female in a 1:2 ratio . Mated females were allowed to lay eggs for four days in 29°C . The number of larvae were counted , and this number was used to determine the number of larvae produced per male per day . Testis and seminal vesicles were dissected ( n = 5 ) , fixed in 4% PFA , and stained with DAPI [88] . Sperm numbers were quantified using images taken on confocal microscope . Actin::GFP was used to label F-actin foci at fusion site . FRAP experiments were performed on a Zeiss LSM710 , with 40x oil objective . 488nm laser was set at 14% . Pinhole was set at AU 1 . 0 . The region of interest ( ROI ) was identified and selected manually , and two scans were taken before bleaching . 100% 488nm and 458nm laser were used to bleach ROI to approximately 30% of the original intensity . After photobleaching , images were acquired every 3s . For data analysis , the size of ROI was fixed for each movie , but the location was adjusted manually for each frame , as the F-actin focus shifts over time . Fluorescence intensity was measured in ImageJ and was normalized to background . Half time was determined by y = ymin+ ( ymax-ymin ) ( 1-e-kt ) , and kinetic curves were plotted according to the calculation [30] . Dominant negative Dia ( DiaDN ) was designed according to [63] . In brief , a dominant negative mDia construct was developed by creating a 21 amino acid deletion ( spanning aa 750–779 ) in the FH2 domain within aa 567–1182 , which contains the FH1 and FH2 domains ( Fig 3 ) . Alignment of mDia1 with Drosophila Dia identified a conserved 497–1029 amino acid sequence containing the FH1 and FH2 domains . The orthologous 21 aa deletion spanning aa 599–619 in FH2 was created; this construct , containing the FH1 domain and part of the FH2 domain , served as our dominant negative Dia ( DiaDN ) in Drosophila . We cloned DiaDN into pUAST vector with a C-terminal GFP or mCherry tag using two pairs of primers: 5’CACCGAATTCATGGGTGTGGCGGCTCCGTC3’ and 5’AAAAGATCTGCCATGAGGCAGAACGGG3’ , 5’CACCGGATCCGTCCCGGCCAAAATGTCC3’ and 5'AAAGGATCCCATCACGCCTTCCTGCG 3' . The DiaDN constructs were validated by sequencing and tested in S2 Cells and in vivo . Embryos were raised at 25°C , collected , dechorinated and mounted on a Teflon membrane in Halocarbon oil 700 ( Halocarbon Products Corp . , Series 700 , 9002-83-9 ) . Images were acquired every 30 or 60 sec as indicated , single z = 0 . 5μm 18-20mm total on an upright Leica SP5 laser scanning confocal microscope equipped with a 63X 1 . 4 NA HCX PL Apochromat oil objective and LAS AF 2 . 2 software . Maximum intensity projections of confocal Z-stacks were rendered using Volocity visualization software ( Improvision ) . S2R+ cells were grown in Schneider’s medium . Transfection was done in Grace’s medium with Cellfectin II reagent ( Invitrogen 10362–100 ) . 0 . 5μg of UAS-DiaDN::GFP and 0 . 5μg of actin-Gal4 were co-transfected in S2R+ cells . Cells were incubated in 25°C for 24h , and fixed in 4% PFA . Phalloidin was used to label F-actin in S2R+ cells . For the rescue experiment , 0 . 5μg of UAS-dia::GFP , 0 . 5μg UAS-diaDN::mCherry and 2 . 5μg of ubi-Gal4 were co-transfected in S2R+ cells . Cells were incubated in 25°C for 24h , and fixed in 4% PFA . Phalloidin was used to label F-actin in S2R+ cells , filopodia number was counted in 5–20 cells . Statistic tests were done in Microsoft Excel and GraphPad Prism . The Student’s t-test was used to compare the mean of two groups . One-way ANOVA test was used to compare mean of three or more groups . The difference between two groups was considered significant when p-value<0 . 1 . | Muscle formation and homeostasis critically depend on fusion between myoblasts to create and maintain multinucleated muscle fibers . Despite the importance of this process , the mechanisms regulating myoblast fusion are not fully understood . Previous studies have shown that actin polymerization factor Arp2/3 plays a critical role during myoblast fusion . However , whether other actin regulators also play a role during fusion , and how they coordinate with Arp2/3 in controlling actin dynamics remain unclear . Taking advantage of the model organism , Drosophila melanogaster , which shares the conserved muscle fiber with mammals , we identify the formin Diaphanous ( Dia ) , which polymerizes linear actin filaments , as essential for myoblast fusion . We show that Dia is present at the fusion site , and with a new dominant negative Dia allele , we demonstrate that Dia functions after myoblast recognition and adhesion , but upstream of Arp2/3 . Moreover , using dia loss and gain of function experiments , we show that Dia regulates myoblast fusion by regulating actin dynamics and by localizing the Arp2/3 regulators , SCAR and WASp , to the fusion site . Our study thus identifies new regulatory factors during muscle formation . It also suggests mechanisms by which Dia and Arp2/3 activities are coordinated to regulate actin dynamics in vivo during development and homeostasis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | The Formin Diaphanous Regulates Myoblast Fusion through Actin Polymerization and Arp2/3 Regulation |
Intellectual Disability ( ID ) disorders , defined by an IQ below 70 , are genetically and phenotypically highly heterogeneous . Identification of common molecular pathways underlying these disorders is crucial for understanding the molecular basis of cognition and for the development of therapeutic intervention strategies . To systematically establish their functional connectivity , we used transgenic RNAi to target 270 ID gene orthologs in the Drosophila eye . Assessment of neuronal function in behavioral and electrophysiological assays and multiparametric morphological analysis identified phenotypes associated with knockdown of 180 ID gene orthologs . Most of these genotype-phenotype associations were novel . For example , we uncovered 16 genes that are required for basal neurotransmission and have not previously been implicated in this process in any system or organism . ID gene orthologs with morphological eye phenotypes , in contrast to genes without phenotypes , are relatively highly expressed in the human nervous system and are enriched for neuronal functions , suggesting that eye phenotyping can distinguish different classes of ID genes . Indeed , grouping genes by Drosophila phenotype uncovered 26 connected functional modules . Novel links between ID genes successfully predicted that MYCN , PIGV and UPF3B regulate synapse development . Drosophila phenotype groups show , in addition to ID , significant phenotypic similarity also in humans , indicating that functional modules are conserved . The combined data indicate that ID disorders , despite their extreme genetic diversity , are caused by disruption of a limited number of highly connected functional modules .
Intellectual Disability ( ID ) is defined by an IQ below 70 , deficits in adaptive behavior and an onset before the age of 18 . ID disorders are among the most common and important unmet challenges in health care due to their tremendous phenotypic and genetic heterogeneity [1] , [2] . Many ID disorders are monogenic , and disease gene identification over the past decade has been very successful . More than 400 causative genes ( referred to as ID genes ) have been identified , providing unique stepping stones for understanding the molecular basis of cognition in health and disease . Some ID genes appear to work together in specific pathways and processes , such as Rho GTPase pathways , MAP kinase signalling and synaptic plasticity [3] , [4] . This has led to the suggestion that ID genes highlight key molecular networks that regulate human cognition [1] , [2] , [5]–[7] . Such networks are of wide interest for both fundamental neuroscience and translational medicine , and can pave the way for developing treatment strategies [2] . However , their identification is limited by the paucity of available information on the function of most ID genes . Model organisms such as the mouse have effectively been used as experimental systems to gain insights into ID gene function and neuropathology [8] . Because such studies are time and cost intensive , ID research , whether in vitro or in vivo , has so far not moved beyond studying individual or small groups of genes . Novel approaches are required to allow functional studies to catch up with disease gene identification . We used Drosophila melanogaster as the model organism for this study . Genes , pathways , and regulatory networks are well-conserved between flies and humans [9] . Drosophila provides numerous approaches to investigate defects in neuronal function and behavior . Furthermore , fly models of selected ID disorders have already provided major insights into ID pathologies and have triggered the first therapeutic approaches [10] , [11] . The efficiency of this organism and its available genome-wide toolboxes [12] , [13] make Drosophila a powerful model to generate comparative phenotype datasets that can provide global insights into ID gene function and connectivity . Here , we present a large-scale in vivo assessment of ID gene function and an in silico analysis of their Drosophila phenotypes and phenotype classes . We investigated the role of 270 evolutionarily conserved ID gene orthologs ( referred to from here on as ‘Drosophila ID genes’ ) in the Drosophila compound eye , a highly organized array of ommatidia and photoreceptor neurons that allows for simultaneous assessment of neuronal function and physiology , and for multiparametric morphological analysis . This comparative survey revealed a large number of novel functions for Drosophila ID genes including previously unappreciated regulatory roles in basal neurotransmission . It identified novel phenogroups in Drosophila that show phenotypic coherence in humans and molecular modules that can predict novel gene functions . Our study demonstrates that ID disorders converge on a limited number of highly connected functional modules .
To generate novel insights into the neuronal and molecular basis of cognitive ( dys ) function , we set out to manipulate established monogenic causes of ID in humans using Drosophila as a model . At the start of this project we conducted a systematic , manually curated disease gene survey . Of the identified 390 ID genes , 285 were conserved in Drosophila ( for curation criteria and orthology see Materials and Methods ) . 95% of these genes , 270 Drosophila ID genes , can be targeted with Drosophila transgenic conditional RNA interference ( RNAi ) lines from an established validated toolbox [12] , [14] , [15] . This approach is a suitable approximation to the human disease conditions since ( partial ) loss of gene function is thought to be the causative mechanism for more than 250 of the 270 ID genes investigated ( see Materials and Methods and Table S1A ) . We used a total of 498 RNAi lines , including two independent RNAi constructs per gene whenever available ( Table S1A ) . To maximize the reliability in our primary screen , we selected lines which exceed previously determined quality criteria that guaranteed high reproducibility ( see Materials and Methods , discussion , and Neumüller et al . [15] ) . Our strategy to ablate Drosophila ID gene expression primarily in the developing eye , including the photoreceptor neurons , was directed at identifying i ) Drosophila ID genes that , if perturbed , cause defects in neuronal function , ii ) Drosophila ID genes that affect viability , and iii ) Drosophila ID genes that control different aspects of eye morphology ( Figure 1A ) . We reasoned that these three classes and their subcategories might break down the large number of Drosophila ID genes into phenogroups , containing genes with a coherent function . Systematic targeting of a defined , larger group of genes in the eye and phenotypic characterization of various phenotypes has to our knowledge not previously been reported . Thus the degree to which phenotypes would be obtained was unknown . The fast phototaxis assay is an efficient and robust test for neuronal function . It is based on the fly's innate behavior to move towards a light source [16] , critically depends on proper performance of photoreceptor neurons , and can be quantified using the Phototaxis Index ( PI ) ( Figure S1A ) . We optimized the assay using known vision mutants and their corresponding RNAi lines ( Figure S1B , C ) . Under the chosen screening conditions ( GMR-Gal4; UAS-dicer2 driver line , 28°C ) all proof of principle RNAi lines showed strong defects , phenocopying their mutant phenotypes ( Figure 1B , Figure S1B , C ) , which validated the efficiency of our approach . In parallel to phototaxis , Drosophila ID gene knockdown progeny were examined for morphological eye phenotypes . As proof of principle for this additional approach , we tested RNAi lines against two Drosophila ID genes with reported eye phenotypes: ubiquitin protein ligase 3a ( ube3a ) , the Drosophila ortholog of UBE3A implicated in Angelman syndrome , and daughterless ( da ) , the ortholog of TCF4 implicated in Pitt-Hopkins syndrome . RNAi lines against both genes resulted in the expected defects , rough eyes [17] and complete loss of interommatidial bristles [18] , respectively ( Figure 1C ) . Progeny of the GMR-Gal4; UAS-dicer2 driver crossed to the genetic background line of the RNAi lines served as controls in all experiments of our study . Controls showed no considerable eye phenotypes ( see Materials and Methods ) and wildtype-like performance in the phototaxis assay . In our screen , RNAi against the majority of all Drosophila ID genes ( 180 genes , 67% ) resulted in lethal , phototactic or morphologic phenotypes ( Figure 1D , Table S1B , C ) . Knockdown of the remaining 90 Drosophila ID genes ( 33% ) did not yield functional or morphological eye phenotypes . The identified phenotype groups are described below . Eighteen Drosophila ID genes ( 7% ) gave rise to ( partial ) lethality and are thus essential in the targeted tissues ( Table S1B , C ) . The eye driver GMR-Gal4 has recently been reported to show some expression outside the eye , which likely accounts for the lethality that was already reported by others [12] , [19] , [20] . Expression of these 18 genes was subsequently knocked down specifically in neurons , using the pan-neuronal driver elav-Gal4 ( Figure 1A , grey asterisk ) . Only ERCC2 ( human gene symbol ) /Xpd ( Drosophila gene symbol ) and TPI/Tpi did not show lethality when ablated in neurons . Sixteen of the 18 GMR-Gal4-induced lethal genes also showed 100% lethality before adult stages upon selective neuronal knockdown ( Table S1B ) . Thus , 16 Drosophila ID genes that are essential in neurons were identified using this strategy . Ablating ID gene orthologs in the Drosophila eye and quantitatively assessing phototaxis yielded PIs between 1 . 1 and 5 . 9 . Using a stringent cut-off of <4 . 0 to define phototaxis defects , we identified 25 phototaxis defective Drosophila ID genes ( Figure 2A , Table S1B ) . Among these is the ortholog of ATP6V0A2 , the vacuolar proton pumping ATPase subunit Vha100-1 , mutations in which have been previously identified in an unbiased large scale phototaxis screen [21] . Electroretinograms ( ERGs ) were performed as a secondary screen to confirm that defects in phototaxis behavior are indeed caused by defective photoreceptor function and to further dissect the cause of defective vision in these ID models . ERGs are extracellular field recordings that measure the potential difference between the photoreceptor layer and the remainder of the fly body during light stimulation , revealing photoreceptor receptor transients ( de- and repolarization ) and synaptic communication ( ‘on’ and ‘off’ transients ) [22] . Of the 24 Drosophila ID genes tested , we confirmed that 21 exhibited defective neuronal physiology . Of these , ATP6V0A2/Vha100-1 and SNAP29/usnp showed isolated synaptic defects characterized by normal receptor potentials but complete absence of ‘on’ and ‘off’ transients ( Figure 2B ) . Two further Drosophila ID genes , DARS2 and GCH1 , exhibited decreased amplitudes of receptor transients and reduced synaptic signalling , whereas the majority ( 17 of 21 ) of phototaxis hits were characterized by nearly absent depolarization and only residual synaptic communication ( Figure 2B ) . In summary , we identified 21 Drosophila ID genes that are required either specifically for synaptic transmission or more broadly for basal neurotransmission and physiology . Only Vha100-1 has been previously demonstrated to be required for synaptic transmission in Drosophila photoreceptors . The majority of genes ( 16 of 21 ) had not been previously implicated in basal neurotransmission in any system or organism ( Figure 2B , Table S2 ) . Internal eye architecture and the state of photoreceptors were monitored in order to obtain further insights into the cellular basis of the identified neurophysiological defects . Each wild-type ommatidium contains eight photoreceptors , organized in a stereotypical pattern ( Figure 3A , B ) . Histological sections of ERG-defective Drosophila ID conditions detected a number of phenotypes ( Figure 3 , Table S1B ) . For example , knockdown of TBCE/tbce , implicated in hypoparathyroidism-retardation-dysmorphism syndrome , showed structural defects of developmental origin . R8 photoreceptors , normally located underneath photoreceptor 7 , failed to be maintained in their appropriate proximal position and thus appeared in distal sections ( Figure 3C ) . Moreover , rhabdomere extension towards the retina base , a process taking place during pupal development , failed in the majority of ommatidia ( Figure 3C′ ) leading to distally accumulated “bulky” rhabdomeres ( Figure 3C ) . This defect has recently been associated with regulators of the actin cytoskeleton that are linked to ID [23] , [24] . In contrast , RNAi against several ERG defective Drosophila ID genes , including PEX7 , ARFGEF2 and PAFAH1B1 caused neuronal degeneration of variable degrees , identifying a role for the encoded proteins in neuronal maintenance ( Figure 3D–F ) . Thirteen of 21 ERG defective Drosophila ID conditions , including NKX2-1 , PRPS1 and ATP6V0A2 knockdown animals , showed intact and properly organized photoreceptors ( Figure 3G–I ) . Some of these conditions showed darker photoreceptor cytoplasm or pigment cell abnormalities ( Figure 3G–I and Table S1B ) . In summary , we identified genes required for neuronal development or maintenance among the ID orthologs that cause neurophysiological defects . In 20% of these cases the data confirm or extend previous findings . In the majority of instances ( 80% ) these functions are novel ( Figure 3 , Table S2 ) . External eye morphology was systematically assessed in the primary screen to determine whether multiparametric phenotyping could identify which Drosophila ID genes work together in common developmental processes or molecular pathways . Thirteen phenotypic categories were identified: mildly rough , rough , partially fused ommatidia , fused ommatidia , fewer bristles , no bristles , stubble bristles , long bristles , necrosis , loss of pigmentation , small eye , wrinkled surface and dented surface ( Figure 4A–M and Table S1B ) . 163 Drosophila ID genes showed at least one of these morphological phenotypes , which were classed as eye morphology defective . Mildly rough and rough phenotypes were the most numerous . Other defects occurred frequently in combination with these and/or with other phenotypes ( Figure 4N ) . In all , RNAi-mediated knockdown of Drosophila ID genes in the eye generated a series of specific phenotype categories and identified a large number of genes with a role in the development of this tissue . Interestingly , the frequency of morphological phenotypes among the phototaxis defective genes was very similar to their overall frequency in our screen . Thus , these phenotype classes do not significantly correlate ( p = 0 . 13 , hypergeometric test ) , which is also illustrated by the random distribution of morphologic phenotypes along the entire spectrum of phototactic performance ( Figure 2A ) . We conclude that vision and external eye morphology do not depend on the same genetic/molecular machineries and provide a largely independent assessment of gene function . We next sought to determine whether Drosophila eye morphology defects could provide insights into conserved functional networks that underlie human ID disorders . To our knowledge , such a correlation has not previously been evaluated . Therefore , we first examined the expression , annotated functions and protein interactions , comparing EMD ( Eye Morphology Defective ) - and NED ( No Eye Defect ) - ID genes ( classes indicated in Figure 1D; the terms EMD- and NED-ID genes refer to Drosophila genes throughout the text ) . Based on EST data from 45 human tissues [25] , the human orthologs of both EMD-ID and NED-ID genes were widely expressed . For each gene we determined the tissue in which its normalized expression is highest ( normalized for overall expression per tissue; see Materials and Methods ) . We found that the largest fraction among EMD-ID orthologs ( 9 . 8% , 16 genes ) had their highest normalized expression in human ‘nerve’ tissue . This was also , among all tissues , the tissue where EMD- and NED-ID gene orthologs differ the most , as only 2 . 2% ( 2 genes ) of NED-ID orthologs had their highest expression in ‘nerve’ ( 4 . 4 fold enrichment EMD-ID over NED-ID , P = 0 . 046 ) . In contrast , the tissue in which most NED-ID orthologs had their highest expression was parathyroid ( 11 . 1% , 10 genes ) ( Figure S2A ) . EMD-ID genes were also enriched for nervous system-related phenotypes in FlyBase , such as neuroanatomy , neurophysiology and photoreceptor defects ( Figure S2B ) as well as for Gene Ontology ( GO ) terms and KEGG pathways related to neuronal processes in humans . In contrast , NED-ID genes were enriched for GO terms related to metabolic processes ( Figure S2C , D ) . The frequencies of human postsynaptic density proteins ( hPSD; 1458 proteins , ∼7% of human genes [25] ) among human orthologs of EMD- versus NED-ID proteins were also compared . In general hPSD proteins were significantly enriched among all ID genes ( 3 fold , χ2 , P = 3 . 65e-18 , ID genes ( 58 ) vs . human genome ( 1458 ) ) but to a different extent among the two eye phenotype-based classes of ID genes: 25% of human orthologs of EMD-ID genes encoded hPSD proteins ( 3 . 4 fold enriched vs . genome , 41 proteins , Table S3 ) , compared to 13% of human orthologs of NED-ID genes ( 1 . 8 fold enriched vs . genome , 12 proteins , Table S3 ) . hPSD proteins are thus enriched by ∼2 fold among human orthologs of EMD-ID genes relative to NED-ID genes ( χ2 , P = 0 . 04 ) . In summary , human orthologs of EMD-ID genes tend to be more specific for the nervous system than the NED-ID gene orthologs with respect to their expression at the RNA and protein levels and with respect to the pathways they are involved in . The above determined fly phenotypes , human gene expression and annotated functions were plotted in a circos diagram to provide a global view of ID gene properties and to illustrate the consistent asymmetry in this composite landscape of ID ( Figure 5 , segments 2–8; a zoomable electronic version of the circos is provided as Figure S3 ) . Annotated genetic interactions ( DroID ) and protein-protein interactions ( PPI; from HPRD ) between ID genes were also retrieved and integrated ( Figure 5 , segments 1 and 9 ) . Interestingly , ID gene-encoded proteins have more than three times as many PPIs with each other as random proteins ( PIE = 3 . 1; p<0 . 0001; taking into account the systematic biases in PPI networks for intensely studied genes that are caused by their high number of measured interactions [26] ) . These data substantiate that ID genes operate in common pathways . Restricting the analysis to human EMD-ID gene orthologs increased this connectivity , not just relative to the PPI database ( PIE = 5 . 8; p<0 . 0001 ) , but also relative to all screened ID genes ( PIE = 1 . 7; p = 0 . 003 ) . NED-ID gene orthologs also showed increased connectivity ( PIE = 8; p<0 . 0001 ) relative to random proteins from the PPI database . The different biology of EMD-ID versus NED-ID orthologs that we observed at the pathway level is therewith supported by an enrichment of protein interactions within each class . The finding that ID genes show a high connectivity is , given their heterogeneity , not trivial . To shed light on the functional connectivity of ID , we further examined Drosophila genetic interactions , comprehensive protein interaction data ( HPRD and human interologs ) and co-purified protein complexes ( DPIM ) and integrated these connections with the phenotypes we obtained . Strikingly , connections among mildly rough and among rough ID genes were each 6 fold enriched over randomly chosen Drosophila ID genes ( p<0 . 0001 ) . Connections between long bristles genes showed 20 fold ( p<0 . 002 ) , and connections between other bristles phenotype categories 24 fold ( p<0 . 001 ) enrichment relative to randomly chosen Drosophila ID genes . This modularity extends beyond the eye morphological phenotypes . Lethal genes showed an 18 fold enrichment ( p<0 . 001 ) , and the most enriched phenotype class , the ERG defective genes , reached 47 fold enrichment in homotypic interactions ( p<0 . 002 ) ( i . e . interactions between genes that fall into the same Drosophila phenotype category ) . Connections within the categories fused ommatidia , necrosis , loss of pigmentation , and small eye , wrinkled or dented surface have not yet been reported in any of the utilized databases . The identified enrichments in known connectivity validate the approach to map molecular modules in ID through Drosophila phenotyping . We next mapped the phenotype-based homotypic ID modules that are underlying the determined enrichments in connectivity among our phenotype categories ( see Materials and Methods ) . In total , we identified 26 functionally coherent ID modules composed of 100 Drosophila ID genes and 200 homotypic connections ( Figure 6A and its high resolution image provided as Figure S4 ) . For the remaining 170 ID genes ( 63% ) , no homotypic connections were annotated . Since Drosophila phenogroups showed high enrichments in known connectivity , they should be able to accurately predict novel gene functions and phenotypically relevant connections . To test this hypothesis , we further investigated the previously undocumented phenotype of abnormally long bristles , which identified a group of eight Drosophila ID genes . Five of these genes , PTEN , TSC2 , RPS6KA3 , MYCN and Myo5A , form a connected module ( Figure 6A , B , module 9 ) associated with cancer biology [27]–[29] . In addition , PTEN , TSC2 , RPS6KA3 and Myo5A also play a role in synapse development and plasticity in post-mitotic neurons [4] , [30] . Therefore our data suggested an unappreciated role for MYCN , the fifth protein in the module , in this process . To address this prediction , synapse development at the Drosophila larval Neuromuscular junction ( NMJ ) was quantified . The NMJ is a well-established model synapse that has already provided a number of fundamental insights into ID gene function and pathways [10] , [24] . Pan-neuronal knockdown of MYCN in larvae caused abnormally small synapses ( Figure 6C ) . We also predicted a role in synapse development for the remaining three long bristles genes PIGV , UPF3B and DMD ( encoding dystrophin ) . Indeed , not only does loss of dystrophin affect synaptic transmission [31] and has recently been found to cause susceptibility to malignant tumors in mice [32] , it also affects activity of Akt [33] , a kinase that directly regulates TSC2 . DMD may thus connect to the long bristles module and act upstream of Akt-TSC2 signalling in tumor and synapse biology . PIGV catalyzes a step in the GPI-anchor biosynthesis pathway , and UPF3B functions in nonsense-mediated mRNA decay ( NMD ) . Both have not yet been implicated in synaptic development or cancer although other members of the PIG family and NMD factors have [34] , [35] . Knockdown of PIGV and UPF3B , like knockdown of MYCN , caused a significant reduction in synaptic size ( Figure 6C ) , consistently observed among RNAi lines . To address whether smaller synapses represent a phenotype that is common among Drosophila ID genes or whether these characterize the long bristles module more specifically , three further Drosophila ID gene sets of equal size were randomly selected from the modules and screened for synaptic growth defects . Of the three gene sets targeted by a total of 16 RNAi lines , only a single RNAi line caused a smaller synapse ( 6% vs . 100% of RNAi lines targeting long bristles genes; p<0 . 001 , χ2 ) ( Figure S5 ) . A further single RNAi line in another gene set caused an increase in synaptic size ( 13% vs . 100% that cause any defect in synapse growth; p<0 . 01 , χ2 ) . No phenotypes were present in the third dataset , see Figure S5 . Thus , Drosophila eye phenogroups can predict novel functions of Drosophila ID genes and connections between them . In addition to this experimental validation , a number of our predictions are further supported by targeted literature search ( Figure 6B dashed lines , Table 1 , 2 and S4 , discussion ) . Further conclusions from our phenotype data and their suggested implications are indicated in Table 1 and 2 . We conclude that our data add considerable information on ID gene functional connectivity , and provide a comprehensive , integrated picture of modular genotype-phenotype networks in our disease model . Are the identified Drosophila phenotype groups relevant to humans ? To test this , we asked whether the corresponding genes showed , in addition to ID , also other similar disease phenotypes . Using the Human Phenotype Ontology ( HPO ) database [36] , we first determined that , relative to human orthologs of NED-ID genes , EMD-ID gene orthologs were enriched for morphological features of the head/neck ( ∼3 fold , 64 vs . 22 of top 200 features , p<10−6 , χ2 ) . In contrast , NED-ID gene phenotypes were enriched for disorders of metabolism and homeostasis ( 17 fold , 17 vs . 1 of top 200 features , p<10−3 , χ2 ) , which is consistent with the associated GO terms discussed above . We further inspected individual fly eye phenotype groups and determined their associated human mean phenotypic similarity scores [37] . This score reflects the degree of overlap between human disease features associated with each gene . To address the phenotypic similarity beyond ID , we excluded ID and all terms residing below it in the HPO hierarchy as features from the calculation of the similarity scores . Comparison of similarity scores in each phenotype group against the background expectation for all genes in the HPO database revealed that the phenotypic classes fused ommatidia , bristle phenotypes other than long bristles and necrosis phenotype classes showed no significant human phenotypic cohesion . In contrast , the remaining phenotype groups , mildly rough , rough , long bristles , loss of pigmentation , small eye and wrinkled or dented surface , lethal and ERG defective were each associated with significantly increased human phenotype similarity ( Figure 6D ) . Moreover , NED-ID genes also showed highly significant coherence in their associated human phenotypes . This is consistent with their enrichment for disorders of metabolism/homeostasis and with the high connectivity among NED-ID genes , together validating them as an independent phenotype category and illustrating that in comparative functional studies also the absence of phenotypes can be informative . Altogether , our findings demonstrate that Drosophila phenotype groups identify coherent disease phenotypes and highly connected functional modules among the large group of genetically heterogeneous ID disorders .
A previously validated transgenic RNAi library [12] was used as discovery toolbox in this study . Because our past work determined significant differences in knockdown levels induced by RNAi using this toolbox ( 20–60% of wt mRNA levels [39]–[42] ) and because we consistently found morphological eye phenotypes with two independent RNAi constructs only for 54% of the investigated ID genes , it seems likely that a number of RNAi lines are not efficient enough to evoke phenotypes . To limit the impact of such false-negatives on our analyses , we included phenotypes caused by single RNAi lines . This strategy has been applied in previous RNAi screens using the same toolbox [14] , [15] , [43] . Although we cannot exclude the occurrence of false-positive and -negative findings on the single gene level , phototaxis and eye morphology proof of principle experiments were successful and reliably recapitulated previously reported mutant phenotypes ( Figures 1D and S1 ) . Twelve percent of Drosophila ID genes ( 33 genes ) have annotated anatomical eye defects in Flybase . Most of these genes were reliably picked up in our screen ( 29 genes , 88% ) , indicating that the degree of false-negative hits is low ( Table S5 ) . High reproducibility of phenotypes was previously reported for RNAi lines with a high s19 specificity score of >0 . 85 [15] . In our screen , we were able to use lines with an s19 value of 0 . 98–1 in 97% of all cases ( Table S1B ) , exceeding this standard . There is evidence from the literature for ( partial or complete ) loss-of-function as the underlying disease mechanism in 93% of the ID genes/disorders investigated in our screen ( see Table S1A ) . Therefore , knockdown by RNAi appears to represent a suitable approach to model most of the studied ID genes . For 6% of the investigated ID genes we found support for gain-of-function mechanisms . Most of these ( affecting 9 of 15 genes ) are activating mutations in the Ras-MAPK pathway . This may limit the conclusions that can be drawn for these genes from our phenotypes . Nonetheless , we note that loss of Ras-MAPK signalling also compromises cognitive functions in mouse and humans [4] . Our phenoclustering approach successfully grouped these nine Ras-MAPK components into a single phenotype module . Close inspection of the determined homotypic modules ( Figure 6A ) showed that in few cases , genes that act in established common pathways or processes are divided over different modules due to their distinct Drosophila eye phenotypes . This is the case for NF1 , a direct negative regulator of Ras proteins that does not group together with HRAS and KRAS genes ( module 1 ) , as well as for mitochondrial NDUF and peroxisomal PEX genes that are divided over different modules ( 5 , 10 and 11 , 20 , respectively ) . Since the NED phenotype is involved , it is possible that some of these ‘splits’ are due to inefficiency of RNAi lines leading to false-negatives , as discussed above . However , others appear to reflect the biology of the genes/gene groups . For example , NF1 , in contrast to the above discussed nine Ras-MAPK genes , is a negative regulator of Ras-MAPK signalling . It is therefore conceivable that its knockdown causes another phenotype ( NED ) than knockdown of the positive Ras-MAPK regulators ( rough eye ) . A second negative regulator of this pathway , SPRED1 , which has recently been found to directly interact with NF1 [44] , is a NED gene as well . For the PEX genes , we would a priori have expected these to cluster together in our screen . It is worth noting though that the distribution of different PEX genes into phenotypic modules matches the molecular architecture of the peroxisomal machinery [45] . PEX1 and PEX6 ( module 20 ) represent the two cytosolic AAA proteins that directly interact to form the peroxisomal export complex . In contrast , PEX10 and PEX12 ( module 11 ) are both ring-finger proteins that directly interact with each other to form the ubiquitin ligase complex . This complex is required for matrix protein import and subsequent release of the cytosolic matrix protein receptor encoded by PEX5 , the third PEX protein in module 11 [45] . In summary , the determined homotypic modules are unlikely to give an error-free and complete picture of biologically meaningful relations between the studied ID genes . However , the consistent properties of EMD- versus NED-ID genes , the high degree of known connectivity among our phenogroups , their increased phenotypic similarity in humans and the demonstrated validation of the predicted synapse phenotypes argue that false ( negative and positive ) discovery rates in this study are limited . In our screen , we identified more than 160 Drosophila ID genes that give rise to aberrant eye morphology , of which only 17% have been described previously on Flybase ( Table S5 ) . Furthermore , we identified 16 Drosophila ID genes that were required in the eye and in neurons for fly viability . Nearly half of these act in transcription or glycosylation-related processes . A further 21 Drosophila ID genes were required specifically for synaptic transmission or , more broadly , for basal neurotransmission . Histological analyses revealed that seven of these genes were essential for neuronal maintenance , whereas the majority was associated with functional defects despite structurally intact photoreceptors , implying that they impact neuronal transmission directly . CG7830 , for example , is orthologous to two human non-syndromic ID genes , TUSC3 and MAGT1 . These two genes encode subunits of oligosaccharyltransferase complexes required for N-glycosylation [46] , which have recently been found to possess Mg2+ transport activity [47] . In neurons , defects in TUSC3 and MAGT1-mediated Mg2+ homeostasis might thus directly impact Mg2+-dependent ion channels . All defects in basal neurotransmission that we identified in our study ( Figure 2B ) provide a cellular mechanism that can directly underlie cognitive deficits in patients . Phenomics , the phenotype correlate of genomics , is an emerging discipline in biomedical research [38] , [48] , [49] . Despite recently established adequate data depositories such as the HPO database , human phenomics lags behind genomics [48] , limiting the recognition of genetic networks based on human phenotype data . Furthermore , the often small number of patients per genetic condition and the impact of environmental factors limit progress in human phenomics and are likely to remain bottlenecks in disease research . Comparative phenomic analyses in model organisms can contribute to the identification of evolutionarily conserved genotype-phenotype correlations in the human disease landscape . Which animal phenotypes are relevant to ID disorders ? Apart from defects of the nervous system such as the synapse , learning and memory defects [50] , [51] , we here show that also less complex phenotypes can be informative . Phenologs are defined as phenotypes enriched among orthologous genes in two organisms [52] . They can be used to unbiasedly identify and predict human disease models , even when the relationship between the phenotypes is not immediately obvious . This is illustrated by the predictive value of a specific yeast growth phenotype as model for mouse angiogenesis defects [52] . In Flybase , the available information on eye phenotypes is limited . However , the total fraction of annotated morphological eye phenotypes is three times higher among Drosophila ID genes than genome-wide ( 12 . 2% of Drosophila ID genes with annotated eye defects ( Table S5 ) vs . 3 . 9% genome-wide , p = 1 . 01e-09 , hypergeometric test ) . Thus , eye phenotypes are more likely to associate with Drosophila ID genes than with random genes , suggesting that to a certain degree they can serve as phenologs of human cognitive dysfunction . Furthermore , genes associated in fly with the same phenotype group show significant phenotypic similarity also in humans , validating Drosophila as a model for human disease phenomics of genetically highly heterogeneous disorders . Using the genotype-phenotype associations generated in this study , we found strong homotypic connectivity among ID genes . Integrating public interaction data with the generated Drosophila eye phenotypes led to novel insights in gene function and functional connectivity . In total , we detected more than two dozen homotypic modules . About half of these ( 14 of 26 ) are pairs . Thus , while informative , these clusters likely represent only a minority of all biologically relevant interactions . Some of the connections within modules are well established , such as the PPIs that delineate the Ras-MAP kinase signalling pathway at the core of the largest phenotype module ( Figure 6A ) . Our phenotypes imply novel gene functions and functional connections within each of the established phenotype categories . The long bristles cluster successfully predicted that MYCN , PIGV and UPF3B are critical for synapse development . Other predictions remain to be tested experimentally , but a number of them are already supported by other studies ( Table 1 , 2 and S4 ) . For example , despite lack of data in the utilized databases , the microtubule and neuronal migration-disorder related rough eye module two can be linked to other rough eye genes such as CC2D2A , TMEM67 and SMC3 , and potentially to other rough eye genes such as Rab3GAP1 , Rab3GAP2 , ARFGEF2 , FKRP , VLDLR and ARX as supported by shared human neuronal migration phenotypes ( Figure 6B , dotted lines ) . CC2D2A- and TMEM67-associated ID disorders are ciliopathies , and apart from its established role in chromosome cohesion , SMC3 has been recently shown to be required for Planar Cell Polarity , a process underlying cilium formation [53] , [54] . These data therefore point to an intimate connection between neuronal migration disorders and ciliopathies . Indeed , a recent paper reported that migrating interneurons display dynamic primary cilia that carry receptors for guidance cues , the dynamics of which are disturbed in a ciliopathy [55] . Another example is the fused ommatidia phenotype ( Figure 3J′ ) , which resembles a phenotype previously reported in the literature as “glossy” . This phenotype has been proposed to identify genes with mitochondrial function [56] , which is required for synaptic energy supply , receptor trafficking and calcium buffering . Indeed , among the twelve Drosophila ID genes in this phenotype category are the fly orthologs of PPOX , SURF1 and DBT , three further genes with established mitochondrial function . Also ASL , a cytosolic enzyme of the urea cycle that partly takes place in mitochondria , gives rise to this phenotype . Four other fused ommatidia Drosophila ID genes encode regulators of transcription including MED12 , a subunit of the mediator complex that in yeast has been shown to regulate transcription of genes with mitochondrial function [57] . In this context , it is important to note that functional connectivity between transcription factors and their target genes remains undetected in many databases , whereas this phenotype-based approach can identify or increase confidence in such relations . The “no bristles” category contains the Drosophila orthologs of FGFR2 , FGFR3 , PAFAH1B1 ( encoding Lis1 ) and the transcription factor TCF4 , and comprises only a single annotated connection ( FGFR2 , FGFR3 , Figure 6A ) . However , ModENCODE data show that the TCF4 ortholog da targets the two Drosophila FGF receptor genes htl and btl [58] ( Figure 6B ) , supporting further functional connections within this mini-cluster . Given the number of ID genes that encode transcription regulators , disruption of gene regulatory networks that comprise several ID genes are likely to contribute to the aetiology of ID . In the era of Next Generation Sequencing in human genomic research and diagnostics , the necessity to provide functional evidence of identified candidate disease genes is increasing exponentially . Here we have demonstrated that human disease phenomics in Drosophila is feasible , despite 1300 million years of evolutionary distance between the two species [59] . The identified genotype-phenotype modules , in combination with efficient fly phenotyping , should be applicable to facilitate identification of causative mutations among multiple DNA variants . Moreover , mapping molecular modules in ID provides a step towards network-based strategies that can target genetically heterogeneous patients with a common treatment . Recent research has demonstrated that cognitive defect in several animal models of ID are reversible in adulthood [60] , [61] . Two of these genes , PTEN and TSC2 , are part of the long bristles cluster , making other partners in this module attractive targets for genetic and pharmacologic rescue experiments and future clinical trials .
ID genes were identified in the literature , in public and in-house databases , and manually curated by clinical specialists . Also conditions that might not be primarily regarded as ID syndromes ( due to other prominent features or partial penetrance ) were considered if independent genetic as well as independent clinical evidence for ID was found . Conditions with clinically or genetically low evidence or treatable metabolic conditions were not considered . To enrich for genes that act in neurodevelopmental processes underlying cognition , also genes associated with neurodegenerative manifestation ( late onset ) , severe neurologic defects and early lethality were excluded . The orthologs of 390 ID genes ( as of beginning of 2011 ) were determined using ENSEMBL's orthology classes ( www . ensembl . org ) and treefam annotations , including manual curation . One-to-one and one ( fly ) -to-many ( human ) orthologs were considered , identifying 285 fly orthologs . RNAi lines were available for 95% of these , which are subject of this study . In eight cases , two human paralogs are implicated in ID and have a common ancestor in Drosophila . Drosophila phenotypes and data associated with these were assigned to both human genes . Of the 270 investigated human ID disorders/genes , 200 are recessive ( OMIM , the Online Mendelian Inheritance in Men database ) , and 28 further ID genes are reported to be haploinsufficient [62] . For 24 of the remaining 42 ID genes , evidence for ( partial ) loss-of-function as the underlying mechanisms exist ( Pubmed , summarized on OMIM ) , illustrating that for >93% of ID disorders the pathomechanism is ( partial ) loss-of-function . In a very few cases ( 4/270 ) no data are available that would allow conclusions about loss versus gain-of-function as ID underlying mechanism . Support for gain-of-function mechanisms accounts for 5% ( 14/270 ) of the investigated ID genes . Conditional knockdown of Drosophila ID genes was achieved with the UAS-GAL4 system [63] , using a w; GMR-Gal4; UAS-dicer2 driver [12] , [19] and UAS-RNAi lines [12] . UAS-RNAi lines , their genetic background controls ( 60000 , 60100 ) and UAS-dicer2 ( 60009 ) were obtained from the Vienna Drosophila RNAi Centre ( VDRC ) . GMR-Gal4 ( 1104 ) , elav-Gal4; UAS-dicer2 ( 25750 ) , nonA4b18 ( 125 ) , norpA45 ( 9051 ) , w*; sr1 ninaE17 es ( 5701 ) and w*; ort1 ninaE1 ( 1946 ) were obtained from the Bloomington Drosophila stock center ( Indiana University ) . Crosses were cultured according to standard procedures and raised at 28°C unless indicated otherwise . Information collected in previous RNAi screens [14] , [15] , [43] was utilized to select genetic tools ( GB and KK collections , see www . vdrc . at ) . ID lines from the site-integrated KK library were included in the primary screen . These lines bear no risk for gene disruption at the integration locus , ensure high expression and represent independent constructs that do not overlap with those of the GB collection . They are also characterized by minimized off-targets , reflected in high s19 values ( Table S1B ) . Including the potent KK library in our screen allowed us to use lines with highly specific s19 scores of 0 . 98–1 in 97% of all cases . A modified countercurrent apparatus was used to fractionate genotypes among six tubes , according to their visual activity ( see Figure S1 ) . The phototaxis index ( PI ) is calculated as ∑i*Ni ) /N , where N is the number of flies , i is the tube number , and Ni is the number of flies in the ith tube . Average PI and standard deviation were calculated from three independent experiments on different test days . Assays were performed under standardized conditions , and progenies from control crosses served as internal controls . Populations of 40–70 flies , mixed sex , at the age of day 3–4 after eclosion and a walking time of 15 seconds were used . Based on the average PI of the control ( PI = 5 . 2 ) , and a maximal standard deviation of 1 . 2 per RNAi line , we defined a stringent cut-off of PI<4 to define a phototaxis hit . Eye morphology defects were scored by two independent experimentators . Despite a reported effect of GMR-Gal4 driver constructs on eye development [64] , our driver controls showed merely mildly rough phenotypes in a maximum of 10% of eyes . A mildly rough phenotype was therefore only scored if present in the majority ( >90% ) of knockdown eyes . No other eye phenotypes were observed in controls . Three to four days old females of the appropriate genotype were fixed in 1% glutaraldehyde , dehydrated by an ethanol series ( 25 , 50 and 75% ) , critically-point dried and mounted on aluminum stubs . Samples were coated in gold by sputter coating and afterwards examined with a JEOL 6310 SEM . Heads from 3–4 days old female progenies raised at 25°C were prefixed for 30 min in 2% glutaraldehyde buffered with 0 . 1 M Sodium cacodylate pH 7 . 4 , bisected and fixed for another 24 hours . Bisected heads were postfixed for 1 hour in 1% Osmium teroxide in Paladebuffer pH 7 . 4 with 1% Kaliumhexacyanoferrat ( III ) -Trihydrat , dehydrated in ethanol and propyleenoxide and embedded in a single drop of Epon . Semi thin , 1 µm thick transverse and longitudinal sections were stained with 1% Toluidine Blue . ERGs were performed as previously described [65] . Flies were tested at day one after eclosion . Per genotype eight to ten flies were recorded and the average of five representative recordings is shown . Segment 2 , 3 and 4 muscle 4 Type 1b neuromuscular junctions ( NMJs ) of wandering L3 panneuronal knockdown larvae were analyzed after dissection , a 30 min fixation in 3 . 7% PFA and immunolabelling with an anti-discs large 1 antibody ( anti-dlg1 , supernatant , 1∶25 ) ( Developmental Studies Hybridoma Bank , University of Iowa ) . NMJ pictures were obtained using a Leica automated brightfield multi-color epifluorescence microscope . Images were automatically processed and the synapse area was measured by an advanced in house-developed Fiji/ImageJ macro . Mutant synapses were compared to their proper genetic background controls . For the X-linked UPF3B RNAi line 31444 and its control , exclusive female knockdown animals were selected . UPF3B RNAi line 31445 was not available at the stock centre for retesting . In contrast , for AP1S2 , NDUFS8 and CHD7 independent RNAi lines were available at the time of synapse evaluation and have been utilized . At least 16 synapses were analyzed per genotype . Random sets of Drosophila ID genes subjected to NMJ analysis were determined from homotypic modules using a PHP script-based random number generator . Constraints were set on the min and max values and previously generated numbers were excluded to avoid duplicates . Independent sets of specified size were generated for subsequent analysis . Drosophila ID genes were assigned to all phenotype categories that describe ( an aspect of ) the observed associated defects . Since RNAi induces variable knockdown that will in some cases not be sufficiently strong to evoke a loss-of-function phenotype , “single hit” genes were included in the further data analysis , as in previous Drosophila RNAi screens [14] , [15] , [43] . In any other scenario , one inefficient RNAi line would disqualify the efficient one , which would likely result in a large amount of false-negatives . For annotations of already known defects associated with EMD- and NED-ID or all Drosophila ID genes , the Drosophila genes annotated with defective phenotypic classes behavior , neuroanatomy , neurophysiology , behavior , photoreceptor , cell cycle and stress response phenotypes as well as with anatomy defective classes retina and photoreceptor cell were fetched from FlyBase ( version march 2012 ) ( www . flybase . org ) [66] . A hypergeometric distribution test was carried out to check the enrichment of these phenotypes within EMD-ID and NED-ID genes against the background of ( fly ) phenotypes associated with all Drosophila genes that have orthologs in human . EST profiles from cDNA libraries of 45 normal human tissues were retrieved from the NCBI UniGene database [67] ( ftp://ftp . ncbi . nih . gov/repository/UniGene/Homo_sapiens/Hs . profiles . gz ) and expression abundance for each gene across the tissues was calculated . Since average expression between tissues varied significantly , we ranked genes in each tissue according to their expression levels . Subsequently we determined for each gene the tissue of its highest normalized expression as the one in which the gene had its highest rank . Overrepresentation of GO biological process and pathway terms for human EMD- and NED-ID gene orthologs against the human genome background data sets were identified using the Database for Annotation , Visualization and Integrated Discovery ( DAVID ) v6 . 7 , web based program [68] , [69] . Direct physical protein-protein interaction data sets ( HPRD_Release9_041310 . tar . gz ) from the Human Protein Reference Database ( HPRD [70] ) were downloaded and used as the standard protein interaction data for our study . Human interologs [71] ( containing interactions from HPRD , BioGRID , IntAct , MINT , and Reactome; version 2012_04 ) , DPIM-coAP complex data ( protein interactions determined in large-scale co-affinity purification screens , Drosophila Protein Interaction Mapping project [72] ( DPIM; version 2012_04 ) , and Drosophila Genetic interaction data ( version 2012_04 ) were downloaded from DroID ( http://www . droidb . org/ ) [73] , [74] . Physical interaction enrichment ( PIE ) scores of human orthologs of EMD- and NED-ID genes were calculated against HPRD , using the PIE algorithm with a minor modification in the normalization factor [26] to account for biases in the number of reported interactions for disease genes . Interaction enrichment scores for the specific phenotype categories within EMD , for lethal and for ERG ID gene products represent the number of unique connections determined from the combined interaction data sets per phenotype ( HPRD , human interologs , DPIM-coAP complex and genetic interactions ) divided by the number of connections for randomly ( 10 , 000 times ) chosen ID genes from the combined interaction data sets . Circos-0 . 56 , a freely available software package [75] was downloaded and used for the depiction of most phenotypes and significantly enriched features , determined as described above . The combined interaction data sets ( see ‘Interaction network datasets and analyses’ above ) were loaded into and visualized with the Cytoscape v2 . 8 . 1 tool [76] . Different phenotypes were colored using the MultiColored Nodes plug-in v2 . 4 . 0 [77] . Homotypic phenotype modules were identified among the entire ID interactome using Cytoscape's v2 . 8 . 1 ‘create new network from attribute’ algorithm . The phenotype-based homotypic ID modules are defined as connected genes with shared phenotype . Thus , genes with a non-overlapping phenotype cannot be part of the same phenotype-based module . The Human Phenotype Ontology ( HPO ) [36] genes-to-phenotype mapping file , build 694 , was downloaded from the HPO website ( www . human-phenotype-ontology . org ) . This file maps genes to lists of standardized phenotypic features organized in a hierarchical structure ( ontology ) . Phenotype similarity was determined based on these feature lists , using an adapted version [37] of a previously published algorithm [78] that takes the hierarchical structure into account . Basically , the human phenotypic similarity per gene pair was determined by calculating the correlation coefficient of the HPO feature vectors associated with each gene . The seven HPO features in the “Intellectual Disability” subtree were excluded from the feature vectors as the analyzed genes were selected based on this feature . Features were weighted according to their rarity and the number of features present in the vector . Before the feature vectors were compared , they were first supplemented with indirectly annotated features based on the feature hierarchy . This was accomplished by recursively adding parent features with progressively lower weights until the root of the feature hierarchy was reached . For each fly phenotype category , the mean pair-wise phenotypic similarity score was determined for all human genes associated with it . As a control , each set's score was compared with those of 1000 equal-sized sets of genes randomly sampled from the full list of HPO genes . For comparing the over-represented individual features of EMD-ID and NED-ID genes , we first identified the top 200 most significantly over-represented human phenotypic features for each gene set . This number was chosen to ensure that all considered features were over-represented at a corrected p-value threshold of 0 . 05 ( Hypergeometric distribution; 206 and 563 features associated with NED-ID and EMD-ID genes respectively meet this threshold ) . Subsequently we determined what percentage of these specific features fall into the various top level HPO phenotypic categories , and compared these between EMD- and NED-ID genes . | Intellectual Disability ( ID ) affects 2% of our population and is associated with many different disorders . Although more than 400 causative genes ( ‘ID genes’ ) have been identified , their function remains poorly understood and the degree to which these disorders share a common molecular basis is unknown . Here , we systematically characterized behavioral and morphological phenotypes associated with 270 conserved ID genes , using the Drosophila eye and photoreceptor neurons as a model . These and follow up approaches generated previously undescribed genotype-phenotype associations for the majority ( 180 ) of ID gene orthologs , and identified , among others , 16 novel regulators of basal neurotransmission . Importantly , groups of genes that show the same phenotype in Drosophila are highly enriched in known connectivity , also share increased phenotypic similarity in humans and successfully predicted novel gene functions . In total , we mapped 26 conserved functional modules that together comprise 100 ID gene orthologs . Our findings provide unbiased evidence for the long suspected but never experimentally demonstrated functional coherence among ID disorders . The identified conserved functional modules may aid to develop therapeutic strategies that target genetically heterogeneous ID patients with a common treatment . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Human Intellectual Disability Genes Form Conserved Functional Modules in Drosophila |
Genomic imprints—parental allele-specific DNA methylation marks at the differentially methylated regions ( DMRs ) of imprinted genes—are erased and reestablished in germ cells according to the individual's sex . Imprint establishment at paternally methylated germ line DMRs occurs in fetal male germ cells . In prospermatogonia , the two unmethylated alleles exhibit different rates of de novo methylation at the H19/Igf2 imprinting control region ( ICR ) depending on parental origin . We investigated the nature of this epigenetic memory using bisulfite sequencing and allele-specific ChIP–SNuPE assays . We found that the chromatin composition in fetal germ cells was biased at the ICR between the two alleles with the maternally inherited allele exhibiting more H3K4me3 and less H3K9me3 than the paternally inherited allele . We determined genetically that the chromatin bias , and also the delayed methylation establishment in the maternal allele , depended on functional CTCF insulator binding sites in the ICR . Our data suggest that , in primordial germ cells , maternally inherited allele-specific CTCF binding sets up allele-specific chromatin differences at the ICR . The erasure of these allele-specific chromatin marks is not complete before the process of de novo methylation imprint establishment begins . CTCF–dependent allele-specific chromatin composition imposes a maternal allele-specific delay on de novo methylation imprint establishment at the H19/Igf2 ICR in prospermatogonia .
Imprinted genes are epigenetically modified during germ cell development , such that their expression in somatic cells depends on the parent of origin [1] , [2] . Allele-specific differential DNA methylation is associated with most imprinted genes [3] . Male or female-specific methylation of the germ line differentially methylated regions ( DMRs ) is inherited from the gametes , survives the global wave of demethylation during early embryogenesis and is faithfully maintained in somatic cells during the life of the individual . Deletion studies showed that some DMRs are critical for allele-specific monoallelic expression of imprinted genes [4]–[8] . The importance of DNA methylation in the establishment and maintenance of genomic imprinting has been demonstrated in mice in which DNA methyltransferase genes have been inactivated [9]–[12] . The paternally expressed insulin-like growth factor 2 ( Igf2 ) and maternally expressed H19 genes on mouse distal chromosome 7 [13] are coordinately expressed during embryonic development , due to shared tissue-specific enhancers ( Figure 1A ) [14] , [15] . A paternally methylated germ line DMR between Igf2 and H19 [16]–[18] is responsible for monoallelic expression of both H19 and Igf2 [19]–[21] , and therefore , is called an imprinting control region ( ICR ) . The regulatory functions of the ICR depend on allele-specific DNA methylation . Inactivation of the H19 promoter takes place in post-implantation development on the paternal chromosome and it depends on ICR methylation [22] . The ICR functions as a methylation regulated enhancer blocker [23]–[27]: CTCF protein [28]–[30] binds in the unmethylated maternal allele and insulates between the Igf2 promoters and the shared enhancers . DNA methylation in the paternal allele inhibits CTCF binding , hence the ICR has no insulator activity , and the Igf2 promoters and the enhancers can interact . Targeted mutagenesis of the CTCF binding sites in the mouse results in a loss of enhancer-blocking activity and increased DNA methylation in the mutant maternal chromosome [31]–[33] . CTCF binding in the ICR is the major organizer of chromatin composition in the maternal allele along the entire imprinted domain [34]–[36] . CTCF recruits active histone tail modification marks to the ICR and to the H19 gene [34] and also recruits at a distance , Polycomb-mediated H3K27me3 repressive marks at the Igf2 promoter and at the Igf2 DMRs [34] , [35] . CpG methylation at DMRs is reset during germ cell development: inherited gametic marks are erased in primordial germ cells ( PGCs ) followed by the establishment of new gametic marks in the female and male germ lines according to the individual's sex ( Figure 1B and 1C ) . The umethylated versus methylated status of the H19/Igf2 ICR in oocytes versus spermatozoa constitutes the female and male gametic mark . Methylation of the paternal allele is erased in female and male germ cells by 13 . 5 days post coitum ( dpc ) [37]–[40] ( Figure 1B and 1C ) . In the female germ line the ICR remains unmethylated during fetal and postnatal stages of oogenesis ( M and P alleles in Figure 1B ) . In male germ cells , the ICR methylation imprint is laid down between 15 . 5–17 . 5 dpc , and is almost fully established by 18 . 5 dpc [31] , [41] . The germ line-specific processes that target differential methylation to the ICR are unknown but are entirely separate from the later somatic ICR functions of chromatin insulation and H19 promoter silencing . CTCF binding is not required to establish an unmethylated ICR during oogenesis or a methylated ICR during spermatogenesis . The ICR that lacks functional CTCF binding sites is unmethylated in female fetal germ cells and ovulated oocytes but is methylated in perinatal spermatogonia [31] , [32] . The timing of DNA methylation between the maternally and paternally inherited alleles ( M and P alleles in Figure 1C ) is different during spermatogenesis , methylation of the paternally inherited allele preceding that of the maternally inherited allele , implying that the two parental alleles can be distinguished from each other by the de novo DNA methylation machinery in the absence of DNA methylation [37] , [39] , [41] , [42] . We sought to investigate the nature of this epigenetic memory in spermatogonia . We hypothesized that differences in CTCF protein binding and/or chromatin composition between the paternally or maternally inherited alleles are responsible for discriminating between the parental alleles in the male germ line . We based this hypothesis on previous observations: We have shown that migratory PGCs exhibit strict imprinted maternal allele-specific H19 expression at 8 . 5 dpc , and paternal allele-specific Igf2 expression at 10 . 5 dpc [43] . Expression of H19 and Igf2 becomes biallelic by the early post-migratory stage of 11 . 5 dpc [43] , [44] and remains biallelic during fetal and postnatal stages of spermatogenesis [44] . Because parental allele-specific expression of both H19 and Igf2 depends on CTCF insulator binding in the maternally inherited ICR allele [31]–[33] , CTCF binding in the ICR must be maternal allele-specific in migratory PGCs and biallelic or missing at later stages of spermatogenesis . It is not known if allele-specific chromatin differences exist in PGCs or if these become erased at the time when DNA methylation marks are erased at DMRs . CTCF binding , however , likely organizes the chromatin composition of the ICR in the maternal allele in PGCs , similarly to its role in somatic cells [34] . The allele-specific chromatin difference may also need to be erased in postmigratory spermatogonia , for example H3K4 methylation would be removed from the maternal allele , because H3K4 methylation is not permissive for de novo DNA methylation [45] . Erasure of chromatin marks may occur synchronously with the global dynamic changes of chromatin reorganization that take place in germ cells around mid-gestation [46]–[48] . If allele-specific chromatin marks are not fully erased in prospermatogonia after methylation imprint erasure is complete , they may influence the rate of de novo methylation . We can test this possibility directly and specifically by perturbing the chromatin bias of the ICR in prospermatogonia . After maternal transmission of the ICR CTCF site mutations [31] , [34] we expect to find loss of allele-specific differences in chromatin composition and methylation establishment in prospermatogonia . Using allele-specific chromatin immunoprecipitation single nucleotide primer extension ( ChIP-SNuPE ) assays we found that in normal prospermatogonia the chromatin composition was biased between the two alleles after complete erasure of CpG methylation . The CTCF site mutant maternal ICR allele , however , no longer exhibited those allele-specific chromatin differences and delayed methylation establishment . Our data suggest that CTCF dependent allele-specific chromatin composition gives de novo methylation imprint establishment an allele-specific bias at the H19/Igf2 ICR .
In prospermatogonia , the paternally inherited ICR allele becomes methylated earlier than the maternally inherited allele in reciprocal crosses between C57BL/6J ( B6 ) and JF1 [39] , [41] . Similarly , when the ICR carries the B6 type allele in the maternal allele and the CAST/Ei type allele in the paternal allele , the B6 type maternal allele is delayed compared to the CAST/Ei type paternal allele in prospermatogonia between 14 . 5 and 18 . 5 dpc [37] . We tested the reciprocal situation when the CAST/Ei type ICR allele is inherited from the mother and the B6 type allele is inherited from the father . Females of FVB/NJ . CAST/Ei ( N7 ) , a distal chromosome 7 partial congenic strain for CAST/Ei ( CS ) [31] were mated with TgOG2 homozygous transgenic males [43] resulting in CS X OG2 fetuses . We isolated male and female germ cells from 13 . 5 , 14 . 5 , 15 . 5 , 16 . 5 and 17 . 5 dpc gonads . We performed two or more independent bisulfite conversion reactions for each sample and sequenced at least twelve clones of each sample . A single nucleotide polymorphism in the CS strain was used to identify the parental alleles . We confirmed previous observations [37]–[40] that DNA methylation erasure is complete by 13 . 5–14 . 5 dpc at the ICR ( Figure 2 and Figure S3 ) . We found that primary oocytes exhibited no methylation of the ICR region between 13 . 5 and 16 . 5 dpc ( Figure S3A ) and prospermatogonia attained CpG methylation gradually ( Figure 2 ) between 15 . 5 dpc and 17 . 5 dpc as expected [37] , [39] . We confirmed that the maternal allele ( CS type ) was delayed compared to the paternal allele ( B6 type ) in CS X OG2 prospermatogonia ( Figure 2 ) similar to the reciprocal B6 X CS situation [37] . Regardless of mouse strains used , there exists a time gap in methylation imprint establishment between the two chromosomes depending on the inheritance from the mother or father ( M and P alleles in Figure 1 ) during spermatogenesis [37] , [39] , [41] , [42] . Therefore , the two parental alleles must be distinguished from each other in 13 . 5–14 . 5 dpc prospermatogonia by epigenetic means other than DNA methylation . We tested the hypothesis that functional CTCF binding sites in the maternally inherited H19/Igf2 ICR allele are responsible for the delayed methylation of the maternally inherited , compared to the paternally inherited allele in male germ cells . Female mice homozygous for CTCF site mutations ( −/− ) [31] were mated with TgOG2 homozygous transgenic males [43] ( wild type ICR ) . In the resulting CTCFm X OG2 fetuses , the maternally inherited ICR allele was mutant , lacking functional binding sites . Germ cells were collected at 13 . 5 , 14 . 5 , 15 . 5 and 16 . 5 dpc . Bisulfite DNA sequencing was performed on agarose-embedded germ cells as described before [31] according to Olek et al . [51] . Nucleotide changes , introduced with the mutations aided discrimination between the mutant and wild type alleles . We found that due to the ICR CTCF site mutations the maternally inherited mutant allele did not lag behind the paternal allele in male germ cells ( Figure 3 ) . The increased rate of methylation in the CTCF site mutant ICR maternal allele ( Figure 3 ) compared to the normal ICR allele ( Figure 2 ) was statistically significant . At 15 . 5 days the p-value = 0 . 0014 and at 16 . 5 days the p-value = 0 . 0183 according to Fisher's exact test . This argues that intact CTCF protein binding sites in the ICR are required for the transient epigenetic memory that delays methylation of the maternally inherited allele during male fetal germ cell development . When the paternal ICR carried the CTCF site mutations in the control OG2 X CTCFm male germ cells ( Figure S4 ) , its rate of methylation was similar to the normal paternal allele in the CS X OG2 cross ( Figure 2 ) , indicating that simply having less CpG sites in the CTCF site mutant ICR is not sufficient to alter the rate of methylation . The control female germ cells did not attain methylation in the mutant allele ( Figure S3B ) . The mutant maternal allele was , unexpectedly , more prone to methylation than the wild-type paternal allele in the same cell . The wild type paternal and mutant maternal alleles are different in two respects , in the strain and in the presence or absence of the CTCF site mutations . The best comparison can be made when the CTCFm allele is compared between paternal and maternal inheritance . The methylation levels of these chromosomes , indeed , were very similar at 14 . 5 and 15 . 5 dpc ( Figure 3 and Figure S4 ) . We noted that the sum level of methylation in the two alleles did not change between the wild type and CTCF site mutant prospermatogonia , indicating perhaps that the two alleles are in competition for a methylation inducing factor that has limited concentration at 15 . 5–16 . 5 dpc . We considered the possibility that differences in CTCF binding and chromatin composition between the paternally or maternally inherited alleles might be responsible for discriminating between the parental ICR alleles in the male germ line . If this is correct , we would expect in spermatogonia a slight bias in chromatin composition between the maternally and paternally inherited alleles at the ICR such that the paternally inherited allele would be more permissive to DNA methylation . We developed ChIP-SNuPE assays based on mass spectrometry Sequenom allelotyping [36] , [52] to distinguish allele-specific incorporation of ddNTPs into the SNuPE primer based on differences in molecular mass at sites of single nucleotide polymorphisms ( SNPs ) between 129 or OG2 and CS mouse genomic sequences along the H19/Igf2 ICR [34] . The Sequenom assay used SNPs at two halves of the ICR at −4 kb and −3 kb distances from the H19 transcriptional start site . Both assays were rigorously quantitative , as shown by DNA mixing experiments ( Figure S5A ) . The number of fetal germ cells is limiting for ChIP assays , we can obtain 100 , 000–300 , 000 germ cells per dissection . We decided to use 100 , 000 germ cells per ChIP . We validated the ChIP-SNuPE assays using 100 , 000 129 X CS mouse embryo fibroblasts ( MEFs ) . We found that CTCF binding and active chromatin ( H3K4me2 enrichment ) was highly specific to the maternal allele in the ICR whereas repressive chromatin ( H3K9me3 ) was highly specific to the paternal allele in MEFs ( Figure S6A ) as we previously reported using large number of the same 129 X CS MEF cells [34] . We found that the assay correctly measured 50% 129 and CS alleles in the input chromatin samples for MEFs ( Figure S6A ) and for CS X OG2 and CTCFm X OG2 fetal germ cells ( Figure S6C ) . We isolated male and control female germ cells from 13 . 5 and 14 . 5 gonads from the CS X OG2 mouse cross and performed ChIP-SNuPE assays using 100 , 000 germ cells per ChIP reaction . The control , nonspecific IgG-precipitated chromatin samples did not exhibit a clear pattern of allele-specific skewing ( Figure S6B ) . The results did not show consistency between the −4 kb and −3 kb regions ( A and B regions , respectively ) or between the 13 . 5 and 14 . 5 dpc stages . Specific antibodies , on the other hand gave reproducible results using germ cell chromatin ( Figure 4 , Figure 5 , Figure 6 ) . CTCF binding was slightly biased toward the maternal ICR allele in male and female germ cells at 14 . 5 dpc ( Figure 4 ) . CTCF binding in the paternal allele would likely be inhibited by DNA methylation in PGCs similarly to somatic cells [23] , [24] , [27] , but not in fetal prospermatogonia at 13 . 5–14 . 5 dpc in the lack of DNA methylation . The slight maternal bias is consistent with the possibility that allele-specific CTCF binding is not completely erased at 14 . 5 dpc , after DNA methylation erasure had been completed . The total level of CTCF binding at the ICR was very low in germ cells at 14 . 5 dpc compared to MEFs ( Figure S7 ) . This suggests that CTCF has been almost completely removed from both ICR alleles in germ cells by 14 . 5 dpc , consistent with biallelic Igf2 expression in the absence of insulation [31]–[33] , [43] . The almost complete lack of CTCF binding , however is not due to the absence of CTCF from prospermatogonia at these stages . This would be expected based on that CTCF and CTCFL ( BORIS ) proteins exhibit mutually exclusive expression in adult male germ cells , round spermatids and spermatocytes , respectively [53] and that CTCFL is expressed in 14 . 5 dpc prospermatogonia [54] . It is not known whether CTCF is expressed in embryonic and fetal germ cells . We addressed this question by performing immunocytochemistry with anti-CTCF antibody using fetal germ cells ( Figure S8 ) . We found that CTCF staining in male and female germ cells was similar to that of control gonadal somatic cells at 12 . 5 dpc and 14 . 5 dpc . The mutually exclusive expression of CTCF and CTCFL , therefore , does not apply in germ cells at 14 . 5 dpc . CTCF may be inhibited to bind in the ICR at these stages because of changes in its covalent modifications [55] , cofactors , or due to an RNA-dependent mechanism [56] . We found a slight ( ∼10% ) , but reproducible bias in the H3K4me2 levels toward the maternally inherited allele in male and female germ cell ChIP samples at 13 . 5 and 14 . 5 dpc ( Figure 5 ) . The bias was present in the ICR at −3 kb and −4 kb positions . H3K4me2 enrichment in germ cells was similar to the level found in MEFs ( Figure S9 ) , suggesting that the ICR had not been stripped of this mark at 13 . 5–14 . 5 dpc . H3K9me3 was reciprocally biased: the paternally inherited allele exhibited about 10% higher enrichment at 13 . 5 and 14 . 5 dpc ( Figure 6 ) . The allele-specificity of the bias for H3K4me2 and H3K9me3 in 13 . 5 dpc germ cells was in agreement with the somatic pattern ( Figure S6A ) , being maternal and paternal specific , respectively , suggesting that it originates in premigratory PGCs ( Figure 7A ) . The amplitude of the bias , however , was smaller than in the soma , consistent with the possibility that the chromatin differences are being erased in germ cells around mid-gestation and only the remnants of the allele-specific differences can be detected at 13 . 5–14 . 5 dpc . H3K9me3 levels at the ICR , however , were very low in germ cells at these stages ( not shown ) , consistent with the possibility that similarly to CTCF but unlike H3K4me2 this mark is almost completely removed by 13 . 5 dpc . The allele-specific bias of H3K4me2 in 14 . 5 dpc germ cells was present with only trace amounts of CTCF binding ( Figure S7 ) in the ICR . We concluded that the H3K4me2 histone mark could be a potential candidate that provides the epigenetic memory of the mother at the ICR in 13 . 5–14 . 5 dpc prospermatogonia in the absence of CpG methylation . CTCF binding is essential for the maternal allele's chromatin composition along the H19/Igf2 imprinted domain in the soma [34] . We decided to analyze if CTCF binding site mutations abolish the enrichment bias of histone covalent modifications between the maternally and paternally inherited alleles in fetal male germ cells . Female mice homozygous for the CTCF site mutations [31] were mated with TgOG2/TgOG2 transgenic males [43] . In the resulting CTCFm X OG2 fetuses , the maternally inherited allele was mutant , lacking functional binding sites . Male and control female germ cells were collected at 13 . 5 and 14 . 5 dpc and ChIP was performed with 100 , 000 germ cells per reaction using the H3K4me2 and H3K9me3 antibodies . Allele-specific precipitation was assessed using ChIP-SNuPE Sequenom assays that can distinguish the CTCF site mutation sites from the normal allele at CTCF binding sites 1 and 3 ( at −4 kb and −3 kb positions , respectively ) in the ICR . Each assay was rigorously quantitative , as shown by DNA mixing experiments ( Figure S5B ) . Contrary to what we found in CS X OG2 fetal germ cells , CTCF did not exhibit a slight bias toward the maternally inherited allele but instead a strong bias toward the paternal allele in 14 . 5 dpc CTCFm X OG2 germ cells ( Figure 4 ) . The reduction of maternal-allele specificity is consistent with impaired binding of CTCF to the mutant sites in the maternal allele . The paternal allele-specificity is likely due to the potential of CTCF binding in the paternal allele in the lack of methylation at 13 . 5–14 . 5 dpc . Using gel shift competition assays [31] and in vivo ChIP analysis [34] we have shown previously that the CTCF site mutations completely abolished CTCF binding in the ICR sequences . The fact that we do not measure a complete lack of maternal allele-specific CTCF binding in 14 . 5 dpc CTCFm X OG2 germ cells is most likely due to the limitation of the assay at extremely low copy numbers ( Figure S7 ) . H3K9me3 was slightly paternally biased at 13 . 5–14 . 5 dpc in CS X OG2 germ cells ( Figure 6 ) but was not consistently biased in CTCFm X OG2 germ cells at 14 . 5 dpc ( Figure 6B ) . We observed a switch from a slight maternal- to a slight paternal H3K4me2 bias at −4 kb and also at −3 kb along the ICR ( Figure 5B ) suggesting that intact CTCF binding sites are required for distinguishing the maternal allele by H3K4 dimethylation in male and female germ cells at 14 . 5 dpc .
We hypothesized that chromatin differences exist between parental alleles of DMRs in PGCs at the time of monoallelic expression of imprinted genes and that these chromatin differences are erased in the germ line . It would be extremely challenging technically to assess allele-specific chromatin in migratory PGCs because of the very low germ cell numbers at those stages . We found , however , evidence that parental allele-specific chromatin bias exists in the H3K4me2 and H3K9me3 residues in postmigratory germ cells at the H19/Igf2 ICR at 13 . 5 and 14 . 5 dpc; thus the erasure of allele-specific chromatin lags behind the erasure of DNA methylation at the H19/Igf2 ICR ( Figure 7 ) . The erasure of allele-specific chromatin at the ICR , therefore , is not required for the erasure of DNA methylation imprint . It will be interesting to investigate the mechanism of how allele-specific chromatin marks are erased at DMRs . It is important to note that fetal germ cells do not divide after 13 . 5 dpc: spermatogonia enter mitotic arrest whereas primary oocytes arrest at the diplotene phase of meiosis , therefore , a passive loss of chromatin marks at DMRs is possible only before 13 . 5 dpc . DMR chromatin erasure might be linked with global chromatin remodeling events [46]–[48] around mid-gestation . The mechanism of global chromatin remodeling in PGCs is not known but is speculated to be mediated by chromatin chaperons [46] . We found that the rate of erasure at the ICR was different for the H3K4me2 and H3K9me3 marks . H3K4me2 overall enrichment appeared to hold on longer whereas H3K9me3 was largely removed by 14 . 5 dpc . This difference suggests that chromatin mark erasure at DMRs likely occurs by specific chromatin modifying enzymes , such as histone demethylases and does not involve nucleosome removal . Overall H3K4me2 erasure is likely completed at a later stage during spermatogenesis , because H3K4 dimethylation is absent at the ICR in postnatal male germ cells spermatocytes , round spermatids and elongating spermatids [57] . We confirmed previous observations [37]–[40] that DNA methylation erasure at the ICR is complete by 13 . 5–14 . 5 dpc . If epigenetic memory existed of the mother or father in prospermatogonia that could distinguish the parental alleles at this time , it had to be distinct from CpG methylation . 5-hydroxy-methyl C ( 5hmC ) emerges as a second covalent DNA modification with potential for epigenetic regulation [58] , [59] . Because bisulfite sequencing recognizes not only 5mC but also 5hmC [60] , our data are consistent with the absence of epigenetic memory of a parent in the form of both of these DNA covalent modifications at 13 . 5–14 . 5 dpc . Prospermatogonia attained CpG methylation at the ICR gradually between 15 . 5 dpc and 17 . 5 dpc with an allele-specific bias in the rate of methylation , confirming that there was epigenetic distinction between the parental alleles . Methylation of the maternal allele was slower than the paternal allele in normal spermatogonia , but not in CTCFm X OG2 spermatogonia where the ICR CTCF sites were mutant , arguing that functional CTCF sites are required in the maternal allele for its delayed methylation . We found maternally biased CTCF binding in the ICR at 13 . 5–14 . 5 dpc , consistent with the possibility that a bias in CTCF binding may provide the epigenetic memory of the mother . However , CTCF binding was only at trace levels suggesting that CTCF is not likely the factor that physically delays DNA methylation in the maternal allele at 15 . 5 dpc . Our data are in agreement with the model ( Figure 7 ) that CTCF binding in the maternal allele organizes allele-specific chromatin differences at the ICR in PGCs and these chromatin marks are erased with a slower rate than the rate of DNA methylation erasure . The remnants of chromatin differences at 13 . 5–14 . 5 dpc may simply reflect their history and may not be responsible for the methylation bias . Alternatively , these marks may constitute the epigenetic memory that distinguishes the parental alleles for de novo methylation , commencing at 15 . 5 dpc . Indeed , in the absence of CTCF binding in the mutant ICR there was no maternal-allele-specific H3K4me2 bias and the methylation rate of the maternal allele was not delayed compared to the paternal allele in prospermatogonia , giving support to our model ( Figure 7 ) . With the erasure of genomic imprints around mid-gestation the female and male germ lines are preparing for the establishment of the new imprints according to the individual's sex . It will be important to find out how the chromatin composition provides clues to the methylation imprint establishment . The chromatin composition at the paternally methylated DMRs is expected to be permissive to de novo methylation in 15 . 5–18 . 5 dpc spermatogonia and refractory to de novo methylation in growing oocytes . Our results argue that the erasure of chromatin clues at the H3K4me2 and H3K9me3 residues overlaps with the initiation phase of de novo methylation imprint establishment at the ICR and the incomplete erasure of these allele-specific chromatin marks can affect the rate of the new methylation imprint establishment in prospermatogonia . Histone covalent modifications could take active part in or influence DNA methylation imprint establishment in the germ line , based on studies describing the interplay between histone methylation and DNA methylation . Histone H3K9 methylation controls DNA methylation in Neurospora crassa [61] , [62] and in Arabidopsis thaliana [63] , [64] . Histone lysine methylation by Suv39h1 is required for DNA methylation at the pericentric heterochromatin in mice [65] . Our genetic system [31] , [34] is uniquely suited for asking the question whether disturbing the bias in chromatin composition specifically at the H19/Igf2 locus would abolish the bias of methylation imprint establishment at the ICR in male fetal germ cells . H3K9me3 was biased toward the paternal ICR allele at 13 . 5 dpc , and H3K4me2 was biased towardF the maternal allele at 13 . 5–14 . 5 dpc in prospermatogonia . In the absence of paternal H3K9me3 bias in the 13 . 5 dpc CTCFm X OG2 prospermatogonia , the paternal allele's methylation rate was reduced , whereas in the lack of maternal H3K4m2 bias in 13 . 5–14 . 5 dpc prospermatogonia , the maternal allele's methylation rate increased . These findings suggest that chromatin composition differences between the parental alleles may influence the rate of their de novo methylation at the ICR . Male and female germ cells behaved similarly with respect to the dynamics of the overall levels and the allele-specificity of H3K4m2 and H3K9me3 enrichment at the H19/Igf2 ICR at 13 . 5 and 14 . 5 dpc , yet methylation imprint establishment was affected only in male germ cells . The maintenance of the unmethylated state of the ICR in fetal female germ cells was not affected by the chromatin bias . It is likely that the chromatin composition provides clues to exclude or target the de novo DNA methyltransferase complex to DMRs . Because Dnmt3a and Dnmt3L are specifically expressed in male versus female fetal germ cells [66]–[69] , these would be affected by allele-specifically biased chromatin in prospermatogonia but not in primary oocytes . The level of H3K4me2 bias toward the maternal allele was about 10% and thus was similar to the average 15% maternal allele-specific bias in delay of DNA methylation at 15 . 5 dpc . The H3K4me2 bias between the parental alleles existed in the lack of DNA methylation and with only a trace amount of CTCF binding in the ICR . We concluded that the H3K4me2 histone mark could provide the epigenetic memory of the mother in prospermatogonia at 13 . 5–14 . 5 dpc that delays de novo CpG methylation in the maternal ICR allele . Significantly , H3K4 demethylase KDM1B is required at certain DMRs for the establishment of maternal methylation imprints in oocytes [45] , indicating that methylated H3K4 is refractory to DNA de novo methylation . Additionally , the DNA de novo methylation cofactor , Dnmt3L [70] requires a DNA substrate in association with histones containing unmethylated H3K4 [71] . Two other paternally methylated DMRs , the Rasgrf1 DMR and the Dlk1/Gtl2 DMR ( IG-DMR ) also exhibit paternal allele-specific bias in de novo methylation imprint establishment [41] . The maternally methylated Snrpn , Zac1 and Peg1/Mest DMRs are methylated faster in the maternal allele in growing oocytes [50] , [72] . Similarly to the H19/Igf2 ICR , allele-specific bias in chromatin composition of PGC origin may be responsible for providing epigenetic memory of the mother or father at these DMRs .
Male mice of the homozygous transgenic TgOG2 line [B6;CBA-Tg ( Pou5f1-EGFP ) 2Mnn] , which expresses the EGFP reporter gene specifically in germ cells [43] were mated to wild type females of FVB/NJ . CAST/Ei ( N7 ) ( CS ) , a distal chromosome 7 partial congenic strain [31] or to females carrying the H19/Igf2 ICR CTCF site mutations ( CTCFm ) where the mutatant allele was derived from the 129SI/ImJ strain [31] . Pregnant females were sacrificed and from the fetuses female or male gonads were isolated and dispersed according to Buehr and McLaren [73] . Isolates were placed into 0 . 15 ml of trypsin-EDTA , incubated for 20 min at 37C° then dissociated into a single cell suspension . A total of 0 . 3 ml of 25% ( v/v ) fetal bovine serum in medium M2 [74] was added before flow cytometry . Cell suspensions were analyzed and sorted on a MoFlo flow cytometer ( Beckman Coulter , Fort Collins , CO ) . Data were acquired using 488 nm excitation from an Innova-306 Argon laser ( Coherrent , Santa Clara , CA ) at 500 mW . EGFP emission was measured through a 530DF30 filter ( Omega Optical , Brattleboro , VT ) . Fetal germ cells were flow-sorted , collected by centrifugation and embedded into agarose beads . Bisulfite sequencing of the ICR A region was done as before [31] according to Olek [51] . The average number of germ cells used per bisulfite reaction was 20 , 000 . The range was between 1 , 200 and 27 , 000 . Chromatin preparation from 129 X CS primary MEFs was done as described earlier [34] . Chromatin was prepared from flow-sorted fetal germ cells similarly with modifications . We used chromatin from 100 , 000 cells per ChIP estimated by the number of sorted EGFP+ cells . We formaldehyde-crosslinked the chromatin in suspension for 2 min , stopped crosslinking by adding glycine , washed the cell pellet in PBS and resuspended the cells in M2 for flow cytometry . After sorting we resuspended the germ cells in lysis buffer , snap froze the chromatin aliquots and kept them deep frozen until sufficient quantities were obtained for several immunoprecipitations . We thawed the chromatin aliquots , sheared the chromatin by sonication and performed ChIP with different antibodies . The following antibodies were used in the chromatin immunoprecipitation ( ChIP ) assays: anti CTCF , 07-729; anti-dimethyl-histone H3 ( Lys4 ) , 07-030; anti-trimethyl-histone H3 ( Lys9 ) , 17–625; were purchased from Millipore and nonspecific IgG , sc-2027; was from Santa Cruz Biotechnology . The chromatin immunoprecipitation was performed as described previously [34] with minor modifications . Pre-blocked A/G beads from Santa Cruz ( Cat#sc-2003 ) were used . Real-time PCR was performed to measure the region-specific overall ChIP enrichment levels at the H19-Igf2 ICR as described [34] . To measure allele-specific chromatin differences we used the MALDI-TOF allelotyping analysis method from Sequenom [52] as we have done earlier [36] . Mass spectrometry was performed to quantify the extended SNuPE primers based on the differences in molecular mass between alleles . SNPs for the H19-Igf2 region were obtained by DNA sequencing of inbred 129S1 ( 129 ) and CAST/Ei ( CS ) at specific regions of interest as described [34] or were provided by the introduced mutations [31] . Polymerase chain reaction and extension primers for the normal ICR ( forward , reverse and UEP , respectively ) were: SNuPE-H19-4kb: 5′-ACGTTGGATGTTGCGCCAAACCTAAAGAGC-3′; 5′-ACGTTGGATGAGGTACTGAACTTGGGTGAC-3′; 5′-CATTTGTGAATTCCAATACC-3′; SNuPE-H19-3kb: 5′-ACGTTGGATGACACTTGTGTTTCTGGAGGG-3′; 5′-ACGTTGGATGATGCCTTCCTATAGTGAGCC-3′; 5′-AAGGGGTCCCTTTGGTC-3′ . Polymerase chain reaction and extension primers for the CTCF site mutant ICR ( forward , reverse and UEP , respectively ) were: SNuPE-CTCFm1#2: 5′-ACGTTGGATGCTTTAGGTTTGGCGCAATCG-3′; 5′-ACGTTGGATGCGTCTGCTGAATCAGTTGTG-3′; 5′-CGCAATCGATTTTGCTG-3′; SNuPE-CTCFm3#1: 5′-ACGTTGGATGGCTGTTATGTGCAACAAGGG-3′; 5′-ACGTTGGATGTGGGCCACGATATATAGGAG-3′; 5′-AAGGGAACGGATGCTAC-3′ . | Allele-specific DNA methylation is considered the primary mark that distinguishes the parental alleles of imprinted genes . Whereas allele-specific chromatin also exists at imprinted genes in the soma , this has not been assessed in the germ line . It will be important to understand what extent the chromatin composition provides clues in the germ line to the erasure and establishment of methylation imprints . Our novel methods provide the sensitivity required to answer these questions . Our results argue that the erasure of the DNA methylation imprint is complete before , and therefore does not depend on , the erasure of allele-specific chromatin marks at the H19/Igf2 imprint control region . Additionally , we show that incomplete erasure of the allele-specific chromatin is responsible for the delayed DNA methylation imprint establishment of the maternal ICR allele in prospermatogonia . The chromatin bias—the transient epigenetic memory of the mother—in fetal germ cells depends on functional CTCF insulator binding sites in this imprint control region . | [
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"and",... | 2010 | CTCF-Dependent Chromatin Bias Constitutes Transient Epigenetic Memory of the Mother at the H19-Igf2 Imprinting Control Region in Prospermatogonia |
Herpes Simplex Virus type 1 ( HSV-1 ) has evolved to disable the cellular DNA damage response kinase , ATR . We have previously shown that HSV-1-infected cells are unable to phosphorylate the ATR substrate Chk1 , even under conditions in which replication forks are stalled . Here we report that the HSV-1 single stranded DNA binding protein ( ICP8 ) , and the helicase/primase complex ( UL8/UL5/UL52 ) form a nuclear complex in transfected cells that is necessary and sufficient to disable ATR signaling . This complex localizes to sites of DNA damage and colocalizes with ATR/ATRIP and RPA , but under these conditions , the Rad9-Rad1-Hus1 checkpoint clamp ( 9-1-1 ) do not . ATR is generally activated by substrates that contain ssDNA adjacent to dsDNA , and previous work from our laboratory has shown that ICP8 and helicase/primase also recognize this substrate . We suggest that these four viral proteins prevent ATR activation by binding to the DNA substrate and obstructing loading of the 9-1-1 checkpoint clamp . Exclusion of 9-1-1 prevents recruitment of TopBP1 , the ATR kinase activator , and thus effectively disables ATR signaling . These data provide the first example of viral DNA replication proteins obscuring access to a DNA substrate that would normally trigger a DNA damage response and checkpoint signaling . This unusual mechanism used by HSV suggests that it may be possible to inhibit ATR signaling by preventing recruitment of the 9-1-1 clamp and TopBP1 .
Eukaryotic cells have evolved a complex set of pathways to repair DNA and ensure the faithful duplication of the genome [1]–[4] . The cellular DNA damage response is orchestrated by the phosphoinositide 3-kinase-related kinases DNA-PK ( DNA-dependent protein kinase ) , ATM ( ataxia-telangiectasia-mutated ) and ATR ( ATM and Rad3 related ) . DNA-PK and ATM are activated in response to DNA double strand breaks ( DSBs ) , and ATR is activated in response to substrates which contain single stranded DNA ( ssDNA ) adjacent to double stranded DNA ( dsDNA ) such as the DNA found at stalled replications forks . An ATR-activating structure is also produced by resection of DSBs in an ATM-dependent manner; thus , if resection occurs , ATM activation generally results in ATR activation as well . The ssDNA at sites of damage is coated by Replication protein A ( RPA ) and recruits ATR through a direct interaction with the ATR interacting protein ( ATRIP ) [5]–[7] . ATR signaling also requires the localization of the 9-1-1 ( Rad9-Rad1-Hus1 ) checkpoint clamp to sites of DNA damage [8]–[10] . A major function of the 9-1-1 clamp is to recruit the ATR kinase activator , TopBP1 [11] , which promotes phosphorylation of ATR-specific substrates such as serine345 on Chk1 ( Checkpoint kinase 1 ) and serine33 on RPA [12] , [13] ( Summarized in Fig . 1 ) . Herpes Simplex Virus type 1 ( HSV-1 ) is a double-stranded DNA virus that replicates in the nucleus of the host cell and as such must contend with the cellular DNA damage response [14] . DNA-PK , a key component of the classical nonhomologous end-joining ( C-NHEJ ) pathway , is degraded by the viral encoded ubiquitin ligase , ICP0 , in some cell types . This degradation likely results in the inactivation of C-NHEJ , at least in cells in which DNA PK is degraded [15]–[17] . In addition , we have previously reported that HSV-1 infection disables ATR activation [7] , [18] a surprising observation given that HSV-1 DNA replication activates the ATM signaling pathway [17] , [19] , [20] . In HSV-1-infected cells , ATR phosphorylation of RPA and Chk1 is inhibited even in the presence of replicative stress [7]; however , ATR/ATRIP and RPA are recruited to viral replication compartments , where they play positive roles during infection [7] , [17] . Furthermore , we have recently shown that all of the ATR pathway proteins are recruited to viral replication compartments and that ATRIP , RPA , TopBP1 , and CINP are required for efficient HSV-1 replication [18] . Thus , it appears that although HSV-1 commandeers ATR pathway proteins , it has evolved to manipulate the host DNA damage response by inactivating DNA-PK and ATR signaling . HSV-1 encodes seven essential replication proteins: an origin binding protein , UL9 , a single-stranded DNA binding protein , ICP8 , a three subunit helicase/primase complex ( UL8/UL5/UL52 ) , a polymerase , UL30 , and a polymerase accessory factor , UL42 [21] , [22] . ICP8 is the nucleating factor of replication compartment formation , and no detectable intra-nuclear structures are formed in its absence [23] , [24] . The helicase/primase complex is a heterotrimer consisting of UL8 , UL5 , and UL52 subunits . All of the catalytic properties of the complex are retained in a subcomplex consisting of UL5 and UL52 [25] , while UL8 appears to be important for the nuclear import of UL5 and UL52 [26] , [27] . UL8 interacts with other replication proteins including ICP8 , UL9 and UL30 and may also mediate protein-protein interactions at a replication fork [28]–[30] . For instance , UL8 is required for ICP8 to stimulate helicase/primase activity [31]–[36] . Here we present evidence that HSV-1 can inhibit ATR signaling by preventing essential ATR pathway proteins from accessing sites of DNA damage . We show that inhibition of ATR signaling during infection is time-dependent and requires the HSV-1 replication proteins ICP8 and helicase/primase . In cells transfected with plasmids encoding ICP8 and helicase/primase , a nuclear complex is formed that we have called the four-protein complex . This complex localizes to sites of DNA damage and recruits ATR/ATRIP and RPA while excluding Rad9 and TopBP1 . We propose that the presence of viral proteins at the sites of DNA damage compete for the loading signals for the 9-1-1 complex . The failure to recruit Rad9 and TopBP1 to sites of damage likely explains the lack of ATR signaling during infection .
We have previously shown that ATR is inhibited during infection even in the presence of hydroxyurea ( HU ) [7] which is known to stall cellular as well as viral replication forks [37] . This observation suggests that ATR cannot sense or respond to stalled forks in HSV-infected cells , and we initiated the current study to identify the viral mechanisms responsible for ATR inhibition . To test if this inhibition was specific to stalled replication forks or can be generalized to other forms of damage known to activate ATR , we treated infected cells with UV , which stalls replication forks and , in addition , creates cross-linked bases that need to be repaired by nucleotide excision repair . Figure 2A shows that HU- and UV-treatment of uninfected cells induced the phosphorylation of Chk1; however , in HSV-1-infected cells , no detectable phosphorylated Chk1 was observed , even after treatment with HU or UV . These data indicate that ATR signaling is inhibited during HSV-1 infection . To determine how soon after infection ATR signaling is disabled , we damaged cells with UV at various times post infection and monitored the phosphorylation of Chk1 and RPA ( Fig . 2B ) . ATR-mediated phosphorylation of Chk1 S345 and RPA32 S33 was detected at 1 and 2 hours post infection , began to decline at 3 and 4 hours post infection , and was greatly reduced by 5 hours post infection . The timing of the inhibition coincides with early events in the virus life cycle and implicates an early gene product or DNA replication itself as the viral factor responsible for ATR inhibition . After repair , RPA is dephosphorylated by phosphatases in order to clear the damage signal . It is possible that , rather than inhibit ATR signaling , HSV-1 potentiates phosphatase activity , thus making ATR-dependent phosphorylation events appear diminished in infected cells . To rule out activation of a phosphatase , we damaged cells and compared the time it took for the damage signal ( RPA phosphorylation ) to be removed in mock and infected cells . We observed that RPA S33 became dephosphorylated with the same kinetics in mock infected and HSV-1 infected cells ( Fig . S1 ) . When cells were damaged prior to infection , the phosphorylation marks persisted up to 6 hours post infection in both mock- and HSV-1-infected cells . On the other hand , in figure 2B , ATR signaling was inhibited when cells were damaged as early as 4 h . p . i . If HSV were activating a phosphatase , we would expect to see the phosphorylation marks in Figure S1 dissipate at the same time point that HSV prevents ATR signaling in response to new damage , and this is not the case . Thus , it appears that HSV-1 prevents the phosphorylation of these sites rather than potentiating dephosphorylation . To identify the viral proteins responsible for ATR inhibition we infected cells with a panel of viruses defective in both immediate early ( IE ) and early ( E ) genes . Infected cells were damaged with HU at 5 hours post infection , and the phosphorylation of Chk1 was monitored by Western blot ( Table 1 ) . Mutants defective in the IE proteins ICP0 and ICP22 were able to inhibit ATR signaling while mutants defective in ICP4 and ICP27 were not . ICP0 and ICP22 are non-essential in cell culture , and at the high multiplicity of infection ( MOI ) used in this study , null-mutants are able to progress to E gene expression and DNA replication . ICP4 and ICP27 are essential in cell culture , and in the absence of these proteins infected cells are unable to carry out E gene expression or DNA replication . To test if E gene expression or DNA replication was required to inhibit ATR signaling , we infected cells with mutants defective in early replication proteins or in the presence of replication inhibitors . Mutants defective in UL5 , UL52 , and UL30 , were able to inhibit ATR signaling ( Table 1 and Figure 2C ) . Signaling was also inhibited during infection in the presence of both helicase/primase inhibitors and polymerase inhibitors ( Table 1 ) . These data suggest that DNA replication per se is not required to inhibit ATR signaling . On the other hand , viral mutants deficient in ICP8 and UL8 failed to inhibit ATR signaling in HeLa and Vero cells ( Table 1 and Fig . 2C ) . Together these data strongly implicate ICP8 and UL8 as the viral proteins responsible for inhibiting ATR signaling . HSV-1 replication proteins such as ICP8 have been implicated in reorganization of the infected cell nucleus resulting in the formation of replication compartments [38] , [39] . Replication compartment formation occurs through an ordered assembly of replication proteins [23] , [24] , [40] , [41] . By adding helicase/primase inhibitors to block DNA replication , the early stages of this protein assembly process can be observed in HSV-1-infected cells [7] , [40] . These assemblies , or prereplicative sites , contain ICP8 , the helicase/primase complex ( UL8/UL5/UL52 ) , and the origin binding protein , UL9 [40] , as well as the cellular proteins ATR/ATRIP and RPA [7] . The observation that ICP8 and UL8 are necessary to inhibit ATR signaling led us to examine whether these proteins are sufficient to inhibit ATR signaling , and whether their ability to reorganize the nucleus is also required for inhibition of ATR signaling . Structures that resemble replication compartments are detected in cells transfected with plasmids expressing the seven replication proteins [24] , [42] . Foci that resemble prereplicative sites can also be reconstituted in cells transfected with plasmids expressing subsets of the replication proteins . Figure S2 shows that as previously described [23] , [24] , when expressed alone either ICP8 or the helicase/primase complex localize in a nuclear diffuse staining pattern . On the other hand , transfection of cells with plasmids expressing ICP8 and helicase/primase results in the formation of punctate nuclear structures that contain all four proteins and resemble prereplicative sites [24] , [40] . The formation of the four-protein complex is consistent with the observation that ICP8 colocalizes with the helicase/primase complex by immunofluorescence and that the four proteins directly interact in a UL8 dependent fashion [23] , [24] , [31] . The four-protein complex forms efficiently in greater than 75% of transfected cells . When ICP8 is expressed with UL8 in the absence of UL5 and UL52 , punctate structures can be detected , which we have termed two-protein complexes ( Fig . S2 ) ; however , formation of these structures is less efficient than formation of the four-protein complex , forming in less than 10% of transfected cells . To determine whether ICP8 and UL8 are sufficient to inhibit ATR signaling , cells expressing the two-protein complex were damaged with UV and monitored for ATR activation by immunofluorescence . Untransfected cells exhibited phosphorylated RPA-S33 in response to UV , while cells expressing the two-protein complex did not ( Fig . 3A ) . This result suggests that ICP8 and UL8 are necessary and sufficient to disable ATR signaling even in the presence of DNA damage that would normally activate ATR . Cells expressing the four-protein complex were also able to inhibit ATR signaling to RPA-S33 and Chk1 in response to UV ( Fig . 3A and B ) . We further quantified this reduction in phosphorylated RPA S33 by counting cells expressing the two- or four-protein complex and scoring them for presence or absence of phosphorylated RPA ( Fig . 3C ) . In UV-treated cells expressing an empty vector greater than 90% of the cells exhibited phosphorylated RPA S33 . In contrast , UV-treated cells expressing the two- or four-protein complex exhibited 33% and 14% of cells with phosphorylated RPA S33 , respectively . These data suggest that the two- and four-protein complexes are efficient inhibitors of ATR signaling . In addition to the ATR specific phosphorylation on RPA S33 after UV damage , RPA is also phosphorylated by DNA-PK on S4/S8 [13] , [43] . To test whether this inhibition was specific for ATR , we treated cells expressing the four-protein complex with UV and looked at phosphorylation of RPA S4/S8 . We observed no difference in RPA S4/S8 phosphorylation between cells expressing an empty vector and cells expressing the two- or four-protein complex ( Fig . 3C ) , indicating that neither complex can inhibit DNA-PK signaling . Thus , the two- and four-protein complexes are specific for inhibition of ATR signaling . As a control we also verified that expression of ICP8 and helicase/primase did not alter the cell cycle profile of these cells ( Fig . S3 ) ; therefore , the inhibition of ATR signaling is not due to a decreased number of cells in S-phase . Consistent with the observation that the four-protein complex forms more efficiently than the two-protein complex , the four-protein complex is more efficient than the two-protein complex at inhibiting ATR signaling ( Fig . 3C ) . Furthermore , UL5/UL52 are unlikely to inhibit ATR signaling on their own since these proteins do not localize to the nucleus in cells that do not express UL8 [26]–[28] , nor do they associate with ICP8 in the absence of UL8 [31] . Thus , we chose to focus the rest of our studies on the four-protein complex . To date , no enzymatic function has been assigned to UL8 , and it is believed to function as a scaffolding protein to link the helicase/primase complex to other replication factors [28] , [31]–[36] . We have previously described a functional EE-epitope tagged version of UL8 ( EE-UL8 ) [44] . Three internal deletion mutants ( Δ6–198 , Δ29–186 , and Δ79–339 ) were generated in EE-UL8 ( Fig . 4A ) . All three mutants are able to express stable protein that can interact with UL5 and UL52; however , they are unable to support origin-dependent DNA replication and four-protein complex formation ( Figure S4 ) . We tested the ability of these mutants to inhibit ATR activation . Vero cells were transfected with ICP8 , UL5 , UL52 , and the indicated EE-UL8 mutant and then damaged with UV . EE-UL8 , Δ29–186 , and Δ79–339 were able to prevent ATR-dependent RPA S33 phosphorylation while Δ6–198 was not ( Fig . 4B ) . These data confirm that DNA replication is dispensable for inhibiting ATR signaling . Furthermore , the inability of Δ6–198 to inhibit ATR signaling suggests that residues at the N-terminus of UL8 ( between 6 and 29 ) may be required to inhibit ATR signaling . Since Δ29–186 and Δ79–339 do not form the four-protein complex but are still able to inhibit ATR , this suggests that the formation of the four-protein complex is not strictly required for this function . Since ICP8 and helicase/primase are able to inhibit ATR signaling , we next asked whether they could localize to sites of DNA damage . Cells were transfected with plasmids encoding ICP8 and the helicase/primase complex , and BrdU was added to the media at the time of transfection to detect sites of cellular DNA replication . Cells were either left undamaged or damaged with UV or HU , and then analyzed for ICP8 and BrdU by immunofluorescence under non-denaturing conditions . Without the denaturation step , BrdU antibodies only detect BrdU in ssDNA . Figure 5 shows that ICP8 colocalizes with BrdU in both undamaged and damaged cells suggesting that the four-protein complex is present at ssDNA regions in undamaged cells and sites of DNA damage in the presence of UV or HU . Although ICP8 colocalizes with BrdU in both damaged and undamaged cells , BrdU staining is much brighter in damaged cells reflecting the increased amount of ssDNA known to be present at sites of DNA damage as a result of helicase and polymerase uncoupling [45] . The observation that ICP8 and helicase/primase localize to sites of ssDNA in undamaged and damaged cells suggests that the four-protein complex may also recognize and be recruited to sites of endogenous DNA damage in transfected cells . Consistent with this notion , the four-protein complex also colocalizes with Rad51 and γH2AX , known markers of DNA damage ( Fig . S5 ) . In order to determine whether cellular ATR pathway proteins such as ATR/ATRIP and RPA are recruited to the four-protein complex after damage , cells expressing ICP8 and helicase/primase were damaged with UV , fixed and analyzed by immunofluorescence . ATRIP and GFP-RPA70 were both strongly recruited to the four-protein complex and precisely colocalized with ICP8 ( Fig . 6 ) , consistent with our previous observation that endogenous RPA32 is recruited to the four-protein complex in 91% of cells expressing the four-protein complex [17] . Interestingly , we found that neither tagged nor endogenous Rad9 , TopBP1 , or Claspin were recruited to the four-protein complex ( Fig . 6 and S5 ) . For instance , 55 of 55 cells expressing the four-protein complex exhibited diffuse Rad9 . As noted previously , the four-protein complex resembles prereplicative sites generated during infection in the presence of helicase/primase inhibitors . The helicase/primase inhibitor used in this study inhibits helicase/primase activity and DNA synthesis but does not alter the expression levels of helicase/primase and does not alter their localization with ICP8 in prereplicative sites . Interestingly , ATR/ATRIP and RPA are recruited to prereplicative sites , and Rad9 is not ( [7] and Figure S6 ) . Exclusion of these essential ATR signaling co-factors from prereplicative sites and from the four-protein complex provides an explanation for the lack of ATR signaling in cells expressing ICP8 and helicase/primase . To further test the hypothesis that lack of 9-1-1 recruitment functionally inactivates ATR by preventing ATR-interaction with TopBP1 , we took advantage of a previously described TopBP1 mutant that overcomes the need for 9-1-1 in ATR activation . The over-expression of the ATR activation domain ( AAD ) of TopBP1 ( amino acids 978–1286 ) specifically activates ATR signaling [12] , [18] , [46] , [47] . TopBP1-AAD lacks the 9-1-1 interacting domain but is still able to bind and activate ATR . Thus , if the four-protein complex inhibits ATR by preventing 9-1-1-mediated recruitment of TopBP1 , then expressing TopBP1-AAD should restore ATR signaling . This is indeed the case , as TopBP1-AAD stimulated ATR signaling when transfected alone and when transfected with the four-protein complex ( Fig . 7 ) . This experiment also indicates that ATR is still functional in cells expressing the four-protein complex . Thus , the inactivation of ATR signaling in HSV-infected cells is not due to inactivation by post-translational modifications or dephosphorylation of ATR substrates . Therefore , the mechanism by which ATR signaling is disabled in HSV-infected cells involves the lack of 9-1-1recruitment to sites of damage .
Several of the proteins described in this study are known to participate in protein-protein as well as protein-DNA interactions . UV- or HU-induced damage is expected to result in a DNA structure containing ssDNA adjacent to dsDNA . This type of structure is also present on the lagging strand during DNA synthesis . We have previously shown that the helicase/primase complex has a higher affinity for forked DNA or dsDNA with a ssDNA overhang than for ssDNA or dsDNA and that dimer or higher-order complexes of helicase/primase could form on forked DNA substrates [48] , [49] . Thus , HSV-1 helicase/primase is known to bind DNA substrates that have similar structures to those recognized by the 9-1-1 clamp in damaged DNA or at stalled replication forks . We suggest that when viral DNA synthesis stalls , helicase/primase binds at ss/dsDNA junctions on the lagging strand and effectively prevents 9-1-1 from loading . Consistent with the proposed model , we also observe the four-protein complex at sites of replication/repair in undamaged cells . Since TopBP1 is generally recruited to sites of DNA damage by interacting with the C-terminal tail of 9-1-1 , the failure to load 9-1-1 would preclude TopBP1 binding . In support of this model , we failed to detect 9-1-1 or TopBP1 at sites of damage in cells transfected with ICP8 and helicase/primase . The inability to recruit TopBP1 , the ATR kinase activator , explains the lack of detectable ATR signaling to RPA and Chk1 . As a further test of the model , we expressed a mutant form of TopBP1 that overcomes the need for 9-1-1 recruitment and restored ATR signaling . Together these observations provide a compelling model to explain the inhibition of ATR signaling by HSV-1 . The model shown in Figure 8 is consistent with the available data; however , validation will require additional experimentation including more direct demonstration of competition between helicase/primase and 9-1-1 for loading onto the ssDNA-dsDNA junction in vitro . Mammalian 9-1-1 loading onto ss/dsDNA junctions has not been reconstituted in vitro , so it is not immediately possible to directly test this part of the model in vitro . Thus , other mechanisms cannot be excluded at this time . The current study was initiated to determine how ATR signaling is disabled in HSV-1-infected cells even though ATM signaling is activated by viral DNA replication . ATR signaling is believed to be important for the stabilization of stalled replication forks . The prevention of ATR signaling during infection may predispose viral replication forks to collapse which in turn may lead to DSB formation , ATM activation and homology directed repair . Since we and others have suggested that HSV-1 utilizes recombination-mediated DNA synthesis [22] , [50] it is possible that inactivation of ATR signaling is beneficial for the virus . This interpretation is also consistent with our previously published observation that constitutively activated ATR inhibits HSV-1 replication-dependent recombination [18] , possibly through stabilization of a stalled fork . It is also important to point out that while TopBP1 and Rad9 are excluded from the four-protein complex , they are recruited to replication compartments [18] . Although we have shown that they do not participate in ATR signaling in infected cells , TopBP1 and Rad9 are known to interact with several other DNA repair and replication proteins that could recruit them to replication compartments . For example , TopBP1 is present in cellular DNA replication complexes and also interacts with ATM after double strand breaks . Likewise , Rad9 binds MLH1 , a protein known to bind viral DNA , and is also recruited to DNA double strand breaks by an interaction with Mre11 and CtIP[51]–[58] . Thus , these proteins could be recruited by distinct mechanisms that do not result in ATR activation . ATR is an essential cellular protein [59] , [60]; however , unlike its related kinase , ATM , the regulation of ATR is poorly understood . Also unlike ATM , there is no consensus regarding a reliable phosphorylation mark that is indicative of ATR activation [61] , [62] , and ATR specific inhibitors are just beginning to emerge [63]–[65] . Many DNA viruses including herpesviruses , adenoviruses and autonomous and non-autonomous parvoviruses disable ATR signaling . Reports from the Weitzman and Turnell labs show that Adenoviruses 5 and 12 disable ATR signaling by E4-mediated degradation of MRN and TopBP1 respectively [66] , [67] This paper presents the first example of a DNA virus that disables cellular ATR signaling by preventing cellular DNA repair proteins from accessing the DNA . This report is also the first to suggest that viral DNA replication machinery can alter the cellular recognition and signaling pathways for DNA damage in infected cells . To date , all other examples of viral manipulation of the DNA damage response have relied on viral proteins such as E4orf3 and E4orf6 in adenovirus or ICP0 in HSV-1 that specifically recognize and degrade substrates irrespective of damage . In this study we have shown that HSV-1 replication proteins can prevent Rad9 and TopBP1 from accessing sites of DNA damage and prevent ATR activation . The use of viruses to target cancer cells ( oncolytic virotherapy ) either alone or in combination with conventional chemotherapy may provide a novel way to inhibit ATR signaling . For example , ATM- and p53-deficient tumor cells are very sensitive to ATR inhibition [63] , and the combination of oncolytic HSV-1 with conventional chemotherapy has resulted in decreased tumor volume and improved long-term survival in animal models of Glioblastoma multiforme [68] , [69] . We suggest that the benefits of combining oncolytic HSV-1 with conventional chemotherapy are due to the ability of HSV-1 to specifically disable ATR signaling and thus sensitize cancer cells to DNA damaging agents .
Vero , HeLa , and U2OS cells were purchased from the American Type Culture Collection ( ATCC ) and maintained as previously described [52] . The Vero cell line stably expressing HA-Rad9 was previously described [18] . The helicase/primase inhibitor BAY 57-1293 ( N- ( 5- ( aminiosulfonyl ) -4-methyl-1 , 3-thiazol-2-yl ) -N-methyl-2- ( 4- ( 2-pyridinyl ) phenyl ) acetamide ) was obtained from Gerald Kleymann ( Bayer Pharmaceuticals; Wuppertal , Germany ) [70] and used at a concentration of 100 µM as described [7] , [40] . The polymerase inhibitor phosphonoacetic acid ( PAA ) was purchased from Sigma and used at a concentration of 400 µg/mL as previously described [17] . In all DNA damage experiments hydroxyurea was purchase from Sigma and used at a concentration of 3 mM or cells were damaged with 50 J/m2 UV . The KOS strain was used as wild type HSV-1 and all mutant viruses used in this study are derived from KOS . The following viruses were previously described: ΔICP0 ( 0β ) [71] , ΔICP4 ( d120 ) [72] , ΔICP22 ( d22lacZ ) [73] , ΔICP27 ( d27-1 ) [74] , ΔICP4/ICP27 ( d92 ) [75] , ΔICP4/22/27/47 ( d106 ) [76] , ΔICP8 ( HD2 ) [77] , ΔUL5 ( hr99 ) [78] , ΔUL8 ( hr80 ) [79] , ΔUL52 ( hr114 ) [80] , and ΔUL30 ( hp66 ) [81] . Proteins expressed from CMV promoters were previously described , ICP8 ( pCM-DBP ) , UL8 ( pCM-UL8 ) , UL5 ( pCM-UL5b ) , UL52 ( pCMV-UL52 ) , GFP-RPA70 ( pEGFP-RPA70 ) , GFP-TopBP1-AAD , and Myc-TopBP1 [18] , [24] , [46] , [82] , [83] . Cells were transfected with Lipofectamine PLUS ( Invitrogen ) according to the manufacturer's suggested protocol . IF analysis was performed as described [7] , [40] , [52] . Briefly , cells adhered to glass coverslips were washed with PBS , fixed with 4% paraformaldehyde , and permeabilized with 1% Triton X-100 . Cells were blocked in 3% normal goat serum and reacted with antibodies as indicated . Staining for BrdU was done as previously described with the omission of a HCl wash to denature DNA [24] . Primary antibodies include polyclonal rabbit anti-ATRIP ( rATRIP Upstate ) ( 1∶200; Upstate ) , monoclonal mouse anti-ICP8 ( 1∶200; Abcam ) , polyclonal rabbit anti-ICP8 367 [84] , monoclonal rabbit anti-phospho-Chk1 S345 ( 1∶200; Cell Signaling ) , polyclonal rabbit anti-phospho-RPA S33 ( 1∶200; Bethyl ) , monoclonal rat anti-BrdU ( 1∶100; Genetex ) , polyclonal rabbit anti-Claspin ( 1∶200; Bethyl ) , monoclonal mouse anti-Myc ( 9B11 ) ( 1∶200; Cell Signaling ) , monoclonal rat anti-HA ( 1∶200; Roche ) , and polyclonal rabbit anti-HA ( 1∶200;Clontech ) . AlexaFluor secondary antibodies ( 1∶200; Molecular Probes ) were used with fluorophores excitable at wavelengths of 488 , 594 , or 647 . Images were captured using a Zeiss LSM 510 confocal NLO microscope equipped with argon and HeNe lasers and a Zeiss 63× objective lens ( numerical aperture , 1 . 4 ) . Images were processed and arranged using Adobe Photoshop CS3 and Illustrator CS3 . Cells in 35 mm dishes were lysed in 2× SDS sample buffer ( 4% SDS , 20% glycerol , 100 mM Tris pH 6 . 8 , 100 mM DTT , 10% β-Mercaptoethanol , 1 mM sodium orthovanadate , 10 mM NaF , 1× protease inhibitor cocktail ( Roche ) , and 0 . 1% bromophenol blue ) and boiled for 5 minutes . Proteins were resolved by SDS-PAGE and transferred to PVDF membranes . Membranes were blocked for 1 hour in 5% non-fat dry milk or 2% BSA dissolved in TBST . Primary antibodies were diluted in blocking solution and incubated overnight at 4°C . Primary antibodies used include polyclonal rabbit anti-ATRIP 403 ( rATRIP 403 ) ( 1∶3 , 000 ) [59] , monoclonal mouse anti-ICP4 ( 1∶10 , 000; US Biologics ) , monoclonal mouse anti-β-actin ( 1∶15 , 000; Sigma ) , polyclonal goat anti-ATR N19 ( 1∶1 , 000; Santa Cruz ) , monoclonal mouse anti-Chk1 ( 1∶1 , 000; Santa Cruz ) , monoclonal mouse anti-HA ( F7 ) ( 1∶3 , 000; Santa Cruz ) , monoclonal mouse anti-RPA32 ( 9H8 ) ( 1∶1 , 000; Genetex ) , monoclonal rabbit anti-phospho-Chk1 S345 ( 1∶5 , 000; Cell Signaling ) , polyclonal rabbit anti-phospho-RPA S33 ( 1∶3 , 000; Bethyl ) , and polyclonal rabbit anti-phospho-RPA S4/S8 ( 1∶3 , 000; Bethyl ) . Polyclonal rabbit antisera to UL8 ( R248 ) and UL52 ( R2403 ) were provided by Mark Challberg . | DNA viruses that replicate in the nucleus have been shown to both activate and inactivate various components of the cellular DNA damage response ( DDR ) . Previous reports from our laboratory and others have demonstrated that Herpes Simplex Virus ( HSV ) utilizes some aspects of the DDR while inactivating others . Paradoxically , HSV utilizes the DDR kinase ATR to complete its life cycle while at the same time disabling the kinase from activating DDR signaling . In this report we provide detail describing the mechanism of ATR inactivation . ATR is normally activated in response to single strand DNA ( ssDNA ) , which serves as a scaffold to recruit several proteins required for complete ATR activation . In this paper we provide evidence that the HSV encoded ssDNA binding protein and helicase/primase complex function to mask the DNA substrate that recruits the ATR kinase activator . This represents the first example of viral DNA replication proteins masking a DNA substrate that could be sensed by the cell as damaged DNA and activate checkpoint signaling . It also explains how ATR can be recruited to sites of viral DNA replication in the absence of checkpoint signaling . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Herpes Simplex Virus Type 1 Single Strand DNA Binding Protein and Helicase/Primase Complex Disable Cellular ATR Signaling |
The ability to adapt to different conditions is key for Mycobacterium tuberculosis , the causative agent of tuberculosis ( TB ) , to successfully infect human hosts . Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage . In latently infected individuals , estimated to include one-third of the human population , the organism exists in a variety of metabolic states , which impedes the development of a simple strategy for controlling or eradicating this disease . Direct knowledge of the metabolic states of M . tuberculosis in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails . Here , we propose an in silico approach to create state-specific models based on readily available gene expression data . The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of M . tuberculosis H37Rv to hypoxia . Given the microarray data for the alterations in gene expression , our model predicted reduced oxygen uptake , ATP production changes , and a global change from an oxidative to a reductive tricarboxylic acid ( TCA ) program . Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth , as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient , low metabolic activity life style . In contrast , the gene expression program in the deletion mutant of dosR , which encodes the immediate hypoxic response regulator , failed to adapt to low-oxygen stress . Our predictions were compatible with recent experimental observations of M . tuberculosis activity under hypoxic and anaerobic conditions . Importantly , alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to differential gene expression of the enzymes catalyzing the related metabolic reactions .
Mycobacterium tuberculosis , the causative agent of tuberculosis ( TB ) , caused 8 . 8 million new TB cases and resulted in the death of 1 . 5 million people worldwide in 2010 [1] . Furthermore , it is estimated that one-third of the human population is latently infected with the disease , with an overall lifetime risk of developing active TB disease of 10% [2] . In the United States , more than 80% of clinically observed TB results from reactivated latent infections [3] , [4] . The latent disease state prevents eradication , confounds diagnosis , increases HIV comorbidity [5] , prolongs existing TB treatment to at least six months [6] , [7] , and increases the risk for the development of drug resistance [8] . The variety of disease states , ranging from dormant to sub-clinical to clinical disease manifestations , complicates the treatment and eradication of the disease [9] . The presence of latent infections results in a dangerous reservoir of the disease . The manifold of latent disease manifestations [10] , [11] is poorly understood and difficult to replicate in model systems of TB , making it quite challenging to reach the United Nation's goal of eradicating TB before 2050 [12] and developing effective therapeutics [13] . Targeting different aspects of metabolism in the latent state is a viable therapeutic strategy that is supported by evidence of differential metabolic activity among several dormant and latent states of M . tuberculosis [14]–[17] . One of the challenges in targeting metabolism in latent disease is the inability of existing experimental model systems to fully capture the range of observed phenotypes . While experimental in vitro persister models can be created based on acid stress , hypoxic stress , and carbon starvation [18] , there is a need for studying the manifold of disease states . Here , we propose an in silico approach to create state-specific models based on readily available gene expression data . The coupling of differential gene expression data with a metabolic network model allows us to metabolically characterize any TB disease state , provided the corresponding microarray data are available . We applied this technique to characterize the metabolic adaptations of M . tuberculosis in response to hypoxia . Similar to the introduction of nitric oxide [19] and carbon monoxide [20] , hypoxia is one of the factors that characterize the onset of persistence and latency in M . tuberculosis [21] . Although hypoxic microenvironments are an important feature of tuberculosis granulomas in guinea pig , rabbit , and nonhuman primate models of the disease , this feature is not present in mouse models [22] , pointing to a link between host-specific factors and latency . In addition to these models , there are established protocols to cultivate the pathogen in artificial low-oxygen conditions in order to create in vitro persistence models [23] . These models , characterized by low-oxygen conditions , exhibit gene expression profiles distinct from those obtained under normoxic conditions [24]–[28] . The immediate response to hypoxic stress is partially governed by the dosR gene , which encodes a transcription factor essential for the hypoxic persistence of mycobacteria [29] . In particular , Park and coworkers measured changes in gene expression under hypoxia of wild type M . tuberculosis H37Rv and its ΔdosR deletion mutant compared to normoxia [25] . Although this work established the connection of the dosR regulator to the hypoxic response , reviewing the list of differentially expressed genes provides a limited view of what the dosR-initiated gene expression pattern entails in terms of M . tuberculosis metabolic adaptation to hypoxia . A systems-level understanding of metabolism requires the identification and reassembly of the constituent components ( metabolites , reactions , transport , and uptake processes ) and methods to analyze metabolic phenotypes [30]–[34] . The most robust and advanced systems biology reconstruction and analysis techniques focus on metabolism . In particular , genome-scale metabolic networks for M . tuberculosis are composed of hundreds of distinct but interconnected chemical reactions , each processing particular metabolites that , taken together , ultimately allow the cell to function and grow [35] , [36] . Metabolic network reconstructions of M . tuberculosis have been used to identify genes essential for growth [35] , [36] , study the importance of mycolic acid production [37] , model quantitative drug-dose response [38] , [39] , deconstruct metabolic responses [40] , and identify metabolic adaptations to different in vitro , ex vivo , and in vivo host conditions [41] , [42] . Traditional metabolic network analysis results in a general description of a cell's steady state metabolism and typically represents an idealized version of the cellular metabolic program under exponential growth conditions . As such , the network description does not take into account different protein or expression levels of individual metabolic genes in the network . Gene expression data captures the transcriptional state of a cell in a particular biological state and it is challenging to interpret this partial information with respect to an altered metabolic program . The strength of a transcriptional approach is that we can capture a specific snapshot of the cell without elaborating the underlying signaling and gene regulatory networks . The weakness is that the transcriptional state is not a direct readout of the metabolic enzyme concentrations that perform metabolic reactions . Efforts to connect transcriptional levels to metabolic activity in network models of metabolism have focused on correlating absolute expression levels to metabolite flows [37] , [43] , [44] , completely suppressing reactions based on pre-defined changes in relative expression levels [45] , or establishing protocols for generating condition-specific metabolic signals of changes in metabolite production based on multiple microarray data sets [40] . Here , we introduce a new method that relies on relative gene expression levels between a metabolically well-characterized reference state ( e . g . , exponential growth under normoxic conditions ) and a perturbed state of interest ( e . g . , reduced growth under hypoxic conditions ) . Although this method ultimately relies on a correlation between gene transcription levels and enzymatic activity , in contrast to previous methods [37] , [43] , [44] , we rely on individual relative relationships between a reference condition and a condition of interest for each gene . It allows for a continuous flow of metabolites , even for down-regulated enzymes , and accommodates variability in biomass composition . This latter feature overcomes the restriction of constraint-based models that the biomass composition remains fixed under the studied conditions , as biomass variability occurs for several bacterial species under different growth conditions [46]–[48] . Thus , based only on the differential gene expression data from M . tuberculosis H37Rv under hypoxic conditions [25] , we mapped out the metabolic response to low-oxygen stress . The model correctly predicted lower oxygen uptake , a lowered ATP production rate , and a higher hypoxic growth rate as compared to its ΔdosR deletion mutant , indicating that the presence of the dosR gene was essential for the pathogen to adapt to hypoxia [49]–[51] . We also predicted that hypoxia induces the production of cell-wall metabolites and alters the biomass composition of M . tuberculosis [50] , [51] . Importantly , our model indicates that the glucose-processing glycolysis pathway and the reductive side of the tricarboxylic acid ( TCA ) cycle contribute to the adaptation of M . tuberculosis to hypoxia [16] , [52] and could serve as a drug target for the elimination of this pathogen in latent disease states .
Figure 1 illustrates the integration of microarray data and a metabolic network description for a small example network that contained six metabolites ( A–F ) , two uptake reactions , six enzymatic reactions , and one biomass reaction . In this set , we were given a metabolic network capable of producing biomass for the reference condition and the gene expression ratios for each metabolic reaction between the reference and the new condition . Given relative gene expression ratios , the approach initially constructed a set of normalized relative fluxes for each metabolic reaction in the reference state ( Figure 1 , Step I ) and then introduced the altered gene expression ( Figure 1 , Step II ) as soft constraints on these fluxes ( Figure 1 , Step III ) . The soft constraints allowed the system to adjust the flow of metabolites as calculated from the entire network to the given altered gene expression state . The procedure then established new flux ranges for all reactions by minimizing violations of the constraints introduced by the gene expression data , modifications to the biomass , and alterations in the uptake reactions ( Figure 1 , Step IV ) . The final metabolic network approximated the altered metabolic state . In the example network , the new condition was compatible with 1 ) increased ( decreased ) uptake of metabolite A ( B ) , 2 ) preferred metabolite flow through reaction B→D over the reaction path B→C→D , 3 ) increased metabolite flow in reaction B→D even though the gene expression of the enzyme catalyzing this reaction was unchanged , and 4 ) increased content of metabolite F in the biomass objective function . A detailed description of the procedure is given in the Materials and Methods Section and the rationale for constructing a relative gene expression methodology is further articulated in the Discussion Section . We performed an initial validation of our approach by successfully predicting experimentally measured reaction fluxes from two separate laboratories . These studies examined metabolic fluxes in yeast grown on four different carbon sources [53] and 13C flux changes upon removal of the gcn4 global regulator gene under histidine starvation conditions [54] ( see Supplemental Text S1 ) . The persistence of M . tuberculosis in human granulomas is partly due to its ability to adapt to a condition of low oxygen availability [23] , a process that requires the transcription factor gene dosR [19] . We modeled an altered metabolic state of M . tuberculosis in response to moving from the reference state of normoxia to an altered hypoxic state as defined by its transcriptional state . We used the iNJ661m metabolic network of M . tuberculosis H37Rv [42] , an enhanced version of the original iNJ661 network [35] that retains the correct predictions of growth rates of H37Rv in different media and includes several reactions missed in iNJ661 , e . g . , in the methylcitrate cycle pathway . We integrated this network with microarray data that included gene expression ratios for both induced and repressed mRNA gene transcription for wild type M . tuberculosis H37Rv , as well as for the ΔdosR deletion mutant , associated with the transfer from normoxic air to hypoxic nitrogen gas with 0 . 2% oxygen ( 1 . 5 mm Hg ) [25] . Furthermore , based on experimentally determined normoxic growth [19] and ATP concentrations in culture [49] as well as the fact that the dosR gene does not directly encode an enzyme in the metabolic network , we assumed that the normoxic metabolic state was equivalent between the two strains , and thus we used the same network for the normoxic simulation . Using the metabolic network/gene-expression integration model , we predicted hypoxia-induced changes in important phenotypes ( oxygen uptake , ATP production , growth ) , biomass composition , and fluxes through the central carbon metabolism for both wild type M . tuberculosis H37Rv and its ΔdosR deletion mutant . We used the observed changes in experimental phenotypes to qualitatively validate and indicate the utility of the proposed method [37] , [43] . Figure 2 shows the predicted normalized oxygen uptake rates and ATP production rates for both wild type M . tuberculosis H37Rv and the corresponding ΔdosR mutant under normoxia and hypoxia 2 hours after switching to a condition of 0 . 2% oxygen . The oxygen uptake rates in Figure 2A were normalized by each strain's biomass production rate , for the different conditions , using the results in Supplemental Table S1 calculated as described in the Materials and Methods Section . While the normoxic conditions between the two strains were equivalent by construction , the hypoxic predictions between the two strains were quite different . The predicted wild type hypoxic oxygen uptake rate was substantially lower than the corresponding normoxic prediction , indicating that the wild type strain had the ability to substantially decrease its oxygen demand . At the same time , we only predicted a modest hypoxia-induced decrease in ATP production , suggestive of this strain's ability to maintain its energy production under low-oxygen stress . These predictions are qualitatively supported by experimentally observed lower ATP concentrations [16] for the wild type strain under hypoxia compared to normoxia . The hypoxic predictions for the ΔdosR strain differed substantially from the wild type predictions , suggesting that the deletion mutant was not able to modulate its metabolism to adapt to hypoxic conditions . In particular , the wild type normalized oxygen uptake rate was considerably lower than in the ΔdosR strain under hypoxic conditions , indicating that the deletion mutant was less able to adapt to the low-oxygen stress than the wild type per unit biomass . Because the deletion mutant is not able to grow efficiently under hypoxia , its overall ATP production rate is relatively lower than that for the wild type ( Figure 2B ) . The predicted lower hypoxic ATP level and slower oxygen depletion in the ΔdosR mutant compared to the wild type have also been observed experimentally [49] . The inability of the ΔdosR strain to adapt to the low-oxygen environment is reflected in the difference in growth characteristics between the wild type and deletion mutant strains [49] . To test whether the strain- and condition-specific metabolic networks contain this growth information , we created corresponding in silico cellular growth predictions using an exponential growth model . We parameterized this model based on calculated growth rates and estimated lysis rates determined by fitting to the experimental cell concentrations of the wild type strain ( see Materials and Methods for details ) . Figure 2C shows the experimentally determined M . tuberculosis cell concentrations during a 200-day growth period in which oxygen was depleted around day five , marking the onset of hypoxic growth [49] . This figure also shows the in silico-modeled cell concentrations of the two strains in the normoxic ( days 0–5 ) and early hypoxic ( days 5–60 ) stages of growth , and allows us , by inspection , to qualitatively estimate the time periods during which our model could capture the growth pattern of M . tuberculosis . The results for the wild type strain indicated that the model successfully reproduced the growth of this strain up to day 60 . After this period , additional cellular reprogramming in response to extended hypoxia occurs [55] , a response that is not dependent on dosR and represents further biological and metabolic adaptations not modeled here . The model results for the ΔdosR mutant provided relatively accurate prediction for the first 12 days . After this initial period , the model begins to break down , presumably due to additional biological and metabolic factors not modeled by the initial dosR gene reprogramming response . To ascertain the robustness and specificity of our approach , we conducted in silico experiments to gauge the influence of fluctuations in the gene expression data and determine whether the metabolic predictions were specific to the expression data or the metabolic network per se . To address fluctuations in the data , we created simulated gene expression data sets where all differential gene expression values were sampled from their corresponding normal distribution defined by their observed means and standard deviations [25] . For each data set , we calculated its hypoxic oxygen uptake rate , allowing us to re-construct a probability distribution of the hypoxic oxygen uptake rates for the wild type strain that is compatible with the given fluctuations of the experimental gene expression data . Figure 3 shows that 98% of the predicted oxygen uptake rates were centered on the hypoxic rate predicted using the mean experimental expression data , indicating that our predicted oxygen uptake was robust to fluctuations in gene expression measurements . Conversely , when we distributed the expression data randomly across genes in the metabolic network , the distribution of uptake rates was far from the originally predicted hypoxic value . This indicated that the predicted decrease in oxygen uptake stemmed directly from the specific changes in the gene expression data and was not an arbitrary result associated with random fluctuations in the metabolic network itself . Together , these results highlighted the strengths and limitations of using a model-based interpretation of metabolic adaptations as captured by differential gene expression data . The model correctly predicted the overall growth phenotype associated with the changed gene expression program , but if the gene expression program was subject to further changes , the model could not capture this without additional expression data . Through the model interpretation of altered gene expression via the metabolic network , we predicted hypoxia-induced changes in biomass composition in both wild type M . tuberculosis H37Rv and the ΔdosR mutant . The prediction qualitatively indicated whether hypoxia induced an increase , decrease , or no change in each metabolite's biomass composition . Figure 4 shows the number of biomass metabolites in different biochemical categories predicted to increase and decrease due to hypoxia ( Supplemental Table S2 provides the detailed list ) . For example , the figure indicates that , in the wild type strain , the biomass composition for five nucleotides ( labeled as NUC in Figure 4 ) was predicted to increase under hypoxia while that for seven amino acids ( AA in Figure 4 ) was predicted to decrease . Nearly half of the wild type predictions were associated with increased biomass composition of metabolites related to cell-wall components , such as mycolates ( MYC ) , phosphatidyl-myo-inositol mannosides ( PIM in Figure 4 ) , and peptidoglycans ( PTD in Figure 4 ) [56] , [57] . These results were compatible with the experimentally observed thickening of the cell walls of mycobacteria during entry into hypoxia-induced dormancy [50] , [51] . Other predictions for the wild type strain included increased nucleotide and decreased amino acid biomass composition , observations that are currently unsupported but should be the subject of future studies . When changes in biomass composition occurred for the ΔdosR mutant , they were similar to the wild type strain . However , the number of metabolites that were predicted to change for ΔdosR was slightly smaller than that for the wild type one ( 42 vs . 51 ) , and the biomass compositions of two MYC-related metabolites that increased under hypoxia in the wild type strain actually decreased in ΔdosR . These results implied that the dosR gene played a role in the modulation of biomass composition , but not as a sole regulator of biomass accumulation . To further test the ability of our approach to alter biomass composition , we qualitatively predicted the biomass changes of Mycobacterium bovis upon transfer from a fast chemostat growth condition [36] to a slow growth condition [58] ( see detailed results in Supplemental Text S2 ) . Given that the metabolic network model provides detailed information of all metabolic fluxes , we examined the resulting hypoxia-induced changes associated with the carbon central metabolism of wild type M . tuberculosis H37Rv and its ΔdosR deletion mutant in more detail . Figure 5 shows that , in the wild type , the metabolite flux through the pathway associated with glucose utilization increased substantially while that through the glycerol utilization pathway decreased . Concomitantly , the flux through the reductive side of the TCA cycle increased considerably while that through the oxidative side only increased moderately . Conversely , the results for the ΔdosR strain indicated only a slight overall decrease in these fluxes . Thus , as captured by our integrated metabolic network model , without the dosR gene the organism fails to adapt its metabolism to cope with low-oxygen stress . Boshoff and coworkers recently confirmed the importance of fermentation in latent hypoxic M . tuberculosis by analyzing metabolite isotopes in the central carbon metabolism to identify the usage of the reductive TCA cycle under anaerobic conditions [16] . The altered flux distribution in the TCA cycle was also accompanied by altered extracellular secretion rates . In particular , the increased flux associated with succinate production at the bottom of the TCA cycle in Figure 5 produced an excess of succinate , which was secreted . We calculated that the hypoxic succinate secretion and accompanying H+ efflux was six times larger than the normoxic value of 9 . 1 millimoles per gram of dry weight of the organism ( mmol/gDW ) . This was in qualitative agreement with experimentally observed succinate accumulation and acidification in the medium in which M . tuberculosis H37Rv is cultured under hypoxia [16] . We further characterized the altered metabolic state of the wild type strain associated with hypoxia by calculating which metabolic genes were essential for adaptation to hypoxic conditions . We defined these genes as those predicted to be nonessential under normoxia , i . e . , removing them from the metabolic network did not prevent the organism from accumulating biomass , but became essential under hypoxia . Figure 6 shows that these genes were either in the glycolysis pathway or in the reductive side of TCA cycle , confirming that the two pathways were required for the hypoxic survival of M . tuberculosis . We further noted that due to the increased secretion of succinate mentioned above , the dctA gene that encodes for the succinate transporter was also predicted to become essential under hypoxia . This suggests that disruption of these pathways could prevent hypoxic adaptation and render the pathogen more susceptible to alternative antibiotic treatments . Many CO2-fixating microbes utilize the reductive TCA cycle [59] , but for hypoxic M . tuberculosis grown in glucose-supplemented Middlebrook 7H9 [25] or Dubos [16] media , the primary reason for utilizing glucose under reductive conditions is to maintain redox balance under low-oxygen availability . Under normoxia , M . tuberculosis oxidizes glucose and glycerol to carbon dioxide via glycolysis and the TCA cycle . At the same time , the oxidized forms of the redox intermediaries are converted to reduced forms in order to maintain chemical balance . Thus , the cell converts nicotinamide adenine dinucleotide ( NAD ) , nicotinamide adenine dinucleotide phosphate ( NADP ) , and flavin adenine dinucleotide ( FAD ) to the reduced forms of NADH , NADPH , and FADH2 , respectively . Under normoxia , the cell maintains this balance by utilizing the constant supply of oxygen from the environment . Under hypoxia , to maintain balance of the redox intermediaries M . tuberculosis must decrease the reduction of NAD , NADP , or FAD and preferentially select pathways that convert the reduced forms back to the oxidized forms . Thus , in our model , hypoxic M . tuberculosis preferred glucose utilization because it reduces less NAD to NADH as compared to glycerol utilization and , as shown in Figure 5 , and increased the flux through the reductive TCA cycle , as this pathway converts NADH and FADH2 back to NAD and FAD , respectively .
There are a number of related methods that use gene expression data to modify the flow of metabolites in a metabolic network . These methods differ in their implementation of how transcription levels of different genes are connected to the reaction fluxes associated with the corresponding translated protein enzymes catalyzing the reactions . All methods deal differently with the general lack of a perfect correlation between transcriptional levels and protein concentrations and , hence , the lack of a direct one-to-one correspondence between expression values and reaction fluxes [60] . For example , in the Gene Inactivity Moderated by Metabolism and Expression ( GIMME ) procedure developed by Becker and Palsson [43] , reactions that are associated with transcription levels lower than a fixed threshold are blocked . The method developed by Shlomi et al . [44] extended this approach by additionally forcing fluxes through reactions associated with high transcriptional levels . To avoid the determination of these somewhat arbitrary thresholds , the E-flux method introduced by Colijn et al . [37] uses transcriptional levels as the upper limits for the corresponding reaction fluxes . This method establishes such upper limits by using the absolute gene expression data to compute the relative changes of genes within the same treatment condition . In the Metabolic Adjustment by Differential Expression ( MADE ) method developed by Jensen and Papin [45] , reaction fluxes are completely removed or unlimitedly allowed based on the corresponding relative gene expression levels between two conditions . The drawback of such a binary on/off approach is the lack of the ability to directly capture a gradual flux increase or decrease from the transcriptional data . Our approach combined different aspects of the above methods by using the concepts of a reference and a treatment condition . For a treatment condition , our assumption was that an existing metabolic network could generate a set of reference fluxes characteristic of the reference condition and that the mRNA transcription data was reflective of the differential gene expression between the reference and treatment conditions . We used the relative expression changes to introduce soft constraints and limits on the relative flux changes , avoiding the introduction of arbitrary thresholds at the price of a more complex optimization problem . Regardless of whether an enzyme functions in either parallel or serial reaction paths in the metabolic network , we captured the notion that if there was a significant change in the expression level of a metabolic gene , it was very likely associated with an attempt to change the related reaction flux , although such a change is not required . In addition , we allowed the biomass composition to change in response to the treatment condition . The advantage of this procedure was the general ability to account for all individual gene expression alterations and provide a detailed interpretation of the metabolic adjustments that capture gradual flux increases or decreases , without using any arbitrary threshold or assuming any correlation between absolute gene expression data and the upper limits of reaction fluxes across different genes under each condition . The disadvantages were in the formulation of a more complex optimization problem and the requirement of the availability of an existing metabolic network for the reference condition and the corresponding differential gene expression data between this reference condition and the treatment condition of interest . In our approach , we made use of differential gene expression data of the changes in the transcriptional program between a well-defined reference condition and a perturbed state . Because both mRNAs and proteins are under control of several different , but possibly correlated , processes , such as transcriptional and post-transcriptional control , degradation , ribosomal capacity , availability of the appropriate metabolites , and energy levels , the relative mRNA level of a gene is not necessarily directly proportional to the concentration of its corresponding protein [61] . However , relative changes in mRNA levels have been shown to be correlated to protein abundance in several studies of Saccharomyces cerevisiae ( yeast ) and prokaryotic bacteria . In yeast , the observed correlations between changes in mRNA level and protein abundance ranged from modest correlations between 0 . 2 and 0 . 5 [62]–[64] to a high of 0 . 7 [65]–[67] . Furthermore , these studies highlight the dependence of these values on the states of the studied organism , e . g . , S . cerevisiae growing under steady state conditions shows a higher correlation between mRNA levels and protein abundance than under transient conditions [66] . In a study of Escherichia coli with a mutation in the pgi gene , transcription levels and corresponding protein abundance of the central metabolism genes changed in a correlated manner , with a coefficient of 0 . 81 [67] . Importantly , even in the studies that found weak correlations [62]–[64] , the most strongly differentially expressed genes frequently displayed changes in mRNA level and protein abundance in the same direction . This has been verified in recent studies exploring transcriptomic and proteomic differences in both eukaryotic and prokaryotic single-celled organisms , with 88% [68] and 97% [69] of the genes with significantly altered transcription levels displaying changes in protein abundance in the same direction for S . cerevisiae and Haemophilus influenzae , respectively . Thus , a formulation that uses a relative change correlation from one steady state to another could provide a practical approach under certain circumstances without requiring full knowledge of all possible regulatory mechanisms that govern the relationship between mRNA level and protein abundance . Our approach was based on the assumption that if the transcriptional mRNA level for a gene changes from a reference ref to an altered condition new , the limit of the corresponding normalized reaction flux catalyzed by the corresponding protein was bound by the ratio of [protein]new/[protein]ref . We approximated this ratio by [mRNA]new/[mRNA]ref+L , where the mRNA levels are taken from microarray experiments and L indicates a slack variable that allows for possible violation of the assumption . The method further assumes that the reference state is associated with a functioning metabolic network description that allows for steady state flux through its metabolite reactions and biomass accumulation . In addition , we assumed that the new state captured by the altered gene expression levels could also be described by a steady state approximation . This makes our model suitable for interpreting gene expression data that describes a transition from one stable condition to another one . In the case studied here , the immediate response of M . tuberculosis H37Rv to hypoxia , the model adaptations appear to be reliable for at least seven days after the insult . Longer-term adaptations that are dependent upon different gene expression programs , e . g . , as in the extended hypoxic response , would have to be modeled by data directly associated with that state . Metabolic adaptations associated with changing from an in vitro environment , where careful characterization of metabolism is possible , to an in vivo environment , where experimental data on metabolism would be more difficult to obtain , could be modeled based solely on differential gene expression measurements . Conversely , the metabolic model interpretation would not be suitable for creating tissue- or cell-specific metabolic networks based on the current overall human metabolic network reconstruction [70] because these use absolute rather than relative enzyme concentrations . However , if a cell-specific metabolic network exists that is amenable to constraint-based modeling , such as flux balance analysis ( FBA ) , a change in the gene expression program of these cells due to some perturbation would be an excellent candidate for applying our methodology . The case study of modeling the immediate metabolic adaptation of M . tuberculosis H37Rv to hypoxia based on an existing metabolic network for normoxic conditions and relative gene expression changes under hypoxic conditions highlighted the type and amount of information that could be extracted from our modeling approach . Phenotypic effects of the hypoxia-induced gene expression program due to low-oxygen stress included adjusting the metabolism to a lower rate of oxygen uptake , lowering ATP utilization , altering biomass composition , increasing cell wall production , engaging the glucose-processing glycolysis pathway , and accommodating anaerobic respiration by using the reductive side of the TCA cycle . The dosR gene controlled this gene program and the gene expression profile of the deletion mutant ΔdosR revealed that it did not accommodate these metabolic adaptations . The mutant was less fit and displayed a substantially reduced growth rate under these conditions compared to the wild type strain . These predictions are supported by observations from previous experimental studies [49]–[51] and the recent confirmation of the importance of the reductive branch of the TCA cycle for latent tuberculosis [16] . The hypotheses regarding the altered importance of different enzymes under hypoxic conditions can be tested with gene knockout studies and , if validated , these enzymes may serve as novel drug target candidates for eliminating latent tuberculosis . In addition to the in vitro-based work presented here , our approach is ideally suited to elucidate the metabolic responses of M . tuberculosis to other stressors , such as nitric oxide [19] and carbon monoxide [20] , as well as metabolic adaptations to animal-model-specific microenvironments [22] .
We used the in vitro iNJ661m metabolic network of M . tuberculosis H37Rv [42] , an enhanced version of the original iNJ661 network [35] , as the reference network to describe cellular metabolism under normoxic growth conditions . The original iNJ661 model was augmented with reactions and metabolites involved in biotin synthesis , fumarate and succinate synthesis , and the methylcitrate cycle and minor changes to the biomass function were made . The iNJ661m network contains 663 genes , 838 metabolites , and 1 , 049 reactions and correctly predicts growth rates of normoxic H37Rv in different media . We used microarray data measured in triplicate from Park et al . [25] as the source for differential gene expression associated with the transcriptomic alteration two hours after the transfer from normoxic air to hypoxic nitrogen gas with 0 . 2% oxygen ( 1 . 5 mm Hg ) for both wild type M . tuberculosis H37Rv and the ΔdosR deletion mutant [25] . Out of the 501 genes that showed a more than 1 . 8-fold change , 96 appeared in the metabolic network , 16 of which were down-regulated and 80 were up-regulated . Figure 1 shows the overall scheme for integrating a given metabolic network compatible with a reference condition and a set of differential gene expression data describing mRNA transcription changes going from the reference state to the new state . Our method depended on developing a set of constraints ( Steps I–IV ) that take into account the known metabolic reference conditions and possible alterations in metabolite flow through any given reaction associated with an expression change to produce a metabolic representation of these constraints ( Step V ) . We performed 500 Monte Carlo simulations to calculate the distribution of normalized oxygen uptake rates of the wild type strain based on experimentally determined gene expression fluctuations . In each simulation , we randomly generated an expression value for each gene based on its assumed normal distribution with mean and standard deviation corresponding to the experimental wild type values [25] , and used Steps I–V to calculate the normalized oxygen uptake . We also performed another set of 500 simulations to calculate the oxygen uptake rates for randomized gene expression data sets . In these simulations , we assigned an expression value for each gene by randomly selecting a value from the experimental data set [25] and used Steps I–V to calculate the normalized oxygen uptake rate for each randomized gene set . We calculated the cell concentrations of wild type M . tuberculosis H37Rv and the ΔdosR deletion mutant under normoxic ( days 0–5 ) and early hypoxic ( days 5–60 ) conditions to compare the model predictions with experimentally determined growth characteristics [49] . Using the initial cell concentrations [49] , we solved the following ordinary differential equation: ( 7 ) where X indicates the cell concentration of M . tuberculosis , t denotes time in days , and μ and d represent biomass production rate and lysis rate , respectively , in units of h−1 . The value for μ differed between the strains of M . tuberculosis ( wild type and ΔdosR ) under the two growth conditions ( normoxic and hypoxic ) , while we assigned d one uniform value and assumed that this value was the same for both strains . The parameters of this equation were determined from matching the calculated growth of the wild type strain to the experimental values . We first performed an FBA of the iNJ661m network [42] to calculate the wild type wt strain's normoxic n biomass production rate μwt , n and then determined the value of d by reproducing the experimental normoxic cell concentrations . Given d , we further matched the calculated cell concentrations under hypoxia of the wild type to determine the hypoxic h biomass production rate μwt , h . To estimate the growth rate of the ΔdosR mutant , we assumed an inverse proportionality between normalized oxygen uptake rate and biomass production rate . Thus , we set the normoxic biomass production rate μΔdosR , n to be equal to that of the wild type strain μwt , n , and obtained the hypoxic rate of the mutant μΔdosR , h via the following equation: ( 8 ) where Owt , h and OΔdosR , h denote the calculated normalized oxygen uptakes of the wild type and ΔdosR strains under hypoxia , respectively . Supplemental Table S1 provides all condition- and strain-specific values for the biomass production rate μ and lysis rate d . We identified a metabolic gene as being essential for adaptation to hypoxia if we predicted that this gene was nonessential under normoxia but essential under hypoxia . To determine gene essentiality under normoxia , we performed an FBA to predict the biomass production rate for the wild type strain and for each individual metabolic gene deletion mutant . We modeled deletion mutants by removing all reaction ( s ) related to the deleted gene . If the ratio for the biomass production rate of a single-gene deletion mutant to wild type was greater than a threshold ( 0 . 10 ) , we categorized the metabolic gene as nonessential under normoxia . Similarly , if the ratio for the biomass production rate calculated under hypoxia ( as approximated in Equation 8 ) was less than the threshold ( 0 . 10 ) , we categorized the corresponding gene as essential under hypoxia . All calculated ratios were either >0 . 25 or <0 . 01 and the choice of 0 . 10 was thus robust with respect to differentiating ratios close to zero from those significantly higher than zero . | Mycobacterium tuberculosis latently infects one-third of the human population and is responsible for millions of deaths worldwide every year . The ability of the pathogen to persist in the human population stems from its capacity to adapt to host-induced stresses and adjust its metabolism to different host environments . We have developed a novel model to interpret M . tuberculosis H37Rv metabolic adjustment by combining gene transcription data with a genome-scale metabolic network model . Using our model , we were able to identify the changes in the metabolic program associated with hypoxia , predict phenotypic change , and determine the critical metabolic enzymes and pathways that are required for pathogen survival . In particular , we predicted the switch in the tricarboxylic acid cycle from an oxidative to a reductive path . The altered importance of different metabolites and pathways under hypoxic conditions may provide guidance for designing novel , adjuvant drug therapies for clearing persistent and latent infections . | [
"Abstract",
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] | [
"systems",
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] | 2012 | Modeling Phenotypic Metabolic Adaptations of Mycobacterium tuberculosis H37Rv under Hypoxia |
Nuclear receptors ( NRs ) are transcription factors that are implicated in several biological processes such as embryonic development , homeostasis , and metabolic diseases . To study the role of NRs in development , it is critically important to know when and where individual genes are expressed . Although systematic expression studies using reverse transcriptase PCR and/or DNA microarrays have been performed in classical model systems such as Drosophila and mouse , no systematic atlas describing NR involvement during embryonic development on a global scale has been assembled . Adopting a systems biology approach , we conducted a systematic analysis of the dynamic spatiotemporal expression of all NR genes as well as their main transcriptional coregulators during zebrafish development ( 101 genes ) using whole-mount in situ hybridization . This extensive dataset establishes overlapping expression patterns among NRs and coregulators , indicating hierarchical transcriptional networks . This complete developmental profiling provides an unprecedented examination of expression of NRs during embryogenesis , uncovering their potential function during central nervous system and retina formation . Moreover , our study reveals that tissue specificity of hormone action is conferred more by the receptors than by their coregulators . Finally , further evolutionary analyses of this global resource led us to propose that neofunctionalization of duplicated genes occurs at the levels of both protein sequence and RNA expression patterns . Altogether , this expression database of NRs provides novel routes for leading investigation into the biological function of each individual NR as well as for the study of their combinatorial regulatory circuitry within the superfamily .
Diverse processes such as reproduction , development , metabolism , and cancer are genetically regulated to a large extent by nuclear hormone receptors ( NRs ) , a prominent transcription factor superfamily [1] . Several small lipophilic molecules , including steroids , thyroid hormones , and retinoids , function by binding members of this superfamily . In addition , a significant fraction of NRs ( approximately 50% in human ) are defined as orphan receptors since the identity of their ligand , if one exists , is unknown [2] . With a few exceptions , such as DAX and SHP in vertebrates , all NRs show a common structural organization with a highly conserved DNA-binding domain , and a less conserved ligand-binding domain . Regardless of their status as orphan or liganded receptors , they interact with hormone response elements in gene promoters or enhancers to regulate transcription [2] . NRs repress or activate the transcription of target genes through varied interactions with numerous transcriptional coregulators , which , together with other transcription factors , mediate chromatin modifications , leading to the repression or activation of target genes [3] . The conservation of several domains of NRs allows for relatively easy isolation of their sequences and permits efficient phylogenetic reconstruction of the superfamily [4 , 5] . This is why several global studies of the whole superfamily have been performed in terms of structural genomics [6–8] . Apart from having implications in evolutionary biology , these comparative approaches have provided an important source of information on the function of human NRs . For example , interspecific comparison of amino acid residues of the ligand-binding domain can help identifying key functional residues required for ligand recognition [9–11] . The number of NR genes present in complete genome sequences has been used as a tool to trace gene duplication and gene loss events that have shaped the structure of the superfamily [4] . Indeed , the number of NR genes varies considerably in metazoan genomes: in humans , 48 receptors were found , 49 in mouse , 21 in Drosophila , 17 in Ciona , 33 in sea urchin , and more than 270 in Caenorhabditis elegans [4 , 6 , 7 , 12 , 13] . In two species of pufferfish , Takifugu rubripes and Tetraodon nigroviridis , at least 71 NR genes were found , thus highlighting the impact of the ancestral fish-specific genome duplication that took place early in evolution of actinopterygian fish [14 , 15] ( Figure 1 ) . In addition to this structural and evolutionary information , several resources are now available to provide functional information on NRs ( e . g . , NURSA , http://www . nursa . org/; NUREBASE , http://www . ens-lyon . fr/LBMC/laudet/nurebase/nurebase . html; and NucleaRDB , http://www . receptors . org/NR/ ) . Several bioinformatic and experimental searches for hormone response elements have led to a better understanding of the transcriptional hierarchies controlled by NRs and their ligands [16–18] . Systematic analysis of NR interactions with themselves and with their coregulators allowed for precise elucidation of each receptor's interactome [19 , 20] . More recently , systematic expression studies using reverse transcriptase PCR ( RT-PCR ) and/or DNA microarrays have been performed in classical model systems such as Drosophila and mouse [21–24] . However , for studying the implications of NRs in development , it is critically important to know when and where individual genes are expressed . This is why we have established the complete spatiotemporal profiles of the expression of all NR genes during embryonic development using the zebrafish as a model system , because the optical transparancy of its embryo allows studies of gene expression with a cellular resolution using whole-mount in situ hybridization [25] . Other studies have been performed on NR expression during embryonic development in vertebrates , mainly in mouse , rat , chicken , and Xenopus [2] . However , most of them are partial and only describe expression by northern blot analysis or by in situ hybridization restricted to one organ or a few developmental stages for a limited number of genes . Moreover , for many NRs , expression during development was only studied regarding their roles in the adult , therefore introducing a bias in the interpretation of the data . To carry out this large-scale project , we isolated all 70 NR genes in zebrafish plus 31 of their coactivators and corepressors . We analyzed the expression of these 101 genes from gastrula to early larval stages by whole-mount in situ hybridization . This allowed us to detect extensive correlation of expression between NR genes and their coregulators . Our results reinforce the notion that NRs are mainly expressed during organogenesis , with few of them expressed at early developmental stages . Our most unexpected finding is that the large majority of NR genes are expressed during central nervous system ( CNS ) and retina development , since classically , the primary role NRs was thought to be metabolism control in endodermal derivatives [2] . Finally , evolutionary analysis of the NR genes that were retained following the fish-specific genome duplication , shows that neofunctionalization of these genes occurred at the levels of both protein sequence and RNA expression patterns . Taken together , our data extend and refresh our vision of NR involvement during vertebrate development , calling for a closer look at metabolic pathways and the control of homeostasis in developmental processes .
Using RT-PCR , we isolated probes corresponding to 70 NR genes from Danio rerio , all of which correspond to a distinct locus in the zebrafish genome , which is publicly available . The assignment of each sequence was done for each NR group by phylogenetic analysis ( Figure S1 ) . Figure 1 gives the complete list of the 70 NR genes that we found ( see also Table S1 ) . When we compared with the mammalian NR complement , we did not find orthologs of RARβ , LXRβ , or CAR using either RT-PCR or database searches . An ortholog of RARβ was found in the complete pufferfish genomes but was apparently lost in zebrafish . Thus far , neither LXRβ nor CAR has been described in any fish . Because it is always difficult to decide on the absence of a gene in a given genome , especially when the complete genome sequence is not published , we performed additional RT-PCR and PCR experiments on various tissues and/or DNA preparations with several primer pairs for these genes . We nevertheless cannot formally rule out that we artifactually missed a specific duplicate . It is now clearly established that actinopterygians underwent a complete genome duplication [14 , 26] . Indeed , compared to mammals , 19 genes are duplicated in zebrafish ( TRα , RARα , RARγ , PPARα , PPARβ , Rev-erbβ , Rev-erbγ , RORα , RORγ , VDR , RXRα , RXRβ , COUP-TFα , EAR2 , one ERRβ or γ , ERβ , SF-1 , GCNF , and SHP ) . Eighty percent of these duplications are shared with pufferfish . For clarity , we name these duplicates with capital letters after the gene name: PPARα-A and PPARα-B are thus the two duplicates of PPARα . Our phylogenetic analysis also reveals five NR paralogues that have no counterparts in mammals . These genes are Rev-erbγ , COUP-TFγ , ERRδ , FF1C , a member of the FTZ-F1 family , and HNF4β . They were also all found in the pufferfish genomes , while HNF4β is present in Xenopus laevis and in chicken . Many different coactivators and corepressors of NRs have been described and these molecules exhibit highly variable functions , specificities , and modes of action [2 , 3 , 27] . Therefore , in contrast to NR genes , we did not attempt to perform an exhaustive analysis and decided to isolate only the most obvious ones . We have isolated representatives of the four main coregulator complexes ( Figure S2 ) , namely , the p160 complex ( containing the three SRC/p160 factors , CBP/P300 , Cited3 , and CARM ) , the SMCC or Mediator complex ( with TRAP220 ) , the SWI/SNF complex ( Baf53 , Baf60 and BRG1 ) , and the corepressor complex containing NCoR , SMRT , and histone deacetylases . Table S1 contains the list of the 31 probes that we have isolated , along with their Genbank accession numbers . As for NR genes , for each coregulator isolated , a tree was constructed to assign clear orthology and in some cases we noticed the presence of actinopterygian-specific duplicates ( Figure S1 ) . However , we cannot exclude that duplicates may exist for some coregulators for which only one copy was detected . We have determined the spatiotemporal expression pattern of the 101 zebrafish genes by whole-mount in situ hybridization at seven different developmental stages that are classically studied [28] . Plates describing individual expression patterns are presented in Figure S3 , and have been deposited in the ZFIN database ( http://zfin . org ) and will be available at the Nurebase Web site . At a global scale , we can define three different types of expression profiles for NRs during zebrafish embryogenesis: ( i ) genes not expressed during embryogenesis and larval stages or expressed under the limit of detection of in toto in situ hybridization , ( ii ) genes expressed ubiquitously , and ( iii ) genes that exhibit a spatially restricted expression pattern . If we compare the expression profiles of NRs at each developmental stage , we observe that the number of spatially restricted NR expression profiles increases dramatically from gastrula to 48 h post-fertilization ( hpf ) ( from 11% to 60% ) , whereas the number of ubiquitously expressed genes is almost constant ( around 20%; see Figure 2A and Table S3 ) . Therefore , it appears that the vast majority of NR genes are not expressed during early embryogenesis but rather late , i . e . , during organogenesis . A similar observation was made in Ciona , where only 17% of NR genes are expressed early , whereas 48% were found expressed during later stages [29] . We did not notice any obvious correlation between the phylogenetic position of NR genes , their orphan versus liganded status , and the type of their expression patterns ( restricted , ubiquitous , or not expressed ) . We then analyzed in detail the expression pattern of NR genes that are spatially restricted during embryogenesis . Strikingly , we observed that many of them are expressed in the retina and in the CNS ( e . g . , spinal cord and/or brain ) , even if for each receptor , the expression is restricted to a part of these organs ( Figure 2B ) . Figure 3 presents a selection of the expression patterns we detected in the brain , stressing the diversity of expression of NR genes in the CNS . At the mid-somitogenesis ( MS ) stage , more than 55% of spatially restricted NRs are expressed in the brain , and this proportion increases up to 71% at 48 hpf . The same picture holds for the retina ( from 29% at MS stage up to 59% at 48 hpf ) . In addition , all genes expressed in the retina , except for TRβ , are also expressed in the brain and/or in the anterior spinal cord . To test whether this high percentage of genes expressed in CNS and retina could be specific to NRs , we analyzed a set of 1 , 900 genes with spatially restricted expression patterns available in the ZFIN database . We found 40% to 54% of these genes expressed in the CNS between 24 and 48hpf , whereas for NR genes , this percentage rose to 71% . Eleven percent to 37% of genes were expressed in the retina , whereas 30% to 59% NR genes were expressed in the same organ ( Figure 2C ) . Therefore , even if many genes are indeed expressed in CNS and retina , NR genes tend to be expressed more often than expected in these organs . In contrast , some organs or tissues express very few NR genes in a restricted manner . This is the case for the lens , blood , somites , and heart , even if these organs express the NRs that show a ubiquitous expression pattern . Phenotypic analyses of mouse knockouts , as well as studies on the implication of NRs in human diseases , have suggested a major role for NRs in the control of homeostasis , and specifically in lipid metabolism , including cholesterol and steroid metabolism ( see [2] for a review ) . These processes occur in organs such as liver , intestine , pancreas , and adipose tissue , all of which are endodermal derivatives , as well as in the adrenal gland , which is derived from neural crest cells . Looking at NR expression in these organs , we found , at various stages , VDR-B , EAR2-B , and FF1C expressed in the intestinal bulb , whereas FXRα , ERβ-A , and LRH1 were found in the liver . In addition , three genes , PPARβ-A , PXR and HNF4α , were detected in both organs . Therefore , we are confident that we did not miss expression of NR genes in endodermal tissues before 5 d of development . The case of PXR , which in mammals is restricted to endodermal derivatives , nicely illustrates this point . In zebrafish , we found this gene expressed at 24 hpf in the pituitary and at 36 hpf with a complex pattern in the telencephalon and diencephalon ( see Figure S3 ) . At 48 hpf , expression remains in the CNS but is also found at a relatively low level in intestine and liver . This demonstrates the power of whole-mount in situ screens in revealing heretofore unsuspected expression patterns . Recently , two analyses of genes of the NR2E , RAR , and RXR groups also revealed extensive expression in retina and CNS , globally supporting our findings [30 , 31] . Another well-known target of NRs in mammals is the sexual organs . Sex determination is a complex and late event in zebrafish and sexual organs are not yet differentiated at the stages examined by whole-mount in situ hybridization . We thus cannot discuss the eventual implication of NRs genes in differentiation of sexual organs in this species and this may account for the lack of expression of AR , PR , and ERα that we noticed in our study . However , at the studied stages , primordial germ cells are present in the embryo and migrate along the body axis , but we did not detect specific NR expression ( e . g . , GCNFs ) in these cells . Because we found frequent and complex restricted expression in the developing retina , we performed high-resolution analysis at 72 hpf , when the retina is already well differentiated . We then analyzed systematically the expression of the 25 NR genes expressed in the retina at this stage ( Figures 4 and S4 ) . At 72 hpf , the retina is divided into three main layers: the outer nuclear layer ( ONL ) containing cell bodies of photoreceptors , the inner nuclear layer ( INL ) which contains four classes of interneurons ( amacrine , bipolar , horizontal and interplexiform ) as well as Müller glia , and finally the ganglion cell layer ( GCL ) , which contains ganglion cells . By examining the retinal expression of these 25 genes , we observed a large diversity of patterns ( Figure 4 ) . TRβ , PNR , COUP-TFα-B are only expressed in the ONL ( Figure 4B ) . RORβ , NURR1 and ERRγ are found only in the INL ( Figure 4C ) , whereas no NRs are expressed only in the GCL . COUP-TFβ and EAR2-B are expressed in an asymmetric manner in the dorsal part of the INL and the ONL , respectively ( Figure 4D ) . COUP-TFγ shows expression in the ventral part of these two layers ( Figure 4E ) . TLL expression is not restricted to a specific layer of the retina , but is associated with cell proliferation ( Figure 4F ) [31] . Finally , the remaining 15 NRs are expressed in more than one layer and often ubiquitously . All these data highlight a diversity of NR gene expression in the retina suggesting that these genes may be implicated in a wide variety of processes . The fact that the retina expresses a large proportion of the members of the NR superfamily has not been noticed in other vertebrates . This may be due to the fact that no global spatiotemporal expression pattern study of this superfamily has been performed with whole-mount in situ hybridization in mammals or Xenopus , or that there are specific differences between mammals and zebrafish concerning NR gene expression in the retina . We thus specifically verified if the genes that are expressed in the zebrafish retina are implicated in retinal development in other vertebrates . By an extensive survey of the literature , we found that among the ten genes that express in specific cell layers or cell types in the retina , four ( TRβ , PNR , RORβ , and TLL ) are known to be important for retina development in mammals . Indeed , retinal phenotypes in knockout mice and mutations in human diseases have been associated with these genes [33–35] . In addition , expression in the retina has been observed in other vertebrates for five more genes: NURR1 [36–38] , ERRγ [39] , COUP-TFγ , COUP-TFβ , and EAR2 [40–42] . Finally only one of these genes , COUP-TFα-B is expressed in zebrafish retina , while its mammalian counterpart is not [43] . We noticed that some genes ubiquitously expressed in the zebrafish retina ( Rev-erb and ROR ) have also been described as expressed in the mammalian retina [44 , 45] . Taken together , our data strongly suggest that some receptors have a conserved role in vertebrate retinal development and that the importance of this organ for the study of NR biological functions has been largely overlooked . This nicely illustrates the power of large expression screens , such as the one we performed here , in unraveling potential functions of NRs in specific organs . In striking contrast with NR genes , most of the CoA/CoR we studied show ubiquitous expression ( CBP-A , CBP-B , P300-A , P300-B , BRG1 , PCAF , NCoA6 , Baf 53 , SRC1 , SRC2 , NCoA4 , Baf 60 , N-CoR , Alien , Sin3A , HDAC1 , HDAC3 , and TIF1α ) or do not display embryonic expression that could be detected by whole-mount in situ hybridization ( TRAP220 , MYST-HAT2 , TRIP13 , and ARA54 ) ( see Figure S3 ) . In fact , only 30% of the coregulators ( SRC3 , RIP140-A , RIP140-B , PGC1 , TRIP7 , TIF1γ , Cited3 , CARM1 , SMRT , and HDAC4 ) show a spatially restricted expression pattern , suggesting that tissue specificity of hormone action is conferred more by the receptors than by their coregulators . Apart from TIF1γ , which is expressed in ventral hematopoietic mesoderm [46] , all other spatially restricted coregulator genes are expressed in the CNS , stressing again the importance of NR signaling in this organ . Among the ten spatially restricted coregulators , we found expression territories that do not correlate with expression of spatially restricted NRs . For example , HDAC4 is expressed in trigeminal ganglia and PGC1 and RIP140-A are expressed in several cranial ganglia , whereas RIP140-B is specifically expressed in the habenula . Some of the coregulators , namely HDAC4 , Cited3 , CARM1 , SMRT , and RIP140-B , also show restricted expression in the retina . It should be noted that TRIP7 is expressed in the lens , where only EAR2-B is expressed in a restricted manner . We also observe expression of SMRT at 5 dpf in the thymus , where we did not find any expression of spatially restricted NR genes . These data support in vivo the notion that coregulators mediate the action of transcription factors other than NRs . Our systematic analysis revealed extensive similarities of expression patterns between NRs and their coregulators . For example , in the p160 family , which contains three members ( SRC1 , SCR2 , and SRC3 ) , SRC3 shows a restricted expression that is reminiscent of that of RXRs and RARs ( Figure S5 ) [30] . This gene is mainly expressed in anterior spinal cord , posterior branchial arches , and tail bud , suggesting possible RAR/RXR interactions with SRC3 in these territories . PGC1 is another coactivator showing a striking correlation of expression with certain NR genes . This gene was identified by its direct interaction with PPARγ and was later shown to be important for other NRs , including ERRα , TRs , and RXRs ( for a review , see [47 , 48] ) . In zebrafish , PGC1 shows a very specific expression pattern in adaxial cells , pronephric ducts , and mucous cells during somitogenesis , and in the epiphysis , olfactory bulb , diencephalic nuclei , hindbrain , heart , pronephric ducts , mucous cells , and slow muscle fibers at 24 hpf . Overall , this expression pattern overlaps extensively with those of the ERR genes ( Figure 5 ) . During somitogenesis stages , ERRα is expressed in adaxial cells , pronephric ducts , and mucous cells , ERRβ and ERRγ are expressed in pronephric ducts , while ERRβ/γ is expressed in mucous cells . At 24 hpf , PGC1 expression overlaps with that of ERRα in pronephric ducts , in slow muscle fibers , of ERRβ in pronephric ducts , epiphysis and in diencephalic nucleus , of ERRγ in epiphysis and diencephalic nucleus and of ERRβ/γ in the mucous cells . In the mouse , no complete embryonic expression pattern of PGC1 has been reported , but complex expression in adult brain was observed in rat [49] . In mouse , PGC1 is preferentially expressed in slow muscle fibers , a situation that we also found in zebrafish [50] . This is consistent with the notion of specific needs for PGC1 in mediating transcriptional activity of ERRs during embryogenesis and with reports highlighting the functional importance of the PGC1/ERR hub [51] . In addition , we identified two other groups of genes ( Rev-erb/ROR and COUP-TF ) sharing extensive similarity of expression suggestive of common functions . Nine of the ten Rev-erb/ROR genes are expressed in retina , optic tectum , hindbrain , and/or epiphysis . We also found that the expression patterns of three coregulators , RIP140-B , SMRT , and HDAC4 , largely overlap with those of Rev-erb/ROR . These expression data strongly suggest that in vivo these genes are regulated in a similar way . In accordance with this notion , we recently observed that Rev-erbα expression is under the control of Rev-erbs and RORs both in vitro and in vivo [52 , 53] . These expression patterns are fully consistent with the important role played by these genes in the generation and control of circadian rhythm [54–56] . Interestingly , SMRT has been shown to interact with Rev-erbs in mammalian cells [57] . Taken together , these observations suggest that the roles played by RIP140-B , SMRT , and HDAC4 in circadian rhythm should be more carefully examined in the future . Similarly , among the six members of the COUP-TF group , COUP-TFα-A , COUP-TFα-B , COUP-TFβ , COUP-TFγ , and EAR2-B are expressed in a similar and complex expression pattern in the CNS ( Figure S6 ) . Once again , this is congruent with the known role of these genes in nervous system development in zebrafish and more generally in vertebrates . We performed hierarchical clustering of regionalized NR and coregulator genes and the anatomical structures expressing them using a binary matrix that quantifies expression pattern divergence between genes ( Tables S2 and S5; Figure 6 ) . This clustering analysis revealed the existence of a higher-order network relating NR genes , their coregulators , and development according to space and time . The anatomical structures expressing NRs and coregulators reveal a clear organization into three clusters ( Figure 6 ) : expression in nervous system at late stages ( I ) , early embryonic expression ( II ) , and late expression in non-nervous system structures ( III ) . Cluster I can be further subdivided: retina and optic tectum ( Ia ) , spinal cord ( Ib ) , and brain structures ( Ic ) . Similarly , cluster II can be divided into an early nervous system ( IIa ) and an early non-nervous system organs ( IIb ) subcluster . These results suggest that during development , NR genes and their coregulators can be categorized depending on their timing of expression ( early/late ) and their expression in nervous or non-nervous tissues . NR and coregulator genes are split into seven clusters ( shown on the vertical axis of Figure 6 ) that follow the previously discussed organ clustering . The genes that we defined above as coexpressed at several developmental stages are clustered within this hierarchy . SRC3 is found in cluster 4 with RARα-A , RARα-B , RARγ-A , RXRα-B , and RXRγ , since they are expressed early ( organ subcluster IIa ) and late ( organ subcluster Ib ) in the spinal cord , a situation illustrated in Figure S5 . Similarly , PGC1 belongs to cluster 6 as ERRβ and ERRγ . Several members of the COUP-TF family ( COUP-TFα-A , COUPTFα-B , COUP-TFβ , COUP-TFγ , and EAR2B ) are grouped in clusters 3 and 8 , and the ten Rev-erb and ROR genes are found together in cluster 5 , since they are expressed late in the retina and in the brain . Furthermore , these genes are never expressed in the spinal cord , a situation explaining their inclusion in cluster 5 . Therefore , this clustering reveals an underlying hierarchy of NR and coregulator genes and suggests that several transcriptional networks are differentially deployed in a spatiotemporal manner during zebrafish development . Our expression dataset gives us the opportunity to analyze the evolution of NR gene expression after duplication . We found in zebrafish 19 pairs of genes specifically duplicated in actinopterygians that account for the increased number of NR genes when compared to tetrapods . According to the Duplication–Degeneration–Complementation ( DDC ) model [58] , duplicated genes have three main fates: in the majority of cases , one of the copies is lost ( 64% for zebrafish NR genes ) , in some cases both duplicated genes are subfunctionalized ( i . e . , they share the function of their nonduplicated ancestor ) , and in other cases one of the copies undergoes neofunctionalization ( i . e . , it acquires a new function ) , while the other retains the function of the ancestor gene . Sub- or neofunctionalization can occur at the level of the expression patterns of the duplicated genes or at the level of their protein coding sequence . Taking into account that we have no expression data from a basal actinopterygian fish that was not subjected to the genome duplication , expression divergence after duplication can only be inferred by comparison with other vertebrates . Of the 19 duplicated couples that we have studied , we found four cases indicative of neofunctionalization at the level of their expression patterns ( RARγ , RORα , RORγ , and GCNF ) . GCNF provides a clear example of such a case: GCNF-A has an expression pattern that is reminiscent of Xenopus and mouse GCNF [59 , 60] , whereas GCNF-B expression is very divergent , with expression observed in head , lateral line neuromasts , and branchial arches . Therefore , it seems that GCNF-A has kept the ancestral expression pattern , whereas GCNF-B has acquired a new one . The acquisition of a new function can be achieved by fixing advantageous mutations within one of the duplicated genes . The neofunctionalized gene will then evolve under positive selection , significantly faster than the other gene in the pair , which will retain the ancestral role and thus evolve under purifying selection ( elimination of deleterious mutations ) . Asymmetric evolution between gene duplicates may thus be interpreted as a sign of neofunctionalization [61 , 62] . We compared the protein sequences of the 19 NR gene pairs to the protein sequence of a nonduplicated outgroup ( Homo sapiens ) and found that the ratios of the evolution rates of the duplicated proteins varied from 1 . 01 ( i . e . , similar rates ) to 6 . 1 . Because the outgroup is very distant , only strong differences in the evolution rate can be detected and evaluated as statistically significant , making our results conservative . We found a significant acceleration of the protein evolution rate ( i . e . , a ratio significantly different from 1 ) , relative to the nonduplicated sequence of the outgroup , for eight out of the 19 gene pairs ( p-values < 0 . 01 in seven out of the eight cases and a p-value = 0 . 03 in the remaining one ) . An alternative explanation for the asymmetry in the evolution rates would be the genomic context , as proposed by Zhang and Kishino [63 , 64] . When two copies have different recombination rates , the copy in the low recombination context accumulates deleterious substitutions because of Hill–Robertson effects ( degeneration ) and thus will evolve faster than the copy in the high recombination context . We have controlled for this effect by estimating , when possible , the recombination rates of the two genes in each pair ( Table S4 ) . The recombination rates were estimated by comparing genetic and physical maps of the zebrafish genome ( A . Popa , personal communication ) . In three out of the eight cases of asymmetrical evolution rates between duplicates , this estimation was not possible at least for one of the genes . Out of the five remaining pairs , only one presented a difference in the recombination rates of the duplicates compatible with the asymmetry in their evolution rates ( SHP-A/SHP-B ) , which suggests that the vast majority of the asymmetrically evolving pairs truly evolved through the neofunctionalization model . We then looked further into this asymmetric evolution of the duplicates by evaluating their expression pattern divergence . Doing this in a quantitative manner allowed us to investigate if there was any correlation between sequence evolution and the evolution of the expression patterns after duplication . The divergence of the expression patterns of the duplicates varied from 0 ( same expression pattern found for both genes , e . g . , RXRβ , an almost ubiquitously expressed pair detected in 162 out of 165 organs considered in the analysis or PPARα , a nondetected pair ) to 1 ( almost completely different expression patterns of the two genes; e . g . , SHP-A is detected in only four of the 165 organs and SHP-B is not detected , see Table S2 ) . We computed the sequence divergence between duplicates by calculating the ratio between nonsynonymous to synonymous substitutions ( Ka/Ks ) between the coding sequences . The Ka/Ks ratio can only be calculated for 17 of the 19 pairs of genes because in two cases ( SHP and RORγ ) the Ks was saturated . Because all the gene duplicates are from the same duplication event ( fish-specific genome duplication ) , differences in Ks values reflect different mutation rates within the genome . By dividing Ka by Ks we corrected for the influence of these mutation rate differences in the evolution of the coding sequence . Strikingly , we observed a significant positive correlation ( Pearson correlation factor R2 = 0 . 69 and p-value = 0 . 04 ) between the expression divergence and the sequence divergence of the duplicates belonging to the pairs ( six ) where a neofunctionalization is suggested by the asymmetrical evolutionary rates of the proteins ( Figure 7B ) . This means that the divergence of the coding sequence was accompanied by a divergence of the regulatory sequences . No significant correlation between the expression divergence and the sequence divergence was found for the pairs ( 11 ) with similar evolutionary rates ( a positive but nonsignificant correlation may be observed in Figure 7B ) . Taken together , our results show that for duplicated NR genes , neofunctionalization occurred in almost half of the cases , both at the protein and RNA expression patterns .
Several systematic analyses of the NR superfamily at the gene expression level have recently been reported . Sullivan and Thummel [23] have conducted a northern blot analysis of all 21 Drosophila melanogaster NRs from egg to adulthood . A systematic quantitative PCR analysis of expression of 49 NR genes in 39 adult tissues and at several circadian times has been reported in the mouse [21 , 22] . These studies revealed NR gene coordinated transcriptional programs in developmental and physiological pathways . Analyzing transcript expression at the tissue level with quantitative PCR or northern blots has the advantage of providing a quantitative measure of transcript abundance . Coupled with hierarchical clustering of the data , this allowed the division of the NR regulatory network in the mouse into two main processes: reproduction , development , and growth on the one hand , and nutrient uptake , metabolism , and excretion on the other . Our analysis of embryonic and larval expression patterns , studied by whole-mount in situ hybridization , allows a direct visualization of the spatiotemporal dynamics of the NR superfamily during development . Our study thus nicely complements these previous global analyses by providing , with unprecedented details , a complete dataset of the embryonic territories where NR-mediated regulation is likely to be deployed . Our data also allow the definition at the global scale of groups of genes expressed in similar locations at several developmental stages and thus highlight the potential transcriptional hierarchies of NRs and coregulators that occur during development . Clustering of the tissues expressing NR and coregulator genes into three main groups according to developmental timing and nature ( neural/nonneural ) of the tissue supports the notion that NR regulation is used differently during embryonic development . There is no extensive overlap between the seven clusters we defined and those found by Bookhout and colleagues [21] . This suggests that the underlying logic of NR deployment during embryonic development in zebrafish and in the adult mouse is different . Nevertheless , one should keep in mind that the two datasets are different ( qualitative versus quantitative data and embryonic versus adult stages ) and are thus difficult to compare . The detection of groups of coexpressed genes suggests that some crossregulation might occur between NR genes and/or their coregulators . The ERR-PGC1 and RAR-RXR-SRC3 groups provide good examples of these potential hubs . Future comparison of the expression patterns reported here with those issued from large-scale gene expression analyses will undoubtedly provide relevant information on NR-regulated networks that control embryonic development . Our exhaustive expression screen reveals that many NRs known to be tightly linked to the control of metabolism in adults are expressed during embryogenesis ( e . g . , PXR , HNF4α , RXRs , COUP-TFs , and ERRs as well as several coactivators such as PGC1 , CITED3 , and RIP140 ) . It is important to stress that most of the expression patterns we describe here are conserved in vertebrates . Given that the methods used to determine expression during development differ from one model organism to another ( e . g . , tissue sections in mouse , whole-mount in situ in zebrafish , and Xenopus ) , and that only a minority of these NR genes have been studied in several organisms , an exhaustive global comparative analysis of the expression patterns is not yet feasible . Nevertheless , of 26 genes for which data are available , we found 22 cases of complete ( TRα-A , TRα-B , PPARβ-A , PPARβ-B , VDR , HNF4α , RXRβ-A , RXRβ-B , TLL , NURR1 , SF1-A , SF1-B , LRH1 , and GCNF-A ) or partial ( TRβ , RARα-A , RARα-B , RARγ-A , RARγ-B , RXRα-A , RXRα-B , and COUP-TFβ ) conservation of expression , whereas in only four cases ( PPARα-A , PPARα-B , PXR , and RXRγ ) we found very different expression patterns between zebrafish and other vertebrates . Therefore , we are confident that most of the data we generated will be transferable to mammals and will thus be relevant for the study of human diseases . Both epidemiological and clinical evidence suggests that prenatal factors play a role in the origin of the metabolic syndrome and its components: hypertension , insulin resistance , obesity , and dyslipidemia ( reviewed in [65] ) . Experimental studies demonstrate that an adverse embryonic or fetal environment can induce structural and functional abnormalities in pancreatic islet cells and can lead to permanent changes in insulin sensitivity [66] . Thus , any developmental perturbation that would affect NR expression and/or the production of NR ligands may be transferred to the NR gene regulatory hierarchy and may impact embryonic development and later on adult physiology and metabolism . Indeed , it is easy to induce insulin resistance and symptoms of the metabolic syndrome by manipulating maternal nutrition ( an event that could easily affect NR ligand production ) or by exposing the mother to synthetic glucocorticoids [67–69] . Therefore , relating the embryonic expression of NRs , including classical pharmacological targets like TR , RAR , RXR , and PXR , to specific developmental processes will help to better understand the mechanisms of the development of metabolic syndrome . Our data provide a unique basis from which to begin such an analysis . Our expression analysis can also be used to identify roles of certain NR or coregulator genes in specific human diseases . For example , since an unexpected number of them are expressed in retina , it could be fruitful to search for their implication in the development of retinal diseases . There are still a large number of mapped but unidentified Mendelian human retinal diseases , some of which match to the chromosomal location of the NR genes , which we found expressed in the retina . For example , we found both RXRα and Rev-erbα in the retina and both have a chromosomal location in humans ( 17q ) that corresponds to the one detected for a specific retina disease , CORD4 ( Cone Rod Dystrophy 4 ) [70] . In sum , this expression screen , performed on a species that resembles humans on the level of organization and physiology and on a protein superfamily that can easily be targeted by drugs , will provide important new information for the identification of interesting targets for drug discovery . The importance of neofunctionalization following gene duplication has been continuously discussed in the literature since Ohno proposed that it was the main mechanism allowing phenotypic diversity [71] . There is no doubt now that subfunctionalization plays an equally or even more important role in the functional evolution of gene pairs [58 , 72] . In contrast , the relative contribution of both mechanisms for functional diversification between gene duplicates is still an open question . Different factors must be taken into account when analyzing gene evolution after duplication , including population characteristics of the species studied [73] . Asymmetric evolutionary rates of duplicates , which may be interpreted as a sign of neofunctionalization [61–64] , have been shown to affect 10% to 56% of duplicated genes analyzed in various species from yeast to fish [62] . In teleost fish , differences in evolution rates were found in 37% of the duplicated genes analyzed [74 , 75] . Here , our analysis revealed that 42% of the 19 NR gene pairs analyzed evolved at different rates ( when compared with an orthologous single copy outgroup ) . Furthermore , the retention of gene duplicates among the NR family ( 36% ) is also higher than the one estimated for the whole genome after the fish whole-genome duplication ( 15% [74] ) . This is consistent with a higher gene retention after duplication and the presence of neofunctionalization , both of which have been reported in regulatory/development-implicated gene families [74 , 76–78] ( e . g . , NRs ) compared with other functional classes of genes . Finally , we also observed a significant positive correlation between coding sequence divergence and expression pattern divergence for the asymmetrical evolving gene pairs . Coupled evolution between coding and regulatory sequences was previously found for single-copy genes , between orthologs of D . melanogaster and D . yakuba [79] and of C . elegans and C . briggsae [80] . In our case , this parallel evolution between coding and regulatory sequences suggests that neofunctionalization affected both the protein function and the expression pattern of the gene . For instance , the evolution rate of GCNF-B is more than two times that of GCNF-A , suggesting that GCNF-B evolved under positive selection , thus acquiring a new function . This is consistent with the divergence of GCNF-B expression patterns suggestive of neofunctionalization: as is the case for the protein sequence , it seems that GCNF-A has kept the ancestral expression pattern , whereas GCNF-B has acquired a new one . It can be hypothesized that following expression divergence of a pair of duplicated genes , the gene that is expressed in novel embryonic territories will accumulate mutations in its coding region more rapidly , because the cognate protein will be exposed to a novel set of interaction partners . One of the striking results of our screen is the widespread expression of NR genes in the nervous system: at 36 hpf , 70% of the spatially restricted NRs are expressed in the CNS , whereas 40% of them are expressed in the retina . This represents an underestimation , because ubiquitously expressed NR genes may also play an important role in these organs . Indeed , the expression of the zebrafish HDAC1 gene is widespread in the embryo at all stages of development , whereas this gene plays an important role in the anterior CNS by maintaining neurogenesis [81] . The developmental role played by these genes is perhaps not connected to their adult function in regulating metabolism , but it has to be emphasized that many other observations focus on an unanticipated link between the control of metabolism and nervous system development . In fact , several large-scale expression screens have revealed expression of metabolic enzymes , cholesterol and fatty acid transport proteins , and hormonal receptors in embryos , even during early embryogenesis . In zebrafish , the brain-type fatty acid binding proteins FABP7a and FABP7b , which intracellularly bind to docosahexaneoic acid ( DHA ) , an RXR ligand [82] , are distributed in the early developing CNS , retina , pharynx , and swim bladder [83] . Similarly , a fatty acid hydroxylase ( FA2H ) is expressed in enveloping layer , pronephric ducts , nose , pharynx , liver , and gut during embryonic development [84] . In a recent genome-scale analysis of genes expressed during mouse retina development , prominent expression of metabolic enzymes has been observed in specific cell types , such as the Müller glia [40] . The reasons for such a widespread spatiotemporal control of metabolic genes may be linked to a variable metabolic demand of developing organs or cell compartments related to differential proliferation or differentiation . Alternatively , metabolic proteins could play a specific developmental role . In the case of NR genes , we have at present no specific indication that , for example , the restricted expression of PXR in specific areas of the zebrafish CNS is linked to its detoxification function in adult liver . Another possibility is that metabolic enzymes may be implicated in the production or delivery of signaling molecules . This is of course the case for the CYP26 , retinaldehyde dehydrogenases , CRBP , and CRABP , the molecules implicated in retinoid metabolism and transport in vertebrate embryos . Clearly , the evidence that continues to accumulate from various experimental model systems suggests that metabolism should no longer be disconnected from the study of embryonic development .
Given the unknown expression patterns of most of NR genes in zebrafish , we used total RNA extracted from various adult tissues ( muscle , gills , liver , etc . ) as well as from embryos at different developmental stages . RNA was extracted from frozen tissues using TRIZOL reagent ( Life Technologies ) . The RNA samples were treated with RQ1 deoxyribonuclease , extracted using phenol/chloroform/isoamylic alcohol ( 25:24:1 ) and chloroform/isoamylic alcohol ( 24:1 ) , and finally precipitated with ethanol . Degenerate or specific primers were designed using an alignment of all published nucleotide sequences for homologs from other vertebrate species according to previously described methods [85] or using available sequences . Many of the primers are degenerate and were used in a touchdown PCR assay [85] . PCR products were cloned into the PCR2 . 1-TOPO vector ( Invitrogen ) and subcloned in pBSK+ or pBKS+ to allow synthesis of sense and antisense probes . A list of studied genes and their sequence accession numbers is given in Table S1 . Predicted amino acid sequences were aligned automatically using ClustalW [86] with manual correction in Seaview [87] . Phylogenetic reconstruction was done using amino acid alignments of the longest sequences found for each gene . Only complete sites ( no gap ) were used . To separate orthologs and paralogs for each sequence , trees were constructed for each group ( see Figure S1 ) with the Phylo_win program [87] using the neighbor-joining method [88] with Poisson-corrected distances on amino acids . Reliability of nodes was estimated by 1 , 000 bootstrap replicates [89] . Alignments of amino acids were also used to calculate the level of sequence similarities with other vertebrate sequences . Whole-mount in situ hybridization was performed as previously described [25] . Several stages were used: gastrula ( G ) , early somitogenesis ( ES , 3–6 somites ) ; mid-somitogenesis ( MS , 14–18 somites ) ; and 24 , 36 , and 48 hpf [28] . For several genes , expression was also studied at 5 d post-fertilization . Sense and antisense RNA probes for each gene tested were prepared from partial cDNA . Probes were made against internal coding regions for most NRs , allowing detection of the different 5′ and 3′ isoforms . After in situ hybridization , embryos were mounted on slides in 100% glycerol . Pictures were taken with a Leica M420 Macroscope or with a microscope ( Leica DM RA2 ) with differential interference contrast using a digital camera ( Coolsnap CCD , Roper Scientific ) . Digital pictures were saved as TIFF files , then adjusted for contrast , brightness , and color balance using Adobe Photoshop software and stored as such or after conversion to JPEG format to reduce the file size . To analyze retinal expression in more detail , embryos previously hybridized with a specific probe were postfixed overnight at 4 °C in 4% paraformaldehyde , 3% glutaraldehyde , and phosphate buffer 0 . 1 M pH 7 . 4; dehydrated in graded ethanol and propylene oxide; embedded in a mix of araldite and epon; and sectioned ( 3 . 5 μm ) on a microtome using standard techniques . The expression patterns were further coded in a binary matrix to quantify their divergence ( see Table S2 ) . In this table , all organs in which at least one gene is expressed , are listed ( a total of 165 organs for the whole set of developmental stages ) , and the presence or absence of each gene transcript in each organ is indicated respectively by a “1” or a “0 . ” All the organs or anatomical structures were labeled with “1” for ubiquitously expressed genes , whereas all organs were marked with “0” for nonexpressed genes . Starting from this matrix , expression divergence between the duplicates was calculated as the number of gene expression differences ( i . e . , the number of organs where only one gene in the pair is detected ) over the total number of organs where at least one of the genes in the pair is expressed . This means that the same number of differences will give a stronger divergence if the genes concerned have a restricted expression pattern ( i . e . , if the pair is expressed in only a few organs ) than if they are broadly expressed . Hierarchical clustering analysis was performed using the binary matrix ( 101 genes versus 166 anatomical structures; Table S2 ) . We excluded 13 genes for which no expression was detected in the 166 organs , and 31 genes ubiquitously expressed in all structures ( except in the yolk syncytial layer ) . Thus , only genes with regionalized expression ( detected here in a number of organs between 1 and 41 ) were included in the analysis . We have verified that the inclusion of ubiquitous and undetected genes in the analysis does not modify the overall conclusions of the hierarchical analysis . Similarities between the expression patterns of the 57 genes and also between the patterns of anatomical structures were computed as Jaccard's coefficient , which is classically employed for species presence–absence data in ecology [90] . Jaccard's coefficient is an asymmetrical binary coefficient , which does not take into account the case of absence/absence in the degree of similarity between two binary patterns . It is suitable in the framework of expression data , because the presence ( i . e . , the detection ) of a gene in an organ is more informative in terms of expression or not than its absence due to the existence of detection thresholds . Distances between the expression patterns of genes and between the patterns of organs were calculated as d = sqrt ( 1 − s ) , with s being the similarity coefficient . Dendrograms were built using the two sets of distances ( genes and organs ) by hierarchical clustering following the Ward's method . We performed all analyses with the R software ( http://www . R-project . org ) using the package ade4 [91] to compute distances between expression patterns . The protein sequences of each pair of actinopterygian-specific paralogs were aligned with the orthologous nonduplicated protein sequence of the outgroup using ClustalX [86] . We used the closest appropriate outgroup ( having diverged before the actinopterygian genome duplication ) being completely sequenced ( H . sapiens ) . We used RRTree [92] on these protein alignments to make relative rate tests and thus evaluate differences in protein evolution rates of the duplicates . Nucleotide alignments of the corresponding coding sequences were obtained based on the protein alignments . We used Gestimator ( analysis-0 . 6 . 6 by K . Thornton ) to compute the Ka/Ks ratios for each pair of duplicates with Comeron's method [93] . | NRs are key molecules controlling development , metabolism , and reproduction in metazoans . Since NRs are implicated in many human diseases such as cancer , metabolic syndrome , and hormone resistance , they are important pharmaceutical targets and are under intense scrutiny to better understand their biological functions . In the present study , we determined the expression patterns of all NR genes as well as their main transcriptional coregulators during zebrafish development . We used zebrafish because the transparency of its embryo allows us to perform whole-mount in situ hybridization from early development to late organogenesis . This complete developmental profiling offers an unprecedented view of NR expression during embryogenesis , uncovering their potential function during central nervous system and retina formation . We observed that in contrast to NR genes , only a few coregulators exhibit a restricted expression pattern , suggesting that tissue specificity of hormone action is conferred more by the receptors than by their coregulators . Lastly , by evolutionary analysis of expression pattern divergence of duplicated genes , we observed that neofunctionalization occurs at the levels of both protein sequence and mRNA expression patterns . Taken together , our data provide the starting point for functional analysis of an entire gene family during development and call for the study of the intersection between metabolism and development . | [
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] | 2007 | Unexpected Novel Relational Links Uncovered by Extensive Developmental Profiling of Nuclear Receptor Expression |
Many core components of the microRNA pathway have been elucidated and knowledge of their mechanisms of action actively progresses . In contrast , factors with modulatory roles on the pathway are just starting to become known and understood . Using a genetic screen in Caenorhabditis elegans , we identify a component of the GARP ( Golgi Associated Retrograde Protein ) complex , vps-52 , as a novel genetic interactor of the microRNA pathway . The loss of vps-52 in distinct sensitized genetic backgrounds induces the enhancement of defective microRNA-mediated gene silencing . It synergizes with the core microRNA components , alg-1 Argonaute and ain-1 ( GW182 ) , in enhancing seam cell defects and exacerbates the gene silencing defects of the let-7 family and lsy-6 microRNAs in the regulation of seam cell , vulva and ASEL neuron development . Underpinning the observed genetic interactions , we found that VPS-52 impinges on the abundance of the GW182 proteins as well as the levels of microRNAs including the let-7 family . Altogether , we demonstrate that GARP complex fulfills a positive modulatory role on microRNA function and postulate that acting through GARP , vps-52 participates in a membrane-related process of the microRNA pathway .
The microRNA ( miRNA ) pathway is a gene regulatory system that uses small non-coding RNAs to target messenger RNAs ( mRNAs ) for post-transcriptional regulation . In the canonical form of miRNA biogenesis , miRNA-containing transcripts are processed through sequential cleavage operated by the Drosha and Dicer enzymes into mature miRNA species ( 21–23 nucleotides long ) that associate with an Argonaute protein ( reviewed in [1] ) . In its effector phase , the miRNA-loaded Argonaute , as part of the core miRNA-induced silencing complex ( miRISC ) , regulates target mRNAs through binding sites in their 3′UTRs . The most detailed repressive effector function of this complex is mediated by its association to GW182 proteins ( reviewed in [2] ) . The miRISC-mediated effector phase of target regulation , may involve the repression of multiple target molecules by each single miRNA through the process of ‘miRNA recycling’ , and this mRNA regulation affects miRNA stability [3]–[7] . Finally , all miRISC components would be subjected to degradation , nucleases have been shown to degrade miRNAs ( reviewed in [8] ) , and autophagy mediates the degradation of Dicer , Argonaute [9] , [10] and GW182 [11] . In the nematode Caenorhabditis elegans ( C . elegans ) , the miRNA pathway comprises over 120 miRNAs [12] , two GW182 homologues ( ain-1 and ain-2 ) [13] , [14] , the Argonautes alg-1 and alg-2 ( both referred to as alg-1/2 ) [15] and single genes for Dicer ( dcr-1 ) [15] , [16] and Drosha ( drsh-1 ) [17] . In worms , as in other animals , the miRNA pathway is essential for development and reproduction . Animals mutant for dcr-1 or drsh-1 genes are sterile [15]–[17] , while at the Argonaute level , the loss of both alg-1/2 results in embryonic arrest [15] , [18] . In contrast , single mutants of alg-1 or alg-2 display differentially penetrant post-embryonic , somatic and germ line defects [19] , [20] . As exemplified here , the existence of these two gene paralogs , with specialized and partially redundant functions provides an opportunity to study the miRNA pathway in a sensitized genetic condition where miRNA activity is reduced albeit not completely abolished , by screening for genetic enhancers of the partial loss-of-miRNA condition . In the present study , we identify the vps-52 gene , encoding a component of the GARP complex , as a genetic interactor of the miRNA-specific alg-1 Argonaute , and establish that this complex fulfills a positive modulatory role in regulating the activity of miRNAs . The loss of vps-52 in distinct sensitized genetic backgrounds induces the reiteration of the miRNA-controlled proliferative seam cell division program , enhances the let-7-related lethal phenotype , exacerbates the abnormal vulval development associated to the lessened miRNA-regulation of the let-60 gene , as well as augments the defective expression of a reporter of the lsy-6 miRNA activity in the ASEL neuron . Our phenotypic analyses thus suggest a broad role for GARP in miRNA function . Underpinning these GARP effects , we found decreased abundance of miRNAs and the GW182 proteins . Based on our data , we propose that the GARP complex operation facilitates a transition of miRISC occurring at endomembranes .
In order to identify new components and modulators of the miRNA pathway , we conducted a forward genetic screen for interactors of the alg-1/2 Argonautes , based on a design that allows the recovery of gene enhancers , including synthetic lethal gene pairs [21] . In brief , we subjected to mutagenesis worms carrying a partially inheritable extrachromosomal array containing a functional GFP-tagged alg-2 gene expressed in the alg-2 ( ok304 ) mutant ( referred to as alg-2 mutant ) background . F2 clones were scored and selected as candidate interactors if their progeny was uniformly transgenic ( i . e . there was no segregation of viable worms lacking the extrachromosomal array in the population ) , indicative of a possible genetic interaction of an unknown mutated factor , in the transgenic setting , with the alg-2 ( ok304 ) background . Upon removal of the screening background ( array and alg-2 mutation ) , a strain with increased growth and fertility defects in alg-1/2 ( RNAi ) was selected , mapped and mutations identified by whole-genome sequencing ( further details in the Materials and Methods section ) . We transgenically rescued the growth and fertility defects of the mutant strain , confirming the identity of the genetic interactor as vps-52 . In addition to the single allele vps-52 ( qbc4 ) retrieved from the screen ( Figure 1A ) , an available deletion allele , vps-52 ( ok853 ) was studied and found to display similar phenotypes ( Figure 2 ) . As expected , vps-52 behaved genetically as an enhancer , but interestingly double mutants of vps-52 with either alg-2 or alg-1 were obtained as viable strains , and the loss of vps-52 induced a visible phenotypic enhancement in combination with the loss-of-function alg-1 ( gk214 ) mutant ( referred to as alg-1 ( 0 ) ) , but not with alg-2 ( ok304 ) as reported below . The mutant strain initially isolated from the screen does not sustain the segregation of viable animals without the extrachromosomal array , which could be accounted for by array-mediated overexpression effects of the GFP::ALG-2 fusion protein or additional secondary background mutations; its segregation was not further investigated . The vps-52 gene encodes a conserved structural protein ( Figure 1A ) , that functions in the traffic of vesicles to the trans-Golgi network ( TGN ) . It forms part of a conserved TGN-localized multimeric complex , known as the GARP ( Golgi Associated Retrograde Protein ) complex [22] , [23] , which comprises in C . elegans the genes vps-51 , vps-52 , vps-53 and vps-54 [24] . Expressing a functional fluorescently tagged VPS-52 from a single copy genomic insert controlled by endogenous gene regulatory elements ( referred to as Si[vps-52] ) , we detected widespread VPS-52 expression in cytoplasmic puncta of many somatic tissues from early embryos on ( Figure 1B–D ) , consistent with a previous report [24] . In particular , VPS-52 is expressed in temporal continuity at all larval stages in the vulval and seam cells ( Figure S1 ) . To address whether the phenotypes of vps-52 mutants reflect an impairment of the GARP complex activity , we included in our study a strain defective for another subunit of this complex , the vps-53 ( ok2864 ) mutant . To analyze the function of vps-52 and vps-53 in the miRNA pathway , we first studied the development of seam cells . These lateral rows of hypodermal cells have a postembryonic developmental program , consisting of patterned rounds of division during each larval stage ( L1 to L4 ) , ended by terminal differentiation encompassing exit from the cell cycle , cell fusion and production of a cuticular structure ( named alae ) at the transition to adult . The seam cell developmental program is controlled at different larval stages by the miRNA lin-4 [25] and those of the let-7 family ( miR-48 , miR-84 , miR-241 and let-7 ) [26] , [27] and their targets lin-14 , lin-28 , hbl-1 , daf-12 and lin-41 [26]–[33] . The repetition of the symmetrical seam cell division program that normally occurs once at the L2 stage is a frequently observed defect in mutants of core components of the miRNA pathway , such as alg-1/2 , dcr-1 and ain-1/2 [13] , [15] . Similarly to these mutants , other pathway modulators and components also display distinctive seam cells defects [34]–[36] . Discontinuities in the cuticular alae ( in particular , gaps ) arise from inappropriate terminal differentiation , and are an indicator of possible alterations in the development of seam cells . We analyzed the vps-52 and vps-53 mutants and noticed mild penetrant defects on the alae structures ( Figure 2A ) that were not exacerbated in the vps-52; vps-53 double mutant , demonstrating epistasis consistent with the affiliation of both gene products to a common complex . We then proceeded to analyze the defects of the vps-52 and vps-53 mutants ( referred to as GARP mutants ) in the absence of functional alg-1/2 Argonautes . Given that defects of the alg-1 null mutant are less penetrant at lower temperatures ( data not shown ) , we conducted the following scoring of alae and seam cell counts under more beneficial condition ( 15°C ) to allow for better phenotypic enhancement . No enhancement of alae defects was observed in vps-52 ( qbc4 ) ; alg-2 double mutant animals ( Figure 2A ) . However , combining either vps-52 or vps-53 with the alg-1 null mutant caused a very prominent increase in alae defects , which were partially rescued by transgenic vps-52 expression ( Figure 2A ) . The enhancement of the alg-1 ( 0 ) alae defects was correlated with an increase in the number of seam cells . While single GARP mutants showed minor deviations from the wild-type lineage ( 16 seam cells at adulthood ) , the mean number of seam cells in alg-1 mutant at 15°C ( 18 cells ) , reached 25 in vps-52 ( qbc4 ) alg-1 ( 0 ) ( Figure 2B–C ) . This increase in seam cells was not observed in the L1 larval stage ( data not shown ) , indicating that it likely results from the reiteration of the L2 stage proliferative division of the seam cells . We then addressed whether the effects of vps-52 mutant on the seam cell phenotype of alg-1 ( 0 ) worms were recapitulated with related mutants of vesicle trafficking processes . We tested the effect of disrupted Golgi trafficking by impairing the action of the worm small GTPase rab-6 . 2 , whose gene product physically interacts with the GARP complex [24] . While single mutants of the putative null rab-6 . 2 ( ok2254 ) did not display any gapped alae , the exposure of rab-6 . 2 ( ok2254 ) to alg-1 ( RNAi ) caused a strong interruption in the continuity of alae ( Table S1 ) . Moreover , this disruption was much stronger than that obtained for vps-52 ( qbc4 ) assayed under the same experimental conditions ( 100% vs 41% gapped alae; Table S1 ) . We conclude that the loss of vps-52 or vps-53 function does not prominently affect seam cell development , but effectively synergizes in the absence of ALG-1 to induce reiteration of seam cell division program . Our observations of similar synergy for the mutant of rab-6 . 2 , implicate the Golgi-associated function of these genes and miRNA-controlled development of the seam cells . The let-7 family members miR-48 , miR-84 and miR-241 redundantly regulate the expression of their target gene hbl-1 during the L2-to-L3 larval stage transition [26] , [28] . Abolishing completely the function of these three miRNAs causes seam cells to reiterate their L2 developmental program [26] . Loss of singleton or pairs of these genes leads to seam cell defects of varied penetrance , thereby constituting useful sensitized genetic backgrounds where the action of pathway modulators can be unveiled , as previously exemplified for the nhl-2 modulator [36] . It was therefore possible that the altered seam cell development observed in alg-1 mutant and enhanced by loss of vps-52 resulted from impaired miR-48 , miR-84 and miR-241 miRNAs function . Consequently , we evaluated whether GARP mutants would alter seam cell development in the absence of miR-48 , with the mir-48 ( n4097 ) mutant allele ( referred to as mir-48 ( 0 ) ) . Although loss of this miRNA did not induce seam cells defects on its own , the concomitant loss of vps-52 did lead to increase gapped or absent alae ( Table 1 ) . We next investigated the genetic interaction of vps-52 with hbl-1 , a main target of the let-7 miRNA family that controls developmental timing at the L2 stage . Reduced hbl-1 function results in the precocious terminal differentiation of the seam cells at the third larval molt evidenced by the production of alae [28] , [36] . The combination of the reduced-function allele , hbl-1 ( ve18 ) , with a vps-52 mutant resulted in the partial suppression of the hbl-1 ( ve18 ) precocious phenotype ( Table 1 and Figure S2 ) . Similarly , suppression of this hbl-1 mutant phenotype has been reported upon concomitant loss of the let-7 miRNA family [36] . Altogether , these results suggest that loss of vps-52 diminishes the activity of let-7 family miRNAs . This effect likely underpins the enhanced defects in the developmental program of the seam cells observed with the alg-1 ( 0 ) and mir-48 ( 0 ) mutants . We further extended our analysis to the let-7 miRNA , which regulates developmental programs at the L4 to adult transition . The complete loss of the let-7 miRNA gene produces a highly penetrant phenotype of bursting through the vulva [27] that is classically used in phenotypic assessments of miRNA functions ( e . g . [37] ) . In this respect , we did not observe any bursting phenotype in the GARP mutants ( Table 2 ) . In addition , the bursting of GARP mutants in combination with alg-1 ( 0 ) was not overtly different from that of single alg-1 mutant ( Table 2 ) . We then investigated if there was any effect on sensitized let-7 genetic backgrounds . We used , to that aim , the hypomorphic let-7 mutation , the n2853 allele , that carries a point mutation in the miRNA seed region , which leads to reduced mature let-7 miRNA level [27] , [38] and to temperature-sensitive reduction in the activity of this miRNA [27] . Remarkably , the GARP mutants strongly enhanced the let-7 ( n2853 ) bursting phenotype at permissive temperature ( Table 2 ) . The penetrance of the defect prevented the propagation of double vps-52 ( qbc4 ) let7 ( n2853 ) mutants to useful populations , but , upon combination with the transgenic vps-52 gene , a rescued viable strain could be obtained ( Table 2 ) . The let-7 miRNA represses expression of lin-41 , one of its major target genes; and reducing lin-41 function suppresses the bursting phenotype of let-7 mutants [27] , [32] , [39] . We therefore decided to verify whether the lin-41 impairment could suppress the penetrant bursting of the double vps-52 ( qbc4 ) let-7 ( n2853 ) mutant . The combination of the lin-41 ( ma104 ) hypomorph with vps-52 ( qbc4 ) let-7 ( n2853 ) resulted in a viable triple mutant and the complete loss of bursting ( Table 2 ) . Thus , the effect of vps-52 on the sensitized let-7 background is dependent on its target , lin-41 . We next expanded our study to an additional target of the let-7 miRNA family through the investigation of the regulation of vulva development by the miRNA-targeted let-60 gene . During vulva development , an inductive signal from the somatic gonad promotes the vulval cell fates among a set of vulva precursor cells ( VPCs ) . A restricted subset of VPCs adopts vulval fates and produces the egg-laying organ , while the remaining VPCs acquire a non-vulval epidermal fate [40] . The response to the inductive gonadal signal in the VPCs , requires the action of the let-60 gene ( RAS homolog ) . Increased let-60 activity results in ectopic adoption of the vulval fate by VPCs and supernumerary vulva-like structures ( known as the Muv phenotype ) [41] , [42] . The let-7 family miRNAs participate in this developmental process through the regulation of their target gene let-60 [36] , [43] . The miRNA-mediated regulation of this gene can modulate the penetrance of the Muv phenotype in let-60 gain-of-function mutants [36] . Similar to wild type worms , GARP mutants did not display the Muv phenotype ( Figure 3A ) . A missense gain-of-function mutation , let-60 ( n1046 ) produces a weakly penetrant Muv phenotype in heterozygous condition ( Figure 3A ) . The concomitant impairment of vps-52 in let-60 ( n1046 ) /+ mutants exacerbated the percentage of defective Muv animals ( 4% vs 32%; Figure 3A ) . This result suggests that vps-52 gene function contributes to the negative regulation of let-60 activity during vulva development , likely through the regulation of the let-7 family miRNAs . In order to address if GARP fulfills a restricted or broad modulatory activity on miRNA function , we then studied the regulation of ASEL neuron development by the lsy-6 miRNA . The lsy-6 miRNA promotes the adoption of a unique cell fate by the ASEL chemosensory neuron . This results in a functional right-left asymmetry between it and its counterpart , the ASER neuron [44] . In the ASEL neuron , the reduced expression of the lsy-6 targeted gene cog-1 leads to a subsequent gene regulatory cascade that activates ASEL-specific gene expression . In particular , the lsy-6-mediated silencing of cog-1 expression leads to transcriptional derepression of lim-6 [44] . Thus , a transcriptional fluorescent reporter of lim-6 ( lim-6p::GFP ) serves as an indicator of achieved lsy-6-mediated cog-1 silencing , when its expression is switched on in ASEL [44] . In wild type and vps-52 ( qbc4 ) animals , this reporter was expressed in the ASEL neuron of every worm ( Figure 3B ) . In contrast , complete absence of lsy-6 activity causes the total loss of expression of the reporter from the ASEL neuron [44] . While the hypomorphic lsy-6 ( ot150 ) mutation induced a mild penetrant loss of expression of this reporter ( Figure 3B ) [45] , the loss of vps-52 in lsy-6 ( ot150 ) mutants augmented the absence of reporter expression in ASEL ( 11% vs 47%; Figure 3B ) . This result suggests that vps-52 gene function facilitates the activity of the lsy-6 miRNA in silencing cog-1 expression that subsequently contributes to the establishment of ASEL-specific gene expression . In summary , our phenotypic analysis indicates that vps-52 and vps-53 mutants display weakly penetrant miRNA-related defects , but synergize with the alg-1 and miRNA mutants in enhancing developmental defects in a miRNA target-dependent manner . In addition , the loss of vps-52 suppresses the precocious phenotypes of a reduced function hbl-1 mutant , increases the misregulation of the miRNA-targeted gene let-60 during vulva development , as well as enhances the defective gene silencing activity of an hypomorphic lsy-6 mutant in the ASEL neuron . These collective results , obtained in the context of distinct miRNA-dependent phenotypes in sensitized genetic backgrounds , suggest therefore , a positive and general role for the GARP complex in miRNA function . In order to gain insights into the steps at which vps-52 regulate miRNA activity , we investigated the physical association of VPS-52 with components of the microRNA pathway in C . elegans . Using immunoprecipitation , we did not observe physical interaction between VPS-52 and the miRISC components AIN-1 or ALG-1 ( data not shown ) . We next investigated if the GARP mutations affected the abundance of key miRNA pathway proteins in C . elegans . While we did not detect any effect on the DCR-1 or ALG-1/2 protein levels ( Figure S3 ) , the abundance of the GW182 AIN-1 protein was reduced in both vps-52 and vps-52 alg-1 mutants ( Figure 4A ) and this without a corresponding decrease in the expression at the mRNA level of the ain-1 gene ( Figure S3B ) . In order to determine if this effect of vps-52 was specific to ain-1 , we addressed genetically the effect of loss of vps-52 in ain-1 function . Interestingly , vps-52 ( qbc4 ) and the loss-of-function ain-1 ( ku322 ) mutant synergized , as their combination induced a strong enhancement of seam cell defects ( Figure 4B ) . Thus , vps-52 may also modulate miRNA activity in parallel to ain-1 . It is likely that the ain-2 gene , which encodes a second GW182 protein that provides redundant miRISC function with AIN-1 [13] , is also affected by the loss of vps-52 . We therefore examined the effect of vps-52 on a AIN-2::GFP translational reporter [13] to overcome the lack of specific antibodies . We observed that the knockdown of alg-1 in the vps-52 ( qbc4 ) mutant induced a moderate decrease of AIN-2::GFP abundance notably in vulva cells ( Figure S4 ) . The moderate effect detected with this transgenic GW182 reporter may be due to its inability to recapitulate the dynamics and expression level of the endogenous AIN-2 protein . Altogether these results support that the GARP complex may impinge on the abundance of the GW182 protein . Next , we determined the effects of the alteration of vps-52 on miRNA levels . Considering that several of the observed genetic interactions are congruent with reduced activity of the let-7 family miRNAs , we monitored their abundance . While in the single vps-52 mutant , we observed a significant decrease of the let-7 family miRNA members miR-48 and miR-241 , their levels were further reduced in vps-52 alg-1 ( 0 ) double mutant ( Figure 5 ) . Moreover , this reduced miR-48 and miR-241 abundance was rescued to the levels found in the single alg-1 ( 0 ) mutant upon expression of a vps-52 rescue transgene ( Figure 5 ) . The level of primary and precursors miRNA molecules remained intact or was mildly altered ( Figures S5A and 5 ) , supporting that the decreased miRNA abundance is not likely due to diminished transcription or biogenesis of these miRNAs . The diminished miRNA abundance induced by the loss of vps-52 was not restricted only to these two let-7 family miRNAs . In vps-52 mutant , the abundance of three other miRNAs tested was also significantly reduced as observed for miR-48 and miR-241 ( Figure S5C ) . This result supports that the loss of vps-52 impinges on a broad or general manner on miRNAs . We conclude that the GARP-mediated modulation of miRNA function , observed upon loss of GARP in alg-1 ( 0 ) and multiple other mutants of miRNAs and their targets , is likely underpinned by the lowered abundance of the miRISC component GW182 as well as reduced miRNA abundance .
Employing a genetic screen for alg-1/2 Argonaute interactors , we have identified the gene vps-52 , encoding a component of the GARP complex , as a genetic enhancer of the miRNA pathway activity in C . elegans . The vps-52 mutants displayed weakly penetrant miRNA defects , and correspondingly mild molecular alterations of the pathway . However , upon vps-52 loss , a positive role was uncovered by the induction of increased defects in sensitized mutant backgrounds for Argonautes , miRNAs and their targets . The phenotypes and interactions we observed for vps-52 , establish it as a modulator of miRNA activity rather than a core pathway component , which is incidentally the type of factors expect to be retrieved from a modifier genetic screen . We encountered similar phenotypes for the mutants of vps-52 and vps-53 , and both synergized with the alg-1 and let-7 mutants . Given that this two genes encode components of the GARP complex , it is likely that the observed regulation of miRNA activity corresponds to the impairment of a GARP complex function , rather than other putative additional role ( s ) of these genes . The GARP complex is involved in the tethering of endosome-derived vesicles reaching the TGN , in a recycling pathway known as ‘retrograde transport’ ( reviewed in [46] ) . This pathway allows the recycling of proteins such as the cation-independent mannose 6-phosphate receptors in mammalian cells , and its impairment in both mammals and yeast leads to protein missorting [22] , [47] . Consequently , we hypothesize that the mechanism underlying the observed regulation of miRNA activity is related to the known function of the GARP complex in tethering endosome-derived vesicles at the TGN and the consequences of it [46] . Noteworthy , a different sensitized let-7 background was previously employed in a genome-wide screen for regulators of the miRNA pathway [37] . Among the top hits identified were components of the COG ( Conserved Oligomeric Golgi ) complex , a vesicle tethering complex [48] , exemplifying again the link between Golgi trafficking functions and miRNA activity . Numerous studies have now reported on several endomembrane-related aspects of the miRNA pathway in diverse organisms: 1 ) the presence of Argonaute at the Golgi of certain cultured cells [49] , [50]; 2 ) the co-fractionation of pathway components with Multivesicular Bodies ( MVBs ) and the negative effects of disrupting components of this compartment [4]; 3 ) the association of Argonautes and Dicer with the endoplasmic reticulum [51] , [52]; 4 ) the miRNA regulatory effects of the BLOC-3 complex [53]; 5 ) those of disrupting the isoprenoid producing enzymes of the mevalonate pathway [54] , [55] and; 6 ) the selective autophagic degradation of Dicer and Argonaute [9] , [10] and C . elegans AIN-1 ( GW182 ) protein [11] . Although all these findings may be underpinned by different mechanisms , they highlight the importance of membrane-regulated aspects on the function of the miRNA pathway . The impairment of autophagy leads to suppression of defective miRNA-mediated gene silencing in C . elegans and affects the abundance of AIN-1 [11] . Thus , it is conceivable that lysosomal mediated pathways , such as autophagy , underpin the lessened GW182 abundance we observed upon the loss of GARP . An interesting possibility regarding the membrane association of the miRNA pathway is that it relates to the sorting and secretion of miRNAs in MVB-derived exosomes . Indeed , circulating miRNAs have been detected in diverse body fluids and proposed to be involved in some form of intercellular communication ( reviewed in [56] ) . Similarly , if miRNA secretion were eventually used to alter gene expression in other cells , it should be expected to alter the cell autonomy of miRNA action to certain extent . In C . elegans this aspect has been only studied for lin-4 in the seam cells , where this miRNA functions cell autonomously [57] . Nonetheless , the sorting of the miRNA and pathway components in exosomes , their putative secretion and role in intercellular communication in C . elegans and other organisms are research topics that require further exploration . An alternative and non-mutually exclusive possibility with that of miRNA secretion , is that the membrane association of the miRNA pathway could be part of a process that facilitates certain transitions occurring during the course of miRNA action [5] . This process being facilitating rather than necessarily required , its absence would only impair , but not completely abolish , miRNA-mediated gene regulation . It can be envisioned that the miRISC components would be associated with endomembranes , such as endosomal vesicles , to facilitate a transition of the miRISC , including , possibly , the recycling of miRISC components , its assembly or disassembly . In this context , impairing the GARP complex function in retrieving endosomal vesicles carrying miRISC components or other factors required for proper miRISC activity would have two foreseeable consequences: i ) missorting of miRISC components , such as the GW182 proteins and; ii ) a ‘block’ of the miRISC at membranes . As a result , the affected complexes would be disallowed from engaging in further repression of target mRNAs , and also from participating in the accumulation of new miRNAs . Although a role for GW182 in regulating miRNA stability has been recently proposed in mammalian cells [58] , other reports indicated that the abundance of miRNAs is not affected by the absence of the GW182 proteins [13] , [59] , suggesting that miRNA abundance and GW182 accumulation can be uncoupled . Consistently with these last reports , we did not find a decrease in miRNA abundance upon the loss of AIN-1 ( Figure S5B–C ) . In addition to the processes of miRNA biogenesis and core effector functions , the understanding of subsequent phase ( s ) of microRNA activity will likely unveil the existence of new components that facilitate miRNA activity and regulate its recycling and turnover . The present trends of discoveries suggest that these facets may be , at least in part , dependent on processes occurring at the interface of endomembranes . Future studies will be required to establish the mechanism by which GARP regulates miRNA activity . Similarly , further research will be helpful to better understand the mechanisms by which membrane-based processes regulate the function of miRNAs .
Worms were cultured in standard conditions [60] . All experiments were performed at 20°C unless otherwise noted . The strains used in the present study were outcrossed four times before analysis ( detailed on the strains can be found in Text S1 ) . The RNAi by feeding was performed on nematode growth media ( NGM ) plates containing 1 mM IPTG ( Isopropyl β-D-1-thiogalactopyranoside ) after overnight induction ( 25°C ) of the bacterial culture . The alg-1/2 ( RNAi ) was performed as described in [15] . The alg-1 ( RNAi ) was performed with a construct targeting the alg-1-specific N-terminal gene region [35] . The genetic screen followed a design to isolate enhancers of the queried gene ( including synthetic lethal interactors ) previously used in C . elegans [21] , [61] . EMS was used to mutagenize alg-2 ( ok304 ) worms carrying an extrachromosomal array containing GFP::alg-2 copies ( strain MJS11 ) . From a pool of 1 , 000 mutagenized fluorescent F1 , F2 clones where the non-integrated array becomes necessary for worm survival , evidenced by a homogeneous population of GFP expressing animals were kept and further investigated . Next , single mutant strains ( with no transgenic array or alg-2 ( ok304 ) ) were obtained and tested as follows . Upon outcrossing with wild type N2 males , a random set of F2 worms ( chosen to be GFP negative , without the alg-2 ( ok304 ) deletion ) , was fed with alg-1/2 ( RNAi ) and the mendelian segregation of a discernible miRNA-related phenotype ( gapped alae or bursting ) in the RNAi condition was assessed . One single mutant strain displaying increased defects upon alg-1/2 ( RNAi ) with the expected segregation frequency was selected for further characterization . Using alg-1/2 ( RNAi ) the mutant locus was SNP mapped to the X chromosome in the genetic interval ( −2 . 9 , −0 . 76 cM ) . Mutations inside the interval were unveiled by whole genome sequencing ( in collaboration with Dr Don Moerman and the British Columbia Cancer Agency ) . Only a single nonsense mutation inside the interval ( in the F08C6 . 3 gene ) was found . A transgenic strain carrying the wild-type vps-52 gene , rescued all the visible defects of the mutant strain . To note , the obtained vps-52 ( qbc4 ) mutant is sensitive to both germline and somatic RNAi ( data not shown ) . The Mos transposase plasmid ( pJL44 ) and co-injection markers ( pGH8 , pCFJ90 , pCFJ104 ) were used following the MosSCI method [62] . To generate the vps-52 rescue plasmid MSp166 ( Pvps-52::vps-52::mCherry::vps-52 3′UTR ) , two genomic regions comprising the whole vps-52 gene were amplified , introducing a NotI site adjacent to the stop codon and terminal restriction digestion sites ( AvrII and BsiWI ) . Upon NotI ligation , the resulting gene fragment was introduced into double digested ( AvrII , BsiWI ) pCFJ151 plasmid and verified by sequencing . Using the introduced C-terminal NotI site , a mCherry NotI cassette was ligated to produce MSp166 . The oligonucleotides used for plasmid construction are listed in Text S1 . A single copy transgenic line containing the promoter , coding sequence and both 5′ and 3′ untranslated regions of the vps-52 gene was obtained as follows . Mutant unc-119 ( ed9 ) worms ( strain EG4322 ) were injected with a mix of the MSp166 plasmid , Mos transposase and marker plasmids following the MosSCI method [62] . The obtained transgenic lines were processed according to the heat-shock protocol of the method . The integrity of the single copy insert was tested by PCR and sequencing . The obtained line ( carrying the qbcSi01 transgene ) was then used for crosses . Synchronized worm populations of the desired stage were disrupted and dissolved in Laemmli buffer . Protein bands in the immunoblots were visualized using the western lighting plus ECL kit ( Perkin-Elmer ) . Antibodies were used at the following dilutions . Actin-HRP ( 1∶10 , 000 ) ; rabbit anti-ALG-1 ( 1∶5 , 000 ) ; rat anti-AIN-1 ( 1∶10 , 000 ) [13]; rabbit anti-ALG-1/2 ( 1∶2 , 000 ) [63]; rabbit anti-DCR-1 ( 1∶5 , 000 ) [64] . Total RNA from synchronized worm populations was prepared using Tri-Reagent ( Sigma-Aldrich ) . Reverse transcription was performed with the high capacity cDNA reverse transcription kit ( Life technologies ) . A 7900HT PCR system was used for quantitative real-time PCR . SYBR Green I ( Invitrogen ) was used to monitor pri-miRNA and mRNA levels . TaqMan small RNA assays ( Life technologies ) were used to measure miRNA levels ( miR-48/miR-241 , let-7 ) following the manufacturer protocol . The sn2841 ( small nucleolar RNA ) Taqman assay was used as control . The oligonucleotides used for qPCR are listed in Text S1 . Total RNA was separated by gel electrophoresis , transferred into a Genescreen plus membrane ( Perkin-Elmer ) and crosslinked using 1-ethyl-3- ( 3-dimethylaminopropyl ) carbodiimide hydrochloride ( EDC ) ( Sigma ) as described in [65] . DNA probes radiolabeled with the Starfire system ( IDT ) were hybridized to the membrane . After washing , the membrane was exposed to an image plate and scanned with the FLA-5100 phosphoimager . Image quantification was done using the ImageGauge 4 . 1 ( Fujifilm ) software . The oligonucleotide probes are listed in Text S1 . | The microRNA pathway is a post-transcriptional gene regulatory system that uses small non-coding RNAs called microRNAs to control multiple developmental and physiological processes . With the goal of unveiling factors modulating this regulatory pathway , we have undertaken the exploration of genetic interactors in the roundworm Caenorhabditis elegans . We identify vps-52 , a component of the Golgi Associated Retrograde Protein or GARP complex , and establish that this complex executes a positive modulatory role on microRNA activity . The absence of vps-52 function exacerbates diverse microRNA-related defects . Molecularly , this effect relates to decreased abundance of microRNAs and the GW182 proteins . Considering that GARP is involved in the traffic of vesicles from endosomes back to the Golgi apparatus , we propose that GARP facilitates a membrane-related process of the microRNA pathway . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | A New Role for the GARP Complex in MicroRNA-Mediated Gene Regulation |
Conventional methods used to characterize multidimensional neural feature selectivity , such as spike-triggered covariance ( STC ) or maximally informative dimensions ( MID ) , are limited to Gaussian stimuli or are only able to identify a small number of features due to the curse of dimensionality . To overcome these issues , we propose two new dimensionality reduction methods that use minimum and maximum information models . These methods are information theoretic extensions of STC that can be used with non-Gaussian stimulus distributions to find relevant linear subspaces of arbitrary dimensionality . We compare these new methods to the conventional methods in two ways: with biologically-inspired simulated neurons responding to natural images and with recordings from macaque retinal and thalamic cells responding to naturalistic time-varying stimuli . With non-Gaussian stimuli , the minimum and maximum information methods significantly outperform STC in all cases , whereas MID performs best in the regime of low dimensional feature spaces .
In recent years it has become apparent that many types of sensory neurons simultaneously encode information about more than one stimulus feature in their spiking activity . Examples can be found across a wide variety of modalities , including the visual [1]–[12] , auditory [13] , olfactory [14] , somatosensory [15] and mechanosensory [16] systems . This discovery was facilitated by the development of dimensionality reduction techniques like spike-triggered covariance ( STC ) [17]–[22] and maximally informative dimensions ( MID ) [23] . These two methods exhibit complementary advantages and disadvantages . For instance , STC can identify many relevant features for stimuli whose parameters are distributed in a Gaussian manner but can fail when natural stimuli are used , whereas MID works well for arbitrary stimuli but requires exponentially larger data sets to find more than a few features . Therefore , there is need for a method that can find relevant features from arbitrary stimulus distributions while bypassing the curse of dimensionality . Here we propose two novel techniques based on minimum and maximum mutual information; these new approaches can be seen as an extension of STC to arbitrary stimuli . Neural coding of multiple stimulus features is typically modeled as a linear-nonlinear Poisson ( LNP ) process [24]–[28] . A stimulus , such as an image with pixels , as well as each of the features for which a neuron is selective are represented by vectors in a dimensional space . The neuron extracts information about the stimulus by projecting onto the linear subspace spanned by the feature vectors . The result is a stimulus of reduced dimensionality , with ; this input is then passed through an nonlinear firing rate function . Spikes are then assumed to be generated by a Poisson process with a rate equal to , which only depends on the relevant dimensions of the stimulus space . Given a set of stimuli , for and the corresponding observed neural responses , where is number of spikes , there are a few commonly used methods available to extract the stimulus features relevant to the neuron . In the STC method , the stimulus covariance matrix and the covariance of the spike-triggered ensemble , are compared to discover the dimensions along which the stimulus variance conditional on a spike is significantly different from the stimulus variance overall . This comparison is done by diagonalizing the matrix . The relevant features can be identified by the eigenvectors that have nonzero eigenvalues . If the stimuli are drawn from a distribution which is Gaussian , then the only limitation to finding the features is having a large enough set of spike data . In practice , the STC procedure can be extended to Gaussian stimuli containing correlations by adding a whitening step [17] , [18] , and can also include a regularization term to smooth the results ( see Methods ) . On the other hand , if is non-Gaussian , as is the case for natural images , then higher order stimulus correlations can greatly affect the results [23] , 29 . The use of Gaussian stimuli makes it possible to find many relevant dimensions using STC , but fully sampling the dynamic range of responses often requires a more similar to the non-Gaussian distributions found in nature [27] , [30] . It has also been suggested that neural representations of stimuli may be optimized in some way [31]–[33] to the statistics of the natural environment . With this in mind , it is important that multidimensional feature extraction methods be extended to stimulus distributions with non-Gaussian statistics . The MID method is an information theoretic dimensionality reduction technique that identifies relevant features based on how much information a linear subspace contains about the observed spikes ( see Methods ) . Unlike STC , the dimensionality of the relevant subspace to be found using MID must be specified a priori , and thus to discover the number of relevant features one must search for additional dimensions until the subspace accounts for a sufficient fraction of the information carried in the neural response . The objective function in MID relies on an empirical construction of the reduced stimulus distribution and the corresponding conditional distribution , and thus suffers from the curse of dimensionality [34] . A related problem that occurs equally for Gaussian and non-Gaussian stimuli , and affects both the STC and MID methods , is that even if one is able to find many relevant dimensions , it is usually not possible to sample the nonlinear gain function simultaneously along all of these dimensions . Here we put forth two new dimensionality reduction techniques applicable to arbitrary stimulus distributions . These methods , much like STC , make use of pairwise correlations between stimulus dimensions and are not hindered by the curse of dimensionality in the same manner as MID . To demonstrate the usefulness of the proposed methods , we apply them to simulated neural data for two biologically inspired model cells , and to physiological recordings of the response of macaque retina and thalamus cells to time-varying stimuli .
If the spiking activity of a neuron is encoding certain aspects of the stimulus , then the corresponding stimulus features must be correlated in some way with the neural response . From an experiment one can estimate specific stimulus/response correlations , such as the spike-triggered average ( STA ) , the spike-triggered covariance ( STC ) , or the mutual information [35] , ( 1 ) which provides a full measure of the degree of dependence between stimulus and response . These estimates can then be used to construct a model of the conditional response probability by constraining to match a given set of observed correlations , as in the STA and STC methods . As there are an infinite number of models that match any given set of experimentally estimated correlations , the values of the unconstrained correlations are necessarily determined by the specific choice of . The minimal model of is the one that is consistent with the chosen set of correlations but is otherwise as random as possible , making it minimally biased with respect to unconstrained correlations [36] . This model can be obtained by maximizing the noise entropy , where denotes an average over . For a binary spike/no spike neuron consistent with an observed mean firing rate , as well as the correlation of the neural response with linear and quadratic moments of the stimulus , the minimal model is a logistic function [36] ( 2 ) where the parameters , and are chosen such that the mean firing rate , STA and STC of the model match the experimentally observed values ( see Methods ) . If correlations between a spike and higher order moments of the stimulus are measured , the argument of the logistic function would include higher powers of . In addition to being as unbiased as possible , also minimizes the mutual information [36] , [37] , which only includes the contribution of the chosen constraints . We note that previously we used this minimal model framework to characterize the computation performed within the reduced relevant subspace [36] , and in particular to quantify in information-theoretic terms the contribution of higher-than-second powers of relevant stimulus features to neural firing . Here , we study whether analysis of the second-order minimal models constructed in the full stimulus space can be used to find the relevant feature subspace itself . The contours of constant probability of the minimal second order models are quadric surfaces , defined by the quadratic polynomial . The diagonalization of involves a change of coordinates such that ( 3 ) This is accomplished through the diagonalization of the matrix , yielding eigenvectors with corresponding eigenvalues . These eigenvectors are the principal axes of the constant probability surfaces , and as such the magnitude of the eigenvalue along a particular direction is indicative of the curvature , and hence the selectivity , of the surface in that dimension . This point is illustrated in Fig . 1 . The linear term in Eq . ( 3 ) may also contain a significant feature . Subtracting off the relevant dimensions found from diagonalizing leaves an orthogonal vector . The magnitude of this vector can be directly compared to the eigenvalue spectrum to determine its relative strength . The minimal models of binary response systems take the form of logistic functions . This restriction can be eliminated if we look for a maximally informative second order model . To accomplish this , we extend the MID algorithm to second order in the stimulus by assuming the firing rate is a function of a quadratic polynomial , . The nonlinear MID ( nMID ) algorithm is then run exactly as linear MID in the expanded dimensional space . Once the maximally informative parameters are found , the matrix can be diagonalized to reveal the relevant features , and the linear term can be analyzed in the same manner as for the minimal sigmoidal model . The ability to construct an arbitrary nonlinearity allows nonlinear MID to include information contained in higher order stimulus/response correlations and to find the linear combination that captures the most information about the neural response . Unlike multidimensional linear MID , nonlinear MID is one-dimensional in the quadratic stimulus space and therefore avoids the curse of dimensionality in the calculation of the objective function . To test and compare the two proposed methods , both to each other and to the established methods such as STC and MID , we created two model cells designed to mimic properties of neurons in primary visual cortex ( V1 ) . The first model cell was designed to have two relevant dimensions , which places it in the regime where the linear MID method should work . The second model was designed to have six relevant dimensions and serves as an example of a case that would be difficult to characterize with linear MID . Using the van Hateren [38] natural image database , a different set of patches of pixels were randomly selected as stimuli for each cell; 100 repetitions of these image sequences were presented during the course of the simulated experiment . To quantify the performance of a given dimensionality reduction method , we calculate the subspace projection [39] ( 4 ) where is an matrix whose rows are the most significant dimensions found from either , or , and is a matrix containing the model cell features . This quantity is the intersection of the volumes spanned by the two sets of vectors . It is bounded between 0 and 1 , with 0 meaning the two subspaces have no overlap and 1 meaning they are identical , and is invariant to a change of basis or rescaling of the vectors in either subspace . The first model cell was constructed to respond to the two Gabor features shown in Fig . 2A in a phase invariant manner . This cell approximates a complex cell in area V1 by responding to the square of the stimulus projections onto the Gabor features , with a firing rate proportional to , as in the energy model [7] , [40]–[45] . Although the firing rate was low for this model cell , there was occasionally more than one spike per stimulus frame . These instances were rare and to simplify the analysis the neural response was binarized by setting all multiple spiking events equal to one . As expected , the STC method performed poorly due to the strong non-Gaussian properties of natural stimuli [30] , [46] . The STC method found a subspace with an overlap of 0 . 77 , whereas the nonlinear MID result had an overlap of 0 . 87 and the minimal model subspace had an overlap of 0 . 90 , as shown in Fig . 2B . For comparison , the conventional MID method searched for the two most informative dimensions and was able to recover a subspace that almost perfectly reproduced the ground truth , with an overlap of 0 . 98 . The feature vectors found by the different methods and the corresponding eigenvalue spectra are shown in Fig . 2C–E . A second model cell was also created to resemble a V1 complex cell , but with a divisive normalization based on inhibitory features with orthogonal orientation in the center and parallel orientation in the surround [7] , [40]–[45] , [47] , as shown in Fig . 3A . The two excitatory features in the center of the receptive field have a specific orientation . The two inhibitory features in the center of the receptive field have an orientation orthogonal to that of the excitatory features , while the two suppressive features in the surround have the same orientation as the excitatory ones in the center . The nonlinear gain function for this cell is ( 5 ) scaled such that the average spike probability over the stimulus set was approximately 0 . 15 . Spiking responses were binarized as for the first model cell . The performance of the various dimensionality reduction methods is shown in Fig . 3B . The spike-triggered covariance approach finds features ( Fig . 3C ) that bear some resemblance to the model features , but have a low overlap of 0 . 29 . In contrast , nonlinear MID and the minimal model find features with much larger overlaps: 0 . 84 and 0 . 85 , respectively . Note that the linear MID was not implemented for this model cell , as the algorithm cannot recover a 6 dimensional feature space . To demonstrate the usefulness of the new approaches proposed here for the analysis of real neural data , we analyzed the responses of 9 macaque retina ganglion cells ( RGC ) and 9 cells from the lateral geniculate nucleus ( LGN ) under naturalistic stimulus conditions [48] ( see Methods ) . In this case , the stimulus was a spot of light filling the center of the RGC or LGN receptive field with non-Gaussian intensity fluctuations . While we cannot know the true features of these neurons as we can for the model cells , this data was previously analyzed using MID [3] and it was found that two stimulus features explain nearly all of the information in the neural response ( an average of 85% information explained across the 18 cells analyzed ) . We can therefore use the two linear MID features as a benchmark for comparing the features recovered with the new algorithms , using the subspace projection quantity in Eq . ( 4 ) . Moreover , the veracity of these new algorithms can be tested by comparison with other studies that have used Gaussian stimuli and STC to investigate feature selectivity of retinal cells . For instance , it was previously shown that salamander RGCs are selective to 2 to 6 significant stimulus features [2] . Here we examine if the new algorithms can find a similar number of features in macaque RGCs . We show the result of fitting the minimal model to one of the RGCs . The parameters are shown in Fig . 4A; the 50 dimensional linear term is plotted as a function of time before a spike and the matrix is shown in the inset . The eigenvalue spectrum of this cell is shown in Fig . 4B . The eigenvectors corresponding to the two largest eigenvalues are shown in Fig . 4C ( solid curves ) ; the MID features ( dashed curves ) , shown for comparison , captured 92% of the information . These two subspaces are very similar , with an overlap of , demonstrating that the minimal model method is able to accurately identify the two features of this cell . Although the two most informative dimensions captured a very large percentage of the information in the neural response [3] , the number of significant features found using the minimal model approach ranged from 2 to 5 , echoing the previous work [2] in salamander retina using white noise stimuli and STC . The number of cells with a given number of significant features is shown in the histogram in Fig . 4B . Most of the cells were dominated by one or two features , with additional weakly influential dimensions having significant curvature , in agreement with previous findings [2] , [3] .
Both of the methods proposed here find relevant subspaces using second order stimulus statistics and can therefore be seen as extensions of the STC method . The minimal model is forced to have a logistic function nonlinearity , which has the benefit of removing unwanted model bias regarding higher than second order stimulus moments . In contrast , nonlinear MID uses an arbitrary nonlinear gain function and is therefore able to make use of higher order statistics to maximize information . Although both methods yield models consistent with first and second order stimulus/response correlations , neither method is guaranteed to work if the underlying neural computation does not match the structure of the model or the assumptions that underlie the estimation of relevant features . In principle , the flexibility in the nonlinear MID gain function means it should perform at least as well as the minimal model . However , what we have observed is that the nonlinear MID subspace projection with these two model cells is slightly smaller than the minimal model subspace . This may be due to the differences in the nature of the optimization problems being solved in the two methods . Maximizing noise entropy under constraints is a convex optimization problem [49] , whereas maximizing mutual information is not convex . This means that the parameter space for nonlinear MID may contain many local maxima . Although the MID algorithm uses simulated annealing to overcome this issue , the number of iterations required to outperform the minimal model may be large . We have observed ( data not shown ) that minimal models can find feature spaces with extremely high dimensionality , i . e . , which corresponds to finding on the order of values of the covariance matrix . Neurons with selectivity for only a few features that are probed with non-Gaussian stimuli , such as the model cell shown in Fig . 2 or the RGC in Fig . 4 , can be characterized very well with MID , as previously shown [23] . Thus , in such cases MID is a useful tool for estimating the relevant features . We have found that for both real and model neurons with a small number of relevant features , the minimum and maximum information models performed quite well , despite the large number of parameters that need to be estimated . In particular , both methods were able to outperform STC in the recovery of the relevant stimulus subspace . On the other hand , when the dimensionality of the feature space is larger , as for the 6 dimensional cell in Fig . 3 , linear MID cannot be used reliably due to the massive amount of data needed to construct a 6 dimensional empirical spike-conditional probability distribution . Because in the case of model cells the relevant features are known , we can verify that the minimal models and nonlinear MID approaches are able to find all of the features , whereas STC performs significantly worse . Furthermore , the fact that the second-order minimal models yielded a similar number ( 2–5 ) of relevant dimensions across the neural population as was previously described with Gaussian stimuli can be viewed as a further validation of the new method . It is our hope that these new techniques will advance the characterization of neural feature selectivity under a variety of stimulus conditions .
Experimental data were collected as part of a previous study using procedures approved by the UCSF Institutional Animal Care and Use Committee , and in accordance with National Institutes of Health guidelines . When applied to stimuli with correlations , a whitening procedure can be used to correct for them [18] . This procedure can still be used if stimuli are non-Gaussian , but the results are biased [29] . The whitening operation can be performed after diagonalization of by multiplying the eigenvectors by , the inverse of the prior covariance matrix . Whitening has the consequence of amplifying noise along poorly sampled dimensions . To combat this effect , we regularize using a technique called ridge regression [50] in our analysis , in which instead of is used in the whitening step . Here is the identity matrix and is a regularization parameter that was varied for both model cells to identify the value which gave the largest overlap . This value of was used to give a best case estimate of STC performance . We note that this procedure gives more credit to STC compared to the other methods used here because it is not possible to evaluate a cross-validation metric such as percent information explained when many dimensions are involved . Maximally informative dimensions [23] is an algorithm that finds one or more linear combinations of the stimulus dimensions , i . e . a reduced stimulus vector , that maximizes the information per spike [51] ( 6 ) where is the total number of stimuli . The mutual information between the stimulus features and the neural response ( the presence of a spike , , or its absence , ) is a sum of contributions from both types of responses: , with defined by replacing with in Eq . ( 6 ) . However , in the limit of small time bins where in most of the bins , , which leads to vanishing contributions from . In this case , one can optimize either or to find the relevant features along which the probability distribution is most different from according to the Kullback-Leibler distance , cf . Eq . ( 6 ) . We note that this optimization is not convex and therefore a standard gradient ascent algorithm may not find the global maximum . An algorithm that combines stochastic gradient ascent with simulated annealing is publicly available at http://cnl-t . salk . edu . To extend the MID algorithm to nonlinear MID ( nMID ) , the stimulus is simply transformed by a nonlinear operation . For the second order nonlinear transformation considered in this paper , , where is a vector whose first components are the components of and the remaining components are the elements of . Due to the symmetry of the outer product matrix , this transformed stimulus dimensionality is . In this new space , the MID algorithm works as before , finding a linear combination of these dimensions , i . e . , such that is maximized . To improve performance and cut down on runtime , the search was started from the minimal model estimate for and for . To prevent overfitting of the parameters , an early stopping mechanism was used whereby the data was broken into two sets: one set was used for training and the other used for testing . The training set was used to search the parameter space , while the test set was used to evaluate the parameters on independent data . The best linear combination for both data sets was returned by the algorithm . This procedure was done four times , using four different quarters of the complete data set as the test set . The resulting parameters found from these four fittings were averaged before diagonalizing and finding the relevant features . Unlike the regularization of STC models , this procedure can be used when analyzing experimental data . The model of the neural response that matches experimental observations in terms of the mean response probability , as well as correlations between the neural response with linear and quadratic moments of stimuli can be obtained by enforcing ( 7 ) where is an average over and is an average over . Because , this reduces to a set of ( 8 ) equations . Simultaneously satisfying these equations is analytically equivalent to maximizing the log likelihood of the data [49] , which is convex and can therefore be maximized using a conjugate gradient ascent algorithm . To prevent overfitting of the parameters , an early stopping procedure was implemented similar to that used in the MID algorithm . Each step of the algorithm increased the likelihood of the training set , but at some point began decreasing the likelihood of the test set , indicating the fitting of noise within the training set . The algorithm then returned the parameters found at the maximum likelihood of the test set . As described above , this was done four times with different quarters of the data serving as the test set and the resulting parameter vectors were averaged before diagonalizing the matrix . Significance testing of the eigenvalues was done by creating 500 Gaussian distributed random matrices with the same variance as that of the set of elements of . These random matrices were each diagonalized to create a random eigenvalue distribution . Eigenvalues of were then said to be significant if they fell below the lower 2 . 5th percentile or above the 97 . 5th percentile . The data analyzed in this paper were collected in a previous study [48] and the details are found therein . The stimulus was a spot of light covering a cell's receptive field center , flickering with non-Gaussian statistics that mimic those of light intensity fluctuations found in natural environments [30] , [38] . The values of light intensities were updated every ( update rate ) . The spikes were recorded extracellularly in the LGN with high signal-to-noise ratio , allowing for excitatory post-synaptic potentials generated by the RGC inputs to be recorded . From such data , the complete spike trains of the RGCs could be reconstructed . | Neurons are capable of simultaneously encoding information about multiple features of sensory stimuli in their spikes . The dimensionality reduction methods that currently exist to extract those relevant features are either biased for non-Gaussian stimuli or fall victim to the curse of dimensionality . In this paper we introduce two information theoretic extensions of the spike-triggered covariance method . These new methods use the concepts of minimum and maximum mutual information to identify the stimulus features encoded in the spikes of a neuron . Using simulated and experimental neural data , these methods are shown to perform well both in situations where conventional approaches are appropriate and where they fail . These new techniques should improve the characterization of neural feature selectivity in areas of the brain where the application of currently available approaches is restricted . | [
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] | 2011 | Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models |
Bacteria within biofilms secrete and surround themselves with an extracellular matrix , which serves as a first line of defense against antibiotic attack . Polysaccharides constitute major elements of the biofilm matrix and are implied in surface adhesion and biofilm organization , but their contributions to the resistance properties of biofilms remain largely elusive . Using a combination of static and continuous-flow biofilm experiments we show that Psl , one major polysaccharide in the Pseudomonas aeruginosa biofilm matrix , provides a generic first line of defense toward antibiotics with diverse biochemical properties during the initial stages of biofilm development . Furthermore , we show with mixed-strain experiments that antibiotic-sensitive “non-producing” cells lacking Psl can gain tolerance by integrating into Psl-containing biofilms . However , non-producers dilute the protective capacity of the matrix and hence , excessive incorporation can result in the collapse of resistance of the entire community . Our data also reveal that Psl mediated protection is extendible to E . coli and S . aureus in co-culture biofilms . Together , our study shows that Psl represents a critical first bottleneck to the antibiotic attack of a biofilm community early in biofilm development .
Hydrogels have broad applications in nature and form the basis of vital selective barriers such as mucus , the tissue extracellular matrix , and nuclear pores [1] . One important hydrogel barrier is found in the extracellular matrix of bacterial biofilms [2]–[4] . The biofilm matrix is secreted by , and surrounds , bacteria within a biofilm . It confers adhesion to substrates and between the cells [5] , [6] , but it also serves as a selective filter , allowing the entry of nutrients [2] , [7] while delaying passage of certain antimicrobials [8]–[10] . The biofilm matrix is essential for bacterial defense against environmental insults , yet the components and mechanisms that govern its selectivity for small molecules , such as nutrients , toxins , or antimicrobials , are still largely unknown . The biofilm matrix is composed of diverse macromolecules including proteins , extracellular DNA , and lipids . In addition , like many other hydrogel barriers [11]–[15] , the biofilm matrix contains different types of polysaccharides . The biological function of sugars outside metabolism is poorly understood: controlling the filtration properties of hydrogels may be one of their central functions . Indeed , alterations in polysaccharide composition and concentration correlate with biofilm development . During initial stages of biofilm formation , exopolysaccharides facilitate surface and cell-to-cell attachment . As the biofilm matures , exopolysaccharide production increases and diversifies , and contributes to the generation of microcolony formation and more complex architecture [16] . Alterations in polysaccharide composition also contribute to changes in biofilm antibiotic resistance [17] , [18] . Overall , the presence of a biofilm matrix , can lead to increased resistance to antimicrobials and the host immune system simply not observed in their free-swimming counterparts [2] . As a result , biofilms can cause particularly devastating chronic infections or facilitate life-threatening nosocomial infections in short time courses [19]–[24] . A biofilm's resilience to eradication can also cause significant damage in environmental and industrial settings , such as on ship hulls [25] and water pipeline systems [26] . Here , we investigate the role of individual polysaccharides on the permeability of Pseudomonas aeruginosa biofilm matrix to antibiotics . The gram-negative bacterium P . aeruginosa is an avid biofilm former that is implicated in both chronic and acute infections [27] . It represents an ideal model system to unravel the barrier function of the biofilm matrix , because several components of its matrix have been identified and partly characterized [18] , [28]–[33] . In addition , clinical and environmental isolates with varying compositions of exopolysaccharides are available , allowing a direct comparison between extracellular defenses evolved in nature and those formed by synthetically derived laboratory strains [34]–[38] . P . aeruginosa produces three major exopolysaccharides found within the matrix: alginate , Pel , and Psl . In the laboratory strains WT PAO1 and WT PA14 , alginate is not a critical matrix component [28] . However , alginate overproduction is a characteristic of mucoid clinical isolates found in the cystic fibrosis lung [39] , [40] . Alginate is comprised of blocks of β-1 , 4-linked d-mannuronic acid residues and its 5-epimer l-guluronic acid [41] , [42] . Pel , a glucose rich exopolysaccharide , is important for air-liquid interface pellicle formation [31] , [32] and provides a structural scaffold during micro- and macro-colony formation in WT PAO1 biofilms [18] , [43] . The charge-neutral exopolysaccharide Psl is comprised of D-mannose , D-glucose , and L-rhamnose arranged in pentasaccharide repeats and provides structural support during biofilm formation , playing a role in both cell to cell and cell to substrate attachment [29] , [30] , [43] . To dissect the contributions of individual polysaccharides to the matrix barrier at selected time points , we use antibiotic tolerance as a reporter . Clinically relevant antibiotics with different charges and mechanisms of action were selected for this study . By comparing the efficacy of antibiotics against biofilms formed by strains that lack different matrix components , we can assess the importance of each polysaccharide in providing tolerance to a specific antibiotic . We found in both static and continuous-flow biofilm experiments , that genetic depletion of Psl result in sensitization toward a range of antibiotics for young biofilms , suggesting that Psl is a critical determinant for the resistance properties of the biofilm matrix at initial developmental stages . We also show that cells devoid of Psl ( P . aeruginosa Δpsl , S . aureus , and E . coli ) can co-exist with Psl-containing biofilms and effectively increase their tolerance . We speculate that Psl can inhibit the function of a range of charged antibiotics by sequestering them , and that removal of Psl in a clinical setting would greatly enhance the efficacy of antibiotic treatments for early onset infections .
To dissect the contribution of individual polysaccharides to the matrix barrier function we first tested their role in tolerance toward the antibiotic colistin , a critical last-resort antibiotic for multidrug resistant P . aeruginosa [44] , [45] . Colistin belongs to the family of polymyxin cationic antimicrobial peptides , which acts by disrupting the cell membrane [44] . Since it is critical to address infections at initial onset , particularly in burn and wound cases , we examined the contribution of polysaccharide components at early stages of biofilm development . [10] , [18] . One important part of our protocol is to examine the killing effect of colistin upon short exposure ( 2-hour ) . This exposure period is significantly shorter than standard over-night and 24-hour treatments [18] , [46] , [47] and approximates the time an antibiotic is available during a one-time treatment before it is metabolized or digested [48] . This is in contrast to other studies that analyze the roles of P . aeruginosa exopolysaccharides toward antibiotic tolerance over longer exposure times in more mature biofilms [10] , [18] . Using a microtiter plate assay [31] , [49] , we determined the minimal colistin concentration required to kill biofilms ( the minimal bactericidal concentration for biofilms , MBC-B ) formed by wild type PAO1 ( WT ) . Experiments were repeated for strains lacking expression of either of the three identified P . aeruginosa exopolysaccharides , alginate ( ΔalgD ) , pel ( ΔpelA ) , and psl ( ΔpslAB ) . Fig . 1A shows that 63 µg/ml colistin were needed to eradicate WT PAO1 biofilms , whereas only 15 µg/ml were required to eradicate biofilms lacking Psl , which was more than a four-fold decrease in MBC-B in the absence of Psl . In contrast , the MBC-B for alginate-free biofilms ( ΔalgD ) and Pel-free biofilms ( Δpel ) were not significantly different from the MBC-B for the wild type . This suggests that Psl , but not Pel or alginate , can form a first line of defense against colistin for short-term antibiotic for 24-hour biofilms . Colistin sensitivity was not altered for cells lacking a functional algD gene product . This result is somewhat expected because alginate is not abundantly expressed in WT PAO1 in vitro laboratory models early in biofilm development [28] . We are therefore cautious in the interpretation of this result . The lack of Pel was previously shown to sensitize 24 to 48-hour biofilms to aminoglycosides in the laboratory strain PA14 , but not for WT PAO1 , consistent with the results presented here [18] . In parallel to the MBC-B assay , which reveals the concentration required to eradicate all cells in biofilm , we also determined the reduction in viable colony forming units ( CFUs ) before and after exposure to a fixed concentration of antibiotic . Biofilms were exposed to 32 µg/ml colistin for two hours ( Figure S1A ) and viable CFUs were quantified on agar plates . At this concentration of colistin , ΔpslAB cells were eradicated , whereas WT PAO1 , ΔalgD , and ΔpelA biofilms were able to persist . This line of experiments confirmed our conclusion that Psl can mediate protection against colistin for 24-hour biofilms . To examine the contribution of each polysaccharide also in more mature biofilms , we assayed the sensitivity to colistin of biofilms that had grown for 48 and 72 hours . These results show that Psl exerts a protective effect for 24-hour old biofilms , but did not greatly influence biofilm susceptibility after 48 and 72 hours of maturation ( Figure S1A ) . Additionally , these data show that neither alg nor pel are critical for biofilm tolerance toward colistin at any time point of development tested here ( Figure S1A ) . ΔpslAB cells form biofilms more slowly than wild type cells [30] , [50] and have a reduced total biomass compared to WT PAO1 ( Figure 2A , S3 ) . Hence , to address the possibility that increased colistin sensitivity for ΔpslAB biofilms was caused by lower cell numbers , rather than an altered matrix composition , we determined the MBC-B for WT and ΔpslAB biofilms at multiple time points during early biofilm development . Figure 2B illustrates that the MBC-Bs after 2-hour colistin exposure were independent of biofilm age and , for WT , remained constant for 6 , 12 , 18 and 24-hour biofilms . Together these results suggest that the increased sensitivity to colistin of the ΔpslAB biofilms was not due to fewer cells present in the biofilm , but , rather , to the lack of Psl in the biofilm matrix , which in turn appears to affect the interaction of the antibiotic with the cells . To determine if Psl tolerance against colistin is effective only if cells are within a biofilm , we examined the minimal inhibitory concentration ( MIC ) of colistin for WT and ΔpslAB stationary phase planktonic cells normalized to the same cell density . Even in the planktonic state , Psl is constitutively expressed and localizes to the cell surface in WT PAO1 [51] . We found a 4-fold reduction in tolerance to colistin for planktonic ΔpslAB ( Table 1 ) . Although this shift in sensitivity is not as pronounced as in the biofilm state , this data suggests that Psl may contribute to protection for planktonic cells , even in the absence of any protective structure and changes in cellular physiology that arise from the biofilm . Differences in MIC relative to WT PAO1 were not observed for planktonic ΔalgD or ΔpelA ( Table 1 ) . Is the barrier effect of Psl specific to colistin or does it extend to other clinically relevant antibiotics ? To address this question we tested if the loss of Psl would also affect sensitivity toward another cationic antimicrobial peptide , polymyxin B . In addition , we tested sensitivity toward the aminoglycoside tobramycin , a vital first-round treatment of Pseudomonal associated infections [52] , [53] , and the fluoroquinolone ciprofloxacin , an antibiotic used commonly in P . aeruginosa infections due to the ease of oral dosing and limited toxicity . As with colistin , we observed an increase in sensitivity ( as determined by the MBC-B ) of ΔpslAB biofilms relative to WT biofilms for polymyxin B ( Figure 1B 32 µg/ml WT PAO1 , 16 µg/ml ΔpslAB ) , tobramycin ( Figure 1C; 785 µg/ml WT PAO1 , 285 µg/ml ΔpslAB ) , and ciprofloxacin ( Figure 1D; 90 µg/ml WT PAO1 , 54 µg/ml ΔpslAB ) . The antibiotic sensitivity was also tested for each antibiotic at different times of biofilm development ( 24 , 48 , and 72 hours ) . As observed before , Psl-mediated protection was critical earlier in biofilm development ( 24 hours ) , but dispensable at later time points ( S1B; S2A , B ) . Of note is that Δpel and ΔalgD biofilms did not show an altered antibiotic sensitivity compared to WT biofilms at 24 hours . However , Δpel biofilms show a modest decrease in viability at 48 hours when treated with tobramycin or colistin , and also at 72 hours when treated with tobramycin . Together , these results suggest that Psl not only protects cells from colistin , but also can suppress the function of additional antibiotics at initial stages of biofilm development . If a deletion of Psl renders biofilms more sensitive to the antibiotics tested here , then we would expect that elevated levels of Psl have the opposite effect and increase antibiotic tolerance . To test this we used a strain derived from WT PAO1 where the native psl promoter was replaced with an arabinose-inducible promoter ( PBAD-psl ) . We found that colistin tolerance directly correlates with the levels of Psl produced , rising from an MBC-B of 24 µg/ml to 125 µg/ml in dependence of the level of Psl overexpression ( Figure 3A , B ) . This result was confirmed for colistin , polymyxin B , and tobramycin with antibiotic sensitivity assays ( S1A , B; S2A ) at 24 hours . We also compared the tolerance of the synthetically derived ΔpslAB to a strain that naturally lacks Psl ( PA14 ) . PA14 does not produce Psl owing to a 3-gene deletion in the psl operon [32] . The lack of Psl in the PA14 matrix was confirmed by staining of the biofilms with fluorescently labeled HHA lectin , which binds to Psl [54] ( Figure S4 ) . 24-hour PA14 biofilms were with a MBC-B of 24 µg/ml similarly sensitive to colistin as ΔpslAB ( Figure 3A ) . This result was supported with viability counts for cells exposed to colistin and polymyxin B ( Figure S1A , B ) . Notably , more mature PA14 biofilms at 48 and 72 hours had developed an increased tolerance to colistin and polymyxin B , similar to ΔpslAB at these later time points ( Figure S1A , B ) . However , in contrast to ΔpslAB biofilms , PA14 biofilms at 24 hours were more tolerant to the aminoglycoside tobramycin . This is in agreement with a previous report , which demonstrated that the Pel rich matrix of PA14 provides protection against aminoglycoside antibiotics [18] . In the converse experiment we measured colistin tolerance of CF127 , a natural isolate that secretes increased levels of Psl compared to WT PAO1 [38] . The CF127 biofilm grows in distinct microcolonies ( Figure S4 ) , and staining with HHA lectin [54] showed that Psl localizes to the CF127 microcolonies ( Figure S4 ) . The MBC-B of CF127 toward colistin was 125 µg/ml and hence , comparable to that of the overproducing PBAD-psl strain ( Figure 3A ) . Interestingly , the increased colistin tolerance of CF127 compared to WT PAO1 was not apparent in viability counts ( Figure S1A , B; S2A , B ) . We speculate that structural differences of CF127 biofilms may result in antibiotic tolerance to a sub-population of cells within these structures , which are not resolved in the viability assay . To obtain mechanistic insight into Psl mediated protection , we considered the possibility that Psl may directly sequester antibiotics to the matrix and thereby limit its access to the cell surface . We compared WT PAO1 , PA14 , ΔpslAB , PBAD-psl , and CF127 biofilms subjected to 5 µg/ml fluorescent polymyxin B after 2 hours of exposure . In the presence of PBAD-psl cells the antibiotic distributed along a fibrous matrix heterogeneously throughout the biofilm matrix , and also associated with matrix material in planktonic culture ( Figure 4 and S5 ) . The fibrous material was less pronounced for WT PAO1 , where the localization of polymyxin B was distributed diffusely within the biofilm ( Figure 4 ) . This distribution of matrix associated polymyxin B was not observed with the Psl deficient ΔpslAB strain or PA14 . ( Figure 4 and S5 ) . Here , fluorescence was detected in close vicinity of the cell periphery , suggesting that polymyxin B may be interacting with the cell membrane . Of note , polymyxin B localized to the periphery of CF127 microcolonies , but was not observed within the structure . The binding of fluorescent polymyxin B to the biofilm matrix may , in part , result from electrostatic interactions with the matrix components . To probe for such interactions , we performed antibiotic sensitivity assays at varying ionic strengths through the addition of NaCl to the challenge medium ( Figure 5 ) . In growth medium or buffer , charged polymers interact with dissolved ions , which to some extent , form a shell of opposite charges around the molecules . This screening of electrostatic interactions becomes more pronounced with increasing salt concentrations and as a result , the ionic strength in the system will influence the interaction between matrix polymers and diffusing molecules . Specifically , if electrostatic interactions occur between the Psl matrix and the antibiotic molecules , an increase in NaCl concentration may affect these interactions . The challenge medium with 32 µg/ml colistin and no further addition of NaCl reduced the amount of viable cells in a 24-hour WT PAO1 biofilm by nearly one half of the total population . However , in the presence of a challenge medium that contained 32 µg/ml colistin and 50 mM NaCl , the total biofilm population was eradicated ( Figure 5A ) . Similar effects were observed for positively charged antibiotics polymyxin B , and tobramycin with a higher concentration of NaCl ( 250 mM; Figure 5B–C ) , but not for the negatively charged ciprofloxacin ( Figure 5D ) . We conclude that electrostatic interactions may partly contribute to the sequestration of the antibiotics by the Psl matrix , and that high ionic strength can suppress these interactions , potentially leading to an increased efficacy of the antibiotics . We note that Psl itself is neutrally charged [29] , hence , it is conceivable that Psl functions when complexed to other matrix components that could provide the negative charge . In many environments biofilms grow under flow conditions and these may affect the biofilms' barrier properties . To address the role of flow on our findings , we assessed antibiotic susceptibility to colistin in a flow-through microfluidic device . The killing dynamics were examined as biofilms were exposed to 20 µg/ml colistin or buffer without antibiotic for 2 hours ( Figure 6; S6 ) . In WT biofilms , >80% of the cells survived a 2-hr exposure to the antibiotic ( Figure 6 A , B ) . In contrast , <20% of ΔpslAB cells survived , providing further support for our findings and demonstrating that the barrier effect was not compromised by flow and hydrodynamic shear ( Figure 6B ) , although some biomass loss was observed ( 7% loss for WT PAO1 and 12% loss for ΔpslAB; Figure S7 ) . Moreover , a biofilm that over-produces Psl ( PBAD-psl ) again shows increased tolerance against colistin compared to a WT biofilm ( Figure 6B ) . Psl is an extracellular product potentially accessible to foreign cells that are natively devoid of this polymer and hence are , by themselves , more sensitive to antibiotic attack . If non-producing cells are able to coexist with the Psl producers they may be able to exploit the protection by Psl and gain tolerance . This scenario could be relevant in natural settings , where biofilms are often not limited to a single strain or species [55] , [56] . We first determined whether ΔpslAB cells and the Psl overproducing PBAD-psl cells could form co-strain biofilms . For this experiment we expressed the fluorescent protein mCherry in ΔpslAB cells , mixed them with PBAD-psl cells to form a co-strain biofilm . Figure 7A shows that ΔpslAB cells ( red ) can indeed grow inside a “Psl donor” biofilm , even if they were incorporated less effectively than the PBAD-psl cells and therefore represent a smaller proportion of the biofilm . One reason for this is the delay of the ΔpslAB cells to attach and mature into biofilms due to their lack of Psl [30] ( Figure S3 ) . The presence of non-producers was not without effect for the entire biofilm , as it weakened the biofilm's tolerance capacity ( Figure 7B ) . We inoculated biofilms with different ratios of ΔpslAB and PBAD-psl cells , and measured the MBC-B for each emerging biofilm . Figure 7B shows that the sensitivity of the composite biofilm toward colistin increased in proportion to the amount of ΔpslAB cells present in the initial inoculum . This result suggests that the inclusion of non-producers can reduce the tolerance of the entire biofilm , and that a critical amount of exopolysaccharides per cell is needed for effective protection . While compromising the overall protective effect from Psl over-producers by becoming part of their biofilm , ΔpslAB cells could benefit from the access to the protective exopolysaccharides . We tested if ΔpslAB cells within a PBAD-psl biofilm would survive higher concentrations of colistin than their counterparts growing in a monoculture . Within a monoculture , ΔpslAB biofilms could survive colistin concentrations at 4 µg/ml ( Figure 7C ) . In contrast , as part of a joint biofilm with Psl donors , ΔpslAB cells were able to survive colistin concentrations up to 32 µg/ml , which would normally kill them ( Figure 7C ) . How many ΔpslAB cells the biofilm was able to host without reducing the effective Psl-mediated protection depended on the intensity of the antibiotic attack . By scanning a range of antibiotic concentrations and counting the number of ΔpslAB cells that survived treatment , we found that at an antibiotic concentration of 8 µg/ml the biofilm contained 13% ΔpslAB cells , while at 32 µg/ml concentration this fraction dropped to 3% ( Figure 7C ) . Thus , ΔpslAB cells can benefit from interacting with PBAD-psl cells , even if at the expense of the performance of the Psl-donors . This implies that certain species that lack protective capacity may become more tolerant to therapy as part of mixed-species biofilms . Biofilms associated with infections are frequently co-populated by multiple species [57]–[60] . Hence , one important question is if Psl can affect the viability of species that coexist within Pseudomonas biofilms . Both gram-negative E . coli and gram-positive Staphylococcus aureus colonize wounds [61]–[64] and are hence good candidates to address this question . First , we tested if E . coli and S . aureus form mixed species biofilms when co-cultured with PBAD-psl and ΔpslAB , respectively ( Figure 8A , B , E , F ) . E . coli readily formed biofilms at the air-liquid interface ( Figure 8E ) as a monoculture and when co-cultured with P . aeruginosa . S . aureus formed biofilms at the bottom of a 96 well plate in the absence of P . aeruginosa . However , when co-cultured with P . aeruginosa , S . aureus was incorporated into the air-liquid interface biofilm . To determine if Psl could provide any advantage for E . coli , we quantified E . coli sensitivity to 32 µg/ml colistin in the presence and absence of Psl-producing cells . As a monospecies biofilm or when incorporated in a ΔpslAB biofilm , E . coli was eradicated by this concentration of colistin ( Figure 8C ) . However , when grown together with the PBAD-psl , E . coli viability was only mildly compromised by the same treatment ( Figure 8C ) , suggesting that E . coli can benefit from the protective effects of Pseudomonas-derived Psl . Supporting this result was the MBC-B assay , which shows that the presence of PBAD-psl enhanced tolerance of E . coli to 104 µg/ml of colistin ( Figure S8A ) . A similar conclusion might be drawn for S . aureus: the monoculture was eradicated with 1 µg/ml of tobramycin and substantially decreased with 0 . 5 µg/ml , but the cells survived even 1 µg/ml of tobramycin when co-cultured with Pseudomonas ( Figures 8D ) . When assessing viable CFUs ( Fig . 8D ) , the protection from the Psl overproducing PBAD-psl strain was only slightly higher compared to the protection from the ΔpslAB strain . However , the difference becomes clearer in the MBC-B data ( Figure S8B ) , which shows that S . aureus can tolerate a higher concentration of tobramycin in PBAD-psl biofilms than in ΔpslAB biofilms , or as monoculture ( Figure S8B ) . The extracellular matrix in biofilms has long been implicated as a barrier for protection [65] , but its exact contribution to resistance is not clear . One reason for this is that the bulk of methods to measure resistance are based on the exposure of cells to antibiotics over long time scales ( over-night to 48 hours ) [10] , [18] , [46] . This allows for many cell divisions to occur , giving the cells time to build adaptive mechanisms at the cellular or genetic level . However , these studies may mask any contribution from a physical barrier , which should be apparent at much shorter time scales: if the matrix acts as a true physical shield then matrix-embedded bacteria should show immediate tolerance on exposure to antibiotics . To focus on the physical barrier effects of the matrix we tested the short-term tolerance response of bacteria , and the contribution of the known matrix polysaccharides within . We found that Psl can provide instant defense and contributes to protecting cells from the action of a broad spectrum of antibiotics with diverse biochemical properties . Psl provides a measure of protection from cationic antimicrobial peptides ( colistin , polymyxin B ) , tobramycin , and to some extent ciprofloxacin . Importantly , this protection is observed in early stages of biofilm development but does not have a profound effect at later time points ( 48 , 72 hour biofilms ) . As the biofilm continues to develop into the characteristic mushroom shaped microcolonies [18] , [30] , [43] resulting in spatio-temporial changes in the matrix [43] , [51] , we conclude that different barrier properties arise from the biofilm structure and other polymers which may be redundant to , or dominate over , Psl function . Supporting our data on the protective effect of Psl is a recent report that shows that strains producing Psl are capable of growth and biofilm formation in the presence of the anti-biofilm agent Polysorbate 80 , a non-ionic surfactant [50] . Psl is found in two forms in the matrix , where large molecular weight oligosaccharide repeats localize around the cell surface [30] and smaller , soluble fractions are distributed throughout the matrix [29] . Based on the localization results of fluorescent polymyxin B , it is possible that the polymer attracts the small antibiotic molecules by direct interaction , as has been proposed for alginate [66] , [67] and ndvB-encoded periplasmic glucans [10] , [68] or reduces affinity of antibiotics to the cell surface . In support of an interaction mechanism , we also show that this attraction may be attributed to , in part , by electrostatic interactions between the antibiotics and the biofilm matrix since the addition of NaCl sensitizes cells with a Psl rich matrix to positively charged antibiotics . Further , the presence of Psl could contribute to indirect effects on antibiotic tolerance such as limiting the diffusion of oxygen or other nutrients , contributing to a more dormant cellular state . However , it is important to note that we did not detect a difference in growth rate for any of the strains . Nevertheless , deciphering the barrier mechanism of Psl may inspire solutions to some vexing treatment challenges in medicine at the initial stages of biofilm associated infections in burns and wounds , where early treatment for bacterial eradication is imperative . An external barrier as the sole defense mechanism is probably risky , as its capacity to sequester molecules is likely limited . However , such a fast-acting physical barrier may offer cells enough time to build up synergistic and longer-term defense systems . The presence of a physical barrier also implies that it is potentially accessible to more sensitive bacterial species that would otherwise succumb to antibiotic exposure . Our in vitro system highlights the possibility that interaction with a protective matrix can render a sensitive strain resistant . Importantly , we observed that Psl mediated protection is extendable to E . coli and S . aureus which also readily colonize burns and wounds . These results may explain why , in many cases , mixed species biofilms are more tolerant to therapy than their monoculture counterparts [69]–[72] . However , the opposite perspective , where co-habitation of a matrix deficient strain compromises the tolerance properties of the biofilm community as a whole , is also important . From a biochemical standpoint this implies that a certain polymer-to-cell ratio is optimal for protection , and that the polymers can become depleted by excessive amounts of non-producers . From a therapeutic outlook , the depletion of the protective polymers may be considered in future treatment strategies of initial onset infections .
The Pseudomonas aeruginosa strains used in this study are as follows: laboratory wild type PAO1 , laboratory wild type strain PA14 , PAO1ΔpslAB ( Psl deficient ) , and PAO1-PBADpsl ( over-producing Psl ) , PAO1ΔalgD , PAO1ΔpelA , and cystic fibrosis isolate CF127 . The mutant strains PAO1ΔpslAB , PAO1-PBADpsl , and PAO1ΔalgD were a generous gift of Daniel J . Wozniak . PAO1Δpel and cystic fibrosis isolate CF127 were a generous gift of Matthew R . Parsek . Other strains include E . coli EMG2 constitutively expressing GFP from pBBR1 ( MCS5 ) -Plac-gfp and Staphylococcus aureus UAMS-1 and were used for co-culture experiments . Details and references for all strains can be found in Text S1 . All of the P . aeruginosa strains and E . coli EMG2 were cultured in 1% Tryptone Broth ( TB ) . S . aureus was cultured in LB broth for both monoculture and co-culture experiments . Selective agar plates were used to evaluate CFU counts for P . aeruginosa ( Cetrimide Agar; Sigma-Aldrich 70887 ) and S . aureus ( Mannitol Salt Phenol Red Agar; Sigma-Aldrich 63567 ) co-culture biofilms . Arabinose was maintained culture medium of PAO1-PBADpsl and in all co-strain/species biofilm experiments at a final concentration of 2% unless otherwise noted . As a control , arabinose was added to the culture medium of WT PAO1 to confirm that arabinose did not influence biomass or antibiotic resistance for each . The strain PAO1ΔpslAB ( Psl deficient ) was transformed with pMP7605-mCherry [73] ( the plasmid construct pMP7605-mCherry was kindly provided by Ellen L . Lagendijk , Institute of Biology , Leiden University , The Netherlands ) via standard methods in bacterial conjugation [74] . For P . aeruginosa and E . coli strains cultures , an OD600 of 0 . 0025 represents a culture density of ∼5 . 0×105 and for S . aureus an OD600 of 0 . 0025 represents a culture density of ∼5 . 0×104 . Antibiotics from three classes that target P . aeruginosa were chosen for investigation ( Text S1 ) : polymyxins ( colistin sulfate salt Sigma-Aldrich #C4461; polymyxin B sulfate Sigma-Aldrich #P0972 ) , aminoglycosides ( Tobramycin Sigma-Aldrich #T4014 ) , fluoroquinolones ( Ciprofloxacin Sigma-Aldrich #17850 ) . They were chosen due to their clinical relevance , difference in net charge , and difference in mechanism of action . The total biofilm biomass for each of the P . aeruginosa strains used in this study was quantified with crystal violet staining as previously described [75] . Briefly , biofilms were grown in 96 well polystyrene microtiter plates in 1% TB medium at room temperature for 24 , 48 and 72 hours ( 150 µl of culture diluted to an OD600 0 . 0025 per well ) . For 48- and 72-hour biofilms , the medium was aspirated and replaced with fresh 1% TB each day ( supplemented with 2% arabinose ) . At the end of each time point , the medium was aspirated and the plates were washed twice with tap water to remove any planktonic cells . 175 µl of 0 . 1% crystal violet was added to each well and remained for 10 minutes at room temperature . After staining , the crystal violet solution was aspirated and the plates were washed twice with tap water to remove any residual stain . The plates were allowed to dry for at least 30 minutes , followed by solubilization of the stained biofilm with 175 µl of 33% acetic acid . The resulting absorbance was recorded at 550 nm . HHA-TRITC ( EY Labs ) was used at a final concentration of 200 µg/ml as previously described [54] . Biofilms were grown at the air-liquid interface on UV sterilized polystyrene surfaces for 24 hours . The biofilms were submerged in the lectin solution for 30 minutes and imaged with a Zeiss LSM 510 Meta Confocal using a 100×/1 . 4 NA oil immersion objective . The MIC was determined by a standard micro-dilution protocol with modifications . Cells grown to stationary phase were normalized to an OD600 0 . 5 and were exposed to 2-fold series of colistin dilutions in order to determine the minimal concentration of colistin that reduced cell viability within two hours . After the challenge , the planktonic cells were centrifuged at 6000 rpm and washed with PBS to remove residual antibiotic . The cultures for each dilution were plated on LB agar plates without antibiotics to determine the minimum concentration of colistin required to inhibit growth within the two hour time frame . This assay was performed as described previously [49] with modifications . Briefly , mid-exponential phase cultures were normalized to an OD600 0 . 0025 in 1%TB . 150 µl of diluted culture was added to each well of a polystyrene 96-well microtiter plate and incubated for 24 hours at room temperature . The medium in each well was aspirated to remove planktonic cells . The resulting biofilms were carefully washed with PBS ( pH 7 . 4 ) to remove any remaining unattached cells . Two-fold dilutions of antibiotics tested were prepared in appropriate solvents and 150 µl of the antibiotic dilutions were added to the biofilm plate ( 0–1 mg/ml for colistin , 0–1 mg/ml for polymyxin B , 0–10 mg/ml for tobramycin , and 0–1 mg/ml ciprofloxacin ) . After 2 hours , the antibiotic was removed and the biofilms were carefully rinsed with PBS . 150 µl of PBS was added to each well along with 150 µl of sterile glass beads ( Sigma-Aldrich #G8772; 425–600 µm ) . The plate was covered with sterile aluminum sealing film ( Sigma-Aldrich #Z722642 ) to prevent any cross-contamination between wells . The plate was then vortexed for 5 minutes to remove adherent cells from the polystyrene well . To quantify cell viability , 35 µl per well was plated on LB agar without antibiotics . The lowest antibiotic concentration that inhibited growth was considered to be the minimal bactericidal concentration for the biofilm ( MBC-B ) . For mixed culture MBC-B analysis , mid-exponential phase cultures were inoculated at different ratios , but the total cell number in solution remained constant when added to each well . To quantify the percentage of Psl deficient survivors after antibiotic challenge , ΔpslAB expressing fluorescent mCherry were quantified with phase contrast and fluorescence microscopy using a Zeiss Observer Z . 1 epifluorescent microscope with a 40×/0 . 75 NA dry objective . The percent survival of Psl deficient cells was calculated by determining the number of fluorescent cells relative to the total cell population . The antibiotic sensitivities of air-liquid interface biofilms on polystyrene 96 well microtiter plates were assessed at 24 , 48 , and 72 hours . The microtiter wells were inoculated with 150 µl of culture at an OD600 of 0 . 0025 . For 48- and 72-hour biofilms , the medium was aspirated and replaced with fresh 1%TB each day . For each time point , the medium was aspirated from the well and gently washed with PBS to removed non-adherent cells . Biofilms were exposed to 32 µg/ml colistin , 32 µg/ml polymyxin B , 650 µg/ml tobramycin , or 50 µg/ml ciprofloxacin for 2 hours . Cells were removed by the glass bead method described above for MBC-B assays . Viability was quantified by serial dilutions and CFU counts of the surviving population . To evaluate the contribution of electrostatic interactions between matrix components and antibiotics , the antibiotic sensitivity was determined for colistin , polymyxin B , tobramycin , and ciprofloxacin with the addition of 50 mM NaCl . 250 mM NaCl was also evaluated for tobramycin . The effect of NaCl on bacterial attachment was quantified by adding the appropriate concentration of NaCl to the challenge medium without antibiotic . Viability was quantified by serial dilutions and CFU counts of the surviving population . For determining cell viability of the P . aeruginosa mixed culture air-liquid interface biofilms with E . coli and S . aureus , cultures were inoculated at a 1∶1 ratio . An independent evaluation ( CFU counts ) of the biofilm population was conducted for each mixed species biofilm to quantify the composition of cells inhabiting the biofilm before antibiotic treatment . For P . aeruginosa and E . coli mixed biofilms , the ratio of colonies expressing GFP ( E . coli strain ) compared to non-fluorescent cells ( P . aeruginosa ) was determined after plating CFUs . For P . aeruginosa and S . aureus mixed biofilms , CFU counts for each species were assessed with selective media for each strain . E . coli expressing GFP and P . aeruginosa strains were inoculated at a 1∶1 ratio ( or as monocultures ) and grown at the air-liquid interface on UV sterilized polystyrene surfaces for 24 hours . Fluorescence and phase contrast images were acquired to determine the biofilm forming capabilities of E . coli at the air-liquid interface on a polystyrene surface both with and without P . aeruginosa . A similar procedure was performed for S . aureus . To determine the biofilm forming capabilities of S . aureus at the air-liquid interface on a polystyrene surface both with and without P . aeruginosa , S . aureus was stained with the gram-positive specific dye , hexidium iodide ( Molecular Probes ) . P . aeruginosa was identified with Syto 9 staining ( Molecular Probes ) . Fluorescently labeled Polymyxin B ( green-fluorescent BODIPY FL-Polymyxin B; Molecular Probes , Invitrogen ) was used at a final concentration of 5 µg/ml . Stationary phase cultures were challenged with 5 µg/ml of Bodipy-polymyxin B for 2 hours . An aliquot of each culture was immobilized on a 1% agarose covered glass slide . Air-liquid interface biofilms grown on UV sterilized polystyrene squares were treated with 5 µg/ml Bodipy-polymyxin B for 2 hours . All images for Bodipy-polymyxin B assays were acquired with a Zeiss LSM 510 Meta Confocal using a 100×/1 . 4 NA oil immersion objective . A PDMS ( Polydimethylsiloxane; Sylgard 184; Dow Corning , MI , USA ) microfluidic device was molded from a silicon master yielding a negative imprint of 10 straight microchannels , 100 µm deep/500 µm wide and then bonded to a glass slide . The device was placed on an inverted Nikon TE2000-E ( Nikon Instruments , Japan ) equipped with an Andor iXon-885 and a 40× long working distance objective for the duration of the experiment . A bacterial suspension ( OD600 0 . 0025 ) was introduced into the microchannels under continuous flow driven by a syringe pump ( PHD Ultra , Harvard Apparatus , MA , USA ) at a flow rate of 0 . 5 µl/min for 18 hours . The biofilms were stained with Bacterial Viability Kit , ( Molecular Probes , Invitrogen Inc . , Eugene , OR ) . Colistin , at a final concentration of 20 µg/ml , was introduced into each channel for 2 hours and one untreated channel served as a control . Phase contrast , green and red fluorescence images were recorded for the same field of view every 5 minutes . The cells absorbed propidium iodide after cell death resulting from colistin exposure . Propidium iodide resulted in fluorescence quenching of Syto 9 , the green fluorescent dye used to identify living cells . As cell death progressed over time , there was a decrease in green fluorescence due to a quenching effect and not a consequence of cell detachment . The coverage of dead cells in the biofilm was calculated in ImageJ [76] by adjusting the threshold of 8 bit binary images and measuring the area coverage . This data was expressed as a percentage of the total biofilm area ( phase-contrast images ) for each time point . A Zeiss 510 confocal laser-scanning microscope ( CLSM ) was used to acquire xyz optical section images before and after colistin treatment of biofilms within the microfluidic device to quantify the amount of biomass loss during treatment . | Many bacteria have the ability to form multicellular communities , termed biofilms . An important characteristic of a biofilm is the ability of cells to synthesize and secrete an extracellular matrix . This matrix offers structural support , community organization , and added protection , often making the cells impervious to desiccation , predation , and antimicrobials . In this study , we investigate the contributions of polysaccharide components found in the extracellular matrix of Pseudomonas aeruginosa at progressive stages in biofilm development . We first show that one specific polysaccharide , Psl , provides an added defense for P . aeruginosa biofilms against antimicrobials of different properties for young biofilms . Then , by cultivating biofilms that contain both Psl producing and Psl non-producing strains , we find that P . aeruginosa , E . coli , and S . aureus species that lack Psl take advantage of the protection offered by cells producing Psl . Collectively , the data indicate that Psl is likely to play a key protective role in early development of P . aeruginosa biofilm associated infections . | [
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The lack of awareness about dog-bite related rabies in the rural population of developing countries , including India , is a major impediment to controlling the incidence of disease in humans . A survey of 127 rural residents was undertaken in Shirsuphal village in western India using a structured questionnaire to assess the influence of demographic and pet/livestock owning characteristics on the knowledge , attitudes and practices of the respondents towards rabies and free roaming dogs ( FRD ) . Multivariable logistic regression models were constructed and the knowledge of the rural residents of Shirsuphal village was found to be significantly influenced by family size ( OR 2 . 1 , 95%CI 1 . 0–4 . 6 , p = 0 . 04 ) and poultry ownership ( OR 2 . 3 , 95%CI 1 . 1–4 . 9 , p = 0 . 03 ) , while their attitudes towards FRD was significantly influenced by age of the respondents ( OR 2 . 6 , 95% CI 1 . 2–5 . 8 ) and ownership of cattle/buffalo ( OR 2 . 2 , 95% CI 1 . 1–5 . 5 ) . Although the knowledge score about rabies was high , a comprehensive understanding of the disease was lacking . Concerted efforts to widen the knowledge about rabies and promote healthier practices towards FRD are recommended .
India has the world’s highest number of dog-bite related rabies deaths , most of whom are people of low socio-economic background from rural areas [1 , 2] . A gross lack of awareness about rabies in rural India is one of the factors that leads to high human mortality from the disease [3] . Although mortality can be prevented through prompt washing of bite wounds with soap and water [4 , 5] , along with timely administration of rabies immunoglobulins ( RIG ) and anti-rabies vaccines ( ARV ) [6 , 7] , these practices are potentially undermined by widespread traditional healing practices , such as application of chilli/turmeric powder to bite wounds [8 , 9] . Policy makers and the general population lack awareness about the impact of rabies [10 , 11] which results in insufficient vaccination coverage of dogs , poor knowledge of post-exposure prophylaxis ( PEP ) amongst medical professionals and unreliable supply of ARV and RIG [12] . Also , insufficient financial resources , poor health care infrastructure and inadequate reporting systems leads to an underestimation of the true public health impact of rabies in India [10 , 13] . Free-roaming dogs ( FRD ) , which are responsible for 96% of all human rabies deaths in India , are ubiquitous in both rural and urban localities/communities [2] . Management of the FRD population , along with responsible ownership of dogs , are key strategies to minimise human deaths from dog-bite related rabies [14] . Although studies in India have assessed the knowledge , attitudes and practices ( KAP ) of communities towards rabies [15–18] , studies on the community’s attitudes and understanding of FRD are lacking . Although India contributes 4 . 4% of the total global research output on rabies , there is a lack of studies focussing on the vector demography , risk factors , epidemiological studies and economic evaluations of the disease [19] . There is also a lack of awareness by the rural population about rabies control programmes [20] . Paucity of activities that can transfer knowledge about the disease to the rural population is a key concern for policy makers [21] and the importance of epidemiological studies to assess the awareness level and practices of people regarding aspects of rabies control is paramount in this context [12 , 20 , 22] . While there have been a number of hospital based studies that have assessed KAP about rabies involving dog-bite victims , community based studies are virtually lacking in India [23] . In view of this deficiency , a cross-sectional community study was designed in rural Baramati , western India , in the village of Shirsuphal to assess the: ( 1 ) KAP of the rural community towards rabies; ( 2 ) KAP of the rural community towards FRD population management; and ( 3 ) KAP of rural dog owners on responsible ownership of dogs .
Recent surveys conducted in rural areas in India near to the present location formed the basis for calculating the sample size for this study . A weighted measure ( 93 . 6% ) of respondents having heard of rabies from four community based cross-sectional studies carried out in neighbouring states was used to calculate the target sample size with 95% confidence and 5% error rate [5 , 16 , 18 , 24] . With 1161 households in Shirsuphal , the required sample size was estimated to be 86 . However , we had sufficient resources to administer the questionnaire to 132 respondents , of which five failed to complete the survey . Consequently the responses of 127 participants were included in the survey analysis , thus achieving a confidence level of 98% at 2% error . A cross-sectional household survey was undertaken in the Shirsuphal village of Baramati Town of Pune District in Maharashtra state in western India from 13th– 21st June 2016 . The village has a population of 5512 in 1161 households ( www . censusindia . gov . in , accessed on 08 October 2015 ) . The majority of the villagers are farmers , although there are some professionals and small business owners . Some farmers have also taken up poultry farming in recent years . No rabies awareness campaign or dog population control measures had been conducted in the area prior to this survey . The houses are divided into four clusters of a similar population size in the village , however they are not numbered . Although the total number of households in each cluster was known , there was limited information available regarding the number of households in each lane , consequently a door-to-door survey method was followed using a rolling sample method where the first randomly selected household provided information about the next available household within the cluster [25 , 26] . To avoid potential bias being introduced by the respondents nominating relatives or friends , they were requested to nominate a household in a different direction to that of their friends and relatives within the cluster . A total of 33 households from each cluster were included for the questionnaire survey . The household head was approached to complete the questionnaire and if he/she were not available or not willing then a household member who was older than 18 years of age was invited to complete the questionnaire . A document outlining informed consent was read out to them in the local language ( Marathi ) and verbal consent obtained before administering the questionnaire . In the event of the household declining to participate in the survey then the adjacent house was selected for inclusion in the study . The KAP survey was designed to: identify gaps in awareness about rabies; assess the practices that potentially contributed to the persistence of the disease in the village; assess the attitudes of the community towards FRD; and assess the attitudes of dog owners towards their pets . The questionnaire consisted of closed questions on: ( 1 ) household information to assess the socio-economic status and resident profile ( age , education , occupation , religion , family size , number of children below 14 years of age , and pet and livestock ownership ) ; ( 2 ) knowledge , attitudes and practices regarding rabies ( a total of 16 questions—11 pertaining to knowledge and five pertaining to attitudes and practices towards rabies , respectively ) ; ( 3 ) attitudes and practices towards FRD ( seven questions ) ; and ( 4 ) pet care practices adopted by dog-owners ( 15 questions asked only to respondents who owned pet dogs ) . The questions were read out to the respondents in their local language ( Marathi ) by the interviewer and their answers were recorded in English ( Appendix ) . Answers to the questions were tabulated in a spreadsheet ( Microsoft Excel , Microsoft Corp . , Redmond , WA , USA ) . “Not sure” responses were combined with the “No” option and “NA ( not applicable ) ” responses were removed from the study prior to subsequent statistical analysis using the R Programming Environment [27] . A matrix was developed to categorise the respondents into high , medium and low socio-economic status on the basis of their educational qualification and occupation on a design based on www . praja . org ( accessed 18 March 2016 ) . Subsequently , the high and medium categories were merged to obtain a binomial distribution of respondents into two socio-economic divisions: low and medium/high . The age of the respondents and the family size of the households was dichotomised into two age groups based on the median age/family size ( S1 File ) .
The demographic and household characteristics of the respondents are presented in Table 1 . The age groups and the family-size was dichotomised at the median age , i . e . 35 years ( ≤34 years and ≥35 years of age ) and the median family size , i . e . 6 members ( <6 and ≥6 ) , respectively . The responses of the participants to the questions pertaining to attitudes towards FRD are presented in Table 5 . The younger respondents ( ≤34 years ) did not consider FRD a threat to human health ( OR 0 . 2 , 95% CI 0 . 04–0 . 97 , p = 0 . 05 ) , and were more likely to feed them ( OR 2 . 2 , 95%CI 1 . 1–4 . 5 , p = 0 . 04 ) than older participants . Participants from the high/middle socio-economic level considered FRD were useful ( OR 3 . 09 , 95%CI 1 . 06–8 . 97 , p = 0 . 03 ) , were likely to feed them ( OR2 . 81 , 95%CI 1 . 34–5 . 88 , p = 0 . 005 ) , and would take an injured stray dog to a veterinarian ( OR 2 . 33 95%CI 1 . 0–5 . 48 , p = 0 . 04 ) . The respondents from the low socio-economic level believed that the responsibility of the health and vaccination of FRD was with the households that fed/sheltered them ( OR 2 . 3 , 95%CI 1 . 04–5 . 1 , p = 0 . 03 ) , a perception similar to dog owners ( OR 2 . 9 , 95%CI 1 . 3–6 . 6 , p = 0 . 04 ) . A significant number of poultry owners reported that FRD attacked their backyard poultry for food ( OR 3 . 2 , 95%CI 1 . 07–12 . 1 , p = 0 . 02 ) . The association between the various descriptive variables and the attitudes and practices of the participants towards FRD is presented in Table 6 . The respondent’s age and ownership of cattle/buffalo ( OR 2 . 6 , 95%CI 1 . 2–5 . 8; OR 2 . 2 , 95%CI 1 . 1–5 . 5 , respectively ) had a positive influence on their attitudes towards FRD in the final multivariable logistic regression model ( Table 7 ) . The model was a good fit of the data with a Likelihood ratio ( χ2 ) test value of 10 . 33 ( p = 0 . 006 ) and a Hosmer—Lemeshow goodness of fit test result of 0 . 008 ( p = 0 . 927 ) . The characteristics of the dog owners in this study are presented in Table 8 . Dog-owners who had a negative perception of FRD were less likely to seek veterinary attention ( OR 0 . 3 , 95%CI 0 . 1–1 , p = 0 . 047 ) for their pets . Dog owners who adopted their pets “off the street” were less likely to get their dogs vaccinated than those who either purchased them or were given them ( OR 0 . 08 , 95%CI 0 . 01–0 . 4 , p = 0 . 001 ) . Dog owners who had an adequate knowledge about rabies ( 76 , 59 . 8% ) or possessed a perception that controlling FRD would help control rabies ( 85 , 66 . 9% ) were not significantly different from those who didn’t own dogs ( p = 0 . 29 , p = 0 . 75 ) .
This study involved survey of rural residents of Shirsuphal village in Baramati town of Pune district , Maharashtra , India and the ethics approval was obtained from the Murdoch University Human Ethics Committee ( permission number: 20/2016 ) . Oral consent was obtained from all respondents prior to their participation ( Appendix ) . | The rural population in developing countries , such as India , are most vulnerable to transmission of rabies , especially due to animal-bites . The widespread trust in traditional healing practices for treating animal-bite injuries undermines the importance of seeking post-bite vaccination against rabies . In spite of a wider acknowledgement of the role of free-roaming dogs in the transmission of rabies virus in rural human populations , the latter’s attitudes towards this vector host remains influenced by the social , cultural and religious beliefs prevalent in the area . This study explores the awareness level and perception of villagers not only towards rabies but also the free-roaming dogs in the locality . Although the rural participants surveyed in this study were aware of the disease , gaps were revealed in the comprehensive knowledge about rabies and its transmission . The villagers were also found lacking in adequate practices towards free-roaming dogs that can remarkably reduce the incidence of the disease . Concerted efforts to widen the knowledge about rabies and promote healthier practices towards free-roaming dogs is recommended . | [
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"healt... | 2019 | Knowledge, attitudes and practices (KAP) towards rabies and free-roaming dogs (FRD) in Shirsuphal village in western India: A community based cross-sectional study |
Eukaryotes , protozoan , and helminth parasites make extensive use of protein kinases to control cellular functions , suggesting that protein kinases may represent novel targets for the development of anti-parasitic drugs . Because of their central role in intracellular signaling pathways , cyclic nucleotide–dependent kinases such as cAMP-dependent protein kinase ( PKA ) represent promising new targets for the treatment of parasitic infections and neoplastic disorders . However , the role of these kinases in schistosome biology has not been characterized and the genes encoding schistosome PKAs have not been identified . Here we provide biochemical evidence for the presence of a PKA signaling pathway in adult Schistosoma mansoni and show that PKA activity is required for parasite viability in vitro . We also provide the first full description of a gene that encodes a PKA catalytic subunit in S . mansoni , named SmPKA-C . Finally we demonstrate , through RNA interference , that SmPKA-C contributes to the PKA activity we detected biochemically and that inhibition of SmPKA-C expression in adult schistosomes results in parasite death . Together our data show that SmPKA-C is a critically important gene product and may represent an attractive therapeutic target for the treatment and control of schistosomiasis .
Schistosomiasis , a disease caused by trematodes of the genus Schistosoma , afflicts approximately 200 million people in tropical and subtropical regions of the world and is responsible for approximately 280 , 000 deaths annually in Sub-Saharan Africa alone [1] . The schistosome life cycle is remarkably complex , involving multiple life cycle stages that are morphologically and physiologically adapted for survival within and transmission between the molluscan and vertebrate hosts these parasites require for life cycle completion . Within each host , evasion of host defenses is balanced with requirements for host resources and signals that are necessary for schistosome growth and development . While specific examples have not been characterized at the molecular level , there is considerable evidence for interactions between schistosomes and host factors , such as hormones and growth factors , which influence aspects of parasite biology such as development and reproduction [2]–[5] . This intimate relationship , where schistosomes exploit host factors to facilitate establishment of infection while simultaneously evading host defenses , is presumably a reflection of extensive host-parasite co-evolution that has occurred since the emergence of the genus Schistosoma some 12–19 million years ago [6] . Currently the anthelminthic praziquantel is the sole drug used for treatment of schistosomiasis , due to its ability to kill the adult worms of all medically important Schistosoma species [7] . However , there are reasons to suspect that reliance on this single drug for all treatment and control of schistosomiasis will not be sustainable in the long term . First , praziquantel-tolerant strains of S . mansoni can be derived in the laboratory by exposure to sub-curative doses of praziquantel [8] , [9] . Second , evidence for decreased sensitivity to praziquantel has been found following mass drug treatment efforts [10] . Thus the potential for praziquantel resistance is real and the increasingly wide scale use of praziquantel , through programs such as the Schistosomiasis Control Initiative , highlight the necessity for the identification of new chemotherapeutic targets in schistosomes [11] . Protein kinases represent a potentially new class of therapeutic targets for the treatment of parasitic diseases [12] . Through the phosphorylation of substrate proteins , protein kinases play a central role in the cellular signaling pathways of eukaryotic organisms and are involved in biological processes as diverse as gene expression , metabolism , apoptosis , and cellular proliferation [13] . The unregulated activity of protein kinases has been implicated in the pathogenesis of several human diseases , including cancer , autoimmune diseases and inflammation [14] , [15] . Consequently , the development of protein kinase inhibitors as therapeutics for cancer and other diseases has been actively pursued [16] . As eukaryotes , protozoan and helminth parasites presumably also make extensive use of protein kinases to control cellular functions , suggesting that protein kinases may represent novel targets for the development of anti-parasitic drugs [17] , [18] . Examples of promising protein kinase targets in parasites include the cyclic guanosine monophosphate- ( cGMP- ) dependent protein kinases ( PKGs ) of Toxoplasma [19] , Eimeria [20] and Plasmodium [21] , and a Plasmodium cyclic adenosine monophosphate- ( cAMP- ) dependent protein kinase ( PKA ) [22] , as inhibition of these kinases resulted in significant anti-parasitic effects in vivo or in vitro [20] , [23] . Regulated by cyclic nucleotide second messengers produced by purine nucleotide cyclases , cyclic nucleotide-dependent protein kinases represent particularly attractive drug targets as , in addition to targeting the kinase domain directly , their activity can also be manipulated by targeting the regulatory cyclic nucleotide binding ( CNB ) domains with cyclic nucleotide analogs [24] . Indeed , an experimental therapy for some cancers in which PKA is implicated utilizes this latter approach and is now in clinical trials [25] . A consistent difference between PKA and PKG across highly divergent taxa is that in PKA , the regulatory and catalytic activities are contained within separate gene products known as PKA-R and PKA-C respectively , whereas in PKG the CNB sites and catalytic domain are usually contained within the same polypeptide . Thus the inactive conformation of PKA is a heterotetramer of two PKA-R and two PKA-C subunits , while PKG exists as a homodimer . Segregation of the catalytic and regulatory functions of PKA into separate proteins provides an opportunity for diversification in the function of PKA , as different PKA-C and PKA-R isoforms can combine to produce holoenzymes with different functions [26] . Mammalian genomes contain as many as three pka-c genes and four pka-r genes , allowing for a variety of different holoenzymes to be formed [27] . While cyclic nucleotide-dependent kinases have been extensively characterized in a variety of eukaryotic organisms , including several parasites , there is relatively little data available on the role of these kinases in the biology of schistosomes . A study by Matsuyama et al . showed that treatment of schistosome miracidia with adenylyl cyclase and PKA inhibitors completely inhibited miracidial locomotion in a dose-dependent manner , suggesting a role for PKA in miracidial swimming [28] . In contrast , Kawamoto et al . found that treatment of miracidia with adenylyl cyclase agonists inhibited miracidium to mother sporocyst transformation , while drugs that decreased cAMP levels triggered transformation [29] . These studies suggest that cAMP and PKA play important roles in the larval stages of the schistosome life cycle . However , no studies have examined the role of PKA in adult schistosome biology and full-length nucleotide sequences encoding schistosome PKAs have not been identified . We hypothesized that PKA plays a vital role in adult worms and that targeting PKA may represent a novel approach to eliminating adult schistosomes from infected mammalian hosts . In this report , we provide a biochemical characterization and molecular identification of a S . mansoni PKA ( SmPKA ) . Furthermore , we show that the schistosome PKA is an essential gene product for adult worms and as such represents an attractive therapeutic target for the treatment and control of schistosomiasis .
All experiments involving mice were performed in accordance with protocols approved by the USUHS Institutional Animal Care and Use Committee . Biomphalaria glabrata snails infected with NMRI/Puerto Rican strain of S . mansoni were supplied by Dr . Fred Lewis ( Biomedical Research Institute , Rockville , MD ) . Cercariae were obtained by exposing infected snails to light for 2 h in 50 mL of filtered water . Schistosomula were prepared by mechanical transformation of cercariae according to published protocols [30] . Adult S . mansoni were obtained from 6 week-infected C57BL/6 mice that were infected with 150 cercariae using the tail immersion method [30] . Freshly isolated adult worms were homogenized in cell extraction buffer ( 100 mM NaCl , 25 mM Tris pH 7 . 5 ) containing a protease inhibitor cocktail ( Sigma; 4- ( 2-aminoethyl ) benzenesulfonyl fluoride hydrochloride ( AEBSF; 1 mM ) , aprotinin ( 0 . 8 µM ) , leupeptin ( 20 µM ) , bestatin ( 40 µM ) , pepstatin A ( 15 µM ) , and E-64 ( 14 µM ) ) . The resulting homogenate was incubated on ice for 30 min and centrifuged at 13 , 000 rpm for 20 min at 4°C to remove insoluble material . The protein concentration of the resulting supernatant ( Sm lysate ) was determined using the Quick Start Bradford Protein Assay . Western blots were performed using the WesternBreeze Chemiluminescent Western Blot Immunodetection Kit ( Invitrogen ) . Briefly , 7 µg of total protein from adult worms , HT1080 , and 293FT cells were used per sample . Reduced samples were separated by SDS-PAGE on 12% Bis-Tris gels and transferred onto polyvinyl difluoride ( PVDF ) membranes . After an initial blocking step in 5% non-fat dried milk , 20 mM Tris pH 7 . 5 , membranes were incubated for 12 h with polyclonal anti-PKA C-α antibody ( Cell Signaling Technology ) diluted 1∶1000 and then with alkaline phosphatase-conjugated goat anti-rabbit secondary antibody ( Invitrogen ) for 2 h . Bound antibody was detected according to manufacturer's instructions using Kodak BioMax Light Film . PKA activity was measured using a fluorescent peptide substrate-based assay ( Omnia Lysate Assay for PKA kit , Biosource ) . Freshly isolated adult worms were used to prepare Sm lysate , in the presence of protease and phosphatase inhibitor cocktails ( Phosphatase Inhibitor Cocktail 1 ( Sigma ) , containing cantharidin , bromotetramisole and microcystin LR , diluted 1∶100 ) as described above . The protein concentration was determined using the Quick Start Bradford Protein Assay and adjusted to a final concentration of 0 . 2 µg/μL with additional extraction buffer . Kinase reactions containing 1 µg total protein ( equivalent to 5 µL Sm lysate ) , 10 mM ATP , 15 µM PKA peptide substrate , 10 mM DTT and a non-PKA inhibitor cocktail ( 64 µM PKC inhibitor peptide , 10 µM GF109203X , 20 µM calmidazolium ) in kinase reaction buffer were assembled in opaque 96-well assay plates , according to the manufacturer's recommendations . Accumulation of phosphorylated substrate was monitored during a 1 h incubation at 30°C by recording fluorescent emissions at a wavelength of 485 nm upon excitation at 360 nm , using a Spectramax M2 microplate fluorometer ( Molecular Devices ) . Fluorescence measurements were recorded every 30 s in relative fluorescence units ( RFUs ) . Kinase reactions containing 2 ng recombinant human PKA-Cα catalytic subunit ( Invitrogen ) were used as positive controls . PKA activity was plotted using GraphPad Prism software version 4 ( Graphpad Software , Inc . ) . Symbols at each time point represent the means of three biological replicates and experiments were performed at least twice . H-89 and protein kinase A inhibitor fragment 14–22 ( PKI 14–22 amide ) were purchased from Invitrogen . H-89 was dissolved in dimethyl sulfoxide ( DMSO ) and PKI 14–22 amide was dissolved in water . Kinase reactions were performed as described above in the presence of H-89 and PKI 14–22 amide or appropriate vehicle control at the following concentrations: 500 , 100 , and 10 µM . The maximum concentration of DMSO in any reaction was less than 5% and no differences in kinase activity were observed between controls treated with water or DMSO . PKA activity in presence and absence of inhibitor was determined as described in the previous section . Forskolin and SQ22536 were purchased from Sigma and stock solutions prepared in DMSO . To examine the effects of forskolin and SQ22536 on PKA activity , triplicate groups of freshly isolated adult worm pairs ( 10 pairs per group ) were incubated for 2 h at 37°C in 24 well tissue culture plates containing Dulbecco's modified Eagle's medium ( DMEM ) in the presence of 100 or 50 µM of forskolin , SQ22536 or DMSO alone . Then Sm lysate was prepared from the treated worms and PKA activity was measured as described above . Effects of SmPKA inhibition in adult worms were assessed using H-89 and PKI 14–22 at the following concentrations: 500 , 250 , 100 , 50 , 25 , 10 , and 1 µM . Individual adult worm pairs ( 6 pairs per concentration ) were placed in the wells of 24 well tissue culture plates containing 1 mL total of DMEM ( with 10% fetal bovine serum and 5% penicillin/streptomycin ) and appropriate concentration of inhibitor . Equal amounts of appropriate vehicle alone were added to the wells containing control worms . Medium containing inhibitor or vehicle was replaced daily . Worms were incubated at 37°C in 5% CO2 and observed every 24 h for a period of 7 d . Worms were considered to be dead when all evidence of motility , including gut peristalsis , had ceased . Kaplan-Meier survival curves were generated using GraphPad Prism software . Photomicrographs were obtained using a Zeiss CL1500ECO dissecting microscope . Total RNA was extracted from adult worms using the RNAzol B Method ( IsoTex Diagnostics , Inc . ) . 1 µg RNA was used to synthesize cDNA using the iScript Select cDNA Synthesis Kit and an oligo ( dT ) 20 primer ( Bio-Rad ) . The full-length cDNA sequence of SmPKA-C was obtained using the RNA ligase-mediated rapid amplification of 5′ and 3′ cDNA ends ( RACE ) kit ( Invitrogen ) and internal gene specific primers designed from the S . mansoni EST Sm11052 . RACE products of interest were purified using the QIAquick Gel Extraction Kit ( Qiagen ) , cloned into the pCR2 . 1-TOPO vector ( Invitrogen ) and sequenced using the Big-Dye Terminator cycle sequencing kit ( Applied Biosystems ) . The SmPKA-C cDNA sequence was compared to genomic sequences available at the National Center for Biotechnology Information ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) and the S . mansoni Genome Project website ( http://www . genedb . org/genedb/smansoni/blast . jsp ) blast servers . Vector NTI software ( Invitrogen ) was used to align the nucleotide and amino acid sequences of SmPKA-C with PKA-C subunit sequences from other eukaryotic organisms . Phylip ( Phylogeny Inference Package ) software [31] was used to construct the phylogenetic tree using the protein parsimony method ( http://mobyle . pasteur . fr/cgi-bin/portal . py ? form=protpars ) . Total RNA was extracted and cDNA was synthesized from male and female adult worms , schistosomula , and cercariae as described above . Egg , miracidium , and sporocyst cDNA were kindly provided by Dr . Conor Caffrey ( Sandler Center for Basic Research in Parasitic Diseases , San Francisco , CA ) . To detect SmPKA-C expression , the following primers were used to amplify a 900 bp fragment of the SmPKA-C cDNA ( nucleotide positions 50–952 ) : forward 5′–GGTAATGCACAAGCTGCTAAA–3 and reverse 5′-CCAATCGGTTGTTGCAAACC-3′ . The S . mansoni alpha tubulin cDNA ( GenBank Accession No . S79195 ) was used as a positive control and a 100 bp fragment ( nucleotide positions 1711–1824 ) was amplified by PCR using the following primers: forward 5′-GGTTGACAACGAGGCCATTTATG-3′ and reverse 5′-TGTGTAGGTTGGACGCTCTATATCT-3′ . Amplicons were visualized by agarose gel electrophoresis . A 900 bp SmPKA-C cDNA fragment was generated by PCR using the primers and cycling parameters described above and the resulting amplicon was cloned into pCRII-TOPO vector ( Invitrogen ) . Plasmid DNA was linearized using SacI or XhoI and used as template to transcribe ssRNA using T7 and Sp6 RNA polymerases and the MEGAscript RNA transcription kit ( Ambion ) [32] . SmPKA-C dsRNA was generated and purified using the MEGAscript RNAi kit ( Ambion ) according to the manufacturer's instructions . A non-schistosome control dsRNA was generated from the pCR-II TOPO vector as described above . Integrity of the final dsRNA products was assessed by agarose gel electrophoresis . 10 or 30 µg of SmPKA-C or control dsRNA , diluted in 100 µl of electroporation buffer ( Ambion ) , were delivered to groups of 12 mixed-sex adult worms via electroporation as described previously [33] . Adult worms , placed in 4 mm cuvettes , were pulsed at room temperature with a single 20 ms square wave pulse at 125 V using the GenePulser Xcell Electroporation system ( Bio-Rad ) . Adult worms were immediately transferred to pre-warmed DMEM ( 10% FBS and 5% penicillin/streptomycin ) and maintained at 37°C . To assess the efficacy of SmPKA-C RNA knockdown , RNA was extracted after 3 d and transcript levels assessed by PCR as described above , except that primers which hybridize outside the targeted region were used ( forward 5′-CGCGTAATATCACTTGAGAGTCAAAATAG-3′ and reverse 5′-AAATTCACTAAATTCTTTTGCACATTTCTCTGTTGTAGCAATACG-3′ , to amplify a fragment corresponding to nucleotide positions 18–1096 ) , and the accumulation of PCR product was monitored in real time by detection of SYBR Green fluorescence , using a M . J . Research Chromo4 PCR cycler ( Bio-Rad ) . Relative SmPKA-C RNA levels were calculated using the 2−ΔΔCt method [34] and S . mansoni alpha tubulin RNA as the control transcript . Data are representative of two independent experiments . The statistical significance of differences between treated and control groups in activity assays was calculated using one-way ANOVA of repeated measures . The statistical significance of differences between Kaplan-Meier survival curves was calculated using the logrank test . P values≤0 . 05 were considered statistically significant . Student's T test with Welch's correction was used to test the significance of differences in expression levels detected by real-time PCR . GraphPad Prism software was used for all statistical analyses .
To detect putative PKA-C subunit homologues in adult S . mansoni protein lysate ( Sm lysate ) , western blot analysis using a polyclonal antibody generated against a conserved epitope of the C-terminus of human PKA-Cα subunit was conducted ( GenBank Accession No . P17612 ) . A band of 40 kDa was detected in Sm lysate , which was similar in molecular weight to the human PKA-Cα detected in the two human cell lines ( Fig . 1A ) . We next sought to determine if protein extracts from adult S . mansoni had measurable PKA activity . Using a fluorescent peptide substrate-based assay , a putative PKA activity was detected in adult Sm lysate , as determined by the accumulation of a fluorescent , phosphorylated peptide reaction product ( Fig . 1B ) . While reaction product accumulated more slowly in the Sm lysate reaction than in a positive control reaction containing recombinant human PKA-Cα ( hereafter referred to as control PKA ) , similar total amounts of product accumulated in both reactions by the end of the assay . While the peptide substrate used in the kinase activity contains a specific PKA target sequence , other protein kinases such as protein kinase C ( PKC ) and calmodulin-dependent protein kinases ( CDPKs ) share similar substrate specificity to PKA [35] . For this reason , a kinase inhibitor cocktail that inhibits PKC and CDPK was included in all assays to eliminate non-PKA mediated phosphorylation . To further confirm that the kinase activity detected in Fig . 1B was attributable to a PKA enzyme , adult Sm lysate was treated with inhibitors that target PKA-C subunits , H-89 and PKI 14–22 amide , and the resulting activity was compared to PKA activity in untreated control lysate . The PKA activity of adult Sm lysate and control PKA was completely inhibited by 10 µM H-89 ( P<0 . 0001 ) ( Figs . 1C and 1D ) , an ATP-competitive inhibitor that is a potent inhibitor of PKA both in vitro and in vivo [36] . Identical results were obtained with 100 and 500 µM H-89 ( data not shown ) . Similar to treatment with H-89 , 10 µM PKI 14–22 amide also significantly inhibited the PKA activity of Sm lysate and the control PKA ( P<0 . 0001 ) ( Figs . 1E and 1F ) . PKI 14–22 amide is a highly specific inhibitor of PKA as it contains a pseudosubstrate site which facilitates high affinity binding to the substrate binding site of PKA-C subunits , preventing docking of the substrate [37] , [38] . Since cAMP binding to PKA-R subunits is required for PKA-C release and activation , we hypothesized that if the kinase activity we detected in Sm lysate was attributable to a PKA , its activity would be sensitive to alterations in the availability of cAMP for R subunit binding . To test this hypothesis , we tested whether the schistosome PKA activity was sensitive to manipulation of endogenous adenylyl cyclase activity [39] . The adenylyl cyclase inhibitor , SQ22536 [40] , and adenylyl cyclase agonist , forskolin [41] , were each used to either inhibit or activate endogenous adenylyl cyclase activity , respectively . In order to maintain the integrity of intracellular signaling pathways in these experiments , intact adult worms were treated with inhibitor or agonist rather than Sm lysate , as cellular structure is lost during preparation of the parasite lysate . 100 µM SQ22536 significantly decreased PKA activity in treated adult S . mansoni worms when compared to the untreated controls ( P<0 . 0001 ) ( Fig . 2A ) , presumably by inhibiting cAMP production and preventing the disassociation of PKA-C subunits from the holoenzyme . In contrast to SQ22536 , 100 µM forskolin significantly increased PKA activity in treated adult worms as compared to the untreated controls ( P<0 . 0001 ) ( Fig . 2C ) . Similar activation and inhibition were seen with 50 µM of forskolin and SQ22536 , respectively ( data not shown ) . As expected , the activity of the recombinant control PKA preparation was not affected by SQ22536 or forskolin , as this preparation does not contain adenylyl cyclase or PKA-R subunits ( Figs . 2B and 2D ) . Taken together , these data further support the conclusion that the kinase activity we detected in adult S . mansoni extracts is attributable to a PKA enzyme , as this activity can be inhibited and activated using inhibitors and agonists of adenylyl cyclase . Furthermore , our data suggest that S . mansoni possesses a functional cAMP signaling pathway , containing adenylyl cyclase and both regulatory and catalytic PKA subunits . To test whether the schistosome PKA activity plays a significant role in parasite biology , we next analyzed the effect of the inhibitors H-89 and PKI 14–22 amide on adult worms in vitro . Treating worms with H-89 at concentrations of 50–500 µM resulted in 100% mortality within 24 h ( Fig . 3A ) . Incubation of worms with H-89 at 25 µM resulted in 75% mortality by Day 3 and 100% by Day 4 ( Fig . 3A ) . Treatment with H-89 at 10 µM resulted in 100% mortality by Day 5 ( Fig . 3A ) . Prior to parasite death , exposure to H-89 caused a cessation in egg production and resulted in dissociation of males and females ( Fig . 3B ) , effects that were evident at H-89 concentrations as low as 1 µM concentration ( data not shown ) , despite the lack of a killing effect ( Fig . 3A ) . Similarly , incubation with 500 and 250 µM of PKI 14–22 amide resulted in 100% worm mortality within 24 h of exposure ( Fig . 3C ) , while at 100 µM some worms survived until Day 3 ( Fig . 3C ) . Incubating worms with 1–50 µM PKI 14–22 amide resulted in 100% survival ( Fig . 3C ) . As with H-89 , PKI 14–22 amide caused cessation of egg production and unpairing prior to parasite death ( Fig . 3D ) . These data show that loss of PKA activity by inhibition of PKA-C subunits is lethal for adult S . mansoni in vitro and suggest that the schistosome PKA is essential for maintaining parasite viability . To identify cDNA sequences that might encode for PKA-C subunits in S . mansoni , BLASTX searches of the S . mansoni genome database ( http://www . genedb . org/genedb/smansoni/blast . jsp ) were performed using PKA-C protein sequences from other organisms . A 622 bp EST ( Sm11052 ) with significant similarity to other PKA-C subunits was identified . Sm11052 contained an incomplete open reading frame ( ORF ) that encoded for the N-terminal portion of a protein kinase domain , as determined by the presence of a complete ATP-binding site ( corresponding to the consensus motif Gly–x–Gly–x–x - Gly–x–Val ) and a serine/threonine kinase active site containing the motif Arg - Asp–Asp–Leu–Lys–x–x–Asn [13] . The complete sequence of the cDNA was obtained by 5′ and 3′ RACE using gene-specific primers that annealed within the Sm11052 sequence and the entire cDNA was then amplified from adult S . mansoni cDNA . The full-length cDNA is 1899 bp long and contains a complete ORF of 1053 bp , encoding for a protein of 350 amino acids in length and with a predicted molecular mass of 40 . 4 kDa ( GenBank Accession No . GQ168377 ) . The ORF encoded for a putative protein kinase , with intact N and C-termini and a C-terminal kinase domain that contained all 12 conserved subdomains characteristic of protein kinase domains [13] . BLAST comparison of the amino acid sequence with the non-redundant protein sequence database at NCBI showed that the putative S . mansoni PKA-C ( SmPKA-C ) protein shared 70% similarity with PKA-C subunits from other eukaryotic organisms ( Caenorhabditis elegans , Drosophila melanogaster , Mus musculus , and Homo sapiens ) ( Fig . 4A ) and was most similar to the PKA-C subunit from Aplysia californica ( Fig . 4B ) . The estimated molecular mass of SmPKA-C protein was also similar to that of other PKA-C proteins and approximately matched the apparent mass of the band detected in S . mansoni extracts by western blot using anti-human PKA-C antibodies in Fig . 1A . Qualitative analysis of SmPKA-C expression in various life cycle stages by reverse transcriptase PCR revealed that SmPKA-C transcript was detectable in all S . mansoni life cycle stages tested ( egg , miracidium , sporocyst , cercaria , schistosomulum , adult male and female; Fig . 4C ) . The ORF of SmPKA-C was then compared using BLAST analysis to the S . mansoni genome database to identify other putative PKA-C subunit sequences encoded by the S . mansoni genome . One sequence , Smp_152330 , was identified that was 95% identical to the SmPKA-C nucleotide sequence . PCR analysis showed that the 3′end of the predicted database sequence was incorrect and , using 3′ SmPKA-C gene-specific primers , we were able to amplify the correct cDNA sequence of Smp_152330 from adult S . mansoni cDNA . Translation of the Smp_152330 nucleotide sequence showed it contained 18 more amino acids at the N-terminus than the SmPKA-C protein , but the remainder of both the nucleotide and amino acid sequences were identical ( Fig . 4A and data not shown ) , suggesting that the corrected Smp_152330 sequence represents an alternatively spliced form of SmPKA-C . To determine the effect of silencing SmPKA-C expression in S . mansoni , adult worms were treated via electroporation with 30 µg of SmPKA-C dsRNA or control dsRNA . We observed 75% mortality of adult worms treated with 30 µg of SmPKA-C dsRNA by Day 3 , while all control dsRNA-treated parasites survived ( Fig . 5A ) . To reduce parasite mortality and provide more surviving worms for subsequent analysis , the amounts of dsRNA were reduced in subsequent experiments . Treatment with 10 µg of SmPKA-C dsRNA resulted in 92% survival of worms , while treatment with 10 µg of control dsRNA again resulted in 100% survival to Day 3 ( data not shown ) . Real-time PCR analysis of SmPKA-C transcript levels in surviving worms on day 3 post-electroporation revealed a significant reduction of SmPKA-C mRNA in SmPKA-C dsRNA-treated worms to approximately 1% of the levels detected in control dsRNA-treated worms ( P = 0 . 0034 ) ( Fig . 5B ) . To determine whether SmPKA-C contributes to the PKA activity detected in Sm lysate , PKA activity was measured in lysates prepared from worms that were treated with 10 µg SmPKA dsRNA or control dsRNA 7 days previously . Worms were treated with 100 µM forskolin for 2 hours prior to lysate preparation in order to reactivate PKA activity . SmPKA activity was significantly reduced in SmPKA-C dsRNA treated worms as compared to the control dsRNA treated worms ( P<0 . 05 ) ( Fig . 5C ) .
In this study , we provide the first direct evidence for the expression of an active PKA in adult S . mansoni worms . First , antibodies to a highly conserved motif from the PKA-C kinase domain of other organisms reacted with a protein of the expected size for a PKA-C subunit in protein extracts from adult worms . Second , a protein kinase assay utilizing a peptide substrate that is preferentially phosphorylated by PKAs demonstrated significant PKA kinase activity in adult worm lysates . Third , the activity of the putative PKA was sensitive to known inhibitors of PKA-C kinase activity , providing further evidence that a PKA-C protein was responsible for the activity we detected in parasite extracts . Finally , exposure of intact parasites to an adenylyl cyclase inhibitor and agonist either decreased or increased , respectively , the PKA activity , demonstrating that the putative parasite PKA exhibits the expected sensitivity to modulation of cAMP levels . Taken together , these data support the conclusion that an active PKA is expressed in adult S . mansoni , and that adult schistosomes also express regulatory proteins that control PKA activation by cAMP , such as adenylyl cyclase and PKA regulatory subunits . We are aware of one other report which demonstrated that targeting protein kinases in adult schistosomes can be detrimental to the parasites . In this report , the authors demonstrated that , while exposure of adult S . mansoni worms to the broad spectrum tyrosine kinase inhibitor herbimycin A in vitro was not lethal to schistosome worms , parasite egg production was significantly inhibited in a dose-dependent manner [42] . Here we demonstrate that exposure of intact adult worms to inhibitors of PKA kinase activity is lethal to the parasites , suggesting that , in contrast to herbimycin-sensitive tyrosine kinases , the schistosome PKA is critically important for viability . H-89 in particular killed schistosomes rapidly at low concentrations in vitro . However , while H-89 is considered a specific inhibitor of PKA and its IC50 for PKA is in the low nanomolar range , it has also been shown to inhibit other protein kinases . For example , previous studies showed that H-89 also inhibited ribosomal protein S6 kinase 1 ( S6K1 ) and mitogen-and stress-activated protein kinase ( MSK1 ) at lower concentrations than PKA [36] , [43] . Since these two kinases play a major role in eukaryotic cell biology , the lethality observed on treating adult worms with H-89 may not be due to PKA inhibition alone , but rather the result of inhibition of several other kinases in addition to PKA . However PKI 14–22 amide , a highly specific inhibitor of PKA-C subunits that does not affect other protein kinases [37] , also caused parasite death , albeit at higher concentrations than H-89 , supporting the conclusion that PKA activity is important for maintaining parasite viability . A possible explanation for the difference in parasite killing we observed with these two inhibitors is that PKI 14–22 amide is a peptide , which would not be expected to penetrate the treated parasites as well as H-89 . Alternatively , inhibition of additional kinases by H-89 may enhance its toxicity for schistosome worms . Partial coding sequences for putative PKA-C genes have been generated by the S . mansoni genome project , but no full-length sequences for a schistosome PKA-C have been identified . Here we report the isolation of a full-length cDNA encoding for a S . mansoni PKA-C subunit we named SmPKA-C . Expression of this transcript was detected in all life cycle stages we examined , including adults , suggesting this cDNA may encode the PKA activity we detected in adult worm extracts . Subsequent targeting of the SmPKA-C transcript in adult schistosomes using RNAi was lethal for the parasites , demonstrating that this gene is essential for parasite viability , at least in vitro , and providing further support for the conclusion that the parasite death we observed with the inhibitors H-89 and PKI 14–22 amide were the result of PKA inhibition . Consistent with this conclusion , RNAi inhibition of SmPKA expression resulted in significant loss of PKA activity in parasite lysates , confirming that at least a portion of the PKA activity in adult worms is encoded by this transcript . Interestingly , not all PKA activity was ablated by RNAi of SmPKA , despite significant knock-down of transcript levels to approximately 1% of the levels observed in control worms , raising the possibility that other PKA isoforms are expressed by adult worms . Alternative splicing in the N-termini of PKA-C subunits has been observed in mammalian species and in invertebrates , such as C . elegans [44] , [45] , and our RACE experiments suggest that alternative splicing results in the expression of at least two SmPKA-C isoforms in adult schistosomes . However , the remaining activity cannot be attributed to either of these splice variants , as the two transcripts share the sequence targeted by the dsRNA fragment we used for RNAi . These observations suggest that S . mansoni expresses other PKA isoforms , perhaps encoded by additional PKA genes or generated by additional , less conservative alternative splicing , and argue that a search for additional PKA-encoding sequences in S . mansoni mRNA and genomic DNA is warranted . Alternatively , the remaining PKA activity detected in RNAi-treated worms may be due to residual SmPKA-C expression , as ablation of SmPKA-C transcript was not absolute ( Fig . 5B ) . Interestingly we were unable , to amplify full-length cDNAs from adult worms that correspond to other putative PKA genes that have been predicted from sequences of S . mansoni genomic DNA ( Smp_080770 , Smp_047150 . 2/1 ) , suggesting that these genes are not expressed in adult schistosomes or that the predicted coding sequences are incorrect . Other putative S . mansoni PKA sequences ( Smp_147450 and Smp_194610 ) appear to lack a functional kinase domain or are missing amino acid residues that are critical for PKA function . Thus the extent to which adult schistosomes express multiple PKA isoforms remains unclear . However , as RNAi knockdown of SmPKA-C resulted in parasite death , we conclude that SmPKA-C is a critically important protein and propose that SmPKA may be an attractive target for the development new schistosomicidal therapeutics . Tight regulation of PKA activity by PKA-R subunits provides additional opportunities for pharmacological manipulation of PKA , beyond targeting the PKA-C subunit directly with kinase inhibitors . To date , there are no published reports that identify or characterize PKA-R subunits in schistosomes , but the cDNA sequence of a putative PKA-R subunit from S . mansoni was recently released by the S . mansoni genome project ( GenBank accession no . CAY17207 ) and others may await identification . In other organisms , variation in CBD sequences amongst PKA-R isoforms results in differential affinity for cAMP and for cAMP analogs that either induce or inhibit holoenzyme dissociation and activation of the PKA-C subunits [46] , [47] . Thus there is considerable potential for pharmacological manipulation of PKA activity using cAMP analogs . One such analog , 8-Cl-cAMP , has been shown to be a potent growth inhibitor in numerous human cancer cell lines and has completed phase I clinical trials for the treatment of some cancers [25] . Identification and analysis of schistosome PKA-R subunits may identify opportunities for the pharmacological targeting of parasite PKA in a similar manner to that now in development for cancer treatment . These observations highlight the obvious parallels between the treatment of cancer and parasitic infections , which both involve the targeting of eukaryotic cells , and suggest that novel approaches to cancer chemotherapy may provide new leads for the development of much needed anti-parasitic drugs . | Schistosomes are parasitic flatworms that inhabit the circulatory system and are the cause of a debilitating and insidious disease for millions of people worldwide . Like other complex organisms , schistosomes and other parasitic worms regulate their cell biology through extensive use of enzymes called protein kinases that phosphorylate other proteins to alter their function . One such protein kinase , cAMP-dependent protein kinase ( PKA ) , has been proposed as a therapeutic target for the treatment of parasitic infections and cancer . Here we use biochemical techniques to show that schistosome worms possess a functional PKA pathway that is required for survival of the parasites . We also identify a parasite gene that encodes a functional PKA enzyme and show that silencing this gene results in both significant loss of PKA activity in schistosome worms and parasite death . These findings suggest that the gene we have identified is critically important to schistosomes and that its protein product may represent a target for the development of much-needed new drugs to treat schistosome infections . | [
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... | 2009 | A Schistosome cAMP-Dependent Protein Kinase Catalytic Subunit Is Essential for Parasite Viability |
Wolbachia are widespread endosymbionts found in a large variety of arthropods . While these bacteria are generally transmitted vertically and exhibit weak virulence in their native hosts , a growing number of studies suggests that horizontal transfers of Wolbachia to new host species also occur frequently in nature . In transfer situations , virulence variations can be predicted since hosts and symbionts are not adapted to each other . Here , we describe a situation where a Wolbachia strain ( wVulC ) becomes a pathogen when transfected from its native terrestrial isopod host species ( Armadillidium vulgare ) to another species ( Porcellio d . dilatatus ) . Such transfer of wVulC kills all recipient animals within 75 days . Before death , animals suffer symptoms such as growth slowdown and nervous system disorders . Neither those symptoms nor mortalities were observed after injection of wVulC into its native host A . vulgare . Analyses of wVulC's densities in main organs including Central Nervous System ( CNS ) of both naturally infected A . vulgare and transfected P . d . dilatatus and A . vulgare individuals revealed a similar pattern of host colonization suggesting an overall similar resistance of both host species towards this bacterium . However , for only P . d . dilatatus , we observed drastic accumulations of autophagic vesicles and vacuoles in the nerve cells and adipocytes of the CNS from individuals infected by wVulC . The symptoms and mortalities could therefore be explained by this huge autophagic response against wVulC in P . d . dilatatus cells that is not triggered in A . vulgare . Our results show that Wolbachia ( wVulC ) can lead to a pathogenic interaction when transferred horizontally into species that are phylogenetically close to their native hosts . This change in virulence likely results from the autophagic response of the host , strongly altering its tolerance to the symbiont and turning it into a deadly pathogen .
Wolbachia pipientis are intracellular α-proteobacteria which are extensively distributed among arthropods and filarial nematodes . These bacteria are vertically transmitted from female hosts to their offspring . The presence of Wolbachia induces a number of reproductive perturbations for their hosts including cytoplasmic incompatibility ( CI ) [1] , [2] , male killing [3] , modification of parthenogenesis [4] and feminization [5] , all enhancing the vertical spread of infection in host populations . The Wolbachia , in their wide range of hosts , are involved in diverse interactions in terms of costs and benefits to their host that can be placed on a continuum from mutualism to parasitism [6] . However , the global picture is that Wolbachia are weakly virulent to their native hosts [7] , [8] . This low virulence can be explained by the evolutionary consequences of vertical transmission that theoretically leads to the attenuation of symbiont virulence through a co-evolutionary process with its host [9] , [10] . However , the general lack of congruence between Wolbachia and host phylogenies suggests that Wolbachia frequently colonize new hosts through horizontal transfer [6] , [11] and that this mode of spreading may be fundamental to their evolutionary dynamics [12] . Immediately after transfer into a new host species , the virulence of Wolbachia is expected to differ from that expressed in its native host because of a lack of host/symbiont co-adaptation , which can only occur after several host generations . Two main non-exclusive situations have been proposed to explain the emergence of a higher level of virulence when a symbiont colonizes a new host: ( 1 ) maladapted symbionts can overexploit their host , by multiplying too fast or in a “wrong” compartment ( e . g . in organs other than gonads , where Wolbachia are typically found ) , ( 2 ) maladapted hosts could respond to the presence of an unknown symbiont by employing inefficient and very costly immune defences [13] . In the first situation , the virulence comes primarily from an overexploitation of host resources by the parasites , and the new host can be considered as less resistant because parasite burden is higher in some tissues of the new host than in the native one . In the second situation , the immune system normally dedicated to the defence of organisms becomes a double-edged sword , turning itself against the host . A disproportionately strong response can lead to an immunopathology that can in turn deeply harm the hosts . Therefore , being able to tolerate the multiplication of parasites to a certain extent by not activating some immune pathways can constitute a good strategy to limit virulence [14] . It is thus possible that through the co-evolutionary process taking place between the Wolbachia and their hosts , especially as prevalence is often high , hosts quickly select either a way to attempt to control Wolbachia proliferation or at the opposite , a way to better tolerate the presence of the symbionts . Being able to decrease invaders load would reduce their cost for the hosts while increasing tolerance would avoid both the direct ( i . e . production of immune effectors ) and indirect costs ( i . e . the damage caused by immune effectors ) of the immune response . To address the question of the causes of drastic change in virulence of a symbiont , we studied a situation where a Wolbachia-host interaction becomes pathogenic when the bacteria ( wVulC ) are transferred from its native terrestrial isopod species host ( Armadillidium vulgare ) to another one ( Porcellio d . dilatatus ) [15] , [16] . In its native host A . vulgare , wVulC not only colonizes gonads for vertical transmission but is also found in somatic tissues [17] , [18] . This extensive colonization could play a role in the virulence of wVulC detected in several A . vulgare's life history traits: Reduction in hemocyte load , phenoloxydase activity , reproduction and survival [19] , [20] . However , though wVulC is clearly involved in a conflicting interaction with A . vulgare having negative impacts on several of its life history traits , this virulence is weak and Wolbachia-infected individuals usually live for years [19] . The aim of the present study was to understand how Wolbachia wVulC becomes a pathogen when introduced in the new host Porcellio d . dilatatus . To do so , we focused our work on the analysis of the different symptoms that appear prior death of the recipient host Porcellio d . dilatatus . For this , several host life history traits have been measured ( gain of weight , mobility , burying behaviour ) . In parallel , we assessed Wolbachia wVulC multiplication in gonads , hemocytes ( i . e immune cells ) and CNS ( i . e . nerve cells and neighbouring adipocytes ) . Moreover , we observed gonads and CNS cells by electron microscopy . We demonstrate that infection with the strain wVulC causes acute disease to P . d . dilatatus with symptoms of paralysis that indicate the existence of nervous system disorders . We suggest that these symptoms and the death that follows are linked to the autophagic reaction observed in the CNS cells of P . d . dilatatus but not in A . vulgare which seems thus more tolerant to its native Wolbachia strain it co-evolved with .
In order to quantify the dose of Wolbachia injected into recipient hosts that could vary due to the donor host species ( A . vulgare for wVulC and P . d . dilatatus for wDil ) , we performed quantification by qPCR on the wsp gene . We found that the quantities of Wolbachia cells inoculated in recipient hosts significantly differed between treatments ( i . e . wVulC versus wDil ) . This is because the natural amount of Wolbachia present in the ovaries of A . vulgare , the donor host species of wVulC ( mean dose ± se = 1 . 66×105±6 . 30×104 wsp copies/injected µL ) is ten times higher than in Porcellio d . dilatatus , the donor host species of wDil ( mean dose ± se = 1 . 14×104±3 . 60×103 wsp copies/injected µL ) ; ( comparison by t-test = 2 . 9956 df = 4 p = 0 . 0401 ) . To evaluate the influence of the injected dose in the effect observed on the recipient hosts , we tested the effect of wVulC with a very small inoculum ( 3 . 02×103±2 . 60×103 wsp copies/injected µL ) by diluting the initial inoculum a hundred times . Moreover , to take into account the differences between injected doses in a statistical framework , the effect of the factor “dose” was tested at first to absorb as much variance as possible , and then it was therefore possible to test the effect of each strain of Wolbachia per se independently from the difference in injected dose . To verify that only the expected Wolbachia strain was present ( i . e . wDil or wVulC ) in the inoculum and in transfected A . vulgare and P . d . dilatatus , we amplified and sequenced 610 bp of the Wolbachia surface protein gene ( wsp ) in ( i ) all inoculum and ( ii ) recipient animals at 60 days Post Injection ( PI ) . In all the cases , the chromatograms obtained showed well resolved peaks indicating that only one matrix has been sequenced per host or inoculum . The comparison of all the obtained sequences with the reference wsp sequences of wDil and wVulC showed no variation . In all cases we therefore found the strain of Wolbachia which was expected in the given sample . After the injections of wVulC or wDil , no significant effects were detected for A . vulgare females on all the different life history traits measured ( gain of weight , activity and survival ) ( Table 1 for statistics; Figure 1 ) . Moreover , no nervous disorders symptoms such as seizures and tremors were observed for A . vulgare . In order to assess the global replication dynamics of Wolbachia in their hosts , Wolbachia quantifications were performed in CNS , gonads and hemocytes ( i . e . immune cells ) . These quantifications were performed at 30 days and 60 days post injection for both recipient host species . The bacterial load of wVulC in CNS , which is composed of a core of nerve cells surrounded by a sheathing of adipocytes but also in gonads and hemocytes did not significantly differ between the two host species ( in CNS at 30 days PI t-test = −1 . 1901 df = 1 p = 0 . 4449 and at 60 days PI t-test = 1 . 048 df = 1 p = 0 . 4851; in gonads at 30 days t-test = −1 . 1758 df = 1 p = 0 . 4489 and at 60 days t-test = 1 . 4303 df = 1 p = 0 . 3884; in hemocytes at 30 days t-test = −0 . 5327 df = 1 p = 0 . 6884 and at 60 days t-test = 1 . 1216 df = 1 p = 0 . 4635; Figure 3A ) . Moreover , the bacterial loads at 60 days post-injection were similar to those obtained in the CNS , gonads and hemocytes of a naturally infected A . vulgare ( i . e . which received wVulC vertically from their mothers ) ( in CNS t-test = −2 . 1411 df = 3 p = 0 . 1217 , in gonads t-test = 2 . 635 df = 3 p = 0 . 0779 and in hemocytes t-test = 1 . 6100 df = 3 p = 0 . 2487; Figure 3A ) . For individuals of both recipient host species injected with wDil , at both 30 days and 60 days post-injections , we showed no significant difference between bacterial load in CNS ( at 30 days t-test = 1 . 0107 df = 1 p = 0 . 4966; at 60 days t-test = 1 . 088 df = 1 p = 0 . 4732; Figure 3B ) . In the other tested tissues ( i . e gonads and hemocytes ) , we obtained significant differences when comparing the wDil quantification at 60 days post-injection between transfected P . d . dilatatus and A . vulgare ( in gonads t-test = 2 . 9053 df = 5 p = 0 . 0335 and in hemocytes t-test = −2 . 9867 df = 5 p = 0 . 0305; Figure 3B ) . In these two cases , wDil densities were higher in the native host P . d . dilatatus than in A . vulgare ( Figure 3B ) . The quantifications also showed that the density of wDil in CNS of all injected animals from both host species or in naturally infected P . d . dilatatus was always ten to a hundred times lower than the density of wVulC injected in the same animals or in naturally infected A . vulgare ( t-test = −37 . 0596 df = 4 p<0 . 0001 ) . For A . vulgare individuals injected with either wDil or wVulC but also for those which were naturally infected with wVulC , nerve cells from the CNS and adipocytes but also gonads observed by transmission electron microscopy at both 30 and 60 days post-injections were normal ( i . e . nucleus and organelles exhibited normal shapes , no autophagic phagosomes and only few lysosomes were present in the cytoplasm ) . The same cell types observed in P . d . dilatatus injected with its native Wolbachia strain wDil at both 30 and 60 days post-injections were also normal . By contrast , strong alterations of the structure of the cells were observed only in the CNS and adipocytes but not in gonads of P . d . dilatatus injected with wVulC . At 30 days post injection , in both adipocytes and nerve cells , we observed some Wolbachia cells and high densities of autophagosomes and autolysosomes which are characteristic of autophagic process ( Figure 4A and 5A ) . Moreover , in one nerve cell , we specifically observed the formation of an autophagosome around a Wolbachia cell ( Figure 4A ) . At 60 days post injection , the autophagic process observed in the nerve cells and adipocytes clearly amplified compared to what was observed at 30 days post-injections . The CNS cells were severely damaged . Both nerve cells and adipocytes were filled with autophagic vesicles and the organelles were less visible because of a complete disorganization of the cytoplasm ( Figure 4B and 5B ) . To have an additional confirmation of the occurrence of autophagy , we applied the antibody labelling kit LC3B ( Invitrogen ) on adipocytes of A . vulgare and P . d . dilatatus injected with wVulC or wDil . This approach confirmed a high autophagic activity in the adipocytes collected in P . d . dilatatus individuals that were injected with wVulC ( Figure 5C ) . For such individuals , we observed highly labeled spherical structures that would reflect the incorporation of the LC3B protein in the phagophores . This kind of phagophore was very rarely observed for A . vulgare and for other treatments in P . d . dilatatus ( Figure 5D ) .
While Wolbachia are typically transmitted vertically within host populations , it is increasingly recognized that horizontal transfer between host species plays an important role in the evolutionary dynamics of these endosymbionts in arthropods [11] . In this study , we aimed at assessing the potential change in virulence of a Wolbachia strain ( wVulC ) immediately after horizontal transfer into a new host , as well as characterizing the causes of this virulence change . We show that after experimental transfer of wVulC from its native terrestrial isopod host species ( A . vulgare ) to a closely related one ( P . d . dilatatus ) , the virulence increases so much that it becomes a pathogen . Such a high virulence is remarkable and is reminiscent of the observations made for another Wolbachia strain , wMelPop which causes early death of both its native and foreign hosts [21]–[23] . In the case of wMelPop , this high virulence has been proposed to be caused by an abnormally high bacterial load in host cells , especially in nerve ones [21] . Along with high loads of wMelpop in nerve cells , important behavioural modifications have been recorded after horizontal transfer followed by vertical transmission in mosquitoes , suggesting an alteration of the central nervous system ( CNS ) [24] , [25] . In the present study , the characterization of symptoms such as paralysis , seizures and leg tremors observed prior to the death of P . d . dilatatus injected by wVulC , led us to hypothesize that the sickness induced by this Wolbachia strain could also involve nervous system disorders . The quantifications of the Wolbachia in the main organs of both A . vulgare and P . d . dilatatus individuals that received the bacteria either vertically from their mothers or horizontally by experimental injection strengthened this hypothesis . We found that wVulC which is virulent in P . d . dilatatus exhibited bacterial loads in the CNS that were at least 10 times higher than those reached by the avirulent wDil . Therefore , similarly to what was observed for wMelpop , the high bacterial loads reached by wVulC in the CNS certainly constitutes one component of its high virulence . The multiplication of intracellular bacteria in nerve cells as a cause of CNS diseases has also been extensively described in vertebrates [26] . Interestingly , most of the intracellular bacteria that cause such diseases belong to the group of Rickettsiales , to which Wolbachia also belongs [26] , [27] . Some of these Rickettsiales , such as Rickettsia sp . have even been shown to be responsible for diseases like encephalitis and meningitidis in humans [26] . It is also noteworthy that symptoms similar to those caused by wVulC and wMelpop in arthropods such as reduction of movements , uncontrolled tremors and inability to feed properly have been described in important CNS diseases caused by Rickettsiales in other animals [24] , [26] , [28] . Nevertheless high bacterial loads in CNS but also in gonads and hemocytes are unlikely to fully explain the pathogenesis caused by wVulC in P . d . dilatatus because A . vulgare individuals infected vertically or horizontally with this strain survive similar infection pattern [19] . Indeed , wVulC reaches similar bacterial loads in the main organs of both its native and foreign host suggesting that both isopod species have an overall similar resistance [14] . We therefore suspected that the pathogenicity of this strain towards P . d . dilatatus could be mostly due to a lower ability of this foreign host to tolerate the multiplication of wVulC compared to A . vulgare [14] , [29] . One hypothesis would be that A . vulgare , through vertical co-evolution with wVulC , has increased its ability to tolerate the high bacterial loads in its CNS . By contrast , P . d . dilatatus could not evolve towards tolerating high bacterial loads in its CNS because it vertically co-evolved with a strain ( wDil ) that exhibits much lower bacterial loads in this organ than wVulC . The main factor explaining the different virulence of wVulC observed between the two isopod species would thus be the way infected tissues respond to the multiplication of wVulC . In agreement with this hypothesis , electron microscopy and LC3B labelling revealed marked differences in the autophagic response of the host cells between A . vulgare and P . d . dilatatus . While in the former species both adipocytes and nerve cells did not show any sign of deregulated autophagy , in P . d . dilatatus , we observed that these two different cell types were completely filled with autophagic vesicles , resulting in a profound disorganization of their cytoplasm . These observations suggest that in addition to high bacterial loads in the CNS cells , the disproportionate autophagic response of these cells is likely to be a major component of the CNS disease caused by wVulC in P . d . dilatatus . Autophagy even if really conserved among eukaryotes has only quite recently been considered as a way to regulate intracellular parasites [30] . A role for autophagy in the response to Wolbachia infection has not previously been clearly described , but a recent paper described the presence of autophagosomes putatively involved in the degradation of dying wMelpop in ovaries of D . melanogaster [31] . Moreover , Anaplasma phagocytophilum , a pathogen closely related to Wolbachia , has been shown to manipulate its host's autophagic machinery [32] . Given the importance of autophagy as a multi-pronged defense against intracellular microbes , we propose that the pathogenic effect of wVulC onto P . d . dilatatus could be an immunopathology characterized by Wolbachia-induced disturbance of host autophagic processes , as already described for other intracellular pathogens [33] , [34] . The mechanism that causes the activation of an apparently unregulated autophagic response in this novel host is not known . However , for Anaplasma phagocytophilum , a previous study has unveiled the importance of the type IV secretion system ( T4SS ) in the induction and subversion of autophagy [35] , [36] . In the genome of wVulC strain , genes corresponding to the minimum components for a typical and functional type IV secretion system ( T4SS ) were identified [37] , [38] . This suggests that wVulC factors that trigger autophagy may use the T4SS system as an effector translocator to reach their targets . We can thus hypothesize that the strong autophagic response in P . d . dilatatus that causes nerve cells destruction could be due to reaction of the host towards the abnormally intense multiplication of wVulC in its CNS but also to the production of particular toxins possibly shuttled via T4SS . Moreover , it cannot be excluded that the expression of some wVulC effectors could change when the bacteria are transfected from native to new host and that this differential expression could play a role in the difference observed in host reactions . In this study , we show that the Wolbachia strain wVulC becomes highly virulent when it jumps from its native host ( A . vulgare ) to a new host ( P . d . dilatatus ) . This increase in virulence is linked to an apparently unregulated autophagic reaction in the new host which is not observed in the same conditions for the native one . These results suggest that A . vulgare , which vertically co-evolved with wVulC better tolerates the presence of this bacterium in its cells . In conclusion , our experiments uphold the hypothesis according to which tolerance could be a better evolutionary strategy to counteract parasite damage than activating a putative resistance pathway such as autophagy , which as a double-edged sword can hurt the host and increases the virulence of the parasites .
All experimental procedures and animal manipulations did not require an ethics statement . All the animals used in this experiment were grown at 20°C in plastic breeding boxes , in natural photoperiod , on moistened potting mix derived from peat from sphagnum moss ( pH = 6 . 4 and conductivity = 50 . 0 mS/m ) with dead lime-tree leaves as a food source . In such laboratory conditions , animals can normally live up to 3 years , whether they are infected or not by Wolbachia . The recipient hosts of all experiments were aposymbiotic ( i . e . without any Wolbachia ) Armadillidium vulgare ( originating from the city of Helsingor in Denmark and reared in the laboratory since 1991 ) and Porcellio dilatatus dilatatus which were collecting at the village of Rom in Deux-Sèvres ( France ) in 1988 and reared since then in the laboratory . As donors of the native strain wDil , we used another lineage of P . d . dilatatus naturally infected by Wolbachia . These animals were originated from the Sainte-Marguerite Island in Alpes-Maritimes ( France ) and reared in the laboratory since 2007 . As donors of the Wolbachia strain wVulC , symbiotic Armadillidium vulgare originating from the city of Helsingor ( Denmark ) and reared in the laboratory since 1991 were used . Aposymbiotic A . vulgare ( females of 6 months-old ) and P . d . dilatatus ( females and males of 6 months-old ) were infected by wVulC , wDil or by a control treatment . For each batch of injection , ovary suspensions were prepared with the ovaries of ( i ) 5 A . vulgare symbiotically associated with wVulC; ( ii ) 5 P . d . dilatatus symbiotically associated with wDil or ( iii ) 5 A . vulgare that did not host Wolbachia for control treatment . The ovaries were collected and crushed into 1 ml of Ringer solution . The resulting suspension was filtered through a 1 . 2 µm pore membrane , and 1 µL of each filtrate was injected in a small hole pierced in each individual cuticle , using a thin glass needle , into the general cavity , at the posterior part of animals [39] . This protocol was applied to inject 3 independent batches of individuals per treatments ( i . e . injection of wVulC , wDil or control ) . Animals were then used for life history traits measurements , Wolbachia quantifications , electron microscopy and LC3B antibody labeling . Thirty individuals per treatment for P . d . dilatatus ( i . e . 15 males and 15 females ) and 15 females per treatment for A . vulgare were monitored for life history measurements . To be able to demonstrate that the effect of wVulC was not due to higher doses injected compared to wDil , 15 P . d . dilatatus females were injected with a wVulC suspension diluted 100 times with filtered sterile Ringer . For all these animals , life-history traits were recorded during 75 days after the injection . Twelve individuals per treatment for P . d . dilatatus ( i . e . 6 males and 6 females ) and 6 females per treatment for A . vulgare were injected for Wolbachia quantifications . Half of the animals were used for Wolbachia quantification at 30 days post-injection and the other half for Wolbachia quantification at 60 days post injection . For each host species 18 individuals per treatment were injected to observe the adipocytes and the nerve cells but also oocytes by electron microscopy at 30 ( 6 individuals for each treatment ) and 60 days ( 6 individuals for each treatment ) post-injection and to perform LC3B labeling antibody kit to characterize autophagic vesicles in adipocytes at 45 days post injections ( 6 individuals for each treatment ) . For each condition and host species the life history traits were recorded for 75 days post-injection ( until the last individuals infected by wVulC died for P . d . dilatatus ) . The first measurement was performed at t = 0 then 30 days after the injection . The following measurements were then performed every 15 days until the final one after 75 days . The gain of weight was calculated for each animal at each point by weighting them on a precision balance ( d = 0 . 001 g ) and dividing the weight at the time t = x by the weight at time t = 0 . The mobility test was performed by measuring the time during which an individual moves in a glass Petri dish onto a period of 180 s . For P . d . dilatatus , only we determined the number of individuals located at the surface versus the number of buried animals after opening the breeding box by counting the number of animals found at the substrate and those which were buried . The survival rate was measured by counting the number of live animals for each treatment . Total DNA was extracted from the Central Nervous system ( CNS ) ( i . e . nerve cells and neighbouring adipocytes ) , the gonads and the hemocytes of each individual as described by Kocher et al . , [40] after dissections . For each sample , the concentration and quality ( ratios OD 260/280 nm and 260/230 nm ) of the extracted DNA were measured using the Nanodrop 1000 spectrophotometer ( Thermo ) . The quantification of Wolbachia by quantitative PCR ( qPCR ) was performed at 30 and 60 days post-injection to follow the time course of the infections before the death of all P . d . dilatatus infected by wVulC but after apparition of the first symptoms . All the qPCR amplifications were performed with DNA sampled from the CNS ( i . e . nerve chord = nerve cells and neighbouring adipocytes ) , the gonads and the hemocytes . To allow a comparison between animals injected by Wolbachia and those which received the bacteria vertically from their mother; we also performed qPCR quantification on respectively 5 females A . vulgare naturally infected with wVulC and 5 females P . d . dilatatus naturally infected with wDil . All the qPCR reactions were performed using Roche LIGHTCYCLER 480 under the following conditions in 10 µL: 5 µL of SYBRGreen MasterMix ( Roche ) , 0 . 5 µL of 10 µM specific primers wsp208f ( 5′- TGG-TGC-AGC-ATT-TAC-TCC-AG-3′ ) and wsp413r ( 5′-TCG-CTT-GAT-AAG-CAA-AAC-CA-3′ ) , which amplified 205 bp of a single-copy of the gene wsp , 3 µL of sterile water and 1 µL of DNA ( between 10 ng and 80 ng of DNA ) . The thermal cycling used an initial denaturation period of 10 min at 95°C , followed by 45 cycles of denaturing temperature at 95°C for 10 s , the annealing temperature for the reaction was 60°C for 10 s and 72°C for 20 s . A melting curve ( 65°C to 97°C ) was recorded at the end of each reaction in order to check that the PCR product was unique . Efficiency of the PCR reaction was calculated . Standard curve was plotted using 7 dilutions of wsp purified PCR product ( wsp copies . µL-1: 2 . 63×100 , 2 . 63×101 , 2 . 63×102 , 2 . 63×103 , 2 . 63×104 , 2 . 63×105 , 2 . 63×106 , 2 . 63×107 ) . wsp copy number was estimated by calculation in reference to the standard curve . The total DNA quantity ( i . e . host+Wolbachia ) of each sample was used to normalize wsp gene copy number . The results are thus given in number of wsp copies by ng of total DNA . For each condition ( individual*organ*Wolbachia strain ) , two independent technical replicates were performed . In the inoculum and in animals injected with Wolbachia 60 days post-injection , we verified that only the expected Wolbachia strain was present ( i . e . wDil or wVulC ) . To do so , a 610 bp fragment of the wsp region of the Wolbachia DNA was amplified using the specific primers wsp81F and wsp691R ( 81F 5′-TGG TCC AAT AAG TGA TGA AGA AAC and 691R 5′- AAA AAT TAA ACG CTA CTC CA ) [41] . The PCR cycling conditions were 95°C for 2 min followed by 35 cycles ( 95°C for 1 min , 55°C for 1 min , 72°C for 1 min ) and 72°C for 5 min . Double strand PCR products were purified using 2 µl of a mix containing two enzymes: 0 . 05 µl of Antarctic Phosphatase ( AnP , 1 u/µl; New England Biolabs , NEB , USA ) and 0 . 1 µl of Exonuclease I ( 0 . 5 u/µl; NEB , USA ) and incubation at 37°C for 1 hour and 80°C for 20 min . Purified DNA fragments were sequenced using the Big Dye v3 . 1 Terminator Sequencing kit ( PE Applied Biosystems , Foster City , CA , USA ) , visualizing results on an ABI 310 automated sequencer . For each sample , wsp sequences were obtained from both strands and compared between them and with the reference sequence of wDil and wVulC . To date , the most reliable way to characterize autophagy is the visualization by TEM of the formation of double membrane cytosolic vesicles called autophagosomes which sequesters cytoplasm and delivers it to the lysosome for degradation and are considered as the hallmark of autophagy [30] , [42] . To observe CNS cells and gonads by TEM , 6 animals from each host species ( i . e A . vulgare and P . d . dilatatus ) and from each treatment ( injected by wVulC , injected by wDil , or injected by an ovary suspension containing no Wolbachia ) were individually sampled 30 and 60 days post-injection . Tissues were fixed ( 9% glutaraldehyde , 0 . 3 M sodium cacodylate , 3% NaCl , v/v/v ) for 2 hours at 4°C . Tissues were washed ( 0 . 3 M sodium cacodylate , 3% NaCl , 0 . 8 M sucrose , v/v/v ) for 2 hours at 4°C . Post-fixation was performed into 4% OsO4 , 0 . 3 M sodium cacodylate , 5 . 5% NaCl for 45 min . Tissues were subsequently dehydrated through a graded series of acetone solutions , infiltrated and embedded in resin ( Spurr , Polyscience Inc . ) . Thick sections ( 0 . 5 µm ) were stained with 1% toluidin blue for light microscopy observation . Thin sections ( 90 nm ) were contrasted by incubation in 1% uranyl acetate in 50% ethanol for 1 min , then stained with lead citrate [43] . Sections were observed using a transmission electron microscope ( JEOL 100C ) . The LC3B protein plays a critical role in autophagy . Normally , this protein resides in the cytosol , but following cleavage and lipidation with phosphatidylethanolamine , LC3B associates with the phagophore . This localization can be used as a general marker for autophagic membranes . To visualize the LC3B , we used the LC3B Antibody Kit for Autophagy ( Invitrogen ) . As this kit was designed to work with cell coming from cell culture it was not possible to perform it on the full CNS . We thus only performed LC3B labeling on adipocytes from the nerve chord which form a loose tissue that can be more easily penetrated by antibodies . To perform this labeling , we first deposited individually part of fat tissue containing adipocytes collected in: 6 A . vulgare naturally infected by wVulC , 6 A . vulgare injected by wVulC , 6 P . d . dilatatus injected by wDil and 6 P . d . dilatatus injected by wVulC ( at 45 days post-injection ) onto microscope slides harboring a 200 µL drop of 3 . 7% formaldehyde in PBS . The fat tissue samples were then incubated in fixative for 15 minutes at room temperature . The fixative was then removed and the adipocytes washed three times with PBS . After the last washing , 200 µL of 0 . 2% Triton X-100 in PBS were deposited onto the cells . The permeabilization buffer was then removed and 200 µL of the primary antibody ( 0 . 5 µg/mL in blocking BSA buffer ) were deposited onto the cells and incubated for 1 hour at room temperature . The primary antibody was then removed and the samples washed again three times with PBS . The samples were then placed with 200 µL of an anti-rabbit secondary antibody ( 0 . 5 µg/mL in blocking BSA buffer ) and incubated for 1 hour at room temperature . Samples were mounted in Citifluor ( AFl antifading , Cititfluor , England ) . Detection was performed with a Carl Zeiss epifluorescent microscope ( Axio Observer . Z1 ) with Apotome and AxioVision 4 . 8 . 1 software ( Zeiss ) equipped with a 63X objective ( oil immersion ) . All statistical analyses were performed using R software ( version 2 . 10 . 1 , 2009-12-14 ) . For all life-history traits , we first adjusted a model to our data which includes sex , dose ( i . e . the number of Wolbachia injected ) , and treatments ( control , wVulC or wDil ) . We then re-adjusted the same model after having removed the control treatment from the data set , which allowed us to compare between the effects of wVulC and wDil . As the experiments were performed on three independent groups of individuals , a randomized block effect has been taken into account in the models when possible . This last analysis showed that the inter-block variance was very low indicating that measures were repeatable between blocks . The gain of weight and mobility were compared between treatments at 60 days post-injection with a Gaussian linear mixed-effects model with randomized block effect fit by maximum likelihood ( ML ) ( lme function in nlme package [44] ) . For comparison of the animals' location in the boxes between treatments through the whole experiment , a generalized linear model with binomial error and logit link function was performed . In these analyses , the data were collected from simultaneous observations of all the individuals from a same rearing box . The comparison was thus made between groups of 6 boxes and not between individuals , it was thus not necessary to add a random effect in this model . Survival curves obtained with the different treatments were compared using a general mixed-effects Cox model with randomized block effect ( coxme fonction in kinship package ) . Wolbachia density in tissues of A . vulgare , P . d . dilatatus and naturally infected animals were compared with t-test because data distribution followed a normal distribution and variances of the samples were homogeneous . | Characterizing the causes of a virulence increase when a parasite jumps from one host species to another is fundamental to the understanding of disease emergence . In this context , we studied the bacterium Wolbachia wVulC , a natural symbiont of one terrestrial isopod species that becomes a pathogen when transfected into individuals of another species . Before death , recipient animals suffer various symptoms including nervous system disorders caused by the multiplication of wVulC . Interestingly , the quantification of wVulC loads showed similar titers in the individuals from both the recipient and native species . The difference between the two host species lies in the way they respond to the invasion of wVulC and not in their resistance per se: While the recipient host species exhibits an acute autophagic response leading to central nervous system cells disorganization , this phenomenon was not observed in the native host species , which seems to better tolerate the bacterium . Together , our results show that tolerance can be a better evolutionary strategy to counteract parasite damage than to activate a putative resistance pathway which , as a double-edged sword , can arm the host itself and increase the virulence of a parasite . | [
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] | 2012 | High Virulence of Wolbachia after Host Switching: When Autophagy Hurts |
Idiopathic epilepsy is a common human disorder with a strong genetic component , usually exhibiting complex inheritance . We describe a new mouse mutation in C57BL/6J mice , called frequent-flyer ( Ff ) , in which disruption of the gene encoding RNA-binding protein Bruno-like 4 ( Brunol4 ) leads to limbic and severe tonic–clonic seizures in heterozygous mutants beginning in their third month . Younger heterozygous adults have a reduced seizure threshold . Although homozygotes do not survive well on the C57BL/6J background , on mixed backgrounds homozygotes and some heterozygotes also display spike-wave discharges , the electroencephalographic manifestation of absence epilepsy . Brunol4 is widely expressed in the brain with enrichment in the hippocampus . Gene expression profiling and subsequent analysis revealed the down-regulation of at least four RNA molecules encoding proteins known to be involved in neuroexcitability , particularly in mutant hippocampus . Genetic and phenotypic assessment suggests that Brunol4 deficiency in mice results in a complex seizure phenotype , likely due to the coordinate dysregulation of several molecules , providing a unique new animal model of epilepsy that mimics the complex genetic architecture of common disease .
Epilepsy , defined by recurrent seizures resulting from abnormal , synchronized neuronal firing in the brain , is a very common neurological disorder . Idiopathic epilepsies do not have any antecedent disease or injury to the brain and many are suspected to have a genetic basis . The difficulty of elucidating defective genes underlying common inherited epilepsies is that they are genetically complex—being caused by multiple variants that are coinherited in affected individuals [1 , 2] . To date , most mutations involved in idiopathic epilepsy have been found in genes encoding ion channels or their accessory subunits with a few exceptions , for example , LGI1 [3] and EFHC1 [4] in humans , [5] and JRK/JH8 [6 , 7] in both humans and mice . Such exceptions are of interest in that they may lead to further understanding of epilepsy disease mechanisms beyond primary excitability defects , for example , by identification of genes that modulate the expression or function of the more proximal candidates for epilepsy—ion channels , neurotransmitter receptors , and synaptic proteins . Here we describe the disruption of the expression of an RNA-binding protein , BRUNOL4 ( Bruno-like 4 ) leading to partial limbic and tonic–clonic seizures in a new mouse model of epilepsy called “frequent-flyer” ( abbreviated Ff; gene symbol: Brunol4Ff ) . BRUNOL4 ( also known as CELF4 , CUG-BP , and ETR-3 like factor 4 ) belongs to a family of RNA-binding proteins involved in multiple aspects of RNA processing such as pre-mRNA splicing [8] , mRNA editing [9] , and RNA stability and translation [10] . There are six family members in both humans and mice with orthologs in nematode and fruit fly [11] . The murine and human BRUNOL4 are 99 . 6% identical at the amino acid level [12] . Mouse knockouts have very recently been published for Brunol1 and Brunol2 , which display spermatagonial and varied developmental defects , respectively [13 , 14] . UNC-75 , a neuron-specific ortholog in C . elegans , shares 47% identity with the human BRUNOL4 protein . UNC-75 deficiency in the nematode leads to behavioral phenotypes indicative of abnormal neurotransmission . Human BRUNOL4 can rescue the unc-75 mutant phenotype , suggesting that UNC-75 and BRUNOL4 may be involved in fine-tuning synaptic transmission through regulating RNA processing in the nervous system [15] . In this study we describe the seizure phenotypes of mice carrying Brunol4 disruption , and begin to explore the molecular consequences using gene expression profiling and genetic interaction tests . Our studies suggest that Brunol4 deficiency alters the expression of several molecules involved in synaptic function , which , when combined , account for the complex seizure disorder of frequent-flyer mice .
The Ff mutation arose from an independent project in which a series of transgenic mouse lines was generated on the C57BL/6J ( B6 ) strain background . One line ( 9/9 transgene carriers ) developed frequent seizures from about three months of age , precipitated by routine handling such as cage transfer . Since the transgene construct was not expressed in all the lines and since other lines using the same construct did not have seizures , together suggested that the seizures were not caused by transgene expression per se . To distinguish between an unlinked spontaneous mutation and insertional mutagenesis , affected mice were outcrossed to normal B6 mice . In the next generation , seizures cosegregated with the presence of the transgene ( 25/28 carriers displayed seizures versus 0/22 non-carriers ) , suggesting a high-penetrance , dominant mode of inheritance for the seizure phenotype . Convulsive seizures ranged in severity , the mildest being muscle twitching in the face and neck , forelimb clonus , and salivation ( Figure 1A ) . More severe seizures included rearing and falling , myoclonic jerks , and arching of the back and tail . In many cases , convulsions were followed by a very wild running–bouncing phase with occasional tonic–clonic hindlimb extension , but which , unlike the equivalent phase of some induced seizures , did not result in lethality . Hence , the allele symbol “frequent-flyer” ( Ff ) was assigned . The incidence of these handling-associated seizures was higher in male than in female mice . Although handling-associated seizures did not begin until the third month of age , by 7 wk heterozygotes had markedly reduced electroconvulsive thresholds ( ECT ) ( Figure 1B ) . In addition to convulsive seizure phenotypes , heterozygotes were also slightly hyperactive , and while slightly smaller at weaning age , they had a late-onset body weight gain in Ff/+ heterozygotes ( on average 10% heavier than littermate controls , Figure 1C ) . Despite the high frequency and the severity of seizures , Ff/+ heterozygotes do not have a reduced life span ( analyzed up to 24 mo of age ) . The morphology of the Ff/+ brain appears normal , as evident in the proper cortical and hippocampal layering and the lack of overt gliosis ( unpublished data ) . Ff/Ff homozygotes , however , had a much more severe phenotype; they were born alive at close to Mendelian ratios but most died during the first day . From matings between heterozygotes , only 1 . 1% ( expect 25% ) survived until 4 wk of age ( Table 1 ) . While alive , homozygotes did not display obvious signs of convulsion or respiratory stress , nor was there any obvious pathology seen in mutant brains ( unpublished data ) . Future work will be needed to clarify the cause of perinatal lethality in Ff/Ff homozygotes . However , when we examined the F2 generation of matings between B6-Ff/+ and six different inbred mouse strains , a range of survival rates of homozygotes were observed was with the highest being 8 . 2% in crosses with 129S1 ( Table 1 ) , suggesting that homozygosity for B6 allele ( s ) makes the homozygous phenotype worse , as is the case with many neurological mutations in mice ( e . g . , see [16] ) . Interestingly , although these F2 hybrid homozygotes often lived for more than 6 mo , they were smaller than littermates and also exhibited spontaneous limbic and tonic–clonic seizures , similar in appearance to those of Ff/+ heterozygotes , except they were observed as early as 8 wk ( and we suspect that lethal seizures occurred as early as 4 wk ) . In addition , Ff/+ heterozygotes on F1 hybrid backgrounds experienced a lower incidence of convulsive seizures later in life ( unpublished data ) . These results show that inbred strains have polymorphisms that attenuate the frequent-flyer phenotypes . The availability of Ff/Ff homozygotes on a mixed genetic background afforded us the opportunity to determine whether they show spike-wave discharges ( SWD ) , the electroencephalographic manifestation of absence seizures—events not observed in Ff/+ heterozygotes on the B6 background ( unpublished data ) . Ff/Ff homozygotes tested on the F2 hybrid backgrounds ( B6 × 129S1 or FVB/NJ ) experienced very frequent SWD ( e . g . , see Figure 1D and 1E ) . Interestingly , heterozygotes in the FVB/NJ cross , but not in the 129S1 cross , also showed a significant rate of SWD ( unpublished data ) . Together , these results suggest that not only do Ff/Ff homozygotes have SWD , but that the penetrance or severity is also modulated by genetic background . Although SWD in Ff/Ff homozygotes were synchronous , rhythmic , and generalized , when compared to those of other SWD-prone mice , such as stargazer or C3H/HeJ ( e . g . , see [17 , 18] ) , the episodes were relatively short , averaging 1 . 5 s in length , and the rhythmicity was more erratic than in other mutants ( Figure 1D ) . Nevertheless , the animals remained motionless during SWD episodes , and SWD were suppressed by the anti-absence drug ethosuximide ( Figure 1E , left ) , suggesting that they are absence seizures . To determine the identity of the gene disrupted by transgenic insertion , we cloned and sequenced a unique transgene-genomic junction fragment ( see Materials and Methods ) and found a 100% match to intron 1 of the Brunol4 gene on mouse Chr 18 . We then evaluated the impact of transgene insertion on Brunol4 expression . The insertion expanded the 74-kb intron 1 of Brunol4 by at least 20 kb , well upstream of the exons encoding RNA binding motifs ( www . ensembl . org/Mus_musculus , Figure 2 ) . Multiple splice donor and acceptor sites were detected in the transgene , suggesting the possibility of the insertion interfering with normal Brunol4 splicing . In total RNA samples from newborn mice , no Brunol4 transcript was detected in Ff/Ff homozygotes and approximately 45% reduction was seen in heterozygotes ( Figure 3A ) ; by real-time reverse-transcriptase PCR ( RT-PCR ) , similar reduction was observed in the adult brain of Ff/+ heterozygotes ( Figure 3B ) . We also examined the potential impact of the transgene on the expression of the neighboring genes . The genomic region where murine Brunol4 resides is gene poor . Genes with strong annotation are at least 0 . 5 Mb either 5′ or 3′ away from Brunol4 . Expression analysis of the neighboring genes with potential brain function did not reveal difference between Ff/Ff homozygous mutants and normal controls ( unpublished data ) , suggesting that Brunol4 is the only brain-expressed gene affected by the transgene insertion . This is consistent with preliminary assessment of a gene-targeted null allele of Brunol4 that we made recently , which displays handling-associated seizures in older adults , resembling those of frequent-flyer mice with both limbic and wild tonic-clonic phases; younger heterozygotes also have an unusually low threshold to electroconvulsion ( C . L . Mahaffey , W . N . Frankel , unpublished results ) . In order to evaluate the expression pattern of Brunol4 in adults , we examined RNA from a variety of tissues . Only brain samples showed robust signal , despite prolonged exposure time , suggesting that BRUNOL4 is brain specific in adults ( Figure 3C ) , consistent with a recent survey of organ protein expression in mice that detected BRUNOL4 only in the brain [19] . At a higher resolution , Brunol4 showed a predominantly neuronal expression pattern in the brain—labeling was seen in the cerebral cortex , hippocampus , olfactory bulbs , and the granule cell layer of the cerebellum ( Figure 4 ) . However , strongest expression in the brain was observed in the hippocampus where high expression was detected in the pyramidal neurons of the CA2 and CA3 region . Pyramidal neurons in CA1 , the dentate gyrus granule cells , and the dentate subgranular zone had weaker expression ( Figure 4 ) ; the latter is interesting in light of the observation that some types of seizure activity may induce neurogenesis in this region ( e . g . , see [20] ) . How might BRUNOL4 deficiency result in a complex seizure phenotype ? We hypothesize that BRUNOL4 is involved in the processing events of one or more mRNA-encoding proteins that are themselves more directly involved in synaptic function . Thus , in the absence or reduction of BRUNOL4 , these molecules become dysregulated , leading to imbalance in neuronal excitability . We carried out microarray analysis to detect genes differentially expressed between coisogenic wild-type and mutant mice . For the primary screen , newborn Ff/Ff homozygous mutants were chosen to optimize the signal differential . Of the approximately 39 , 000 transcripts interrogated , changes in only 459 transcripts ( corresponding to approximately 350 independent genes ) were considered statistically significant in the Ff/Ff homozygotes compared with controls ( Table S1 ) . Of the 94 down-regulated transcripts ( from approximately 70 independent genes ) , the most reduced was Brunol4 itself , with a significant decrease in Ff/Ff homozygotes . Four genes from the down-regulated list were of obvious interest for an excitability disorder . One encodes serotonin receptor 2c ( Htr2c ) ; Htr2c-null mice are known to experience frequent spontaneous seizures , at least on a mixed background , as well as a reduced seizure threshold [21] . The second encodes synapsin II ( Syn2 ) ; seizures precipitated by sensory stimuli were found previously in Syn2-knockout mice [22] . The third encodes N-ethylmaleimide-sensitive factor ( Nsf ) , which regulates exocytosis in synaptic transmission , as well as AMPA receptor trafficking [23 , 24] . The fourth encodes α-synuclein ( Snca ) , a neuron-specific presynaptic protein [25] . All four molecules have been found in the pyramidal neurons in the hippocampus where Brunol4 is highly expressed [25–28] . In particular , the expression of NSF [27] and synapsin II [28] were enriched in the CA2-CA3 region , similar to that of Brunol4 . After confirming the differential expression of all four transcripts in Ff/Ff-homozygous newborn mice ( unpublished data ) , expression levels were assessed in adult Ff/+ heterozygous brain regions . All four RNAs had , on average , a 20%–25% reduction in the B6-Ff/+ hippocampus compared with controls ( Figure 5A and 5B ) . Moreover , these candidates showed a significant reduction at the protein level in adult hippocampus of B6-Ff/+ mice ( 30%–39%; unpublished data ) , and on a mixed background in Ff/+ and Ff/Ff , ( 25%–32% Ff/+; 32%–56% Ff/Ff , Figure 5C and 5D ) . These region-specific decreases before the onset of seizures were consistent with the limbic-seizure phenotype and the overlapping hippocampal expression of the four genes with that of Brunol4 . Null mutants of the Htr2c receptor gene have several phenotypic similarities with Brunol4Ff/+ heterozygotes , including reduced seizure threshold , hyperactivity , and late-onset weight gain [21 , 29] . However , the fact that Brunol4Ff/+ mice on the B6 strain background experience frequent handling-provoked convulsive seizures after 3 mo of age , whereas Htr2c null mutants do not ( Y . Yang , W . Frankel , unpublished data ) , suggests that down-regulating Htr2c receptor expression is not sufficient to account for the handling-induced seizures . To determine whether Htr2c deficiency is sufficient to account for the ECT of Brunol4Ff/+ mice , we compared seizure threshold in wild-type , single-mutant , and double-mutant mice ( Figure 6 ) . These studies were all done on the B6 strain , to avoid the confounding effect of genetic background . Although the average seizure threshold of Htr2c null mutants was lower than that of Brunol4Ff/+ mice by approximately 2 mA , the threshold of double mutants was 1 mA lower still ( p = 0 . 0003; |t|-test ) . This suggests that factors in addition to reduced expression of Htr2c contribute to seizure threshold in Brunol4Ff/+ mice . Another difference between Htr2c and Brunol4 mutant mice is that Htr2c-null mutants do not have SWD ( [21]; our unpublished observations ) . However , we found that blocking serotonin reuptake in Brunol4Ff/Ff homozygotes by fluoxetine ( Prozac ) treatment lowers the SWD incidence by about 50% ( Figure 1E , right ) —again suggesting that Htr2c down-regulation combines with other Brunol4-downstream deficiencies to cause the seizure disorder of frequent-flyer mutants . Although further tests are required to determine whether the other causative factors are Syn2 , Nsf , or Snca specifically , it is clear that the seizure phenotypes of Brunol4Ff are determined in a genetically complex manner .
Here , we report on the causal association between Brunol4 , encoding a brain-specific RNA-binding protein , and the seizure disorder of frequent-flyer mouse mutants . The origin of the disorder is a transgenic insertion in the Brunol4 gene , resulting in very little Brunol4 transcript in homozygotes and accordingly reduced amount in heterozygotes—suggesting haploinsufficiency . We do not know why the transcript levels were very low , but one possibility is due to the inverted repeat in the transgene cluster , creating a potential hairpin structure that may prevent read-through transcription ( Figure 2 ) . Brunol4Ff mutant mice have several different kinds of seizures , depending upon genotype , age , and strain background . In heterozygotes on a B6 inbred strain background , these include recurrent limbic and tonic–clonic seizures—observed readily following routine animal handling after 3 mo of age—and a significantly lower ECT at an earlier age . Although homozygotes do not usually survive on a C57BL/6J strain background , on F2 hybrid backgrounds homozygotes ( and some heterozygotes ) that survived also displayed spike-wave discharges , the hallmark of absence epilepsy . The prospect of a defective RNA-binding protein such as BRUNOL4 causing a complex seizure disorder suggests a way in which a single gene defect can mimic a complex genetic disease , by impairing the function of multiple molecules simultaneously . This could happen either as a direct consequence of its absence , or secondarily , e . g . , a cascade of effects . Microarray analysis between homozygous mutants and coisogenic control brain yielded a small number of down-regulated transcripts that were statistically significant , two of which ( Htr2c and Syn2 ) are already known to cause seizure-related phenotypes when knocked out in mice [21 , 22] , and two other ( Nsf and Snca ) have obvious functions that relate to synaptic transmission . The down-regulation of each was confirmed in adults , and was greatest in the hippocampus , where Brunol4 expression is high . Interestingly , in addition to seizure susceptibility , Ff/+ heterozygotes display two nonseizure phenotypes like those of Htr2c null mutants: mild hyperactivity and late-onset obesity [21 , 30 , 31] . This might suggest that compromised serotonergic transmission is the major factor of the frequent-flyer phenotype . However , because Htr2c expression is reduced only modestly ( ∼25% RNA , ∼35% protein ) in Ff/+ heterozygotes , compared with complete loss in Htr2c-null mice , it seems more likely that a combination of compromised serotonergic transmission and other defects is responsible for the disorder . Further evidence for this idea was obtained in the ECT paradigm ( additive phenotypic effects of double mutants ) , and by observing that SWD phenotype of Brunol4 homozygotes was partially mitigated by up-regulation of serotonergic transmission through blocking the reuptake of serotonin . With any of the seizure paradigms , it is plausible that reduced synaptic efficacy , e . g . , due to reduced expression of the other three candidates singled out—Syn2 , Nsf , or Snca—is the other contributing variable . However , we cannot ignore other genes misregulated in Brunol4 mutants , some with unknown function ( Table S1 ) , since many also expressed selectively within the hippocampus in B6 mice ( Allen Brain Atlas [32] ) . We do not know why Brunol4 deficiency results in the decreased expression of these and other transcript RNAs . BRUNOL4 has been shown to regulate alternative splicing in cells and tissues without endogenous BRUNOL4 [33–35] , but we did not detect aberrant splice variants in mutant brains , at least in the subset of transcripts that we analyzed ( our unpublished results ) . Since members of the Bruno gene family are involved in RNA editing , stability , and translation , the possibility exists that Brunol4 is involved in other aspects of RNA metabolism , for example , in stabilizing transcripts for translation , a possibility that is supported , in part , by the fact that the degree of reduction at the RNA level and at the protein level was not 100% concordant in the mutant hippocampus ( ∼20%–25% and ∼31%–39% , respectively ) . However , since many RNA processing steps are believed to be coupled with transcription [36] , BRUNOL4 may indeed serve multiple roles in RNA metabolism . Most genes known to cause idiopathic epilepsy encode ion channels . Brunol4 joins a growing list of non-ion-channel epilepsy genes in both humans and mice [3–5 , 7] . It is noteworthy that the human BRUNOL4 gene is in a region on human Chromosome 18 showing strong evidence for linkage with adolescent-onset idiopathic generalized epilepsy [37] , suggesting that BRUNOL4 may be a candidate gene for these seizure disorders .
Origin of Brunol4Ff mice . C57BL/6J ( B6 ) transgenic mice were generated at The Jackson Laboratory ( http://www . jax . org/ ) using a construct where the expression of murine Ighmbp2 cDNA and enhanced green fluorescent protein ( EGFP ) was driven by a bidirectional tetracycline-responsive promoter . Briefly , the coding region of the Ighmbp2 cDNA was cloned in the pBI-EGFP vector ( Clontech , http://www . clontech . com/ ) . The PvuI linearized transgene ( ∼8 . 3 kb ) was microinjected into pronuclei of single cell B6 mouse embryos , which were subsequently implanted into pseudopregnant mice . B6 . 129-Htr2ctm1Jul mice , derived from mice published in 1995 [21] , were obtained from The Jackson Laboratory's Induced Mutant Resource and are now fully congenic on the B6 background after backcrossing for ten generations . All animals were fed standard National Institutes of Health diet containing 6% fat and acidified water ad libitum . All animal procedures followed Association for Assessment and Accreditation of Laboratory Animal Care guidelines and were approved by institutional Animal Care and Use Committee . As previously described [38] , mice were restrained , a drop of anesthetic containing 0 . 5% tetracaine and 0 . 9% NaCl was placed onto each eye , and a preset current was applied via silver transcorneal electrodes using a electroconvulsive stimulator ( Ugo Basile model 7801; http://www . ugobasile . com ) . The stimulator was set to produce rectangular wave pulses with the following parameters: 299 Hz , 0 . 2 s duration , 1 . 6 ms width . Sixty Ff/+ and 57 littermate +/+ male mice ( ages 6–9 wk ) were tested for ECT over a range of electric current settings for minimal clonic forebrain seizure and each ECT response was recorded . The data were analyzed in the computer program MiniTab ( Minitab , http://www . minitab . com/ ) and a response curve was generated using the log-Probit procedure . To determine ECT in double mutants , male mice ( ages 6–9 wk ) were tested by increasing the stimulus once daily until at least a minimal clonic seizure was observed , and the average threshold determined for each genotype . Mice were anesthetized with tribromoethanol ( 400mg/kg i . p . ) Small burr holes were drilled ( 1 mm anterior to the bregma and 2 mm posterior to the bregma ) on both sides of the skull 2 mm lateral to the midline . Electroencephalogram ( EEG ) activity was measured by four Teflon-coated silver wires soldered onto a microconnector . The wires were placed between the dura and the brain and a dental cap was then applied . The mice were given a post-operative analgesic of carprofen ( 5 mg/kg subcutaneous ) and were given a 48-h recovery period before recordings were made . The mice were recorded for a 2-h period on each of the following two days using the Grass EEG Model 12 Neurodata Acquisition System and PolyViewPro software program ( Grass-Telefactor , http://www . grasstechnologies . com/ ) . For mice that were treated with ethosuximide ( 200 mg/kg; Sigma-Aldrich , http://www . sigmaaldrich . com ) or fluoxetine ( 20 mg/kg , Sigma-Aldrich ) , on the day following their second standard EEG recording , mice were recorded for 90 min and then injected intraperitoneally . They were then recorded for a minimum of one additional hour . The control mice were injected intraperitoneally with saline and recorded in the same manner . Matched pairs tests were done using the program JMP 6 . 0 . 3 ( SAS , http://www . sas . com/ ) . Genomic DNA from transgenic mice and control mice was digested with BclI , SpeI , BglII , SphI , and StuI and electrophoresed and blotted onto a Nytron Plus membrane ( Schleicher & Schuell , http://www . whatman . com/ ) . The blot was probed with an EGFP probe and a unique transgene-genomic junction fragment was present in StuI-digested DNA at about 3 kb . The StuI-digested fragment of ∼3 kb was cloned into the pBluescript II-SK vector ( Stratagene , http://www . stratagene . com ) . A vector-specific primer and an EGFP primer were used to amplify the junction fragment . Automated sequencing confirmed the presence of the EGFP cDNA as well as other vector sequence . A 652-nt fragment did not match with the pBluescript II-SK vector sequence and was used as a query to BLAST search the mouse genome . A single perfect match to intron 1 of the Brunol4 gene was found on mouse Chromosome 18 . The other breakpoint of the transgene insertion was cloned by a PCR strategy using a transgene-specific primer and a Brunol4 intron 1 primer . Based on the sequence information around the insertion breakpoints in the Ff allele , a 3-primer PCR assay was designed to detect the Ff allele and wild-type allele . Primers for this assay are: s3gtf , 5′-CTCTTCATCCCTTCTGGCAAGTAG-3′; s3gtr , 5′-GTATTCAACAATTCCGTGTCGCCC-3′; and s3gtr2 , 5′-CCACACAGAGACCAAGAAGATTCC-3′ . At 55 °C annealing temperature , 35 cycles , standard PCR conditions , the s3gtf/s3gtr2 primers produce a 672-bp wild-type allele , and the s3gtf/s3gtr primers produce a 464-bp mutant allele . Total RNA was prepared from newborn brain , adult brain and dissected brain regions using TRIzol reagent ( Invitrogen , http://www . invitrogen . com ) . Two probes were generated for Brunol4 , p1 containing 5′ UTR and exon1 5′ to the insertion site and p2 containing the region between BRUNOL4′s second and third RNA recognition motif . Both probes detected a single transcript on northern blots and p2 was also used for in situ analysis . Hybridization was carried out in formamide-based solution at 42 °C overnight and the blot was washed and exposed to an X-ray film at −80 °C . The same blot was stripped and reprobed with a mouse β-actin probe . Films were imaged by Fuji Luminescent Image Analyzer LAS-1000 Plus ( http://fujifilmlifescienceusa . com ) and subsequently quantified by Fuji Image Gauge Ver . 3 . 4 . Total RNA was prepared from the cerebral cortex of adult B6-Ff/+ and and B6-+/+ /littermates with Trizol ( Invitrogen , http://www . invitrogen . com ) and treated with DNase I ( Promega , http://www . promega . com ) under the manufacturer's suggested conditions . RNA ( 2 μg ) was reverse transcribed with AMV reverse transcriptase ( Promega ) . The cDNA was diluted 20-fold , and 1 . 5 μl was added to qPCR Mastermix Plus for SYBR Green I ( Eurogentec , http://www . eurogentec . be ) with pairs of the following primers: beta-actinF ( 5′-CATTGCTGACAGGATGCAGAA-3′ ) and beta-actinR ( 5′-GCCACCGATCCACACAGAGT-3′ ) , Be1u ( 5′-TCGCAGTAGGTGAGGAAAGCGCAG-3′ ) and Be2d ( 5′-TCGCAGTAGGTGAGGAAAGCGCAG-3′ ) , corresponding to Brunol4 exon 1 forward and exon 2 reverse , respectively . The PCR reactions were analyzed on an ABI Prism 7000 Sequence Detection System ( PerkinElmer , http://www . perkinelmer . com/ ) . The PCR amplifications from three pairs of age-matched mice were run in triplicate . Amplification of the correct size products was confirmed by agarose gel electrophoresis . Thin brain sections ( 7 μm ) from 10-wk-old B6 male mice were hybridized with 33P probes overnight at 50 °C and washed , RNase A treated , dehydrated , and air dried . Slides were dipped in liquid emulsion ( Kodak , http://www . kodak . com/ ) and images were developed 5 d afterwards . For DIG-based in situ analysis , 15-μm cryosections were hybridized with DIG probes overnight at 65 °C and washed extensively before an overnight incubation of alkaline phosphatase–conjugated anti-DIG antibody ( 1:2 , 000 , Roche , http://www . roche . com/ ) . Staining signal was developed using BM purple ( Roche ) at room temperature for 12 h . Total RNA was prepared from six male newborn heads ( 3 Ff/Ff and 3 +/+ ) . 10 μg of total RNA was used to generate 15 μg of cRNA for hybridization to the Affymetrix 430 v2 . 0 Gene Chip ( Affymetrix , http://www . affymetrix . com/ ) according to manufacturer's recommendation . Using the R/maanova package [39] , an analysis of variance ( ANOVA ) model was applied to the data , and F1 , F2 , F3 , and Fs test statistics were constructed along with their permutation p-values . Changes in 459 transcripts were considered statistically significant among the 39 , 000 transcripts interrogated . The primary antibodies and the dilutions were: α-HTR2A , 1:500 ( BD Pharmingen , http://www . bdbiosciences . com/ ) ; α-HTR2C , 1:500 ( Immunostar , http://www . immunostar . com/ ) ; α-NSF , 1:1 , 000 ( H-300 , Santa Cruz Biotechnology , http://www . scbt . com/ ) ; α-Synapsin 2 , 1:5 , 000 ( Stressgen , http://www . nventacorp . com/ ) ; α-alpha-synuclein , 1:250 ( BD Transduction , http://www . bdbiosciences . com/ ) ; and α-beta-tubulin , 1:1 , 000 ( Sigma ) . The secondary antibodies and the dilutions were: HRP α-mouse , 1:6 , 000 ( Zymed , http://www . invitrogen . com/ ) and HRP α-rabbit , 1:2 , 000 ( PerkinElmer ) . The following probes were designed to detect expression differences among the putative BRUNOL4 target RNAs between Ff/+ and +/+ brains: ( 1 ) Htr2c , 437-nt probe in exon 6 , the last nucleotide is 43 nt 3′ of the TAA stop codon . ( 2 ) Nsf , 470-nt probe as described in [27] . ( 3 ) Syn2 , 234-nt probe in exon 1 , the first nucleotide is 90 nt 3′ of the ATG start codon . This probe was able to detect the two alternatively polyadenylated Syn2 transcripts . ( 4 ) Snca , 416-nt probe in exon 6 , the first nucleotide is 37 nt 3′ of the TAA stop codon . ( 5 ) Gabrb3 , 412-nt probe specific to the 3′ end of the coding sequence , the last nucleotide is 14 nt 5′ of the TGA stop codon . Hippocampi were dissected from three male Ff/+ mice ( before the onset of handling-provoked seizures ) and three +/+ littermates . Protein extracts were made using RIPA buffer with proteinase inhibitors ( Roche ) and subsequently quantified using the Bradford reagent ( Bio-Rad , http://www . bio-rad . com/ ) . Protein ( 50 μg ) from each sample was loaded and probed with the primary antibody and a secondary peroxidase-conjugated antibody and visualized with the ECL plus kit ( Amersham , http://www . amersham . com/ ) . The nitrocellulose membrane was incubated with Restore Western blot stripping buffer ( Pierce , http://www . piercenet . com/ ) at 37 °C for 30 min to remove all the antibodies . The membrane was washed and subsequently reprobed with a different set of antibodies . Signals were quantified using the method described above .
The National Center for Biotechnology Information ( NCBI ) Nucleotide database ( http://www . ncbi . nlm . nih . gov/sites/entrez ? db=Nucleotide ) accession number for intron 1 of the Brunol4 gene on mouse Chr 18 is EF639873 . | Epilepsy is a very common brain disorder characterized by recurrent seizures , resulting from abnormal nerve cell activity in the brain . Some cases of epilepsy are caused by brain trauma , such as stroke , infection , tumor , or head injury . Others—so called “idiopathic”—do not have a clear cause . Many idiopathic epilepsies run in families , but the inheritance patterns and complex seizure types suggest that they are not due to a single defective gene but instead are caused by multiple gene defects that are inherited simultaneously in a patient . This complex inheritance makes it difficult to pinpoint the underlying defects . Here , we describe a new mutant mouse , called “frequent-flyer , ” which has several different types of seizures . Although these seizures are caused by a mutation in a single gene , because this gene regulates the expression of many other genes , which , in turn , cause abnormal nerve cell activity , frequent-flyer mice provide a unique animal model of epilepsy—mimicking the complex genetic architecture of common disease . | [
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] | 2007 | Complex Seizure Disorder Caused by Brunol4 Deficiency in Mice |
Cortical responses to sensory inputs vary across repeated presentations of identical stimuli , but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood . Using multi-channel local field potential ( LFP ) recordings in primary somatosensory cortex ( S1 ) of the awake mouse , we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses , based on features of the spontaneous , ongoing LFP recorded across cortical layers . Our findings show that , by utilizing an ongoing prediction of the sensory response generated by this state classifier , an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain , which could have implications for variability in the performance of detection tasks across brain states .
The large majority of what we know about sensory cortex has been learned by averaging the response of individual neurons or groups of neurons across repeated presentations of sensory stimuli . However , multiple studies in the last three decades have clearly demonstrated that sensory-evoked activity in primary cortical areas varies across repeated presentations of a stimulus , particularly when the sensory stimulus is weak or near the threshold for sensory perception [1–3] , and have suggested that this is an equally important aspect of sensory coding as the average response [4–6] . Variability is thought to arise from a complex network-level interaction between sensory-driven synaptic inputs and ongoing cortical activity , and single-trial response variability is partially predictable from the ongoing activity at the time of stimulation . A large body of work has focused on characterizing this relationship between notions of cortical “state” and sensory-evoked responses [7–13] , establishing some simple models of local cortical dynamics [14] . Less is known about the impact of this relationship for downstream circuits ( though see [15 , 16] ) . As an example , consider the detection of a sensory stimulus , which has been foundational in the human [17–22] and non-human primate psychophysical literature [23 , 24] and serves as one of the most widely utilized behavioral paradigms in rodent literature [25–27] . In an attempt to link the underlying neural variability to behavior , the principal framework for describing sensory perception of stimuli near the physical limits of detectability is signal detection theory [28] . A key prediction of signal detection theory is that , on single trials , detection of the stimulus is determined by whether the neural response to the stimulus crosses a threshold . Particularly large responses would be detected but smaller responses would not , so variability in neural responses would lead to , and perhaps predict , variability in the behavioral response . From the perspective of an ideal observer , if variability in the sensory-evoked response can be forecasted using knowledge of cortical state , the observer could potentially make better inferences , but in traditional ( state-blind ) observer analysis , the readout of the ideal observer is not tied to the ongoing cortical state . In this work , using network activity recordings from the whisker sensitive region of the primary somatosensory cortex in the awake mouse , we develop a data-driven framework that predicts the trial-by-trial variability in sensory-evoked responses in cortex by classifying ongoing activity into discrete states that are associated with particular patterns of response . The classifier takes as inputs features of network activity that are known to be predictive of single-trial response from previous studies [9 , 14] , as well as more complex spatial combinations of such features across cortical layers , to generate ongoing discrete classifications of cortical state . We optimize the performance of this state classifier by systematically varying the selection of predictors . Finally , embedding this classification of state in a state-aware ideal observer analysis of the detectability of the sensory-evoked responses , we analyze a downstream readout that changes its detection criterion as a function of the current state . We find that state-aware observers outperform state-blind observers and , further , that they equalize the detection accuracy across states . Downstream networks in the brain could use such an adaptive strategy to support robust sensory detection despite ongoing fluctuations in sensory responsiveness during changes in brain state .
The foundation upon which the state-aware observer is constructed is a prediction of the sensory-evoked cortical response . This prediction is based on classifying elements of the ongoing , pre-stimulus activity into discrete “states , ” and the goal is to find the features of ongoing activity and the classification rules that generate the best prediction of sensory-evoked responses . Treating this as a discrete problem was a methodological choice motivated by the rationale that such an approach could find rules that are not linear in the features of ongoing activity and could lend more flexibility in the rules relating features of ongoing activity to variability in the response . The features of ongoing activity include the power spectrum of pre-stimulus LFP and the instantaneous “LFP activation” ( Fig 2A ) . To describe sensory-evoked responses , we define a parameterization of the LFP response using principal components analysis ( Fig 2B ) . The state classifier is a function that takes as inputs features of pre-stimulus LFP and produces an estimate of the principal component ( PC ) weights and thus of the single-trial evoked response ( Fig 2C ) . In the following sections , we describe this process in detail . Next , within the general class of pre-stimulus features considered–power ratio and LFP activation–we optimized several choices: the range of frequencies used to compute the power ratio; the cortical depth from which the ongoing LFP signal is taken; and possible combinations of LFP signals across the cortical depth . Changes in pre-stimulus features resulted in changes in the boundaries between states , and ultimately in changes in prediction performance . First , we varied the bounds of the low-frequency range ( “L range” , Fig 3A ) . The increase in fVE was on average 0 . 09 ± 0 . 05 ( N = 11 recordings ) ( Fig 3B; classifier boundaries shown in S2 Fig ) , with a significant increase in 10 of 11 recordings ( Fig 3C , asterisks ) . We found that the optimal L range could extend to frequencies up to 40 Hz ( Fig 3C ) , with the median bounds of the optimal L being from 1 to 27 Hz . Using for each recording the power ratio based on the optimized range of low-frequency power ( Fig 3 ) , we next determined where along the cortical depth the most predictive activity was and whether taking spatial combinations of LFP activity could improve the prediction . Note that in this analysis , the channel for the stimulus-evoked response was held fixed ( L4 ) and thus the parameterization of the evoked response using principal components did not change , but the pre-stimulus channel was varied . For each recording , we thus built a series of classifiers , using single- and multi-channel LFP activity from across the array ( Fig 4A , S3 Fig ) , which again were optimized for prediction of the single-trial L4 sensory-evoked response . Classifiers built from a single channel of LFP performed best when the channel was near L4 ( Fig 4B , single example; Fig 4C , average profile ) . Because the LFP represents a volume-conducted signal , we also examined the current source density ( CSD ) [34–36] , estimated on single trials using the kernel method [37] . There was no improvement in fVE using CSD to build classifiers ( fVE difference , CSD minus LFP: -0 . 07; range: ( -0 . 12 , -0 . 01 ) ) . For each recording , we defined an optimal classifier channel based on the spatial profile of fVE for single-channel predictors ( Fig 4B; S3 Fig ) . In the “pair” combination , we paired the optimal classifier channel with each of the other possible 31 channels ( Fig 4B; green dashed line ) . We optimized the classifier in the 3-dimensional space defined by power ratio ( on the optimal channel only ) and LFP activation from each of the two channels and compared the fVE to that obtained using the optimal classifier channel only ( Fig 4D ) . We found no improvement in the prediction using the pair combination compared to using the optimal channel alone ( Fig 4D , mean fVE difference: 0 . 00 ± 0 . 01; 0/11 recordings with significant change , pair vs . single ) or using more complex combinations of channels ( S3 Fig ) . To summarize , we optimized classifiers based on pre-stimulus features to predict single-trial sensory-evoked LFP responses in S1 cortex of awake mice . We found that the classifier performance was improved by changing the definition of the power ratio ( L/W ) such that the low-frequency range ( L ) extended from 1 Hz to 27 Hz , depending on the recording , which differed from the range typically used from anesthetized recordings in S1 ( 1–5 Hz ) [8 , 9] . We also found that the most predictive pre-stimulus LFP activation was near layer 4 . After establishing a clear enhanced prediction of the single-trial stimulus-evoked response within the LFP by considering the pre-stimulus activity , we investigated the impact of this relationship on the detection of sensory stimuli from cortical LFP activity using a state-aware ideal observer analysis . We first considered a simple matched-filter detection scheme [38] in which the ideal observer operated by comparing single-trial evoked responses to the typical shape of the sensory evoked response ( Methods , Detection ) . The matched filter was defined by the trial-average evoked LFP response , and this filtered the raw LFP ( Fig 5A ) to generate the LFP score ( Fig 5B ) . For the state-blind observer , a detected event was defined as a peak in the LFP score that exceeded a fixed threshold ( Fig 5B , stars ) . The LFP score distributions from time periods occurring during known stimulus-evoked responses and from the full spontaneous trace were clearly distinct but overlapping ( Fig 5C ) , and detected events ( Fig 5B , stars ) included both “hits” ( detection of a true sensory input ) and “false alarms” ( detection of a spontaneous fluctuation as a sensory input ) . Next , using the state classifier constructed in the first half of the paper , we analyzed the performance of a state-aware observer on a reserved set of trials , separate from those used for fitting and optimizing the state classifiers ( Methods ) . Specifically , using the optimized state classifier ( Figs 3 and 4 ) , we continuously classified “state” at each time point in the recording ( Fig 5D ) . The state-aware observer detects events exceeding a threshold , which changed as a function of the current state ( Fig 5E ) . Instead of a single LFP score distribution , we now have one for each predicted state ( Fig 5F ) , leading to many possible strategies for setting the thresholds for detecting events across states . In general , the overall hit rate and false alarm rate will depend on hits and false alarms in each individual state ( Fig 6A and 6B for single example; S4 and S5 Figs show all recordings ) , as well as the overall fraction of time spent in each state ( Fig 6A , inset ) . We walk through the analysis for a single example , selected as one of the clearest examples of how state-aware detection worked . While this example recording shows a relatively large improvement , it is not the recording with the largest improvement , and , moreover , the corresponding plots for all recordings are shown in S4 and S5 Figs . To compare between traditional ( state-blind ) and state-aware observers , we compared hit rates at a single false alarm rate , determined for each recording as the false alarm rate at which 80%-90% detection was achieved by a state-blind ideal observer . To select thresholds for the state-aware observer , we systematically varied the thresholds in state 1 and state 3 , while adjusting the state-2 threshold such that average false alarm rate was held constant . For each combination of thresholds , we computed the overall hit rate ( Fig 6C ) . For the example recording highlighted in Fig 6 , the state-aware observer ( hit rate: 96% ) outperformed the traditional one ( hit rate: 90% ) . This worked because the threshold in state 3 could be increased with very little decrease in the hit rate ( Fig 6B ) , and this substantially decreased the false alarm rate in state 3 ( Fig 6A ) . Because the overall false alarm rate is fixed , this meant more false alarms could be tolerated in states 1 and 2 . Consequently , thresholds in states 1 and 2 could be decreased , which increased their hit rates . Across recordings , we found that the state-aware observer outperformed the state-blind observer in 9 of 11 recordings ( Fig 6D; S4 and S5 Figs ) . Hit rates slightly but significantly increased from a baseline of 81% for the state-blind observer to 84% for state-aware detection , or an average change of +3 percentage points ( SE: 3%; signed-rank test , p < 0 . 01 , N = 11 ) . The overall change in hit rate reflects both the fraction of time spent in each state ( some fixed feature of an individual mouse ) and the changes in state-dependent hit rates . To separate these factors , we analyzed the hit rate of the state-blind and state-aware observers by computing , for each observer , the hit rate conditioned on each pre-stimulus state ( Fig 6E ) . For this recording , the state-blind observer had very low hit rate in state 1 and high hit rates in states 2 and 3 . In comparison , hit rates were similar across the three state for the state-aware observer ( Fig 6D ) . Thus , in state 1 ( smallest responses , blue ) , we observed a large increase in the hit rate depending on whether the observer used state-blind or state-aware thresholds . Averaged across all recordings , the state-1 hit rates increased from 60% to 76% , which is a relative increase of 26% ( SE 11% ) . Because this is weighted by the fraction of time spent in state 1 , the overall impact on the hit rate is smaller . Hit rates increased slightly on average in state 2 ( + 2% , SE 4% ) and decreased slightly in state 3 ( -7% , SE 9% ) . The net impact of this is that across the majority of recordings , the cross-state range of hit rates for the state-blind ideal observer was much larger than that for the state-aware ideal observer ( Fig 6D and 6F; 19% , average state-blind minus state-aware hit rate range in percentage points ( SE: 5% ) ; p < 0 . 01 , signed-rank test , N = 11 ) . Thus , while the overall differences between state-aware and state-blind hit rates are modest , the state-aware observer has more consistent performance across all pre-stimulus states than a state-blind observer .
Due to the rapid development of tools that enable increasingly precise electrophysiology in the awake animal , there is a growing appreciation that the “awake brain state” encompasses a wide range of different states of ongoing cortical activity , and that this has a large potential impact on sensory representations during behavior [39–44] . Here , we constructed a framework for the prediction of highly variable , single-trial sensory-evoked responses in the awake mouse based on a data-driven classification of state from ongoing cortical activity . In related work , past studies have used some combination of LFP/MUA features to predict future evoked MUA response [9 , 14] . We used a similar approach for state classification and response prediction in cortical recordings in the awake animal , extending this to allow complex combinations of ongoing activity in space and different features of the pre-stimulus power spectrum as predictors . We found that simple features of pre-stimulus activity sufficed to enable state classification that yielded single-trial prediction of sensory evoked responses . These predictive features were analogous to the synchronization and phase variables found in previous studies [8 , 9 , 14] , though we found a revised definition of synchronization was more predictive . In particular , we found that the very low-frequency band of the LFP power spectrum ( 1–5 Hz ) was less predictive of single-trial evoked responses in our recordings than a wider band ( e . g . 1 to 27 Hz ) . This is consistent with findings from a recent study [40] that surveyed the power spectrum of LFP across different behavioral states in the awake animal and demonstrated differences in the power spectrum between quiet and active wakefulness up to 20 Hz . While we have focused on the problem of state classification and prediction from the perspective of an internal observer utilizing neural activity alone , future work could investigate whether the state classifier is also tracking external markers of changes in state , such as those indicated by changes in pupil diameter [42 , 45] , whisking [40] , or other behavioral markers in the awake animal . We fit classifiers for each individual recording rather than pooling responses across animals and recording sessions . The structure of the classification rules was similar across recordings , showing that the relationship between pre-stimulus features and evoked responses is robust . This suggests that a single classifier could be fit , once inputs and outputs are normalized to standard values . This normalization could be accomplished by determining the typical magnitude of LFP sensory responses and rescaling accordingly . Moreover , the ordered structure of the classification rules suggests that a continuous model of state , rather than a discrete model , would have worked as well . To implement as a continuous model , one would fit a regression of the evoked response coefficients using as independent variables LFP activation and power ratio . Judging by the classification boundaries shown in S1 and S2 Figs , keeping only linear terms in activation and power ratio would give a good prediction . In its current formulation , this framework utilizes only the features of ongoing cortical activity that are reflected in the LFP in order to classify state and predict the evoked LFP response . Both as features underlying the state classifier and as the sensory-evoked response being predicted , LFP must be interpreted carefully , as the details of how underlying sinks and sources combine depend on the local anatomy and population spiking responses [46] . In barrel cortex , the early whisker-evoked LFP response ( 0 to 25 ms ) is characterized by a current sink in L4 initially driven by thalamic inputs to cortex , but also reflecting cortical sources of activity: the evoked LFP is highly correlated with the layer-4 multi-unit activity response [47 , 48] . We restricted our predictive framework to the high degree of variability in this initial response . It remains to determine how LFP response variability is reflected in the sensory-evoked single-unit cortical spiking activity patterns . Further , regarding LFP as a predictor used by the state classifier , LFP is a mesoscopic marker of cortical state that neglects finer details of cortical state organization . In addition to establishing whether better predictions are made from more detailed representations of cortical state , it is an interesting question how microcircuit spiking dynamics are related to the mesoscopic markers of cortical state , or how much can be inferred about population spiking dynamics from the LFP . Finally , thalamic and cortical activity are tightly linked , and the results presented here may also reflect variations in ongoing thalamic activity . Disentangling thalamic and cortical sources of variability in the evoked response will require paired recordings and perturbative experimental approaches designed to address issues of causality . In the second part of the paper , we used ideal observer analysis to show that state-aware observers , with oracle knowledge of the spontaneous , ongoing state fluctuations informative of the single-trial sensory-evoked response , can out-perform a state-blind ideal observer . Our analysis relied on classification of the markers of ongoing state . This is not to suggest that this specific estimation takes place in the brain , but instead could potentially be achieved dynamically by a downstream network through the biophysical properties of the circuitry . Theoretically , the gain and threshold of such a readout neuron or network could be dynamically modified on the basis of the ongoing activity as a biophysical manifestation of the adaptive state-aware ideal observer , though the identification of specific mechanisms was beyond the scope of the current study . We found that the state-aware observer had higher accuracy than the traditional , state-blind observer , but the absolute gain in hit rate ( at fixed false alarm rate ) averaged across all states was modest . When pre-stimulus states were analyzed separately , however , we found that accuracy in the low-response state was substantially higher for the state-aware observer , where there was a relative increase of 25% in the hit rate for this state . Because small sensory responses are predictable from the ongoing activity , transiently lowering the threshold for detection resulted in more “hits” in the low-response state , while false alarms in high-response states could be avoided by raising the threshold when the state changed . However , the cortical activity was classified to be in this particular state approximately 20% of the time , and thus had a relatively modest impact on the overall performance , averaged across all states . What is not currently known is the overall statistics associated with the state transitions ( i . e . distribution of time spent in each state , rate of transitions , etc . ) during engagement within perceptual tasks , but in any case , what we observe here is a normalization of detectability across brain states . For near-threshold sensory perception , the signal detection theory framework asserts that single-trial responses are predictive of perceptual report [28] . While there are many previous studies that seem to support this [49–52] , several animal studies have called this into question , showing that primary sensory neural activity does not necessarily co-vary with perceptual report on simple detection tasks [23 , 25 , 27] . It is possible that the conflicting findings in the literature are due to behavioral state effects , and that more consistent reports would emerge if the analysis of the neural activity incorporated elements of the state-classification approach developed here . Our results show how single-trial response size can be decoupled from perception , if a downstream network predicts and then accounts for the variability in sensory responses . Moreover , our analysis showed that some states of pre-stimulus activity should be associated with higher or lower performance on a near-threshold detection task , which has been observed in near-threshold detection studies in the rodent [26] and monkey [24] . It should be noted that there is controversy regarding the relevance of primary sensory cortex in simple behavioral tasks [53 , 54] , but this is likely related to the task difficulty [55] , where a large body of literature has resolutely shown that processing in primary cortical areas is critical for difficult tasks that increase cognitive load , and we suspect that near threshold stimuli such as those shown here fall in that category . Many studies have demonstrated a link between pre-stimulus cortical activity and perceptual report on near-threshold detection tasks in humans [17 , 18 , 56–59] . Currently , it is not entirely clear how far the parallel in cortical dynamics between the mouse and human can be taken . One challenge is that connecting invasive recordings in the mouse to non-invasive recordings in human studies is non-trivial . Here , at the level of LFP , we observed similarities between species in the interaction between ongoing and evoked activity: the largest evoked responses tended to be preceded by positive deflection in the LFP , and the smallest evoked responses were preceded by negative deflection in the LFP . This relationship , the negative interaction phenomenon , points to a non-additive interaction between ongoing and evoked activity and is also observed in both invasive and non-invasive recordings in humans [33 , 56 , 60 , 61] . Establishing parallels between cortical dynamics on a well-defined task , such as sensory detection , between humans and animal models is an important direction for future studies . In summary , we have developed a framework for the prediction of variable single-trial sensory-evoked responses and shown that this prediction , based on cortical state classification , can be used to enhance the readout of sensory inputs . Utilizing state-dependent decoders for brain-machine interfaces has been shown to greatly improve the readout of motor commands from cortical activity [62 , 63] , at the very end-stage of cortical processing . Others have raised the possibility of using state knowledge to ‘cancel out’ variability in sensory brain-machine interfaces , with the idea that this could generate a more reliable and well-controlled cortical response [64 , 65] , which would in theory transmit information more reliably . This is intriguing , though our analysis suggests a slightly different interpretation: if downstream circuits also have some knowledge of state , canceling out encoding variability may not be the appropriate goal . Instead , the challenge is to target the response regime for each state . This could be particularly relevant if structures controlling state , including thalamus [66] , are upstream of the cortical area in which sensory BMI stimulation occurs . The simple extension of signal detection theory we explored suggests a solution to the problem that the brain faces at each stage of processing: how to adaptively read out a signal from a dynamical system constantly generating its own internal activity .
All procedures were approved by the Institutional Animal Care and Use Committee at the Georgia Institute of Technology ( Protocol Number A16104 ) and were in agreement with guidelines established by the National Institutes of Health . Six nine to twenty-six week old male C57BL/6J mice were used in this study . Mice were maintained under 1–2% isoflurane anesthesia while being implanted with a custom-made head-holder and a recording chamber . The location of the barrel column targeted for recording was functionally identified through intrinsic signal optical imaging ( ISOI ) under 0 . 5–1% isoflurane anesthesia . Recordings were targeted to B1 , B2 , C1 , C2 , and D2 barrel columns . Mice were habituated to head fixation , paw restraint and whisker stimulation for 3–7 days before proceeding to electrophysiological recordings . Following termination of the recordings , animals were anesthetized ( isoflurane , 4–5% , for induction , followed by a euthanasia cocktail injection ) and perfused . Local field potential was recorded using silicon probes ( A1x32-5mm-25-177 , NeuroNexus , USA ) with 32 recording sites along a single shank covering 775 μm in depth . The probe was coated with DiI ( 1 , 1’-dioctadecyl-3 , 3 , 3′3’-tetramethylindocarbocyanine perchlorate , Invitrogen , USA ) for post hoc identification of the recording site . The probe contacts were coated with a PEDOT polymer [67] to increase signal-to-noise ratio . Contact impedance measured between 0 . 3 MOhm and 0 . 7 MOhm . The probe was inserted with a 35° angle relative to the vertical , until a depth of about 1000 μm . Continuous signals were acquired using a Cerebus acquisition system ( Blackrock Microsystems , USA ) . Signals were amplified , filtered between 0 . 3 Hz and 7 . 5 kHz and digitized at 30 kHz . Mechanical stimulation was delivered to a single contralateral whisker corresponding to the barrel column identified through ISOI using a galvo motor ( Cambridge Technologies , USA ) . The galvo motor was controlled with millisecond precision using a custom software written in Matlab ( Mathworks , USA ) . The whisker stimulus followed a sawtooth waveform ( 16 ms duration ) of various velocities ( 1000 deg/s , 500 deg/s , 250 deg/s , 100 deg/s ) delivered in the caudo-rostral direction . To generate stimuli of different velocity , the amplitude of the stimulus was changed while its duration remained fixed . Whisker stimuli of different velocities were randomly presented in blocks of 21 stimuli , with a pseudo-random inter-stimulus interval of 2 to 3 seconds and an inter-block interval of a minimum of 20 seconds . The total number of whisker stimuli across all velocities presented during a recording session ranged from 196 to 616 stimuli . For analysis , the LFP was down-sampled to 2 kHz . The LFP signal entering the processing pipeline is raw , with no filtering beyond the anti-aliasing filters used at acquisition , enabling future use of these methods for real-time control . Prior to the analysis , signal quality on each channel was verified . We analyzed the power spectrum of LFP recorded on each channel for line noise at 60 Hz . In some cases , line noise could be mitigated by fitting the phase and amplitude of a 60-Hz sinusoid , as well as harmonics up to 300 Hz , over a 500-ms period in the pre-stimulus epoch , then extrapolating the sinusoid over the stimulus window and subtracting . A small number of channels displayed slow , irregular drift ( 2 or 3 of 32 channels ) and these were discarded . All other channels were used . Current source density ( CSD ) analysis was used for two different purposes: first , to functionally determine layers based on the average stimulus-evoked response , and second , to analyze the pre-stimulus activity ( in single trials ) to localize sinks and sources generating the predictive signal . We describe the general method used here . Prior to computing the current source density ( CSD ) , each channel was scaled by its standard deviation to normalize impedance variation between electrodes . We then implemented the kernel CSD method [37] to compute CSD on single trials . This method was chosen because it accommodates irregular spacings between electrodes , which occurs when recordings on a particular contact do not meet quality standards outlined above . To determine the best values for the kernel method parameters ( regularization parameter , λ; source extent in x-y plane , r; and source extent in z-plane , R ) we followed the suggestion of Potworowski ( 2012 ) and selected the parameter choices that minimize error in the reconstruction of LFP from the CSD . These parameters were similar across recordings , so for all recordings we used: λ = 0 . 0316; r = 200μm; R = 37 . 5μm . The trial-averaged evoked response was computed on each trial by subtracting the pre-stimulus baseline ( average over 200 ms prior to stimulus delivery ) and computing the average across trials . The CSD of this response profile was computed as described above . The center of layer 4 was determined by finding the largest peak of the trial-averaged evoked LFP response as well as the location of the first , large sink in the trial-averaged sensory-evoked CSD response . We assume a width of 205 μm for layer 4 , based on published values for mice [32] . The matched filter ideal observer analysis [38] is implemented as follows . The score s ( t ) is constructed by taking the dot product of the evoked responses yt with a filter matched to the average evoked response: s ( t ) =yt∙ξ0 This is equivalent to computing the sum s ( t ) =∑t'=1Nξ ( x ( t+t' ) -x ( t ) ) ξ ( t' ) In the standard encoding model , if η is zero-mean white noise , this gives a signal distribution P ( s ) ~N ( ∥ξ0∥ , σ2 ) where σ2=∥ξ0∥2ση2 and a noise distribution with mean 0 . In practice , we do not parameterize the distribution , because η is not uncorrelated white noise , and work from the score distribution directly . For the state-aware decoder , we use the prediction α^t , k of evoked responses yt=ξ0+∑k=1NCα^t , kξk+η' This changes the score to s ( t ) =|ξ0|2+∑kα^t , kξk∙ξ0+η'∙ξ0 Typically , one of the first two PCs ( ξ1 or ξ2 ) has a very similar shape to ξ0 , while the other one has both positive and negative components ( Fig 2 , S1 and S2 Figs ) . For the state-aware threshold , we use state predictions for the component that is more similar to ξ0 , as indicated in S1 and S2 Figs . An event is detected at time t for threshold θ when s ( t ) > θ is a local maximum that is separated from the nearest peak by at least 15 ms and has a minimum prominence ( i . e . drop in s before encountering another peak that was higher than the original peak ) of |ξ0|2/2 . | Establishing the link between neural activity and behavior is a central goal of neuroscience . One context in which to examine this link is in a sensory detection task , in which an animal is trained to report the presence of a barely perceptible sensory stimulus . In such tasks , both sensory responses in the brain and behavioral responses are highly variable . A simple hypothesis , originating in signal detection theory , is that perceived inputs generate neural activity that cross some threshold for detection . According to this hypothesis , sensory response variability would predict behavioral variability , but previous studies have not born out this prediction . Further complicating the picture , sensory response variability is partially dependent on the ongoing state of cortical activity , and we wondered whether this could resolve the mismatch between response variability and behavioral variability . Here , we use a computational approach to study an adaptive observer that utilizes an ongoing prediction of sensory responsiveness to detect sensory inputs . This observer has higher overall accuracy than the standard ideal observer . Moreover , because of the adaptation , the observer breaks the direct link between neural and behavioral variability , which could resolve discrepancies arising in past studies . We suggest new experiments to test our theory . | [
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"m... | 2019 | State-aware detection of sensory stimuli in the cortex of the awake mouse |
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